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DESCRIPTION:# Overview\nThe huge amount of generated research data has urge
 d the scientific community to consider developing efficient FAIR Research 
 Data Management Strategies with an “Open Data” philosophy and implemen
 ting robust Data Management Plans (DMP) for research projects. This need i
 s also reflected in the requirements of funding agencies\, amongst which t
 he Swiss National Science Foundation (SNFS)\, Horizon Europe\, and publish
 ing platforms. Making research data FAIR - Findable\, Accessible\, Interop
 erable and Reusable **1** - provides many benefits\, including to increase
  the visibility and to improve the reproducibility\, reuse\, and the confi
 dence towards the data **2-4**\, as well as to enable new research questio
 ns and collaborations. \n\n  \n\nThis course\, given by researchers and pr
 ofessionals involved in Research Data Management and in Data Management Pl
 an preparation at ELIXIR-CH\, SIB/Vital-IT and FBM-UNIL/CHUV\, will provid
 e you with the knowledge and the tools **5** to generate robust data and e
 xcellent quality studies that follow the FAIR principles. This course will
  also provide you with effective support to build high quality DMP complyi
 ng with the guidelines established by funding agencies. \n\n*Sources of in
 formation*\n\n**1** Wilkinson\, M.\, Dumontier\, M.\, Aalbersberg\, I. et 
 al. The FAIR Guiding Principles for scientific data management and steward
 ship. Sci Data 3\, 160018 (2016). DOI: [https://doi.org/10.1038/sdata.2016
 .18](https://doi.org/10.1038/sdata.2016.18)\n\n**2** Baker\, M. 1\,500 sci
 entists lift the lid on reproducibility. Nature 533\, 452–454 (2016). DO
 I: [https://doi.org/10.1038/533452a](https://doi.org/10.1038/533452a)\n\n*
 *3** Begley\, C G\, and Ioannidis\, J. PA. “Reproducibility in science i
 mproving the standard for basic and preclinical research.” Circulation r
 esearch. 2015\; 116.1: 116-126. DOI: [10.1161/CIRCRESAHA.114.303819](https
 ://doi.org/10.1161/circresaha.114.303819) \n\n**4**  Asher Mullard\, “Pr
 eclinical cancer research suffers another reproducibility blow” Nature R
 eviews Drug Discovery 21\, 89 (2022). DOI: [https://doi.org/10.1038/d41573
 -022-00012-6](https://doi.org/10.1038/d41573-022-00012-6)\n\n**5** RDMkit:
  RDMkit The ELIXIR Research Data Management Kit.2022. [https://rdmkit.elix
 ir-europe.org/index.html](https://rdmkit.elixir-europe.org/index.html).  \
 n\n# Schedule\n**First day- 09:00-17:00 CET**\n\nAt first\, participants w
 ill be introduced to the notion of research reproducibility and to the nee
 d for a Data Management Plan (DMP) preparation\, an evolving document repo
 rting how the research data will be managed during and after a research pr
 oject. You will learn best practices in FAIR Research Data Management (RDM
 ) with a focus on data collection and data documentation. \nDuring the exe
 rcises\, participants will directly apply what they have learned. \n\n \n\
 nIn the afternoon\, you will learn additional steps of the RDM cycle conce
 rning ethics\, legal\, security issues\, data preservation and data sharin
 g\, as well as an overview of the FAIR principles. During the exercises yo
 u will learn how to share your published data on adapted repositories\, su
 ch as Zenodo. \n\n**Second day- 09:00-17:00 CET**\n\nOn the second day\, p
 articipants will learn how to fill a DMP corresponding to their own resear
 ch using the “Data Stewardship Wizard” tool. Participants will also be
  able to present and discuss their draft DMPs with the group. \n\n# Audie
 nce\nThe course is addressed to post-graduate students and researchers who
  plan to apply for SNFS funds and want to be trained on how to efficiently
  complete the Data Management Plan form. At the same time\, this course ai
 ms at educating the participants on FAIR Data Management principles in gen
 eral. \n\n# Learning objectives\nAt the end of the course you will be able
  to:\n* Understand the requirements of a Data Management Plan (DMP) \n* Ma
 nage the main steps of your research rata using good practices and guideli
 nes (RDM) \n* Understand the FAIR guiding principles and Open Data foundat
 ions \n* Use the [DSW](https://ds-wizard.org/) tool to complete your own D
 MP \n\n# Prerequisites\n##### ***Knowledge / competencies***\nTo be involv
 ed in Life Sciences research.\n\n##### ***Technical***\nYou will need a la
 ptop with a web browser installed. \n\n\n# Trainers\n* Cécile Lebrand - H
 ead of Open Science service at FBM UNIL/CHUV\; Data Steward at UNIRIS 
 \n\n* Vassilios Ioannidis - Lead Computational Biologist at SIB/Vital-IT\;
  Data Steward – FAIR Specialist at UNIRIS \n\n* Grégoire Rossier - Trai
 ning Manager &amp\; Project Manager at SIB/Vital-IT &amp\; SIB/Training\n\
 n# Application\n\n\n\n\n\n\nWhile participants are registered on a first c
 ome\, first served basis\, exceptions may be made to ensure diversity and 
 equity\, which may increase the time before your registration is confirmed
 .\n\nDeadline for cancellation is set to **17/09/2024**.\n\nYou will be in
 formed by email of your registration confirmation. \n\n # Venue and time\n
 \n\nThis course will ONLY be held in person at the University of Lausanne 
 (Metro M1 line\, Sorge station). **No online streaming will be offered.**\
 n\nIt will start at 9:00 and end around 17:00 on each day.\n\nPrecise info
 rmation will be provided to the participants before the course.\n\n#  Addi
 tional information\nCoordination: Valeria Di Cola\, SIB Training Group\n\n
 You are welcome to register to the SIB courses mailing list to be informed
  of all future courses and workshops\, as well as all important deadlines 
 using the form [here](https://lists.sib.swiss/postorius/lists/courses.list
 s.sib.swiss/).\n\nPlease note that participation in SIB courses is subject
  to our [general conditions](https://www.sib.swiss/training/terms-and-cond
 itions).\n\nSIB abides by the [ELIXIR Code of Conduct](https://elixir-euro
 pe.org/events/code-of-conduct). Participants of SIB courses are also requi
 red to abide by the same code.\nFor more information\, please contact [tra
 ining@sib.swiss](mailto://training@sib.swiss).
SUMMARY:Introduction to FAIR Research Data Management &amp\; Data Managemen
 t Plan
URL;VALUE=URI:https://www.sib.swiss/training/course/20241001_FAIRD
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