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377 results for “Temperature response”
Dataset: Strong isoprene emission response to temperature in tundra vegetation
<p>Dataset used in the article "<em>Strong isoprene emission response to temperature in tundra vegetation</em>" published in the journal <em><strong>Proceedings of the National Academy of Sciences of the USA </strong></em><strong>119: e2118014119</strong> <a href="https://doi.org/10.1073/pnas.2118014119">https://doi.org/10.1073/pnas.2118014119</a></p> <p>The tab-delimited file contains direct surface-atmosphere isoprene fluxes, measured every 30-minutes by Eddy Covariance with a Proton Transfer Reaction -Time of Flight- Mass Spectrometer (PTR-ToF-MS) during the whole growing season at two different tundra sites in Scandinavia (near Abisko, Sweden in 2018, and near Finse, Norway during 2019). It also contains the MEGANv2.1 biogenic model predicted isoprene emissions for the same periods and sites. In addition, air temperature, vegetation surface temperature, and photosynthetic photon flux density (PPFD) measured at the sites are also reported, together with their past 24h and 240h averages (needed to run the MEGAN simulation accounting for the recent past environmental conditions).</p>
Temperature-related mortality exposure-response functions for 854 cities in Europe
<p>This repository contains data to reconstruct the exposure-response functions (ERF) of temperature-related mortality by five 5 age groups in 854 cities in Europe.</p><p>These ERFs have been derived in the study by Masselot et al. 2023, <i>Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe</i>, The Lancet Planetary Health (<a href="https://protect-eu.mimecast.com/s/zqg2Cg204i4ZMYKf3NUKN?domain=doi.org">https://doi.org/10.1016/S2542-5196(23)00023-2</a>). An associated semi-replicable GitHub repository is available at <a href="https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM">https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM</a> to reproduce part of the analysis and the full results, as well as to provide technical details on the derivation of these ERFs.</p><p><strong>Note: </strong>This updated version contains revised data after the correction of an error in the code related to the computation of the age-specific baseline mortality rates. Details about the error can be found in the GitHub repository linked above. This correction only affects the figures of excess mortality (found in the `results.zip` archive) while the ERFs are negligibly affected. The originally published results can be found in V1.0.0 of this repository.</p><p><strong>Extraction of the ERFs</strong></p><p>The ERFs are provided as coefficients of B-spline functions that can be used to reconstruct the ERFs, along with variance-covariance matrices and quantiles from location-specific temperature distributions. The parametrisation associated with these coefficients is a quadratic B-spline (degree 2), with knots located at the 10th, 75th and 90th percentiles of the temperature distribution. In R, the associated basis can be constructed using the <i>dlnm</i> package, with a temperature series <i>x</i>, as follows:</p><blockquote><p>library(dlnm) </p><p>basis <- onebasis(x, fun = "bs", degree = 2, knots = quantile(x, c(.1, .75, .9)))</p></blockquote><p>The main files associated with ERFs are the following:</p><p><i>coefs.csv</i>: The B-spline coefficients for each age group and city.</p><p><i>vcov.csv</i>: The variance-covariance matrix of the coefficients in each city and age group. It is provided here as the lower triangular part of the matrix with names indicating the position of each value (v[row][column]). In R, assuming <i>x</i> is a row of this file, the matrix can be reconstructed using <i>xpndMat(x)</i> after loading the <i>mixmeta</i> package.</p><p><i>coef_simu.csv</i>: 1000 simulations from the distribution of each city and age-specific coefficients. Useful to derive empirical confidence intervals for derived measures such as excess deaths or attributable fractions.</p><p><i>tmean_distribution.csv</i>: The city-specific temperature percentiles representing the distribution of the data derived from the ERA5-Land dataset.</p><p><strong>Health impact assessment results</strong></p><p><i>results.zip</i>: A summary of the results from the health impact assessment reported in the analysis. The dataset includes several impact measures provided in files representing different geographical levels, including city, country and regional level. Different files are also provided for age-group specific or all age results.