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

State and Topology Estimation for Unobservable Distribution Systems Using Deep Neural Networks

IEEE Transactions on Instrumentation and Measurement(2022)10.1109/tim.2022.3167722Source: DataRank Database

State and Topology Estimation for Unobservable Distribution Systems Using Deep Neural Networks is a research paper published in IEEE Transactions on Instrumentation and Measurement (2022). On theSindex it has a DataRank of 0. It has been cited 94 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access94 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 (11 grants)Topics (12)IDs (PubMed, PMC)

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 (6)

Reetam Sen Biswas ORCID,Shiva Moshtagh ORCID,Anamitra Pal ORCID,Lang Tong ORCID,Gautam Dasarathy ORCID

Live enrichment

PubMed:36277673 PMC:PMC9585895

Department of Energy

Grant: DE-AR00001858-1631

Department of Energy

Grant: DE-EE0009355

Power Systems Engineering Research Center (PSERC), Arizona State University, Tempe, AZ

Grant: T-63

National Science Foundation

Grant: OAC-1934766

National Science Foundation

Grant: ECCS-2145063

National Science Foundation

Grant: CNS-2003111

National Institutes of Health

Grant: 1R01GM140468-01

Graphical Models from Partially Observed Interactions with Biomedical Applications

NIGMS NIH HHS

Grant: R01 GM140468

National Science Foundation

Grant: 2003111

Collaborative Research: MLWiNS: Distributed Learning over Multi-Access Channels: From Bandlimited Coordinate Descent to Gradient Sketching

National Science Foundation

Grant: 1934766

Collaborative Research: High-Dimensional Spatio-Temporal Data Science for a Resilient Power Grid: Towards Real-Time Integration of Synchrophasor Data

National Science Foundation

Grant: 2145063

CAREER: Time-Synchronized Estimation in Power Systems: Unique Challenges and Innovative Solutions

Fields of Study

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

Keywords

State estimationDeep Neural Network (Dnn)Topology Identification (Ti)Synchrophasor MeasurementsSignal Processing (eess.SP)FOS: Computer and information sciencesComputer Science - Machine LearningFOS: Electrical engineering, electronic engineering, information engineeringElectrical Engineering and Systems Science - Signal ProcessingMachine Learning (cs.LG)