Search Results for "data science" - Page 20

Here we introduce a Genomic Epidemiology Ontology (GenEpiO) that covers vocabulary necessary to identify, document and research food-borne pathogens and associated outbreaks. We envision various subdomains including genomic laboratory testing, specimen and isolate metadata, and epidemiological case investigations. The project files are currently hosted at https://github.com/GenEpiO/genepio/ . Here we provide the genepio-consortium@lists.sourceforge.net listserve. Terms for these subdomains...

hemcuveGUI is a graphical interface for HEMCUVE++ (see http://tsc.unex.es/~llandesa/page17/page17.html). More specifically, hemcuveGUI lets you · to collect all the data required by HEMCUVE++, · to manage a materials database, · to call the external executable HEMCUVE++ and · to visualize its results.

GeoSciPy is a set of C and Python functions that provide an API for all aspects of Geoscience and Remote Sensing Applications. Functions include reading/writing of many data formats, images, vectors, ..., and processing of images, vectors, ... including map projections, ... The library is written C, using HDF5 as the file format for storage of all remotely-sensed data and ancillary data, as well as processed data. The C functions assume a particular format for the HDF5 file contents, and...

In Green Cloud here is a central global server which does the work of global database management along with providing complex analysis services for the users (farmers, scientists) through the client machines and the public access web portal. The existing agricultural databases throughout the world can also be used for inference purposes and their data may also be updated by our server. The meteorological servers can be accessed for climate data.This server-client architecture mandatory in...

This project seeks to integrate knowledge, methods and data from various disciplines through the design and implementation of a system that allows for communications between languages and different platforms.

This project collect several data mining methods, such as: 1) clustering methods; 2) biclustering methods; 3) FCA methods and etc.