1. Social aspects of data management Leen Vandepitte On behalf of WoRMS data management team

3. Back to basic biology: Symbiosis = Close interaction between 2 different species Free translation: Relationship between 2 people or groups of people that work with each other

4. Symbiosis – three forms Commensalism relationship between two living organisms where one benefits and the other is not significantly harmed or helped Mutualism relationship between two living organisms where both benefit Freely translated as “co-operation” or “collaboration” Parasitism relationship between two living organisms where one benefits while the other is harmed

5. Analogies… Data manager / parasite Data

6. Why should we manage data? Selfish reasons: – Work more efficiently – Avoid data corruption and loss Altruistic reasons: – Facilitates data exchange – Avoid data loss Where oh where did I leave that information ??!! => Altruistic reasons become selfish in the long run!

7. Why should we conserve data? Moral obligation – Price of data collection – Uniqueness of observations => you can’t measure a 2001 temperature in 2009! – Ensure long-term integrity of the data (= avoid data loss) Solution: archiving your data in a central information system

8. Documentation? Importance of metadata => “data about the data” Store metadata together with the data = working more efficiently Information on: – Who – What – Where – When – How

9. Data sharing The sense and non-sense of data sharing… How to convince data custodians to share their data? – “Stick” Legal obligations written into contracts – “Carrot” Make it advantageous for everyone to share data => breaking the prisoner’s dilemma

10. The prisoners’ dilemma Best choice => both stay silent (= both serve 1 year) BUT: no guarantee that the other one will remain silent! Actual outcome => “rational choice” is to betray or ‘cheat’ on companion

11. The prisoners’ dilemma: how does this relate to data sharing? “Cheater” earns the highest rewards: – Scientist A & B each have a dataset – A shares data, B doesn’t – B can write papers on combined dataset, A can’t How to break the prisoner’s dilemma? – Building up trust relationships – Provide in good data policies – Increase advantages of data sharing / data publication Citation of datasets Co-authorship Data sharing = iterated prisoner’s dilemma

12. How do you convince possible data providers? … or ….

13. How do you convince possible data providers? Be positive Know what you are talking about… Be honest – Data do go into the public domain: they are not 100% waterproof against mis-uses – Scientific code of conduct => ethical approach of data and information Highlight advantages of data sharing / data publishing: – Higher visibility of the research, data and scientists and their institute – Possible opportunities for collaboration with other scientists – Data go through thorough quality control Good for provider => extra QC on the data Good for OBIS & provider => high quality data offered to user

14. Explain different options on data content level: – Share only presence data – Share abundance data – Share abundance and biomass data – Moratorium period is possible Data of last 5 years not available Data of last 5 years as “presence only” available Explain different options on data exchange level: – Send files to a data manager / OBIS node Mostly done for small-scale datasets, which are no longer updated – Set-up of data exchange tool, such as IPT Explain involved work for the provider: – Limited => only asked for feedback during processing & QC – Extended => provider keeps his data and works through IPT

15. Let us analyse some situations… … statements, responses and arguments from potential data providers and data managers

16. How providers might respond… People will copy my work from the web and plagiarize it Where can one publish data? Journals will not publish primary raw data. It’s my data, why should I make it available? The data I used were not my own and I did not get permission to publish them. I have not finished analyzing the data and I may do further analysis on them. Somebody will use my data and benefit from this. Worst-case-scenario: they may be from a commercial organization! Based on Costello (2009). Motivating online publication of data. BioScience, 59 (5): 418-427.

17. The publisher may profit I fear that my data will be used for an incorrect purpose I do not have the skills to publish data on the internet Intellectual property rights related to data and databases differ between countries I will not get due recognition for creating the data … Based on Costello (2009). Motivating online publication of data. BioScience, 59 (5): 418-427.

18. The benefits of online data publication For individual scientists as a data creator Additional publications Greater citation rate Wider recognition among peers Invitations to meetings Invitations to collaborate Invitations to provide consultancy For individual scientists as a researcher and author Creators of data are known from citation and so are contactable for more information Citation of data sources adds authority that indicates their quality For editors & peer reviewers Independent verification and qualification of research findings is possible Based on Costello (2009). Motivating online publication of data. BioScience, 59 (5): 418-427.

19. For the scientific community Data can be reused for similar and new purposes Data can be integrated with other data to create new data resources For funding agencies Better financial return from research investment as a data can be used again For governments Data are easily accessible to government science advisors For society Better science Based on Costello (2009). Motivating online publication of data. BioScience, 59 (5): 418-427.

20. Society – an additional argument Societal pressure: Society expects that scientists will make their data available because most data are: – paid for by public funds – collected for the public good => public health, product safety, environmental monitoring data) Payment: – Government (directly) – University salaries (indirectly)

21. “open” versus “closed” data: A comparison of national policies regarding the availability of government data showed that open access conferred significant economic benefits by stimulating entrepreneurial use of the data by commercial companies. In contrast, restrictive data-release policies and fees for data use (which provide negligible financial return) discouraged innovation and development of data products. “small” versus “large” datasets: A large amount of more diverse data spread through many small data sets and individual scientists is not being professionally curated, yet the size of a data set is not necessarily an indicator of the data's value to science now or in the future. Based on Costello (2009). Motivating online publication of data. BioScience, 59 (5): 418-427.

22. Questions?