Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

A database housing longitudinal relational research data from over 4,000 research subjects. The database includes the following types of data: physical and neurological exam findings, neurocognitive test scores, personal and family history of dementia, personal demographic genotypes (APOE, HLA), age at service evaluations, age at onset, age at death, clinical diagnosis, neuropathology diagnosis, tissue inventory information (when available), health status, medications, laboratory tests, and MRI data.

The Urban Big Data Centre (UBDC) is an ESRC research centre based at the University of Glasgow that promotes the use of smart data and innovative methods to improve social, economic & environmental well-being in cities.

From 2014-26, it was also funded by ESRC to provide a national data service to enhance access to 'smart data'. UBDC focuses on six main research themes (labour market, housing and neighbourhoods, transport and mobility, urban governance, urban sustainability, and education) as well as two research methods (urban sensing and participatory analytics).

You can find more information about UBDC by visiting https://www.ubdc.ac.uk. To explore UBDC’s data offerings, please visit https://data.ubdc.ac.uk/.

Some of UBDC’s data collections are only available with permission from UBDC. These collections have been archived with the University of Glasgow repository. Details on the hosting and availability of safeguarded datasets can be found in the attached metadata sheet (snapshot as of 07/05/2025).

The proposed UBDRC will bring together an internationally outstanding combination of researchers, data resources and engaged local and national stakeholders to establish a unique linked data resource based in the University of Glasgow (UoG). Through extensive partnerships with other key academic institutions, data-owning organizations, and other scientific, governmental, third sector and business organizations, the UBDRC will: (i) establish a world leading facility to create an multi-sectoral urban linked data resource from local government authorities and business owners in Glasgow; (ii) provide outstanding training and research support services to ensure wide exploitation of the data; and (iii) deliver a strategic approach to knowledge transfer and training to build capacity and engage with policy, business, and the wider public. The UBDRC will provide a unique facility for researching cross-cutting urban issues and complex urban challenges by enabling access to multi-sectoral linked data from local government, business and other sources. This vision will be achieved by: (1) Data Services: UBDRC will focus on bringing together myriad of datasets many of which are unique and hard to obtain, from multiple urban sectors to create a linked urban data resource that allows comprehensive and cross-sectoral research. The centre will provide data curation services and the necessary metadata and provide a range of data access services to users, including, where necessary, secure access to confidential data. (2) Methods and social science research: UBDRC will develop, test and evaluate a wide range of methodological approaches including urban and regional modeling, agent-based models, machine learning and other methods and will support research leading to new cross-cutting theoretical insights, hypotheses and understanding of urban systems, thereby stimulating foundational research on new models of urban behaviour, processes and service provision. The data resource will be used to develop spatially-indexed (and perhaps temporally-indexed) urban indicators on myriad aspects describing the quality and character of urban spaces, and the spatial distribution of the urban processes, eg, on environmental risks, mobility and accessibility patterns, housing and educational aspects, and other aspects that desribe the socio-demographic, economic, environmental, built environment, physical and other aspects of urban areas.. The data would further allow policy research on a wide range of urban sectors and the derivation of a multitude of approaches for urban governance and business development. Additionally, new insights may be derived for capacity-building, innovations and learning strategies to better equip citizens to meet a diversity of challenges in cities of the future. (3) Knowledge Exchange, Outreach and Public Engagement: The UBDRC will be an important node in a growing network of UK-wide and international initiatives on cities. The networks include: international centres on urban research and cities, international research Networks, and networks of governmental, private, non-profit and other organizations. The UBDRC will undertake a research programme to advance the state-of-the-art of methods related to the use of the data resource, as well as an applied urban research stream to demonstrate the use of the linked urban Big Data resource and to derive understanding towards theory, planning and policy. Research Group 1: Methods Research: A series of computational, data management, statistical, and urban analytics projects will be undertaken to make the data more easily accessible and usable. Group 2: Urban Research Projects (URPs): Research projects on substantive urban issues such as transport, housing, migration and education will demonstrate to data owners and policy makers the value of large-scale, cross-sectoral data linkage and lead to policy insights for public, private and non-profit decision-makers.

The SweGen contains whole-genome variant frequencies for 1000 Swedish individuals generated within the SweGen project. The data is intended to be used as a resource for the research community and clinical genetics laboratories.

DNA from blood samples were whole genome sequenced using Illumina X technology at SciLifeLab Uppsala and SciLifeLab Stockholm. The sequencing data was analyzed with the GATK best practices pipeline to obtain a joint called variant frequency dataset. For more information, see: https://www.nature.com/articles/ejhg2017130

This dataset lists out all software in use by NASA

This database refers to the data collected by the European University Association (EUA) for its Open Access Survey 2017-2018, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html.

