PUBLICATION OF RESEARCH DATA MANAGEMENT IN OPEN ACCESS JOURNAL ANALYSIS BASED ON SCOPUS DATA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study aims to determine: (1) the number of open access resources for research data management publications indexed by Scopus, including the year of publication, source of publication, authors, institutions, countries, types of documents and funding agencies; (2) mapping research data management based on keywords. The results of the study showed that the number of open access resources for research data management publications has started since 1981 and the number has continued to increase starting in 2014 and the highest number occurred in 2019, namely 49 publications. The most publicized journals that open access to research data management was the Data Science Journal, which was 11 publications. The most productive author of conducting research data management publications was Cox, A.M. and Pinfield, S. The largest institutions contributing to the publication of open access research data management were the University of Toronto and New York University. The countries that contributed the most were the United States with 50 publications, then China with 38 publications. The most open access research data management in the form of articles as many as 107 and 37 conference paper publications. The institutions that provided the most funding sponsors were the Deutsche Forschungsgemeinschaft and the National Science Foundation. The results of keyword mapping with VOSViewer showed that big data, research data management, information management, data management, medical research topics, software, information processing, and metadata were the most researched topics.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.006 | 0.024 |
| Open science | 0.015 | 0.014 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it