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

Chalcopyrite semimagnetic semiconductors: From nanocomposite to homogeneous material

Currently, complex ferromagnetic semiconductor systems are of significant interest due to their potential applicability in spintronics. A key feature in order to use semiconductor materials in spintronics is the presence of room temperature ferromagnetism. This feature was recently observed and is intensively studied in several Mn-alloyed II-IV-V2 group diluted magnetic semiconductor systems. The paper reviews the origin of room temperature ferromagnetism in II-IV-V2 compounds. In view of our recent reports the room temperature ferromagnetism in Mn-alloyed chalcopyrite semiconductors with more than 5 molar % of Mn is due to the presence of MnAs clusters. The solubility of magnetic impurities in bulk II-IV-V2 materials is of the order of a few percent, depending on the alloy composition. High values of the conducting hole - Mn ion exchange constant Jpd have significant value equal to 0.75 eV for Zn0.997Mn0.003GeAs2. The sample quality has significant effect on the magnetotransport of the alloy. The magnetoresistance of the alloy change main physical mechanism from spin-disorder scattering and weak localization for homogeneous samples to cluster-related geometrical effect observed for nanocomposite samples. The magnetoresistance of the II-IV-V2 alloys can be then tuned up to a few hundreds of percent via changes of the chemical composition of the alloy as well as a degree of disorder present in a material. [Projekat Ministarstva nauke Republike Srbije, br. III45003

A high resolution electromagnetic calorimeter based on lead-tungstate crystals

A large-scale prototype of the PHOS electromagnetic spectrometer, which is part of the ALICE detector, has been built and tested. This prototype has 256 detector channels and is operated at −25 °C. Each detector channel is a lead-tungstate crystal coupled to an Avalanche Photo-Diode with a low-noise preamplifier. The prototype includes a 16×16 crystal matrix, photo-detectors, analog and digital electronics, a thermo-stabilized cooling system, a light-emitting diode monitoring system, and a charged-particle detector acting as veto counter. Results of measurements using electron and hadron beams of the CERN PS and SPS accelerators are discussed, and the performance of the prototype is evaluated

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, 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