The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=266). All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available:

Questionnaire

Database in the following formats: .sav (IBM SPSS Statistics), .xlsx (Microsoft Excel) and .csv

Codebook: includes information on all the variables and their coding.

The Database Platform as a Service (DBPaaS) market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and cost-effective database solutions, and the rising demand for data analytics. The market's expansion is fueled by businesses migrating legacy on-premise databases to cloud-based alternatives, seeking enhanced agility, and leveraging the advantages of pay-as-you-go models. Major players like Amazon Web Services, Microsoft Azure, and Google Cloud Platform dominate the market, offering a wide range of DBPaaS options catering to diverse needs, from relational databases to NoSQL solutions. The market is segmented by deployment model (public cloud, private cloud, hybrid cloud), database type (SQL, NoSQL, NewSQL), and industry vertical (BFSI, healthcare, retail, etc.). Competition is fierce, with established players constantly innovating and new entrants emerging to challenge the status quo. Factors like data security concerns and integration complexities pose some challenges to market growth. However, advancements in serverless computing and the increasing adoption of artificial intelligence (AI) and machine learning (ML) are expected to drive further expansion. The forecast period (2025-2033) is projected to witness substantial growth, driven by ongoing digital transformation initiatives across various industries. The increasing adoption of cloud-native applications and microservices architectures further necessitates robust and scalable DBPaaS solutions. While the initial investment in migrating to the cloud can be significant, the long-term cost savings and improved efficiency make DBPaaS an attractive option. The market's growth is expected to be particularly strong in regions with high cloud adoption rates and robust digital infrastructure. The competitive landscape will likely remain dynamic, with mergers and acquisitions, strategic partnerships, and continuous product innovation shaping the market's trajectory. Overall, the DBPaaS market is poised for substantial growth, driven by a confluence of technological advancements and evolving business needs. Assuming a conservative CAGR of 20% (a reasonable estimate considering the high growth sectors involved), and a 2025 market size of $50 Billion, we can project substantial future growth.

This study uses historical records from 36 archives in the United States to analyze 8,437 enslaved people's sale and/or appraisal prices from 1797 to 1865.

The Swedish Contextual Database provides a large number of longitudinal and regional macro-level indicators primarily assembled to facilitate research on the effects of contextual factors on family and fertility behavior. It can be linked to the individual-level data of the Swedish GGS as well as to data of other surveys. It can also be used for other types of research and for teaching. The comparative data will also be integrated into the international Contextual Database of the GGP. The contextual data are available open-access through the GGP webpage: www.ggp-i.org and through the webpage of Stockholm University Demography Unit www.suda.su.se

Purpose:

The Swedish contextual database (CDB) was established to accompany the Swedish Generations and Gender Survey (GGS) and to complement the contextual database of the international Generations and Gender Programme (GGP).

The Swedish Contextual Data Collection is available in xls format. In addition to that, the internationally comparative data will be integrated into the Contextual Database (CDB) of the GGP in 2018. These data can be exported in other formats, as well (e.g. CSV, XML). The indicators can also be accessed in a single file in STATA or SPSS format. The data can be matched with the Swedish GGS. International regional coding schemes are also supported, such as NUTS, OECD.

A database containing details of water-related researchers located in Irish academic institutions

The Natural Resources Management (NRM) Boundaries define the area of responsibility for each of the State's eight NRM Boards. These Boards are responsible for the planning and management of the region's Natural Resources and undertake many of the roles formally performed by the Catchment Water Management Boards, Soil Conservation Boards, Animal and Plant Control Boards etc.

Data set consists of daily logs by menhaden purse-seine vessels w/ data on individual purse-seine set size, location, and date

This repo contains the npz files of the database that is required by the RANGE model. This dataset is associated with the paper RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings (CVPR 2025). Code: https://github.com/mvrl/RANGE

This statistic depicts the annual compensation among neurologists in the U.S. according to different sources (organizations), as of 2018. According to Integrated Healthcare Strategies, annual salaries for neurologists averaged some *** thousand U.S. dollars.

R code and data for a landscape scan of data services at academic libraries. Original data is licensed CC By 4.0, data obtained from other sources is licensed according to the original licensing terms. R scripts are licensed under the BSD 3-clause license. Summary This work generally focuses on four questions:

Which research data services does an academic library provide? For a subset of those services, what form does the support come in? i.e. consulting, instruction, or web resources? Are there differences in support between three categories of services: data management, geospatial, and data science? How does library resourcing (i.e. salaries) affect the number of research data services?

