Accessing, analyzing and visualizing research data metadata using DataCite and Jupyter Notebooks
Bibliographic record
Abstract
Interest in measuring data citation and developing metrics for data is increasing. Despite this interest, research investigating data sharing, data reuse and data citation practices remains relatively nascent. In this presentation, we will look at some of the work that has been done in the Meaningful Data Counts project in this effort. As such, the presentation will primarily focus on how the DataCite GraphQL API can be leveraged to access, analyze, and visualize research data metadata (using a Jupyter notebook). Anton will walk through the various resources and explain the functionality. Additionally, we will look the broader Meaningful Data Counts project and discuss subject classification mapping done to improve subject classification metadata as well as provide an overview of an ongoing survey about data sharing behaviours of academics will be presented. This was presented at the DH Toolbox series at the University of Ottawa.
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.040 | 0.052 |
| Open science | 0.009 | 0.083 |
| 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 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".