Field-wide assessment of differential HT-seq from NCBI GEO database

We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. - Fixed some missing RAW files, that had failed to download. - This release includes GEO series up to Dec-31, 2020; - Fixed xlrd missing optional dependency, which affected import of some xls files, previously we were using only openpyxl (thanks to anonymous reviewer); - All files in supplementary _RAW.tar files were checked for p values, previously _RAW.tar files were completely omitted, alas (thanks to anonymous reviewer). Archived dataset contains following files: - output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0). - output/document_summaries.csv, document summaries of NCBI GEO series - output/publications.csv, publication info of NCBI GEO series - output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series - output/single-cell.csv, single cell experiments - spots.csv, NCBI SRA sequencing run metadata - suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row. - suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row. Workflow to produce this dataset is available on Github at rstats-tartu/geo-htseq.