Self-paced Courses
Starting in 2026, our learning platform will offer a variety of self-paced courses on working with natural science collections and object-related data. The courses cover key topics such as the handling and reuse of research data, legal and ethical issues in fieldwork, data improvement and enrichment, text recognition, machine learning, and Open Science. For more details, check out the course descriptions.
WiNoDa Courses
In this course you will receive an introduction to advanced concepts, principles, and frameworks in research data management (RDM) specifically tailored for natural science collections. It will highlight the importance of professional standards, best practices, and data curation strategies that enhance the quality and reuse of natural collection data. Get foundational knowledge to critically evaluate and improve current data management practices in science natural collections. Learn to navigate and effectively use Diversity Workbench in managing natural science collections data.
The online self-study 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-centered research data and deals with the necessary metadata and persistent identifiers for researching and publishing datasets.
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.
Recordings of WiNoDa Webinars
Title: Introduction to Data Science. In this video you will learn the basics of Data Science and recommendations for your own data sets.
Date of recording: 01.10.2024. Speaker: Tim Conrad. Part of playlist: Data Science. Language: English.
Title: Machine Learning: A Brief Introduction. In this video you will learn the basics of Machine Learning.
Date of recording: 15.10.2024. Speaker: Tim Conrad. Part of playlist: Data Science. Language: English.
Title: Hands-on Machine Learning with Python. In this video you will actively work with and learn Python.
Date of recording: 05.11.2024. Speaker: Tim Conrad. Part of playlist: Data Science. Language: English.
Title: Deep Learning: A Brief Introduction. In this video you will learn the Basics of Deep Learning.
Date of recording: 12.11.2024. Speaker: Tim Conrad. Part of playlist: Data Science. Language: English.
Title: Deep Learning with Python. In this hands-on video you will learn and work with Python in the topic of Deep Learning.
Date of recording: 26.11.2024. Speaker: Tim Conrad. Part of playlist: Data Science. Language: English.
Title: Data Visualization: Introduction and Hands-on. In this hands-on video you will learn the basics of visualization of data.
Date of recording: 10.12.2024. Speaker: Tim Conrad. Part of playlist: Data Science. Language: English.
Title: Data Standards for Natural Science Collections. This video presents standards for collection management in natural science collections.
Date of recording: 29.04.2025. Speaker: Caitlin Thorn. Language: English.
Title: Specify 7 for Natural Science Collection Data: More Than Just a Database. In this video you are presented different criteria for choosing your own database. Date of recording: 06.05.2025. Speaker: Franziska Böttger. Language: English.
Title: Data Management with SQL. In this hands-on video you will take your first steps in data management with SQL.
Date of recording: 03.06.2025. Speaker: Fabian Riebschläger. Language: English.
Title: Be FAIR and CARE: Core Principles for Open Science in Object-related Research. In this video you are presented the basics of Open Science.
Date of recording: 20.05.2025. Speaker: Philipp Kandler. Language: English.
Title: Animal Sound Archive: An Open Data Source for Scientific Research.
Date of recording: 17.06.2025. Speaker: Karl-Heinz Frommolt. Language: English.
Titel: Artificial Intelligence Meets Biodiversity Science – Mining Museum Labels. 4-part playlist. Date of recording: 18.06.2025. Spreakers: Christian Bölling; Franziska Schuster; Phillip Lücking; Théo Léger. Language: English.
Title: Data Ethics and Open Science: Navigating the Complexities of Research Data Accessibility and Transparancy.
Date of recording: 01.07.2025. Speaker: Lambert Heller. Language: English.
Title: Interactive Data Visualizations in R: Create Your First Shiny App.
Date of recording: 08.07.2025. Speaker: Nicolas Antunes. Language: English.
Titel: R as a Mini-GIS: Build Interactive Maps From Scratch.
Date of recording: 15.07.2025. Spreaker: Nicolas Antunes. Language: English. Video: Will be available soon.