7 Easy Steps to Building your Own Shiny App from Scratch

Shiny is a powerful tool in R for you to show off your work to the world, without explaining all the complicated code behind your analysis. Because of its free and open-source development and deployment structure, sharing your methods or work online was never easier. For example, in our recent publication in the Journal of European Psychology Students my colleagues and me used Shiny to implement a network method in which we used the concept of network centrality to determine the most relevant articles in a research field. Because I believe there are a lot of benefits in sharing one’s methods, my hopes are that this blog post has the possibility to also inspire you to share your own work through Shiny. I will walk you though developing your own R Shiny application from scratch, tailoring it to your design choices, and publishing it online, in 7 easy steps. Together we will create an application that has a pleasant layout, allows the user to upload a data file, performs some calculations on selected variables and returns the output in a nice format. Code snippets will be given at every step along the way, which you can paste directly into R to see how the application develops from basic functionality to a fully functioning web app. The result can be found here.

Step 1: The Idea

The first step in any design process is thinking of an idea. This, for me, is also the most important step in the process. I that it helps me a great deal when the application and the features that I want in it are completely defined in my head. I will often draw the layout and the features that I have in mind on a piece of paper. The reason I do this is that, because you will build your Shiny application step-by-step, it would be a waste if you would have to go back and change things in the layout. So, I drew our imagined application on a piece of paper. I will make a one-page application that allows the user to upload some data, perform a correlation analysis and inspect the results.

Step 2: Launching Shiny

Shiny itself is an R package that works best in RStudio, which is the most used R environment among users. For RStudio to work properly, you need to separately install R on your computer. Once you open RStudio, in the new file menu, click the option to open a new Shiny Web App. RStudio will now prompt you with the screen below, asking you to specify the folder name for the application, whether you want a single file app or multiple file app, and the directory on your computer in which you want the app folder to be created. For ease of illustration we will create the application in a single file, but both options are okay to use. I am going to name the app directory “correlation” and it will go into one of my many folders, probably never to see the light of day again.

Tip. If you are familiar with Github, this is probably a good time to set up a repository in the application directory for version control.

Step 3: The Shiny Structure

Any Shiny application has two parts, the user interface (UI) function and the server function, which interact with each other. The UI function has an important visual task. It creates the html environment that allows the user to interact with the server function using buttons, sliders and other custom inputs while also rendering the visual output. All these components are placed in a layout in the UI function. The server function is responsible for all the calculations according to the input options that is receives from the UI function. The server function is where all the computational heavy-lifting is performed, like calculating the statistics for our correlation analysis. For example, if the user enables a checkbox (UI) that controls whether a regression line should be drawn in a scatter plot, the plot should be created (server) with a regression line and rendered in html (UI) to be displayed on the screen. The picture below shows how input and output are bounced around between the UI function and the server function and hopefully gives you an idea about the interaction between the two.

Tip. Try to get a feeling of the interaction between the UI and server functions. Ask questions like: what options should I have, and what should happen to the output when the user changes these options?

Step 4: Creating Your Layout

Let me say that there is a lot of packages out there that make shiny applications look pretty, and I agree that these downloadable add-ons can really improve the quality to your work and are worth looking into. Packages like Shiny Dashboard will make your applications look and feel fantastic. For now, I consider these topics advanced knowledge and I will provide you with a list of them at the end, so you can explore them yourself. However, they still require a basic understanding of Shiny concepts and therefore I will stick to the basics in this post.

When you first create a new Shiny app, R presents you with a default template application drawing a histogram. Check it out if you want, by pressing the “Run App” button in RStudio. However, the first step to any good and original app is to start from scratch. So, to start off, completely empty the pre-defined ui() and server() functions that R presents you with. We are going to make our own, better functions. All the information that I discuss here can also be found at the Shiny layout guide, which is a great resource when starting with Shiny.

The number of pages or tabs of your application will be defined by one of the page functions. Page functions are the top level of your UI function. For an application with multiple tab-pages, you can assign the navbarPage() function to the UI object. However, the default page equals a fluidPage() layout, which gives you a single page to work with. Since our application requires only one tab, let’s keep the default page function. The page will be assigned to the UI function. You can check out code snippet #1 for how I applied this in R. Remember you can check the application at any time by running the app through RStudio.

