This course introduces the principles and practices of open science, with an emphasis on reproducible research workflows, transparent reporting, and collaborative scholarship. Students will critically examine reproducibility, explore tools that support openness (such as Git, GitHub, and Quarto), and apply these tools in hands-on assignments and a final open project. Students will also explore how open science practices vary across disciplines, including education, humanities, social sciences, industry and government, and STEM contexts. This course emphasizes applying open science principles to real-world data problems in alignment with the principles of producing reproducible research.
Center for Open Science
At the Center for Open Science, our mission is to increase openness, integrity, and reproducibility of scholarly research. Promoting these practices within the research funding and publishing communities accelerates scientific progress
FORRT - Framework for Open and Reproducible Research Training
Integrating open and reproducible science into higher education
Reproducible research: methodological principles for transparent…
This Mooc proposes methodological principles for open and transparent science. It deals in a practical way with note-taking, computational documentation, replicability of analyses.
Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.
Improving scientific mentorship with "Open Labs" — science better
Open Labs would be a place for experienced scientists to post their open questions and curiosities — a list of the ideas they don't have time to pursue personally but wish someone would.
Cataloguing and theorising open research practices in the arts, humanities and social sciences: Problematising and diversifying ‘Open Science’
Background Discourses of ‘Open Science’ have been criticised for privileging the quantitative and positivist methodologies of science, technology, engineering and mathematics (STEM) research at the expense of qualitative, interpretive, critical-theoretical, practice-based, and other forms of research more common in the arts, humanities and social sciences (AHSS). Methods In this article, which emerged from a work component of the MORPHSS (Materialising Open Research Practices in the Humanities and Social Sciences) project, we document the process, outcomes and practical implications of work to develop a catalogue of open research practices in these disciplines, which proceeded via a wide-ranging literature review followed by targeted desk research and a conceptual mapping exercise. Results Key findings include the fact that open research practices in AHSS are diverse, extending beyond the suite of practices emphasised within dominant accounts of Open Science. We identify among these practices a range of forms of openness including those focused on mobilising the involvement and expert knowledge of diverse participants and communities. Conclusions Presenting a typology of forms of openness in AHSS that is consistent with the epistemic logics of these disciplines, we conclude that openness in AHSS is highly situated and context-dependent, as well as resisting quantification and binary measurement. Drawing on these conclusions, we offer a series of recommendations for institutions, open research monitoring initiatives, funders, publishers, learned societies and researchers to enhance the inclusivity of their policy and practice around openness.
Open Responses: What you need to know
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Open Source Explained — Open Indie
Musings on the concept of ‘open source’, in the broadest possible definition of the term. Also an invitation to become a participant in ...
A Research Lab for Open Source ✸ Software Stewardship Lab
Today, we're launching a world-class research lab dedicated to Open Source sustainability with a team of some of the most experienced people in the world.
Open Source
These GitHub repositories contain open-source software developed by the Observatory on Social Media for various projects
Citizen science in environmental and ecological sciences
Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental and ecological sciences, in which non-professional participants contribute to data collection to advance scientific research. We present contributory citizen science as a valuable method to scientists and practitioners within the environmental and ecological sciences, focusing on the full life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction, as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. The aspects of reproducibility and data sharing are considered, placing citizen science within an encompassing open science perspective. Finally, we discuss its limitations and challenges and present an outlook for the application of citizen science in multiple science domains.
DataColada: No Comments - Replicability-Index
Science is like an iceberg. The published record is only a fraction of the things that university -paid academics do. Some time ago, Brian Nosek dreamed about a scientific utopia of open science that would make the workings of academia more transparent, but all we got was preprints and some badges - that are apparently DataColada: Open Science, but Closed Comments?
Mixture of Experts Explained
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
