Thinking about evidence and vice versa
Data Colada (datacolada.org) scores 76 out of 100 on Brand Analyzer, placing it in the Brand-Ready tier among Education brands, Media & Entertainment brands, Other brands, in the 53rd percentile of 21712 Education brands.
Data Colada is an academic blog that scrutinizes behavioral science research, focusing on statistical and methodological critiques. Run by academic experts, it investigates published studies for errors, questionable research practices, and fraud, aiming to promote transparency, rigor, and integrity in the field. Its target audience includes researchers, scientists, and journal editors interested in research integrity and open science.
| Brand Name | Data Colada |
|---|---|
| Domain | datacolada.org |
| Industry | Education, Media & Entertainment |
| Main Competitors | Retraction Watch (87/100), PubPeer (82/100), For Better Science (71/100), Replication Index, Center for Open Science |
Moz Domain Authority 56/100 vs category average 47 / leader 99 - a strong backlink profile, so AI systems frequently encounter mentions of the brand.
Ranked #438,410 on the Tranco list of most-visited sites - modest traffic makes the brand easy for AI models to overlook.
Data Colada appears on Wikipedia but without a dedicated company entity, so AI models can confuse it with similarly named subjects.
The homepage meta description reads "Thinking about evidence and vice versa" (No meta description. OG description present but brief) - this is the summary AI engines are most likely to quote.
Structured data on the homepage: og:title, og:description, og:image, og:type. Adding Organization and FAQ schema would further help AI crawlers parse the brand's identity.
AI-crawler access: robots.txt present, no AI bot restrictions - robots.txt controls whether engines like GPTBot and ClaudeBot can read the site at all.
Detected tech stack: Cloudflare, WordPress, Ghost.
Social footprint: verified profiles on Facebook, X (Twitter), LinkedIn, Instagram, GitHub; no detected presence on YouTube - consistent profiles reinforce the brand's identity across the web.
0 Reddit mentions - community discussion signals real-world reputation to AI models.
Live results from asking a general-purpose AI assistant about the brand, checked October 2026.
When asked "What is Data Colada?", Claude could identify the brand as of October 2026. Data Colada is a blog run by social psychologists Uri Simonsohn, Leif Nelson, and Joe Simmons that scrutinizes research methods and statistical practices, often highlighting flawed or fraudulent data in published studies. Best known for Exposing data fraud and questionable research practices in behavioral science, including the Francesca Gino and Dan Ariely cases.
When asked "Best brands similar to Data Colada?", Claude would recommend Data Colada as of October 2026. It's one of the most influential and well-known outlets for research integrity and methodological critique in the social sciences.
Common questions people ask in Google, ChatGPT, Claude, Gemini, Perplexity, and other AI search engines.
Data Colada's main competitors in Education: Retraction Watch (87/100), PubPeer (82/100), For Better Science (71/100).
Sources: Brand Analyzer scan
Data Colada has limited AI-search visibility, scoring 38/100 on Brand Analyzer's AI visibility composite (visibility 38, trust 29, recommendation 50). This estimates how likely AI engines like ChatGPT, Claude, Gemini and Perplexity are to know, trust, and recommend the brand.
Sources: Data Colada official site
Data Colada scores 50/100 on Brand Analyzer's AI recommendation signal, indicating it is reasonably likely to be surfaced when AI assistants like ChatGPT suggest Education options. Recommendation depends on crawlability, structured data, and category authority; a Wikipedia presence helps.
Sources: Data Colada official site
Data Colada is an academic blog dedicated to scrutinizing behavioral science research through statistical and methodological analysis. It is run by academic experts who investigate published studies to identify errors, questionable research practices, and in some cases fraud. The blog positions itself as an independent watchdog within the academic community, aiming to improve transparency and rigor in behavioral science research.
Sources: Data Colada official site
Data Colada analyzes and critiques published behavioral science research, focusing on statistical methods and research design. It investigates studies for errors, questionable research practices, and potential data fraud, publishing detailed findings publicly. Through this work, it aims to promote transparency, rigor, and integrity in academic research, serving as a check on the quality of published scientific findings in the behavioral sciences.
Sources: Data Colada official site
Data Colada can improve AI discoverability by strengthening structured data (Organization and FAQ schema), maintaining an accurate Wikipedia/Wikidata entity, earning authoritative citations, and keeping content crawlable for AI bots. Brand Analyzer measures these as visibility, trust, and recommendation signals.
Sources: Data Colada official site
Data Colada offers blog content featuring statistical and methodological critiques of published behavioral science research. Its primary output includes detailed analyses that identify errors, questionable research practices, and potential fraud in academic studies. Rather than offering products in a commercial sense, it provides publicly accessible research commentary and investigative content aimed at supporting transparency, rigor, and integrity within the behavioral science research community.
Sources: Data Colada official site
Data Colada is known for its detailed statistical and methodological critiques of published behavioral science research, often uncovering errors, questionable research practices, and instances of data fraud that other outlets overlook. It has built a reputation as a rigorous, independent watchdog within academia, scrutinizing high-profile studies and researchers to hold the field accountable for research integrity and transparency.
Sources: Data Colada official site
Data Colada is used by academic researchers, scientists, journal editors, and others interested in research integrity, open science, and behavioral science methodology. These individuals read the blog to stay informed about methodological critiques, potential issues in published research, and broader discussions on improving the credibility and transparency of behavioral science research.
Sources: Data Colada official site
Data Colada scores 38/100 for AI visibility - how well ChatGPT, Claude, Perplexity and Google AI Overviews can discover, identify and cite the brand.
Data Colada has a weak AI-visibility profile at 38/100 - a proxy for how readily ChatGPT, Perplexity and Google's AI Overviews can recognise and cite it. Its strongest area is recommendation likelihood (50/100) and its weakest is trust (29/100). It benefits from a Wikidata knowledge-graph entry, a global traffic rank of #438,410 (Tranco) and a Moz Domain Authority of 56/100. The main gaps holding it back: no clear Wikipedia entity to anchor it in LLM training data, thin schema.org structured data and no llms.txt to steer AI to its best pages. In a live check, Claude could already identify Data Colada from memory (October 2026) - a sign these signals are paying off.
The score combines seven dimensions - name quality, digital presence, visual identity, messaging clarity, trust foundation, AI discoverability, and brand authority - into a single objective benchmark.
Ranked closest to Data Colada: Dar-us-Salam (76/100), Donald Danforth Plant Science Center (76/100), Data Trace Publishing Company (76/100), Davis Furniture (76/100).
A step up - brands to learn from: Zymo Research (81/100), Zutobi (81/100).
Category leader: Canva (97/100).
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