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

MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT

Rare & Special e-Zone (The Hong Kong University of Science and Technology)(2025)10.1145/3715014.3722053Source: DataRank Database

MMBind: Unleashing the Potential of Distributed and Heterogeneous Data for Multimodal Learning in IoT is a research paper published in Rare & Special e-Zone (The Hong Kong University of Science and Technology) (2025). 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

Free full text

Cite:

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 (5 grants)OA: goldTopics (5)IDs (PubMed)

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 (7)

Jason Wu ORCID,Tomoyoshi Kimura ORCID,Yihan Lin ORCID,Gunjan Verma ORCID,Tarek Abdelzaher ORCID

Live enrichment

PubMed:19910364

DEVCOM Army Research Laboratory

Grant: W911NF-17-2- 0196

National Institutes of Health

Grant: 1P41EB028242

Air Force Office of Scientific Research

Grant: FA95502210193

Air Force Office of Scientific Research

Grant: FA95502310559

National Institutes of Health

Grant: 3P41EB028242-04S1

Administrative Supplement to P41EB028242 award (The mDOT Center): WristPrint: Bioethical Policy Implications of the Emerging Re-identifiability Risks from Wrist-worn Activity Data.

goldLicense: cc-by

https://doi.org/10.1145/3715014.3722053

publisherhttps://dl.acm.org/doi/10.1145/3715014.3722053

publisherhttps://dl.acm.org/doi/abs/10.1145/3715014.3722053

https://dx.doi.org/10.48550/arxiv.2411.12126

http://arxiv.org/abs/2411.12126

Fields of Study

0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

FOS: Computer and information sciencesComputer Science - Machine LearningMachine Learning (cs.LG)