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Found 37 result(s)
Academic Commons provides open, persistent access to the scholarship produced by researchers at Columbia University, Barnard College, Jewish Theological Seminary, Teachers College, and Union Theological Seminary. Academic Commons is a program of the Columbia University Libraries. Academic Commons accepts articles, dissertations, research data, presentations, working papers, videos, and more.
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Sikt archives research data on people and society to make sure the data can be shared and is made available for reuse. We continuously enrich our data collections to provide a richer basis for research. Sikt’s main focus is quantitative data matrices on individuals, organisations, administrative, political, and geographical actors. The archive specialise in survey data, which undergoes extensive curation at the variable level and detailed metadata is produced and published in Norwegian and English.
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The Health Atlas is an alliance of medical ontologists, medical systems biologists and clinical trials groups to design and implement a multi-functional and quality-assured atlas. It provides models, data and metadata on specific use cases from medical research projects from the partner institutions.
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MDM-Portal (Medical Data Models) is a meta-data registry for creating, analyzing, sharing and reusing medical forms. It serves as an infrastructure for academic (non-commercial) medical research to contribute a solution to this problem. It contains forms in the system-independent CDISC Operational Data Model (ODM) format with more than 500,000 data-elements. The Portal provides numerous core data sets, common data elements or data standards, code lists and value sets. This enables researchers to view, discuss, download and export forms in most common technical formats such as PDF, CSV, Excel, SQL, SPSS, R, etc.
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The interdisciplinary data platform INPTDAT provides easy access to research data and information from all fields of applied plasma physics and plasma medicine. It aims to support the findability, accessibility, interoperability and re-use of data for the low-temperature plasma physics community.
<<<!!!<<< OFFLINE >>>!!!>>> A recent computer security audit has revealed security flaws in the legacy HapMap site that require NCBI to take it down immediately. We regret the inconvenience, but we are required to do this. That said, NCBI was planning to decommission this site in the near future anyway (although not quite so suddenly), as the 1,000 genomes (1KG) project has established itself as a research standard for population genetics and genomics. NCBI has observed a decline in usage of the HapMap dataset and website with its available resources over the past five years and it has come to the end of its useful life. The International HapMap Project is a multi-country effort to identify and catalog genetic similarities and differences in human beings. Using the information in the HapMap, researchers will be able to find genes that affect health, disease, and individual responses to medications and environmental factors. The Project is a collaboration among scientists and funding agencies from Japan, the United Kingdom, Canada, China, Nigeria, and the United States. All of the information generated by the Project will be released into the public domain. The goal of the International HapMap Project is to compare the genetic sequences of different individuals to identify chromosomal regions where genetic variants are shared. By making this information freely available, the Project will help biomedical researchers find genes involved in disease and responses to therapeutic drugs. In the initial phase of the Project, genetic data are being gathered from four populations with African, Asian, and European ancestry. Ongoing interactions with members of these populations are addressing potential ethical issues and providing valuable experience in conducting research with identified populations. Public and private organizations in six countries are participating in the International HapMap Project. Data generated by the Project can be downloaded with minimal constraints. The Project officially started with a meeting in October 2002 (https://www.genome.gov/10005336/) and is expected to take about three years.
ETH Data Archive is ETH Zurich's long-term preservation solution for digital information such as research data, digitised content, archival records, or images. It serves as the backbone of data curation and for most of its content, it is a “dark archive” without public access. In this capacity, the ETH Data Archive also archives the content of ETH Zurich’s Research Collection which is the primary repository for members of the university and the first point of contact for publication of data at ETH Zurich. All data that was produced in the context of research at the ETH Zurich, can be published and archived in the Research Collection. An automated connection to the ETH Data Archive in the background ensures the medium to long-term preservation of all publications and research data. Direct access to the ETH Data Archive is intended only for customers who need to deposit software source code within the framework of ETH transfer Software Registration. Open Source code packages and other content from legacy workflows can be accessed via ETH Library @ swisscovery (https://library.ethz.ch/en/).
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bonndata is the institutional, FAIR-aligned and curated, cross-disciplinary research data repository for the publication of research data for all researchers at the University of Bonn. The repository is fully embedded into the University IT and Data Center and curated by the Research Data Service Center (https://www.forschungsdaten.uni-bonn.de/en). The software that bonndata is based on is the open source software Dataverse (https://dataverse.org)
ModelDB is a curated database of published models in the broad domain of computational neuroscience. It addresses the need for access to such models in order to evaluate their validity and extend their use. It can handle computational models expressed in any textual form, including procedural or declarative languages (e.g. C++, XML dialects) and source code written for any simulation environment. The model source code doesn't even have to reside inside ModelDB; it just has to be available from some publicly accessible online repository or WWW site.
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The Universidad del Rosario Research data repository is an institutional iniciative launched in 2019 to preserve, provide access and promote the use of data resulting from Universidad del Rosario research projects. The Repository aims to consolidate an online, collaborative working space and data-sharing platform to support Universidad del Rosario researchers and their collaborators, and to ensure that research data is available to the community, in order to support further research and contribute to the democratization of knowledge. The Research data repository is the heart of an institutional strategy that seeks to ensure the generation of Findable, Accessible, Interoperable and Reusable (FAIR) data, with the aim of increasing its impact and visibility. This strategy follows the international philosophy of making research data “as open as possible and as closed as necessary”, in order to foster the expansion, valuation, acceleration and reusability of scientific research, but at the same time, safeguard the privacy of the subjects. The platform storage, preserves and facilitates the management of research data from all disciplines, generated by the researchers of all the schools and faculties of the University, that work together to ensure research with the highest standards of quality and scientific integrity, encouraging innovation for the benefit of society.
The Research Collection is ETH Zurich's publication platform. It unites the functions of a university bibliography, an open access repository and a research data repository within one platform. Researchers who are affiliated with ETH Zurich, the Swiss Federal Institute of Technology, may deposit research data from all domains. They can publish data as a standalone publication, publish it as supplementary material for an article, dissertation or another text, share it with colleagues or a research group, or deposit it for archiving purposes. Research-data-specific features include flexible access rights settings, DOI registration and a DOI preview workflow, content previews for zip- and tar-containers, as well as download statistics and altmetrics for published data. All data uploaded to the Research Collection are also transferred to the ETH Data Archive, ETH Zurich’s long-term archive.
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The German Neuroinformatics Node's data infrastructure (GIN) services provide a platform for comprehensive and reproducible management and sharing of neuroscience data. Building on well established versioning technology, GIN offers the power of a web based repository management service combined with a distributed file storage. The service addresses the range of research data workflows starting from data analysis on the local workstation to remote collaboration and data publication.