SPARClink: an interactive tool to visualize the impact of the SPARC program [version 1; peer review: 2 approved, 1 approved with reservations]

The National Institutes of Health (NIH) Stimulating Peripheral Activity to Relieve Conditions (SPARC) program seeks to accelerate the development of therapeutic devices that modulate electrical activity in nerves to improve organ function. SPARC-funded researchers are generating rich datasets from n...

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Vydáno v:F1000 research Ročník 11; s. 124
Hlavní autoři: Soundarajan, Sanjay, Kuruppu, Sachira, Singh, Ashutosh, Kim, Jongchan, Achalla, Monalisa
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Faculty of 1000 Ltd 2022
F1000 Research Limited
F1000 Research Ltd
Témata:
Algorithms
Biotechnology
Citations
Data Curation
data sharing
Databases, Factual
Datasets
eng
FAIR
Funding
Interfaces
Keywords
Machine learning
Medical research
Metadata
Nerves
Neuromodulation
Osteonectin
Researchers
Software
Software Tool
User experience
Visualization
Visualization
machine-learning
citations
FAIR
data sharing
ISSN:2046-1402, 2046-1402
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Shrnutí:The National Institutes of Health (NIH) Stimulating Peripheral Activity to Relieve Conditions (SPARC) program seeks to accelerate the development of therapeutic devices that modulate electrical activity in nerves to improve organ function. SPARC-funded researchers are generating rich datasets from neuromodulation research that are curated and shared according to FAIR (Findable, Accessible, Interoperable, and Reusable) guidelines and are accessible to the public on the SPARC data portal. Keeping track of the utilization of these datasets within the larger research community is a feature that will benefit data-generating researchers in showcasing the impact of their SPARC outcomes. This will also allow the SPARC program to display the impact of the FAIR data curation and sharing practices that have been implemented. This manuscript provides the methods and outcomes of SPARClink, our web tool for visualizing the impact of SPARC, which won the Second prize at the 2021 SPARC FAIR Codeathon. With SPARClink, we built a system that automatically and continuously finds new published SPARC scientific outputs (datasets, publications, protocols) and the external resources referring to them. SPARC datasets and protocols are queried using publicly accessible REST application programming interfaces (APIs, provided by Pennsieve and Protocols.io) and stored in a publicly accessible database. Citation information for these resources is retrieved using the NIH reporter API and National Center for Biotechnology Information (NCBI) Entrez system. A novel knowledge graph-based structure was created to visualize the results of these queries and showcase the impact that the FAIR data principles can have on the research landscape when they are adopted by a consortium.
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Competing interests: NIH SPARC is the primary funder for the publication of this research article.
ISSN:2046-1402
2046-1402
DOI:10.12688/f1000research.75071.1