Trainer

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.

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. 

Youtube playlist of past webinars organised by the UK Data Service. Topics include:

• mapping crime data in R
• longitudinal data
• census and population data
• getting started with secondary analysis
• data management
• how to anonymise quantitative and qualitative data
• working with twitter data
• web scraping
• consent issues in data sharing

Youtube playlist of online video tutorials demonstrating practical data management aspects such as writing a data management plan, encrypting data files, backing up data, etc.

Introduction to Systematic Reviews in International Development