A Cloud-Based National Resource for FAIR Microscopy Research Data Management, Analysis and Sharing in Canada
Bibliographic record
Abstract
Advanced light microscopy is used globally as an essential tool for recording quantitative measurements of biological structure, function and/or dynamics in the life and biomedical sciences. Its increasing use in fundamental research generates substantial and ever increasing, amounts of image data from diverse imaging modalities. The sheer volume of image data and the speed at which it is generated presents a major data management and storage challenge. This is especially true for individual laboratories that typically do not have the hardware infrastructure or domain knowledge to implement an effective image-data-management strategy by themselves. At the same time, effective curation, management and storage of image data and associated metadata are crucial to the success of the research enterprise and for fulfilling the Research Data Management (RDM) mandates of public funding bodies worldwide. Any proposed data management and storage solution should follow the FAIR – Findable, Accessible, Interoperable and Reusable [ 1] principles. To that end, open access tools and software that embed data management technologies as an integral part of ongoing research projects, rather than as an afterthought, are pivotal. Therefore, we have deployed an implementation of an existing open-source tool [ 2] to enable researchers that generate image data by facilitating the management of these data. The system was developed by the Open Microscopy Environment (OME) consortium and is called OME’s Remote Objects (OMERO) [ 3]. It is a platform for managing images in a secure central repository where data can be viewed, organized, analyzed and shared [ 3]. OMERO is the most popular RDM system for microscopy image data [ 4] with 1000+ production instances of OMERO worldwide [ 5].
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.055 | 0.022 |
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".