We intend to publicly publish a data set in the machine learning (ML) area, which consists of sensory data gathered in a technical process.
The first and most common possibility is the UCI ML repository (http://archive.ics.uci.edu/ml/index.html). One drawback of this repository is that no persistent identifier like digital object identifier (DOI) is offered.
Do you know an alternative in the ML area offering the possibility publishing a data set publicly and persistently accessible? My research did not bring up any alternative.
Thanks a lot!
Best regards,
Uwe
While not specific to machine learning, figshare allows you to publish an arbitrary piece of research output and make it citable with a DOI. This includes data. Take a look at this blog entry, for instance:
http://figshare.com/blog/All+research+outputs+should+be+citable/32
While not specific to machine learning, figshare allows you to publish an arbitrary piece of research output and make it citable with a DOI. This includes data. Take a look at this blog entry, for instance:
http://figshare.com/blog/All+research+outputs+should+be+citable/32
Hi Uwe,
Another option for you might be Zenodo.org. It is developed by CERN under the EU FP7 project OpenAIREplus. The portal allows to publish any kind of dataset with an upper limit of 2GB at the moment. According to the project, the limit might be extended to 10GB in the future. All uploads get a DOI.
It might be worth taking a look at it.
Cheers,
Henning
Hi Uwe Mönks, ResearchGate offers free DOI for publications. Why don't you try?
Thank you so much for all your answers! I took a look at both, figshare and Zenodo. As Zenodo is hosted by a European research organisation, this is presumably my favourite. Nevertheless, figshare appears to be a promising alternative.
Thank you also for the hint on ResearchGate's DOI functionality. I did not know this until now.
Cheers,
Uwe
Dear Nada,
If I understand correctly, Microsoft Azure is to setup a whole machine learning process chain. Here, my problem is regarding data set storage and publication only, with the constraint of an offered DOI. Thus, Azure does not seem appropriate to me at all.
Nevertheless, thank you very much for your input!
Uwe
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