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Open laboratory notebooks: good for science, good for society, good for scientists

2019· preprint· en· W2913466914 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueF1000Research · 2019
Typepreprint
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsStructural Genomics ConsortiumUniversity of Toronto
FundersEshelman Institute for Innovation, University of North Carolina at Chapel HillNovartis PharmaOntario Genomics InstituteCanada Foundation for InnovationInternational Seafood Sustainability FoundationAgence Nationale de la RechercheWellcome TrustOntario Ministry of Research, Innovation and ScienceOntario GenomicsFundação de Amparo à Pesquisa do Estado de São PauloGenome CanadaUniversity of TorontoEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAInnovative Medicines InitiativeWellcomePfizerHuntington's Disease Society of America
KeywordsOpen scienceOpen peer reviewOpen dataCitizen scienceOpen access journalEngineering ethicsOpen researchPlant biologyOpen societyScientific progressComputer scienceData sciencePolitical sciencePublic relationsWorld Wide WebBiologyEngineeringMEDLINEMathematicsEpistemology

Abstract

fetched live from OpenAlex

The fundamental goal of the growing open science movement is to increase the efficiency of the global scientific community and accelerate progress and discoveries for the common good. Central to this principle is the rapid disclosure of research outputs in open-access peer-reviewed journals and on pre-print servers. The next bold step in this direction is open laboratory notebooks, where research scientists share their research - including detailed protocols, negative and positive results - online and in near-real-time to synergize with their peers. Here, we highlight the benefits of open lab notebooks to science, society and scientists, and discuss the challenges that this nascent movement is facing. We also present the implementation and progress of our own initiative at openlabnotebooks.org, with more than 20 active contributors after one year of operation.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.089
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science
Consensus categoriesMetaresearch, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.443
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0890.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0030.002
Scholarly communication0.0180.001
Open science0.0260.033
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.350
GPT teacher head0.531
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it