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Found 12 result(s)

The SuiteSparse Matrix Collection

UFL, Sparse Matrix Collection

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The SuiteSparse Matrix Collection is a large and actively growing set of sparse matrices that arise in real applications. The Collection is widely used by the numerical linear algebra community for the development and performance evaluation of sparse matrix algorithms. It allows for robust and repeatable experiments. Its matrices cover a wide spectrum of domains, include those arising from problems with underlying 2D or 3D geometry (as structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations) and those that typically do not have such geometry (optimization, circuit simulation, economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, power networks, and other networks and graphs.

WorldData.AI

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WorldData.AI comes with a built-in workspace – the next-generation hyper-computing platform powered by a library of 3.3 billion curated external trends. WorldData.AI allows you to save your models in its “My Models Trained” section. You can make your models public and share them on social media with interesting images, model features, summary statistics, and feature comparisons. Empower others to leverage your models. For example, if you have discovered a previously unknown impact of interest rates on new-housing demand, you may want to share it through “My Models Trained.” Upload your data and combine it with external trends to build, train, and deploy predictive models with one click! WorldData.AI inspects your raw data, applies feature processors, chooses the best set of algorithms, trains and tunes multiple models, and then ranks model performance.

International Satellite Cloud Climatology Project

ISCCP

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The International Satellite Cloud Climatology Project (ISCCP) is a database of intended for researchers to share information about cloud radiative properties. The data sets focus on the effects of clouds on the climate, the radiation budget, and the long-term hydrologic cycle. Within the data sets the data entries are broken down into entries of specific characteristics based on temporal resolution, spatial resolution, or temporal coverage.

SURFRAD

Surface Radiation

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To understand the global surface energy budget is to understand climate. Because it is impractical to cover the earth with monitoring stations, the answer to global coverage lies in reliable satellite-based estimates. Efforts are underway at NASA and universities to develop algorithms to do this, but such projects are in their infancy. In concert with these ambitious efforts, accurate and precise ground-based measurements in differing climatic regions are essential to refine and verify the satellite-based estimates, as well as to support specialized research. To fill this niche, the Surface Radiation Budget Network (SURFRAD) was established in 1993 through the support of NOAA's Office of Global Programs.

BAliBASE

Benchmark Alignment dataBASE

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A collection of high quality multiple sequence alignments for objective, comparative studies of alignment algorithms. The alignments are constructed based on 3D structure superposition and manually refined to ensure alignment of important functional residues. A number of subsets are defined covering many of the most important problems encountered when aligning real sets of proteins. It is specifically designed to serve as an evaluation resource to address all the problems encountered when aligning complete sequences. The first release provided sets of reference alignments dealing with the problems of high variability, unequal repartition and large N/C-terminal extensions and internal insertions. Version 2.0 of the database incorporates three new reference sets of alignments containing structural repeats, trans-membrane sequences and circular permutations to evaluate the accuracy of detection/prediction and alignment of these complex sequences. Within the resource, users can look at a list of all the alignments, download the whole database by ftp, get the "c" program to compare a test alignment with the BAliBASE reference (The source code for the program is freely available), or look at the results of a comparison study of several multiple alignment programs, using BAliBASE reference sets.

PSnpBind

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PSnpBind is a large database of protein–ligand complexes covering a wide range of binding pocket mutations and small molecules’ landscape. This database can be used as a source of data for different types of studies, for example, developing machine learning algorithms to predict protein–ligand affinity or mutation's effect on it which requires an extensive amount of data with a wide coverage of mutation types and small molecules. Also, studies of protein-ligand interactions and conformer orientation changes across different mutated versions of a protein can be established using data from PSnpBind.

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.

studyforrest

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This project is an open invitation to anyone and everyone to participate in a decentralized effort to explore the opportunities of open science in neuroimaging. We aim to document how much (scientific) value can be generated from a data release — from the publication of scientific findings derived from this dataset, algorithms and methods evaluated on this dataset, and/or extensions of this dataset by acquisition and incorporation of new data. The project involves the processing of acoustic stimuli. In this study, the scientists have demonstrated an audiodescription of classic "Forrest Gump" to subjects, while researchers using functional magnetic resonance imaging (fMRI) have captured the brain activity of test candidates in the processing of language, music, emotions, memories and pictorial representations.In collaboration with various labs in Magdeburg we acquired and published what is probably the most comprehensive sample of brain activation patterns of natural language processing. Volunteers listened to a two-hour audio movie version of the Hollywood feature film "Forrest Gump" in a 7T MRI scanner. High-resolution brain activation patterns and physiological measurements were recorded continuously. These data have been placed into the public domain, and are freely available to the scientific community and the general public.

ChemSpider

Search and share chemistry

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ChemSpider is a free chemical structure database providing fast access to over 58 million structures, properties and associated information. By integrating and linking compounds from more than 400 data sources, ChemSpider enables researchers to discover the most comprehensive view of freely available chemical data from a single online search. It is owned by the Royal Society of Chemistry. ChemSpider builds on the collected sources by adding additional properties, related information and links back to original data sources. ChemSpider offers text and structure searching to find compounds of interest and provides unique services to improve this data by curation and annotation and to integrate it with users’ applications.

DTU Bioinformatics

Center for Biological Sequence Analysis (formerly)

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<<<!!!<<< DTU Bioinformatics was closed on 31 December 2018 as part of a reorganization at DTU >>>!!!>>>

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.

ECCAD - the GEIA database

Emissions of atmospheric Compounds & Compilation of Ancillary Data

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The main goal of the ECCAD project is to provide scientific and policy users with datasets of surface emissions of atmospheric compounds, and ancillary data, i.e. data required to estimate or quantify surface emissions. The supply of ancillary data - such as maps of population density, maps of fires spots, burnt areas, land cover - could help improve and encourage the development of new emissions datasets. ECCAD offers: Access to global and regional emission inventories and ancillary data, in a standardized format Quick visualization of emission and ancillary data Rationalization of the use of input data in algorithms or emission models Analysis and comparison of emissions datasets and ancillary data Tools for the evaluation of emissions and ancillary data ECCAD is a dynamical and interactive database, providing the most up to date datasets including data used within ongoing projects. Users are welcome to add their own datasets, or have their regional masks included in order to use ECCAD tools.