Optimization of quality assured dataflow from biosensors : Time series analysis of plankton respiration by oxygen optode

Data analysis can be a time consuming part of an experimental method, especially when the method is used frequently and large amounts of data are produced each time. In this study, an application software was developed to improve work flow and data management for respiration rate measurements using an optical oxygen sensor. The application was used to analyze data files from the oxygen sensor without the need to manually enter and analyze the data in a spreadsheet application. The software was written in the Python programming language and utilized available scientific computing packages as well as a graphical user interface framework to provide user friendly access to all functions. Any number of files with experimental data were imported into the program and a linear regression analysis was done for each file and viewed to verify the quality of the data. Tables and summarizing graphs were used to display the key information and statistical results. The final results were exported for use in other applications. Data processing that used to take an hour to complete was done with the new application in five to ten minutes and the risk of introducing human errors in the data was simultaneously reduced. User tests indicated that learning the basics of the program was easy. This study shows the usefulness of a bioinformatics approach and the tools provided by Python and its related software to solve problems that arise with managing large volumes of numerical data.Älvburet organiskt kol och bakteriers syre respiratio