While I am quite proficient at creating places, projects and making identifications on iNaturalist as well as training others on these aspects, I’m a bit ill-informed on how I can process a project’s data once I have downloaded it in excel form.
Are there any specialist apps/programs that you or iNaturalist use to easily create graphs/tables/trends/projections (rather than excel itself!) etc or are there any tutorials, guidance, advice that iNat have posted on this topic? Like ‘Analyse iNat data 101’ or something! (I’ve had a look for tutorials and other threads but can’t any, unless I’m blind!)
Any advice you could give would be really appreciated as I work a lot with schools and community groups and I want to give them the opportunity to further analyse their own data and draw comparisons with others.
While I haven’t done it with iNaturalist data, if you are trying to start doing data analysis on large datasets I suggest learning some python. Packages in python such as Numpy, Pandas, Scipy, Scikit-learn and Matplotlib go awfully far for data analysis.
I suspect it is possible to use pyinaturalist and the API reference for getting data out of iNaturalist as well, but it seems like you were already able to extract to excel already.
Data analysis and visualization is really a field of its own. There are many different software applications, like R, python, etc, but you really need to have a working knowledge of these programming languages. If you’re only familiar with Excel, that might be the best place to start. There are not any programs specifically for easily working with iNat data.
To analyze data in a meaningful way you need to have an idea of what sorts of questions you’re trying to answer and a really good understanding of the limitations and biases of your data.
The software and the programming language are just tools, nothing more. They don’t magically give you meaningful results, in fact, they often provides utterly worthless results when used without a very specific goal, understanding of the data limitations and biases, and the purpose and use of that specific tool.
Once you have a clear understanding of those limitations the question of what exact additional tool becomes less important and you’ll find that many different tools are more than adequate to get meaningful results.
One of the issues with iNat data is that it’s heavily biased toward common species in areas with lots of human activity. In and of itself it’s not especially good for many types of analysis, but when combined with other types of data (tourism/population density info, for example) or when specific aspects of the data (phenology in a well populated area with a common species a long use-time, for example) you can get at some interesting questions and use simple tools (excel, for example) to do so.
Like a lot of data analysis, you’re limited mainly by your imagination and the quality of the data, not by the tools at your disposal.