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Found 7 result(s)
The Federal Interagency Traumatic Brain Injury Research (FITBIR) informatics system was developed to share data across the entire TBI research field and to facilitate collaboration between laboratories, as well as interconnectivity with other informatics platforms. Sharing data, methodologies, and associated tools, rather than summaries or interpretations of this information, can accelerate research progress by allowing re-analysis of data, as well as re-aggregation, integration, and rigorous comparison with other data, tools, and methods. This community-wide sharing requires common data definitions and standards, as well as comprehensive and coherent informatics approaches.
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The Research Documentation Centre of the Centre for Social Sciences at the Hungarian Academy of Sciences provides information on and access to research conducted at the Centre. The metadata and many of the documents of the Research Documentation Centre (RDC) are available to all visitors. External researchers may ask for access to restricted collections
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>>>!!!<<< Data originally published in the JCB DataViewer has been moved BioStudies. Please note that while the majority of data were moved, some authors opted to remove their data completely. >>>!!!<<< Migrated data can be found at https://www.ebi.ac.uk/biostudies/JCB/studies. Screen data are available in the Image Data Resource repository. http://idr.openmicroscopy.org/webclient/?experimenter=-1 >>>!!!<<< The DataViewer was decommissioned in 2018 as the journal evolved to an all-encompassing archive policy towards original source data and as new data repositories that go beyond archiving data and allow investigators to make new connections between datasets, potentially driving discovery, emerged. JCB authors are encouraged to make available all datasets included in the manuscript from the date of online publication either in a publicly available database or as supplemental materials hosted on the journal website. We recommend that our authors store and share their data in appropriate publicly available databases based on data type and/or community standard. >>>!!!<<<
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The UWA Profiles and Research Repository contains research publications, research datasets, theses, equipment, grants and activities created by researchers and postgraduates affiliated with the University of Western Australia (UWA). It is managed by the University Library and provides access to research datasets held at UWA. The information about each dataset has been provided by UWA research groups. Dataset metadata is harvested into Research Data Australia (RDA) https://researchdata.edu.au/.
This library is a public and easily accessible resource database of images, videos, and animations of cells, capturing a wide diversity of organisms, cell types, and cellular processes. The Cell Image Library has been merged with "Cell Centered Database" in 2017. The purpose of the database is to advance research on cellular activity, with the ultimate goal of improving human health.
Country
The German Central Health Study Hub is a platform that serves two different kinds of users. First, it allows scientists and data holding organizations (data producers) to publish their project characteristics, documents and data related to their research endeavour in a FAIR manner. Obviously, patient-level data cannot be shared publicly, however, metadata describing the patient-level data along with information about data access can be shared via the platform (preservation description information). The other kind of user is a scientist or researcher (data consumer) that likes to find information about past and ongoing studies and is interested in reusing existing patient-level data for their project. To summarize, the platforms connect data providers with data consumers in the domain of clinical, public health and epidemiologic health research to foster reuse. The platform aggregates and harmonizes information already entered in various public repositories such as DRKS, clinicaltrials.gov, WHO ICTRP to provide a holistic view of the German research landscape in the aforementioned research areas. In addition, data stewards actively collect available information from (public) resources such as websites that cannot be automatically integrated. The service started during the COVID-19 pandemic.
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A machine learning data repository with interactive visual analytic techniques. This project is the first to combine the notion of a data repository with real-time visual analytics for interactive data mining and exploratory analysis on the web. State-of-the-art statistical techniques are combined with real-time data visualization giving the ability for researchers to seamlessly find, explore, understand, and discover key insights in a large number of public donated data sets. This large comprehensive collection of data is useful for making significant research findings as well as benchmark data sets for a wide variety of applications and domains and includes relational, attributed, heterogeneous, streaming, spatial, and time series data as well as non-relational machine learning data. All data sets are easily downloaded into a standard consistent format. We also have built a multi-level interactive visual analytics engine that allows users to visualize and interactively explore the data in a free-flowing manner.