Evidence Summary
An Online Community of Data
Enthusiasts Collaborates to Seek, Share, and Make Sense of Data
A Review of:
Stvilia, B., & Gibradze, L. (2022). Seeking and sharing datasets in
an online community of data enthusiasts. Library
& Information Science Research 44(3). https://doi.org/10.1016/j.lisr.2022.101160
Reviewed by:
Jordan Patterson
Associate Librarian
A. P. Mahoney Library
St. Peter’s Seminary
London, Ontario, Canada
Email: jpatte46@uwo.ca
Received: 16 Nov. 2022 Accepted: 25 Jan. 2023
2023 Patterson. This is an Open Access article distributed under
the terms of the Creative Commons‐Attribution‐Noncommercial‐Share Alike License 4.0
International (http://creativecommons.org/licenses/by-nc-sa/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly attributed, not used for commercial
purposes, and, if transformed, the resulting work is redistributed under the
same or similar license to this one.
DOI: 10.18438/eblip30280
Abstract
Objective – To understand the major activities,
tools, sources, and challenges of online communities focused on datasets.
Design – Content analysis informed by activity
theory.
Setting – The r/Datasets subreddit, a web forum
for sharing, seeking, and discussing datasets.
Methods – The researchers used Reddit’s API to collect their sample of threads.
Using a random subset of the sample, the researchers developed a coding scheme
for content analysis, which identified major themes in the data. Through this
process, they controlled for quality: each researcher coded half the subset
independently, then together evaluated their intercoder reliability and
discussed and resolved disagreements. The researchers also employed labelled
latent Dirchlet allocation to construct topic models corresponding to the
theme’s manual content analysis, which produced profiles of the top 100 terms
most likely to appear in that topic. Finally, the researchers extracted URLs
from threads in the sample to ascertain types of information and data sources
used by the community. Presenting their findings, the researchers discussed
notable themes and proposed a metadata model for describing datasets, the Data
Q&A metadata (DQAM) model.
Main Results – The r/Datasets community engages in
three distinct activities: asking and answering questions, disseminating
information, and community building. The closely related Q&A and
dissemination activities shared themes of obtaining and aggregating data,
sensemaking, collaborating and crowdsourcing, and data evaluation. Community
members frequently discussed tools, competencies, and sources for data work.
Major challenges for members of the community related to the general themes of
data quality, accessibility, ethics, and legality. A proposed 16-element
metadata schema should meet the needs of data enthusiasts.
Conclusion – The content analysis reveals a
dedicated community engaged in an array of data-seeking and data-sharing
activities. Data producers should be mindful of how their data can be accessed
and used outside of their original professional or scholarly contexts.
Commentary
Where once datasets were the preserve of ivory-tower
statisticians and scholars, the tech competencies of digital natives and a
trend toward openness in the Information Age have made data wrangling a viable
hobby, to which the existence of the subreddit r/Datasets stands as a
testament. The great proliferation of data has been attended by a movement to
regulate and systematize the sharing and seeking of data. In Canada, for
instance, federal fund-granting agencies CIHR, NSERC, and SSHRC (2021) require
researchers to have research data management (RDM) plans in place in order to qualify for grants, hastening the development of
data infrastructure online. The researchers ask how data enthusiasts operate in
this landscape.
Assessed
with Glynn’s (2006) critical appraisal tool, this study meets commonly accepted
standards of validity. The researchers took evident care in their research
design to ensure their content analysis coding scheme was reliable and free
from bias; for instance, in developing their coding scheme, the researchers
first worked independently to identify themes before coming together to compare
their results and generate a final set of thematic categories.
The
researchers’ DQAM model is sure to be welcomed by casual and professional data
enthusiasts alike, but it enters the study abruptly. A short literature review
covering the state of dataset metadata schemas would have been a welcome
addition. In writing about r/Datasets, the researchers focus on a novel,
non-academic context, but the problems they uncover in their study are not so
novel—the issues identified by data enthusiasts plague data professionals as
well, and have for some time. The researchers propose their own solution to these
ills with the DQAM model, but they could have fruitfully considered what other
solutions were on offer first; for instance, they could have described how
major players in other more typically academic areas such as RDM have
approached the issue of dataset metadata.
For
one example, the Dataverse Project (Institute for Quantitative Social Science
[IQSS], n.d.) “is an open source web application to share, preserve,
cite, explore, and analyze research data” that has seen great adoption in the
scholarly realm, with Harvard at the forefront. When submitting their datasets
to an instance of Dataverse, researchers must input descriptive metadata, and
for this purpose the project’s documentation includes a dataset citation
metadata schema with 78 elements (IQSS, 2019). How does the DQAM model differ
from this earlier Dataverse schema? What are the merits and deficiencies of
each compared to the other? What makes DQAM stand out? The need for a metadata
model custom-built for the non-academic context may exist, but it seems like an
oversight to present the DQAM model without discussing other dataset metadata
schemas first.
Looking
to future studies, the researchers hope to “expand and triangulate” their
findings by interviewing r/Datasets community members. The demographic
composition of this community would be a compelling question for further
research. There seems to be an implicit assumption in this study that community
members are just data enthusiasts,
and it would be interesting to know how many also operate in a professional or
scholarly context. The findings of this study demonstrate that r/Datasets
community members are highly capable, technically proficient, and ethically
concerned -- hallmarks of a commitment to data beyond the amateur. Ultimately,
this study underscores the need for researchers and other data-producers to
handle their data with care; there is no telling where their data may end up.
References
Canadian
Institutes of Health Research (CIHR), Natural Sciences and Engineering Research
Council of Canada (NSERC), & Social Sciences and Humanities Research
Council of Canada (SSHRC). (2021, March 15). Tri-agency research data
management policy. Government of Canada. https://science.gc.ca/site/science/en/interagency-research-funding/policies-and-guidelines/research-data-management/tri-agency-research-data-management-policy
Glynn,
L. (2006). A critical appraisal tool for library and information research. Library Hi Tech, 24(3), 387-399. https://doi.org/10.1108/07378830610692154
Institute
for Quantitative Social Science. (n.d.). About. The Dataverse Project. https://dataverse.org/about
Institute
for Quantitative Social Science (2019, November 21). Dataverse documentation
v.4.18.1: User guide: Appendix. The
Dataverse Project. https://guides.dataverse.org/en/4.18.1/user/appendix.html
Stvilia, B., & Gibradze, L. (2022). Seeking and sharing datasets in
an online community of data enthusiasts. Library
& Information Science Research 44(3). https://doi.org/10.1016/j.lisr.2022.101160