Opening up translational data impact through the Data Citation Corpus
Bibliographic record
Abstract
The metadata for 5 million data citations in the Make Data Count Data Citation Corpus serves as a source for this exploratory analysis of the use of datasets from biomedical fields across a number of facets, including date, affiliations, funders and domain. To gather insights about translational data impact, we focused on array-based gene expression profiling datasets produced between 2005-2009 in Homo sapiens from the Gene Expression Omnibus in the Corpus (n=3,427). This time period was selected to allow a 15-20 year timeframe to allow follow-up studies/publications to accrue. GEO is a public functional genomics data repository by the National Library of Medicine’s National Center for Biotechnology Information. GEO archives and freely distributes microarray, next-generation sequencing, and other forms of high-throughput functional genomics data submitted by the research community. GEO identifiers were selected for this exploratory work because they are unambiguous (see Limitations) and can be easily filtered by human data to more easily highlight clinically-relevant studies. Scatter plots of sample count versus number of citations for these datasets were created (Figure 1) allowing identification of two candidate datasets: “Breast cancer relapse free survival” (GSE2034, 2005) and “Strong Time Dependence of the 76-Gene Prognostic Signature” (GSE7390, 2007) to explore in more depth based on citations in papers that report on clinical trials (Figure 2). The primary articles about these two datasets were also cited in subsequent articles, including articles reporting on clinical trials. Dataset citations were collected through the Data Citation Corpus v3.0, while inter-article citations were collected through iCite v32.
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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 | 0.022 | 0.189 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.107 | 0.141 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".