Dozenten: Mahadia Tunga; Dr. Jessica Daikeler; Ignace Kabano, PhD
Mahadia Tunga is a co-founder of the Tanzania Data Lab (dLab), which aims to strengthen the data ecosystem in Tanzania and Africa through capacity development. Mahadia is trained as a computer scientist and specialised in data science. She has vast experience in managing capacity development projects, gender-based and youth engagement programs with a special interest in young girls. Kindly visit sample projects in twitter pages (@dlabtz and @TungaMahadia) and through https://bit.ly/2K1kA3e and https://bit.ly/2lHPO2q. Since 2015, Mahadia has delivered a number of strategic consulting in research and capacity building programs on open data, data innovation, management, visualisation and analysis to several governments, non-government organisations and private entities. She has trained over 2000 individuals and 50 organisations in Tanzania, Uganda, Congo, South Africa, Egypt, and other countries.
Jessica Daikeler is a survey methodologist and works at GESIS in the Survey Operations and Survey Statistics teams in the Survey Design and Methodology department. Jessica has already collected her own data and made it available for further processing and has a broad expertise to work with different research data sets. At GESIS she is involved in the application of evidence-based methods, in particular experiments and meta-analyses as well as in developing open science structures. Her research is currently focused on data quality in web and mobile surveys, data linkage and, methods for the accumulation of evidence.
Kabano H. Ignace is a senior lecturer of Demography and Statistics in the department of Applied Statistics, School of Economics, College of Business and Economics and is the head of training at the African Centre of Excellence in Data Science at the University of Rwanda. He holds a Msc in Demography from State University of Groningen and PhD in Demography from Utrecht in the Netherlands. He has completed a Training of Trainers (ToT) on Data Management with Software Application from Harvard University (USA). With 18 years in academia, he has taught numerous courses related to Demography, Statistics, Economics and research methodology in both Social and Data sciences. He is an expert on resettlement action plans and has consulted public and private institutions, international organizations and local NGOs.
The course “Research Data Management” introduces participants to strategies, processes, and measures required to assure the quality, understandability, and (re)usability of research data from an Open Science and Open Data perspective. Not only is replicability of research data and research findings considered an integral part of good scientific practice. More and more research funders require active data management to ensure that data is of high quality and can be re-used by researchers for new research purposes. Participants will gain relevant information on openness in science and replicability ensuring that their research data is FAIR (findable, accessible, interoperable, and reusable).
The course will cover a) researching data, b) data management plans, c) data collection, d) data processing, e) ethical and legal aspects of data sharing, f) documentation and metadata, g) data storing and archiving. The course will introduce the FAIR principles to guide researchers in creating re-usable research data, increasing transparency as well as replicability of research findings.
Each day will take six hours of classroom instructions, combining lectures in which the theoretical foundations of the literature are discussed, with discussions and practical examples, giving participants the opportunity to discuss their research projects and data.
The course is targeted at researchers and practitioners who produce qualitative or quantitative data and want to learn how to efficiently manage this data and ensure its reusability or how to work with quantitative data and want to understand how the FAIR principles can be implemented in their research. In the course, participants will develop a) familiarity with the idea of Open Science and the principles of FAIR data; b) an understanding of research data management; c) the skills to set up and implement a data management plan; d) the ability to efficiently handle research data; and e) the skills to prepare data in a way that makes it re-usable by other researchers.
Introduction to Open Science, the FAIR principles, and research data management
Exploring existing data sources; Ethical and legal aspects of data collection and sharing
Data collection and cleaning
Preparing data for reuse
Data storing, archiving, and sharing
Participants should be experienced in working with quantitative research data and be well-versed in using one of the main statistical software packages, such as Stata, SPSS or R.
Participants will find the course useful if:
- are social science researchers at an early stage of study planning or data collection, working with quantitative data (principal investigators, researchers who are part of project teams, individual researchers and PhD students):
- are faced with challenges related to data protection, data cleaning and documentation and have little experience in dealing with them so far;
- aim to share their data for re-use after the end of the research project and/or want to learn how to ensure reproducibility of their research findings.
By the end of the course participants will:
- have gained a basic understanding of research data management in social science research within the larger data lifecycle;
- be familiar with techniques of data cleaning and data documentation, as well as preparing their data for re-use
- be aware of ethical and legal challenges to data sharing resulting from data protection regulations and intellectual property rights
- be familiar with applying re-use licenses to their data.