Update 2023-02-03-my-experience-as-editor.md

writing/_posts/2023-02-03-my-experience-as-editor.md

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 These months are marking the end of my experience as a Section Editor for the [Journal of Water Resources Planning and Management](https://ascelibrary.org/journal/jwrmd5). Here's a list of common issues I found in the papers I handled during the past years:
 
 1. A VERY common issue I found were papers with a poor Introduction. What does that mean?
-
-1.1 Weak or confusing description of the macro problem(s) addressed in the study --> Make your problem statement clear, tangible, and accessible to a broad audience.
-1.2 Unstructured literature review, namely a long list of previously-published articles that does not identify any specific issue --> Identify the gaps in the existing literature and map them to the problem statement.
-1.3 Unclear description of the specific contributions --> Having identified macro problem(s) and gaps in the state of the art, point the reader to your contributions. What is novel? Why does it matter?
+  1.1 Weak or confusing description of the macro problem(s) addressed in the study --> Make your problem statement clear, tangible, and accessible to a broad audience.
+  1.2 Unstructured literature review, namely a long list of previously-published articles that does not identify any specific issue --> Identify the gaps in the existing literature and map them to the problem statement.
+  1.3 Unclear description of the specific contributions --> Having identified macro problem(s) and gaps in the state of the art, point the reader to your contributions. What is novel? Why does it matter?
 
 2. A second issue concerns the experimental setup.
-2.1 Unclear descriptions of the data used --> This may sound basic, but make an effort to help the reader understand which data you used, and why.
-2.2 Lack of a benchmarking exercise. Many papers introduce an algorithm / model to improve the current methodological approach to a given problem .. shouldn't we compare it against the existing approaches? --> Consider a benchmarking exercise!
-2.3 Unclear description of modelling assumptions --> Don't be shy here ... describe the assumptions and explain why you adopted them. I found that doing this works much better than avoiding to introduce them. Reviewers will catch them ..
+  2.1 Unclear descriptions of the data used --> This may sound basic, but make an effort to help the reader understand which data you used, and why.
+  2.2 Lack of a benchmarking exercise. Many papers introduce an algorithm / model to improve the current methodological approach to a given problem .. shouldn't we compare it against the existing approaches? --> Consider a benchmarking exercise!
+  2.3 Unclear description of modelling assumptions --> Don't be shy here ... describe the assumptions and explain why you adopted them. I found that doing this works much better than avoiding to introduce them. Reviewers will catch them ..
 
 3. Lack of a proper discussion. Often, discussions presented a summary of methods and results ... that's an extended abstract, not a Discussion. So, what should one write?
-3.1 --> Summarise your results, but then explain why they matter ... what's their broader impact? What are the implications of your findings?
-3.2 --> Openly discuss your findings in light of modelling assumptions and limitations.
-3.3 --> race back to the opening of your paper--to the big problem(s)--and ask yourself how your work helped tackle it ... and, finally, create a roadmap for what should be done next.
+  3.1 --> Summarise your results, but then explain why they matter ... what's their broader impact? What are the implications of your findings?
+  3.2 --> Openly discuss your findings in light of modelling assumptions and limitations.
+  3.3 --> race back to the opening of your paper--to the big problem(s)--and ask yourself how your work helped tackle it ... and, finally, create a roadmap for what should be done next.
 
 4. This last point is not an issue, just a wish: several papers are not making their data, code, and protocols available. What's the point of proposing a new algorithm if nobody can use it?