</p><p><strong>Additional data</strong></p><p>We provide additional data that are useful to reproduce or extend the analysis. Please note that due to restrictive data-sharing agreements for the mortality series, only a part of the code is reproducible. See the <a href="https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM">associated GitHub repository</a> for more details.</p><p><i>metadata.csv</i>: City-specific metadata used to create the ERFs and perform the health impact assessment.</p><p><i>additional_data.zip</i>: contains further data used to replicate the second stage of the analysis and the final health impact assessment. It includes the full city-level daily temperature series (<i>era5series.csv</i>), the detail of extracted metadata for available years (<i>metacityyear.csv</i>), a description of the city-level characteristics (<i>metadesc.csv</i>), and the first-stage ERF coefficients for all available city and age-groups (<i>stage1res.csv</i>). Additionally, the file <i>meta-model.RData</i> contains R object defining the second-stage model that can be used to predict new ERFs. </p>
Dataset for: Wood et al Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article Wood et al (2020) 'Role of sea surface temperature patterns for the Southern hemisphere jet stream response to CO2 forcing' published in Environmental Research Letters (<a href="https://doi.org/10.1088/1748-9326/abce27">https://doi.org/10.1088/1748-9326/abce27</a>).</p> <p>To isolate the role of sea surface temperature (SST) patterns for the Southern Hemisphere circulation response in the abrupt-4xCO2 experiments in CMIP5 and CMIP6, we perform experiments using IGCM4.</p> <p>Five 120-year long simulations were performed following a 5-year spin-up period. In the control simulation (CTRL) we prescribe an annually repeating cycle of climatological monthly mean SSTs using the multi-model mean (MMM) of the ‘ts’ field for the first 200 years of the CMIP5 piControl simulations. Following the CMIP6 protocol (Eyring et al., 2016), greenhouse gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) concentrations are set at preindustrial (year 1850) values and ozone is prescribed as a zonally averaged monthly mean preindustrial climatology.</p> <p>In two perturbation simulations (4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub>) the same boundary conditions are used as in CTRL, but with an annually repeating cycle of climatological monthly mean SST anomalies added using the MMM ‘ts’ field for either the CMIP5 or CMIP6 FAST (years 4-10) responses. In both the 4xCO2-FULL<sub>CMIP5</sub> and 4xCO2-FULL<sub>CMIP6</sub> simulations CO<sub>2</sub> is quadrupled from its preindustrial concentration. This enables a like-for-like comparison with the CMIP5 and CMIP6 abrupt-4xCO2 simulations. Two further perturbation simulations (SHET-only<sub>CMIP5</sub> and SHET-only<sub>CMIP6</sub>) are used to isolate the effect of differences in SH extratropical SST patterns alone. In both simulations CO<sub>2</sub> is kept at preindustrial values, and CTRL SSTs are used with the SST anomalies from either 4xCO2-FULL<sub>CMIP5</sub> or 4xCO2-FULL<sub>CMIP6</sub> added poleward of 18°S. Similarly to McCrystall et al. (2020), the SST anomalies are smoothed between 18°S and 29°S using a cosine squared weighting function with weights of 0 at 18°S and 1 at 29°S. This minimizes sharp gradients in SST across the tropical-extratropical boundary.</p> <p>To enable a clean determination of the effects of SST patterns alone, in all perturbation simulations we keep sea ice fixed at preindustrial values by only adding SST anomalies where the MMM sea ice concentration in the CMIP5 piControl simulations is less than 15% (i.e., equatorward of the sea ice edge). Furthermore, to remove the effect of differences in the change in global mean SST, the SST anomalies in each CMIP model are normalised by the respective global mean SST anomaly and then scaled to a global mean value of 2.2 K (the pooled MMM of CMIP5 and CMIP6). The CMIP6 FAST SST anomalies are added to the CMIP5 preindustrial control SSTs, so as to isolate the effect of differences in the fast SST responses between CMIP5 and CMIP6, and not the effect of differences in the base state.</p>
Locally adaptive temperature response of vegetative growth in Arabidopsis thaliana