We are now going to further layout our page, since it still empty. In my applications, I often use a sidebar to indicate the difference between the input (options) and the output (result). With a sidebar, the application is divided in two parts; the sidebar for the input options and the main panel for the output display. The sidebar layout can be set by inserting the sidebarLayout() function into the fluid page. Since this layout can include a sidebar and a main panel, we will insert them into the sidebar layout by calling their appropriate layout functions as well, see code snippet #2. Finally, a title panel can be inserted above the sidebar layout with titlePanel(“Correlation”).

This skeleton will be the framework of the application. We will fill these layouts with drop-down menus, upload buttons, tables and plots. Remember that at this point, the server() function is still empty. We don’t need it yet, since we want to build up the entire user-interface first.

The Sidebar

Let’s start with filling the sidebar. Shiny is rendered in an html environment and uses recognizable html functions for text rendering. For example, we can display our intro text with the p() function, displaying regular text. However, if we would like to adjust the font size of the text, we could also use one of h1() through h6() to enlarge the font size. We can add our first interactive element, the upload button, by inserting the fileInput() function in the sidebar. Every interactive UI element has an inputId argument, which basically names the element. This name is used to refer to the element in the server function and should always be filled. For our data input, inputid=”datafile” will be an appropriate name. The file input allows the user to browse their computer for a file to upload to your application. The two drop-down menu’s for selecting variables can be inserted via the selectInput() function. We’ll leave the choices argument in these functions empty for now, I’m going to show you a neat little trick I’ve come across to automatically read in the variable names from the uploaded data set. Going down, we are going to insert a bold title for the plot header with p(strong(“Plot”)). Underneath this header there are two checkboxes, one for the regression line and one for the residuals, for which we use checkboxInput(). We repeat this process for the table header. Lastly, we add an update button using the actionButton() function. I find these buttons working very nicely, as they allow the application to only perform a calculation when all options are set correctly by the user. This saves computation time and makes your application faster, and easier to work with.

The Main Panel

The main panel is where we are going to display the output of our correlation analysis, namely the plot and the results table. It basically works the same as filling the sidebar panel. First, we want to display a header for the results which is slightly larger than regular text, so we’ll use h3(“Result”) for that. Adding plots and tables is extremely easy in Shiny and is done by output functions. These functions reserve a spot for the specific output in the main panel. The plot is reserved with the plotOutput() function and the table with tableOutput(). Remember that we have to give them appropriate input id’s. The result when we run our application is now a nice and clean layout, visually and in code.

Tip Online resources are your friend. Try to use them as much as possible, as they will increase your understanding of the process of making a Shiny application. Look for YouTube clips and blog posts to clarify the material for you.

Step 5: Creating Functionality

Let’s give the application some functionality by filling in the server function. The first logical step is to make sure that the data is read in correctly. For this purpose, we are going to make the drop-down menus recognize the variable names by performing a neat little trick in the server function. When we insert the following code snippet in our server function and run the application, you can see that it now recognizes our variable names in the data file as inputs.

Now it’s time to create the true functionality within the app, namely creating a scatter plot of two user-specified variables, inserting an optional regression line and residuals, and creating a table of the results. As stated in the previous paragraphs, an action button is ideal for monitoring user actions. Only when this button is clicked, the output will be updated. Monitoring buttons is done with the observeEvent() function, which links functionality to an event. In our case, the event is the click on the action button and the functionality is all that I mentioned above. The functionality will only get activated when the action button is clicked by the user. The code for what happens when the button is clicked is inside the second part of the observeEvent() function. This way of thinking makes Shiny a very intuitive and chronological way of working for me. For example, when the button is clicked, the following happens in order (see code snippet #4): 1) a data frame is made with both variables in it, 2) the correlation analysis is run, 3) the output data frame is made, 4) the table is rendered, 5) the plot is created, 6) the additional elements are drawn conditionally and, 7) the plot is rendered. Code snippet #4 shows this chronological order within the server function. Check out how it works by running the app locally if you feel like it.

Tip Programming in Shiny will be a lot of trial-and-error when you do it for the first couple of times. Refresh the browser page in which you are running the application to see the effect of your code changes immediately, instead of closing and opening it each time.

