Towards an Open Research Information Ecosystem: Synergies with Open Software and Open Metadata Providers
Bibliographic record
Abstract
VIVO and OpenAlex are complementary initiatives in the realm of open research information. VIVO, an open-source software, focuses on the aggregation of profiles of actors in science to enable visualization of an institution’s scholarly activities through comprehensive profiles of researchers, their publications, projects, and aQiliations. OpenAlex serves as a global index of scholarly publications with a massive database of scholarly works, authors, institutions, and accompanying metadata. In this presentation, we will highlight the power and benefit to all involved stakeholders of connecting opensource software, built with open standards, to open research information. These benefits include sovereignity over the metadata of an institution’s research activities and output, plus improved and enriched metadata for OpenAlex, the institution using VIVO, and all potential consumers of that data across the global research community. VIVO can utilize OpenAlex’s vast metadata to fill its profiles, ensuring that researchers’ outputs are more accurately represented and more easily discoverable. At the same time, OpenAlex can benefit from the rich, structured data provided by VIVO, such as affiliation details and project specifics, to deepen the context and accuracy of its records.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.111 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.036 | 0.097 |
| Open science | 0.007 | 0.066 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".