Where is the best standard place to store single-cell datasets to be accessible for the community?
Is GEO the right place? I looked up some single-cell datasets and the number of samples is quite low does each sample include all the cells fastq files?

 - [GSE57249][1]: Has just 56 samples 
 - [GSE57872][2]: Has 587 samples 
 - [GSE145926][3]: Has 21 samples
 - ?

Related to sharing the data set, is it worth to publish a Data Note of a single cell data set? (Tradeoff between salami-splicing publications and work on a new manuscript, citation/reference of the data set for multiple purposes...)


  [1]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57249
  [2]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57872
  [3]: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE145926
I was running on the internet with the same query and I think yes GEO is the best place to deposit raw sequencing data. There are other single cell specific data repositories, for example, Broad Institute's [Single Cell Portal][1], [UCSC Cell Browser][2], [PanglaoDB][3], [Singel Cell Studies][4], and perhaps plenty of others. Usually, these portal has an advantage over GEO, where you can interactively access the scRNA-Seq data like visualizing cluster embeddings, visualizing gene expression, cluster-specific pathways distribution, etc. Nevertheless, you can upload the processed data (expression matrices, metadata, R objects, etc) to GEO, but the user needs to download and visualize it by themselves.

I have uploaded 10X genomics data to the GEO once and in my opinion, you can directly submit the samples (multiplexed; raw sequencing data) so that users can start processing your data from the scratch. 

So in my case, we had 3 samples with approximately 14k cells in each. I submitted 3 samples with the processed data (by CellRanger, feature count matrix, metadata, cell barcodes, and Seurat object in .rds format).

I hope this would help you during your submission.

Cheers!!

;)


  [1]: https://singlecell.broadinstitute.org/single_cell
  [2]: https://cells.ucsc.edu/
  [3]: https://panglaodb.se/papers.html
  [4]: https://www.nxn.se/single-cell-studies/