The Learning in Neural Circuits Research Environment: Managing Living Specimens and Laboratory Data in Islandora
Bibliographic record
Abstract
Learning In Neural Circuits (LINC) is a new repository and virtual research environment for the department of biological sciences research cluster at the University of Toronto Scarborough Campus. The system serves the Blake Richards neuroscience research lab, which is comprised of early career researchers (ECRs) who would benefit from training and practices in research data management. The repository is designed to institute best practices for research data management with this audience in mind as well as foster new insights by replacing traditional paper and spreadsheet based systems with a more complex relational metadata system and robust Solr index. LINC is the locus of development for a new Islandora Living Research Lab Solution Pack developed in UTSC Library’s Digital Scholarship Unit (DSU). The code is currently available on the DSU github, with the hope that the module can be contributed to the Islandora project in 2016. The solution pack Digital Scholarship Unit primary use case is to a record living specimen and to reveal the related experimental data associated with that specimen.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".