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11,506 results for “Dynamics”
Mutually Beneficial Combination of Molecular Dynamics Computer Simulations and Scattering Experiments - DATA
<p>Specular reflectivities of the SoyPC bilayer stack measured at the vertical reflectometer MARIA at Heinz Maier-Leibnitz Zentrum (MLZ) in Garching, Germany.</p> <p>Offspecular reflectivity map (log scale) of the multilayer sample as a function of theangle of incidence (θi) and of the reflection angle (θi).</p> <p>Specular reflectivities of the Si/SiO<sub>2</sub>/DMPC/H2O at 4 different contrasts (H<sub>2</sub>O, D<sub>2</sub>O, SMW and 4MW)</p> <p>Small-angle neutron scattering of the unilamellar SoyPC</p>
Experimental results dataset reported in the paper: "Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement."
<p><strong>Temporal teleportation with pseudo-density operators: how dynamics emerges from temporal entanglement.</strong></p> <p>Experimental results data-set (as reported in Figure 2 of the main paper).</p> <p>Caption of the Figure: Classical bound violation ∆S<em><sup>(l)</sup></em>(n) =S<em><sup>(l)</sup></em>(2n)−(2n−2) (<em>l=T,H,S</em>) for the multi-parameter <em>CHSH</em> inequalities in the temporal (red), spatial (grey) and “hybrid” (blue) domain. The dots represent the experimental results, with the uncertainty bars evaluated as statistical fluctuations among repeated measurement sets, while the solid curves show the theoretically-expected values (for the correlations belonging to the spatial domain, deviations from the ideal case due to the V<em><sub>s</sub></em>= 0.982 estimated visibility of the generated |<em>ψ<sub>−</sub></em>〉state were considered)</p> <p>Data-set description:</p> <p>Column 1 = X-coordinate: n (Number of Settings)</p> <p>Column 2 = Y-coordinate: ∆S<em><sup>(S)</sup></em>(n), Space-like (S) CHSH Violation [Grey color in the figure on the paper]</p> <p>Column 3 = Uncertainty on ∆S<em><sup>(S)</sup></em>(n) (Space-like, S, Grey in the figure on the paper), </p> <p>Column 4 = Y-coordinate: ∆S<em><sup>(H)</sup></em>(n), Hybrid (H) CHSH Violation [Blue color in the figure on the paper]</p> <p>Column 5 = Uncertainty on ∆S<em><sup>(H)</sup></em>(n) (Hybrid, H, Blue in the figure on the paper)</p> <p>Column 6 = Y-coordinate: ∆S<em><sup>(T)</sup></em>(n), Time-like (T) CHSH Violation [Red color in the figure on the paper]</p> <p>Column 7 = Uncertainty on ∆S<em><sup>(T)</sup></em>(n) (Time-like, T, Red in the figure on the paper)</p>
Data from: Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing data
<p>Data from the paper:</p> <p><em>Amos, S., Mengistu, S., Kleinschroth, F. (2021): Three decades of pastoralist settlement dynamics in the Ethiopian Omo Delta based on remote sensing.</em></p> <p>Based on Landsat 5, 7, 8, RapidEye Ortho, and Sentinel-2 satellite imagery, we manually mapped the settlements of the Dasanech people in the most populated parts of the Omo River Delta in Ethiopia from 1992 to 2019 using QGIS. We used the data to answer the following questions: (1) How have pastoralist settlements in the delta changed in extent and persistence over the past three decades? And (2) how have the settlements changed structurally during the construction, filling, and operation of Gibe III Dam?</p> <p>We conducted two independent remote sensing analyses. Firstly, we used Landsat data from 1992 to 2019 to track land that is inhabited by pastoralists people within the evergreen part of the Delta. Secondly, the higher spatial resolution of the RapidEye Ortho (5m) and Sentinel-2 (10m) images allowed the detailed identification of settlements as well as infrastructure (tin-roof houses and road) in the Delta during a shorter period from 2009 to 2019. <strong>For more information on the data, please refer to the README.txt or the paper.</strong></p>
Coarse-grained molecular dynamics simulations of SARS-CoV-2 envelope protein E in the pentameric form
