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http://www.fastfig.com/

FastFig is a computational tool that lets users seamlessly combine numerical models so that using science is easier than ever. Sign up now for our limited beta release to be among the first to experience FastFig. At Fig Labs, Inc. we develop solutions for scientific data management, analysis and distribution. We specialize in scientific computing solutions and information management systems. Currently, we are developing a web application called FastFig that helps engineers and scientists to collaboratively create, solve and share numerical models. With FastFig you can: * Perform numerical analysis on everything from simple equations to complex models using our dynamic online interface. * Share the functions that you have built along with documentation so that others can use your function. * Search and browse functions to discover what people are calculating all over the world.

Proper citation: FastFig (RRID:SCR_005915) Copy   

•   Source: SciCrunch Registry

http://researchdata.4tu.nl/en/home/

Multidisciplinary data repository for a consortium of universities in the Netherlands housing over datasets with a focus on scientific and technical data. Most data were produced by Dutch researchers including datasets from doctoral research. Users can deposit up to 1G by completing an upload form. Collection development foci include applied sciences, biomedical technology, earth sciences, and technology and construction. 4TU.Datacentrum is a collaboration of the libraries of the three leading technical universities - Delft University of Technology, Eindhoven University of Technology and the University of Twente.

Proper citation: 4TU.Datacentrum (RRID:SCR_006295) Copy   

•   Source: SciCrunch Registry

http://galaxyproject.org/

Open, web-based platform providing bioinformatics tools and services for data intensive genomic research. Platform may be used as a service or installed locally to perform, reproduce, and share complete analyses. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Community has created Galaxy instances in many different forms and for many different applications including Galaxy servers, cloud services that support Galaxy instances, and virtual machines and containers that can be easily deployed for your own server.The Galaxy team is a part of BX at Penn State, and the Biology and Mathematics and Computer Science departments at Emory University.Training Infrastructure as a Service (TIaaS) is a service offered by some UseGalaxy servers to specifically support training use cases.

Proper citation: Galaxy (RRID:SCR_006281) Copy   

•   Source: SciCrunch Registry

http://datastar.mannlib.cornell.edu/

A single library software prototype transitioning to a to an open-source platform ready for adoption and extension at other institutions wishing to provide research data sharing and discovery services. Datastar''''s ability to expose metadata about research datasets in a standard semantic format called Linked Data will be enhanced to support selective interchange of related information with VIVO, an open-source semantic researcher networking tool gaining prominence through adoption at multiple U.S. universities, in the federal government, and internationally.

Proper citation: DataStaR (RRID:SCR_006381) Copy   

•   Source: SciCrunch Registry

http://www.neuroinf.jp/

The Japan Node of the INCF coordinates neuroinformatics activities within Japan and represents Japanese efforts in INCF. This site provides information about Japanese neuroinformatics platforms (NI Platforms) and the techniques and tools available from the International Neuroinformatics Coordinating Facility (INCF). The Neuroinformatics Japan Center (NIJC) will also supply techniques and tools developed at RIKEN BSI and at other research groups in Japan. INCF expects each national node to: 1. Actively formulate and implement the INCF Work Programs, 2. Coordinate and facilitate local neuroinformatics research activities at the national level, 3. Encourage neuroinformatics data sharing that conforms with INCF standards, and 4. Promote neuroinformatics development that supports the goals of INCF. The Neuroinformatics Japan Center (NIJC) represents the Japan Node. Together with the Japan Node Committee and the Platform Subcommittees, we promote domestic activities of neuroinformatics. Platform Subcommittee members collaborate to develop databases that are available for use on the website. Standing at the intersection of neuroscience and information science, the field of neuroinformatics develops the tools to house, share and analyze neuroscientific data, and to create computational models of brain. NIJC supports researchers developing and maintaining neuroscience databases, provides a portal for these databases and Neuroinformatics, and is designing the infrastructure for Neuroinformatics. It is also developing database technologies, and facilitates cooperation and distribution of the information stored in those databases. The activities of the Japan Node * Shaping domestic neuroinformatics research and directions (Japan Node Committee) * Advising on Intellectual Property Rights and protecting experimental subjects (Japan Node Committee) * Developing and publishing brain science databases (Platform Subcommittee) * Coordinating database management (Platform Subcommittee) * Disseminating neuroinformatics information via the web portal * Developing the infrastructure for brain science information and neuroinformatics * Supporting the development and diffusion of neuroinformatics technology

