YummyData: providing high-quality open life science data
By Yasunori Yamamoto, Atsuko Yamaguchi, Andrea Splendiani. 2018
36 citations recorded by the source corpus.
Abstract Many life science datasets are now available via Linked Data technologies, meaning that they are represented in a common format (the Resource Description Framework), and are accessible via standard APIs (SPARQL endpoints). While this is an important step toward developing an interoperable bioinformatics data landscape, it also creates a new set of obstacles, as it is often difficult for researchers to find the datasets they need. Different providers frequently offer the same datasets, with different levels of support: as well as having more or less up-to-date data, some providers add metadata to describe the content, structures, and ontologies of the stored datasets while others do not. We currently lack a place where researchers can go to easily assess datasets from different providers in terms of metrics such as service stability or metadata richness. We also lack a space for collecting feedback and improving data providers’ awareness of user needs. To address this issue, we have developed YummyData, which consists of two components. One periodically polls a curated list of SPARQL endpoints, monitoring the states of their Linked Data implementations and content. The other presents the information measured for the endpoints and provides a forum for discussion and feedback. YummyData is designed to improve the findability and reusability of life science datasets provided as Linked Data and to foster its adoption. It is freely accessible at http://yummydata.org/. Database URL: http://yummydata.org/
Subfields: Artificial Intelligence, Molecular Biology
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- Christophe Dessimoz
- Natasha M. Glover
- David Moi
- Adrian M. Altenhoff
- Alex Warwick Vesztrocy
- Clément-Marie Train
- Tarcisio Mendes de Farias
Citations in the corpus
- OMA orthology in 2021: website overhaul, conserved isoforms, ancestral gene order and more (2020, background): “Recent years have witnessed an increasing adoption of Resource Description Framework (RDF) among bioinformatics databases to model their data, as evidenced by the YummyData monitor (33), which currently highlights more than 65 biological and biomedical resources with SPARQL endpoints for querying th”