Open Science

Science works best by exchanging ideas and building on them. Most efficient science involves both questions and experiments being made as fully informed as possible, which requires the free exchange of data and information.

All practices that make knowledge and data freely available fall under the umbrella-term of Open Science/Open Research. It makes science more reproducible, transparent, and accessible. As science becomes more open, the way we conduct and communicate science changes continuously.

What is Open Science

Open science is the movement to make scientific research (including publications, data, physical samples, and software) and its dissemination accessible to all levels of an inquiring society, amateur or professional.

Open Science represents a new approach to the scientific process based on cooperative work and new ways of diffusing knowledge by using digital technologies and new collaborative tools

Open science is transparent and accessible knowledge that is shared and developed through collaborative networks.

Characteristics:

Using web-based tools to facilitate information exchange and scientific collaboration
Transparency in experimental methodology, observation, and collection of data
Public availability and reusability of scientific data, methods and communications

What is the Open Science movement?

Sharing of information is fundamental for science. This began at a significant scale with the invention of scientific journals in 1665. At that time this was the best available alternative to critique & disseminate research, and foster communities of like-minded researchers.

Whilst this was a great step forward, the journal-driven system of science has led to a culture of ‘closed’ science, where knowledge or data is unavailable or unaffordable to many.

The distribution of knowledge has always been subject to improvement. Whilst the internet was initially developed for military purposes, it was hijacked for communication between scientists, which provided a viable route to change the dissemination of science.

The momentum has built up with a change in the way science is communicated to reflect what research communities are calling for – solutions to the majority of problems (e.g. impact factors, data reusability, reproducibility crisis, trust in the public science sector etc…) that we face today.

Open Science is the movement to increase transparency and reproducibility of research, through using the open best practices.

/Figure%201.%20Open%20Science%20Building%20Blocks

Attribution Gema Bueno de la Fuente

Open Science Building Blocks

Open Access: Research outputs hosted in a way that make them accessible for everyone. Traditionally Open Access referred to journal articles, but now includes books, chapters or images.
Open Data: Data freely and readily available to access, reuse, and share. Smaller data sets were often accessible as supplemental materials by journals alongside articles themselves. However, they should be hosted in dedicated platforms for more convenient and better access.
Open Software: Software where the source code is made readily available; others are free to use, change, and share. Some examples of these including the coding language and supporting software R and RStudio, as well as image analysis software such as Fiji/ImageJ.
Open Notebooks: Lab & notebooks hosted online, readily accessible to all. These are popular among some of the large funding bodies and allow anyone to comment on any stage of the experimental record.
Open Peer Review: A system where peer review reports are published alongside the body of work. This can include reviewers’ reports, correspondence between parties involved, rebuttals, editorial decisions etc…
Citizens Science: Lay people become involved in scientific research, most commonly in data collection or image analysis. Platforms such as zooniverse.org help connect projects with lay people interested in playing an active role in research, which can help generate and/or process data which would otherwise be unachievable by one single person.
Scientific social networks: Networks of researchers, which often meet locally in teams, but are also connected online, foster open discussions on scientific issues. Online, many people commonly use traditional social media platforms for this, such as Twitter, Instagram, various sub-reddits, discussion channels on Slack/Discord etc…, although there are also more dedicated spaces such as researchgate.net.
Open Education resources: Educational materials that are free for anyone to access and use to learn from. These can be anything from talks, instructional videos, and explanations posted on video hosting websites (e.g. YouTube), to entire digital textbooks written and then published freely online.
Citizen science: Citizen participation of various stages of research process from project funding to collecting and analysing data.

Benefits of Open Science

Exercise: Open Science

Being open has other outcomes/consequences beyond giving the free access to information. For example, Open educational resources:

Enables collaborative development of courses
Improves teachers/instructors skills by sharing ideas

Select one or two of the following OS parts:

Open Access
Open Data
Open Software
Open Notebooks
Open Peer Review

and discuss what are the benefits or what problems are solved by adaption of those Open initiatives.

