Data from the second part of the FAIRqual workshop where groups were combined (a+c and b+d) to identify key challenges in sharing qualitative data and propose approaches to address them. This exercise focused on synthesizing insights from part 1 into actionable challenges and solutions.
Format
A tibble with 9 rows and 3 variables:
- group
Combined workshop groups. Character variable with values "ac" (groups a and c combined) or "bd" (groups b and d combined).
- challenge
Data sharing challenge identified by the group. Character variable containing specific challenges such as anonymization difficulties, data misuse concerns, ethics alignment, AI implications, and politicization risks.
- approach
Proposed approach or solution to address the identified challenge. Character variable containing strategies like obtaining proper ethics approval, metadata documentation, participant co-creation, and approval processes. Some entries may be "na" where no specific approach was proposed.
Source
FAIRqual Workshop at the International Transdisciplinary Conference (ITD24), Utrecht, Netherlands, November 4-8, 2024.
Details
In this workshop phase, the four original groups were merged into two larger groups to facilitate deeper discussion and synthesis. Each combined group identified multiple challenges and attempted to propose practical approaches for addressing them. The challenges reflect key concerns about sharing qualitative data in transdisciplinary research contexts.
See also
codebook_qualitative for the analyzed results
flipcharts1 for the workshop part 1 data
Examples
# Load the data
data(flipcharts2)
# View all challenges and approaches
flipcharts2
#> # A tibble: 9 × 3
#> group challenge approach
#> <chr> <chr> <chr>
#> 1 bd annonymisation does not work loss of…
#> 2 bd misue & decontextualisation of data ethical…
#> 3 bd != ethics considerations to be aligned what is…
#> 4 bd qualitatvie data and AI? are we afraid? na
#> 5 ac politization of data it is n…
#> 6 ac moral responsibility to care (do no harm) partici…
#> 7 ac diffuse ownership of raw data + i.p. (intellectual property ri… na
#> 8 ac transfer across projects and between partners consort…
#> 9 ac ethics and permission (amendments) legal p…
# Challenges by group
library(dplyr)
flipcharts2 %>%
group_by(group) %>%
summarise(
n_challenges = n(),
challenges = paste(challenge, collapse = "; ")
)
#> # A tibble: 2 × 3
#> group n_challenges challenges
#> <chr> <int> <chr>
#> 1 ac 5 politization of data; moral responsibility to care (do no …
#> 2 bd 4 annonymisation does not work; misue & decontextualisation …
# Filter out entries without specific approaches
flipcharts2 %>%
filter(approach != "na")
#> # A tibble: 7 × 3
#> group challenge approach
#> <chr> <chr> <chr>
#> 1 bd annonymisation does not work loss of context (as main …
#> 2 bd misue & decontextualisation of data ethical concerns; get eth…
#> 3 bd != ethics considerations to be aligned what is data; do not publ…
#> 4 ac politization of data it is not allowed to take…
#> 5 ac moral responsibility to care (do no harm) participating actors give…
#> 6 ac transfer across projects and between partners consortium agreement
#> 7 ac ethics and permission (amendments) legal partner checks; in…