"Transparency is a fetish" In our new segment, #RigorTakes, Dr. Zac Parker discusses scientific transparency with C4R founder Konrad Kording, professor at University of Pennsylvania. Agree, disagree, or somewhere in between?

What can well designed controls tell us about an experiment? From guarding against false positives to strengthening confidence in results, thoughtful control design is a pillar of rigorous research. #ExperimentalDesign

Careful design and thoughtful reporting are cornerstones of rigorous research. Careful experimental design incorporates several principles like: selecting appropriate controls, choosing the right randomization method, avoiding bias, and choosing the best sample size for the experiment. Writing readable code, creating visualizations to tell your data story, and sharing data effectively are all pieces of thoughtful reporting. C4R has created free online modules for each of these topics for you to teach or learn from. What other topics are cornerstones of rigorous research? #ScientificRigor #OpenScience

Avoiding these 3 common control issues produces more rigorous research: 1. Insufficient negative controls Solution: add targeted negative controls 2. Human bias hindering controls Solution: apply a masking design 3. Uneven variable distribution Solution: use randomization What control issues do you most often see?

What would change if your lab or classroom fully adopted C4R's free scientific rigor materials? We asked researchers this question. They shared hopes of better, more rigorous, and higher quality science. To them, adoption would mean improved collaboration, increase awareness, and promotion of higher quality experimental designs like randomization. What would change if your lab or classroom adopted C4R's materials? #ResearchCulture #TeamScience

Controlling for variables is a cornerstone of rigorous research. How do researchers control for them? The framework we use is has three categories, constrain, distribute, and test. Constrain Limiting the range of the variable Distribute Distributing the range of the variable as equally as possible across groups Test Making a comparison group or adjusting for effect of the variable during analysis All three are ways to address a variable's potential influence on an experiment. How are you controlling for variables in your research? #ExperimentalDesign #ResearchDesign

You're testing Compound X, which you dissolve in DMSO. After treatment, you observe a difference. What comparison best separates the effect of Compound X and the solvent (DMSO)? A. Compare to untreated cells B. Compare with cells treated with the same concentration of DMSO alone C. Compare different concentrations of Compound X D. Compare the effect across several independent experimental replicates

What are controls doing in a research design? C4R Senior Curriculum Developer, Karen Word, breaks down Constrain, Distribute, Test, the framework used in our Controls unit. Next week we'll share why we developed it. #ScientificRigor #ResearchMethods