<p>The trajectories of coarse-grained (CG) molecular dynamics (MD) simulations of<br> 1) unmodified (FeigLab_NMR; FeigLab_PentamerNoPTM_POPC_Martini3b: 5 μs; 5 μs); <br> 2) palmitoylated (FeigLab_PentamerCYSP43; PentamerCYSP44_POPC_Martini3b: 5 μs; 5 μs); <br> SARS-CoV-2 E protein pentamer in a POPC bilayer.</p> <p>The trajectory of CG MD of system containing 2 pentamers in the membrane buckled in a single direction (BuckledMembrane_FeigLab_2xPentamerNoPTM_POPC_Martini3b: 1 μs).</p> <p>FeigLab_Pentamer: https://github.com/feiglab/sars-cov-2-proteins/blob/master/Membrane/E_protein.pdb<br> FeigLab_NMR_Pentamer is assembled based on the transmembrane domain determined by NMR (PDB ID: 7K3G) and FeigLab model for the rest.</p>
Open data and power dynamics
<p>Podcast with an expert -Mor Rubenstein- about the management of open data and the power dynamics that are at play in society. In particular we are referring mainly to western Europe as the societal context.</p>
Supplementary material for "High turn-over rates at the upper range limit and elevational source-sink dynamics in a widespread songbird"
<p><strong>Abstract</strong></p> <p>The formation of an upper distributional range limit for species breeding along mountain slopes is often based on environmental gradients resulting in changing demographic rates towards high elevations. However, we still lack an empirical understanding of how the interplay of demographic parameters forms the upper range limit in highly mobile species. Here, we study apparent survival and within-study area dispersal over a 700 m elevational gradient in barn swallows (<em>Hirundo rustica</em>) by using 15 years of capture-mark-recapture data. Annual apparent survival of adult breeding birds decreased while breeding dispersal probability of adult females, but not males increased towards the upper range limit. Individuals at high elevations dispersed to farms situated at elevations lower than would be expected by random dispersal. These results suggest higher turn-over rates of breeding individuals at high elevations, an elevational increase in immigration and thus, within-population source-sink dynamics between low and high elevations. The formation of the upper range limit therefore is based on preference for low-elevation breeding sites and immigration to high elevations. Thus, shifts of the upper range limit are not only affected by changes in the quality of high-elevation habitats but also by factors affecting the number of immigrants produced at low elevations.</p>
Source data for Synthetic dynamic hydrogels promote degradation-independent in vitro organogenesis
<p>Source data and statistical analysis results for Synthetic dynamic hydrogels promote degradation-independent in vitro organogenesis</p>
Data and code for: Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats, Suricata suricatta
<p>Data and code to go with our publication "Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats, <em>Suricata suricatta", </em>Nature Communications (2021).</p> <p><strong>FILE DESCRIPTIONS</strong></p> <p><em>****** DATA ******</em></p> <p><strong>meerkat_16S_data.tar.gz</strong> # 16S V4 amplicon sequences sequenced on an Illumina MiSeq platform using primer pair 515F and 806R, including all faecal samples, controls, and sand samples. Sequence identifiers and basic metadata are in <strong>sequence_identifiers.csv.</strong></p> <p><strong>sequence_identifiers.csv </strong># Simple metadata and identifiers for all sequences/samples (what type of sample/sequencing run, etc), required for QIIME2 processing of the raw fasta.gz files contained in meerkat_16S_data.tar.gz. It contains a column for whether the sample was included in the final analysis. Does not include sample biological metadata as generating this data requires access to Kalahari Meerkat Project database. Biological metadata for samples included in the final analysis are instead provided in <strong>processed_data_phyloseq.RDS </strong>and can be accessed via <em>phyloseq::sample_data(processed_data_phyloseq)</em>.</p> <p><strong>processed_data_phyloseq.RDS</strong> # Phyloseq object containing the processed data used in the presented analysis. Contains data for 1109 samples, and includes the ASV table, the taxonomic classification, the phylogenetic tree, and the sample metadata used in the analysis.</p> <p><strong>technical_replicate_data_phyloseq.RDS</strong> # Phyloseq object containing data from the 16 technical replicates.</p> <p><strong>pilot_study_data_phyloseq.RDS</strong> # Phyloseq object containing data from the pilot study on captive meerkats.</p> <p><em>****** CODE ******</em></p> <p><strong>CODE1_QIIME_script.R</strong> # QIIME2 script to generate ASV table, taxonomy, and phylo tree from <strong>meerkat_16S_data.tar.gz. </strong>Requires a reference taxonomy (SILVA) and a reference phylogeny (SEPP) for taxonomic and phylogenetic placements.</p> <p><strong>CODE2_processing_QIIME_output.Rmd</strong> # R markdown script that processes the QIIME2 output generated by <strong>CODE1_QIIME_script.R</strong>. Does not generate meerkat metadata as this requires access to the Kalahari Meerkat Project database. This metadata is provided in <strong>processed_data_phyloseq.RDS.</strong></p> <p><strong>CODE3_data_analysis_script.Rmd </strong># R markdown script that generates data and figures presented in paper, using data from <strong>processed_data_phyloseq.RDS, technical_replicate_data_phyloseq.RDS, </strong>and<strong> pilot_study_data_phyloseq.RDS.</strong></p> <p><em>****** R MARKDOWN REPORTS ******</em></p> <p>The following reports are html files that show the code output for the two RMD files above.</p> <p><strong>RMARKDOWN_data_processing.html </strong># R markdown report for<strong> CODE2_processing_QIIME_output.Rmd</strong></p> <p><strong>RMARKDOWN_data_analysis.html </strong># R markdown report for <strong>CODE3_data_analysis_script.Rmd</strong></p> <p>*****************************</p> <p>For general queries, unexpected errors and/or inconsistencies, please contact riselya@gmail.com.</p> <p> </p>
Simulated daily weather dynamics and gross primary production in 3 locations for 100,000 years
<p>IMPORTANT NOTE: The data in version 1 of this record, due to an error of units in the precipitation, had a non-physical vegetation growth and gross primary production. This has been fixed in version 2 of the record/dataset. Further, version 2 of the dataset contains 3 locations because the sites called "Grassland" and "Temperate" site produced the same type of vegetation (just grasses) in version 2, that contains therefore only a "Temperate" site.</p> <p>-------------------------------------------------</p> <p>The dataset reports daily temperature, precipitation, radiation and gross primary production in 3 different geographic locations (denoted as Temperate, Boreal and Tropical), representative of different climates and vegetation distributions, for 100,000 years. Each of the .nc files contains the dataset corresponding to one particular site.</p> <p>The weather data was produced using the AWE-GEN stochastic weather generator model ( Fatichi et al., Water Resources, 34(4):448–467 (2011) ). Vegetation dynamics and gross primary production are simulated via the dynamic global vegetation model LPX-Bern v1.4 ( Lienert and Joos, Biogeosciences, 15(9):2909–2930 (2018) ). The foliar projective cover is also reported at an annual scale.</p> <p> </p>
CRCM5-CMIP6 : A dynamically-downscaled ensemble of CMIP6 simulations.
<h1>CRCM-CMIP</h1> <h2>Data reference</h2> <p>Paquin, D., C. McCray, C. B. Gauthier, M. Giguère, O. Asselin, P .Bourgault, M.-P. Labonté and D. Matte. The CRCM5-CMIP6 Ouranos’ ensemble : A dynamically-downscaled ensemble of CMIP6 simulations over North America. Accepted in Scientific Data.</p> <p><a href="https://www.ouranos.ca/en">Ouranos</a> : Canadian Regional Climate Model – version 5</p> <p><strong>Martynov et al. 2013, Separovic et al. 2013</strong></p> <p>Based on GEM 3.3.3.1</p> <h3>Configuration</h3> <p>NAM-11 CORDEX North American domain at 0.11° 695x668 grid points including a 20-point sponge (and halo) zone surrounding the domain, 5-minute time steps, xlat1=28.525 xlon2=145.955. 56 vertical levels and a top at 10 hPa. 17 surface levels and a bottom at 15 m.</p> <h3>Spectral Nudging</h3> <p>A spectral nudging is applied to the horizontal wind component with a half-response wavelength of 1177km and a relaxation time of 13.34 h. The nudging strength is set to zero from the surface to a height of 500 hPa and increases linearly onward to the top of the model’s simulated atmosphere (10 hPa).