I would strongly encourage research students, scientists and science writers to read Noah Gray’s excellent piece at the Huffington Post science section. Titled Abstract Science, it takes apart the key elements of good scientific abstracts.
Those that write science for general readers, including on blogs, should read it too. In breaking down his example abstract Gray, is a senior editor at Nature, has also offered advice that might serve as a check-list to ensure they address each of elements their readers need.
Readers are best to read his article, my condensed presentation here is limiting and you’d miss his writing and tips. He notes five key elements, presenting the value of each along with advice (the explanations here are paraphrased from his article):
- Context – the background or history of a field establishes a framework to conceptualize the purpose of a study
- Question or Purpose – make explicit the objectives (ultimate objectives may need to be inferred)
- Methodology – writers addressing general audiences will usually obtain this from the body of the paper
- Results – the ‘factual’ results, the data observed
- Interpretation – what was inferred from the data observed
- Conclusions – these might include pointers at future research or speculation (which should be clearly identified as such)
One additional element in presenting science to a general audience is interpretation of domain-specific phrases, casting these into more widely-understood phrasing whilst avoiding words that confuse general readers. Andrew Revkin, writing at the New York Times Opinion Pages, raised this issue using as his example “here we show”,
I think that [NASA climate scientist and Real Climate blogger, Gavin Schmidt’s] conclusion misses the reality that, particularly in the world of online communication of science, abstracts are not merely for colleagues who know the shorthand, but have different audiences who’ll have different ways of interpreting phrases such as “here we show.”
This might be re-phrased “here we argue” – research papers present to the wider scientific community the research team’s case for their interpretations and conclusions. (Authors can, however, show data,* as data ‘just is’. It is the methodology, interpretations and conclusions that are argued.)
* When researchers argue over data, it would be more correct to say that they are arguing over the methodology that the data was derived from. Data itself are ‘merely’ recorded observations. You can argue how the data was generated, how it was recorded or observed, but the numbers themselves should be just whatever they are. (Baring fraud, of course… See also Ivan Oransky’s coverage of a PNAS paper arguing that the majority of retractions are due to misconduct. As I was writing this fellow sciblogger, Aimee, has put up a post about this issue.)
Other articles on science writing at Code for life: