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Announcer: Examining the latest research and telling you about the latest breakthroughs. The Science and Research Show is on The Scope.

Interviewer: I'm talking with Dr. Bryan Jones, an investigator at the Moran Eye Center at the University of Utah. Now anyone who sees you knows that you carry a camera around with you wherever you go.

Dr. Jones: I do.

Interviewer: And I suspect your love for photography might have come before you delved into science.

Dr. Jones: It did, yeah.

Interviewer: I'm wondering if that passion for photography and how maybe we see the world or how the camera sees the world sort of influenced what type of research you're doing now?

Dr. Jones: I came to photography initially in college. My first major was film studies. And I spent a lot of time with a Leica M6 camera and a Canon 1D camera that my parents had. And I fell in love with photography. I've had the privilege of knowing a couple of friends who have retinitis pigmentosa. One of my friends is an army veteran, and I've watched him go blind. So for me, the ability to do photography and to share what I see is a constant reminder of why the work we do is so important.

Interviewer: And what are you looking at these days? What's driving your research?

Dr. Jones: So our lab has two main focus areas. The first is understanding how the retina is wired. So the retina is this piece of gauzy tissue at the back of your eye that is an extension of the brain. And this brain tissue at the back of the eye is sort of a layer cake-like structure with 7,200 neurons or more in our human eyes. And it captures light, photons of light and then computes all the parameters required for vision. Captures contrast and luminance and edges and movement. And calculates all these parameters and sends that information on to others and brain and cortex and sub-cortex for higher processing.

People have been studying the retina for about 150 years, and we still don't know precisely how all those 7,200 or more neurons are wired. And that's the first mission of our lab is to try and pull apart that wiring and identify all the neurons and figure out how they are precisely wired together. That's a field called connectomics.

The second main mission of our lab is to understand how that structure and how the neuron identity and how the wiring is altered in diseases like retinitis pigmentosa and age-related macular degeneration that steal vision from us.

Interviewer: So you're looking at circuitry itself?

Dr. Jones: Yes, ma'am.

Interviewer: And how are you doing that?

Dr. Jones: So there are two new approaches that have been pioneered. The first one that was pioneered by Dr. Robert Mark here at the University of Utah called computation molecular phenotyping. We basically use antibodies to serially label tissues and pull out smaller molecule fingerprints that uniquely identify populations of neurons. And then we insert those data into electron microscopy datasets.

So we basically take the retina and section it very thin. Each section is thinner than the wavelength of light. There are about 90 nanometers. And then we section in the case of the mouse retina that we recently finished, about 1,490 nanometer sections, and then we reconstructed those 1,400 sections into a digital layer cake and create a volume and then we go in by hand and trace neurons all the way through the dataset along with all their connections, their synapses and their gap junctions.

Interviewer: So the hope is to create like a three dimensional representation?

Dr. Jones: Yeah. A three dimensional representation that we then extract out circuit topologies. So it turns out the circuit topologies are far more complex than we thought they were. And so we're now having to develop new software that can allow us to visualize how complex some of these topologies are. The circuit topologies are things that give us excitation and inhibition and ultimately end up tuning response profiles in retina.

Interviewer: You know, if you think about circuitry in how most of us think about circuitry, I mean, if you look at the circuitry like in a computer or in the electronic system of a car, I mean, it doesn't exactly tell you how it works. Or does it?

Dr. Jones: So there was a pretty famous paper that came out a few months ago where they took a standard computer integrated circuit chip and they took it apart digitally and they tried to predict how it would work. And it turns out they didn't have a whole lot of information. It didn't function the way it should have functioned. Or at least their models, their predictive models that they thought how it would behave, it didn't actually behave the way the chip actually did.

So while the circuitry is what we're after and the circuitry in any neural system is sort of the substrate that gives us a basic performance profile and ultimately makes us who we are, there is still a lot we don't know. And so for us the connectomics framework, the circuitry framework we're trying to obtain is only the basic starting point.

It is a framework and on that framework we have to hang physiology, we have to hang physics and we have to put molecular biology and we have to put genetics into it. And so for us it's only the very beginning of understanding how neural systems are wired. Even though the science community has been studying the retina for 150 years. In a lot of ways, we are still right at the very beginning.

