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30,334 results for “Responsiveness”
DATA (part 2): Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations
<p>Data used for the peer-reviewed article published in the Journal of Climate, titled: "Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations."</p> <p>The publication is available at: https://journals.ametsoc.org/view/journals/clim/aop/JCLI-D-21-0172.1/JCLI-D-21-0172.1.xml.</p> <p>The software developed for the data herein is available at: https://github.com/mariajmolina/climatico.</p>
DATA (part 1): Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations
<p>Data used for the peer-reviewed article published in the Journal of Climate, titled: "Response of Global SSTs and ENSO to the Atlantic and Pacific Meridional Overturning Circulations."</p> <p>The publication is available at: https://journals.ametsoc.org/view/journals/clim/aop/JCLI-D-21-0172.1/JCLI-D-21-0172.1.xml.</p> <p>The software developed for the data herein is available at: https://github.com/mariajmolina/climatico.</p>
Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement (Data)
<p>Origin projects, figures and COMSOL simulation used for the article "Improvement of frequency responses of an in-plane electro-thermal cantilever sensor for real-time measurement", published in <em>Journal of Micromechanics and Microengineering </em>on 05 Nov 2019.</p>
Bark Beetle Behavioral Response to 4-Allylanisole
<p><strong>Experiment 1: </strong>Experiment 1 was established in May 2018 and was a dose response study that evaluated the behavioral response of southern pine beetle (<em>Dendroctonus frontalis</em>), black turpentine beetle (<em>Dendroctonus terebrans</em>), and clerid predator beetles (<em>Thanasimus dubius</em>) to 4-allylanisole when combined with bark beetle pheromone components and a demonstrated host-produced synergist (<em>alpha</em>-pinene). </p> <p>This experiment contained four different collection dates, four sites in Oconee National Forest in Georgia, four traps per site, and four different treatments/lure combinations.</p> <p>Treatments: 1) Control with pheromone components (frontalin and brevicomin) + <em>alpha</em>-pinene; 2) pheromone components + <em>alpha</em>-pinene + low release rate of 4-allylanisole (4.8 mg/day) (LOW4AA); 3) pheromone components + <em>alpha</em>-pinene + medium release rate of 4-allylanisole (48 mg/day) (MED4AA); and 4) pheromone components + <em>alpha</em>-pinene + high release rate of 4-allylanisole (500 mg/day) (HIGH4AA).</p> <p>Variables in data include date of collection (Date), date of collection with dummy codes for each of the four collection times (Time), collection site (Site), trap number (Trap), lure combination (Treatment), number of southern pine beetle (SPB), number of black turpentine beetles (BTB), and number of clerid predator beetles (Clerids).</p> <p> </p> <p><strong>Experiment 2: </strong>Experiment 2 was established in April 2019 and was a dose response study that assessed the capacity of 4-allylanisole to influence beetle response when combined with attractive bark beetle pheromone components in the absence of other host-produced odors.</p> <p>This experiment contained four different collection dates, four sites in Oconee National Forest in Georgia, four traps per site, and four different treatments/lure combinations.</p> <p>Treatments: 1) Control with only pheromone components (frontalin and brevicomin); 2) pheromone components + low release rate of 4-allylanisole (4.8 mg/day) (LOW4AA); 3) pheromone components + medium release rate of 4-allylanisole (48 mg/day) (MED4AA); and 4) pheromone components + high release rate of 4-allylanisole (500 mg/day) (HIGH4AA).</p> <p>Variables in data include date of collection (Date), date of collection with dummy codes for each of the four collection times (Time), collection site (Site), trap number (Trap), lure combination (Treatment), number of southern pine beetle (SPB), number of black turpentine beetles (BTB), and number of clerid predator beetles (Clerids).</p> <p> </p> <p><strong>Experiment 3: </strong>Experiment 3 assessed the efficacy of 4-allylanisole to enhance the standard lure for <em>D. frontalis </em>and whether the presence of <em>alpha</em>- and <em>beta-</em>pinene and 4-allylanisole simultaneously enhances attraction over either host odor component when present singly<em>. </em></p> <p>Treatments: 1) pheromone components (frontaline and <em>endo</em>-brevicomin (A); 2) pheromone components + 4-allylanisole (B); 3) pheromone components + <em>alpha-/beta-</em>pinene (C); 4) pheromone components + <em>alpha-/beta-</em>pinene + 4-allylanisole (D); 5) pheromone components + turpentine sock (E); and 6) pheromone components + turpentine + 4-allylanisole (F).</p> <p>Variable in the data include date of collection (Date), time of collection which is a dummy code for each collection date (Time), number of days before collection (Days), site (Block), trap number (Trap), lure combination (Treatment), number of male southern pine beetles (Male), number of female southern pine beetles (Female), number of southern pine beetles (SPB), and number of clerid predator beetles.</p> <p> </p> <p>Questions regarding this data can be e-mailed to hmunro@uga.edu.</p>
Fifty-year changes of the world ocean's surface layer in response to climate change
<p>This object includes two files. One (GlobalML_Trend_1970_2018.mat) contains the 1970-2018 trends of mixed-layer depth, 0-200 stratification, and pycnocline stratification, as described in: Sallée, J.B., Pellichero, V., Akhoudas, C., Pauthenet, E., Vignes, L., Schmidtko, S., Naveira Garabato, A., Sutherland, P., Kuusela, M., 2020, Fifty-year changes of the world ocean’s surface layer in response to climate change, 591, 592–598, https://doi.org/10.1038/s41586-021-03303-x. The second one (<a href="https://zenodo.org/api/files/15c80d5f-a6f9-4b7b-bde1-12de43732195/GlobalML_Climato_1970_2018.mat">GlobalML_Climato_1970_2018.mat</a>) contains a climatology of mixed-layer depth based on the same methodology as in Sallée et al., 2021 (nature; doi:https://doi.org/10.1038/s41586-021-03303-x) but without regressing a trend. The climatological field is therefore different than in the paper; more robust in region where trends are unphysical (e.g. winter high latitude)</p>
Dataset of plasma non-esterified fatty acid concentrations response of a suckling cow exposed to a feed restriction
<p>The detaset describes the response of a suckler cow in terms of plasma non-esterified fatty-acids (NEFA) concentrations, that was exposed to a feed restriction that consisted in the reduction of net energy requirements by 50%. The dataset has two columns, one for time (t) in days (d) and another column for plasma NEFA concentrations (g·L<sup>-1</sup>). The feed restriction started at t = 1 d and lasted untill t = 4 d. Negative values for t represent the pre-challenge period. </p>
Dataset of milk yield response of a suckling cow exposed to feed restrictions
<p>The detaset describes the response of a suckler cow in terms of milk yield (MY, kg·d<sup>-1</sup>), which was exposed to two feed restrictions (FR) consisting in the reduction of net energy requirements by 50%. The dataset has three columns, the first one is time (t) in days (d), the second one is also time (t2, d) and the third column is MY. The first FR started at t = 0 d and lasted untill t = 3 d, the second FR started at t = 21 d until t = 31 d. The second column for time is obtained from the first column by omitting data points corresponding to restriction and recovery periods. Negative values for t and t2 represent the pre-challenge period. </p>
Passenger kinematics and muscle responses from autonomous braking events from Olafsdottir et al. 2013
<p>This data was extracted by the "authors" of this dataset from the publication</p> <p>Olafsdottir, J. M., Östh, J., Davidsson, J., & Brolin, K. (2013). Passenger kinematics and muscle responses in autonomous braking events with standard and reversible pre‐tensioned restraints. In Ircobi conference proceedings 2013, IRC-13-70, pp. 602-617; <a href="http://www.ircobi.org/wordpress/downloads/irc13/pdf_files/70.pdf">http://www.ircobi.org/wordpress/downloads/irc13/pdf_files/70.pdf</a></p> <p>within the OSCCAR project and made publicly available for future validations of active Human Body Models.</p> <p>If you use this data, please cite the original paper.</p> <p>To use the data, a generic model of the vehicle environment is also openly available: <a href="https://openvt.eu/osccar/precrash_seat_models/safer-ahbm_2-3">https://openvt.eu/osccar/precrash_seat_models/safer-ahbm_2-3 </a></p> <p> </p>
The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh
<p><strong>Note: Downloading the large data file could have a timeout issue. If you cannot directly download it here, please use the following link as a complementary method for getting the data. </strong></p> <p><a href="https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing">https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing</a></p> <p> </p> <p>This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America.</p> <p>In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313): <br> LRV 4306 has 5 acceleration channels, corresponding to the two uni-axial accelerometers inside the train and the three channels of the tri-axial accelerometer on the wheel truck.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5<br> - Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial</em></p> <p><br> LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train.</p> <p><em>- The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8<br> - Corresponding sensor channels: front cabinet uni-axial, back cabinet uni-axial, front tri-axial x, front tri-axial y, front tri-axial z, back tri-axial x, back tri-axial y, back tri-axial z.<br> - x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical.</em></p> <p>The dataset contained in this repository is a condensed version of the original raw data. While the accelerometers on the train were sampled continuously, this dataset contains only those measurements for when the train was actually moving along the track (i.e. not idling at a terminal).</p> <p>The data is stored in binary MAT-files (a MATLAB/Octave data format). These files contain MATLAB objects of the class "pass", which is defined in the file pass.m that can be found in the "code" folder. Specifically, two MAT-files named "obj_dic.mat", and found in the "LRV4306" and "LRV4313" folders, contain the "pass" objects of the two trains, respectively.</p> <p>Each category is described in detail. For more detail on the regions of the track, refer to the 'region.fig' file in this folder. The track was divided into distinct regions so that the data over specific sections of track could be compared. These regions were chosen for two reasons: <br> (1) within a region, the train always followed the same track and <br> (2) there are no tunnels in them so the GPS data is relatively consistent. </p> <p>To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder.</p> <p>A data descriptor paper with details of the data collection process was published.</p> <p>Please cite as</p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Scientific Data, 6, 146. <a href="https://doi.org/10.1038/s41597-019-0148-9">https://doi.org/10.1038/s41597-019-0148-9</a>(2019)</strong></p> <p><strong>Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Zenodo, <a href="https://doi.org/10.5281/zenodo.1432702">https://doi.org/10.5281/zenodo.1432702</a>(2018).</strong></p> <p>For questions or suggestions please e-mail Jingxiao Liu <liujx@stanford.edu></p>
Data for "Phenotypic responses to climate change are significantly dampened in big-brained birds"
<p>Anthropogenic climate change is rapidly altering local environments and threatening biodiversity throughout the world. Although many wildlife responses to this phenomenon appear largely idiosyncratic, a wealth of basic research on this topic is enabling the identification of general patterns across taxa. Here we expand those efforts by investigating how avian responses to climate change are affected by the ability to cope with ecological variation through behavioral flexibility (as measured by relative brain size). After accounting for the effects of phylogenetic uncertainty and interspecific variation in adaptive potential, we confirm that although climate warming is generally correlated with major body size reductions in North American migrants, these responses are significantly weaker in species with larger relative brain sizes. Our findings suggest that cognition can play an important role in organismal responses to global change by actively buffering individuals from the environmental effects of warming temperatures.</p>
Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot
<p>Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot</p> <blockquote> <p><strong>X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil </strong>- [<a href="https://doi.org/10.1002/ppj2.20036">https://doi.org/10.1002/ppj2.20036</a>]</p> </blockquote> <p>The ZIP file contains:</p> <ul> <li><code>MCT1_Rcode.R</code> - Statistics script for candidate single-timepoint experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT1... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>MCT2_Rcode.R</code> - Statistics script for time-series experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.</li> <li><code>MCT2... .csv</code> - 3 CSV data files required by the R script.</li> <li><code>R_RooThProcessing.R</code> - R code for aggregating root traits from RooTh software.</li> <li><code>Modelling folder</code> - OpenSimRoot with model parameters and root data used in manuscript.</li> </ul>
Evolution of Indian Ocean Paleoceanography and South-East Asian Climate during the Miocene in response to change in regional topography
<p>This directory contain outputs of 9 paleo-climate simulations performed with the IPSL-CM5A2 and PISCES-v2 models. The simulations have used in a paper to be published in Nature Geoscience (2022) entitled "Divergent South Asian Monsoon Rainfall and Wind Histories due to topography effects" (Sarr et al.) that investigates the co-evolution of Arabian Sea upwelling and South Asian Monsoon rainfall and winds over the Miocene. It includes simulations with both early Miocene and late Miocene paleogeography.</p> <p>SimulationsOutputs.tar directory contains NetCDF files with ocean, ocean biogeochemistry and atmosphere variables. Data are monthly average over the last 100 years of each simulation.</p> <p>TopoMiocene.tar contains the paleogeographies used for the simulations.</p> <p> More informations on output contents can be find in README_detailsOutput.md document as well as within the Methods section of the publication.</p> <p>PISCES_update.tar contains updated routines for the PISCES-offline model (Aumont et al., 2015) that have been used for the publication. It contains a REAME.md file that explain how to include those updates within the reference code.</p> <p> </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>
Variation in DNA methylation and response to short-term herbivory in Thlaspi arvense
<p>Plant metabolic pathways and gene networks involved in the response to herbivory are well-established, but the impact of epigenetic factors as modulators of those responses is less understood. Here, we studied the role of DNA cytosine methylation on phenotypic responses after short-term herbivory in <em>Thlaspi arvense</em> plants with two contrasting flowering phenotypes. We investigated the effect of experimental demethylation and herbivory treatments following a 2x3 factorial design. First, half the seeds were submerged in a water solution of the demethylating agent 5-azacytidine and the other half only in water, as controls. Then, we assigned control and demethylated plants to three herbivory categories (i) insect herbivory, (ii) artificial herbivory, and (iii) undamaged plants. The effects of the demethylation and herbivory treatments were assessed by quantifying genome-wide global DNA cytosine methylation, concentration of leaf glucosinolates, final stem biomass, fruit and seed production, and seed size. For most of the plant traits analysed, individuals from the two plant-types responded differently. In late-flowering plants, global DNA methylation did not differ between control and demethylated plants but it was significantly reduced by herbivory. Conversely, in early-flowering plants, demethylation at seed stage was still evident in leaf genomes of reproductive individuals whereas herbivory did not affect their global DNA methylation.</p>
Dataset of Spatial Room Impulse Responses in a Variable Acoustics Room for Six Degrees-of-Freedom Rendering and Analysis
<p>Room acoustics measurements are used in many areas of audio research, from physical acoustics modelling and speech enhancement to virtual reality applications. This paper documents the technical specifications and choices made in the measurement of a dataset of spatial room impulse responses (SRIRs) in a variable acoustics room. Two spherical microphone arrays are used: the mh Acoustics Eigenmike em32 and the Zylia ZM-1, capable of up to fourth- and third-order Ambisonic capture, respectively. The dataset consists of three source and seven receiver positions, repeated with five configurations of the room's acoustics with varying levels of reverberation. Possible applications of the dataset include six degrees-of-freedom (6DoF) analysis and rendering, SRIR interpolation methods, and spatial dereverberation techniques. </p> <p>Accompanying paper on details of the dataset measurement: https://arxiv.org/abs/2111.11882</p> <p>Changelog:</p> <p>V 1.0 - Initial version.<br> V 1.1 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition and ListenerPosition z data corrected. Changed ListenerPosition and SourcePosition x data so that it follows the convention of origin in bottom left corner (rather than the previous bottom right). Fixed the swapped x and y labels in 6dof_source_and_receiver_positions.pdf.</p>
Diversity in the Expressed Genomic Host Response to Myocardial Infarction - Validation Dataset
<p>External validation was performed by separately hierarchically clustering 934 patients with STEMI in an independent cohort[1] into 2 groups (232 and 702 individuals) based on Illumina HT12v4-profiled PBMC expression (median time 21 hour between cardiac catheterization and blood sampling). Probes with most variable expression intensities (SD≥0.5, 216 probes, excluding ribosomal genes) were used. From the 20 most differentially expressed genes in the discovery cohort described in the manuscript Toma et al. 2022 [2], 19 were available in the validation cohort.</p> <p>Column names include the Illumina identifyer and the mapped gene name as used in the discovery cohort. Values are log2-transformed, quantile-normalized, batch-corrected values, see also [1] for methodological details.</p> <p>Acknowledgement:</p> <p>This work is supported by LIFE – Leipzig Research Center for Civilization Diseases, Universität Leipzig. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERDF) and by means of the Free State of Saxony within the framework of the excellence initiative.</p> <p> </p> <p>1) Teren A, Kirsten H, Beutner F, Scholz M, Holdt LM, Teupser D, Gutberlet M, Thiery J, Schuler G, Eitel I. Alteration of multiple leukocyte gene expression networks is linked with magnetic resonance markers of prognosis after acute st-elevation myocardial infarction. <em>Scientific Reports</em>. 2017;7:41705</p> <p>2) Toma A, dos Santos C, Burzyńska B, Góra M, Kiliszek M, Stickle N, Kirsten H, Kosyakovsky L, Wang B, van Diepen S, Epelman S, Szekely Y, Marshall JC, Billia F, Lawler PR (2022), Diversity in the Expressed Genomic Host Response to Myocardial Infarction, submitted.</p>
Data analysis source code and measurement data of chemosensor salt-responsiveness
