Xenbase: Core features, data acquisition, and data processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Xenbase, the Xenopus model organism database (www.xenbase.org), is a cloud-based, web-accessible resource that integrates the diverse genomic and biological data from Xenopus research. Xenopus frogs are one of the major vertebrate animal models used for biomedical research, and Xenbase is the central repository for the enormous amount of data generated using this model tetrapod. The goal of Xenbase is to accelerate discovery by enabling investigators to make novel connections between molecular pathways in Xenopus and human disease. Our relational database and user-friendly interface make these data easy to query and allows investigators to quickly interrogate and link different data types in ways that would otherwise be difficult, time consuming, or impossible. Xenbase also enhances the value of these data through high-quality gene expression curation and data integration, by providing bioinformatics tools optimized for Xenopus experiments, and by linking Xenopus data to other model organisms and to human data. Xenbase draws in data via pipelines that download data, parse the content, and save them into appropriate files and database tables. Furthermore, Xenbase makes these data accessible to the broader biomedical community by continually providing annotated data updates to organizations such as NCBI, UniProtKB, and Ensembl. Here, we describe our bioinformatics, genome-browsing tools, data acquisition and sharing, our community submitted and literature curation pipelines, text-mining support, gene page features, and the curation of gene nomenclature and gene models.
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.
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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it