Cyberinfrastructure in Canada: challenges, opportunities, and threats
Bibliographic record
Abstract
Cyberinfrastructure is used variously to encompass a wide variety of developments, including-infrastructure, cloud computing, cyberenvironments, grid computing, virtual research environments, e-research and e-science. Cyberinfrastructure has been used in Canada to describe the various underpinnings of data acquisitions, data storage, data management, data mining and other online manipulations of data. Another layer has been added by the need to link researchers around the globe and to provide the means for collaborative activity to advance knowledge. This paper presents an overview of recent cyberinfrastucture initiatives within Canada and compares Canadian activity with developments elsewhere in the world. Is Canada behind, ahead, or about in the same place as others? What are the challenges and the opportunities? Canada’s developments are being facilitated by CANARIE’s investments through its network-enabled platform program (NEP) which is providing the platforms for analysis of data. Are Canadian libraries seizing the opportunities provided by these new challenges? Initiatives like ODESI and Synergies are helping and the paper will address additional efforts which could be made by research libraries to deal with the data deluge.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".