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17,474 results for “Complexes”
Scored protein-protein interactions accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p><a href="http://plants.proteincomplexes.org/static/data/panplant_cfms_scores_annot.txt.gz">All scored pairwise protein-protein interactions with CF-MS scores (3,076,999 unique pairwise interactions)</a></p> <ul> <li>Description: Scores between Orthogroups with the corresponding CF-MS score and eggNOG generated orthogroup descriptions.</li> <li>Note: Only the highest scoring pairs are considered significant. A CF-MS score >= 0.509 corresponds to 10% FDR, >= 0.207 corresponds to 50% FDR</li> <li>Format: OrthogroupID1 [tab] OrthogroupID2 [tab] Score [tab] Annotation1 [tab] Annotation2</li> </ul>
An agent-based model of the origins of modern linguistic complexity – supplementary information
<p>A central question in the evolution of human language is whether it emerged as a result of one specific event or from a mosaic-like constellation of different phenomena and their interactions. Three potential processes have been identified by recent research as the potential <em>primum mobile</em> for the origins of modern linguistic complexity: Self-domestication, characterized by a reduction in reactive aggression and often associated with a gracilization of the face; changes in early brain development manifested by globularization of the skull; and demographic expansion of H. sapiens during the Middle Pleistocene. We developed an agent-based model to investigate how these three factors influence transmission of information within a population. Our model shows that there is an optimal degree of both hostility and mental capacity at which the amount of transmitted information is the largest. It also shows that linguistic communi- ties formed within the population are strongest under circumstances where individuals have high levels of cognitive capacity available for information processing and there is at least a certain degree of hos- tility present. In contrast, we find no significant effects related to population size.</p>
FESOM2 simulations with increasing sea-ice model complexity under different atmospheric forcings
<p><strong>Introduction</strong></p> <p>This dataset has been compiled in support of the paper "Impact of sea-ice model complexity on the performance of an unstructured sea-ice/ocean model under different atmospheric forcings" by Zampieri et al., submitted to the Journal of Advances in Modeling Earth Systems (JAMES) published by the American Geophysical Union (AGU).</p> <p><strong>Scientific description of the dataset</strong></p> <p>The dataset contains the results of sea-ice simulations performed with the Finite-volumE Sea ice-Ocean Model version 2 (FESOM2), based on six model configurations: C1-E, C1-N, C2-E, C2-N, C3-E, and C3-N. As described in the paper, the complexity of the sea-ice model increases from the setup C1 to C3. The suffix -E and -N indicate respectively the ERA5 and NCEP atmospheric forcings used as boundary conditions for the FESOM2 model. As two iterations of the Green's function approach for the optimization of the parameter space have been performed, each configuration features three separate simulations: a control run (cnt), a first-round of optimization (opt_1), and a second and final round of optimization (opt_2). The parameter optimization is based on various sea-ice observations retrieved over the period 2002–2015. In total, 18 simulations compose the dataset (6 configurations x 3 realizations). The following 2D monthly-averaged variables are provided: the sea-ice concentration, the sea-ice thickness, the meridional and zonal components of the sea-ice velocity, and the snow thickness on top of the sea ice. The fields are defined on a global unstructured mesh denominated "CORE2", which is also included in the database.</p> <p><strong>Technical description of the dataset</strong></p> <p>As an unstructured model output is not widely diffused in the sea-ice community, we include here some suggestions for handling and analyzing the simulation results.