Abstract

Dataset quality **: Medium/high quality dataset, not quality checked or modified by the EIDC team

The climate TARCK uses satellites, other remote sensing techniques, and artificial intelligence to deliver a detailed, independent look at global emissions that gets more granular over time.

This dataset track greenhouse gas emissions from 39 subsectors in agriculture, buildings, manufactoring, maritime, mineral extraction, forestry and land use, oil and gas, power, transport, and waste for all countries from 2015-2020.

Usage

**Time: **2015-2021

**Coverage: **US

Frequency: Annual

Source type: Downloadable csv

This dataset contains time series observations of surface-atmosphere exchanges of carbon, water, and energy, as well as supporting micrometeorological, soil physics, and vegetation measurements. Data have been obtained at ten eddy covariance (EC) flux observation sites across England and Wales. Sites were active for different time periods between 2018 and 2023. Flux data include net ecosystem carbon dioxide exchange (NEE), sensible heat (H), and latent heat (LE). Examples of ancillary and vegetation data include measurements of air temperature, relative humidity, barometric pressure, wind speed and direction, components of radiation, soil heat flux, soil temperature and moisture, precipitation, water table depth, biomass, leaf area index (LAI), and canopy height. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

This dataset contains the Standardised Streamflow Index (SSI) data for 303 catchments across the United Kingdom from 1891 to 2015. The SSI is a drought index based on the cumulative probability of a given monthly mean streamflow occurring for a given catchment. Here, the SSI is calculated for the following accumulation periods: 1, 3, 6, 9, 12, 18 and 24 months. Each accumulation period is calculated for calendar end-months. The standard period used to fit the Tweedie distribution is 1961-2010. The SSI was produced by the RCUK-funded Historic Droughts project in order to characterise and explore hydrological drought severity over the period 1891-2015. This dataset is an outcome of the Historic Droughts Project (grant number: NE/L01016X/1).

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

The Massive Data Institute is partnering with BlocPower, who has created an open building data set for 121 million buildings across America. This is the largest building-level dataset in the country. This data enables researchers, policymakers, and community leaders to harness information on building characteristics to make buildings greener, smarter, and healthier.

Buildings produce over 30% of US greenhouse gas (GHG) emissions. Correctly analyzing, sizing, engineering, and commissioning projects to reduce GHG emissions requires accurate and precise data. However, that data is currently highly fragmented, inaccessible, and unreliable.

In this initial data release, EIDC and BlocPower are making a subset of the data accessible at a building-level resolution. The data is accessible to registered users.

Methodology

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The data was provided by BlocPower based on multiple sources, including Oak Ridge National Laboratory (ORNL)’s Model America project, tax assessment records and building permits.

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After receiving the data from BlocPower, EIDC transformed and subsetted the data to focus on 16 variables most relevant for potential users.

The following list provides a brief description of the variables in the current table, 'BlocPower Core'.

%3Cu%3E%3Cstrong%3EGeographic characteristics:%3C/strong%3E%3C/u%3E

%3Cu%3E%3Cstrong%3E%3C/strong%3E%3C/u%3E

building_id: unique identifier for each building

state: includes all 50 U.S. states and Washington D.C.

county: includes 1,810 U.S. counties

city: includes 15,391 U.S. cities

zip: includes 25,837 U.S. zipcodes

address: includes 68 million building addresses

%3Cu%3E%3Cstrong%3EBuilding characteristics:%3C/strong%3E%3C/u%3E

%3Cu%3E%3Cstrong%3E%3C/strong%3E%3C/u%3E

area_sq_ft: total area of building in square feet

year_built: year in which building was built

building_type: type of building, includes values such as single family residential, multi family residential, and small commercial.

%3Cu%3E%3Cstrong%3EBuilding system types:%3C/strong%3E%3C/u%3E

%3Cu%3E%3Cstrong%3E%3C/strong%3E%3C/u%3E

cooling_system_type: type of cooling system, includes values such as central air, chilled water, evaporative cooler, wall unit, and window unit.

heating_system_type: type of heating system, includes values such as central air, electric, forced air, gas, heat pump, hot water, and solar.

heating_fuel_type: type of heating fuel, includes values such as electric, wood, oil, propane, electric, coal, and gas.

