Kursliste

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Regulatory Frameworks for Data Acquisition in Fieldwork

The online self-study course introduces the key international frameworks relevant to biodiversity research, genetic resources, specimen transport, and Indigenous knowledge and data governance.

Participants will learn the basic principles and practical implications of:

the Nagoya Protocol and Access and Benefit-Sharing (ABS),
CITES regulations for international transport of species and specimens,
the CARE Principles and UNDRIP in the context of Indigenous rights and data governance.

The course explains core concepts, common procedures, and practical research scenarios, including the use of relevant online resources such as the ABS Clearing-House and Species+ databases.

Upon completion of the course, participants should be able to identify which frameworks may apply to their own research projects, understand key compliance and ethical considerations, and navigate essential information resources independently.

Estimated time: 1 hour 

Level: Basic
Suitable for beginners with little or no prior knowledge of international biodiversity frameworks, access and benefit-sharing regulations, or Indigenous data governance principles

The Basics of Automatic Text Recognition (ATR)

The self-paced online course explains the basic concepts and workflows of automatic text recognition, including the compilation and preparation of the text corpus, common software and transcription platforms, and best practices for fine-tuning existing text models to your own corpus.
Upon completion of the course, participants should be able to assess the benefits and costs of ATR for their own research projects and to try out common platforms (e.g., eScriptorium, OCR4All, Transkribus) for themselves.

Estimated time for the whole course series: 5-6 hours

  • ATR 1 - Introduction: Basic concepts, terminology and workflow of Automatic Text Recognition (ATR) - 2 hours
  • ATR 2 - Deep Dive: Preprocessing and software selection - 1 hour
  • ATR 3 – Deep Dive: Transcription and semantic enrichment - 1 hour
  • ATR 4 - Deep Dive: Model Training and Publishing - 1 hour

Level: Basic
Suitable for beginners who have little or no prior knowledge of automatic text recognition or machine learning.

RDM - Data Governance. Strategies for quality improvement in collection data

This self-paced online course introduces participants to the benefits of (institutional) data governance concepts for monitoring data quality. The focus is on operational techniques for detecting and preventing issues, e.g. automated checks, manual review with checklists, and rule‑based validation. Participants will learn how to design a lightweight monitoring approach.

Upon completion, participants should be able to understand the roles, tasks and workflows necessary for ensuring continuous improvement of data quality. They should know common tools and methods for monitoring data quality, analyse any detected issues, identify root courses and implement (semi-) automated measures to prevent repeat occurrences.

Estimated time: ca. 2 hours

Level: Intermediate
Suitable for researchers and professionals with a solid knowledge of basic Research Data Management who wish to systematise their approach for larger projects or institutional contexts.