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3,101 results for “Historical”
Man's #3 costume from a historical engraving
The 3D model presents a digital look of a historical costume from a Russian lubok print "Domostroj" dating to the 18th century. The costume was made for a theatrical performance «Indecent is decent» (Ivanovo State Museum of Local History named after D.G. Burylin, October 30, 2019 – March 1, 2020). The 3D reconstruction was made by using historical block patterns, 2D scans of textile materials and 3d scans of contemporary actors. Anthroscan, Clo3D, SubstancePainter and Unreal Engine 4 software programs were applied. The authors of the 3D model are: Victor Kuzmichev https://www.researchgate.net/profile/Viktor-Kuzmichev Aleksei Moskvin https://sketchfab.com/alekseimoskvin1 https://independent.academia.edu/AlekseiMoskvin Mariia Moskvina https://sketchfab.com/mariia89 https://independent.academia.edu/MariiaMoskvina The authors thank Tatiana Ponomareva and Anastasia Kornilovich for their important contribution to the project. Source: Objaverse 1.0 / Sketchfab
Bursa Mursel Village - Historical Fountain
The fountain was built in 1715 by Ali Ağa, one of the trustees who managed the foundation of Murat I. Source: Objaverse 1.0 / Sketchfab
Historic Grave of Sailor
Harry Osmon, 1913, buried at Cape Fish Company (today-Cape Canaveral Air Force Station) in 1913. Source: Objaverse 1.0 / Sketchfab
Paramoshka's costume from historical engraving
The 3D model presents a digital look of a historical costume from a Russian lubok print "Paramoshka and Avoska" dating to the 18th century. The costume was made for a theatrical performance «Indecent is decent» (Ivanovo State Museum of Local History named after D.G. Burylin, October 30, 2019 – March 1, 2020). The 3D reconstruction was made by using historical block patterns, 2D scans of textile materials and 3d scans of contemporary actors. Anthroscan, Clo3D, SubstancePainter and Unreal Engine 4 software programs were applied. The authors of the 3D model are: Victor Kuzmichev https://www.researchgate.net/profile/Viktor-Kuzmichev Aleksei Moskvin https://sketchfab.com/alekseimoskvin1 https://independent.academia.edu/AlekseiMoskvin Mariia Moskvina https://sketchfab.com/mariia89 https://independent.academia.edu/MariiaMoskvina The authors thank Tatiana Ponomareva and Anastasia Kornilovich for their important contribution to the project. Source: Objaverse 1.0 / Sketchfab
Nioza Historical Road-2600k-pts
Nioza Historical Road -- TokyoStreetView [[ 2.6m points decimated down from 24m ]] This is a medium-density #pointcloud made from 4K video footage originally produced and published by TokyoStreetView here: https://www.youtube.com/watch?v=avct9RMjOvc We thank TSV for their wonderful videos!! This signal was analyzed by Organic. Additional data may be found here: https://www.youtube.com/watch?v=u3J_XpFz_xs and https://www.youtube.com/watch?v=hQoTGqKke6w and https://www.instagram.com/p/B1i0KXEHLRs/ and https://www.instagram.com/p/B1jDSBPn6-u/ Tools used: youtube-dl, ffmpeg, python, colmap, houdini https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
Historic Staircase at The John Rylands Library
The main staircase leads up to the beautiful Historic Reading Room and provides stunning views of the Gothic architecture of the Entrance Hall below and the Lantern Gallery above. This is a temporary test of a 360 panoramic Jpeg from a Ricoh ThetaS camera. Image displayed on a Spherical object from Blender. Thanks to @LOUIS for the spherical template and instructions Source: Objaverse 1.0 / Sketchfab
Figure 7 in The death adder Acanthophis antarcticus (Shaw & Nodder, 1802) in Victoria: historical records and contemporary uncertainty
Figure 7. First edition of the Dangerous Snakes of Victoria poster, produced in 1877.
Figure 4 in The death adder Acanthophis antarcticus (Shaw & Nodder, 1802) in Victoria: historical records and contemporary uncertainty
Figure 4. Dorsal perspective of the head and neck (including neck wound) of specimen D4349.
