Appendix A: Tables of results

Topics of the trainings in 2010–2021.

Sourcea Planning and organisation Sharing and reuse Storage, backup, security Metadata and data description Preservation Legal and ethical issues Quality and documentation Types and Formats Life cycle Discovery Policies, requirements, incentives Processing Cultures of Practice
Adamick et al., 2013 1 1 1 1 1
ARDS, 2018b 1 1 1 1 1 1 1
Borycz & Carroll, 2021 1 1 1 1 1 1
Carpentries, n.d. 1 1
Castle, 2019 1 1 1
CESSDA, 2017c 1 1 1 1 1 1 1 1
Clement et al., 2017 1 1 1 1 1 1 1 1 1 1
Cole & Evans, 2014 1 1 1 1 1 1 1
Corti & Van den Eynden, 2015 1 1 1 1 1 1 1
EDINAd 1 1 1 1 1 1 1 1 1 1
Kafel et al., 2014 1 1 1 1 1 1 1 1 1 1
Mustajoki, n.d. 1 1 1 1 1 1 1 1
Pascuzzi & Nelson, 2018 1 1 1 1 1 1 1
Petters et al., 2019 1 1 1 1 1
Piorun et al., 2012 1 1 1 1 1 1 1 1 1 1
Qin & D’ignazio, 2010 1 1 1 1 1 1 1 1 1 1 1 1 1
Rantasaari & Kokkinen, 2019 1 1 1 1 1 1 1 1 1 1
Read, 2019 1 1 1 1 1 1 1 1 1
Read et al., 2019 1 1 1 1 1 1 1 1 1 1
Schmidt & Holles, 2018 1 1 1 1 1 1 1 1 1 1
Schöpfel & Prost, 2016 1 1 1 1 1 1
Shadbolt et al., 2014 1 1 1 1 1 1
Southall & Scutt, 2017 1 1 1 1
Surkis et al., 2017 1 1
Thielen et al., 2017 1 1 1 1 1 1 1 1
Wang & Fong, 2015 1 1 1 1 1 1 1 1
Whitmire, 2015 1 1 1 1 1
Wiley et al., 2017 1 1 1 1 1 1 1
Wiljes & Cimiano, 2019 1 1 1 1 1 1 1 1
Wright & Andrews, 2015 1 1 1 1 1 1
SUM 27 25 21 21 21 17 17 14 14 13 12 8 7

 

Source Visualisation and representation Conversion and interoperability Collection planning Analysis Budgeting Cleaning Databases Encoding Planning research project Big data Cloud computing Planning curation profile
Adamick et al., 2013
ARDS, 2018
Borycz & Carroll, 2021
Carpentries, n.d. 1 1 1 1 1
Castle, 2019
CESSDA, 2017
Clement et al., 2017 1
Cole & Evans, 2014
Corti & Van den Eynden, 2015 1
EDINA 1 1 1
Kafel et al., 2014 1
Mustajoki, n.d.
Pascuzzi & Nelson, 2018 1 1 1
Petters et al., 2019 1 1
Piorun et al., 2012 1
Qin & D’ignazio, 2010 1 1 1 1 1 1
Rantasaari & Kokkinen, 2019 1 1
Read, 2019
Read et al., 2019 1
Schmidt & Holles, 2018 1 1
Schöpfel & Prost, 2016
Shadbolt et al., 2014
Southall & Scutt, 2017
Surkis et al., 2017 1 1 1
Thielen et al., 2017 1
Wang & Fong, 2015
Whitmire, 2015
Wiley et al., 2017
Wiljes & Cimiano, 2019
Wright & Andrews, 2015 1 1 1
SUM 7 4 4 4 3 3 2 2 2 2 1 1

aFor details of source, see references of main text.

bAustralian Research Data Commons: https://ardc.edu.au/.

chttps://www.cessda.eu/DMGuide.

dhttps://mantra.ed.ac.uk/.

Participants by their disciplines 2019–2021.

Year 2019 2020 2021 Sum
Law 1 0 1 2
Education, Welfare 3 8 8 19
Humanities, Psychology, Theology 4 16 12 32
Social Sciences, Business, Economics 6 22 38 66
Science and Engineering 5 28 30 63
Health Sciences 23 22 32 77
Sum 42 96 121 259

What are the three things you have learned?

Category 2019 2020 2021
What, why and when in RDM 100 112 55
Importance of legal considerations 64 19 9
Making a sound research plan 38 26 10
Securing data privacy 29 17 24
Using data collecting or organizing software 17 7 10
Other comments 4 13 11
Sum 252 194 119

How will the things you have learned change your practices?

