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Created and managed by the Library, DataSpace@HKUST is the data repository and workspace service for HKUST research community. Faculty members and research postgraduate students can use the platform to store, share, organize, preserve and publish research data. It is built on Dataverse, an open source web application developed at Harvard’s Institute for Quantitative Social Science. Using Dataverse architecture, the repository hosts multiple "dataverses". Each dataverse contains datasets; while each dataset may contain multiple data files and the corresponding descriptive metadata.
From now on you no longer deposit archaeological data here in EASY . Please see: https://archaeology.datastations.nl/ EASY is the online archiving system of Data Archiving and Networked Services (DANS). EASY offers you access to thousands of datasets in the humanities, the social sciences and other disciplines. EASY can also be used for the online depositing of research data.
In order to meet the needs of research data management for Peking University. The PKU library cooperate with the NSFC-PKU data center for management science, PKU science and research department, PKU social sciences department to jointly launch the Peking University Open Research Data Platform. PKU Open research data provides preservation, management and distribution services for research data. It encourage data owner to share data and data users to reuse data.
The Odum Institute Archive Dataverse contains social science data curated and archived by the Odum Institute Data Archive at the University of North Carolina at Chapel Hill. Some key collections include the primary holdings of the Louis Harris Data Center, the National Network of State Polls, and other Southern-focused public opinion data. Please note that some datasets in this collection are restricted to University of North Carolina at Chapel Hill affiliates. Access to these datasets require UNC ONYEN institutional login to the Dataverse system.
The Harvard Dataverse is open to all scientific data from all disciplines worldwide. It includes the world's largest collection of social science research data. It is hosting data for projects, archives, researchers, journals, organizations, and institutions.
The Society of American Archivists (SAA) Dataverse is an SAA data service that was established to support the needs and interests of SAA’s members and the broader archives community. The SAA Dataverse supports the reuse of datasets for purposes of fostering knowledge, insights, and a deeper understanding of archival organizations, the status of archivists, and the impact of archives and archival work on the broader society. Deposited datasets should be “actionable” in that they should support direct analysis and interpretation. The SAA Dataverse welcomes deposits of collections of quantitative or qualitative data and associated documentation. SAA membership is not required to deposit or use data in the SAA Dataverse.
Arch is an open access repository for the research and scholarly output of Northwestern University. Log in with your NetID to deposit, describe, and organize your research for public access and long-term preservation. We'll use our expertise to help you curate, share, and preserve your work.
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.
The UCD Digital Library is a platform for exploring cultural heritage, engaging with digital scholarship, and accessing research data. The UCD Digital Library allows you to search, browse and explore a growing collection of historical materials, photographs, art, interviews, letters, and other exciting content, that have been digitised and made freely available.
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 JRC Data Catalogue gives access to the multidisciplinary data produced and maintained by the Joint Research Centre, the European Commission's in-house science service providing independent scientific advice and support to policies of the European Union.