Open Source Windows Data Analytics Tools - Page 10
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Data Analytics Tools for Windows
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OpenData
Open source coronavirus 2019 sample application using open data
Open source software using open data with template code to demonstrate getting open government data such as the coronavirus 2019 (COVID-19). It takes data in feed format such as CSV, JSON, etc. to process maps, pie charts, line/bar graphs, HTML table that can be copied and pasted to spreadsheets such as Excel. The purpose is to provide template code to access open data readily available from official government websites useful for regional data analysis, latest information at a glance with data visualisation, etc. Sample includes region like Australia, Austria, Belgium, Brazil, Canada, Chile, Czechia, Denmark, Estonia, Europe, Finland, France, Germany, Greece, Hong Kong, Iceland, Indonesia, Ireland, Israel, Italy, Japan, Luxembourg, Macau, Malaysia, Netherlands, Norway, Korea, Philippines, Poland,Portugal,Qatar,Romania,Saudi Arabia,Singapore,Slovenia,Spain,Sweden,Switzerland,Taiwan,Thailand,United Kingdom,United States of America,etc. with various data such as face mask maps, and more. -
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OpenDinTest is a OpenOffice-Calc-Sheet for statistical Data-Analysis according to the german industry-norm DIN 32645. <br> <br> OpenDinTest ist eine OpenOffice-Calc-Datei zur statistischen Auswertung von Messdaten nach der DIN 32645. <br>
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OptiMIClass
Automatic n-dimensional data clustering tool powered by five advanced
This software is an automatic n-dimensional quantitative data clustering tool based on five optimization heuristics (GA, PSO, ACO, SA, and TS). It enables performance comparisons across heuristics and parameter testing within each method, analyzing solution quality, runtime, and convergence; it also includes a repository of benchmark test tables and an intuitive interface for research and operational use. It was developed by M.Sc. Jeffry Chavarría-Molina and M.Sc. Juan José Fallas-Monge at the Instituto Tecnológico de Costa Rica (TEC), School of Mathematics, with support from the Vice-Rectory for Research and Extension (VIE). The software was a direct outcome of “Combinatorial optimization heuristics for data classification” (Code: 5402-1440-3901), and its development continued within the related project “Disease propagation: heuristics applied to optimizing control measures” (Code: 540114404201). -
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PINCIS
PINCIS.pl is a Perl bioinf. script to analyze PICS data
PINCIS.pl (PIcs N-/C-terminal Inferred Substrates perl script) is a small, command line tool to designate and analyze PICS (Schilling et al., Nat. Protocols, 2011) data to gain the prime and non-prime site specificity of proteases. Thus, the script filters given peptide lists for library peptides (generated by the digestion protease in the proteomics workflow rather then the protease of interest) and prints out lists of inferred N- and C-terminal cleavage window extensions which can be concurrently used to generate cleavage specificity visualizations like the iceLogo (https://iomics.ugent.be/icelogoserver/create). -
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PMM-Lab
Predictive Microbial Modeling plug-in for KNIME
PMM-Lab is an open-source extension to the Konstanz Information Miner (KNIME). It consists of three components: • a library of KNIME nodes (called PMM-Lab), • a library of “standard” workflows • an HSQL database.to store experimental data and microbial models. Altogether these components are designed to ease and standardize the statistical analysis of experimental microbial data and the development of predictive microbial models (PMM). Users can apply PMM-Lab to proprietary or public data and create bacterial growth / survival / inactivation models. The framework can easily be extended to other model types, e.g. growth/no-growth boundary models. PMM-Lab has been initiated and provided by the Federal Institute for Risk Assessment - BfR (Berlin, Germany). The software is in Beta status. Before using the software you have to read and accept the license and disclaimer (https://sourceforge.net/p/pmmlab/wiki/Disclaimer/). If you do not agree, do not use this software. -
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PRADA
PRADA : Pipeline for RNA-Sequencing Data Analysis
Massively parallel sequencing of cDNA reverse transcribed from RNA (RNASeq) provides an accurate estimate of the quantity and composition of mRNAs. To characterize the transcriptome through the analysis of RNA-seq data, we developed PRADA. PRADA focuses on the processing and analysis of gene expression estimates, supervised and unsupervised gene fusion identification, and supervised intragenic deletion identification. PRADA currently supports 7 modules to process and identify abnormalities from RNAseq data: preprocess: Generates aligned and recalibrated BAM files. expression: Generates gene expression (RPKM) and quality metrics. fusion: Identifies candidate gene fusions. guess-ft: Supervised search for fusion transcripts. guess-if: Supervised search for intragenic fusions. homology: Calculates homology between given two genes. frame: Predicts functional consequence of fusion transcript -
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The Physics eXtension Library (PXL) is a C++ toolkit for fourvector analysis and hypothesis evolution in high energy physics data analysis.
