GREENER bridges the gap between raw geospatial data and actionable research. We curate, process, and link decades of environmental exposure data to research cohorts.
Explore our comprehensive collection of environmental, health, and social datasets.
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Transforming raw environmental data into actionable, easy-to-understand insights.
GREENER maintains a continuously growing catalog of standardized environmental and social datasets — from satellite-derived air quality and greenspace indices to federal vulnerability and housing indicators. Every dataset is harmonized to CONUS census geographies and validated for spatial and temporal completeness before publication.
Participant addresses are geocoded entirely on the Mount Sinai institutional server using GIS — no protected health information leaves our network. Each address is matched to a 2020 TIGER/Line census tract and returned with a quality flag, match score, and match type, enabling transparent downstream QC.
GREENER links participant cohorts to multiple environmental exposure layers simultaneously — air quality, greenspace, social vulnerability, climate, wildfire smoke, and more. Linkage is performed at the census tract level or at ~100m residence-level resolution, depending on the dataset and your study design.
Researchers can define their geographic area of interest by drawing a polygon, entering a bounding box, or selecting standard geographies — state, county, ZIP code, or census tract. Extraction is available at multiple spatial resolutions, from ZCTA-level aggregates to fine-scale ~100m grid exposures.
Every linked dataset is delivered with a complete data dictionary, match rate table, and QA flag columns. Temporal gaps, geographic mismatches, and missing values are documented before data leaves the platform — so your methods section reflects the actual state of the data.
How GREENER Works
A secure, four-step pipeline — run on institutional infrastructure
Upload a de-identified participant list with address fields through your secure GREENER account. The platform deduplicates records, flags formatting issues, and prepares the data for on-server geocoding. No protected health information is transmitted or stored externally.
Addresses are geocoded using the Mount Sinai institutional GIS deployment. Each participant is matched to a geography (e.g. census tract, zip code, or custom polygon) and assigned a match score and quality flag. The geocoding process is entirely server-side and produces no outbound data transfer.
Explore the GREENER Data Library and add datasets to your request cart. Choose from air quality, vegetation indices, social vulnerability, climate, and built environment indicators. Specify the years, temporal resolution, and spatial aggregation level that fit your study design.
Your request is processed by the GREENER team and delivered as a CSV or Apache Parquet file. Every delivery includes a data dictionary, match rate report, QA flag columns, and metadata describing the source, processing steps, and spatial resolution of each variable.
By abstracting away the complex geospatial joins and harmonizing diverse datasets, GREENER enables you to move directly to statistical modeling and visualization. Build reproducible pipelines and accelerate your environmental health research.
GREENER curates data from peer-reviewed models and federal agencies, processed on institutional HPC infrastructure and validated before publication.
GREENER is available to Mount Sinai investigators and CTSA-affiliated institutions. Register for an account to browse the full Data Library, or contact the team to discuss a cohort linkage request.
Request Access Contact The TeamThis platform is supported by the Clinical and Translational Science Awards (CTSA) program, Grant UL1TR004419, from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Icahn School of Medicine at Mount Sinai | ConduITS — Conduits for the Integration of Translational Science
