Reviewer response for version 1
Weber and Soneson present HDCytoData, a Bioconductor data package providing pre-formatted high-dimensional cytometry data. 
The preparation of the datasets as SummarizedExperiment and flowSet objects makes these amendable for benchmarking, a crucial step when developing new methods.
My main comment centres around the contribution of new data. 
While the curated/formatted data in the package have already been useful to the authors in their previous work, the ambition is to make it possible for others to benefit from them and, to enable this in the longer term, to expand the package with additional data. 
These contributions are anticipated to come from the original authors and, ideally, also by new contributors.
The contribution procedure, while crucial, (1) isn't described very clearly and, at least in its current form, (2) only applies to seasoned R users/programmers. 
These two points constitute a serious barrier to external contributions.
Indeed, the only information that is provided are a list of three required artefacts (objects, scripts and documentation), without details as to how to produce these, nor how to provide them. 
I would suggest to add a 'How to contribute' vignette to the package, describing all these aspects, including an example for one of the existing data. 
I would also suggest to include a contribution code of conduct, given that external contributions are explicitly advertised.
I would suggest asking new contributors to send a pull request (PR) on Github, with possible alternative methods for those that aren't familiar with GitHub. 
The use a PR provides traceability (as opposed to an email, for instance) and publicly recognises the external contribution, as PRs are publicly recorded on GitHub. 
I would also suggest to explicitly define how external contributions are to be acknowledged in the contribution guide (for example addition as a 'contributor' in the DESCRIPTION file).
These additions will clarify what is expected for a contribution to be considered, how it will be managed by the authors, and how it will be acknowledged, thus hopefully facilitating the process.
Minor suggestions:
- How can a potential user find out if/when new data have been added to the package? 
While `?HDCytoData` gives a list of dataset, a function returning a vector or dataframe with dataset names and possibly some annotation would be useful for programmatic access (given here that `data(package = "HDCytoData")` doesn't work for data on ExperimentHub).
- It could be useful to expand the 'Use cases' section with (1) example calculations of the F1 scores and Rand indices for the clustering example and (2) adding a similar short example for the differential analysis use case.
- I am curious as to why the content of the lmweber/HDCytoData-example isn't included as a vignette in the HDCytoData package (and thus lacking the usual control and documentation that comes with R packages).
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