FAIR Data Management, Fall I (2023)

    Description

    Welcome to the course – FAIR Data Management

     

    In this course you will get a wide and thorough introduction to the FAIR principles, the importance of a Data Management Plan (DMP) and how to create one yourself.

     

    The course objective is thus to promote good research practices by helping the participant make informed choices in relation to planning and executing research processes such as data collecting, data analysis, data storing and sharing. 

     

    The course consists of two half-day workshops aimed at supporting the individual PhD student in planning the data handling that is inherent to the PhD project. 

    15 hours of homework drafting a data management plan (DMP) and working on a dataset is to be expected between 1st and 2nd day of teaching. At sign-up, participants will be enrolled in both workshops, and attendance at both are required. 

    Teaching day #1                            

    Welcome and introduction to the course FAIR Data Management
    FAIR principles (making data that is Findable, Accessible, Interoperable and Reusable)
    Exercise LEGO
    What is a DMP and why is it important?
    Introduction to AAU generic DMP template
    Question 1-4 on the DMP template
    Question 5-8 on the DMP template
    Workshop
    Questions and explanation of homework.

    Teaching day #2

    Welcome and status on homework
    How did you make the dataset FAIR?
    How did you approach making your own DMP?
    Closing, including the option of individual follow up arrangements. 

    Literature and exercises needed to be done prior to the first day of teaching (estimated time requirement: 4 hours):

    1.      Video with introduction to DMPonline and how to log on

    2.      https://howtofair.dk/what-is-fair/ - in this link, you will find an introduction to the FAIR principles. Prior to the course, you will need to have read the information contained in this link and seen the three videos at the bottom - module 1 (Introduction), module 2 (FAIR principles) and module 3 (Data Management Plans).

    3.      https://howtofair.dk/why-fair/ - in this link, you will find an introduction to the purpose of knowing and using the FAIR principles. Prior to the course, you will need to have read the information contained in this link and seen the four videos with Susanna and Barend.

    4.      https://howtofair.dk/how-to-fair/ - in this link, you will get an introduction to how you can make your research data more FAIR by taking you through six FAIRification practices. Prior to the course, you will need to have read the information contained in this link and seen the four videos with research projects used as examples. Moreover, you must read the six attached documents in the bottom of the link.

    5.      https://fair-office.at/lernen-sie-mehr/?lang=en – in this link, you will find nine videos lasting between 5-10 minutes. Prior to the course, you will need to have seen eight of the nine (not including the video on metadata, as it is in German).

     

    6.      https://howtofair.dk/quiz/ - in this link, you will find three quizzes. One on qualitative, one on quantitative and one on sensitive quantitative data. Please go through all three and please note, that you will not be held accountable for you correct/incorrect answers during the course.

     

    Literature and exercises needed to be done between the 1st and 2nd day of teaching (estimated time requirement: 15 hours in total):

     

    FAIRification of data

     

    1.      For this exercise you must use this dataset; https://doi.org/10.5061/dryad.7d7wm37wp

     

    2.      Evaluate the FAIRness of the dataset.

     

    Go through each of the letters in FAIR and assess, whether the dataset follows the four principles.

     

    3.      When assessing the different principles in FAIR, you can use the following guide to refresh the principles; “How FAIR are your data[1] ”; https://doi.org/10.5281/zenodo.1065991

     

     

    Start writing your own Data Management Plan

     

    Start writing a DMP on your PhD. Project. Use the AAU generic template to write your DMP. You will find the template at DMPonline.

     

    First versions do not have to be perfect or done. If you need inspiration to start writing your DMP, you can have a look at the following materials for inspiration.

     

     

    Materials for inspiration.

    1.      You can use the following guidance from Science Europe to help reflect on what to write in the different questions of your Data Management Plan; https://scienceeurope.org/media/4brkxxe5/se_rdm_practical_guide_extended_final.pdf 

     

    2.      You can browse through existing Data Management Plans for inspiration:

    -          REPAIR draft Data Management Plan

    -          Data Management Plan: Empowering Indigenous Peoples and Knowledge Systems Related to Climate Change and Intellectual Property Rights

     

     

    Additional reading

     

    Bellow you will find suggestions for additional reading materials. This is not part of the 15 hours dedicated for homework and therefore voluntary.

     

    -          A FAIRy tale; https://doi.org/10.5281/zenodo.2248200     

    -          FAIR Principles: Interpretations and Implementation Considerations: https://doi.org/10.1162/dint_r_00024

    -          A basic introduction to Data management; Managing and sharing research data: a guide to good practice. Corti, Louise, author. 2020; 2nd edition – you can get this book at AUB.

    Organizer: 
    CLAAUDIA (www.claaudia.aau.dk)

    Lecturers: 
    Dagmar Knudsen Fallesen
    Carina Ollerup Christensen

    Kamilla Hall Kragelund

    Thomas Andersen
    Dennis Aagaard Pedersen

     

    ECTS: 
    1

    Time, Fall 1:

    Teaching day #1: September 19 - 09:00 – 15:00
    Teaching day #2: October 3 - 09:00 – 12:00

    Place:

    Aalborg campus, 9220 East

    Fibigerstræde 5/33

    Number of seats: 

    12
    A minimum of 5


    Deadline, Fall 1: 
    August 29, 2023



    Important information concerning PhD courses:
    We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 1 week before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

     

     


    [1] Jones, Sarah, & Grootveld, Marjan. (2017, November 24). How FAIR are your data?. Zenodo.