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Found 7 result(s)
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TUL Open Research Data Repository (RDB.open) is a service addressed to the scientific and research community of the Lodz University of Technology. The main purpose of RDB.open is to collect, share and store the open research data, both during the research and after its completion, at least for the minimum period indicated by the funder or the scientists. The RDB.open is a place where research data can be openly shared, accessed and then reused by others.
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Repository "Open Science Resource Atlas 2.0" aims to increase the accessibility, improve the quality and extend the reusability of science resources. Repository focuses on the digital sharing of resources of great importance to the field of science and economy. These include publications, scripts, lectures, 3D models, audio and video recordings, photos, input and output files of various computer programs, databases collecting data from various fields, machines, systems, language corpora and many others. The target group, apart from academics, students and doctoral students, is everyone interested, including entrepreneurs and, what is important and unique - disabled, blind, visually impaired and deaf people.
SLAPIS is an integrated Flood Early Warning System that aims to promote decision-making and behavioral changes from reactive to proactive at several levels, from the community to the administration, for the reduction of flood risk in the Communes of the Sirba (main tributary of the Niger River and cause of the main floods in the region)
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.
The University of Guelph Research Data Repositories provide long-term stewardship of research data created at or in cooperation with the University of Guelph. The Data Repositories are guided by the FAIR Guiding Principles for scientific data management and stewardship which aim to improve the Findability, Accessibility, Interoperability and Reuse of research data. The Data Repositories is composed of two main collections: the Agri-environmental Research Data collection which houses agricultural and environmental research data, and the Cross-disciplinary Research Data collection which houses all other disciplinary research data.