Step 6: Fine-tuning

When the functionality of your application is finished, it is time for the most fun part: making your Shiny app pretty. For example, use shinycssloaders to display some pretty graphics as plots are being created within the server function. It is extremely easy to use, just put withSpinner() around the plotOutput() part of code in the UI function. Other R packages that are be very useful for making your app beautiful are Shinyjs for JavaScript features like showing and hiding options, ShinyWidgets for upgraded and additional widgets, like turning checkboxes into toggle switches (picture below) and ShinyBS to add bootstrap html elements to your app. It is important to remember to load additional packages into your application by calling the library() function at the top of your R file.

Step 7: Publishing Your App

When every component of your application is finished, you are ready to publish your shiny app online. Next to the “Run App” button in RStudio you find a little blue icon   which, when clicked, brings you to the next screen:

If you do not have an account on https://shinyapps.io, create one as it is needed for publishing your application, and connect it to RStudio. When this is done, life gets easy. You just have to click the “Publish” button and all work will be done for you. The application will be uploaded to the shiny server and is accessible from your account, but also from a specific URL. The URL for my application for example, is https://koenderks.shinyapps.io/correlation/. Share your work and enjoy!

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Koen Derks

Koen Derks is a PhD student at Nyenrode Business University. Before he started researching Bayesian statistics in financial auditing, he studied psychological methods at the University of Amsterdam (UvA), where his interests for statistics and programming developed itself. Today, he is still collaborating with the UvA to develop software tools for students and practitioners to make data analysis fun and intuitive.

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Publishing the results of coursework research: An interview with Julian Burger and Koen Derks

submit-you-mustBeing an undergrad is hard. With the days spent in lecture rooms and the nights devoted to catching up with essays and assignments, one wonders how is it even possible for undergrads to do any research – let alone publish it. While there is no expectation from undergrads to publish, a rough (and very anecdotal) approximation is that around 1 in 100 students publish during their undergraduate studies in either a peer-reviewed journal or other online outlets. (However, this highly depends on the field and publishing culture of the affiliated institution). There are also many benefits to publishing as undergrad; as illustrated by Griffith (2001), an early publication – regardless of the importance of the findings or prominence of the outlet – can increase student’s confidence and inspire a prolific academic career in the future. So how do these acclaimed one-in-a-hundred undergrads manage to publish amid challenges of the student life?

One way is to publish the outputs of coursework assignments – be it an empirical study or a review article. This is precisely what Julian Burger and Koen Derks from the University of Amsterdam did with the group assignment from one Image result for r shiny logoof their Research Masters courses. Together with their classmates, they developed a method for ranking publications in a literature review, and wrote it all up in an article that was recently published in JEPS. In addition, they created an interactive tool in R Shiny – a package implemented in R that allows for effective and didactic illustration of one’s analysis tools and methods. We were curious about the process of publishing the results of coursework assignments, so we invited Julian and Koen to share some of their insights. We hope you enjoy the read – and hopefully get inspired to publish some of your own coursework research as well!

Could you tell us a little bit about the study you recently published in JEPS?

Our recently published study in JEPS has its origin in our 2016 Research Master course “Good Research Practices”, which elaborated on the methodological do’s and don’ts of scientific research. Our course coordinator at the time based the course around the topic of a prevalent misunderstanding in the use of ANCOVA, namely that when groups differ on a covariate, removing the variance associated with the covariate also removes the variance associated with the group (Miller & Chapman, 2001). As such, an ANCOVA with covariates that are too intimately related to group membership yields unreliable results. As preparation for group-projects, every student searched for 40 articles (20 before and 20 after Miller & Chapman) related to the issue under consideration. Out of a need for a concise literature overview, we started thinking about possible solutions to aggregate this literature. We were under the impression that there had to be a way to statistically find the top-something relevant articles that everybody could read to get a simple, but complete, overview of the topic. This is how the idea of a network model of our literature search was born. We came up with two distinct methods with which we created separate networks, one of their citation structure and one of their co-occurrence frequency. These networks, as they describe the relationships between articles, give relevant insight in the relative importance of the articles in the literature.

Creating a Shiny App sounds like an exciting way of presenting one’s research. Was it hard for you and your colleagues to build the app?

We have had some experience with programming in R during the Bachelor and Master programmes. However, we had never implemented a Shiny app to demonstrate our work publicly. Programming the network method itself was the most difficult part. The Shiny app is of course a nice way to promote the network method and let other people benefit from it, and since Shiny is well documented, it was a fairly straightforward task. Shiny has so many advantages for demonstrating your work and making it publicly available for others, it is amazing for these projects.

derks-app-viewA view inside the Shiny app for network visualization of literature search. The app accompanies the publication, and instructions for use can be found in Appendix C of online supplementary materials.

Publishing a paper with 52 co-authors sounds like a challenging collaborative endeavour. Could you recap the process for us – how did it all go, from the first idea to final manuscript edits?

Within one group of the course, we worked on a way to aggregate the literature collected by all students. In this group we applied the two different network models to the articles collected by all contributors. From our experience it is very important to assign a clear role distribution from an early time-point on and communicate the obligations that come with this well. Throughout the course, we presented the results of the analyses in class, collected feedback by the students and after the course wrote the article in a team of two. We appointed a main contributor who took care of the main writing and fine-tuning and a second contributor who wrote another part of the article. The course coordinator took a supervising role and was available for meetings and feedback on the writing.

And what were the most difficult aspects?

Coordinating the feedback and rewriting the article. If you work with multiple authors that takes a lot of time. Because some of our classmates were following other courses, it was not always easy to contact them for feedback in the later stages of the publishing process.

Do you have any tips for students who are thinking about publishing the results of their coursework research? Or maybe for lecturers who consider structuring their course assignments in a similar way?

From our experience in working on this project, we think these points might help in coordinating a project with a large group of students:

1. A clear role distribution. To prevent misunderstandings regarding responsibility, we advise to spend enough time on clarifying who is working on what.

2. Not too many contributors involved in the actual writing. From our experience, it worked well to have two people involved in the main writing. It is of course useful to collect feedback from the group, but to have a coherent story, not too many writers should be involved.

3. Have one main responsible contributor. It worked very well for us to appoint one main contributor, who took the main responsibility of incorporating and coordinating feedback.

4. Make use of feedback sessions. From our experience, the main benefit of working in a big group is that you can use a lot of input from different perspectives.

What are your future career plans?

Koen: This year I started a PhD focused on statistical auditing. My main topic is developing Bayesian alternatives to classical audit methods and implementing these methods in JASP for Audit (JfA), which is built to support auditors in their statistical journey. In the future, I plan on staying in academia and continue learning about Bayesian statistics.

Julian: I started my PhD this year on network models/dynamical systems in psychopathology. My plan is to make these models more accessible for clinical practice and at some point in the future to combine the research with working as a therapist myself.

Any final comments?

The Journal of European Psychology Students is a great outlet for publishing findings from such a student course project, so we think it is definitely worthwhile trying to write down your work and get to know the process of scientific practice and publishing!

If you’re inspired by Julian’s and Koen’s story and wish to put some of your own coursework research in writing, check out out submission guidelines at jeps.efpsa.org!

Karla Matić

Karla Matić is a psychology graduate of University of Leuven with interests in cognitive neuroscience, large-scale neuroimaging methodology, and science policy. She is currently an intern in the European Research Council (ERC) in Brussels. If she didn't aspire for an academic career, she would be running a book-café on a small Croatian island.

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Between science and policy: an interview with Dr Toby Wardman

Even though scientists are oftentimes lost in the ivory towers of their scientific work, academic research in any discipline – and especially psychology – is tightly connected to the society. It contributes to the improvement of the living conditions in the population. It supports the decision-making process of policy-makers with scientific evidence. And it is paid for by the tax-payers’ money. In an attempt to ensure that this natural relationship between science and society is always well-balanced, we make policies – governmental policies, international policies, institutional policies. The field at the interplay between science and policy-making – very intuitively coined ‘science policy’ – therefore concerns itself with topics such as the allocation of resources for scientific research, the careers of scientists, and the systems of efficient communication between scientists and policy-makers (Pielke, 2005).

Precisely this area of science communication is the field of interest of Dr Toby Wardman. He works in SAPEA – Scientific Advice for Policy by European Academies. SAPEA is a part of European Commission’s strivings to efficiently communicate with academic researchers and inform the decisions on new policies with scientific evidence. Its role is to provide timely, independent and evidence-based scientific opinion on a diverse set of relevant topics to both the EU policy-makers and the wider public. By bringing together the knowledge and expertise of scientists from Academies and Learned Societies in over 40 countries across Europe, SAPEA plays a crucial role in bringing the scientific findings from the lab bench to the policy desk.

When it comes to big questions, such as genetic modification, or cybersecurity, the quality of the policy will necessarily depend on the quality of the scientific evidence. In an attempt to gather comprehensive evidence and policy advice in a robust and efficient system, European Union relies on an in-house advisory body called Scientific Advice Mechanism. This body is run by 7 prominent scientists, and through SAPEA (and several other mechanisms) it provides independent scientific advice directly to European Commissioners. By acquiring scientific evidence on relevant policy initiatives and debates from many hubs of expertise in different European countries, this mechanism optimises the objectivity of evidence-based policy recommendations.

Scientific Advice Mechanism (SAM) – the process of closing the gap between scientific evidence and policy in the European Commission.

The scientists’ role in this process is pivotal; after all, each researcher is the best connoisseur of their own research. Communicating research outside narrow academic circuits is also a highly valuable skill within the academic community – it improves academic prospects and brings new perspectives into researchers’ scientific work. Yet many believe that scientists still do not engage enough in public outreach, and call for more education on science communication (including strategies to counteract the post-truth culture that propagates misinformation). In an interview following our meeting at a workshop on science communication, I talked with Toby about science, policy, communication – and everything in-between. Enjoy the interview! (And want to know more? Check out the ‘Further readings’ below.)

 

You started off as an academic studying philosophy of science, but then took a turn towards science and policy communication. Tell us a bit about your background, where did you start from, and how did you end up where you are today?

Actually, I started work in communications straight after my Bachelors degree and have worked full-time ever since – I studied both my masters and my PhD part-time alongside working. My jobs have been a mix of science communication (which I do now) and political communications, mostly in the UK. I worked on the EU referendum on the Remain side, and when it all went wrong, my wife and I decided that enough was enough and we moved to Brussels. Now I work for SAPEA, part of the European Commission’s Scientific Advice Mechanism that provides science advice for EU policy-makers.

Many people these days argue that political decisions should be more informed by the results of scientific research. However, scientists and politicians tend to speak completely different languages – which poses a massive challenge to evidence-based policy. How does the EU bridge this gap between science the policy?

The EU actually has a pretty robust setup so that policy-makers can get advice from scientists. EU policies generally start life as drafts from the European Commission, so that’s where the Scientific Advice Mechanism comes in. Before drafting a new policy, Commissioners can ask us any question about the state of science in that area. In response, we give them two documents: a comprehensive and independent review of the evidence, which is done by SAPEA (‘Science Advice for Policy by European Academies’), consisting of experts from more than 100 European academies working together; and a Scientific Opinion which is drafted by the Group of Chief Scientific Advisors based on SAPEA’s evidence review. Those two documents are then used by the Commission to decide what policy action to take. Equally, the scientists themselves can offer advice to the Commission on topics which they think are important, rather than waiting to be asked the question.

And do you think that this mechanism works well? In other words, can we say that EU policies are evidence-based, or is there still space for improvement?

Yes, I think the EU sets a pretty good example in this field. The Commission has access to very high-quality science advice, and the impact of that advice can be clearly seen in the proposals they write, especially in technical fields, which are often very closely based on the science.

Of course, that’s not to say that every new EU law looks exactly how scientists might want it to look. Firstly, there is always room for improvement. And secondly, it’s important to remember that we don’t really want our public policy to be dictated exclusively by science. We live in a democracy, not a technocracy. The role of scientists is to provide evidence and advice, and the role of politicians is to weigh up that evidence and advice, along with many other considerations, when deciding what policy measures to adopt. Science is value-neutral: it can give us information about the way the world works and what effect certain actions might have, but it can’t tell us what is the right thing to do, or what the public supports.

Many of our readers are students and young researchers who might be thinking about continuing their careers outside of academia. What are the pros and cons of your current job, as compared to doing full-time academic work?

For me, science communication in general sits in the sweet spot between academia and the rest of the world. On the plus side, I get to work closely with Europe’s top researchers and dive into all kinds of interesting scientific areas, while also keeping a foot in the policy side of things and seeing our work have a real, concrete impact on legislation and people’s behaviour. The obvious downside, compared to academia, is the fact that you are always working on someone else’s research – never doing your own.

One of the projects you worked on included training early-stage social science researchers in communication. Why is it important for the early-career researchers to work on their communication skills? And do you have some good resources to recommend to our readers?

We need scientists who can communicate! And most importantly, we need good young communicators who are able to reach out authentically to people of their own generation. That means early career researchers. The training programmes I ran were only a few years ago, and when I recall the content, it already seems embarrassingly out-of-date.

