In this thought‑provoking episode, host Dr Emily Goodall speaks with Dr Jack Wilkinson, Senior Lecturer in Clinical Trial Statistics at the University of Manchester. Together, they explore the growing challenge of problematic and fraudulent research polluting the published literature, what it means for trust in evidence, and how the research community can respond.

From “paper mills” to AI‑generated publications, this episode shines a light on how research integrity is being challenged—and what researchers, reviewers, and institutions can do to protect it.

Key themes

• The scale of problematic research is growing: Fraudulent and low‑quality studies, driven by paper mills, AI and pressured research environments, are increasingly entering the literature.
• Quality checks don’t guarantee authenticity: Peer review and standard appraisals focus on quality, but often miss whether research is authentic.
• Structured tools can help spot issues: Tools like INSPECT‑SR guide peer reviewers to identify inconsistencies, implausibility, and red flags.
• Culture and incentives drive the problem: Publish‑or‑perish pressures and limited transparency make problematic studies more likely and harder to detect.

Final reflections

This episode raises an important challenge: if publications are the currency of academia, what happens when we can’t fully trust them?

Dr Wilkinson’s work shows that while the problem is complex, there are practical steps we can take, from better tools and reviewer support to systemic cultural change, to strengthen trust in research.

Find out more:

• INSPECT-SR PrePrint: a tool for assessing trustworthiness of randomised controlled trials, medRxiv 2025.
• INSPECT-SR: Resources for users.
• A recording of the Introduction to INSPECT-SR workshop is available on YouTube.
• Dr Jack Wilkinson on Bluesky.
• Our open research playlist.

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Welcome to the Research Culture Uncovered podcast, where in every episode we explore what is research culture and what should it be? You'll hear thoughts and opinions from a range of contributors to help you change research culture into what you want it to be

Hello, and welcome to "Research Culture Uncovered."

I'm Emily Goodall, a Researcher Developer from the University of Leeds. My episodes focus on research integrity, responsible research practices, and ethics. So today, I'm delighted to be joined by Dr. Jack Wilkinson, a Senior Lecturer in Clinical Trial Statistics from the University of Manchester. Jack is a specialist in research authenticity and integrity, focused on improving the trustworthiness of clinical evidence and strengthening methods in health research.

It's such an important topic, so I'm really looking forward to diving in and hearing your views.

Thank you. Great to be here, thanks, Emily.

So could you tell us a bit more about yourself and where this interest in research integrity came from?

Yeah, no problem at all. So my background, I am a statistician or a biostatistician.

Normally a bit wary of leading off with that, because I imagine a lot of people will just turn off the podcast immediately once they hear that they are gonna listen to a statistician talk for a while. But I think people don't really understand what that means, so a lot of my work is around the question of how can we find out whether or not treatments, various health interventions actually work, right?

How can we find out whether or not health interventions make things better and make sure that they don't make things worse? And we do that by designing research studies, such as clinical trials, to answer those questions, and by analysing data from those studies to find out about whether or not treatments help or harm.

So I do a lot of work around that, basically. Now, my interest in research integrity, I guess that probably started around quite a while ago now. Seven years, eight years, something like this, I think. And basically, I was a, a statistical editor for a clinical journal, and as part of that role, I would be sent various published or not published clinical trials or manuscripts describing clinical studies that were being considered for publication, and I'd often give an opinion on the methodological integrity or quality of those studies, and that's a key part of what a statistician does.

However, at some point, a few complaints were made to the journal about some studies that they had published previously. And these complaints were basically questioning not just the methodological quality of these studies, but really the authenticity of these studies. So the suggestion was that these studies had perhaps been fabricated, so they may have been entirely false.

What the journal did was they said, "Well, okay, we're gonna bring in a real expert in identifying fake or fraudulent research." And they brought in a guy, Professor Stephen Evans. From the London School of Hygiene and Tropical Medicine. Steven has been working on this field for a few decades now, I'd say.

Basically what they said is, "Well, okay, we're gonna bring Steven in." Uh, and Steven said, "Hey, I'm gonna come, and I'm gonna agree to look at these studies for you. However, my condition is that you're gonna give me a couple of your statistical editors, and I'm gonna train them up in methods to detect fake or fabricated studies so that going forward you'll have some people who know how to do this."

So I stuck my hand up that, "Oh, that sounds pretty interesting. Finding out how to detect fraudulent or fake studies, count me in." So I volunteered, received some initial training from Steven Evans that gave me my start, and then the rest was history, I suppose. From there, I began undertaking lots of these kinds of investigations for various journals and publishers, and as I'm sure we'll talk about, that ended up becoming a major focus of my research as well.

That's such a fantastic opportunity to get that specialist training. Do you think there's increased awareness of fake research and increased complaints over the last few years in, in sort of the publishing sector?

Oh, definitely. So I think awareness of this problem that some of the research that people use to make quite serious decisions about people's healthcare It turns out that some, perhaps a substantial portion, is fake.

And I really think awareness of that has increased over the past five years in particular. I think there are lots of reasons for that. There have been some really high profile examples. So the COVID-19 pandemic provided lots of examples of this happening, basically. So there were some clinical trials of the drug ivermectin, which was this really sort of hotly debated, politicized topic.

Uh, does ivermectin make things better or worse? And there were some clinical trials that suggested it looks like, uh, ivermectin will save your life if you have COVID-19, okay? Some researchers got hold of the data underlying some of these studies, and without being specific, it seems pretty clear that at least some of those studies were not authentic.

And once we removed the problematic studies from the evidence base, we were basically left with uncertainty about whether or not ivermectin made things better or worse. And I'm not making any claim one way or the other about the effectiveness or lack thereof of ivermectin, but it seems pretty clear that at least some of the trials that were suggesting a, a major benefit were inauthentic, possibly due to fraud.

And that wasn't the only example. There were a number of large studies from a US-based company, uh, that were claiming to have tested various interventions for COVID-19. But concerns were raised about the plausibility of what the authors were saying they had done. That resulted in those studies being retracted.

However, they'd had quite a big impact before they were retracted. So for example, one of those studies had been used by the Peruvian government to recommend ivermectin again as a treatment for COVID-19. That's kind of a big problem, right? There it was. The study's subsequently retracted. That means that was based on evidence that we do not have trust in.

So I think it helps. There've been some really high profile examples like that that have put the problem of fraudulent or otherwise problematic research on people's radar. So that's one big reason. I think another big reason is that it looks like the scale of the problem appears to be increasing. It now seems pretty clear that we have a huge problem with fraud on an industrial scale in the field of health and medicine, basically.

So there are companies called paper mills, and these are commercial enterprises who will basically sell authorship positions on fabricated or extremely low quality or junk publications. And there's evidence of thousands upon thousands of these paper mill products being published and polluting the literature So I think there's a number of factors.

High-profile examples, the scale of the problem appears to be increasing. It also helps that there's been some really sort of, uh, loud and prominent voices calling attention to the problem as well. All of these things combine, I think, to mean that, yeah, people are starting to get the message and starting to realize that this is potentially quite a big problem.

Do you have any feel for how big the problem is? Because it sounds quite scary on the scale that you're talking about.

It's difficult to come up with a direct estimate because, well, if we're talking about fraudulent research, I suppose by definition a lot of these studies are sort of in disguise. They're disguised as genuine studies, and they've at least, you know, not been noticed when they've been accepted for publication.

So that makes it difficult to just say, "Look, this is how many problematic studies there are." Nonetheless, there are various somewhat indirect, uh, sources of evidence we could use. So for example, a, a study's just been published in The Lancet where the authors looked at how many of the studies that have been published in PubMed this year appear to have hallucinated references, okay?

These are studies which appear to have been generated by large language models, by AI, and sometimes what will happen when AI writes a paper is it will make stuff up. It will create stuff that isn't real or wasn't really there, and that includes references. So what the authors did is they said, "Let's look at all the studies published or indexed in PubMed," which is a sort of a database indexing all of these health research studies, "and see how many of these appear to contain hallucinated references."

And the proportion of studies containing fabricated references is pretty alarming, so it's increased hugely over the past few years. So far in the first few months of 2026, they estimate that I think 1 in 277 studies contain hallucinated references. That's quite a high number. When we consider how many studies are published each year, that means that there are a huge number in absolute terms of these studies with which appear to have been generated by large language models in circulation, and that's just obviously just the, the tip of the iceberg, right?

Because here I've described one particular sort of flavour of problematic study, just these ones that were created using large language models and which happen to contain hallucinated references, but it gives a, an indication of the scale of the problem, I think.

And what role do you think publishers and journals have in checking what they are publishing?

Because obviously we do have the peer review system, but should they be doing more?

Oh, absolutely. I mean, it is a complex issue because in, in some sense, I do understand that publishers have been tricked, I suppose, by people doing this. I'm not someone who thinks it's completely black and white. Nonetheless, publishers clearly have a responsibility to try to do more to prevent the publication of these studies in the first place.

The question is, what should they do? Now, many publishers are using a sort of professional software to try to detect some of the common red flags that we see in these paper mill products. That could include all different types of things. For example, some of these paper mill products will have very unusual or natural text or phrasing in them.

That may have happened because somebody has taken an existing paper and used some software to try to create synonyms or paraphrase text, right? So to duplicate the original paper while avoiding plagiarism detection, and that can result in some really unusual wording phrases. This gets called tortured phrases.

So many journals will implement software to try to detect tortured phrases. That's just one example. Now that's all well and good, and it probably will stop or detect some of these problematic studies. However, it seems like different types of problematic health research actually have quite different sort of warning signs or red flags, okay?

I'm particularly interested in problematic or fraudulent clinical trials, and actually it seems like The majority of these problematic clinical trials haven't been created by paper mills, right? They've been created by lone researchers or small teams of researchers rather than these sort of large corporate entities.

And as a result, the methods they use to create the papers are very different and therefore the warning signs that we see in these papers are very different as well. So unfortunately, it seems like a lot of the checks that publishers are doing probably aren't gonna detect some of these problematic clinical trials.

This prompts the question of what should they be checking for instead? And that's a major focus of my own research.

So you lead on an NIHR-funded project called Inspect-SR, which aims to help spot these problematic trials, the ones that might not be trustworthy. Can you tell us a little bit more about the tool and what it's designed to do?

So we think that the best way to test whether or not a treatment works is using something called a randomized control trial. And this is a type of study where we randomly allocate people to receive this new treatment that we're interested in or to some sort of control group, which is often the usual standard of care.

By making a comparison between what happens in these two randomized groups, that basically is our best way we have of saying, "Does this intervention make things better or not?" So that's the randomized control trial or the RCT. That's our gold standard. Now, when more than one RCT has been performed on a given topic, it makes sense to bring them all together, right?

To review all of the evidence and to reach an overall conclusion based on the totality of the evidence, and we do that using something called a systematic review of the evidence. Systematic reviews, what we do is we say, "Hey, we really wanna be comprehensive here." It's really important that when we're doing a systematic review, we identify all of the trials that have been done on the given topic, right?

Otherwise, we're gonna end up with a distorted subset if we only find some of the studies. We wanna be comprehensive. We wanna consider everything. Now, that's okay, but what it means is if there are any fraudulent or we use the term problematic clinical trials, that means they're gonna be sucked up, I suppose.

They're gonna be hoovered up and included in this systematic review. So that's a bad start. As systematic reviewers, we're used to assessing the quality of the evidence. So a standard step in systematic review is to look at all the clinical trials and to critically appraise them in terms of their methodological quality.

And we have good tools and frameworks in place already, but the problem is that those assessments are largely predicated on the assumption that the studies are authentic, that they more or less took place as described, okay? And that's a problem because, well, it turns out that many fake or fabricated studies actually describe methods that would be okay if they were genuine, and so they're not detected.

They're not picked up by these established methods of critical appraisal And then that's a big problem because it means that these fabricated studies go undetected. They're included in these systematic reviews, and these systematic reviews can actually be extremely influential. They're often used to, uh, inform healthcare policy.

They're often included in influential guidance, so they have a big effect on people's healthcare, basically. So there's a real concern that these systematic reviews might be acting as a sort of pipeline for these fake or problematic clinical trials to influence patient care. So Cochrane are the leading publisher of these systematic reviews in the world, I'd say.

Cochrane realized that this was a problem a few years back, and so they introduced this policy. They call it the Policy for Managing Potentially Problematic Studies. And what they said is, "You should not include problematic studies in a systematic review," and that's a good policy, okay? But it, it prompts a follow-up question.