</p> <p>The files can be interpolated to a regular grid using the following <strong><a href="https://code.mpimet.mpg.de/projects/cdo">CDO</a></strong> commands:</p> <ol> <li>Add grid description to model file: <strong><em>cdo setgrid,CORE2_mesh.nc var.fesom.yyyy.nc temp.nc</em></strong></li> <li>Interpolate to regular grid: <strong>cdo remapycon,r360x180 temp.nc var.fesom.interpolated.yyyy.nc</strong></li> </ol> <p>Furthermore, the python package<strong> <a href="https://code.mpimet.mpg.de/projects/cdo">pyfesom2</a></strong> can be used for plotting the unstructured model data and for interpolating it to a regular grid. The R package <strong><a href="https://github.com/FESOM/spheRlab">spheRlab</a></strong> can be used for plotting the model data directly on its unstructured grid and for performing further analysis. More information can be found on the <strong><a href="https://fesom.de/cmip6/work-with-awi-cm-unstructured-data/">FESOM website</a></strong>.</p> <p>The following naming convention is adopted for the model variables:</p> <ul> <li><strong><em>a_ice</em></strong> → sea-ice concentration</li> <li><strong><em>m_ice</em></strong> → sea-ice volume per unit area of ice</li> <li><strong><em>m_snow </em></strong>→ snow-volume per unit area of ice</li> <li><strong><em>vice</em></strong> → meridional component of the sea-ice velocity</li> <li><strong><em>uice</em></strong> → zonal component of the sea-ice velocity</li> </ul> <p>Three types of simulation are included:</p> <ul> <li><strong>cnt </strong>→ control run before the parameters optimization (2000–2019)</li> <li><strong>opt_1 </strong>→ after the first iteration of the parameter optimization method (2000–2015)</li> <li><strong>opt_2</strong> → after the second iteration of the parameter optimization method (2000–2019)</li> </ul> <p>Do not hesitate to contact the corresponding author (lorenzo.zampieri@awi.de) for additional information about the data processing and for any other issue with this dataset.</p> <p> </p> <p> </p>
Data for investigating structural complexity of individual Scots pine trees
<p>Tree functional traits together with processes such as forest regeneration, growth, and mortality affect forest and tree structure. Forest management inherently impacts these processes. Moreover, forest structure, biodiversity, resilience, and carbon uptake can be sustained and enhanced with forest management activities. To assess structural complexity of individual trees, comprehensive and quantitative measures are needed, and they are often lacking for current forest management practices. Fractal analysis and a single scale, independent metric called box dimension offer means for assessing structural complexity of individual trees. Terrestrial laser scanning (TLS) point clouds provide three-dimensional (3D) information on trees that can be utilized in generating the box dimension metric. This data set includes information needed for generating the box dimension from 741 individual Scots pine (<em>Pinus sylvestris</em> L.) trees from 9 sample plots with different thinning treatments located in southern boreal forests. The thinning treatments include two intensities of thinning and control treatment (i.e., no thinning treatment since the establishment). The data set can be used in characterizing structural complexity of individual Scots pine trees of various size as well as assessing effects of various thinning treatments on it.</p> <p>Please see the data descriptor for more information on the data structure and its possibilities.</p> <p>Please keep the designated corresponding author informed of any plans to use the data. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the data must include proper acknowledgement.</p>
X-ray diffraction images for PDB 6Z5G: The RSL - sulfonato-calix[8]arene complex, I23 form, citrate pH 4.0, solved by S-SAD
<p>Anomalous diffraction data collected at 5.975 KeV at Swiss Light Source beam line X06DA using a Pilatus 2M-F detector. </p> <p> </p>
Supplementary data for "The subgenual organ complex in stick insects: Functional morphology and mechanical coupling of a complex mechanosensory organ"
