A community effort to optimize sequence-based deep learning models of gene regulation is a dataset published in Nature Biotechnology (2024). On theSindex it has a DataRank of 0.676, placing it in the top 29.6% of the data-sharing corpus. It has been cited 35 times, with 25 citing works in its 1-hop citation network. Its calibrated FAIR score is 71/100.
Ranks in the top 30% for downstream scientific impact
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Full FAIR picture · advisory
The headline score is computed from the scored criteria — the fact-shaped checks (a repository, an accession, a licence) that two independent models agree on. The advisory criteria below are real FAIR guidance but rest on judgment calls that models read differently, so they inform without moving the number.
“Data generated for this study are available from the National Center of Biotechnology Information Gene Expression Omnibus (GEO) under accession number GSE254493 . The processed datasets are available from Zenodo (10.5281/zenodo.10633252) 70 .”
The paper provides a persistent identifier (GEO accession and Zenodo DOI) for its own dataset. [majority verdict 'yes' (3/5 passes agreed)]
RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit · RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier' · FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'
“Data generated for this study are available from the National Center of Biotechnology Information Gene Expression Omnibus (GEO) under accession number GSE254493 . The processed datasets are available from Zenodo (10.5281/zenodo.10633252) 70 .”
The paper names GEO and Zenodo, both curated repositories listed in re3data/FAIRsharing. [majority verdict 'yes' (3/5 passes agreed)]
RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed ( · NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived · NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten
“Rafi, A. M. Random Promoter DREAM Challenge 2022: predicting gene expression using millions of random promoter sequences. Zenodo 10.5281/zenodo.10633252 (2024).”
The dataset appears as a reference-list entry in the bibliography. [majority verdict 'yes' (3/5 passes agreed)]
FORCE11 Joint Declaration of Data Citation Principles (2014) — data should be cited as a first- · RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes · FsF-F3-01M — F-UJI: 'Metadata includes the identifier of the data it describes'
Advisory · not in the published score
“Data generated for this study are available from the National Center of Biotechnology Information Gene Expression Omnibus (GEO) under accession number GSE254493 . The processed datasets are available from Zenodo (10.5281/zenodo.10633252) 70 .”
The data availability statement points to repository records with accessions, matching Colavizza category 3. [majority verdict 'yes' (3/5 passes agreed)]
Colavizza, Hrynaszkiewicz, Staden, Whitaker & McGillivray (2020), 'The citation advantage of li · Springer Nature research data policy — Data Availability Statements: standard statement templat · RDA-F3-01M — metadata clearly and explicitly includes the identifier of the data it describes
“This resulted in a training dataset of 6,739,258 random promoter sequences and their corresponding mean expression values.”
The dataset is described in a sentence in the Results, not in an itemized list. [majority verdict 'partial' (3/5 passes agreed)]
RDA-F2-01M — 'Rich metadata is provided to allow discovery' (priority Essential) · FsF-F2-01M — F-UJI: 'Metadata includes descriptive core elements to support data findability' · FsF-R1-01MD — F-UJI: 'Metadata specifies the content of the data'
“Data generated for this study are available from the National Center of Biotechnology Information Gene Expression Omnibus (GEO) under accession number GSE254493 .”
The text gives a route to the data with no stated precondition; the data are publicly available at a repository. [majority verdict 'yes' (3/5 passes agreed)]
RDA-A1.1-01D — 'Data is accessible through a free access protocol' · FsF-A1-01M — F-UJI: 'Metadata contains access level and access conditions of the data' · NSTC Desirable Characteristics of Data Repositories (2022) — 'Free and Easy Access'
Advisory · not in the published score
“Data generated for this study are available from the National Center of Biotechnology Information Gene Expression Omnibus (GEO) under accession number GSE254493.”— not found in the paper; verdict downgraded
No access-level label is used; only an action is described. [downgraded to 'no' — no verifiable quote from the paper] [majority verdict 'no' (3/5 passes agreed)]
FsF-A1-01M — F-UJI: 'Metadata contains access level and access conditions of the data' · RDA-A1-01M — metadata contains information to enable the user to get access to the data · COAR Controlled Vocabularies — Access Rights v1.0 (open / embargoed / restricted / metadata-onl
The data are not sensitive human-subject data, and no gatekeeper is named.
NIH Genomic Data Sharing Policy (NOT-OD-14-124) — controlled-access via a Data Access Committee · RDA-A1.2-01D — 'Data is accessible through an access protocol that supports authentication and · NIH DMS Policy Element 5 (NOT-OD-21-014) — Access, Distribution, or Reuse Considerations (conse
“Data generated for this study are available from the National Center of Biotechnology Information Gene Expression Omnibus (GEO) under accession number GSE254493.”— not found in the paper; verdict downgraded
The data are stated to be available now, but no persistence period is given. [downgraded to 'no' — no verifiable quote from the paper]
NIH DMS Plan Element 4 (NOT-OD-21-014) — Data Preservation, Access, and Associated Timelines · NSTC Desirable Characteristics (2022), Organizational Infrastructure: 'Retention Policy' · RDA-A2-01M — 'Metadata is guaranteed to remain available after data is no longer available'
No file format is named for the released data.
FsF-R1.3-02D — F-UJI: 'Data is available in a file format recommended by the target research co · RDA-R1.3-02D — data is expressed in a machine-understandable community standard · RDA-I1-01D — data uses a knowledge representation expressed in a standardised format
Advisory · not in the published score
No community data standard is named.
RDA-R1.3-01M — 'Metadata complies with a community standard' (priority Essential) · RDA-R1.3-01D — 'Data complies with a community standard' · RDA-I2-01M — '(Meta)data use vocabularies that follow FAIR principles'
“The Drosophila STARR-seq data are available from the GEO under accession number GSE183939 .”
The paper provides identifiers for external datasets used in benchmarks (e.g., GEO accession GSE183939). [majority verdict 'yes' (3/5 passes agreed)]
RDA-I3-01M — '(meta)data include references to other (meta)data' · RDA-I3-03M — 'metadata includes qualified references to other metadata' · FsF-I3-01M — F-UJI: 'Metadata includes links between the data and its related entities'
No licence is stated for the data.
RDA-R1.1-01M — 'Metadata includes information about the licence under which the data can be reu · RDA-R1.1-02M — 'Metadata refers to a standard reuse licence' · RDA-R1.1-03M — 'Metadata refers to a machine-understandable reuse licence'
No version token or date is provided for the dataset.
DataCite Metadata Schema 4.6 — the 'Version' property · RDA-R1.2-01M — provenance information (which version was used is provenance) · NSTC Desirable Characteristics of Data Repositories (2022) — 'Provenance', 'Retention Policy'
“Open-source code for our models is available from GitHub ( https://github.com/de-Boer-Lab/random-promoter-dream-challenge-2022 ).”
A GitHub repository URL is provided for the code.
NIH DMS Policy Element 2 (NOT-OD-21-014) — 'Related Tools, Software and/or Code' · FAIR4RS Principles v1.0 (Chue Hong et al., 2022; RDA/FORCE11/ReSA) — FAIR Principles for Resear · FORCE11 Software Citation Principles (Smith, Katz & Niemeyer, 2016, PeerJ CS 2:e86)
“RGPIN-2020-05425”
An award number is given for the funding. [majority verdict 'yes' (4/5 passes agreed)]
DataCite Metadata Schema 4.6 — 'FundingReference' property (funderName, funderIdentifier, award · Crossref Funder Registry — canonical funder identifiers for funding metadata · RDA-F2-01M — rich metadata provided to allow discovery (funding is part of the descriptive reco
Advisory · not in the published score
“sequenced on an Illumina NextSeq using 2 × 76-bp paired-end reads with 150-cycle kits.”
The paper names specific instruments and kits used in data generation. [majority verdict 'yes' (3/5 passes agreed)]
RDA-R1.2-01M — 'Metadata includes provenance information according to community- specific standa · FsF-R1.2-01M — F-UJI: 'Metadata includes provenance information about data creation or generati · W3C PROV-O (W3C Recommendation, 2013) — the entity/activity/agent model of provenance
No documentation object (e.g., README, data dictionary) is named as accompanying the data; field definitions are not provided. [majority verdict 'no' (3/5 passes agreed)]
RDA-R1-01M — '(Meta)data are richly described with a plurality of accurate and relevant attribu · FsF-R1-01MD — F-UJI: 'Metadata specifies the content of the data' · NIH DMS Policy Element 3 (NOT-OD-21-014) — Standards (documentation and metadata to accompany t
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.538
From this paper's citation signal
Citation Network Contribution
0.138
From 12 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 25 citers.
Novo Nordisk Fonden
Grant: NNF21OC0068929
NLM NIH HHS
Grant: R01 LM013722
NHGRI NIH HHS
Grant: K99 HG009920
Natural Sciences and Engineering Research Council of Canada
Grant: unidentified
unidentified
National Institutes of Health
Grant: 5K99HG009920-02
Learning the rules of enhancer activity to understand non-coding genetic variation in autoimmune disease
Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
Fields of Study
MeSH Terms
Keywords
Improving variant effect predictions of regulatory sequences in human disease using Machine Learning and High-throughput Assays
Improving variant effect predictions of regulatory sequences in human disease using Machine Learning and High-throughput Assays