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Record W2162154158 · doi:10.1371/journal.pcbi.1002549

Data Sharing in the Post-Genomic World: The Experience of the International Cancer Genome Consortium (ICGC) Data Access Compliance Office (DACO)

2012· article· en· W2162154158 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

VenuePLoS Computational Biology · 2012
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcGill Genome CentreGenome CanadaMcGill University
FundersOntario Institute for Cancer ResearchGenome Canada
KeywordsData sharingOpen access publishingOpen scienceInternet privacyData accessGenomicsOpen dataInformation privacyComputer scienceData scienceWorld Wide WebBusinessGenomeBiologyMedicineGenetics

Abstract

fetched live from OpenAlex

<p>The scientific community, research funders, and governments have repeatedly recognized the importance of open access to genomic data for scientific research and medical progress. Open access is becoming a well-established practice for large-scale, publicly funded, data-intensive community science projects, particularly in the field of genomics. Given this consensus, restrictions to open access should be regarded as exceptional and treated with caution. Yet, several developments have led scientists and policymakers to investigate and implement open access restrictions. Notably, there are privacy concerns within the genomics community and critiques from some researchers that open access, if left completely unregulated, could raise significant scientific, ethical, and legal issues (e.g., quality of the data, appropriate credit to data generators, relevance of the system for small and medium projects, etc.). A recent paper by Greenbaum and colleagues in this journal identified protecting the privacy of study participants as the main challenge to open genomic data sharing.</p>One possible way to reconcile open data sharing with privacy concerns is to use a tiered access system to separate access into open and controlled. Open access remains the norm for data that cannot be linked with other data to generate a dataset that would uniquely identify an individual. A controlled access mechanism, on the other hand, regulates access to certain, more sensitive data (e.g., detailed phenotype and outcome data, genome sequences files, raw genotype calls) by requiring third parties to apply to a body (e.g., custodian, original data collectors, independent body, or data access committee) and complete an access application that contains privacy safeguards. This mechanism, while primarily designed to protect study participants, can also be used to protect investigators, database hosting institutions, and funders from perceptions or acts of favoritism or impropriety. The experience of controlled access bodies to date has been only minimally documented in the literature. To address this lacuna, we present the experience of the Data Access Compliance Office (DACO) of the International Cancer Genome Consortium (ICGC). The goal is to provide information on this increasingly important type of database governance body.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0070.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.770
GPT teacher head0.603
Teacher spread0.167 · 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