Methods for the Analysis of 26 Million VOC Area Measurements during the Deepwater Horizon Oil Spill Clean-up is a dataset published in Annals of Work Exposures and Health (2021). On theSindex it has a DataRank of 0.470, placing it in the top 40.4% of the data-sharing corpus. It has been cited 18 times, with 1 citing works in its 1-hop citation network.
Ranks in the top 40% for downstream scientific impact
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.442
From this paper's citation signal
Citation Network Contribution
0.0287
From 1 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 1 citer.
National Institute of Environmental Health Sciences
Grant: ZO1 ES 102945
National Institute of Environmental Health Sciences
Grant: NIH/NIEHSR01ES027027-01
National Institute of Environmental Health Sciences
Grant: NIH/NIEHS R01ES030210-01
National Institute of Environmental Health Sciences
Grant: NSF DMS-1513654
National Institute of Environmental Health Sciences
Grant: NSF IIS-1562303
National Institute of Environmental Health Sciences
Grant: NSF DMS-1916349
NIEHS NIH HHS
Grant: R01 ES030210
NIEHS NIH HHS
Grant: ZO1 ES 102945
NIEHS NIH HHS
Grant: R01 ES027027
National Institutes of Health
Grant: 1ZIAES102945-10
Gulf Longitudinal Follow-up (GuLF) Study
National Institutes of Health
Grant: 1R01ES030210-01
Flexible Bayesian Hierarchical Models for Estimating Inhalation Exposures
National Institutes of Health
Grant: 5R01ES027027-03
Hierarchical Modeling and Analysis for Large Spatially and Temporally Misaligned Data in Environmental Health Applications
National Science Foundation
Grant: 1916349
Collaborative Research: High-Dimensional Spatial-Temporal Modeling and Inference for Large Multi-Source Environmental Monitoring Systems
National Science Foundation
Grant: 1513654
Collaborative Research: Hierarchical Sparsity-Inducing Gaussian Process Models for Bayesian Inference on Large Spatiotemporal Datasets
National Institutes of Health
FWCI
1.75
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals