MétaCan
Menu
Back to cohort
Record W4285597203 · doi:10.1101/2022.07.12.495092

MIADE metadata guidelines: Minimum Information About a Disorder Experiment

2022· preprint· en· W4285597203 on OpenAlexaff
Bálint Mészáros, András Hatos, Nicolás Palópoli, Federica Quaglia, Edoardo Salladini, Kim Van Roey, Haribabu Arthanari, Zsuzsanna Dosztányi, Isabella C. Felli, Patrick D. Fischer, Jeffrey C. Hoch, Cy M. Jeffries, Sonia Longhi, Emiliano Maiani, Sandra Orchard, Rita Pancsa, Elena Papaleo, Roberta Pierattelli, Damiano Piovesan, Iva Pritišanac, Thibault Viennet, Péter Tompa, Wim Vranken, Silvio C. E. Tosatto, Norman E. Davey

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsHospital for Sick Children
FundersNational Research, Development and Innovation OfficeDanmarks GrundforskningsfondCancer Research UK
KeywordsMetadataComputer scienceDisseminationInterpretabilityKey (lock)Information retrievalExperimental dataData scienceComplement (music)AnnotationData miningWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract An unambiguous description of an experimental setup and analysis, and the subsequent biological observation is vital for accurate data interpretation and reproducible results. Consequently, experimental analyses should be described in a concise, unequivocal, and digestible manner. The aim of minimum information guidelines is to define the fundamental complement of data that can support an unambiguous conclusion on experimental observations. In this document, we present the Minimum Information About Disorder Experiments (MIADE) guidelines to define the minimal fundamental parameters required for non-experts to understand the key findings of an experiment studying intrinsically disordered proteins (IDPs) or intrinsically disordered protein regions (IDRs). MIADE guidelines provide recommendations for data producers to describe the results of their experiments at source, for curators to annotate experimental data to community resources and for database developers maintaining community resources to disseminate the data. We give examples of the application of these guidelines in common use cases and describe the implementation of an update to the DisProt IDP database to allow MIADE-compliant annotation. The MIADE guidelines will improve the interpretability of experimental results for data consumers, facilitate direct data submission, simplify data curation, improve data exchange among repositories and standardise the dissemination of the key metadata on an IDP experiment by IDP data sources.

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 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)0.010
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0050.008
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.090
GPT teacher head0.345
Teacher spread0.255 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)Same topicScientific Computing and Data ManagementFrench-language works237,207