<p>We investigated early vegetative growth of natural <em>Arabidopsis thaliana</em> accessions in cold, non-freezing temperatures, similar to temperatures these plants naturally encounter in fall at northern latitudes.</p> <p>Dataset includes:<br> - rosette area measurements over 3 weeks in a 16ºC and a 6ºC treatment. First phenoptying time point is at 14 days after stratification. Measurements were take twice per day.<br> These data are in file <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/rawdata_combined_annotation.txt?versionId=7b707f81-723f-4059-b72b-9dfb9f5ddd2e">rawdata_combined_annotation.txt</a> and go together with <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/outliers.csv?versionId=7287c919-1ed1-4b65-8e25-a75bb312c8fa">outliers.csv</a>, which contains outlying datapoints.</p> <p>- Seed Size measurements.<br> These data are in file <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/seed_size_swedes_lab_updated.csv?versionId=fb739477-862b-45cb-8074-7a1d8e1650bb">seed_size_swedes_lab_updated.csv </a><br> </p> <p>The remainnig files are required to rerun the analyses and recreate figures.<br> Scripts to do so can be found in https://github.com/picla/growth_16C_6C/</p> <p><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/1001genomes-accessions.csv?versionId=ee605038-bd9e-448f-9c96-1a8e980c1755">1001genomes-accessions.csv</a>: lists all accession from the 1001genomes project and their respective subpopulations.</p> <p><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/2029_modified_MN_SH_wc2.0_30s_bilinear.csv?versionId=73c6c2bf-97bd-425f-bf7e-14b5a7cb162f">2029_modified_MN_SH_wc2.0_30s_bilinear.csv</a>: contains climate data for each accession, downloaded and prcocessed from www.worldclim.org</p> <p><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/metabolic_distance.csv?versionId=8456f998-d0dc-4a80-b96f-c0c66c1c9731">metabolic_distance.csv</a>: contains the metabolic distance as calculated in Weiszmann et al. (https://www.biorxiv.org/content/10.1101/2020.09.24.311092v1)</p> <p><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/RNAseq_samples.txt?versionId=6ae1518b-1a70-440d-b0bd-0ccdcb66665e">RNAseq_samples.txt</a>: sample description of the RNA-seq samples (data is downloadable from <a href="http://www.ncbi.nlm.nih.gov/bioproject/807069">http://www.ncbi.nlm.nih.gov/bioproject/807069)</a></p> <p><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/ZAT12_downregulated_table10.csv?versionId=c6f7aa54-cb07-4378-a5a0-de12c6979b9b">ZAT12_downregulated_table10.csv</a>, <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/ZAT12_upregulated_table9.csv?versionId=911a2aa2-f08f-4a20-85de-cfa7c58b73a8">ZAT12_upregulated_table9.csv</a>, <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/CBF_regulon_DOWN_ParkEtAl2015.txt">CBF_regulon_DOWN_ParkEtAl2015.txt</a>, <a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/CBF_regulon_UP_ParkEtAl2015.txt?versionId=f7cacbda-eea6-4ac9-8f71-5ba74e3a67c4">CBF_regulon_UP_ParkEtAl2015.txt, </a><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/CBF2_downregulated_table8.csv">CBF2_downregulated_table8.csv, </a><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/CBF2_upregulated_table7.csv">CBF2_upregulated_table7.csv, </a><a href="https://zenodo.org/api/files/54fc7139-99a8-4e7f-af87-d6754031f5d9/HSFC1_regulon_ParkEtAl2015.txt">HSFC1_regulon_ParkEtAl2015.txt</a>: these files list genes that are involve din cold acclimation as described by Park et al. (https://onlinelibrary.wiley.com/doi/10.1111/tpj.12796), and Vogel et al.(https://onlinelibrary.wiley.com/doi/10.1111/j.1365-313X.2004.02288.x).</p> <p><strong>Material and Methods</strong></p> <p><em><strong>Rosette growth</strong></em></p> <p>Seeds of 249 natural accessions (Suppl. Data 1) of <em>Arabidopsis thaliana</em> described in the 1001 genomes project <a href="https://paperpile.com/c/UDgV3V/DUBI">(1001 Genomes Consortium 2016)</a> were sown on sieved (6 mm) substrate (Einheitserde ED63). Pots were filled with 71.5 g ±1.5 g of soil to assure homogenous packing. The prepared pots were all covered with blue mats <a href="https://paperpile.com/c/UDgV3V/1WUv">(Junker et al. 2014)</a> to enable a robust performance of the high-throughput image analysis algorithm. Seeds were stratified (4 days at 4ºC in darkness) after which they germinated and left to grow for 2 weeks at 21ºC (relative humidity: 55 %; light intensity: 160 µmol m-2 s-1; 14 h light). The temperature treatments were started by transferring the seedlings to either 6 °C or 16 °C. To simulate natural conditions temperatures fluctuated diurnally between 16-21 °C, 0.5-6 °C and 8-16 °C for the 21 °C initial growth conditions and the 6 °C and 16 °C treatments, respectively (<a href="https://docs.google.com/document/d/1Bmr7p24ZMh4yPFVV5oPeH2-T5S41TOFDS3au8JhtwsU/edit#fig_design">Fig.2</a>). Light intensity was kept constant at 160 µmol m-2 s-1 throughout the experiment. Relative humidity was set at 55% but in colder temperatures it rose uncontrollably to maximum 95%. Daylength was 9h during the 16°C and 6°C treatments.