Open badges to acknowledge open science, now uploaded to OSF: https://osf.io/sxb4t/
The badges were inspired by:
and drawn in LibreOffice: source file on OSF
In the age of datalinkage, protecting microdata is as relevant as ever. Fortunately, there are R packages available to help:
- https://github.com/sdcTools/sdcMicro, also offering access via a Shiny interface
- https://github.com/J-PAL/PII-Scan is an R script scanning Stata (.dta),SPSS (.sav), CSV, and even SAS (.sas7bdat) datafiles and flags potentially personally identifiable information
- https://github.com/PovertyAction/PII_detection is a similar tool
That’s another excuse for not sharing data busted.
Shouldn’t we know more about the journals we submit to? When starting out in academia, I found it quite difficult to judge journals: who reads which journals, what kinds of research is appreciated by which journals, etc. Most journals advertise their impact factors, but that’s probably not the most important information. SciRev is probably the most useful service out there for this (beyond senior colleagues), giving information on the time journals take to make a decision (which of course greatly depends on the reviewers, but also what they let the reviewers get away with), the number of reviewer reports, and some subjective quality score. Some reviews justify their score in a couple of words. What would be even better is if SciRev made its non-profit objectives clearer (it’s run by the SciRev Foundation), user-contributed information on the journals, and perhaps a forum to discuss the scope of journals. Submitting reviews is very easy, by the way!
Pre-registration plans (PAP) rightly become more common (they are still not common enough yet, I think), but here’s a reason to write up a PAP that I have never seen mentioned before: Pre-registration plans can be immensely useful for yourself!
So, you have come up with a clever analysis, and writing the PAP has helped sharpen your mind what exactly you are looking for. You then collect your data, finish off another project, and … what was it exactly I was going to do with these data? Did I need to recode the predictor variable? etc.? Yes it happens, and a pre-analysis plan would be an ideal reminder to get back into the project: PAP can be like a good lab journal or good documentation of the data and analysis we do — a reminder to our future selves.