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Found 33 result(s)
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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.
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.
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.
The Purdue University Research Repository (PURR) provides a virtual research environment and data publication and archiving platform for its campuses. Also supports the publication and online execution of software tools with DataCite DOIs.
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.
The Genomic Observatories Meta-Database (GEOME) is a web-based database that captures the who, what, where, and when of biological samples and associated genetic sequences. GEOME helps users with the following goals: ensure the metadata from your biological samples is findable, accessible, interoperable, and reusable; improve the quality of your data and comply with global data standards; and integrate with R, ease publication to NCBI's sequence read archive, and work with an associated LIMS. The initial use case for GEOME came from the Diversity of the Indo-Pacific Network (DIPnet) resource.
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In the digital collections, you can take a look at the digitized prints from the holdings of the GWLB Hannover free of cost. In special collections, the GWLB unites rare, valuable and unique parts of holdings that are installed as an ensemble. Deposita, unpublished works, donations, acquisition of rare books etc. were and are an important source for the constant growth of the library. These treasures and specialties - beyond their academic value - also contribute substantially to the profile of the GWLB.
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B2SHARE allows publishing research data and belonging metadata. It supports different research communities with specific metadata schemas. This server is provided for researchers of the Research Centre Juelich and related communities.
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Rodare is the institutional research data repository at HZDR (Helmholtz-Zentrum Dresden-Rossendorf). Rodare allows HZDR researchers to upload their research software and data and enrich those with metadata to make them findable, accessible, interoperable and retrievable (FAIR). By publishing all associated research software and data via Rodare research reproducibility can be improved. Uploads receive a Digital Object Identfier (DOI) and can be harvested via a OAI-PMH interface.
WikiPathways was established to facilitate the contribution and maintenance of pathway information by the biology community. WikiPathways is an open, collaborative platform dedicated to the curation of biological pathways. WikiPathways thus presents a new model for pathway databases that enhances and complements ongoing efforts, such as KEGG, Reactome and Pathway Commons. Building on the same MediaWiki software that powers Wikipedia, we added a custom graphical pathway editing tool and integrated databases covering major gene, protein, and small-molecule systems. The familiar web-based format of WikiPathways greatly reduces the barrier to participate in pathway curation. More importantly, the open, public approach of WikiPathways allows for broader participation by the entire community, ranging from students to senior experts in each field. This approach also shifts the bulk of peer review, editorial curation, and maintenance to the community.
Reference anatomies of the brain and corresponding atlases play a central role in experimental neuroimaging workflows and are the foundation for reporting standardized results. The choice of such references —i.e., templates— and atlases is one relevant source of methodological variability across studies, which has recently been brought to attention as an important challenge to reproducibility in neuroscience. TemplateFlow is a publicly available framework for human and nonhuman brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to distribute their resources under FAIR —findable, accessible, interoperable, reusable— principles. TemplateFlow supports a multifaceted insight into brains across species, and enables multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species, thereby contributing to increasing the reliability of neuroimaging results.
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MIDAS is a national research data repository. The aim of MIDAS is to collect, process, store and analyse research data and other relevant information in all fields of knowledge, enabling free, easy and convenient access to the data via the Internet. MIDAS provides services for registered and unregistered users: students, listeners, academics, researchers, scientists, research administrators, other actors of the research and studies ecosystem, and all individuals interested in research data. MIDAS consists of the MIDAS portal and MIDAS user account. The MIDAS portal is a public space accessible to anyone interested in discovering and viewing published research Data and their metadata, whereas MIDAS user account is available to registered users only. MIDAS is managed by Vilnius University.
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Macquarie University's Institutional Research Data Repository (RDR) allows researchers to upload, publish, search and download research data. The RDR promotes collaboration, data sharing and discovery amongst researchers globally according to FAIR data principles. The RDR is based on Figshare for Institutions, which has been specifically tailored to suit the needs of the Macquarie University research community.
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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 U.S. Department of Energy’s (DOE) Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) data archive serves Earth and environmental science data. ESS-DIVE is funded by the Data Management program within the Climate and Environmental Science Division under the DOE’s Office of Biological and Environmental Research program (BER), and is maintained by the Lawrence Berkeley National Laboratory. ESS-DIVE will archive and publicly share data obtained from observational, experimental, and modeling research that is funded by the DOE’s Office of Science under its Subsurface Biogeochemical Research (SBR) and Terrestrial Ecosystem Science (TES) programs within the Environmental Systems Science (ESS) activity. ESS-DIVE was launched in July 2017, and is designed to provide long-term stewardship and use of data from observational, experimental and modeling activities in the DOE in the Subsurface Biogeochemical Research (SBR) and Terrestrial Ecosystem Science (TES) Programs in the Environmental System Science (ESS) activity.
The University of Amsterdam (UvA) and the Amsterdam University of Applied Sciences (AUAS/HvA) cooperate to connect academic research with the insights and experiences from professional practice, and together the UvA and AUAS offer students a wide range of education pathways.
Biological collections are replete with taxonomic, geographic, temporal, numerical, and historical information. This information is crucial for understanding and properly managing biodiversity and ecosystems, but is often difficult to access. Canadensys, operated from the Université de Montréal Biodiversity Centre, is a Canada-wide effort to unlock the biodiversity information held in biological collections.
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.
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This repository accepts data from life science researchers and service units in Sweden. The repository is operated by SciLifeLab, which is the national infrastructure for life science and environmental research in Sweden. This repository replaces NBIS DOI repository: https://doi.org/10.17616/R3CW52
The CERN Open Data portal is the access point to a growing range of data produced through the research performed at CERN. It disseminates the preserved output from various research activities, including accompanying software and documentation which is needed to understand and analyze the data being shared.
The FAIRDOMHub is built upon the SEEK software suite, which is an open source web platform for sharing scientific research assets, processes and outcomes. FAIRDOM (Web Site) will establish a support and service network for European Systems Biology. It will serve projects in standardizing, managing and disseminating data and models in a FAIR manner: Findable, Accessible, Interoperable and Reusable. FAIRDOM is an initiative to develop a community, and establish an internationally sustained Data and Model Management service to the European Systems Biology community. FAIRDOM is a joint action of ERA-Net EraSysAPP and European Research Infrastructure ISBE.
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 Radboud Data Repository (RDR) is an institutional repository for archiving and sharing of data collected, processed, or analyzed by researchers working at or affiliated with the Radboud University (Nijmegen, the Netherlands). The repository allows safe long-term (at least 10 years) storage of large datasets. The RDR promotes findability of datasets by providing a DOI and rich metadata fields and allows researchers to easily manage data access.