|Reproducible Research and Data Analysis||
Reproducible research is at the heart of science. There has been an increased need and willingness to open and share research from the data collection right through to the interpretations of results. This has come with its own set of challenges, which include designing workflows that can be adopted by collaborators in a way that does not compromise the integrity of their contribution. This module will introduce the necessary tools required for transparent reporting which is reproducible and readable.
|Open Research Software and Open Source||
Software and technology underpin modern science. There is an increasing demand for more sophisticated open source software, matched by an increasing willingness for researchers to openly collaborate on new tools. These developments come with a specific ethical, legal and economic challenges that impact upon research workflows. This module will introduce the necessary tools required for transforming software into something that can be openly accessed and re-used by others.
|Open Research Data||
Open research data refers to the publishing the data underpinning scientific research results so that they have no restrictions on their access. Openly sharing data opens it up to inspection and re-use, forms the basis for research verification and reproducibility, and opens up a path to broader collaboration. In this module, you will gain insight into the importance of data sharing for reproducible research and how to curate and share your own research data.
|Managing Evidence: A CAEL Module||
In this era of evidence-driven reform, school leaders must learn to harness an array of data to drive improvement. In this module, you will explore key concepts in performance measurement, research design, and data analysis (qualitative and quantitative) to understand what can be gleaned from different sources. With your cohort, you will discuss the data you have available and learn how to draw on multiple forms of evidence to make more informed policy and programmatic decisions.
This Train-the-Trainers (TTT) package forms an addition to the CESSDA Data Management Expert Guide which was developed by the CESSDA Training Working Group in 2017 and 2018.
This package contains different materials that trainers can use in developing and giving Research Data Management and Discovery trainings for (social science) researchers.
Materials for instance include workshop outlines, slides and exercises that can be reused and adapted by local RDM trainers. The package contains five different types of materials: Workshop Outlines, Exercises, Presentations, Documents and Handouts, and Images. A full overview of all materials in this TTT Package is giving below.
|Open Science: Sharing Your Research with the World||
Explore ways to apply Open Science principles to academic work - including your own. Learn how to share your research effectively and responsibly, building greater visibility and impact.
Geneva Center for Security Center Governance (DCAF) is dedicated to making states and people safer, within a framework of democratic governance, the rule of law and respect for human rights. Since 2000, the Centre has facilitated, driven and shaped security sector reform (SSR) policy and programming around the world.
DCAF creates innovative knowledge products, promotes norms and good practices, and provides legal and policy advice. DCAF supports capacity building of state, civil society and private sector stakeholders by providing access to independent expertise and information on Security Sector Governance and Reform (SSG/R).
DCAF's publications include a number of toolkits for trainers, training tips, and training modules on security issues.
Research is getting a global makeover, in part thanks to the power of the internet and the tools it provides for us, and in part due to a growing call for accountability (e.g., reproducibility and data provenance) in research. Global policies are emerging at different levels that include some aspect of Open Research, Open Scholarship, or Open Science, and inclusive of all research disciplines. But our universities are often letting us down, and they are not teaching us the knowledge, tools and skills we need to do research effectively in the 21st century.
“Open Science” has many interpretations, but at its core it is about increased rigour, accountability, and reproducibility for research. For us, it is based on the principles of inclusion, fairness, equity, and sharing. Open Science can be viewed as research simply done properly, and it extends across the Life and Physical Sciences, Engineering, and Mathematics, to Social Science and Humanities.
This MOOC is designed to help equip students and researchers with the skills they need to excel in a modern research environment. It brings together the efforts and resources of hundreds of researchers and practitioners who have all dedicated their time and experience to create a community to help propel research forward.
|RDNL/DCC/University of Edinburgh||
Collaboratively developed MOOC by RDNL, DCC and the University of Edinburgh, published on FutureLearn
MOOC on delivering RDM services
MOOC, offering diverse online teaching including single courses, specializations, professional certificates, mastertrack certificates and online degrees.
It offers an array of syllabuses on "Data Science".