United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

This dataset lists various data sources used within the Department of Community Resources & Services for various internal and external reports. This dataset allows individuals and organizations to identify the type of data they are looking for and to which geographical level they are trying to get the data for (i.e. National, State, County, etc.). This dataset will be updated every quarter and should be utilized for research purposes

This document describes a dataset that aggregates information about 135 data journals.
Data journals focus on the publication of data papers -- a specialized publication type describing datasets, their collection and reuse potential that is peer-reviewed, citable and indexed.
This dataset includes a comprehensive list of data journals that was compiled by aggregating existing sources, as well as an overview of these sources.

The list is continually updated on GitHub, where additional information on data journals (URLs of data journal homepages) is provided: https://github.com/MaxiKi/data-journals

Data sources list for Occurrence records of tropical Asian butterflies (1970-2024)

*H: human, R: rat, M: mouse, F: fruit fly (Drosophila), E: E. coli.†OMIM data are presently not distributed with the TargetMine demonstration version.

This PDF lists the data sources used for the AK DOT CIDD dashboards.

This database table is the Dixon Master List of Radio Sources (Version 43, dated November 1981) which contains flux densities for known radio sources detected at a variety of frequencies. The Master List of Radio Sources was prepared by combining about thirty catalogs of radio sources that were available as of that date into a common format. Notice that this is a list of observations, not of individual sources, and that an entry in this table corresponds to an observation of a radio source at a particular frequency from a particular source catalog: also, no attempt was made by the author to use the same name for the same source, e.g., the source 3C 273 appears more than a dozen times under a variety of names such as PKS 1226+02, NRAO400, CTA 53, etc. This database table was recreated at the HEASARC in June 2005 after it was discovered that the positions had been incorrectly precessed. The original input table used for both the previous and current HEASARC Dixon tables was the 43rd version of the Master List, dated November 1981. It was obtained from the Colorado node of the Astrophysics Data System (ADS), the now-defunct HTTP link

A comprehensive list of data sources relating to violence against women and girls, bringing together a range of different sources from across government, academia and the voluntary sector.

The LAT team monitors flux values for a number of bright sources and transient sources that have shown flares during the mission. (See up-to-date weekly reports on flaring sources at the Fermi-LAT Flare Advocate Blog.) As sources cross the monitoring flux threshold of 1x10-6 cm-2s-1, they are added to the monitored source list. (The initial flux threshold was 2x10-6 cm-2s-1, but this value was lowered in June 2009.) In addition to the light curves below, the flux values in several bands are available via Browse. This list will continue to grow as the mission progresses.

The Planck list of high-redshift source candidates (PHZ) is a list of 2151 sources located in the cleanest 26% of the sky and identified as point sources exhibiting an excess in the submillimeter compared to their environment. It has been built using the 48 months Planck data at 857, 545, 353 and 217 GHz combined with the 3 THz IRAS data, as it is described in Planck-2015-XXXIX. These sources are considered as high-z source candidates (z>1.5-2), given the very low contamination by Galactic cirrus, and their typical colour-colour ratio. A subsample of the PHZ list has already been followed-up with Herschel, and chararcterized as overdensities of red galaxies for more than 93% of the population, and as strongly lensed galaxies in 3% of the cases, as detailed in Planck-2014-XXVIII.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

Key Features:

✅232M+ Job Postings Tracked – Data sourced from 92 Million company websites worldwide. ✅7,1M+ Active Job Openings – Updated in real-time to reflect hiring demand. ✅Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes:

• id (string, UUID) – Unique identifier for the job posting.
• type (string, constant: "job_opening") – Object type.
• title (string) – Job title.
• description (string) – Full job description, extracted from the job listing.
• url (string, URL) – Direct link to the job posting.
• first_seen_at – Timestamp when the job was first detected.
• last_seen_at – Timestamp when the job was last detected.
• last_processed_at – Timestamp when the job data was last processed.

