The Sub-metered HVAC Implemented for Demand Response (SHIFDR) dataset is a massive dataset that captures the response of individual commercial building HVAC system components to demand response events. The dataset includes device-level power consumption during demand response events as well as during normal operation. We have organized the data into subsets, with each subset containing data from buildings in different parts of the world. Kindly refer to the README file within each subsection for specific information about how data is organized. Please reach out if you have data that you would like to share, find any mistakes in the data, or have any questions. We are always trying to improve SHIFDR.
The following works contain the databases, field notebooks, unit and profile drawings, photographs, photo descriptions, radiocarbon dates, and geophysical survey data related to the Zagorë settlement excavation.
The following works contain the databases, field notebooks, unit and profile drawings, photographs, photo descriptions, radiocarbon dates, and geophysical survey data related to the Kodër Boks settlement excavation.
All databases, field notebooks, paper maps, GIS files, photographs, and photo descriptions related to the intensive survey, of tracts and tumuli, and the collection of sites have been made available in PASH Deep Blue Data Realm 1. The data are broadly organized by team (A-K). The surveyed land was divided up into “tracts”. Tracts are labeled with team letter and a consecutive number: e.g., A-001, A-002, B-003, C-122, D-035.
Fossil energy production, processing, flaring, and transmission all can harm climate and air quality by emitting greenhouse gases and air pollutants. Studies now show that onshore oil and gas production emit much more methane than what is inventoried, and that local air quality impacts can be significant, however, natural gas flaring and offshore systems have been largely overlooked.
The F3UEL (Flaring & Fossil Fuels: Uncovering Emissions & Losses) project aims to address these gaps by improving our understanding of offshore emissions, characterizing how flares behave in the real world, identifying what portion of the offshore system is responsible for emissions, and determining how such systems can be monitored.
Spanning three years (2020-2022), the project employed an aircraft platform to measure including both greenhouse gas and air quality measurements. To sample the largest regions of current and potential future offshore production and flaring, airborne measurements targeted the Gulf of Mexico, offshore California and Alaska, the Bakken Formation (North Dakota) and the Permian and Eagle Ford Basins (Texas).
Data provided here includes the airborne measurements collected using Scientific Aviation’s Mooney aircraft platform, equipped with spectroscopic instrumentation to measure methane, carbon dioxide, water vapor, nitrous oxide, and nitrogen oxide, in addition to meteorological variables such as wind speed and direction. Data products from our analysis of these airborne measurements are also provided, including estimated flare destruction removal efficiency for the Bakken, Eagle Ford, and Permian basins.
Each data file is in .csv format and is accompanied by a readme file with further information and descriptors of the variables included. All users should cite the papers and datasets provided in the readme files for each individual dataset.
Website: https://graham.umich.edu/f3uel
This project is funded by the Alfred P. Sloan Foundation with additional support from the Environmental Defense Fund, Scientific Aviation, and University of Michigan (College of Engineering, Climate and Space Sciences and Engineering; Graham Sustainability Institute).
This collection was produced as part of the project, “A ‘Big Data’ Approach to Understanding Neighborhood Effects in Chronic Illness Disparities.” The Investigators for the project are Tiffany Veinot, Veronica Berrocal, Phillipa Clarke, Robert Goodspeed, Daniel Romero, and VG Vinod Vydiswaran from the University of Michigan. The study took place from 2015-2016, with funding from the University of Michigan’s Social Sciences Annual Institute, MCubed, and the Sloan and Moore Foundations.
Contact: Tiffany Veinot, MLS, PhD
Office: 3443 North Quad
Phone: 734/615-8281
Email: tveinot@umich.edu
MCubed project page:
https://mcubed.umich.edu/projects/%E2%80%9Cbig-data%E2%80%9D-approach-understanding-neighborhood-effects-chronic-illness-disparities