</p> <p>Each temperature treatment was repeated in three independent experiments. Five replicate plants were grown for every genotype per experiment. Plants were randomly distributed across the growth chamber with an independent randomisation pattern for each experiment. During the temperature treatments (14 DAS – 35 DAS), plants were photographed twice a day (1 hour. after/before lights switched on/off), using an RGB camera (IDS uEye UI-548xRE-C; 5MP) mounted to a robotic arm. At 35 DAS, whole rosettes were harvested, immediately frozen in liquid nitrogen and stored at -80 °C until further analysis. Rosette areas were extracted from the plant images using Lemnatec OS (LemnaTec GmbH, Aachen, Germany) software.</p> <p><em><strong>Seed size</strong></em></p> <p>We used the seeds produced by <a href="https://paperpile.com/c/UDgV3V/Jqsd">(Kerdaffrec et al. 2016)</a> and limited our measurements to the set of 123 Swedish accessions that overlapped with our growth dataset. After seed stratification for four days at 4ºC in darkness, mother plants were grown for 8 weeks at 4ºC under long-day conditions (16h light; 8h dark) to ensure proper vernalization. Temperature was raised to 21ºC (light) and 16ºC (dark) for flowering and seed ripening. Seeds were kept in darkness at 16ºC and 30% relative humidity, from the harvest until seed size measurements. For each genotype three replicates were pooled and about 200-300 seeds were sprinkled on 12 x 12 cm square, transparent Petri dishes. Image acquisition was performed as described in <a href="https://paperpile.com/c/UDgV3V/WH1e">(Exposito-Alonso et al. 2018)</a> by scanning dishes on a cluster of eight Epson V600 scanners. The resulting 1200 dpi .tiff images were analyzed in the Fiji software. Images were converted to 8-bit binary images and thresholded with the <em>setAutoThreshold("Defaultdark”) </em>command, and seed area was measured in squared mm by running the <em>Analyse Particles</em> command (inclusion parameters: size=0.04-0.25 circularity=0.70-1.00).</p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset: High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes
<p>Dataset used in the article "High emission rates and strong temperature response make boreal wetlands a large source of isoprene and terpenes"</p> <p>The tab-delimited file contains direct surface-atmosphere Volatile Organic Compound fluxes, measured by Eddy Covariance with a Vocus- proton transfer reaction mass spectrometer (Vocus-PTR) at a subarctic fen during 2021. It also contains PAR (Photosynthetic Active Radiation), temperature and flux quality criteria.</p>
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study
<p>Open data for "Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study" Angew. Chem.Int. Ed. 2023,62, e202214032(1 of 11) <a href="https://doi.org/10.1002/anie.202214032">https://doi.org/10.1002/anie.202214032</a></p>
Elevated temperature effects on animal personality: hormonal stress response underlying behavioural differences in the American bullfrog
<p>Dataset for research paper submitted to Animal Behaviour</p> <p>Behavioural_data.csv: raw data for how individual bullfrogs performed in six different trials on an 8-arm maze before and after they were submitted to thermal stress. Behaviours analyzed: movements against the wall of the maze, posture changes, total ambulatory distance (m), and time on the centre of the arena (s).</p> <p>Hormone_data.csv: raw hormone (corticosterone and testosterone) data collected from individual bullfrogs in four different time points: baseline, 12 hours after stress, 24 days after stress, and 47 days after stress.</p> <p>Mass_data.csv: raw mass data collected from individual bullfrogs at the beginning and end of the experiment. SVL = snout-vent length. Body index is calculated as the residuals of a linear regression between mass as dependent variable and SVL as independent variable.</p>
DATASET: Near-Infrared Photothermal Ablation of Biofilms using Protein-Functionalized Gold Nanospheres with a Tunable Temperature Response
<p>This dataset contains the DLS, TEM, temperature data, and other experimental data to accompany the manuscript.</p>
Temperature response of dark respiration from the 1980-82 Eriophorum vaginatum reciprocal transplant experiment along Dalton Highway, Alaska.
