Measuring the time spent on data curation

Abstract

PurposeBudgeting data curation tasks in research projects is difficult. In this paper, we investigate the time spent on data curation, more specifically on cleaning and documenting quantitative data for data sharing. We develop recommendations on cost factors in research data management.Design/methodology/approachWe make use of a pilot study conducted at the GESIS Data Archive for the Social Sciences in Germany between December 2016 and September 2017. During this period, data curators at GESIS - Leibniz Institute for the Social Sciences documented their working hours while cleaning and documenting data from ten quantitative survey studies. We analyse recorded times and discuss with the data curators involved in this work to identify and examine important cost factors in data curation, that is aspects that increase hours spent and factors that lead to a reduction of their work.FindingsWe identify two major drivers of time spent on data curation: The size of the data and personal information contained in the data. Learning effects can occur when data are similar, that is when they contain same variables. Important interdependencies exist between individual tasks in data curation and in connection with certain data characteristics.Originality/valueThe different tasks of data curation, time spent on them and interdependencies between individual steps in curation have so far not been analysed.

Low Research-Data Availability in Educational-Psychology Journals: No Indication of Effective Research-Data Policies

“…First, the authors might not be aware of such a policy and, hence, do not know how research data should be handled. Second, although researchers know how to manage research data according to the institutional research-data policy, they balk at the effort of storing them in a repository because the curation of research data is related to significant costs (Perry & Netscher, 2022). This is a reasonable strategy in case the nonadherence to such guidelines is not sanctioned.…”

“…Note that making research data available is related to costs (Perry & Netscher, 2022). Data-curation costs and implementing transparency standards vary depending on—among other things—disciplines, study design, data complexity, and the personal information included in the data (Hensel, 2021).…”

Low Research Data Availability in Educational Psychology Journals: No Indication of Effective Research Data Policies

“…It is important to note that making research data available is related to costs (Perry & Netscher, 2022). Data curation costs and implementing transparency standards vary depending on -among others -disciplines, study design, data complexity, and the personal information included in the data (Hensel, 2021).…”

“…First, the authors might not be aware of such a policy and, hence, do not know how research data should be handled. Second, although researchers know how to manage research data according to the institutional research data policy, they balk at the effort of storing them in a repository because the curation of research data is related to significant costs (Perry & Netscher, 2022). This is a reasonable strategy in case the non-adherence to such guidelines is not sanctioned.…”

ChatGPT usage in the Reactome curation process

“…This process is crucial in scientific research, data-driven decision-making, and knowledge dissemination [1]- [4]. However, data curation is often a time-intensive, manual process including data gathering, annotation and cross-referencing in a defined data model [5]. The recent successful application of Large Language Models (LLMs) such as GPT3.5 to fields like social sciences [6], education [7], art [8], software [9], health care [10], clinical research [11], and medicine [12] provides a compelling motive to develop good benchmarking exercises to test the capabilities of current LLMs to streamline biological data curation and to track improvements in these capabilities as new LLMs and training strategies develop.…”