Series of Jupyter notebook tutorials showing how to use sparc-me, a python tool to explore, enhance, and expand SPARC datasets and their descriptions in accordance with FAIR principles.
Study Purpose: To create the first publicly programmatic approach for: (1) accessing and interrogating all metadata fields in SDS datasets. (2) Creating new SDS datasets.
Data Collection: Not applicable; computational study.
Primary Conclusion: sparc-me will elevate the impact of the SPARC program by providing the fundamental tools needed by users to programmatically interact with SDS datasets and efficiently build novel resources and tools from SPARC data. This includes: (1) Supporting SPARC Data and Resource Centre (DRC) and communnity developments, (2) Supporting and promoting reuse/harmonisation/compatibility with other research initiatives. (3) Enabling extensions of the SDS specification to be proposed/explored (similar to BIDS extensions).
Experimental Design:This This project was developed during the 2022 SPARC FAIR Codeathon. Researchers developed a python module called the SPARC Metadata Editor (sparc-me) that can be used to enhance the FAIRness of SPARC data.
Completeness: This dataset is complete.
Subjects & Samples: This is a computational dataset; thus no subjects are described.
Primary vs derivative data: Not applicable. This is a computational study. Only a configuration file to view and run the simulation on the oSPARC platform is provided.
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