SLIMS—a user-friendly sample operations and inventory management system for genotyping labs
Bibliographic record
Abstract
SUMMARY: We present the Sample-based Laboratory Information Management System (SLIMS), a powerful and user-friendly open source web application that provides all members of a laboratory with an interface to view, edit and create sample information. SLIMS aims to simplify common laboratory tasks with tools such as a user-friendly shopping cart for subjects, samples and containers that easily generates reports, shareable lists and plate designs for genotyping. Further key features include customizable data views, database change-logging and dynamically filled pre-formatted reports. Along with being feature-rich, SLIMS' power comes from being able to handle longitudinal data from multiple time-points and biological sources. This type of data is increasingly common from studies searching for susceptibility genes for common complex diseases that collect thousands of samples generating millions of genotypes and overwhelming amounts of data. LIMSs provide an efficient way to deal with this data while increasing accessibility and reducing laboratory errors; however, professional LIMS are often too costly to be practical. SLIMS gives labs a feasible alternative that is easily accessible, user-centrically designed and feature-rich. To facilitate system customization, and utilization for other groups, manuals have been written for users and developers. AVAILABILITY: Documentation, source code and manuals are available at http://genapha.icapture.ubc.ca/SLIMS/index.jsp. SLIMS was developed using Java 1.6.0, JSPs, Hibernate 3.3.1.GA, DB2 and mySQL, Apache Tomcat 6.0.18, NetBeans IDE 6.5, Jasper Reports 3.5.1 and JasperSoft's iReport 3.5.1.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.087 | 0.077 |
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".