In the light of the growing concerns about COVID-19, we strongly warn all our prospective participants NOT to make any travel or accommodation arrangements for this event, until further notice. We are closely monitoring the COVID-19 situation, and any update or change in arrangements will be communicated.  

ENVRI Community International Winter School DATA FAIRness

Lecce, date to be defined

 

In recent years, one of the major challenges in the Environmental and Earth Science has been managing and searching larger volumes of data, collected across multiple disciplines. Many different standards, approaches, and tools have been developed to support the phases of the Data Lifecycle (Data Acquisition, Data Curation, Data Publishing, Data Processing and Data Use). In particular, modern semantic technologies provide a promising way to properly describe and interrelate different data sources in ways that reduce barriers to data discovery, integration, and exchange among biodiversity and ecosystem resources and researchers.

After having explored Findability during the latest edition, this year, the course will focus on the Accessibility, in particular on the use of FAIR data in ENVRI Community, and for Environmental and Earth sciences research. It is built as a five-day winter school where leading scientists will address this topic from different perspectives.

The attempt is to gather what we consider the most interesting perspectives of our time.

We offer a cutting edge and high-quality programme, aimed at fostering a rich and lively intellectual exchange.

Target audience:

Since the focus should is about supporting end users in how to make the best use of the data, understanding the end user perspective is very important to develop good user interfaces and services to interact with data.

The main target group is the ENVRI-FAIR project partner data center staff and some junior scientists (6-7). The maximum number of participants is 30 (to be confirmed).

Fill in the application form to register to the winter school.

 

ENVRI IWS DATA FAIRness