This article is about the importance of recognizing and accepting intercultural differences in the way science is understood and interpreted. By taking individual routines of interpreting science into account, science communication and science management will become more efficient and successful in terms of integrating local communities and non-scientific audiences. We provide examples of how to consider intercultural context while planning field research.

The European Climate Research Alliance is a bottom-up association of European research institutions helping to facilitate the development of climate change research, combining the capacities of national research institutions and inducing closer ties between existing national research initiatives, projects and infrastructures. This article briefly introduces the network's structure and organisation, as well as project management issues and prospects.

Freely and open distributed optical satellite images used to obtain bathymetric data for shallow waters based on timeseries analysis of multispectral Sentinel-2 datasets. The ratio transform algorithm was implemented for twelve monthly images covering a year. Bathymetric maps were generated and compared with LIDAR measurements. The results showed that bathymetry can be obtained from satellite data within a Root Mean Square Error while more accurate results were generated during the summer.

The use of medium resolution free-of-charge multi-spectral satellite imagery for purposes of flood extension and impact monitoring can be extremely valuable due to their ability to offer a remote access to information The authors propose a workflow that uses Copernicus Sentinel-2 data to provide the comparison changes firstly in the single relevant bands and successively in the indexes NDVI e NDWI, suitable for the estimation of water component.

We believe that software project management tools can support and improve the development of scientific software. Its integration in project management needs coordination and planning. In this paper, we discuss opportunities and possible limitations of recent software project management tools with respect to their application in scientific projects in geoscience and climate modelling.

The paper examines a machine learning algorithm (Random Forests) in comparison with Multivariate Linear Regression, for a data-set of 3500 transactions of residential apartments in Nicosia District in Cyprus. The methodology suggested, indicated high accuracy of the Random Forests Method, that can be applied in automated valuation models and CAMA systems.