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Found 10 result(s)
Water DAMS (Water Data Analysis and Management System) provides access to foundational water treatment technology data that enable researchers and decision-makers to identify and quantify opportunities for technology innovations to reduce the cost and energy intensity of desalination. It is the submission point for all data generated by research conducted by the National Alliance for Water Innovation (NAWI) and is designed to be used by the broader water research community. With publicly accessible contributions from a variety of academic and industrial partners, Water DAMS seeks to enable data discoverability, improve accessibility, and accelerate collaboration that contributes to pipe parity and innovation in water treatment technologies.
OEDI is a centralized repository of high-value energy research datasets aggregated from the U.S. Department of Energy’s Programs, Offices, and National Laboratories. Built to enable data discoverability, OEDI facilitates access to a broad network of findings, including the data available in technology-specific catalogs like the Geothermal Data Repository and Marine Hydrokinetic Data Repository.
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DataverseNO (https://dataverse.no) is a curated, FAIR-aligned national generic repository for open research data from all academic disciplines. DataverseNO commits to facilitate that published data remain accessible and (re)usable in a long-term perspective. The repository is owned and operated by UiT The Arctic University of Norway. DataverseNO accepts submissions from researchers primarily from Norwegian research institutions. Datasets in DataverseNO are grouped into institutional collections as well as special collections. The technical infrastructure of the repository is based on the open source application Dataverse (https://dataverse.org), which is developed by an international developer and user community led by Harvard University.
Brainlife promotes engagement and education in reproducible neuroscience. We do this by providing an online platform where users can publish code (Apps), Data, and make it "alive" by integragrate various HPC and cloud computing resources to run those Apps. Brainlife also provide mechanisms to publish all research assets associated with a scientific project (data and analyses) embedded in a cloud computing environment and referenced by a single digital-object-identifier (DOI). The platform is unique because of its focus on supporting scientific reproducibility beyond open code and open data, by providing fundamental smart mechanisms for what we refer to as “Open Services.”
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RADAR4Culture is a low-threshold and easy-to use service for sustainable publication and preservation of cultural heritage research data. It offers free publication for any data type and format according to the FAIR principles, independent of the researcher´s institutional affiliation. Through persistent identifiers (DOI) and a guaranteed retention period of at least 25 years, the research data remain available, citable and findable long-term. Currently, the offer is aimed exclusively at researchers at publicly funded research institutions and (art) universities as well as non-commercial academies, galleries, libraries, archives and museums in Germany. No contract is required and no data publication fees are charged. The researchers are responsible for the upload, organisation, annotation and curation of research data as well as the peer-review process (as an optional step) and finally their publication.
The Open Energy Information (OpenEI.org) initiative is a free, open source knowledge-sharing platform created to facilitate access to data, models, tools, and information that accelerate the transition to clean energy systems through informed decisions. Sponsored by the Department of Energy, and developed by the National Renewable Energy Lab, in support of the Open Government Initiative, OpenEI strives to make energy-related data and information searchable, accessible, and useful to both people and machines
Online materials database (known as PAULING FILE project) with nearly 2 million entries: physical properties, crystal structures, phase diagrams, available via API, ready for modern data-intensive applications. The source of these entries are about 0.5M peer-reviewed publications in materials science, processed during the last 30 years by an international team of PhD editors. The results are presented online with a quick search interface. The basic access is provided for free.
The MHKDR is the repository for all data collected using funds from the Water Power Technologies Office (WPTO) of the U.S. Department of Energy (DOE). It was established to receive, manage, and make available all water power relevant data generated from projects funded by the DOE Water Power Technologies Office. This includes data from WPTO-funded projects associated with any portion of the water power project life-cycle (exploration, development, operation), as well as data produced by WPTO-funded research.
US Department of Energy’s Atmospheric Radiation Measurement (ARM) Data Center is a long-term archive and distribution facility for various ground-based, aerial and model data products in support of atmospheric and climate research. ARM facility currently operates over 400 instruments at various observatories (https://www.arm.gov/capabilities/observatories/). ARM Data Center (ADC) Archive currently holds over 11,000 data products with a total holding of over 3 petabytes of data that dates back to 1993, these include data from instruments, value added products, model outputs, field campaign and PI contributed data. The data center archive also includes data collected by ARM from related program (e.g., external data such as NASA satellite).
OpenML is an open ecosystem for machine learning. By organizing all resources and results online, research becomes more efficient, useful and fun. OpenML is a platform to share detailed experimental results with the community at large and organize them for future reuse. Moreover, it will be directly integrated in today’s most popular data mining tools (for now: R, KNIME, RapidMiner and WEKA). Such an easy and free exchange of experiments has tremendous potential to speed up machine learning research, to engender larger, more detailed studies and to offer accurate advice to practitioners. Finally, it will also be a valuable resource for education in machine learning and data mining.