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Found 37 result(s)
ScholarWorks preserves and provides access to the research and creative scholarship created by Brandeis faculty, students, and staff. The research papers, theses, dissertations, books, reports, interviews, data and multimedia here represent Brandeis’s rich intellectual and cultural community.
The Radboud Data Repository (RDR) is an institutional repository for archiving and sharing of data collected, processed, or analyzed by researchers working at or affiliated with the Radboud University (Nijmegen, the Netherlands). The repository allows safe long-term (at least 10 years) storage of large datasets. The RDR promotes findability of datasets by providing a DOI and rich metadata fields and allows researchers to easily manage data access.
The DesignSafe Data Depot Repository (DDR) is the platform for curation and publication of datasets generated in the course of natural hazards research. The DDR is an open access data repository that enables data producers to safely store, share, organize, and describe research data, towards permanent publication, distribution, and impact evaluation. The DDR allows data consumers to discover, search for, access, and reuse published data in an effort to accelerate research discovery. It is a component of the DesignSafe cyberinfrastructure, which represents a comprehensive research environment that provides cloud-based tools to manage, analyze, curate, and publish critical data for research to understand the impacts of natural hazards. DesignSafe is part of the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI), and aligns with its mission to provide the natural hazards research community with open access, shared-use scholarship, education, and community resources aimed at supporting civil and social infrastructure prior to, during, and following natural disasters. It serves a broad national and international audience of natural hazard researchers (both engineers and social scientists), students, practitioners, policy makers, as well as the general public. It has been in operation since 2016, and also provides access to legacy data dating from about 2005. These legacy data were generated as part of the NSF-supported Network for Earthquake Engineering Simulation (NEES), a predecessor to NHERI. Legacy data and metadata belonging to NEES were transferred to the DDR for continuous preservation and access.
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UBC Dataverse Collection is a free research data repository for our faculty, students and staff. The platform makes it possible for researchers to deposit data, create appropriate metadata, obtain DOIs for permanent links, and maintain version control of their datasets. All files are held in a secure environment on Canadian servers. Researchers are encouraged to make their data available publicly, but can choose to restrict access to their data if they wish.
figshare allows researchers to publish all of their research outputs in an easily citable, sharable and discoverable manner. All file formats can be published, including videos and datasets. Optional peer review process. figshare uses creative commons licensing. figshare+ repository allows figshare users to share larger datasets, over 20GB up to many TBs, see: https://plus.figshare.com/
ZENODO builds and operates a simple and innovative service that enables researchers, scientists, EU projects and institutions to share and showcase multidisciplinary research results (data and publications) that are not part of the existing institutional or subject-based repositories of the research communities. ZENODO enables researchers, scientists, EU projects and institutions to: easily share the long tail of small research results in a wide variety of formats including text, spreadsheets, audio, video, and images across all fields of science. display their research results and get credited by making the research results citable and integrate them into existing reporting lines to funding agencies like the European Commission. easily access and reuse shared research results.
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.