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Zoekresultaten (13)

HBO/WO

MBO

2023

Workshop FAIR Data [ april 2023]

The FAIR principles contain guidelines for good data management practices that aim at making data FAIR: findable, accessible, interoperable, and reusable. In this presentation these four foundational principles (findability, accessibility, interoperability, and reusability) are explained with practical examples.

Presentatie

Ingrid van van Gorkum, Jellie Visser

25 januari 2023

FAIR essentials

As a researcher, you have probably heard about the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Nevertheless, how do you make research data FAIR? Sign up for this hands-on training on 8 February 2022 and learn what FAIR is and what it means for different disciplines. This course lies its basis within the PBL approach of Maastricht University. In this sense, we will build both a collective discussion of what FAIR is and what it means to different disciplines. We will then integrate what we have learned collectively by doing FAIR in breakout room sessions. The participants will have a tutor available to answer questions about the exercises in the breakout rooms. However, it is up to the participants to do the exercises. The intention is to build a collective roadmap of FAIR points in an ending plenary session. Learning objectives: Get basic theoretical knowledge about what FAIR is and if and how it could be applied to all scientific disciplines. Articulate benefits, barriers, and challenges in making data and other outputs FAIR. Learn collectively through independent hands-on experience with techniques, services, and technologies to make your data FAIR or help others make their data FAIR. Together come up with a collective roadmap of ideas on what it means to be FAIR. This last point will be used to keep building the course in the future.

Document

Pedro V Hernandez-Serrano

25 januari 2023

FAIR for Qualitative Data

The following course is for researchers working with qualitative research material and willing to explore how FAIR (Findable-Accessible-Inter-operable-Re-usable) could perhaps fit their work. The course will be informative but also hands-on and practice base. Course Description The course covers the basics of FAIR practices and helps qualitative researchers to think how far and if the FAIR principles could be applied to their work. The course is a co-constructive exercise, so your work and active participation will be key to keep designing the course. Learning objectives : Theoretically discuss FAIR and its possible limits for non-readable machine research data. In breakout rooms look into FAIR repositories with qualitative data and constructively be critical to the researcher’s approaches in terms of becoming findable and accessible. We will use a real case scenario to explore how inter-operability and re-usability can become useful aids while working with teams of qualitative researchers.

Website

Pedro V Hernandez-Serrano

23 augustus 2023

Lecture: Fair Operating Practices - AI Recruitment and Malpractices of AI Monitoring

Understand the principles of fair operating practices in AI recruitment, including the importance of transparency, accountability, and bias mitigation. Identify responsible AI monitoring practices, including informed consent and transparent policies. Recognize potential ethical concerns and legal frameworks for using AI in recruitment and monitoring.

Website

HCAIM Consortium

15 mei 2025

Write Your DMP: online module materials

These are the raw materials used in the online training module "Write Your DMP" created within Utrecht University's Moodle platform, by Utrecht University's Research Data Management Support. This module was developed to help Utrecht University researchers to write their Data Management Plan. It is a self-paced module that takes about 1.5 hours to complete. The module is structured as follows: 0: Introduction 1: Prepare: data collection, data reuse, data documentation and metadata 2: Handle: data privacy, security, ownership and storage 3: Share: data preservation, data sharing, FAIR data 4: Evaluation and glossary In the deposit, you can find the following files: The module content in the form of a storyboard per chapter in the online platform (WriteYourDMP_raw_materials_20250509.pdf) Self-created images used in the platform (materials_Ulearning.zip). A raw Moodle export of the module that can be imported in any other Moodle platform (backup-moodle2-course-1203-w_dmp_ub-20250515.mbz)

Document

Utrecht University Research Data Management Support

15 juli 2024

Managing your data: the basics

In this module you will learn to state what data management is, explain why management of (research) data is crucial for academics, describe the principles of FAIR, and practice good data management.

Website

Maastricht University Library

2 september 2025

Intro to Open Science

Created for the GSLS Master introduction Days 2025, Utrecht University. This lecture will introduce you to the principles and practices of Open Science, a global movement aimed at making scientific research more accessible, transparent, and collaborative. You will learn why open science matters for accelerating discoveries, improving research quality, and enhancing reproducibility. Key topics include open access publishing, open and FAIR data, preprints, open peer review, and sharing research tools and methods. The session will also cover practical tips on how you can integrate open science practices into your own research from the start of your graduate journey. By embracing open science, you contribute to a more inclusive and impactful scientific community.

Presentatie

Annetrude de Mooij-van Malsen

25 januari 2023

Circular Research Data Course book

The theme, “Circular Research Data”, is inspired by the socio-economic transition we live through, moving from a linear to a circular model acknowledging sustainability. One of the barriers to research reproducibility is precisely this “linear” mindset. A significant share of the research output produced in the last decades has followed the linear model: 1) Take the data, 2) Analyze and publish it, 3) Dispose of the data. We have the responsibility to change into a circular model. In this week-long summer camp training which will take place between July 25-29,2022, you will look at real-world examples and learn the steps you need to take in your research to close the circle and achieve data sustainability following the FAIR principles of research data management. Link to the original event page: Circular Research Data

Website

Pedro V Hernandez-Serrano

1 augustus 2026

Introduction to data management as part of Open Science

Data management is part of the research cycle. It is crucial to think about it in the design phase of any experiment. This self-study module gives you hands on advice on what to do (and what NOT) with research data. It is not about (statistical) data analysis. This self-study module helps students get familiar with setting up their data management plan. As it’s an introduction to this topic, it is for students at beginner level. After completing this self-study module, you should be able to answer the following questions: What do I store? Where do I store? How can others understand what I stored? Why should I care?

Website

Susanne Kirchhoff

5 juni 2022

Ethical matrixes and how to design them - Educational format

This exercise is about the ethical matrix as a tool for reflection and evaluation in ethics. Students will learn what such a matrix entails and will design matrixes themselves. They will specify the cells or their matrixes and discuss the usefulness of this conceptual tool. Ethical matrixes can help to analyse the different duties and responsibilities of moral actors involved in a specific ethical situation. In principle, an ethical matrix can be used to make a morally neutral analysis. The matrixes require a specific topic to be filled in. They can also help to make a rational political decision-process easier by formulating the issues at stake in a transparent and generally understandable and acceptable manner. They can be used for individual considerations as well. As ethical matrixes help to explore the moral positions of all involved stakeholders, they offer a just/fair assessment. Finally, ethical matrixes can aid in tracing weaker principles and possible compromises.

Document

Bart Gremmen