´╗┐´╗┐ GESIS Training Courses
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Scientific Coordination

Dr.
Marlene Mauk
Tel: +49 221 47694-579

Administrative Coordination

Claudia O'Donovan-Bellante
Tel: +49 621 1246-221

Tools for Efficient Workflows, Smooth Collaboration and Optimized Research Outputs

About
Location:
Mannheim B6, 4-5
 
General Topics
Course Level
Format
Software used
Duration
Language
Fees
Students: 500 €
Academics: 750 €
Commercial: 1500 €
 
Keywords
Additional links
Lecturer(s): Dr. Julia Schulte-Cloos, Lukas Lehner

About the lecturer - Dr. Julia Schulte-Cloos

About the lecturer - Lukas Lehner

Course description

How can we create efficient workflows and facilitate optimal collaboration in teams? How can we ensure that our research processes, our data collection, and our complex (Big) Data analyses can be re-traced by ourselves and other researchers, both in the near and distant future? How can we build our analyses in a way that they can be run reliably and stably on other researchers' computers, regardless of the hardware and software environment? Efficient and reproducible workflows are essential to keep up with the increasing amount of data and complexity of analyses. In recent years, exciting new tools have emerged that enable effective data management and research collaboration. Not only do these new tools help us streamline our workflows, but they also make our research outputs more visible, citable, and sustainable. From the first day we begin adapting our research practices, we benefit from greater efficiency, ease of tracing our research progress, and smoother collaboration with other researchers. In the long run, these practices help us maintain a high quality of research outputs and meet the replicability and transparency standards that an increasing number of journals require for publication. This course provides participants with the skills to harness the potential of new tools that help create efficient workflows and optimize research outputs. It equips them with a toolkit to conduct research that is well organized and documented, and can be readily disseminated and reproduced, both when working on independent projects and in collaborations with others.
 
For additional details on the course and a day-to-day schedule, please download the full-length syllabus.


Target group

  • Researchers at any stage of their career, who rely on data-driven approaches in their work.


  • Learning objectives

    By the end of the course participants will:
  • confidently master tools that enable efficient workflows and collaboration;
  • be able to write executable code and create automatable reports using RMarkdown, Pandoc, and Lua;
  • be able to collaborate effectively with other researchers and document work processes with version control through Git and DVC;
  • have an in-depth understanding of key Git operations, including branching, merging, forking, resolving merge conflicts;
  • rely on Veracrypt for advanced data protection and encryption;
  • be able to effectively disseminate their findings online, e.g. on their own academic website created using GitHub Pages, Hugo, and Blogdown;
  • successfully containerize their projects using Docker and Binder;
  • understand how to ensure interoperability of programming languages when generating reports;
  • be able to rely on the command line and shell scripts for advanced programming and to solve tricky computational issues.
  •  
    Organizational structure of the course
    This course is a one-week full-time program designed to turn participants into experts in modern approaches to workflow management. Participants are expected to do some essential preparatory reading and install the required software before attending the course. All necessary instructions and tutorials will be provided in advance. The seminars consist of lectures, laboratory exercises, and group exercises. In the lab sessions, participants work on practical exercises and complete tasks both individually and in small groups while the lecturers assist them. This allows participants with different levels of prior knowledge to acquire new skills and progress at their own pace. The instructors are also available for one-on-one meetings to clarify questions and give advice on participants' projects.


    Prerequisites

  • Basic knowledge of a statistical programming language such as R or a general-purpose programming language such as Python.
  • This course is based on open-source programming languages and software environments and supports the principles of 'Open Data', 'Open Code' and the integration of narrative text and code. We will use a variety of software and tools, such as:
  • R and R Studio;
  • TinyTex and Pandoc;
  • Git, GitHub, and GitHub Desktop;
  • Veracrypt;
  • Docker Desktop.
  • Participants will also need to register for free online accounts with GitHub and DockerHub. Course participants will receive
    detailed instructions on the required software, packages and how to install them in sufficient time prior to the start of the
    course.
     
    Software and hardware requirements
    Participants should bring their own laptops. More information on software/packages will be provided before the course.
    Recommended related courses
  • R 101 (Workshop, online, 31.08. - 01.09.2022)


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