4DN-BINA-OME (NBO) Tiered Microscopy Metadata Specifications - v2.01 - XLS Spreadsheet and Entity Relationship schemas
Bibliographic record
Abstract
This an XLS Spreadsheet and three Entity-Relationship Schemas representing the latest version (v02.01) of the 4DN-BINA-OME (NBO) tiered system of Microscopy Metadata Specifications extending the OME Data Model. Note: v2.01 is a minor release that corrects minor errors that were found in v2.00. While the power of modern microscopy techniques is undeniable, rigorous record-keeping and quality control are required to ensure that imaging data may be properly interpreted (quality), reproduced (reproducibility), and used to extract reliable information and scientific knowledge which can be shared for further analysis (value). In the absence of agreed guidelines, it is inherently difficult for scientists to create comprehensive records of imaging experiments and ensure the quality of resulting image data or for manufacturers to incorporate standardized reporting and performance metrics. To solve this problem, the 4D Nucleome (4DN) Initiative and BioImaging North America (BINA) here propose light Microscopy Metadata specifications that scale with experimental intent and with the complexity of the instrumentation and analytical requirements. They consist of a set of three extensions of the latest version of the Open Microscopy Environment (OME) Data Model, which was released in 2016. Because of their scalable nature, the 4DN-BINA-OME Microscopy Metadata Specifications clearly specify which provenance and quality control metadata should be recorded for a given experiment. Last but not least, this endeavor is closely aligned with the undertakings of the recently established global community initiative dedicated to Quality Assessment and Reproducibility in Light Microscopy (QUAREP-LiMi; quarep.org). As a result, the ensuing flexible 4DN-BINA-OME (NBO) framework represents a turning point towards achieving community-driven Microscopy Metadata standards that would increase data fidelity, improve repeatability and reproducibility, ease future analysis and facilitate the verifiable comparison of different datasets, experimental setups, and assays. The intention of this proposal is to encourage participation, critiques, and contributions from the entire imaging community and all stakeholders, including research and imaging scientists, facility personnel, instrument manufacturers, software developers, standards organizations, journals, and funding agencies.
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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.144 | 0.136 |
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".