As for resources, honestly the best advice I can give is to practise! Communication, outreach, dissemination – whatever you want to call it, it should be a key part of every academic’s skillset, not an optional extra. So seize every opportunity to write for, or speak to, a non-specialist audience. There are loads of outlets out there which are crying out for good quality content. And even academic journals increasingly have an ‘editorial’ or ‘magazine’ section, or an online companion magazine, which welcomes contributions. As a professional science communicator, I probably shouldn’t say this, but communicating isn’t rocket science. You can just jump in and do it, and learn on the job. That’s how I learned, anyway!

You had the pleasure – or maybe the displeasure – of working on the EU referendum campaign in the UK. How do you think Brexit will affect science and academic collaboration in Europe?

Ugh. It’s already damaging it. Even before the Brexit vote happened, there was clear evidence that UK researchers were starting to get overlooked when it came to putting together project collaborations and applying for funds. That’s really bad news for the UK, obviously, which has always done disproportionately well when it comes to winning EU research funding. But it’s also bad news for research across Europe, because Britain has traditionally had a lot to offer.

It’s depressing, because research collaboration is really one of the most obvious benefits of EU membership. And no matter what you think about issues of sovereignty or migration or whatever, you surely have to agree that combining our firepower – both in terms of collaborations and funding – is obviously a good idea when it comes to research. It simply makes no sense for 28 countries to spend money 28 times over.

Now that Brexit is happening, it’s not just loss of collaborations and loss of funding, but also the very real danger of “brain drain”. If you’re a talented researcher from anywhere in the world, and European universities are competing to attract you, why would you choose the UK? Of course, individual researchers will still be able to collaborate, but without the EU’s framework, it will just make everything much harder and therefore less likely to happen.

 Any last comments, thoughts, or recommendations for our readers?

Sometimes I think of science communication as a necessary and temporary evil: we only need science communicators because scientists haven’t learned to communicate, and if only the next generation of researchers would learn how to communicate themselves directly, then they wouldn’t need people like me. So if you have an interest in outreach as well as an interest in your particular research area, then great! Being able to sell what you do to a wider audience will always be a boost to your academic career, and it’s only going to get more important.

But then at other times I think that’s nonsense. Sure, some researchers are good at communicating, and that’s wonderful. But many others aren’t, because they don’t have the right skillset or the right interests – and why should they? If you’re good at doing something, you should be able to focus on it even if you’re not also good at talking about it. So I guess I’m saying: if you don’t have an interest in outreach, that’s ok too. It will keep people like me in a job!

 

Want to embrace Toby’s advice and try out your science communication skills by writing a blogpost for JEPS Bulletin? Write to us at bulletin@efpsa.org!

 

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Karla Matić

Karla Matić is a psychology graduate of University of Leuven with interests in cognitive neuroscience, large-scale neuroimaging methodology, and science policy. She is currently an intern in the European Research Council (ERC) in Brussels. If she didn't aspire for an academic career, she would be running a book-café on a small Croatian island.

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Publishing a Registered Report as an Undergraduate: An Interview with Tatiana Kvetnaya

In the past, we have talked a lot about Registered Reports and their potential to increase the rigor and reproducibility of psychological science (see here, here, and here). In a previous blog post, James Bartlett interviewed Dr. Hannah Hobson, who published a Registered Report as part of her PhD project.

In this blog post, we talk with Tatiana Kvetnaya who received her Bachelor degree from the University of Tübingen, and who is currently pursuing her graduate studies at the Goethe University Frankfurt. Excitingly, Tatiana recently published her bachelor thesis as a Registered Report with the Journal of European Psychology Students. Below, she recounts how she first came in contact with Registered Reports, her experience publishing one herself, and tips for students thinking about doing the same. Continue reading

Fabian Dablander

Fabian Dablander just finished his Masters in Cognitive Science at the University of Tübingen. He is interested in innovative ways of data collection, Bayesian statistics, and open science. You can find him on Twitter @fdabl.

