The first Open Call for the NEANIAS project will be launched on February 8, 2021 in order to utilize the NEANIAS servicesdevelop novel technologies and strengthen European researches in the Atmosphere, Underwater and Space service sectors. 

NEANIAS participated in OpenAIRE Week, held from October 12th until October 16th 2020, where a mix of internal meetings in the morning for OpenAIRE partners and public sessions consisted of external webinars in the afternoon.

Game engines are continuously evolving toolkits that assist in communicating with underlying frameworks and APIs for rendering, audio and interfacing. A game engine core functionality is its collection of libraries and user interface used to assist a developer in creating an artifact that can render and play sounds seamlessly, while handling collisions, updating physics, and processing AI and player inputs in a live and continuous looping mechanism.

The first version of the NEANIAS Space portal is now live!. You can find it at:

The portal is provided by the NEANIAS WP4 Space community (made up of INAF, UoP, JACOBS, ALTEC, MEEO, UNIMIB and SZTAKI). This first release has been designed and developed by Michelle Osubor of the School of Creative Technologies at University of Portsmouth for her MSc dissertation in Digital Media.

Projects like NEANIAS are meant to guide the astrophysics community in the transition toward the Open Science paradigm. By promoting Open Science practices, delivering innovative services and tools, and enriching workflows for data management, visualisation and analysis, NEANIAS will lay solid scientific and technological foundations to face the challenges of future astronomy. And that is only possible if we see the big picture first: where we are right now, and where we want to go.

Suppose you want to teach a concept to someone: a possible way, instead of trying to provide a formal definition (which can be hard to provide or ineffective to communicate), is to give examples and counterexamples. Basically, this is the approach adopted by supervised machine learning; based on the complexity of the objects to be classified, the number of examples can be very high and, in general, the choice of the number of examples from the different classes that must be managed is not simple. So producing a good dataset for training supervised machine learning models is difficult and time consuming.

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EU Flag  NEANIAS is a Research and Innovation Action funded by European Union under Horizon 2020 research and innovation programme via grant agreement No.863448.