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GENCODE is a scientific project in genome research and part of the ENCODE (ENCyclopedia Of DNA Elements) scale-up project. The GENCODE consortium was initially formed as part of the pilot phase of the ENCODE project to identify and map all protein-coding genes within the ENCODE regions (approx. 1% of Human genome). Given the initial success of the project, GENCODE now aims to build an “Encyclopedia of genes and genes variants” by identifying all gene features in the human and mouse genome using a combination of computational analysis, manual annotation, and experimental validation, and annotating all evidence-based gene features in the entire human genome at a high accuracy.
The Humanitarian Data Exchange (HDX) is an open platform for sharing data across crises and organisations. Launched in July 2014, the goal of HDX is to make humanitarian data easy to find and use for analysis. HDX is managed by OCHA's Centre for Humanitarian Data, which is located in The Hague. OCHA is part of the United Nations Secretariat and is responsible for bringing together humanitarian actors to ensure a coherent response to emergencies. The HDX team includes OCHA staff and a number of consultants who are based in North America, Europe and Africa.
The Bremen Core Repository - BCR, for International Ocean Discovery Program (IODP), Integrated Ocean Discovery Program (IODP), Ocean Drilling Program (ODP), and Deep Sea Drilling Project (DSDP) cores from the Atlantic Ocean, Mediterranean and Black Seas and Arctic Ocean is operated at University of Bremen within the framework of the German participation in IODP. It is one of three IODP repositories (beside Gulf Coast Repository (GCR) in College Station, TX, and Kochi Core Center (KCC), Japan). One of the scientific goals of IODP is to research the deep biosphere and the subseafloor ocean. IODP has deep-frozen microbiological samples from the subseafloor available for interested researchers and will continue to collect and preserve geomicrobiology samples for future research.
The Malaria Atlas Project (MAP) brings together researchers based around the world with expertise in a wide range of disciplines from public health to mathematics, geography and epidemiology. We work together to generate new and innovative methods of mapping malaria risk. Ultimately our goal is to produce a comprehensive range of maps and estimates that will support effective planning of malaria control at national and international scales.
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sciencedata.dk is a research data store provided by DTU, the Danish Technical University, specifically aimed at researchers and scientists at Danish academic institutions. The service is intended for working with and sharing active research data as well as for safekeeping of large datasets. The data can be accessed and manipulated via a web interface, synchronization clients, file transfer clients or the command line. The service is built on and with open-source software from the ground up: FreeBSD, ZFS, Apache, PHP, ownCloud/Nextcloud. DTU is actively engaged in community efforts on developing research-specific functionality for data stores. Our servers are attached directly to the 10-Gigabit backbone of "Forskningsnettet" (the National Research and Education Network of Denmark) - implying that up and download speed from Danish academic institutions is in principle comparable to those of an external USB hard drive. Data store for research data allowing private sharing and sharing via links / persistent URLs.
TCIA is a service which de-identifies and hosts a large archive of medical images of cancer accessible for public download. The data are organized as “collections”; typically patients’ imaging related by a common disease (e.g. lung cancer), image modality or type (MRI, CT, digital histopathology, etc) or research focus. Supporting data related to the images such as patient outcomes, treatment details, genomics and expert analyses are also provided when available.
Chapman University Digital Commons is an open access digital repository and publication platform designed to collect, store, index, and provide access to the scholarly and creative output of Chapman University faculty, students, staff, and affiliates. In it are faculty research papers and books, data sets, outstanding student work, audiovisual materials, images, special collections, and more, all created by members of or owned by Chapman University. The datasets are listed in a separate collection.
The Australian Drosophila Ecology and Evolution Resource (ADEER) from the Hoffmann lab and other contributors is a nationally significant life science collection. The Drosophila Clinal Data Collection contains data on populations along the eastern coast of Australia. It remains an excellent resource for understanding past and future evolutionary responses to climate change. The Drosophila Genomic Data Collection hosts Drosophila genomes sequenced as part of the Genomic Basis for Adaptation to Climate Change Project. 23 genomes have been sequenced as part of this project. Currently assemblies and annotations are available for Drosophila birchii, D. bunnanda, D. hydei, and D. repleta. The Drosophila Species Distribution Data Collection contains distribution data of nine drosophilid species that have been collected in Australia by the Hoffmann lab and other research groups between 1924 and 2005. More than 300 drosophilid species have been identified in the tropical and temperate forests located on the east coast of Australia. Many species are restricted to the tropics, a few are temperate specialists, and some have broad distributions across climatic regions. Their varied distribution along the tropical - temperate cline provide a powerful tool for studying climate adaptation and species distribution limits.
The SICAS Medical Image Repository is a freely accessible repository containing medical research data including medical images, surface models, clinical data, genomics data and statistical shape models. The data can freely be organized and shared on SMIR and made publicly accessible with a DOI. Dedicated data sets are organized as collections of anatomical regions (e.g Cochlea). The data can be filtered using a modular search and accessed on the web or through the SMIR API.