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Found 21 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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data.public.lu is Luxembourg's central and official platform for data from the public sector, from research institutes and the private sector.
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.
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.
The Arctic Permafrost Geospatial Centre (APGC) is an Open Access Circum-Arctic Geospatial Data Portal that promotes, describes and visualizes geospatial permafrost data. A data catalogue and a WebGIS application allow to easily discover and view data and metadata. Data can be downloaded directly via link to the publishing data repository.
Kaggle is a platform for predictive modelling and analytics competitions in which statisticians and data miners compete to produce the best models for predicting and describing the datasets uploaded by companies and users. This crowdsourcing approach relies on the fact that there are countless strategies that can be applied to any predictive modelling task and it is impossible to know beforehand which technique or analyst will be most effective.
VertNet is a NSF-funded collaborative project that makes biodiversity data free and available on the web. VertNet is a tool designed to help people discover, capture, and publish biodiversity data. It is also the core of a collaboration between hundreds of biocollections that contribute biodiversity data and work together to improve it. VertNet is an engine for training current and future professionals to use and build upon best practices in data quality, curation, research, and data publishing. Yet, VertNet is still the aggregate of all of the information that it mobilizes. To us, VertNet is all of these things and more.
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.
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depositar — taking the term from the Portuguese/Spanish verb for to deposit — is an online repository for research data. The site is built by the researchers for the researchers. You are free to deposit, discover, and reuse datasets on depositar for all your research purposes.
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In 2018, the Ministry of Higher Education, Research and Innovation has included in its roadmap the creation of a new infrastructure called the National Biodiversity Data Centre (PNDB). The PNDB's missions are part of a FAIR (Easy to Find, Accessible, Interoperable, Reusable) approach, and consist in - providing access to datasets and metadata, associated services and products derived from the analyses - promoting scientific leadership to identify gaps and foster the emergence of community-driven systems of users and producers - facilitate the sharing of practices with other research communities, encourage the sharing of data and their reuse, and be part of the reflection on the future Earth System infrastructure. - promote coherence with national, European and international efforts concerning access to and use of biodiversity research data and the promotion of products and services. The PNDB is supported by the Muséum national d'Histoire naturelle, more specifically by the UMS 2006 PatriNat, a MNHN CNRS and AFB unit. The project is closely linked with the FRB and several of its founding institutions (AFB, BRGM, CIRAD, CNRS, Ifremer, INERIS, INRA, IRD, IRSTEA, MNHN, Univ. Montpellier).
The KNB Data Repository is an international repository intended to facilitate ecological, environmental and earth science research in the broadest senses. For scientists, the KNB Data Repository is an efficient way to share, discover, access and interpret complex ecological, environmental, earth science, and sociological data and the software used to create and manage those data. Due to rich contextual information provided with data in the KNB, scientists are able to integrate and analyze data with less effort. The data originate from a highly-distributed set of field stations, laboratories, research sites, and individual researchers. The KNB supports rich, detailed metadata to promote data discovery as well as automated and manual integration of data into new projects. The KNB supports a rich set of modern repository services, including the ability to assign Digital Object Identifiers (DOIs) so data sets can be confidently referenced in any publication, the ability to track the versions of datasets as they evolve through time, and metadata to establish the provenance relationships between source and derived data.
The Registry of Open Data on AWS provides a centralized repository of public data sets that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge to their users. Anyone can access these data sets from their Amazon Elastic Compute Cloud (Amazon EC2) instances and start computing on the data within minutes. Users can also leverage the entire AWS ecosystem and easily collaborate with other AWS users.
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RepOD is a general-purpose repository for open research data, offering all members of the academic community in Poland the possibility to deposit their work. It is intended for scientific data from all disciplines of knowledge and in all formats. The purpose of RepOD is to create a place where research data can be safely stored and openly shared with others.
The Astronomy data repository at Harvard is currently open to all scientific data from astronomical institutions worldwide. Incorporating Astroinformatics of galaxies and quasars Dataverse. The Astronomy Dataverse is connected to the indexing services provided by the SAO/NASA Astrophysical Data Service (ADS).
CLARIN.SI is the Slovenian node of the European CLARIN (Common Language Resources and Technology Infrastructure) Centers. The CLARIN.SI repository is hosted at the Jožef Stefan Institute and offers long-term preservation of deposited linguistic resources, along with their descriptive metadata. The integration of the repository with the CLARIN infrastructure gives the deposited resources wide exposure, so that they can be known, used and further developed beyond the lifetime of the projects in which they were produced. Among the resources currently available in the CLARIN.SI repository are the multilingual MULTEXT-East resources, the CC version of Slovenian reference corpus Gigafida, the morphological lexicon Sloleks, the IMP corpora and lexicons of historical Slovenian, as well as many other resources for a variety of languages. Furthermore, several REST-based web services are provided for different corpus-linguistic and NLP tasks.
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The Climate Change Centre Austria - Data Centre provides the central national archive for climate data and information. The data made accessible includes observation and measurement data, scenario data, quantitative and qualitative data, as well as the measurement data and findings of research projects.
Ag Data Commons provides access to a wide variety of open data relevant to agricultural research. We are a centralized repository for data already on the web, as well as for new data being published for the first time. While compliance with the U.S. Federal public access and open data directives is important, we aim to surpass them. Our goal is to foster innovative data re-use, integration, and visualization to support bigger, better science and policy.
Regionaal Archief Alkmaar (RAA) is a joint arrangement that operates within a large region in the province of Noord-Holland. The first purpose of this arrangement is to fulfill the function of a regional knowledge and information center through the acquisition and preservation of a broad collection of historical sources. The second purpose is to make these sources actively available. It does so according to the Dutch Public Records Act (Archiefwet 1995). At the time of writing, the joint arrangement services include 9 municipalities, namely: Alkmaar, Bergen, Castricum, Den Helder, Heiloo, Hollands Kroon, Schagen, Dijk en Waard and Texel. The arrangement also includes other joint arrangements. These are the GGD Hollands Noorden and Veiligheidsregio Noord-Holland Noord. Also, the RAA keeps the archives of the water authority Hoogheemraadschap Hollands Noorderkwartier and its predecessors. This is being done on the basis of a service agreement. Finally many archives of families, persons of interest, companies and non-governmental organizations are being collected and managed. This is a secondary task of the RAA, but these archives are also being managed on the ground of the Dutch Public Records Act.
Launched in December 2013, Gaia is destined to create the most accurate map yet of the Milky Way. By making accurate measurements of the positions and motions of stars in the Milky Way, it will answer questions about the origin and evolution of our home galaxy. The first data release (2016) contains three-dimensional positions and two-dimensional motions of a subset of two million stars. The second data release (2018) increases that number to over 1.6 Billion. Gaia’s measurements are as precise as planned, paving the way to a better understanding of our galaxy and its neighborhood. The AIP hosts the Gaia data as one of the external data centers along with the main Gaia archive maintained by ESAC and provides access to the Gaia data releases as part of Gaia Data Processing and Analysis Consortium (DPAC).