These data were collected in July 2011 for tussocks transplanted in 1980-82 in a reciprocal transplant experiment and harvested in 2011. Important variables are garden name, source population, and dark respiration.
Response of psychrophilic plant endosymbionts to experimental temperature increase
<p>Countless uncertainties remain regarding the effects of global warming on biodiversity, including the ability of organisms to adapt and how that will affect obligate symbiotic relationships. The present study aimed to determine the consequences of temperature increase on the adaptation of plant endosymbionts (endophytes) that grow better at low temperatures (psychrophilic). We isolated fungal endophytes from a high-elevation (paramo) endemic plant. Initial growth curves were constructed at different temperatures (4–25°C). Then, experiments were carried in which only the psychrophilic isolates were subjected to repeated increments in temperature. After the experiments, the final growth curves showed significantly slower growth than the initial curves, and some isolates even ceased to grow. While most studies suggest that the distribution of microorganisms will expand as temperatures increase because most of these organisms grow better at 25°C, the results from our experiments demonstrate that psychrophilic fungi were negatively affected by temperature increases. These outcomes raise questions concerning the potential adaptation of beneficial endosymbiotic fungi in the already threatened plant ecosystems. Assessing the consequences of global warming at all trophic levels is urgent because many species on Earth depend on their microbial symbionts for survival. Associations are mutually beneficial, results from our study have implications on the potential survival of these plants and high-elevation ecosystems.</p>
Fig. 2 in Diurnal activity, temperature responses and endothermy in three South American cicadas (Homoptera: Cicadidae: Dorisiana bonaerensis, Quesada gigas and Fidicina mannifera)
Fig. 2. Tb of Dorisiana bonaerensis during a light rain under a heavy overcast on the 21st December 1986. These animals were not exposed to direct solar radiation before the Tb was recorded. Closed circles, represent body temperatures of singing D. bonaerensis; open circles, represent body temperatures of D. bonaerensis engaged in other activities (including no activity). Details as in Fig. 1.
Fig. 3 in Diurnal activity, temperature responses and endothermy in three South American cicadas (Homoptera: Cicadidae: Dorisiana bonaerensis, Quesada gigas and Fidicina mannifera)
Fig. 3. Distribution of Dorisiana bonaerensis Tb as a function of Ta. The slope of the linear regression for "active" animals (closed circles) is significantly different from one, suggesting thermoregulation. The slope of the linear regression for "inactive" animals (open circles) is not significantly different from one, suggesting no thermoregulation is occurring.
Fig. 4 in Diurnal activity, temperature responses and endothermy in three South American cicadas (Homoptera: Cicadidae: Dorisiana bonaerensis, Quesada gigas and Fidicina mannifera)
Fig. 4. Distribution of Tb of as a function of Ta when Dorisiana bonaerensis were not exposed to solar radiation. The slope of the linear regression for " active " animals (closed circles) is significantly different from one, suggesting thermoregulation is occurring with endogenous heat. The slope of the linear regression for " inactive " animals (open circles) is not significantly different from one, suggesting no thermoregulation is occurring when the animals do not generate heat for activity.
Fig. 1 in Diurnal activity, temperature responses and endothermy in three South American cicadas (Homoptera: Cicadidae: Dorisiana bonaerensis, Quesada gigas and Fidicina mannifera)
Fig. 1. Diurnal distribution of Tb of Dorisiana bonaerensis and Quesada gigas on the 13th December 1986. A rainstorm from 07.00 to 08.00 h followed by a misting rain until 09.45 h delayed the start of activity approx. 2 h. Body temperatures are below ambient temperature before activity begins. Body temperatures are elevated at the start of activity (10.00 h) and regulated throughout the day. Body temperatures of quiescent animals decrease with ambient temperature after sunset (20.10 h). Animals participating in the evening chorus have body temperatures in or near the body temperature range measured during the day. Closed circles, Tb of singing D. bonaerensis; open circles, Tb of D. bonaerensis engaged in other activities (including no activity); closed triangles, Tb of singing Q. gigas; open triangles, Tb of Q. gigas performing other activities; solid line, ambient air temperature.
Investigating the Utility of Potato (Solanum tuberosum L.) Canopy Temperature and Leaf Greenness Responses to Water-Restriction for the Improvement of Irrigation Management Data
<p><span>Traits that rapidly respond to stress in important agricultural crops have the potential to provide growers with actionable feedback. E.g., traits that respond to water-restriction could inform irrigation systems by identifying crop water status and requirements in real-time. This would be particularly useful for potato, which is extremely susceptible to drought. We conducted two pot experiments and one field experiment to evaluate the utility of two traits, canopy temperature and leaf greenness, for informing irrigation management in potatoes. We also evaluated the efficacy of Phenospex PlantEye F500 sensors for the remote sensing of leaf greenness. We found that canopy temperatures of the cvs. Maris Piper (Spring Pot Experiment, +0.8°C; Autumn Pot Experiment, +5.3°C) and Désirée (Autumn Pot Experiment, +2.5°C) increased with water-restriction and that the canopy temperatures of Maris Piper return to baseline within three days after the resumption of well-watered conditions. We also found that these responses varied between cultivars, with predictable outcomes based on reported and corroborated drought tolerance ratings. We found inconclusive evidence of leaf greenness increasing due to water-restriction (Spring Pot Experiment, +0.8°C; Autumn Pot Experiment, +5.3°C) and found no evidence that post-drought recovery periods return this trait to baseline. However, leaf greenness measurements from the Phenospex PlantEye F500 were moderately to strongly correlated with SPAD values, suggesting this tool might be useful in the screening of drought-tolerant cultivars in the future.</span></p>
Data from: Annual species' experimental germination responses to light and temperature do not correspond with their microhabitat associations in the field
<p>Annual species have evolved sets of germination cues that are thought to be predictive of the post-germination environment. In naturally patchy environments, germination microsites often vary considerably in the amount of light they receive and in the diurnal temperature fluctuations they experience. However, whether species' differential germination responses to light and temperature are associated with their spatial patterns of occurrence remains largely untested.</p> <p>We surveyed species' occurrences in annual plant communities in 150 quadrats across gradients of canopy cover and litter cover. Nineteen species recorded in this survey were then included in a germination experiment that manipulated (1) Light vs. Dark (12h light or continuous dark) approximating seeds near the soil surface versus those covered by litter and (2) Cold vs. Warm temperature regimes (7/18 °C and 7/24 °C) approximating diurnal fluctuations experienced in shaded versus sun-exposed microsites, respectively.</p> <p>In the germination experiment, six species had highest germination probabilities in the Light treatment (regardless of temperature), five in <em>Cold</em> + <em>Light</em>, one in <em>Warm</em> + <em>Light</em>, two were indifferent to the treatments, and four did not germinate at all. Binomial linear mixed-effects models showed that species' maximum responses to light and temperature did not explain their spatial distributions along canopy cover and litter cover gradients, contrary to theoretical expectations of germination being a strong driver of species' occurrences.</p> <p>Despite variation in species' responses to experimental treatments, no association was found with their field microsite associations. Germination strategies in our system were wider than expected for Mediterranean systems. Our results support that germination cues are not strong drivers of microhabitat associations in this system.</p>
Hippocampal CA1 pyramidal cell membrane voltage recorded in response to noise stimuli at two temperatures.
