VIVO software 1.10.0 release
Bibliographic record
Abstract
Summary VIVO [Pronunciation: vee-voh] is member-supported, enterprise open source software and an ontology for representing scholarship. VIVO supports recording, editing, searching, browsing and visualizing scholarly activity. VIVO encourages research discovery, expert finding, network analysis and assessment of research impact. VIVO is easily extended to support additional domains of scholarly activity. VIVO uses an ontology to represent people, papers, grants, projects, datasets, resources, and other elements of research and scholarship as linked open data. The ontology can be used to create RDF that can be loaded into VIVO. VIVO RDF data is easily exported for use in other applications. VIVO includes Vitro, a domain-free engine for managing linked open data, the JFact reasoner, SolR for search, SPARQL query, Jena as a triple store, supporting both TDB and SDB on MySQL, uses D3 for visualizations, and provides multiple APIs, including Triple Pattern Fragments for rapid remote access to specified data. Using VIVO, organizations can represent the activities and accomplishments of their scholars as linked open data, and share that data with others. Acknowledgements The authors wish to acknowledge the foundational work done on VIVO, and VIVO concepts by the team at the Mann Agricultural Library, Cornell University, led by Jon Corson-Rikert. The authors also wish to acknowledge NIH grant 1U24RR029822-01 to the first author, which funded the work of more than 120 co-investigators in the further development of the VIVO software, and NIH grant xxxxxxx to Dr. Melissa Haendel of Oregon Health Science University which funded significant advances in the VIVO Integrated Semantic Framework, which VIVO uses to represent scholarship. Finally, the authors wish to acknowledge the many hundreds of members of the VIVO community around the world, who volunteer their time and effort to advance the art of representing scholarship as linked open data. The work described here builds on the work of many others.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.325 | 0.283 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".