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1,393 results for “Traces”
HPC Production Trace
<p>Job trace includes 14M jobs from a production high performance computing cluster consisting of 14,376 cores. Each job entry includes its submission time, user ID, maximum running time limit, requested number of cores and memory, and running time. We have formatted the trace in hierarchical data format (hdf) format).</p> <p>## Important Data Fields</p> <p>* *userid*: The user identification number. (Integer)</p> <p>* *wallclock_runtime_sec*: Actual job runtime in seconds. (Integer)</p> <p>* *wallclock_limit_sec*: Maximum runtime limit specified by the user. (Integer)</p> <p>* *num_cores*: Number of CPU requested for the job. (Integer)</p> <p>* *total_MB_req*: Memory in MB requested for the job. (Integer)</p> <p>* *status*: Status of the job (String)</p> <p>* *time*: Timestamp when job queued. (Timestamp)</p>
Trace gas mixing ratios measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>The authors would highly appreciate to be contacted if the data is used for any purpose.</p> <p>We measured mixing ratios of CO, CO<sub>2</sub> and CH<sub>4</sub> with a PICARRO G2401 Gas Analyzer. Ozone (O<sub>3</sub>) mixing ratios were measured with a 2B Technology ozone monitor, model 205. We report five-minute averaged data cleaned from exhaust gas influence. Temporal coverage is from December 20, 2016 to April 10, 2017.</p> <p>The trace gas concentrations represent a large number of atmospheric processes that happen on different time scales. CO for example, has basically no sources other than combustion and can hence be used as tracer for air mass transport from regions with combustion activities (e.g., South Africa). CO has a lifetime of a few weeks. CO<sub>2</sub> and CH<sub>4</sub> are longer-lived trace gases which disperse globally. The data set shows that concentrations in the Northern Hemisphere are higher than in the Southern Hemisphere. Both trace gases are emitted by anthropogenic activities as well as natural sources. Over the cruise track, areas of the Southern Ocean were passed where these trace gases either outgas or are absorbed. Ozone is a secondary trace gas, meaning that it is formed in the atmosphere. It’s concentrations are relatively low.</p> <p>All trace gases data have been cleaned from exhaust gas influence.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_trace_gas_concentration.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>change_log.txt, metadata, text</li> </ul> <p>NaN values denote missing values because of e.g., ship exhaust contamination or instrument maintenance. For latitude and longitude, NaN values are noted in cases where position data was not available for the given time period.</p> <p><strong>Dataset license</strong></p> <p>This trace as mixing ratio dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p> <p><strong>Change log</strong></p> <p>v1.1 - data file updated<br> - Ozone was corrected because a wrong calibration factor was applied in version 1.0<br> - CO2 and CH4 are now dry mixing ratios, the previous data were not corrected for water vapour<br> - README updated accordingly with details of changes to processing<br> - added change_log.txt file</p> <p>v1.0 - initial release of dataset</p>
Dataset: The SPIKE II experiment - Tracing the water balance
<p>This repository holds data collected during the “SPIKE II” tracer experiment. The experiment was carried out on a large vegetated lysimeter (2.5 m<sup>3</sup>) planted with two willow trees (clones) (<em>Salix viminalis</em>) within the EPFL campus (CH), in Switzerland. SPIKE II took place from May 10 to June 29 in 2018. This composite dataset contain stable isotopic composition (δ<sup>2</sup>H and δ<sup>18</sup>O) of more than 900 water samples of precipitation, soil water, bulk soil collected at different depths in the soil profile, xylem from willow, and leakage flow in the bottom of the lysimeter. The dataset comprises environmental conditions and water fluxes recorded during the experiment. This includes: meteorological conditions, soil moisture and tension, evapotranspiration in the lysimeters, and tree transpiration recorded at high resolution. Finally, the repository holds tree hydraulic and growth measurements and root traits.</p> <p>Specifically, this dataset contains six files:</p> <ul> <li>“METADATA_spikeII.txt” contains specific information about each recorded variable and data point collected throughout the experiment.