Managing and sharing confidential data in Australian social science
1 like118 views
The document discusses the management and sharing of sensitive data in Australian social science research, focusing on the 5 Safes model which ensures data safety, appropriate use, and secure settings. It outlines the principles of open and transparent research, emphasizing the need for data accessibility and its usefulness for secondary users, while presenting the Australian Data Archive (ADA) as a model for a data access system. The document also highlights guidelines for data depositors and frameworks for responsible research conduct, proposing the ADA Dataverse as a solution for securely sharing and preserving research data.
Managing and sharing confidential data in Australian social science
- 2. Overview • The “problem” of “sensi/ve data” - the 5 Safes model • The “problem” of open and transparent research – the FAIR principles • From problems to solu/ons – Access to sensi/ve data in Australia – ADA as a model for journal data access system
- 3. Sensi/ve data • “Sensi/ve data are data that can be used to iden/fy an individual, species, object, process, or loca/on that introduces a risk of discrimina/on, harm, or unwanted aSen/on.” – ANDS Guide on Publishing and Sharing Sensi/ve Data, p.7 – hSp://www.ands.org.au/__data/assets/pdf_file/ 0010/489187/Sensi/ve-data.pdf
- 4. The 5 safes 1. Safe people: Can the researchers be trusted to do the right thing? 2. Safe projects: Is the data to be used for an appropriate purpose? 3. Safe se^ngs: Is the environment in which the analysis takes place safe? 4. Safe data: Is the data appropriately protected? 5. Safe output: Is there a low risk of disclosure in research outputs? Desai, T., F. Ritchie and R. Welpton (2016) Five Safes: designing data access for research. Economics Working Paper Series 1601, University of the West of England. hSp://www2.uwe.ac.uk/facul/es/BBS/Documents/1601.pdf
- 5. Open and transparent research • “It is the responsibility of everyone involved to ensure that the published record is an unbiased, accurate representa/on of research” – PLoS Medicine Editors. (2009) An unbiased scien/fic record should be everyone’s agenda. PLoS Medicine, 6:e1000038. DOI: 10.1371/journal.pmed.1000038 • “Improving the reliability and efficiency of scien/fic research will increase the credibility of the published scien/fic literature and accelerate discovery.” – Munafo et al. (2017) A manifesto for reproducible science. Nature Human Behaviour, 1, Ar/cle No. 0021. doi: 10.1038/s41562-016-0021
- 7. What do secondary users “need”? (or What is wanted? :-) • “We emphasize that direct access to micro-data is cri/cal for success. Alterna/ves such as access to synthe/c data or submission of computer programs to agency employees will not address the key problem of restoring US leadership with cu^ng-edge policy-relevant research.” • Card, CheSy, Feldstein and Saez, 2010 (emphasis in original) – hSp://rajcheSy.com/cheSyfiles/NSFdataaccess.pdf
- 8. What is expected? • “Here's what you need to do if you want an anonymised 1% sample of the US Census – Go to Google and type US Census 1% sample, click on link to the Census. – Download each of the state files from the FTP site and merge them yourself. Or just check things out for one of the states. Whatever you like. – Start mucking about to test whether your pet theory is plausible. • Here's what you need to do if you want an anoymised sample of the NZ Census, or a Confiden/alised Unit Record File (CURF) of any of big Stats series: – Go to Stats NZ's site, here. – Follow the instruc/ons below: …” • (Followed by several pages of instruc/ons, Applica/on Process, Assessment Criteria, Methods of Access, …) Eric Crampton, the New Zealand Ini/a/ve, Wellington, formerly University of Canterbury. hSp://offse^ngbehaviour.blogspot.com.au/2015/10/curf-and-turf.html
- 9. What do depositors want? • Is my data safe and secure? • Can others iden/fy my par/cipants? • Is my data being used “appropriately”? • Can my data really be “understood” by others? • Will someone else get there before me? • Will I be “gazumped”? • Will someone else find out what I did wrong? • Can we bridge depositor and user expecta9ons? – Remember: these are ooen THE SAME PEOPLE
- 10. Frameworks for research prac/ce • There are exis/ng Australian frameworks for researcher accountability and responsibili/es: – the Australian Code for the Responsible Conduct of Research (ACRCR), which sets out ins/tu/onal and researcher responsibili/es for conduct of research – (Note that this is currently under review) – Human Research Ethics CommiSees (HREC) • Increasingly, professional and journal requirements for data sharing: – E.g. PLOS One, AEA, DA-RT (poli/cal science) – hSps://www.aeaweb.org/aer/data.php – hSp://journals.plos.org/plosone/s/data-availability
