Data are the foundation of science, and there is an increasing focus on how data can be reused and enhanced to drive scientific discoveries. However, most seemingly “open data” do not provide legal permissions for reuse and redistribution. The inability to integrate and redistribute our collective data resources blocks innovation and stymies the creation of life-improving diagnostic and drug selection tools. To help the biomedical research and research support communities (e.g. libraries, funders, repositories, etc.) understand and navigate the data licensing landscape, the (Re)usable Data Project (RDP) (http://reusabledata.org) assesses the licensing characteristics of data resources and how licensing behaviors impact reuse. We have created a ruleset to determine the reusability of data resources and have applied it to 56 scientific data resources (e.g. databases) to date. The results show significant reuse and interoperability barriers. Inspired by game-changing projects like Creative Commons, the Wikipedia Foundation, and the Free Software movement, we hope to engage the scientific community in the discussion regarding the legal use and reuse of scientific data, including the balance of openness and how to create sustainable data resources in an increasingly competitive environment.

The Learning Resources Database is a catalog of interactive tutorials, videos, online classes, finding aids, and other instructional resources on National Library of Medicine (NLM) products and services. Resources may be available for immediate use via a browser or downloadable for use in course management systems

Dataset Description

It contains 520 rows and 13 variables as listed below - - Resource ID : Alphanumeric identifier - Resource Name : Title of the resource - Resource URL : Link of the resource - Description : Brief explanation on the reource - Archived : Flagged as False for all data points - Format : Format of the resource ex. HTML, PDF, MP4 video , MS Word, Powerpoint etc. - Type : Type of the resource ex Webinar, document, tutorial, slides etc. - Runtime : Runtime of the resource - Subject Areas : Topic covered in reource - Authoring Organization : Name of the Authoring Organization - Intended Audiences : Profile of the intended audience - Record Modified : Timestamp info on record last modification - Resource Revised : Timestamp info on resource last modified

The NYSERDA-funded Integrated Energy Data Resource (IEDR) provides a single statewide platform to securely collect, integrate, analyze, and make accessible a large and diverse set of energy-related information from New York's electric, gas, and steam utilities and other sources. Useful access to useful energy data provided by the IEDR enables analyses that informs investment decisions, identifies operational inefficiencies, monitors the effectiveness of policy objectives, promotes innovation, and encourages new business models. The IEDR includes analytic tools to enable energy stakeholders to design and run useful queries and calculations that can operate across all data types in the IEDR. Those tools' number and functionality should increase over time to align with, and support the use cases, that become operational as part of the IEDR. Additionally, relational information that describes the relationships among the various data elements in the IEDR materially affects the depth potential of users' ability to find, analyze, and generate useful information. User access to the IEDR data and analytic tools will be governed by the access controls that reflect and align with each type of user's legitimate needs while preventing unwarranted access to information that does not serve those legitimate needs. Public, utility-managed, and commercial datasets processed by the platform and made available or planned to be made available to approved users in various forms include: • Feeder and sub-feeder hosting capacity • Installed and queued DER projects • Utility Rates and Tariffs • Customer billing and usage • Aggregated building usage • Disadvantaged Community Characteristics • Land, Parcel, and Terrain attributes The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, accelerate economic growth, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

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

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.

Image Data Resource (IDR) is an online, public data repository that seeks to store, integrate and serve image datasets from published scientific studies. We have collected and are continuing to receive existing and newly created “reference image" datasets that are valuable resources for a broad community of users, either because they will be frequently accessed and cited or because they can serve as a basis for re-analysis and the development of new computational tools.

This list includes major sources of data collected by the U.S. government and available for research on small business. It includes business data from private, nonprofit, university, international, and other sources.

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

The New York State Energy Research and Development Authority (NYSERDA) hosts a web-based Distributed Energy Resources (DER) integrated data system at https://der.nyserda.ny.gov/. This site provides information on DERs that are funded by and report performance data to NYSERDA. Information is incorporated on more diverse DER technology as it becomes available. Distributed energy resources (DER) are technologies that generate or manage the demand of electricity at different points of the grid, such as at homes and businesses, instead of exclusively at power plants, and includes Combined Heat and Power (CHP) Systems, Anaerobic Digester Gas (ADG)-to-Electricity Systems, Fuel Cell Systems, Energy Storage Systems, and Large Photovoltaic (PV) Solar Electric Systems (larger than 50 kW). Historical databases with hourly readings for each system are updated each night to include data from the previous day. The web interface allows users to view, plot, analyze, and download performance data from one or several different DER sites. Energy storage systems include all operational systems in New York including projects not funded by NYSERDA. Only NYSERDA-funded energy storage systems will have performance data available. The database is intended to provide detailed, accurate performance data that can be used by potential users, developers, and other stakeholders to understand the real-world performance of these technologies. For NYSERDA’s performance-based programs, these data provide the basis for incentive payments to these sites. How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

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.

