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Found 7 result(s)

BioVel

Biodiversity Virtual e-Laboratory

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<<<!!!<<< This repository is no longer available. >>>!!!>>> BioVeL is a virtual e-laboratory that supports research on biodiversity issues using large amounts of data from cross-disciplinary sources. BioVeL supports the development and use of workflows to process data. It offers the possibility to either use already made workflows or create own. BioVeL workflows are stored in MyExperiment - Biovel Group http://www.myexperiment.org/groups/643/content. They are underpinned by a range of analytical and data processing functions (generally provided as Web Services or R scripts) to support common biodiversity analysis tasks. You can find the Web Services catalogued in the BiodiversityCatalogue.

Zenodo

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ZENODO builds and operates a simple and innovative service that enables researchers, scientists, EU projects and institutions to share and showcase multidisciplinary research results (data and publications) that are not part of the existing institutional or subject-based repositories of the research communities. ZENODO enables researchers, scientists, EU projects and institutions to: easily share the long tail of small research results in a wide variety of formats including text, spreadsheets, audio, video, and images across all fields of science. display their research results and get credited by making the research results citable and integrate them into existing reporting lines to funding agencies like the European Commission. easily access and reuse shared research results.

OpenML

Open Machine Learning

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OpenML is an open ecosystem for machine learning. By organizing all resources and results online, research becomes more efficient, useful and fun. OpenML is a platform to share detailed experimental results with the community at large and organize them for future reuse. Moreover, it will be directly integrated in today’s most popular data mining tools (for now: R, KNIME, RapidMiner and WEKA). Such an easy and free exchange of experiments has tremendous potential to speed up machine learning research, to engender larger, more detailed studies and to offer accurate advice to practitioners. Finally, it will also be a valuable resource for education in machine learning and data mining.

Leiden Open Variation Database

LOVD

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LOVD portal provides LOVD software and access to a list of worldwide LOVD applications through Locus Specific Database list and List of Public LOVD installations. The LOVD installations that have indicated to be included in the global LOVD listing are included in the overall LOVD querying service, which is based on an API.

KONECT

Koblenz Network Collection

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This is the KONECT project, a project in the area of network science with the goal to collect network datasets, analyse them, and make available all analyses online. KONECT stands for Koblenz Network Collection, as the project has roots at the University of Koblenz–Landau in Germany. All source code is made available as Free Software, and includes a network analysis toolbox for GNU Octave, a network extraction library, as well as code to generate these web pages, including all statistics and plots. KONECT contains over a hundred network datasets of various types, including directed, undirected, bipartite, weighted, unweighted, signed and rating networks. The networks of KONECT are collected from many diverse areas such as social networks, hyperlink networks, authorship networks, physical networks, interaction networks and communication networks. The KONECT project has developed network analysis tools which are used to compute network statistics, to draw plots and to implement various link prediction algorithms. The result of these analyses are presented on these pages. Whenever we are allowed to do so, we provide a download of the networks.

COEMS Open Data

Continuous Observation of Embedded Multicore Systems Data

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<<<!!!<<< This repository is no longer available. >>>!!!>>>

Gaia@AIP

Gaia@AIP Services

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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).