Posted 8/2/2021, last updated 7/31/2023
The abstract is your paper’s most-read section. Many readers will skim your abstract, so you have roughly 200 words to make a sales pitch for readers to keep reading your paper (or come to your talk) . Write clearly, don’t assume that your audience has a particular perspective, and don’t use acronyms or unnecessary jargon. A compelling message free of minutia or tangents will nudge your reader to stay and keep reading.
Your abstract should be a self-contained, stand-alone description of your study and results. It should provide the following information, in order.
Motivation: what problem are you solving, and why should the reader care? Writers often let this section dominate the abstract, feeling a need to warm up slowly before the real story starts, but you should keep it almost uncomfortably short. Don’t state widely known ideas (“recent events have shown that earthquakes can be destructive”) or provide detailed background (“in the 1990s, researchers approached this problem by…”). Spend one or at most two sentences on motivation. If the problem is self-evident to the audience based on the remainder of the abstract, I sometimes skip the motivation entirely.
Approach: what is the scope of the study, and what method did you use to solve the problem? Try to get to this as quickly as possible, as it is more valuable than the motivation. You can indicate whether the research was numerical or experimental, what types of data you utilized, and what techniques you employed. You might not initially think about this topic (maybe all of your research is numerical, so you wouldn’t consider stating this). But explaining your approach helps readers evaluate whether the paper relates to their interests.
Results: what did you learn? Try to state some specific findings. Specifics are tricky, as you can’t provide much context. Aim for statements like “the results show that A increases B by 40%,” instead of “the results show the relationship between A and B.” The former provides more information and greater encouragement to keep reading.
Conclusions: what are the implications of your findings? Comment on why your results matter and how they move your field forward.
Your abstract also serves as a personal roadmap: you can monitor that your manuscript aligns with your stated aims. So draft the abstract after your paper outline, but before you start writing other text. It forces you to assert your approach and results, which can then guide your writing. It also forces you to articulate a compelling problem and results before you write. You will undoubtedly revise the abstract during editing, but it’s invaluable to formulate your vision before embarking on too much writing.
The following examples illustrate these ideas. The coloring and notes [in brackets] point out their structure. Try labeling your abstracts like this a few times as you get used to this structure.
Estimation of fragility functions using dynamic structural analysis is an important step in a number of seismic assessment procedures. [Motivation] This paper discusses the applicability of statistical inference concepts for fragility function estimation, describes appropriate fitting approaches for use with various structural analysis strategies, and studies how to fit fragility functions while minimizing the required number of structural analyses. [Approach] Illustrative results show that multiple stripe analysis produces more efficient fragility estimates than incremental dynamic analysis for a given number of structural analyses, provided that some knowledge of the building's capacity is available prior to analysis so that relevant portions of the fragility curve can be approximately identified. [Results] This finding has other benefits, given that the multiple stripe analysis approach allows for different ground motions to be used for analyses at varying intensity levels, to represent the differing characteristics of low-intensity and high-intensity shaking. The proposed assessment approach also provides a framework for evaluating alternate analysis procedures that may arise in the future. [Conclusions]
Ground shaking intensity varies spatially in earthquakes, and many studies have estimated correlations of intensity from past earthquake data. [Motivation] This paper presents a framework for quantifying uncertainty in the estimation of correlations and true variability in correlations from earthquake to earthquake. A procedure for evaluating estimation uncertainty is proposed and used to evaluate several methods that have been used in past studies to estimate correlations. [Approach. In hindsight I would have tried to be a bit more explicit here.] The results indicate that a weighted least squares algorithm is most effective in estimating spatial correlation models and that earthquakes with at least 100 recordings are needed to produce informative earthquake-specific estimates of spatial correlations. The proposed procedure is also used to distinguish between estimation uncertainty and the true variability in model parameters that exist in a given data set. [Results] The estimation uncertainty is seen to vary between well-recorded and poorly recorded earthquakes, whereas the true variability is more stable. [Conclusions]
Following a disaster, crucial decisions about recovery resources often prioritize immediate damage, partly due to a lack of detailed information on who will struggle to recover in the long term. [Motivation] Here, we develop a data-driven approach to provide rapid estimates of non-recovery, or areas with the potential to fall behind during recovery, by relating surveyed data on recovery progress with data that would be readily available in most countries. [Approach] We demonstrate this approach for one dimension of recovery—housing reconstruction—analyzing data collected five years after the 2015 Nepal earthquake to identify a range of ongoing social and environmental vulnerabilities related to non-recovery in Nepal. If such information were available in 2015, it would have exposed regional differences in recovery potential due to these vulnerabilities. [Results] More generally, moving beyond damage data by estimating non-recovery focuses attention on those most vulnerable sooner after a disaster to better support holistic and nuanced decisions. [Conclusions]
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