Seasonal Schools
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Whether you are a PhD student, a researcher, a data collector or a data center staff member, our Seasonal Schools offer a broad range of basic and advanced knowledge in the management of biodiversity, ecology and environmental data. The carefully designed intensive courses combine exciting input from experts with hands-on exercises.
As soon as a new Seasonal School is scheduled, you’ll find it listed in our Events section.
What the Seasonal Schools offer
Data is of central importance for scientific progress. Accordingly, it is increasingly necessary to be able to competently manage one's own research data. Our one-week intensive courses, which take place once a year, are designed to teach participants the skills they need for research data management of biodiversity, ecology and environmental data. The Seasonal Schools alternate between expert input from practitioners and practical exercises to directly apply what has been learned. It is important to us to create an open, collaborative environment that invites networking and knowledge sharing among participants.
The number of places is limited in order to be able to guarantee individual support for the course participants.
Die Seasonal Schools are held online and in English.
What participants learn
Renowned experts will guide the participants, for example, in
- Creation of a data management plan
- Data collection with digital apps in the field
- Using Jupyter in combination with R and Python
- Data standardisation
- Legal aspects
- Handling spatial data
- Extraction of data from freely accessible databases
- Securing data quality
- Integration, harmonisation and analysis of data
and other topics.
In practical workshops, course participants gain hands-on experience with modern tools (e.g. Jupyter Notebook, R, Python).
The knowledge acquired in our Seasonal Schools is directly applicable to ongoing research projects and future ventures.
Who can attend
Our trainings are based on the previous knowledge and interests of the participants and are aimed at students and researchers from biodiversity research, data management of biodiversity, ecology and environmental data and related fields.
Possible course contents
The exact course content depends on the background and knowledge of the participants.
A good impression on what a Seasonal School might look like offers the programme of our Seasonal School 2025.
For all those who are interested in more detailed information, we recommend the accompanying in-depth Seasonal School concept publication available on Zenodo.
Next Seasonal School
The next Seasonal School is scheduled for October 2026.
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Publications
Template for the NFDI4Biodiversity & GfÖ Winter School (Röder, Juliane; Fischer, Marlen; Tschink, Daniel; Brand, Ortrun), 2023. Zenodo, https://doi.org/10.5281/zenodo.8221511
This publication showcases the NFDI4Biodiversity & GfÖ Winter School 2022, a transformative one-week course program designed to equip young scientists with essential Research Data Management (RDM) skills tailored for biodiversity and environmental data. Organized by the NFDI4Biodiversity consortium, the Winter School fostered data literacy and promoted sustainable research practices within the ecological and environmental science communities.
The publication offers an overview of the Winter School's structure, curriculum, and highlights. Throughout the course, participants engaged in a series of expert-led lectures and hands-on practical sessions, delivered by renowned academics, data specialists, and research institutions in the biodiversity domain. These sessions covered critical aspects of RDM, encompassing data collection, curation, analysis, visualization, and long-term preservation.
The curriculum extensively explored state-of-the-art software tools for data handling, including Jupyter notebook, R, Python, RightField, and OpenRefine. By providing hands-on training with these tools, the Winter School empowered participants to enhance their data management workflows and produce robust research outcomes.
Targeting PhD students and Early-Career Researchers, the Winter School nurtured the next generation of scientists, equipping them with the necessary skills to navigate the challenges posed by increasingly complex and diverse ecological datasets.
By sharing insights, lessons learned, and practical guidance, this publication serves as an invaluable reference and guide for organizers, educators, and researchers seeking to create impactful and inclusive training programs in research data management for biodiversity and related studies.
The Winter School's legacy lies not only in the skills acquired by participants but also in the collaboration across the biodiversity and environmental science community. It exemplifies the commitment of NFDI4Biodiversity to advance data-driven research and contribute to a more sustainable and biodiverse planet.
Template for the NFDI4Biodiversity & iDiv Seasonal School on Data Management in Ecology and Environmental Science 2024 (Röder, Juliane; Signer, Johannes; Scherreiks, Pascal; Figueiredo, Ludmilla; Kusch, Erik; Linares Gómez, Jimena; Müller, Wolfgang; Nozik, Alexandra; Schellenberger Costa, David; Scherer, Cédric; Schindler, Uwe; Tschink, Daniel), 2025. Zenodo, https://doi.org/10.5281/zenodo.15585748
This document describes a one-week course program for a Seasonal School on research data management (RDM) from 2024 which was organised for the second time by partners from the NFDI4Biodiversity consortium. The course aimed to provide young scientists in ecology and environmental science with state-of-the-art skills and knowledge in handling scientific data throughout the data life cycle. Lectures and practical sessions were given by experts from renowned universities, data centers, and research institutions in the environmental research community, covering general aspects of RDM and aspects specifically important for handling ecological data. The participants were introduced to Jupyter notebooks and learned how to combine software tools like Jupyter, R, GitHub, spreadsheet tools, and NFDI4Biodiversity Tools and Services. The course was conducted remotely and targeted PhD students (R1). This publication summarises detailed information about the structure, organisation, and lessons learned, and is supposed to be used as a guide for similar events.
We list all course materials (presentation slides, video recordings, code, Jupyter notebooks, trainings data sets) and accounts required to follow the course program in your own pace. Additional code and training data sets are available here: https://doi.org/10.5281/zenodo.15594632. The course requires a minimum of programming skills in R (Python is optional).