1.2 • Introduction to Open Science •

In this section, materials from the Open Science Training Handbook and the Open Science MOOC are presented.

According to FOSTER, Open Science is about increased transparency, re-use, participation, cooperation, accountability and reproducibility for research. It is aimed at improving the quality and reliability of research through the principles of inclusion, fairness, equity, and sharing. Open Science can be viewed as research simply done properly, extending across the life and physical sciences, engineering, mathematics, social sciences, and humanities (Open Science MOOC). In practice, Open Science includes changes to the way science is done – including opening access to research publications, data sharing, open notebooks, transparency in research evaluation, ensuring the reproducibility of research (where possible), transparency in research methods, open source coding, software and infrastructure, citizen science and open educational resources.

One of the key elements of Open Science is reproducibility, which pertains to research data and codes being made available to others, who are able to obtain the same results as ascertained in scientific outputs. Closely related is the concept of replicability; the act of repeating a scientific methodology to reach similar conclusions. These concepts are core elements of empirical research.

Improving reproducibility leads to increased rigour and quality of scientific outputs and thus, to greater trust in science. There has been a growing need and willingness to expose research workflows from initiation of a project and data collection right through to the interpretation and reporting of results. These developments have come with their own sets of challenges, including designing integrated research workflows that can be adopted by collaborators, while maintaining high standards of integrity.

The concept of reproducibility means being able to do or apply a method again in another piece of research. It is directly applied to the scientific method by providing clear and open documentation, thus making the study transparent and reproducible.

Goodman, Fanelli, & Ioannidis (2016) note that, in epidemiology, computational biology, economics, and clinical trials, reproducibility is often defined as the ability of a researcher to duplicate the results of a prior study using the same materials as were used by the original investigator. That is, a second researcher might use the same raw data to build the same analysis files and implement the same statistical analysis in an attempt to yield the same results.

This is distinct from replicability, which refers to the ability of a researcher to duplicate the results of a prior study if the same procedures are followed but new data are collected. A simpler way of thinking about this might be that reproducibility is methods-oriented, whereas replicability is results-oriented.

Reproducibility can be assessed at several different levels: an individual project (e.g. a paper, an experiment, a method or a dataset), an individual researcher, a lab or research group, an institution, or even a research field. Slightly different kinds of criteria and points of assessment might apply to these different levels. For example, an institution upholds reproducibility practices, if it institutes policies that reward researchers who conduct reproducible research. Further, a research field might be considered to have a higher level of reproducibility, if it develops community-maintained resources that promote and enable reproducible research practices, such as data repositories, or common data-sharing standards.

rainbow-of-open-science-practices (click to see the image)

It is key to critically enabling open science practices in teaching and learning because it fosters research-driven learning opportunities through open data, which is a condition sine qua non for reproducibility and scientific progress, facilitating reuse. For Ioannidis and Khoury (2011), Opening up data enables to detect false claims and inaccuracies and allows for replicability tests. In sum, opening up research data can have a wide societal impact.

According to LIBER EU, The FAIR Data Principles is a set of guiding principles aimed at making data findable, accessible, interoperable and reusable. It provides guidance for scientific data management and stewardship that is relevant to all stakeholders in the current digital ecosystem. They directly encourage data producers and data publishers to promote maximum use of research data.

During the Lorentz Workshop Jointly Designing a Data FAIRport (2014), participants formulated the FAIR data vision to optimise data sharing and reuse by humans and machines, which resulted in the publication of The FAIR Guiding Principles for scientific data management and stewardship in “Scientific Data“. The FAIR principles can be understood as Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e. the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention), because humans are increasingly relying on computational support to deal with data as a result of the increase in volume, complexity and speed of creation.

For OpenAire,  the FAIR principles are needed to increase the availability of online resources means that data need to be created with longevity in mind. Providing other researchers with access to your data facilitates knowledge discovery and improves research transparency. The FAIR principles describe how research outputs should be organised so they can be more easily accessed, understood, exchanged and reused. Major funding bodies, including the European Commission, promote FAIR data to maximise the integrity and impact of their research investment.

The EU proposes six Recommendations for Implementation of FAIR practice, which can be summarised as follows.

1. Fund awareness-raising, training, education and community-specific support.
2. Fund development, adoption and maintenance of community standards, tools and infrastructure.
3. Incentivise development of community governance.
4. Translate FAIR guidelines for other digital objects.
5. Reward and recognise improvements in FAIR practice.
6. Develop and monitor adequate policies for FAIR data and research objects.

It is important to promote ethical practices in education to promote scientific data creation and sharing. This can be done by mapping and showcasing the impact it can have when teaching the basics of data management as transversal