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Record W2106831214 · doi:10.1093/database/bar023

The modENCODE Data Coordination Center: lessons in harvesting comprehensive experimental details

2011· article· en· W2106831214 on OpenAlexaff
Nicole Washington, Eo Stinson, Marc D. Perry, Peter Ruzanov, Sergio Contrino, Richard Smith, Zheng Zha, Rachel Lyne, Adrian R. Carr, Paul Lloyd, Ellen Kephart, Sheldon McKay, Gos Micklem, Lincoln Stein, Suzanna Lewis

Bibliographic record

VenueDatabase · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsOntario Institute for Cancer Research
FundersBasic Energy SciencesOffice of ScienceNational Institutes of HealthWellcome TrustNational Human Genome Research InstituteU.S. Department of Energy
KeywordsComputer scienceMetadataProtocol (science)Research centerEncyclopediaCaenorhabditis elegansSet (abstract data type)World Wide WebInformation retrievalLibrary scienceBiologyProgramming language

Abstract

fetched live from OpenAlex

The model organism Encyclopedia of DNA Elements (modENCODE) project is a National Human Genome Research Institute (NHGRI) initiative designed to characterize the genomes of Drosophila melanogaster and Caenorhabditis elegans. A Data Coordination Center (DCC) was created to collect, store and catalog modENCODE data. An effective DCC must gather, organize and provide all primary, interpreted and analyzed data, and ensure the community is supplied with the knowledge of the experimental conditions, protocols and verification checks used to generate each primary data set. We present here the design principles of the modENCODE DCC, and describe the ramifications of collecting thorough and deep metadata for describing experiments, including the use of a wiki for capturing protocol and reagent information, and the BIR-TAB specification for linking biological samples to experimental results. modENCODE data can be found at http://www.modencode.org.

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 imitation

Not 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.

metaresearch head score (Codex)0.156
metaresearch head score (Gemma)0.316
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.823

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1560.316
Meta-epidemiology (narrow)0.0030.005
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0130.024
Science and technology studies0.0060.007
Scholarly communication0.0230.039
Open science0.0250.018
Research integrity0.0050.012
Insufficient payload (model declined to judge)0.0310.044

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 head0.101
GPT teacher head0.332
Teacher spread0.231 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainReproducibility
GenreEmpirical

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".

Quick stats

Citations34
Published2011
Admission routes1
Has abstractyes

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