How are we meant to identify these problematic studies in the first place, right? That's the big question, and so we partnered with Cochrane to undertake the INSPECT-SR project, and the goal of that project was to develop a tool called INSPECT-SR to help researchers to identify these problematic clinical trials so that they could spot them and make sure that they didn't get included in these systematic reviews.

That's what I've been working on over the past few years. Now, when I say a tool, a, a natural question is, what does that mean? What, what's a tool? So basically, what the INSPECT-SR does is it includes a series of checks that can be performed by a researcher or by a reviewer when they're assessing the trustworthiness, we call it, of a trial, of a published RCT.

And INSPECT-SR will guide the user through a series of checks to check for different features of the paper to try and identify problems, and then it prompts the user to make a judgment about the trustworthiness of the paper. At the end, it sort of says, "Hey, do you have concerns about the trustworthiness of the paper?"

And the reviewer has the options of saying, "I have no concerns," "I have some concerns," or, "I have pretty serious concerns about the trustworthiness of the paper." And if you have serious concerns, maybe we shouldn't use this study to influence important healthcare decisions, and this allows the reviewer to put that one on the shelf, okay?

Rather than taking it at face value.

So is there a mechanism in there to then report the ones that you feel are problematic back to the journal where it was originally published, or flag it with the researchers or authors of the paper?

Yeah, absolutely. The first thing to say is that whenever we're doing these systematic reviews and doing any sort of critical appraisal or trustworthiness assessment- Communication with the authors can be quite important, right?

Sometimes something will look like a problem, but actually communication with the authors can clarify things and resolve some of those concerns. So that, that's always really important. Now, in terms of reporting any problems you find back to the journal, yep, that's really important as well. And actually Cochrane provides a lot of templates that researchers can use once they've spotted those problems.

They can use those to write to the journal, say, "Look, we've assessed this study. We have these concerns," and then it's over to the journal to investigate.

And for those listeners that are involved in the peer review process, or perhaps even just reading the academic literature, what do you think are the warning signs that might flag that a study is not reliable?

Yeah, that's a tough one because we think that the red flags probably depend on the nature of the study itself. So the red flags for looking at problematic clinical trials might be quite different to the red flags for looking at a different type of health research study, for example. Now, I think a good starting point is for peer reviewers to just sort of question the authenticity or the plausibility of what they're reading, and I think that's a really important starting point.

I think part of the problem is that it just hasn't occurred to peer reviewers previously that the study that they're reviewing or reading might not be authentic, that maybe they shouldn't take it at face value, and I think that's a really important first step. So if we can just raise awareness of the problem, hopefully more people will at least be aware of the possibility that the research they're reading might not be authentic, and that's a really good start.

However, it's not enough because unfortunately, sometimes people will convince themselves that there are problems when actually there aren't any problems, right? So it's not enough to say, "Be sceptical, be cautious." We need to give people a little bit more guidance about what exactly they should be looking for.

So let me give a few different examples of the types of things that we are asking people to look for when they're looking or assessing the trustworthiness of clinical trials. So we ask people to do some basic things, first of all. So we ask people to do things like when someone does a clinical trial, there's an expectation that the clinical trial will be registered on a clinical trial registry.

That is now mandatory for clinical trials taking place. Now, the clinical trial registration should include all the key details about what's gonna take place in the study. Information like how many people are gonna be recruited, the nature of the treatments that are gonna be used in the study, the eligibility criteria, right?

Who is eligible to be in this study, et cetera. Okay, that should all be detailed in the trial registration record. Now, actually, what we find with a lot of problematic studies is sometimes there are quite major inconsistencies, right, between what the trial registration says, if there is a registration page at all, I should add.

If there is one there, there are often major inconsistencies between what the registration page says and what the published manuscript describing the trial says. Okay? So that's a very basic thing that we can tell people to, to check. Are these things consistent? That's an easy one. What are some other things we often see?

Well, actually, problematic studies often contain numerical inconsistencies throughout the paper, and that can take various forms. Sometimes the same result is reported in multiple places in the same paper. And actually, we're just asking people to say, "Hey, is that consistent where it's reported throughout the paper?"

Okay? Unfortunately, sometimes the answer is no. This includes asking people to check they've written this result in the text or in a table, and they've also displayed it visually using a graph or a plot of the data. Are those things consistent? You know, things like this. There are some slightly more sophisticated checks we can ask people to do.

