Welcome to The Ethos of Open Science
Welcome to this introductory module on open science. Open Science is the principle and practice of making research products and processes available to all, while respecting diverse cultures, maintaining security and privacy, and fostering collaborations, reproducibility, and equity. In this module, you take a closer look at what open science is, the current landscape as well as the benefits and challenges. You then get a glimpse into the practice of open science including a case study. To start your journey with open science, you are presented with actions that you can take starting today, such as exploring communities that you can engage with.
This module is for anyone who is interested in open science and would like to learn the benefits and ways to get started today.
After completing this module, you should be able to:
Select the term to see the description.
Open Science – The principle and practice of making research products and processes available to all, while respecting diverse cultures, maintaining security and privacy, and fostering collaborations, reproducibility and equity.
Open Data – Data that can be freely used, re-used and redistributed by anyone. Learn more here.
Open Source – Computer programs in which the source code is available to the general public for use or modification from its original design. Learn more here.
Open Access – Free access to information and unrestricted use of electronic resources for everyone. Learn more here.
Interdisciplinary – Combining multiple academic disciplines into one activity, such as a research project. Learn more here.
Equitable – Indicates the absence of unfair, avoidable or remediable differences among groups of people, whether those groups are defined socially, economically, demographically, or geographically or by other dimensions of inequality (e.g. sex, gender, ethnicity, disability, or sexual orientation). Learn more here.
Citizen Science or Community Science – The practice of public participation and collaboration in scientific research to increase scientific knowledge. Learn more here.
Open Research – How research is performed and how knowledge is shared based on the principle that research should be as open as possible. Learn more here.
Open Scholarship – An expansive term meant to encompass the rapid and widespread sharing of a range of scholarly activities and outputs across multiple disciplines. Learn more here.
Reproducibility and Replicability – Reproducibility is defined as obtaining consistent results using the same data and code as an original study (synonymous with computational reproducibility). Replicability means obtaining consistent results across studies aimed at answering the same scientific question using new data or other new computational methods. Learn more here.
Peer Review – The evaluation of work by one or more people with similar competencies as the producers of the work - that is, the authors’ peers. Learn more here.
FAIR principles – Principles to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasize machine- actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.
Metrics (in context of scientific merit) – Quantitative tools used to help assess the quality and impact of research outputs (eg. scientific articles, researchers, and more). Learn more here and here.
Altmetrics – Alternative tools to assess the impact of a scientific article that do not involve journal-level usage information like impact factors. Learn more here.
Openness – A concept or personality trait that involves transparency, collaboration, honesty, and receptivity to new ideas and experiences.
Transparency – The quality of being easy to perceive or detect.
Rigor – Widely used by educators to describe instruction, schoolwork, learning experience, and educational expectations that are academically, intellectually, and personally challenging.
Computational Provenance - Seeks to develop systematic, computationally-based processes and standards for capturing, and making available for us, information about who created an object, when it was created or modified, and the process or procedure that modified the object.