In this session, Post-Doctoral Research Fellow Dr. Ralitsa Madsen covers why using an Electronic Lab Notebook (ELN) is a great idea. Dr. Madsen suggests that there are many rewards in using ELN for a reproducible workflow and they include:
Saving a lot of time while reading, searching the documents and so on.
If you are a postgraduate or graduate student, it will be more convenientwhile retrieving the details that you need for materials and methods section of your research.
ELN makes it easier to collaborate not only within but also outside of the group.
Lab members can pick up where you left off, therefore it ensures the continuity of the research.
It is much safer to rely on an ELN rather than your hard drive. Your documents will be accessible even if your computer gets damaged/stolen.
It is necessary to have an extensive documentation, version control and traceability of your work if you would like to make a patent application.
In addition, RSpace is well-integrated with many other services like Mendeley, Microsoft Office, Dropbox, Google Drive as well as data repositories like Git Hub.
After naming several great reasons, Dr. Madsen goes on to do a walk-through of the tool and gives useful tips to facilitate RSpace adoption within the lab:
First, you should think FAIR: are your documents easily findable? Are they accessible to researchers inside or outside of the lab? Is it interpretable? Can others read through the lab book and reuse your protocol for their experiment?
But for this to work, says Dr. Madsen, you also need to create;
A lab book entry template which will ensure consistency and make it easier to collaborate,
Notebook based project organisation,
Data storage rules that are motivating to use external repositories and
Consistent file naming rules
Do not forget to check Dr. Ralitsa Madsen’s RSpace demonstration on Edinburgh Reproducibility’s YouTube channel if you haven’t already!
Edinburgh ReproducibiliTea held another great session last Friday, with Dr. Rosalind Attenborough from University of Edinburgh – Science, Technology and Innovation Studies! Her research is focused on the researchers’ attitudes towards open science and here are the main points of her insightful talk for those who have missed;
For her PhD project, Dr. Attenborough interviewed 54 individuals from various career stages, genders and disciplines in biology. She mainly explored what does open science mean to them. Although the interviewees came up with various responses to her question, majority of them fell under three category: open access, open data and interpersonal openness.
In general, researchers tends to be positive while talking about open access and believes that it is a good idea. Yet, it does not go without mentioning the monetary and bureaucratic issues around it.
Open data is a completely different story. While interviewing scientists and policymakers, Dr. Attenborough saw that people’s attitudes varied immensely. Some of the interviewees perceived it as a norm and embraced it with passion, while the others were cautious. What makes people refrain from sharing data seems to be stemming from the possibility of receiving destructive criticism and getting scooped.
The last category, interpersonal openness, refers to willingness and ability to talk about unpublished research ideas. Like data sharing, interpersonal openness also gets negatively affected by the competitive research culture as well as unsupportive mentorship.
Dr. Attenborough’s work is particularly insightful as it sheds a light on in which ways academia has to change so that the researchers , especially the ECR’s, can feel more comfortable embracing open science practices.
Dr. Wallace argued that all researchers need to learn how to analyse their data reproducibly, reliably and efficiently, regardless of which career stage they are at.
Researchers need some foundational skills like coding, data science and project organisation in order to practice open science. However, the many of the group leaders, Postdocs, PhD students and RAs across the university stated that they do not have formal training in computing (45%) or statistics (35%) at all. This, says Dr. Wallace, was one of the main reasons to work with the Carpentries for him.
The Carpentries relies on the open community, ethos and pedagogical drive. Here, all the resources are developed by volunteers on GitHub and learning as well as teaching is well structured.
One very important reason to get involved with the Edinburgh Carpentries is that the funding bodies are interested in open science as much as the researchers. For instance, UKRI-BBSRC plans to “take actions to increase the capacity in computational skills within the biosciences”. In fact, Edinburgh Carpentries is now funded by UKRI for two years to expand their trainings.
In the session, Dr. Wallace informed us that the Edinburgh Carpentries are currently developing new teaching materials for statistics, FAIR principles and data management and data science computing with reproducible workflows. In one of these workshops, the instructors are teaching some skills that can save a lot of time and improve your work, such as how to organise and document your code efficiently which is also discussed in the “Good Enough Practices in Scientific Computing”.
Edinburgh Carpentries is a community that is growing every day and is in need for more instructors. Some of the benefits of getting involved with the community is that;
You can get better at coding and teaching
The training helps to get funding and
You will be a part of a nice, supportive community.
Many of our attendees seemed to be interested in participating the Edinburgh Carpentries and thinking of ways to engage their own labs in open research. To receive updates about EdCarp workshops and/or sign up as an Instructor and/or Helper for Edinburgh Carpentries you can sign up to the Edinburgh Carpentries mailing list. If you would like to help Dr. Wallace and his colleagues in developing workshop on FAIR practices in biosciences (and be paid for that), please email them at firstname.lastname@example.org
Reproducibility is not a newly adopted principle, in fact, it dates back to 1600s. The Irish chemist Robert Boyle was the first to emphasize the importance of obtaining the same results when the study is re-created. Since then, scientists consistently reflected on how adopting a reproducible workflow helps advancing the science. Question is, does it only benefit science? During an invited talk at Edinburgh ReproducibiliTea Journal Club, Kaitlyn Hair explained how sharing your data, materials and code is also in your own interest.
Perhaps the most important reason for adopting a reproducible workflow is simply to avoid a disaster, according to Hair. Researchers all around the world publish their works continuously. These publications are leading up to other ideas, discoveries and products like vaccines and cancer therapeutics. A few years earlier, scientists from Duke University published a paper in which they claimed to find a way to efficiently target tumours based on their genetic sequencing. It is not very hard to imagine that this was a very important achievement at the time. However, after some failed attempts to replicate the results, scientists came to a shocking realisation; the promising findings were only a by-product of a technical problem which occurred in the process of copying the data from an excel sheet to another statistical program. Being unable to provide evidence against fraud claims, “this mistake was career ending for some of its authors”, says Hair. These kinds of mistakes are not as rare as we wish it to be. However, adopting open science principles can be life-saving in such situations. This was the case for Dr. Julia Strand. In 2018, she published the greatest achievement of her career. As it turns out, there was a way to improve speech perception and diminish the cognitive effort that we spend in noisy environment. Simply presenting a modulating circle which got expanded when the speech got louder made participants respond faster, or she thought so. In 2020, she published a blog post which drew lots of attention from scientists. “The central finding was the result of a software glitch…” she said. She found what she found only because she made a mistake while programming the timing clock. In this case, however, the code was openly available, the study was pre-registered and her work was fully transparent. Detecting her own mistake too, helped her prove that this was not a scientific fraud.
Another reason for working reproducibly according to Hair is that, it makes writing easier. Writing a paper is not a smooth process if you don’t use tools like R, OSF and GitHub in the process. Having to copy your figures and paste it to your Word document, going back and forth to create a table for your analysis can be overwhelming, especially on a tight schedule. One of the best features of R is that you can run your analysis, make tables and figures and write your results up at the same time. On top of that, Hair explains that the rticles package in RMarkdown comes with many different paper formats such as PLOS or Frontiers style and knitr package allows you to save your document as PDF, word document or HTML file when you are done with it.
In addition, if you are collaborating with other scientists, you may easily end up with dozens of updated versions of the same code. GitHub is a great tool to avoid getting lost in a pool of code files in such situations. All you need to do is to upload your file on a remote GitHub repository so that the co-authors can pull it to their own computer, make changes and push it back to the repository. It tracks the changes that are being made and who made them, which means that you don’t need to keep all the versions on your computer.
Working reproducibly also ensures continuity, according to Hair. Especially as you make progress on your career and publish more often, you will notice that you forgot what you did, what the variable X in your dataset refers to and how it is different than the variable Y which looks just like it. Uploading our notes on OSF or creating a bookdown page can help us remember what we did and easily inform our team without having to go through everything again and again. Likewise, you can use GitHub to share your codes and readme files in which you can explain what your code exactly does.
Furthermore, it helps you get through the peer review. “If you are using RMarkdown, it helps the reviewers understand what you have done.”, says Hair. Reviewers can just download your data and RMarkdown file, re-run the whole analysis which could improve the reviewing process and help to avoid misunderstandings.
Reproducibility can help building your reputation and future-proof your work as well. Open science practices such as sharing your code and data are increasingly adopted by not only scientists but also the stakeholders. There are some funding available specifically for open science projects, therefore adopting open science practices can help you secure a funding for your studies. Hair explains that some journals like eLIFE are also sharing “living figures”. Here, the text and the figures are getting updated in the light of new data and information. It is possible to create such work by using RMarkdown and you can even submit it directly to journals like PLOS One.
Finally, Hair underlines that adopting open science practices eventually leads to getting more citations, recognitions and opportunities therefore helps you build a career as a scientist. She makes a convincing case and finishes her talk by saying;
“It’s not just good for science – it’s good for you!”
Adopting open science practices like reproducible and transparent workflow is becoming widespread among scientists. Whatever your reason is, -be it a selfless act of advancing the science or sparing yourself quite a lot of time- the best time to start is now.