Dataset for "Emergence of Interfacial Magnetism in Strongly-Correlated Nickelate‐Titanate Superlattices"
Bibliographic record
Abstract
This dataset is a collection of data that support the findings of the following publication: T. C. Asmara et al., Adv. Mater. 36, 2310668 (2024) DOI: https://doi.org/10.1002/adma.202310668 This dataset is persistently available in the following links: All versions: https://doi.org/10.5281/zenodo.12745079 Version 1 (only .pxp file): https://doi.org/10.5281/zenodo.12745080 If this data is re-used elsewhere, please correctly cite the related publication (T. C. Asmara et al., Adv. Mater. 36, 2310668 (2024)) and include the DOI links to both the publication (https://doi.org/10.1002/adma.202310668) and the dataset repository (https://doi.org/10.5281/zenodo.12745079). Description of the data and file structure This dataset has been collected using the following experimental methods: Resonant inelastic x-ray scattering (RIXS) X-ray absorption spectroscopy (XAS) Muon spin rotation (μSR) X-ray diffraction (XRD) X-ray reflectivity (XRR) Electron energy loss spectroscopy (EELS) Electrical transport The data have only been minimally processed, mainly to present them as figures in the related scientific publication. The data has been collected in a .pxp file, which needs a software called IGOR Pro to open. Text-based files of the dataset will be available in the next version. Code/Software The dataset has also been analysed using variety of fitting analysis and theoretical calculations using the following software packages: lmfit package of Python ATHENA MUSRFIT TRIM.SP DIFFRAC.XRR The fitting and calculation results are also included in the .pxp file.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 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".