Bibliographic record
Abstract
Research infrastructures enabling scalable sharing of data are proliferating, at the institutional, project/consortium and national/international levels. Many of these are domain-specific, but there also is a growing focus on Research Commons that enable general data sharing across domains. Examples include the EUDAT Collaborative Data Infrastructure in Europe, the Gakunin RDM platform in Japan, and the Research Commons planned by Canada’s Digital Research Alliance (DRA). In some cases, such as Gakunin RDM, Research Commons are architected as an integrated set of complementary services, but in most existing and planned Research Commons a disparate set of unconnected services is offered. This paper describes the integration between RSpace, an active content management digital research platform, and iRODS, a policy-driven data management platform, and explains how it is designed to serve as a flexible connecting component that ties together other services that make up a Research Commons. The paper discusses how, by tying together otherwise unconnected services, inclusion of RSpace + iRODS in Research Commons enables streamlined flows of data and metadata between services, enhancing FAIR principles. This will be illustrated by considering inclusion of RSpace + iRODS in the two specific examples of Canada’s proposed Research Commons and the EUDAT Collaborative Data Infrastructure. In both cases the interaction between RSpace, iRODS, and individual services provided by the Commons will be discussed, and a comparison will be made between the two Commons and the benefits derived from the inclusion of RSpace + iRODS.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.053 |
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".