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5,506 results for “variability”
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) precipitation and transpiration variable outputs (precipitation, total, potential and actual evapotranspiration), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of precipitation, total evapotranspiration and actual evapotranspiration Outputs of snow water equivalent, snow melt, and runoff, as well as the model configuration file, as well as model inputs are archived separately on the Environmental Data Initiative.
SBC LTER: Reef: California kelp canopy and environmental variable dynamics
This dataset contains time series of kelp canopy area for giant kelp, Macrocystis pyrifera, and bull kelp, Nereocystis luetkeana, and canopy biomass of giant kelp derived from Landsat satellite imagery along with time series of environmental variables known to be associated with kelp forest dynamics. Data are organized into a single NetCDF file and kelp canopy data are co-located with the nearest environmental data point.
Data to accompany "Exploring the Complexity of Ocean Acidification: An Ecosystem Comparison of Coastal pH Variability"
The goal of this project was to create a science lesson at the middle school level with data illustrating the variablilty of pH and temperature in nature. The lesson allows students to interpret pH data and gain knowledge of abiotic and biotic processes that contribute to pH differences between tropical, temperate and polar marine ecosystems. Students use what they have learned to interpret data from a 'mystery' site and develop a hypothesis as to which ecosystem the unknown data was collected from. The full cirriculum is described in Kapsenberg, L, AL Kelley, LA Francis, and SB Raskin (2015) Exploring the complexity of ocean acidification: an ecosystem comparison of coastal pH variability. Science Scope 39(3): 51-60. doi: 10.2505/4/ss15_039_03_51 This dataset contains an Excel workbook with five worksheets: A "Readme" tab with citations for additional reading and data attribution.Three time-series of pH and temperature from coastal locations: a temperate kelp forest in the Santa Barbara Channel (Spring season, 2 months, 20 min interval). a coral reef near the island of Moorea, Tahiti (Summer season, 3 weeks, 30 min interval), and the polar ocean of Cape Evans, McMurdo Bay, Antarctica (Spring/Summer, 6 months, bi-hourly interval). A fourth worksheet contains data for a "mystery site" for student examination. Data for the study were contributed by the Santa Barbara Coastal LTER, Moorea Coral Reef LTER and the G. Hofmann lab (University of California, Santa Barbara). Additional pH data are available from both LTER sites. Antarctic data in this dataset are also available from NSF's Biological and Chemical Oceanographic Data Management Office (see Cape Evans Mooring, 2012).
Rodent declines track regional climate variability in North American drylands
Regional long-term monitoring can enhance the detection of biodiversity declines associated with climate change, improving future projections by reducing reliance on space-for-time substitution and increasing scalability. Rodents are diverse and important consumers in drylands, which cover ~45% of Earth’s land surface and face increasingly drier and more variable climates. Here, we analyzed abundance data for 22 rodent species across grassland, shrubland, ecotone, and woodland habitats in the southwestern USA. We captured two time series: 1995-2006 and 2004-2013 that coincide with phases of the Pacific Decadal Oscillation (PDO), which influences drought in southwestern North America. Regionally, rodent species diversity declined 20-35%, with greater losses during the later time period. Abundance also declined regionally, but only during 2004-2013, with losses of ~5% of animals captured. During the first time series (PDO wet phase), plant productivity outranked climate variables as the best regional predictor of rodent abundance for 70% of taxa, whereas during the second period (dry phase), climate best explained rodent abundance for 60% of taxa. Temporal dynamics in rodent diversity and abundance differed spatially among habitats and sites, with the largest declines in woodlands and shrublands of central New Mexico and Colorado. Both habitat type and phase of the PDO modulated which species were winners or losers under increasing drought and amplified interannual variability in drought. Fewer taxa were significant winners (18%) than losers (30%) under drought, but the identities of winners and losers differed among habitats for 70% of taxa. Our results suggest that the sensitivities of rodent species to climate contributed to regional declines in diversity and abundance during 1995 - 2013. Whether these changes portend future declines in drought-sensitive consumers in the southwestern USA will depend on the climate during the next major phase of the PDO.
The human Voice Areas: spatial organisation and inter-individual variability in temporal and extra-temporal cortices
Open the record for dataset details and reuse information.
