The document discusses the limitations of current data management practices in laboratory environments, highlighting the need for a collective approach rather than focusing on individual projects. By utilizing concept mapping to visualize workflows, it emphasizes the importance of understanding the interconnectedness of various research activities and their data management needs. The author proposes a structured method for identifying data management strategies that address issues such as decision fatigue and staff training to enhance effective implementation.

1. Project Mapping VariablesIntroduction
Acknowledgements
I appreciate the critical reading of the abstract and poster by Sandy De Groote,
Carmen Caswell, and Abigail Goben.
How data management issues manifest:
Using concept mapping to discover the information network in a laboratory environment
Tina Griffin
University of Illinois at Chicago
Library of the Health Sciences
• Current data management instruction and practices
focus at an individual or project level.
• These approaches fail to consider the collaborative
nature of laboratory research work, as well as,
supporting documentation management needs (e.g.
inventory control).
• In a laboratory environment, research work seldom
exists in an isolated context. Multiple projects in
different stages are progressing simultaneously, by
individuals with various levels of data management
experience.
• However, their workflows and outputs may converge
and diverge in many ways, affecting their individual data
management activities.
• By mapping the information workflow as a laboratory
group instead of as an individual or project, these
convergences/divergences can elucidate complex data
management needs.
1. Pre-questionnaire to determine which data aspects to
cover and obtain demographics of the group
2. Presentation/workshop data management principles at
lab meeting or some group event
3. Accompanying worksheet prompts questions regarding
specifics to their work, to be filled out simultaneously
with presentation/ workshop
4. Moderate discussion as the group reveals their answers
to worksheet post-presentation
5. Map the results
6. Identify themes or common pathways
7. Determine data management need(s)
Approach
Person
Project
Workflow
Habits
Data
Mgmt
Need
Output • Develop strategies
for data management
implementation that
address:
 Volume Issues
 Decision Fatigue
 Staff Buy-in
 Training
• Mapping laboratory workflow
can identify complicating
factors affecting data
management implementation.
• Once mapped, strategies can
be developed to account for
the whole workflow, easing
adoption.
Future directions
Sample Worksheet
Summary
Habit
Project
Output
Person
Workflow
NEED
Effective
laboratory data
management
addresses
intersecting
workflows from
diverse projects
in different
stages.