| Hana Kubickova
We would like to cordially invite you to the upcoming webinar dedicated to metadata and FAIR sharing principles for best practice in research data management that is scheduled on Tuesday 23rd June at 10 AM CEST.
During the webinar you will learn:
Metadata Introduction The importance of metadata is often underestimated. For data users are metadata commonly regarded as a necessary evil, and something mandatory to the data creating and dataset publishing process. This presentation offers a different point of view: metadata are a red line connecting the geographical tools and applications. No matter when searching for relevant data, Web services, explaining underlying models, displaying predicted situation in a map etc. This presentation will guide you a way to revoke an artificial border between data and metadata.
Predicting cloudiness of satellite images through (meta)data Metadata does not have to be texts! Metadata in a textual way have in some cases a low information value for a user; e.g. a satellite image is covered from 60% by clouds. Will my farm be covered with clouds or not? This presentation will guide you through another (meta)data use case: metadata as geometry/graphics. Moreover, the presentation deals with cloudiness predictions as well as notification mechanisms through e-mail and SMS.
Micka MIcKa is a complex solution for metadata management and for Spatial Data Infrastructure (SDI) and geoportal building. It contains tools for editing and management of metadata for spatial information, web services and other sources (documents, web sites, etc.). It includes their search on the Internet, portrayal in map or download to local computer. OpenMicka is freely available while Micka is a commercial tool. The designation (Open)Micka is used hereinafter for features similar to both licensing versions. Commercial version of Micka is also available free of charge for the duration of the SIEUSOIL project. The full documentation to both versions is stored at https://github.com/hsrs-cz/Micka. MicKa is now part of SmartAfriHub
FAIR data principles for best practice in research data management The FAIR data principles identify four important characteristics of datasets (Findable, Accessible, Interoperable and Reusable) that will make them easier to use. These principles have been developed to help publishers assess whether individual datasets are published in a FAIR and open way. They have been adopted by the research community, where they capture a set of best practices that apply when publishing any type of dataset. FAIR data principles can be applied to data that exists at any point on the data spectrum. The principles emphasise clear licensing and recommend standard licences ‒ like those of creative commons ‒ but do not suggest data should be either closed, shared or open. For instance, sensitive personal data only available to researchers under limited data sharing agreements can still benefit from being FAIR to ensure researchers can easily find, access and reuse that data.
You will hear from:
The webinar is free and open to everyone - students, researchers, data analytics, NGO, African and European projects, anybody in Africa and beyond, who is dealing with data management.