ReproducibiliTea Blog

The replication crisis in psychology: Pre-registration and Registered Reports as crusaders for a brighter future | 20/01/23 | Dr Roman Briker

In our first Edinburgh ReproducibiliTea session of 2023, Dr Roman Briker gave a talk on pre-registration and Registered Reports. Dr Briker is an Assistant Professor in Organisational Behaviour at Maastricht University School of Business and Economics, and an Open Science Ambassador at the School of Business and Economics.

Reproducibility crisis and questionable research practices

Many academic journals are interested in significant results, those with a P-value of less than 0.05. Dr Briker shared a personal experience of writing his first paper and spotting an error in his draft that would impact the results of his statistical analyses. He was worried that correcting the error would lead to his findings being non-significant, and that journals would no longer be interested in his work.

This led Dr Briker to realise that this is not the way research should work. In his talk, Dr Briker suggested that this current model of academic publishing – the culture of “publish or perish” – contributes to the reproducibility crisis, as significant results are published and non-significant results are filed away. Dr Briker also gave examples of scientific fraud, including Dan Ariely and Daryl Bem, and reports from a survey which suggested that 8% of Dutch scientists have at some point falsified data. Overall, the focus on significant outcomes reduces our focus on rigorous methodology.

Dr Briker mentioned that the issue of irreproducibility impacts all fields of research, and that only 25% to 60% of scientific findings are replicable. He spoke about questionable research practices which have been allowed to thrive in our current research culture, including HARKing (Hypothesising After Results are Known), selective reporting, optimal/selective stopping of experiments, changing control variables, playing around with outliers, changing the inclusion or exclusion criteria, using different analytical methods, and rounding off P-values (e.g. reporting a P value of 0.53 as P = < 0.5).

Pre-registration and registered reports

Dr Briker suggested pre-registration and Registered Reports as potential solutions to these problems.

A pre-registration is a publicly time-stamped pre-specification of a research study design, including hypotheses, required sample sizes, exclusion criteria, and planned analyses. It is completed prior to data collection and is not peer-reviewed (Logg & Dorison, 2021).

A Registered Report goes further than a pre-registration, including the introduction, theory and hypothesis, proposed methods and analyses (Chambers & Tzavella, 2022). This is submitted to a journal, or platform such as Peer Community In Registered Reports, for peer-review prior to data collection. Once the Registered Report is approved by reviewers, it gains in-principle acceptance for publication in a journal, and the results will be published whether they are significant or not, as long as the plan outlined in the Registered Report is followed.

In his talk, Dr Briker explained what parts of a study design should be pre-registered, and gave an example of his own pre-registration. He also highlighted a number of templates available, and busted some myths surrounding concerns researchers may have about pre-registering a study.

Slides, references and pre-registration templates mentioned in Dr Briker’s talk are available on OSF: https://osf.io/cgkua/

The session recording is available on our YouTube channel.

This blog is written by Emma Wilson

SOCIALS:

For any questions/suggestions, please send us an email at edinburgh.reproducibilitea@ed.ac.uk

ReproducibiliTea Blog

Open Research Across Disciplines | 16/12/22 | Emma Wilson

In our December session of Edinburgh ReproducibiliTea, Emma Wilson presented a session on open research practices across disciplines. Emma is a PhD student at the Centre for Clinical Brain Sciences.

The session was focused on the UK Reproducibility’s list of open research case studies, examples, and resources for various research disciplines: https://www.ukrn.org/disciplines/

The list of resources can be cited as follows:

Farran EK, Silverstein P, Ameen AA, Misheva I, & Gilmore C. 2020. Open Research: Examples of good practice, and resources across disciplines. https://doi.org/10.31219/osf.io/3r8hb

What is open research?

Open research is all about making research practices and findings more transparent and accessible. The University of Edinburgh defines open research as “research conducted and published via a combination of two or more of the following attributes:

  • Open Access publication
  • Open research data
  • Open source software and code
  • Open notebooks
  • Open infrastructure
  • Pre-registration of studies

We use the term open research instead of open science as it is more inclusive of the broad spectrum of work that takes place at the University.