Sediment Properties Drive Spatial Variability of Potential Methane Production and Oxidation in Small Streams
<ul> <li>This dataset contains 20 data tables (Fig.2.csv, Fig.3.csv, data_PLS_stream-main-stem.csv, Fig.4_a.csv, data_PLS_subcatch.-stream-sect.csv, Fig.4_b.csv, Fig.5.csv, Fig.S1.csv, Fig.S2.csv, Fig.S3.csv, Fig.S4.csv, Fig.S5_a.csv, Fig.S5_b.csv, Fig.S6_a.csv, Fig.S6_b.csv, Fig.S6_c.csv, Fig.S6_d.csv, TableS1_data-adjustment.csv, TableS1_lit-data.csv, PMO_surface-water.csv), we separated our data tables in the respective figures/analyses presented in our paper</li> <li>We added the units to each column title of each respective data table</li> <li>Please see "Metadata.pdf" and our paper (same title as the dataset) for more information</li> </ul> <p> </p>
alexandru-uta/cloud_network_variability_data: Cloud Network Variability Data
<p># Dataset on Cloud Network Variability</p> <p>This is the dataset obtained while benchmarking public and private clouds for the following article:</p> <p>### Alexandru Uta, Alexandru Custura, Dmitry Duplyakin, Ivo Jimenez, Jan Rellermeyer, Carlos Maltzahn, Robert Ricci, Alexandru Iosup. Is Big Data Performance Reproducible in Modern Cloud Networks?. In proceedings of 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2020, February 25-27, Santa Clara, USA.</p> <p>The dataset contains bandwidth variability data for:<br> - Amazon EC2<br> - Google Compute Engine<br> - Microsoft Azure (limited data)<br> - Scaleway (limited data)<br> - SURFsara HPCCloud</p> <p>The dataset contains TCP latency (RTT) data for:<br> - Amazon EC2<br> - Google Compute Engine</p> <p>The dataset contains data regarding token bucket sizes (explained in depth in the aforementioned article) for Amazon EC2.</p> <p>Please note that the full archive is over 3.5 GB of data, made up of hundreds of thousands of small files. This is why we decided to archive the data, which we then split into smaller files, to accommodate for the Github max file size of 100 MB.</p> <p>===== DETAILS ON THE ARCHIVE FORMAT =====</p> <p><strong>1. The Bandwidth Variability data:</strong><br> - archived in the bandwidth_variability_data.tar.bz2.parta, .partb, .partc<br> - after unpacking the archive, the data is split in directories per machine type, and experiment type, for example:<br> --- perfvar-aws-m5xlarge-fullspeed: contains iperf3 output files for continuous communication between 2 m5.xlarge VMs in Amazon EC2.<br> --- perfvar-google-4cpu-bursty-5s30s: contains iperf3 output files for bursty communication (5 seconds communication, 30 seconds break; repeat)</p> <p><strong>2. The Latency Variability data:</strong><br> - archived in the file latency_study.tar.bz2<br> - after unpacking the archive, the directories contain TCP dump RTT data and iperf3 outputs</p> <p><strong>3. The Token Bucket AWS study:</strong><br> - archived in the file token_bucket_study.tar.bz2<br> - contains files of form INSTANCE_TYPE-REGION-TIMESTAMP.{bw, raw, tb}<br> - files with extension .raw contain raw iperf3 client output<br> - files with extension .bw contain bandwidth samples taken at 1 second intervals from the iperf3 utility<br> - files with extension .tb contain a triple of form <time_to_token_bucket_depletion, high_token_bucket_bandwidth, low_token_bucket_bandwidth><br> - example of a file name: c5.2xlarge-us-west-1-1567733720.raw</p> <p> </p>
Alpine ice sheet glacial cycle simulations aggregated variables
<p>These data contain time-integrated and otherwise time-reduced glacier model output variables.</p> <p><strong>Reference:</strong></p> <ul> <li>Seguinot, J., Ivy-Ochs, S., Jouvet, G., Huss, M., Funk, M., and Preusser, F.: Modelling last glacial cycle ice dynamics in the Alps, <em>The Cryosphere</em>, 12, 3265-3285, doi:<a href="https://doi.org/10.5194/tc-12-3265-2018">10.5194/tc-12-3265-2018</a>, 2018.</li> </ul> <p><strong>File names:</strong></p> <pre><code>alpcyc.{1km|2km}.{epic|grip|md01}.