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Found 17 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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Lithuanian Data Archive for Social Sciences and Humanities (LiDA) is a virtual digital infrastructure for SSH data and research resources acquisition, long-term preservation and dissemination. All the data and research resources are documented in both English and Lithuanian according to international standards. Access to the resources is provided via Dataverse repository. LiDA curates different types of resources and they are published into catalogues according to the type: Survey Data, Aggregated Data (including Historical Statistics), Encoded Data (including News Media Studies), and Textual Data. Also, LiDA holds collections of social sciences and humanities data deposited by Lithuanian science and higher education institutions and Lithuanian state institutions (Data of Other Institutions). LiDA is hosted by the Centre for Data Analysis and Archiving of Kaunas University of Technology (data.ktu.edu).
WBG Finances is a World Bank Group digital platform that provides our clients and partners access to public financial data and portfolio information from across all Group entities in one place. WBG Finances simplifies the presentation of financial information in an ‘easy to consume’ and in the context of Country and Portfolio across WBG. Open Finances makes World Bank Group’s financials available for everybody to explore. All the data presented is available to everybody to analyze, visualize, and share with others. We invite you to explore the numerous tools, build your own visualizations or download the data in multiple formats. If you are a developer, connect to it through the APIs associated with all the datasets.
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.”
A premier source for United States cancer statistics, SEER gathers information related to incidence, prevalence, and survival from specific geographic areas that represent 28 percent of the population, as well as compiles related reports and reports on the national cancer mortality rates. Their aim is to provide information related to cancer statistics and decrease the burden of cancer in the national population. SEER has been collecting data from cancer cases since 1973.
The datacommons@psu was developed in 2005 to provide a resource for data sharing, discovery, and archiving for the Penn State research and teaching community. Access to information is vital to the research, teaching, and outreach conducted at Penn State. The datacommons@psu serves as a data discovery tool, a data archive for research data created by PSU for projects funded by agencies like the National Science Foundation, as well as a portal to data, applications, and resources throughout the university. The datacommons@psu facilitates interdisciplinary cooperation and collaboration by connecting people and resources and by: Acquiring, storing, documenting, and providing discovery tools for Penn State based research data, final reports, instruments, models and applications. Highlighting existing resources developed or housed by Penn State. Supporting access to project/program partners via collaborative map or web services. Providing metadata development citation information, Digital Object Identifiers (DOIs) and links to related publications and project websites. Members of the Penn State research community and their affiliates can easily share and house their data through the datacommons@psu. The datacommons@psu will also develop metadata for your data and provide information to support your NSF, NIH, or other agency data management plan.
The Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC) is a team of researchers, data specialists and computer system developers who are supporting the development of a data management system to store scientific data generated by Gulf of Mexico researchers. The Master Research Agreement between BP and the Gulf of Mexico Alliance that established the Gulf of Mexico Research Initiative (GoMRI) included provisions that all data collected or generated through the agreement must be made available to the public. The Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC) is the vehicle through which GoMRI is fulfilling this requirement. The mission of GRIIDC is to ensure a data and information legacy that promotes continual scientific discovery and public awareness of the Gulf of Mexico Ecosystem.
The DesignSafe Data Depot Repository (DDR) is the platform for curation and publication of datasets generated in the course of natural hazards research. The DDR is an open access data repository that enables data producers to safely store, share, organize, and describe research data, towards permanent publication, distribution, and impact evaluation. The DDR allows data consumers to discover, search for, access, and reuse published data in an effort to accelerate research discovery. It is a component of the DesignSafe cyberinfrastructure, which represents a comprehensive research environment that provides cloud-based tools to manage, analyze, curate, and publish critical data for research to understand the impacts of natural hazards. DesignSafe is part of the NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI), and aligns with its mission to provide the natural hazards research community with open access, shared-use scholarship, education, and community resources aimed at supporting civil and social infrastructure prior to, during, and following natural disasters. It serves a broad national and international audience of natural hazard researchers (both engineers and social scientists), students, practitioners, policy makers, as well as the general public. It has been in operation since 2016, and also provides access to legacy data dating from about 2005. These legacy data were generated as part of the NSF-supported Network for Earthquake Engineering Simulation (NEES), a predecessor to NHERI. Legacy data and metadata belonging to NEES were transferred to the DDR for continuous preservation and access.
UNC Dataverse is an open-source repository software application for archiving, sharing, and accessing research data of all kinds. Each dataverse within the larger repository contains a multitude of datasets, and each dataset contains descriptive metadata and data files. UNC Dataverse is hosted by Odum Institute for Research in Social Science.
Apollo (previously DSpace@Cambridge) is the University of Cambridge’s Institutional Repository (IR), preserving and providing access to content created by members of the University. The repository stores a range of content and provides different levels of access, but its primary focus is on providing open access to the University’s research publications.
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.