14/09/2020.
NEANIAS team is already registered and excited about this webinar programme. Interesting applications for us related to the development of our thematic services (Underwater, Atmospheric and Space), the business models and EOSC integration.
10/09/2020.
The video introducing the work entitled “Towards porting Astrophysics Visual Analytics Services in the European Open Science Cloud” is available on our YouTube channel.
8/09/2020.
With this article, we continue to share our experience in organizing online events that allow us to overcome the limitations derived from the global COVID-19 pandemic with minimal impact on the project.
7/09/2020.
Our page presenting our General Meeting held on September 02 & 03, 2020 has been updated with some pictures.
4/09/2020.
The global pandemic is still there, but also our work. NEANIAS team has held a General Tele-meeting in order to continue making progress towards our goals. Two full days with many sessions of e-cooperation, e-discussions and e-agreements.
28/08/2020.
In our day and age, with the current pandemic ongoing, alternative ways of organizing events are a necessity instead of cancelling and stalling everything. Thus they are a variety of options and ways to set-up and organize a Hackathon in the days of the pandemic. Online Hackathons seem to be the new trend with currently multiple active ones ongoing.
21/08/2020.
We are looking to recruit an enthusiastic Research Assistant in the School of Creative Technologies (CT) to work as coder within our NEANIAS research team.
12/08/2020.
The deliverable “D8.1 EOSC integration plan” has been successfully uploaded on the EE platform. The document belongs to Work Package 8 "EOS Integration" and introduces the integration plan (roadmap) for NEANIAS.
10/08/2020.
La Commission européenne (CE) soutient la vision d'une science ouverte, mondiale, transparente et axée sur les données, en l'articulant dans le cadre de sa stratégie pour le marché unique numérique et de l'initiative European Open Science Cloud (EOSC).
5/08/2020.
"Machine learning methods in Smartphone-Based Activity Recognition" has been published. In this paper, we present a system for human physical Activity Recognition (AR) using smartphone with embedded sensors.
