Sustainability During Instability: Long-Lived Life Science Databases and Science Funding Outlook in the United States
Imker, H. J.
Show abstract
For decades, life science researchers have had cost-free, unrestricted access to data through online databases. However, the sustainability of even well-established resources was already tenuous, and abrupt changes in science funding in the United States seems poised to exacerbate these challenges. This study employed a multiple mini case study approach, triangulating semi-structured interviews with supplemental documentation to investigate 9 diverse, long-standing databases that had previously received support from U.S. federal agencies. The research explored each databases purpose and use, examined current and emerging funding strategies, and considered the potential consequences if any of these databases were forced to shut down. Participating databases include: 1) BHL: Biodiversity Heritage Library, 2) MorphoBank, 3) OMIM: Online Mendelian Inheritance in Man, 4) ORDB: Olfactory Receptor Database, 5) rrnDB: ribosomal RNA operon copy number database, 6) VEuPathDB: Eukaryotic Pathogen, Vector, and Host Informatics Resources, 7) WormAtlas, and 8-9) two databases that wished to remain anonymous. Findings revealed anticipation of higher barriers to data access and reuse, loss of subject matter expertise now and into the future, and lost or interrupted opportunities. Additionally, real impacts have already begun through redirection of energy, abrupt reductions in support, and increased competition for funding. Sustainability models in light of the current funding outlook in the US are discussed.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Extraction of biological terms using large language models enhances the usability of metadata in the BioSample database 92%
- Open Humans: A platform for participant-centered research and personal data exploration 92%
- Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.