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Ycasd - a tool for capturing and scaling data from graphical representations.

Arnd Gross¹, Sibylle Schirm, Markus Scholz

¹Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Haertelstrasse 16-18, 04107 Leipzig, Germany. arnd.gross@imise.uni-leipzig.de.

BMC Bioinformatics

|June 27, 2014

Summary

ycasd is an open-source tool that extracts data points from graphical representations in scientific literature. This facilitates biological modeling and other analyses requiring raw data from publications.

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Area of Science:

Computational Biology
Bioinformatics
Scientific Data Extraction

Background:

Mathematical modeling in biology necessitates diverse datasets for parameter estimation and validation.
Clinical data are often presented graphically, requiring extraction for computational analysis.
Existing methods for data extraction from figures can be cumbersome.

Purpose of the Study:

To introduce ycasd, a freely available open-source tool for extracting data points from graphical representations.
To provide a user-friendly solution for converting visual data into usable formats for analysis.
To offer an efficient alternative to manual data extraction from scientific publications.

Main Methods:

ycasd establishes a coordinate system via simple axes definitions for data point retrieval.
The tool processes figures without requiring specific input file formats.
All functionalities are accessible through a single, intuitive window for streamlined operation.

Main Results:

ycasd enables convenient and accurate data retrieval from arbitrary figures.
Extracted data can be formatted as Matlab or R matrices for subsequent analysis.
Comparative analysis indicates ycasd offers a balance between ease of use and functionality.

Conclusions:

ycasd is suitable for accurate data extraction from graphical representations in scientific papers.
The tool provides a practical compromise between usability and complexity for scientific data capture.
ycasd is valuable for biological modeling and other analyses requiring raw data from publications, including systematic reviews and meta-analyses.