Deduplicate Dataset (#3)

- Add deduplicated Master file. (20ac450ed2abab34093fb60441b3fc8f930da367)
- Add deduplicated Heliconius and dorsal master files. (bb27758632dc14a3687cb50b500de4d52142faf0)
- Add notebook performing deduplication of the master files (ba3ca2d853729fd8c578b1c57773dec11dbb4f49)
- Update README stats following deduplication (e570b24364ea206a44452f6c2d3542ca8e7def4b)
- Rename data generation notebooks to reflect their order of operation. (57250d31eb33674a86351ddca62050668d65cd8b)
- Rename deduplication notebook to reflect its order of operation (c3c671dd46ed0ad097e3cc1f8436bbb6ec5d329c)
- Update description to align with renaming for workflow order (7fa480b9236503c832e7fdb28ca8fd7c5005b037)
- Update download script with better logging and retry loop (5253c8cbefe3884261b8439551272095ac375f3a)
- Update license file to reflect updated metadata, also more human-readable (1e9e3039693d2259ee1687ab815d368d45ba8ccf)
- Add notebook that generates patch file for deduplication (328796a1e26d4409eafc91f2aaf64996151583ad)
- Add large CSVs to LFS tracking (c15b558fa1090b647d508f8c1c0a64226501d19e)
- Add files relating to the deduplication process (afe2302952e0130d0a53f72d274e115fbf2b96f5)
- Add original Zenodo master file for record (282eed14617709e3c655d96bba14703f45eac320)
- Add links for new files and description of final curation process. (ff1996f41ce125cae54aebb8abb3e909683361f9)
- Update master files entries labeled only to genus level. (3e4f1f2bd0021905761d5ae9a6e74908548b7c08)
- Add bib file for all incldued records (9bb99e162e3c5c7d54fdcb80a461f99ae9aac5be)
- Update read location for initial file (c3bf57ed7524e3f98afcfba8f695a0d3353857d6)
- Small clarity fixes in Dataset Card (9ae0c5ddd8aba594e3577bdc1a28cd6fb8ce2bdf)
- Move deduplication related files (EDA & docs) into appropriate root folders or subfolders. (b5dfeddec2e75bb576305e25e1b8c6fd53bb61be)
- Adjust links and filepaths according to re-organization in b5dfedde (cbd69490da91b78c7bb16df9d7199cf1c54a8f4d)

.gitattributes

@@ -54,5 +54,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 
+# Large CSVs
 Jiggins_Zenodo_Img_Master.csv filter=lfs diff=lfs merge=lfs -text
 Jiggins_Heliconius_Master.csv filter=lfs diff=lfs merge=lfs -text
+metadata/Jiggins_Zenodo_Master.csv filter=lfs diff=lfs merge=lfs -text
+deduplication_process/Jiggins_Zenodo_Img_Master_3477891Patch.csv filter=lfs diff=lfs merge=lfs -text
+deduplication_process/metadata/Jiggins_Zenodo_Img_Master_3477891Patch_downloaded.csv filter=lfs diff=lfs merge=lfs -text

Jiggins_Heliconius_Master.csv

@@ -1,3 +1,3 @@
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-oid sha256:c694500af92fb2f19a8a78a5a370686fcf94bb5bcbbc095da1d8d94da9312ad7
-size 12124839
+oid sha256:d7116b8aa211d770eb64787fe8c605f926db493bd47e8ea9957177efa18aa75e
+size 13499313

Jiggins_Zenodo_Img_Master.csv

@@ -1,3 +1,3 @@
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-oid sha256:fe79fdb62909be50eff6f7016f02301c56ce09c9c0b164dce680810ec4149b2a
-size 15528648
+oid sha256:a43867adb30f411fcaf663ba8a43e15e2b13a458a8602b9bcd29b5f7e4149b21
+size 16448423

README-supplemental.md

@@ -0,0 +1,49 @@
+# Summary of Deduplication of Jiggins Data
+
+We explored our downloaded images in `notebooks/EDA-DL-0-3.ipynb`, and established the following causes of duplication along with remedies:
+1. The "patch" record (record 3477891) which required alignment in `notebooks/Data-gen-1-1.ipynb`, resulting in `metadata/Jiggins_Zenodo_Img_Master_3477891Patch.csv` used for download, is a duplication of all listed records. Additionally, it introduced extra copies of 4 images with incorrect labels for their views (i.e., dorsal vs ventral).
+ - Solution: This record will be removed.
+2. The remaining duplication (430 images x2) comes from 5 records: [4291095](https://zenodo.org/records/4291095), [2813153](https://zenodo.org/records/2813153), [5526257](https://zenodo.org/records/5526257), [2553977](https://zenodo.org/records/2553977), and [2552371](https://zenodo.org/records/2552371).
+  - Though there is nothing in their descriptions on Zenodo to indicate this overlap occurred, record 4291095 duplicated 415 images from record 2813153. 104 of these images had their view/side labels (dorsal vs ventral) updated to reflect an earlier mislabeling in record 2813153; these were all RAW copies of the images. All other metadata was consistent across records.
+  - Solution: Copies of these images and their metadata will be retained from the newer record, record 4291095.
+  - Record 5526257 has 10 images that were added twice. All their metadata is consistent, so we will just keep the first instance of each image.
+  - Records 2553977 and 2552371 are both "Miscellaneous Heliconius wing photographs (2001-2019)" Parts 3 & 1, respectively. Record 2553977 has duplicated images with matching metadata, though 3 of the 4 images have different filenames (`Image_name`). It seems to have resulted from typos or not indicating `cut`, as they are all close-up crops of a single wing. From this perspective, the view labels (dorsal) don't seem appropriate when we have more fine-grained indicators such as "forewing dorsal".
+    - Suggestion that the entries in this record with `cut` in the filename not be used for general classification unless this variety is desired. These also appear to be all the `tif` images, which are often ignored.
+    - Record 2552371 has one image duplicated with two different `CAMID`s assigned (different views as well). These will both be removed as the image has no indicator of the specimen ID in it. Note that these also were labeled `Heliconius sp.`.
+3. Looking at images that were not duplicates, there are clearly still multiple images of the same specimen from the same perspective (eg., two dorsal images) that are also of the same file type (eg., both jpgs). Duplication was checked at the pixel level, so there is no guarantee that they are truly different images, but due to the scope of this collection (over 20 years), it does not seem unlikely that multiple images could have been taken of the same specimen. 
+  - Suggestion/Solution: When determining splits using this data, follow these steps:
+    1. Ensure you are looking at only **_one_** view (eg., dorsal or ventral).
+    2. Ensure you are only looking one file type (`raw`, `jpg`, or `tif`).
+    3. Reduce to only unique `CAMID`s.
+    4. Generate splits as desired.
+    5. Add images to splits based on matched `CAMID`s if multiple views are desired (i.e., if a `CAMID` is present in the dorsal training, add the matching ventral image to the training set).
+    6. Be sure to check only desired categories are included, such as excluding hybrids or cross types, or specimens not labeled to the level of classification.
+
+
+Final deduplication following this process was completed in `notebooks/Data-gen-1-2.ipynb`.
+
+# Other Notes on Using this Data
+
+Not all images are labeled with the same detail. For instance, there are images labeled to the subspecies and species levels, while some images are not labeled past genus (eg., `Heliconius sp.`, `Heliconius hybrid`). If classifying to the subspecies level, be sure to only include images that have labels in the subspecies column, similarly, when doing species classification, don't include those not labeled past genus (exclude species labels `<Genus> sp.` and `Heliconius hybrid`).
+
+The `Taxonomic_Name` column has been standardized to `<Genus> <species> ssp. <subspecies>` (where species and subspecies are available). If species label is unavailable, `Taxonomic_Name` and `species` columns are labeled `<Genus> sp.` or `Heliconius hybrid`.
+
+Cross type specimens (those mixed in a lab to generate hybrids or back-crosses) are indicated by a non-null value in the `Cross_Type` column. Their subspecies column is written out in full indicating parentage (eg., `(plesseni x malleti) x malleti`), unlike in the `Taxonomic_Name` column, where they're standardized to match the formatting of other hybrids. Use the following code to filter back crosses and F2 hybrids if just F1s are desired:
+
+```
+import pandas as pd
+
+df = pd.read_csv(MASTER_FILE_PATH, low_memory = False)
+
+cross_types_rm = []
+for ssp in list(df.loc[df["Cross_Type"].notna(), "subspecies"].unique()):
+    if " x " in ssp:
+        split_x = ssp.split(" x ")
+        if len(split_x) > 2:
+            cross_types_rm.append(ssp)
+
+print(len(cross_types_rm)) 
+
+non_bcross = df.loc[~df["subspecies"].isin(cross_types_rm)]
+```
+

README.md

@@ -51,11 +51,11 @@ configs:
 ### Dataset Summary
 
 Subset of the collection records from Chris Jiggins' research group at the University of Cambridge, collection covers nearly 20 years of field studies. 
-This subset contains approximately 44,809 RGB images of 11,991 specimens (34,265 images of 10,109 specimens across all Heliconius). Many records have both images and locality data. 
+This subset contains approximately 36,189 RGB images of 11,962 specimens (29,134 images of 10,086 specimens across all Heliconius). Many records have both images and locality data. 
 
 Most images were photographed with a DSLR camera with a 100 mm macro-lens in standardized conditions. 
 More information can be found at the individual Zenodo record pages.
-Images and full records with data are stored in the [EarthCape database](https://heliconius.ecdb.io/default.aspx) and on [Zenodo](https://zenodo.org/communities/butterfly?q=&l=list&p=1&s=10&sort=newest) (across 31 records from the Butterfly Genetics Group).
+Images and full records with data are stored in the [EarthCape database](https://heliconius.ecdb.io/default.aspx) and on [Zenodo](https://zenodo.org/communities/butterfly?q=&l=list&p=1&s=10&sort=newest) (across 29 records from the Butterfly Genetics Group).
 
 Both dorsal and ventral images available. Contains both whole butterfly, and 4 wings separate. Large variation in image content.
 
@@ -73,37 +73,38 @@ English, Latin
 
 ## Dataset Structure
 
-* **Jiggins_Zenodo_Img_Master.csv:** Information for the approximately 45,0000 unprocessed image files included in the Jiggins Heliconius Collection. Image types are `jpg`, `raw` (.CR2) and `tif`. `genus`, `species`, and `subspecies` are included columns.
+* **Jiggins_Zenodo_Img_Master.csv:** Information for the approximately 36,000 unprocessed image files included in the Jiggins Heliconius Collection. Image types are `jpg`, `raw` (.CR2) and `tif`. `genus`, `species`, and `subspecies` are included columns.
 
-* **Jiggins_Zenodo_dorsal_Img_Master.csv:** Subset of 22,175 images from `Jiggins_Zenodo_Img_Master.csv` with a dorsal view of the butterflies (note that some have both dorsal and ventral). This subset includes 12,296 unique specimens. Image types and columns are the same as for the Master file.
+* **Jiggins_Zenodo_dorsal_Img_Master.csv:** Subset of 17,748 images from `Jiggins_Zenodo_Img_Master.csv` with a dorsal view of the butterflies (note that some have both dorsal and ventral). This subset includes 11,746 unique specimens. Image types and columns are the same as for the Master file.
 
-* **Jiggins_Heliconius_Master.csv:** The 34,265-image subset of all Heliconius images from `Jiggins_Zenodo_Img_Master.csv`. This subset includes 10,109 unique specimens. IImage types and columns are the same as for the Master file.
+* **Jiggins_Heliconius_Master.csv:** The 29,134-image subset of all Heliconius images from `Jiggins_Zenodo_Img_Master.csv`. This subset includes 10,086 unique specimens. Image types and columns are the same as for the Master file.
 
 
 **Notes:**
-- The notebooks that generated these files and stats are included in the `notebooks` folder.
-    - The original Jiggins Zenodo Master file was compiled from the CSVs provided with the included Zenodo records from the Butterfly Genetics Group. Christopher Lawrence selected which of these provided columns to include. Further processing and standardization (all documented in the Jupyter Notebooks) was performed by Elizabeth Campolongo.
+- The notebooks that generated these files and stats are included in the `notebooks` folder, their only requirement is `pandas`.
+    - The [original Jiggins Zenodo Master file](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/metadata/Jiggins_Zenodo_Master.csv) was compiled from the CSVs provided with the included Zenodo records from the Butterfly Genetics Group. Christopher Lawrence selected which of these provided columns to include. Further processing and standardization (all documented in the Jupyter Notebooks) was performed by Elizabeth Campolongo.
     - Taxonomic information for records [5526257](https://zenodo.org/record/5526257), [2554218](https://zenodo.org/record/2554218), and [2555086](https://zenodo.org/record/2555086) was recovered from information on their Zenodo pages, as the provided CSVs did not contain that information. 
     - Be advised that there may be overlap between images in [record 2548678](https://zenodo.org/records/2548678) and [record 3082688](https://zenodo.org/records/3082688).
-- The `scripts` folder has a download and checksum script. 
+- The `scripts` folder has a download and checksum script, their only requirement is `pandas`. 
     - Images are downloaded to the provided images directory with subfolders labeled by the `Taxonomic_Name`, with filenames `<X>_<Image_name>`.
-    - The checksum script is called by `download_jiggins_subset.py` to generate an MD5 for all download images and creates a CSV with `filepath`, `filename`, and `md5` columns in the same folder as the source CSV (named `<source CSV>_checksums.csv`). This helps to ensure FAIR and Reproducible results, though this will _**not**_ distinguish between the raw and jpg versions of the same image.
+    - The checksum script is called by `download_jiggins_subset.py` to generate an MD5 for all downloaded images and creates a CSV with `filepath`, `filename`, and `md5` columns in the same folder as the source CSV (named `<source CSV>_checksums.csv`). This helps to ensure FAIR and Reproducible results, though this will _**not**_ distinguish between RAW and JPG versions of the same image.
     - A log of the download is also generated in the same folder as the source CSV (named `<source CSV>_log.json`).
-- `metadata/Missing_taxa_Jiggins_Zenodo_Master.csv` contains a record of the images that did not have easily reconcilable taxonomic information (see `notebooks/standardize_datasets.ipynb` for more information on this data). There are 1,630 such images distributed across 18 records.
+- `metadata/Missing_taxa_Jiggins_Zenodo_Master.csv` contains a record of the images that did not have easily reconcilable taxonomic information (see `notebooks/Data-gen-0-3.ipynb` for more information on this data). There are 1,630 such images distributed across 18 records.
+    - `metadata/Missing_taxa_download.csv` contains the 22 entries that had `Unknown` or `Stratiomyidae` (not a butterfly) as their `Taxonomic_Name`. Their specimen IDs did not appear elsewhere in the record, so this information was not easily reconcilable (see `notebooks/Data-gen-1-2.ipynb` for more information).
 
 ### Data Instances
 
 `Jiggins_Heliconius_Master.csv` contains multiple species of Heliconius (including erato and melpomene), most are labeled down to the subspecies level. The `Jiggins_Zenodo_Img_Master.csv` also contains species from other genera, with just over half labeled to the subspecies level (these are predominantly Heliconius subspecies).
 
-Detached wings in four quadrants (generally).
+Detached wings in four quadrants (generally). Many include a label indicating the specimen ID (`CAMID`). There is variation in formatting both across and within records, but overall setup is relatively consistent.
 Some subspecies may be photographed differently, needs segmentation preprocessing.
 
-* **Type:** JPG/jpg/tif(very few)
+* **Type:** RAW (`.CR2`), JPG, and TIFF (very few)
 * **Size (x pixels by y pixels):** Unknown yet
 * **Background (color or none):** multiple (needs to be normalized, often grey or lime green)
-* **Fit in frame:**
+* **Fit in frame:** varies
 * **Ruler or Scale:** Some with Ruler
-* **Color (ColorChecker, white-balance, None):** None
+* **Color (ColorChecker, white-balance, None):** some with ColorChecker, many with white reflectance standard in the bottom right corner.
 
 #### Preprocessing steps (to be done):
 1. Hybrid separation - some images labeled as _H. erato_ and _H. melpomene_ without subspecies names are hybrids and need to be determined what subspecies they are.
@@ -119,9 +120,9 @@ CSV Columns are as follows:
 
 - `CAMID`: Unique identifier for each specimen that was photographed. Each `CAMID` corresponds to multiple images (based on factors such as `View` and `file_type`).
 - `X`: Unique identifier for each line in the master CSV.
-- `Image_name`: Filename of image (not unique, often `CAM<CAMID>_<v or d>`).
-- `View`: View of the butterfly in the image: `dorsal`, `ventral`, `forewing dorsal`, `hindwing dorsal`, `forewing ventral`, `hindwing ventral`, `dorsal and ventral`.
-- `zenodo_name`: Name of the CSV file with metadata used to populate this file from the associated Zenodo record.
+- `Image_name`: Filename of image (unique, often `CAM<CAMID>_<v or d>`).
+- `View`: View of the butterfly in the image: `dorsal`, `ventral`, `forewing dorsal`, `hindwing dorsal`, `forewing ventral`, `hindwing ventral`, or `dorsal and ventral`.
+- `zenodo_name`: Name of the CSV file with metadata from the associated Zenodo record used to populate the information about this image.
 - `zenodo_link`: URL for the Zenodo record of the image.
 - `Sequence`: Mostly numeric IDs, not unique, please see the associated Zenodo record for more information on the meaning of these designations.
 - `Taxonomic_Name`: Indication of the Genus, species, and possibly, subspecies, of the specimen. For Cross Types, the hybrid names are reduced to just the two subspecies (from the `Cross_Type` column) and non-specified crosses are labeled `<Genus> <species> cross hybrid`.
@@ -133,24 +134,27 @@ CSV Columns are as follows:
 - `Dataset`: Overall collection the images belong to: `Heliconiine Butterfly Collection Records from University of Cambridge` (largely, but not entirely Heliconius), `Patricio Salazar`, `Nadeau Sheffield`, `Bogota Collection (Camilo Salazar)`, `Cambridge Collection`, `Mallet`, `Merril_Gamboa`, `STRI Collection (Owen)`. Please see the associated Zenodo record for more information on the meaning of these designations.
 - `Store`: Storage location for specimen (`Box ###`).
 - `Brood`: Likely indicator of batch of butterflies. Please see the associated Zenodo record for more information on the meaning of these designations.
-- `Death_Date`: Date of specimen death. Only noted for 318 images.
-- `Cross_Type`: Laboratory cross breeding information. There is a mix of F1 (subspecies x subspecies), F2 (F1 x F1), and backcross (F1 x subspecies) hybrids. Generally, the order of the subspecies listed in the cross corresponds to the sexes of the parents (Maternal x Paternal). There are approximately 5,000 such images; on average, there are both raw and jpg images of the specimens for each view, so this covers 820 unique specimens.
-- `Stage`: Life stage of the specimen. Only 15 specimens have a non-null value for this feature, and they are all labeled as `Adult`.
-- `Sex`: Sex of the specimen: `Male`, `Female`, or `Unknown`.
+- `Death_Date`: Date of specimen death. Only noted for 269 images.
+- `Cross_Type`: Laboratory cross breeding information. There is a mix of F1 (subspecies x subspecies), F2 (F1 x F1), and backcross (F1 x subspecies) hybrids; these are all crosses of _Heliconius erato_ and _Heliconius melpomene_ subspecies. Generally, the order of the subspecies listed in the cross corresponds to the sexes of the parents (Maternal x Paternal). There are approximately 4,400 such images; on average, there are both raw and jpg images of the specimens for each view, so this covers 820 unique specimens.
+- `Stage`: Life stage of the specimen. Only 6 images (3 specimens) have a non-null value for this feature, and they are all labeled as `Adult`.
+- `Sex`: Sex of the specimen: `Male`, `Female`, or `Unknown`; there are also null values.
 - `Unit_Type`: Type of the specimen: `wild`, `reared`, `Mutant`, `Wild`, `e11`, or `e12`. Please see the associated Zenodo record for more information on the meaning of these designations. 
 - `file_type`: Image type: `jpg`, `raw` (.CR2), or `tif`.
 - `record_number`: The number associated with the Zenodo record that the image came from.
-- `species`: Species of the specimen. There are 246 species represented in the full collection, 37 of these are species of Heliconius.
+- `species`: Species of the specimen. There are 242 species represented in the full collection, 36 of these are species of Heliconius. Note that 25 of these are `<Genus> sp.` indicating that they are a species of the designated genus, but have not been classified at the species level, this includes `Heliconius sp.` and `Heliconius hybrid` designations.
 - `subspecies`: Subspecies of the specimen (where available, mostly labeled for Heliconius). There are 155 subspecies represented in the full collection, 110 of which are Heliconius subspecies.
-- `genus`: Genus of the specimen. There are 94 unique genera represented in the full collection.
+- `genus`: Genus of the specimen. There are 92 unique genera represented in the full collection.
 - `file_url`: URL to download image from Zenodo: `zenodo_link + "/files/" + Image_name`. Allows for sample image display in [data dashboard](https://huggingface.co/spaces/imageomics/dashboard-prototype).
 - `hybrid_stat`: Hybrid status of the sample: `hybrid`, `non-hybrid`, or `None`. Hybrids are determined by an ` x ` or `hybrid` in the `Taxonomic_Name` column, all other images classified to the _subspecies_ level are labeled as `non-hybrid`, and the parent species of the one species-level hybrid is labeled as `non-hybrid` (only one of them is present in the dataset).
+- `filename`: Unique filename assigned to image at download (`<X>_<Image_name>.<jpg/tif/CR2>`) using `scripts/download_jiggins_subset.py`.
+- `filepath`: Filepath of the downloaded image (`<image_folder>/<Taxonomic_Name>/<filename>`) using `scripts/download_jiggins_subset.py`.
+- `md5`: MD5 of the downloaded image. This was used as the measure of uniqueness (at the pixel level) to address duplication of images across Zenodo records.
 
 **Note:**
- - `Jiggins_Zenodo_dorsal_Img_Master.csv` also has a column `CAM_dupe` indicating whether the `CAMID` has multiple images within this subset.
-    - We do not leave the first instance as a non-duplicate, so to have a clear assessment of all duplication (eg., is it just across a couple records, file types, etc).
-    - `CAMID`s are necessarily duplicated for the images that are of just a dorsal forewing or hindwing, so we label those as `single_wing`.
-    - There are multiple jpg images & multiple raw images of the same specimen. Note that this does not necessarily mean these are duplicates of the same images. There are also jpg copies provided alongside raw images.
+ - `Jiggins_Zenodo_dorsal_Img_Master.csv` also has a column `CAM_dupe` indicating whether the `CAMID` has multiple images of the same file type within this subset. Most (11,446) specimens have only one dorsal image per file type.
+    - `CAMID`s are necessarily duplicated for the images that are of just a dorsal forewing or hindwing, so we label those as `single_wing`. There are one RAW and one JPG image for each single wing. None of these are Heliconius butterflies.
+    - Instances of both dorsal and ventral wings in the same image are labeled `both-wings`; there are 18 such images, all of Heliconius butterflies.
+    - There are multiple JPG images with the same `CAMID`. Note that this does not necessarily mean these are duplicates of the same images; check the views to confirm. There are also JPG copies provided alongside RAW images. Generally, RAW images will be unique up to `View`, as there is only one CAMID with two RAW images that aren't of just a single wing.
 
 <!--
 ### Data Splits
@@ -162,7 +166,7 @@ CSV Columns are as follows:
 
 ### Curation Rationale
 
-The Butterfly Genetics Group has a large collection of butterfly images distributed across 31 Zenodo records. They do not all have the same information, and it is sometimes only provided in the record, but not the metadata. With this collection, we combine the provided information (metadata) into a shared format that is easily ingested into ML pipelines. We also add some other labels of interest (based on identification determined by the Butterfly Genetics Group), and endeavor to remove duplication, noting potential points of duplication and providing some assessment tools to help prevent data leakage. 
+The Butterfly Genetics Group has a large collection of butterfly images distributed across 31 Zenodo records. They do not all have the same information, and it is sometimes only provided in the record, but not the metadata. With this collection, we combine the provided information (metadata) into a shared format that is easily ingested into ML pipelines. We also add some other labels of interest (based on identification determined by the Butterfly Genetics Group), and endeavor to remove duplication, noting potential points of duplication and providing some assessment tools to help prevent data leakage. This de-duplication effort reduced the overall dataset to covering only 29 of these records, and it is documented in the `deduplication_process` directory.
 
