Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dictionary describing what each numeric digit means within each classification. The “Category” column uses numeric digits (2-6, depending on the factor) defined in the “Classification” column.

Metro vs. Non-Metro – “Metro_Rural” Metro vs. Non-Metro classification type is an aggregation of the 6 National Center for Health Statistics (NCHS) Urban-Rural classifications, where “Metro” counties include Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro areas and “Non-Metro” counties include Micropolitan and Non-Core (Rural) areas. 1 – Metro, including “Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro” areas 2 – Non-Metro, including “Micropolitan, and Non-Core” areas

Urban/rural - “NCHS_Class” Urban/rural classification type is based on the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. Levels consist of:

1 Large Central Metro
2 Large Fringe Metro 3 Medium Metro 4 Small Metro 5 Micropolitan 6 Non-Core (Rural)

American Community Survey (ACS) data were used to classify counties based on their age, race/ethnicity, household size, poverty level, and health insurance status distributions. Cut points were generated by using tertiles and categorized as High, Moderate, and Low percentages. The classification “Percent non-Hispanic, Native Hawaiian/Pacific Islander” is only available for “Hawaii” due to low numbers in this category for other available locations. This limitation also applies to other race/ethnicity categories within certain jurisdictions, where 0 counties fall into the certain category. The cut points for each ACS category are further detailed below:

Age 65 - “Age65”

1 Low (0-24.4%) 2 Moderate (>24.4%-28.6%) 3 High (>28.6%)

Non-Hispanic, Asian - “NHAA”

1 Low (<=5.7%) 2 Moderate (>5.7%-17.4%) 3 High (>17.4%)

Non-Hispanic, American Indian/Alaskan Native - “NHIA”

1 Low (<=0.7%) 2 Moderate (>0.7%-30.1%) 3 High (>30.1%)

Non-Hispanic, Black - “NHBA”

1 Low (<=2.5%) 2 Moderate (>2.5%-37%) 3 High (>37%)

Hispanic - “HISP”

1 Low (<=18.3%) 2 Moderate (>18.3%-45.5%) 3 High (>45.5%)

Population in Poverty - “Pov”

1 Low (0-12.3%) 2 Moderate (>12.3%-17.3%) 3 High (>17.3%)

Population Uninsured- “Unins”

1 Low (0-7.1%) 2 Moderate (>7.1%-11.4%) 3 High (>11.4%)

Average Household Size - “HH”

1 Low (1-2.4) 2 Moderate (>2.4-2.6) 3 High (>2.6)

Community Vulnerability Index Value - “CCVI” COVID-19 Community Vulnerability Index (CCVI) scores are from Surgo Ventures, which range from 0 to 1, were generated based on tertiles and categorized as:

1 Low Vulnerability (0.0-0.4) 2 Moderate Vulnerability (0.4-0.6) 3 High Vulnerability (0.6-1.0)

Social Vulnerability Index Value – “SVI" Social Vulnerability Index (SVI) scores (vintage 2020), which also range from 0 to 1, are from CDC/ASTDR’s Geospatial Research, Analysis & Service Program. Cut points for CCVI and SVI scores were generated based on tertiles and categorized as:

1 Low Vulnerability (0-0.333) 2 Moderate Vulnerability (0.334-0.666) 3 High Vulnerability (0.667-1)

Dataset Card for Dataset Name

A data set of images of faces of people affected with Bell's palsy (Facial palsy).

  Dataset Details





  Dataset Description

A data set of images of faces of people affected with Bell's palsy (Facial palsy). Created using curating and editing publically available youtube videos. Also included are images from people not affected by it, using the same method.

License: CC-BY-4.0

  Uses

Can be used to train image models to detect… See the full description on the dataset page: https://huggingface.co/datasets/jasir/palsynet-data.

Every two years the WECC (Western Electricity Coordinating Council) releases an Anchor Data Set (ADS) to be analyzed with a Production Cost Models (PCM) and which represents the expected loads, resources, and transmission topology 10 years in the future from a given reference year. For hydropower resources, the WECC relies on members to provide data to parameterize the hydropower representation in production cost models. The datasets consist of plant-level hydropower generation, flexibility, ramping, and mode of operations and are tied to the hydropower representation in those production cost models.

In 2022, PNNL supported the WECC by developing the WECC ADS 2032 hydropower dataset [1]. The WECC ADS 2032 hydropower dataset (generation and flexibility) included an update of the climate year conditions (2018 calendar year), consistency in representation across the entire US WECC footprint, updated hydropower operations over the core Columbia River, and a higher temporal resolution (weekly instead of monthly)[1] associated with a GridView software update (weekly hydro logic). Proprietary WECC utility hydropower data were used when available to develop the monthly and weekly datasets and were completed with HydroWIRES B1 methods to develop the Hydro 923 plus (now RectifHydPlus weekly hydropower dataset) [2] and the flexibility parameterization [3]. The team worked with Bonneville Power Administration to develop hydropower datasets over the core Columbia River representative of the post-2018 change in environmental regulation (flex spill). Ramping data are considered proprietary, were leveraged from WECC ADS 2030, and were not provided in the release, nor are the WECC-member hydropower data.

This release represents the WECC ADS 2034 hydropower dataset. The generator database was first updated by WECC. Based on a review of hourly generation profiles, 16 facilities were transitioned from fixed schedule to dispatchable (380.5MW). The operations of the core Columbia River were updated based on Bonneville Power Administration's long-term hydro-modeling using 2020-level of modified flows and using fiscal year 2031 expected operations. The update was necessary to reflect the new environmental regulation (EIS2023). The team also included a newly developed extension over Canada [4] that improves upon existing data and synchronizes the US and Canadian data to the same 2018 weather year. Canadian facilities over the Peace River were not updated due to a lack of available flow data. The team was able to modernize and improve the overall data processing using modern tools as well as provide thorough documentation and reproducible workflows [5,6]. The datasets have been incorporated into the 2034 ADS and are in active use by WECC and the community.

WECC ADS 2034 hydropower datasets contain generation at weekly and monthly timesteps, for US hydropower plants, monthly generation for Canadian hydropower plants, and the two merged together. Separate datasets are included for generation by hydropower plant and generation by individual generator units. Only processed data are provided. Original WECC-utility hourly data are under a non-disclosure agreement and for the sole use of developing this dataset.

[1] Voisin, N., Harris, K. M., Oikonomou, K., Turner, S., Johnson, A., Wallace, S., Racht, P., et al. (2022). WECC ADS 2032 Hydropower Dataset (PNNL-SA-172734). See presentation (Voisin N., K.M. Harris, K. Oikonomou, and S. Turner. 04/05/2022. "WECC 2032 Anchor Dataset - Hydropower." Presented by N. Voisin, K. Oikonomou at WECC Production Cost Model Dataset Subcommittee Meeting, Online, Utah. PNNL-SA-171897.).

[2] Turner, S. W. D., Voisin, N., Oikonomou, K., & Bracken, C. (2023). Hydro 923: Monthly and Weekly Hydropower Constraints Based on Disaggregated EIA-923 Data (v1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8212727

[3] Stark, G., Barrows, C., Dalvi, S., Guo, N., Michelettey, P., Trina, E., Watson, A., Voisin, N., Turner, S., Oikonomou, K. and Colotelo, A. 2023 Improving the Representation of Hydropower in Production Cost Models, NREL/TP-5700-86377, United States. https://www.osti.gov/biblio/1993943

[4] Son, Y., Bracken, C., Broman, D., & Voisin, N. (2025). Monthly Hydropower Generation Dataset for Western Canada (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14984725

[5] https://github.com/HydroWIRES-PNNL/weccadshydro/

Dataset Files:

Data Description:

Each dataset contains the following column headers:

Data Source (Method)

The dataset contains data from four different data sources, developed using different methods:

VisDrone Dataset (YOLO Format)

  Overview

This repository contains the VisDrone dataset converted into the YOLO (You Only Look Once) format. The VisDrone dataset is a large-scale benchmark for object detection, segmentation, and tracking in drone videos. The dataset includes a variety of challenging scenarios with diverse objects and backgrounds.

  Dataset Details

Classes: 0: pedestrian 1: people 2: bicycle 3: car 4: van 5: truck 6: tricycle 7: awning-tricycle 8:… See the full description on the dataset page: https://huggingface.co/datasets/banu4prasad/VisDrone-Dataset.