Advanced Skills for Research Data Management and Open Science in Social Sciences

Instructors: Dr. Agata A. Lambrechts, Università della Svizzera italiana; Dr Alexandra Stam, FORS; Carmen Borrat-Besson, FORS; Emilie Morgan de Paula, FORS; Dr. Igor Sarman, Università della Svizzera italiana; Mario Gay, Università della Svizzera italiana; Meret Hildebrandt, FORS; Dr. Pablo A. Diaz-Venegas, University of Lausanne

Modality: Online

Dates: 26-30 August 2024 and 9 September 2024 (am only)

Should you prefer to attend the in-presence edition of this course in Lugano (25-29 November 2024) you can find the dates and details here.


Course contents and objectives

The evolving landscape of research, publishing, and career trajectories presents both new opportunities and fresh challenges for those engaged in the social sciences. Increased emphasis on transparency, collaboration, and data-driven insights is redefining how we conduct, communicate, and measure the impact of scholarly work. As such, developing a robust understanding of Research Data Management (RDM) and Open Science (OS) practices has become a critical skillset for early-career researchers. Navigating these shifting demands is key not only for completion of doctoral thesis and publishing success but also for enhancing research productivity and seizing broader career opportunities. 

Research Data Management and Open Science Training at the Summer School for Social Sciences Methods  

This intensive, comprehensive summer school course focused on research data management and open research data practices is specifically tailored for doctoral and postdoctoral researchers in the social sciences. Designed to follow the research data cycle, the program seamlessly blends academic theory with hands-on activities. We use a dynamic data management plan as the central project, allowing you to apply the learnings to your ongoing research directly. 

By bringing your own research data (or research ideas) to the program, you can actively engage in the learning process, ensuring all concepts and techniques have immediate relevance to your specific project and research field.

This immersive experience goes beyond just technical skills. We delve into the ethical considerations, values, and professional attitudes associated with the application of responsible RDM and OS principles and practices, equipping you for a successful career path whether within academia or beyond. 

The program's focus on fostering research efficiency and improving your research workflow translates into tangible benefits. You will gain the necessary tools and strategies to effectively manage your data, expedite the publication process, and enhance collaboration opportunities. Ultimately, by understanding how RDM and OS principles contribute to research transparency, reproducibility, and impact, you can strategically utilise these practices to advance your research career and contribute meaningfully to the wider academic community. 


Daily schedule

DAY 1:​ 26th of August 2024

Introduction to Research Data Management and Open Science in Social Sciences

Instructor(s): Dr. Agata A. Lambrechts & Dr. Igor Sarman

  • Introduction – the evolving landscape of RDM and Open Science policies and mandates​
  • Course overview​
  • Research data lifecycle​
  • FAIR data principles and RDM​
  • RDM and the Open Science movement – positions and challenges​

Data Management Planning

Instructor(s): Dr. Agata A. Lambrechts & Dr. Igor Sarman

  • Purpose and benefits of data management planning in social sciences​
  • Standard elements of a DMP – introduction to DMPonline​
  • Funders data management planning requirements ​
  • Types and purpose of data – your research, your data​
  • Allocation of resources – expenses and responsibilities ​
  • Data management in collaborations

DAY 2:​ 27th of August 2024

Data Collection and Active Data Curation

Instructor(s): Dr. Agata A. Lambrechts & Dr. Igor Sarman

  • Systematic data collection (planned, deliberate, using standardised procedures)
  • File and folder structuring​
  • Data cleaning​
  • Data documentation during different stages of the research cycle​ (e.g. codebooks, data dictionaries, user guides (README files), and project reports)
  • File formats, naming, versioning 

DAY 3:​ 28th of August 2024

Data Ethics and Privacy​           

Instructor(s): Pablo A. Diaz-Venegas & Dr Alexandra Stam

  • Protecting personal and sensitive data​​
  • Anonymisation and pseudonymisation principles and practices
  • Management of informed consent​​
  • Sensitive data and Open Data requirements​​
  • Copyright and Intellectual Property Rights – reusing existing data

DAY 4: 29th of August 2024

Data Storage and Security

Instructor(s): Mario Gay, Università della Svizzera italiana

  • Storage solutions for digital and physical data​
  • Data security​
  • Data encryption and access control mechanisms​
  • Backups​
  • Security incident prevention and data recovery strategies

Data Sharing and Open Science

Instructor(s): Meret Hildebrandt, FORS 

  • Power of data sharing and Open Science
    • benefits to data producers
    • value to users
  • Requirements and conditions for data sharing
  • Data preservation and long-term access
  • Using a data repository

DAY 5​ (am only) 30th of August 2024

Reproducible Research and Data Citation

Instructor(s): Emilie Morgan de Paula & Carmen Borrat-Besson, FORS

  • Background of reproducibility and replication in the social sciences
  • Some practices of reproducible research in social sciences
    • Preregistration and registered reports: why it is important and how to do it
    • Replication and coding best practices: why is it important and how to do it
    • Pre preregistration and registered report: why it is important and how to do it
  • Impact of Open Sciences practices on research careers and incentives

DAY 6 (am only) 9th of September 2024

Presentations and Peer Review

Instructor(s): Dr Agata A. Lambrechts & Dr Igor Sarman


Class materials

All course materials will be shared with participants via a dedicated online learning space a few weeks before the start of the course.



Participants should have a foundation in research design and data collection techniques commonly used in their field (e.g., surveys, interviews, focus groups). This ensures they can readily connect RDM and Open Science practices to their own research activities.