Global Research Trends in Research Data Management (RDM) – A Scientometric View
Bibliographic record
Abstract
The study focuses on Research Data Management (RDM), aiming to demonstrate how RDM is evolving globally. It provides a systematic mapping of the current literature to aid in identifying core coverage and reflecting on potential trends using the scientometric technique. For detailed thematic analysis, performance analysis, network representation, science mapping, and scientific collaboration, this study used bibliometric tools such as the R package Biblioshiny, ScientoPy, and VOSviewer. Two premier indexing databases, SCOPUS and Web of Science, which extensively cover RDM literature, are appropriately considered. Furthermore, all retrieved documents are refined by language, document type, and irrelevant keywords for practical analysis. This study covers the Research Data Management (RDM) literature from 1926 to 2020, with 6263 documents published from 1666 sources and 15,545 authors. The most common domains discovered in RDM research are Computer Science, Library and Information Science. RDM is primarily familiar and cultivated in nations such as the United States, China, the United Kingdom, Canada, Germany, and Australia are pioneers in RDM research. The findings are vital for researchers working on RDM projects and policy formulation. This research helps to identify the literature's strengths and potential gaps. This secondary data contributes significantly to the scientific landscape in scientific production, network architecture, source clustering, and international collaboration, the evolution of dominant subjects and countries, and science mapping of productive word frequency. https://dorl.net/dor/20.1001.1.20088302.2022.20.4.8.7
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 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 | 0.026 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.074 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.020 | 0.041 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 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; both teacher heads agree on what is shown here.
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".