|Review Comment: |
This manuscript was submitted as 'Tools and Systems Report' and should be reviewed along the following dimensions: (1) Quality, importance, and impact of the described tool or system (convincing evidence must be provided). (2) Clarity, illustration, and readability of the describing paper, which shall convey to the reader both the capabilities and the limitations of the tool.
Traits are an important topic in biodiversity research and have gained considerable interest in the last few years. Tools to ease integration of trait data are desperately needed. Thus, the paper addresses a real need of the research community.
Overall, the paper is well-written and very readable. However, in some sections, I would have liked some further explanations (see below).
The SWJ journal is read primarily by computer scientists. For those readers, providing a few examples of traits (plant height, leaf length, fur color or whatever else) might be helpful.
You mention that automated methods for trait measurement accelerate data generation. That is, of course, true - but don't they also promote standardisation?
You state that no project provides trait data across organisational groups. Is that really such a big disadvantage? Aren't traits mostly rather group specific and thus, wouldn't it suffice to have global systems for plants (like TRY) and other groups and build bridges between those? What is the advantage of having the integrated system from the start?
Figure 1: Please provide an explanation of the meaning of the arrows and their direction in Figure 1. To me, for instance an arrow from Taxon to Occurence seems to indicate that I can move/reason/.. from a specific taxon to occurences of that taxon, but not the other way round. Is that the case? If so: Please provide the rationale behind it.
You build on quite a number of existing ontologies. Notably missing from this list are OBOE or BCO. Wouldn't one of those two have been a good starting point to model occurences, events, and measurements?
I would have liked to see a more complete description of the data model, in particular the associated meta data which seems to carry quite a bit of the relevant information. Please extend this description.
What are "computability opportunities and requirements for future research"?
In computer science, "computability" describes the principle possibility to compute something effectively (thus is a measure of problem complexity) - I don't think this is what you are refering to here, is it?
When describing the ingest process, you do not talk about data quality issues. Do you assume that data is cleaned prior to being added? For instance, are you sure that the sample columen "body length" in a specific data sets contains values that follow exactly one definition of body length (.eg. the head-body length)?
3.5. Do you have a way to deal with changes in the defintions?
You write that the results of your informal evaluations were mostly positive. While this is good, of course, it would be interesting to also get some information on which areas could do with some improvement and what that could be.