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Zoekresultaten (22)

HBO/WO

MBO

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

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

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

2 maart 2024

Data management

Introductie op research data management, met onder meer aandacht voor Open Science, FAIR data management, een aantal praktische tips en persoonlijke data. De slides zijn voorzien van het UvA-logo, en een aantal slides hebben betrekking op de lokale situatie van de UvA.

Presentatie

Casper Fermin, Joost Oosterhuis, Ira Lenters

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

23 januari 2023

An introduction to research data management- training

As a researcher, you invest a great deal of your time and energy in obtaining valuable research data. Good data management is important to ensure that your data remain accessible and usable in the future, for yourself and others. But what does that involve? We would like to help you get started with data management in the Introduction to Research Data Management course, which is available for all Maastricht University researchers. During this training, we will explain more about what research data management involves, good data management practices, advantages of good research data management, what it means to make your data FAIR, and more. You can register for the May workshop here.

Website

Maria Vivas-Romero, Pedro V Hernandez-Serrano, Erik Jansen

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

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

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