Niimpy : A toolbox for behavioral data analysis

Arsi Ikäheimonen*, Ana M. Triana, Nguyen Luong, Amirmohammad Ziaei, Jarno Rantaharju, Richard Darst, Talayeh Aledavood

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

46 Downloads (Pure)

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.

Original languageEnglish
Article number101472
JournalSoftwareX
Volume23
DOIs
Publication statusPublished - Jul 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • Data analysis toolbox
  • Digital behavioral studies
  • Mobile sensing
  • Python package

Fingerprint

Dive into the research topics of 'Niimpy : A toolbox for behavioral data analysis'. Together they form a unique fingerprint.

Cite this