sameAs.cc: The Closure of 500M owl: sameAs Statements

The owl:sameAs predicate is an essential ingredient of the Semantic Web architecture. It allows parties to independently mint names, while at the same time ensuring that these parties are able to understand each other’s data. An online resource that collects all owl:sameAs statements on the Linked Open Data Cloud has therefore both practical impact (it helps data users and providers to find different names for the same entity) as well as analytical value (it reveals important aspects of the connectivity of the LOD Cloud). This paper presents sameAs.cc: the largest dataset of identity statements that has been gathered from the LOD Cloud to date. We describe an efficient approach for calculating and storing the full equivalence closure over this dataset. The dataset is published online, as well as a web service from which the data and its equivalence closure can be queried

EO Big Data connectors and analytics for understanding the effects of climate change on migratory trends of marine wildlife

This paper describes the current ongoing research activities concerning the intelligent management and processing of Earth Observation (EO) big data together with the implementation of data connectors, advanced data analytics and Knowledge Base services to a Big Data platform in the EO4Wildlife project (www.eo4wildlife.eu). These components support on the discovery of marine wildlife migratory behaviours, some of which may be a direct consequence of the changing Met-Ocean resources and the globe climatic changes. In EO4wildlife, we specifically focus on the implementation of web-enabled advanced analytics web services which comply with OGC standards and make them accessible to a wide research community for investigating on trends of animal behaviour around specific marine regions of interest. Big data connectors and a catalogue service are being installed to enable access to COPERNICUS sentinels and ARGOS satellite big data together with other in situ heterogeneous sources. Furthermore, data mining services are being developed for knowledge extraction on species habitats and temporal behaviour trends. Also, high level fusion and reasoning services which process big data observations are deployed to forecast marine wild-life behaviour with estimated uncertainties. These will be tested and demonstrated under targeted thematic scenarios in EO4wildlife using a Big Data platform a cloud resources

Assessment of nitrogen diagnosis methods in sunflower

Nitrogen deficiency can severely limit sunflower (Helianthus annuus L.) grain yield and quality. Our objective was to evaluate N diagnosis methods based on: (a) pre-plant soil nitrate-nitrogen (NO3––N) test (PPSNT) and soil N mineralized in short-term anaerobic incubation (Nan), (b) Greenness index (GI) and the normalized difference vegetation index (NDVI) measured at 6 (V6) and 12 (V12) leaves, and (c) grain nitrogen concentration (Nc). Seventeen experiments were carried out between 2010 and 2019 in Argentina, evaluating nine N rates (0, 30, 40, 60, 80, 90, 120, 150, and 160 kg N ha–1). The GI, NDVI, N sufficiency index and relative normalized difference vegetation index (NDVIr) were determined at V6 and V12 growth stages. On average, yield response to N was 492 kg ha–1 and Nc response was 0.25% in 9 and 11 responsive experiments, respectively. The inclusion of Nan improved the PPSNT diagnosis method. The critical N availability (PPSNT + fertilizer N) threshold was 115 kg N ha–1 for experiments with low Nan (60 mg kg–1). The NDVIr at V12 allowed monitoring the crop N status with a 0.95 critical threshold. The Nc adequately diagnosed N deficiencies and the critical threshold was 2.26%. Also, Nc was predicted from the ratio between N availability and grain yield (R2 = .39). Our results would allow to better estimate N availability to recommend adequate N fertilizer rates for sunflower aiming to optimize grain yield and quality, and minimize the economic and environmental cost of fertilization.EEA BalcarceFil: Tovar Hernandez, Sergio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Diovisalvi, Natalia. Laboratorio de Suelos Fertilab; Argentina.Fil: Carciochi, Walter Daniel. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Izquierdo, Natalia. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Izquierdo, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Sainz Rozas, Hernán René. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce; Argentina.Fil: Sainz Rozas, Hernán René. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Sainz Rozas, Hernán René. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: García, Fernando. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Reussi Calvo, Nahuel Ignacio. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; Argentina.Fil: Reussi Calvo, Nahuel Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: Reussi Calvo, Nahuel Ignacio. Laboratorio de Suelos Fertilab; Argentina