Access to scientific data: The Social and Technical Challenges and strategies
Bibliographic record
Abstract
Abstract The practice of science has changed in the last three decades due to the rapid development of information and communication technologies and massive increases in computing capacity, made manifest by the Internet. As the International Council for Science (ICSU) describes in its recently released five‐year strategic plan, there is now more scientific data and information that is freely and openly available. This environment enables scientists around the world access to the most up‐to‐date data and information from his or her desktop. “Secondary analyses of data, and the combining of data from multiple sources, are opening up exciting new scientific horizons. Scientific publication practices are changing rapidly.” (ICSU, 2005, 16‐17) These revolutionary changes in the creation, management, and use of scientific data and information have significant economic and social implications. First among them are the economic and legal aspects provoked by open sharing versus intellectual property protection of scientific data. In addition to the impact of these issues, there are technical challenges in managing the life cycle of scientific data. Long‐term preservation strategies are evolving to ensure that the authenticity of scientific data can be verified, and to enable knowledge discovery and interoperability via metadata representations of the data collections. To maximize the impact of scientific data, the information community needs to promote new thinking and structures in society to properly collect, preserve and distribute this resource. In response to the issues and challenges in access to scientific data and information, we have arranged for a comprehensive session that examines the topic from a holistic view. For full coverage, two panels are required: one that covers the current social and policy contexts and one that covers the system developments being driven by these broader issues. The experts on both panels will contribute their experience from conducting social, economic, and technical research of scientific data and information and invite the ASIS&T annual meeting attendees to join them in discussion.
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 | 0.134 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.024 | 0.060 |
| Scholarly communication | 0.055 | 0.080 |
| Open science | 0.007 | 0.041 |
| Research integrity | 0.030 | 0.028 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".