 Additionally, these datasets are prepared in a format that allows for easy integration with the Imageomics Institute's [Data Dashboard](https://huggingface.co/spaces/imageomics/dashboard-prototype) for distribution statistics and easy sampling of images by taxonomic information and view.
 
@@ -172,6 +176,8 @@ These images are a subset of the [Butterfly Genetics Group's Cambridge butterfly
 
 Data is pulled from the Zenodo Records in [`licenses.json`](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/metadata/licenses.json). This file also contains full citation information for all records.
 
+The [original Master file](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/metadata/Jiggins_Zenodo_Master.csv), created by Christopher Lawrence, was fed into notebook Data-gen-0-1, and further processed in Data-gen-0-2 and Data-gen-0-3. The next data generation step (deduplication by MD5) involved downloading all images in `metadata/Jiggins_Zenodo_Img_Master_3477891Patch.csv` (created in Data-gen-1-1). MD5s of all downloaded images were taken and the results were explored in the EDA-DL series of notebooks; information and conclusions from this EDA are documented in [`README-supplemental`](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/README-supplemental.md). The outline described there was then implemented in the [Data-gen-1-2 notebook](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/notebooks/Data-gen-1-2.ipynb), regenerating all current datasets. This notebook also has another round of taxonomic standardization so that all entries labeled only to the genus level are labeled as `<Genus> sp.` in both `Taxonomic_Name` and `species` columns, 4 entries with a non-butterfly genus name were noted and removed, as were 18 entries with `Unknown` as their taxonomic label. 
+
 
 #### Initial Data Collection and Annotation
 
@@ -187,9 +193,13 @@ None
 ### Discussion of Biases and Other Known Limitations
 
 - This dataset is imbalanced. Even among the Heliconius subset, some subspecies are more heavily represented than others. 
-- There are a mix of valid subspecies and hybrids that are labeled as such, but there are also images of butterflies classified only to the genus or species level, for which such a designation may not be clearly made (there are also instances of "_Heliconius <species> hybrid_", where the parent subspecies are not indicated. 
+- Not all images are labeled with the same detail. There are a mix of valid subspecies and hybrids that are labeled as such, but there are also images of butterflies classified only to the genus or species level, for which such a designation may not be clearly made. These images are limited to classification tasks only down to the level of their label. There are also instances of "_Heliconius <species> hybrid_", where the parent subspecies are not indicated because it was labeled only as a hybrid of that species. 
+    - All entries labeled only to the genus level can be recognized by ` sp.` following the genus in the `Taxonomic_Name` and `species` columns or are labeled as `Heliconius hybrid` in those columns. 
 - There may be overlap between images in [record 2548678](https://zenodo.org/records/2548678) and [record 3082688](https://zenodo.org/records/3082688).
-- There are multiple images of the same specimen for many specimens; sometimes this is due to different views (eg., dorsal or ventral side), sometimes it is due to JPG copies of the RAW photos, though it is also sometimes that new photos were taken of the same specimen at a different times.
+- There are multiple images of the same specimen for many specimens; sometimes this is due to different views (eg., dorsal or ventral side), sometimes it is due to JPG copies of the RAW photos, though it seems it is also sometimes that new photos were taken of the same specimen at a different times.
+  - The master files contain only images that were determined to be unique (at the pixel level) through MD5 checksum. This does _**not**_ guarantee that there are not images that are cropped copies of other photos. For instance, [record 2553977](https://zenodo.org/records/2553977) has a number of images with `_cut_` in their name, some of which are close-up crops of the butterfly wings (though not all, some are just close-ups of the full butterfly).
+
+Please see [`README-supplemental.md`](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/README-supplemental.md) for more details on the deduplication process and recommendations on how to split this data effectively without data leakage.
 
 
 ## Additional Information
@@ -199,19 +209,19 @@ None
 * Christopher Lawrence (Princeton University) - ORCID: 0000-0002-3846-5968
 * Chris Jiggins (University of Cambridge) - ORCID: 0000-0002-7809-062X
 * Butterfly Genetics Group (University of Cambridge)
-* Elizabeth G. Campolongo - ORCID: 0000-0003-0846-2413
+* Elizabeth G. Campolongo (Imageomics Institute, The Ohio State University) - ORCID: 0000-0003-0846-2413
 
 ### Licensing Information
 
 The data (images and text) are all licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/). Each image and text in this dataset is provided under the least restrictive terms allowed by its licensing requirements as provided to us (i.e, we impose no additional restrictions past those specified by this license on the original source files). Modified images are only restricted by this original license.
 
-Images can be matched to their source record through the `zenodo_link` column in the Master CSVs to the `url` in the licenses.json file.
+Images can be matched to their source record through the `zenodo_link` or `record_number` column in the Master CSVs to the `url` or `record_number` in the [`licenses.json`](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/metadata/licenses.json) file, respectively.
 
 ### Citation Information
 
-Christopher Lawrence, Chris Jiggins, Butterfly Genetics Group (University of Cambridge), Elizabeth Campolongo. (2024). Jiggins Heliconius Collection. Hugging Face. https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection.
+Christopher Lawrence, Chris Jiggins, Butterfly Genetics Group (University of Cambridge), Elizabeth G. Campolongo. (2024). Jiggins Heliconius Collection. Hugging Face. https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection.
 
-If you use this dataset, please cite the original datasets (citations for all 31 Zenodo records are in licenses.json) as well as this curated subset. 
+If you use this dataset, please cite the original datasets (bibtex citations for all 29 included Zenodo records are in [`jiggins.bib`](https://huggingface.co/datasets/imageomics/Jiggins_Heliconius_Collection/blob/main/metadata/jiggins.bib)) as well as this curated subset. 
 
 ### Contributions
 

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metadata/jiggins.bib

@@ -0,0 +1,292 @@
+@misc{gabriela_montejo_kovacevich_2020_4289223,
+author = {Gabriela Montejo-Kovacevich and Eva van der Heijden and Nicola Nadeau and Chris Jiggins},
+title = {Cambridge butterfly wing collection batch 10},
+month = nov,
+year = 2020,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.4289223},
+url = {https://doi.org/10.5281/zenodo.4289223}
+}
+
+@misc{patricio_a_salazar_2020_4288311,
+author = {Patricio A. Salazar and Nicola Nadeau and Gabriela Montejo-Kovacevich and Chris Jiggins},
+title = {{Sheffield butterfly wing collection - Patricio Salazar, Nicola Nadeau, Ikiam broods batch 1 and 2}},
+month = nov,
+year = 2020,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.4288311},
+url = {https://doi.org/10.5281/zenodo.4288311} 
+}
+
+@misc{montejo_kovacevich_2019_2677821,
+author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian},
+title = {Cambridge butterfly wing collection batch 2},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2677821},
+url = {https://doi.org/10.5281/zenodo.2677821} 
+}
+
+@misc{jiggins_2019_2682458,
+author = {Jiggins, Chris and Montejo-Kovacevich, Gabriela and Warren, Ian and Wiltshire, Eva},
+title = {Cambridge butterfly wing collection batch 3},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2682458},
+url = {https://doi.org/10.5281/zenodo.2682458} 
+}
+
+@misc{montejo_kovacevich_2019_2682669,
+author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian},
+title = {Cambridge butterfly wing collection batch 4},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2682669},
+url = {https://doi.org/10.5281/zenodo.2682669} 
+}
+
+@misc{montejo_kovacevich_2019_2684906,
+author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva},
+title = {Cambridge butterfly wing collection batch 5},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2684906},
+url = {https://doi.org/10.5281/zenodo.2684906} 
+}
+
+@misc{warren_2019_2552371,
+author = {Warren, Ian and Jiggins, Chris},
+title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 1}},
+month = feb,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2552371},
+url = {https://doi.org/10.5281/zenodo.2552371} 
+}
+
+@misc{warren_2019_2553977,
+author = {Warren, Ian and Jiggins, Chris},
+title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 3}},
+month = feb,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2553977},
+url = {https://doi.org/10.5281/zenodo.2553977} 
+}
+
+@misc{montejo_kovacevich_2019_2686762,
+author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva},
+title = {Cambridge butterfly wing collection batch 6},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2686762},
+url = {https://doi.org/10.5281/zenodo.2686762} 
+}
+
+@misc{jiggins_2019_2549524,
+author = {Jiggins, Chris and Warren, Ian},
+title = {{Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 1}},
+month = jan,
+year = 2019,
+publisher = {Zenodo},
+version = 1,
+doi = {10.5281/zenodo.2549524},
+url = {https://doi.org/10.5281/zenodo.2549524}
+}
+
+@misc{jiggins_2019_2550097,
+author = {Jiggins, Chris and Warren, Ian},
+title = {{Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 2}},
+month = jan,
+year = 2019,
+publisher = {Zenodo},
+version = 1,
+doi = {10.5281/zenodo.2550097},
+url = {https://doi.org/10.5281/zenodo.2550097}
+}
+
+@misc{joana_i_meier_2020_4153502,
+author = {Joana I. Meier and Patricio Salazar and Gabriela Montejo-Kovacevich and Ian Warren and Chris Jggins},
+title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild specimens batch 3}},
+month = oct,
+year = 2020,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.4153502},
+url = {https://doi.org/10.5281/zenodo.4153502}
+}
+
+@misc{montejo_kovacevich_2019_3082688,
+author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian},
+title = {{Cambridge butterfly wing collection batch 1- version 2}},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.3082688},
+url = {https://doi.org/10.5281/zenodo.3082688} 
+}
+
+@misc{montejo_kovacevich_2019_2813153,
+author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Salazar, Camilo and Elias, Marianne and Gavins, Imogen and Wiltshire, Eva and Montgomery, Stephen and McMillan, Owen},
+title = {{Cambridge and collaborators butterfly wing collection batch 10}},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2813153},
+url = {https://doi.org/10.5281/zenodo.2813153} 
+}
+
+@misc{salazar_2018_1748277,
+author = {Salazar, Patricio and Montejo-Kovacevich, Gabriela and Warren, Ian and Jiggins, Chris},
+title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 1}},
+month = dec,
+year = 2018,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.1748277},
+url = {https://doi.org/10.5281/zenodo.1748277} 
+}
+
+@misc{montejo_kovacevich_2019_2702457,
+author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva},
+title = {Cambridge butterfly wing collection batch 7},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2702457},
+url = {https://doi.org/10.5281/zenodo.2702457}
+}
+
+@misc{salazar_2019_2548678,
+author = {Salazar, Patricio and Montejo-Kovacevich, Gabriela and Warren, Ian and Jiggins, Chris},
+title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 2}},
+month = jan,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2548678},
+url = {https://doi.org/10.5281/zenodo.2548678}
+}
+
+@misc{pinheiro_de_castro_2022_5561246,
+author = {Pinheiro de Castro, Erika and Jiggins, Christopher and Lucas da Silva-Brand\u00e3o, Karina and Victor Lucci Freitas, Andre and Zikan Cardoso, Marcio and Van Der Heijden, Eva and Meier, Joana and Warren, Ian},
+title = {{Brazilian Butterflies Collected December 2020 to January 2021}},
+month = feb,
+year = 2022,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.5561246},
+url = {https://doi.org/10.5281/zenodo.5561246}
+}
+
+@misc{montejo_kovacevich_2019_2707828,
+author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva},
+title = {Cambridge butterfly wing collection batch 8},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2707828},
+url = {https://doi.org/10.5281/zenodo.2707828}
+}
+
+@misc{montejo_kovacevich_2019_2714333,
+author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva and Gavins, Imogen},
+title = {Cambridge butterfly wing collection batch 9},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2714333},
+url = {https://doi.org/10.5281/zenodo.2714333} 
+}
+
+@misc{gabriela_montejo_kovacevich_2020_4291095,
+author = {Gabriela Montejo-Kovacevich and Eva van der Heijden and Chris Jiggins},
+title = {{Cambridge butterfly collection - GMK Broods Ikiam 2018}},
+month = nov,
+year = 2020,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.4291095},
+url = {https://doi.org/10.5281/zenodo.4291095}
+}
+
+@misc{gabriela_montejo_kovacevich_2019_3569598,
+author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins},
+title = {Cambridge butterfly collection - Loreto, Peru 2018},
+month = dec,
+year = 2019,
+publisher = {Zenodo},
+version = {1.0.0},
+doi = {10.5281/zenodo.3569598},
+url = {https://doi.org/10.5281/zenodo.3569598}
+}
+
+@misc{gabriela_montejo_kovacevich_2020_4287444,
+author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins},
+title = {{Cambridge butterfly collection - Loreto, Peru 2018 batch2}},
+month = nov,
+year = 2020,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.4287444},
+url = {https://doi.org/10.5281/zenodo.4287444}
+}
+
+@misc{gabriela_montejo_kovacevich_2020_4288250,
+author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins},
+title = {{Cambridge butterfly collection - Loreto, Peru 2018 batch3}},
+month = nov,
+year = 2020,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.4288250},
+url = {https://doi.org/10.5281/zenodo.4288250} 
+}
+
+@misc{montejo_kovacevich_2021_5526257,
+author = {Montejo-Kovacevich, Gabriela and Paynter, Quentin and Ghane, Amin},
+title = {{Heliconius erato cyrbia, Cook Islands (New Zealand) 2016, 2019, 2021}},
+month = sep,
+year = 2021,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.5526257},
+url = {https://doi.org/10.5281/zenodo.5526257}
+}
+
+@misc{warren_2019_2553501,
+author = {Warren, Ian and Jiggins, Chris},
+title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 2}},
+month = feb,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2553501},
+url = {https://doi.org/10.5281/zenodo.2553501} 
+}
+
+@misc{salazar_2019_2735056,
+author = {Salazar, Camilo and Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Gavins, Imogen},
+title = {{Camilo Salazar and Cambridge butterfly wing collection batch 1}},
+month = may,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2735056},
+url = {https://doi.org/10.5281/zenodo.2735056} 
+}
+
+@misc{mattila_2019_2554218,
+author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian},
+title = {{University of Helsinki butterfly wing collection - Anniina Mattila field caught specimens}},
+month = feb,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2554218},
+url = {https://doi.org/10.5281/zenodo.2554218} 
+}
+
+@misc{mattila_2019_2555086,
+author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian},
+title = {{University of Helsinki butterfly collection - Anniina Mattila bred specimens}},
+month = feb,
+year = 2019,
+publisher = {Zenodo},
+doi = {10.5281/zenodo.2555086},
+url = {https://doi.org/10.5281/zenodo.2555086} 
+}

metadata/licenses.json

@@ -1 +1,205 @@
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Zenodo. https://doi.org/10.5281/zenodo.2548678", "bibtex": "@misc{salazar_2019_2548678, author = {Salazar, Patricio and Montejo-Kovacevich, Gabriela and Warren, Ian and Jiggins, Chris}, title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 2}}, month = jan, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2548678}, url = {https://doi.org/10.5281/zenodo.2548678} }"}, {"record_number": "5561246", "url": "https://zenodo.org/record/5561246", "license": "cc-by-4.0", "citation": "Pinheiro de Castro, E., Jiggins, C., Lucas da Silva-Brand\u00e3o, K., Victor Lucci Freitas, A., Zikan Cardoso, M., Van Der Heijden, E., Meier, J., & Warren, I. (2022 , February). Brazilian Butterflies Collected December 2020 to January 2021. Zenodo. https://doi.org/10.5281/zenodo.5561246", "bibtex": "@misc{pinheiro_de_castro_2022_5561246, author = {Pinheiro de Castro, Erika and Jiggins, Christopher and Lucas da Silva-Brand\u00e3o, Karina and Victor Lucci Freitas, Andre and Zikan Cardoso, Marcio and Van Der Heijden, Eva and Meier, Joana and Warren, Ian}, title = {{Brazilian Butterflies Collected December 2020 to January 2021}}, month = feb, year = 2022, publisher = {Zenodo}, doi = {10.5281/zenodo.5561246}, url = {https://doi.org/10.5281/zenodo.5561246} }"}, {"record_number": "2707828", "url": "https://zenodo.org/record/2707828", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 8. 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Zenodo. https://doi.org/10.5281/zenodo.2714333", "bibtex": "@misc{montejo_kovacevich_2019_2714333, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva and Gavins, Imogen}, title = {Cambridge butterfly wing collection batch 9}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2714333}, url = {https://doi.org/10.5281/zenodo.2714333} }"}, {"record_number": "4291095", "url": "https://zenodo.org/record/4291095", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., van der Heijden, E., & Jiggins, C. (2020 , November). Cambridge butterfly collection - GMK Broods Ikiam 2018. Zenodo. https://doi.org/10.5281/zenodo.4291095", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4291095, author = {Gabriela Montejo-Kovacevich and Eva van der Heijden and Chris Jiggins}, title = {{Cambridge butterfly collection - GMK Broods Ikiam 2018}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4291095}, url = {https://doi.org/10.5281/zenodo.4291095} }"}, {"record_number": "5731587", "url": "https://zenodo.org/record/5731587", "license": "cc-by-4.0", "citation": "Meier, J., Barker, A., Jiggins, C., Warren, I., & Blow, R. (2021 , November). Cambridge butterfly wing collection - Ecuador, August 2019. Zenodo. https://doi.org/10.5281/zenodo.5731587", "bibtex": "@misc{joana_meier_2021_5731587, author = {Joana Meier and Annalie Barker and Chris Jiggins and Ian Warren and Rachel Blow}, title = {{Cambridge butterfly wing collection - Ecuador, August 2019}}, month = nov, year = 2021, publisher = {Zenodo}, version = {version 1}, doi = {10.5281/zenodo.5731587}, url = {https://doi.org/10.5281/zenodo.5731587} }"}, {"record_number": "3569598", "url": "https://zenodo.org/record/3569598", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2019 , December). Cambridge butterfly collection - Loreto, Peru 2018. Zenodo. https://doi.org/10.5281/zenodo.3569598", "bibtex": "@misc{gabriela_montejo_kovacevich_2019_3569598, author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins}, title = {Cambridge butterfly collection - Loreto, Peru 2018}, month = dec, year = 2019, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.3569598}, url = {https://doi.org/10.5281/zenodo.3569598} }"}, {"record_number": "4287444", "url": "https://zenodo.org/record/4287444", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2020 , November). Cambridge butterfly collection - Loreto, Peru 2018 batch2. Zenodo. https://doi.org/10.5281/zenodo.4287444", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4287444, author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins}, title = {{Cambridge butterfly collection - Loreto, Peru 2018 batch2}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4287444}, url = {https://doi.org/10.5281/zenodo.4287444} }"}, {"record_number": "4288250", "url": "https://zenodo.org/record/4288250", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2020 , November). Cambridge butterfly collection - Loreto, Peru 2018 batch3. Zenodo. https://doi.org/10.5281/zenodo.4288250", "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4288250, author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins}, title = {{Cambridge butterfly collection - Loreto, Peru 2018 batch3}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4288250}, url = {https://doi.org/10.5281/zenodo.4288250} }"}, {"record_number": "5526257", "url": "https://zenodo.org/record/5526257", "license": "cc-by-4.0", "citation": "Montejo-Kovacevich, G., Paynter, Q., & Ghane, A. (2021 , September). Heliconius erato cyrbia, Cook Islands (New Zealand) 2016, 2019, 2021. 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Zenodo. https://doi.org/10.5281/zenodo.2735056", "bibtex": "@misc{salazar_2019_2735056, author = {Salazar, Camilo and Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Gavins, Imogen}, title = {{Camilo Salazar and Cambridge butterfly wing collection batch 1}}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2735056}, url = {https://doi.org/10.5281/zenodo.2735056} }"}, {"record_number": "2554218", "url": "https://zenodo.org/record/2554218", "license": "cc-by-4.0", "citation": "Mattila, A., Jiggins, C., & Warren, I. (2019 , February). University of Helsinki butterfly wing collection - Anniina Mattila field caught specimens. Zenodo. https://doi.org/10.5281/zenodo.2554218", "bibtex": "@misc{mattila_2019_2554218, author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian}, title = {{University of Helsinki butterfly wing collection - Anniina Mattila field caught specimens}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2554218}, url = {https://doi.org/10.5281/zenodo.2554218} }"}, {"record_number": "2555086", "url": "https://zenodo.org/record/2555086", "license": "cc-by-4.0", "citation": "Mattila, A., Jiggins, C., & Warren, I. (2019 , February). University of Helsinki butterfly collection - Anniina Mattila bred specimens. Zenodo. https://doi.org/10.5281/zenodo.2555086", "bibtex": "@misc{mattila_2019_2555086, author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian}, title = {{University of Helsinki butterfly collection - Anniina Mattila bred specimens}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2555086}, url = {https://doi.org/10.5281/zenodo.2555086} }"}]
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+        "record_number": "4289223",
+        "url": "https://zenodo.org/record/4289223",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., van der Heijden, E., Nadeau, N., & Jiggins, C. (2020 , November). Cambridge butterfly wing collection batch 10. Zenodo. https://doi.org/10.5281/zenodo.4289223",
+        "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4289223, author = {Gabriela Montejo-Kovacevich and Eva van der Heijden and Nicola Nadeau and Chris Jiggins}, title = {Cambridge butterfly wing collection batch 10}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4289223}, url = {https://doi.org/10.5281/zenodo.4289223} }"
+    },
+    {
+        "record_number": "4288311",
+        "url": "https://zenodo.org/record/4288311",
+        "license": "cc-by-4.0",
+        "citation": "Salazar, P. A., Nadeau, N., Montejo-Kovacevich, G., & Jiggins, C. (2020 , November). Sheffield butterfly wing collection - Patricio Salazar, Nicola Nadeau, Ikiam broods batch 1 and 2. Zenodo. https://doi.org/10.5281/zenodo.4288311",
+        "bibtex": "@misc{patricio_a_salazar_2020_4288311, author = {Patricio A. Salazar and Nicola Nadeau and Gabriela Montejo-Kovacevich and Chris Jiggins}, title = {{Sheffield butterfly wing collection - Patricio Salazar, Nicola Nadeau, Ikiam broods batch 1 and 2}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4288311}, url = {https://doi.org/10.5281/zenodo.4288311} }"
+    },
+    {
+        "record_number": "2677821",
+        "url": "https://zenodo.org/record/2677821",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Jiggins, C., & Warren, I. (2019 , May). Cambridge butterfly wing collection batch 2. Zenodo. https://doi.org/10.5281/zenodo.2677821",
+        "bibtex": "@misc{montejo_kovacevich_2019_2677821, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian}, title = {Cambridge butterfly wing collection batch 2}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2677821}, url = {https://doi.org/10.5281/zenodo.2677821} }"
+    },
+    {
+        "record_number": "2682458",
+        "url": "https://zenodo.org/record/2682458",
+        "license": "cc-by-4.0",
+        "citation": "Jiggins, C., Montejo-Kovacevich, G., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 3. Zenodo. https://doi.org/10.5281/zenodo.2682458",
+        "bibtex": "@misc{jiggins_2019_2682458, author = {Jiggins, Chris and Montejo-Kovacevich, Gabriela and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 3}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2682458}, url = {https://doi.org/10.5281/zenodo.2682458} }"
+    },
+    {
+        "record_number": "2682669",
+        "url": "https://zenodo.org/record/2682669",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Jiggins, C., & Warren, I. (2019 , May). Cambridge butterfly wing collection batch 4. Zenodo. https://doi.org/10.5281/zenodo.2682669",
+        "bibtex": "@misc{montejo_kovacevich_2019_2682669, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian}, title = {Cambridge butterfly wing collection batch 4}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2682669}, url = {https://doi.org/10.5281/zenodo.2682669} }"
+    },
+    {
+        "record_number": "2684906",
+        "url": "https://zenodo.org/record/2684906",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 5. Zenodo. https://doi.org/10.5281/zenodo.2684906",
+        "bibtex": "@misc{montejo_kovacevich_2019_2684906, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 5}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2684906}, url = {https://doi.org/10.5281/zenodo.2684906} }"
+    },
+    {
+        "record_number": "2552371",
+        "url": "https://zenodo.org/record/2552371",
+        "license": "cc-by-4.0",
+        "citation": "Warren, I. & Jiggins, C. (2019 , February). Miscellaneous Heliconius wing photographs (2001-2019) Part 1. Zenodo. https://doi.org/10.5281/zenodo.2552371",
+        "bibtex": "@misc{warren_2019_2552371, author = {Warren, Ian and Jiggins, Chris}, title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 1}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2552371}, url = {https://doi.org/10.5281/zenodo.2552371} }"
+    },
+    {
+        "record_number": "2553977",
+        "url": "https://zenodo.org/record/2553977",
+        "license": "cc-by-4.0",
+        "citation": "Warren, I. & Jiggins, C. (2019 , February). Miscellaneous Heliconius wing photographs (2001-2019) Part 3. Zenodo. https://doi.org/10.5281/zenodo.2553977",
+        "bibtex": "@misc{warren_2019_2553977, author = {Warren, Ian and Jiggins, Chris}, title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 3}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2553977}, url = {https://doi.org/10.5281/zenodo.2553977} }"
+    },
+    {
+        "record_number": "2686762",
+        "url": "https://zenodo.org/record/2686762",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 6. Zenodo. https://doi.org/10.5281/zenodo.2686762",
+        "bibtex": "@misc{montejo_kovacevich_2019_2686762, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 6}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2686762}, url = {https://doi.org/10.5281/zenodo.2686762} }"
+    },
+    {
+        "record_number": "2549524",
+        "url": "https://zenodo.org/record/2549524",
+        "license": "cc-by-4.0",
+        "citation": "Jiggins, C. & Warren, I. (2019 , January). Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 1. Zenodo. https://doi.org/10.5281/zenodo.2549524",
+        "bibtex": "@misc{jiggins_2019_2549524, author = {Jiggins, Chris and Warren, Ian}, title = {{Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 1}}, month = jan, year = 2019, publisher = {Zenodo}, version = 1, doi = {10.5281/zenodo.2549524}, url = {https://doi.org/10.5281/zenodo.2549524} }"
+    },
+    {
+        "record_number": "2550097",
+        "url": "https://zenodo.org/record/2550097",
+        "license": "cc-by-4.0",
+        "citation": "Jiggins, C. & Warren, I. (2019 , January). Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 2. Zenodo. https://doi.org/10.5281/zenodo.2550097",
+        "bibtex": "@misc{jiggins_2019_2550097, author = {Jiggins, Chris and Warren, Ian}, title = {{Cambridge butterfly wing collection - Chris Jiggins 2001/2 broods batch 2}}, month = jan, year = 2019, publisher = {Zenodo}, version = 1, doi = {10.5281/zenodo.2550097}, url = {https://doi.org/10.5281/zenodo.2550097} }"
+    },
+    {
+        "record_number": "4153502",
+        "url": "https://zenodo.org/record/4153502",
+        "license": "cc-by-4.0",
+        "citation": "Meier, J. I., Salazar, P., Montejo-Kovacevich, G., Warren, I., & Jggins, C. (2020 , October). Cambridge butterfly wing collection - Patricio Salazar PhD wild specimens batch 3. Zenodo. https://doi.org/10.5281/zenodo.4153502",
+        "bibtex": "@misc{joana_i_meier_2020_4153502, author = {Joana I. Meier and Patricio Salazar and Gabriela Montejo-Kovacevich and Ian Warren and Chris Jggins}, title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild specimens batch 3}}, month = oct, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4153502}, url = {https://doi.org/10.5281/zenodo.4153502} }"
+    },
+    {
+        "record_number": "3082688",
+        "url": "https://zenodo.org/record/3082688",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Jiggins, C., & Warren, I. (2019 , May). Cambridge butterfly wing collection batch 1- version 2. Zenodo. https://doi.org/10.5281/zenodo.3082688",
+        "bibtex": "@misc{montejo_kovacevich_2019_3082688, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian}, title = {{Cambridge butterfly wing collection batch 1- version 2}}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.3082688}, url = {https://doi.org/10.5281/zenodo.3082688} }"
+    },
+    {
+        "record_number": "2813153",
+        "url": "https://zenodo.org/record/2813153",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., Salazar, C., Elias, M., Gavins, I., Wiltshire, E., Montgomery, S., & McMillan, O. (2019 , May). Cambridge and collaborators butterfly wing collection batch 10. Zenodo. https://doi.org/10.5281/zenodo.2813153",
+        "bibtex": "@misc{montejo_kovacevich_2019_2813153, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Salazar, Camilo and Elias, Marianne and Gavins, Imogen and Wiltshire, Eva and Montgomery, Stephen and McMillan, Owen}, title = {{Cambridge and collaborators butterfly wing collection batch 10}}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2813153}, url = {https://doi.org/10.5281/zenodo.2813153} }"
+    },
+    {
+        "record_number": "1748277",
+        "url": "https://zenodo.org/record/1748277",
+        "license": "cc-by-4.0",
+        "citation": "Salazar, P., Montejo-Kovacevich, G., Warren, I., & Jiggins, C. (2018 , December). Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 1. Zenodo. https://doi.org/10.5281/zenodo.1748277",
+        "bibtex": "@misc{salazar_2018_1748277, author = {Salazar, Patricio and Montejo-Kovacevich, Gabriela and Warren, Ian and Jiggins, Chris}, title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 1}}, month = dec, year = 2018, publisher = {Zenodo}, doi = {10.5281/zenodo.1748277}, url = {https://doi.org/10.5281/zenodo.1748277} }"
+    },
+    {
+        "record_number": "2702457",
+        "url": "https://zenodo.org/record/2702457",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 7. Zenodo. https://doi.org/10.5281/zenodo.2702457",
+        "bibtex": "@misc{montejo_kovacevich_2019_2702457, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 7}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2702457}, url = {https://doi.org/10.5281/zenodo.2702457} }"
+    },
+    {
+        "record_number": "2548678",
+        "url": "https://zenodo.org/record/2548678",
+        "license": "cc-by-4.0",
+        "citation": "Salazar, P., Montejo-Kovacevich, G., Warren, I., & Jiggins, C. (2019 , January). Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 2. Zenodo. https://doi.org/10.5281/zenodo.2548678",
+        "bibtex": "@misc{salazar_2019_2548678, author = {Salazar, Patricio and Montejo-Kovacevich, Gabriela and Warren, Ian and Jiggins, Chris}, title = {{Cambridge butterfly wing collection - Patricio Salazar PhD wild and bred specimens batch 2}}, month = jan, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2548678}, url = {https://doi.org/10.5281/zenodo.2548678} }"
+    },
+    {
+        "record_number": "5561246",
+        "url": "https://zenodo.org/record/5561246",
+        "license": "cc-by-4.0",
+        "citation": "Pinheiro de Castro, E., Jiggins, C., Lucas da Silva-Brand\u00e3o, K., Victor Lucci Freitas, A., Zikan Cardoso, M., Van Der Heijden, E., Meier, J., & Warren, I. (2022 , February). Brazilian Butterflies Collected December 2020 to January 2021. Zenodo. https://doi.org/10.5281/zenodo.5561246",
+        "bibtex": "@misc{pinheiro_de_castro_2022_5561246, author = {Pinheiro de Castro, Erika and Jiggins, Christopher and Lucas da Silva-Brand\u00e3o, Karina and Victor Lucci Freitas, Andre and Zikan Cardoso, Marcio and Van Der Heijden, Eva and Meier, Joana and Warren, Ian}, title = {{Brazilian Butterflies Collected December 2020 to January 2021}}, month = feb, year = 2022, publisher = {Zenodo}, doi = {10.5281/zenodo.5561246}, url = {https://doi.org/10.5281/zenodo.5561246} }"
+    },
+    {
+        "record_number": "2707828",
+        "url": "https://zenodo.org/record/2707828",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Wiltshire, E. (2019 , May). Cambridge butterfly wing collection batch 8. Zenodo. https://doi.org/10.5281/zenodo.2707828",
+        "bibtex": "@misc{montejo_kovacevich_2019_2707828, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva}, title = {Cambridge butterfly wing collection batch 8}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2707828}, url = {https://doi.org/10.5281/zenodo.2707828} }"
+    },
+    {
+        "record_number": "2714333",
+        "url": "https://zenodo.org/record/2714333",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Jiggins, C., Warren, I., Wiltshire, E., & Gavins, I. (2019 , May). Cambridge butterfly wing collection batch 9. Zenodo. https://doi.org/10.5281/zenodo.2714333",
+        "bibtex": "@misc{montejo_kovacevich_2019_2714333, author = {Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Wiltshire, Eva and Gavins, Imogen}, title = {Cambridge butterfly wing collection batch 9}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2714333}, url = {https://doi.org/10.5281/zenodo.2714333} }"
+    },
+    {
+        "record_number": "4291095",
+        "url": "https://zenodo.org/record/4291095",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., van der Heijden, E., & Jiggins, C. (2020 , November). Cambridge butterfly collection - GMK Broods Ikiam 2018. Zenodo. https://doi.org/10.5281/zenodo.4291095",
+        "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4291095, author = {Gabriela Montejo-Kovacevich and Eva van der Heijden and Chris Jiggins}, title = {{Cambridge butterfly collection - GMK Broods Ikiam 2018}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4291095}, url = {https://doi.org/10.5281/zenodo.4291095} }"
+    },
+    {
+        "record_number": "3569598",
+        "url": "https://zenodo.org/record/3569598",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2019 , December). Cambridge butterfly collection - Loreto, Peru 2018. Zenodo. https://doi.org/10.5281/zenodo.3569598",
+        "bibtex": "@misc{gabriela_montejo_kovacevich_2019_3569598, author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins}, title = {Cambridge butterfly collection - Loreto, Peru 2018}, month = dec, year = 2019, publisher = {Zenodo}, version = {1.0.0}, doi = {10.5281/zenodo.3569598}, url = {https://doi.org/10.5281/zenodo.3569598} }"
+    },
+    {
+        "record_number": "4287444",
+        "url": "https://zenodo.org/record/4287444",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2020 , November). Cambridge butterfly collection - Loreto, Peru 2018 batch2. Zenodo. https://doi.org/10.5281/zenodo.4287444",
+        "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4287444, author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins}, title = {{Cambridge butterfly collection - Loreto, Peru 2018 batch2}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4287444}, url = {https://doi.org/10.5281/zenodo.4287444} }"
+    },
+    {
+        "record_number": "4288250",
+        "url": "https://zenodo.org/record/4288250",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Cookson, L., van der Heijden, E., Warren, I., Edwards, D. P., & Jiggins, C. (2020 , November). Cambridge butterfly collection - Loreto, Peru 2018 batch3. Zenodo. https://doi.org/10.5281/zenodo.4288250",
+        "bibtex": "@misc{gabriela_montejo_kovacevich_2020_4288250, author = {Gabriela Montejo-Kovacevich and Letitia Cookson and Eva van der Heijden and Ian Warren and David P. Edwards and Chris Jiggins}, title = {{Cambridge butterfly collection - Loreto, Peru 2018 batch3}}, month = nov, year = 2020, publisher = {Zenodo}, doi = {10.5281/zenodo.4288250}, url = {https://doi.org/10.5281/zenodo.4288250} }"
+    },
+    {
+        "record_number": "5526257",
+        "url": "https://zenodo.org/record/5526257",
+        "license": "cc-by-4.0",
+        "citation": "Montejo-Kovacevich, G., Paynter, Q., & Ghane, A. (2021 , September). Heliconius erato cyrbia, Cook Islands (New Zealand) 2016, 2019, 2021. Zenodo. https://doi.org/10.5281/zenodo.5526257",
+        "bibtex": "@misc{montejo_kovacevich_2021_5526257, author = {Montejo-Kovacevich, Gabriela and Paynter, Quentin and Ghane, Amin}, title = {{Heliconius erato cyrbia, Cook Islands (New Zealand) 2016, 2019, 2021}}, month = sep, year = 2021, publisher = {Zenodo}, doi = {10.5281/zenodo.5526257}, url = {https://doi.org/10.5281/zenodo.5526257} }"
+    },
+    {
+        "record_number": "2553501",
+        "url": "https://zenodo.org/record/2553501",
+        "license": "cc-by-4.0",
+        "citation": "Warren, I. & Jiggins, C. (2019 , February). Miscellaneous Heliconius wing photographs (2001-2019) Part 2. Zenodo. https://doi.org/10.5281/zenodo.2553501",
+        "bibtex": "@misc{warren_2019_2553501, author = {Warren, Ian and Jiggins, Chris}, title = {{Miscellaneous Heliconius wing photographs (2001-2019) Part 2}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2553501}, url = {https://doi.org/10.5281/zenodo.2553501} }"
+    },
+    {
+        "record_number": "2735056",
+        "url": "https://zenodo.org/record/2735056",
+        "license": "cc-by-4.0",
+        "citation": "Salazar, C., Montejo-Kovacevich, G., Jiggins, C., Warren, I., & Gavins, I. (2019 , May). Camilo Salazar and Cambridge butterfly wing collection batch 1. Zenodo. https://doi.org/10.5281/zenodo.2735056",
+        "bibtex": "@misc{salazar_2019_2735056, author = {Salazar, Camilo and Montejo-Kovacevich, Gabriela and Jiggins, Chris and Warren, Ian and Gavins, Imogen}, title = {{Camilo Salazar and Cambridge butterfly wing collection batch 1}}, month = may, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2735056}, url = {https://doi.org/10.5281/zenodo.2735056} }"
+    },
+    {
+        "record_number": "2554218",
+        "url": "https://zenodo.org/record/2554218",
+        "license": "cc-by-4.0",
+        "citation": "Mattila, A., Jiggins, C., & Warren, I. (2019 , February). University of Helsinki butterfly wing collection - Anniina Mattila field caught specimens. Zenodo. https://doi.org/10.5281/zenodo.2554218",
+        "bibtex": "@misc{mattila_2019_2554218, author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian}, title = {{University of Helsinki butterfly wing collection - Anniina Mattila field caught specimens}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2554218}, url = {https://doi.org/10.5281/zenodo.2554218} }"
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+    {
+        "record_number": "2555086",
+        "url": "https://zenodo.org/record/2555086",
+        "license": "cc-by-4.0",
+        "citation": "Mattila, A., Jiggins, C., & Warren, I. (2019 , February). University of Helsinki butterfly collection - Anniina Mattila bred specimens. Zenodo. https://doi.org/10.5281/zenodo.2555086",
+        "bibtex": "@misc{mattila_2019_2555086, author = {Mattila, Anniina and Jiggins, Chris and Warren, Ian}, title = {{University of Helsinki butterfly collection - Anniina Mattila bred specimens}}, month = feb, year = 2019, publisher = {Zenodo}, doi = {10.5281/zenodo.2555086}, url = {https://doi.org/10.5281/zenodo.2555086} }"
+    }
+]

notebooks/{make_Jiggins_Img_Master.ipynb → Data-gen-0-1.ipynb}

@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "jiggins = pd.read_csv(\"../Jiggins_Zenodo_Master.csv\", low_memory = False)"
+    "jiggins = pd.read_csv(\"../metadata/Jiggins_Zenodo_Master.csv\", low_memory = False)"
    ]
   },
   {

notebooks/Data-gen-1-1.ipynb

@@ -0,0 +1,1704 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 5683 entries, 0 to 5682\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column      Non-Null Count  Dtype  \n",
+      "---  ------      --------------  -----  \n",
+      " 0   coreid      5683 non-null   object \n",
+      " 1   type        0 non-null      float64\n",
+      " 2   identifier  5683 non-null   object \n",
+      " 3   license     0 non-null      float64\n",
+      "dtypes: float64(2), object(2)\n",
+      "memory usage: 177.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "multimedia = pd.read_csv(\"../metadata/deduplication/Zenodo_meta_files/multimedia__(rec_3477891).csv\", low_memory=False)\n",
+    "multimedia.info(show_counts=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>coreid</th>\n",
+       "      <th>type</th>\n",
+       "      <th>identifier</th>\n",
+       "      <th>license</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>275ad2e7-bc7e-4e74-832e-869825f5bf0b</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2684906/files/CAM008...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cf02ac3a-6204-417c-b342-6f84eab48931</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2714333/files/CAM041...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7be80267-dbe9-4f4b-8f73-c7355447d5e1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2686762/files/CAM008...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>b97011cb-c4fd-4ea9-8828-dc920c7b900a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2684906/files/CAM008...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6375bf74-3333-4cb6-a0dc-f95c3794edae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2714333/files/CAM040...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 coreid  type  \\\n",
+       "0  275ad2e7-bc7e-4e74-832e-869825f5bf0b   NaN   \n",
+       "1  cf02ac3a-6204-417c-b342-6f84eab48931   NaN   \n",
+       "2  7be80267-dbe9-4f4b-8f73-c7355447d5e1   NaN   \n",
+       "3  b97011cb-c4fd-4ea9-8828-dc920c7b900a   NaN   \n",
+       "4  6375bf74-3333-4cb6-a0dc-f95c3794edae   NaN   \n",
+       "\n",
+       "                                          identifier  license  \n",
+       "0  https://zenodo.org/record/2684906/files/CAM008...      NaN  \n",
+       "1  https://zenodo.org/record/2714333/files/CAM041...      NaN  \n",
+       "2  https://zenodo.org/record/2686762/files/CAM008...      NaN  \n",
+       "3  https://zenodo.org/record/2684906/files/CAM008...      NaN  \n",
+       "4  https://zenodo.org/record/2714333/files/CAM040...      NaN  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multimedia.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://zenodo.org/record/2684906/files/CAM008538_d.JPG'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multimedia.identifier[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's get a filename and record number recorded. Would like to add a `zenodo_link` column to see how that matches up to the master file as well. David said these were mostly resolution for records from [3477891](https://zenodo.org/records/3477891) (where these files are from) at download.\n",
+    "\n",
+    "`identifier` is non-null for all entries, but there is one non-Zenodo link."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>coreid</th>\n",
+       "      <th>type</th>\n",
+       "      <th>identifier</th>\n",
+       "      <th>license</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>record_number</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>275ad2e7-bc7e-4e74-832e-869825f5bf0b</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2684906/files/CAM008...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2684906</td>\n",
+       "      <td>CAM008538_d.JPG</td>\n",
+       "      <td>2684906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cf02ac3a-6204-417c-b342-6f84eab48931</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2714333/files/CAM041...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2714333</td>\n",
+       "      <td>CAM041048_v.JPG</td>\n",
+       "      <td>2714333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7be80267-dbe9-4f4b-8f73-c7355447d5e1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2686762/files/CAM008...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2686762</td>\n",
+       "      <td>CAM008842_d.JPG</td>\n",
+       "      <td>2686762</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>b97011cb-c4fd-4ea9-8828-dc920c7b900a</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2684906/files/CAM008...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2684906</td>\n",
+       "      <td>CAM008539_v.JPG</td>\n",
+       "      <td>2684906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6375bf74-3333-4cb6-a0dc-f95c3794edae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2714333/files/CAM040...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2714333</td>\n",
+       "      <td>CAM040771_v.JPG</td>\n",
+       "      <td>2714333</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 coreid  type  \\\n",
+       "0  275ad2e7-bc7e-4e74-832e-869825f5bf0b   NaN   \n",
+       "1  cf02ac3a-6204-417c-b342-6f84eab48931   NaN   \n",
+       "2  7be80267-dbe9-4f4b-8f73-c7355447d5e1   NaN   \n",
+       "3  b97011cb-c4fd-4ea9-8828-dc920c7b900a   NaN   \n",
+       "4  6375bf74-3333-4cb6-a0dc-f95c3794edae   NaN   \n",
+       "\n",
+       "                                          identifier  license  \\\n",
+       "0  https://zenodo.org/record/2684906/files/CAM008...      NaN   \n",
+       "1  https://zenodo.org/record/2714333/files/CAM041...      NaN   \n",
+       "2  https://zenodo.org/record/2686762/files/CAM008...      NaN   \n",
+       "3  https://zenodo.org/record/2684906/files/CAM008...      NaN   \n",
+       "4  https://zenodo.org/record/2714333/files/CAM040...      NaN   \n",
+       "\n",
+       "                         zenodo_link       Image_name record_number  \n",
+       "0  https://zenodo.org/record/2684906  CAM008538_d.JPG       2684906  \n",
+       "1  https://zenodo.org/record/2714333  CAM041048_v.JPG       2714333  \n",
+       "2  https://zenodo.org/record/2686762  CAM008842_d.JPG       2686762  \n",
+       "3  https://zenodo.org/record/2684906  CAM008539_v.JPG       2684906  \n",
+       "4  https://zenodo.org/record/2714333  CAM040771_v.JPG       2714333  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def get_link_filename(identifier):\n",
+    "    if \"zenodo\" not in identifier:\n",
+    "        link_list = identifier.split(\"com/\")\n",
+    "        link_list[0] = np.nan\n",
+    "    else:\n",
+    "        link_list = identifier.split(\"/files/\")\n",
+    "    # link is first part, filename at end\n",
+    "    return pd.Series(link_list)\n",
+    "\n",
+    "def get_record_number(zenodo_link):\n",
+    "    if type(zenodo_link) != float:\n",
+    "        link = zenodo_link.split(\"record/\")\n",
+    "        return link[1]\n",
+    "\n",
+    "multimedia[[\"zenodo_link\", \"Image_name\"]] = multimedia[\"identifier\"].apply(get_link_filename)\n",
+    "multimedia[\"record_number\"] = multimedia[\"zenodo_link\"].apply(get_record_number)\n",
+    "multimedia.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "http://earthcape-heliconius.s3-eu-west-1.amazonaws.com/F1FD2804C9E643798A7C1B0D9FBDE4AB.JPG\n"
+     ]
+    }
+   ],
+   "source": [
+    "for link in list(multimedia.identifier):\n",
+    "    if \"zenodo\" not in link:\n",
+    "        print(link)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So there is one image that does not have a Zenodo link."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 5683 entries, 0 to 5682\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column         Non-Null Count  Dtype  \n",
+      "---  ------         --------------  -----  \n",
+      " 0   coreid         5683 non-null   object \n",
+      " 1   type           0 non-null      float64\n",
+      " 2   identifier     5683 non-null   object \n",
+      " 3   license        0 non-null      float64\n",
+      " 4   zenodo_link    5682 non-null   object \n",
+      " 5   Image_name     5683 non-null   object \n",
+      " 6   record_number  5682 non-null   object \n",
+      "dtypes: float64(2), object(5)\n",
+      "memory usage: 310.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "multimedia.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "coreid           2794\n",
+       "type                0\n",
+       "identifier       5683\n",
+       "license             0\n",
+       "zenodo_link        12\n",
+       "Image_name       5683\n",
+       "record_number      12\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multimedia.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `coreid` is repeated, but the `Image_name` is unique across entries, so this could (hopefully) connect us to the source images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "record_number\n",
+       "2707828    1276\n",
+       "2714333    1113\n",
+       "2686762     986\n",
+       "2684906     863\n",
+       "2677821     703\n",
+       "2702457     276\n",
+       "2682458     158\n",
+       "2682669     124\n",
+       "2552371      91\n",
+       "2550097      50\n",
+       "2553977      22\n",
+       "2813153      20\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multimedia.record_number.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Interesting, it would seem that record 3477891 is a collection of these 12 other records. It matches with this [GBIF Collection](https://www.gbif.org/dataset/34f8683a-dfc0-46b8-acf6-390fe5ca6b92) that is the \"collection records from the research group of Chris Jiggins at the University of Cambridge derived from almost 20 years of field studies. Many records include images as well as locality data.\" released in October 2019."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4372 entries, 0 to 4371\n",
+      "Data columns (total 29 columns):\n",
+      " #   Column                         Non-Null Count  Dtype  \n",
+      "---  ------                         --------------  -----  \n",
+      " 0   id                             4372 non-null   object \n",
+      " 1   occurrenceID                   4372 non-null   object \n",
+      " 2   catalogNumber                  4372 non-null   object \n",
+      " 3   datasetName                    4372 non-null   object \n",
+      " 4   recordNumber                   0 non-null      float64\n",
+      " 5   otherCatalogNumbers            33 non-null     object \n",
+      " 6   basisOfRecord                  4372 non-null   object \n",
+      " 7   eventDate                      3806 non-null   object \n",
+      " 8   locality                       4372 non-null   object \n",
+      " 9   country                        4372 non-null   object \n",
+      " 10  decimalLatitude                4372 non-null   float64\n",
+      " 11  decimalLongitude               4372 non-null   float64\n",
+      " 12  geodeticDatum                  4372 non-null   int64  \n",
+      " 13  year                           3806 non-null   float64\n",
+      " 14  sex                            3882 non-null   object \n",
+      " 15  lifeStage                      39 non-null     object \n",
+      " 16  recordedBy                     0 non-null      float64\n",
+      " 17  individualCount                4372 non-null   int64  \n",
+      " 18  taxonId                        1105 non-null   float64\n",
+      " 19  scientificName                 4360 non-null   object \n",
+      " 20  scientificNameAuthorship       0 non-null      float64\n",
+      " 21  taxonRank                      4358 non-null   object \n",
+      " 22  genus                          4358 non-null   object \n",
+      " 23  family                         4086 non-null   object \n",
+      " 24  order                          4086 non-null   object \n",
+      " 25  class                          4086 non-null   object \n",
+      " 26  kingdom                        4086 non-null   object \n",
+      " 27  coordinateUncertaintyInMeters  0 non-null      float64\n",
+      " 28  dynamicProperties              4372 non-null   object \n",
+      "dtypes: float64(8), int64(2), object(19)\n",
+      "memory usage: 990.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "occurrence = pd.read_csv(\"../metadata/deduplication/Zenodo_meta_files/occurrences__(rec_3477891).csv\",low_memory=False)\n",
+    "occurrence.info(show_counts=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id               4372\n",
+       "occurrenceID     4372\n",
+       "catalogNumber    4372\n",
+       "datasetName         1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "occurrence[(occurrence.columns)[:4]].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Are `id` and `occurrenceID` all equal?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4372, 29)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "occurrence.loc[occurrence[\"id\"] == occurrence[\"occurrenceID\"]].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This has a record number column, but there are no non-null values, so we'll try to fill that in. Except there is nothing to use to fill it in...we have to connect on `catalogNumber` to the `id` to the `coreid`, but `catalogNumber` is just the CAMID and we have more unique IDs than there are in the multimedia file..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>occurrenceID</th>\n",
+       "      <th>catalogNumber</th>\n",
+       "      <th>datasetName</th>\n",
+       "      <th>recordNumber</th>\n",
+       "      <th>otherCatalogNumbers</th>\n",
+       "      <th>basisOfRecord</th>\n",
+       "      <th>eventDate</th>\n",
+       "      <th>locality</th>\n",
+       "      <th>country</th>\n",
+       "      <th>...</th>\n",
+       "      <th>scientificName</th>\n",
+       "      <th>scientificNameAuthorship</th>\n",
+       "      <th>taxonRank</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>family</th>\n",
+       "      <th>order</th>\n",
+       "      <th>class</th>\n",
+       "      <th>kingdom</th>\n",
+       "      <th>coordinateUncertaintyInMeters</th>\n",
+       "      <th>dynamicProperties</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>00075b7f-3920-4987-a3e4-e98568a38558</td>\n",
+       "      <td>00075b7f-3920-4987-a3e4-e98568a38558</td>\n",
+       "      <td>CAM040599</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PreservedSpecimen</td>\n",
+       "      <td>2017-03-07</td>\n",
+       "      <td>Mashpi to Pachijal 2</td>\n",
+       "      <td>Ecuador</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Heliconius cydno ssp. alithea</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Subspecies</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>Nymphalidae</td>\n",
+       "      <td>Lepidoptera</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>000dc8ca-5d60-4aff-9823-33d20d52c7cd</td>\n",
+       "      <td>000dc8ca-5d60-4aff-9823-33d20d52c7cd</td>\n",
+       "      <td>CAM040277</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PreservedSpecimen</td>\n",
+       "      <td>2017-01-30</td>\n",
+       "      <td>Km 119 Baeza - Lago Agrio</td>\n",
+       "      <td>Ecuador</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Actinote sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Species</td>\n",
+       "      <td>Actinote</td>\n",
+       "      <td>Nymphalidae</td>\n",
+       "      <td>Lepidoptera</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>001b4619-bfe3-4a89-9709-45a70c1fa380</td>\n",
+       "      <td>001b4619-bfe3-4a89-9709-45a70c1fa380</td>\n",
+       "      <td>CAM120368</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PreservedSpecimen</td>\n",
+       "      <td>2005-11-15</td>\n",
+       "      <td>Anangu Boca del Rio ECD OR</td>\n",
+       "      <td>Ecuador</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Pseudoscada timna ssp. utilla</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Subspecies</td>\n",
+       "      <td>Pseudoscada</td>\n",
+       "      <td>Nymphalidae</td>\n",
+       "      <td>Lepidoptera</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0021e86f-64b3-4ce8-b872-f783f00f5f6a</td>\n",
+       "      <td>0021e86f-64b3-4ce8-b872-f783f00f5f6a</td>\n",
+       "      <td>CAM014638</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PreservedSpecimen</td>\n",
+       "      <td>2009-11-23</td>\n",
+       "      <td>Puerta Lara</td>\n",
+       "      <td>Panamá</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Heliconius melpomene ssp. melpomene</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Subspecies</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>Nymphalidae</td>\n",
+       "      <td>Lepidoptera</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{}</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0034a857-9ed6-45be-b437-8e20eef541bb</td>\n",
+       "      <td>0034a857-9ed6-45be-b437-8e20eef541bb</td>\n",
+       "      <td>CAM008071</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PreservedSpecimen</td>\n",
+       "      <td>2000-12-17</td>\n",
+       "      <td>Gamboa #183</td>\n",
+       "      <td>Panamá</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Anthanassa drusilla</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Species</td>\n",
+       "      <td>Anthanassa</td>\n",
+       "      <td>Nymphalidae</td>\n",
+       "      <td>Lepidoptera</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{}</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     id                          occurrenceID  \\\n",
+       "0  00075b7f-3920-4987-a3e4-e98568a38558  00075b7f-3920-4987-a3e4-e98568a38558   \n",
+       "1  000dc8ca-5d60-4aff-9823-33d20d52c7cd  000dc8ca-5d60-4aff-9823-33d20d52c7cd   \n",
+       "2  001b4619-bfe3-4a89-9709-45a70c1fa380  001b4619-bfe3-4a89-9709-45a70c1fa380   \n",
+       "3  0021e86f-64b3-4ce8-b872-f783f00f5f6a  0021e86f-64b3-4ce8-b872-f783f00f5f6a   \n",
+       "4  0034a857-9ed6-45be-b437-8e20eef541bb  0034a857-9ed6-45be-b437-8e20eef541bb   \n",
+       "\n",
+       "  catalogNumber                                        datasetName  \\\n",
+       "0     CAM040599  Heliconiine Butterfly Collection Records from ...   \n",
+       "1     CAM040277  Heliconiine Butterfly Collection Records from ...   \n",
+       "2     CAM120368  Heliconiine Butterfly Collection Records from ...   \n",
+       "3     CAM014638  Heliconiine Butterfly Collection Records from ...   \n",
+       "4     CAM008071  Heliconiine Butterfly Collection Records from ...   \n",
+       "\n",
+       "   recordNumber otherCatalogNumbers      basisOfRecord   eventDate  \\\n",
+       "0           NaN                 NaN  PreservedSpecimen  2017-03-07   \n",
+       "1           NaN                 NaN  PreservedSpecimen  2017-01-30   \n",
+       "2           NaN                 NaN  PreservedSpecimen  2005-11-15   \n",
+       "3           NaN                 NaN  PreservedSpecimen  2009-11-23   \n",
+       "4           NaN                 NaN  PreservedSpecimen  2000-12-17   \n",
+       "\n",
+       "                     locality  country  ...  \\\n",
+       "0        Mashpi to Pachijal 2  Ecuador  ...   \n",
+       "1   Km 119 Baeza - Lago Agrio  Ecuador  ...   \n",
+       "2  Anangu Boca del Rio ECD OR  Ecuador  ...   \n",
+       "3                 Puerta Lara   Panamá  ...   \n",
+       "4                 Gamboa #183   Panamá  ...   \n",
+       "\n",
+       "                        scientificName  scientificNameAuthorship   taxonRank  \\\n",
+       "0        Heliconius cydno ssp. alithea                       NaN  Subspecies   \n",
+       "1                         Actinote sp.                       NaN     Species   \n",
+       "2        Pseudoscada timna ssp. utilla                       NaN  Subspecies   \n",
+       "3  Heliconius melpomene ssp. melpomene                       NaN  Subspecies   \n",
+       "4                  Anthanassa drusilla                       NaN     Species   \n",
+       "\n",
+       "         genus       family        order    class   kingdom  \\\n",
+       "0   Heliconius  Nymphalidae  Lepidoptera  Insecta  Animalia   \n",
+       "1     Actinote  Nymphalidae  Lepidoptera  Insecta  Animalia   \n",
+       "2  Pseudoscada  Nymphalidae  Lepidoptera  Insecta  Animalia   \n",
+       "3   Heliconius  Nymphalidae  Lepidoptera  Insecta  Animalia   \n",
+       "4   Anthanassa  Nymphalidae  Lepidoptera  Insecta  Animalia   \n",
+       "\n",
+       "   coordinateUncertaintyInMeters dynamicProperties  \n",
+       "0                            NaN                {}  \n",
+       "1                            NaN                {}  \n",
+       "2                            NaN                {}  \n",
+       "3                            NaN                {}  \n",
+       "4                            NaN                {}  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "occurrence.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many unique CAMIDs do we have in `multimedia`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2802"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def get_camid(image_name):\n",
+    "    if \"_\" in image_name:\n",
+    "        return image_name.split(\"_\")[0]\n",
+    "    else:\n",
+    "        # We have at least one record with image name that doesn't have CAMID (the non-zenodo record)\n",
+    "        return np.nan\n",
+    "\n",
+    "multimedia[\"CAMID\"] = multimedia[\"Image_name\"].apply(get_camid)\n",
+    "multimedia[\"CAMID\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, so there are more unique `CAMID`s than there are unique `coreid`s, but less than there are unique CAMIDs (`catalogNumber`) in `occurrence`...\n",
+    "\n",
+    "What do I get if I merge these on `CAMID` and `coreid`?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 5407 entries, 0 to 5406\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                         Non-Null Count  Dtype  \n",
+      "---  ------                         --------------  -----  \n",
+      " 0   coreid                         5407 non-null   object \n",
+      " 1   type                           0 non-null      float64\n",
+      " 2   identifier                     5407 non-null   object \n",
+      " 3   license                        0 non-null      float64\n",
+      " 4   zenodo_link                    5407 non-null   object \n",
+      " 5   Image_name                     5407 non-null   object \n",
+      " 6   record_number                  5407 non-null   object \n",
+      " 7   CAMID                          5407 non-null   object \n",
+      " 8   id                             5407 non-null   object \n",
+      " 9   occurrenceID                   5407 non-null   object \n",
+      " 10  catalogNumber                  5407 non-null   object \n",
+      " 11  datasetName                    5407 non-null   object \n",
+      " 12  recordNumber                   0 non-null      float64\n",
+      " 13  otherCatalogNumbers            0 non-null      object \n",
+      " 14  basisOfRecord                  5407 non-null   object \n",
+      " 15  eventDate                      4937 non-null   object \n",
+      " 16  locality                       5407 non-null   object \n",
+      " 17  country                        5407 non-null   object \n",
+      " 18  decimalLatitude                5407 non-null   float64\n",
+      " 19  decimalLongitude               5407 non-null   float64\n",
+      " 20  geodeticDatum                  5407 non-null   int64  \n",
+      " 21  year                           4937 non-null   float64\n",
+      " 22  sex                            5307 non-null   object \n",
+      " 23  lifeStage                      0 non-null      object \n",
+      " 24  recordedBy                     0 non-null      float64\n",
+      " 25  individualCount                5407 non-null   int64  \n",
+      " 26  taxonId                        1473 non-null   float64\n",
+      " 27  scientificName                 5389 non-null   object \n",
+      " 28  scientificNameAuthorship       0 non-null      float64\n",
+      " 29  taxonRank                      5387 non-null   object \n",
+      " 30  genus                          5387 non-null   object \n",
+      " 31  family                         5211 non-null   object \n",
+      " 32  order                          5211 non-null   object \n",
+      " 33  class                          5211 non-null   object \n",
+      " 34  kingdom                        5211 non-null   object \n",
+      " 35  coordinateUncertaintyInMeters  0 non-null      float64\n",
+      " 36  dynamicProperties              5407 non-null   object \n",
+      "dtypes: float64(10), int64(2), object(25)\n",
+      "memory usage: 1.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_merge = pd.merge(multimedia,\n",
+    "                      occurrence,\n",
+    "                      left_on = [\"coreid\", \"CAMID\"],\n",
+    "                      right_on = [\"id\", \"catalogNumber\"],\n",
+    "                      how = \"inner\")\n",
+    "test_merge.info(show_counts=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So there are about 270 images listed in `multimedia` that are unaccounted for in `occurences`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>coreid</th>\n",
+       "      <th>type</th>\n",
+       "      <th>identifier</th>\n",
+       "      <th>license</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>id</th>\n",
+       "      <th>occurrenceID</th>\n",
+       "      <th>catalogNumber</th>\n",
+       "      <th>datasetName</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>275ad2e7-bc7e-4e74-832e-869825f5bf0b</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2684906/files/CAM008...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2684906</td>\n",
+       "      <td>CAM008538_d.JPG</td>\n",
+       "      <td>2684906</td>\n",
+       "      <td>CAM008538</td>\n",
+       "      <td>275ad2e7-bc7e-4e74-832e-869825f5bf0b</td>\n",
+       "      <td>275ad2e7-bc7e-4e74-832e-869825f5bf0b</td>\n",
+       "      <td>CAM008538</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>275ad2e7-bc7e-4e74-832e-869825f5bf0b</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2684906/files/CAM008...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2684906</td>\n",
+       "      <td>CAM008538_v.JPG</td>\n",
+       "      <td>2684906</td>\n",
+       "      <td>CAM008538</td>\n",
+       "      <td>275ad2e7-bc7e-4e74-832e-869825f5bf0b</td>\n",
+       "      <td>275ad2e7-bc7e-4e74-832e-869825f5bf0b</td>\n",
+       "      <td>CAM008538</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>cf02ac3a-6204-417c-b342-6f84eab48931</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2714333/files/CAM041...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2714333</td>\n",
+       "      <td>CAM041048_v.JPG</td>\n",
+       "      <td>2714333</td>\n",
+       "      <td>CAM041048</td>\n",
+       "      <td>cf02ac3a-6204-417c-b342-6f84eab48931</td>\n",
+       "      <td>cf02ac3a-6204-417c-b342-6f84eab48931</td>\n",
+       "      <td>CAM041048</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>cf02ac3a-6204-417c-b342-6f84eab48931</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2714333/files/CAM041...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2714333</td>\n",
+       "      <td>CAM041048_d.JPG</td>\n",
+       "      <td>2714333</td>\n",
+       "      <td>CAM041048</td>\n",
+       "      <td>cf02ac3a-6204-417c-b342-6f84eab48931</td>\n",
+       "      <td>cf02ac3a-6204-417c-b342-6f84eab48931</td>\n",
+       "      <td>CAM041048</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7be80267-dbe9-4f4b-8f73-c7355447d5e1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2686762/files/CAM008...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>https://zenodo.org/record/2686762</td>\n",
+       "      <td>CAM008842_d.JPG</td>\n",
+       "      <td>2686762</td>\n",
+       "      <td>CAM008842</td>\n",
+       "      <td>7be80267-dbe9-4f4b-8f73-c7355447d5e1</td>\n",
+       "      <td>7be80267-dbe9-4f4b-8f73-c7355447d5e1</td>\n",
+       "      <td>CAM008842</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 coreid  type  \\\n",
+       "0  275ad2e7-bc7e-4e74-832e-869825f5bf0b   NaN   \n",
+       "1  275ad2e7-bc7e-4e74-832e-869825f5bf0b   NaN   \n",
+       "2  cf02ac3a-6204-417c-b342-6f84eab48931   NaN   \n",
+       "3  cf02ac3a-6204-417c-b342-6f84eab48931   NaN   \n",
+       "4  7be80267-dbe9-4f4b-8f73-c7355447d5e1   NaN   \n",
+       "\n",
+       "                                          identifier  license  \\\n",
+       "0  https://zenodo.org/record/2684906/files/CAM008...      NaN   \n",
+       "1  https://zenodo.org/record/2684906/files/CAM008...      NaN   \n",
+       "2  https://zenodo.org/record/2714333/files/CAM041...      NaN   \n",
+       "3  https://zenodo.org/record/2714333/files/CAM041...      NaN   \n",
+       "4  https://zenodo.org/record/2686762/files/CAM008...      NaN   \n",
+       "\n",
+       "                         zenodo_link       Image_name record_number  \\\n",
+       "0  https://zenodo.org/record/2684906  CAM008538_d.JPG       2684906   \n",
+       "1  https://zenodo.org/record/2684906  CAM008538_v.JPG       2684906   \n",
+       "2  https://zenodo.org/record/2714333  CAM041048_v.JPG       2714333   \n",
+       "3  https://zenodo.org/record/2714333  CAM041048_d.JPG       2714333   \n",
+       "4  https://zenodo.org/record/2686762  CAM008842_d.JPG       2686762   \n",
+       "\n",
+       "       CAMID                                    id  \\\n",
+       "0  CAM008538  275ad2e7-bc7e-4e74-832e-869825f5bf0b   \n",
+       "1  CAM008538  275ad2e7-bc7e-4e74-832e-869825f5bf0b   \n",
+       "2  CAM041048  cf02ac3a-6204-417c-b342-6f84eab48931   \n",
+       "3  CAM041048  cf02ac3a-6204-417c-b342-6f84eab48931   \n",
+       "4  CAM008842  7be80267-dbe9-4f4b-8f73-c7355447d5e1   \n",
+       "\n",
+       "                           occurrenceID catalogNumber  \\\n",
+       "0  275ad2e7-bc7e-4e74-832e-869825f5bf0b     CAM008538   \n",
+       "1  275ad2e7-bc7e-4e74-832e-869825f5bf0b     CAM008538   \n",
+       "2  cf02ac3a-6204-417c-b342-6f84eab48931     CAM041048   \n",
+       "3  cf02ac3a-6204-417c-b342-6f84eab48931     CAM041048   \n",
+       "4  7be80267-dbe9-4f4b-8f73-c7355447d5e1     CAM008842   \n",
+       "\n",
+       "                                         datasetName  \n",
+       "0  Heliconiine Butterfly Collection Records from ...  \n",
+       "1  Heliconiine Butterfly Collection Records from ...  \n",
+       "2  Heliconiine Butterfly Collection Records from ...  \n",
+       "3  Heliconiine Butterfly Collection Records from ...  \n",
+       "4  Heliconiine Butterfly Collection Records from ...  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_merge[list(test_merge.columns)[:12]].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "coreid           2713\n",
+       "CAMID            2713\n",
+       "identifier       5407\n",
+       "record_number      10\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_merge[[\"coreid\", \"CAMID\", \"identifier\", \"record_number\"]].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Uniqueness counts from `multimedia`:\n",
+    "```\n",
+    "CAMID            2802\n",
+    "coreid           2794\n",
+    "identifier       5683\n",
+    "Image_name       5683\n",
+    "record_number      12\n",
+    "```\n",
+    "It seems there are 2 records that don't match on IDs, which is a loss of 81 unique listings in `multimedia`.\n",
+    "\n",
+    "How will this compare to the entries from record 3477891 in our master file? Also, are these other records in there?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 5501 entries, 3235 to 42852\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             5501 non-null   object\n",
+      " 1   X                 5501 non-null   int64 \n",
+      " 2   Image_name        5501 non-null   object\n",
+      " 3   View              5501 non-null   object\n",
+      " 4   zenodo_name       5501 non-null   object\n",
+      " 5   zenodo_link       5501 non-null   object\n",
+      " 6   Sequence          5501 non-null   object\n",
+      " 7   Taxonomic_Name    5501 non-null   object\n",
+      " 8   Locality          5501 non-null   object\n",
+      " 9   Sample_accession  925 non-null    object\n",
+      " 10  Collected_by      0 non-null      object\n",
+      " 11  Other_ID          12 non-null     object\n",
+      " 12  Date              5025 non-null   object\n",
+      " 13  Dataset           5501 non-null   object\n",
+      " 14  Store             5421 non-null   object\n",
+      " 15  Brood             4 non-null      object\n",
+      " 16  Death_Date        0 non-null      object\n",
+      " 17  Cross_Type        0 non-null      object\n",
+      " 18  Stage             0 non-null      object\n",
+      " 19  Sex               5435 non-null   object\n",
+      " 20  Unit_Type         5501 non-null   object\n",
+      " 21  file_type         5501 non-null   object\n",
+      " 22  record_number     5501 non-null   int64 \n",
+      " 23  species           5501 non-null   object\n",
+      " 24  subspecies        3673 non-null   object\n",
+      " 25  genus             5501 non-null   object\n",
+      " 26  file_url          5501 non-null   object\n",
+      " 27  hybrid_stat       3705 non-null   object\n",
+      "dtypes: int64(2), object(26)\n",
+      "memory usage: 1.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../Jiggins_Zenodo_Img_Master.csv\", low_memory = False)\n",
+    "\n",
+    "odd_record = df.loc[df[\"record_number\"] == 3477891]\n",
+    "odd_record.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "id_cols = [\"CAMID\", \"X\", \"Image_name\", \"zenodo_name\", \"zenodo_link\", \"file_url\", \"Dataset\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID          2704\n",
+       "X              5501\n",
+       "Image_name     5497\n",
+       "zenodo_name       1\n",
+       "zenodo_link       1\n",
+       "file_url       5497\n",
+       "Dataset           1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "odd_record[id_cols].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This falls somewhere between the `multimedia` & `occurrence` merge, and the `multimedia` file. Let's see a sample of these images then try aligning it with `multimedia` on `Image_name`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>Dataset</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3235</th>\n",
+       "      <td>CAM000001</td>\n",
+       "      <td>44387</td>\n",
+       "      <td>CAM000001_v.JPG</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM000...</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3236</th>\n",
+       "      <td>CAM000001</td>\n",
+       "      <td>44386</td>\n",
+       "      <td>CAM000001_d.JPG</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM000...</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3237</th>\n",
+       "      <td>CAM000003</td>\n",
+       "      <td>44388</td>\n",
+       "      <td>CAM000003_d.JPG</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM000...</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3240</th>\n",
+       "      <td>CAM000003</td>\n",
+       "      <td>44389</td>\n",
+       "      <td>CAM000003_v.JPG</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM000...</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3242</th>\n",
+       "      <td>CAM000004</td>\n",
+       "      <td>44390</td>\n",
+       "      <td>CAM000004_d.JPG</td>\n",
+       "      <td>occurences_and_multimedia.csv</td>\n",
+       "      <td>https://zenodo.org/record/3477891</td>\n",
+       "      <td>https://zenodo.org/record/3477891/files/CAM000...</td>\n",
+       "      <td>Heliconiine Butterfly Collection Records from ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          CAMID      X       Image_name                    zenodo_name  \\\n",
+       "3235  CAM000001  44387  CAM000001_v.JPG  occurences_and_multimedia.csv   \n",
+       "3236  CAM000001  44386  CAM000001_d.JPG  occurences_and_multimedia.csv   \n",
+       "3237  CAM000003  44388  CAM000003_d.JPG  occurences_and_multimedia.csv   \n",
+       "3240  CAM000003  44389  CAM000003_v.JPG  occurences_and_multimedia.csv   \n",
+       "3242  CAM000004  44390  CAM000004_d.JPG  occurences_and_multimedia.csv   \n",
+       "\n",
+       "                            zenodo_link  \\\n",
+       "3235  https://zenodo.org/record/3477891   \n",
+       "3236  https://zenodo.org/record/3477891   \n",
+       "3237  https://zenodo.org/record/3477891   \n",
+       "3240  https://zenodo.org/record/3477891   \n",
+       "3242  https://zenodo.org/record/3477891   \n",
+       "\n",
+       "                                               file_url  \\\n",
+       "3235  https://zenodo.org/record/3477891/files/CAM000...   \n",
+       "3236  https://zenodo.org/record/3477891/files/CAM000...   \n",
+       "3237  https://zenodo.org/record/3477891/files/CAM000...   \n",
+       "3240  https://zenodo.org/record/3477891/files/CAM000...   \n",
+       "3242  https://zenodo.org/record/3477891/files/CAM000...   \n",
+       "\n",
+       "                                                Dataset  \n",
+       "3235  Heliconiine Butterfly Collection Records from ...  \n",
+       "3236  Heliconiine Butterfly Collection Records from ...  \n",
+       "3237  Heliconiine Butterfly Collection Records from ...  \n",
+       "3240  Heliconiine Butterfly Collection Records from ...  \n",
+       "3242  Heliconiine Butterfly Collection Records from ...  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "odd_record[id_cols].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "They are all labeled as that dataset with the `zenodo_name` \"occurrences_and_multimedia.csv\" because it was a combo of these used by Christopher to populate the CSV."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 5501 entries, 0 to 5500\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   CAMID_master        5501 non-null   object \n",
+      " 1   X                   5501 non-null   int64  \n",
+      " 2   Image_name          5501 non-null   object \n",
+      " 3   zenodo_name         5501 non-null   object \n",
+      " 4   zenodo_link_master  5501 non-null   object \n",
+      " 5   file_url            5501 non-null   object \n",
+      " 6   Dataset             5501 non-null   object \n",
+      " 7   coreid              5501 non-null   object \n",
+      " 8   type                0 non-null      float64\n",
+      " 9   identifier          5501 non-null   object \n",
+      " 10  license             0 non-null      float64\n",
+      " 11  zenodo_link_media   5501 non-null   object \n",
+      " 12  record_number       5501 non-null   object \n",
+      " 13  CAMID_media         5409 non-null   object \n",
+      "dtypes: float64(2), int64(1), object(11)\n",
+      "memory usage: 601.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "odd_multimedia = pd.merge(odd_record[id_cols],\n",
+    "                          multimedia,\n",
+    "                          on = \"Image_name\",\n",
+    "                          how = \"inner\",\n",
+    "                          suffixes = (\"_master\", \"_media\"))\n",
+    "odd_multimedia.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID_master          2704\n",
+       "X                     5501\n",
+       "Image_name            5497\n",
+       "zenodo_name              1\n",
+       "zenodo_link_master       1\n",
+       "file_url              5497\n",
+       "Dataset                  1\n",
+       "coreid                2704\n",
+       "type                     0\n",
+       "identifier            5497\n",
+       "license                  0\n",
+       "zenodo_link_media       12\n",
+       "record_number           12\n",
+       "CAMID_media           2712\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "odd_multimedia.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like all the images were captured (there are more entries than unique `Image_name` & URL), so we should be able to replace the URLs in the master file with the multimedia image URLs directly.\n",
+    "\n",
+    "We do want to compare record numbers to the master file first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "media_records = list(multimedia.record_number.unique())\n",
+    "media_imgs = list(multimedia.Image_name.unique())\n",
+    "master_records = list(df.record_number.unique())\n",
+    "\n",
+    "overlap_records = [record for record in media_records if record in master_records]\n",
+    "len(overlap_records)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ahhh no duplication then. Interesting (and good!)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5501, 55)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "non_odd_df = df.loc[df[\"record_number\"] != 3477891]\n",
+    "\n",
+    "test_odd_merge = pd.merge(odd_record,\n",
+    "                          non_odd_df,\n",
+    "                          on = \"Image_name\",\n",
+    "                          how = \"inner\")\n",
+    "test_odd_merge.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://zenodo.org/record/2677821/files/CAM000003_v.JPG'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multimedia.loc[multimedia[\"Image_name\"] == \"CAM000003_v.JPG\", \"identifier\"].values[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Duplication in `Image_name`, though that's not necessarily unexpected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               2704\n",
+       "X                   5501\n",
+       "Image_name          5497\n",
+       "View                   2\n",
+       "zenodo_name            1\n",
+       "zenodo_link            1\n",
+       "Sequence            2704\n",
+       "Taxonomic_Name       195\n",
+       "Locality             205\n",
+       "Sample_accession     446\n",
+       "Collected_by           0\n",
+       "Other_ID               5\n",
+       "Date                 200\n",
+       "Dataset                1\n",
+       "Store                 55\n",
+       "Brood                  2\n",
+       "Death_Date             0\n",
+       "Cross_Type             0\n",
+       "Stage                  0\n",
+       "Sex                    3\n",
+       "Unit_Type              2\n",
+       "file_type              1\n",
+       "record_number          1\n",
+       "species              121\n",
+       "subspecies            93\n",
+       "genus                 38\n",
+       "file_url            5497\n",
+       "hybrid_stat            2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for image_name in list(odd_record.Image_name.unique()):\n",
+    "    url = multimedia.loc[multimedia[\"Image_name\"] == image_name, \"identifier\"].values[0]\n",
+    "    df.loc[(df[\"record_number\"] == 3477891) & (df[\"Image_name\"] == image_name), \"file_url\"] = url\n",
+    "\n",
+    "df.loc[df[\"record_number\"] == 3477891].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID          11991\n",
+       "X              44809\n",
+       "Image_name     36281\n",
+       "zenodo_name       33\n",
+       "zenodo_link       30\n",
+       "file_url       39297\n",
+       "Dataset            8\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[id_cols].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dataset\n",
+       "Heliconiine Butterfly Collection Records from University of Cambridge    25211\n",
+       "Patricio Salazar                                                          7519\n",
+       "Nadeau Sheffield                                                          3233\n",
+       "Bogota Collection (Camilo Salazar)                                         982\n",
+       "Cambridge Collection                                                        47\n",
+       "Mallet                                                                      22\n",
+       "Merril_Gamboa                                                                6\n",
+       "STRI Collection (Owen)                                                       4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Dataset.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID                6538\n",
+       "X                   25211\n",
+       "Image_name          17362\n",
+       "View                    7\n",
+       "zenodo_name            17\n",
+       "zenodo_link            17\n",
+       "Sequence             6538\n",
+       "Taxonomic_Name        287\n",
+       "Locality              372\n",
+       "Sample_accession      485\n",
+       "Collected_by            0\n",
+       "Other_ID             1123\n",
+       "Date                  282\n",
+       "Dataset                 1\n",
+       "Store                 102\n",
+       "Brood                 102\n",
+       "Death_Date              0\n",
+       "Cross_Type              0\n",
+       "Stage                   0\n",
+       "Sex                     3\n",
+       "Unit_Type               2\n",
+       "file_type               2\n",
+       "record_number          17\n",
+       "species               207\n",
+       "subspecies             99\n",
+       "genus                  82\n",
+       "file_url            19710\n",
+       "hybrid_stat             2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HBCRUC = \"Heliconiine Butterfly Collection Records from University of Cambridge\"\n",
+    "df.loc[df.Dataset == HBCRUC].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"../metadata/Jiggins_Zenodo_Img_Master_3477891Patch.csv\", index = False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "std",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/Data-gen-1-2.ipynb