Open research across disciplines resource

The UK Reproducibility Network (UKRN) have produced a document and webpage with examples of open research practices across different research disciplines. The document is updated over autumn and was last updated in October 2022.

The resource covers 28 disciplines from Archaeology & Classics to Veterinary Science. New resources can be added to the collection via this Google Form.

Examples of open research across different disciplines

Emma chose a few example resources to talk about in her presentation.

Art & Design: Open Access at the National Gallery of Art

The National Gallery of Art have an open access policy for public domain artworks. You can search and download over 50,000 artworks on their website, and they have made a dataset of information on over 130,000 artists and artworks available on GitHub.

Artificial Intelligence: recommendations on creating reproducible AI

In 2018, Gundersen, Gil and Aha published an article describing recommendations on creating reproducible artificial intelligence.

Economics: case study from a PhD student

Dr Marcello De Maria, a graduate from the University of Reading, describes the benefits of open research within economics.

Engineering: open source 3D printing toolkit

Slic3r is an open source software that allows anyone to convert 3D models into printing instructions for a 3D printer. They have a large GitHub community involved in creating and maintaining code and take pride in the fact that the provide resource for free to the community.

Music, Drama and Performing Arts, Film and Screen Studies: podcast on making music research open

Alexander Jensenius, Associate Professor at the Department of Musicology – Centre for Interdisciplinary Studies in Rhythm, Time and Motion (IMV) at the University of Oslo, discusses open research within the context of music research in a podcast hosted by the University Library at UiT, the Arctic University of Norway. He also discusses MusicLab, an event-based project which aims to collect data during musical performances and analyse it on the fly.

Physics: citizen science project case study

In this case study, Professor Chris Scott, Dr Luke Barnard, and Shannon Jones discuss a citizen science project they ran on the online platform Zoonverse. Their project focused on analysing images of solar storms and four thousand members of the public took part.

Barriers to open research

In the final section of her presentation, Emma then discussed some of the barriers that may prevent researchers from working openly. These included:

  • Funding and finances (e.g. to pay open access publishing fees)
  • Time and priorities (e.g. time required to learn new skills, and supervisor / lab cultures around open research practices)

Finally, the session closed with a discussion around the implementation of open research in different disciplines, and whether all researchers and disciplines should be judged the same when it comes to this implementation.

The slides for Emma’s talk are available on our OSF page and the session recording is available on YouTube.

This blog is written by Emma Wilson

SOCIALS:

For any questions/suggestions, please send us an email at edinburgh.reproducibilitea@ed.ac.uk

ReproducibiliTea Blog

Introducing FAIRPoints and FAIR + Open Research for Beginners | 18/11/22 | Dr Sara El-Gebali

In our November session of Edinburgh ReproducibiliTea, we were joined by Dr Sara El-Gebali. Sara is a Research Data Manager, Co-Founder of FAIRPoints and Project Leader of LifeSciLab. In her talk, Sara introduced FAIRPoints, an event series highlighting pragmatic community-developed measures towards the implementation of the FAIR data principles, and some of the projects currently ongoing at FAIRPoints.

What is FAIR?

FAIR stands for Findable, Accessible, Interoperable, and Reproducible. FAIR is a set of best practices rather than a set of rules.

FAIR + Open Research for Beginners

FAIR + Open Research for Beginners is a new community-led effort towards the inclusion of education on open and FAIR principles at earlier time points, such as in high school and undergraduate curriculums.

Through this initiative, Sara and the FAIRPoints community are launching a set of Google Flash Cards related to FAIR and open data. The flash cards help students better find answers to educational questions they have searched for on Google. The group are also working on developing slide decks and accompanying scripts that can be delivered in schools, undergraduate teaching, and public lectures.

Anyone with an interest in FAIR and open data can join the community and get involved in the initiative by subscribing to events and joining the FAIRPoints Slack channel:

You can find out more about FAIRPoints on their website. The slides for Sara’s talk are available on our OSF page and the session recording is available on YouTube.

