Niimpy : A toolbox for behavioral data analysis
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2023-07
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Language
en
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SoftwareX, Volume 23
Abstract
Behavioral studies using personal digital devices typically produce rich longitudinal datasets of mixed data types. These data provide information about the behavior of users of these devices in real-time and in the users’ natural environments. Analyzing the data requires multidisciplinary expertise and dedicated software. Currently, no generalizable, device-agnostic, freely-available software exists within Python scientific computing ecosystem to preprocess and analyze such data. This paper introduces a Python package, Niimpy, for analyzing digital behavioral data. The Niimpy toolbox is a user-friendly open-source package that can quickly be expanded and adapted to specific research requirements. The toolbox facilitates the analysis phase by offering tools for preprocessing, extracting features, and exploring the data. It also aims to educate the user on behavioral data analysis and promotes open science practices. Over time, Niimpy will expand with new data analysis features developed by the core group, new users, and developers. Niimpy can help the fast-growing number of researchers with diverse backgrounds who collect data from personal and consumer digital devices to systematically and efficiently analyze the data and extract useful information. This novel information is vital for answering research questions in various fields, from medicine to psychology, sociology, and others.Description
Funding Information: We thank Professor Jari Saramäki for providing valuable feedback. We also thank Aalto Science-IT for providing computational resources and Aalto Research Software Engineers for their support. We thank Anna Hakala for their help with the project in its early stages. TA acknowledges the support of Professor Erkki Isometsä and other collaborators in the MoMo-Mood project, which has motivated the creation of the Niimpy toolbox. Publisher Copyright: © 2023 The Author(s)
Keywords
Data analysis toolbox, Digital behavioral studies, Mobile sensing, Python package
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Citation
Ikäheimonen, A, Triana, A M, Luong, N, Ziaei, A, Rantaharju, J, Darst, R & Aledavood, T 2023, ' Niimpy : A toolbox for behavioral data analysis ', SoftwareX, vol. 23, 101472 . https://doi.org/10.1016/j.softx.2023.101472