Bibliographic record
Abstract
Transparency lies at the heart of the open lab notebook movement.Open notebook scientists publish laboratory experiments and findings in the public domain in real time, without restrictions or omissions.Research on rare diseases is especially amenable to the open notebook model because it can both increase scientific impact and serve as a mechanism to engage patient groups in the scientific process.Here, I outline and describe my own success with my open notebook project, LabScribbles, as well as other efforts included in the openlabnotebooks.orginitiative.The term "open notebook science" was first coined in 2006 by Professor Jean-Claude Bradley, a Canadian chemistry researcher at Drexel University.In defining the practice, Bradley said, "It is essential that all of the information available to the researchers to make their conclusions is equally available to the rest of the world" [1], meaning that the notebook must be a complete and honest representation of the scientist's findings.Despite the benefits of openly documenting research projects in real time, scientists have been slow to adopt open notebook science.Of those who have, many quickly abandon the practice or fail to update their notebook regularly or share it with restrictions.Starting my own open notebook for my postdoctoral research project was appealing for a number of reasons.I am a postdoctoral fellow at the Structural Genomics Consortium (SGC), where open science is a critical part of the laboratory ethos.SGC scientists not only make their work as open as possible through extensive data and material sharing but have also recently implemented an open publication strategy, in which all manuscripts are submitted systematically to open access preprint servers.Piloting innovative open science strategies is well supported and is encouraged for scientists working within the SGC.My particular research focus is Huntington disease (HD), a devastating inherited neurodegenerative disease.Although scientists mapped the causative mutation 25 years ago [2], successful development of disease-modifying or curative therapeutics has not materialized as hoped [3].Open science and open notebooks promise to accelerate the process of scientific discovery.I hoped that by documenting my research project through an open notebook and sharing data ahead of traditional publication timelines, I would speed up the research process for HD (Fig 1).My specific aim was to create an open and collaborative network of researchers
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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 | 0.077 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.028 | 0.031 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.185 | 0.083 |
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".