This blog is written by Emma Wilson

SOCIALS:

For any questions/suggestions, please send us an email at edinburgh.reproducibilitea@ed.ac.uk

ReproducibiliTea Blog

Errors in Research

“Fallibility in Science- Responding to Errors in the Work of Oneself and Others”

This was the first session of year 2022 and revolved around a paper discussion on Errors in Research. It was led by Laura Klinkhamer, a PHD student at The University of Edinburgh. Her research interests lie at the intersection of neuroscience and psychology. The discussion was on Professor Dorothy Bishop’s 2018 commentary paper ‘Fallibility in Science: Responding to Errors in the Work of Oneself and Others’. Apart from the paper discussion, the session involved interactive sessions with anonymous polls on Mentimeter.com and some interesting discussions in the breakout rooms. 

The session began with imagining a scenario where a PHD student runs a series of studies to find a positive effect. After getting null findings in three studies, the student changed the design and found a statistically significant effect in the fourth study. This resulted in paper publication in a prestigious journal with student as first author. The study was also featured on National Public Radio. However, after two weeks the student realized as a consequence of preparing for a conference talk that the groups in the study were miscoded and the study was a faulty one. The same scenario was asked to be imagined by the participants in the session and to report their answers anonymously on Mentimeter.com. 

According to Azoulay, Bonatti and Krieger (2017), there was an average decline of 10% in subsequent citations of early work of authors who publicly admitted their mistake. However, the effect was small when the mistake made was an honest one. Moreover, there was no reputational damage in case of junior researchers. According to Hosseini, Hillhorst, de Beaufort & Fanelli (2018), 14 authors who self-retracted their papers believed their reputation would be damaged badly. However, in reality, self-retraction did not damage their reputation but improved it. 

Incentives for Errors in Research or Research Misconduct:

  1. Pressure from colleagues, institutions and journal editors to publish more and more papers
  2. Progression in academic career is determined greatly by metrics that incentivize publications and not retractions

Unfortunately, according to Bishop (2018) there are very few incentives for honesty in academic careers. Participants were encouraged to share their opinions on Mentimeter.com on what would they do to incentivize scientific integrity. 

Open Research:

  1. Research that is publicly accessible does not indicate that it is free from errors. However, open data and open code enhances the chances of error detection by the other authors
  2. Open research encourages scientists to double check their data and code before publication
  3. Open research helps normalize error detections and reduces stigma which eventually leads to scientific accuracy 

How to Respond to Errors in the Work of Other Researchers:

There are different platforms to do that including-

  • Contacting researchers directly
  • Contacting researchers via journal (if possible)
  • Preprint servers
  • PubMed Commons (discontinued)
  • PubPeer (commentators can be anonymous)
  • Twitter
  • Personal blogs
  • OSF and Octopus (emerging platforms)

One of the drawbacks of anonymous platforms is that they often result in criticism of someone’s work that can be harsh and discouraging. When responding to errors in the work of other scientists it is important to make no assumptions. Because a failure to replicate an original study can be due to reasons beyond incompetence or fraudulent intentions. The scale of error can be useful while approaching the situation.

Scale of errors:

  • Honest errors- coding mistakes
  • Paltering- using a truthful statement to mislead by failing to provide the relevant contextual information
  • P-hacking
  • Citing only a part of literature that matches with one’s position. Commonly referred to as confirmation bias
  • Inaccurate presentation of results from cited studies
  • Inventing fake data
  • Paper mills- businesses producing fake studies for profits

There was a little discussion on the case of Diederik Stapel who was fired instantly after it was discovered that he faked a large-scale data during his academic career. Moreover, some discussion was done on paper mills that are polluting the scientific literature for profits. An important question remains: who are/should be responsible for detecting and responding to large errors? 

  1. At an internal level, head of the department/lab, whistleblowing policy and research misconduct policy
  2. Journals 
  3. Separate institutes like UKRIO (UK Research Integrity Office
  4. Technology
  5. External researchers

There was a lot more to be discussed and hopefully the discussion can continue in later discussions and/or the conference. There is a ‘Edinburgh Open Research Conference’ on Friday 27 May, 2022 organised by the Library Research Support Team and EORI/Edinburgh ReproducibiliTea. SAVE THE DATE!!!!

Anonymous responses the participants on Mentimeter.com:

This blog is written by Sumbul Syed

SOCIALS:

Edinburgh RT Twitter

Edinburgh RT OSF page

Edinburgh RT mailing list

For any questions/suggestions, please send us an email at edinburgh.reproducibilitea@ed.ac.uk

ReproducibiliTea Blog

Bayesian data analysis and preregistration 17/12/2021 with Dr Zachary Horne

This session was the final session of the year 2021. The speaker was Dr Zachary Horne, a lecturer at School of Philosophy, Psychology & Language Sciences, The University of Edinburgh. Dr Horne talked about Bayesian statistics and preregistration in the context of open research practices. Dr Horne started his presentation by talking about what is Bayesian data analysis and very broadly it is a data analysis that takes into consideration prior information about a particular domain, in addition to data collection. Sometimes it is also called prior distribution. 

There are different aspects to keep in mind when it comes to preregistration in Bayesian data analysis:

  • How the data is going to be collected
  • Why is the data being collected in a particular way?
  • Sample size
  • Operationalization of constructs
  • Specifying key analyses
  • Aspects of analysis that will be exploratory

Bayesian workflow (Gelman et al., 2020)

  1. Choosing an initial model
  2. Prior predictive checking
  3. Fitting the model
  4. Computational problems and algorithm diagnostics
  5. Posterior predictive checking
  6. Prior robustness

Dr Horne talked about prior predictive checking in a bit detail and it covers the following features:

  • Prior to data collection, is the model consistent with what is already known about the world?
  • What distribution is implied for an outcome variable given prior and likelihood?
  • Assessing the credibility of model before collecting the data

A question ‘Do tweets from activist groups (e.g., PETA, Greenpeace, etc.) with photos get liked more than tweets without photos?’ was central during the session to discuss models in Bayesian data analysis. Analysis showed that photos are better as far as likes are concerned on twitter. With respect to which model is the ‘right’ model, the regularizing model provided better estimates of central tendency of distribution. However, none of the priors (optimistic, regularizing and improper) captured that the larger central tendency is coming out just from many tweets getting 200 or so likes, but also from tweets getting huge numbers of likes! Moreover, models have a room for improvement. 

The session was concluded with pre-registration priors in Bayesian data analysis and Dr Horne suggested using regularizing priors for the parameters of interest especially when those parameters are expected to ‘do something’ and incorporating posterior information in the priors of subsequent related models.

This blog is written by Sumbul Syed

SOCIALS:

Edinburgh RT Twitter

Edinburgh RT OSF page

Edinburgh RT mailing list

For any questions/suggestions, please send us an email at edinburgh.reproducibilitea@ed.ac.uk

ReproducibiliTea Blog

Edinburgh University Research Optimisation Course (EUROC) 19/11/2021 with Dr Gillian Currie

In this session, Dr Gillian Currie who is a Postdoctoral Research Fellow in the CAMARADES group, Centre for Clinical Brain Sciences at The University of Edinburgh talked about EUROC (Edinburgh University Research Optimisation Course) which encourages open research practices in animal research. Dr Gillian Currie is a meta-researcher and her research interests include improvement in research methodology.

Dr Currie began talking about EUROC (Edinburgh University Research Optimization Course), a course with a focus on rigorous design, conduct, analysis and reporting of research using animals. She further mentioned some key points on research using animals:

  • In the year 2020, 2.8 million animals were used in research across the UK
  • The studies helped understand basic biology, complex diseases and potential treatments development
  • However, there were certain concerns regarding difficulties in replication, reproducibility and translation

Dr Currie talked briefly about the translational pipeline which aims to translate pre-clinical research into clinical research which further results in improved health. A survey conducted by Nature involving 1,576 researchers found that 52% of the researchers agreed there is a ‘reproducibility crisis’. The problem of ‘replication crisis’ can be attributed to the following reasons:

  1. Smaller sample size in studies
  2. Publication bias
  3. Limited randomization and blinding

Dr Currie carried on with a discussion by talking about new opportunities in open research practices including:

  1. An increased focus on methodological rigour which involves ensuring appropriate power, appropriate statistics and p values
  2. An increased transparency through pre-registration of studies, reporting of methods as well as sharing of data
  3. Measures to reduce risks of biases

It is important to realise that a small improvement manifested across large number of researchers can help make sure to have a substantial effect overall. 

Course structure of EUROC:

EUROC comprises of 3 modules which can be completed across multiple sessions. Every module consists of 1 core and 1 extended lecture.

MODULE 1: Study Design and Data Analysis

In module 1, ‘Study Design’ section will comprise of internal validity, Risks of bias, Construct and external validity and Exploratory vs confirmatory research. ‘Data Analysis’ section consists of Statistical analysis, Significance testing, Sample size and statistical power, Outliers, Unit of analysis and Multiple outcome testing.

MODULE 2: Experimental Procedure

Module 2 is divided into two sections: Maximizing Study Validity and Study Design. The former section includes topics like Risks of bias, Pilot studies, Confounding characteristics and variables, Validity of outcome and Optimization of complex treatment parameters. The latter section will have Use of reference compounds, Statistical Analysis Tips, Replication and Standardisation.

MODULE 3: Pre-registration and Reporting

The final module will deal with Pre-registration (including Study protocols) and Reporting (Data sharing, Statement of conflict of interest, Reporting standards).

The course is a contribution by The University of Edinburgh towards an improvement in research. Therefore, the course is also available to researchers outside the university through this link edin.ac/2SZvY4U.

How to access EUROC on Learn (for people within the University of Edinburgh):

  1. Log in to Learn
  2. Click on ‘self-enrol’ (available on top right of the screen)
  3. Scroll down to Research Improvement
  4. Click on EUROC (Edinburgh University Research Optimisation Course)

The session was concluded with Dr Currie talking about a research improvement project that is coming up soon. Delays in dissemination of research findings act as impediments in scientific progress, therefore one of the most important aims of research improvement project is to increase the speed at which findings are shared with the use of pre-prints. A Pre-print is an early version of a scholarly article that has not gone under peer-review. It is open to comments and is a good means to prioritise new ideas.

This blog is written by Sumbul Syed

SOCIALS:

Session’ video on YouTube

Edinburgh RT Twitter

Edinburgh RT OSF page

Edinburgh RT mailing list

For any questions/suggestions, please send us an email at edinburgh.reproducibilitea@ed.ac.uk

ReproducibiliTea Blog

Building an Open Research Culture 29/10/2021 with Dr Will Cawthorn

In this session, Dr Will Cawthorn at The University of Edinburgh Centre for Cardiovascular Science talked about Building an Open Research Culture. Dr Cawthorn began by talking about the conflicts of interest in research which can be extrinsic and intrinsic and how important it is in open research to have no potential conflicts of interest. Here are the key points which were discussed during the session:

  • Value of a research study these days is based a lot more on if it’s published in a high impact journal and whether it is highly cited
  • Experts opinions are usually flawed especially in flawed and noisy environments
  • There are many consequences of mismeasurement of science including devaluation and ignoring of valuable research, publication delays, incentivization of poor research practice and external pressures killing inner motivation to do good research 
  • Researchers pay to publish their research they produced in the first place in a journal only to ask others to pay in order to access it. This is the opposite of open research

Dr Cawthorn further talked about how he is taking the steps to bring about an open research culture in his own lab. 

  • Encouraging members of his lab to follow their own ideas
  • Publishing negative results because there is no such thing as ‘positive’ and ‘negative’ results. Just the ‘conclusive’ and ‘inconclusive’ results.
  • Encouraging more preprints and open access papers

However, open research culture is easier said than done and there are additional practices that Dr Cawthorn wants to introduce/further improve in his lab. And these include:

  • Electronic lab notebook
  • Writing a lab manual
  • Robust data management

The session was concluded with a question regarding if there is any brighter future of open research culture. There is probably a brighter future with many initiatives coming up like DORA (The Declaration on Research Assessment) and LERU (The League of European Research Universities).

Dr Cawthorn is the LERU Open Science Ambassador for The University of Edinburgh and he is collaborating with many others on writing an Open Science Roadmap for the University which is due to be published soon.

This blog is written by Sumbul Syed


SOCIALS-

Session’s video on YouTube

Edinburgh RT Twitter

Edinburgh RT OSF page

Edinburgh RT mailing list

For any questions/suggestions, please send us an email at edinburgh.reproducibilitea@ed.ac.uk


ReproducibiliTea Blog

Easing Into Open Science 17/09/2021 with Dr Priya Silverstein

written by Laura Klinkhamer (co-organiser of Edinburgh ReproducibiliTea)

Dr. Priya Silverstein

In this session we took a look at the following paper:
Ummul-Kiram Kathawalla, Priya Silverstein, Moin Syed; Easing Into Open Science: A Guide for Graduate Students and Their Advisors. Collabra: Psychology 4 January 2021; 7 (1): 18684. doi: https://doi.org/10.1525/collabra.18684

and we were joined by one of the authors, Dr Priya Silverstein for a live Q&A.

The paper is a great place to start for people who are new to open research concepts and provides a very useful summary and guide for some practices you could consider applying to your research. It introduces open science (now often referred to as open research, to include the academic disciplines that would not describe themselves as a “science”), as a “broad term that refers to a variety of principles and behaviors pertaining to transparency, credibility, reproducibility, and accessibility” (Kathawalla et al., 2021, p. 2). The paper is written specifically for the types of situations that graduate students are more likely to encounter, but the practices described are broadly applicable to researchers of any career stage.

Paper Summary

Eight Open Research (OR) practices are outlined in this guide and classified according to the author’s perception of the difficulty of implementation.

Practice 1 is to set up or join an open research (journal) club, such as the with the ReproducibiliTea organisation. This can be a quick and efficient way of getting to grips with some key concepts of the reproducibility and OR movement, while meeting new people along the way and increasing your network.
For researchers in Edinburgh – we encourage you to join the Edinburgh Open Research Initiative Teams group, which serves as a hub for bringing people interested in OR together. The University of Edinburgh now also has an Open Research Blog and newsletter that you can sign up for here.

Practice 2 refers to thinking about your project workflow, in particular setting up your file organisation, data access regulations and keeping clear records so that (future) you and others can quickly get an overview of your project and are able to reproduce the outcomes. For more information and tips on how to work reproducibly, we refer you to Kaitlyn Hair’s talk (Edinburgh ReproducibiliTea session Nov 2020) on selfish reasons to work reproducibly and Ralitsa Madsen’s talk on RSpace, a platform that you could consider to set up a project workflow in addition to the freely accessible Open Science Framework.

Practice 3 is about preprints, which refers to the practice of publishing your manuscript before or during peer review. Check with the journal where you intend to publish your work first on what their policy on pre-prints is by either messaging them or checking this source suggested by Priya. Pre-prints are a way to bring your research out to the world, even if publication is delayed or rejected. It also increases the number of times your work will be cited. There are free servers that you can upload your manuscript as a pre-print to, such as bioRxiv for biology.

Practice 4 refers to creating reproducible code/analyses. It is very helpful for your project workflow and reproducibility to write your code/analysis plans in such a way that it is clear beyond a doubt for others and your future self what you did. Annotating your steps and writing README files, basic text files describing for instance what files are in your project space/folder and what role they play in your project (e.g. data_spreadsheet_version3 contains the clean data on x number of participants that is used for Analysis B.), are very useful practices.

Practice 5 Sharing data is very useful to the scientific community and there are many platforms that you could upload your project’s anonymized data set to (e.g. OSF again). However, it is very important to make sure you are legally allowed to share the data. This will depend on your local and wider data regulation guides (e.g. in the EU GDPR applies) as well as what has exactly been put into the consent forms (if applicable to your project of course).
There are also options to upload only part of your data set or set up a system so that others can access more sensitive data. Talk to your supervisors/collaborators and check University research support services to see what would be most suitable in your case (for instance see this resource for the school of PPLS).

Practice 6 Being very open in your manuscript writing. In a way it’s fascinating how the norm in manuscript writing is that the research story gets presented as an almost perfect execution of a plan with a happy ending (i.e. significant results), whereas in reality you often hear researchers struggling with all kinds of issues and ending up with a manuscript that is only very slightly connected to the original research idea. It’s not very realistic, and actually harmful to scientific integrity. So if we allow ourselves to be humans, who make mistakes, and allow others to read about and learn from our mistakes, wouldn’t that make life easier?

Practices 7 & 8 are related.
Pre-registration: a time-stamped, read-only version of your research plan created before you begin data collection/analysis.
Registered report: similar to pre-registration but your research plan undergoes peer review before results are known. Helpful resource on the Centre for Open Science website here.
Both practices are very useful ways that make you sit down and plan your research before executing it. In the case of registered reports, you will also obtain feedback before executing it, which may be much more useful than receiving feedback after the fact in the regular peer-review system. Although you state what you intend to research in a pre-registration or registered report, it is important to realise that you do not sign a binding contract. If it turns out that another method or additional exploratory analysis are interesting to your research question, you are of course able to make changes. It is however your responsibility to transparently report and justify these changes.
As Niamh summarised: these practices do not stifle creativity, but create accountability.

It is important to realise that engaging is in OR practices is not an all-or-nothing approach. It’s much more about adopting a certain critical mindset and taking (small) steps that are suitable for you and your specific project.

Discussion

During the discussion Priya mentioned that if the paper were to get written this year she would probably include the same practices, but elaborate on the increased number of options in which they could be applied. For example, one thing that has changed in recent years is that registered reports have become available for projects with secondary data analysis (rather than it just being available for projects where the data still is to be collected).

Another interesting development is that of Peer Community In Registered Reports, which facilitates scheduled peer review. You indicate beforehand when you intend to hand in Stage 1 of your registered report (Introduction & Methods) and the community tries to arrange reviewers for that particular time frame, meaning that the peer review process can be completed much more rapidly. Priya mentioned that in the past this has been one of the main criticisms of the registered report that it was unclear when researchers could start their research analysis/data collection because of uncertainty regarding the peer review duration. This new facility makes registered reports an even more attractive option.

Will Cawthorn added that Review Commons is another place you can send your manuscript to for general peer review. If the manuscript then passes review, you can choose from a list of journals where to publish your paper. This approach decreases redundancy in peer review (i.e. if rejected from one journal, you don’t go through the roulette wheel of another round of completely new peer review).
Priya confirmed that this procedure is also in place for PCI registered reports. From the website: “Following the completion of peer review, authors of RRs that are positively recommended have the option to publish their articles in the growing list of PCI RR-friendly journals that have committed to accepting PCI RR recommendations without further peer review.”

We also had a group discussion about how we could further promote OR in the University. One of the suggested routes was to include more OR practices in undergraduate and postgraduate course curriculae. Will Cawthorn also referred to an OR roadmap for the University of Edinburgh that he and several others (including many from the Library and Research Support Services) are working on that is due to be published soon. Priya emphasised that is important create momentum both through bottom-up and top-down initiatives at the same time to bring about real change in the research culture. This nicely connected to our next session on Friday 15 October, which will be on how to build an open research culture in your lab/research group, by Dr. Will Cawthorn (LERU Open Science Ambassador for the University of Edinburgh).

Then I said something silly about how we should all jump aboard the Open Research train and Priya kindly replied with a “choo choo!” making me feel slightly less embarrassed.

All things considered, we look back at a successful first session of this academic year!

The presentation slides and meeting recording can be found on our OSF page. The meeting recordings can also be found on our YouTube channel.

If you’d like to stay up to date with Edinburgh ReproducibiliTea, please consider joining our mailing list by filling in this form and/or following us on Twitter.

For any questions/suggestions, please send us an email: edinburgh.reproducibilitea@ed.ac.uk

ReproducibiliTea Blog

A selfish guide to RSpace: Why and How?

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!

This blog is written by Bengü Kalo

Find more information about the RSpace here

Edinburgh RT YouTube Channel

Edinburgh RT OSF page

Edinburgh RT Mailing List

ReproducibiliTea Blog

How (some) scientists talk about openness

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.

This blog is written by Bengü Kalo

Edinburgh RT YouTube Channel

Edinburgh RT OSF page

Edinburgh RT Mailing List