Model output from historical and future scenarios related to 'Carbon Dioxide Removal: Tradeoffs and Lags'
Open the record for dataset details and reuse information.
Data from: Evaluating the impact of historical climate and early human groups in the Araucaria Forest of Eastern South America
<p>It has been hypothesized that the Araucaria Forest in Southern Brazil underwent expansions in the past, driven either by human groups or by climate fluctuations of the Holocene and Pleistocene. Fossil pollen records of the Paraná Pine (<em>Araucaria angustifolia</em>), a dominant tree in that forest, provide some insights into when those may have occurred. Still, the timing of those expansions has never been estimated. To infer past range shifts and shed light on their main drivers, we employed next-generation DNA sequencing (ddRADseq), machine learning, and a comprehensive database of fossil pollen records in a study of historical demographic inference and paleo-distribution modeling of the Paraná Pine. We found that <em>A. angustifolia</em> comprises two populations expanding at different times: one in the Mantiqueira mountain chain, and the other in the southern Brazilian plateau. The Southern population began to expand during the Last Glacial Period ~70kya, long before human arrival in South America. Still, genetic analyses support that humans later impacted this population, resulting in lower genetic diversity, higher inbreeding, and high levels of gene flow over large distances with a weak pattern of isolation by distance. It is possible this resulted from human influence on seed dispersal and germination on the Southern Brazilian plateau. The Mantiqueira population, in contrast, expanded only recently (~3kya). This timing coincides with Holocene climatic changes and human settlements established further south, although, to date, there is little archeological evidence of human impact in the Mantiqueira. In addition, multitemporal species distribution models built from a combination of present-day and pollen records infer range expansion of the Araucaria Forest during glacial times until the cold humid HS1 event (~16kya), when the forest was most widespread, with no evidence of glacial refugia. The combination of genomic and spatial analyses suggests that both human and climatic controls played a role in the dynamics of the Araucaria Forest.</p>
Data from: Historical field records reveal habitat as an ecological correlate of locomotor phenotypic diversity in the radiation of Neotropical Geophagini fishes
<p>Phenotypic macroevolutionary studies provide insight into how ecological processes shape biodiversity. However, the complexity of phenotype-ecology relationships underscores the importance of also validating phenotype-based ecological inference with direct evidence of resource use. Unfortunately, macroevolutionary scale ecological studies are often hindered by the challenges of acquiring taxonomically and spatially representative ecological data for large and widely distributed clades. The South American cichlid fish tribe Geophagini represents a continentally distributed radiation whose early locomotor morphological divergence suggests habitat as one ecological correlate of diversification, but an association between locomotor traits and habitat preference has not been corroborated. Field notes accumulated over decades of collecting across South America provide first-hand environmental records that can be mined for habitat data in support of macroevolutionary ecological research. In this study, we applied a newly developed method to transform descriptive field note information into quantitative habitat data, and used it to assess habitat preference and its relationship to locomotor morphology in Geophagini. Field note-derived data shed light on geophagine habitat use patterns and reinforced habitat as an ecological correlate of locomotor morphological diversity. Our work emphasizes the rich data potential of museum collections, including often overlooked material such as field notes, for evolutionary and ecological research.</p>
Vlachs historical transhumance trail route
