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Datasets for the Results of Scratch Tests of Green Wood and Results of Scratch Tests of Timber Components (D2.1) and Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk (D2.2) of 5G-TIMBER EU Project
<p>7 June 2024: added D2.2_data_statistics.zip and D2.2_analysis_results.zip, which are the datasets for D2.2 "<span>Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains and Stresses and Crack Risk" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</span></p> <p>D2.2 presents the Hygro-Thermo-Mechanical (HTM) models and the finite element (FE) analyses of selected wooden components that use the material properties of wood presented in deliverable D2.1 "Input database for selected wood parts and timber components: material properties, representative environmental conditions,<br>and loads" (see below). </p> <p>------</p> <p>Figures_22_23_24_25.xlsx : Results of Scratch Tests of Green Wood</p> <p>corrected_Figures_26_27_28_29_30.xlsx : Results of results of Scratch Tests of Timber Components (new version, uploaded on 26 October 2023)</p> <p>This dataset consists of 2 Excel files that correspond to the scratch test results reported in the deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" of the Horizon Europe Innovation Action project "5G-TIMBER: Secure 5G-Enabled Twin Transition for Europe's TIMBER Industry Sector" (project reference: 101058505).</p> <p>The purpose of D2.1, to which this dataset is related, is to present the input data needed for the Hygro-Thermo-Mechanical (HTM) models and the related finite element (FE) analyses planned for a follow-up deliverable, i.e., the D2.2. (Output Database for Selected Wood Parts and Timber Components: Moisture Contents and Temperatures, Moisture Induced Strains And Stresses And Crack Risk). The data include the material properties for green wood and selected wooden components, as well as the plans to collect environmental conditions and loads to be considered in the analyses for prediction of the crack risk of timber components under moisture variations. In additions, new results of scratch tests of wood and wooden components, supported by computed tomography (CT) investigations, are collected to define a model for shear failure risk to be added to the HTM computational models.</p> <p>In D2.1, scratch tests carried out at VTT are described and their results are collected to provide information about the moisture effects of wood logs during cutting operations in sawmills, as well as on relevant fracture and shear properties for wooden components in sawing centres before using them to produce wooden elements of modular buildings in the production. The scratch tests are supported by CT tomography investigations and these results are also reported in the deliverable.</p> <p>D2.1 is available here: <a title="Deliverable D2.1 "Input Database for Selected Wood Parts and Timber Components: Material Properties, Representative Environmental Conditions and Loads" " href="../records/10577505" target="_blank" rel="noopener">https://zenodo.org/records/10577505</a> </p>
Robust quantum dots charge autotuning using neural network uncertainty - Output data
<p>Outputs of the model training and the offline autotuning experiments presented in the paper: "<em>Robust quantum dots charge autotuning using neural network uncertainty</em>".</p> <p>For convenience, the results are splitted in several zipped files:</p> <ul> <li><strong>run_outputs_light.zip</strong>: contains only settings and results text files (sufficient for compiling result tables).</li> <li><strong>run_outputs_full_scan.zip</strong>: contains complete scan of the diagrams (for qualitative analyse)</li> <li><strong>run_outputs_part<N>.zip</strong>: contains all autotuning simulation output, grouped by seed (images and video output types might vary between seeds)</li> </ul> <p>Each folder in the zipped files represent a run that includes:</p> <ul> <li>log file</li> <li>plots / images</li> <li>run settings</li> <li>performance results</li> <li>pytorch model parameters</li> </ul> <p>See README.txt for more information about the file strucutre.</p>
ImpactX+MADX input and output files for a thin-kick model of the FNAL Booster
<p>Input and output files for a thin-kick model of the Fermilab Booster ring in MAD-X (expressed in SXF format), together with a Python script to parse and run ImpactX using the SXF lattice file.</p>
Data from: Higher thermal plasticity in flowering phenology increases flowering output
<p>Ongoing climate change poses an increasing threat to biodiversity. To avoid decline or extinction, species need to either adjust or adapt to new environmental conditions or track their climatic niches across space. In sessile organisms such as plants, phenotypic plasticity can help maintain fitness in variable and even novel environmental conditions and is therefore likely to play an important role in allowing them to survive climate change, particularly in the short term. Understanding a species' response to rising temperature is crucial for planning well-targeted and cost-effective conservation measures. We sampled seeds of three <em>Hypericum</em> species (<em>H. maculatum</em>, <em>H. montanum</em>, and <em>H. perforatum</em>), from a total of 23 populations originating from different parts of their native distribution areas in Europe. We grew them under four different temperature regimes in a greenhouse to simulate current and predicted future climatic conditions in the distribution areas. We measured flowering start, flower count, and subsequent seed weight, allowing us to study variations in the thermal plasticity of flowering phenology and its relation to fitness. Our results show that individuals flowered earlier with increasing temperature, while the degree of phenological plasticity varied among species. More specifically, the plasticity of <em>H. maculatum</em> varied depending on population origin, with individuals from the leading range edge being less plastic. Importantly, we show a positive relationship between higher plasticity and increased flower production, indicating adaptive phenological plasticity. The observed connection between plasticity and fitness supports the idea that plasticity itself may be adaptive. This study underlines the need for information on plasticity for predicting species' potential to thrive under global change and the need for studies on whether higher phenotypic plasticity is currently being selected for as natural populations experience a rapidly changing climate.</p>
Figure 7 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 7. GLMM diagrams illustrating the effect of the main and alternative prey taxa and the two derived indices on the number of fledglings (A: Common vole, B: Apodemus genus, C: Microtinae/Murinae ratio, D: Trophic level index).
Figure 5 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 5. GLMM diagrams illustrating the effect of the main and alternative prey taxa and the two derived indices on clutch size (A: Common vole, B–C: Apodemus genus, D–E: Microtinae/Murinae ratio, F: Trophic level index).
Figure 4 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 4. Box plots of the relative frequency of the main and alternative prey taxa. The bottom and top limits of each box are the lower and upper quartiles; error bars equal ±1.5 times the interquartile range; the horizontal black band within each box is the median; and the red triangle is the mean.
Figure 6 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 6. GLMM diagrams illustrating the effect of the main and alternative prey taxa and the two derived indices on the number of hatchlings (A: Common vole, B: Apodemus genus, C: Microtinae/Murinae ratio, D: Trophic level index).
Figure 3 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 3. Rank abundance curves of the prey composition of the common barn-owl in different outbreak and crash years, and cumulative results of these two periods.
Figure 2 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 2. Box plots of barn owls' breeding parameters (A: clutch size; B: number of hatchlings; C: number of fledglings). The bottom and top limits of each box are the lower and upper quartiles; error bars equal ±1.5 times the interquartile range; the horizontal black band within each box is the median; and the red triangle is the mean.
Figure 1 in Diet and reproductive outputs of common barn-owl (Tyto alba) during the common vole (Microtus arvalis) outbreak and crash
Figure 1. Study area in Baranya County (Hungary), showing the location of sampled nesting pairs (settlements).
Data for lodubay/galactic-dtd: Multi-Zone Galactic Chemical Evolution Model Outputs and APOGEE Sample
<p>Data for <a href="github.com/lodubay/galactic-dtd">lodubay/galactic-dtd</a>, a project exploring different models for the Type Ia supernova delay-time distribution (DTD) in multi-zone galactic chemical evolution models with the <a href="github.com/giganano/VICE">VICE</a> package. This dataset contains two files: <code>multizone.tar.gz</code> is a compressed archive of all 33 multi-zone outputs (combinations of 8 DTDs x 4 star formation histories, plus one with an alternate stellar migration scheme), and <code>sample.csv</code> contains chemical abundance data from the <a href="https://www.sdss4.org/surveys/apogee/">APOGEE survey</a> (data release 17) and stellar ages from <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.tmp.1191L">Leung et al. (2023)</a>. This project is made reproducible with <code>showyourwork</code>, which will automatically download and extract all data from this deposit when it builds the article.</p> <p>This version contains a minor update to the APOGEE sample file.</p>
Fig. 11 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 11: RMSE profiles for temperature (A) and salinity (C). All associated profiles differences (Argo-model) for temperature (B) and salinity (D) in 6 discrete depths (10 m dark blue, 20 m light blue, 30 m red, 40 m pink, 50 m green, 60 m yellow).
Fig. 10 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 10: Temperature (A) and salinity (C) average profiles with the associated STD for model (red) and Argo (blue), calculated from all the associated profiles of the study area (Fig. 1). Profile differences (Argo – model) of the average temperature (green line) and salinity (brown line) (B). T-S diagram of all Argo and model associated profiles for two depth layer zones (Argo: 200-800m light blue, 800-2000 m dark blue) (Model: 200-800 m pink, 800-2000 m red) (D).
Fig. 9 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 9: Temperature (A) and salinity (C) average profiles with the associated STD for model (red) and Argo (blue), calculated from the associated profiles during the "winter" periods (November – April). The associated profiles for the "summer" periods (May – October) are shown in (B) and (D) for the temperature and salinity respectively.
Fig. 7 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 7: A: Argo salinity average profiles in Southern Adriatic (SA) and Otranto Strait (OS) for the years 2010 (green) and 2012 (purple). B: Argo salinity average profiles in the Northern Ionian (NI) for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple). C: Model salinity average profiles in Southern Adriatic (SA) and Otranto Strait (OS) for the years 2010 (green) and 2012 (purple). D: Argo salinity average profiles in the Northern Ionian (NI) for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple).
Fig. 8 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 8: A: Argo salinity average profiles in the south-eastern Ionian for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple). B: Model salinity average profiles in the south-eastern Ionian for the years 2008 (light blue), 2009 (dark blue), 2010 (green), 2011 (red) and 2012 (purple).
Fig. 6 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 6: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the southern Ionian region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the southern Ionian.
Fig. 5 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 5: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the northern Ionian region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the northern Ionian.
Fig. 3 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 3: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the southern Adriatic region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the south Adriatic.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.