Interviewer: So it's kind of new frontier. Something that hasn't really been done this way before.

Dr. Jones: Yeah, exactly.

Announcer: Interesting, informative and all in the name of better health. This is The Scope Health Sciences Radio.

Interviewer: Systematic reviews or meta-analyses may be trickier to carry out than you think. But there's help out there. We'll talk about that next on The Scope.

Announcer: Examining the latest research and telling you about the latest breakthroughs. "The Science and Research Show" is on The Scope.

Interviewer: I'm talking with Melissa Rethlefsan and Mellanye Lackey from the Eccles Health Sciences Library at the University of Utah. Melissa and Mellanye you're putting a lot of efforts into improving the quality of systematic reviews. And that's a very particular type of research. First of all, can you tell me what systematic reviews are?

Melissa: A systematic review is a pre-specified methodology that looks at the literature to answer a very focused question, usually about patient care.

Interviewer: So can you give a certain example?

Melissa: So for example, usually it's a PICOT type of question with a patient intervention, comparison and outcome that a researcher might be looking to answer. So for example, it might be looking at teenagers who are depressed and whether or not SSRIs would be better than placebo or a different drug class in preventing the onset of further depression or suicide or some other outcomes that would be of interest to a researcher.

Interviewer: So it's really important that these studies are set up correctly. How can not taking some of those steps lead to issues in research reproducibility?

Melissa: If you do not have a librarian or someone who's extremely familiar with literature searching methods involved, then what you can produce is the systematic review that may answer the question, but that because it was not well documented, it can't be replicated. So people, we can go in and they can look at the systematic review and they can think that they're getting an unbiased answer. But then, when you look at the details of the study, you realize that you have no idea how the study was actually performed.

It's very similar to in a clinical trial that's not well reported. You might have no idea if the results that they're getting are actually true because you can't tell enough detail from the methods to be able to ascertain that for yourself.

Interviewer: Really, it starts with the very question that they decide to ask in the first place. What are some of the issues that come about in that arena and how can you address those?

Mellanye: Sure. One of the things we do as librarians is help make sure that people are asking questions that can be answered by literature that aren't too big or that aren't too narrow. And once they have a question that there's adequate literature to support studying that question and so we can do initial searches into the databases to identify gaps in the literature, to see if that study has already been done, if it needs to be updated, if it hasn't been done in a while. We can find if it has been done, was it done accurately? Was it done well? And if not, what areas they could . . . if they need to change their question slightly so as to carve out a unique area of research for themselves. We can help identify just the body of literature that addresses their question.

Interviewer: And how you do those studies, of course, is important too.

Mellanye: Absolutely. And so we as librarians encourage people to register their study in a protocol registry and this helps them ensure that they're going to be doing . . . that they've thought about as a research team, do they have the capacity to do the whole study? It makes them think about every single step of what they will do in their study and outline it and submit that to an international body that is open to anyone to read. And it just helps the researchers go through the entire process, think through the entire process before they actually get started.

Interviewer: So how does it do that? Does it prompt with certain questions or . . .

Mellanye: Definitely. It's a lengthy, not too lengthy, adequately lengthy set of questions that asks them to describe previous studies in their field, that asks them to describe their search strategy and describes their approach and anything that makes their contribution unique or any limitations that their study will have. And then it puts it out into the Internet, into the field for their peers to look at and they can comment on it. It also secures their place, so this says, it lets them set a flag, "Yes. We are doing this study, " helps them find out if anyone else has already started on that study and it prevents them from being scooped.

Interviewer: What is that registry called?

Mellanye: PROSPERO. P-R-O-S-P-E-R-O.

Interviewer: And do you feel like people are using that or is this something that's kind of new?

Melissa: I think people are using it. I think it is somewhat new. People don't have to publish a protocol in PROSPERO. A lot of times, people will also publish their protocols in a journal. And there is a specific type of systematic review called the Cochrane systematic review. And in order to do a Cochrane systematic review, you have to publish your protocol in the Cochrane database of systematic reviews prior to the publication of the full systematic review.