<p>µCT-scans of the upper tibial regions of the foreleg (T1) and the midleg (T2) of <em>Ramulus artemis</em> (Westwood, 1859), <em>Carausius morosus</em> (Sinéty, 1901), and <em>Sipyloidea sipylus</em> (Westwood, 1859). For use of scans, please cite the following publication:</p> <p>Strauß, J., Moritz, L. & Rühr, P.T. (<strong>2021</strong>): The subgenual organ complex in stick insects: Functional morphology and mechanical coupling of a complex mechanosensory organ. <em>Frontiers in Ecology and Evolution (Research Topic “Evolutionary Biomechanics of Sound Production and Reception”)</em>. doi: <a href="https://doi.org/10.3389/fevo.2021.632493">10.3389/fevo.2021.632493</a>.</p> <p>All scans were performed with a commercial μCT desktop system (Skyscan 1272, Bruker microCT, Kontich, Belgium) at the Zoological Research Museum Alexander Koenig, Leibniz Institute for Animal Biodiversity, Bonn, Germany.</p> <p><strong>µCT scan settings of all samples:</strong></p> <p><em>Ramulus artemis:</em></p> <ul> <li>tube voltage = 30 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1980 ms</li> <li>binning = 1x1</li> <li>averaging = 8</li> <li>random movement = 15 px</li> <li>voxel size = 1.8 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Ramulus_artemis_T1.tif; Ramulus_artemis_T2.tif</li> </ul> <p><em>Carausius morosus:</em></p> <ul> <li>tube voltage = 29 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1900 ms</li> <li>binning = 1x1</li> <li>averaging = 5</li> <li>random movement = 15 px</li> <li>voxel size = 1.0 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Carausius_morosus_T1.tif; Carausius_morosus_T2.tif</li> </ul> <p><em>Sipyloidea sipylus:</em></p> <ul> <li>tube voltage = 29 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1900 ms</li> <li>binning = 1x1</li> <li>averaging = 7</li> <li>random movement = 15 px</li> <li>voxel size = 1.8 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Sipyloidea_sipylus_T1.tif; Sipyloidea_sipylus_T2.tif</li> </ul>
Radiative transfer modeling in structurally-complex stands: what aspects matter most?: Dataset
<p>This repository is linked to the paper "Radiative transfer modeling in structurally-complex stands: what aspects matter most?" submitted to Annals of Forest Science and written by Frédéric ANDRÉ (corresponding author), Louis DE WERGIFOSSE, François DE COLIGNY, Nicolas BEUDEZ, Gauthier LIGOT, Vincent GAUTHRAY-GUYÉNET, Benoit COURBAUD and Mathieu JONARD.</p> <p>The repository contains the three following files :</p> <ul> <li>CalibrationResults.csv: Bayes factors and summary statistics of parameter estimates for each calibration run</li> <li>ParameterPosteriorDistributions.csv: median values and 90% credible intervals for the parameter posterior distributions</li> <li>StatisticalComparison.csv: statistics (Fractional bias, Root mean square error, Paired Student test, Pearson correlation coefficient, Parameters of the Deming regression between observed and predicted values) used to compare the 'Best model configurations'</li> </ul> <p>For more information concerning this repository or the study, please do not hesitate to contact Frédéric ANDRÉ (frederic.andre@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>
SIRAH-CoV2 initiative: nsp7-nsp8 complex (PDB id:6YHU)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 nsp7-nsp8 complex (PDB id: 6YHU, Bioassembly 1). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. </p> <p>The file 6YHU_SIRAHcg_rawdata.tar contains all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 6YHU_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 6YHU_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 6YHU_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6yhu_SIRAHcg_prot.prmtop 6yhu_SIRAHcg_prot.ncrst 6yhu_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martín Soñora (msonora@pasteur.edu.uy) Sergio Pantano (spantano@pasteur.edu.uy).</p>
X-ray diffraction images for an MDM2/Nutlin-3a complex
<p>This submission includes a zip archive of diffraction images recorded with the MARMOSAIC 225 mm CCD detector at the ESRF beam line ID23-2. Relevant meta data can be found in the headers of those diffraction images or in the Protein Data Bank entry 4HG7.</p>
Dataset: Neural correlates of error prediction in a complex motor task