%3Cu%3E%3Cstrong%3EModeled variables:%3C/strong%3E%3C/u%3E

%3Cu%3E%3Cstrong%3E%3C/strong%3E%3C/u%3E

The energy use variables were modeled by BlocPower based on ORNL’s Model America building energy profiles.

total_site_energy_GJ: Total energy - amount of heat and electricity - consumed by a building on site, in gigajoules.

total_source_energy_GJ: Total amount of raw fuel that is required to operate the building, in gigajoules. It incorporates all transmission, delivery, and production losses. Recommended by the EPA as the best unit of evaluation for comparing different buildings.

energy_use_intensity: Total energy use normalized by building area, in units of thousand British Thermal Units (kBTUs) per square foot.

energy_efficiency_potential: Predicted energy efficiency potential of a building, classified as low, medium or high.

Usage

There are missing values for several variables. Users can inspect the number of missing values for a variable by the following steps:

1. Click on ‘Tables’ at the top of this landing page.

2. Click on the table named ‘BlocPower.Core’.

3. Click on the variable of interest,

4. Look for the distribution of possible variable values (including missing values) will appear on the lower right-hand corner of the popup window (scroll down as needed).

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Addresses are available for 68 million buildings. Modeled data is matched to addresses using nearest neighbor search methods. The modeling process is unable to link buildings to specific addresses for states that do not provide building coordinates. Because the modeled data is based on a limited set of publicly available addresses, some specific street addresses may be duplicated.

Consequently, data is completely missing for 8 states - DC, DE, FL, GA, MD, NC, SC, WV. Data is also sparse for VA and TX (%3E90% missing values). An upcoming priority is to identify ways to source addresses for these states. Addresses are mostly available for AK, CA, CO, CT, HI, IL, LA, MA, MI, NH, NJ, NY, NV, OR, PA, RI, TN, WA (less than 25% missing).

Building system data is most complete for AK, CO, CT, MA, NH, NY, NV, RI, WA

(average of %3C30% missing). Missing data for building sys

Explore the historical Whois records related to eidc-k.org (Domain). Get insights into ownership history and changes over time.

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

This repo contains the code, processes, and documentation for the data and tech powering the Justice40 Climate and Economic Justice Screening Tool (CEJST) provided by the Justice40 Open Source Community.

Methodology

The core Justice40 team building this tool is a small group of designers, developers, and product managers from the US Digital Service in partnership with the Council on Environmental Quality (CEQ).

This dataset presents estimates of mean values within selected habitats and parent material characteristics made using Countryside Survey (CS) data from 1978, 1998 and 2007 using a mixed model approach (see Scott, 2008 for further details of similar statistical analysis - http://nora.nerc.ac.uk/id/eprint/5202/1/CS_UK_2007_TR4%5B1%5D.pdf ). Countryside Survey topsoil carbon data is representative of 0-15 cm soil depth and includes Loss-on-ignition (%), Carbon concentration (g kg-1) and Carbon density (t ha-1). A total of 2614 cores from 591 1km x 1km squares across Great Britain were collected and analysed in 2007 (see Emmett et al. 2010 for further details of sampling and methods http://nora.nerc.ac.uk/id/eprint/5201/1/CS_UK_2007_TR3%5B1%5D.pdf ). Loss-on-ignition (LOI) was determined by combustion of 10g dry soil at 375 deg C for 16 hours; carbon concentration was estimated by multiplying LOI by a factor of 0.55, and carbon density was estimated by combining carbon concentration with bulk density estimates. The estimated means of habitat/parent material combinations using 2007 data are modelled on dominant habitat and parent material characteristics derived from the Land Cover Map 2007 and Parent Material Model 2009, respectively. The parent material characteristic used was that which minimised AIC in each model (see Supporting Information). Areas, such as urban and littoral rock, are not sampled by CS and therefore have no associated data. Also, in some circumstances sample sizes for particular habitat/parent material combinations were insufficient to estimate mean values. The Countryside Survey looks at a range of physical, chemical and biological properties of the topsoil from a representative sample of habitats across the UK.

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

Each state zip file contains a single CSV file of key facility-level information including facility name and address, geospatial information, EPA and State programs the facility is associated with, and facility industry classifications (SIC and NAICS codes and descriptions). Complete documentation of the CSV file is included within the zip archive.

Siemens Healthcare Nv Sa Eidc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

These are the standard time aggregations EPA calculates and stores (we do not have monthly data). All have data files grouped by parameter: Criteria Gases, and Particulates Each group has data listed by year, in reverse order, back to 1990.

Each table entry has the file name, linked to the file, the size of the (zipped) file, the number of data rows in the file, and the date the file was last modified. EPA will update these files twice per year; in the spring and fall (late May and November). Keep in mind, data collection agencies have up to 6 months to report their data.

