Digging deeper into data citations: Recognizing and rewarding data work
Bibliographic record
Abstract
Citations and metrics are central features in evaluating academic careers. As researchers increasingly engage in open science, data citations have emerged as potential mechanisms for evaluating and rewarding data sharing and reuse in academic assessments. Despite this, we still lack critical information about the data citation practices and motivations of researchers themselves, information which is needed to contextualize the use of such metrics. Here, we present the results of a semi-structured interview study with researchers across disciplines exploring their data referencing practices and motivations, as well as how they would like their ‘data work’ (including data sharing) to be rewarded and evaluated. As a whole, our findings confirm a lack of standard practices for referencing data and provide new insights into the social and scientific reasons motivating data referencing. While our results show an overall skepticism toward the use of citation-based metrics in evaluations, they also suggest that researchers are caught between traditional and emergent modes of assessment for recognizing data work. Furthermore, we find that rather than valuing data citations as rewards, our participants value creating data objects which are useful for their (often small) research communities. Ultimately, we conclude that data work is a cornerstone of research practice which needs to be evaluated and considered, but one which also requires context-aware approaches.
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 | 0.230 | 0.584 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier 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".