{cp|pp}.agg.nc</code></pre> <ul> <li>Horizontal resolution: <ul> <li><em>1km</em>: 1 km horizontal resolution</li> <li><em>2km</em>: 2 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li><em>epic</em>: EPICA ice core temperature forcing</li> <li><em>grip</em>: GRIP ice core temperature forcing</li> <li><em>md01</em>: MD01-2444 core temperature forcing</li> </ul> </li> <li>Precipitation forcing: <ul> <li><em>cp</em>: constant precipitation</li> <li><em>pp</em>: palaeo-precipitation reduction</li> </ul> </li> </ul> <p><strong>Data format:</strong></p> <p>The data use compressed netCDF format. For quick inspection I recommend ncview. Conversion to GeoTIFF (and other GIS formats) can be achieved with e.g. GDAL::</p> <pre><code>gdal_translate NETCDF:filename.nc:variable filename.variable.tif</code></pre> <p>The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. To convert all variables to separate files use:</p> <pre><code>gdalinfo $filename | grep NETCDF | cut -d '=' -f 2 | egrep -v '(lat|lon|time_bounds)' | while read sub do gdal_translate $sub ${filename%.nc}.${sub##*:}.tif done</code></pre> <p>Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata. Also see <a href="https://doi.org/10.5281/zenodo.1423175">continuous</a> variables.</p> <p><strong>Changelog:</strong></p> <ul> <li>Version 2: <ul> <li>Add age coordinate in kiloyears (ka) before present.</li> <li>Use ka units for covertime, deglacage and maxthkage.</li> </ul> </li> <li>Version 1: <ul> <li>Initial version</li> </ul> </li> </ul>
Datos curados de variables de producción por departamento colombiano (2015-2018).
<p>El siguiente dataset contiene información sobre el rendimiento (t/ha), producción (t), área cosechada (ha), área sembrada (ha) y temperatura (ºC) de cada departamento colombiano por semestre desde 2015 hasta el primer semestre de 2018.</p>
Quantification of 3D spatial correlations between state variables and distances to the grain boundary network in full-field crystal plasticity spectral method simulations
<p>This repository provides supplementary material to our paper: <a href="https://doi.org/10.1088/1361-651X/ab7f8c">https://doi.org/10.1088/1361-651X/ab7f8c</a></p> <p><strong>DAMASKPhenoPowerLaw75x75x75TestCase.zip</strong><br> An exemplary DAMASK simulation and corresponding output, generated from DAMASK v2.0.3. We used this to debug more productively the implementation of the post-processing tools. Furthermore we employed this simulation in the paper to identify why the graph clustering grain reconstruction method in many cases fuses neighboring grains in similar orientation.</p> <p><strong>DAMASKPhenoPowerLaw256x256x256ProductionRun.zip</strong><br> All input to run the DAMASK simulation that we discussed in the paper.</p> <p><strong>DAMASKPDTSettings256x256x256ProductionRun.zip</strong><br> All damaskpdt settings files to execute the individual post-processing studies of the paper.</p> <p><strong>DAMASKPDTSlurmSubmissionScripts256x256x256ProductionRun.zip</strong><br> All SLURM scripts we used to execute the compilation of damaskpdt and post-processing on TALOS.</p> <p><strong>DAMASKPDTSlurmLogs256x256x256ProductionRun.zip</strong><br> All logs from the SLURM job management system from the individual post-processing runs.</p> <p><strong>DAMASKPDTSourceCode_USedForAnalyticalDistanceToVoronoiCellFacets.zip</strong><br> The source code to the tool we developed during the revision process of our paper to verify the methods<br> via computing analytically exact distances to the facets of the Poisson-Voronoi tessellation from the<br> DAMASK microstructure instantiation.<br> <br> <strong>DAMASKPDTSourceCode_Production.zip</strong><br> The source code we used to post-process all results from the DAMASK simulations.</p> <p><strong>GitHub repository:</strong><br> https://github.com/mkuehbach/damaskpdt</p>
Estimate of the atmospherically-forced contribution to sea surface height variability based on altimetric observations
<p>This repository contains the estimate of the atmospherically-forced contribution to sea level variability described in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>, and derived from the Ssalto/Duacs altimeter products produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (<a href="http://www.marine.copernicus.eu">http://www.marine.copernicus.eu</a>).</p> <p>The files contain successive 5-day averages of sea level anomaly, with the same global coverage and 0.25° grid as the Ssalto/Duacs altimeter products. The estimate is created using a spatial bandpass filter, with cutoff scales of ~1.5° and 10.5°. Zeros in the mask file indicate regions in which it has not been possible to evaluate the quality of the estimate.</p> <p>The cutoff scales applied to the altimetry data were determined through analysis of output from the OceaniC Chaos – ImPacts, strUcture, predicTability (Penduff et al, 2014) experiment, comprising a 50-member ensemble of ocean-sea ice model hindcasts with 0.25° horizontal resolution (<a href="http://doi.org/10.5194/gmd-10-1091-2017">Bessières et al., 2017</a>). The spatiotemporal coherence between the model-based estimates of the atmospherically-forced (ensemble mean) and total simulated sea surface height signals was analysed, and found to exhibit distinct partitioning between the atmospherically-forced and intrinsic contributions in a spatial (but not temporal) sense, thus suggesting that meaningful estimation of the two components can be achieved based on simple spatial filtering. Verification of the method using the model data indicates good accuracy, with a global mean correlation of 0.9 between the estimate based on spatial filtering and the ensemble mean sea surface height. Full details of the methodology and verification may be found in <a href="http://doi.org/10.1016/j.pocean.2020.102314">Close et al, 2020</a>.</p> <p>----</p> <p><strong>References</strong>:</p> <p>Bessières, L., Leroux, S., Brankart, J.-M., Molines, J.-M., Moine, M.-P., Bouttier, P.-A., Penduff, T., Terray, L., Barnier, B., and Sérazin, G., 2017. Development of a probabilistic ocean modelling system based on NEMO 3.5: application at eddying resolution, Geosci. Model Dev., 10, 1091–1106, <a href="https://doi.org/10.5194/gmd-10-1091-2017">doi: 10.5194/gmd-10-1091-2017</a>.</p> <p>Close, S., Penduff, T., Speich, S. and Molines J.-M., 2020. A means of estimating the intrinsic and atmospherically-forced contributions to sea surface height variability applied to altimetric observations. Progr. Oceanogr. <a href="https://doi.org/10.1016/j.pocean.2020.102314">doi: 10.1016/j.pocean.2020.102314</a></p> <p>Penduff, T., Barnier, B. , Terray, L., Bessières, L., Sérazin, G., Grégorio, S., Brankart, J., Moine, M., Molines, J., Brasseur, P., 2014. Ensembles of eddying ocean simulations for climate, CLIVAR Exchanges, Special Issue on High Resolution Ocean Climate Modelling, 19.</p>
The Cassandra retrotransposon landscape in sugar beet (Beta vulgaris): Recombination and re-shuffling leads to a high structural variability
<p>Here we provide supplementary data for our study of non-autonomous Cassandra terminal-repeat retrotransposons in miniature (TRIMs) in sugar beet and related genomes.</p> <p>Cassandra sequences are distributed across the plant kingdom and share a unique feature: conserved 5S rDNA promoter motifs within their long terminal repeats (LTRs). This dataset contains two multiple sequence alignments and a sequence list of tandemly-arranged (TA) Cassandra sequences in FASTA format. Alignments cover LTR and internal regions of all Amaranthaceae Cassandra (Ama-Cassandra) from our study. This includes Cassandra full-length sequences from <em>B. vulgaris</em> (Ama_Cassandra_Beet_full-length) and <em>C. quinoa</em> (Ama_Cassandra_Quinoa_full-length). Sequence names include information on host plant, subfamily classification, localisation (scaffold), start and stop position, a Lab-unique TE identifier and sequence orientation. For the tandemly-arranged Cassandra sequences from sugar beet, we provide a sequence list of twelve sequences (Ama_Cassandra_TA_Beet_list). Here, sequence names refer to TA copy number, host, localisation (scaffold), start and stop position, a Lab-unique TE identifier and sequence orientation.</p> <p>All sequences were identified in the recent genome assemblys of <em>B. vulgaris</em> (RefBeet1.2; Dohm <em>et al</em>. 2014) and <em>C. quinoa</em> (ASM168347v1; Jarvis <em>et al</em>., 2017).</p>
Centennial clonal stability of asexual Daphnia in Greenland lakes despite climate variability
<p><strong>Daphnia_microsatellite_data_Dane_etal.2020.csv: </strong></p> <p><strong>Microsatellite genotypes from three study lakes (SS4, SS1381, and SS1590) in the Kangerlussuaq area, West Greenland. </strong>Microsatellite loci were amplified in single, 12.5 µl multiplex reactions (Type-it PCR kit, Qiagen Inc, Valencia, CA, USA), using an Eppendorf Nexus Thermal Cycler with thermal cycle conditions recommended in the Type-it PCR kit manual. Ten microsatellite primers representing genome-wide loci were used for genotyping; details in (Colbourne et al. 2004; Frisch et al., 2014). Two primers (Dp90, Dp377) failed to amplify in a consistent manner and were therefore excluded from further analysis. Amplified microsatellites were genotyped on an Applied Biosystems 3730 genetic analyser. We used the microsatellite plugin for Geneious 7.0.6 (https://www.geneious.com) for peak calling and binning. Called peaks were visually inspected and manually adjusted when necessary. </p> <p><strong>SS4_sediment.core_data_Fig2_Dane_et_al2020.xlsx</strong>: </p> <p><strong>Information on various parameters of sediment cores collected in Lake SS4, Kangerlussuq area, West Greenland. </strong>Data used in Dane et al. 2020, Figure 2 (panels B and C) are derived from two sediment cores: one for fluorescence (section at 0.5 cm intervals, <em>Depth</em>) and one for <em>Daphnia </em>ephippia analyses (1-cm intervals). Percentage organic matter content (loss-on-ignition at 550 °C,<em>OM%</em>) was used to correlate the two cores to each other and to a previously-dated sediment core (see Dane et al. 2020, Methods). The fluorescence derived parameter Parafac component C2 was used as an indicator of the abundance of purple sulphur bacteria. The organic carbon burial rate (<em>OC AR</em>, g C m–2 yr–1) was also calculated for this core (see Anderson et al. 2019). The <em>Daphnia</em> core was used for the microsatellite analyses and the accumulation rate of ephippia (<em>ephippia AR</em>) at the core site was estimated.</p> <p>For further details please see associated publication in Ecology and Evolution.</p> <p> </p>
SuperWASP Variable Stars: Classifying Light Curves Using Citizen Science
<p>Table of 301 previously unidentified SuperWASP stellar variables and related characteristics, not including rotators and unknown variables. The variable type has been decided by citizen scientists through the SuperWASP Variable Stars Zooniverse project. The types and periods of each object have been assessed by the authors to correct for mis-classifications; whilst they have been corrected as much as possible, some types periods remain best guesses. All periods have an uncertainty of 0.1%.</p>
Global dataset for evaluating impact of topographic factors on hydrologic response to climate variability
<p>The dataset contained here was used to document the biomes in the world that show high sensitivity in their hydrologic response to interannual changes in climatic forcing during the 2001-2016 period, while evaluating the role of major topoclimatic factors in modulating these responses. To do this we generated a hydrologic sensitivity index (HSi). HSi evaluates the absolute ratio between the changes of the climatic conditions (dryness index, DI) and hydrologic response (evaporative index, EI<sub>R</sub>) between consecutive years (e.g. HSi= |∆ EI<sub>R</sub> /∆ DI|). HSi was computed for every successive pair of years from 2001 to 2016. A total of 15 HSi maps were obtained representing the HSi for each consecutive pair of years. For each map, where HSi >1, regions are classified as <strong><em>Sensitive</em></strong> and for HSi ≤1, <strong><em>Resilient</em></strong>. To provide a synthesis of the general trend of global hydrologic sensitivity, we display the frequency of HSi, showing the recurrence of HSi >1 for every non-ocean location with a range of 0 (low frequency) to 15 (high frequency). Regions where frequency HSi≥7 are considered highly recurring and as such are deemed as the most hydrologically sensitive. </p> <p><strong>This dataset includes the code and raster data to evaluate the effect of the topography on HSi to plot the average frequency HSi for all elevations, aspects, and slope steepness against latitudinal change.</strong> We used global digital elevation models (DEMS) from the Shuttle Radar Topography Mission (SRTM) data (90 m resolution; version 4, for latitudes < 60◦ N and GTOPO30 (1◦ resolution; http://lta.cr.usgs.gov/GTOPO30) for latitudes > 60◦ N. Slope and aspect maps were derived from the DEMs using standard GIS-based methods in ArcMap 10.7.Elevation range used is [0,7000] meters above sea level (m.a.s.l), aspect (N, NE, E, SE, S, SW, W, NW) specifically above slope values greater than 10-degrees (no flat areas used), and slope [0,90] degrees.</p> <p><strong>Contents:</strong></p> <ul> <li>1 MATLAB with the code ready to use</li> <li>1 PDF file with the same code</li> <li>27 geotiff files for elevation (dem#1-27.tif)</li> <li>27 geotiff files for frequency HSi (freq#1-27.tif) </li> </ul> <p>Note: the following files of slope and aspect could not upload in repository due to exceedance in storage limit: 50MG. The DEM files must be run in ArcMap using slope and aspect tool to produce the following files with the following names.</p> <ul> <li>27 geotiff files for slope (slope#1-27.tif)</li> <li>27 geotiff files for aspect (aspect#1-27.tif)</li> </ul>