So I think it is something that's been around for a long time. I think what we often find, though, is the lower quality systematic reviews out there aren't doing that. And we're trying to really help elevate the quality of systematic reviews much in the same way that clinical trials are trying to do by pre-registering their trial protocols, by making sure that all of the inclusion and exclusion criteria are out there from the beginning, that they know the outcomes that they're going to be looking for so that people can't go back later and switch the outcomes or make up new outcomes or decide because they're an expert that they want to include a certain study that doesn't actually meet their eligibility criteria.

So it's a quality measure, people are definitely accepting of it. And registering your protocol is actually part of the PRISMA guidelines, which is a reporting guideline for systematic reviews. It stands for Preferred Reporting Items for Systematic Reviews and meta-analyses. And it was published in 2009, I believe. And since then, the incidence of protocol registration has gone up quite a bit. It is still not great, but it has gone up significantly.

Interviewer: And you just touched on reporting guidelines. Tell me a little bit more about that and what that involves and why it's useful.

Melissa: Sure. Well, reporting guidelines, there are hundreds of them out there these days. But for systematic reviews, there are two that are out there that are really well disseminated and used. PRISMA, which I already mentioned, and the other one is MOOSE.

Interviewer: Oh. So many acronyms.

Melissa: Meta-Analysis Of Observation of Studies in Epidemiology. But reporting guidelines are really there to guide a researcher through the process of what things are really key to the reporting process. When they're actually writing that final journal article, what has to be in there so that this study can be reproduced and understood by the reader? And for systematic reviews, research time and time and time again shows that people are just not reporting their systematic reviews in such a way that is actually reproducible. And this is one of the ways that I think that librarians are really key, is because we can really help increase the reproducibility of this specific type of methodology.

Interviewer: Well, Mellanye, we've quite a bit about another common pitfall, which is how to search through data.

Mellanye: [inaudible] has 25 million citations in it. And you don't want to ask a question, you don't want to have a search strategy that's so large, you get lots of irrelevant results and you bring up a lot of static. That can be a real burden on the research team to have to go through extra thousands of results. But you don't want to ask the search strategy in such a way that it misses very relevant results. So as librarians, we can work on the search strategies to make sure we get exactly the right amount, not too many, not few, just right.

Interviewer: So give me an example of a search that might give you the wrong . . . maybe not the wrong information, but not enough, or too much. Either one.

Mellanye: Sure. One of the searches that I have worked on before, the research team did their search strategy with just the term "developing countries" and they missed a lot of highly relevant research. When they did their search they got about 17,000 results. When I added my search strategy in, including country names, including directions to the database and then form field tags and only searching for specific words in certain fields, it's kind of technical, but it really improved the results that came back. And it brought back about 3,500 results, many of which were actually relevant and would have been missed by that group.

Interviewer: You know, we're kind of going through the beginning, through to the end. What sort of an end step that you can help with?

Melissa: I think the end step, really, is the production of that final manuscript. Here in our systematic reviews core team, we do require authorship on manuscripts. And that's so that we can actually control how our literature searches are being reported. Because there's a tendency if you're not an expert in that area that you might not know what things actually need to be reported in order to make a literature search reproducible. So that really is our final step.

Announcer: Discover how the research of today will affect you tomorrow. The Science and Research Show is on The Scope.

Interviewer: What's becoming loud and clear is that most scientific studies are, well, unreliable. But there's help out there for scientists. We'll talk about that next on The Scope.

Announcer: Examining the latest research, and telling you about the latest breakthroughs. "The Science and Research Show" is on The Scope.

Interviewer: I'm talking with Darell Schmick, research librarian at the Eccles Health Sciences Library at the University of Utah. So there are a number of reasons for what some people are calling a research reproducibility crisis, including fraud. But even scientists with the best intentions are at risk for doing sloppy work and there are a lot of reasons for that as well. One of the things that you're interested in looking at is data management. I really like this example of the Postdoc who leaves the lab.

Schmick: So it was it an age-old issue where you do a lot of work and it happens to be on your personal computer. It happens to be in a folder with poorly-named files and you produce a bunch of research on behalf of the institution, but then, once you're done, you obviously take your computer with you, right, and then head off to that next position. Now, that seemingly innocuous, however, has a lot of implications. The data that you produce on behalf the university has ownership concerns. Is it the Postdoc's? Is it the University's?

