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Found 17 result(s)
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Launched in February 2020, data.sciencespo is a repository that offers visibility, sharing and preservation of data collected, curated and processed at Sciences Po. The repository is based on the Dataverse open-source software and organised into collections: CDSP Collection This collection managed by the Centre des données socio-politiques (CDSP) includes the catalogue of surveys, in the social science and humanities, processed and curated by CDSP engineers since 2005. This catalogue brings together surveys produced at Sciences Po and other French and international institutions. - Sciences Po collection (self-deposit) This collection, which is managed by the Direction des ressources et de l'information scientifique (DRIS), is intended to host data produced by researchers affiliated with Sciences Po, following the self-deposit process assisted by the Library's staff.
The National Sleep Research Resource (NSRR) is an NHLBI-supported repository for sharing large amounts of sleep data (polysomnography, actigraphy and questionnaire-based) from multiple cohorts, clinical trials, and other data sources. Launched in April 2014, the mission of the NSRR is to advance sleep and circadian science by supporting secondary data analysis, algorithmic development, and signal processing through the sharing of high-quality data sets.
The HSRC Research Data Service provides a digital repository facility for the HSRC's research data in support of evidence based human and social development in South Africa and the broader region. It includes both quantitative and qualitative data. Access to data is dependent on ethical requirements for protecting research participants, as well as on legal agreements with the owners, funders or in the case of data owned by the HSRC, the requirements of the depositors of the data.
ICRISAT performs crop improvement research, using conventional as well as methods derived from biotechnology, on the following crops: Chickpea, Pigeonpea, Groundnut, Pearl millet,Sorghum and Small millets. ICRISAT's data repository collects, preserves and facilitates access to the datasets produced by ICRISAT researchers to all users who are interested in. Data includes Phenotypic, Genotypic, Social Science, and Spatial data, Soil and Weather.
Brainlife promotes engagement and education in reproducible neuroscience. We do this by providing an online platform where users can publish code (Apps), Data, and make it "alive" by integragrate various HPC and cloud computing resources to run those Apps. Brainlife also provide mechanisms to publish all research assets associated with a scientific project (data and analyses) embedded in a cloud computing environment and referenced by a single digital-object-identifier (DOI). The platform is unique because of its focus on supporting scientific reproducibility beyond open code and open data, by providing fundamental smart mechanisms for what we refer to as “Open Services.”
The Humanitarian Data Exchange (HDX) is an open platform for sharing data across crises and organisations. Launched in July 2014, the goal of HDX is to make humanitarian data easy to find and use for analysis. HDX is managed by OCHA's Centre for Humanitarian Data, which is located in The Hague. OCHA is part of the United Nations Secretariat and is responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. The HDX team includes OCHA staff and a number of consultants who are based in North America, Europe and Africa.
The projects include airborne, ground-based and ocean measurements, social science surveys, satellite data use, modelling studies and value-added product development. Therefore, the BAOBAB data portal enables to access a great amount and a large variety of data: - 250 local observation datasets, that have been collected by operational networks since 1850, long term monitoring research networks and intensive scientific campaigns; - 1350 outputs of a socio-economics questionnaire; - 60 operational satellite products and several research products; - 10 output sets of meteorological and ocean operational models and 15 of research simulations. Data documentation complies with metadata international standards, and data are delivered into standard formats. The data request interface takes full advantage of the database relational structure and enables users to elaborate multicriteria requests (period, area, property…).
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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.
The Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Data and Specimen Hub (DASH) is a centralized resource that allows researchers to share and access de-identified data from studies funded by NICHD. DASH also serves as a portal for requesting biospecimens from selected DASH studies.
A service of the Inter-university Consortium for Political and Social Research (ICPSR), openICPSR is a self-publishing repository for social, behavioral, and health sciences research data. openICPSR is particularly well-suited for the deposit of replication data sets for researchers who need to publish their raw data associated with a journal article so that other researchers can replicate their findings.
DataFirst's open research data repository, based at the University of Cape Town, gives open access to disaggregated administrative and survey data from African governments and research entities. DataFirst also operates a secure centre at the university to give researchers access to highly-disaggregated South African data.
The African Development Bank Group (AfDB) is committed to supporting statistical development in Africa as a sound basis for designing and managing effective development policies for reducing poverty on the continent. Reliable and timely data is critical to setting goals and targets as well as evaluating project impact. Reliable data constitutes the single most convincing way of getting the people involved in what their leaders and institutions are doing. It also helps them to get involved in the development process, thus giving them a sense of ownership of the entire development process. The AfDB has a large team of researchers who focus on the production of statistical data on economic and social situations. The data produced by the institution’s statistics department constitutes the background information in the Bank’s flagship development publications. Besides its own publication, the AfDB also finances studies in collaboration with its partners. The Statistics Department aims to stand as the primary source of relevant, reliable and timely data on African development processes, starting with the data generated from its current management of the Africa component of the International Comparison Program (ICP-Africa). The Department discharges its responsibilities through two divisions: The Economic and Social Statistics Division (ESTA1); The Statistical Capacity Building Division (ESTA2)
The focus of PolMine is on texts published by public institutions in Germany. Corpora of parliamentary protocols are at the heart of the project: Parliamentary proceedings are available for long stretches of time, cover a broad set of public policies and are in the public domain, making them a valuable text resource for political science. The project develops repositories of textual data in a sustainable fashion to suit the research needs of political science. Concerning data, the focus is on converting text issued by public institutions into a sustainable digital format (TEI/XML).
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 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.