How to Write a Really Great Presentation Abstract
This conference welcomes people with no, or little experience presenting at conferences. Whether this is your first abstract submission or you just need a refresher on best practices when writing a conference abstract, these tips are for you.
Note: If you want to present about a project–in the Project Slam or at the Citizen Science Festival–the abstract does not need to include these portions, this is for scholarly presentations. If you are submitting a Project Slam Abstract or are submitting to Table at the Citizen Science Festival, your abstract will be a simple description of your project and why it is interesting.
An abstract for a presentation should include most the following sections. Sometimes they will only be a sentence each since abstracts are typically short (250 words):
- What (i.e. the focus): Clearly explain your idea or question your work addresses (i.e. how to recruit participants in a retirement community, a new perspective on the concept of “participant” in citizen science, a strategy for taking results to local government agencies).
- Why (i.e., the purpose): Explain why your focus is important (i.e. older people in retirement communities are often left out of citizen science; participants in citizen science are often marginalized as “just” data collectors; taking data to local governments is rarely successful in changing policy, etc.)
- How (i.e., Methods): Describe how you collected information/data to answer your question. Your methods might be quantitative (producing a number-based result, such as a count of participants before and after your intervention), or qualitative (producing or documenting information that is not metric-based such as surveys or interviews to document opinions, or motivations behind a person’s action) or both.
- Results: Share your results–the information you collected. What does the data say? (e.g. Retirement community members respond best to in-person workshops; participants described their participation in the following ways, 6 out of 10 attempts to influence a local government resulted in policy changes ).
- Conclusion: State your conclusion(s) by relating your data to your original question. Discuss the connections between your results and the problem (retirement communities are a wonderful resource for new participants; when we broaden the definition of “participant” the way participants describe their relationship to science changes; involvement of a credentialed scientist increases the likelihood of success of evidence being taken seriously by local governments.). If your project is still ‘in progress’ and you don’t yet have solid conclusions, use this space to discuss what you know at the moment (ie. lessons learned so far, emerging trends, etc).
Here is a sample abstract submitted to a previous conference as an example:
Giving participants feedback about the data they help to collect can be a critical (and sometimes ignored) part of a healthy citizen science cycle. One study on participant motivations in citizen science projects noted “When scientists were not cognizant of providing periodic feedback to their volunteers, volunteers felt peripheral, became demotivated, and tended to forgo future work on those projects” (Rotman et al, 2012). In that same study, the authors indicated that scientists tended to overlook the importance of feedback to volunteers, missing their critical interest in the science and the value to participants when their contributions were recognized. Prioritizing feedback for volunteers adds value to a project, but can be daunting for project staff. This speed talk will cover 3 different kinds of visual feedback that can be utilized to keep participants in-the-loop. We’ll cover strengths and weaknesses of each visualization and point people to tools available on the Web to help create powerful visualizations.
Rotman, D., Preece, J., Hammock, J., Procita, K., Hansen, D., Parr, C., et al. (2012). Dynamic changes in motivation in collaborative citizen-science projects. the ACM 2012 conference (pp. 217–226). New York, New York, USA: ACM. doi:10.1145/2145204.2145238