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Zoekresultaten (110)

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HBO/WO

MBO

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.

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Pedro V Hernandez-Serrano

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

2024

Workshop FAIR data [january 2024]

This presentation contains the following topics: Open Science Looking back at the Data Management Workshop FAIR F – Findability A- Accessiblity I- Interoperability R- Reuseability

Presentatie

Ingrid van van Gorkum

12 november 2025

FAIR enough - Kwaliteitscriteria voor leermaterialen

Aan de slag met de levenscyclus digitale leermaterialen en de FAIR-principes voor leermaterialen. Ontdek hoe je binnen je instelling kan zorgen voor kwaliteitsgericht en duurzaam werken met digitale leermaterialen.

Mbo, Wo, HboDocument

Suzanne Unck

10 juli 2023

PROTOTYPE FAIR

“Show, don’t tell” is de basis voor een Prototype Fair. Studenten presenteren en testen tijdens zo'n event hun prototypes onder medestudenten, docenten, voorbijgangers en experts uit het werkveld. De studenten kunnen op basis van de opgehaalde feedback keuzes maken om tot het beste eindproduct te komen.

Document

Esther Bouw

23 augustus 2023

Lecture: Consumer Issues - Filter Bubbles, Data Storage, AI monitoring, fair practices

Identify and define key consumer issues Analyse the implications of filter bubbles and AI monitoring on consumer issues. Explore the fair practices used when dealing with AI consumers

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HCAIM Consortium

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.

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HCAIM Consortium

23 augustus 2023

Interactive session: Consumer Issues - Filter Bubbles, Data Storage, AI monitoring, fair practices

Understand the implications of filter bubbles on consumer access to information. Explore the ethical considerations surrounding data storage and privacy. Discuss the impact of AI monitoring and fair practices on consumer rights.

Website

HCAIM Consortium

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