Classification of Mars Terrain Using Multiple Data Sources Alan Kraut1, David Wettergreen1 ABSTRACT. Images of Mars are being collected faster than they can be analyzed by planetary scientists. Automatic analysis of images would enable more rapid and more consistent image interpretation and could draft geologic maps where none yet exist. In this work we develop a method for incorporating images from multiple instruments to classify Martian terrain into multiple types. Each image is segmented into contiguous groups of similar pixels, called superpixels, with an associated vector of discriminative features. We have developed and tested several classification algorithms to associate a best class to each superpixel. These classifiers are trained using three different manual classifications with between 2 and 6 classes. Automatic classification accuracies of 50 to 80% are achieved in leave-one-out cross-validation across 20 scenes using a multi-class boosting classifier.

This dataset compiles heat flow and temperature gradient data from over 44,000 wells across the United States, along with more than 6,000 related geothermal exploration resources. Originally assembled prior to 2014 for the now-retired National Geothermal Data System (NGDS), the collection includes curated well data, scanned field notes, temperature-depth curves, publications, maps, and other supporting documents. SMU Geothermal Laboratory contributed two different nationwide heat flow databases to the project. One is based on equilibrium temperature measurements (over 14,000 sites) and the other is based on corrected bottom hole temperature (BHT) data from oil and gas industry wells (over 30,000 sites). In addition, scanned field notes and temperature-depth curves were associated with approximately 6,000 specific sites in the heat flow database. Records were corrected and overlapping sites in the equilibrium heat flow database were linked between the original SMU National database and the UND Global Heat Flow database. New or related sites, which were not previously published because they lacked full heat flow content, are now included as gradient only information along with their detailed temperature data to fill in data gaps. Finally, SMU submitted over 920 scanned publications, reports, and maps suitable for full text searching. The dataset is provided in two flat-structured zip archives: one containing the curated well data and another containing related resources. An Excel index file is provided for each archive, allowing filtering by well name, location, and description. Data files are labeled with state or institutional origin where available.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

For the latest Land Use Legend, 2022-DWR-Standard-Land-Use-Legend-Remote-Sensing-Version.pdf, please see the Data and Resources section below.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Comprehensive dataset containing 2 verified Denbury Resources Inc locations in Montana, United States with complete contact information, ratings, reviews, and location data.

This coverage contains land-cover information for all of Ohio and portions of Indiana, Michigan, Kentucky, West Virginia, Pennsylvania, and New York. This dataset was derived from the U.S. Geological Survey's National Land Cover Dataset (NLCD). NLCD raster grids were downloaded from the USGS EROS Data Center web server at http://landcover.usgs.gov/natllandcover.html, by state. These grids were then reprojected, mosaiced and clipped against a polygon coverage representing the study area. Grid cell resolution is approximately 30 meters or 1 arc-second.

Comprehensive dataset containing 22 verified Human resources businesses in North Carolina, United States with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 15 verified Human resources businesses in Louisiana, United States with complete contact information, ratings, reviews, and location data.

This dataset is used to:

• assess biodiversity levels
• define seed zones
• map ecosystem types
• set targets for the identification of natural heritage systems

Quantitative Data from Different Groundwater locations The Data is provided ‘as-is’, without any guarantee of correctness.

This dataset shows the locations of Cool Spaces and free drinking water fountains in Camden. Cool Spaces are free places where you can stay cool and take a break from the heat. They are open to everyone from June 1 to September 30, 2025. Each Cool Space has its own opening hours and facilities, just hover over the icons to see the details! Please note that Cool Spaces are not for emergencies. If you think you might have heatstroke or are unsure, please visit https://www.nhs.uk/conditions/heat-exhaustion-heatstroke for information on how to recognise the symptoms of heat exhaustion and heatstroke. If you would like to join and become a Cool Space or support in any way (spread the news on tips to beat the heat, contributing to drinking water machines, donation for water or spray bottles / hats/ sunscreen), please find the expression of interest form on https://www.camden.gov.uk/preparing-for-heatwaves.