This course introduces advanced research data management (RDM) concepts and practices tailored to natural science collections, emphasizing standards, best practices, and data curation for improved quality and reuse. Participants will also learn how to effectively apply the Diversity Workbench for managing collection data.

The course introduces the concept of data reusability and provides information on licenses and citation methods for datasets. It provides an overview of the relevant repositories and aggregators for natural history-, collection- and object-related research data and covers the necessary metadata and persistent identifiers for researching and publishing datasets on physical objects.

This course provides an overview of the legal, ethical, and institutional frameworks for field research and sampling, including key principles and regulatory instruments such as CARE, FPIC, and the Nagoya Protocol. Through case studies and simulations, participants learn to identify stakeholders, manage authorization and participation processes, and critically evaluate risks and planning in research projects.

This course introduces methods for harmonizing, validating, enriching, and contextualizing object and find data. Through practical examples, participants learn how semantic enrichment with standards and taxonomies transforms heterogeneous raw data into structured and linked research data.

This course introduces the basic concepts and workflows of automatic text recognition (ATR), 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.

This course introduces the basic concepts and workflows of machine learning, including supervised and unsupervised models. The course presents use cases for the recognition or classification of objects and the analysis of enriched data using no-code/low-code standard solutions. Examples are drawn from the fields of natural history collections and archaeology, but can be applied to other areas of research. Participants will learn to assess the benefits and costs of machine learning with retrained or proprietary models for their own research projects and carry out such projects in collaboration with computer scientists as subject matter experts.

This course explores the ethical challenges of collections and their data, focusing on provenance, cultural sensitivities, and the digital management of sensitive information across various object types. Through theoretical insights and hands-on exercises, participants learn to identify, handle, and ethically digitize collection data while balancing openness, cultural respect, and historical context.

This course introduces the principles of Open Science and how it promotes scientific and societal progress, focusing on integrating Open Science into research with object-related natural history data. Participants learn how to publish according to Open Science principles and reflect on practical, policy, and cultural challenges in its implementation.