id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-102835-71ome9h8	Levinson, Maxwell Adam	FAIRSCAPE: A Framework for FAIR and Reproducible Biomedical Analytics	2020-08-15		.txt	text/plain	4772	288	51	All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software. We set out to construct a provenance-aware computational data lake, as described above, by significantly extending and refactoring the identifier and metadata services framework we and our colleagues developed in the NIH Data Commons Pilot Project Consortium (Timothy Clark et al. We extended and re-engineered this framework over time to track and visualize computations and their evidence, to manage the computational objects (such as data and software) as well as their metadata, to analyze very large datasets with horizontal scale-out, to support neuroimaging workflows, and to make it generally more easy for scientists and computational analysts to use, by providing Binder and Notebook services (Jupyter et al. It supports transparent disclosure of the Evidence Graphs of computed results, with access to the persistent identifiers of the cited data or software, and to their stored metadata.	./cache/cord-102835-71ome9h8.txt	./txt/cord-102835-71ome9h8.txt