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DataPipeline: Automated importing and fitting of large amounts of biophysical data
Author(s) -
Farrell Damien,
Nielsen Jens Erik
Publication year - 2012
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.23066
Subject(s) - computer science , software , workflow , raw data , data mining , source code , process (computing) , data file , source lines of code , mit license , visualization , file format , code (set theory) , variety (cybernetics) , database , programming language , set (abstract data type) , artificial intelligence
Raw data from experiments across the biological sciences comes in a large variety of text formats. In small or medium sized laboratories researchers often use an assorted collection of software to interpret, fit, and visualize their data. The spreadsheet is commonly the core component of such a workflow. The limitations of such programs for large amounts of heterogeneous data can be frustrating. We report the construction of DataPipeline, a desktop and command-line application that automates the tasks of importing, fitting, and plotting of text-based data. The software is designed to simplify the process of importing text data from various sources using simple configuration files to describe raw file formats. Once imported, curve fitting can be performed using custom fitting models designed by the user inside the application. Fitted parameters can be grouped together as new datasets to be fitted to other models and experimental uncertainties propagated to give error estimates. This software will be useful for processing of data from high through-put biological experiments or for rapid visualization of pilot data without the need for a chain of different programs to carry out each step. DataPipeline and source code is available under an open source license. The software can be freely downloaded at http://code.google.com/p/peat/downloads/list.