Open Source Linux Data Analytics Tools - Page 5

Automated Microarray Data Analysis

An on-line GIS-enabled infrastructure for spatially integrated social science. A web-based open source e-research facility for social-spatial data analysis that incorporates GIS-enabled visualisation (mapping and classification) and statistical modelling capabilities.

Advanced Online Surveys for a sophisticated market research and other kinds of professional data collection by using the world wide web. The potential for charmelionic data analysis and aquisition of additional data distinguishes it from other products.

This library contains various Apache Flink connectors to connect to AWS data sources and sinks. This repository contains various Apache Flink connectors to connect to AWS Kinesis data sources and sinks. Flink maintain backwards compatibility for the Sink interface used by the Firehose Producer. This project is compatible with Flink 1.x, there is no guarantee it will support Flink 2.x should it release in the future. An Apache Flink application is a Java or Scala application that is created with the Apache Flink framework. You author and build your Apache Flink application locally. Applications primarily use either the DataStream API or the Table API. The other Apache Flink APIs are also available for you to use, but they are less commonly used in building streaming applications. The Apache Flink DataStream API programming model is based on two components.

An Introduction to Data Analysis in R [Book] A guide for learning the basic tools on data anaylisis: process, visualize and learn from your data using R programming. This repository holds the necessary data sets for the book "An introduction to Data Analysis in R", to be published by Springer series Use R!. The book can be purchased in XXX. The book is meant as an introductory guide to manipulate data sets in the Big Data paradigm. One of the main goals of this book is to take the analyst from the very first moment when she/he contacts with data to the final conclusion and presentation of results of analysis. We take into account the variety of fields where data analysis occurs nowadays. We pay special attention to the different ways to obtain data and how to make it manageable before starting the analysis. The data analysis includes most of the basic visualization options and some advanced extra options. Finally, basic statistics is used to learn from the processed data.

This project is a package of different algorithms developped for cortical-based functional data analysis.

Apache Doris is a modern MPP analytical database product. It can provide sub-second queries and efficient real-time data analysis. With it's distributed architecture, up to 10PB level datasets will be well supported and easy to operate. Apache Doris can meet various data analysis demands, including history data reports, real-time data analysis, interactive data analysis, and exploratory data analysis. Make your data analysis easier! Support standard SQL language, compatible with MySQL protocol. The main advantages of Doris are the simplicity (of developing, deploying and using) and meeting many data serving requirements in a single system. Doris mainly integrates the technology of Google Mesa and Apache Impala, and it is based on a column-oriented storage engine and can communicate by MySQL client.

Apache InLong is a one-stop integration framework for massive data that provides automatic, secure and reliable data transmission capabilities. InLong supports both batch and stream data processing at the same time, which offers great power to build data analysis, modeling and other real-time applications based on streaming data. InLong (应龙) is a divine beast in Chinese mythology who guides the river into the sea, and it is regarded as a metaphor of the InLong system for reporting data streams. InLong was originally built at Tencent, which has served online businesses for more than 8 years, to support massive data (data scale of more than 80 trillion pieces of data per day) reporting services in big data scenarios. The entire platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting, and Management, so that the business only needs to provide data sources, data service quality, data landing clusters and data landing formats.

A collection of scripts and functions for the analysis of atom probe tomography data. Currently, this includes: standard file operations, plotting mass spectra, mass spectra analysis and mass peak overlap solving. For more information see the Wiki: https://sourceforge.net/p/atomprobelab/wiki/Home/ Quick overview video: https://youtu.be/8rJHce7M5UU More in-depth video using the APT school demos live script: https://www.youtube.com/watch?v=cyrLHCzZwuw For details of the maximum likelihood method used to solve peak overlaps see the following papers: https://doi.org/10.1017/S1431927616012782 and https://doi.org/10.1017/S1431927618016276 for the quantification of the uncertainty. If you are looking for Dan Haley's "weights" program, go here https://sourceforge.net/projects/apttools/files/extras/

BigPlg allows the collection of the IPv4 Internet routing table which may be viewed by service providers and/or ISP customers to validate IP routing across autonomous systems. Routing updates may also have attributes aliased to become (graph) readable. This function exists to allow the exporting of RIB updates to Splunk, or any such data collector. A beneficial side-effect of collecting BGP routing information is the ability to track BGP updates/events local or remote(upstream). To analyze events Splunk is supported. A JSON/Socket API is currently under development. Custom graphs can be easily implemented if taken the time to do so. There's certainly no need to rely on Splunk if one developed another way of displaying data. This project is meant to be nothing more than a route collector, and it's interface is solely CLI(command line interface). This project does not have graph data "out of the box".

BIRBU (BIological Relationship BUilder) is a Java tool for microarray gene expression data analysis. BIRBU identifies biologically significant relationship between genes using microarray data and prior knowledge on relationships between genes.