Proper citation: INCF Japan Node (RRID:SCR_006569) Copy   

•   Source: SciCrunch Registry

http://www.gigasciencejournal.com/

An online open-access open-data journal, publishing ''big-data'' studies from the entire spectrum of life and biomedical sciences whose publication format links standard manuscript publication with its affiliated database, GigaDB, that hosts all associated data, provides data analysis tools, cloud-computing resources, and a DOI assignment to every dataset. GigaScience covers not just ''omic'' type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale sharable data. Supporting the open-data movement, they require that all supporting data and source code be publicly available in a suitable public repository and/or under a public domain CC0 license in the BGI GigaScience database. Using the BGI cloud as a test environment, they also consider open-source software tools / methods for the analysis or handling of large-scale data. When submitting a manuscript, please contact them if you have datasets or cloud applications you would like them to host. To maximize data usability submitters are encouraged to follow best practice for metadata reporting and are given the opportunity to submit in ISA-Tab format.

Proper citation: GigaScience (RRID:SCR_006565) Copy   

•   Source: SciCrunch Registry

http://dataup.cdlib.org/

An open source tool to help researchers document, manage, and archive their tabular data that integrates with Microsoft Excel. The tool will parse .xlsx or .csv file to detect the presence of potential issues that do not comply with data management best practices, assign a unique identifier to a data set and deposit it within the DataONE repository system.

Proper citation: DataUp (RRID:SCR_006905) Copy   

•   Source: SciCrunch Registry

http://btris.nih.gov

Provides NIH clinical investigators with access to identifiable data for the subjects on their own active protocols, while providing all NIH investigators with access to de-identified data across all protocols. BTRIS provides users with advanced search, filtering, and aggregation methods to create data sets to support ongoing studies and stimulate ideas for new research. BTRIS is two distinct but interrelated applications, BTRIS Data Access and BTRIS Preferences. * BTRIS Data Access is the data repository where principal investigators or their designee create reports on their active protocols with identified subject data. Reports include the IRB Inclusion Enrollment Report, demographics, patient lists, laboratory and microbiology results, vital signs, medication orders and administration, diagnoses, and radiology reports (with links to images in the CC PACS system). * BTRIS Preferences is a Web based application that allows principal investigators or their designees to verify subject enrollment in their protocol(s). This ensures that reports created in BTRIS Data Access include all subjects. It also allows the principal investigator to designate an alternate investigator from the protocol to manage subject enrollment and create reports in BTRIS Data Access. BTRIS contains subject data from CRIS/MIS (the Clinical Center Medical Information Systems) and research data from NIAID (Crimson), NIAAA, and NCI. Data are available from 1976 to the present.

Proper citation: BTRIS: NIH Biomedical Translational Research Information System (RRID:SCR_006838) Copy   

•   Source: SciCrunch Registry

http://www.1000genomes.org/

International collaboration producing an extensive public catalog of human genetic variation, including SNPs and structural variants, and their haplotype contexts, in an effort to provide a foundation for investigating the relationship between genotype and phenotype. The genomes of about 2500 unidentified people from about 25 populations around the world were sequenced using next-generation sequencing technologies. Redundant sequencing on various platforms and by different groups of scientists of the same samples can be compared. The results of the study are freely and publicly accessible to researchers worldwide. The consortium identified the following populations whose DNA will be sequenced: Yoruba in Ibadan, Nigeria; Japanese in Tokyo; Chinese in Beijing; Utah residents with ancestry from northern and western Europe; Luhya in Webuye, Kenya; Maasai in Kinyawa, Kenya; Toscani in Italy; Gujarati Indians in Houston; Chinese in metropolitan Denver; people of Mexican ancestry in Los Angeles; and people of African ancestry in the southwestern United States. The goal Project is to find most genetic variants that have frequencies of at least 1% in the populations studied. Sequencing is still too expensive to deeply sequence the many samples being studied for this project. However, any particular region of the genome generally contains a limited number of haplotypes. Data can be combined across many samples to allow efficient detection of most of the variants in a region. The Project currently plans to sequence each sample to about 4X coverage; at this depth sequencing cannot provide the complete genotype of each sample, but should allow the detection of most variants with frequencies as low as 1%. Combining the data from 2500 samples should allow highly accurate estimation (imputation) of the variants and genotypes for each sample that were not seen directly by the light sequencing. All samples from the 1000 genomes are available as lymphoblastoid cell lines (LCLs) and LCL derived DNA from the Coriell Cell Repository as part of the NHGRI Catalog. The sequence and alignment data generated by the 1000genomes project is made available as quickly as possible via their mirrored ftp sites. ftp://ftp.1000genomes.ebi.ac.uk ftp://ftp-trace.ncbi.nlm.nih.gov/1000genomes

Proper citation: 1000 Genomes: A Deep Catalog of Human Genetic Variation (RRID:SCR_006828) Copy   

•   Source: SciCrunch Registry

http://platform.visiome.neuroinf.jp/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 4, 2023.Analytical tools. Archive files may be written in any format and may include explanatory figures, program sources, readme files, and other related files. The readme file describes the purpose and usage of the archive file. This data sharing framework allows users to improve the reproducibility of simulations. Users can browse the platform contents via branch sites (A catalogue of illusions, Visitope), which introduce user friendly view of items such as basic images and original artworks of visual illusions with high resolution. The items in Visiome Platform are useful not only for reproducing the published results, but also for advancing and expanding the research in Vision Science.