So in clinical trials, there'll be a lot of statistical testing of results, and if anyone's done any introductory statistics class, they might remember things like performing statistical tests and producing P values and things like this. And actually, we can do things like saying, "Hey, look, they've done these statistical tests and they've produced these P values, but are these P values consistent with the data that they're reporting in the paper," for example.

And again, quite often in problematic studies, the answer is no. For example, if somebody's just sat down, started making up numbers or typing numbers and these statistical P values into a table It doesn't occur to them that they need to make all of these things consistent, I suppose. So quite often we find flaws by drawing attention and asking people to check for these consistencies.

There's various details like that. We also like to ask people to consider sort of like the clinical plausibility of what was done. So how plausible is it that the authors would have managed to recruit this number of people in this period of time? How plausible is it that they could have administered the protocol that they said they administered, given the resources that were available to them?

Things like this. That's often quite hard for me to make a judgment on as a statistician or as a methodologist with no knowledge of the clinical context or the setting in which the study took place. It's much easier for people with some expertise in the topic, clinical expertise, for example, to make a judgment on things like this.

But sometimes clinical experts are able to say, "Look, what they are saying they have done here, there is just no way it could have happened, okay? There is absolutely no way they could have administered this protocol to this many people without any funding in this period of time. It just doesn't add up."

So sometimes there are major sort of concerns about the plausibility of what was done. So I've just mentioned a couple of different things there that we might use to detect problems, inconsistencies in documentation compared to the manuscript, numerical inconsistencies within a study itself, or serious doubts about the plausibility of what the authors said that they did.

You know, there are a few different examples of things that can highlight problems.

I think peer review is often seen as kind of the main safeguard to research quality and integrity. Is it fit for purpose? Does it fall short? How do you think we can address some of these issues with our peer reviewers?

Beause it is a difficult job, it's a big responsibility, but I'm just wondering, what are your views on how the peer review system is working at the moment generally?

That's a really tricky and big question. Sorry. I mean, I think, as you say, it's a big one. But, uh, I'll try to reduce something very complicated into a few concise thoughts.

My, my overall view is that, yes, uh, peer review is very flawed, right? Peer reviewers will often fail to spot serious errors. Sometimes they will make suggestions or corrections that are incorrect, et cetera. So peer review is deeply flawed. I, I think on average it's probably still better than having nothing at all, however, right?

So that, that's my view. It might be deeply flawed, but I suppose the overall effect It is probably still positive. It's probably still better than nothing. What I will say, however, is though that it comes back to the point I made before that we often think of peer reviewers, and I think peer reviewers have often thought of their role as being to, in some sense, assess the quality of what they're reviewing.

But again, it hasn't occurred to people that they need to assess the authenticity. That's something slightly different, right? It hasn't occurred to people that they should be assessing the authenticity of the papers that they're reviewing. Now, I can't be really specific, but I know that some journals and some publishers have started to prompt peer reviewers specifically as part of the peer review process to question these things.

So they will include prompting questions to say things like, "Do you have any doubts about the integrity of the data? Do you have any concerns about the plausibility of things like recruitment of the things that people said that they did?" And that's a really positive step, I think. Okay. Now, we could go one step further, right?

And we are interested in this question. So we have created this tool, this list of checks, if you like, that can be applied by systematic reviewers. But a related question is, what should that protocol, that list of checks look like, um, if it were gonna be deployed by journals basically in the editorial assessment context?

What should that look like? Um, the checks probably need to be slightly different. Some of the checks don't make sense in the post-publication context compared to the pre-publication context. There are other considerations like who should do the checks at the journal? Should they be split amongst different parties?

Maybe a clinical reviewer should be responsible for checking some aspects. Maybe a statistical reviewer should be responsible for some checks based on data or results, things like this. Maybe some editorial staff could be responsible for doing some more, uh, of the checks that are looking at things like, uh, details of registration documents and making sure that's all in order and stuff like that.

But this is something we're really interested in. So we're actually at the start of a new project, which we're calling Inspect-JR, and we're trying to create a version of the tool that could be used in the, in journal editorial assessment context. So hopefully we'll have a bit more, uh, to say about what that should look like in the next year or two.

