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Found 62 result(s)
We present the MUSE-Wide survey, a blind, 3D spectroscopic survey in the CANDELS/GOODS-S and CANDELS/COSMOS regions. Each MUSE-Wide pointing has a depth of 1 hour and hence targets more extreme and more luminous objects over 10 times the area of the MUSE-Deep fields (Bacon et al. 2017). The legacy value of MUSE-Wide lies in providing "spectroscopy of everything" without photometric pre-selection. We describe the data reduction, post-processing and PSF characterization of the first 44 CANDELS/GOODS-S MUSE-Wide pointings released with this publication. Using a 3D matched filtering approach we detected 1,602 emission line sources, including 479 Lyman-α (Lya) emitting galaxies with redshifts 2.9≲z≲6.3. We cross-match the emission line sources to existing photometric catalogs, finding almost complete agreement in redshifts and stellar masses for our low redshift (z < 1.5) emitters. At high redshift, we only find ~55% matches to photometric catalogs. We encounter a higher outlier rate and a systematic offset of Δz≃0.2 when comparing our MUSE redshifts with photometric redshifts. Cross-matching the emission line sources with X-ray catalogs from the Chandra Deep Field South, we find 127 matches, including 10 objects with no prior spectroscopic identification. Stacking X-ray images centered on our Lya emitters yielded no signal; the Lya population is not dominated by even low luminosity AGN. A total of 9,205 photometrically selected objects from the CANDELS survey lie in the MUSE-Wide footprint, which we provide optimally extracted 1D spectra of. We are able to determine the spectroscopic redshift of 98% of 772 photometrically selected galaxies brighter than 24th F775W magnitude. All the data in the first data release - datacubes, catalogs, extracted spectra, maps - are available at the website.
Data deposit is supported for University of Ottawa faculty, students, and affiliated researchers. The repository is multidisciplinary and hosted on Canadian servers. It includes features such as permanent links (DOIs) which encourage citation of your dataset and help you set terms for access and reuse of your data. uOttawa Dataverse is currently optimal for small to medium datasets.
The ACSS Dataverse is a repository of interdisciplinary social science research data produced in and on the Arab region. The ACSS Dataverse, part of an initiative of the Arab Council for the Social Sciences in collaboration with the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill, preserves and facilitates access to social science datasets in and on the Arab region and is open to relevant research data deposits.
UCLA Library is adopting Dataverse, the open source web application designed for sharing, preserving and using research data. UCLA Dataverse will allow data, text, software, scripts, data visualizations, etc., created from research projects at UCLA to be made publicly available, widely discoverable, linkable, and ultimately, reusable
University of Alberta Dataverse is a service provided by the University of Albert Library to help researchers publish, analyze, distribute, and preserve data and datasets. Open for University of Alberta-affiliated researchers to deposit data.
The CBU Dataverse is a research data repository for Cape Breton University. Files are held securely on Canadian servers, and can be made openly accessible to further research, gain citations and promote our world class research.
The mission of World Data Center for Climate (WDCC) is to provide central support for the German and European climate research community. The WDCC is member of the ISC's World Data System. Emphasis is on development and implementation of best practice methods for Earth System data management. Data for and from climate research are collected, stored and disseminated. The WDCC is restricted to data products. Cooperations exist with thematically corresponding data centres of, e.g., earth observation, meteorology, oceanography, paleo climate and environmental sciences. The services of WDCC are also available to external users at cost price. A special service for the direct integration of research data in scientific publications has been developed. The editorial process at WDCC ensures the quality of metadata and research data in collaboration with the data producers. A citation code and a digital identifier (DOI) are provided and registered together with citation information at the DOI registration agency DataCite.
The University of Toronto Dataverse is a research data repository for our faculty, students, and staff. Files are held in a secure environment on Canadian servers. Researchers can choose to make content available publicly, to specific individuals, or to restrict access.
Open access to macromolecular X-ray diffraction and MicroED datasets. The repository complements the Worldwide Protein Data Bank. SBDG also hosts reference collection of biomedical datasets contributed by members of SBGrid, Harvard and pilot communities.
The Agri-Environmental Research Data Repository includes datasets from several studies conducted by researchers at the University of Guelph. This repository includes data on topics such as crop yield, soil moisture, weather and agroforestry.
In keeping with the open data policies of the U.S. Agency for International Development (USAID) and Bill & Melinda Gates Foundation, the Cereal Systems Initiative for South Asia (CSISA) has launched the CSISA Data Repository to ensure public accessibility to key data sets, including crop cut data- directly observed, crop yield estimates, on-station and on-farm research trial data and socioeconomic surveys. CSISA is a science-driven and impact-oriented regional initiative for increasing the productivity of cereal-based cropping systems in Bangladesh, India and Nepal, thus improving food security and farmers’ livelihoods. CSISA generates data that is of value and interest to a diverse audience of researchers, policymakers and the public. CSISA’s data repository is hosted on Dataverse, an open source web application developed at Harvard University to share, preserve, cite, explore and analyze research data. CSISA’s repository contains rich datasets, including on-station trial data from 2009–17 about crop and resource management practices for sustainable future cereal-based cropping systems. Collection of this data occurred during the long-term, on-station research trials conducted at the Indian Council of Agricultural Research – Research Complex for the Eastern Region in Bihar, India. The data include information on agronomic management for the sustainable intensification of cropping systems, mechanization, diversification, futuristic approaches to sustainable intensification, long-term effects of conservation agriculture practices on soil health and the pest spectrum. Additional trial data in the repository includes nutrient omission plot technique trials from Bihar, eastern Uttar Pradesh and Odisha, India, covering 2012–15, which help determine the indigenous nutrient supplying ability of the soil. This data helps develop precision nutrient management approaches that would be most effective in different types of soils. CSISA’s most popular dataset thus far includes crop cut data on maize in Odisha, India and rice in Nepal. Crop cut datasets provide ground-truthed yield estimates, as well as valuable information on relevant agronomic and socioeconomic practices affecting production practices and yield. A variety of research data on wheat systems are also available from Bangladesh and India. Additional crop cut data will also be coming online soon. Cropping system-related data and socioeconomic data are in the repository, some of which are cross-listed with a Dataverse run by the International Food Policy Research Institute. The socioeconomic datasets contain baseline information that is crucial for technology targeting, as well as to assess the adoption and performance of CSISA-supported technologies under smallholder farmers’ constrained conditions, representing the ultimate litmus test of their potential for change at scale. Other highly interesting datasets include farm composition and productive trajectory information, based on a 20-year panel dataset, and numerous wheat crop cut and maize nutrient omission trial data from across Bangladesh.
The Polinsky Language Sciences Lab at Harvard University is a linguistics lab that examines questions of language structure and its effect on the ways in which people use and process language in real time. We engage in linguistic and interdisciplinary research projects ourselves; offer linguistic research capabilities for undergraduate and graduate students, faculty, and visitors; and build relationships with the linguistic communities in which we do our research. We are interested in a broad range of issues pertaining to syntax, interfaces, and cross-linguistic variation. We place a particular emphasis on novel experimental evidence that facilitates the construction of linguistic theory. We have a strong cross-linguistic focus, drawing upon English, Russian, Chinese, Korean, Mayan languages, Basque, Austronesian languages, languages of the Caucasus, and others. We believe that challenging existing theories with data from as broad a range of languages as possible is a crucial component of the successful development of linguistic theory. We investigate both fluent speakers and heritage speakers—those who grew up hearing or speaking a particular language but who are now more fluent in a different, societally dominant language. Heritage languages, a novel field of linguistic inquiry, are important because they provide new insights into processes of linguistic development and attrition in general, thus increasing our understanding of the human capacity to maintain and acquire language. Understanding language use and processing in real time and how children acquire language helps us improve language study and pedagogy, which in turn improves communication across the globe. Although our lab does not specialize in language acquisition, we have conducted some studies of acquisition of lesser-studied languages and heritage languages, with the purpose of comparing heritage speakers to adults.
LibraData is a place for UVA researchers to share data publicly. It is UVA's local instance of Dataverse. LibraData is part of the Libra Scholarly Repository suite of services which includes works of UVA scholarship such as articles, books, theses, and data.
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The National Archives and Records Administration (NARA) is the nation's record keeper. Of all documents and materials created in the course of business conducted by the United States Federal government, only 1%-3% are so important for legal or historical reasons that they are kept by us forever. Those valuable records are preserved and are available to you, whether you want to see if they contain clues about your family’s history, need to prove a veteran’s military service, or are researching an historical topic that interests you.
The range of CIRAD's research has given rise to numerous datasets and databases associating various types of data: primary (collected), secondary (analysed, aggregated, used for scientific articles, etc), qualitative and quantitative. These "collections" of research data are used for comparisons, to study processes and analyse change. They include: genetics and genomics data, data generated by trials and measurements (using laboratory instruments), data generated by modelling (interpolations, predictive models), long-term observation data (remote sensing, observatories, etc), data from surveys, cohorts, interviews with players.
Additionally to the institutional repository, current St. Edward's faculty have the option of uploading their work directly to their own SEU accounts on stedwards.figshare.com. Projects created on Figshare will automatically be published on this website as well. For more information, please see documentation
Western University's Dataverse is a research data repository for our faculty, students, and staff. Files are held in a secure environment on Canadian servers. Researchers can choose to make content available publicly, to specific individuals, or to keep it locked.
PSI is a global health organization dedicated to improving the health of people in the developing world by focusing on serious challenges like a lack of family planning, HIV and AIDS, barriers to maternal health, and the greatest threats to children under five, including malaria, diarrhea, pneumonia and malnutrition. A hallmark of PSI is a commitment to the principle that health services and products are most effective when they are accompanied by robust communications and distribution efforts that help ensure wide acceptance and proper use. PSI works in partnership with local governments, ministries of health and local organizations to create health solutions that are built to last. We use original data to monitor and evaluate our programs, generate consumer insight, estimate the impact of our solutions, and evaluate the health of the markets we work to strengthen.
A data repository for researchers affiliated with Toronto Metropolitan University. This resource is part of Borealis which is a service provided by the Ontario Council of University Libraries.
Queen's University Dataverse is the institutional open access research data repository for Queen's University, featuring Queen's University Biological Station (QUBS) which includes research related to ecology, evolution, resource management and conservation, GIS, climate data, and environmental science.