Bibliographic record
Abstract
In 2013, Research Data Canada (RDC) established an Infrastructure subcommittees (RDC-I) to tackle research data management infrastructure issues. This document summarizes the findings of the subcommittee related to activity 1: “identity infrastructure available for data in Canada”. The expected outcome of the activity is a “list of infrastructure in existence or planned, with capabilities and management structure outlined”. The document presents the results of the RDC-I team investigation, to the best of their knowledge. It does not claim to provide an exhaustive list of facilities and infrastructures but makes every effort to cover the subject with ample details. The document is divided up in three sections and three appendices. The first section establishes a few definitions that frame the concepts behind “Digital Infrastructure” when it relates to research data. The second section presents some of the existing actors on the Canadian scene who play an enabling role by providing some of the necessary technical elements. In a third section, the document lists the larger such infrastructures i.e., those with a national scope that cover one or more connected science disciplines, have a reasonably long life expectancy and reasonably sustained funding. The appendices provide more details and compare research data support infrastructures briefly listed in the sections below. The last appendix covers more specifically research data that are typically attached to short- to medium duration research projects. Those will typically require either continued funding beyond the project end or a well planned transfer of their data holdings to long-lived data centres to ensure their maintenance and accessibility.
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 | 0.025 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.026 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.024 |
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".