1. Consider non-proprietary format as a good practice
You might find an argument in the Tim Berners-Lee 5 star approach. When discussing Open Data (something we should be embracing more in academia as well), he presents the following:
Under the star scheme, you get one (big!) star if the information has
been made public at all, even if it is a photo of a scan of a fax of a
table -- if it has an open licence. The you get more stars as you make
it progressively more powerful, easier for people to use.
★ Available on the web (whatever format) but with an open licence, to be Open Data
★★ Available as machine-readable structured data (e.g. excel instead of image scan of a table)
★★★ as (2) plus non-proprietary format (e.g. CSV instead of excel)
★★★★ All the above plus, Use open standards from W3C (RDF and SPARQL) to identify things, so that people can point at your stuff
★★★★★ All the above, plus: Link your data to other people’s data to provide context
It is best to publish in an non-proprietary format (csv would be fine) as you claimed.
Stars 4 & 5 are for Linked Data structure which is a nice thing to aim.
2. Publish in a reliable repository thinking long-term preservation
Citation and versioning are very important if you want to alter something on your data-sets in the future.
I would recommend you publish your data in Figshare. Research made publicly available of figshare gets allocated a DataCite DOI at point of publication. It supports versioning as well.
Another alternative for a repository is DataVerse suggested by Thomas below