ELN basics training materials
Under the subtitle “Getting into using ELNs for experimental and computational workflows”, participants learned how to establish a workflow using Scinote-based electronic lab notebooks. It started with an introduction to the Scinote inventory and continues with hands-on instructions 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 in reports that facilitate data FAIRness.
Technology advancement, the emergence of nanoinformatics and FAIR data principles implementation have increased the need for high-quality datasets. To achieve this, the data produced through academia, industry and regulatory bodies needs to be properly curated, to contain sufficient metadata and to be semantically annotated. In this way, data can be accessible and readable from both humans and machines, making it possible to be queried and mined using appropriate systems.
One of the main objectives of NanoCommons is to promote the FAIR data principles, cross-project collaboration and data interoperability. This will make it possible to offer the nanosafety community high quality data that can be combined to produce big datasets and be used in novel modelling, machine learning, deep learning and AI techniques. The University of Birmingham (UoB) aims to achieve this by implementing data management processes covering the entire data lifecycle, and by moving the data curation process to the data generators. Capturing the data and metadata as they are produced will save substantial time and resources, while resulting in higher quality datasets. ELNs can be implemented, through cloud services or locally, into everyday experimental practice streamlining and simplifying experimental and computational workflows, practices and data capturing.
Resource type: Presentation, hackathon
Target audience: experimental nanoscientists
Difficulty level: Beginner
Licence: Creative Commons Attribution 4.0