How to argue with a closed scientist

• Counter argument 1: A lot of the invested time is “pre-loaded” to an earlier point in time and if you continously apply them it should not feel like a huge effort at the end (“growing pains” when changing the infrastuctre)
  • Connected counter argument: if it prevents problems from occuring, it might actually safe time on the long run
• Counter argument 2: Also, if it takes more time, but the studies are objectively better the time investment should not matter that much; might be good to have an example ready
• Counter argument 3: You can use extra output (e.g., published datasets) as an academic output on your CV
• Counter argument 4: It is part of your work (might not work)

• Counter argument 1: If any part of your work is open it is actually more difficult to be scooped as they are often time stamped
  • Timestamping is not sure-fire. A researcher could scoop you and publish in journal X. Journal X may not care about your timestamp and refuse to retract. If you then try to publish in journal Y, journal Y may say “this is already published in journal X”. Suggested response to this: Journal or university lawyers can help you get scooping articles retracted if you can prove it, and time stamps will help with that.
• You could still share meta-data/information about what data you have. Would still be useful to researchers to avoid double-work, and they could contact you to ask for specific data, which would make reuse at least possible in principle.
• You could share data after you have published. If you share data bit by bit over multiple papers, resources like DANS can help you link data across multiple projects together.

• Sometimes it is not true: Preregistration just moves the effort on the timeline (prior to data collection instead of after)
• Sometimes it is true:
  1. “Yes you are right, and it should not be the case” —> we should make it more easy and efficient;
  2. “You are saving time for later” does not always hold, but it might be worth it to take this extra time now (e.g. for reuse later)
• It is beneficial for the community
• You can get help (data stewards, consultants)
• Question back: “How can I help you?”
• Advocate for ‘slow science’ (DORA: quality over quantity)

• sustainability - tuition from students, not education/learning materials
• selling courses/materials
• materials - IP & copyrights
• quality questioned
• to convince - carrot & stick; top & bottom
• open publication

Vriend of Vijand Podcast (https://www.nporadio1.nl/podcasts/vriend-of-vijand): Sharing data is dangerous - this is how Pakistan got the nuclear bomb.

• Results can be a harm to society if they come out and misuse.
• Ethical issue: If there is such risk should the initial research be even done? or should you keep the research data closed due to the risk of misuse?
• Should you even publish on the data at all if it poses such a risk?
• Nothing is black or white, some bad things can happen but several good things can also happen (such us in the case of open Covid data).
• in the DMP there is a question of reasons not to publish data, point at which it could be considered (perhaps in a committee) whether it should be published.

The costs: you need money to keep OS publishing going (website, editing, DOIs)

• You shouldn’t publish in ‘pay to publish’ journals
• There are open source publishing templates, editors can be funded through their unis

• Two extremes of either doing everything of OS or if too difficult - do nothing at all
• Opportunities naming instead of the checklist “Am I open enough?”
• Communicate practical examples of why OS is good for your research
• How to protect individual information in shared data? – Work on access conditions, i.e. not completely open, but already some info is open (e.g. metadata)
  • Open enough to prevent too many requests for closed data
• Requests about correctness of entries rather than reuse —> an ideal of reusable OS is still in an unforeseeable future

• The first time it is, then you get used to it.
• Learning something new is always an effort.
• Think about your values, is it worth it? It gets easier.
• OS is about making conscious decisions, no checklist.

• Its TRUE, but is the institution support you?
• Where are your priorities: conscious decisions to use the most convenient
• Most valued by your institution
• Things like learning RStudio
• What are the quick wins for making your data open or fair?
• Institutionalized decisions:
  • Current code of conduct: institutions fair data
  • To offer support and the tools the offer
• ABOUT PRIORITIES

the value of equity in OS infrastructures (os preprint infrastructures are not accessible for everyone in the world, because of infrastructural issues (internet speed)).