Investigation of spatial and temporal variability in lower tropospheric ozone from RAL Space UV-Vis satellite products - Dataset
<p>This data set represents a long-term (1996-2017) harmonised record of lower tropospheric ozone (surface - 450 hPa or surface - approximately 6 km) from satellite instruments. These instruments include the Global Ozone Monitoring Experiment (GOME-1, 1996–2002), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2004) and the Ozone Monitoring Instrument (OMI, 2005–2017). These original products were produced by the Rutherford Appleton Laboratory (RAL) Space using the retrieval scheme described by Miles et al., (2015 - doi:10.5194/amt-8-385-2015). Pre-print of accepted manuscript can be found at https://doi.org/10.5194/egusphere-2023-1172.</p>
Metadata for ecological condition variables for use in Norway
<p>The OpenDocument spreadsheet "metadata.ods" contains the metadata for variables measuring ecological condition that are in use or have been suggested for use i Norway. The columns and the nature of the variables are explained in remarks in the column headings.</p><p> </p>
Extracted Source Properties Catalog for "Monitoring the X-ray Variability of Bright X-ray Sources in M33"
<p>Supplemental data to the article "Monitoring the X-ray Variability of Bright X-ray Sources in M33" accepted for publication in ApJ. Contains all extracted source properties for the 56-source final catalog, including single-ObsID extractions and merged values. See ReadMe for column descriptions and additional comments.</p>
Additional steady-state simulations of Miocene Antarctic ice-sheet variability using 3D thermodynamical ice-sheet model IMAU-ICE
<div> </div> <div> <div> <div>We supplement our previous dataset (<a href="https://doi.pangaea.de/10.1594/PANGAEA.939114">doi:10.1594/PANGAEA.939114</a>), with six additional steady-state simulations of the Miocene Antarctic ice sheet using the reference Miocene settings.</div> <div> </div> <div>IMAU-ICE was run using a 40x40km grid covering the Antarctic continent. Initial conditions were obtained from reconstructions of the Antarctic bathymetry and bedrock topography pertaining to 23 to 24 million years (Myr) ago (dataset <a href="https://doi.pangaea.de/10.1594/PANGAEA.923109" target="_self">doi:10.1594/PANGAEA.923109</a>). The simulations were forced by climate input data obtained from GENESIS simulations with varying CO2 levels (280 to 840 ppm) and Antarctic ice sheet cover (no ice to a large East-Antarctic ice sheet), and with present-day insolation. We utilized a matrix interpolation method to construct the time-varying climate forcing, based on the prescribed CO2 levels and ice cover simulated by IMAU-ICE.</div> <div> </div> <div>For each simulation, we provide the run script, 1D output variables including CO2 level and the sea level contribution of the Antarctic ice sheet, and 3D output variables including ice thickness, bedrock and surface height, surface mass balance, basal mass balance, ice velocities, and ice temperatures. For more information, please contact L.B. Stap at l.b.stap@uu.nl.</div> </div> </div>
Range expansion is slower and more variable with rapid evolution across a spatial gradient in temperature
<p><span>Rapid evolution in colonizing populations can alter our ability to predict future range expansions. Recent theory suggests that the dynamics of replicate range expansions are less variable, and hence more predictable, with increased selection at the expanding range front. Here, we test whether selection from environmental gradients across space produces more consistent range expansion speeds, using the experimental evolution of replicate duckweed populations colonizing landscapes with and without a temperature gradient. We found that range expansion across a temperature gradient was slower on average, with range-front populations displaying higher population densities, and genetic signatures and trait changes consistent with directional selection. Despite this, we found that with a spatial gradient range expansion speed became more variable and less consistent among replicates over time. Our results therefore challenge current theory, highlighting that chance can still shape the genetic response to selection to influence our ability to predict range expansion speeds.</span></p>
ScienceDex guides
Understand access before you commit
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.