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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>
Winter wheat yield analysis using the Light Use Efficiency model in LandKlif project
<p><span>The crop yield of Winter wheat is calculated using the light use efficiency (LUE) model for the state of Bavaria (adopted from Dhillon et al 2020). The yield output is received by inputting the synthetic dataset of Sentinel-2-MODIS with 10-meter spatial and 8-day temporal resolution (generated by Dhillon et al 2022) plus climate elements. The model output is validated using the Landesamt regional crop yield data of Bavaria for 2019 with an R2 of 0.86 and RMSE of 5.03 dt/ha. The NDVI and the yield product was created by Carina Kübert-Flock, Thorsten Dahms, and Maninder Singh Dhillon from TP 7 in LandKlif project.<br></span></p> <p><span>LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Oil Seed Rape yield analysis using the Light Use Efficiency model in LandKlif project
<p>The crop yield of Oil Seed Rape (OSR) is calculated using the light use efficiency (LUE) model for the state of Bavaria (adopted from Dhillon et al 2020). The yield output is received by inputting the synthetic dataset of Sentinel-2-MODIS with 10-meter spatial and 8-day temporal resolution (generated by Dhillon et al 2022) plus climate elements. The model output is validated using the Landesamt regional crop yield data of Bavaria for 2019 with an R2 of 0.82 and RMSE of 2.14 dt/ha. The NDVI and the yield product was created by Carina Kübert-Flock, Thorsten Dahms, and Maninder Singh Dhillon from TP 7 in LandKlif project.</p> <p>LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Biomass estimation of oilseed rape using the light use efficiency model in Bavaria (Atlas) in LandKlif project
<p>This dataset shows the predicted biomass (g/m2) of oilseed rape (OSR) using the light use efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.79 with an RMSE of 2.21 dt/ha. </p> <p>This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Biomass estimation of winter wheat using the Light Use Efficiency model in Bavaria (Atlas) in LandKlif project
<p>This dataset shows the predicted biomass (g/m2) of winter wheat (WW) using the Light Use Efficiency (LUE) model for Bavaria in 2019. The LUE model uses satellite data (Landsat-8, MODIS) and climate data (temperature and solar radiation) to calculate the plant biomass. The crop yield is predicted and validated at district level using LfStat data. The validation results show an R2 of 0.82 with an RMSE of 5.46 dt/ha. </p> <p>This dataset is conducted under LandKlif project. LandKlif is funded by the <a href="https://www.stmwk.bayern.de/englisch.html"><strong>Bavarian State Ministry of Science and the Arts</strong></a> within the <a href="https://www.bayklif.de/"><strong>Bavarian Climate Research Network (bayklif)</strong></a><strong>. </strong> Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. <strong>LandKliF</strong>, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Synthetic aggregate projected maximum dimensions
<p>These files contain the projected maximum dimensions for the Kuo et al. (2016) aggregates at a variety of particle orientation angles and sensor view angles. The 'aggregate_standard.nc' file contains view angles for standard ice particle probe sensor configurations and the 'aggregate_leb.nc' file contains the view angles for probes following the Lebedev order 7 quadrature rule.</p>
Data from: Designing eco-evolutionary experiments for restoration projects: Opportunities and constraints revealed during Stickleback introductions
<p>Eco-evolutionary experiments are typically conducted in semi-unnatural controlled settings, such as mesocosms; yet inferences about how evolution and ecology interact in the real world would surely benefit from experiments in natural uncontrolled settings. Opportunities for such experiments are rare but do arise in the context of restoration ecology – where different "types" of a given species can be introduced into different "replicate" locations. Designing such experiments requires wrestling with consequential questions. Q1. Which specific "types" of a focal species should be introduced to the restoration location? Q2. How many sources of each type should be used – and should they be mixed together? Q3. Which specific source populations should be used? Q4. Which type or population(s) should be introduced into which restoration sites? We recently grappled with these questions when designing an eco-evolutionary experiment with threespine stickleback (<em>Gasterosteus aculeatus</em>) introduced into nine small lakes and ponds on the Kenai Peninsula in Alaska that required restoration. After considering the options at length, we decided to use benthic versus limnetic ecotypes (Q1) from a mixture of four source populations of each ecotype (Q2) selected based on trophic morphology (Q3), and introduced into restoration lakes in a paired design (Q4). We hope that the present paper outlining the alternatives and resulting choices will provide the rationales clear for future studies leveraging our experiment, while also proving useful for investigators considering similar experiments in the future.</p>
IuliaMargineanGitHub/Projecting-Heat-Stress-Vulnerable-Populations-at-Intra-Urban-Scales: Projecting heat stress vulnerable populations at intra-urban scales
<p>This repository provides the data and scripts necessary for full reproduction of results, as well as example datasets that were used for data generation and analysis in the manuscript Marginean et al., "High-resolution Modelling and Projecting Local Dynamics of Differential Vulnerability to Urban Heat Stress".</p> <p>The folders in this repository contain the following:</p> <ol> <li>The input datasets: <ul> <li>shares of educational attainment in 2012 and 2020, by sex, age group and census tract</li> <li>internal migration by sex, age group and census tracts</li> <li>decadal mortality and fertility, by sex, age group, scenario, and census tract</li> </ul> </li> <li>Projected decadal data (2012 to 2050) by sex, age group, scenario and census tract</li> <li>Scripts for generating high resolution projections for three Shared Socioeconomic Pathways (SSPs): one script for SSP 2 projections and one scripts for SSP 1 and SSP 3 projections</li> <li>Example input data to reconstruct the projections in SSP 1, 2 and 3 for females ages 25 to 64</li> </ol>
Dataset: Aura FAT Projects Acquisition Corp (AFARW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Aura FAT Projects Acquisition Corp (AFARU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Aura FAT Projects Acquisition Corp (AFAR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Aura FAT Projects Acquisition Corp (AFARU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Aura FAT Projects Acquisition Corp (AFARW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Aura FAT Projects Acquisition Corp (AFAR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Project Energy Reimagined Acquisition Corp. (PEGR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Project Energy Reimagined Acquisition Corp. (PEGRW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Project Energy Reimagined Acquisition Corp. (PEGRU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Images from the textile study visit at the Yale University Art Gallery, New Haven, USA for the TEX-KR project
<p>These pictures were taken at the Yale University Art Gallery storage facility as part of a study trip in March 2022 to research on the Cambodian textiles in the Yale collection for the EU-funded MSCA TEX-KR project.<br>The objects photographed by Magali An BERTHON are part of Helen Ibbitson Jessup's gift to Yale University Art Gallery.</p> <p>These objects are also accessible on the Yale University Art Gallery database: <a href="https://artgallery.yale.edu/collection?query=jessup&page=0" target="_blank" rel="noopener">Jessup gift textiles Yale</a></p> <p>To find out more: <br><span>"Acquisitions July 1, 2017–June 30, 2018," <em>Yale University Art Gallery Bulletin: Online Supplement</em> (accessed December 1, 2018), 21.</span></p> <p>This repository presents what has been relevant to the TEX-KR project, that is, images of textile details, marks of wear and tears, repairs, and motifs in the resist-dyed ikat technique. </p> <p>To credit the images appropriately:<br>- The photos with BERTHON in the file names are to credit © Magali An Berthon for TEX-KR. <br>- The photos with Yale in the file names come from the Yale University Art Gallery and are to credit ©Yale University Art Gallery with <a href="https://rightsstatements.org/page/UND/1.0/?language=en">Copyright undetermined</a>.</p>
Images from field research in Cambodia - October/November 2022 for the TEX-KR project
<p>These pictures were taken by Magali An Berthon for the EU-funded MSCA TEX-KR project, as part of a period of fieldwork in Phnom Penh, Cambodia in October-November 2022. Following copyright restrictions for this institution, this repository only shows a selection of images from the Tuol Sleng Genocide Museum. It includes images of the public exhibition Remembering S-21 Victims through their Clothes: Textile Preservation at Tuol Sleng Museum; images from a workshop (October 24-27, 2022) on textile research and curatorial practices led by Magali An Berthon and interpreter Moeung Seyha with the textile conservation team at Tuol Sleng Genocide Museum and two conservators from the National Museum of Cambodia; and finally images from the museum permanent galleries, especially the rooms showcasing paintings of artist Vann Nath (1946-2011). </p> <p>All these images must be credited © Magali An Berthon for TEX-KR / courtesy of Tuol Sleng Genocide Museum.</p>
Dataset for the TEX-KR project
<p>Listing of all the different data collected, produced, and published that are available open-access for the EU-funded MSCA TEX-KR project with links to access the data, combined in a table format (in .cvs and .xlsx versions).<br>Please note that the data with anonymised identities, sensitive or personal content and data from institutions and archives protected by copyrights are not included. See the file "List of archives and museums accessed for research for TEX-KR project" for more information, available here: <a href="https://doi.org/10.5281/zenodo.11537111" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11537111</a></p>
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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.