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The Magnetics Information Consortium (MagIC) improves research capacity in the Earth and Ocean sciences by maintaining an open community digital data archive for rock magnetic, geomagnetic, archeomagnetic (archaeomagnetic) and paleomagnetic (palaeomagnetic) data. Different parts of the website allow users access to archive, search, visualize, and download these data. MagIC supports the international rock magnetism, geomagnetism, archeomagnetism (archaeomagnetism), and paleomagnetism (palaeomagnetism) research and endeavors to bring data out of private archives, making them accessible to all and (re-)useable for new, creative, collaborative scientific and educational activities. The data in MagIC is used for many types of studies including tectonic plate reconstructions, geomagnetic field models, paleomagnetic field reversal studies, magnetohydrodynamical studies of the Earth's core, magnetostratigraphy, and archeology. MagIC is a domain-specific data repository and directed by PIs who are both producers and consumers of rock, geo, and paleomagnetic data. Funded by NSF since 2003, MagIC forms a major part of https://earthref.org which integrates four independent cyber-initiatives rooted in various parts of the Earth, Ocean and Life sciences and education.
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Repository of the Faculty of Science is institutional repository that gathers, permanently stores and allows access to the results of scientific and intellectual property of the Faculty of Science, University of Zagreb. The objects that can be stored in the repository are research data, scientific articles, conference papers, theses, dissertations, books, teaching materials, images, video and audio files, and presentations. To improve searchability, all materials are described with predetermined set of metadata.
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<<<!!!<<< The digital archive of the Historical Data Center Saxony-Anhalt was transferred to the share-it repositor https://www.re3data.org/repository/r3d100013014 >>>!!!>>> The Historical Data Centre Saxony-Anhalt was founded in 2008. Its main tasks are the computer-aided provision, processing and evaluation of historical research data, the development of theoretically consolidated normative data and vocabularies as well as the further development of methods in the context of digital humanities, research data management and quality assurance. The "Historical Data Centre Saxony-Anhalt" sees itself as a central institution for the data service of historical data in the federal state of Saxony-Anhalt and is thus part of a nationally and internationally linked infrastructure for long-term data storage and use. The Centre primarily acquires individual-specific microdata for the analysis of life courses, employment biographies and biographies (primarily quantitative, but also qualitative data), which offer a broad interdisciplinary and international analytical framework and meet clearly defined methodological and technical requirements. The studies are processed, archived and - in compliance with data protection and copyright conditions - made available to the scientifically interested public in accordance with internationally recognized standards. The degree of preparation depends on the type and quality of the study and on demand. Reference studies and studies in high demand are comprehensively documented - often in cooperation with primary researchers or experts - and summarized in data collections. The Historical Data Centre supports researchers in meeting the high demands of research data management. This includes the advisory support of the entire life cycle of data, starting with data production, documentation, analysis, evaluation, publication, long-term archiving and finally the subsequent use of data. In cooperation with other infrastructure facilities of the state of Saxony-Anhalt as well as national and international, interdisciplinary data repositories, the Data Centre provides tools and infrastructures for the publication and long-term archiving of research data. Together with the University and State Library of Saxony-Anhalt, the Data Centre operates its own data repository as well as special workstations for the digitisation and analysis of data. The Historical Data Centre aims to be a contact point for very different users of historical sources. We collect data relating to historical persons, events and historical territorial units.
The Cornell Center for Social Sciences (CCSS) houses an extensive collection of research data files in the social sciences with particular emphasis on data that matches the interests of Cornell University researchers. CCSS intentionally uses a broad definition of social sciences in recognition of the interdisciplinary nature of Cornell research. CCSS collects and maintains digital research data files in the social sciences, with a current emphasis on Cornell-based social science research, Results Reproduction packages, and potentially at-risk datasets. Our archive historically has focused on a broad range of social science data, including data on demography, economics and labor, political and social behavior, family life, and health. You can search our holdings or browse studies by subject area.
HydroShare is a system operated by The Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) that enables users to share and publish data and models in a variety of flexible formats, and to make this information available in a citable, shareable and discoverable manner. HydroShare includes a repository for data and models, and tools (web apps) that can act on content in HydroShare providing users with a gateway to high performance computing and computing in the cloud. With HydroShare you can: share data and models with colleagues; manage access to shared content; share, access, visualize, and manipulate a broad set of hydrologic data types and models; publish data and models and obtain a citable digital object identifier (DOI); aggregate resources into collections; discover and access data and models published by others; use the web services application programming interface (API) to programmatically access resources; and use integrated web applications to visualize, analyze and run models with data in HydroShare.