<p>There are two files for each subject:</p> <p>1. errorsegments_sub##.mat -> Contains EEG Segments, that were recorded while the subject executed a clear target miss (minimal distance between the center of the ball and target > 12 cm) in the task (segment and electrode information can be found below).</p> <p>2. hitsegments_sub##.mat -> Contains EEG Segments, that were recorded while the subject executed a clear target hit (minimal distance between the center of the ball and the target < 5 cm) in the task (segment and electrode information can be found below).</p> <p>The data in the *.mat-files are stored in a three dimensional matrix: 1300 datapoints x n segments x 14 electrodes</p> <p>datapoints: The first dimension contains 1300 data points for each segment which translates to 2600 ms (500 Hz sampling frequency). The time of the ball´s release set at the 301st data point in each segment.</p> <p>segments: The second dimension stands for the number of segments. Since the number of trials which satisfy the above described distance criterion for hit and error trials differ for participants size, n is variable. </p> <p>electrodes: The third dimension consists of the 14 different electrodes that were used during data recording in this exact order: [F3 Fz F4 C4 Cz C3 P3 Pz P4 VEOGu VEOGo HEOGre HEOGli FCz]</p> <p> </p> <p> </p>
SeMRA Protein Complex Mappings Database
<p>Analyze the landscape of protein complex nomenclature resources, species-agnostic. See instructions for reproduction and usage in the attached README.md.</p>
IrCytoToxDB: a dataset of iridium(III) complexes cytotoxicities against various cell lines
<h1><strong>If you use this dataset, please cite our paper</strong>: <a href="https://doi.org/10.1038/s41597-024-03735-w">https://doi.org/10.1038/s41597-024-03735-w</a></h1> <p>IrCytoToxDB contains 4546 experimentally measured cytotoxicity values of 1295 unique iridium(III) complexes against 177 different cell lines reported in the 389 literature papers from 2008 to 2025.</p> <p>The 15 columns of this dataset are explained as follows:</p> <ol> <li>L1 — SMILES representation of the L1 ligand attached to the iridium ion</li> <li>L2 — SMILES representation of the L2 ligand attached to the iridium ion</li> <li>L3 — SMILES representation of the L3 ligand attached to the iridium ion</li> <li>L4 — SMILES representation of the L4 ligand attached to the iridium ion</li> <li>Counterion — SMILES representation of the counterion (if the complex molecule is charged)</li> <li>Abbreviation_in_the_article — the original abbreviation depicting the complex in the article</li> <li>IC50Dark(M*10^-6) — value of IC<sub>50</sub> originally presented in the article</li> <li>IC50Dark_standard_error(M*10^-6) — standard error of IC<sub>50</sub> originally presented in the article</li> <li>IC50Light(M*10^-6) — value of IC<sub>50</sub> under irradiation originally presented in the article</li> <li>IC50Light_standard_error(M*10^-6) — standard error of IC<sub>50 </sub>under irradiation originally presented in the article</li> <li>Excitation_Wavelength(nm) — excitation wavelength related to IC50Light values</li> <li>Irradiation_Time(minutes) — irradiation time related to IC50Light values</li> <li>Irradiation_Power(W*m^-2) — power of light source related to IC50Light values</li> <li>Cell_line — cell line (HeLa, A549, etc.)</li> <li>Time(h) — time of exposure of the complexes to the cell line</li> <li>DOI — doi of a data source for given values</li> <li>Year — year of a data source for given values </li> <li>Comments — additional comments regarding the data</li> </ol> <p>Additional remarks:</p> <ul> <li>The array of iridium(III) complexes could be formally mainly in two parts – <em>bis</em>-cyclometalated Ir(III) complexes and half-sandwich Ir(III) complexes. The former usually contain two cyclometalated ligands and one or two ancillary (or third cyclometalated) ligand; for these L1 and L2 correspond to the cyclometalated ligands and L3 (or L3 and L4) corresponds to the ancillary ligand. The latter usually contain one cyclopentadiene<sup>-</sup>(Cp<sup>-</sup>)-based ligand, one bidentate ligand and one monodentate ligand; for these L1 corresponds to the Cp<sup>-</sup>-based ligand, L2 corresponds to the bidentate ligand and L3 corresponds to the monodentate ligand.</li> <li>Some ligands make formally covalent bonds with the Ir(III) ion. For these a negatively charged bond-forming atom is drawn in the SMILES of corresponding ligand.</li> </ul>
Datasets of sequences, alignments and structural models generated for the structural prediction of complexes mediated by intrinsically disordered regions.
