TY - JOUR

T1 - Ontology of Experimental Variables as an Extension of Infrastructure for Behavioral Research Data FAIRification

AU - Begler, Alena

AU - Anufriev, Grigoriy

AU - Leshcheva, Irina

PY - 2022/1/1

Y1 - 2022/1/1

N2 - Data sharing is becoming a common practice in behavioral research. Thousands of experimental datasets can be found in open repositories; however, most of them cannot be properly reused due to lack of documentation. We present a structured review of ontologies for experimental research data with a description of 16 ontologies that we divided into three groups according to their approach to variable descriptions: general data description with no attention to variables, scientific research description with either abstract representation of variables or focus on their measurement, and domain-specific ontologies with classes for biological and cognitive fields. The structured resources review can be found at https://doi.org/10.17632/xw288mx2ws.1. We propose an Empirion ontology that provides a variables description that makes it possible to integrate variables from different datasets. To do this, the ontology inherits three-level variable description and enriches it with (1) connections with information about the variable’s measurements, and (2) typology of variables based on their role in the experiment. The ontology source code together with supportive materials can be found at our GitHub repository: https://github.com/jimijimiyo/empirion.

AB - Data sharing is becoming a common practice in behavioral research. Thousands of experimental datasets can be found in open repositories; however, most of them cannot be properly reused due to lack of documentation. We present a structured review of ontologies for experimental research data with a description of 16 ontologies that we divided into three groups according to their approach to variable descriptions: general data description with no attention to variables, scientific research description with either abstract representation of variables or focus on their measurement, and domain-specific ontologies with classes for biological and cognitive fields. The structured resources review can be found at https://doi.org/10.17632/xw288mx2ws.1. We propose an Empirion ontology that provides a variables description that makes it possible to integrate variables from different datasets. To do this, the ontology inherits three-level variable description and enriches it with (1) connections with information about the variable’s measurements, and (2) typology of variables based on their role in the experiment. The ontology source code together with supportive materials can be found at our GitHub repository: https://github.com/jimijimiyo/empirion.

KW - Conceptual modeling

KW - Experimental data integration

KW - FAIR data

KW - Ontology

KW - Research data

UR - http://www.scopus.com/inward/record.url?scp=85128489818&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-98876-0_24

DO - 10.1007/978-3-030-98876-0_24

M3 - Article

AN - SCOPUS:85128489818

VL - 1537 CCIS

SP - 268

EP - 279

JO - Communications in Computer and Information Science

JF - Communications in Computer and Information Science

SN - 1865-0929

ER -