Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges is a dataset published in Entropy (2022). On theSindex it has a DataRank of 1.2, placing it in the top 17.3% of the data-sharing corpus. It has been cited 58 times, with 34 citing works in its 1-hop citation network. Its calibrated FAIR score is 4/100.
Ranks in the top 17% 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.
The paper does not assign a persistent identifier to any dataset because it generated no data.
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'
No repository is named as holding the study's data because the study generated no data.
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
No dataset identifier appears anywhere in the paper because the review generated no data.
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 Availability Statement Not applicable.”
The existing statement declares no data are available, offering no route to any repository record.
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
The paper does not describe the content or size of a dataset because it generated no data.
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 Availability Statement Not applicable.”
The paper states no data are available, providing no access route.
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 Availability Statement Not applicable.”
The paper does not label the access level of any dataset because it is a review that generated no data.
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 paper generated no data, so 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
The paper makes no statement about when or how long its data are available because it generated no data.
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 because the review generated no 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
The paper does not name a data or metadata community standard applied to its own data because it generated no 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'
The paper does not provide identifiers for external resources that its own data depend on because it generated no data.
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 is attached to the paper's own data because none were generated.
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 given for the data because the paper generated no data.
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 paper does not provide a locator for code it wrote because it is a review and no code is claimed.
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)
“This study was supported in part by the Science Education Research Board (SERB), New Delhi, India (grant CRG/2021/004960); the ICAR-Indian Agricultural Statistics Research Institute (ICAR-IASRI), New Delhi, India (grant AGEDIASRISIL202101800189); and the Wendell Cherry Chair of the Clinical Trial Research Fund (SNR).”
The paper provides specific grant numbers attached to named funders.
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
The paper does not describe how its own data were produced because it generated no data.
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 or variable-definition table exists because the paper generated no data.
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
0.612
From this paper's citation signal
Citation Network Contribution
0.573
From 30 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 34 citers.
Science Education Research Board (SERB), New Delhi, India
Grant: CRG/2021/004960
Science Education Research Board (SERB), New Delhi, India
Grant: AGEDIASRISIL202101800189
FWCI
4.62
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords
Sustainable Development Goals
Additional file 1 of Kernel-based testing for single-cell differential analysis
Additional file 1 of Kernel-based testing for single-cell differential analysis
Additional file 3 of Kernel-based testing for single-cell differential analysis
Additional file 3 of Kernel-based testing for single-cell differential analysis
Additional file 2 of Leveraging gene correlations in single cell transcriptomic data
Additional file 2 of Leveraging gene correlations in single cell transcriptomic data
Additional file 3 of Leveraging gene correlations in single cell transcriptomic data
Additional file 3 of Leveraging gene correlations in single cell transcriptomic data
Additional file 4 of Leveraging gene correlations in single cell transcriptomic data
Additional file 4 of Leveraging gene correlations in single cell transcriptomic data
Additional file 5 of Leveraging gene correlations in single cell transcriptomic data
Additional file 5 of Leveraging gene correlations in single cell transcriptomic data
Additional file 6 of Leveraging gene correlations in single cell transcriptomic data
Additional file 6 of Leveraging gene correlations in single cell transcriptomic data
Additional file 7 of Leveraging gene correlations in single cell transcriptomic data
Additional file 7 of Leveraging gene correlations in single cell transcriptomic data
Additional file 8 of Leveraging gene correlations in single cell transcriptomic data
Additional file 8 of Leveraging gene correlations in single cell transcriptomic data
Additional file 9 of Leveraging gene correlations in single cell transcriptomic data
Additional file 9 of Leveraging gene correlations in single cell transcriptomic data