@@ -0,0 +1,2566 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "We explored our downloaded images in `EDA-DL-0-3.ipynb`, and established the following causes of duplication along with remedies:\n",
+    "1. The \"patch\" record (record 3477891) which required alignment in `Data-gen-1-1.ipynb`, resulting in `../metadata/Jiggins_Zenodo_Img_Master_3477891Patch.csv` used for download, is a duplication of all listed records. Additionally, it introduced extra copies of 4 images with incorrect labels for their views (i.e., dorsal vs ventral).\n",
+    " - Solution: This record will be removed.\n",
+    "2. The remaining duplication (430 images x2) comes from 5 records: [4291095](https://zenodo.org/records/4291095), [2813153](https://zenodo.org/records/2813153), [5526257](https://zenodo.org/records/5526257), [2553977](https://zenodo.org/records/2553977), and [2552371](https://zenodo.org/records/2552371).\n",
+    "  - Though there is nothing in their descriptions on Zenodo to indicate this overlap occurred, record 4291095 duplicated 415 images from record 2813153. 104 of these images had their view/side labels (dorsal vs ventral) updated to reflect an earlier mislabeling in record 2813153; these were all RAW copies of the images. All other metadata was consistent across records.\n",
+    "  - Solution: Copies of these images and their metadata will be retained from the newer record, record 4291095.\n",
+    "  - Record 5526257 has 10 images that were added twice. All their metadata is consistent, so we will just keep the first instance of each image.\n",
+    "  - Records 2553977 and 2552371 are both \"Miscellaneous Heliconius wing photographs (2001-2019)\" Parts 3 & 1, respectively. Record 2553977 has duplicated images with matching metadata, though 3 of the 4 images have different filenames (`Image_name`). It seems to have resulted from typos or not indicating `cut`, as they are all close-up crops of a single wing. From this perspective, the view labels (dorsal) don't seem appropriate when we have more fine-grained indicators such as \"forewing dorsal\".\n",
+    "    - Suggestion that the entries in this record with `cut` in the filename not be used for general classification unless this variety is desired. These also appear to be all the `tif` images, which are often ignored.\n",
+    "    - Record 2552371 has one image duplicated with two different `CAMID`s assigned (different views as well). These will both be removed as the image has no indicator of the specimen ID in it. Note that these also were labeled `Heliconius sp.`.\n",
+    "3. Looking at images that were not duplicates, there are clearly still multiple images of the same specimen from the same perspective (eg., two dorsal images) that are also of the same file type (eg., both jpgs). Duplication was checked at the pixel level, so there is no guarantee that they are truly different images, but due to the scope of this collection (over 20 years), it does not seem unlikely that multiple images could have been taken of the same specimen. \n",
+    "  - Suggestion/Solution: When determining splits using this data, follow these steps:\n",
+    "    1. Ensure you are looking at only **_one_** view (eg., dorsal or ventral).\n",
+    "    2. Ensure you are only looking one file type (`raw`, `jpg`, or `tif`).\n",
+    "    3. Reduce to only unique `CAMID`s.\n",
+    "    4. Generate splits as desired.\n",
+    "    5. Add images to splits based on matched `CAMID`s if multiple views are desired (i.e., if a `CAMID` is present in the dorsal training, add the matching ventral image to the training set).\n",
+    "    6. Be sure to check only desired categories are included, such as excluding hybrids or cross types, or specimens not labeled to the level of classification.\n",
+    "\n",
+    "\n",
+    "## Generate \"Clean\" Master Metadata File\n",
+    "\n",
+    "We'll now generate a cleaned master metadata file from `../metadata/deduplication/Jiggins_Zenodo_Img_Master_3477891Patch_downloaded.csv` (generated in `EDA-DL-0-3.ipynb` from our download master file and checksums of successful downloads) following the solutions outlined above. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "STATS_COLS = [\"X\",\n",
+    "              \"CAMID\",\n",
+    "              \"Image_name\",\n",
+    "              \"md5\",\n",
+    "              \"record_number\",\n",
+    "              \"Taxonomic_Name\",\n",
+    "              \"species\",\n",
+    "              \"subspecies\",\n",
+    "              \"genus\",\n",
+    "              \"Cross_Type\",\n",
+    "              \"View\",\n",
+    "              \"Locality\",\n",
+    "              \"Sex\",\n",
+    "              \"Dataset\",\n",
+    "              \"file_type\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 42143 entries, 0 to 42142\n",
+      "Data columns (total 15 columns):\n",
+      " #   Column          Non-Null Count  Dtype \n",
+      "---  ------          --------------  ----- \n",
+      " 0   X               42143 non-null  int64 \n",
+      " 1   CAMID           42143 non-null  object\n",
+      " 2   Image_name      42143 non-null  object\n",
+      " 3   md5             42143 non-null  object\n",
+      " 4   record_number   42143 non-null  int64 \n",
+      " 5   Taxonomic_Name  42143 non-null  object\n",
+      " 6   species         42143 non-null  object\n",
+      " 7   subspecies      24502 non-null  object\n",
+      " 8   genus           42143 non-null  object\n",
+      " 9   Cross_Type      4447 non-null   object\n",
+      " 10  View            41364 non-null  object\n",
+      " 11  Locality        29047 non-null  object\n",
+      " 12  Sex             33254 non-null  object\n",
+      " 13  Dataset         34924 non-null  object\n",
+      " 14  file_type       42143 non-null  object\n",
+      "dtypes: int64(2), object(13)\n",
+      "memory usage: 4.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../metadata/deduplication/Jiggins_Zenodo_Img_Master_3477891Patch_downloaded.csv\", low_memory = False)\n",
+    "df[STATS_COLS].info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "X                 42143\n",
+       "CAMID             11968\n",
+       "Image_name        36216\n",
+       "md5               36212\n",
+       "record_number        30\n",
+       "Taxonomic_Name      366\n",
+       "species             246\n",
+       "subspecies          155\n",
+       "genus                94\n",
+       "Cross_Type           30\n",
+       "View                  7\n",
+       "Locality            644\n",
+       "Sex                   3\n",
+       "Dataset               7\n",
+       "file_type             3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[STATS_COLS].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observations:\n",
+    "- Half of the Zenodo records wind up with duplicated images. \n",
+    "- `Image_name` uniqueness is close to `md5`, but there are 4 more unique image names than unique images. I believe this was from the duplication in reocrd 2553977. We can check when cleaning it. \n",
+    "- None of the cross types are duplicated.\n",
+    "- We do have multiple unique images for a single `CAMID`, more than just dorsal vs ventral since there are 11968 unique CAMIDs and 36212 unique `md5` values (36212 = **3**x11968 + 308)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Remove Record 3477891\n",
+    "\n",
+    "This is just a duplication of images and we already have from earlier records, along with some extra duplication that gets mislabeled."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "X                 36642\n",
+       "CAMID             11968\n",
+       "Image_name        36216\n",
+       "md5               36212\n",
+       "record_number        29\n",
+       "Taxonomic_Name      366\n",
+       "species             246\n",
+       "subspecies          155\n",
+       "genus                94\n",
+       "Cross_Type           30\n",
+       "View                  7\n",
+       "Locality            644\n",
+       "Sex                   3\n",
+       "Dataset               7\n",
+       "file_type             3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_reduced = df.loc[df[\"record_number\"] != 3477891].copy()\n",
+    "df_reduced[STATS_COLS].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The only change is in `X` (our total entry count) and in `record_number` (expected since we removed one)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Address Remaining Duplicates\n",
+    "\n",
+    "The remaining duplicates are across 5 records and will require a bit more care to reslove (following the plan outlined at the top of the notebook and in the README).\n",
+    "\n",
+    "### Record 4291095 & 2813153 Duplicates\n",
+    "\n",
+    "There are 415 duplicates across these two records, with `View` corrections in record 4291095, so we will remove the duplicates from record 2813153."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['CAMID', 'X', 'Image_name', 'View', 'zenodo_name', 'zenodo_link',\n",
+       "       'Sequence', 'Taxonomic_Name', 'Locality', 'Sample_accession',\n",
+       "       'Collected_by', 'Other_ID', 'Date', 'Dataset', 'Store', 'Brood',\n",
+       "       'Death_Date', 'Cross_Type', 'Stage', 'Sex', 'Unit_Type', 'file_type',\n",
+       "       'record_number', 'species', 'subspecies', 'genus', 'file_url',\n",
+       "       'hybrid_stat', 'filename', 'filepath', 'md5'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_reduced.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by marking all duplicate images since the decision of which to keep is dependent on comparisons between them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "md5_duplicated\n",
+       "False    35782\n",
+       "True       860\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_reduced[\"md5_duplicated\"] = df_reduced.duplicated(\"md5\", keep = False)\n",
+    "df_reduced[\"md5_duplicated\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "415"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicates = df_reduced[df_reduced[\"md5_duplicated\"]]\n",
+    "dupes_2813153 = duplicates.loc[duplicates[\"record_number\"] == 2813153]\n",
+    "#print(duplicates.loc[duplicates[\"record_number\"] == 4291095])\n",
+    "dupes_2813153.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will record the `X` value from these 415 entries to remove them from the overall dataset in favor of the updated entries from record 4291095."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_dupes_2813153 = list(dupes_2813153.X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "X                 36227\n",
+       "CAMID             11968\n",
+       "Image_name        36216\n",
+       "md5               36212\n",
+       "record_number        29\n",
+       "Taxonomic_Name      366\n",
+       "species             246\n",
+       "subspecies          155\n",
+       "genus                94\n",
+       "Cross_Type           30\n",
+       "View                  7\n",
+       "Locality            644\n",
+       "Sex                   3\n",
+       "Dataset               7\n",
+       "file_type             3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_reduced_2 = df_reduced.loc[~df_reduced[\"X\"].isin(X_dupes_2813153)]\n",
+    "df_reduced_2[STATS_COLS].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As expected, our numbers are holding steady with a reduction in `X` values of 415."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Record 5526257 Duplicates\n",
+    "\n",
+    "For record 5526257, the 10 duplicated entries are all the same, so we will just check `.duplicated` and keep the first instance of each."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dupe_entries\n",
+      "False    10\n",
+      "True     10\n",
+      "Name: count, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dupes_5526257 = duplicates.loc[duplicates[\"record_number\"] == 5526257].copy()\n",
+    "dupes_5526257[\"dupe_entries\"] = dupes_5526257.duplicated([\"CAMID\", \"Image_name\", \"View\", \"Taxonomic_Name\", \"Locality\", \"md5\"], keep = \"first\")\n",
+    "print(dupes_5526257[\"dupe_entries\"].value_counts())\n",
+    "\n",
+    "X_dupes_5526257 = list(dupes_5526257.loc[dupes_5526257[\"dupe_entries\"], \"X\"])\n",
+    "len(X_dupes_5526257)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "X                 36217\n",
+       "CAMID             11968\n",
+       "Image_name        36216\n",
+       "md5               36212\n",
+       "record_number        29\n",
+       "Taxonomic_Name      366\n",
+       "species             246\n",
+       "subspecies          155\n",
+       "genus                94\n",
+       "Cross_Type           30\n",
+       "View                  7\n",
+       "Locality            644\n",
+       "Sex                   3\n",
+       "Dataset               7\n",
+       "file_type             3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_reduced_3 = df_reduced_2.loc[~df_reduced_2[\"X\"].isin(X_dupes_5526257)]\n",
+    "df_reduced_3[STATS_COLS].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reduction in `X` by 10, consistency in all other numbers.\n",
+    "\n",
+    "### Record 2552371 Duplicates\n",
+    "\n",
+    "We are removing both duplicates from this record as their metadata is contradictory and cannot be verified. (Two different CAMIDs, different filenames, different views, etc.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "      <th>filename</th>\n",
+       "      <th>filepath</th>\n",
+       "      <th>md5</th>\n",
+       "      <th>md5_duplicated</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11978</th>\n",
+       "      <td>CAM010354</td>\n",
+       "      <td>23471</td>\n",
+       "      <td>10354d.jpg</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2552371</td>\n",
+       "      <td>10,354</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2552371</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2552371/files/10354d...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23471_10354d.jpg</td>\n",
+       "      <td>images/Heliconius sp./23471_10354d.jpg</td>\n",
+       "      <td>84d4ac6527458786cdb33166cd80e0a8</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12010</th>\n",
+       "      <td>CAM010362</td>\n",
+       "      <td>23484</td>\n",
+       "      <td>10362v.jpg</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
+       "      <td>https://zenodo.org/record/2552371</td>\n",
+       "      <td>10,362</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2552371</td>\n",
+       "      <td>Heliconius sp.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2552371/files/10362v...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23484_10362v.jpg</td>\n",
+       "      <td>images/Heliconius sp./23484_10362v.jpg</td>\n",
+       "      <td>84d4ac6527458786cdb33166cd80e0a8</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X  Image_name     View  \\\n",
+       "11978  CAM010354  23471  10354d.jpg   dorsal   \n",
+       "12010  CAM010362  23484  10362v.jpg  ventral   \n",
+       "\n",
+       "                                          zenodo_name  \\\n",
+       "11978  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
+       "12010  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
+       "\n",
+       "                             zenodo_link Sequence  Taxonomic_Name Locality  \\\n",
+       "11978  https://zenodo.org/record/2552371   10,354  Heliconius sp.      NaN   \n",
+       "12010  https://zenodo.org/record/2552371   10,362  Heliconius sp.      NaN   \n",
+       "\n",
+       "      Sample_accession  ... record_number         species subspecies  \\\n",
+       "11978              NaN  ...       2552371  Heliconius sp.        NaN   \n",
+       "12010              NaN  ...       2552371  Heliconius sp.        NaN   \n",
+       "\n",
+       "            genus                                           file_url  \\\n",
+       "11978  Heliconius  https://zenodo.org/record/2552371/files/10354d...   \n",
+       "12010  Heliconius  https://zenodo.org/record/2552371/files/10362v...   \n",
+       "\n",
+       "      hybrid_stat          filename                                filepath  \\\n",
+       "11978         NaN  23471_10354d.jpg  images/Heliconius sp./23471_10354d.jpg   \n",
+       "12010         NaN  23484_10362v.jpg  images/Heliconius sp./23484_10362v.jpg   \n",
+       "\n",
+       "                                    md5 md5_duplicated  \n",
+       "11978  84d4ac6527458786cdb33166cd80e0a8           True  \n",
+       "12010  84d4ac6527458786cdb33166cd80e0a8           True  \n",
+       "\n",
+       "[2 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dupes_2552371 = duplicates.loc[duplicates[\"record_number\"] == 2552371]\n",
+    "X_dupes_2552371 = list(dupes_2552371[\"X\"])\n",
+    "dupes_2552371"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "X                 36215\n",
+       "CAMID             11968\n",
+       "Image_name        36214\n",
+       "md5               36211\n",
+       "record_number        29\n",
+       "Taxonomic_Name      366\n",
+       "species             246\n",
+       "subspecies          155\n",
+       "genus                94\n",
+       "Cross_Type           30\n",
+       "View                  7\n",
+       "Locality            644\n",
+       "Sex                   3\n",
+       "Dataset               7\n",
+       "file_type             3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_reduced_4 = df_reduced_3.loc[~df_reduced_3[\"X\"].isin(X_dupes_2552371)]\n",
+    "df_reduced_4[STATS_COLS].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Removing these two has reduced our `X` count by 2, as well as our number of unique `Image_name`s, and it reduced our unique image count (`md5`) by 1. Observe that our unique `CAMID` count has remained constant, so both specimens indicated by these duplicate images are still represented in the dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Record 2553977 Duplicates\n",
+    "\n",
+    "The duplicated entries for record 2553977 are the most complicated in that their metadata is nearly exact, though a few have different filenames (`Image_name`) and we would like to preserve the version that has `_cut` in the name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>X</th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>md5</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>Cross_Type</th>\n",
+       "      <th>View</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Dataset</th>\n",
+       "      <th>file_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>40141</th>\n",
+       "      <td>26197</td>\n",
+       "      <td>CAM050147</td>\n",
+       "      <td>CAM050147_DS1_HW_IMG_8537_cut_3.tif</td>\n",
+       "      <td>21e726df7076f5c13fe1441126bac4d5</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius sara ssp. sara</td>\n",
+       "      <td>Heliconius sara</td>\n",
+       "      <td>sara</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Madingley</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tif</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40144</th>\n",
+       "      <td>26196</td>\n",
+       "      <td>CAM050147</td>\n",
+       "      <td>CAM050147_DS1_HW_IMG_8537_cut_3.tif</td>\n",
+       "      <td>21e726df7076f5c13fe1441126bac4d5</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius sara ssp. sara</td>\n",
+       "      <td>Heliconius sara</td>\n",
+       "      <td>sara</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Madingley</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tif</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40112</th>\n",
+       "      <td>26102</td>\n",
+       "      <td>CAM050036</td>\n",
+       "      <td>CAM050036_S1_9_FW_IMG_8547_wb_2.tif</td>\n",
+       "      <td>34adbd5596f977834fb2c6876d2f4493</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius sara ssp. sara</td>\n",
+       "      <td>Heliconius sara</td>\n",
+       "      <td>sara</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Gamboa_insectaries</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tif</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40116</th>\n",
+       "      <td>26115</td>\n",
+       "      <td>CAM050036</td>\n",
+       "      <td>CAM050036_S1_9_FW_IMG_8547_cut_2.tif</td>\n",
+       "      <td>34adbd5596f977834fb2c6876d2f4493</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius sara ssp. sara</td>\n",
+       "      <td>Heliconius sara</td>\n",
+       "      <td>sara</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Gamboa_insectaries</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tif</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40138</th>\n",
+       "      <td>26112</td>\n",
+       "      <td>CAM050147</td>\n",
+       "      <td>CAM050147_DS1_IMG_8513_4.tif</td>\n",
+       "      <td>464801449aa127933d9619cb4e6f0a01</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius sara ssp. sara</td>\n",
+       "      <td>Heliconius sara</td>\n",
+       "      <td>sara</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Madingley</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tif</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40145</th>\n",
+       "      <td>26117</td>\n",
+       "      <td>CAM050147</td>\n",
+       "      <td>CAM050147_DS1_FW_IMG_8513_cut_4.tif</td>\n",
+       "      <td>464801449aa127933d9619cb4e6f0a01</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius sara ssp. sara</td>\n",
+       "      <td>Heliconius sara</td>\n",
+       "      <td>sara</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Madingley</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tif</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40113</th>\n",
+       "      <td>26105</td>\n",
+       "      <td>CAM050036</td>\n",
+       "      <td>CAM050036_S1_9_HW_wt_IMG_8541_cut3.tif</td>\n",
+       "      <td>6d63d659fbc02369010938bbc229ad4d</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius sara ssp. sara</td>\n",
+       "      <td>Heliconius sara</td>\n",
+       "      <td>sara</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Gamboa_insectaries</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tif</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40120</th>\n",
+       "      <td>26116</td>\n",
+       "      <td>CAM050036</td>\n",
+       "      <td>CAM050036_S1_9_HW_wt_IMG_8541_cut_3.tif</td>\n",
+       "      <td>6d63d659fbc02369010938bbc229ad4d</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius sara ssp. sara</td>\n",
+       "      <td>Heliconius sara</td>\n",
+       "      <td>sara</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Gamboa_insectaries</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tif</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           X      CAMID                               Image_name  \\\n",
+       "40141  26197  CAM050147      CAM050147_DS1_HW_IMG_8537_cut_3.tif   \n",
+       "40144  26196  CAM050147      CAM050147_DS1_HW_IMG_8537_cut_3.tif   \n",
+       "40112  26102  CAM050036      CAM050036_S1_9_FW_IMG_8547_wb_2.tif   \n",
+       "40116  26115  CAM050036     CAM050036_S1_9_FW_IMG_8547_cut_2.tif   \n",
+       "40138  26112  CAM050147             CAM050147_DS1_IMG_8513_4.tif   \n",
+       "40145  26117  CAM050147      CAM050147_DS1_FW_IMG_8513_cut_4.tif   \n",
+       "40113  26105  CAM050036   CAM050036_S1_9_HW_wt_IMG_8541_cut3.tif   \n",
+       "40120  26116  CAM050036  CAM050036_S1_9_HW_wt_IMG_8541_cut_3.tif   \n",
+       "\n",
+       "                                    md5  record_number  \\\n",
+       "40141  21e726df7076f5c13fe1441126bac4d5        2553977   \n",
+       "40144  21e726df7076f5c13fe1441126bac4d5        2553977   \n",
+       "40112  34adbd5596f977834fb2c6876d2f4493        2553977   \n",
+       "40116  34adbd5596f977834fb2c6876d2f4493        2553977   \n",
+       "40138  464801449aa127933d9619cb4e6f0a01        2553977   \n",
+       "40145  464801449aa127933d9619cb4e6f0a01        2553977   \n",
+       "40113  6d63d659fbc02369010938bbc229ad4d        2553977   \n",
+       "40120  6d63d659fbc02369010938bbc229ad4d        2553977   \n",
+       "\n",
+       "                  Taxonomic_Name          species subspecies       genus  \\\n",
+       "40141  Heliconius sara ssp. sara  Heliconius sara       sara  Heliconius   \n",
+       "40144  Heliconius sara ssp. sara  Heliconius sara       sara  Heliconius   \n",
+       "40112  Heliconius sara ssp. sara  Heliconius sara       sara  Heliconius   \n",
+       "40116  Heliconius sara ssp. sara  Heliconius sara       sara  Heliconius   \n",
+       "40138  Heliconius sara ssp. sara  Heliconius sara       sara  Heliconius   \n",
+       "40145  Heliconius sara ssp. sara  Heliconius sara       sara  Heliconius   \n",
+       "40113  Heliconius sara ssp. sara  Heliconius sara       sara  Heliconius   \n",
+       "40120  Heliconius sara ssp. sara  Heliconius sara       sara  Heliconius   \n",
+       "\n",
+       "      Cross_Type    View            Locality  Sex Dataset file_type  \n",
+       "40141        NaN  dorsal           Madingley  NaN     NaN       tif  \n",
+       "40144        NaN  dorsal           Madingley  NaN     NaN       tif  \n",
+       "40112        NaN  dorsal  Gamboa_insectaries  NaN     NaN       tif  \n",
+       "40116        NaN  dorsal  Gamboa_insectaries  NaN     NaN       tif  \n",
+       "40138        NaN  dorsal           Madingley  NaN     NaN       tif  \n",
+       "40145        NaN  dorsal           Madingley  NaN     NaN       tif  \n",
+       "40113        NaN  dorsal  Gamboa_insectaries  NaN     NaN       tif  \n",
+       "40120        NaN  dorsal  Gamboa_insectaries  NaN     NaN       tif  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dupes_2553977 = duplicates.loc[duplicates[\"record_number\"] == 2553977]\n",
+    "dupes_2553977[STATS_COLS].sort_values(\"md5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each of these, we want the one with `_cut_` in the `Image_name`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[26102, 26105, 26112]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_dupes_2553977 = [x for x in list(dupes_2553977[\"X\"]) if \"_cut_\" not in dupes_2553977.loc[dupes_2553977[\"X\"] == x, \"Image_name\"].values[0]]\n",
+    "\n",
+    "# Check we got the ones expected\n",
+    "X_dupes_2553977"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add 26196, since both those entries (26196 and 26197) are the same\n",
+    "X_dupes_2553977.append(26196)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "X                 36211\n",
+       "CAMID             11968\n",
+       "Image_name        36211\n",
+       "md5               36211\n",
+       "record_number        29\n",
+       "Taxonomic_Name      366\n",
+       "species             246\n",
+       "subspecies          155\n",
+       "genus                94\n",
+       "Cross_Type           30\n",
+       "View                  7\n",
+       "Locality            644\n",
+       "Sex                   3\n",
+       "Dataset               7\n",
+       "file_type             3\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_reduced_5 = df_reduced_4.loc[~df_reduced_4[\"X\"].isin(X_dupes_2553977)]\n",
+    "df_reduced_5[STATS_COLS].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "4 entries were removed, reducing our number of unique `Image_name`s by 3 (one of the duplicated images had all its metadata duplicated), and everything else has remained consistent. Now the number of unique image names and entries matches the number of unique images. We have a total of 36,211 images, and we will save this reduced CSV as our new master file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save Reduced Master File of Unique Images\n",
+    "\n",
+    "Don't include last column indicating duplicate MD5s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_reduced_5[list(df_reduced_5.columns)[:-1]].to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Generate New Master Heliconius and Dorsal Subsets\n",
+    "\n",
+    "## Read in New Master File\n",
+    "\n",
+    "First check on our stats noted in the README."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 36211 entries, 0 to 36210\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             36211 non-null  object\n",
+      " 1   X                 36211 non-null  int64 \n",
+      " 2   Image_name        36211 non-null  object\n",
+      " 3   View              35432 non-null  object\n",
+      " 4   zenodo_name       36211 non-null  object\n",
+      " 5   zenodo_link       36211 non-null  object\n",
+      " 6   Sequence          35279 non-null  object\n",
+      " 7   Taxonomic_Name    36211 non-null  object\n",
+      " 8   Locality          23127 non-null  object\n",
+      " 9   Sample_accession  3647 non-null   object\n",
+      " 10  Collected_by      3043 non-null   object\n",
+      " 11  Other_ID          11329 non-null  object\n",
+      " 12  Date              23040 non-null  object\n",
+      " 13  Dataset           29114 non-null  object\n",
+      " 14  Store             27813 non-null  object\n",
+      " 15  Brood             13847 non-null  object\n",
+      " 16  Death_Date        269 non-null    object\n",
+      " 17  Cross_Type        4447 non-null   object\n",
+      " 18  Stage             6 non-null      object\n",
+      " 19  Sex               27514 non-null  object\n",
+      " 20  Unit_Type         25060 non-null  object\n",
+      " 21  file_type         36211 non-null  object\n",
+      " 22  record_number     36211 non-null  int64 \n",
+      " 23  species           36211 non-null  object\n",
+      " 24  subspecies        20400 non-null  object\n",
+      " 25  genus             36211 non-null  object\n",
+      " 26  file_url          36211 non-null  object\n",
+      " 27  hybrid_stat       20948 non-null  object\n",
+      " 28  filename          36211 non-null  object\n",
+      " 29  filepath          36211 non-null  object\n",
+      " 30  md5               36211 non-null  object\n",
+      "dtypes: int64(2), object(29)\n",
+      "memory usage: 8.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "master = pd.read_csv(\"../Jiggins_Zenodo_Img_Master.csv\", low_memory = False)\n",
+    "master.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Taxonomic Labeling Check\n",
+    "\n",
+    "As noted above, there are instances of `<Genus> sp.` in the `Taxonomic_Name` column. These get reproduced in the `species` column as well. We also have at least one instance of just `Heliconius`, and an instance of `Unknown` was observed outside this process. To ensure the taxonomic information is indeed clear, we will print and check all 246 unique species listed in the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.Series(master.species.unique()).to_csv(\"../metadata/Unique_species_labels.txt\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We have 24 'species' designations really labeled only to genus, 5 species designations that may not be labeled at the species level, and 1 with 'hybrid' in the name.\n"
+     ]
+    }
+   ],
+   "source": [
+    "species_list = list(master.species.unique())\n",
+    "\n",
+    "sp_list = []\n",
+    "one_word_list = []\n",
+    "hybrid_label = []\n",
+    "for species in species_list:\n",
+    "    if \"sp.\" in species:\n",
+    "        sp_list.append(species)\n",
+    "    if len(species.split(\" \")) == 1:\n",
+    "        one_word_list.append(species)\n",
+    "    if \"hybrid\" in species.lower():\n",
+    "        hybrid_label.append(species)\n",
+    "\n",
+    "print(f\"We have {len(sp_list)} 'species' designations really labeled only to genus, {len(one_word_list)} species designations that may not be labeled at the species level, and {len(hybrid_label)} with 'hybrid' in the name.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Pteronymia', 'Unknown', 'Ithomiinae', 'Stratiomyidae', 'Heliconius']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_word_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Heliconius hybrid']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hybrid_label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This matches my manual check:\n",
+    "\n",
+    "Non-standard labels found:\n",
+    "\n",
+    "- 'Heliconius hybrid'\n",
+    "- \"sp.\":\n",
+    "    >'Heliconius sp.', 'Melinaea sp.', 'Oleria sp.', 'Ithomia sp.', 'Godyris sp.', 'Hypothyris sp.', 'Pteronymia sp.', 'Greta sp.', 'Dircenna sp.', 'Actinote sp.', 'Eresia sp.', 'Eueides sp.', 'Pteronymia sp.nov.', 'Patricia sp.', 'Hyalyris sp.', 'Olyras sp.', 'Mechanitis sp.', 'Euides sp.', 'Taygetis sp.', 'Euselasia sp.', 'Eunica sp.', 'Ascia sp.', 'Euptychia sp.', 'Megaleas sp.'\n",
+    "- One name: 'Heliconius', 'Stratiomyidae', 'Ithomiinae', 'Pteronymia', + 'Unknown'.  \n",
+    "    - This is becuase `species` was just a split at `ssp.` and take the first element of that list, hence the duplication of single names.\n",
+    "\n",
+    "### Taxonomic Labeling Fix:\n",
+    "- We'll adjust the genera to be `<Genus> sp.` to be consistent. Then to avoid any labeled only to the genus level, one must simply exclude those with `sp.` in the species name. \n",
+    "- The `Heliconius hybrid` should be labeled as a hybrid, but have null `subspecies` value, so that should be avoidable or useable as well. \n",
+    "- We will remove the `Unknown` values as their CAMIDs don't appear elsewhere so the taxonomic information is non-reconcilable. \n",
+    "- Christopher confirmed that `Ithomiinae` and `Pteronymia` are genera in the family `nymphalidae`, like `Heliconius`, but `Stratiomyidae` is a \"Soldier Fly\" (Google, Christopher did not recognize it as a butterfly), clearly not a butterfly from the images. This label is present in [record 5561246](https://zenodo.org/records/5561246), attached to `CAMID: CAM036326`, which does not appear elsewhere in our dataset (see check below). Looking at the record, it is labeled as `BSF blue wings` in the CSV, and the images are clearly of a butterfly's wings. We have concluded it is a mislabeling and will drop it for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 18 entries with the taxonomic label 'Unknown', and 18 entries of that/those CAMID(s).\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "zenodo_link\n",