</p> <h2>Parameterization</h2> <h3>Atmosphere</h3> <p>Precipitation: modified Sundqvist (1998); precipitation partition Bourgouin (2000) ; Implicit vertical diffusion. <br>Shallow convection: Kuo (1965) transient shallow, Non‐cloudy boundary layer formulation. <br>Deep convection: Kain-Fritsch (1990); <br>Radiation: Li & Barker (2005)</p> <h3>Surface</h3> <p>CLASS3.5c (Verseghy, 1993)</p> <p>Lake model: FLake</p> <h3>Ocean</h3> <p>Prescribed SST & sea ice fraction</p> <h3>Aerosol</h3> <p>Prescribed</p> <h2>Data Access</h2> <p>Due to its large size, the full dataset can't yet be shared publicly.</p> <p>A subset of the variables are stored on Ouranos' THREDDS server.</p> <p>- Annual files : <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/disk2/ouranos/CORDEX/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/birdhouse/disk2/ouranos/CORDEX/catalog.html</a><br>- Aggregated datasets : <a href="https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/RCM-CMIP6/catalog.html">https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/catalog/datasets/simulations/RCM-CMIP6/catalog.html</a></p> <p>Other variables can be provided upon request by writing to simulations_ouranos@ouranos.ca.</p> <p>All data are available through a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC-BY 4.0</a> license.</p> <h2>Acknowlegments</h2> <p>Developed by the <a href="https://escer.uqam.ca/">ESCER Centre</a> at UQAM (Université du Québec à Montréal) with the collaboration of Environment and Climate Change Canada (ECCC). <strong>CRCM5; Martynov et al. 2013, Separovic et al. 2013</strong></p> <p>The CRCM5 data has been generated and supplied by Ouranos.</p> <p>CRCM5 computations were made on the supercomputers beluga and narval managed by Calcul Québec and the <a href="https://alliancecan.ca/en">Digital Research Alliance of Canada</a>. The operation of this supercomputer received financial support from Innovation, Science and Economic Development Canada and the Ministère de l’Économie et de l’Innovation du Québec.</p> <h2>Some references for CRCM5</h2> <p>Asselin, M. Leduc, D. Paquin, K. Winger, A. Di Luca, M. Bukovsky, B. Music, and M. Giguère (2022). On the Intercontinental Transferability of Regional Climate Model Response to Severe Forestation. MDPI's Climate <br><a href="https://doi.org/10.3390/cli10100138">https://doi.org/10.3390/cli10100138</a> </p> <p>Bresson, E., R. Laprise, D. Paquin, J. M. Thériault, R. de Elia, 2017: Evaluating CRCM5 ability to simulate mixed precipitation. Atmosphere-Ocean. 55(2); 79-93. <a href="http://dx.doi.org/10.1080/07055900.2017.1310084">http://dx.doi.org/10.1080/07055900.2017.1310084</a> </p> <p>Leduc, M., A. Mailhot, A. Frigon, J.-L. Martel, R. Ludwig, G.B. Brietzke, M. Giguère, F. Brissette, R. Turcotte, M. Braun, (2019) ClimEx project: a 50-member ensemble of climate change projections at 12-km resolution over Europe and northeastern North America with the Canadian Regional Climate Model (CRCM5). Journal of Applied Meteorology and Climatology. <a href="https://doi.org/10.1175/JAMC-D-18-0021.1" target="_blank" rel="noopener">https://doi.org/10.1175/JAMC-D-18-0021.1</a></p> <p>Martynov A, R Laprise, L Sushama, K Winger, L Separovic, B Dugas. 2013. Reanalysis-driven climate simulation over CORDEX North America domain using the Canadian Regional Climate Model, version 5: model performance evaluation. Clim Dyn 41:2973-3005. <a href="https://doi.org/10.1007/s00382-013-1778-9">https://doi.org/10.1007/s00382-013-1778-9</a></p> <p>Martynov A, L Sushama, R Laprise, K Winger, B Dugas. 2012. Interactive lakes in the Canadian regional climate model version 5: the role of lakes in the regional climate of North America. Tellus A 64, 016226. <a href="https://doi.org/10.3402/tellusa.v64i0.16226">https://doi.org/10.3402/tellusa.v64i0.16226</a>.</p> <p>Martynov A, L Sushama, R Laprise. 2010. Simulation of temperate freezing lakes by one-dimensional lake models: performance assessment for interactive coupling with regional climate models. Boreal Env Res 15:143-164.</p> <p>Matte, D., Thériault, J. M., & Laprise, R. (2019). Mixed precipitation occurrences over southern Québec, Canada, under warmer climate conditions using a regional climate model. Climate Dynamics, 53(1), 1125–1141. <a href="https://doi.org/10.1007/s00382-018-4231-2">https://doi.org/10.1007/s00382-018-4231-2</a></p> <p>McCray, C. D., D. Paquin, J. M. Thériault, É. Bresson (2022). A multi-algorithm analysis of projected changes to freezing rain over North America in an ensemble of regional climate model simulations. Journal of Geophysical Research -Atmospheres <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD036935">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD036935</a></p> <p>McCray, D. C., J. M. Thériault, D. Paquin, É. Bresson, 2022. Quantifying the impact of precipitation-type algorithm selection on the representation of freezing rain in an ensemble of regional climate model simulations. Journal of Applied Meteorology and Climatology. <a href="https://journals.ametsoc.org/view/journals/apme/aop/JAMC-D-21-0202.1/JAMC-D-21-0202.1.xml">https://journals.ametsoc.org/view/journals/apme/aop/JAMC-D-21-0202.1/JAMC-D-21-0202.1.xml</a> </p> <p>McCray, C.D., G. Schmidt, D. Paquin, M. Leduc, Z. Bi, M. Radiyat, C. Silverman, M. Spitz, B. Brettschneider (2023). Changing Nature of High-Impact Snowfall Events in Eastern North America. Journal of Geophysical Research: Atmospheres. <a href="https://doi.org/10.1029/2023JD038804">https://doi.org/10.1029/2023JD038804</a></p> <p>Mironov D, E Heise, E Kourzeneva, B Ritter, N Schneider, A Terzhevik. 2010. Implementation of the lake parameterisation scheme FLake into the numerical weather prediction model COSMO. Boreal Env Res 15:218-230.</p> <p>Mittermeier, M., E. Bresson, D. Paquin, R. Ludwig, 2021 A deep learning approach for the identification of long-duration mixed precipitation in Montréal (Canada). Atmosphere-Ocean. <a href="https://doi.org/10.1080/07055900.2021.1992341">https://doi.org/10.1080/07055900.2021.1992341</a></p> <p>Riette S, D Caya. 2002. Sensitivity of short simulations to the various parameters in the new CRCM spectral nudging. – In: RITCHIE, H. (Ed.): Research activities in Atmospheric and Oceanic Modeling, WMO/TD No. 1105, Report No. 32: 7.39–7.40.</p> <p>Pérez Bello, A., A. Mailhot and D. Paquin, 2021 The response of daily and sub-daily extreme precipitations to changes in surface and dew point temperatures. Journal of Geophysical Research – Atmospheres <a href="http://dx.doi.org/10.1029/2021JD034972">http://dx.doi.org/10.1029/2021JD034972</a></p> <p>Pérez Bello, A., A. Mailhot, D. Paquin and D. Paquin-Ricard (2022). Temperature-precipitation scaling rates: a rainfall event-based perspective. Journal of Geophysical Research – Atmospheres. <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD037873">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022JD037873</a></p> <p>Separovic L, A Alexandru, R Laprise, A Martynov, L Sushama, K Winger, K Tete, M Valin. 2013. Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model. Clim Dyn 41:3167-3201. <a href="https://doi.org/10.1007/s00382-013-1737-5">DOI 10.1007/s00382-013-1737-5</a>.</p> <p>St-Pierre, M., J. Thériault and D. Paquin, 2019. Influence of the model spatial resolution on atmospheric conditions leading to freezing rain in regional climate simulations. Atmosphere-Ocean, <a href="https://doi.org/10.1007/s00382-013-1737-5">https://doi.org/10.1080/07055900.2019.1583088</a>.</p>
Molecular dynamics of ALDH1/2
<p>Members of the aldehyde dehydrogenase (ALDHs) superfamily have unique characteristics at their substrate binding site, where they catalyze the NAD+-dependent oxidation of aldehydes to their respective carboxylic acids. Molecular dynamics simulations, performed with the GROMACS 2022 package, have investigated the dynamics of the substrate binding site, using ALDH1 (PDB ID: 6B5G) and ALDH2 (PDB ID: 5L13) crystallographic structures. The tetrameric structures were simulated for 100 ns in: (1) APO (without any ligand bounded) state; (2) NAD-bounded state; (3) subtrate-bounded state; and (4) NAD- and substrate-bounded state. A total of 101 frames were extracted at regular intervals of 100 ps from the molecular dynamics trajectory. The ligands were removed from the substrate binding site after production to ease analysis.</p>
Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America.
