1) Put it on figshare or github. Your website might be dead next year. Those services have a better chance of survival.

2) CSV, JSON works fine if your dataset is just a few numbers and strings. GitHub will preview csv files as HTML tables.

3) If you need the efficiency of binary data and more robust data containers I would look into - Parquet https://parquet.apache.org/documentation/latest/ and - Arvo http://avro.apache.org/ http://blog.cloudera.com/blog/2009/11/avro-a-new-format-for-...

4) DataScientists work with R/Pandas "DataFrames". If you are familiar with either one, import the data into a data frame and use an export method to do the serialization for you: https://pandas.pydata.org/pandas-docs/stable/api.html#id12

I vote CSV.

- simple

- lightweight

- open

It's easy for the consumer of your data to convert a CSV to whatever format they need.

- spreadsheet, for personal analysis

- SQL database, for industrial-strength analysis

- HTML, for pretty output to their users

Oh, and don't forget that CSV is a rigorous data format, with many tricks. Don't just append some text together, separated by commas, and call it CSV. Instead, use a dedicated lib to create the CSV for you.

Suggest CSV plus a Table Schema in a nice little Data Package: https://frictionlessdata.io/data-packages/

There's a wide set of tooling for Tabular Data Packages, plus underlying data is CSV which anyone can use.

If you want this done automatically you can just publish your CSV to https://datahub.io/ and your Tabular Data Package is made automatically for you.

Csv and a permitive license like CC0. Provide good meta data to make provenance easier. Mark up your dataset with schema.org.

CSV is your best option. CSVW is a CSV+a metadata file to convert the tabular data to graph data (plus it types the nodes and relationships of the graph). You may want to have a look at it.

You probably ask here the most controversial question of the Web today. The fact that CSV is the prefered answer is a HUUUGE fail.