Proper citation: Visiome Platform (RRID:SCR_003049) Copy   

•   Source: SciCrunch Registry

http://www.xnat.org

Software platform designed to facilitate common management and productivity tasks for neuroimaging and associated data.

Proper citation: XNAT - The Extensible Neuroimaging Archive Toolkit (RRID:SCR_003048) Copy   

•   Source: SciCrunch Registry

http://datasharing.net

The U.S. National Institutes of Health Final NIH Statement on Sharing Research Data (NIH-OD-03-032) is now in effect. It specifies that all high-direct-cost NIH grant applications include plans for sharing of research data. To support and encourage collegial, enabling, and rewarding data sharing for neuroscience and beyond, the Laboratory of Neuroinformatics at Weill Medical College of Cornell University has established this site. A source of, and portal to, tools and proposals supporting the informed exchange of neuroscience data.

Proper citation: Datasharing.net (RRID:SCR_003312) Copy   

•   Source: SciCrunch Registry

http://www.humanvariomeproject.org/

Project facilitating the establishment and maintenance of standards systems and infrastructure for the worldwide collection and sharing of all genetic variations effecting human disease. The Human Variome Project produces two categories of recommendations: HVP Standards and HVP Guidelines. HVP Standards are those systems, procedures and technologies that the Human Variome Project Consortium has determined should be used by the community. These carry more weight than the less prescriptive HVP Guidelines, which cover those systems, procedures and technologies that the Human Variome Project Consortium has determined would be beneficial for the community to adopt. HVP Standards and Guidelines are central to supporting the work of the Human Variome Project Consortium and cover a wide range of fields and disciplines, from ethics to nomenclature, data transfer protocols to collection protocols from clinics. They can be thought of as both technical manuals and scientific documents, and while the impact of HVP Standards and Guidelines differ, they are both generated in a similar fashion. A document has been generated both as a guide for those collecting and distributing data and for those developing policy. Items should include those generated by HGVS/HVP collaborators as well as those generated by groups of individual Societies and Standards bodies in all relevant fields worldwide.

Proper citation: Human Variome Project (RRID:SCR_003492) Copy   

•   Source: SciCrunch Registry

http://connectome.ch/

A Python-based open source toolkit for magnetic resonance connectome mapping, data management, sharing, visualization and analysis. The toolkit includes the connectome mapper (a full DMRI processing pipeline), a new file format for multi modal data and metadata, and a visualization application.

Proper citation: Connectome Mapping Toolkit (RRID:SCR_001644) Copy   

•   Source: SciCrunch Registry

http://dynamicbrain.neuroinf.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 19. 2022. Platform to promote studies on dynamic principles of brain functions through unifying experimental and computational approaches in cellular, local circuit, global network and behavioral levels. Provides services such as data sets, popular research findings and articles and current developments in field. This site has been archived since FY2019 and is no longer updated.

Proper citation: Dynamic Brain Platform (RRID:SCR_001754) Copy   

•   Source: SciCrunch Registry

http://tbi.ci.uchicago.edu/

Project to define a roadmap for diffusion MR imaging of traumatic brain imaging and design an infrastructure to implement the recommendations and tested to ensure feasibility, disseminate results, and facilitate deployment and adoption. The research roadmap and infrastructure development will concentrate on three areas: 1) standardization of diffusion imaging methodology, 2) trial design and patient selection for acute or chronic therapy, and 3) development of multi-center collaborations and repositories for evaluating whether advanced diffusion imaging does improve decision making and TBI patients' outcomes. # DTI MRI reproducability: One of the major areas of investigation in this project is to study the reproducibility of data acquisition and image analysis algorithms. Understanding reproducibility defines a base level of deviation from which scans can be analyzed with statistical significance. As part of this work they are also developing site qualification criteria with the intention of setting limits on the MR system minimal performance for acceptable use in TBI evaluation. # Infrastructure for image storage, analysis and visualization: There is a continuing need to refine and extend software methods for diffusion MRI data analysis and visualization. Not only to translate tools into clinical practice, but also to encourage continuation of the innovation and development of new tools and techniques. To deliver upon these goals they are designing and implementing a storage and computational infrastructure to provide access to shared datasets and intuitive interfaces for analysis and visualization through a variety of tools. A strong emphasis has been placed on providing secure data sharing and the ability to add community defined common data elements. The infrastructure is built upon a Software-as-a-Service model, in which tools are hosted and managed remotely allowing users access through well-defined interfaces. The final service will also facilitate composition or orchestration of workflows composed of different analysis and processing tasks (for example using LONI or XNAT pipelines) with the ultimate goal of providing automated no-click evaluations of diffusion MRI data. # Tool development: The final aspect of this project aims to facilitate and encourage tool development and contribution. By providing access to open datasets, they will create a platform on which tool developers can compare and improve and their tools. When tools are sufficiently mature they can be exposed in the infrastructure mentioned above and used by researchers and other developers.

