BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Research Data Management for Junior Researchers: Essential Knowled
 ge and Fundamental Steps
DTSTART:20221201T090000
DTEND:20221201T170000
DTSTAMP:20260509T215429Z
UID:49528e1755bde56a607d49128fcdecfaa0cc9deebb0f70470c298f8b
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Francesco Varrato \; Dr Jessica Pidoux \; Simon Dürr\nOver
  the last few years\, research data and its good management have become in
 creasingly important. Proper data management and publication of research d
 ata is often required by funding bodies (e.g.\, the SNSF or EC) as well as
  journals. It ensures reproducibility\, it facilitates reuse by other rese
 archers and paves the way for automated analysis and text mining. Articles
  containing data on average receive about 25% more citations. Moreover\, a
 s professionals\, researchers can no longer risk the loss of a dataset\, n
 or the confusion over the way they obtained their results. Research Data M
 anagement (RDM) enhances the necessary\, transversal skills to boost and i
 mprove research outputs\, while fostering collaborations. Whether research
 ers' interest lies in the challenges of digital humanities or the advancem
 ents of machine learning\, for a career in academia or in industry\, they 
 need to be equally aware of the recent developments in RDM and ready to pr
 ovide the data that underpin their analyses and research results.\n\nLearn
 ing Outcomes:\nThis workshop will provide the participants with the essent
 ial knowledge and concrete examples to tackle these requirements and to ma
 nage the entire data life cycle covering both qualitative and quantitative
  research.\n\nUltimately\, participants will be able to:\n\n	Understand th
 e latest developments in Open Science\, especially FAIR principles\n	Plan 
 their research and ensure compliance with policies and funders' requiremen
 ts\, by writing a Data Management Plan (DMP)\n	Use digital formats that im
 prove collaborations and increase research reproducibility\n	Organize and 
 document their datasets\, considering naming conventions and metadata stan
 dards\n	Analyze and improve their own data workflow\, considering storage 
 solutions\, security issues\, collaborative sharing\, and back-ups\n	Impro
 ve the data workflow by integrating specific tools such as Electronic Lab 
 Notebooks\, surveying platforms\, anonymization software\, etc.\n	Understa
 nd the pros and cons of various platforms for data publication\, such as d
 ata repositories\, code repositories\, databanks\, or data papers\n	Tackle
  possible legal and ethical issues\, with reference to privacy by-design a
 nd specific data masking techniques\n	Understanding issues when handling p
 ersonal and sensitive data\n	Annotate a dataset and go through the publica
 tion procedure on Zenodo\n	Identify and use the most appropriate data lice
 nse for publishing their datasets\n
LOCATION:University of Lausanne
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR