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Found 15 result(s)
All ADNI data are shared without embargo through the LONI Image and Data Archive (IDA), a secure research data repository. Interested scientists may obtain access to ADNI imaging, clinical, genomic, and biomarker data for the purposes of scientific investigation, teaching, or planning clinical research studies. "The Alzheimer’s Disease Neuroimaging Initiative (ADNI) unites researchers with study data as they work to define the progression of Alzheimer’s disease (AD). ADNI researchers collect, validate and utilize data, including MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers as predictors of the disease. Study resources and data from the North American ADNI study are available through this website, including Alzheimer’s disease patients, mild cognitive impairment subjects, and elderly controls. "
The OpenMadrigal project seeks to develop and support an on-line database for geospace data. The project has been led by MIT Haystack Observatory since 1980, but now has active support from Jicamarca Observatory and other community members. Madrigal is a robust, World Wide Web based system capable of managing and serving archival and real-time data, in a variety of formats, from a wide range of ground-based instruments. Madrigal is installed at a number of sites around the world. Data at each Madrigal site is locally controlled and can be updated at any time, but shared metadata between Madrigal sites allow searching of all Madrigal sites at once from any Madrigal site. Data is local; metadata is shared.
The WashU Research Data repository accepts any publishable research data set, including textual, tabular, geospatial, imagery, computer code, or 3D data files, from researchers affiliated with Washington University in St. Louis. Datasets include metadata and are curated and assigned a DOI to align with FAIR data principles.
This interface provides access to several types of data related to the Chesapeake Bay. Bay Program databases can be queried based upon user-defined inputs such as geographic region and date range. Each query results in a downloadable, tab- or comma-delimited text file that can be imported to any program (e.g., SAS, Excel, Access) for further analysis. Comments regarding the interface are encouraged. Questions in reference to the data should be addressed to the contact provided on subsequent pages.
CDAAC is responsible for processing the science data received from COSMIC. This data is currently being processed not long after the data is received, i.e. approximately eighty percent of radio occultation profiles are delivered to operational weather centers within 3 hours of observation as well as in a more accurate post-processed mode (within 8 weeks of observation).
VertNet is a NSF-funded collaborative project that makes biodiversity data free and available on the web. VertNet is a tool designed to help people discover, capture, and publish biodiversity data. It is also the core of a collaboration between hundreds of biocollections that contribute biodiversity data and work together to improve it. VertNet is an engine for training current and future professionals to use and build upon best practices in data quality, curation, research, and data publishing. Yet, VertNet is still the aggregate of all of the information that it mobilizes. To us, VertNet is all of these things and more.
SuperDARN is an international HF radar network designed to measure global-scale magnetospheric convection by observing plasma motion in the Earth’s upper atmosphere. This network consists of more than 20 radars operating on frequencies between 8 and 20 MHz that look into the polar regions of Earth. These radars can measure the position and velocity of charged particles in our ionosphere, the highest layer of the Earth's atmosphere, and provide scientists with information regarding Earth's interaction with the space environment.
OpenWorm aims to build the first comprehensive computational model of the Caenorhabditis elegans (C. elegans), a microscopic roundworm. With only a thousand cells, it solves basic problems such as feeding, mate-finding and predator avoidance. Despite being extremely well studied in biology, this organism still eludes a deep, principled understanding of its biology. We are using a bottom-up approach, aimed at observing the worm behaviour emerge from a simulation of data derived from scientific experiments carried out over the past decade. To do so we are incorporating the data available in the scientific community into software models. We are engineering Geppetto and Sibernetic, open-source simulation platforms, to be able to run these different models in concert. We are also forging new collaborations with universities and research institutes to collect data that fill in the gaps All the code we produce in the OpenWorm project is Open Source and available on GitHub.
Additionally to the institutional repository, current St. Edward's faculty have the option of uploading their work directly to their own SEU accounts on stedwards.figshare.com. Projects created on Figshare will automatically be published on this website as well. For more information, please see documentation
In response to emerging pathogens, LabKey launched the Open Research Portal in 2016 to help facilitate collaborative research. It was initially created as a platform for investigators to make Zika research data, commentary and results publicly available in real-time. It now includes other viruses like SARS-CoV-2 where there is a compelling need for real-time data sharing. Projects are freely available to researchers. If you are interested in sharing real-time data through the portal, please contact LabKey to get started.
A service of the Inter-university Consortium for Political and Social Research (ICPSR), openICPSR is a self-publishing repository for social, behavioral, and health sciences research data. openICPSR is particularly well-suited for the deposit of replication data sets for researchers who need to publish their raw data associated with a journal article so that other researchers can replicate their findings.
This site is now made possible by University of Canterbury, New Zealand, and serves as historical repository of collections of files once housed at datacenterhub (without the interactive tools that were available on that site). Former description: We offer a public platform to help researchers organize, share, and explore their research data. DataCenterHub provides a simple, standardized yet flexible platform to preserve and share data. In the future, this platform will offer data visualization tools and the ability to compare directly data from different sources.
EM-DAT is a global database on natural and technological disasters, containing essential core data on the occurrence and effects of more than 26,000 disasters in the world, from 1900 to present. EM-DAT provides geographical, temporal, human and economic information on disasters at the country level. The database is compiled from various sources, including UN agencies, non-governmental organisations, insurance companies, research institutes and press agencies.