Proper citation: Diffusion MRI of Traumatic Brain Injury (RRID:SCR_001637) Copy   

•   Source: SciCrunch Registry

http://www.myexperiment.org/

Community repository and virtual research environment where scientists can safely publish their workflows and experiment plans, share them with groups and find and use those of others. Workflows, other digital objects and collections (called Packs) can be swapped, sorted and searched. It supports Linked data, has a SPARQL Endpoint and REST API and is based on an open source Ruby on Rails codebase. Scientific workflows in various formats can be uploaded. Specific support is provided for Taverna workflows for which the system displays relevant metadata, components and visual previews, that are retrieved directly from workflow files. Version history for workflows is collected. This feature allows the contributor to keep previous versions of the workflow available, when the latest one is uploaded. This brings additional benefit for the users by allowing them to view the development stages of the workflow towards its latest implementation.

Proper citation: myExperiment (RRID:SCR_001795) Copy   

•   Source: SciCrunch Registry

https://datashare.nida.nih.gov

Website which allows data from completed clinical trials to be distributed to investigators and public. Researchers can download de-identified data from completed NIDA clinical trial studies to conduct analyses that improve quality of drug abuse treatment. Incorporates data from Division of Therapeutics and Medical Consequences and Center for Clinical Trials Network.

Proper citation: NIDA Data Share (RRID:SCR_002002) Copy   

•   Source: SciCrunch Registry

https://portal.gdc.cancer.gov/

A unified data repository of the National Cancer Institute (NCI)'s Genomic Data Commons (GDC) that enables data sharing across cancer genomic studies in support of precision medicine. The GDC supports several cancer genome programs at the NCI Center for Cancer Genomics (CCG), including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI). The GDC Data Portal provides a platform for efficiently querying and downloading high quality and complete data. The GDC also provides a GDC Data Transfer Tool and a GDC API for programmatic access.

Proper citation: Genomic Data Commons Data Portal (GDC Data Portal) (RRID:SCR_014514) Copy   

•   Source: SciCrunch Registry

http://www.genesis-sim.org/hbp/

Software system to assist computational neuroscientists in interacting with databases of models and with neural simulation packages such as GENESIS. There are three components in the system: a user interface, a database server, and a global registry and repository. The collection of software tools with a graphical user interface enables users to interact over the WWW with databases of models and data. It provides facilities for searching multiple remote databases for model components based on various criteria; visualizing the characteristics of the components retrieved; creating new components, either from scratch or derived from existing models; combining components into new models; linking models to experimental data as well as online publications; and interacting with simulation packages such as GENESIS to simulate the new constructs. Although the initial version uses GENESIS as the simulator, the design permits the use of multiple simulation systems, with or without the use of a database. This allows modeling at multiple levels of scale from the molecular level, through the subcellular (e.g. ion channel), single cell, and network levels, to the systems level (e.g. relating models to fMRI studies). The system is intended to help users create and organize models and to interact with databases of models and neuronal simulation software. More specifically, the Modeler's Workspace is designed to provide the following: * Search and retrieval facilities for interacting with databases of models and other information; * Facilities for creating, editing and visualizing the characteristics of models; * Facilities for combining model components together and translating them into formats suitable for simulation systems such as GENESIS and NEURON; * Facilities for managing a personal database where a user can collect models and other objects; and * Collaboration facilities for connecting one or more users together, to allow them to simultaneously edit objects in a shared database and communicate with each other using real-time chat. The Modeler's Workspace is written in Java for portability and extensibility. It is modular in design and uses pluggable components for supporting different data formats, which means that new data types can be supported by loading an appropriate plug-in. To increase the probability that the Modeler's Workspace will be compatible with future databases and tools, they are using the eXtensible Markup Language (XML) as the interchange format for communicating with databases.

Proper citation: GENESIS Neural Database and Modelers Workspace (RRID:SCR_002357) Copy   

•   Source: SciCrunch Registry