Tools and Resources for Open Science
Preregistration template for scoping reviewsGeneral OSF template
Transparent Processes and Research Design
Example guidelines for anonymizing data
DOCA - Database of Variables for Content Analysis
ASReview - Open source software for systematic reviews
Data Storage and Sharing
Open Access Publications
Other awesome things to help good quality research, dissemination
Miro - Collaborative multimedia whiteboards (for sync or async work)OBS Studio – Screen capture; Open broadcaster softwareKialoEdu – For asynchronous, structured debates and discussionsWorld Health Organization – Informed consent formsCOS Reproducible Research Training + IRB and Consent form examplesOpen (qualitative) data consent supplement for informed consent forms (draft)European Charter & Code for Researchers – General ethics considerationsInkScape – Free and open source vector graphics editor; tutorials
DreamPuf - Synchronous mind mapping with code
Figma - Free vector graphics editor
Otter.ai – Transcription toolKaggle – Machine learning and data science communityGuidelines on picking colors for visualizationsCRediT – Contributor roles taxonomyRegular expression tutorialRStudio cheatsheets
Some thoughts on Open Science in qualitative and quantitative ethnographic research
Many people think Open Science = Open Data, and precisely because of this reason, it is less feasible in qualitative research
I've heard colleagues say: Open Science (OS) principles don't apply to qualitative research. What exactly would we make public? Our data can be anonymized to an extent (e.g. proper nouns omitted or replaced with pseudonyms), but can it really, truly be anonymized so that the interviewee is unrecognizable, even to their own social environment? Sometimes we analyze life stories, personal experiences, sensitive topics...how is it possible to anonymize these narratives? And wouldn't we be omitting exactly the things that qualitative researchers are so keen on scrutinizing? Supposing we could anonymize texts to a full extent (beyond proper nouns), would that even be useful to other researchers?
These are incredibly important questions and things to discuss more widely among qualitative and quantitative ethnographic researchers. I'm not going to address open qualitative data here, it's far too complex. I want to talk a bit about everything else. OS is much more than "just" making your data publicly accessible. It is about being open concerning the entire research process, well, as open as possible. OS is a spectrum; it is not a dichotomy.
Qualitative researchers might ask: Well, what else IS there to share besides data? There's a lot you can make public that other researchers can benefit from! Without attempting to compile ALL aspects of qual research that can be made open, below are some ideas on what you CAN share with others; and although each researcher should consider the implications for their own project, these probably don't entail any legal, ethical, or moral complications.
So, what can be made public in a qualitative research project?
- Conceptualization (key terms and their descriptions/definitions, theoretical framework, etc.)
- Research design/Operationalization (research questions and type of measurement, sampling strategy, sample size planning, recruitment, etc.)
- Process of data collection (tools: interview structure, focus group discussion guide, etc., stopping criteria, type of data collected)
- Codes (code development, different versions of code structure and round/s of triangulation, final codebook, etc.)
- Segmentation and data transformation (if qual data was segmented what considerations were behind that, how was it achieved, etc.)
- Coding process (changes in codes while coding: merges, splits, additions, etc., software used, number of raters, inter-rater reliability measures, etc.)
- Analysis (analytical approach, analytical tools, analysis scripts, etc.)
- Credibility strategies (respondent validation, peer debriefing, etc.)
- If you are using Epistemic Network Analysis: model parameters: unit, conversation, stanza window, SVD/means rotation, edge weights, etc.
...And, of course, not merely stating the decisions you have made in your process, but also the justifications and reasoning behind these decisions. Making these aspects of qualitative research initiatives public not only facilitates the transparency, reproducibility, and interoperability of a project, but also helps future researchers make better decisions. Sharing our challenges and the solutions we came up with adds to a pool of knowledge that will advance science in general.