About
AQUA (Advanced Query Architecture for the SPARC Portal) an application that aims at improving the search capabilities of the SPARC Portal.
What is SPARC?
- The NIH's Stimulating Peripheral Activity to Relieve Conditions (SPARC) program seeks to accelerate development of therapeutic devices that modulate electrical activity in nerves to improve organ function.
What are the FAIR SPARC Data Guidelines?
- All SPARC-funded researchers must curate their datasets following the SPARC Data Standards (SDS) and share them openly on the Pennsieve data platform as per their funding agreement with SPARC.
What are the challenges?
- Currently, the search feature of the SPARC Portal is very limited. It does not recognize nearby words (typos and close-matches) or synonyms and provides limited result information.
What does AQUA do?
- AQUA makes the current SPARC Portal search engine smarter at understanding user query and improve search result display. The end goal is to improve exponentially the visibility of the SPARC datasets.
Technology
AQUA for SPARC utilized 2 main tool groups to develop the User interface and the Back end. The former includes the HTML-CSS-JS trio using: VueJS and NuxtJS. The latter is implemented using Python, Docker, SciGraph, and SQLite.
Development Approach
AQUA for SPARC is distributed as an open-source application with an MIT License. Anyone is free to fork our GitHub repository and make their own changes if they would like. If you would like to submit a feature modification, or feature suggestion, please feel free to submit an issue on the repository.
Origin Story
The AQUA project was first born as an idea at the 2021 NIH SPARC Codeathon. The idea was to improve user query understandability and result display of the SPARC Portal search engine. AQUA received the fourth-place prize at the Codeathon.
Impact
AQUA: an Advanced QUery Architecture for the SPARC Portal
Citation
Shahidi, N., Lin, X., Munarko, Y., Rasmy, L., & Ngo, T. (2021). AQUA: an Advanced QUery Architecture for the SPARC Portal. F1000Research, 10, 930. https://doi.org/10.12688/f1000research.73018.1