Based on the book Scientific Writing 2.0: a Reader and Writer’s guide, this course promotes clarity, fluidity, conciseness, and organization in scientific writing. As readers, we often approach scientific literature with a sense of dread. It is as if by the very nature of being scientific, journal papers become dry, tedious, and generally unpleasant to read. After this course, you will see that this correlation is not a necessity - with the proper writing skills, any journal paper can be both clear and interesting, including yours.
More than just a course on scientific writing, we will arm you with the tools to increase your chances of publication. Take a sneak peek into the reviewer's process, and understand the editor's considerations. With some tweaks, your paper can make it to the editor's desk without being "vertically filed" into the trash can. You will learn how to write for the reader, not just in theory, but in practice. In order to do this, course participants will participate in rewriting exercises with the reader scientist in mind (and especially the reviewer and editor). You will be given checklists and open-source assessment tools created specifically for this class (SWAN, etc.) to control the quality of your figures and of your manuscript’s title, abstract, introduction, structure, conclusions and references. Finally, you will learn how to write fluidly to maintain the attention of the reader. |
Put yourself to the test
Take a look at the two paragraphs below
Original manuscript |
Revised manuscript |
If molecular typing data pass through curating without issue, they are labeled as ‘approved’ and put at the disposal of the epidemiologists for further analysis. If the data do not meet the criteria set according to the relevant standard operating procedures that define the minimum required quality acceptable, for example, when the gel electrophoresis image is not considered appropriate for cluster detection/analysis, they are labeled as ‘rejected’ and will not be included in the cluster analysis exercises. Molecular typing data that meet the minimum required quality criteria in parts only may be labeled as ‘acceptable for outbreak’, only when related to an ongoing event, to support scenario generation analysis. For the data labeled as ‘rejected’, an open comment text field for the entire electrophoresis image is filled in by the curators in order to explain the reasons for the rejection. If there is a need for better quality data, in particular when data are labeled as ‘rejected’, the curators can correspond through email with the data provider in order to explain why the data was rejected and to recommend possible ways to correct that situation. (185 words)
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All molecular typing data approved at the end of the curating process, are marked accepted and made available for epidemiologic analysis. Data partially meeting the minimum required quality criteria are marked acceptable for outbreak and only used to generate scenarios related to an ongoing event. Data not meeting the minimum required quality criteria (e.g. the curator considers a gel electrophoresis image unsuitable for cluster detection/analysis) are marked rejected and excluded from cluster analysis. The reasons for rejection are stated in the image comment field. Should better quality data be needed, the curator corresponds with the data provider to suggest possible corrections. (102 words)
45% shorter. –Sentences on average 16 words shorter. |
The revised manuscript is visibly far ahead of the original in both clarity and conciseness. To conduct this transformation, ten problems with the original text were corrected. Can you determine all ten?
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