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Technology-assisted Therapy: An Interview with “Aaron T. Beck” Professor Daniel David

The technological developments we see today set a whole new view of life as we know it. Starting with the Industrial Revolution, and getting to robot assisted mass production of goods, we get to use intelligent machines in order to make life easier and evolve as a species. And psychology is not an exception. Ever since ELIZA was developed to simulate a psychotherapist in the ‘60s (try it for yourself here) computers have been widely used within clinical psychology and psychotherapy. Today, we will be talking about the efforts of the Clinical Psychology and Psychotherapy School of “Babeș-Bolyai” University of Cluj-Napoca, Romania in pursuing Virtual Reality (VR) and Artificial Intelligence (AI) research and practice excellence.

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Ioana Piscoi

Currently a first year Master Student of Clinical Psychology, Counselling and Psychotherapy at "Babes-Bolyai" University of Cluj-Napoca, Romania, Ioana is interested in developing her skills to become an ACT Therapist. She is particularly interested in the field of Personality Disorders and Techlonogy Assisted Psychotherapies, wishing to pursue a PhD in the future.

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Writing a Systematic Literature Review

Investigating concepts associated with psychology requires an indefinite amount of reading. Hence, good literature reviews are an inevitably needed part of providing the modern scientists with a broad spectrum of knowledge. In order to help, this blog post will introduce you to the basics of literature reviews and explain a specific methodological approach towards writing one, known as the systematic literature review. Continue reading

Eva Štrukelj

Eva Štrukelj is currently studying Clinical and Health Psychology at the University of Algarve in Portugal. Her main areas of interest are social psychology and health psychology. Regarding research, she is particularly curious about stigma and with it related topics.

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“WHAT REALLY MATTERS IS SCIENTIFIC PROGRESS AND NOT PERSONAL SUCCESS.” — AN INTERVIEW WITH PROF. DORTHE BERNTSEN

Take a minute to think about the following question. Who are you?

In trying to come up with an answer, you most likely have relied on knowledge about your past experiences. You might have thought about where you grew up, where you went to school or university, your current career, or your particular interests and hobbies. Most of these memories are autobiographical. Continue reading

Nicola Falzon

After finishing her Bachelor's in Psychology at the University of Malta, Nicola Falzon currently works at YMCA Homeless Shelter, working with diverse clients presenting various difficulties and at Willingness Malta, organising various scientific events and being involved in various projects related to sexuality. She intends to sit for a Masters in Counselling Psychology in the near future and go on to further her studies in the field of sexuality and gender diversity.

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Accelerating Psychological Science with Large-Scale Collaborations

Science is the collaborative attempt to understand ourselves and the world around us better by gathering and evaluating evidence. Ironically enough, we are pretty bad at evaluating evidence. Luckily, others rejoice in pointing out our flaws. It is this reciprocal corrective process which is at the core of science, and the reason why it works so well. Working collaboratively helps us catch and correct each other’s mistakes.
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Fabian Dablander

Fabian Dablander just finished his Masters in Cognitive Science at the University of Tübingen. He is interested in innovative ways of data collection, Bayesian statistics, and open science. You can find him on Twitter @fdabl.

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“Bullied Into Bad Science”: An Interview with Corina Logan

The last two years have seen a lot of talk about the issues of science and scientific publishing – and how the incentives prevalent in science (publish or perish, preferably with high-impact stories with lots of news coverage) are actually bad for science. Corina Logan, a zoologist and part of a group of postdocs from the University of Cambridge is eager to push for a change in the publishing culture. They argue that the current way of publishing is hindering the progress of science. A recent column by Brian Martinson in Nature summarises the problem nicely: “[The fact that researchers need publications encourages] all manner of corner-cutting, sloppiness in research, and other degradations in the quality of publications, not to mention an obvious motive for plagiarism. A quest for high-profile papers leads researchers to favour a spectacular result, even if it is specious. Authors cite themselves to boost the impact of publications, and cite colleagues to curry favour.” Continue reading

Katharina Brecht

Katharina Brecht

After finishing her PhD at the University of Cambridge, Katharina is currently a Postdoc in the Institute of Neurobiology at the University of Tübingen. Her research interests revolve around the mechanisms of social and causal cognition in animals.

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A conceptual introduction to mathematical modeling of cognition

Psychological researchers try to understand how the mind works. That is, they describe observable phenomena, try to induce explanatory theories, and use those theories to deduce predictions. The explanatory value of a theory is then assessed by comparing theoretical predictions to new observations. Continue reading

Frederik Aust

Frederik Aust

Frederik Aust is pursuing a PhD in cognitive psychology at the University of Cologne. He is interested in mathematical models of memory and cognition, open science, and R programming.

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