+       "https://zenodo.org/record/5561246    12\n",
+       "https://zenodo.org/record/4291095     4\n",
+       "https://zenodo.org/record/2702457     2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cams_unknown = list(master.loc[master[\"species\"] == \"Unknown\", \"CAMID\"])\n",
+    "print(f\"There are {len(cams_unknown)} entries with the taxonomic label 'Unknown', and {master.loc[master['CAMID'].isin(cams_unknown)].shape[0]} entries of that/those CAMID(s).\")\n",
+    "master.loc[master[\"CAMID\"].isin(cams_unknown), \"zenodo_link\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 4 entries with the taxonomic label 'Stratiomyidae', and 4 entries of that/those CAMID(s).\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "      <th>filename</th>\n",
+       "      <th>filepath</th>\n",
+       "      <th>md5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>21877</th>\n",
+       "      <td>CAM036326</td>\n",
+       "      <td>41866</td>\n",
+       "      <td>CAM036326_d.CR2</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Filelist.csv</td>\n",
+       "      <td>https://zenodo.org/record/5561246</td>\n",
+       "      <td>36,326</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>Reserva Serra Bonita - BSF02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>5561246</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>https://zenodo.org/record/5561246/files/CAM036...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>41866_CAM036326_d.CR2</td>\n",
+       "      <td>images/Stratiomyidae/41866_CAM036326_d.CR2</td>\n",
+       "      <td>4d823bf9308f967d8c1b2ea9325dac1d</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21878</th>\n",
+       "      <td>CAM036326</td>\n",
+       "      <td>41867</td>\n",
+       "      <td>CAM036326_d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Filelist.csv</td>\n",
+       "      <td>https://zenodo.org/record/5561246</td>\n",
+       "      <td>36,326</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>Reserva Serra Bonita - BSF02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5561246</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>https://zenodo.org/record/5561246/files/CAM036...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>41867_CAM036326_d.JPG</td>\n",
+       "      <td>images/Stratiomyidae/41867_CAM036326_d.JPG</td>\n",
+       "      <td>23d2d33583c31c83b3e36563418204b8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21879</th>\n",
+       "      <td>CAM036326</td>\n",
+       "      <td>41868</td>\n",
+       "      <td>CAM036326_v.CR2</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Filelist.csv</td>\n",
+       "      <td>https://zenodo.org/record/5561246</td>\n",
+       "      <td>36,326</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>Reserva Serra Bonita - BSF02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>raw</td>\n",
+       "      <td>5561246</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>https://zenodo.org/record/5561246/files/CAM036...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>41868_CAM036326_v.CR2</td>\n",
+       "      <td>images/Stratiomyidae/41868_CAM036326_v.CR2</td>\n",
+       "      <td>d4a628aa306aa7deafa93d2b913ba4de</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21880</th>\n",
+       "      <td>CAM036326</td>\n",
+       "      <td>41869</td>\n",
+       "      <td>CAM036326_v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Filelist.csv</td>\n",
+       "      <td>https://zenodo.org/record/5561246</td>\n",
+       "      <td>36,326</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>Reserva Serra Bonita - BSF02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>5561246</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Stratiomyidae</td>\n",
+       "      <td>https://zenodo.org/record/5561246/files/CAM036...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>41869_CAM036326_v.JPG</td>\n",
+       "      <td>images/Stratiomyidae/41869_CAM036326_v.JPG</td>\n",
+       "      <td>0a8ed38b8ad9755831bb796febaa624e</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X       Image_name     View   zenodo_name  \\\n",
+       "21877  CAM036326  41866  CAM036326_d.CR2   dorsal  Filelist.csv   \n",
+       "21878  CAM036326  41867  CAM036326_d.JPG   dorsal  Filelist.csv   \n",
+       "21879  CAM036326  41868  CAM036326_v.CR2  ventral  Filelist.csv   \n",
+       "21880  CAM036326  41869  CAM036326_v.JPG  ventral  Filelist.csv   \n",
+       "\n",
+       "                             zenodo_link Sequence Taxonomic_Name  \\\n",
+       "21877  https://zenodo.org/record/5561246   36,326  Stratiomyidae   \n",
+       "21878  https://zenodo.org/record/5561246   36,326  Stratiomyidae   \n",
+       "21879  https://zenodo.org/record/5561246   36,326  Stratiomyidae   \n",
+       "21880  https://zenodo.org/record/5561246   36,326  Stratiomyidae   \n",
+       "\n",
+       "                           Locality Sample_accession  ... file_type  \\\n",
+       "21877  Reserva Serra Bonita - BSF02              NaN  ...       raw   \n",
+       "21878  Reserva Serra Bonita - BSF02              NaN  ...       jpg   \n",
+       "21879  Reserva Serra Bonita - BSF02              NaN  ...       raw   \n",
+       "21880  Reserva Serra Bonita - BSF02              NaN  ...       jpg   \n",
+       "\n",
+       "      record_number        species subspecies          genus  \\\n",
+       "21877       5561246  Stratiomyidae        NaN  Stratiomyidae   \n",
+       "21878       5561246  Stratiomyidae        NaN  Stratiomyidae   \n",
+       "21879       5561246  Stratiomyidae        NaN  Stratiomyidae   \n",
+       "21880       5561246  Stratiomyidae        NaN  Stratiomyidae   \n",
+       "\n",
+       "                                                file_url hybrid_stat  \\\n",
+       "21877  https://zenodo.org/record/5561246/files/CAM036...         NaN   \n",
+       "21878  https://zenodo.org/record/5561246/files/CAM036...         NaN   \n",
+       "21879  https://zenodo.org/record/5561246/files/CAM036...         NaN   \n",
+       "21880  https://zenodo.org/record/5561246/files/CAM036...         NaN   \n",
+       "\n",
+       "                    filename                                    filepath  \\\n",
+       "21877  41866_CAM036326_d.CR2  images/Stratiomyidae/41866_CAM036326_d.CR2   \n",
+       "21878  41867_CAM036326_d.JPG  images/Stratiomyidae/41867_CAM036326_d.JPG   \n",
+       "21879  41868_CAM036326_v.CR2  images/Stratiomyidae/41868_CAM036326_v.CR2   \n",
+       "21880  41869_CAM036326_v.JPG  images/Stratiomyidae/41869_CAM036326_v.JPG   \n",
+       "\n",
+       "                                    md5  \n",
+       "21877  4d823bf9308f967d8c1b2ea9325dac1d  \n",
+       "21878  23d2d33583c31c83b3e36563418204b8  \n",
+       "21879  d4a628aa306aa7deafa93d2b913ba4de  \n",
+       "21880  0a8ed38b8ad9755831bb796febaa624e  \n",
+       "\n",
+       "[4 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cams_stratiomyidae = list(master.loc[master[\"species\"] == \"Stratiomyidae\", \"CAMID\"])\n",
+    "print(f\"There are {len(cams_stratiomyidae)} entries with the taxonomic label 'Stratiomyidae', and {master.loc[master['CAMID'].isin(cams_stratiomyidae)].shape[0]} entries of that/those CAMID(s).\")\n",
+    "master.loc[master[\"species\"] == \"Stratiomyidae\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "missing_label_cams = cams_unknown + cams_stratiomyidae\n",
+    "master.loc[master[\"CAMID\"].isin(missing_label_cams)].to_csv(\"../metadata/Missing_taxa_download.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36193"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop Unknown\n",
+    "master = master.loc[master[\"species\"] != \"Unknown\"]\n",
+    "master.shape[0] # should now be 36,193"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36189"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop Stratiomyidae\n",
+    "master = master.loc[master[\"species\"] != \"Stratiomyidae\"]\n",
+    "master.shape[0] # should now be 36,189"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Rename 'Pteronymia', 'Ithomiinae', and 'Heliconius' as '<Genus> sp.' for consistency\n",
+    "rename_sp = ['Pteronymia', 'Ithomiinae', 'Heliconius']\n",
+    "\n",
+    "for species in rename_sp:\n",
+    "    master.loc[master[\"species\"] == species, \"Taxonomic_Name\"] = species + \" sp.\"\n",
+    "    master.loc[master[\"species\"] == species, \"species\"] = species + \" sp.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We have 25 'species' designations really labeled only to genus, 0 species designations that may not be labeled at the species level.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check, sp_list should now be 25, as Heliconius sp. and 'Pteronymia sp.' were already on there, and our one_word_list should now be zero\n",
+    "species_list = list(master.species.unique())\n",
+    "\n",
+    "sp_list = []\n",
+    "one_word_list = []\n",
+    "for species in species_list:\n",
+    "    if \"sp.\" in species:\n",
+    "        sp_list.append(species)\n",
+    "    if len(species.split(\" \")) == 1:\n",
+    "        one_word_list.append(species)\n",
+    "\n",
+    "print(f\"We have {len(sp_list)} 'species' designations really labeled only to genus, {len(one_word_list)} species designations that may not be labeled at the species level.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Stats Checks & Save\n",
+    "\n",
+    "We'll now save our updated master file, then check all our stats to update the Dataset Card appropriately before regenerating our other subsets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "master.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 36189 entries, 0 to 36210\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   CAMID             36189 non-null  object\n",
+      " 1   X                 36189 non-null  int64 \n",
+      " 2   Image_name        36189 non-null  object\n",
+      " 3   View              35410 non-null  object\n",
+      " 4   zenodo_name       36189 non-null  object\n",
+      " 5   zenodo_link       36189 non-null  object\n",
+      " 6   Sequence          35257 non-null  object\n",
+      " 7   Taxonomic_Name    36189 non-null  object\n",
+      " 8   Locality          23105 non-null  object\n",
+      " 9   Sample_accession  3647 non-null   object\n",
+      " 10  Collected_by      3043 non-null   object\n",
+      " 11  Other_ID          11325 non-null  object\n",
+      " 12  Date              23022 non-null  object\n",
+      " 13  Dataset           29112 non-null  object\n",
+      " 14  Store             27807 non-null  object\n",
+      " 15  Brood             13847 non-null  object\n",
+      " 16  Death_Date        269 non-null    object\n",
+      " 17  Cross_Type        4447 non-null   object\n",
+      " 18  Stage             6 non-null      object\n",
+      " 19  Sex               27510 non-null  object\n",
+      " 20  Unit_Type         25058 non-null  object\n",
+      " 21  file_type         36189 non-null  object\n",
+      " 22  record_number     36189 non-null  int64 \n",
+      " 23  species           36189 non-null  object\n",
+      " 24  subspecies        20400 non-null  object\n",
+      " 25  genus             36189 non-null  object\n",
+      " 26  file_url          36189 non-null  object\n",
+      " 27  hybrid_stat       20948 non-null  object\n",
+      " 28  filename          36189 non-null  object\n",
+      " 29  filepath          36189 non-null  object\n",
+      " 30  md5               36189 non-null  object\n",
+      "dtypes: int64(2), object(29)\n",
+      "memory usage: 8.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "master.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               11962\n",
+       "X                   36189\n",
+       "Image_name          36189\n",
+       "View                    7\n",
+       "zenodo_name            31\n",
+       "zenodo_link            29\n",
+       "Sequence            10876\n",
+       "Taxonomic_Name        362\n",
+       "Locality              643\n",
+       "Sample_accession     1559\n",
+       "Collected_by           12\n",
+       "Other_ID             3080\n",
+       "Date                  804\n",
+       "Dataset                 7\n",
+       "Store                 137\n",
+       "Brood                 224\n",
+       "Death_Date             79\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               4\n",
+       "file_type               3\n",
+       "record_number          29\n",
+       "species               242\n",
+       "subspecies            155\n",
+       "genus                  92\n",
+       "file_url            36189\n",
+       "hybrid_stat             2\n",
+       "filename            36189\n",
+       "filepath            36189\n",
+       "md5                 36189\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that a total of 4 \"unique\" Taxonomic Names and species were removed above: \"Unknown\", \"Heliconius\", \"Pteronymia\", \"Ithomiinae\", and \"Stratiomyidae\" were removed and \"Ithomiinae sp.\" was added. This process also resulted in the removal of 2 \"unique\" genera (\"Unknown\" and \"Stratiomyidae\").\n",
+    "\n",
+    "No records were removed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "      <th>filename</th>\n",
+       "      <th>filepath</th>\n",
+       "      <th>md5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>13300</th>\n",
+       "      <td>CAM013566</td>\n",
+       "      <td>26040</td>\n",
+       "      <td>13566_H_m_thelxiopeia_V.JPG.jpg</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "      <td>13,566</td>\n",
+       "      <td>Heliconius melpomene ssp. thelxiopeia</td>\n",
+       "      <td>Maripasoula</td>\n",
+       "      <td>ERS977708</td>\n",
+       "      <td>...</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>thelxiopeia</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2553977/files/13566_...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "      <td>26040_13566_H_m_thelxiopeia_V.JPG.jpg</td>\n",
+       "      <td>images/Heliconius melpomene ssp. thelxiopeia/2...</td>\n",
+       "      <td>b5ed68f937132cbbb04a81d550c9f17e</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13301</th>\n",
+       "      <td>CAM013566</td>\n",
+       "      <td>26039</td>\n",
+       "      <td>13566_H_m_thelxiopeia_D.JPG.jpg</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "      <td>13,566</td>\n",
+       "      <td>Heliconius melpomene ssp. thelxiopeia</td>\n",
+       "      <td>Maripasoula</td>\n",
+       "      <td>ERS977708</td>\n",
+       "      <td>...</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>thelxiopeia</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2553977/files/13566_...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "      <td>26039_13566_H_m_thelxiopeia_D.JPG.jpg</td>\n",
+       "      <td>images/Heliconius melpomene ssp. thelxiopeia/2...</td>\n",
+       "      <td>cb107e2d6a2c3c84b68bbf4e3cfa7af7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13302</th>\n",
+       "      <td>CAM013715</td>\n",
+       "      <td>26041</td>\n",
+       "      <td>13715_H_m_meriana_D.JPG.jpg</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "      <td>13,715</td>\n",
+       "      <td>Heliconius melpomene ssp. meriana</td>\n",
+       "      <td>N. Wakapou, Maripausala FR GUIANA</td>\n",
+       "      <td>ERS977704</td>\n",
+       "      <td>...</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>meriana</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2553977/files/13715_...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "      <td>26041_13715_H_m_meriana_D.JPG.jpg</td>\n",
+       "      <td>images/Heliconius melpomene ssp. meriana/26041...</td>\n",
+       "      <td>8c77ecb2d8d878f8ac8b097ad361deda</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13303</th>\n",
+       "      <td>CAM013715</td>\n",
+       "      <td>26042</td>\n",
+       "      <td>13715_H_m_meriana_V.JPG.jpg</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "      <td>13,715</td>\n",
+       "      <td>Heliconius melpomene ssp. meriana</td>\n",
+       "      <td>N. Wakapou, Maripausala FR GUIANA</td>\n",
+       "      <td>ERS977704</td>\n",
+       "      <td>...</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>meriana</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2553977/files/13715_...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "      <td>26042_13715_H_m_meriana_V.JPG.jpg</td>\n",
+       "      <td>images/Heliconius melpomene ssp. meriana/26042...</td>\n",
+       "      <td>fcf74c73537323bb90127cf92775ac08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13304</th>\n",
+       "      <td>CAM013819</td>\n",
+       "      <td>26044</td>\n",
+       "      <td>13819_H_m_meriana_V.JPG.jpg</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "      <td>13,819</td>\n",
+       "      <td>Heliconius melpomene ssp. meriana</td>\n",
+       "      <td>N. Wakapou, Maripausala FR GUIANA</td>\n",
+       "      <td>ERS977703</td>\n",
+       "      <td>...</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>meriana</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2553977/files/13819_...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "      <td>26044_13819_H_m_meriana_V.JPG.jpg</td>\n",
+       "      <td>images/Heliconius melpomene ssp. meriana/26044...</td>\n",
+       "      <td>601b67512a8c25e5f25a9da89841e8c2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13305</th>\n",
+       "      <td>CAM013819</td>\n",
+       "      <td>26043</td>\n",
+       "      <td>13819_H_m_meriana_D.JPG.jpg</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
+       "      <td>https://zenodo.org/record/2553977</td>\n",
+       "      <td>13,819</td>\n",
+       "      <td>Heliconius melpomene ssp. meriana</td>\n",
+       "      <td>N. Wakapou, Maripausala FR GUIANA</td>\n",
+       "      <td>ERS977703</td>\n",
+       "      <td>...</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>2553977</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>meriana</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/2553977/files/13819_...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "      <td>26043_13819_H_m_meriana_D.JPG.jpg</td>\n",
+       "      <td>images/Heliconius melpomene ssp. meriana/26043...</td>\n",
+       "      <td>fe4cf831c5203ad6c9010fc047b7fa10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X                       Image_name     View  \\\n",
+       "13300  CAM013566  26040  13566_H_m_thelxiopeia_V.JPG.jpg  ventral   \n",
+       "13301  CAM013566  26039  13566_H_m_thelxiopeia_D.JPG.jpg   dorsal   \n",
+       "13302  CAM013715  26041      13715_H_m_meriana_D.JPG.jpg   dorsal   \n",
+       "13303  CAM013715  26042      13715_H_m_meriana_V.JPG.jpg  ventral   \n",
+       "13304  CAM013819  26044      13819_H_m_meriana_V.JPG.jpg  ventral   \n",
+       "13305  CAM013819  26043      13819_H_m_meriana_D.JPG.jpg   dorsal   \n",
+       "\n",
+       "                                          zenodo_name  \\\n",
+       "13300  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "13301  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "13302  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "13303  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "13304  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "13305  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
+       "\n",
+       "                             zenodo_link Sequence  \\\n",
+       "13300  https://zenodo.org/record/2553977   13,566   \n",
+       "13301  https://zenodo.org/record/2553977   13,566   \n",
+       "13302  https://zenodo.org/record/2553977   13,715   \n",
+       "13303  https://zenodo.org/record/2553977   13,715   \n",
+       "13304  https://zenodo.org/record/2553977   13,819   \n",
+       "13305  https://zenodo.org/record/2553977   13,819   \n",
+       "\n",
+       "                              Taxonomic_Name  \\\n",
+       "13300  Heliconius melpomene ssp. thelxiopeia   \n",
+       "13301  Heliconius melpomene ssp. thelxiopeia   \n",
+       "13302      Heliconius melpomene ssp. meriana   \n",
+       "13303      Heliconius melpomene ssp. meriana   \n",
+       "13304      Heliconius melpomene ssp. meriana   \n",
+       "13305      Heliconius melpomene ssp. meriana   \n",
+       "\n",
+       "                                Locality Sample_accession  ... file_type  \\\n",
+       "13300                        Maripasoula        ERS977708  ...       jpg   \n",
+       "13301                        Maripasoula        ERS977708  ...       jpg   \n",
+       "13302  N. Wakapou, Maripausala FR GUIANA        ERS977704  ...       jpg   \n",
+       "13303  N. Wakapou, Maripausala FR GUIANA        ERS977704  ...       jpg   \n",
+       "13304  N. Wakapou, Maripausala FR GUIANA        ERS977703  ...       jpg   \n",
+       "13305  N. Wakapou, Maripausala FR GUIANA        ERS977703  ...       jpg   \n",
+       "\n",
+       "      record_number               species   subspecies       genus  \\\n",
+       "13300       2553977  Heliconius melpomene  thelxiopeia  Heliconius   \n",
+       "13301       2553977  Heliconius melpomene  thelxiopeia  Heliconius   \n",
+       "13302       2553977  Heliconius melpomene      meriana  Heliconius   \n",
+       "13303       2553977  Heliconius melpomene      meriana  Heliconius   \n",
+       "13304       2553977  Heliconius melpomene      meriana  Heliconius   \n",
+       "13305       2553977  Heliconius melpomene      meriana  Heliconius   \n",
+       "\n",
+       "                                                file_url hybrid_stat  \\\n",
+       "13300  https://zenodo.org/record/2553977/files/13566_...  non-hybrid   \n",
+       "13301  https://zenodo.org/record/2553977/files/13566_...  non-hybrid   \n",
+       "13302  https://zenodo.org/record/2553977/files/13715_...  non-hybrid   \n",
+       "13303  https://zenodo.org/record/2553977/files/13715_...  non-hybrid   \n",
+       "13304  https://zenodo.org/record/2553977/files/13819_...  non-hybrid   \n",
+       "13305  https://zenodo.org/record/2553977/files/13819_...  non-hybrid   \n",
+       "\n",
+       "                                    filename  \\\n",
+       "13300  26040_13566_H_m_thelxiopeia_V.JPG.jpg   \n",
+       "13301  26039_13566_H_m_thelxiopeia_D.JPG.jpg   \n",
+       "13302      26041_13715_H_m_meriana_D.JPG.jpg   \n",
+       "13303      26042_13715_H_m_meriana_V.JPG.jpg   \n",
+       "13304      26044_13819_H_m_meriana_V.JPG.jpg   \n",
+       "13305      26043_13819_H_m_meriana_D.JPG.jpg   \n",
+       "\n",
+       "                                                filepath  \\\n",
+       "13300  images/Heliconius melpomene ssp. thelxiopeia/2...   \n",
+       "13301  images/Heliconius melpomene ssp. thelxiopeia/2...   \n",
+       "13302  images/Heliconius melpomene ssp. meriana/26041...   \n",
+       "13303  images/Heliconius melpomene ssp. meriana/26042...   \n",
+       "13304  images/Heliconius melpomene ssp. meriana/26044...   \n",
+       "13305  images/Heliconius melpomene ssp. meriana/26043...   \n",
+       "\n",
+       "                                    md5  \n",
+       "13300  b5ed68f937132cbbb04a81d550c9f17e  \n",
+       "13301  cb107e2d6a2c3c84b68bbf4e3cfa7af7  \n",
+       "13302  8c77ecb2d8d878f8ac8b097ad361deda  \n",
+       "13303  fcf74c73537323bb90127cf92775ac08  \n",
+       "13304  601b67512a8c25e5f25a9da89841e8c2  \n",
+       "13305  fe4cf831c5203ad6c9010fc047b7fa10  \n",
+       "\n",
+       "[6 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master.loc[master.Stage.notna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID                820\n",
+       "X                   4447\n",
+       "Image_name          4447\n",
+       "View                   2\n",
+       "zenodo_name            4\n",
+       "zenodo_link            4\n",
+       "Sequence             820\n",
+       "Taxonomic_Name         8\n",
+       "Locality               0\n",
+       "Sample_accession       0\n",
+       "Collected_by           0\n",
+       "Other_ID               0\n",
+       "Date                 160\n",
+       "Dataset                1\n",
+       "Store                 10\n",
+       "Brood                 49\n",
+       "Death_Date             0\n",
+       "Cross_Type            30\n",
+       "Stage                  0\n",
+       "Sex                    2\n",
+       "Unit_Type              1\n",
+       "file_type              2\n",
+       "record_number          4\n",
+       "species                2\n",
+       "subspecies            21\n",
+       "genus                  1\n",
+       "file_url            4447\n",
+       "hybrid_stat            2\n",
+       "filename            4447\n",
+       "filepath            4447\n",
+       "md5                 4447\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master.loc[master.Cross_Type.notna()].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                17224\n",
+       "ventral               17156\n",
+       "hindwing dorsal         254\n",
+       "hindwing ventral        254\n",
+       "forewing dorsal         252\n",
+       "forewing ventral        252\n",
+       "dorsal and ventral       18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sex\n",
+       "Male       15904\n",
+       "Female     11323\n",
+       "Unknown      283\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master.Sex.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate master_licenses CSV file to re-run [`get_licenses.py`](https://huggingface.co/datasets/imageomics/Comparison-Subset-Jiggins/blob/main/scripts/get_licenses.py) for nicely formatted licensing info of the 29 included records.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(29, 2)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master_licenses = master[[\"record_number\", \"zenodo_link\"]]\n",
+    "master_licenses = master_licenses.loc[~master_licenses.duplicated([\"record_number\", \"zenodo_link\"], keep = \"first\")]\n",
+    "master_licenses.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>url</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4289223</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>4288311</td>\n",
+       "      <td>https://zenodo.org/record/4288311</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     record_number                                url\n",
+       "0          4289223  https://zenodo.org/record/4289223\n",
+       "145        4288311  https://zenodo.org/record/4288311"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master_licenses.rename(columns = {\"zenodo_link\": \"url\"}, inplace = True)\n",
+    "master_licenses.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save to CSV to run through get licenses script\n",
+    "master_licenses.to_csv(\"../metadata/master_licenses.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make Heliconius Master Subset\n",
+    "\n",
+    "Now make the Heliconius master CSV, and check stats to update README."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               10086\n",
+       "X                   29134\n",
+       "Image_name          29134\n",
+       "View                    3\n",
+       "zenodo_name            31\n",
+       "zenodo_link            29\n",
+       "Sequence             9008\n",
+       "Taxonomic_Name        131\n",
+       "Locality              471\n",
+       "Sample_accession     1559\n",
+       "Collected_by           12\n",
+       "Other_ID             1865\n",
+       "Date                  773\n",
+       "Dataset                 7\n",
+       "Store                 121\n",
+       "Brood                 224\n",
+       "Death_Date             79\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               4\n",
+       "file_type               3\n",
+       "record_number          29\n",
+       "species                36\n",
+       "subspecies            110\n",
+       "genus                   1\n",
+       "file_url            29134\n",
+       "hybrid_stat             2\n",
+       "filename            29134\n",
+       "filepath            29134\n",
+       "md5                 29134\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heliconius_master = master.loc[master.genus.str.lower() == \"heliconius\"].copy()\n",
+    "heliconius_master.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "View\n",
+       "dorsal                14202\n",
+       "ventral               14135\n",
+       "dorsal and ventral       18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heliconius_master.View.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save Heliconius Subset to CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "heliconius_master.to_csv(\"../Jiggins_Heliconius_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Make Dorsal Subset\n",
+    "\n",
+    "We'll now make a CSV of all dorsal images."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAMID               11746\n",
+       "X                   17748\n",
+       "Image_name          17748\n",
+       "View                    4\n",
+       "zenodo_name            31\n",
+       "zenodo_link            29\n",
+       "Sequence            10683\n",
+       "Taxonomic_Name        358\n",
+       "Locality              640\n",
+       "Sample_accession     1552\n",
+       "Collected_by           12\n",
+       "Other_ID             2889\n",
+       "Date                  788\n",
+       "Dataset                 7\n",
+       "Store                 137\n",
+       "Brood                 215\n",
+       "Death_Date             61\n",
+       "Cross_Type             30\n",
+       "Stage                   1\n",
+       "Sex                     3\n",
+       "Unit_Type               4\n",
+       "file_type               3\n",
+       "record_number          29\n",
+       "species               241\n",
+       "subspecies            152\n",
+       "genus                  92\n",
+       "file_url            17748\n",
+       "hybrid_stat             2\n",
+       "filename            17748\n",
+       "filepath            17748\n",
+       "md5                 17748\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_views = [view for view in list(master.View.dropna().unique()) if \"dorsal\" in view]\n",
+    "\n",
+    "dorsal_master = master.loc[master[\"View\"].isin(dorsal_views)].copy()\n",
+    "dorsal_master.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAM_dupe\n",
+       "False    15449\n",
+       "True      2299\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_master[\"CAM_dupe\"] = dorsal_master.duplicated([\"CAMID\", \"file_type\"], keep = False)\n",
+    "dorsal_master.CAM_dupe.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAM_dupe\n",
+       "False          15431\n",
+       "True            1793\n",
+       "single-wing      506\n",
+       "both-wings        18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Adding label for both wings alters counts, as in, some only have the double wing image\n",
+    "dorsal_master.loc[dorsal_master[\"View\"].isin([\"forewing dorsal\", \"hindwing dorsal\"]), \"CAM_dupe\"] = \"single-wing\"\n",
+    "dorsal_master.loc[dorsal_master[\"View\"] == \"dorsal and ventral\", \"CAM_dupe\"] = \"both-wings\"\n",
+    "dorsal_master.CAM_dupe.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "881\n",
+      "11446\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(dorsal_master.loc[dorsal_master[\"CAM_dupe\"] == True, \"CAMID\"].nunique())\n",
+    "print(dorsal_master.loc[dorsal_master[\"CAM_dupe\"] == False, \"CAMID\"].nunique())\n",
+    "dorsal_master.loc[dorsal_master[\"CAM_dupe\"] == True, \"record_number\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "raw    253\n",
+       "jpg    253\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_master.loc[dorsal_master[\"CAM_dupe\"] == \"single-wing\", \"file_type\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    17\n",
+       "tif     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_master.loc[dorsal_master[\"CAM_dupe\"] == \"both-wings\", \"file_type\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    1775\n",
+       "tif      16\n",
+       "raw       2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_master.loc[dorsal_master[\"CAM_dupe\"] == True, \"file_type\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "file_type\n",
+       "jpg    10700\n",
+       "raw     4713\n",
+       "tif       18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_master.loc[dorsal_master[\"CAM_dupe\"] == False, \"file_type\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False    10717\n",
+       "True      2028\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_master.loc[dorsal_master[\"file_type\"] == \"jpg\"].duplicated(subset = \"CAMID\", keep = False).value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False    4714\n",
+       "True      254\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dorsal_master.loc[dorsal_master[\"file_type\"] == \"raw\"].duplicated(subset = \"CAMID\", keep = False).value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generally speaking, it seems RAW images will be unique, as there is only one CAMID that gets duplicated across the raw images (2 photos of that specimen that are RAW) outside of the single wing views.\n",
+    "\n",
+    "### Save Dorsal Subset to CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dorsal_master.to_csv(\"../Jiggins_Zenodo_dorsal_Img_Master.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "std",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/EDA-DL-0-2.ipynb

@@ -0,0 +1,843 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 50 entries, 0 to 49\n",
+      "Data columns (total 4 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   File name   50 non-null     object\n",
+      " 1   ID          49 non-null     object\n",
+      " 2   Side        49 non-null     object\n",
+      " 3   Unnamed: 3  49 non-null     object\n",
+      "dtypes: object(4)\n",
+      "memory usage: 1.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Record 4289223 is most of our initial 404s (ultimately just second most), \n",
+    "# looks like they align with 0.2.rachel.blow.mel.tim.ikiam.csv, so we'll check\n",
+    "fof_df = pd.read_csv(\"../metadata/deduplication/Zenodo_meta_files/0.2.rachel.blow.mel.tim.ikiam.csv\", low_memory = False)\n",
+    "fof_df.info(show_counts=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>File name</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Side</th>\n",
+       "      <th>Unnamed: 3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CAM028501d.JPG</td>\n",
+       "      <td>CAM028501</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>d.JPG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CAM028501v.JPG</td>\n",
+       "      <td>CAM028501</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>v.JPG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CAM028502d.JPG</td>\n",
+       "      <td>CAM028502</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>d.JPG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CAM028502v.JPG</td>\n",
+       "      <td>CAM028502</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>v.JPG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CAM028503d.JPG</td>\n",
+       "      <td>CAM028503</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>d.JPG</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        File name         ID     Side Unnamed: 3\n",
+       "0  CAM028501d.JPG  CAM028501   dorsal      d.JPG\n",
+       "1  CAM028501v.JPG  CAM028501  ventral      v.JPG\n",
+       "2  CAM028502d.JPG  CAM028502   dorsal      d.JPG\n",
+       "3  CAM028502v.JPG  CAM028502  ventral      v.JPG\n",
+       "4  CAM028503d.JPG  CAM028503   dorsal      d.JPG"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fof_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These don't have a URL, but it could be reconstructed from the record number. Let's check if these are the files that had the 404 error from that record."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Image</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>dataset</th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>Response_status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Heliconius melpomene ssp. plesseni x malleti/5...</td>\n",
+       "      <td>https://zenodo.org/record/3082688/files/CAM017...</td>\n",
+       "      <td>3082688</td>\n",
+       "      <td>Patricio Salazar</td>\n",
+       "      <td>CAM017505</td>\n",
+       "      <td>404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Heliconius erato ssp. notabilis x lativitta/54...</td>\n",
+       "      <td>https://zenodo.org/record/3082688/files/CAM017...</td>\n",
+       "      <td>3082688</td>\n",
+       "      <td>Patricio Salazar</td>\n",
+       "      <td>CAM017693</td>\n",
+       "      <td>404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Heliconius erato ssp. notabilis x lativitta/55...</td>\n",
+       "      <td>https://zenodo.org/record/3082688/files/CAM017...</td>\n",
+       "      <td>3082688</td>\n",
+       "      <td>Patricio Salazar</td>\n",
+       "      <td>CAM017821</td>\n",
+       "      <td>404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Heliconius melpomene ssp. plesseni x malleti/1...</td>\n",
+       "      <td>https://zenodo.org/record/1748277/files/CAM017...</td>\n",
+       "      <td>1748277</td>\n",
+       "      <td>Patricio Salazar</td>\n",
+       "      <td>CAM017932</td>\n",
+       "      <td>404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Heliconius melpomene ssp. plesseni x malleti/1...</td>\n",
+       "      <td>https://zenodo.org/record/1748277/files/CAM017...</td>\n",
+       "      <td>1748277</td>\n",
+       "      <td>Patricio Salazar</td>\n",
+       "      <td>CAM017971</td>\n",
+       "      <td>404</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               Image  \\\n",
+       "0  Heliconius melpomene ssp. plesseni x malleti/5...   \n",
+       "1  Heliconius erato ssp. notabilis x lativitta/54...   \n",
+       "2  Heliconius erato ssp. notabilis x lativitta/55...   \n",
+       "3  Heliconius melpomene ssp. plesseni x malleti/1...   \n",
+       "4  Heliconius melpomene ssp. plesseni x malleti/1...   \n",
+       "\n",
+       "                                            file_url record_number  \\\n",
+       "0  https://zenodo.org/record/3082688/files/CAM017...       3082688   \n",
+       "1  https://zenodo.org/record/3082688/files/CAM017...       3082688   \n",
+       "2  https://zenodo.org/record/3082688/files/CAM017...       3082688   \n",
+       "3  https://zenodo.org/record/1748277/files/CAM017...       1748277   \n",
+       "4  https://zenodo.org/record/1748277/files/CAM017...       1748277   \n",
+       "\n",
+       "            dataset      CAMID Response_status  \n",
+       "0  Patricio Salazar  CAM017505             404  \n",
+       "1  Patricio Salazar  CAM017693             404  \n",
+       "2  Patricio Salazar  CAM017821             404  \n",
+       "3  Patricio Salazar  CAM017932             404  \n",
+       "4  Patricio Salazar  CAM017971             404  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ERROR_PATH = \"../metadata/deduplication/Jiggins_Zenodo_Img_Master_3477891Patch_error_log_(readable).json\"\n",
+    "with open(ERROR_PATH, \"r\") as error_file:\n",
+    "    errors = json.loads(error_file.read())\n",
+    "\n",
+    "error_df = pd.DataFrame(data = errors)\n",
+    "error_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "rec_4289223_df = error_df.loc[error_df[\"record_number\"] == \"4289223\"]\n",
+    "\n",
+    "missing_camids = []\n",
+    "for camid in list(rec_4289223_df[\"CAMID\"].unique()):\n",
+    "    if camid not in list(fof_df.ID):\n",
+    "        missing_camids.append(camid)\n",
+    "\n",
+    "print(rec_4289223_df.CAMID.nunique())\n",
+    "print(len(missing_camids))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like they're all captured in this CSV, so we can check their URLs?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['https://zenodo.org/record/4289223/files/CAM028509v.JPG',\n",
+       " 'https://zenodo.org/record/4289223/files/CAM028504d.JPG',\n",
+       " 'https://zenodo.org/record/4289223/files/CAM028508v.JPG',\n",
+       " 'https://zenodo.org/record/4289223/files/CAM028509d.JPG',\n",
+       " 'https://zenodo.org/record/4289223/files/CAM028502v.JPG',\n",
+       " 'https://zenodo.org/record/4289223/files/CAM028513v.CR2',\n",
+       " 'https://zenodo.org/record/4289223/files/CAM028514d.JPG']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(rec_4289223_df.file_url.sample(7))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are listed in the CSV, but do not show up on the [record page](https://zenodo.org/records/4289223)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We saw in `EDA-DL-0-1.ipynb` that none of the error images were in the multimedia file from record 3477891, so we don't have a clear patch. All error records were saved to a CSV through that notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Image', 'file_url', 'record_number', 'dataset', 'CAMID',\n",
+      "       'Response_status', 'filename', 'view'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "error_df = pd.read_csv(\"../metadata/deduplication/Jiggins_Zenodo_Img_Master_3477891Patch_error_log.csv\", low_memory = False)\n",
+    "print(error_df.columns)\n",
+    "\n",
+    "master = pd.read_csv(\"../metadata/Jiggins_Zenodo_Img_Master_3477891Patch.csv\", low_memory = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Image</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>dataset</th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>Response_status</th>\n",
+       "      <th>filename</th>\n",
+       "      <th>view</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Heliconius melpomene ssp. malleti/14972_CAM028...</td>\n",
+       "      <td>https://zenodo.org/record/4289223/files/CAM028...</td>\n",
+       "      <td>4289223</td>\n",
+       "      <td>Cambridge Collection</td>\n",
+       "      <td>CAM028501</td>\n",
+       "      <td>404</td>\n",
+       "      <td>14972_CAM028501v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Heliconius melpomene ssp. malleti/14971_CAM028...</td>\n",
+       "      <td>https://zenodo.org/record/4289223/files/CAM028...</td>\n",
+       "      <td>4289223</td>\n",
+       "      <td>Cambridge Collection</td>\n",
+       "      <td>CAM028501</td>\n",
+       "      <td>404</td>\n",
+       "      <td>14971_CAM028501d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Heliconius melpomene ssp. malleti/14973_CAM028...</td>\n",
+       "      <td>https://zenodo.org/record/4289223/files/CAM028...</td>\n",
+       "      <td>4289223</td>\n",
+       "      <td>Cambridge Collection</td>\n",
+       "      <td>CAM028502</td>\n",
+       "      <td>404</td>\n",
+       "      <td>14973_CAM028502d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Heliconius melpomene ssp. malleti/14974_CAM028...</td>\n",
+       "      <td>https://zenodo.org/record/4289223/files/CAM028...</td>\n",
+       "      <td>4289223</td>\n",
+       "      <td>Cambridge Collection</td>\n",
+       "      <td>CAM028502</td>\n",
+       "      <td>404</td>\n",
+       "      <td>14974_CAM028502v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Heliconius melpomene ssp. plesseni/14975_CAM02...</td>\n",
+       "      <td>https://zenodo.org/record/4289223/files/CAM028...</td>\n",
+       "      <td>4289223</td>\n",
+       "      <td>Cambridge Collection</td>\n",
+       "      <td>CAM028503</td>\n",
+       "      <td>404</td>\n",
+       "      <td>14975_CAM028503d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               Image  \\\n",
+       "5  Heliconius melpomene ssp. malleti/14972_CAM028...   \n",
+       "6  Heliconius melpomene ssp. malleti/14971_CAM028...   \n",
+       "7  Heliconius melpomene ssp. malleti/14973_CAM028...   \n",
+       "8  Heliconius melpomene ssp. malleti/14974_CAM028...   \n",
+       "9  Heliconius melpomene ssp. plesseni/14975_CAM02...   \n",
+       "\n",
+       "                                            file_url  record_number  \\\n",
+       "5  https://zenodo.org/record/4289223/files/CAM028...        4289223   \n",
+       "6  https://zenodo.org/record/4289223/files/CAM028...        4289223   \n",
+       "7  https://zenodo.org/record/4289223/files/CAM028...        4289223   \n",
+       "8  https://zenodo.org/record/4289223/files/CAM028...        4289223   \n",
+       "9  https://zenodo.org/record/4289223/files/CAM028...        4289223   \n",
+       "\n",
+       "                dataset      CAMID  Response_status              filename  \\\n",
+       "5  Cambridge Collection  CAM028501              404  14972_CAM028501v.JPG   \n",
+       "6  Cambridge Collection  CAM028501              404  14971_CAM028501d.JPG   \n",
+       "7  Cambridge Collection  CAM028502              404  14973_CAM028502d.JPG   \n",
+       "8  Cambridge Collection  CAM028502              404  14974_CAM028502v.JPG   \n",
+       "9  Cambridge Collection  CAM028503              404  14975_CAM028503d.JPG   \n",
+       "\n",
+       "      view  \n",
+       "5  ventral  \n",
+       "6   dorsal  \n",
+       "7   dorsal  \n",
+       "8  ventral  \n",
+       "9   dorsal  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_42 = error_df.loc[error_df[\"record_number\"] == 4289223].copy()\n",
+    "error_42.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_x(filename):\n",
+    "    return int(filename.split(\"_\")[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>24194</th>\n",
+       "      <td>CAM028501</td>\n",
+       "      <td>14972</td>\n",
+       "      <td>CAM028501v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>0.2.rachel.blow.mel.tim.ikiam.csv</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "      <td>28,501</td>\n",
+       "      <td>Heliconius melpomene ssp. malleti</td>\n",
+       "      <td>Río Pusuno</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4289223</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>malleti</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4289223/files/CAM028...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24195</th>\n",
+       "      <td>CAM028501</td>\n",
+       "      <td>14971</td>\n",
+       "      <td>CAM028501d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>0.2.rachel.blow.mel.tim.ikiam.csv</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "      <td>28,501</td>\n",
+       "      <td>Heliconius melpomene ssp. malleti</td>\n",
+       "      <td>Río Pusuno</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4289223</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>malleti</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4289223/files/CAM028...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24196</th>\n",
+       "      <td>CAM028502</td>\n",
+       "      <td>14973</td>\n",
+       "      <td>CAM028502d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>0.2.rachel.blow.mel.tim.ikiam.csv</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "      <td>28,502</td>\n",
+       "      <td>Heliconius melpomene ssp. malleti</td>\n",
+       "      <td>Río Pusuno</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4289223</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>malleti</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4289223/files/CAM028...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24197</th>\n",
+       "      <td>CAM028502</td>\n",
+       "      <td>14974</td>\n",
+       "      <td>CAM028502v.JPG</td>\n",
+       "      <td>ventral</td>\n",
+       "      <td>0.2.rachel.blow.mel.tim.ikiam.csv</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "      <td>28,502</td>\n",
+       "      <td>Heliconius melpomene ssp. malleti</td>\n",
+       "      <td>Río Pusuno</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4289223</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>malleti</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4289223/files/CAM028...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24198</th>\n",
+       "      <td>CAM028503</td>\n",
+       "      <td>14975</td>\n",
+       "      <td>CAM028503d.JPG</td>\n",
+       "      <td>dorsal</td>\n",
+       "      <td>0.2.rachel.blow.mel.tim.ikiam.csv</td>\n",
+       "      <td>https://zenodo.org/record/4289223</td>\n",
+       "      <td>28,503</td>\n",
+       "      <td>Heliconius melpomene ssp. plesseni</td>\n",
+       "      <td>Huaymayaco</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Wild</td>\n",
+       "      <td>jpg</td>\n",
+       "      <td>4289223</td>\n",
+       "      <td>Heliconius melpomene</td>\n",
+       "      <td>plesseni</td>\n",
+       "      <td>Heliconius</td>\n",
+       "      <td>https://zenodo.org/record/4289223/files/CAM028...</td>\n",
+       "      <td>non-hybrid</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CAMID      X      Image_name     View  \\\n",
+       "24194  CAM028501  14972  CAM028501v.JPG  ventral   \n",
+       "24195  CAM028501  14971  CAM028501d.JPG   dorsal   \n",
+       "24196  CAM028502  14973  CAM028502d.JPG   dorsal   \n",
+       "24197  CAM028502  14974  CAM028502v.JPG  ventral   \n",
+       "24198  CAM028503  14975  CAM028503d.JPG   dorsal   \n",
+       "\n",
+       "                             zenodo_name                        zenodo_link  \\\n",
+       "24194  0.2.rachel.blow.mel.tim.ikiam.csv  https://zenodo.org/record/4289223   \n",
+       "24195  0.2.rachel.blow.mel.tim.ikiam.csv  https://zenodo.org/record/4289223   \n",
+       "24196  0.2.rachel.blow.mel.tim.ikiam.csv  https://zenodo.org/record/4289223   \n",
+       "24197  0.2.rachel.blow.mel.tim.ikiam.csv  https://zenodo.org/record/4289223   \n",
+       "24198  0.2.rachel.blow.mel.tim.ikiam.csv  https://zenodo.org/record/4289223   \n",
+       "\n",
+       "      Sequence                      Taxonomic_Name    Locality  \\\n",
+       "24194   28,501   Heliconius melpomene ssp. malleti  Río Pusuno   \n",
+       "24195   28,501   Heliconius melpomene ssp. malleti  Río Pusuno   \n",
+       "24196   28,502   Heliconius melpomene ssp. malleti  Río Pusuno   \n",
+       "24197   28,502   Heliconius melpomene ssp. malleti  Río Pusuno   \n",
+       "24198   28,503  Heliconius melpomene ssp. plesseni  Huaymayaco   \n",
+       "\n",
+       "      Sample_accession  ... Stage   Sex Unit_Type file_type record_number  \\\n",
+       "24194              NaN  ...   NaN  Male      Wild       jpg       4289223   \n",
+       "24195              NaN  ...   NaN  Male      Wild       jpg       4289223   \n",
+       "24196              NaN  ...   NaN  Male      Wild       jpg       4289223   \n",
+       "24197              NaN  ...   NaN  Male      Wild       jpg       4289223   \n",
+       "24198              NaN  ...   NaN  Male      Wild       jpg       4289223   \n",
+       "\n",
+       "                    species subspecies       genus  \\\n",
+       "24194  Heliconius melpomene    malleti  Heliconius   \n",
+       "24195  Heliconius melpomene    malleti  Heliconius   \n",
+       "24196  Heliconius melpomene    malleti  Heliconius   \n",
+       "24197  Heliconius melpomene    malleti  Heliconius   \n",
+       "24198  Heliconius melpomene   plesseni  Heliconius   \n",
+       "\n",
+       "                                                file_url hybrid_stat  \n",
+       "24194  https://zenodo.org/record/4289223/files/CAM028...  non-hybrid  \n",
+       "24195  https://zenodo.org/record/4289223/files/CAM028...  non-hybrid  \n",
+       "24196  https://zenodo.org/record/4289223/files/CAM028...  non-hybrid  \n",
+       "24197  https://zenodo.org/record/4289223/files/CAM028...  non-hybrid  \n",
+       "24198  https://zenodo.org/record/4289223/files/CAM028...  non-hybrid  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error_42[\"X\"] = error_42[\"filename\"].apply(get_x)\n",
+    "\n",
+    "master_42 = master.loc[master[\"X\"].isin(list(error_42[\"X\"]))].copy()\n",
+    "master_42.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAMID</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Image_name</th>\n",
+       "      <th>View</th>\n",
+       "      <th>zenodo_name</th>\n",
+       "      <th>zenodo_link</th>\n",
+       "      <th>Sequence</th>\n",
+       "      <th>Taxonomic_Name</th>\n",
+       "      <th>Locality</th>\n",
+       "      <th>Sample_accession</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Stage</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Unit_Type</th>\n",
+       "      <th>file_type</th>\n",
+       "      <th>record_number</th>\n",
+       "      <th>species</th>\n",
+       "      <th>subspecies</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>file_url</th>\n",
+       "      <th>hybrid_stat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [CAMID, X, Image_name, View, zenodo_name, zenodo_link, Sequence, Taxonomic_Name, Locality, Sample_accession, Collected_by, Other_ID, Date, Dataset, Store, Brood, Death_Date, Cross_Type, Stage, Sex, Unit_Type, file_type, record_number, species, subspecies, genus, file_url, hybrid_stat]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "master.loc[(master[\"Image_name\"].isin(list(master_42.Image_name.unique()))) & (~master[\"record_number\"] == 4289223)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Can't align these elsewhere based on `Image_name` either."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "std",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

scripts/download_jiggins_subset.py

@@ -1,7 +1,11 @@
-# Modified code from https://huggingface.co/datasets/imageomics/Comparison-Subset-Jiggins/blob/main/scripts/download_jiggins_subset.py
-# For downloading Jiggins images from any of the master CSV files
-# Generates Checksum file for all images download
-# logs image download in json file
+# Built on Michelle's download script: https://huggingface.co/datasets/imageomics/Comparison-Subset-Jiggins/blob/977a934e1eef18f6b6152da430ac83ba6f7bd30f/download_jiggins_subset.py
+# with modification of David's redo loop: https://github.com/Imageomics/data-fwg/blob/anomaly-data-challenge/HDR-anomaly-data-challenge/notebooks/download_images.ipynb
+# and expanded logging and file checks. Further added checksum calculation for all downloaded images at end.
+
+# Script to download Jiggins images from any of the master CSV files.
+# Generates Checksum file for all images downloaded (<master filename>_checksums.csv).
+# Logs image downloads and failures in json files (<master filename>_log.json & <master filename>_error_log.json).
+# Logs record numbers and response codes as strings, not int64.
 
 import requests
 import shutil
@@ -12,9 +16,26 @@ from checksum import get_checksums
 
 from tqdm import tqdm
 import os
+import sys
+import time
 import argparse
 
 
+EXPECTED_COLS = ["CAMID",
+                 "X",
+                 "Image_name",
+                 "file_url",
+                 "Taxonomic_Name",
+                 "record_number",
+                 "Dataset"
+                 ]
+
+REDO_CODE_LIST = [429, 500, 502, 503, 504]
+
+# Reset to appropriate index if download gets interrupted.
+STARTING_INDEX = 0
+
+
 def parse_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--csv", required=True, help="Path to CSV file with urls.", nargs="?")
@@ -23,52 +44,122 @@ def parse_args():
     return parser.parse_args()
 
 
-def update_log(log_data, index, image, url, response_code):
+def log_response(log_data, index, image, url, record_number, dataset, cam_id, response_code):
     # log status
     log_entry = {}
     log_entry["Image"] = image
-    log_entry["zenodo_link"] = url
-    log_entry["Response_status"] = response_code
+    log_entry["file_url"] = url
+    log_entry["record_number"] = str(record_number) #int64 has problems sometimes
+    log_entry["dataset"] = dataset
+    log_entry["CAMID"] = cam_id
+    log_entry["Response_status"] = str(response_code)
     log_data[index] = log_entry
 
     return log_data
 
 
-def download_images(csv_path, image_folder, log_filepath):
-    #load csv 
-    jiggins_data = pd.read_csv(csv_path)
+def update_log(log, index, filepath):
+    # save logs
+    with open(filepath, "a") as log_file:
+        json.dump(log[index], log_file, indent = 4)
+        log_file.write("\n")
+
+
+def download_images(jiggins_data, image_folder, log_filepath, error_log_filepath):
     log_data = {}
+    log_errors = {}
 
     for i in tqdm(range(0, len(jiggins_data))) : 
+        # species will really be <Genus> <species> ssp. <subspecies>, where subspecies indicated
         species = jiggins_data["Taxonomic_Name"][i]
         image_name = jiggins_data["X"][i].astype(str) + "_" + jiggins_data["Image_name"][i]
+        record_number = jiggins_data["record_number"][i]
 
-        #download the image from url is not already downloaded
+        # download the image from url if not already downloaded
+        # Will attempt to download everything in CSV (image_name is unique: <X>_<Image_name>), unless download restarted
         if os.path.exists(f"{image_folder}/{species}/{image_name}") != True:
             #get image from url
-            url = jiggins_data["zenodo_link"][i]
-            response = requests.get(url, stream=True)
-
-            # log status
-            log_data = update_log(log_data,
-                                  index = i,
-                                  image = species + "/" + image_name,
-                                  url = url,
-                                  response_code = response.status_code
-                                  )
-            
-            #create the species appropriate folder if necessary
-            if os.path.exists(f"{image_folder}/{species}") != True:
-                os.makedirs(f"{image_folder}/{species}", exist_ok=False)
+            url = jiggins_data["file_url"][i]
+            dataset = jiggins_data["Dataset"][i]
+            cam_id = jiggins_data["CAMID"][i]
 
             #download the image
-            if response.status_code == 200:
-                with open(f"{image_folder}/{species}/{image_name}", "wb") as out_file:
-                    shutil.copyfileobj(response.raw, out_file)
+            redo = True
+            max_redos = 2
+            while redo and max_redos > 0:
+                try:
+                    response = requests.get(url, stream=True)
+                except Exception as e:
+                    redo = True
+                    max_redos -= 1
+                    if max_redos <= 0:
+                        log_errors = log_response(log_errors,
+                                        index = i,
+                                        image = species + "/" + image_name,
+                                        url = url,
+                                        record_number = record_number,
+                                        dataset = dataset,
+                                        cam_id = cam_id,
+                                        response_code = str(e))
+                        update_log(log = log_errors, index = i, filepath = error_log_filepath)
+                        
+                if response.status_code == 200:
+                    redo = False
+                    # log status
+                    log_data = log_response(log_data,
+                                        index = i,
+                                        image = species + "/" + image_name,
+                                        url = url,
+                                        record_number = record_number,
+                                        dataset = dataset,
+                                        cam_id = cam_id,
+                                        response_code = response.status_code
+                                        )
+                    update_log(log = log_data, index = i, filepath = log_filepath)
+                    
+                    #create the species appropriate folder if necessary
+                    if os.path.exists(f"{image_folder}/{species}") != True:
+                        os.makedirs(f"{image_folder}/{species}", exist_ok=False)
+                    
+                    # save image to appropriate folder
+                    with open(f"{image_folder}/{species}/{image_name}", "wb") as out_file:
+                        shutil.copyfileobj(response.raw, out_file)
+            
+                # check for too many requests
+                elif response.status_code in REDO_CODE_LIST:
+                    redo = True
+                    max_redos -= 1
+                    if max_redos <= 0:
+                        log_errors = log_response(log_errors,
+                                        index = i,
+                                        image = species + "/" + image_name,
+                                        url = url,
+                                        record_number = record_number,
+                                        dataset = dataset,
+                                        cam_id = cam_id,
+                                        response_code = response.status_code)
+                        update_log(log = log_errors, index = i, filepath = error_log_filepath)
+
+                    else:
+                        time.sleep(1)
+                else: #other fail, eg. 404
+                    redo = False
+                    log_errors = log_response(log_errors,
+                                            index = i,
+                                            image = species + "/" + image_name,
+                                            url = url,
+                                            record_number = record_number,
+                                            dataset = dataset,
+                                            cam_id = cam_id,
+                                            response_code = response.status_code)
+                    update_log(log = log_errors, index = i, filepath = error_log_filepath)
+
             del response
     
-    with open(log_filepath, "w") as log_file:
-        json.dump(log_data, log_file, indent = 4)
+        else:
+            if i > STARTING_INDEX:
+                # No need to print if download is restarted due to interruption (set STARTING_INDEX accordingly).
+                print(f"duplicate image: {jiggins_data['X']}, {jiggins_data['Image_name']}, from record {record_number}")
 
     return
 
@@ -79,20 +170,32 @@ def main():
     csv_path = args.csv #path to our csv with urls to download images from
     image_folder = args.output  #folder where dataset will be downloaded to
 
-    # log file location
+    # log file location (folder of source CSV)
     log_filepath = csv_path.split(".")[0] + "_log.json"
+    error_log_filepath = csv_path.split(".")[0] + "_error_log.json"
+
+    #load csv 
+    jiggins_data = pd.read_csv(csv_path, low_memory = False)
+
+    # Check for required columns
+    missing_cols = []
+    for col in EXPECTED_COLS:
+        if col not in list(jiggins_data.columns):
+            missing_cols.append(col)
+    if len(missing_cols) > 0:
+        sys.exit(f"The CSV is missing column(s): {missing_cols}")
 
     #dowload images from urls
-    download_images(csv_path, image_folder, log_filepath)
+    download_images(jiggins_data, image_folder, log_filepath, error_log_filepath)
 
     # generate checksums and save CSV to same folder as CSV used for download
     checksum_path = csv_path.split(".")[0] + "_checksums.csv"
     get_checksums(image_folder, checksum_path)
 
     print(f"Images downloaded from {csv_path} to {image_folder}.")
-    print(f"Checksums recorded in {checksum_path} and download log is in {log_filepath}.")
+    print(f"Checksums recorded in {checksum_path} and download logs are in {log_filepath} and {error_log_filepath}.")
 
     return
 
 if __name__ == "__main__":
-    main() 
+    main()