Benchmarking atlas-level data integration in single-cell genomics is a dataset published in Nature Methods (2021). On theSindex it has a DataRank of 6.5, placing it in the top 2.9% of the data-sharing corpus. It has been cited 1,462 times, with 100 citing works in its 1-hop citation network. Its calibrated FAIR score is 58/100.
Ranks in the top 3% 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.
“10.6084/m9.figshare.12420968”
The paper provides a DOI for the dataset deposited on Figshare. [majority verdict 'yes' (4/5 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'
“Figshare”
Figshare is a recognised data repository named as the holder of the data. [majority verdict 'yes' (4/5 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
“Our reprocessed versions of these datasets are publicly available as preprocessed Anndata objects on Figshare (10.6084/m9.figshare.12420968).”— not found in the paper; verdict downgraded
The dataset identifier appears only in the body text (Data Availability section), not in the reference list. [downgraded to 'no' — no verifiable quote from the paper] [majority verdict 'no' (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
“Our reprocessed versions of these datasets are publicly available as preprocessed Anndata objects on Figshare (10.6084/m9.figshare.12420968). The output data from all metric runs are available in Supplementary Data 1.”— not found in the paper; verdict downgraded
The statement points to a repository record with a DOI (Figshare) and a supplementary file, matching Colavizza category 3. [downgraded to 'partial' — no verifiable quote from the paper]
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
“Table 1 Integration tasks for benchmarking”
The paper includes an itemised table describing the datasets (cell numbers, batches, etc.). [majority verdict 'yes' (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'
“Our reprocessed versions of these datasets are publicly available as preprocessed Anndata objects on Figshare (10.6084/m9.figshare.12420968).”— not found in the paper; verdict downgraded
The data are stated to be publicly available on Figshare with no precondition. [downgraded to 'partial' — no verifiable quote from the paper]
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
“publicly available”
The paper labels the data as 'publicly available', which is an access-level label equivalent to open access. [majority verdict 'yes' (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 and no gatekeeper is named; the data are publicly available on Figshare.
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
No sentence states how long the data will remain available or when they become obtainable beyond being publicly available.
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'
The paper does not name an open file format from the recognised list; 'Anndata objects' is not a standard format token. [majority verdict 'no' (4/5 passes agreed)]
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 data/metadata community standard (e.g., MIAME, MINSEQE, a controlled vocabulary) is named for the data.
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'
“pancreas GSE81076 , GSE85241 , GSE86469 , GSE84133 , GSE81608 (Gene Expression Omnibus (GEO))”
The paper provides identifiers (GEO accessions) for external source datasets used in the study.
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 license for the data is stated; the article's CC BY license does not apply to 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'
“10.6084/m9.figshare.12420968.v7”
The Figshare DOI includes a version suffix (.v7), identifying the snapshot. [majority verdict 'yes' (3/5 passes agreed)]
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'
“The preprocessing, integration and evaluation metric functions with relevant parameterizations have been made available is our scIB Python package at https://github.com/theislab/scib”
A machine-resolvable code repository URL is given for the study's own code. [majority verdict 'yes' (4/5 passes agreed)]
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)
“ExNet-0041-Phase2-3”
A specific award number is provided for a named funder.
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
“We used scran pooling normalization 40 (v.1.10.2 unless otherwise specified) and log +1 transformation on count data.”
The paper names specific tools (scran) and versions used to produce the data. [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 (README, data dictionary, codebook) is named as accompanying the data. [majority verdict 'no' (4/5 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
1.1
From this paper's citation signal
Citation Network Contribution
5.4
From 100 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 100 citers.
European Commission
Grant: 874656
Discovering the cellular landscape of the airways and the lung
Wellcome Trust
Grant: 108413
WTSI Science and IT Platforms, Translation, Administration and Estates
Fields of Study
Keywords
Additional file 1 of Identifying tumor cells at the single-cell level using machine learning
Additional file 1 of Identifying tumor cells at the single-cell level using machine learning
Additional file 4 of Identifying tumor cells at the single-cell level using machine learning
Additional file 4 of Identifying tumor cells at the single-cell level using machine learning
Additional file 1 of Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
Additional file 1 of Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
Additional file 2 of Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
Additional file 2 of Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
Additional file 1 of The shaky foundations of simulating single-cell RNA sequencing data
Additional file 1 of The shaky foundations of simulating single-cell RNA sequencing data
Additional file 2 of The shaky foundations of simulating single-cell RNA sequencing data
Additional file 2 of The shaky foundations of simulating single-cell RNA sequencing data
Additional file 3 of The shaky foundations of simulating single-cell RNA sequencing data
Additional file 3 of The shaky foundations of simulating single-cell RNA sequencing data
Additional file 4 of scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing
Additional file 4 of scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing
Additional file 4 of Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability
Additional file 4 of Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability
Additional file 5 of Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability
Additional file 5 of Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability