Linked hereto is the Comptroller's Quarterly New York City Economic Update, which includes a variety of data sets (nearly all of which are created separately by other other federal, state or city agencies and private entities) that provide information about the economy of New York City.

Independently derived sea level data based on satellite altimeters. Data runs from 1993 to latest update, which is typically April and October each year.

Under the "Services" tab in this collection we provide THREDDS, HTTP and FTP links to - Global Mean Sea Level (GMSL) annual values in .txt or .csv and .jpg plots - Regional Sea Level (RSL) monthly gzipped netCDF files with various corrections applied or not, as described in https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/#OurData - Images plotting these data, including many seen in https://research.csiro.au/slrwavescoast/sea-level/sea-level-changes/ Lineage: Refer to Church and White (2011).

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

(Includes MeSH 2023 and 2024 changes) The MeSH 2025 Update - Add Report lists new Descriptors and Supplementary Concept Records (SCRs) that have been added to MeSH for the upcoming new release. It also includes terms that replace deleted terms, as well as new entry combinations (new precoordinated descriptor heading that replaces an existing descriptor/subheading combination). This report includes MeSH changes from previous years, starting from 2023.

Note: 11/1/2023: Publication of the COVID data will be delayed because of technical difficulties. Note: 9/20/2023: With the end of the federal emergency and reporting requirements continuing to evolve, the Indiana Department of Health will no longer publish and refresh the COVID-19 datasets after November 15, 2023 - one final dataset publication will continue to be available. Note: 5/10/2023: Due to a technical issue updates are delayed for COVID data. New files will be published as soon as they are available. Note: 3/22/2023: Due to a technical issue updates are delayed for COVID data. New files will be published as soon as they are available. Note: 3/15/2023 test data will be removed from the COVID dashboards and HUB files in recognition of the fact that widespread use of at-home tests and a decrease in lab testing no longer provides an accurate representation of COVID-19 spread. Number of Indiana COVID-19 cases and deaths by age group, gender, race and ethnicity by day. All data displayed is preliminary and subject to change as more information is reported to IDOH. Expect historical data to change as data is reported to IDOH. Historical Changes: 1/11/2023: Due to a technical issue updates are delayed for COVID data. New files will be published as soon as they are available. 1/5/2023: Due to a technical issue the COVID datasets were not updated on 1/4/23. Updates will be published as soon as they are available. 9/29/22: Due to a technical difficulty, the weekly COVID datasets were not generated yesterday. They will be updated with current data today - 9/29 - and may result in a temporary discrepancy with the numbers published on the dashboard until the normal weekly refresh resumes 10/5. 9/27/2022: As of 9/28, the Indiana Department of Health (IDOH) is moving to a weekly COVID update for the dashboard and all associated datasets to continue to provide trend data that is applicable and usable for our partners and the public. This is to maintain alignment across the nation as states move to weekly updates. 2/10/2022: Data was not published on 2/9/2022 due to a technical issue, but updated data was released 2/10/2022. 12/30/21: This dataset has been updated, and should continue to receive daily updates. 12/15/21: The file has been adjusted with data through 12/13, and regular updates will resume to it today. 11/12/2021: Historical re-infections have been added to the case counts for all pertinent COVID datasets back to 9/1/2021 and new re-infections will be added to the total case counts as they are reported in accordance with CDC guidance. 06/23/2021: COVID Hub files will no longer be updated on Saturdays. The normal refresh of these files has been changed to Mon-Fri. 06/10/2021: COVID Hub files will no longer be updated on Sundays. The normal refresh of these files has been changed to Mon-Sat. 6/03/2021 : A batch of historical negative and positive test results added 16,492 historical tests administered, 7,082 tested individuals, and 765 historical cases to today's counts. These cases are not included in the new positive counts but have been added to the total positive cases. Today’s total case counts include historical cases received from other states. 2/4/2021 : Today’s dataset now includes 1,507 historical deaths identified through an audit of 2020 and 2021 COVID death records and test results.

Updates to Website: (Please add new items at the top of this description with the date of the website change) May 9, 2012: Uploaded experimental data in matlab format for HIRENASD November 8, 2011: New grids, experimental data for HIRENASD configuration, new FEM for HIRENASD configuration. (JHeeg) Oct 13: Uploaded BSCW grids (VGRID) (PChwalowski) Oct 5: Added HIRENASD experimental data for test points #159 and #132 (JHeeg, PChwalowski)

(Includes MeSH 2023 and 2024 changes) The MeSH 2025 Update - Replace Report lists Descriptors and Supplementary Concept Records (SCRs) where an existing MeSH term has been replaced by another term. Note that replacement terms are not necessarily new to the vocabulary; terms are sometimes consolidated under or moved to other existing terms. This report includes MeSH changes from previous years, starting from 2023.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

The Forager.ai - US Company Dataset is a leading source of firmographic data, backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Over 16M total US company records, the highest volume in the industry today.
• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

• Map the comprehensive universe of companies globally, and acquire the latest firmographic data for all those records.
• Track company size and measure growth to determine sales propensity or to create an internal growth score.
• Enrich your existing company database with fresh, comprehensive data.

B2B Tech Companies:

• Enrich leads that sign-up through the Company Search API (available separately).
• Identify and map every company that fits your core personas and ICP.
• Build audiences to target, using key fields like location, company size, industry, and description.

Venture Capital and Private Equity:

• Discover new investment opportunities using company descriptions and industry-level data.
• Review the growth of private companies and benchmark their strength against competitors.
• Create high-level views of companies competing in popular verticals for investment.

| Delivery Options |

• Flat files via S3 or GCP
• PostgreSQL Shared Database
• PostgreSQL Managed Database
• API
• Other options available upon request, depending on the scale required

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.

This dataset contains the digitized treatments in Plazi based on the original journal article Price, Michelle J. (2014): Hedwig-Schwägrichen types catalogue update (1): new typifications and typification corrections. Candollea 69 (2): 119-133, DOI: 10.15553/c2014v692a3

Analysis of ‘COVID-19 Data Update’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9c05876a-e7ce-46c3-bdbb-c16c2e2dc595 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

This data package aims to pilot an approach for providing usable data for analyses related to drought planning and management for urban water suppliers--ultimately contributing to improvements in communication around drought. This project was convened by the California Water Data Consortium in partnership with the Department of Water Resources (DWR) and the State Water Resources and Control Board (SWB) and is one of two use cases of this working group that aim to improve data submitted by urban water suppliers in terms of accessibility and useability. The datasets from DWR and the SWB are compiled in a standard format to allow interested parties to synthesize and analyze these data into a cohesive message. This package includes a data management plan describing its development and maintenance. All code related to preparing this data package can be found on GitHub. Please note that the "org_id" (DWR's Organization ID) and the "pwsid" (SWB's Public Water System ID) can be used to connect to the various data tables in this package. We acknowledge that data quality issues may exist. Making these data available in a usable format will help identify and address data quality issues. If you identify any data quality issues, please contact the data steward (see contact information). We plan to iteratively update this data package to incorporate new data and to update existing data with quality fixes. The purpose of this project is to demonstrate how data from two agencies, when made publicly available, can be used in relevant analyses; if you found this data package useful, please contact the data steward (see contact information) to share your experience.

Local Health contains indicators related to:

• population and demographic factors, including age, sex and deprivation
• health outcomes
• wider determinants of health, including housing and living environment

It presents data for middle super output areas (MSOAs), electoral wards, clinical commissioning groups (CCGs), local authorities, and England as a whole.

The tool allows users to map data and provides spine charts and reports for small areas. Users can also define their own geographies and add their own data.

This update contains:

• 47 indicators updated with data for the latest available time period and the latest available boundaries
• 13 indicators with no new source data have been updated to new ward and CCG 2021 boundaries
• an update of wards and CCGs to 2021 boundaries – this includes those indicators for which more recent data is not available

See the attached ‘Local Health: indicator updates, August 2022’ document for a full list of the available indicators, geographies and any other changes in this release.

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 is an updated and simplified version of the New Mexico heat flow data already on the NGDS that was used for Play Fairway analysis. This is an updated and simplified version of the New Mexico heat flow data already on the NGDS that was used for Play Fairway analysis.

Public Health England (PHE) has published an update to Local Alcohol Profiles for England (LAPE).

The LAPE interactive tool presents a range of alcohol-related indicators and allows users to view and analyse data in a user-friendly format.

The aim of the profile is to provide information for local government, health organisations, commissioners and other agencies to monitor the impact of alcohol on local communities, and to monitor the services and initiatives that have been put in place to prevent and reduce the harmful impact of alcohol.

This release includes new data for the following indicators:

• admission episodes for alcohol-related conditions
• admission episodes for alcohol-specific conditions
• admission episodes for alcohol-related unintentional injuries
• admission episodes for mental and behavioural disorders due to use of alcohol
• admission episodes for intentional self-poisoning by and exposure to alcohol
• admission episodes for alcohol-related cardiovascular disease
• admission episodes for alcoholic liver disease
• successful completion of alcohol treatment
• proportion waiting more than 3 weeks for alcohol treatment

The following new indicators have been added:

• quarterly admission episodes for alcohol-related conditions (Broad)
• quarterly admission episodes for alcohol-related conditions (Narrow)

View previous Local Alcohol Profiles for England updates.

Relevant information from LAPE to aid in the understanding of alcohol-related harm in a local population is available from the https://webarchive.nationalarchives.gov.uk/20171107173418/http://www.lape.org.uk/" class="govuk-link">UK Government Web Archive.

Data file for the third release of the Data Citation Corpus, produced by DataCite and Make Data Count as part of an ongoing grant project funded by the Wellcome Trust. Read more about the project.

The data file includes 5,322,388 data citation records in JSON and CSV formats. The JSON file is the version of record.

For convenience, the data is provided in batches of approximately 1 million records each. The publication date and batch number are included in the file name, ex: 2025-02-01-data-citation-corpus-01-v3.0.json.

The data citations in the file originate from the following sources:

DataCite Event Data

A project by Chan Zuckerberg Initiative (CZI) to identify mentions to datasets in the full text of articles

Data citations identified Aligning Science Across Parkinson’s (ASAP)

Each data citation record is comprised of:

A pair of identifiers: An identifier for the dataset (a DOI or an accession number) and the DOI of the publication (journal article or preprint) in which the dataset is cited

Metadata for the cited dataset and for the citing publication

The data file includes the following fields:

Field

Description

Required?

id

Internal identifier for the citation

Yes

created

Date of item's incorporation into the corpus

Yes

updated

Date of item's most recent update in corpus

Yes

repository

Repository where cited data is stored

No

publisher

Publisher for the article citing the data

No

journal

Journal for the article citing the data

No

title

Title of cited data

No

publication

DOI of article where data is cited

Yes

dataset

DOI or accession number of cited data

Yes

publishedDate

Date when citing article was published

No

source

Source where citation was harvested

Yes

subjects

Subject information for cited data

No

affiliations

Affiliation information for creator of cited data

No

funders

Funding information for cited data

No

Additional documentation about the citations and metadata in the file is available on the Make Data Count website.

Notes on v3.0:

The third release of the Data Citation Corpus data file reflects a few changes made to add new citations, including those from a new data source (ASAP), update and enhance citation metadata, and improve the overall usability of the file. These changes are as follows:

Add and update Event Data citations:

Add 65,524 new data citations created in DataCite Event Data between August 2024 and December 2024

Add ASAP citations:

Add 750 new data citations provided by Aligning Science Across Parkinson’s (ASAP), identified through processes to evaluate compliance with ASAP’s for open science practices, which involve a partnership with DataSeer and internal curation (described here).

Citations with provenance from ASAP are identified as “asap” in the source field

Metadata enhancements:

Reconcile and normalize organization names for affiliations and funders in a subset of records with the Research Organization Registry (ROR)

Add ror_name and ror_id subfields for affiliations and funders in JSON files. Unreconciled affiliation and funder strings are identified with values of null

Add new columns affiliationsROR and fundersROR in CSV files. Unreconciled affiliation and funder strings are identified with values of NONE NONE (this is to ensure consistency in number and order of values in cases where some strings have been reconciled and others have not)

Normalize DOI formats for articles and papers as full URLs

Additional details about the above changes, including scripts used to perform the above tasks, are available in GitHub.

Additional enhancements to the corpus are ongoing and will be addressed in the course of subsequent releases. Users are invited to submit feedback via GitHub. For general questions, email info@makedatacount.org.