Bibliographic record
Abstract
The increasing volume and complexity of X-ray absorption spectroscopy (XAS) data generated at synchrotron facilities worldwide require robust infrastructure for data management, sharing, and analysis. This paper introduces the XAS Database (XASDB), a comprehensive web-based platform developed and hosted by the Canadian Light Source (CLS). The database houses more than 1000 reference spectra spanning 40 elements and 324 chemical compounds. The platform employs a Node.js/MongoDB architecture designed to handle diverse data formats from multiple beamlines and synchrotron facilities. A key innovation is the XASproc JavaScript library, which enables browser-based XAS data processing including normalization, background sub- traction, extended X-ray absorption fine structure (EXAFS) extraction, and preliminary analysis traditionally limited to desktop applications. The integrated XASVue spectral viewer provides installation-free data visualization and analysis with broad accessibility across devices and operating systems. By offering standardized data output, comprehensive metadata, and integrated analytical ca- pabilities, XASDB facilitates collaborative research and promotes FAIR (Findable, Accessible, In- teroperable, and Reusable) data principles. The platform serves as a valuable resource for linear combination fitting (LCF) analysis, machine learning applications, and educational purposes. This initiative demonstrates the potential for web-centric approaches in XAS data analysis, accelerating advances in materials science, environmental research, chemistry, and biology.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, 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".