</li> <li>“spike.hydrometric.II.csv” contains information about meteorological and soil conditions, evapotranspiration fluxes, and tree stem radius, including growth and tree water deficit.</li> <li>"spike.isotopes.II.csv” contains stable isotope data.</li> <li>“fineroots_spike.II.csv” contains root traits information.</li> <li>“events_chronology.csv” summarizes the main events that occurred during SPIKE II.</li> <li>“Figure1_SpikeII_Aerial_Image.PNG” illustrates the location and spatial display of the experiment at the EPFL campus.</li> </ul> <p>This data repository was used in the following SPIKE II publications:</p> <p>Nehemy, M. F., Benettin, P., Asadollahi, M., Pratt, D., Rinaldo, A., & McDonnell, J. J. (2021). Tree water deficit and dynamic source water partitioning. <em>Hydrological Processes</em>, <em>35</em>(1), e14004. doi:10.1002/hyp.14004</p> <p>Benettin, P., Nehemy, M. F., Cernusak, L. A., Kahmen, A., & McDonnell, J. J. (2021). On the use of leaf water to determine plant water source: A proof of concept. <em>Hydrological Processes</em>, <em>35</em>(3), e14073. doi:10.1002/hyp.14073</p> <p>Benettin, P., Nehemy, M. F., Asadollahi, M., Pratt, D., Bensimon, M., McDonnell, J. J., & Rinaldo, A. (2021). Tracing and closing the water balance in a vegetated lysimeter. <em>Water Resources Research</em>, 57, e2020WR029049. doi:org/10.1029/2020WR029049</p> <p>For any further inquiry, please contact Magali Nehemy or Paolo Benettin.</p> <p>We thank Kim Janzen for assistance with laser and mass spec analysis. We thank the Laboratory of Ecohydrology at EPFL (ECHO/IIE/ENAC/EPFL) for assistance throughout the experiment. We also thank Pierre Queloz and Scott Allen for precious help, Gabriel Cotte and Torsten Vennemann from University of Lausanne (CH) for the collection and analysis of atmospheric vapor samples. This research was supported by the American Geophysical – Horton Research Grant 2019 awarded to MFN, an NSERC CREATE in Water Security and an NSERC Discovery Grant to JJM, AR and PB thank ENAC school at EPFL for financial support and acknowledge the Swiss National Science Foundation grant number CRSII5\_186422.</p> <p> </p>
Scalasca trace analysis of HemeLB application execution with 13824 MPI processes on SuperMUC-NG
<p>The CompBioMed HPC CoE flagship application HemeLB was run with a 6.4 micron resolution "circle of Willis" geometry dataset on LRZ's SuperMUC-NG supercomputer, and its execution performance with 13824 MPI processes on 288 dual 24-core compute nodes measured by Score-P (using SIONlib) and analysed by Scalasca trace analyzer.</p>
Container Registry Benchmark experiments measurements and trace workload samples
<p>Measurements for experiments using Container Registry Benchmark, CReB. 4 experiments: Long running, small experiment stress mode, small experiment delay mode, and large workload experiment.</p> <p> </p> <p>Structure:</p> <ol> <li><strong>full-measurements-long-running-pull.csv : </strong>measurements for long running pull experiment</li> <li><strong>full-measurements-long-running-push.csv: </strong>measurements for long running push experiment</li> <li><strong>result-bug-analysis.zip: </strong>results from bug analysis of trace replayer</li> <li><strong>results-1hr-experiment.zip: </strong>measurements for the large experiment (4 registries)</li> <li><strong>results-small-delay.zip: </strong>measurements for the delay mode, small experiment with real workload</li> <li><strong>results-small-stress.zip: </strong>measurements for the stress mode, small experiment with real workload</li> <li><strong>traces.zip: </strong>traces used for pen-and-paper experiment, 1 hour sample, and the trace used for small experiment (selected are first 405 requests)</li> </ol>
Zonal mean of atmospheric water vapour and water vapour perturbation by emitted trace gases of hypersonic aircraft
<p>This short movie (no sound) shows two figures with time steps of five days over a period of fourteen years (2000-2014). On the left the atmospheric mixing ratio of water vapour is presented in parts per million. On the right the perturbation of stratospheric water vapour is depicted in parts per million. The perturbation is created by emitted water vapour of hypersonic aircraft flying at high altitudes (35 km). Over the years the accumulation of water vapour up to equilibrium is shown.</p>
Data and code for the publication "Tracing the horizontal transport of microplastics on rough surfaces"
<p><strong>Background</strong></p> <p>The data set contains images of fluorescent PMMA (Polymethyl methacrylate) particles that are moved by water on rough surfaces in an irrigation experiment. The experiments were done in the laboratory at the Institute of Geography, University of Cologne, Germany, in Septembre 2020. The images were taken with an sCMOS (advanced scientific complementary metal-oxide-semiconductor) high resolution pco.panda 4.2 camera (PCO AG, Kehlheim, Germany).</p> <p>The data set was analysed in the publication: Laermanns, H., Lehmann, M., Klee, M., Löder, M.G.J., Gekle, S. and Bogner, C., 2021, “Tracing the horizontal transport of microplastics on rough surfaces,” Microplastics and Nanoplastics, <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a></p> <p>Additionally to the data, this collection of files contains the Python and R scripts/notebooks used to analyse the images and create graphics for the publication. The code for the simulation of flow patterns can be obtained from the authors upon request.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p>Experimental parameters</p> <ul> <li> <p>Surface roughness: two levels, fine and course</p> </li> <li> <p>Inclination: 6 levels, 2.5°, 5°, 7.5°, 10°, 12.5° and 15°</p> </li> <li> <p>Irrigation: three levels, 4.8, 7.2 and 10.44 L/h</p> </li> <li> <p>Repetitions: three</p> </li> </ul> <p>More details on the experimental setup are given in the publication.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The folder <strong>images.zip</strong> contains the images. They are organized as follows:</p> <ul> <li><strong>Feinsand_10_Partikel</strong>: images of PMMA particles on the fine surface</li> <li><strong>Grobsand_10_Partikel</strong>: images of PMMA particles on the rough surface <ul> <li> <p>Both folders contain six subfolders <strong>_XX_Grad_Gefaelle</strong>, XX being 2_5, 5, 7_5, 10, 12_5, 15. These folders refer to inclinations of 2.5°, 5°, 7.5°, 10°, 12.5° and 15° of the rough surfaces, respectively.</p> </li> <li> <p>every folder _XX_Grad_Gefaelle contains three subfolders <strong>Fliessgeschwindigkeit_YY</strong>, with YY being 20mlx4, 30mlx4 and 43_5mlx4, the parameters of the peristaltic pump, corresponding to irrigation rates of 4.8, 7.2 or 10.44 L/h, respectively.</p> </li> <li> <p>every folder Fliessgeschwindigkeit_YY contains three subfolders <strong>Z_Durchgang</strong> with Z being 1, 2 or 3 corresponding to the tree repetitions of the experiment.</p> </li> </ul> </li> <li><strong>stained_flow_patterns</strong>: images of flow patterns of the fluorescent dye Nile Red (in methanol), an mp4 video and a text file with parameters to produce the video based on the images. The images were produced for the following experimental parameters: <ul> <li> <p><strong>Feinsand_2_5_Grad_20_ml</strong>: fine surface, inclined by 2.5° and irrigated with 7.2 L/h</p> </li> <li> <p><strong>Grobsand_7_5_Grad_20_ml</strong>: coarse surface, inclined by 7.5° and irrigated with 7.2 L/h</p> </li> </ul> </li> </ul> <p>The file <strong>experimental_data.csv</strong> links the concatenated folder names to experimental parameters.</p> <p> </p> <p><strong>Description of the code</strong></p> <p>The images were first processed in Python to locate the PMMA particles and calculate particle sizes. The Python code is located in the <strong>py_scripts.zip</strong> folder. It contains the following files:</p> <ul> <li> <p><strong>find_XYZ</strong>: locates PMMA particles. XYZ stands for different experimental parameters (see above). Scripts containing the string <strong>_problems</strong> locate PMMA particles for images with possible artefacts (smeared particles, residual light etc.). You need to uncomment the appropriate lines in the files to rerun the code because it was run piece by piece.</p> </li> <li> <p><strong>pickle_to_csv.py</strong>: converts pickle files to csv files</p> </li> <li> <p><strong>calculate_sizes.py</strong>: calculates the sizes of PMMA particles from the first image of each experiment</p> </li> <li> <p><strong>py_functions_new.py</strong>: contains custom functions</p> </li> </ul> <p>Further analysis run in a mixture of R and Pyhton in one working document (R Notebook):</p> <ul> <li> <p><strong>Analysis_with_loops.Rmd</strong>: tracking of the PMMA particles by PtrakPy version 0.4.2 (Allan et al. 2019). Python 3.8 (Python Software Foundation, <a href="https://www.python.org/">https://www.python.org/</a>) was called directly from R using the R package reticulate (<a href="https://rstudio.github.io/reticulate/">https://rstudio.github.io/reticulate/</a>) in RStudio (<a href="https://www.rstudio.com/">https://www.rstudio.com/</a>).</p> </li> <li> <p><strong>Analysis_for_paper.Rmd</strong>: R code for analysis of tracking, statistical analysis, plotting. We used the R version 4.0.3 (R Core Team 2020).</p> </li> <li> <p><strong>helper_function.R</strong>: contains custom R functions for the analysis</p> </li> </ul> <p> </p> <p><strong>Results</strong></p> <p>The file <strong>results.zip</strong> contains the folders:</p> <ul> <li> <p><strong>data</strong>: *.pickle files produced by Python containing the trajectories of PMMA particles</p> </li> <li> <p><strong>data_csv</strong>: *.pickle files converted to *.csv files</p> </li> <li> <p><strong>figures</strong>: figures produced by the code during the analysis, organized in different subfolders</p> </li> <li> <p><strong>RData</strong>: large computational results produced and saved during analysis</p> </li> <li> <p><strong>sizes_csv</strong>: *.csv files containing PMMA particle sizes and further morphological characteristics; produced during analysis</p> </li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors thank Julia Horn for support in the laboratory and Florian Steininger for technical assistance.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 391977956, SFB 1357, subprojects B04 and B06.</p> <p> </p> <p><strong>References</strong></p> <p>Allan, Dan, Casper van der Wel, Nathan Keim, Thomas A Caswell, Devin Wieker, Ruben Verweij, Chaz Reid, et al. 2019. <em>Soft-Matter/Trackpy: Trackpy V0.4.2</em> (version v0.4.2). Zenodo. <a href="https://doi.org/10.5281/zenodo.3492186">https://doi.org/10.5281/zenodo.3492186</a>.</p> <p>Laermanns, Hannes, Moritz Lehmann, Marcel Klee, Martin GJ Löder, Stephan Gekle, and Christina Bogner. 2021. “Tracing the Horizontal Transport of Microplastics on Rough Surfaces.” <em>Microplastics and Nanoplastics</em>. <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a>.</p> <p>R Core Team. 2020. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.</p>
Data for: Scalable and Live Trace Processing with Kieker Utilizing Cloud Computing
<p>Knowledge of the internal behavior of applications often gets lost over the years. This circumstance can arise, for example, from missing documentation. Application-level monitoring, e.g., provided by Kieker, can help with the comprehension of such internal behavior. However, it can have large impact on the performance of the monitored system. High-throughput processing of traces is required by projects where millions of events per second must be processed live. In the cloud, such processing requires scaling by the number of instances.</p> <p>In this paper, we present our performance tunings conducted on the basis of the Kieker monitoring framework to support high-throughput and live analysis of application-level traces. Furthermore, we illustrate how our tuned version of Kieker can be used to provide scalable trace processing in the cloud.</p> <p>This is the dataset containing the results of our conducted benchmarks.</p>
Traces used for calibration of NPB LU with SMPI / SimGrid
<p>These traces were used to calibrate the NAS NPB Benchmark LU with SMPI.</p> <p>The archives contain the traces for 12 cores, all run on 1 single node (that contained 12 cores).</p> <p>The extracted size should be around 7 GB for the MPI one and 600 MB for SMPI.</p> <p> </p> <p>The .org-file contains the analysis used to obtain the required input files for SimGrid. You can load them in org-mode and then execute via "C-c C-v b" the whole buffer. Make sure to extract the archives in /tmp/ or change the paths accordingly.</p>
Mobile broadband speedtest traces
<p>The goal of this research is to collect a wide range speedtest traces for the mobile broadband (MBB) networks, as seen from actual users while moving around the city using public or private vehicles. For collecting this dataset we ask students to participate and run Mobile BroadBand speedtest. You can find the instruction of our test here. Traces were mostly collected in the city of Torino in Italy, and refer to three technologies (WiFi, 3G, and 4G), and multiple Mobile Network Operators (MNO). The networks were in normal operating conditions (and unaware of our tests). Our terminals (both Android and iOS smartphones) accessed the mobile networks to upload data to a server on campus, using both TCP and UDP at the transport layer.<br> <br> We used a hybrid method in the trace collection process: we run repetitive active measurements from mobile terminals using iperf2, and we collect passive traces on server side using tcpdump. In each experiment, the mobile terminal runs iperf2 in the upload direction for 600 seconds while tcpdump captures packets at the server.<br> We collected traces for different MNOs in Italy (Tim, Wind, and Vodafone). For WiFi, we considered the open WiFi community WoW-Fi offered automatically by Fastweb customers that share their DLS or FTTH home network via the access gateway. Mobile phones automatically authenticate using IEEE 802.1x with no action from the user. Traces shorter than 300 seconds are iperf2 experiments run from the stationary MONROE nodes or failed experiments.<br> </p>
Soil respiration data measured with LI-7810 CH4/CO2/H2O Trace Gas Analyzer in Tangermuende/Germany in August 2022
<p>The dataset is associated with the publication Koschorreck, M., Kamjunke, N., Koedel, U., Rode, M., Schuetze, C., and Bussmann, I.: Diurnal versus spatial variability of greenhouse gas emissions from an anthropogenic modified German lowland river, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2023-176, in review, 2023. </p> <p>It shows CO2 and CH4 initial concentrations and flux data measured with the LICOR 7810 instrument and calculated with the SoilFluxPro software V5.3.</p>
Contact tracing of binary stars: Pathways to stellar mergers (online data)
<p><strong># Data for Henneco et al. (2024)</strong></p> <p>This repository contains the input files required to reproduce the MESAbinary models from Henneco et al. (2024). It also contains the full machine-readable version of Table G.1. For an overview of the quantities in each column, we refer to the notes underneath Table G.1 in the paper.</p> <p>MESA r12778<br>MESA SDK 20.3.2</p> <p><strong>## MESA_inlists</strong></p> <p>- <strong>inlist1</strong>: inlist for the initially more massive primary star</p> <p>- <strong>inlist2</strong>: inlist for the initially less massive secondary star</p> <p>-<strong> inlist_project</strong>: inlist for the binary system</p> <p> </p> <p><strong>## run_extras</strong></p> <p>- <strong>run_star_extras.f</strong>: subroutines and functions for the individual stars</p> <p>- <strong>run_binary_extras.f</strong>: subroutines and functions for the binary system</p> <p> </p> <p><strong>## MESA_ZAMS_models</strong></p> <p>Precomputed ZAMS models read in through <strong>inlist1</strong> and <strong>inlist2</strong>.</p> <p> </p> <p><strong>## table_G1_full.txt</strong></p> <p>Full machine-readable version of Table G.1.<br> </p> <p><strong>## MESA_models_output</strong></p> <p>Detailed output of the MESAbinary calculations. <em>Will be added in due time.</em></p>
A Stereo Camera Simulator for Large-Eddy Simulations of Continental Shallow Cumulus clouds based on three-dimensional Path-Tracing
<p>Dataset to produce the results of the publication: "A Stereo Camera Simulator for Large-Eddy Simulations of Continental Shallow Cumulus clouds based on three-dimensional Path-Tracing"</p><p>The dataset contains:</p><ul><li>Large-Eddy Simulation (LES) model configuration files</li><li>Selected output data of the LES experiments</li><li>Data and analysis scripts for the figures</li><li>The rendered camera images</li><li>The cloud field, cloud hulls, and reconstructed hulls</li><li>A frozen version of the open-source Blender code (version 2.90) as used in this study</li></ul><p>For the latest version of Blender, please visit:</p><p><a href="https://chat.openai.com/c/www.blender.org">www.blender.org</a></p><p>It is important to note that the method was specifically tested only on version 2.90.</p><p> </p><p>This research is supported by the German Research Foundation (DFG) under project number 430226822 (https://gepris.dfg.de/gepris/projekt/430226822). This research was supported by the U.S. Department of Energy's Atmospheric System Research, an Office of Science Biological and Environmental Research program, under grant DE-SC0022126. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bb1086. The Gauss Centre for Supercomputing e.V. (https://www.gauss-centre.eu/) is acknowledged for providing computing time on the Gauss Centre for Supercomputing (GCS) supercomputer JUWELS at the Jülich Supercomputing Centre (JSC) under projects VIRTUALLAB and RCONGM.</p>
Updated Supplementary Figures for Can leafhoppers help us trace the impact of climate change on agriculture?
<p>Supplementary Figures for: <strong>Can</strong> <strong>leafhoppers help us trace the impact of climate change on agriculture? </strong>to be posted in bioRxiv.</p><p><strong>Figure S1. </strong>Diversity indexes calculated in this study to compare leafhopper diversity each growing season investigated in this study and the geographic regions where the strawberry fields were located. Statistical analyses were performed for Shannon and Simpson finding that in both cases there is no interaction between years and regions with <i>p</i> = 0.0889 and <i>p</i> = 0.7139, respectively.</p><p><strong>Figure S2.</strong> Distinctive RFLP patterns obtained with <i>Cpn</i>ClassiPhyR from <i>in silico</i> digestion of <i>cpn60</i>UT from SbGPQ clones and AY-Col. Lanes labelled MW in <i>in silico</i> RFLP represent <i>Hae</i>III-digested phage <i>ϕ</i>X174 DNA.</p><p><strong>Figure S3.</strong> Phylogenetic tree using neighbour-joining method of the <i>16S, secY, nusA, rp, secA, cpn60 </i>and<i> tuf</i> sequences obtained in this study for the SbGP phytoplasma and sequences retrieved from Genbank. <i>Acholeplasma laidlawii</i> PG8 was used as an outgroup. The phylogenetic tree was bootstrapped 1000 times to achieve reliability. Bar, 1 substitution in 100 or 500 positions. </p><p><strong>Fig. S3 Panel 1: </strong>cpn60UT, tuf, and secY trees.</p><p><strong>Fig. S3 Panel 2:</strong> nusA, rp, and secA trees.</p><p><strong>Fig. S3 Panel 3:</strong> 16S tree with subtree showing heterogeneity of SbGPQ and 'Ca. P. tritici'.</p><p><strong>Figure S4.</strong> Leafhopper feeding-associated damages observed in strawberry plants. <strong>A</strong>, in the field. <strong>B</strong>, in the greenhouse after incubation with leafhoppers.</p><p><strong>Figure S5.</strong> Alpha diversity indexes were calculated to study <i>Macrosteles quadrilineatus</i> microbiome observed for each growing season. No statistical difference was observed among the sites for any of the indexes calculated.</p><p><strong>Figure S6.</strong> Effect of insecticides leafhopper population control. Only those with a number of applications higher or equal to five are presented. We did not find statistical differences among the treatments before and after the application of the insecticides with <i>p</i> = 0.8488.</p><p><strong>Figure S7.</strong> Effect of insecticides on <i>Macrosteles quadrilineatus</i> and <i>Empoasca fabae</i> population control. All insecticides (n = 12) are represented but the statistical analysis was only performed with those that the number of applications was higher than 5. We did not find statistical differences among the treatments before and after the application of the insecticides with <i>p</i> = 0.1781 for the aster leafhopper <i>M.</i> <i>quadrilineatus </i>and <i>p</i> = 0.6540 for the potato leafhopper <i>E. fabae</i>.</p><p><strong>Figure S8.</strong> Comparison among the Shannon index obtained for leafhopper populations in vineyards in 2007 and 2008 and for leafhopper populations in strawberry fields in 2021 and 2022 in Quebec.</p>
Tracing evolutionary trajectories in the presence of gene flow in South American temperate lizards (Squamata: Liolaemus kingii group)
<p>Evolutionary processes behind lineage divergence often involve multidimensional differentiation. However, in the context of recent divergences, the signals exhibited by each dimension may not converge. In such scenarios, incomplete lineage sorting, gene flow, and scarce phenotypic differentiation are pervasive. Here, we integrated genomic (RAD loci of 90 individuals), phenotypic (linear and geometric traits of 823 and 411 individuals, respectively), spatial, and climatic data to reconstruct the evolutionary history of a speciation continuum of liolaemid lizards (<em>Liolaemus kingii</em> group). Specifically, we (i) inferred the population structure of the group and contrasted it with the phenotypic variability; (ii) assessed the role of post-divergence gene flow in shaping phylogeographic and phenotypic patterns; and (iii) explored eco-geographic drivers of diversification across time and space. We inferred eight genomic clusters exhibiting leaky genetic borders coincident with geographic transitions. We also found evidence of post-divergence gene flow resulting in transgressive phenotypic evolution in one species. Predicted ancestral niches unveiled suitable areas in southern and eastern Patagonia during glacial and interglacial periods. Our study underscores integrating different data and model-based approaches to determine the underlying causes of diversification, a challenge faced in the study of recently diverged groups. We also highlight <em>Liolaemus</em> as a model system for phylogeographic and broader evolutionary studies.</p>
SNSPD traces given varying incident mean photon numbers
<p>The data set consists of 1.1 million electrical output signals (traces) from a superconducting nanowire single-photon detector (SNSPD) from Single Quantum. These traces were recorded with an oscilloscope (21 GHz bandwidth, 128GSa/s) for varying incident mean photon numbers between 0.5 and 5 in steps of 0.5 photons per pulse (generated with a laser, i.e., coherent states). More information can be found in the accompanying publication.</p>
Fig. 5 in Parasitic gastropod bioerosion trace fossil on Cenomanian oysters from Le Mans, France and its ichnologic and taphonomic context
Fig. 5. Parasitic gastropod bioerosion and perforation trace Loxolenichnus stellatocinctus igen. et isp. nov., MHNLM 2015.2.244, holotype, Marnes à Pycnodonte biauriculata Formation, Upper Cenomanian, Lycée Bellevue earthmoving works, Le Mans, Sarthe Department, France; on LV of Rhynchostreon suborbiculatum (Lamarck, 1801). A. Entire LV shell with the arrow showing the perforation. B. LV (viewed from inside), the arrow shows the opening of the perforation on the inner side of the shell, diascopic illumination. Outer (C) and inner (D) sides of the shell, close-ups of the perforation, the dashed line delimitates approximately the course of the perforation through the shell, diascopic illumination. E. Positive X-ray print of the perforation.
Fig. 6 in Parasitic gastropod bioerosion trace fossil on Cenomanian oysters from Le Mans, France and its ichnologic and taphonomic context
Fig. 6. Parasitic gastropod bioerosion trace Loxolenichnus stellatocinctus igen. et isp. nov., MHNLM 2015.2.346 and MHNLM 2015.2.347, paratypes; lower Campanian Inoceramus lingua–Goniotheuthis quadrata Zone, quarry near Höver, Germany. A. Outer side of an oyster valve, accommodating two specimens of L. stellatocinctus (arrows). B. Close-up of the two specimens and the multiple perforations. C. Inner side of the oyster valve showing two of the perforations reaching the adductor muscle pad. Outer (D) and inner (E) sides of an oyster valve with a marginal L. stellatocinctus. F. Close-up of D, note the two concentric stellate rims and the marginal notch.
Major and trace elements abundance of the Belbashani Pumice and other Hasandag deposits, Central Anatolian Volcanic Province
<p>Glass microchemical data, including major and trace elements, of the Belbashani Pumice, a Plinian eruption produced by Hasandag volcano about 400ka ago within the Central Anatolian Volcanic Province (CAVP)</p>
Wrapper Impact Workloads and BSC Slurm Simulator Output of Static Traces based on Data from LUMI Supercomputer
<p>This dataset contains the workloads, with the workflow added to them, and the results of the simulations of the static trace utilizing LUMI fitted data carried out using <a href="https://ieeexplore.ieee.org/abstract/document/8641556">BSC's SLURM Simulator</a>.</p> <p>It is organized in two folders: workloads and results. In the first, we find a folder per experiment, which is a different randomly generated workload file. Within each experiment we find a folder per fair share inidicating the target platform, the workflow it was based on, and the characteristics of the tracked job: number of cores and runtime. The results folder follows the same scheme but with a file extension of ".trace".</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.