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This short course will enable you to find the answers and empower yourself to defend and promote the rights of refugees and discover how you can be part of the solution.

This is Amnesty International's second human rights MOOC. Be prepared for active learning, connecting with course participants from across the world and to become part of a global community campaigning for the rights of refugees to be upheld everywhere.

This course will provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data lifecycle, be able to identify the components of good data management plans, and be familiar with best practices for working with data including the organization, documentation, and storage and security of data. Learners will also understand the impetus and importance of archiving and sharing data as well as how to assess the trustworthiness of repositories.

Guidelines to help researchers make their research data findable, accessible, interoperable and reusable (FAIR), to ensure sound management. Good research data management is not a goal in itself, but rather the key conduit leading to knowledge discovery and innovation, and to subsequent data and knowledge integration and reuse.

easySHARE is a simplified HRS-adapted dataset for student training, and for researchers who have little experience in quantitative analyses of complex survey data. While the main release of SHARE is stored in more than 100 single data files, easySHARE stores information on all respondents and of all currently released data collection waves in one single dataset. Moreover, for the subset of variables covered in easySHARE, the complexity was considerably reduced. For example the information collected only from one person of a couple or in a household was transferred to all respective respondents; time constant information collected only in the first interview was transferred to all later interviews; the coding of missing values was enriched to provide an easier understanding of the routing and filtering of the interviews; etc. In addition, several ready to analyse variables have been added, such as health indexes, demographic information, or economic measures. When possible measures have been selected or recoded to facilitate comparative analyses with the US Health and Retirement Study (HRS).

Upon completion of this course, participants should have an understanding of what Research Data Management is, and why it is important in academic research.  They should have an understanding of the FAIR data principles, and how they can make data more FAIR. They should be able to successfully manage all types of research data and to document both the research itself, as well as the data in a comprehensive way. 

This two-day course (27 and 28 May, 2020) is aimed at project managers, researchers and graduate students in the biomedical sciences who wish to improve their skills on data handling. The course will introduce the concepts of the FAIR principles for data, the concept and implementation of data stewardship as well as practical aspects of day to day data management and data management plans, which are required in many grant applications.

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 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.

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.

The workshop will cover the ethical and operational issues associated with linking Twitter and survey data. We will begin by thinking about what new and exciting opportunities this new form of linked data provides. We will then move on to the challenges associated with designing, collecting, analyzing, publishing and sharing this type of linked data. Drawing on recent experiences of three UK studies (British Social Attitudes 2015, the Understanding Society Innovation Panel 2017 (IP10) and the NatCen Panel July 2017) we will explore issues around informed consent, disclosure, security and archiving. The workshop will be interactive with a focus on participant engagement and dialogue.

Please note, this is an example of a possible workshop. Some workshops are offered regularly. An updated workshop list is to be found under the link provided under the Access point.