Becoming an open responsible scientist is not a single action, but a path built step by step. Each step helps make research clearer, safer and more useful: understanding openness, organizing data, sharing code, collaborating with others and involving society when it makes sense.
Step 1: Understand what Open Responsible Science Means
The first step is to understand that Open Science is not just “putting things online”. It is a way of doing research with more transparency, collaboration and social responsibility.
An open responsible scientist tries to make research more accessible to colleagues, institutions and society. But at the same time, this scientist respects privacy, ethics, authorship, licences and possible restrictions.
So, the idea is not: “everything must be public”.
The idea is: as open as possible, as closed as necessary.
This means that when data, code or results can be shared, they should be shared. But when there are ethical, legal or privacy reasons, the researcher must protect them.
Step 2: Opening up Your Research
Opening up your research means making your research process more visible, organized and reusable. Remember that science is not only the final article… It is also about showing the materials that are behind the paper.
You can open different parts of your research, for example:
- Your research question;
- Your project plan;
- Your protocols;
- Your datasets;
- Your analysis scripts;
- Your software;
- Your preprints;
- Your final publication;
- Your communication materials for the public.
- …
Data can help other researchers to test new questions. Data and software can help others reproduce your results. Protocols can show exactly how you did the work. Even negative results can be useful, because they can prevent other people from repeating the same errors.
There are a lot of Open Science tools that can help you to organize this process. A very useful example is the Open Science Framework (OSF). OSF allows researchers to manage the whole research lifecycle of a project, store materials, collaborate with other people, and decide what remains private, what is shared with collaborators, and what becomes public.
In this sense, OSF is not only a repository. It is a tool to manage the opening of your research in a responsible way.
Step 3: Do not be Afraid of Sharing your Research
Okay, now that you have decided to use Open Science tools like OSF, it is time to stop being afraid of sharing your research. Some researchers may worry about sharing early drafts, ideas, data or protocols. They may fear that someone could steal their ideas, use their work without permission, or judge a project before it is finished. This fear is understandable, especially for young researchers or PhD students. Sometimes we think: What if someone uses my idea before me? What if my work is not perfect yet? What if I lose control of my project?
However, sharing research does not mean losing control. In fact, if it is done properly, it can give you more protection. One good example is preregistration. Preregistration means writing down your research question, hypotheses, methods and analysis plan before collecting or analysing the data. This creates a clear record of what you planned to do from the beginning (and you can do that with OSF!). So, it is useful because it shows that your decisions were not invented after seeing the results (HARKing, etc.). And it also helps other people understand your research process better.
So, instead of putting your work at risk, Open Science platforms, repositories and project management tools can help you to:
- Prove you had the idea first;
- Show a record of your work;
- Keep a clear timeline of your project;
- Preregister your hypotheses and methods;
- Ensure your authorship is permanently attached;
- Control how others can use your work;
- Decide what remains private, what is shared with collaborators, and what becomes public.
So, instead, sharing your research provide security.
Step 4: Create a good Data Management Plan
Another very important step is creating a Data Management Plan (DMP).
Why? Because a proper Data Management Plan explains how you will manage your data during and after the research project. It is not only a bureaucratic document for funding agencies, and it is not only an exercise of transparency for other people. It is also a practical guide for yourself.
A good DMP can help you avoid confusion and problems later. For example, maybe after some months, or even after some years, you need to go back to your project and understand where a dataset came from, which version of a file you used, how you cleaned the data, or why you made a specific decision. If everything was planned and documented, it will be much easier to reconstruct your own research process. And this is very important, because many times it is impossible to remember everything by memory!!
In this sense, a DMP is like a map of your project. It helps other people understand your data, but it also helps your future self. Research projects are long, files change, collaborators come and go, and memory is not always perfect. A good DMP protects you from losing important information and saves time when you need to review, reuse or explain your own work.
A good DMP should answer questions such as:
- What type of data will I collect?
- How will I organize my files?
- Where will I store the data?
- Who will have access to it?
- Can the data be shared openly?
- Are there ethical or privacy limitations?
- What metadata will I use?
- What licence will I choose?
- How will the data be preserved after the project finishes?
- …
There are also specific tools that can help you create a DMP. For example, ARGOS, developed in the OpenAIRE ecosystem, is a platform for creating structured and shareable plans to manage data, software and other digital research outputs. It can guide researchers step by step, help them use templates from institutions, projects or funders, and keep data, software, workflows and responsibilities connected.
Step 5: Open your Code and Software when Possible
In many research areas, software and code are essential parts of the work, especially now that AI has emerged and made it easier to create code to collect or analyze data, even through practices like vibe coding. Scripts, notebooks, algorithms and analysis pipelines can be as important as the final paper.
If the code is not shared, other researchers may not be able to reproduce the results. They can read the paper, but they cannot fully understand how the analysis was made.
Opening code can help to:
- Improve transparency
- Allow reproducibility
- Receive feedback from other researchers
- Avoid duplicated work
- Give visibility to the person who developed the code
- Help other people adapt the method to new projects
- …
Tools like GitHub can be useful for this. But code should not be shared without explanation. A responsible open scientist should include a short documentation, a licence, basic instructions and, if possible, example data. Because sharing code is good. But sharing understandable code is much better.
Step 6: Collaborate with other Researchers, but Define Rules
Open Science is also about collaboration. The idea that “two heads are better than one” is very true in research. Working with other scientists can improve the quality of a project, reduce bias and bring new perspectives.
But collaboration needs organization. Before starting a project with other people, it is useful to define some basic rules:
- Who does what
- Who has access to the data
- What tools will be used
- What deadlines exist
- When materials can be shared
- What should remain private
This is especially important in interdisciplinary projects. Researchers from different fields may use different methods, different words and different expectations. A good collaboration needs clear communication from the beginning.
Open tools such as shared documents, collaborative platforms, project management tools and file-sharing systems can help. Here, the Open Science Framework (OSF) is again very interesting, because it covers many of these areas in the same platform: it helps researchers organize files, document decisions, manage collaborators, connect different tools, and decide what parts of the project are private or public. But even with good tools, the most important element is still trust.
Step 7: Involve the Public when it Makes Sense
Sometimes the public can do more than listen. In some projects, citizens can help collect data, classify information, observe local problems or participate in discussions about research priorities. This is called citizen science.
Involving the public can be very positive because it creates mutual learning. Scientists learn from society, and society learns from science. It can also make research more democratic and closer to real problems.
However, public participation is not always useful for every project. A responsible scientist should ask: does this project really benefit from public involvement? Can we guarantee data quality? Are participants well informed? Are ethical issues considered?
Again, openness must be connected with responsibility.
Small Steps can Change Science
Becoming an open responsible scientist does not happen in one day. You can start with small steps: organize your data better, create a Data Management Plan, use OSF, share a preprint, upload your code, choose a licence, write documentation, or explain your research in a simple blog post.
The most important idea is to understand that research is more than a paper. The paper is important, but data, software, methods, protocols and communication are also part of the scientific contribution.
Open Responsible Science is not about losing control. It is about making science more transparent, more useful and more trustworthy.