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DTSTAMP:20260616T070041Z
UID:1533ba2b-f144-49e0-8e10-861dc44b2291
DTSTART:20200714T100000Z
DTEND:20200714T140000Z
DESCRIPTION:On Tuesday\, July 14th 2020\, the NanoCommons team\, in a joint
  initiative with the NanoSafety Cluster\, is offering an interactive onlin
 e workshop on implementing Electronic Laboratory Notebooks (ELNs) for nano
 safety assessment.  \n\nUnder the subtitle “Getting into using ELNs for 
 experimental and computational workflows”\, you will learn how to establ
 ish a workflow using Scinote-based electronic lab notebooks. This starts w
 ith an introduction to the Scinote inventory and continues with hands-on i
 nstructions on how to manage\, modify\, create\, and import protocols for 
 assays. Tasks can be defined and assigned to different users and/or groups
 . Finally\, data (incl. all relevant metadata) can be exported resulting i
 n reports that facilitate data FAIRness.\n\nTechnology advancement\, the e
 mergence of nanoinformatics and FAIR data principles implementation have i
 ncreased the need for high-quality datasets. To achieve this\, the data pr
 oduced through academia\, industry and regulatory bodies needs to be prope
 rly curated\, to contain sufficient metadata and to be semantically annota
 ted. In this way\, data can be accessible and readable from both humans an
 d machines\, making it possible to be queried and mined using appropriate 
 systems.\n\nOne of the main objectives of NanoCommons is to promote the FA
 IR data principles\, cross-project collaboration and data interoperability
 . This will make it possible to offer the nanosafety community high qualit
 y data that can be combined to produce big datasets and be used in novel m
 odelling\, machine learning\, deep learning and AI techniques. The Univers
 ity of Birmingham (UoB) aims to achieve this by implementing data manageme
 nt processes covering the entire data lifecycle\, and by moving the data c
 uration process to the data generators. Capturing the data and metadata as
  they are produced will save substantial time and resources\, while result
 ing in higher quality datasets. ELNs can be implemented\, through cloud se
 rvices or locally\, into everyday experimental practice streamlining and s
 implifying experimental and computational workflows\, practices and data c
 apturing.
SUMMARY:Getting into using ELNs for experimental and computational workflow
 s
URL;VALUE=URI:https://infrastructure.nanocommons.eu/events/41/online-electr
 onic-lab-notebook-basics-hackathon/
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