Solution

Possible benefits and consequences for each part:

Open Access

Speed of knowledge distribution
Leveling field for underfunded sites which otherwise wouldn’t be able to navigate the paywall
Prevent articles being paid for ‘thrice’ (first to produce, second to publish, third to access) by institutions.
Greater access to work by others, increasing chance for exposure & citations
Access to work by lay audiences, thus increases social exposure of research

Open Data

Ensures data isn’t lost overtime - reusability
Acceleration of scientific discovery rate
Value for money/reduced redundancy
Permits statistical re-analysis of the data to validate findings
Gives access to datasets which were not published as papers (e.g. negative results, large screening data sets)
Provides an avenue to generate new hypotheses
Permits combination of multiple data sources to address questions, provides greater power than a single data source

Open Software

Great source to learn programming skills
The ability to modify creates a supportive community of users and rapid innovation
Saves time
Faster bug fixes
Better error scrutiny
Use of the same software/code allows better reproducibility between experiments
Need funds to maintain and update software

Open Notebooks

100% transparent science, allowing input from others at early stages of experiments
Source of learning about the process of how science is actually conducted
Allows access to experiments and data which otherwise never get published
Provides access to ‘negative’ results and failed experiments
Anyone, anywhere around the world, at any time, can check in on projects, including many users simultaneously
Possibility of immediate feedback
Thorough evidence of originality of ideas and experiments, negating effect of ‘scooping’

Open Peer Review

Visibility leads to more constructive reviews
Mitigates against editorial conflicts of interest and/or biases
Allows readers to learn/benefit from comments of the reviewers

Open Educational Materials

Foster collaboration between educators/others
Show clearly how method was taught (e.g. Carpentries materials) which can be reproduces anywhere, anytime
Protects materials from becoming technologically obsolete
Authors preparing the material or contribute all earn credit (e.g. GitHub)
Recycle animations and material that is excellent (why reinvent the wheel?)

Motivation: Money

One has to consider the moral objectives that accompany the research/publication process: charities/taxpayers pay to fund research, these then pay again to access the research they already funded.

From an economic point of view, scientific outputs generated by public research are a public good that everyone should be able to use at no cost.

According to EU report “Cost-benefit analysis for FAIR research data”, €10.2bn is lost every year because of not accessible data (plus additional 16bn if accounting for re-use and research quality).

The goals of Open Science is to make research and research data available to e.g. charities/taxpayers who funded this research.

Motivation: Reproducibility

The inherited transparency of Open Science and the easy access to data, methods and analysis details naturally help to address part of the Reproducibility crisis. The openness of scientific communications and of the actual process of evaluation of the research (Open Peer Review) increases confidence in the research findings.

Personal motivators

Open Science is advantageous to many parties involved in science (including researcher community, funding bodies, the public even journals), which is leading to a push for the widespread adoption of Open Science practices.

Funding bodies are also becoming big supporters of Open Science. We can see with the example of Open Access, that once enforced by funders (the stick) there is a wide adoption. But what about the personal motivators, the carrots.

The main difference between the public benefits of Open Science practices and the personal motivators of outputs creators, that the public can benefit almost instantly from the open resources. However, the advantages for data creator comes with a delay, typically counted in years. For example, building reputation will not happen with one dataset, the re-use also will lead to citations/collaboration after the next research cycle.

Why we are not doing Open Science already?

Exercise: Open Science Barriers

Discuss Open Science barriers, mention the reasons for not already being open:

Solution

Possible benefits and consequences for each part:

Sensitive data (anonymising data from administrative health records can be difficult)
IP
Misuse (fake news)
Lack of confidence (the fear of critics)
Lack of expertise
The costs in $ and in time
Novelty of data
It is not mandatory
Lack of credit (publishing negative results is of little benefit to you)

It may seem obvious that we should adopt open science practices, but there are associated challenges with doing so.

Sensitivity of data is sometimes considered a barrier. Shared data needs to be compliant with data privacy laws, leading many to shy away from hosting it publicly. Anonymising data to desensitise it can help overcome this barrier.

The potential for intellectual property on research can dissuade some from adopting open practices. Again, much can be shared if the data is filtered carefully to protect anything relating to intellectual property.

Another risk could be seen with work on Covid19: pre-prints. A manuscript hosted publicly prior to peer review, may accelerate access to knowledge, but can also be misused and/or misunderstood. This can result in political and health decision making based on faulty data, which is counter to societies’ best interest.

One concern is that opening up ones data to the scientific community can lead to the identification of errors, which may lead to feelings of embarrassment. However, this could be considered an upside - we should seek for our work to be scrutinized and errors to be pointed out, and is the sign of a competent scientist. One should rather have errors pointed out rather than risking that irreproducible data might cause even more embarrassment and disaster.

One of the biggest barriers are the costs involved in "being Open". Firstly, making outputs readily available and usable to others takes time and significant effort. Secondly, there are costs of hosting and storage. For example, microscopy datasets reach sizes in terabytes, making such data accessible for 10 years involves serious financial commitment.

For more information

Here are some links to further readings:

The content in this page was adapted from: