Charlotte Sudduth

Tile chart showing 36 barriers to practicing open science, grouped by barrier type and by open science practice. The five barrier categories, based on National Academies of Sciences, Engineering, and Medicine (2018), are: costs and infrastructure (3 barriers); structure of scholarly communications (8); lack of supportive culture, incentives, and training (15); privacy, security, and proprietary barriers to sharing (8); and (intra)disciplinary differences (2). Barriers are also grouped by nine open science practices: publishing open access (4 barriers), publishing preprints (5), sharing open code (4), sharing open data (6), sharing open materials (2), conducting open peer review (4), using open source software (4), pre-registering research (3), and disclosing contribution roles (4).

Researchers often perceive 'barriers' to practicing #OpenScience, whether it’s publishing open access, sharing data, or pre-registering studies.

Last week at @esc-eurocrim.bsky.social, I presented our work at NSCR identifying 36 such distinct barriers.

Do you recognize them in your own work?

Call for Papers: Advances in Methods and Practices in Psychological Science is calling for proposals for manuscripts that discuss, demonstrate, and work to enhance replicability and reproducibility of methods research in psychology.

Proposals are due by September 15!

A do-or-die moment for the scientific enterprise Reflecting on our paper “The entities enabling scientific fraud at scale are large, resilient, and growing rapidly”

Today, our article "The entities enabling scientific fraud at scale are large, resilient, and growing rapidly" is finally published in PNAS. I hope that it proves to be a wake-up-call for the whole scientific community.

reeserichardson.blog/2025/08/04/a...

The Love Consortium Statistics Decision Tree launched today! Think of it like a choose-your-own adventure guide to help you identify the best analyses for your specific dataset and research question.

theloveconsortium.org/so/9fPWyVE6Z...

#DataScience #Statistics

Sometimes, a descriptive figure is worth more than a thousand model coefficients: the importance of data description in social research Many social research questions aim at understanding relationships between different phenomena, and increasingly complex multivariate statistical models are often employed to address these questions...

Finally got this banger of a title out: 𝑆𝑜𝑚𝑒𝑡𝑖𝑚𝑒𝑠, 𝑎 𝑑𝑒𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑣𝑒 𝑓𝑖𝑔𝑢𝑟𝑒 𝑖𝑠 𝑤𝑜𝑟𝑡ℎ 𝑚𝑜𝑟𝑒 𝑡ℎ𝑎𝑛 𝑎 𝑡ℎ𝑜𝑢𝑠𝑎𝑛𝑑 𝑚𝑜𝑑𝑒𝑙 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝑠.
If your descriptive figures and your model tell different stories, trust the figure. (And maybe rethink the model.)
👉 📄 www.tandfonline.com/doi/full/10....

With more opportunities for diverse interactions, little is known about how social interactions involving people of different socioeconomic status (SES) may unfold. We investigated social-attunement patterns in dyadic interactions involving SES. Unacquainted adults recruited from a community in the United States interacted with similar-or-different-SES partners in the lab ( N = 130 dyads). Attunement was assessed throughout the interaction by examining physiological linkage —how much a person’s physiological change is predicted by another’s physiological change over time. Overall, low-SES participants showed stronger physiological linkage—indicating greater attunement—to partners across SES. Participants also appeared more comfortable when interacting with low-SES partners. There were no SES differences in dominance during the conversation. After the interaction, participants reported liking similar-SES partners more than different-SES partners. These patterns suggest that during interactions, lower-SES individuals are more other-focused than high-SES individuals, and in-group preference prevails. We note limitations in the racial representation of our sample.

Socioeconomic Status in Social Interactions

"Low-SES individuals exhibited greater attunement to others and elicited greater comfort in others than high-SES individuals, but impressions were formed in line with homophily effects."

doi.org/10.1177/0956...

#SocialPsyc #Sociology

screenshot of our public handbook

every year my lab does a re-read + edit of our Handbook, a documentation resource for how we do science

this year we also updated our Public Handbook, an open-access version for folks wanting to improve their own docs

it's at handbook-public.themusiclab.org and available for noncommercial re-use

Trump officials take steps toward a radically different NSF Efforts to shrink staff, budget, and focus have alarmed members of Congress

NSF grant terminations are disproportionately affecting underrepresented scientists. 58% of canceled grants have women PIs, despite making up only 34% of awards. 17% of canceled grants have Black PIs (versus 4% of the pool). Hispanic and disabled PIs are twice as likely to have NSF grants canceled.

Don’t BE the documentation

COMPARE: the results of fighting back to the results of not fighting back

Under Pressure, Psychology Accreditation Board Suspends Diversity Standards (Gift Article) As the Trump administration threatens to strip accrediting bodies of their power, many are scrambling to purge diversity requirements.

HEY APA! Didn’t you know that the first rule of fighting tyranny is DO NOT OBEY IN ADVANCE?! So why the fuck are you obeying in advance?!??

www.nytimes.com/2025/03/27/h...

Excellent post, my close colLEAGUE

Massive team work in replicating papers and cross checking samples and treatments across multiple papers — what a massive undertaking!
This is evidence that journals *must* require data and code sharing.
I’m wondering if anything in the peer review process could have prevented this

Gotta catch ‘em all

going to SPSP this week

I find it hilarious that the Interpersonal Relations and Group Processes section of JPSP--ostensibly a "premier outlet"--cannot fill its page allocation. The rest of the editorial is all about how every paper must meet a high bar for "theoretical innovation." Good times. doi.org/10.1037/pspi...

Investigating fraud and errors in health research is obviously laudable. But we need to maintain high methodological standards. That means scrutinising each others’ work, and calling each other out when we err. There are so many errors in error detection. A few recurring errors:

In a new paper, my colleagues and I argue that it's time for relationship science to have its own credibility revolution.

osf.io/preprints/os...

Coauthored with @davekhera.bsky.social and @pauleastwick.bsky.social

no funding if it seems like the research MIGHT involve a woman or a nonwhite person

Asking PIs about their stats is like

Idk why people freak out about the tik tok ban when you can just post videos using R.

The brand new `legendry` package by @teunbrand.bsky.social takes ggplots2 guides to the next level 🤩

teunbrand.github.io/teunbrand_bl...