Research data management and FAIR compliance through popular research data repositories: an exploratory study
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
Purpose The present study examines the features and services of four research data repositories (RDRs): Dataverse, Dryad, Zenodo and Figshare. The study explores whether these RDRs adhere to the FAIR principles and suggests the features and services that need to be added to enhance their functionality. Design/methodology/approach An online survey was conducted to identify the features of four popular RDRs. The study evaluates the features of four popular RDRs using the specially designed checklist method based on FAIR principles. The checklist is based on 11 construct progressions used to evaluate the features and services of four popular RDRs. The final checklist contains 11 constructs with 199 check spots. Findings Figshare has attained the highest features for findability, accessibility, interoperability and reusability. It is identified that Figshare, with 116 (58.3%) scored the highest points and ranked no 1. It has also been found that Figshare recorded the highest features in 6 constructs out of the 11. Dataverse, with 90 (45.2%) features, ranked 2nd; Zenodo, with 86 (43.2%), ranked 3rd. The lowest features are found in Dryad, with 85 (42.7%). Furthermore, the study found that all four popular RDRs have poor features relating to “research data access metrics” features 23.3%, “output, data license and other advanced features” 22.6%. The very less features recorded in the category “services in RDRs” are 15.9%. Therefore, the features of these three constructs framed under FAIR need to be upgraded to improve the functionalities of the four popular RDRs. Practical implications The findings of the study are useful for researchers in choosing the appropriate RDR for accessing and sharing data and can be used by data scientists, librarians and policymakers in starting the research data management services in academic and research institutions. Furthermore, the study can also help impart research data literacy instructions to researchers and faculty members. Originality/value This study has prepared a special checklist based on FAIR principles to evaluate the features and services of RDRs. No prior study has been conducted to explore the features of popular RDRs and their compliance with FAIR principles based on the checklist method.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.031 |
| Open science | 0.039 | 0.158 |
| Research integrity | 0.000 | 0.001 |
| 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 it