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11,837 results for “Stress;”
Data_Figure4_Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice
<p>Data of Fig4, “Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice”</p> <p>The Dataset contains the original figure 4 as PNG-format (PNTD-D-21-00134R2_Fig4.png). Related information (meta-data) are provided as one file in TXT format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_Fig4_M_1.txt).</p>
Data_Figure3_Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice
<p>Data of Fig3, “Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice”</p> <p>The Dataset contains the original figure 3 as PNG-format (PNTD-D-21-00134R2_Fig3.png), original supp. table S1 (31003A-179400_PNTD-D-21-00134R2_FJ_MW_SS_Echinococcus_F1_M_3.pdf) and raw blots as the original supplemental file S1 (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_F1_M_4.pdf). Corresponding raw data, subsequent data analysis and all further experiment related information (meta-data) from Western Blot analysis provided as one file in TXT format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_F3_2_13-14_M_1.txt) and one file in PDF format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_F3_M_2.pdf).</p>
Data_Figure2_Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice
<p>Data of Fig2, “Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice”</p> <p>The Dataset contains the original figure 2 as PNG-format (PNTD-D-21-00134R2_Fig2.png). Corresponding raw data and subsequent data analysis obtained from Multiplex Luminex Cytokine measurement analysis provided as one files in CSV format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_F2_22_1-2_1.csv). All further experiment related information (meta-data) provided as one file in TXT format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_F2_22_1-2_M_1.txt) and one file in PDF format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_F2_M_2.pdf).</p>
Data_Figure1_Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice
<p>Data of Fig1, “Albendazole reduces endoplasmic reticulum stress induced by Echinococcus multilocularis in mice”</p> <p> </p> <p>The Dataset contains the original figure 1 as PNG-format (PNTD-D-21-00134R2_Fig1.png), original supp. table S1 (31003A-179400_PNTD-D-21-00134R2_FJ_MW_SS_Echinococcus_F1_M_3.pdf), original supp. Figure S1 (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_F1_M_4.pdf) and the original supplemental file S1 (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_F1_M_5.pdf).</p> <p>Corresponding raw data, subsequent data analysis and all further experiment related information (meta-data) from Western Blot analysis provided as one file in TXT format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_F1_2_1-26_M_1.txt) and one file in PDF format (31003A-179400_PNTD-D-21-00134R2 _FJ_MW_SS_Echinococcus_F1_M_2.pdf).</p>
Raw data: Diversity in root architecture of durum wheat at stem elongation under drought stress
<p>Raw data on above and below ground traits from a greenhouse drought stress experiment with six durum wheat varieties performed at Tuscia University, Viterbo, Italy. Measurements were performed at stem elongation stage; recorded traits: plant shoot length, dry weight, number of leaves and tillers; total root length, root surface area, mean diameter, volume, number of tips, forks, crossings, root dry weight and root angle. Root measurments were performed on the whole root system and the topsoil area (upper 5 cm). </p>
Dataset for the publication "Size matters: small biochar particles hardly disintegrate under cryo-stress"
<p>Corresponding dataset for the OA publication "Small biochar particles hardly disintegrate under cryo-stress" published in Geoderma</p>
Predicted stress development during cure and subsequent cooling
<p>Predicted stress development during cure and subsequent cool-down to room temperature. Stress-time curves for a path dependent model and the visco-elastic VisCoR model.<em> </em></p>
Complete datasets and code for "Hungry or angry? Experimental evidence for the effects of food availability on two measures of stress in developing wild raptor nestlings"
<p><strong>Abstract</strong></p> <p>Food shortage challenges the development of nestlings; yet, to cope with this stressor, nestlings can induce stress responses to adjust metabolism or behaviour. Food shortage also enhances the antagonism between siblings, but it remains unclear whether the stress response induced by food shortage operates via the individual nutritional state or via the social environment experienced. In addition, the understanding of these processes is hindered by the fact that effects of food availability often co-vary with other environmental factors. We used a food supplementation experiment to test the effect of food availability on two complementary stress measures, feather corticosterone (CORTf) and Heterophil/Lymphocyte-ratio (H/L) in developing red kite (Milvus milvus) nestlings, a species with competitive brood hierarchy. By statistically controlling for the effect of food supplementation on the nestlings’ body condition, we disentangled the effects of food and ambient temperature on nestlings during development. Experimental food supplementation increased body condition, and both CORTf and H/L were reduced in nestlings of high body condition. Additionally, CORTf decreased with age in non-supplemented nestlings. H/L decreased with age in all nestlings and was lower in supplemented last-hatched nestlings compared to non-supplemented ones. Ambient temperature showed a negative effect on H/L. Our results indicate that food shortage increases the nestlings’ stress levels through both, a reduced food intake affecting nutritional state and the nestlings’ social environment. Thus, food availability in conjunction with ambient temperature shape between- and within nest differences in stress load, which may have carry-over effects on behaviour and performance in further life-history stages.</p>
Olive orchard stress assessment with supplementary Sentinel-2 data
<p>This file contains ground truthing data from olive orchards in Halkidiki, N.Greece and Sentinel-2 data for the noted samples. The samples collected for ground truthing are polygons inside the borders of olive orchards in Halkidiki. Polygons or Sampling units contain information associated with biotic and abiotic stress-related assessments carried out by visual inspection and laboratory analysis of samples with ongoing symptoms. Assessments were recorded as percentages of symptoms observed in the total vegetation surface present in each sampling unit. Symptom percentages were attributed to three possible classes: Verticillium dahliae, Spilocaea oleaginea, Unidentified Stress Factors. These percentage values were used to characterize healthy trees and the incidence of V. dahliae, S. oleaginea and unidentified stress factors (USF) in the sample. USF was used for all other non-classified surveyed symptoms attributed to diseases, pests, or abiotic-related damage. Percentages were summed to compute the total stress present in each sampling unit. “Total stress” refers to the stress incidence value used together with different thresholds to create binary labels for each sample of “stressed” or “not stressed”.</p> <p>The polygon geographical information for each sample were recorded and stored in shapefile format using SW maps, a mobile mapping and GIS app.</p> <p>Sentinel-2 data was paired with each sample using the Feature Info Service (FIS) available from sentinel hub, now upgraded into the Statistical API tool ( & ). This API enables acquisition of statistics calculated based on satellite imagery without having to download images. In the Statistical API request the area of interest, time period, evalscript and statistical measures of interest can be calculated. The requested statistics are returned in the API response.</p> <p>Statistical API deployments:</p> <p><a href="https://creodias.sentinel-hub.com/api/v1/statistics">https://creodias.sentinel-hub.com/api/v1/statistics</a></p> <p><a href="https://services.sentinel-hub.com/api/v1/statistics">https://services.sentinel-hub.com/api/v1/statistics</a></p> <p><a href="https://services-uswest2.sentinel-hub.com/api/v1/statistics">https://services-uswest2.sentinel-hub.com/api/v1/statistics</a></p>
Stressful crystal histories recorded around melt inclusions in volcanic quartz
<p>Magma ascent and eruption are driven by a set of internally and externally generated stresses that act upon the magma. We present microstructural maps around melt inclusions in quartz crystals from six large rhyolitic eruptions using synchrotron Laue X-ray microdiffraction to quantify elastic residual strain and stress. We measure plastic strain using average diffraction peak width and lattice misorientation, highlighting dislocations and subgrain boundaries. Quartz crystals preserve similar and relatively small magnitudes of elastic residual stress (mean 53-135 MPa, median 46-116 MPa) in comparison to the strength of quartz (~10 GPa). However, the distribution of strain in the lattice around inclusions varies between samples. We hypothesize that dislocation and twin systems may be established during compaction of crystal-rich magma, which affects the magnitude and distribution of preserved elastic strains. Given the lack of stress-free haloes around faceted inclusions, we conclude that most residual strain and stress was imparted after inclusion faceting. Fragmentation may be one of the final strain events that superimposes stresses of ~100 MPa across all studied crystals. Overall, volcanic quartz crystals preserve complex, overprinted deformation textures indicating that quartz crystals have prolonged deformation histories throughout storage, fragmentation, and eruption. The data collected using Laue microdiffraction at Lawrence Berkeley National Laboratory Advanced Light Source beamline 12.3.2 are included below as .xlsx files. Data was processed and analyzed using XMAS (Tamura, 2014) and XtalCAMP (Li et al., 2020).</p>
