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NCBI Datasets is a continually evolving platform designed to provide easy and intuitive access to NCBI’s sequence data and metadata. NCBI Datasets is part of the NIH Comparative Genomics Resource (CGR). CGR facilitates reliable comparative genomics analyses for all eukaryotic organisms through an NCBI Toolkit and community collaboration.
A data repository and social network so that researchers can interact and collaborate, also offers tutorials and datasets for data science learning. "data.world is designed for data and the people who work with data. From professional projects to open data, data.world helps you host and share your data, collaborate with your team, and capture context and conclusions as you work."
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Scans of plates obtained at Landessternwarte Heidelberg-Königstuhl and German-Spanish Astronomical Center (Calar Alto Observatory), Spain, 1900 through 1999.
KDP has replaced the KNMI Data Centre (KDC), which was turned off on the 27th of July 2020. Not only a change of name, but also a transition to new technologies. Initially, the KDP will be more primitive than KDC. To fulfill future ambitions, a digital KNMI transformation has been initiated. Part of this transition is the development of a new KDP as a successor of the KDC. All data on the KNMI Data Platform is free to use. For some datasets a service agreement is available, which is indicated on the page of the dataset. The KNMI Data platform provides access to KNMI data on weather, climate and seismology. Here you will find KNMI data on various subjects such as the most recent 10-minute observations, historical series, data about meteorological measuring stations, model calculations, earthquake data and satellite products. In addition to KNMI datasets, we also make datasets from other parties available, such as ECMWF, ECOMET, EUMETSAT and WMO.
The CDAWeb data system enables improved display and coordinated analysis of multi-instrument, multimission data bases of the kind whose analysis is critical to meeting the science objectives of the ISTP program and the InterAgency Consultative Group (IACG) Solar-Terrestrial Science Initiative. The system combines the client-server user interface technology of the World Wide Web with a powerful set of customized IDL routines to leverage the data format standards (CDF) and guidelines for implementation adopted by ISTP and the IACG. The system can be used with any collection of data granules following the extended set of ISTP/IACG standards. CDAWeb is being used both to support coordinated analysis of public and proprietary data and better functional access to specific public data such as the ISTP-precursor CDAW 9 data base that is formatted to the ISTP/IACG standards. Many data sets are available through the Coordinated Data Analysis Web (CDAWeb) service and the data coverage continues to grow. These are largely, but not exclusively, magnetospheric data and nearby solar wind data of the ISTP era (1992-present) at time resolutions of approximately a minute. The CDAWeb service provides graphical browsing, data subsetting, screen listings, file creations and downloads (ASCII or CDF). Public data from current (1992-present) space physics missions (including Cluster, IMAGE, ISTP, FAST, IMP-8, SAMPEX and others). Public data from missions before 1992 (including IMP-8, ISIS1/2, Alouette2, Hawkeye and others). Public data from all current and past space physics missions. CDAWeb ist part of "Space Physics Data Facility" (https://www.re3data.org/repository/r3d100010168).
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.