Ana Neves | Reality Bending Lab

Ana Neves

Wed, 02 Jul 2025 00:00:00 +0000

The ultimate, hidden truth of the world is that it is something that we make, and could just as easily make differently.

How to collect and save data with DataPipe in OSF

Wed, 02 Jul 2025 00:00:00 +0000

Hello there! 👋 Let’s learn how to set up DataPipe to collect and save data in OSF.

Lets start with some basics!

What is DataPipe?

DataPipe is a tool that allows you to collect and save data in OSF (Open Science Framework). It is designed to help researchers manage their data collection process efficiently, ensuring that data is stored securely and can be easily accessed for analysis.

How to set up DataPipe in OSF

Create an OSF Project: Start by creating a new project in OSF. This will be the container for your data and any related files. You can set up an account if you don’t have one already, quite easily!
- Go to the OSF homepage and log in or create an account. You can easily sign up through institutional access.
- Click on “Create New Project” and fill in the necessary details such as project title, description, and visibility settings. Choose “Germany - Frankfurt” as the server location; this is important for data privacy and compliance with regulations such as GDPR.DO NOT SET YOUR PROJECT AS PUBLIC as the data being saved will not be anonymized and may contain sensitive information.
Create OSF Token: You will need to create a token to grant DataPipe the necessary permissions to access your OSF project.
- Go to your OSF “Settings” tab and navigate to the “Personal Access Tokens” section.
- Click on “Create Token” and give it a name (e.g., “DataPipe Token”).
- Set the permissions for the token, ensuring it has access to read and write data in your project.
- Copy the generated token; you will need it later.
Link OSF to DataPipe: In DataPipe, you will need to link your OSF project using the token you created.
- Open DataPipe, click “Account” in the top-right corner and select “Settings”.
- Click on the “Set OSF Token” button and paste the token you copied earlier from OSF.
Create new experiment on DataPipe: Now that your OSF project is linked, you can create a new experiment in DataPipe.
- In the “My Experiments” DataPipe tab, click on the “Create New Experiment” button.
- Give the experiment a name - I recommend using the same name as your OSF project for consistency.
- Add the OSF project ID to the experiment settings. You can find the project ID in the URL of your OSF project (it is the alphanumeric string after osf.io/)
- Create a New OSF Data Component called “data”. This will create a folder - named “data” - in your OSF project where all the data collected will be saved.
- Again, choose “Germany - Frankfurt” as the server location for your DataPipe experiment.
Configure Data Collection: Once the experiment is set up on DataPipe, enable data collection on the “Status” section. You can optionally enable base64 data collection if you wish to encode any video, audio, or image files as strings. “Condition assignment” can also be enabled - this makes DataPipe loop through the conditions when it requests the data. When deciding whether these features are suitable, it’s best to consider how you will preprocess the data. It’s advised that you only enable the minimum needed as a security measure.
Save the data from the experiment hosted on GitHub: If you are using a GitHub repository to host your experiment, you can save the data collected by writing the following code within the experiment HTML file. Here is what that code might look like…
- Ensure you load the jsPsych DataPipe plugin, along with the rest of your plugins, within the head of the HTML script:
```
        <script src="https://unpkg.com/@jspsych-contrib/plugin-pipe"></script>
```
- After initializing your jsPsych timeline, to generate a random participant ID for your study, you can code the following:
```
// Initialize timeline
    var timeline = []
    participant_ID = jsPsych.randomization.randomID(10)
```
- This next bit of code should be called at the end of your experiment (albeit before running the timeline) to ensure that all data is saved to the OSF project, using the unique participant ID generated from the step above:
```
// Save data via DataPipe
    timeline.push({
        type: jsPsychPipe,
        action: "save",
        experiment_id: "xxxxxxxxxx", // This in generated in the DataPipe interface
        filename: `${participant_ID}.csv`,
        data_string: () => jsPsych.data.get().csv(),
    })
```
- On the experiment created in DataPipe, there is an ‘Experiment ID’ field. This is the ID you need to add to the experiment_id field in the code above.
- The filename field can be customized to include the participant ID or any other identifier you prefer.
- If publishing your experiment to GitHub, make sure the link is
  
  ‘https://[your username].github.io/[your repository name]’
  
  or ‘https://[your username].github.io/[your repository name]/[name of experiment’s html file]’ if the html file for your experiment is named anything other than 'index.html'
Run Your Experiment: With everything set up, you can now run your experiment. DataPipe will automatically collect and save the data to your OSF project as specified.

Give it a try! If you’d like further clarification, the DataPipe website includes a useful outline.

Too beautiful to be fake: Attractive faces are less likely to be judged as artificially generated

Thu, 19 Dec 2024 00:00:00 +0000

Audio Summary

Listen to a podcast summary of the paper!

