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

Multi-fidelity Hierarchical Neural Processes

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(2022)10.1145/3534678.3539364Source: DataRank Database

Multi-fidelity Hierarchical Neural Processes is a research paper published in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022). On theSindex it has a DataRank of 0. It has been cited 7 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access7 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 (4 grants)OA: bronzeTopics (7)IDs (PubMed)SDGs (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 (5)

Matteo Chinazzi ORCID,Alessandro Vespignani ORCID,Yi-An Ma ORCID,Rose Yu ORCID,Dongxia Wu ORCID

Live enrichment

PubMed:19162232

National Science Foundation

Grant: 2134209

Collaborative Research: Robust Deep Learning in Real Physical Space: Generalization, Scalability, and Credibility

National Institutes of Health

Grant: 5U01IP001137-04

RFA-IP-20-003, FluMod - Center for the Multiscale Modeling of Pandemic and seasonal Flu Prevention and Control

National Science Foundation

Grant: 2112665

AI Institute for Learning-Enabled Optimization at Scale (TILOS)

National Science Foundation

Grant: 2037745

CRII: III: Multiresolution Tensor Learning for Scalable and Interpretable Spatiotemporal Analysis

bronzeLicense: CC BY NC SA

publisherhttps://dl.acm.org/doi/pdf/10.1145/3534678.3539364

repositoryhttps://arxiv.org/pdf/2206.04872

https://doi.org/10.1145/3534678.3539364

https://dx.doi.org/10.48550/arxiv.2206.04872

http://arxiv.org/abs/2206.04872

Fields of Study

0202 electrical engineering, electronic engineering, information engineering02 engineering and technology01 natural sciences0105 earth and related environmental sciences

Keywords

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

Sustainable Development Goals

SDG 13: 13. Climate action