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Fig. 12 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 12: Trammel net landing biomass per vessel for the main target species.
Fig. 4 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 4: Number of active vessels using traps.
Fig. 3 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 3: Number of active vessels using trammel nets.
Fig. 1 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 1: Study area: the Adriatic Sea subdivided into GFCM Geographical Sub-Area (GSA) 17 and 18.
Fig. 2 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 2: Number of active vessels using gillnets.
Fig. 11 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 11: Gillnet landing biomass per vessel for the main target species.
Fig. 19 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 19: Total value and selling price per unit weight of traps landings.
Fig. 10 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 10: Biomass of traps landings.
Fig. 13 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 13: Trap landing biomass per vessel for S. officinalis.
Fig. 9 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 9: Biomass of trammel net landings.
Fig. 18 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 18: Total value and selling price per unit weight of trammel net landings.
Fig. 16 in Seasonal dynamics of small-scale fisheries in the Adriatic Sea
Fig. 16: Size-frequency distribution of trap landings. ML = mantle length.
Fig. 4. Transversotrema licinum MANTER, 1970 from Diplodus noct. Ventral view. Scale 1.00 in Digenetic Trematodes From Marine Fishes Off The Coast Of Kuwait, Arabian Gulf: Fellodistomidae And Some Smaller Families, New Host And Geographic Records
Fig. 4. Transversotrema licinum MANTER, 1970 from Diplodus noct. Ventral view. Scale 1.00 mm
Fig. 5. Treptodemus latus MANTER, 1961 from Hemiramphus marginatus. Ventral view. Scale 1.00 in Digenetic Trematodes From Marine Fishes Off The Coast Of Kuwait, Arabian Gulf: Fellodistomidae And Some Smaller Families, New Host And Geographic Records
Fig. 5. Treptodemus latus MANTER, 1961 from Hemiramphus marginatus. Ventral view. Scale 1.00 mm
Fig. 6 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 6: Scatterplot matrices for Large (A) and Linear (B) FM functions.
Fig. 8 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 8: High values of the FP c index as estimated by Local Moran's I test.
Fig. 5 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 5: Spatial representation of coastal vessels activity indexes (Ac).
Fig. 4 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 4: Spatial representation of the Coastal fishery suitability index (Sc).
Fig. 3 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive
Fig. 3: Spatial representation of the criteria ranking taken into account in MCDA.
PiezoTensorNet: Crystallography informed multi-scale hierarchical machine learning model for rapid piezoelectric performance finetuning
<h2>Description:</h2> <p>a. <strong>Feature Engineering</strong>:</p> <p> The file <strong>data_of_145_features.csv</strong> consists of the datasets of the features. The datasets correspond to Fig. 1(a) of the paper.</p> <p>b. <strong>HierCrystalNet of PiezoTensorNet</strong>:</p> <p> The saved models after training HierCrystalNet are placed inside the zip folders, namely, (i)<strong> classification_saved_models.zip</strong>, (ii) PG1_cubic_saved_models.zip, (iii) PG2_tetragonal42m_saved_models.zip, (iv) PG3_orthorhombic222_saved_models.zip, (v) PG4_hex6tetra4mm_saved_models.zip, and (vi) PG5_orthorhombicmm2_saved_models.zip.</p> <p>c. <strong>ModularEnsembleNet of PiezoTensorNet </strong>:</p> <p> The prediction_results.zip folder consists the prediction results of the ModularEnsembleNet. </p> <p>d. <strong>Input data of piezoelectric tensors and stiffness tensors for the</strong> <strong>finite element analysis</strong>: </p> <p>(i) <em><strong>piezoelectric_tensors_AlN_B0.3Er0.5Al0.2N_alloy.zip</strong></em>: The coefficients of piezoelectric tensors (array shape: 3x6) for AlN alloy and B0.3Er0.5Al0.2N alloy are respectively presented in <strong>undoped_AlN_piezoelectric_tensor.csv</strong> and <strong>B0.3Er0.5Al0.2N_alloy_unrotated_piezoelectric_tensor.csv</strong> files. Both of these files contain the data for samples whose laboratory coordinate system is same with that of the orientation of crystal lattice (all of the three Euler angles theta, psi and phi = 0 degree ), and so the compositional effect on the piezoelectric behavior can be assessed solely without considering the orientation effect. Rotation or other transformation can cause the vector value of (theta, phi, phis) other than (0,0,0) which indicates that the laboratory coordinate system of a sample being different than its lattice orientation. The effect of two different magnitudes of rotation on the coefficients of piezoelectric tensors for B0.3Er0.5Al0.2N alloy are revealed through the files <strong>B0.3Er0.5Al0.2N_alloy_rotated_at_orientation_1_piezoelectric_tensor.csv</strong> and <strong>B0.3Er0.5Al0.2N_alloy_rotated_at_orientation_2_piezoelectric_tensor.csv</strong>. Orientation 1 refers to<strong> theta = 0 degree, psi = 268.47 degree, 91.52 degree, 0 <= phi <= 360 degree</strong> whereas <strong> theta = 180 degree, psi = 268.47 degree, 91.52 degree, 0 <= phi <= 360</strong> for orientation 2. The units of the coefficients are C/m^2. The description of the methodology on how to generate this data in this format is provided at https://piezoelectrictensorsdatabase.streamlit.app/ .</p> <p>(ii) <em><strong>stiffness_tensors_AlN_B0.3Er0.5Al0.2N_alloy.csv</strong></em>: The stiffness tensors (array shape: 6x6) for the two materials AlN alloy and B0.3Er0.5Al0.2N alloy at room temperature are presented through the four csv files. The elements of stiffness tensors in the datasets are presented in GPa. The Cijkl tensors are provided first for isotropy and then for orthotropy assumptions. The description of these tensors for AlN and B0.3Er0.5Al0.2N materials are provided in the web apps: https://isotropic-elasticity.streamlit.app/ and https://orthotropic-elasticity.streamlit.app/</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.