Description
Researchers routinely deal with datasets that are not created and owned by them, that contain sensitive information, or may have different degrees of limitations for reuse. This training offers an introduction to and an overview of the best practices for working with this type of “restricted datasets”. We discuss how to improve openness and comply with the national and international recommendations for open science and institutional open science policies when the data has restrictions.
We cover relevant issues to consider when working with such data, including data ownership and licensing (rights and restrictions of reuse), the importance of attribution and proper documentation (metadata, data management plan, data availability statement), reporting of data use, and alternatives to opening data (shared software, synthetic or recoded data). Throughout, we illustrate the content with examples and use cases from different research fields.
Who can participate?
The webinar is aimed at anyone who is working with or planning to work with restricted datasets, i.e., data that is not owned by the researcher or cannot be openly shared due to privacy or ethical concerns.
Learning outcomes
- Benefits of open science and how openness relates to research that utilises restricted data.
- Recognise potential restrictions when reusing data from paid as well as free sources.
- Diverse types of restricted data and various settings through examples and use cases.
- Learn about good research design practices (attribution, documentation, and sharing) from the viewpoint of working with restricted data.
- Data availability statements in research articles.
Format
1-hour webinar with a presentation and a Q&A. The webinar is free of charge.
Schedule and location
The training will be held online via Zoom on March 24, 2022, at 1:00–2:00 PM Eastern European Time (EET).
Registration
Instructor
Dr. Kunal Bhattacharya is a Staff Scientist at the School of Science (SCI), Department of Industrial Engineering and Management (DIEM). He has been working with different socio-technical datasets for some time and supports a technology-based data science infrastructure at DIEM. He helps researchers who are conducting quantitative research with business and industry-related information, like transcripts, ownership data, and questionnaires.