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Operational Earth Observation (EO) satellite missions are entering their 5th lifetime decade, and the need to access historical data has strongly increased, particularly for long-term science and environmental monitoring applications. This trend that drives users to request long time-series of data will increase even more in the future, in particular regarding the interest on global change assessment and monitoring to support policy makers decisions on atmosphere, ocean, cryosphere, carbon and other biogeochemical cycles safeguard.The Copernicus initiative (https://www.copernicus.eu) is playing a unique and unprecedented role form the point of view of amount, relevance and quality of provided environmental data. In the frame of the European Commission funded activities, the Data and Information Access Service (DIAS) are operated by five different consortia to acquire, process, archive and distribute data from Copernicus and Third-Party Missions.With this enormous availability of past, present, and future geospatial environmental data, there is the need to make users able to identify the datasets that best fit with their needs and obtain these data in fastest and easiest-to-use possible way. The Advanced geospatial DAta Management - ADAM platform (https://adamplatform.eu/) provides discovery, access, processing and visualization services for data in the distributed cloud environment, significantly reducing the burden of data usability.ADAM allows the exploitation of the content of EO data archives extended from a few years to decades and therefore makes their continuously increasing scientific value fully accessible. The advances in satellite sensor characteristics (spatial resolution, temporal frequency, spectral sensors) as well as in all related technical aspects (data and metadata format, storage, infrastructures) underline the strong need to preserve the EO space data without time constraints and to keep them accessible and exploitable, as they constitute a humankind asset. This is a typical big data challenge that ADAM can face. This paper describes the ADAM platform and various application domains supported with its data science analytics and visualization capabilities.

This study aimed to set up a method for the long-term management of Adriatic sturgeon, through the analysis of sex steroids and the genetic profiling of individuals in order to maximize the already reduced genetic variability of this species. Ten A. naccarii adults (nine of captive origin, one captured in the Ticino river and then moved into captivity) and eight subadults located in a semi-natural land-locked pond in Abbiategrasso (Milan, Italy) were analysed. Plasma testosterone differed significantly in the eight analysed subadults allowing their differentiation into two groups: the first group with an average testosterone concentration of 5.42 ± 1.31 ng/ml (probably female) and the second group with an average of 423.14 ± 75.97 ng/ml (probably male), as subsequently confirmed by artificial stripping. The plasma testosterone level was also significantly different between adult males and females (371.37 ± 43.58 vs. 95.34 ± 51.10 ng/ml), while the E2 levels showed no significant differences. Animals were genotyped on the basis of 10 microsatellite loci and their parental relationships were defined: four adults, two females and two males, generated the eight subadults. On the basis of pedigree analyses and genetic distances, 15 unrelated couples were identified for the future breeding seasons. Finally, the adult female captured in the Ticino River showed an interesting genetic profile, widely different from all of the other 17 specimens analysed, and represents a valuable source of genetic diversity.

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.