Discovering research data management trends from job advertisements using a text-mining approach
Bibliographic record
Abstract
In today’s data-driven culture, research data management (RDM) is essential for the research community. The demand for reusing research datasets is a challenging and diverse process for the scientific community. Despite this, it is essential in RDM to discover trends and themes using text mining, which is scarce. The purpose of this study is to employ text mining to discover insights from job advertisements associated with RDM profiles, which collected 810 advertisements. We found RDM-related patterns using latent Dirichlet allocation (LDA) and identified three key contexts. The first is ‘research services in libraries’, with the topics of research services, research information, research universities, collection processes and library services. The second context is ‘research data’, which includes RDM, business data, university data, research data, health research, science research, social science research, data centres, data services, statistical software, digital scholarship and digital preservation. The third context is ‘workplace environment’, and the topics are leadership, work development and scientific position. Job title normalisation reveals names such as ‘data librarian’, ‘librarian’, ‘director’, ‘data curator’, ‘data manager’, ‘research data librarian’, ‘data specialist’ and ‘data officer’ are frequently employed. Focusing on titles with a single or double occurrence is new and interesting for developing nations. Reputable institutions such as Harvard, Stanford and the Massachusetts Institute of Technology, as well as countries such as the United States, the United Kingdom, Canada and Germany, are the major participants in RDM practises and services. This discovery will assist higher education institutions, RDM stakeholders, which aid in the formulation of curriculum, and job seekers to familiarise themselves with the themes.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".