Quantifying the distribution of materials data types in scientific literature across text, tables, and figures
Bibliographic record
Abstract
Materials science research is a multifaceted field, with valuable data scattered across the pages of research papers in various formats. The efficient extraction of data from these papers is of paramount importance for further analysis and research. This study aims to shed light on the distribution of data in materials science papers and their interconnections. In this preliminary analysis, we systematically examined 10 random materials science papers to discern where key data types—composition, processing conditions, characterization, and performance properties—reside within the textual content, tables, and figures. Our findings reveal intriguing patterns in the presentation of data, ranging from conventional text-based descriptions to detailed tabular presentations and visually informative figures. The analysis encompasses diverse materials and highlights cases where data types are isolated or interconnected across different sources. We also address the challenges and limitations faced during the annotation process. This investigation underscores the importance of understanding data distribution within materials science papers, as it has profound implications for data accessibility and integration in the field. Furthermore, these insights pave the way for future research, particularly in the development of advanced NLP models tailored to the unique characteristics of materials science research papers and other machine learning techniques for more efficient data extraction and analysis in materials science research.
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 | 0.028 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.057 | 0.059 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".