SESAM 2023 Workshop on "Open Access data sharing - How to make simulation-based training data Findable, Accessible, Interoperable and Reusable (FAIR)?"

F.R. Halfwerk*, Zafer Öztürk

*Corresponding author for this work

Research output: Contribution to conferenceOtherAcademic

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Abstract

Introduction & Aims

Open Access publishing is getting more and moreattention. Research is made available to the public and developing countries,giving more exposure. The underlying data, which can be qualitative (i.e.interview data) or quantitative (test scores, time to perform a task, simulatorparameters) are however often not shared. Publishing these data improvesreproducibility, accelerates innovation and allows for sharing of unique datanot available to everyone. We believe this is the next step forward inmaturation of the field of simulation applied to medicine and nursing. Thereare however perceived concerns regarding privacy and access under the EU generaldata protection regulation (GDPR). At the same time anonymization techniquesallow for data sharing and reuse under the GDPR.

So how to publish your simulation-based studiesand practices according to the so called FAIR principles, making it Findable,Accessible, Interoperable and Reusable?

The aim of this workshop is to show bestpractices for publishing simulation-based training data. We use a graduatesurgical skills curriculum study previously published as use case.

 Context (reading not required before theworkshop):

1.     Wilkinson, M.,Dumontier, M., Aalbersberg, I. et al. (2016). The FAIR Guiding Principlesfor scientific data management and stewardship. Sci Data 3, 160018. https://doi.org/10.1038/sdata.2016.18

2.     Halfwerk, F.,Groot Jebbink, E., & Groenier, M. (2020). Development and Evaluation ofa Proficiency-based and Simulation-based Surgical Skills Training for TechnicalMedicine Students. MedEdPublish, 9(1), [3523]. https://doi.org/10.15694/mep.2020.000284.1

3.     Halfwerk, F., Groot Jebbink, E., & Groenier, M. (2021): Dataunderlying the Research on "Development and Evaluation of aProficiency-based and Simulation-based Surgical Skills Training for TechnicalMedicine Students". 4TU.ResearchData. Dataset. https://doi.org/10.4121/14837907.v1

 Learning objectives

After this workshop, participants will be able to:

1.    Discussadvantages and limitations of publishing your simulation data

2.     Recall the FAIR principles of makingdata Findable, Accessible, Interoperable and Reusable

3.     Address anonymization techniques forpersonal data sharing of simulation participants or patients, and learn which openor restricted licenses are available for data reuse

4.     Apply FAIR principles and review how(well) these were applied on a published simulation-based training dataset fromgraduate surgical skills training

5.    Usethe FAIR framework in your own simulation practice and simulation research

 Session Description (planned activities: how learning objectives will be reached, time distribution ofinteractive vs presented content)

This interactive 90-minute workshop consists of:

1.     Introductory lecture with welcome, learning objectivesand workshop format. Workshop participants share experience with simulationdata publishing (10 min)

2.     Lecture with theoretical framework on the FAIRprinciples (10 min)

3.     Introduction of a best-practice case with following small group discussion: applying one of the FAIR principles per group on simulation-based data (25 min)

4.     Plenary group presentation and discussion of small group results on applying the FAIR-framework (20 min)

5.     Small group discussion: Start planning data sharing for your own practice and studies (15 min)

6.     Plenary closure and wrap up: Where can you find support on data sharing? (10 min)

Educational methods (e.g. group dynamics, interactive methods)

Faculty presentations on framework, context, and data publishing support

Small group discussions with expert facilitation
Plenary group discussion with small group findings and tips for use in own practice and research

Expected impact

Participants will consider sharing data best-practices when designing and conducting their simulation practices and research studies. Data sharing in simulation appliedto medicine and nursing allows for comparison between learner groups,benchmarking data, finding best practices, and potentially norms and standardsfor specific task trainers and levels of proficiency.

Target audience

All delegates involved in simulation design, simulation practice, simulation evaluation, simulation research.

Level (Introductionary / intermediate / advanced)

Introductory

Conflict of interest: None

Declaration:

This abstract has not been previously published and will only be presented at the SESAM annual meeting.

Acknowledgements:

The authors appreciate the icons “group discussion” by AVAM, and “meeting” byIcon Z from Noun Project, thenounproject.com.

Ethics statement:

The authors declare that all procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975 (In its most recently amended version).

The use case in this workshop was received post-hoc ethical review from the Natural Sciences and Engineering Sciences Ethics Committee of the University of Twente without having ethical concerns regarding this research. Written informed consent was obtained from all students before the study.

Original languageEnglish
DOIs
Publication statusPublished - 15 Jun 2023
Event28th Annual Meeting of the Society for Simulation Applied to Medicine, SESAM 2023: Shaping the future of simulation together - Lisbon Congress Centre, Lisbon, Portugal
Duration: 14 Jun 202316 Jun 2023
Conference number: 28
https://www.sesam-web.org/events/event/sesam-lisbon-2023/

Conference

Conference28th Annual Meeting of the Society for Simulation Applied to Medicine, SESAM 2023
Abbreviated titleSESAM 2023
Country/TerritoryPortugal
CityLisbon
Period14/06/2316/06/23
Internet address

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