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External Resources

Additional data resources to supplement your data science needs.

1 - Data for Use in R&D

Additional data resources to supplement your data science needs.


Air Quality System (AQS) | US EPA

The Air Quality System (AQS) is EPA’s repository of ambient air quality data. AQS stores data from over 10,000 monitors, 5000 of which are currently active.


Atmospheric Deposition and Critical Loads Data

NADP Maps and Data

National Atmospheric Deposition Program

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EPA’s Critical Loads Mapper Tool enables access to information on atmospheric deposition, critical loads, and their exceedances to better understand vulnerability to atmospheric pollution.

CLAD Database Access

National Atmospheric Deposition Program

Emissions

National Emissions Inventory (NEI) | US EPA

Detailed estimate of air emissions of both criteria and hazardous air pollutants from all air emissions sources.

Toxics Release Inventory (TRI) Program | US EPA

The Toxics Release Inventory tracks the management of certain toxic chemicals that may pose a threat to human health and the environment.

Satellite Air Quality Measurements

https://earthdata.nasa.gov/earth-observation-data/near-real-time/hazards-and-disasters/air-quality

https://www.usgs.gov/centers/eros/science/national-land-cover-database

https://www.nodc.noaa.gov/ocads/oceans/

https://www.ncdc.noaa.gov/cdo-web/datatools/lcd

Meteorology

https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/automated-surface-observing-system-asos

https://www.nrel.gov/analysis/jedi/index.html

Using JEDI, you can analyze the energy impacts of wind, biofuels, concentrating solar power, geothermal, marine and hydrokinetic power, coal, and natural gas power plants.

https://openei.org/wiki/Utility_Rate_Database

Rate structure information from utilities in the U.S. maintained by the U.S. Department of Energy

2 - Tools, Tips & Cheat Sheets

Additional data resources to supplement your data science needs.

Tools to Help you Get Started

A. 3 Major Steps to Data Science Project

B. Data Analytics Pyramid

C. Data Science Project Scorecard

D. 10 Questions to Understand Your Data

E. Data Analytics Lifecycle with Questionnaire

F. Data Analytics Planning Template

3 Major Steps to a Data Science Project



  1. Planning

    • Pose your operational problem as a data question
    • Make sure you have a good problem to solve with data science
    • Determine where your project falls in the lifecycle
  2. Execution

    • Stage the data
    • Select an analytic approach
    • Do the analytic work
    • Hand off the patterns to Action Team
    • Communicate the benefits to stakeholders
  3. Action Team

    • Share insights with your Action Team
    • Action Team will use them to implement change

Data Analytics Pyramid

Data Science Project Score

10 Questions to Understand Your Data

Data Analytics Lifecycle with Questionnaire

Data Analytics Planning Template