<p>The Vlachs are an ethnolinguistic community who spoke a dialect of the neo-Latin language, and are distinguished by a way of living that was based primary on pastoral transhumance. For centuries, groups of Vlachs have traveled long-distance transhumant movement from lowland winter pastures in the Ionian coast to upland summer pastures in the mountain areas of south-east Albania. Once arrived in the summer pastures, the Vlachs build their yearly encampment, consisting of several familiar dwellings, known as ‘kalive’. During their movement, they set up daily camps of simple woolen tents, which were normally arranged close to inhabited areas.<br>Part of this movement includes the route that passes from Dhëmbel Mt pass to Mt Gramoz. The entire track lasts around 4 to 5 days, marching a distance of c. 90 km. Some of the suggested camping spots correspond with the daily camps that Vlachs set up during their treks. The camps suggested in this trial are situated in the vicinity of villages of Sheper, Ogren, Orgockë, and Skorovot. This journey offers the visitor the possibility to travel along one of the Vlachs seasonal movement. Additionally, it allows the travelers to look at various attractions of the area, such as are the villages vernacular architecture, Medieval churches, as well as to enjoy the astonishing nature, as yet unstained, of this corner of south-east Albania.<br>As walking along the route, you are recommended to stopover the Albturist Ecocamp, in Përmet, where you can visit a reconstructed Vlach historical dwelling, the kalive, and learn more about the history of the Vlach community.<br>This trail is of average difficulty due to mountain terrain and remoteness of the area. You can find available natural water springs along the track, with the exception of the hiking path from Frashër to Shëmtir (Çlirim).<br>Certain locations may not have mobile network signal or have poor connectivity.<br>Also be aware of shepherd dogs accompanying large flocks of sheep and goats. If it happens you encounter them on the way, stay calm and try to call and ask help to shepherds.</p>
Anthropogenic Climate Forcers Historical Emissions Analysis
<p>[DRAFT - final version May 2024] Input data necessary to run the jupyter notebook code script located at the <a href="https://github.com/bwalkowiak/Anthropogenic-Climate-Forcer">Anthropogenic Climate Forcer GitHub</a></p> <p>Files include:</p> <ul> <li>Final Emissions Data for major GHGs and air pollutants from <a href="../records/10904361">Community Emissions Data System (CEDS)</a> version v_2024_04_01. Emissions pre-1970 are from v_2021_04_21 fuel data with sector partitioning from latest version 1970 allocation. <ul> <li>Emissions data files by emission species (SO2, NOx, BC, OC, NH3, NMVOC, CO, CO2, CH4, N2O), country, and sector produced by the April-01-2024 release of CEDS.</li> <li>See the <a href="https://github.com/JGCRI/CEDS/">CEDS GitHub</a> site for details including journal paper reference information and any known issues with this data.</li> </ul> </li> <li> HC (CFC, HCFC) emissions data from <a href="https://csl.noaa.gov/assessments/ozone/2022/">WMO Ozone 2022 Report</a>, HFC and HCFC emission data from <a href="https://edgar.jrc.ec.europa.eu/emissions_data_and_maps">EDGAR - Emissions Database for Global Atmospheric Research</a>, specifically EDGAR v8.0, with other species from <a href="https://ozone.unep.org/countries/data-table">UNEP Ozone Depleting Substance databases</a>]. HC emissions sectoral fractions calculated using UNFCCC AFEAS database and UNEP ODS. See HC Section for specific details.</li> <li><a href="https://github.com/IPCC-WG1/Chapter-6_Fig12_22_24">IPCC AR6 Ch6</a> ouput data for effective radiative forcing (ERF) and temperture response (del_T) over the calculation time period (1750-2022)</li> <li><a href="https://www.iea.org/data-and-statistics/data-product/world-energy-balances">IEA World Energy Balances 2023</a> data used to allocate sectoral emissions data</li> <li>Species lifetime from IPCC AR6</li> <li>CMIP model thermal reponse parameters from <a href="https://gmd.copernicus.org/articles/14/3007/2021/">Leach et al., (2021)</a></li> </ul>
Replication file for: "Understanding money using historical evidence"
<p>Replication file for:</p> <p>Brzezinski, A., Palma, N., and Velde, François (2024). Understanding money using historical evidence. <strong>Annual Review of Economics</strong></p>
A domesticated phage suppresses competitors in historical and modern metapopulations of pathogenic bacteria
<p>Bacteria have repeatedly repurposed the machinery of their viruses (bacteriophage) to kill strains of neighboring bacteria. These bacteriophage (phage)-derived elements are strain-specific in their killing activity, and this specificity has been proposed to drive bacterial population dynamics. Direct evidence of the impact of phage-derived elements on natural populations over time is limited. Here we identified viral sequences in a metapopulation of wild plant-associated <em>Pseudomonas</em> spp. genomes. We discovered that the most abundant viral cluster does not encode an intact phage but instead encodes a tailocin: a phage-derived element that bacteria use to kill bacterial competitors. Each pathogenic <em>Pseudomonas</em> sp. strain carries one of a few distinct tailocin variants, which target variable polysaccharides in the outer membrane of co-occurring pathogenic strains. Analysis of historic herbarium samples from the past 170 years revealed that the same tailocin and receptor variants have persisted in the <em>Pseudomonas</em> populations for at least two centuries, suggesting the continued use of a defined set of tailocin haplotypes and receptors. These results indicate that tailocin genetic diversity can be mined to develop targeted "tailocin cocktails" for microbial control.</p>
HexaLCSeg: Hexagon-based Historical Land Cover Benchmark Dataset
<p>This dataset is a research outcome of a European Research Council, Proof of Concept Grant funded (Grant Number 101100837, A GeoAI-based Land Use Land Cover Segmentation Process to Analyse and Predict Rural Depopulation, Agricultural Land Abandonment, and Deforestation in Bulgaria and Turkey, 1940-2040, <a href="https://cordis.europa.eu/project/id/101100837">GeoAI_LULC_Seg</a>) project.</p> <p>We introduce a new benchmark dataset derived from very high-resolution historical Hexagon (KH-9) reconnaissance satellite images for use in deep learning-based image segmentation tasks. Our dataset comprises high-resolution monochromatic Hexagon images from the 1970s and 1980s covering Turkish and Bulgarian territories, encompassing a large geographic area.</p> <p>Land cover (LC) classes used in this study:<br>Our dataset is inspired by the European Space Agency (ESA) WorldCover project and includes eight LC classes and related RGB codes were set for each class but we adjusted the 0-pixel value as no data and replaced the 0 values with 1 in the ESA RGB code palette. Additionally, a new sub-class for the trees, named Permanent Cropland is defined and its RGB code was set to 1-207-117. This class is important to differentiate permanent fruit trees from other trees, specifically crucial for past agricultural mapping purposes.</p> <p>The HexaLCSeg dataset comprises eight panchromatic images accompanied by corresponding 3-channel RGB Ground Truth Masks, all with 8-bit radiometric resolution and a spatial resolution of 1 meter. The dataset is organized into a total of 10,000 patches, each sized at 256x256 pixels. We split our dataset into 70% training (7000 patches), 15% validation (1500 patches), and 15% testing (1500 patches).</p> <p>Methodology:<br>In our study, we employed the geographic object-based image analysis (GEOBIA) approach to generate accurate land cover (LC) maps, which serve as the ground truth masks for our dataset.</p> <p>For deep learning-based image segmentation, we employed a total of 9 CNN models, implementing U-Net++ and DeepLabv3+ segmentation architectures with different hyperparameters, paired with SE-ResNeXt50 backbone that pre-trained with weight values from the 2012 ILSVRC ImageNet dataset.