<p>Dataset with measurement data of salt-responsiveness of macrocyclic chemosensors and the Python source code for data analysis.</p>
Multi-omic machine learning predictor of breast cancer therapy response
<p>H&E slides used in the training dataset described in "Multi-omic machine learning predictor of breast cancer therapy response" published in <em>Nature</em>: <a href="https://www.nature.com/articles/s41586-021-04278-5">https://www.nature.com/articles/s41586-021-04278-5</a></p> <p>Metadata associated with these images also included in file Slide metadata.xlsx</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Survey responses
<p>The spreadsheets in the present dataset (CSV format) include the anonymised responses to our online survey of signatories of the Joint Statement on open research and data sharing. Responses have been split into quantitative responses (i.e., closed survey questions) and qualitative responses (i.e., free text survey questions).</p> <p>This data has been used to inform our final report, which is available in our <a href="https://zenodo.org/communities/data-sharing-in-public-health-emergencies">Zenodo Project Community</a>.</p>
Temnothorax rugatulus ants do not change their nest walls in response to environmental humidity
<p><strong>Overview</strong></p> <p>Data used for manuscript: <em>Temnothorax rugatulus</em> ants do not change their nest walls in response to environmental humidity</p> <p> </p> <p><strong>Structure of the data</strong></p> <p>SupplementalHygrometerDatabase.csv</p> <p>Raw hygrometer data that is used to calculate the average environmental humidity and temperature for each Trial:Salt combination</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>TrialNumber: Sequential trial number (1-4) that is NOT unique for each colony - see "Trial"</li> <li>Salt: Saturated salt solution used</li> <li>"Date Time, GMT-07:00": Date and time of each observation</li> <li>Temp: Temperature in celcius</li> <li>RH: Relative humidity (%)</li> <li>Trial: The trial number for each individual colony, each colony underwent two trials</li> </ul> <p>HumidityExperimentalDatabase.csv</p> <p>Raw experimental data with nest features and colony size</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>Trial: The trial number for each individual colony, each colony underwent two trials</li> <li>TrialNumber: Sequential trial number (1-4) that is NOT unique for each colony - see "Trial"</li> <li>Day: The day in the experimental timeline (always 10, but days 1 and 5 were captured and not considered)</li> <li>Area: Area of the built nest wall (mm<sup>2</sup>)</li> <li>Length: Length of the built nest wall (mm)</li> <li>Nest.Area: Area of the internal nest space (mm<sup>2</sup>)</li> <li>HumLevel: Whether the colony started with a higher or lower relative humidity (High/Low)</li> <li>Number.Ant: The number of workers in the colony</li> <li>Number.Brood: The number of brood in the colony</li> <li>Number.Queens: The number of brood in the colony</li> <li>Salt: Saturated salt solution used</li> <li>SubstrateISide: Substrate I placement in the container from the perspective of looking out from the nest entrance</li> <li>StartWtI: The initial weight (g) of the available substrate I building nest wall material</li> <li>UsedWtI: The weight (g) of the available substrate I building nest wall material following the experimental building phase</li> <li>StartWtII: The initial weight (g) of the available substrate II building nest wall material</li> <li>UsedWtII: The weight (g) of the available substrate II building nest wall material following the experimental building phase</li> <li>CollWallWt: The weight (g) of the experimental nest wall that each colony built</li> </ul> <p>HumidMortalityRaw.csv</p> <p>Raw experimental data with proportion of workers and brood dead after each colony underwent Trial 1</p> <ul> <li>Colony: Unique experimental colony identifiers</li> <li>WorkerDeath: Proportion of workers that died</li> <li>BroodDeath: Proportion of brood that died</li> <li>TrialNumber: Sequential trial number (1-4) that is NOT unique for each colony</li> </ul> <p>PorosityComparisonRaw.csv</p> <p>Data used for comparing the porosities of each experimental substrate, experimentally built walls, and collected <em>Temnothorax rugatulus</em> walls</p> <p>Porosity is the percentage of void space in compact substrate - PoreVolume/TotalVolume</p> <ul> <li>SubstrateID: A unique identifier for each substrate replicate</li> <li>Trial: The trial number for each individual colony, each colony underwent two trials (only applicable for experimentally built walls)</li> <li>SubCategory: The type of substrate (Sub I, Sub II, Built, Natural)</li> <li>TotalVolume: The combined pore (void space in compact substrate grains) and soil volume (ml) of a substrate</li> <li>PoreVolume: The void space in between compact substrate grains (ml)</li> </ul>
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.