Interviewer: How can that lead to issues with research reproducibility?

Schmick: So if the Postdoc takes all that data with him or her and hasn't been saved into the department bio or anything like that, how can you ensure that you have records of all the work that Postdoc has done? They could have taken just a little bit of it, they could have taken a substantial chunk of it. And it really leaves the PI as well as the rest of the members of the lab with potentially a significant disadvantage.

Interviewer: So there's a lot of knowledge that can be lost?

Schmick: Absolutely.

Interviewer: So what are some ways to avoid that?

Schmick: We do teach a research administration training class that talks about just the basic fundamentals of just good data management, which involves things like where to properly back up your files and how often to do that. Myself and a couple of the librarians on campus have been working on a pilot for electronic lab notebook technology.

So if, say for instance, you happen to have the perennial issue of lab members recording data in their own personal computers because it's inconvenient to share it, this sort of technology allows for a lab to share in a collaborative notebook technology something that has all those questions that we're talking about answered, like how frequently will it be backed up? Is it going to be backed up in a trustworthy source?

Interviewer: Right. Well, and not to mention that most lab notebooks that I've seen are kind of a disaster.

Schmick: Are you saying that scientists don't uniformly have amazing handwriting?

Interviewer: Exactly. Right, we're all human. And even how the information is recorded is varied from person to person. So the idea is that this would become more standardized?

Schmick: You hit the nail on the head there.

Interviewer: Any other approaches to make their data more accessible or reliable?

Schmick: When we're talking about optimizing the mechanics of anything in the research process, we want to ensure that we're doing it in a way that is not only accessible by us because we can look at our notes, presumably, and be able to understand what we were saying, understand what we were recording, understand what we were encountering in that process. But to think about how the results that you're producing are going to be read by somebody else that's not in that same context. So if you're doing an experiment, you do it for you, but you also ensure that you're doing it in a way that if somebody wants to reproduce that experiment, they can do that.

Interviewer: You've talked about ways about preserving information within a lab group, for example, a research group. What about sharing information more broadly with the scientific community?

Schmick: That's a great question, Julie. And a lot of people think that all you are able to really produce is that end product, that finalized article. And we don't realize, many times, that when we're doing experiments, when we're producing all this data that we're talking about, that data could be good data. It could be good information and good intel for another scientist that's stumbling across that same issue.

If you're embarking on answering a research question and you come across a dataset that has already sought to ask that question, you find out that maybe those results weren't satisfactory enough to produce something into a finished article, that could potentially save you years in otherwise reinventing the wheel.

So another thing that we like to talk about is the idea of ensuring that researchers know that the data that they produce is of value and there are places that you can store that. So one example that comes to mind is figshare. And figshare is a repository that you can actually assign a DOI to the data sets that you're uploading on there. Figshare are all about open science so they say as long as you're making it public, I mean, you can upload it for free.

Interviewer: So how can sharing data with the scientific community help with research reproducibility?

Schmick: There's a lot of news as of late in the way of that openness toward science where folks on a peer review panel want to see the steps you were able to take in order to draw the conclusions that you were able to take or able to make. And if they're able to go ahead and see that data right there from the start, it answers all those questions.

It's when things like that data being withheld presents a larger problem not only for you as author but greater implications for science in general. When we start to withhold that data, when we start to conceal certain steps in that recipe toward what we ended up with the final product, it leads towards a slippery slope of was it not open science and that closed scholarly environment, I think, is something that is well worth fighting against.

Interviewer: Where can people go to learn more about best data management practices?

Schmick: There are a lot of places. If you're embarking on a data management plan there's a great tool called DMPTool. That's dmptool.org, and it'll take you through the steps of the processes of, "Let's take you through the steps of the data management process." By asking you in a 20 questions format, "What's this data going to be for? Which agencies are going to be seeing this data?" And it'll give you recommendations at the end from there. If you're at the University of Utah's campus, I'd encourage you to talk to me or one of our other fine staff at Eccles Health Sciences Library. I'd be delighted answer any questions regarding data management plans.

Announcer: Interesting, informative and all in the name of better health. This is The Scope Health Sciences Radio.