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Found 82 result(s)
Welcome to Smithsonian Open Access, where you can download, share, and reuse millions of the Smithsonian’s images—right now, without asking. With new platforms and tools, you have easier access to nearly 3 million 2D and 3D digital items from our collections—with many more to come. This includes images and data from across the Smithsonian’s 19 museums, nine research centers, libraries, archives, and the National Zoo.
Ag-Analytics is an online open source database of various economic and environmental data. It automates the collection, formatting, and processing of several different commonly used datasets, such as the National Agricultural Statistics Service (NASS), the Agricultural Marketing Service (AMS), Risk Management agency (RMA), the PRISM weather database, and the U.S. Commodity Futures Trading Commission (CFTC). All the data have been cleaned and well-documented to save users the inconvenience of scraping and cleaning the data themselves.
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This is the County of Huron's public platform for exploring and downloading open data, discovering and building apps, and engaging to solve important local issues. You can analyze and combine Open Datasets using maps, as well as develop new web and mobile applications.
The Tromsø Repository of Language and Linguistics (TROLLing) is a FAIR-aligned repository of linguistic data and statistical code. The archive is open access, which means that all information is available to everyone. All data are accompanied by searchable metadata that identify the researchers, the languages and linguistic phenomena involved, the statistical methods applied, and scholarly publications based on the data (where relevant). Linguists worldwide are invited to deposit data and statistical code used in their linguistic research. TROLLing is a special collection within DataverseNO (http://doi.org/10.17616/R3TV17), and C Centre within CLARIN (Common Language Resources and Technology Infrastructure, a networked federation of European data repositories; http://www.clarin.eu/), and harvested by their Virtual Language Observatory (VLO; https://vlo.clarin.eu/).
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.
CLOSER Discovery is a research tool for locating the variables that best suit your research interests and testing their robustness. Metadata repository for Longitudinal Population Studies in the United Kingdom
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The main focus of tambora.org is Historical Climatology. Years of meticulous work in this field in research groups around the world have resulted in large data collections on climatic parameters such as temperature, precipitation, storms, floods, etc. with different regional, temporal and thematic foci. tambora.org enables researchers to collaboratively interpret the information derived from historical sources. It provides a database for original text quotations together with bibliographic references and the extracted places, dates and coded climate and environmental information.
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The vocabulary of forenames is a simple, multilingual vocabulary (i.e. without hierarchies etc.) in which the forenames of the project partners’ persons and the forenames’ spelling variants, both historical and dialectal, are documented with references or passages. As a rule, each forename is assigned one or more persons bearing that name. There is a hit list of the most frequent forenames between 200 BC and AD 2016 as well as a visualisation in word clouds and the occurrences in a timeline.
The Social Science Data Archive is still active and maintained as part of the UCLA Library Data Science Center. SSDA Dataverse is one of the archiving opportunities of SSDA, the others are: Data can be archived by SSDA itself or by ICPSR or by UCLA Library or by California Digital Library. The Social Science Data Archives serves the UCLA campus as an archive of faculty and graduate student survey research. We provide long term storage of data files and documentation. We ensure that the data are useable in the future by migrating files to new operating systems. We follow government standards and archival best practices. The mission of the Social Science Data Archive has been and continues to be to provide a foundation for social science research with faculty support throughout an entire research project involving original data collection or the reuse of publicly available studies. Data Archive staff and researchers work as partners throughout all stages of the research process, beginning when a hypothesis or area of study is being developed, during grant and funding activities, while data collection and/or analysis is ongoing, and finally in long term preservation of research results. Our role is to provide a collaborative environment where the focus is on understanding the nature and scope of research approach and management of research output throughout the entire life cycle of the project. Instructional support, especially support that links research with instruction is also a mainstay of operations.
The gift of the Stowell Datasets, a digital archive of psychographic data, to the College of Liberal Arts (and continued gift of new datasets) provide a unique opportunity for WSU to facilitate access to a valuable research resource. The datasets include over 350 individual major media market surveys (CATI, Random Digit Dialing telephone surveys) collected over the period 1989-2001 and feature approximately n=1,000+ respondents for each market for each year.
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NAKALA is a repository dedicated to SSH research data in France. Given its generalist and multi-disciplinary nature, all types of data are accepted, although certain formats are recommended to ensure longterm data preservation. It has been developed and is hosted by Huma-Num, the French national research infrastructure for digital humanities.
FRED is an online database consisting of hundreds of thousands of economic data time series from scores of national, international, public, and private sources. FRED, created and maintained by the Research Department at the Federal Reserve Bank of St. Louis, goes far beyond simply providing data: It combines data with a powerful mix of tools that help the user understand, interact with, display, and disseminate the data. In essence, FRED helps users tell their data stories.
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.
UNAVCO promotes research by providing access to data that our community of geodetic scientists uses for quantifying the motions of rock, ice and water that are monitored by a variety of sensor types at or near the Earth's surface. After processing, these data enable millimeter-scale surface motion detection and monitoring at discrete points, and high-resolution strain imagery over areas of tens of square meters to hundreds of square kilometers. The data types include GPS/GNSS, imaging data such as from SAR and TLS, strain and seismic borehole data, and meteorological data. Most of these can be accessed via web services. In addition, GPS/GNSS datasets, TLS datasets, and InSAR products are assigned digital object identifiers.
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The CSIRO National Collections and Marine Infrastructure (NCMI) Information and Data Centre has managed marine data for Australia's government research organisation for over 30 years. They have an enduring archive of marine and climate research data, and regularly publish data (including physical, chemical, bathymetric and biological data) collected on board RV Investigator as part of the Marine National Facility. Data from the MNF is freely and publicly available.
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The City of Burnaby maintains a wide range of data. As part of our commitment to engagement, transparency and accountability, we want to share our data with you. Please explore this site and use Burnaby’s data to analyze information, build apps, combine open datasets using maps, develop new web and mobile applications, and to meet your specific data needs. Together, using open data, we’ll make our great community even better!
The DARIAH-DE repository is a digital long-term archive for human and cultural-scientific research data. Each object described and stored in the DARIAH-DE Repository has a unique and lasting Persistent Identifier (DOI), with which it is permanently referenced, cited, and kept available for the long term. In addition, the DARIAH-DE Repository enables the sustainable and secure archiving of data collections. The DARIAH-DE Repository is not only to DARIAH-DE associated research projects, but also to individual researchers as well as research projects that want to save their research data persistently, referenceable and long-term archived and make it available to third parties. The main focus is the simple and user-oriented access to long-term storage of research data. To ensure its long term sustainability, the DARIAH-DE Repository is operated by the Humanities Data Centre.
The Repository of Psychological Instruments in Serbian (REPOPSI), run by the Laboratory for Research of Individual Differences at the University of Belgrade and hosted on the Open Science Framework, is an open-access repository of psychological instruments. REPOPSI is a collection of psychological measures, scales, tests, and other research instruments commonly used in social and behavioral science research. Documented are Serbian, English and multilingual instruments, which can be used free of charge for non-commercial purposes (e.g., academic research or education).
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.