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Many authors, especially in manufacturing, use data from real manufacturing companies (not from a lab experiment) for their research. They are asked to report only the results from their analysis on those datasets in journal papers without publishing the underlying data due to confidentiality agreements with the companies. However, I see that the journals are increasingly asking the authors to deposit the raw data so that the research is reproducible. On the other hand, the companies are not ready to make their internal data go public but they are ok with publishing the summary statistics on the datasets. As a result, authors face difficulty in publishing their research results in journal papers.

Any strategies to handle this situation effectively and convince editors about the non-availability of the datasets to other researchers?

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The recommendation I always give is to clearly state where the data was obtained from and what strategy was used to select it. If it’s data owned by a commercial partner who is not willing to allow open sharing of the data, you’re unlikely to get them to change their mind, but for the end goal of supporting the reproducibility of the research, clearly stating your sources is a good second best. That will give other researchers the opportunity to approach the company directly and sign their own NDA to get access to the data.

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    This is a good answer and a reasonable solution. The decision to share data should rest with the company/institute/individual author, not the journal. – AppliedAcademic May 28 '18 at 14:12
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I am going to question the premise:

You ask: Any strategies to handle this situation effectively and convince editors about the non-availability of the datasets to other researchers?

I'm going to say: You can't and you shouldn't.

It isn't that the journal editors don't believe you when you say "This data is not able to be released for other researchers." They don't really care why it isn't available, but they do believe that without that data, it is not the kind of article they want to publish.

Some options which may be valid depending on circumanstances:

  • Find a journal that is Ok publishing without data. (For understandable reasons these might not be as good as the more stringent ones)
  • Create a new synthetic dataset, that has similar properties to your real data, and present your primary results on that. (And mention as a secondary point your results on the real data)
  • Work out what it would take to make the company happy to release the data:
    • Perhaps removing identifiers (E.g. for personal identifying data k-anonymity is a common technique)

See also the related question: Can you publish studies based on confidential customer data that comes from private companies?

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    Finding the proper journal, or other medium, is the critical thing here. But I've had journals that ask for data welcome submissions with a statement that data are restricted due to prior agreements. – mightypile May 28 '18 at 4:52
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    +1 for "You can't and you shouldn't". It's important that data's available to other researchers so that they can reproduce your results, and so that they can compare their method with yours. Publishing results based on private data is unhelpful - how is anyone supposed to evaluate/trust them? If anything, working to convince the company of this is far more useful than working to convince the journal that they should accept unreproducible work. – Stuart Golodetz May 28 '18 at 15:49
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    @Stuart Golodetz- While I'm not disputing the importance and desirability of reproducible data, the 'how is anyone supposed to trust' question seems a little odd to me. I'd like to think that in academia we operate on a certain level of trust, that people are generally being honest with their research. We maintain this by keeping a steep deterrent- loss of credibility if exposed. It would otherwise be quite impossible to check every aspect of research work and evaluate it's accuracy. There are potentially so many ways something could be misrepresented, we couldn't proceed without trust. – AppliedAcademic May 28 '18 at 16:33
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    @user153812: It's probably too strongly worded - I wasn't intending to imply that I automatically assume research based on closed data to be untrustworthy (99% of the time that's not the case). That said, data openness is one of the things that would encourage me to trust a piece of research more, all other things being equal. – Stuart Golodetz Jun 1 '18 at 8:00
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    @StuartGolodetz I think simulating a synthetic data-set and providing your source code to run with that data-set and then producing a few extra figures using real-world data too goes a long way if you can't publish the data – WetlabStudent Jan 21 '19 at 9:56
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@Lyndon White makes a good point about the journal reserving the right to decide what kind of articles they would like to publish. Though I'm in agreement, I would like to raise one point of caution.

Publishing is a commercial enterprise, and is driven by economic considerations and profit margins more than increasing reproducibility. The fact that these aspects are actively discussed on ASE is a sign that gradually researchers are becoming aware of how skewed the market is.

In this scenario, a journal could demand data on the grounds of scientific reproducibility, and make this data available to subscribers only. This feature could increase subcriptions from rival industries as well as research labs. In effect, the journal would then be selling data from one source to another. The authors would be willing to turn a blind eye because the journal is prestigious, while the source of data (eg. industry) would grow increasingly distrustful of this journal, widening the industry-academia gap. I find it difficult to see this as a good sign.

The only solution that comes to mind is the erection of mutual non-disclosure agreements between the journal and the author, that permit the author to decide (atleast in part) who gets to see/use the data. This has its own flaws, but it could be a start.

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    That said, most journals which do demand data availability expect it to be open, not behind a paywall, whether or not that paywall belongs to the publisher in question. Most publishers are unwilling to accept stewardship of data, instead expecting authors to use existing public archives to make the data available. Where publishers do facilitate data sharing themselves its through a separate product they’ve invested in, like figshare (Springer Nature) or Mendeley Data (Elsevier). – Jez May 28 '18 at 11:34
  • @Jez- thanks, I'm glad you brought up this point. I'm also glad someone down-voted my answer, because it shows that people hurt by this viewpoint are reading this. At present, publishers 'facilitate' data-sharing by shepherding authors to a separate, free service. They may (hence the 'could' in my answer) at any point, choose to charge for this. Since you mentioned Mendeley (now owned, not 'invested in' by Elsevier), I quote from their FAQs: (1/2) – AppliedAcademic May 28 '18 at 14:06
  • (1) "In future, we plan to offer paid-for versions of our repository service to academic institutions." (2)"The terms which you accept by creating a Mendeley user account and posting data, grant our service permission to 'publish, extract, reformat, adapt, build upon, index, re-distribute, link to and otherwise use [published data]'". These two points pretty much allow the potential misuse I am talking about in my answer. (2/2) – AppliedAcademic May 28 '18 at 14:07
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    I downvoted this answer not because I would be hurt by this viewpoint (I'm not), but because your "scenario" (1) is speculative at best and (2) does not help in answering the original question. – silvado May 29 '18 at 15:04
  • @silvado - Thanks for clarifying. A downvote is constructive only when you comment about the reason for downvoting, as seems to be the general practice on ASE. Hence my impression that it was either a vested or a 'troll' downvote. Anyway, I respect your opinion, but (1) my comment to Jez contains a statement from one such publisher that describes a very similar scenario ("in future we plan to..."). So while it may not have happened yet, it may be shortsighted to dismiss it as "speculative at best". – AppliedAcademic May 30 '18 at 4:57

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