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

Resonant anomaly detection with multiple reference datasets

Journal of High Energy Physics(2023)10.1007/jhep07(2023)188Source: DataRank Database

Resonant anomaly detection with multiple reference datasets is a research paper published in Journal of High Energy Physics (2023). On theSindex it has a DataRank of 0. It has been cited 11 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access11 citations

Download PDF

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)Impact (FWCI 2.41)OA: goldTopics (29)IDs (PubMed, OpenAlex)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 (3)

Benjamin Nachman ORCID,Frédéric Sala ORCID,Mayee F. Chen

Live enrichment

PubMed:19910364 OpenAlex:https://openalex.org/W4385267341

National Science Foundation

Grant: 2106707

SHF: Medium: Program Synthesis for Weak Supervision

National Science Foundation

Grant: 1763315

AF: Medium: Collaborative Research: Beyond Sparsity: Refined Measures of Complexity for Linear Algebra

National Institutes of Health

Grant: 5U54EB020405-03

Mobility Data Integration to Insight

National Science Foundation

Grant: 1937301

RTML: Large: Continuous Adaptation for Decision Streams

FWCI

2.41

Citation Percentile

0.9%

Citation Trend

2023

2024

2025

goldLicense: cc-by

journalhttps://link.springer.com/content/pdf/10.1007/JHEP07(2023)188.pdf

publisherhttps://link.springer.com/content/pdf/10.1007/JHEP07(2023)188.pdf

publisherhttps://link.springer.com/article/10.1007/JHEP07(2023)188/fulltext.html

journalhttps://doi.org/10.1007/jhep07(2023)188

repositoryhttps://www.osti.gov/biblio/2007191

Fields of Study

Particle physics theoretical and experimental studiesNeutrino Physics ResearchComputational Physics and Python Applications01 natural sciences0103 physical sciences

Keywords

ExploitAnomaly detectionAnomaly (physics)PhysicsData miningClass (philosophy)Computer scienceMachine learningArtificial intelligenceFOS: Computer and information sciencesComputer Science - Machine LearningOther Weak Scale BSM ModelsFOS: Physical sciencesMachine Learning (stat.ML)QC770-798High Energy Physics - ExperimentMachine Learning (cs.LG)High Energy Physics - PhenomenologyHigh Energy Physics - Experiment (hep-ex)High Energy Physics - Phenomenology (hep-ph)Statistics - Machine LearningJets and Jet SubstructureNuclear and particle physics. Atomic energy. RadioactivitySpecific BSM Phenomenology

Sustainable Development Goals

SDG 0: Affordable and clean energy