- 11. Relevant content from ACRCR • S.2: Management of research data and primary materials – E.g. 2.7 Maintain confiden/ality of research data and primary materials – Researchers given access to confiden/al informa/on must maintain that confiden/ality. Primary materials and confiden/al research data must be kept in secure storage. Confiden/al informa/on must only be used in ways agreed with those who provided it. Par/cular care must be exercised when confiden/al data are made available for discussion. • S.4: Publica/on and dissemina/on of research findings – E.g. 4.2.3 Ins/tu/ons must ensure that the sponsors of research understand the importance of publica/on in research and do not delay publica/on beyond the /me needed to protect intellectual property and other relevant interests. • S.9: Breaches of the Code and misconduct in research
- 12. Current models in Australia ABS: • Confiden/alised Unit Record Files (CURFs) • RADL • ABS Remote Data Lab • TableBuilder ADA: • Confiden/alised Unit Record Files (CURFs) Shared (ooen remote access) infrastructure: • AURIN • SURE (PHRN) • Data linkage facili/es Ad hoc arrangements: • “Secure rooms” • Departmental arrangements
- 13. Applying the 5 Safes People Projects SeEngs Data Output CURFs Yes? Yes? Yes? YES YES TableBuilder No No YES YES YES RADL Yes? Yes? YES YES YES ABSDL Yes? Yes? YES YES YES ABS Remote Data Lab Yes Yes? YES YES YES ADA Yes? No No YES No AURIN No No YES YES Yes? SURE (PHRN) Yes Yes YES No? ??? Data Linkage facili9es No? YES Yes? YES ??? Secure rooms Yes? Yes? YES No? ???
- 16. ADA model • Safe data: data is anonymised/de- iden/fied/“confiden/alised” either prior to deposit or by ADA archivists • Safe people: data access can mediated, and users must be iden/fied and provide contact and supervisor details • Safe projects: users provide a project descrip/on • Safe se^ngs: NOT applied (but could be) • Safe outputs: terms of use
- 18. Incen/ves for Dataverse • Developed under the direc/on of Gary King (head of the Ins/tute of Quan/ta/ve Social Science) at Harvard • “The idea is to facilitate the public distribu/on of persistent, authorized, and verifiable data, with powerful but easy-to-use technology, even when the data are confiden/al or proprietary. • We intend to solve some of the sociological problems of data sharing via technological means, with the result intended to benefit both the scien/fic community and the some/mes apparently contradictory goals of individual researchers.” – Gary King. 2007. “An Introduc/on to the Dataverse Network as an Infrastructure for Data Sharing.” Sociological Methods and Research, 36: 173–199. DOI: 10.1177/0049124107306660. – Copy at hSp://j.mp/2owjuRr
- 23. Gustavo Durand Technical Lead IQSS, Harvard University August 31, 2016 CONNECTING JOURNALS TO THE DATAVERSE
- 24. WHAT IS DATAVERSE? Open Source Software Since 2006 https://github.com/IQSS/dataverse 18+ data repositories worldwide Example: Harvard Dataverse open to researchers, journals and research institutions worldwide to deposit data. http://dataverse.org https://dataverse.harvard.edu
- 25. STEP 1 – RECOMMEND A DATAVERSE REPOSITORY Some examples of Journals that recommend Dataverse: Journals can: • Recommend Dataverse in their data policy for authors to deposit data • Ask authors to send them the data citation to include in article
- 26. STEP 2 – JOURNAL DATAVERSE Journals setup a Dataverse to: • Organize all datasets per article / issue • Can review datasets before publishing them • Get a data citation w/ PID and link to the article Over 90 journals in Harvard Dataverse. Some examples of Journals/Publishers w/ Dataverses in Harvard Dataverse:
- 27. STEP 3 – DATA CURATION+ REPLICATION VERIFICATION Journals currently using this service from UNC Odum: For Journals w/ Dataverses: • That would like independent third-party curation and verification of replication data Contact Odum Archive: odumarchive@unc.edu
- 28. STEP 4 – AUTOMATED INTEGRATION (SWORD API) For Journals with a Dataverse that would like: • Authors to submit data at the same time as the article in journal system. • Data is automatically archived in Dataverse w/ data citation added to article Currently available for journals using OJS. Otherwise contact support@dataverse.org so we can work with you! SWORD
- 29. FEATURES MADE FOR JOURNALS Data File Widget to insert in article (Fall 2016) Private URL For Dataset Review (Summer 2016)