Information Technologies and Infrastructures for Research Data Aggregation. The Experience of Canada, Netherlands and Germany
Bibliographic record
Abstract
In the paper we focus on the experience of some Networked Readness Index (NRI) leading countries. We are interested in their actions aimed at creating and using e-infrastructures and IT for the research data aggregation. As far as quite many of NRI leaders are EU countries, we also take into account EU approaches to the task mentioned. We study some steering papers and some particular IT-projects in the field as well. As a result of the analysis we ascertain that some approaches in different countries have much in common. All of the countries examined acknowledge the problem of e-infrastructures for research data storage and processing to be vital. The countries work hard in order to build and integrate it with the others. We can clearly see some differences caused by traditions and authorities. Sometimes we even could estimate the level of success of a particular country on the way to the problem solution. We also indicate some common elements for all e-infrastructures for research data storage and processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".