metadata
license: apache-2.0
language:
- en
tags:
- NASA
- Earth
- Satellite
- Knowledge Graph
- Machine Learning
NASA Knowledge Graph Dataset
Dataset Summary
The NASA Knowledge Graph Dataset is an expansive graph-based dataset designed to integrate and interconnect information about satellite datasets, scientific publications, instruments, platforms, projects, data centers, and science keywords. This knowledge graph is particularly focused on datasets managed by NASA's Distributed Active Archive Centers (DAACs), which are NASA's data repositories responsible for archiving and distributing scientific data. In addition to NASA DAACs, the graph includes datasets from 184 data providers worldwide, including various government agencies and academic institutions.
The primary goal of the NASA Knowledge Graph is to bridge scientific publications with the datasets they reference, facilitating deeper insights and research opportunities within NASA's scientific and data ecosystem. By organizing these interconnections within a graph structure, this dataset enables advanced analyses, such as discovering influential datasets, understanding research trends, and exploring scientific collaborations.
What's Changed (v1.1.0) - Feb 25th, 2025
1. Node Changes
- Total Nodes: Increased from 135,764 to 145,678 (+9,914)
- New Node Counts:
- Dataset: Increased from 6,390 to 6,821 (+431)
- DataCenter: Increased from 184 to 197 (+13)
- Instrument: Increased from 867 to 897 (+30)
- Platform: Increased from 442 to 451 (+9)
- Project: Increased from 333 to 351 (+18)
- Publication: Increased from 125,939 to 135,352 (+9,413)
- ScienceKeyword: Remained the same at 1,609
2. Relationship Changes
- Total Relationships: Increased from 365,857 to 406,515 (+40,658)
- Updated Relationship Counts:
- CITES: Decreased from 208,670 to 208,429 (-241)
- HAS_APPLIED_RESEARCH_AREA: Increased from 89,039 to 119,695 (+30,656)
- HAS_DATASET: Increased from 9,017 to 9,834 (+817)
- HAS_INSTRUMENT: Increased from 2,469 to 2,526 (+57)
- HAS_PLATFORM: Increased from 9,884 to 10,398 (+514)
- HAS_SCIENCEKEYWORD: Increased from 20,436 to 21,571 (+1,135)
- HAS_SUBCATEGORY: Remained the same at 1,823
- OF_PROJECT: Increased from 6,049 to 6,378 (+329)
- USES_DATASET: Increased from 18,470 to 25,861 (+7,391)
3. Property Changes
- Removed Properties:
pagerank_globalfastrp_embedding_with_labels
- Added Properties:
pagerank_publication_dataset(Float) forDatasetandPublicationnodes
These changes reflect the addition of new datasets, publications, and relationships, alongside the streamlined properties to improve data efficiency and analysis potential.
Data Integrity
Each file in the dataset has a SHA-256 checksum to verify its integrity:
| File Name | SHA-256 Checksum |
|---|---|
graph.cypher |
99710792de412ce1977f1172d0458de1b0ea3d387e33adcd32adbec63c2238c1 |
graph.graphml |
00f2848602104c073a97ca65ff7d3e10c5bdddcc1fe5ede69cb21d4bfbbe8a89 |
graph.json |
bb415c8579c31b2ec80cc83dedc5660ead6be2f5f2fcd3c162433ec73d6f5280 |
Verification
To verify the integrity of each file, calculate its SHA-256 checksum and compare it with the hashes provided above.
You can use the following Python code to calculate the SHA-256 checksum:
import hashlib
def calculate_sha256(filepath):
sha256_hash = hashlib.sha256()
with open(filepath, "rb") as f:
for byte_block in iter(lambda: f.read(4096), b""):
sha256_hash.update(byte_block)
return sha256_hash.hexdigest()
Dataset Structure
Nodes and Properties
The knowledge graph consists of seven main node types, each representing a different entity within NASA's ecosystem:
Dataset Structure
Nodes and Properties
1. Dataset
- Description: Represents satellite datasets, particularly those managed by NASA DAACs, along with datasets from other governmental and academic data providers. These nodes contain metadata and attributes specific to each dataset.
- Properties:
globalId(String): Unique identifier for the dataset.doi(String): Digital Object Identifier.shortName(String): Abbreviated name of the dataset.longName(String): Full name of the dataset.abstract(String): Brief summary of the dataset.cmrId(String): Common Metadata Repository ID.daac(String): Distributed Active Archive Center associated with the dataset.temporalFrequency(String): Frequency of data collection (e.g., daily, monthly).temporalExtentStart(Date): Start date of the dataset's temporal coverage.temporalExtentEnd(Date): End date of the dataset's temporal coverage.nwCorner(Geo-Coordinate): Northwest corner coordinate of spatial coverage.seCorner(Geo-Coordinate): Southeast corner coordinate of spatial coverage.landingPageUrl(URL): Webpage link to the dataset.pagerank_publication_dataset(Float): PageRank score based on dataset-publication influence.
2. Publication
- Description: This node type captures publications that reference or use datasets, particularly those from NASA DAACs and other included data providers. By linking datasets and publications, this node type enables analysis of scientific impact and research usage of NASA’s datasets.
- Properties:
globalId(String): Unique identifier for the publication.DOI(String): Digital Object Identifier.title(String): Title of the publication.abstract(String): Summary of the publication's content.authors(List): List of authors.year(Integer): Year of publication.pagerank_publication_dataset(Float): PageRank score based on publication-dataset influence.
3. ScienceKeyword
- Description: Represents scientific keywords used to classify datasets and publications. Keywords provide topical context and aid in identifying research trends and related areas.
- Properties:
globalId(String): Unique identifier.name(String): Name of the science keyword.
4. Instrument
- Description: Instruments used in data collection, often mounted on platforms like satellites. Instruments are linked to the datasets they help generate.
- Properties:
globalId(String): Unique identifier.shortName(String): Abbreviated name.longName(String): Full name.
5. Platform
- Description: Platforms, such as satellites or airborne vehicles, that host instruments for data collection.
- Properties:
globalId(String): Unique identifier.shortName(String): Abbreviated name.longName(String): Full name.Type(String): Type of platform.
6. Project
- Description: NASA projects associated with datasets, often indicating a funding source or collaborative initiative for data collection and research.
- Properties:
globalId(String): Unique identifier.shortName(String): Abbreviated name.longName(String): Full name.
7. DataCenter
- Description: Represents data centers, primarily NASA DAACs, as well as other data providers in academia and government who manage and distribute datasets.
- Properties:
globalId(String): Unique identifier.shortName(String): Abbreviated name.longName(String): Full name.url(URL): Website of the data center.
Relationships
The knowledge graph includes several relationship types that define how nodes are interconnected.
1. HAS_DATASET
- Description: Connects a
DataCenterto itsDataset(s). - Properties: None.
2. OF_PROJECT
- Description: Links a
Datasetto aProject. - Properties: None.
3. HAS_PLATFORM
- Description: Associates a
Datasetwith aPlatform. - Properties: None.
4. HAS_INSTRUMENT
- Description: Connects a
Platformto anInstrument. - Properties: None.
5. HAS_SCIENCEKEYWORD
- Description: Links a
Datasetto aScienceKeyword. - Properties: None.
6. HAS_SUBCATEGORY
- Description: Defines hierarchical relationships between
ScienceKeywordnodes. - Properties: None.
7. CITES
- Description: Represents citation relationships between
Publicationnodes, indicating how research builds on previous work. - Properties: None.
8. HAS_APPLIED_RESEARCH_AREA
- Description: Associates a
Publicationwith aScienceKeyword. - Properties: None.
Statistics
Data Statistics
Total Counts
| Type | Count |
|---|---|
| Total Nodes | 145,678 |
| Total Relationships | 406,515 |
Node Label Counts
| Node Label | Count |
|---|---|
| Dataset | 6,821 |
| DataCenter | 197 |
| Project | 351 |
| Platform | 451 |
| Instrument | 897 |
| ScienceKeyword | 1,609 |
| Publication | 135,352 |
Relationship Label Counts
| Relationship Label | Count |
|---|---|
| CITES | 208,429 |
| HAS_APPLIEDRESEARCHAREA | 119,695 |
| HAS_DATASET | 9,834 |
| HAS_INSTRUMENT | 2,526 |
| HAS_PLATFORM | 10,398 |
| HAS_SCIENCEKEYWORD | 21,571 |
| HAS_SUBCATEGORY | 1,823 |
| OF_PROJECT | 6,378 |
| USES_DATASET | 25,861 |
Data Formats
The Knowledge Graph Dataset is available in three formats:
1. JSON
- File:
graph.json - Description: A hierarchical data format representing nodes and relationships. Each node includes its properties, such as
globalId,doi, andpagerank_global. - Usage: Suitable for web applications and APIs, and for use cases where hierarchical data structures are preferred.
Loading the JSON Format
To load the JSON file into a graph database using Python and multiprocessing you can using the following script:
import json
from tqdm import tqdm
from collections import defaultdict
from multiprocessing import Pool, cpu_count
from neo4j import GraphDatabase
# Batch size for processing
BATCH_SIZE = 100
# Neo4j credentials (replace with environment variables or placeholders)
NEO4J_URI = "bolt://<your-neo4j-host>:<port>" # e.g., "bolt://localhost:7687"
NEO4J_USER = "<your-username>"
NEO4J_PASSWORD = "<your-password>"
def ingest_data(file_path):
# Initialize counters and label trackers
node_label_counts = defaultdict(int)
relationship_label_counts = defaultdict(int)
node_count = 0
relationship_count = 0
with open(file_path, "r") as f:
nodes = []
relationships = []
# Read and categorize nodes and relationships, and count labels
for line in tqdm(f, desc="Reading JSON Lines"):
obj = json.loads(line.strip())
if obj["type"] == "node":
nodes.append(obj)
node_count += 1
for label in obj["labels"]:
node_label_counts[label] += 1
elif obj["type"] == "relationship":
relationships.append(obj)
relationship_count += 1
relationship_label_counts[obj["label"]] += 1
# Print statistics
print("\n=== Data Statistics ===")
print(f"Total Nodes: {node_count}")
print(f"Total Relationships: {relationship_count}")
print("\nNode Label Counts:")
for label, count in node_label_counts.items():
print(f" {label}: {count}")
print("\nRelationship Label Counts:")
for label, count in relationship_label_counts.items():
print(f" {label}: {count}")
print("=======================")
# Multiprocess node ingestion
print("Starting Node Ingestion...")
node_batches = [nodes[i : i + BATCH_SIZE] for i in range(0, len(nodes), BATCH_SIZE)]
with Pool(processes=cpu_count()) as pool:
list(
tqdm(
pool.imap(ingest_nodes_batch, node_batches),
total=len(node_batches),
desc="Ingesting Nodes",
)
)
# Multiprocess relationship ingestion
print("Starting Relationship Ingestion...")
relationship_batches = [
relationships[i : i + BATCH_SIZE]
for i in range(0, len(relationships), BATCH_SIZE)
]
with Pool(processes=cpu_count()) as pool:
list(
tqdm(
pool.imap(ingest_relationships_batch, relationship_batches),
total=len(relationship_batches),
desc="Ingesting Relationships",
)
)
def ingest_nodes_batch(batch):
with GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD)) as driver:
with driver.session() as session:
for node in batch:
try:
label = node["labels"][0] # Assumes a single label per node
query = f"""
MERGE (n:{label} {{globalId: $globalId}})
SET n += $properties
"""
session.run(
query,
globalId=node["properties"]["globalId"],
properties=node["properties"],
)
except Exception as e:
print(
f"Error ingesting node with globalId {node['properties']['globalId']}: {e}"
)
def ingest_relationships_batch(batch):
with GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD)) as driver:
with driver.session() as session:
for relationship in batch:
try:
rel_type = relationship[
"label"
] # Use the label for the relationship
query = f"""
MATCH (start {{globalId: $start_globalId}})
MATCH (end {{globalId: $end_globalId}})
MERGE (start)-[r:{rel_type}]->(end)
"""
session.run(
query,
start_globalId=relationship["start"]["properties"]["globalId"],
end_globalId=relationship["end"]["properties"]["globalId"],
)
except Exception as e:
print(
f"Error ingesting relationship with label {relationship['label']}: {e}"
)
if __name__ == "__main__":
# Path to the JSON file
JSON_FILE_PATH = "<path-to-your-graph.json>"
# Run the ingestion process
ingest_data(JSON_FILE_PATH)
2. GraphML
- File:
graph.graphml - Description: An XML-based format well-suited for complex graph structures and metadata-rich representations.
- Usage: Compatible with graph visualization and analysis tools, including Gephi, Cytoscape, and databases that support GraphML import.
Loading the GraphML Format
To import the GraphML file into a graph database with APOC support, use the following command:
CALL apoc.import.graphml("path/to/graph.graphml", {readLabels: true})
3. Cypher
- File:
graph.cypher - Description: A series of Cypher commands to recreate the knowledge graph structure.
- Usage: Useful for recreating the graph in any Cypher-compatible graph database.
Loading the Cypher Format
To load the Cypher script, execute it directly using a command-line interface for your graph database:
neo4j-shell -file path/to/graph.cypher
4. Loading the Knowledge Graph into PyTorch Geometric (PyG)
This knowledge graph can be loaded into PyG (PyTorch Geometric) for further processing, analysis, or model training. Below is an example script that shows how to load the JSON data into a PyG-compatible HeteroData object.
