The ten points you proposed are very interesting and important - definitely a great resource! Here's my two cents:
I particularly like point 10, as it is not often done but so many patterns change with time and cost/reward can be high.
I believe the first point is of particular relevance, and it would be hard but important to determine the scope/boundary of your study and how many studies would actually be needed to cover the entire spectrum. Performing a literature review beforehand is essential in order to gain valuable insights and plan the study, but also by knowing the methodologies used. Here, an approach based on systematic quantitative literature review (https://doi.org/10.1080/07294360.2013.841651) that could help for example. There are various tools available to do a detailed literature search. It could be beneficial to have a separate point dedicated to this!
It's the current trend by the use of AI could probably be advantageous for the first points. Actually, something chatGPT suggested that my be relevant: "Gain permission to access necessary sites" and "consider the ethical nature of the research".
With respect to the statistical analyses, it would be useful to run analyses on previous data obtained from literature or even toy examples in order to evaluate if the experimental design is appropriate and well adapted to the question. Additionally, it may be useful to conduct a power analysis to know if any signal could actually be detected and therefore if the hypothesis would be tested appropriately. Finally, when all is well prepared, on might consider preregistering the study (https://www.cos.io/initiatives/prereg), it is likely that in the future, the study with such considerations, will become the norm, for good reasons.
1. Application of modeling approach to make precise predictions beforehand. Not always is it necessary to assess expensive and valuable data, but playing through the scenario would help a lot.
2. Handling every Ressource as if they are absolutely valuable, and lost irreversibly after use. They actually are and keeping this in mind would change the way we deal with resources of any kind.
For any global ecological study, we must make the sampling efforts global. An ecological question, theory, or phenomenon fit for the global north may not necessarily represent the ecological context of the global south.
The ten points you proposed are very interesting and important - definitely a great resource! Here's my two cents:
I particularly like point 10, as it is not often done but so many patterns change with time and cost/reward can be high.
I believe the first point is of particular relevance, and it would be hard but important to determine the scope/boundary of your study and how many studies would actually be needed to cover the entire spectrum. Performing a literature review beforehand is essential in order to gain valuable insights and plan the study, but also by knowing the methodologies used. Here, an approach based on systematic quantitative literature review (https://doi.org/10.1080/07294360.2013.841651) that could help for example. There are various tools available to do a detailed literature search. It could be beneficial to have a separate point dedicated to this!
It's the current trend by the use of AI could probably be advantageous for the first points. Actually, something chatGPT suggested that my be relevant: "Gain permission to access necessary sites" and "consider the ethical nature of the research".
With respect to the statistical analyses, it would be useful to run analyses on previous data obtained from literature or even toy examples in order to evaluate if the experimental design is appropriate and well adapted to the question. Additionally, it may be useful to conduct a power analysis to know if any signal could actually be detected and therefore if the hypothesis would be tested appropriately. Finally, when all is well prepared, on might consider preregistering the study (https://www.cos.io/initiatives/prereg), it is likely that in the future, the study with such considerations, will become the norm, for good reasons.
Thanks!
Thanks very much for writing. These are great points, thanks a lot! I agree with all of them.
1. Application of modeling approach to make precise predictions beforehand. Not always is it necessary to assess expensive and valuable data, but playing through the scenario would help a lot.
2. Handling every Ressource as if they are absolutely valuable, and lost irreversibly after use. They actually are and keeping this in mind would change the way we deal with resources of any kind.
Nice additions, thanks!
For any global ecological study, we must make the sampling efforts global. An ecological question, theory, or phenomenon fit for the global north may not necessarily represent the ecological context of the global south.
Would appreciate hearing your thoughts on this list and in particular if you have any ideas about what points can be added.