<p>Köppen - Geiger scripts and resulting datasets for the publication entitled "Population dynamics shifts by Climate Change: High resolution future mid-century trends for South America." This scripts can be adapted to any geographic scale and region. Works with climate change scenarios.</p> <p>Original publication: <a href="https://doi.org/10.1016/j.gloplacha.2023.104155">https://doi.org/10.1016/j.gloplacha.2023.104155</a></p> <p>Dataset description</p> <p><strong>Scripts.rar</strong>: R Scripts used in this publication, as well they are reproducible</p> <p><strong>Readme_Köppen.txt</strong>: README file that explain the requisites and data formatting to run the scripts</p> <p><strong>Output datasets.zip</strong>: Output GIS datasets of this publication. Coordinate system GCS WGS 1984</p> <p> </p>
Genotype Data for "A genomic snapshot of demographic and cultural dynamism in Upper Mesopotamia during the Neolithic Transition"
<p>This repository contains genotype data from the article "<a href="https://www.science.org/doi/10.1126/sciadv.abo3609">A genomic snapshot of demographic and cultural dynamism in Upper Mesopotamia during the Neolithic Transition</a>". Dataset preparation protocols are described in the article. Here, we only include the genotype files of 13 newly published Çayönü samples in eigenstrat format.</p> <p>The 1KGYoruba suffix refers to the dataset prepared using variable positions in the Yoruba population (see the paper for details). Others are well known Human Origins and 1240K panels. </p> <p>Code and processed data related to the paper has been deposited <a href="http://doi.org/10.5281/zenodo.7086441">here</a>.</p> <p>* The first version has missing individuals. </p>
Stand structure and tree population dynamic attribute dataset of long abandoned strict forest reserves
<p>We provide an integrated dataset of two consecutive forest inventories, both containing plot-level, and individual tree-level data. The first provides the descriptions and measuring units (or categories) of plot-level variables (Table 1). The plot level table contains 233 records (rows), one for each selected permanent plot of six strict forest reserves located in Hungary. This dataset is georeferenced and contains information on inventories and basic stand structure attributes (Table_1_Plots ESRI shape format). </p> <p>The individual tree-level datasets were acquired by the sampling procedure, detailed in section 2.2. Species, dendrometric attributes, relative crown position, health, and decay status were documented for each tree belonging to the samples. Table 2 provides the descriptions and measuring units (or categories) of tree-level datasets in detail. Furthermore, it provides a tree history classification based on the interpretation of tree status changes. According to a simple scheme of the life and dead history of a tree, it could be classified into four main phases: establishment/regeneration phase; developmental phase; death and gradual decay of the tree trunk; terminated in decomposed/disintegrated state. The main events along these phases are ingrowth regeneration; death of the tree (mortality); disaggregation and decomposition of deadwood. We classify each sampled tree individuals into tree history categories (events and phases, Table 3) that can provide population dynamic aspects at stand level by appropriate tree aggregation functions.</p> <p>Relational link can be set between the plot-level and tree-level datasets based on the unique identification code of the site and sampling plots.</p>
Leaf water and stem cellulose oxygen isotope ratios simulated with global dynamic vegetation model LPX-Bern
<p>Description of leaf water and stem cellulose oxygen isotope ratios simulated with LPX-Bern</p> <p>Citation of describing paper:</p> <p>Keel SG, Joos F, Spahni R, Saurer M, Weigt RB, Klesse S. 2016. Simulating oxygen isotope ratios in tree ring cellulose using a dynamic global vegetation model, Biogeosciences, 13, 3869–3886, 2016 doi:10.5194/bg-13-3869-2016</p> <p>download: www.biogeosciences.net/13/3869/2016/</p> <p>General Information: Format: NetCDF, gridded</p> <p>Model: Dynamic global vegetation model LPX-Bern Version 1.0 (Land surface Processes and eXchanges, Bern) (Spahni et al., 2013; Stocker et al., 2013)</p> <p>Resolution: 3.75° x 2.5° lat/lon global Time: Monthly from Jan 1960 to Dec 2012</p> <p>Variables:</p> <p>cellu18: monthly stem cellulose δ18O (per mil) lw18: monthly leaf water δ18O (per mil) -2 NPP: monthly net primary production (g C m ) FPC: monthly fractional plant cover</p> <p>Dimensions: i=longitude, j=latitude, l=time, k=plant functional type Codes for plant functional types (k):</p> <ol> <li> <p>1 tropical broad-leaved evergreen</p> </li> <li> <p>2 tropical broad-leaved deciduous (raingreen)</p> </li> <li> <p>3 temperate needle-leaved evergreen</p> </li> <li> <p>4 temperate broad-leaved evergreen</p> </li> <li> <p>5 temperate broad-leaved deciduous (summergreen)</p> </li> <li> <p>6 boreal needle-leaved evergreen</p> </li> <li> <p>7 boreal needle-leaved deciduous (summergreen)</p> </li> <li> <p>8 boreal broad-leaved deciduous (summergreen)</p> </li> <li> <p>9 temperate herbaceous</p> </li> <li> <p>10 tropical herbaceous</p> </li> </ol>
Supplementary material for "Productivity drives the dynamics of a red kite source population that depends on immigration"
<p>Data files, code and custom functions for all analyses and figures presented in the paper. The seven data files are provided in csv format (CMRJuvRing.csv, CMRJuvDraht.csv, CMRAdDraht.csv, CMRAdSat.csv, RingRecoveries.csv, Bruten.csv, Condition.csv). The code file (RedKiteCode.txt) and the function file (Custom_functions.txt) are space delineated text files. The code file is written for R, but some models are run in JAGS from R. The code file also contains descriptions of the data files and code for data management.</p> <p> </p>
Scalably learning quantum many-body Hamiltonians from dynamical data
<p>Our <a href="https://arxiv.org/abs/2209.14328">paper</a> on Hamiltonian learning for large quantum systems contains several numerical results. The results were produced with the <a href="https://github.com/frederikwilde/differentiable-tebd">differentiable-tebd package</a> which we developed for this study. The scripts and raw output data, as well as Jupyter notebooks for generating the plots shown in the paper are contained in this repository. For more information please refer to the <a href="https://github.com/frederikwilde/scalable-dynamical-hamiltonian-learning/">guiding repository</a>.</p> <p>For funding information please refer to the acknowledgement section of the <a href="https://arxiv.org/abs/2209.14328">paper</a>.</p>
Data used in the "H-FISTA: A hierarchical algorithm for phase retrieval with application to pulsar dynamic spectra" publication
<p>Dynamic spectra used in the "A new approach to phase retrieval for pulsar dynamic spectra" publication. These data can be used to reproduce the results in the paper</p>
Volumetric imaging of cellular dynamics with deep learning enhanced bioluminescence microscopy
<p>The low photon emission of known luciferases, currently limit their widespread use as contrast agents in live cell microscopy because they demand long exposure times that are prohibitive for imaging fast biological dynamics. To increase the versatility of bioluminescence microscopy as an alternative for fluorescence microscopy, we present an improved low-light microscope in combination with deep learning methods to image extremely photon-starved samples enabling subsecond exposures for timelapse and volumetric imaging. Here, we leverage a versatile training data set for deep learning based bioluminescence microscopy including paired images of noisy and ground thruth fluorescence data of body wall muscle labeled <em>Caenorhabditis elegans</em> animals. These data include light-field images and their ground truth reconstructions for training a CNN for fast light fiel deconvolution.</p>
Classifying the generation and formation channels of dynamically-formed gravitational-wave events
<p>This dataset contains all the simulations of dynamically-formed binaries performed with the software <a href="https://github.com/Kkritos/Rapster">rapster</a>, together with trained machine-learning classification models from (Antonelli, Kritos et al, in prep.), see <a href="https://github.com/aantonelli94/TheBHClassifier">the public codes online</a>.</p> <p>All items starting with "mergers_*" are simulations of clusters and they follow the structure reported in the documentation of <a href="https://github.com/Kkritos/Rapster">rapster</a>. The simulations differ in the choice of the hyperparameters for the distribution of the cluster mass, half-mass radius and initial spin distribution for the binaries.</p> <p>All items starting from "RFClassifier_*" are machine-learning classification models that use a Random Forest Classifier and that are trained with the simulations above. The models ending with "*_gen" predict the generation of the black holes, those with "*_form" predict their formation channels. </p> <p> </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.