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Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase, separator), scripts (Latin, Cyrillic) and blocks (ASCII, Cyrilic). File sizes, creation dates, dimensions, indication of truncated images and existance of EXIF metadata. Mostly global details about the dataset (number of records, number of variables, overall missigness and duplicates, memory footprint). Comprehensive and automatic list of potential data quality issues (high correlation, skewness, uniformity, zeros, missing values, constant values, between others). -
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Parallel Colt is a multithreaded version of Colt - a library for high performance scientific computing in Java. It contains efficient algorithms for data analysis, linear algebra, multi-dimensional arrays, Fourier transforms, statistics and histogramming
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Pathomx
Workflow-based data analysis built on IPython
Pathomx is a workflow-based tool for the analysis and visualisation of experimental data. Initially created as a tool for metabolomic data analysis is has been extended and can now be used for any scientific and non-scientific data analysis. -
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PivotTable.js
Open-source Javascript Pivot Table
PivotTable.js is a powerful JS library (originally CoffeeScript) that brings Excel-style pivot tables to the browser. Built atop jQuery and jQueryUI, it allows users to drag and drop fields into rows, columns, and aggregators to slice and summarize datasets. It also integrates with Python and R via modules like pivottablejs, making it versatile for data analysis across platforms. -
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PlateEditor
PlateEditor, a free web application to work with multi-well plates
PlateEditor is a free web application to work with multi-well plates, from creation of layouts to data visualization and aggregation. It is primarily designed as a tool to help scientists working in the field of biology to simplify and fasten the process of data analysis. It is fully client-side: files are never transferred to the server, ensuring complete security and privacy of research data. It doesn't require any installation. - PlateEditor is available at this address: https://plateeditor.sourceforge.io - The source code is also available on GitHub, visit: https://github.com/vindelorme/PlateEditor More information about the source code and the API are available there (the wiki is still under construction, thanks for your patience!) For a list of recent updates, visit: https://sourceforge.net/p/plateeditor/wiki/Updates/ Hoping PlateEditor will make your life easier in the lab! If yes, please cite us: https://doi.org/10.1371/journal.pone.0252488 -
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Poli
An easy-to-use BI server built for SQL lovers. Power data analysis
An easy-to-use BI server built for SQL lovers. Power data analysis in SQL and gain faster business insights. Platform independent web application. Single JAR file + Single SQLite DB file. Get up and running in 5 minutes. PostgreSQL, Oracle, SQL Server, MySQL, Elasticsearch... You name it. No ETLs, no generated SQL, polish your own SQL query to transform data. Pixel-perfect positioning + Drag'n'Drop support to customize the reports and charts in your own way. Utilize the power of dynamic SQL with query variables to connect Filters and Charts. Capture the snapshot of historical data. Free up space in your own database. Three system level role configurations + Group based report access control. Custom the language pack and translations just for your audience. Auto refresh, drill through, fullscreen, embeds, color themes + more features in development. -
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Pyphant is a framework for scientific data analysis. It features a flexible plugin architecture allowing for the application in many different fields. For the analysis a graph of processing steps is constructed, that can be applied via the cli as well.
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Python Laboratory Operations Toolkit
many useful snippets for using python in a laboratory
A toolkit of Python software useful in a laboratory data acquisition and analysis environment. Includes support for such protocols as VXI-11 (and its extension, LXI), Vernier LabPro (now very old), and National Instruments DSTP (now very old). Also includes data analysis and modelling tidbits. Python3 updates are on the way in the very near future for the biggest packages. the vxi11 package is fully up-to-date, although see the blog post about python 3.13 -
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PythonStatsLab
A collection of useful statistical functions written in Python
There are functions for numerical integration of the standard normal probability distribution. They can be used to find probabilities, find z-alpha values, calculate confidence interval, etc. New functionality I wish to add are hypothesis testing and calculation of p-value, type I & II errors, etc. These functions would come in handy to anyone taking introductory or intermediate-level courses in probability, statistics and data analysis. There are several numerical integration techniques (Riemann sum, Simpson's method, Taylor expansion, etc.) that can be employed in this task, which I hope to add soon. Projected Future Improvements: -Estimation with 2 means -Student's t-distribution -Hart's algorithm for integrating CDF -
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QME-DEV Workbench (wxScipy) is a data analysis workbench based on Python, SciPy, Numpy and MatPlotLib(PyLab) and uses wxPython for the GUI. It can be used for non-linear analysis with a large set of experimental or generated data. Load/Acquire / Save/ Gr
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Query2Report
Simple open source business intelligence and reporting solution
Query2Report provides a simple opensource business intelligence platform that allows users to build report/dashboard for business analytics or enterprise reporting. The application transforms bunch SQL queries to beautiful google charts. The application caters to real time reporting with automatic refresh functionality. Refer to video tutorials Concepts - https://youtu.be/NdEUZ2suiv8 Data Analytics Demo - https://youtu.be/evCf74Ou7kg Data Forecast Demo - https://youtu.be/Nmi1UIDpFpM Report Showcase - https://youtu.be/gxlEGq5iSm8 Getting Started - https://youtu.be/vyU7BUE5rbs Building First Report - https://youtu.be/MZm6rhf2_Ts Source Repo GitHub : https://github.com/yogeshsd/query2report -
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QuickNGS
Next-Generation Sequencing workflow system
QuickNGS is a software for efficient and highly reproducible data analysis for Next-Generation Sequencing. -
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QuickRNASeq
A pipeline for large scale RNA-seq data analysis
We have implemented QuickRNASeq, an open-source based pipeline for large scale RNA-seq data analysis. QuickRNASeq takes advantage of parallel computing resources, a careful selection of previously published algorithms for RNA-seq read mapping, counting and quality control, and a three-stage strategy to build a fully automated workflow. We also implemented built-in functionalities to detect sample swapping or mislabeling in large-scale RNA-seq studies. Our pipeline significantly lifts large-scale RNA-seq data analysis to the next level of automation and visualization. Please take a look at our test run project report and get a taste of its rich interactive visualization features. http://baohongz.github.io/QuickRNASeq QuickRNASeq user guide can be downloaded or is accessible online as well http://baohongz.github.io/QuickRNASeq/guide.html For more on QuickRNASeq, please see our paper http://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-015-2356-9 Enjoy QuickRNASeq -
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QuiclickRNAseq
An integrated pipeline for RNA-seq analysis
Quiclick is a python framework for RNA-seq data analysis, including data trimming, mapping, counting and RNA editing analysis. -
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R Hadoop for Big Data
Download Free Associated R open source script files for big data analy
Download Free Associated R open source script files for big data analysis with Hadoop and R These are R script source file from Ram Venkat from a past Meetup we did at http://www.meetup.com/R-Matlab-Users/events/85160532/ Also, there is a long video and Powerpoint presentation slide PDF with R files at: http://quantlabs.net/blog/2012/11/how-to-use-hadoop-and-r-for-big-data-parallel-processing-free-download-pdf/ Download source files from http://quantlabs.net/blog/2012/11/download-free-associated-r-open-source-script-files-for-big-data-analysis-with-hadoop-and-r-rstats-hadoop/ -
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A collection of code, bundled into a single R package, to perform several aspects of data management, image pre-processing, data analysis and statistical inference related to the quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI).
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RCFPD
Random Collection of Functions for Proteomics Data Analysis
An R package for distribution of data analysis functionality used by the Proteomics Core at Weill Cornell Medical College in Qatar. -
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ROOTSNNS is a set of C++ classes which allows one to use the Stuttgart Neural Network Simulator kernel (ansi-C) within the ROOT, a data analysis package. Multiple ANNs can be built, trained, and tested, while results and ANN performance can be saved.