Well, that sounds like a really important and interesting development, especially to help just support peer reviewers, because like I said, it can feel like a big responsibility and spotting all these things in increasingly complex publishing world, especially with the advent of AI and just how difficult it is to detect.

So yeah, maybe we'll have you back on the podcast to talk about JR.

We do have to be careful though because, as I said, sometimes what will happen is well-intentioned people who are aware that this is a problem, sometimes they will spot things and flag these up that are actually just misunderstandings or mistakes on the part of the reviewer.

So I kind of briefly mentioned at the start that I do lots of integrity investigations for journals and publishers, and that's where people have written to the journal and said, "Look, I have these concerns. I've spotted these errors in this published paper." And sometimes they've just made a mistake basically.

So for example, when they've been checking for errors or inconsistencies in statistical results, they forget things like when you're reading a paper, the numbers you see that the authors have put in a table, they have rounded those numbers, right? They're not reporting all decimal places or whatever, and sometimes that's the explanation for the apparent inconsistency that you've flagged, i.e.,

it's not a inconsistency or contradiction at all. And that's a problem because we don't want people making these sort of false positive accusations of error or fraud or anything like that for various reasons. First of all, it's not good if publishers are inundated with lots of spurious complaints that are based purely on misunderstandings, right?

That one's gonna distract from the investigation of the genuinely problematic articles. But also sometimes, you know, these kinds of spurious complaints might be successful actually because the person doing the investigation or reviewing the complaint might not have a good understanding of this stuff either.

So that could actually lead to genuine studies being, you know, retracted, removed, et cetera, which isn't a good state of affairs either. So we do need to, yes, spread awareness. It's really good that people are aware of the problem. It's really good to promote the problem, but we also need to promote good and responsible methods for actually identifying these problematic studies.

Yeah, I think that's a really important point. So I wanted to finish today by thinking about the aspects of research culture and the publishing system. What changes do you think would make the most difference towards a positive research culture in the system that we have at the moment?

It's another really big question, Emily.

I'll try my best to say a few bits. Obviously, it's a big problem that the majority of these cases happen is because people are desperate for publications. They need that to advance. Uh, this is true whether or not people are primarily researchers or even if people who are primarily clinicians, quite often it's beneficial to their clinical career to have some publications, and that seems to be the main explanation for why people do this.

We put pressure on people to just publish as much stuff as possible, and as a result, people take shortcuts, right? That's human nature. So that's probably the main motivation that people have. Can we change that? I don't know. That's a really big problem. I think some of this happens or a lot of this goes undetected is because we don't really require people to show all the details of what they've done.

We don't really require people to For example, uh, supply the data that they used, the data underlying the study. We take it on trust that's there somewhere, but we don't actually typically require people to submit that or to make it publicly available. We take it on trust that people have written, you know, some code in some statistical software to analyse that data, but we don't normally require the authors to show that to us, right?

Again, we're taking it on trust that that exists somewhere and it's doesn't have errors in, et cetera, et cetera. So we could make really positive changes just by, uh, improving requirements to be transparent when we publish research, by requiring researchers to do things like sharing anonymized study data, by sharing the statistical code they've used to analyse their data, for example.

It makes it much harder for fabricators to get away with things, okay? Because we're asking them to, to show us everything that they've done, but it also increases the chance of problems being detected, including genuine errors, which happens, right? It improves the chance of detecting genuine but serious errors if everybody has access to the data and code used to produce the results in the published study.

Yeah, that links into quite a few themes that we've picked up in the podcast over the last few years, thinking about more open research- Mm-hmm ... and the barriers to reporting misconduct or even raising concerns around questionable research practices. There's some work to do in those areas- Yeah, no ... I would say.

Absolutely.

Unfortunately, that's all we've got time for today. It's been a really thought-provoking episode. I think ultimately, publications is the currency in academia, and if we can't trust the published record, it's just so fundamental. I, I also get it. There are some real challenges out there, and there are a lot of pressures on researchers to get those results, as you've said.

And there is increasing complexity in how that knowledge is generated and how that knowledge is produced. So yeah, some really good food for thought. Thank you so much for joining us today, and thank you to our listeners. I, uh, hope you will join us for future episodes on the podcast.

Thanks, Emily.

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