Data supporting: Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem
<p>Data used to obtain the results of the research paper entitled: "Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem", published in the journal "Water Research". The data derives from an outdoor (meso-) cosm experiment in Spain (Imdea Water, Alcala de Henares) in which the transportable temperature and heatwave control device (TENTACLE) was used to investigate the multiple stressors effects of two different climate change scenarios related to temperature (i.e., elevated temperature and reoccurring heatwaves) in combination with the neonicotinoid insecticide imidacloprid.</p>
Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data
<p>This dataset contains valuable information on earthquake events, including their location, magnitude, depth, and focal mechanism solutions. This README file provides detailed explanations of each header in the dataset, as well as information about the files included in the repository.<br><br><em>"Stress regimes in the Himalaya-Karakoram-Tibet, the western part of India-Eurasia collision: stress field implications based on focal mechanism solution data"</em> <strong>(Under Review)</strong><br> </p>
Data archive for Pepper, Bateson and Nettle, 'Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis'
<p>Data archive for the paper 'Telomeres as integrative markers of exposure to stress and adversity: A systematic review and meta-analysis' by Gillian Pepper, Melissa Bateson and Daniel Nettle. This version was uploaded in July 2018 after peer-review in the journal Royal Society Open Science. Compared to earlier version, it incorporates some minor error correction to the dataset, and reflects the revised analyses we performed after peer review. </p> <p>Our protocol and recording guide, which were preregistered on the Open Science Framework in 2016, are also included here, as is our PRISMA diagram.</p> <p>The data file 'unprocessed data' contains the data as extracted from the literature, with associations shown both as provided in the original papers, and converted to correlation coefficients. The algorithms for converting all the different associations to correlation coefficients are described in the flowchart and implemented in the R script 'effect conversion algorithms.r'.</p> <p>The data file 'processed data.csv' is the dataset analysed in the paper. Compared to 'unprocessed data.csv', it excludes: associations from studies of non-human animals; duplicate associations; a small number of associations from studies of medical treatments; and associations considered subparts or subscales of other associations. These exclusions are outlined in Methods section of the paper. In addition, in the processed data file, all correlations are aligned in direction so as to make them comparable (variable 'ValencedEffect'); and all associations are assigned to broad and fine categories.The script 'unprocessed to processed.r' makes the processed data file from the unprocessed one, or you can simply work from the processed one directly. </p> <p>The R script 'telomere metanalysis script RSOS REVISED.r' reproduces the analyses found in the paper.</p> <p>This version of the archive (July 17 2018) contains one small correction in the data files compared to all earlier versions. </p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2018_DiDonFranceesco
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign the magnet S0805 and S0808 were used. The tests were conducted for several bottom configurations (smooth bottom; thin sand d<sub>50</sub>=0.24 mm; coarse sand d<sub>50</sub>=0.56 mm; and mixed sand 70% thin sand and 30% coarse sand). The goals of such tests were: to study the effects of the type of magnets and to carry out a preliminary analysis the ferrofluid behavior over sandy bottom.</p>
A ferrofluid-based sensor to measure bottom shear stresses under currents and waves. Data set: Ferrofluids_Opt_2017_Privitera
<p>The experimental calibration of the system for measuring bed shear stresses under currents was carried out at the Hydraulic Laboratory of the University of Catania.</p> <p>In this experimental campaign magnet type S0805 and a number of magnets equal to 2,3 and 4 were used. The tests were conducted both over a fixed bed (Perspex<sup>©</sup>) and in the presence of mobile beds. The goals of such tests were: to study of the velocity profiles for some fixed and mobile bottoms; to study the effects of the number of magnets on the ferrofluid behavior; preliminary analysis of the bed shear stress over sandy bottom.</p>
EC 5th Framework ENPOWER austenitic edge welded beam contour cut metrology for assessing residual stress
<p>Data from contour method cut surfaces collected from an autogenously edge-welded AISI 316H stainless steel beam produced as part of ENPOWER. Surfaces were generated as part of a slitting experiment and then subsequently measured with a coordinate measurement machine. This dataset forms the basis for <a href="https://doi.org/10.1115/1.4004626">"<em>Slitting and Contour Method Residual Stress Measurements in an Edge Welded Beam</em>" Hosseinzadeh et al. (2012)</a>, and further information on the specimen background and diffraction based results can be found in <a href="https://doi.org/10.1115/PVP2008-61339">"<em>A statistical framework for analysing weld residual stresses for structural integrity assessment</em>" Nadri et al. (2008)</a>.</p> <p>Datasets are in the form of lists of x,y.z coordinates, with one point per line, whitespace delimited in millimeters. The *Perimeter1.txt file coincides with *Surface1.txt, with the former an outline identifying the cut surface periphery, and the latter points lying on the surface. The same format is employed for the other side of the cut.</p>
Processed data from SnoHATS and METCRAX II: anisotropic turbulence and geometry of the Reynolds stress tensor in a streamline coordinate system
<p>Datasets used for the paper 'Interpreting turbulence anisotropy in a streamline coordinate system'. Data from SnoHATS and METCRAX II field campaigns. Datasets include turbulent quantities calculated on 30- and 1-min averaging windows for unstable and stable conditions, with prior linear detrending. Planar fit was used in METCRAX II and double rotation in SnoHATS to rotate the flow into the mean wind direction. Datasets include quantities to characterize the anisotropy of the Reynolds stress tensor, such as eigenvalues, eigenvectors, and the angles between the eigenvectors and the streamline coordinate system, defined in the direction of the mean wind vector.</p> <p>1c: one-component Reynolds stress tensor</p> <p>2c: two-component axisymmetric Reynolds stress tensor</p> <p>3c: isotropic Reynolds stress tensor</p>
Metabolomics data associated with "Glial swip-10 controls systemic mitochondrial function, oxidative stress, and neuronal viability via copper ion homeostasis"
<p>Raw feature tables used for metabolomic analysis of the <em>Caenorhabditis elegans</em> mutant <em>swip-10</em>. The data were generated using liquid chromatography coupled high-resolution mass spectrometry. Two different columns were used: HILIC (+ ESI) and C18 (-ESI), coupled to a Thermo Q-Exactive Orbitrap mass spectrometer. The feature tables were generated using open-source peak peaking and alignment R packages: apLCMS and xMAanalyzer. See more details in the associated manuscript.</p>
Stress-Strain Analysis of Polycrystalline Copper with Goss Texture Using Crystal Plasticity FEM
<pre>Stress-strain analysis of single-phase polycrystalline copper with a Goss texture using a cubic representative volume element (RVE) and periodic boundary conditions, performed with Abaqus through the crystal plasticity finite element method.</pre>
Daily time series of 12 human thermal stress indices in Greece aggregated at commune level (1998-2022)
<p>The overview table of the dataset containing 12<strong> </strong>Human Thermal Stress Indices in Greece (<strong>HTSI-GR</strong>):</p> <div> <table> <tbody> <tr> <th> <p>Heat indices names</p> </th> <th> <p>Description</p> </th> <th> <p>Units</p> </th> <th> <p>Reference</p> </th> <th> <p>Dataset file names</p> </th> </tr> <tr> <td> <p><strong>AT</strong></p> </td> <td> <p>Apparent Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Steadman, R. G. Norms of apparent temperature in Australia. Aust. Met. Mag. 43, 1–16 (1994).</p> </td> <td> <p>AT_min_1998-01-01_2022-12-31.csv, AT_mean_1998-01-01_2022-12-31.csv, AT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>HI</strong></p> </td> <td> <p>Heat Index</p> </td> <td> <p>°C</p> </td> <td> <p>Rothfusz, L.P. Te heat index equation. National Weather Service Technical Attachment. Report No. SR 90–23 (1990).</p> </td> <td> <p>HI_min_1998-01-01_2022-12-31.csv, HI_mean_1998-01-01_2022-12-31.csv, HI_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>Humidex</strong></p> </td> <td> <p>Humidity Index</p> </td> <td> <p>°C</p> </td> <td> <p>Masterson, J. & Richardson, F.A. Humidex: a method of quantifying human discomfort due to excessive heat and humidity (Environment Canada, 1979).</p> </td> <td> <p>Humidex_min_1998-01-01_2022-12-31.csv, Humidex_mean_1998-01-01_2022-12-31.csv, Humidex_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>NET</strong></p> </td> <td> <p>Normal Effective Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Landsberg HE. The assessment of human bioclimate: a limited review of physical parameters. Technical Note No. 123, WMO-No. 331 (World Meteorological Organization, 1972).</p> </td> <td> <p>NET_min_1998-01-01_2022-12-31.csv, NET_mean_1998-01-01_2022-12-31.csv, NET_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WBGT</strong></p> </td> <td> <p>Wet Bulb Globe Temperature (simple)</p> </td> <td> <p>°C</p> </td> <td> <p>Australian Bureau of Meteorology. Thermal comfort observations http://bom.gov.au/info/thermal_stress/ (2020).