Document describing the data sources and methodology used to produce the Veteran Population Projection Model 2023 (VetPop2023).

Summary of the searchable data fields in FReD and examples of the data format used in each.

This template covers section 2.5 Resource Fields: Entity and Attribute Information of the Data Discovery Form cited in the Open Data DC Handbook (2022). It completes documentation elements that are required for publication. Each field column (attribute) in the dataset needs a description clarifying the contents of the column. Data originators are encouraged to enter the code values (domains) of the column to help end-users translate the contents of the column where needed, especially when lookup tables do not exist.

The Government has published the Business Plan Quarterly Data Summaries (QDS) The QDS are designed to fit on a single page to provide a quarterly snapshot on how each department is spending its budget, the results it has achieved and how it is deploying its workforce. The QDS follows commitments made at Budget 2011 and the Written Ministerial Statement on Business Plans. Their primary purpose is to make more of the management information currently held by government available to members of the public on a regular basis. This information is not audited and the quality and accuracy of the data needs to dramatically improve. However, over time with improvements in data quality and timeliness the public will be able to judge the performance of each department in a meaningful and understandable manner. We intend for an annual version of this information to be formally laid in Parliament in the Annual Report and Accounts for July 2011/12 onwards. The information is presented in excel format to allow the information to be presented in a re-usable format. The QDS template is the same for all departments, though many of the individual indicators are unique to the department (especially input and impact indicators). This is the first time Government has published this kind of information, and while this is a good start, there is room for improvement. Before using this data people should ensure they take full note of the caveats noted in each Department’s measurement annex and treat with necessary caution. At the moment, people should not be using this data to make direct comparisons between departments for several reasons. Firstly, the business of each department is unique and it does not make sense to compare some measures across all departments. Secondly, many of the measures are not directly comparable because they do not have common definitions, time periods, or data collection processes. We will be updating regularly the QDS each quarter with the next publication following in October 2011.

This dataset is in a user-friendly human-readable format. To download the source dataset that contains raw data values, go here: https://data.transportation.gov/dataset/Form55a-Source-Table/kuvg-3uwp.

This file contains participant response data to Likert scale, open-ended responses and self-reported time taken to complete various tasks related to the extraction exercise. This Excel file also contains:

1) Examples of the Interactive HAWC Visuals that can be created after extracting data into the template. 2) The Initial Post-Extraction Survey Tool ("Survey 1") 3) The Final Post-Pilot Survey Tool ("Survey 2") 4) Survey 2 Results: Willingness to Consider Structured Data During Publication Process (Table 2) 5) Survey 1 Results: Participant Self-Reported Time Spent Performing Various Pilot Tasks (Table 3) 6) Survey 1 Results: Summary of Technical Assistance Provided by Team Members (Table 4) 7) Survey 2 Results: Participant Responses Describing Pilot's Impact on Future Research Activities (Table 5) 8) Survey 1 Results: Initial Survey Likert Scale Results (Table 6) 9) Repeat Extraction: Comparison of the First and Second Data Extraction Experience (Among the Same Participant) 10) Survey 1 Results: Problematic & Easy Fields to Extract.

This dataset is associated with the following publication: Wilkins, A., P. Whaley, A. Persad, I. Druwe, J. Lee, M. Taylor, A. Shapiro, N. Blanton, C. Lemeris, and K. Thayer. Assessing author willingness to enter study information into structured data templates as part of the manuscript submission process: A pilot study. Heliyon. Elsevier B.V., Amsterdam, NETHERLANDS, 8(3): 1-9, (2022).

This dataset contains templates of policies and MoU's on data sharing. You can download the Word-templates and adapt the documents to your national context.

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Dataset Details





  Dataset Description

Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed]

  Dataset Sources [optional]

Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/templates/dataset-card-example.

Data Dictionary template for Tempe Open Data.

The RMP summary should include specific sections, which mirror the EU RMP Summary format sections.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

This dataset contains model-based county-level estimates for the PLACES 2021 release in GIS-friendly format. PLACES is the expansion of the original 500 Cities Project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2019 or 2018 data, Census Bureau 2019 or 2018 county population estimates, and American Community Survey (ACS) 2015–2019 or 2014–2018 estimates. The 2021 release uses 2019 BRFSS data for 22 measures and 2018 BRFSS data for 7 measures (all teeth lost, dental visits, mammograms, cervical cancer screening, colorectal cancer screening, core preventive services among older adults, and sleeping less than 7 hours a night). Seven measures are based on the 2018 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2015 county boundary file in a GIS system to produce maps for 29 measures at the county level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=024cf3f6f59e49fe8c70e0e5410fe3cf

not available

This dataset is comprised of a collection of example DMPs from a wide array of fields; obtained from a number of different sources outlined below. Data included/extracted from the examples include the discipline and field of study, author, institutional affiliation and funding information, location, date created, title, research and data-type, description of project, link to the DMP, and where possible external links to related publications or grant pages. This CSV document serves as the content for a McMaster Data Management Plan (DMP) Database as part of the Research Data Management (RDM) Services website, located at https://u.mcmaster.ca/dmps. Other universities and organizations are encouraged to link to the DMP Database or use this dataset as the content for their own DMP Database. This dataset will be updated regularly to include new additions and will be versioned as such. We are gathering submissions at https://u.mcmaster.ca/submit-a-dmp to continue to expand the collection.

The data is published using the Summary Data Template, Version 1.1 as of 05 March 2015.

According to the EITI Standard 5.3.b: "Summary data from each EITI Report should be submitted electronically to the International Secretariat according to the standardised reporting format provided by the International Secretariat" This template should be completed in full and submitted by email by the national secretariat to the International EITI Secretariat following the publication of the report. The data will be used to populate the global EITI data repository, available on the international EITI website.

NB: The data available on ResourceData is republished from the EITI API and covers one section of the Summary Data, Part 3 which is comprised of data on government revenues per revenue stream and company.

Notes for consideration:

Disclaimer: The EITI Secretariat advice that users consult the original reports for detailed information. Where figures are not available in US dollars, the annual average exchange rate is used. Any questions regarding the data collection and Summary Data methodology can be directed to the EITI Secretariat: data@eiti.org or by visiting eiti.org/summary-data.

2017, 2016. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census tract-level data in GIS-friendly format can be joined with census tract spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-Census-Tract-Boundaries/x7zy-2xmx) in a geographic information system (GIS) to produce maps of 27 measures at the census tract level. There are 7 measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) in this 2019 release from the 2016 BRFSS that were the same as the 2018 release.

Summary Viewer is a configurable app template that summarizes the numeric attributes of features in a specified operational layer that are within the visible map area. The summaries can be configured to show the sum, average, minimum and maximum of specified field values.Configurable OptionsThe template can be configured using the following options:Text TitleThe application name displayed in the application header.Logo URL: The URL location for the application icon (logo).Color: Choose the color schemes for the application.Summary Layer: The operational layer in the web map used to generate the summary.Filter Field: (Optional) The field from which a value can be chosen from a dropdown list to filter map features.Summary fields: One or more fields used for the display of a sumAverage Fields: One or more fields used for the display of an averageMinimum Fields: One or more fields used for which a minimum value will be displayedMaximum Fields(s): One or more fields used for which a maximum value will be displayedCluster Summary Layer: Toggle the display of a cluster rather than unique feature, when features are in close proximity.Data RequirementsThis application requires a feature layer with at least one numeric field. For more information, see the Layers help topic for more details.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.