Job Metadata:

• contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
• categories (array of strings) – Job categories (e.g., "engineering", "marketing").
• seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
• status (string) – Job status (e.g., "open", "closed").
• language (string) – Language of the job posting.
• location (string) – Full location details as listed in the job description.
• Location Data (location_data) (array of objects)
• city (string, nullable) – City where the job is located.
• state (string, nullable) – State or region of the job location.
• zip_code (string, nullable) – Postal/ZIP code.
• country (string, nullable) – Country where the job is located.
• region (string, nullable) – Broader geographical region.
• continent (string, nullable) – Continent name.
• fuzzy_match (boolean) – Indicates whether the location was inferred.

Salary Data (salary_data)

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Currency of the salary (e.g., "USD", "EUR").
• salary_low_usd (float, nullable) – Converted minimum salary in USD.
• salary_high_usd (float, nullable) – Converted maximum salary in USD.
• salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

Occupational Data (onet_data) (object, nullable)

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (e.g., "Computer and Mathematical").
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (e.g., "Python", "JavaScript").

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Dataset: https://docs.predictleads.com/v3/guide/job_openings_dataset

The Spitzer Science Center and IRSA have released a set of Enhanced Imaging Products (SEIP) from the Spitzer Heritage Archive. These include Super Mosaics (combining data from multiple programs where appropriate) and a Source List of photometry for compact sources. The primary requirement on the Source List is very high reliability -- with areal coverage, completeness, and limiting depth being secondary considerations. The SEIP include data from the four channels of IRAC (3.6, 4.5, 5.8, 8 microns) and the 24 micron channel of MIPS. The full set of products for the Spitzer cryogenic mission includes around 42 million sources.

List of monographic and matrix publications used in this analysis along with anatomical focus of the study and the number of fin or limb and girdle EQs (phenotypes) associated with each taxon.

A list of the data sources that currently feed into the Quarterly Sector Accounts and UK Economic Accounts tables.

United States - Sources of Revenue: Directories, Databases, and Other Collections of Information - Advertising Space for Directory and Mailing List Publishers, All Establishments, Employer Firms was 1781.00000 Mil. of $ in January of 2020, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Directories, Databases, and Other Collections of Information - Advertising Space for Directory and Mailing List Publishers, All Establishments, Employer Firms reached a record high of 9226.00000 in January of 2010 and a record low of 1618.00000 in January of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Directories, Databases, and Other Collections of Information - Advertising Space for Directory and Mailing List Publishers, All Establishments, Employer Firms - last updated from the United States Federal Reserve on November of 2025.

Usage

This dataset can be used for Data Visualization and Data analytics purpose.

Context This dataset contains the sanctions imposed by the Countries.

Content | Column | Description | | --- | --- | | id | the unique identifier of the given entity | | schema| the entity type | | name| the display name of the given entity | | aliases| any alias names (e.g. other scripts, nom de guerre) provided by the data sources | | birth_date | for people, their birth date | | countries | Includes countries of residence, nationalities and corporate jurisdictions | | addresses | a list of known addresses for the entity | | identifiers | identifiers such as corporate registrations, passport numbers or tax identifiers linked to this sanctions target | | sanctions | details regarding the sanctions designation if any | | phones | a list of phone numbers in E.164 format | | emails | a list of email addresses linked to the entity | | dataset | the dataset this entity is in | | address | address | | last_seen | the last time this entity was observed in source data | | first_seen | the earliest date this entity has been noticed by OpenSanctions |

Acknowledgment This data is collected from the Open Sanction Project

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The list of the data sources.

This database table consists of a preliminary source list for the Einstein Observatory's High Resolution Imager (HRI). The source list, obtained from EINLINE, the Einstein On-line Service at the Smithsonian Astrophysical Observatory (SAO), contains basic information about the sources detected with the HRI. This is a service provided by NASA HEASARC .

A table listing all elevational data sets of small mammal diversity, including data specifics, null model and analysis values, and a list of data sources used in the review.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)