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Found 195 result(s)
>>>>!!!<<< As stated 2017-06-27 The website http://researchcompendia.org is no longer available; repository software is archived on github https://github.com/researchcompendia >>>!!!<<< The ResearchCompendia platform is an attempt to use the web to enhance the reproducibility and verifiability—and thus the reliability—of scientific research. we provide the tools to publish the "actual scholarship" by hosting data, code, and methods in a form that is accessible, trackable, and persistent. Some of our short term goals include: To expand and enhance the platform including adding executability for a greater variety of coding languages and frameworks, and enhancing output presentation. To expand usership and to test the ResearchCompendia model in a number of additional fields, including computational mathematics, statistics, and biostatistics. To pilot integration with existing scholarly platforms, enabling researchers to discover relevant Research Compendia websites when looking at online articles, code repositories, or data archives.
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The German Neuroinformatics Node's data infrastructure (GIN) services provide a platform for comprehensive and reproducible management and sharing of neuroscience data. Building on well established versioning technology, GIN offers the power of a web based repository management service combined with a distributed file storage. The service addresses the range of research data workflows starting from data analysis on the local workstation to remote collaboration and data publication.
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GBIF is an international organisation that is working to make the world's biodiversity data accessible everywhere in the world. GBIF and its many partners work to mobilize the data, and to improve search mechanisms, data and metadata standards, web services, and the other components of an Internet-based information infrastructure for biodiversity. GBIF makes available data that are shared by hundreds of data publishers from around the world. These data are shared according to the GBIF Data Use Agreement, which includes the provision that users of any data accessed through or retrieved via the GBIF Portal will always give credit to the original data publishers.
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.
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SMU Research Data Repository (SMU RDR) is a tool and service for researchers from Singapore Management University (SMU) to store, share and publish their research data. SMU RDR accepts a wide range of research data and outputs generated from research projects.
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The Libraries offer members of the Université de Montréal community the opportunity to publish their research data in a Dataverse repository space
HepSim is a public repository with Monte Carlo simulations for particle-collision experiments. It contains predictions from leading-order (LO) parton shower models, next-to-leading order (NLO) and NLO with matched parton showers. It also includes Monte Carlo events after fast ("parametric") and full (Geant4) detector simulations and event reconstruction.
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Dataverse for faculty, researchers, and students at St. Francis Xavier University or affiliated institutions. Hosted by Borealis.
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The Concordia University Dataverse is a research data repository for Concordia faculty, students, and staff. Files are held in a secure environment on Canadian servers.
The Arizona State University (ASU) Research Data Repository provides a platform for ASU-affiliated researchers to share, preserve, cite, and make research data accessible and discoverable. The ASU Research Data Repository provides a permanent digital identifier for research data, which complies with data sharing policies. The repository is powered by the Dataverse open-source application, developed and used by Harvard University. Both the ASU Research Data Repository and the KEEP Institutional Repository are managed by the ASU Library to ensure research produced at Arizona State University is discoverable and accessible to the global community.
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SODHA is the federal Belgian data archive for social sciences and the digital humanities. SODHA is a new service of the State Archives of Belgium and acts as the Belgian service provider for the Consortium of European Social Science Data Archives (CESSDA).
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DUnAs is the institutional research data repository of the University of Aveiro. This repository is intended to share, archive, preserve, cite, access, and explore research data produced in the university scientific research activities.
CERN, DESY, Fermilab and SLAC have built the next-generation High Energy Physics (HEP) information system, INSPIRE. It combines the successful SPIRES database content, curated at DESY, Fermilab and SLAC, with the Invenio digital library technology developed at CERN. INSPIRE is run by a collaboration of CERN, DESY, Fermilab, IHEP, IN2P3 and SLAC, and interacts closely with HEP publishers, arXiv.org, NASA-ADS, PDG, HEPDATA and other information resources. INSPIRE represents a natural evolution of scholarly communication, built on successful community-based information systems, and provides a vision for information management in other fields of science.
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The MacEwan University Data Repository provides a place to store, share, and explore data and supports the teaching and scholarly activity of MacEwan University.
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The University of Northern British Columbia Dataverse is a research data repository for research data from UNBC researchers. Files are held in a secure environment on Canadian servers. The platform makes it possible for researchers to deposit data, create appropriate metadata, and version documents as they work. Researchers can choose to make content available publicly, to specific individuals, or to keep it locked.
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GRO.data is a research data repository for the Göttingen Campus. Belonging researchers can use it for free. It serves different purposes such as: to simply preserve datasets, to keep track of changes across several versions, to share data with colleagues, to make data itself publicly available, to receive a persistent identifier upon publications.
The Research Collection is ETH Zurich's publication platform. It unites the functions of a university bibliography, an open access repository and a research data repository within one platform. Researchers who are affiliated with ETH Zurich, the Swiss Federal Institute of Technology, may deposit research data from all domains. They can publish data as a standalone publication, publish it as supplementary material for an article, dissertation or another text, share it with colleagues or a research group, or deposit it for archiving purposes. Research-data-specific features include flexible access rights settings, DOI registration and a DOI preview workflow, content previews for zip- and tar-containers, as well as download statistics and altmetrics for published data. All data uploaded to the Research Collection are also transferred to the ETH Data Archive, ETH Zurich’s long-term archive.
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DABAR (Digital Academic Archives and Repositories) is the key component of the Croatian e-infrastructure’s data layer. It provides technological solutions that facilitate maintenance of higher education and science institutions' digital assets, i.e., various digital objects produced by the institutions and their employees.
For datasets big and small; Store your research data online. Quickly and easily upload files of any type and we will host your research data for you. Your experimental research data will have a permanent home on the web that you can refer to.
RAVE (RAdial Velocity Experiment) is a multi-fiber spectroscopic astronomical survey of stars in the Milky Way using the 1.2-m UK Schmidt Telescope of the Anglo-Australian Observatory (AAO). The RAVE collaboration consists of researchers from over 20 institutions around the world and is coordinated by the Leibniz-Institut für Astrophysik Potsdam. As a southern hemisphere survey covering 20,000 square degrees of the sky, RAVE's primary aim is to derive the radial velocity of stars from the observed spectra. Additional information is also derived such as effective temperature, surface gravity, metallicity, photometric parallax and elemental abundance data for the stars. The survey represents a giant leap forward in our understanding of our own Milky Way galaxy; with RAVE's vast stellar kinematic database the structure, formation and evolution of our Galaxy can be studied.