The files are all comma separated text with a header. Each aggregate level has a different format.

Methodology

For site description data, each unique geographic location that contains monitors is called a "site" in AQS. Information about the geographic setting is store in the site record, which are presented here. A unique site is identified by the combination of state code, county code, and site number (within county). It can also be identified by the latitude and longitude.

For monitor description data, each parameter that is measured at a site is considered a "monitor" in AQS. (So a "monitor" does not necessarily correspond to a physical instrument/sampler.) AQS tracks administrative information about monitors including who operates them, the methods being used, the networks they belong to, etc. That information is available in this file. A unique monitor is identified by the combination of state code, county code, site number (within county), parameter code, and parameter occurrence code ("POC", used to differentiate when a parameter is measured more than once at a site).

For daily summary data, each daily summary file contains data for every monitor (sampled parameter) in our database for each day. These files are separated by parameter (or parameter group) to make the sizes more manageable.

This file will contain a daily summary record that is:

1) The aggregate of all sub-daily measurements taken at the monitor.

2) The single sample value if the monitor takes a single, daily sample (e.g., there is only one sample with a 24-hour duration). In this case, the mean and max daily sample will have the same value.

The daily summary files contain (at least) one record for each monitor that reported data for the given day. There may be multiple records for the monitor if:

• There are calculated sample durations for the pollutant. For example, PM2.5 is sometimes reported as 1-hour samples and EPA calculates 24-hour averages.

• There are multiple standards for the pollutant (q.v. pollutant standards).

• There were exceptional events associated with some measurements that the monitoring agency has or may request be excluded from comparison to the standard.

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Credit report of Siemens Healthcare Nv Sa Eidc contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

The Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) dataset is a global hydrological dataset containing publicly available daily flow data for 2,386 gauging stations across the globe which have natural or near-natural catchments. Metadata is also provided alongside these stations for the Full ROBIN Dataset consisting of 3,060 gauging stations. Data were quality controlled by the central ROBIN team before being added to the dataset, and two levels of data quality are applied to guide users towards appropriate the data usage. Most records have data of at least 40 years with minimal missing data with data records starting in the late 19th Century for some sites through to 2022. ROBIN represents a significant advance in global-scale, accessible streamflow data. The project was funded the UK Natural Environment Research Council Global Partnership Seedcorn Fund - NE/W004038/1 and the NC-International programme [NE/X006247/1] delivering National Capability

This dataset is model output from the GR4J lumped catchment hydrology model. It provides 500 model realisations of daily river flow, in cubic metres per second (cumecs, m3/s), for 303 UK catchments for the period between 1891-2015. The modelled catchments are part of the National River Flow Archive (NRFA) (https://nrfa.ceh.ac.uk/) and provide good spatial coverage across the UK. These flow reconstructions were produced as part of the Research Councils UK (RCUK) funded Historic Droughts and IMPETUS projects, to provide consistent modelled daily flow data across the UK from 1891-2015, with estimates of uncertainty. This dataset is an outcome of the Historic Droughts Project (grant number: NE/L01016X/1). The data are provided in two formats to help the user account for uncertainty: (1) a 500-member ensemble of daily river flow time series for each catchment, with their corresponding model parameters and evaluation metric scores of model performance. (2) a single river flow time series (one corresponding to the top run of the 500), with the maximum and minimum daily limits of the 500 ensemble members.

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

The United States Environmental Protection Agency (EPA) collects occurrence data for contaminants that may be present in drinking water, but are not currently subject to the agency's drinking water regulations.

Methodology

How does EPA select the contaminants for UCMR?

In establishing the list of contaminants for each Unregulated Contaminant Monitoring Rule (UCMR) cycle, EPA considers the Contaminant Candidate List (CCL) and other priority contaminants. Further, EPA considered the opportunity to use multi-contaminant methods to collect occurrence data in an efficient, cost-effective manner.

EPA evaluates candidate UCMR contaminants using a multi-step prioritization process. The first step includes identifying contaminants that:

(1) were not monitored under prior UCMR cycles

(2) may occur in drinking water

(3) are expected to have a completed, validated drinking water method in time for rule proposal.

The next step is to consider the following: availability of health assessments or other health-effects information (e.g., critical health endpoints suggesting carcinogenicity); public interest (e.g., PFAS); active use (e.g., pesticides that are registered for use); and availability of occurrence data.

During the final step, EPA considers stakeholder input; looks at cost-effectiveness of the potential monitoring approaches; considers implementation factors (e.g., laboratory capacity); and further evaluates health effects, occurrence, and persistence/mobility data to identify the list of proposed UCMR contaminants.

