Gaia Early Data Release 3: The celestial reference frame (Gaia-CRF3)

Context. Gaia-CRF3 is the celestial reference frame for positions and proper motions in the third release of data from the Gaia mission, Gaia DR3 (and for the early third release, Gaia EDR3, which contains identical astrometric results). The reference frame is defined by the positions and proper motions at epoch 2016.0 for a specific set of extragalactic sources in the (E)DR3 catalogue. Aims. We describe the construction of Gaia-CRF3 and its properties in terms of the distributions in magnitude, colour, and astrometric quality. Methods. Compact extragalactic sources in Gaia DR3 were identified by positional cross-matching with 17 external catalogues of quasi-stellar objects (QSO) and active galactic nuclei (AGN), followed by astrometric filtering designed to remove stellar contaminants. Selecting a clean sample was favoured over including a higher number of extragalactic sources. For the final sample, the random and systematic errors in the proper motions are analysed, as well as the radio-optical offsets in position for sources in the third realisation of the International Celestial Reference Frame (ICRF3). Results. Gaia-CRF3 comprises about 1.6 million QSO-like sources, of which 1.2 million have five-parameter astrometric solutions in Gaia DR3 and 0.4 million have six-parameter solutions. The sources span the magnitude range G = 13-21 with a peak density at 20.6 mag, at which the typical positional uncertainty is about 1 mas. The proper motions show systematic errors on the level of 12 μas yr-1 on angular scales greater than 15 deg. For the 3142 optical counterparts of ICRF3 sources in the S/X frequency bands, the median offset from the radio positions is about 0.5 mas, but it exceeds 4 mas in either coordinate for 127 sources. We outline the future of Gaia-CRF in the next Gaia data releases. Appendices give further details on the external catalogues used, how to extract information about the Gaia-CRF3 sources, potential (Galactic) confusion sources, and the estimation of the spin and orientation of an astrometric solution

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Generalized World Entities as an Unifying IoT Framework: A Case for the GENIUS Project

International audienceAfter having briefly discussed some possible interpretations of the (still at least partially ambiguous ambiguous) ”IoT” term, this Chapter sums up the aims and the main characteristics of an on-going IoT-inspired project, GENIUS. GENIUS concerns the creation of a flexible, internet-based, IoT cognitive architecture, able to support a wide range of ‘intelligent’ applications focused on the recognition and interaction with the so-called Generalized World Entities (GWEs). The GWE paradigm intends to fill up the present fracture between the detection of entities at the sensor/physical level and their representation/management at the conceptual level. It deals in a unified way with physical objects, humans, robots, media objects and low-level events generated by sensors and with GWEs at higher level of abstraction corresponding to complex, structured events/situations/behaviours implying mutual relationships among GWEs captured at lower conceptual level. GWEs of both classes will be recognised and categorised by using, mainly, a conceptual “representation of the world”, ontology-based, auto-evolving and general enough to take into account both the “static” and “dynamic” characteristics of the GWEs. When all the GWEs (objects, agents, events, complex events, situations, circumstances, behaviours etc.) involved in a given application scenario have been recognised, human-like reasoning procedures in the form of “set of services”, general enough to be used in a vast range of GWE-based applications, can be used to solve real-life problems. Details about the use of the GWE paradigm to set up an “Ambient Assisted Living (AAL)” application for dealing with the “elderly at home problem” are provided in the Chapter