<p>Electrophysiological recording of the membrane voltage (whole-cell patch-clamp) of three hippocampal CA1 pyramidal cells. Cells are stimulated with a current step chosen to ensure a firing rate around 5-10Hz (amplitude of the current step is given in the filenames) and a noise stimulus with zero mean (Ornstein-Uhlenbeck process with 4ms timescale). Each CSV file contains three columns, the timepoints (saved at 10000Hz), the noise stimulus, and the voltage trace recorded in response to the given noise stimulus. Voltages are recorded at low temperatures (around 32 degrees Celsius) and at high temperatures (around 37 degrees Celsius for cell 1, around 40 degrees Celsius for cells 2 and 3), exact temperatures are given in the filenames. The file metadata.csv contains additional information.</p>
Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use
<p>This repository contains all necessary raw data as well as the R code used to conduct statistical analysis and create figures of the publication<br> <br><strong>Unexpected microbial metabolic responses to elevated temperatures and nitrogen addition in subarctic soils under different land-use</strong></p><p>Julia Schroeder1, Tino Peplau1, Edward Gregorich2, Christoph C. Tebbe3, Christopher Poeplau1</p><p>1 Thünen Institute of Climate-Smart Agriculture, Bundesallee 68, 38116 Braunschweig, Germany<br>2 Research and Development Centre, Central Experimental Farm, Agriculture and Agri-Food Canada, Ottawa, Canada<br>3 Thünen Institute of Biodiversity, Bundesallee 65, 38116 Braunschweig, Germany</p><p>DOI: https://doi.org/10.1007/s10533-022-00943-7 </p><p>This study investigated how subarctic soils under different land use will respond to warming and increasing N availability to allow for better predictions of C cycling under global change. The short-term temperature sensitivity as well as N-input effects on microbial CUE, respiration, growth and turnover were assessed in a one-day incubation experiment according to the 18O-CUE approach. The warming and N response of SOM decomposition were assessed in a 50-days incubation experiment via measurement of cumulative respiration. Both experiments were conducted with the following three treatments: incubation at 10 °C, incubation at 20 °C, and incubation at 20 °C plus N-fertiliser addition at an amendment rate of 100 kg N ha-1. The response to warming or N addition were expressed as response ratios RRT = 20°C/10°C and RRN = 20°C+N/20°C for warming and N response, respectively.</p><p>The R code was developed under R v3.6.3 and adapted to work under version R v.4.1.2.</p><p>The repository includes the following files:</p><ul><li>general_soil_parameters_per_sample.csv - general soil data for each field sample (n=27)</li><li>general_soil_parameters_per_plot.csv - general soil data assessed on pooled replicated field samples (n=9)</li><li>respiration_over_50d_incubation.csv - respiration rate and cumulative respiration for each time-point and laboratory sample over the 50-days incubation</li><li>sample_data.csv - data measured for each laboratory sample (n=81)</li></ul><p> </p><ul><li>Warming_and_nitrogen_response_of_CUE_in_subarctic_soils.Rproj - Rproject (load project to work on provided scripts and data)</li><li>load_data_script.R - loads required data</li><li>absolute_values_script.R - summary of absolute ranges of parameters per land-use type and site</li><li>absolute_linear_mixed_effects_model_script.R - run statistical analysis</li><li>correlograms_absolute_soil_params_script.R - correlation analysis to identify what drives absolute values</li><li>plot_correlations_absolute_soil_params_script.R - plot drivers of CUE and cumulative respiration</li><li>RRT_RRN_calculation_script.R - calculates response ratios</li><li>plot_RRT_RRN_script.R - plot response ratios</li><li>RRT_RRN_linear_mixed_effects_models_script.R - run statistical analysis</li><li>correlograms_RRT_RRN_soil_param_script.R - correlation analysis to identify drivers of response ratios</li><li>plot_correlations_RRT_RRN_soil_params_script.R - plot drivers of response ratios</li><li>RRT_RRN_resprate_cumulresp_over_time_50d_incubation_script.R - plot response ratios over time course</li></ul>
Survival, wet weight and muscle cellular stress responses of Palaemon varians shrimps exposed to combined temperature and salinity variations
<p>Shrimps were exposed to a full factorial experiment combining different temperatures (20, 23 and 26 ºC) and salinities (20, 40). Cellular stress response biomarkers were assessed in the shrimps muscle at several time-points, namely the 7th, 14th, 21st and 28th days of exposure. Wet weight (as proxy for growth) and survival were also assessed during the experiment. These datasets refer to the publication of an article in STOTEN (<a href="https://doi.org/10.1016/j.scitotenv.2022.158732">https://doi.org/10.1016/j.scitotenv.2022.158732</a>).</p> <p>Note: the biomarker dataset contained 3.2% of missing values, which were replaced by group averages for the purpose of the statistical analyses in the article.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.