</p> <p>Models, metric results and weights:</p> <table> <tbody> <tr> <th>Model No</th> <th>Architecture</th> <th>Loss Function</th> <th>Augmentation</th> <th>Loss</th> <th>Accuracy</th> <th>IoU</th> <th>F-1 Score</th> <th>Precision</th> <th>Recall</th> </tr> </tbody> <tbody> <tr> <td>Model 1</td> <td>U-Net++</td> <td>Focal Loss</td> <td>No Augmentation</td> <td>0.1252</td> <td>0.9734</td> <td>0.8052</td> <td>0.8804</td> <td>0.8805</td> <td>0.8803</td> </tr> <tr> <td>Model 2</td> <td>U-Net++</td> <td>Focal Loss</td> <td>Horizontal Flip</td> <td>0.1253</td> <td>0.9728</td> <td>0.8008</td> <td>0.8776</td> <td>0.8778</td> <td>0.8774</td> </tr> <tr> <td>Model 3</td> <td>DeepLabv3+</td> <td>Focal Loss</td> <td>No Augmentation</td> <td>0.1255</td> <td>0.9720</td> <td>0.7959</td> <td>0.8739</td> <td>0.8744</td> <td>0.8734</td> </tr> <tr> <td>Model 4</td> <td>U-Net++</td> <td>Focal Loss</td> <td>Random BC</td> <td>0.1256</td> <td>0.9717</td> <td>0.7938</td> <td>0.8725</td> <td>0.8727</td> <td>0.8723</td> </tr> <tr> <td>Model 5</td> <td>DeepLabv3+</td> <td>Dice Loss</td> <td>Horizontal Flip</td> <td>0.1292</td> <td>0.9714</td> <td>0.7928</td> <td>0.8714</td> <td>0.8717</td> <td>0.8711</td> </tr> <tr> <td>Model 6</td> <td>DeepLabv3+</td> <td>Dice Loss</td> <td>No Augmentation</td> <td>0.1307</td> <td>0.9711</td> <td>0.7906</td> <td>0.8699</td> <td>0.8702</td> <td>0.8697</td> </tr> <tr> <td>Model 7</td> <td>DeepLabv3+</td> <td>Focal Loss</td> <td>Horizontal Flip</td> <td>0.1257</td> <td>0.9711</td> <td>0.7897</td> <td>0.8698</td> <td>0.8704</td> <td>0.8692</td> </tr> <tr> <td>Model 8</td> <td>DeepLabv3+</td> <td>Focal Loss</td> <td>Random BC</td> <td>0.1259</td> <td>0.9704</td> <td>0.7871</td> <td>0.8667</td> <td>0.8673</td> <td>0.8662</td> </tr> <tr> <td>Model 9</td> <td>DeepLabv3+</td> <td>Dice Loss</td> <td>Random BC</td> <td>0.1401</td> <td>0.9691</td> <td>0.7793</td> <td>0.8608</td> <td>0.8612</td> <td>0.8604</td> </tr> </tbody> </table> <p> </p> <p>System-specific notes and configuration:</p> <p>The code was implemented in Python (3.10) Programming Language.</p> <p>- torch == 2.1.2<br>- segmentation-models-pytorch == 0.3.3<br>- Albumentations == 1.4.0</p> <p>Apart from main data science libraries, RS-specific libraries such as GDAL, rasterio, and tifffile are also required.</p> <p>Citation:<br>Please kindly cite our paper if this code and the dataset used in the study are useful for your research.</p> <p>Elif Sertel et al., “HexaLCSeg: A Historical Benchmark Dataset from Hexagon Satellite Images for Land Cover Segmentation [Software and Data Sets],” <em>IEEE Geoscience and Remote Sensing Magazine</em> 12, no. 3 (September 2024): 197–206, <a href="https://doi.org/10.1109/MGRS.2024.3394248">https://doi.org/10.1109/MGRS.2024.3394248</a>.</p>
Resources for "BMF CP20: Cultural-historical knowledge, social skills, and school events for the young generation in the globalization era"
<p>The current study has two objectives:<br><br></p> <ol> <li>Examine whether discussions with parents about cultural-historical knowledge and social skills and participation in multicultural school events improve children’s and youth’s tolerance toward different viewpoints;</li> <li>Examine whether discussions with parents about cultural-historical knowledge and social skills and participation in multicultural school events improve children’s and youth’s general inclusiveness.</li> </ol>
Global Water Quality Data: Historical Nitrogen and Phosphorus Loads and Concentrations Simulated by CoSWAT-WQ v1.0
<p>Output data on global TN and TP water quality and scripts for the historical period (1995 - 2016) generated by CoSWAT-WQ v1.0. Please check the readme file for data description. The data includes river flow, , nitrogen and Phosphorus loads and concetrations at monthly timestep.</p>
GODEEEP-hydro - Historical and projected power system ready hydropower data for the United States
<p>This dataset contains monthly and weekly hydropower generation and generation constraints (min, max, daily range) for over 1,400 hydropower plants in the conterminous United States. The dataset includes a historical period (1982-2019) and a future period (2020-2099) with 4 future warming scenarios.</p> <p>For more information please refer to Bracken et al. 2024, godeeep_hydro: Historical and projected power system ready hydropower data for the United States, in prep, or refer to the Github repository https://github.com/GODEEEP/tgw-hydro</p> <h3>Data description</h3> <p>The dataset contains 10 data files with the naming convention <code><scenario>_<monthly/weekly>.csv</code> where scenario can be either "historical", "rcp45cooler", "rcp45hotter", "rcp85cooler", or "rcp85hotter". "monthly" or "weekly" refers to the timestep of the data.</p> <ul> <li>datetime - The datetime stamp of the current timestep</li> <li>eia_id - An integer value with the EIA plant code that represents the facility</li> <li>plant - The name of the facility according to the EIA</li> <li>power_predicted_mwh - The total energy gnerated over the period in MWh, aka the energy target</li> <li>n_hours - The number of hours in the period, useful for converting between power and energy</li> <li>p_avg - Average power generation for the period</li> <li>p_max - Maximum allowable power generation for the period</li> <li>p_min - Minimum allowable power generation for the period</li> <li>ador - Average daily operational range for any given day in the period</li> <li>scenario - The name of the scenario, either "historical", "rcp45cooler", "rcp45hotter", "rcp85cooler", or "rcp85hotter"</li> </ul> <p>Also included is the metadata file <code>godeeep_hydro_plants.csv</code> which contains metadata for each hydropower plant that is included in the dataset. Each row in this file refers to one hydropower facility. This file has the following columns:</p> <ul> <li>eia_id - An integer value with the EIA plant code that represents the facility</li> <li>plant - The name of the facility according to the EIA</li> <li>mode - Either "Storage" or "RoR" indicating if the plant is primarily operated as a storage or ron-of-river facility</li> <li>state - Two letter U.S. state name</li> <li>lat - Latitude of the facility</li> <li>lon - Longitude of the facility</li> <li>nameplate_capacity - The total nameplate capacity of the facility according to the EIA</li> <li>nerc_region - Four letter code for the NERC region of the facility</li> <li>ba - Balacing authority of the facility</li> <li>max_param - Value of the a_{max} parameter used to derive p_max</li> <li>min_param - Value of the a_{min} parameter used to derive p_min</li> <li>ador_param - Value of the a_{ador} parameter used to derive ador</li> <li>huc2 - Two digit hydrologic unit code (HUC) which contains the facility</li> </ul> <h3>Funding</h3> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p>Corresponding author:</p> <p>Cameron Bracken, cameron.bracken@pnnl.gov</p> <p>v1.1.0 - Update file naming convention</p>
FORSITE-Clim Europe: European-wide climate indicators for historical periods and climate projections at high resolution
<h2>Overview</h2> <p>This meteorological data set consists of climatologies (climate indicators) on 30-year average basis for Europe and covers two historical periods as well as two periods for three selected climate scenarios with a high spatial resolution of less than 1 km. The two 30-year periods provided for the observations allow the analysis of the climate change that has already happened. </p> <p><strong>Resolution</strong>: 30x30 arcsec<br><strong>Projection</strong>: EPSG 4326<br><strong>Extent for historical data</strong>: 10.67°W – 47.67°E, 33.68°N – 71.33°N<br><strong>Extent for scenario data</strong>: 10.67°W – <em>39.33°E</em>, 33.68°N – 71.33°N<br><strong>Periods for historical data</strong>: 1961-1990 and 1991-2020<br><strong>Periods for scenario data</strong>: 2036-2065 and 2071-2100<br><strong>Format:</strong> GeoTIFF</p> <p><strong>List of climatologies (climate indicators) </strong> </p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>Short name</strong></p> </td> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Unit</strong></p> </td> <td> <p><strong>Yearly (Y) or monthly (M)<br></strong></p> </td> </tr> <tr> <td> <p><em>1</em></p> </td> <td> <p>tasmin</p> </td> <td> <p>Average daily minimum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>2</em></p> </td> <td> <p>tasmax</p> </td> <td> <p>Average daily maximum temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>3</em></p> </td> <td> <p>tas</p> </td> <td> <p>Average temperature</p> </td> <td> <p>Arithmetic mean</p> </td> <td> <p>°C</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>4</em></p> </td> <td> <p>tas_warmest_month</p> </td> <td> <p>Average temperature mean in the warmest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>5</em></p> </td> <td> <p>tas_coldest_month</p> </td> <td> <p>Average temperature mean in the coldest month</p> </td> <td> <p>Calculation of the mean temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>6</em></p> </td> <td> <p>tasmin_coldest_month</p> </td> <td> <p>Average temperature minimum in the coldest month</p> </td> <td> <p>Calculation of the mean minimum temperature over the climate period for all months and then selection of the lowest value for the coldest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>7</em></p> </td> <td> <p>tasmax_warmest_month</p> </td> <td> <p>Average temperature maximum in the warmest month</p> </td> <td> <p>Calculation of the mean maximum temperature over the climate period for all months and then selection of the highest value for the warmest month</p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>10</em></p> </td> <td> <p>GSL</p> </td> <td> <p>Average length of the growing season</p> </td> <td> <p>The growing season is the duration in days of the longest continuous period of days with an average temperature of at least 5°C. However, an earlier or later period of such warm days is included in the growing season if it lasts longer than the sum of all intervening cooler days</p> </td> <td> <p>days</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>12</em></p> </td> <td> <p>GDD</p> </td> <td> <p>Average Growing Degree Days per year above 5°C</p> </td> <td> <p>Σ(Tmean – 5°C) per year. </p> </td> <td> <p>°C</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>13</em></p> </td> <td> <p>FD_first</p> </td> <td> <p>Average date of the first frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>14</em></p> </td> <td> <p>FD_last</p> </td> <td> <p>Average date of the last frost occurrence</p> </td> <td> <p>Frost is defined by a temperature of 0°C at a height of 2 meters (arithmetic mean). Years without frost are excluded from the calculation of the mean. If no frost occurs at all, the value is indeterminate</p> </td> <td> <p>day of year</p> </td> <td> <p>Y</p> </td> </tr> <tr> <td> <p><em>20</em></p> </td> <td> <p>GLO_hori</p> </td> <td> <p>Average sum of global radiation</p> </td> <td> <p> </p> </td> <td> <p>kWh</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>33</em></p> </td> <td> <p>pr</p> </td> <td> <p>Average precipitation sum</p> </td> <td> <p> </p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>39</em></p> </td> <td> <p>ET0</p> </td> <td> <p>Average annual potential evapotranspiration</p> </td> <td> <p>Calculation according to FAO Penman-Monteith: fao.org/3/X0490E/x0490e08.htm</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> <tr> <td> <p><em>40</em></p> </td> <td> <p>WBAL</p> </td> <td> <p>Average climatic water balance</p> </td> <td> <p>Precipitation minus potential evapotranspiration</p> </td> <td> <p>mm</p> </td> <td> <p>Y, M</p> </td> </tr> </tbody> </table> <h2>Data sources</h2> <p>The raw historical data is a combination or extension of daily CHELSA (Climatologies at high resolution for the earth’s land surface areas) with ERA5-Land to fully cover 1961-2020. </p> <ul> <li>CHELSA-W5E5 v1.0 (https://doi.org/10.5194/essd-15-2445-2023) for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), maximum temperature (tasmax) and minimum temperature (tasmin) for the period 1979-2016</li> <li>CHELSA V2.1 for climatological average monthly wind speed (sfcWind_01, ..., sfcWind_12) and for climatological mean vapor pressure deficit (vpd_01, ..., vpd_12)</li> <li>ERA5-Land for daily variables precipitation (pr), global radiation (rsds), mean temperature (tas), dew point (tds), and wind speed (sfcWind) for the period 1961-2020</li> <li><em>v2.0: WorldClim version 2.1 for climatological monthly minimum, maximum, and average temperatures.</em></li> </ul> <p><strong>Climate models from EURO-CORDEX </strong>(doi.org/10.1007/s10113-013-0499-2<strong>)</strong>:</p> <ul> <li>MPI-M-MPI-ESM-LR_rcp45_r1i1p1_CLMcom-CCLM4-8-17</li> <li>MPI-M-MPI-ESM-LR_rcp85_r1i1p1_CLMcom-CCLM4-8-17</li> <li>ICHEC-EC-EARTH_rcp85_r12i1p1_SMHI-RCA4</li> </ul>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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