Approach Using direct survey of web resources, we investigated the services offered at 25 Research 1 universities in the United States of America. Please refer to the included README.md files for more information.

For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu

Information for how to cite the MTE bundle.

Dataset

This dataset was created by Timerkhanov Yuriy

Released under CC0: Public Domain

Contents

This database contains data on the isoflavone content of 560 food items. Isoflavones included in the database are daidzein, genistein, glycitein and total isoflavones. Other phytoestrogens (coumestrol, biochanin A, and formononetin) are included as a separate table in the PDF report and in the database. Resources in this dataset:Resource Title: READ ME - USDA Database for the Isoflavone Content of Selected Foods, Release 2.1. File Name: Isoflav_R2-1.pdfResource Description: Information about the release history, documentation, format of the database, sources of data, and references cited in the data.Resource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: Data Dictionary. File Name: Isoflav_R21_DD.pdfResource Title: Isoflav_R2-1.accdb . File Name: Isoflav_R2-1.zipResource Description: This file contains the Isoflavone Database imported into a MS Access database, version 2007 or later. The file structure is the same as that of the USDA National Nutrient Database for Standard Reference.

Benthic fauna data has been collected from 1881 to the present by the National Marine Fisheries Service Laboratories at Woods Hole, MA and Sandy Hook, NJ. The data includes the work by Wigley and Theroux on the macrofauna of the Northeastern United States. Other major studies include Ocean Pulse, the Northeast Monitoring Program, New York Bight, 12 Mile Dumpsite, Long Island Sound and Raritan Bay surveys. Parameters included in these surveys include depth, sediment type, gear type, number, weight, family, class, genus, species name, and abundance. A total of 21,000 sample sites are included in this data set with 4,000 meters being the maximum depth sampled.

The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Water quality data can be downloaded in Excel, CSV, TSV, and KML formats. Fourteen site types are found in the WQP: aggregate groundwater use, aggregate surface water use, atmosphere, estuary, facility, glacier, lake, land, ocean, spring, stream, subsurface, well, and wetland. Water quality characteristic groups include physical conditions, chemical and bacteriological water analyses, chemical analyses of fish tissue, taxon abundance data, toxicity data, habitat assessment scores, and biological index scores, among others. Within these groups, thousands of water quality variables registered in the EPA Substance Registry Service (https://iaspub.epa.gov/sor_internet/registry/substreg/home/overview/home.do) and the Integrated Taxonomic Information System (https://www.itis.gov/) are represented. Across all site types, physical characteristics (e.g., temperature and water level) are the most common water quality result type in the system. The Water Quality Exchange data model (WQX; http://www.exchangenetwork.net/data-exchange/wqx/), initially developed by the Environmental Information Exchange Network, was adapted by EPA to support submission of water quality records to the EPA STORET Data Warehouse [USEPA, 2016], and has subsequently become the standard data model for the WQP. Contributing organizations: ACWI The Advisory Committee on Water Information (ACWI) represents the interests of water information users and professionals in advising the federal government on federal water information programs and their effectiveness in meeting the nation's water information needs. ARS The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief in-house scientific research agency, whose job is finding solutions to agricultural problems that affect Americans every day, from field to table. ARS conducts research to develop and transfer solutions to agricultural problems of high national priority and provide information access and dissemination to, among other topics, enhance the natural resource base and the environment. Water quality data from STEWARDS, the primary database for the USDA/ARS Conservation Effects Assessment Project (CEAP) are ingested into WQP via a web service. EPA The Environmental Protection Agency (EPA) gathers and distributes water quality monitoring data collected by states, tribes, watershed groups, other federal agencies, volunteer groups, and universities through the Water Quality Exchange framework in the STORET Warehouse. NWQMC The National Water Quality Monitoring Council (NWQMC) provides a national forum for coordination of comparable and scientifically defensible methods and strategies to improve water quality monitoring, assessment, and reporting. It also promotes partnerships to foster collaboration, advance the science, and improve management within all elements of the water quality monitoring community. USGS The United States Geological Survey (USGS) investigates the occurrence, quantity, quality, distribution, and movement of surface waters and ground waters and disseminates the data to the public, state, and local governments, public and private utilities, and other federal agencies involved with managing the United States' water resources. Resources in this dataset:Resource Title: Website Pointer for Water Quality Portal. File Name: Web Page, url: https://www.waterqualitydata.us/ The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Links to Download Data, User Guide, Contributing Organizations, National coverage by state.