| ArthurPSmith added a comment. |
Some references on why CC0 is essential for a free public database:
https://wiki.creativecommons.org/wiki/CC0_use_for_data
"Databases may contain facts that, in and of themselves, are not protected by copyright law. However, the copyright laws of many jurisdictions cover creatively selected or arranged compilations of facts and creative database design and structure, and some jurisdictions like those in the European Union have enacted additional sui generis laws that restrict uses of databases without regard for applicable copyright law. CC0 is intended to cover all copyright and database rights, so that however data and databases are restricted (under copyright or otherwise), those rights are all surrendered"
https://www.nature.com/nature/journal/v461/n7261/full/461171a.html
"Although it is usual practice for major public databases to make data freely available to access and use, any restrictions on use should be strongly resisted and we endorse explicit encouragement of open sharing, for example under the newly available CC0 public domain waiver of Creative Commons."
https://blog.datadryad.org/2011/10/05/why-does-dryad-use-cc0/
"Dryad’s policy ultimately follows the recommendations of Science Commons, which discourage researchers from presuming copyright and using licenses that include “attribution” and “share-alike” conditions for scientific data.
Both of these conditions can put legitimate users in awkward positions. First, specifying how “attribution” must be carried out may put a user at odds with accepted citation practice:
“when you federate a query from 50,000 databases (not now, perhaps, but definitely within the 70-year duration of copyright!) will you be liable to a lawsuit if you don’t formally attribute all 50,000 owners?” Science Commons Database Protocol FAQ)
While “share-alike” conditions create their own unnecessary legal tangle:
“ ‘share-alike’ licenses typically impose the condition that some or all derivative products be identically licensed. Such conditions have been known to create significant “license compatibility” problems under existing license schemes that employ them. In the context of data, license compatibility problems will likely create significant barriers for data integration and reuse for both providers and users of data.” (Science Commons Database Protocol FAQ)
Thus,
“… given the potential for significantly negative unintended consequences of using copyright, the size of the public domain, and the power of norms inside science, we believe that copyright licenses and contractual restrictions are simply the wrong tool [for data], even if those licenses and contracts are used with the best of intentions.” (Science Commons Database Protocol FAQ)"
https://pietercolpaert.be/open%20data/2017/02/23/cc0.html
"Requiring that you mention the source of the dataset in each application that reuses my data, still complies to the Open Definition. There is no need to argue with anyone that uses for example the CC BY license: you will only have the annoying obligation that you have to mention the name in a user interface. This is useful for datasets which are closely tied to their document or database: when for example reusing and republishing a spreadsheet, I can understand you will want that someone attributes you for created that spreadsheet. However, for data on the Web, the borders between data silos are fading and queries are evaluated over plenty of databases. Then requiring that each dataset is mentioned in the user interface is just annoying end-users."
"The share alike requirement, as the name implies, requires that when reusing a document, you share the resulting document under the same license. I like the idea for “viral” licenses and the fact that all results from this document will now also become open data. However, what does it mean exactly for an answer that is generated on the basis of 2 or more datasets? And what if one of these datasets would be a private dataset (e.g., a user profile)? It thus would make it even more unnecessarily complex to reuse data, while the goal was to maximize the reuse of our dataset."
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