-What is BRIC-seq? BRIC-seq, short for 5'-bromo-uridine (BrU) immunoprecipitation chase-deep sequencing analysis, is a novel method in Next Generation Sequencing (NGS) to determine the RNA stability of each transcript. It employs metabolic labeling by BrU of newly synthesized RNAs to chase chronological decreases of BrU-labeled RNA (Pulse-chase labeling), followed by immunoprecipitation with antibody for BrU, construction of cRNA library for High-throughput sequencing and computational data analysis. -What is BriPy? BriPy package is a BRIC-seq data analysis tool written in Python. It can calculate RNA half-life for each transcript and draw graphs describing the kinetics of RNA decay. In the future, it will provide several useful modules(e.g. calculating RPKM of each transcript from BAM file or ELAND output file). -Prerequisites To use BriPy package, you will need Python and the following extension packages. Python2.7 or higher NumPy SciPy Matplotlib

A collection of tools for working with the comparative data analysis ontology including import/export facilities for common phylogenetic file formats, and also a triple-store framework.

CNV-WebStore is an integrated platform to analyse, store, visualise and interpret CopyNumber Variation data. Analysis is supported for Illumina data, all CNV-reports and raw data can be imported after third-party analysis. Contact system admin at http://cnv-webstore.uantwerpen.be for installation scripts

* Introduction: - This Module is a Library based Perl code. - The library provide: - Convert INFIX expressions to POSTFIX; - Convert POSTFIX expressions to INFIX and; - Perform POSTFIX context validations. - Context validation can be implemented in item selection routines or data context validation, when it is possible to identify data to be selected or ignored in some data analysis process. * Support: The Support Service is FREE. Do you need support? Open a ticket and I will get back to you as soon as possible.

CT4AM is a cross-platform toolkit for 3D-image processing in the domain of additive manufacturing for toolpath visualisation or correlation to tomographic data and related materials science. It is written in C++ and provides small, problem-specific applications for viewing, analysing and visualising of volumetric data from micro-CT scans with respect to the complex process parameters and characteristics of additive manufacturing. Moreover, data analysis and processing of G-code are key assets of the project. The main objective is not to provide yet another complex application for volume data visualization and medical image processing. Instead, CT4AM is a collection of small and simple apps that have already been successfully used to solve various problems in materials science and process visualization. You can either execute these tools directly or use their source codes to develop your own application for toolpath analysis or correlation.

CalPyLab is an open source data acquisition and (light) analysis software suite for scientific laboratories. The goal of this project is to provide a lightweight replacement for lab-view, to provide a quick and easy way to connect to your equipment.

The Canopy project is an initiative to merge and expand the functionality of Perl-speaks-NONMEM (PsN), Census, Xpose and PopED. The goal is to produce a coherent, inclusive and convenient platform for pharmacometric data analysis.

CPFP provides a pipeline for the analysis of MS/MS proteomic data, targeted at the needs of central proteomics facilities. == Project Status - Updated January 3rd 2019 == CPFP has not been actively developed since 2014, when I left the proteomics group at UTSW. To access up-to-date algorithms for the analysis of proteomics data, other tools would now be preferred. However: * Limited fixes have been made on the main master branch by Phil Charles at the University of Oxford. * As of 2019, limited maintenance is being carried out by myself (now in the BioHPC high performance computing group at UTSW), to allow CPFP to install and run on RHEL 7 / CentOS 7, with updated TPP 5.1.0 and search tools. This updated code can be found in the utsw-biohpc branch of the git repository, and may become a 2.2.0 release if time and testing allows. -- David Trudgian - 2019/01/03

International School Bus Operation Specification

A Clinical Data Analytics Language

CloudQuery extracts, transforms and loads your cloud assets into normalized PostgreSQL tables. CloudQuery enables you to assess, audit, and monitor the configurations of your cloud assets. Use standard SQL to find any asset based on any configuration or relation to other assets. Connect CloudQuery standard PostgreSQL database to your favorite BI/Visualization tool such as Grafana, QuickSight, etc. Codify your security & compliance rules with SQL as the query engine. Integrate CloudQuery with your current visualization, monitoring, and alerting such as Grafana. CloudQuery supports the TimescaleDB PostgreSQL extension, giving you full historical snapshots of your cloud asset inventory. Data analysis, security, auditing, and compliance. Leverage SQL to get visibility into your cloud infrastructure and SaaS applications. Build a cloud-asset inventory across any of our supported official or community providers.

The Collaborative Computational Project for NMR (CCPN) provides tools and knowledge to maximise the impact of the biological NMR studies. The CCPN software facilitates data analysis and software integration. The project actively promotes the exchange of knowledge and provides training and best practices for the NMR community. CCPN also has a leading role in the development of a NMR data-sharing standard and coordination of NMR instrumentation proposals for RCUK and BIS. The 28 partners of CCPN jointly cover all aspects of biomolecular NMR and together they promote excellence in science in their respective fields.

High performance, real-time data analysis engine capable of processing from various sources simultaneously (syslog, files, sockets, etc). Triggers internal and user-defined events based on built-in and user-defined rules.

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards, ultimately, data science code quality is about correctness and reproducibility. It's no secret that good analyses are often the result of very scattershot and serendipitous explorations. Tentative experiments and rapidly testing approaches that might not work out are all part of the process for getting to the good stuff, and there is no magic bullet to turn data exploration into a simple, linear progression.