Registration is now open for D-Lab R, Python, and Stata workshops August 15-18. General consulting will re-open the following week, but in the meantime, faculty needing consulting support please email: dlab-frontdesk@berkeley.edu
Are you starting a research project at UC Berkeley that involves human subjects? If so, one of the first steps you will need to take is getting IRB approval.
If you are planning on conducting a systematic review consider using Covidence for your next project! Your UC Berkeley account gives you access to an unlimited number of reviews and citations per review, as well enhanced support.
As part of my Masters of Public Health Capstone Project, I completed a systematic review and meta-analysis to assess suicides among previously incarcerated persons. Covidence streamlined the most time-consuming and error-prone parts...
Openness, transparency, and reproducibility in research are critical to scientific progress. Yet, according to a 2016 internet-based survey of 1,576 researchers, 90% of respondents felt there was a slight or significant reproducibility crisis.1 Moreover, there are longstanding concerns that scientific journals favor positive or significant results—often referred to as publication bias—which may produce bodies of evidence that are incomplete or misleading (i.e., because studies which report null findings are not published).2 Publication bias can also arise when researchers choose...
When future historians try to piece together social life in the twenty-first century, they won’t be combing through faded newspaper clippings or handwritten letters. They’ll be clicking through digital archives that have stored remnants of our real and virtual lives: email collections, tweets, Facebook messages, maybe even Google Calendar entries.
For the time being, preservation of these records is the burden of the user. If one chooses, they can download their Facebook archive or back-up their emails and photos, and maybe choose to share them with a scholar in the...
Computational and data-driven research increasingly requires developing complex codebases. At the same time, many scientists don’t receive training in software engineering practices, resulting in, for some, the perception that scientists write terrible software. As scientists, good software should accelerate our work and facilitate its reproducibility. While building good coding practices takes some time and experience, it doesn’t require a...