</p> </td> <td> <p>WBGT_min_1998-01-01_2022-12-31.csv, WBGT_mean_1998-01-01_2022-12-31.csv, WBGT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>thermofeelWBGT</strong></p> </td> <td> <p>Wet Bulb Globe Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).</p> </td> <td> <p>thermofeelWBGT_min_1998-01-01_2022-12-31.csv, thermofeelWBGT_mean_1998-01-01_2022-12-31.csv, thermofeelWBGT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WBT</strong></p> </td> <td> <p>Wet Bulb Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).</p> </td> <td> <p>WBT_min_1998-01-01_2022-12-31.csv, WBT_mean_1998-01-01_2022-12-31.csv, WBT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WCT</strong></p> </td> <td> <p>Wind Chill Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Office of the Federal Coordinator for Meteorological services and supporting research (OFCM). Report on Wind Chill Temperature and extreme heat indices: evaluation and improvement projects. Report No. FCM-R19-2003 (U.S. Office of the Federal Coordinator for Meteorological Services and Supporting Research, 2003).</p> </td> <td> <p>WCT_min_1998-01-01_2022-12-31.csv, WCT_mean_1998-01-01_2022-12-31.csv, WCT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>MRT</strong></p> </td> <td> <p>Mean Radiant Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Weihs, P. et al. The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from measured and observed meteorological data. Int. J. Biometeorol. 56, 537–555 (2012).</p> </td> <td> <p>MRT_min_1998-01-01_2022-12-31.csv, MRT_mean_1998-01-01_2022-12-31.csv, MRT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI</strong></p> </td> <td> <p>Universal Thermal Climate Index (UTCI)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI_min_1998-01-01_2022-12-31.csv, UTCI_mean_1998-01-01_2022-12-31.csv, UTCI_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI2</strong></p> </td> <td> <p>Indoor environment UTCI with 2 parameters (air temperature and humidity)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI2_min_1998-01-01_2022-12-31.csv, UTCI2_mean_1998-01-01_2022-12-31.csv, UTCI2_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI3</strong></p> </td> <td> <p>Outdoor shaded space environment UTCI with 3 parameters (air temperature, humidity, and wind speed)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI3_min_1998-01-01_2022-12-31.csv, UTCI3_mean_1998-01-01_2022-12-31.csv, UTCI3_max_1998-01-01_2022-12-31.csv</p> </td> </tr> </tbody> </table> </div> <p> </p> <p>The overview table of the <strong>HTSI-GR</strong> additional resources folder containing support files and instructions for dataset replication:</p> <table> <tbody> <tr> <th> <p><strong>File names</strong></p> </th> <th> <p>Description</p> </th> </tr> <tr> <td> <p><strong>0. Calculate thermofeelWBGT.py</strong></p> </td> <td> <p>A python script that calculates the Wet Bulb Globe Temperature (WBGT) using the Thermofeel library. Processes NetCDF files containing daily meteorological data and outputs WBGT values in new NetCDF files for each day.</p> </td> </tr> <tr> <td> <p><strong>1. Merge_HI_by_max-mean-min.py</strong></p> </td> <td> <p>A python script that merges daily NetCDF files containing heat index (HI) data into three separate files based on mean, maximum and minimum values for further processing.</p> </td> </tr> <tr> <td> <p><strong>2. QGIS_zonal_statistics.py</strong></p> </td> <td> <p>A python script that calculates zonal statistics for heat indices using QGIS python console. Uses a shapefile of Greek communes and a raster NetCDF file containing daily index values, and outputs daily CSV files with computed statistics.</p> </td> </tr> <tr> <td> <p><strong>3. Zonal_format.py</strong></p> </td> <td> <p>A python script that formats the zonal statistics results into a comprehensive dataset. Combines daily CSV files into a single CSV, fills in missing data using nearest neighbour values, and produces a final formatted dataset.</p> </td> </tr> <tr> <td> <p><strong>Greek Communes.ZIP</strong></p> </td> <td> <p>Contains the shapefile of Greek communes derived from the Hellenic Statistical Authority (ELSTAT) required for zonal statistics calculations. KALCODE and Commune names are linked in the .shp.</p> </td> </tr> <tr> <td> <p><strong>Nearest Neighbour data table.csv</strong></p> </td> <td> <p>A support table to script <strong>3.Zonal</strong><strong>_format.py</strong> that lists communes with missing data and their nearest neighbour with data.</p> </td> </tr> <tr> <td> <p><strong>Read me.txt</strong></p> </td> <td> <p>Provides an overview and instructions for using the scripts. Describes the purpose of each script, lists prerequisites, and provides step-by-step instructions for replicating the dataset.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
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.