I've just started reading papers about speech recognition and algorithms on medium sized graphs (~800,000 nodes and 4,400,000 edges with some connected text data). I think a problem of these papers is quite often, that it is difficult to check the experimental results. It is difficult to check them because of two reasons:
- The source code that was used to generate the results is not (publicly) available
- The data is either not at all available or it is not clear which version of the data was used
When I start writing papers, I would like to make it easier to check the results.
The first problem is easy to solve: I can simply provide the source code (e.g. on GitHub or my personal web space).
The second problem seems to be much harder to solve. The data is often quite big (speech recognition: several GB; graphs: about 2GB). This is too much to upload it on my personal webspace / GitHub.
How can I show which data I'm using in my paper? (Currently I give a link to the data source and note the data when I've downloaded it. Additionally, I note the date/version of the source if possible.)
Are there projects that try to solve this issue? (e.g. by providing space for important / interesting projects like dblp, a version history and good download speeds)