Starrydata: from published plots to shared materials data
Bibliographic record
Abstract
We have developed the Starrydata2 web system, an open, web-based database for collecting and organizing experimental material property data from the literature. It assists users worldwide in extracting and sharing curve data from plot images in published papers, along with relevant sample information such as chemical compositions and fabrication methods. Starrydata2 streamlines the manual data collection process through partial automation. Currently, Starrydata encompasses over 194,000 curves extracted from more than 82,000 physical samples, as reported in over 13,000 publications on functional inorganic materials, including thermoelectric and magnetic materials. All data in Starrydata are openly accessible to the public for both commercial and non-commercial purposes. In this paper, we introduce the web interface, data curation workflow, data structure, and system architecture of Starrydata2. We then described in detail the datasets currently included in Starrydata2 and discuss their use cases. We also present the methods for applying the collected dataset, including a unique large-scale data representation method called ‘all-data plots’, which provides a comprehensive overview of the entire dataset. Finally, we report on how the collected datasets are being utilized in data-driven materials research through machine learning, modelling and simulation.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.042 |
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".