Sussex Psychological Methods MRes: Tips and Advice

Tue, 19 Mar 2024 00:00:00 +0000

Ola! I’m Ana. As I’m starting to approach the end of the year, it might be a good time to reflect and share my experience of doing a research masters in psychological methods at the University of Sussex, during the 2023/2024 academic year. First, I will talk a little bit about the modules I took; then I will mentioned all the reasons why you should choose to work with the Reality Bending Lab (ReBeL) and lastly, I will share some gems on how to survive the masters 💎. Hopefully this blog will help you decide whether this degree is for you! Shall we start?

Overview of the Modules

Since this is a research masters (MRes) aiming to prepare students for a future career as psychology researchers, the modules will have a significant focus on different research frameworks and practices, statistics and coding. During the Autumn semester you will have three main modules: 1) a (re)introduction to statistical models; 2) an introduction to Qualitative Methods; and 3) an introduction to better quality research practices. This term is super heavy on its content (no jokes) and will feel like a lot to do and learn (see below for tips on how to survive). However, there are plenty of materials to help you through this term, such as the R tutorials from our own in-house celebrity Professor Andy Field.

The Spring semester is less content heavy and more practical focus. There are again, three main modules: 1) a theoretical and practical module on how to use advanced statistical methods; 2) an introduction to the Bayesian framework; and 3) an introduction to Python programming and how to use it to implement experiments. This has been a delightful term, not because it is easy, but because the focus is less on memorising and more on learning how. Similarly, there are plenty of amazing materials to help you through this term such as optional zoom meetings to help you understand the materials and continuous communication on discord between lecturers and students.

Additionally, there will be a research module that runs both in Autumn and Spring, and a dissertation module that starts in January and ends in August (i.e., when the dissertation project is due).

The Internship

Critically, you will also do an “internship” as part of this masters (named the “research process” module 🤷‍♀️). This is by far the most exciting part of this masters as you will learn first-hand what is like to be a researcher. You can essentially chose any psychology researcher from Sussex to work with, providing you with a great network and experience. Now… you may be wondering what lab to choose? And oh boy, do I have the answer for you!

Introducing the Reality Bending Lab (ReBeL)

Rebel is led by Dr. Dominique Makowski. He will be your Mr. Miyagi during the Autumn and Spring term (and also your lecturer for the Bayesian Module). His patience, humour, straightforwardness and unmatched theoretical and pragmatic knowledge will be one of the big reasons why you will desire to be a researcher at the end of this masters (PS: no payment has been received in exchange for this testimony). The lab focus a lot on innovation hence you will learn new ways to collect neuroscientific data and use new statistical methods. There will also be a big focus on collaboration. Yes you will work independently, however more likely than not you will have the support of everyone in the lab, and you will be giving support yourself (getting a bit of experience on supervision and mentoring). Curiosity is welcome and encouraged. Ask your questions, get involved in all aspects of the process if possible, and take advantage of the fact you will have a ‘mentor’ for the whole academic year.

During my time at ReBeL, I have been involved in various projects, such as “Exploring the Correlation between Interoception and Primal World Beliefs” and a meta-analysis of a widely used questionnaire of Interoception. These projects have taught me a lot, from how to collect and analyse both physiological and behavioural data, access and collect data for a meta-analysis, and report the work I did in oral and written format. Throughout the year, with the guidance and expertise of everyone involved in the lab, I gained a lot of confidence in my abilities as a researcher. Which is why I found this internship the most influential aspect of my masters. Ultimately, at the ReBeL lab, you will not only investigate exciting concepts and topics but you will also have first hand experience on what it actually takes to be a researcher (including the need to have a twitter account, apparently).

Survival Tips

Now… You might be wondering.. “How in the world will I do all of this in one academic year?” Here are some tips that helped me gain the most of this masters without loosing my mind.

Unsurprising tip: DO THE WEEKLY WORKSHOPS/TUTORIALS. They will provide with the majority of code, steps and knowledge necessary to complete the assignments.
Life saving tip: do meal prep for the 48-hour assignments. If you are anything like me you will rather lose sleep then a delicious home-made meal. However, with the short time window to complete these assignments, meal prepping will help you feel less anxious about “not having enough time” to complete it all whilst still giving your mind everything it needs to function (i.e., sleep and nutrients).
Qualitative tip: as part of the January assignments, you will be asked to analyse 5 interviews using a qualitative method. If you come from a mostly quantitative background like me, you will be unfamiliar to how long it takes to code qualitative data. Do not make the same mistakes as I did and start that assignment as early as possible.
Student formatting tip: when lectures say “I want it in APA format” some will expect you to write a piece of work that equates a publication level piece of work. When in doubt, ask them!
Practical life skills tip: communication is key with your supervisors. Especially during your internship; be honest about what you can and can not do, your preferred ways of working, your goals and dreams, and mostly important when you need help.
Ultimate tip: do consider part-time , especially if you want/need to be working more than 20 hours a week on top of doing this masters. It is full on, and even as part-time all the lectures will be taught in the first year and hence there is still a lot of work to do. But it is possible, and can even be enjoyable.