Filter
Reset all

Subjects

Content Types

Countries

AID systems

API

Data access

Data access restrictions

Database access

Database access restrictions

Database licenses

Data licenses

Data upload

Data upload restrictions

Enhanced publication

Institution responsibility type

Institution type

Keywords

Metadata standards

PID systems

Provider types

Quality management

Repository languages

Software

Repository types

Versioning

  • * at the end of a keyword allows wildcard searches
  • " quotes can be used for searching phrases
  • + represents an AND search (default)
  • | represents an OR search
  • - represents a NOT operation
  • ( and ) implies priority
  • ~N after a word specifies the desired edit distance (fuzziness)
  • ~N after a phrase specifies the desired slop amount
  • 1 (current)
Found 18 result(s)
The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. It is used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times.
CiteSeerx is an evolving scientific literature digital library and search engine that focuses primarily on the literature in computer and information science. CiteSeerx aims to improve the dissemination of scientific literature and to provide improvements in functionality, usability, availability, cost, comprehensiveness, efficiency, and timeliness in the access of scientific and scholarly knowledge. Rather than creating just another digital library, CiteSeerx attempts to provide resources such as algorithms, data, metadata, services, techniques, and software that can be used to promote other digital libraries. CiteSeerx has developed new methods and algorithms to index PostScript and PDF research articles on the Web.
The SuiteSparse Matrix Collection is a large and actively growing set of sparse matrices that arise in real applications. The Collection is widely used by the numerical linear algebra community for the development and performance evaluation of sparse matrix algorithms. It allows for robust and repeatable experiments. Its matrices cover a wide spectrum of domains, include those arising from problems with underlying 2D or 3D geometry (as structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations) and those that typically do not have such geometry (optimization, circuit simulation, economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, power networks, and other networks and graphs.
The JPL Tropical Cyclone Information System (TCIS) was developed to support hurricane research. There are three components to TCIS; a global archive of multi-satellite hurricane observations 1999-2010 (Tropical Cyclone Data Archive), North Atlantic Hurricane Watch and ASA Convective Processes Experiment (CPEX) aircraft campaign. Together, data and visualizations from the real time system and data archive can be used to study hurricane process, validate and improve models, and assist in developing new algorithms and data assimilation techniques.
WorldData.AI comes with a built-in workspace – the next-generation hyper-computing platform powered by a library of 3.3 billion curated external trends. WorldData.AI allows you to save your models in its “My Models Trained” section. You can make your models public and share them on social media with interesting images, model features, summary statistics, and feature comparisons. Empower others to leverage your models. For example, if you have discovered a previously unknown impact of interest rates on new-housing demand, you may want to share it through “My Models Trained.” Upload your data and combine it with external trends to build, train, and deploy predictive models with one click! WorldData.AI inspects your raw data, applies feature processors, chooses the best set of algorithms, trains and tunes multiple models, and then ranks model performance.
The International Satellite Cloud Climatology Project (ISCCP) is a database of intended for researchers to share information about cloud radiative properties. The data sets focus on the effects of clouds on the climate, the radiation budget, and the long-term hydrologic cycle. Within the data sets the data entries are broken down into entries of specific characteristics based on temporal resolution, spatial resolution, or temporal coverage.
Content type(s)
BioSimulators is a registry of containerized biosimulation tools that provide consistent command-line interfaces. The BioSimulations web application helps investigators browse this registry to find simulation tools that have the capabilities (supported modeling frameworks, simulation algorithms, and modeling formats) needed for specific modeling projects.
CSDMS is a virtual home for a vibrant and growing community of about 1,000 international modeling experts and students who study the dynamic interactions of lithosphere, hydrosphere, cryosphere, and atmosphere at Earth’s surface. Participating in cross-disciplinary groups, members develop integrated software modules that predict the movement of water, sediment, and nutrients across landscapes and into the ocean. We share an open library of models, software, and access to high-performance computing. We also share knowledge that helps create higher-resolution simulations, often involving higher complexity algorithms. Together, we support the discovery, use, and conservation of natural resources; mitigation of natural hazards; geotechnical support of commercial and infrastructure development; environmental stewardship; and terrestrial surveillance for global security.
Using a combination of remote sensing data and ground observations as inputs, CHC scientists have developed rainfall estimation techniques and other resources to support drought monitoring and predict crop performance in parts of the world vulnerable to crop failure. Policymakers within governments and non-governmental organizations rely on CHC decision-support products to make critical resource allocation decisions. The CHC's scientific focus is "geospatial hydroclimatology," with an emphasis on the early detection and forecasting of hydroclimatic hazards related to food-security droughts and floods. Basic research seeks an improved understanding of the climatic processes that govern drought and flood hazards in FEWS NET countries (https://fews.net/). The CHC develops better techniques, algorithms, and modeling applications in order to use remote sensing and other geospatial data for hazards early warning.
To understand the global surface energy budget is to understand climate. Because it is impractical to cover the earth with monitoring stations, the answer to global coverage lies in reliable satellite-based estimates. Efforts are underway at NASA and universities to develop algorithms to do this, but such projects are in their infancy. In concert with these ambitious efforts, accurate and precise ground-based measurements in differing climatic regions are essential to refine and verify the satellite-based estimates, as well as to support specialized research. To fill this niche, the Surface Radiation Budget Network (SURFRAD) was established in 1993 through the support of NOAA's Office of Global Programs.
The ASTER Project consists of two parts, each having a Japanese and a U.S. component. Mission operations are split between Japan Space Systems (J-spacesystems) and the Jet Propulsion Laboratory (JPL) in the U.S. J-spacesystems oversees monitoring instrument performance and health, developing the daily schedule command sequence, processing Level 0 data to Level 1, and providing higher level data processing, archiving, and distribution. The JPL ASTER project provides scheduling support for U.S. investigators, calibration and validation of the instrument and data products, coordinating the U.S. Science Team, and maintaining the science algorithms. The joint Japan/U.S. ASTER Science Team has about 40 scientists and researchers. Data access via NASA Reverb, ASTER Japan site, earth explorer, GloVis,GDEx and LP DAAC. See here https://asterweb.jpl.nasa.gov/data.asp. In Addition data are availabe through the newly implemented ASTER Volcano archive (AVA) https://ava.jpl.nasa.gov/ .
ChemSpider is a free chemical structure database providing fast access to over 58 million structures, properties and associated information. By integrating and linking compounds from more than 400 data sources, ChemSpider enables researchers to discover the most comprehensive view of freely available chemical data from a single online search. It is owned by the Royal Society of Chemistry. ChemSpider builds on the collected sources by adding additional properties, related information and links back to original data sources. ChemSpider offers text and structure searching to find compounds of interest and provides unique services to improve this data by curation and annotation and to integrate it with users’ applications.
This project is an open invitation to anyone and everyone to participate in a decentralized effort to explore the opportunities of open science in neuroimaging. We aim to document how much (scientific) value can be generated from a data release — from the publication of scientific findings derived from this dataset, algorithms and methods evaluated on this dataset, and/or extensions of this dataset by acquisition and incorporation of new data. The project involves the processing of acoustic stimuli. In this study, the scientists have demonstrated an audiodescription of classic "Forrest Gump" to subjects, while researchers using functional magnetic resonance imaging (fMRI) have captured the brain activity of test candidates in the processing of language, music, emotions, memories and pictorial representations.In collaboration with various labs in Magdeburg we acquired and published what is probably the most comprehensive sample of brain activation patterns of natural language processing. Volunteers listened to a two-hour audio movie version of the Hollywood feature film "Forrest Gump" in a 7T MRI scanner. High-resolution brain activation patterns and physiological measurements were recorded continuously. These data have been placed into the public domain, and are freely available to the scientific community and the general public.
!!! We will terminate ASTER Products Distribution Service in March 2016 although we have been providing ASTER Products since November 20, 2000. !!! ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) is the high efficiency optical imager which covers a wide spectral region from the visible to the thermal infra-red by 14 spectral bands. ASTER acquires data which can be used in various fields in earth science. ASTER was launched from Vandenberg Air Force Base in California, USA in 1999 aboard the Terra, which is the first satellite of the EOS Project. The purpose of ASTER project is to make contributions to extend the understanding of local and regional phenomena on the Earth surface and its atmosphere. The followings are ASTER related information, which includes ASTER instrument, ASTER Ground Data System, ASTER Science Activities, ASTER Data Distribution and so on. ASTER Search provides services to search and order ASTER data products on the website.
OASIS-3 is the latest release in the Open Access Series of Imaging Studies (OASIS) that aimed at making neuroimaging datasets freely available to the scientific community. By compiling and freely distributing this multi-modal dataset, we hope to facilitate future discoveries in basic and clinical neuroscience. Previously released data for OASIS-Cross-sectional (Marcus et al, 2007) and OASIS-Longitudinal (Marcus et al, 2010) have been utilized for hypothesis driven data analyses, development of neuroanatomical atlases, and development of segmentation algorithms. OASIS-3 is a longitudinal neuroimaging, clinical, cognitive, and biomarker dataset for normal aging and Alzheimer’s Disease. The OASIS datasets hosted by central.xnat.org provide the community with open access to a significant database of neuroimaging and processed imaging data across a broad demographic, cognitive, and genetic spectrum an easily accessible platform for use in neuroimaging, clinical, and cognitive research on normal aging and cognitive decline. All data is available via www.oasis-brains.org.
The main goal of the ECCAD project is to provide scientific and policy users with datasets of surface emissions of atmospheric compounds, and ancillary data, i.e. data required to estimate or quantify surface emissions. The supply of ancillary data - such as maps of population density, maps of fires spots, burnt areas, land cover - could help improve and encourage the development of new emissions datasets. ECCAD offers: Access to global and regional emission inventories and ancillary data, in a standardized format Quick visualization of emission and ancillary data Rationalization of the use of input data in algorithms or emission models Analysis and comparison of emissions datasets and ancillary data Tools for the evaluation of emissions and ancillary data ECCAD is a dynamical and interactive database, providing the most up to date datasets including data used within ongoing projects. Users are welcome to add their own datasets, or have their regional masks included in order to use ECCAD tools.