Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Communication lower-bounds for distributed-memory computations for mass spectrometry based omics data

Journal of Parallel and Distributed Computing(2021)10.1016/j.jpdc.2021.11.001Source: DataRank Database

Communication lower-bounds for distributed-memory computations for mass spectrometry based omics data is a research paper published in Journal of Parallel and Distributed Computing (2021). On theSindex it has a DataRank of 0. It has been cited 4 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access4 citations

Download PDF

Cite:

Linked data & code

pxd

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (16 grants)Impact (FWCI 0.29)OA: greenTopics (27)IDs (PubMed, PMC, OpenAlex)Annotations (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (3)

Muhammad Haseeb ORCID,S.S. Iyengar,Fahad Saeed ORCID

Live enrichment

PubMed:34898836 PMC:PMC8658624 OpenAlex:https://openalex.org/W3090870634

National Institute of General Medical Sciences

Grant: R01GM134384

National Science Foundation

Grant: TG-ASC200004

National Science Foundation

Grant: TG-CCR150017

Computer and Information Science and Engineering Office of Advanced Cyberinfrastructure

Grant: OAC-1925960

National Institutes of Health

Grant: 1R01GM134384-01A1

Multimodal Machine-Learning and High Performance Computing Strategies for Big MS Proteomics Data

National Science Foundation

Grant: 1925960

CAREER: Towards Fast and Scalable Algorithms for Big Proteogenomics Data Analytics

National Institutes of Health

Oak Ridge National Laboratory

U.S. Naval Research Laboratory

National Aeronautics and Space Administration

Office of Naval Research

U.S. Department of Energy

Florida International University

Defense Advanced Research Projects Agency

Western Michigan University

Army Research Office

FWCI

0.29

Citation Percentile

0.4%

Citation Trend

2020

2021

greenLicense: Elsevier TDM

repositoryhttps://arxiv.org/pdf/2009.14123

publisherhttps://api.elsevier.com/content/article/PII:S0743731521002082?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S0743731521002082?httpAccept=text/plain

repositoryhttp://arxiv.org/abs/2009.14123

Fields of Study

Advanced Proteomics Techniques and ApplicationsMetabolomics and Mass Spectrometry StudiesGene expression and cancer classification0301 basic medicine03 medical and health sciences0206 medical engineering02 engineering and technology

Keywords

ProteomicsOmegaComputer scienceComputationMass spectrometryUpper and lower boundsAlgorithmParallel algorithmParallel computingChemistryMathematicsPhysicsGenomics (q-bio.GN)FOS: Computer and information sciencesComputer Science - Distributed, Parallel, and Cluster ComputingMolecular Networks (q-bio.MN)FOS: Biological sciencesQuantitative Biology - GenomicsQuantitative Biology - Molecular NetworksDistributed, Parallel, and Cluster Computing (cs.DC)

Communication lower-bounds for distributed-memory computations for mass spectrometry based omics data

FAIR Checklist

Authors (3)

Funding (16 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)