Usage

There are 3 different UCMR waves in this dataset: UCMR 2 (2008 - 2010), UCMR 3 (2013 - 2015), UCMR 4 (2018 - 2020). All three have their unique key identifiers to be the combination of %3Cu%3ESample ID and Contaminant Name and Public Water System ID%3C/u%3E

. NOTE: The first two variables can be combined to uniquely identify most observations. The third variable is added to ensure absolute uniqueness.

For UCMR 2, we have one main table corresponding.

For UCMR 3, in addition to the main table, we have two additional tables for residual disinfectant type detected in some of the PWSs that are subject to such regulations, and for the service area zipcodes reported by some PWSs.

For UCMR 4, in addition to the main table, we have four additional tables for additional results for total organic carbon and bromide from select PWSs, for additional data elements for cyanotoxins, for additional disinfectant type information for some PWSs, and for the service area zipcodes reported by some PWSs. NOTE: UCMR 4 has no Associated Facility information.

Context

The EPA uses the UCMR to collect data for contaminants that are suspected to be present in drinking water and do not have health-based standards set under the Safe Drinking Water Act (SDWA).

Occurrence data are collected through UCMR to support the Administrator's determination of whether to regulate particular contaminants in the interest of protecting public health. The program was developed in coordination with the Contaminant Candidate List (CCL) a list of contaminants that:

• Are not regulated by the National Primary Drinking Water Regulations
• Are known or anticipated to occur at public water systems (PWS)
• May warrant regulation under the SDWA

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What are the public health benefits of UCMR?

UCMR provides EPA and others with scientifically valid data on the occurrence of these contaminants in drinking water. This permits assessment of the population being exposed and the levels of exposure.

UCMR data represent one of the primary sources of national occurrence data in drinking water that EPA uses to inform regulatory and other risk management decisions for drinking water contaminant candidates. This data will ensure science-based decision-making and help prioritize protection of disadvantaged communities.

Abstract

Dataset quality **: Medium/high quality dataset, not quality checked or modified by the EIDC team

The Local Area Unemployment Statistics (LAUS) program produces monthly and annual employment, unemployment, and labor force data for Census regions and divisions, States, counties, metropolitan areas, and many cities, by place of residence.

Methodology

A script to extract these is given here.

This data product combines macroinvertebrate taxonomic abundance for 1,519 monitoring sites across English rivers for the period between 1965 and 2018, with concentrations of 41 water quality determinands, river flow measurements and air temperature derived values. It also includes site variables such as sewage effluent exposure, habitat quality, land cover in the upstream catchment and other physical parameters measured at the sampling point such as altitude, slope, distance from source, width and depth of the channel, and type of substrate. Developed as part of the ChemPop (2018-2023), a NERC-funded project investigating the impact of chemical exposure on wildlife populations in rivers, this research output is an open data product for use in a broad spectrum of environmental data and modelling analyses.

This dataset was created on 2022-06-17.

Abstract

Dataset quality **: Medium/high quality dataset, not quality checked or modified by the EIDC team

The National Risk Index is a dataset and online tool to help illustrate the United States. communities most at risk for 18 hazard types: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather.

It was designed and built by FEMA in close collaboration with various stakeholders and partners in academia; local, state, and federal governments; and private industry. The Risk Index leverages available source data for natural hazard and community risk factors to develop a baseline relative risk measurement for each United States county and Census tract. The National Risk Index is intended to help users better understand the natural hazard risk of their communities.

Abstract

All states (including the District of Columbia) are required to provide data to The Centers for Medicare & Medicaid Services (CMS) on a range of indicators related to key application, eligibility, and enrollment processes within the state Medicaid and Children’s Health Insurance Programs (CHIP). These data reflect enrollment activity for all populations receiving comprehensive Medicaid and CHIP benefits in all states, as well as state program performance.

The data set contains vertical profiles of diffuse light transmittance measured within six forest plots in montane Atlantic forest, São Paulo state, Brazil. The plots measured include intact, previously logged and secondary forest in a large continuous forest block of the Serra do Mar State Park (Parque Estadual de Serra do Mar), and two forest fragments outside the park. In each plot 10 - 12 individual light profiles were recorded; the data set contains these individual profiles and averages profiles for each plot based on both height above the ground and depth below the canopy. Each profile was measured only once. Data was collected March - November 2015 as part of the NERC Human modified Tropical Forest Programme.