Category 2019 2020 2021
I will pay notice to IPR, agreements and licenses 25 9 5
I will improve data management planning and documenting 18 73 31
I will collect, produce or process data with REDCap or Nvivo 17 6 3
I will pay more notice to data privacy and security 13 22 16
I will improve my research plan 13 17 5
Other comments 1 5 3
Sum 87 132 63

How would you suggest the module be developed?

Category 2019 2020 2021
Increase practicality, e.g., good and bad examples and check lists 34 30 29
Clarifying and standardizing procedures, practices, and course platform 23 53 42
Increase discussions and interactivity 12 22 4
Possibility to prepare one’s own study plan and DMP 7 0 0
Differentiating the course contents according to discipline, data type, methods 6 9 10
Turning to hybrid or contact course 0 12 0
Good as it is 0 0 12
Other comments 8 10 4
Sum 90 136 101

Competencies before and after BRDM 2019 (medians, custom quantiles, and p-values).

Competence Median, before Q1; Q3 Median, after Q1; Q3 p-value (Fit Y by X; Wilcoxon rank-sum test)
Discovery and acquisition of data 1.97 1.78; 2.14 2.39 2.12; 2.84 0.02
Databases and data formats 2.02 1.82; 2.22 2.38 2.07; 2.89 0.04
Data conversion and interoperability 1.83 1.17; 1.98 2.08 1.81; 2.65 0.07
Data management and organization 1.95 1.76; 2.14 2.63 2.16; 3 0.001
Data quality and documentation 2.01 1.98; 2.06 2.62 2.11; 2.94 0.02
Metadata and data description 1.91 1.76; 2 2.72 2.21; 2.91 <0.001
Cultures of practice 1.96 1.81; 2.08 2.22 1.86; 3 0.07
Ethics and attribution 2.11 2.03; 2.69 2.89 2.37; 3.10 0.01
Data curation and reuse 1.89 1.31; 1.97 2.15 2.04; 2.68 0.001
Data preservation 1.93 1.80; 2 2.62 2.11; 2.94 0.001
Median, custom quantiles, p-value 1.96 1.82; 2.09 2.32 2.12; 2.84 0.003

Competencies before and after BRDM 2020–2021 (medians, custom quantiles, and p-values).

Knowledge, skill, or ability Median, before Q1; Q3 Median, after Q1; Q3 p-value (Distributions; Wilcoxon signed-rank test)
Describing your research and data collection process to identify your data lifecycle 2.07 2.03; 2.14 3.08 3.05; 3.13 <0.0001
Recognizing the necessary components of a data management plan 1.95 1.89; 1.99 3.11 3.07; 3.82 <0.0001
Creating a data management plan to manage and curate your own data 1.83 1.12; 1.93 3.06 3.03; 3.10 <0.0001
Documenting your data for yourself and others 2.06 2.02; 2.13 3.07 3.04; 3.12 <0.0001
Applying the relevant laws, agreements, permits, and licenses to your data 1.87 1.14; 1.93 2.93 2.88; 2.97 <0.0001
Applying the basic anonymization methods for qualitative and quantitative research when needed 2.05 2; 2.13 3.01 2.97; 3.06 <0.0001
Recognizing the importance of data protection for collecting, processing, storage and sharing of data 2.08 2.03; 2.20 3.11 3.06; 3.83 <0.0001
Creating a data privacy statement and a risk analysis when needed 1.9 1.16; 1.97 2.94 2.89; 2.98 <0.0001
Creating a database or a survey for capturing and maintaining your data using REDCap software 1.13 1.06; 1.23 2.99 2.83; 3.14 <0.0001
Organizing and coding your qualitative data for analyzing using NVivo software 1.59 1.29; 2.23 3.01 2.88; 3.16 <0.0001
Creating a storage and backup plan, and applying it to your data using the services of your organization, or the services of The IT Center for Science (CSC) 1.96 1.82; 2.09 3 2.88; 3.12 <0.0001
Evaluating data repositories for depositing and publishing your data and discovering other researchers’ data for re-use 1.18 1.1; 1.92 2.97 2.93; 3.01 <0.0001
Applying FAIR principles to your data when possible 1.9 1.15; 1.97 2.98 2.86; 3.08 <0.0001
Applying data management best practices concerning collecting, organizing, documenting, storing, long-term preserving, and sharing (when possible) to your own data 2.01 1.91; 2.12 3.03 2.98; 3.09 <0.0001
Median, custom quantiles, p-value 1.97 1.93; 2.01 3.03 2.98; 3.08 <0.0001