![“Um, yeah [laughs]”
Challenges in opening up qualitative research data
Paula Goodale ¦ Information School ¦ University of Sheffield
@PaulaGoodale ¦ @LifeOfData
#iFutures 7/7/15](https://image.slidesharecdn.com/ifutures2015pechakucha-150710141911-lva1-app6891/85/Challenges-in-opening-up-qualitative-research-data-1-320.jpg)
![Open research data:
Web site, online data archive
Image: Jonathan Gray [Flickr]](https://image.slidesharecdn.com/ifutures2015pechakucha-150710141911-lva1-app6891/85/Challenges-in-opening-up-qualitative-research-data-5-320.jpg)
![Anonymising participants:
“Who’s that girl [boy]?”:](https://image.slidesharecdn.com/ifutures2015pechakucha-150710141911-lva1-app6891/85/Challenges-in-opening-up-qualitative-research-data-10-320.jpg)
![“Umm, yeah [laughs]”
(or, a transcript is a transcript, is not a transcript…)](https://image.slidesharecdn.com/ifutures2015pechakucha-150710141911-lva1-app6891/85/Challenges-in-opening-up-qualitative-research-data-13-320.jpg)
![Lessons learned [1]: informed consent
Make sure participants read and understand the small print!
I have read the small print](https://image.slidesharecdn.com/ifutures2015pechakucha-150710141911-lva1-app6891/85/Challenges-in-opening-up-qualitative-research-data-17-320.jpg)
![Lessons learned [2]: Put yourself in their shoes…
No ‘one-size-fits-all’](https://image.slidesharecdn.com/ifutures2015pechakucha-150710141911-lva1-app6891/85/Challenges-in-opening-up-qualitative-research-data-18-320.jpg)
![Lessons learned [3]: Compromise
Some open data better than no open data?](https://image.slidesharecdn.com/ifutures2015pechakucha-150710141911-lva1-app6891/85/Challenges-in-opening-up-qualitative-research-data-19-320.jpg)
Challenges in opening up qualitative research data
- 1. “Um, yeah [laughs]” Challenges in opening up qualitative research data Paula Goodale ¦ Information School ¦ University of Sheffield @PaulaGoodale ¦ @LifeOfData #iFutures 7/7/15
- 2. The Secret Life of a Weather Datum www.lifeofdata.org.uk
- 3. Four contrasting, but inter-connected case studies Data Production Citizen Science Financial Markets Climate Science
- 4. Thematic analysis Policy issues The journey of the weather datum Socio-cultural values
- 5. Open research data: Web site, online data archive Image: Jonathan Gray [Flickr]
- 6. Data collection: “The best laid plans of mice and men…”
- 7. Data collection: “Nothing to see here…”
- 9. Open research data: Which data? Which licence?
- 10. Anonymising participants: “Who’s that girl [boy]?”:
- 11. Redactions: Commercially sensitive information, controversial points of view, humour, personal information about 3rd parties
- 12. Modifications: Personal & professional opinions. criticisms, humour, 3rd party anonymisation, clarifications…
- 13. “Umm, yeah [laughs]” (or, a transcript is a transcript, is not a transcript…)
- 14. Audio: All or nothing No audio Full audio Clips
- 15. “Our technology, our machines, is part of our humanity. We created them to extend ourselves, and that is what is unique about human beings.” (Ray Kurzweil)
- 16. People and places: Individual permissions, public spaces
- 17. Lessons learned [1]: informed consent Make sure participants read and understand the small print! I have read the small print
- 18. Lessons learned [2]: Put yourself in their shoes… No ‘one-size-fits-all’
- 19. Lessons learned [3]: Compromise Some open data better than no open data?
- 20. Would I do it again? $1 million question?
Editor's Notes
- #9: Limited advance notice of interviewees No / limited observation Accompanied by the press officer Use of powerpoint to steer the narrative No video
- #10: Transcripts, audio recording of interview, photos, observation notes Full text/audio and quotes/clips Usage Attribution-Noncommercial-No Derivative Works 3.0
- #11: Anonymising participants Person level – option – full details inc. name, title/org, org only, full anonymisation Some organisations could not be anonymised – e.g. the Met Office Citizens, academics, weather station not anonymised – Met Office, commercial, govt – mostly anonymised
- #13: Not deleted, but changed in some way
- #14: Figures of speech, colloquialisms, hesitations,
- #18: Black and white Make sure they read and understand Follow up on permissions/multimedia release sooner rather than later Keep good records
- #19: Citizens vs academics, vs scientists vs govt vs commercial Disciplinary differences Level of formality Level of risk
- #20: Negotiate, compromise, flexibility, some data better than no data? We archived 73 images (most), 10 transcripts (half) and 5 audio files (quarter) – some edited