Researcher

An introduction to R for non-programmers using gapminder data

The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation.

Spanish version available here

Github repository available here

The Questionnaire Design and Documentation Tool is intended to provide an interactive and dynamic web-based tool which can be used to both document and retrieve information on the complex process of designing a cross-national survey questionnaire. It should provide a more streamlined and user friendly replacement for the current paper-based word template used to document the European Social Survey’s questionnaire design process.

The QDDT is intended to improve the efficiency of the questionnaire design process. The tool will provide a means of managing the design process, tracking the development of question items over different stages of pre-testing and review and incorporating input from multiple stakeholders. The QDDT should also provide the basis for generating the final field ready questionnaire from the question items developed and recorded within the tool.

This introductory webinar is for anyone who is involved in the collection of data and is considering making (some of) their data available in accordance with funders’ requirements. More and more funders are requiring that research data be made available after completion of the research project, usually through the archiving of data in a trusted repository. However, research teams often still lack the appropriate skills and knowledge regarding how to properly prepare their data for archiving and sharing. This webinar aims to raise awareness about relevant key data management practices for sharing, specifically regarding data documentation, gaining consent, and data anonymisation. Addressing each of these three topics, it provides a short theoretical introduction, including what FAIR means and how it is implemented, as well as practical illustrations drawing on a large-scale cross-national survey (the European Social Survey). It also provides some practical tips with respect to data archiving, in particular how to choose an appropriate archive or repository.

This webinar is intended for everyone who wants to learn about ways of finding and reusing research data. Managing your research data in a FAIR and transparent manner is important. It helps researchers to meet requirements of funding institutions and ensures long-term re-usability of their data. The webinar introduces the CESSDA ERIC Data Catalogue as a means of finding and accessing research data. It enables participants to understand conditions for reuse (licenses) and introduces use-cases. This event is part of a workshop/webinar series organised by members of the SERISS project.

DDI-Lifecycle is designed to document and manage data across the entire life cycle, from conceptualization to data publication, analysis and beyond. It encompasses all of the DDI-Codebook specification and extends it. Based on XML Schemas, DDI-Lifecycle is modular and extensible. This version also supports improvements in Classification management (based on GSIM / Neuchatel), non-survey data collection (Measurements), sampling, weighting, questionnaire Design and support for DDI as a Property Graph.

Part of the Introduction to longitudinal studies Module.

Introduction to RDM primarily for researchers. Can be seen as primer to all other materials in this catalogue.

This presentation was delivered virtually for Botswana Open University Library on 17th May 2021 as part of a Foundational Data Stewardship Workshop. It is primarily aimed at data stewards but can also be useful to researchers and RDM service providers and should be viewed in conjunction with these two other presentations that were part of the same workshop:

• DOI:10.5281/zenodo.4665390 (Open and Responsible Research: Roles and Responsibilities for Data Stewards)
• DOI:10.5281/zenodo.4561728 (Developing and Implementing a Research Data Policy)

This is an introductory guide to the main types of data with a time element. The guide is a brief overview of key elements in need of consideration when using these types of data. It also covers data availability and some commonly used methods for studying change over time quantitatively. 

This guide covers the basics of sampling as well as other related topics such as clustering, stratification and weighting. 

This guide explains the main reasons for using weights, how weights work and how to use weighting variables in statistical analysis.