The script first reads the JSON data, processes nodes and relationships, and then loads everything into a HeteroData object for use with PyG.
import json
import torch
from torch_geometric.data import HeteroData
from collections import defaultdict
# Load JSON data from file
file_path = "path/to/graph.json" # Replace with your actual file path
graph_data = []
with open(file_path, "r") as f:
for line in f:
try:
graph_data.append(json.loads(line))
except json.JSONDecodeError as e:
print(f"Error decoding JSON line: {e}")
continue
# Initialize HeteroData object
data = HeteroData()
# Mapping for node indices per node type
node_mappings = defaultdict(dict)
# Temporary storage for properties to reduce concatenation cost
node_properties = defaultdict(lambda: defaultdict(list))
edge_indices = defaultdict(lambda: defaultdict(list))
# Process each item in the loaded JSON data
for item in graph_data:
if item['type'] == 'node':
node_type = item['labels'][0] # Assuming first label is the node type
node_id = item['id']
properties = item['properties']
# Store the node index mapping
node_index = len(node_mappings[node_type])
node_mappings[node_type][node_id] = node_index
# Store properties temporarily by type
for key, value in properties.items():
if isinstance(value, list) and all(isinstance(v, (int, float)) for v in value):
node_properties[node_type][key].append(torch.tensor(value, dtype=torch.float))
elif isinstance(value, (int, float)):
node_properties[node_type][key].append(torch.tensor([value], dtype=torch.float))
else:
node_properties[node_type][key].append(value) # non-numeric properties as lists
elif item['type'] == 'relationship':
start_type = item['start']['labels'][0]
end_type = item['end']['labels'][0]
start_id = item['start']['id']
end_id = item['end']['id']
edge_type = item['label']
# Map start and end node indices
start_idx = node_mappings[start_type][start_id]
end_idx = node_mappings[end_type][end_id]
# Append to edge list
edge_indices[(start_type, edge_type, end_type)]['start'].append(start_idx)
edge_indices[(start_type, edge_type, end_type)]['end'].append(end_idx)
# Finalize node properties by batch processing
for node_type, properties in node_properties.items():
data[node_type].num_nodes = len(node_mappings[node_type])
for key, values in properties.items():
if isinstance(values[0], torch.Tensor):
data[node_type][key] = torch.stack(values)
else:
data[node_type][key] = values # Keep non-tensor properties as lists
# Finalize edge indices in bulk
for (start_type, edge_type, end_type), indices in edge_indices.items():
edge_index = torch.tensor([indices['start'], indices['end']], dtype=torch.long)
data[start_type, edge_type, end_type].edge_index = edge_index
# Display statistics for verification
print("Nodes and Properties:")
for node_type in data.node_types:
print(f"\nNode Type: {node_type}")
print(f"Number of Nodes: {data[node_type].num_nodes}")
for key, value in data[node_type].items():
if key != 'num_nodes':
if isinstance(value, torch.Tensor):
print(f" - {key}: {value.shape}")
else:
print(f" - {key}: {len(value)} items (non-numeric)")
print("\nEdges and Types:")
for edge_type in data.edge_types:
edge_index = data[edge_type].edge_index
print(f"Edge Type: {edge_type} - Number of Edges: {edge_index.size(1)} - Shape: {edge_index.shape}")
Citation
Please cite the dataset as follows:
NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC). (2024). Knowledge Graph of NASA Earth Observations Satellite Datasets and Related Research Publications [Data set]. DOI: 10.57967/hf/3463
BibTeX
@misc {nasa_goddard_earth_sciences_data_and_information_services_center__(ges-disc)_2024,
author = { {NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC)} },
title = { nasa-eo-knowledge-graph },
year = 2024,
url = { https://huggingface.co/datasets/nasa-gesdisc/nasa-eo-knowledge-graph },
doi = { 10.57967/hf/3463 },
publisher = { Hugging Face }
}
References
For details on the process of collecting these publications, please refer to:
Gerasimov, I., Savtchenko, A., Alfred, J., Acker, J., Wei, J., & KC, B. (2024). Bridging the Gap: Enhancing Prominence and Provenance of NASA Datasets in Research Publications. Data Science Journal, 23(1). DOI: 10.5334/dsj-2024-001
For any questions or further information, please contact:
- Armin Mehrabian: [email protected]
- Irina Gerasimov: [email protected]
- Kendall Gilbert: [email protected]