BRISK—research-oriented storage kit for biology-related data
Bibliographic record
Abstract
MOTIVATION: In genetic science, large-scale international research collaborations represent a growing trend. These collaborations have demanding and challenging database, storage, retrieval and communication needs. These studies typically involve demographic and clinical data, in addition to the results from numerous genomic studies (omics studies) such as gene expression, eQTL, genome-wide association and methylation studies, which present numerous challenges, thus the need for data integration platforms that can handle these complex data structures. Inefficient methods of data transfer and access control still plague research collaboration. As science becomes more and more collaborative in nature, the need for a system that adequately manages data sharing becomes paramount. RESULTS: Biology-Related Information Storage Kit (BRISK) is a package of several web-based data management tools that provide a cohesive data integration and management platform. It was specifically designed to provide the architecture necessary to promote collaboration and expedite data sharing between scientists. AVAILABILITY AND IMPLEMENTATION: The software, documentation, Java source code and demo are available at http://genapha.icapture.ubc.ca/brisk/index.jsp. BRISK was developed in Java, and tested on an Apache Tomcat 6 server with a MySQL database. CONTACT: denise.daley@hli.ubc.ca.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.075 |
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".