<p>This repository contains input and ouput files used and generated for the scanning of intrinsically disordered region and the prediction of their binding sites to receptor proteins using the <a href="https://github.com/i2bc/SCAN_IDR">SCAN_IDR</a> pipeline with AlphaFold2-Multimer.</p><p>It contains two archives: </p><ol><li><a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content"><i><strong>scanidr_data_repository_corr6J08.tar</strong></i></a> dedicated to the analysis of a dataset of 42 protein complexes non redundant with the dataset used for AlphaFold2 training,</li><li><a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content"><i><strong>923_elm_cases_repository.tar.gz</strong></i></a> dedicated to the analysis of 923 complexes from the ELM database.</li></ol><p>These data can be used to rerun specific sections of the pipeline and scripts provided in: <a href="https://github.com/i2bc/SCAN_IDR">https://github.com/i2bc/SCAN_IDR</a></p><h4><strong>Dataset of 42 non redundant complexes</strong></h4><p>The first archive <a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content"><i><strong>scanidr_data_repository_corr6J08.tar</strong></i></a> contains 3 compressed directories and a README file detailing their contents :</p><ul><li>the initial raw sequence and alignment data for every chain -> DIRECTORY <strong>fasta_msa/</strong></li><li>the input and output data of every Alphafold run for every complex -> DIRECTORY <strong>af2_runs/</strong></li><li>the native reference structures -> DIRECTORY <strong>ref_capri_curated/</strong></li></ul><p>The protein-peptide complex cases have been assigned a distinct index number, from 1 to 42, consistent across the several directories of the archive. Their corresponding directories are labelled as <i><index>_<pdbcode></i>.</p><p><i>The models in this archive were generated using AlphaFold2-Multimer v2.2</i></p><h4><strong>Dataset of 923 complexes selected from the ELM database</strong></h4><p>The second archive <a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content"><i><strong>923_elm_cases_repository.tar.gz</strong></i></a> contains input and ouput files used and generated for the analysis of 923 Eukaryotic Linear Motifs (ELM) database entries.</p><p>Each ELM entry is indexed with specific integer id and is composed of a receptor and a ligand protein. </p><p>The archive contains a Table associating ELM indexes with the ELM entry information, 5 directories and a README file detailing their contents:</p><ul><li>the table describing ELM entries -> FILE <strong>Table_923ELM_uid_delimitations_info_for_archive.txt</strong></li><li>the initial raw sequence and multiple sequence alignment (MSA) data for every chain -> DIRECTORY <strong>fasta_msa/</strong></li><li>the concatenated MSA model for every ELM complex and protocol used -> DIRECTORY <strong>af2_elm_coali_inputs/</strong></li><li>the best model of every AF2 protocol for every complex according to the AF2 -> DIRECTORY <strong>af2_elm_models/</strong></li><li>the best model cut in the ligand part to select only the ELM motifs as used for the evaluation of the models -> DIRECTORY <strong>elm_cut_models/</strong></li><li>the reference structures used for the evaluation of the models -> DIRECTORY <strong>ref_capri_curated/</strong></li></ul><p><i>The models in this archive were generated using AlphaFold2-Multimer v2.3</i></p>
Deep and complex vascular anatomy in the rat brain described with Ultrasound Localization Microscopy in 3D
<p><strong>Abstract:</strong></p><p>Ultrasound Localization Microscopy (<strong>ULM</strong>) enables imaging microvessels in the brain with a resolution of a few tens of micrometers <i>in vivo</i>. The planar architecture of arterioles and venules was revealed with a 2D ultrasound scanner in the cortex of the rat brain. However, deeper in the brain, where the vascularization becomes tri-dimensional, 2D imaging remains limited by the elevation projection. In this study, volumetric ultrasound imaging was performed in the craniotomized rat brain to yield 3D ULM<i> in vivo</i> within 7.5 min of acquisition with a commercial system. For instance, it highlighted the thalamus or the circle of Willis with small vessels down to 21 µm. Microbubbles tracking also gave access to the 3D velocity vector of blood flow allowing to distinguish flow directions. Volumetric ULM resolved deep complex tri-dimensional vascular structures and was compared to 2D ULM. It is a safe, simple and repeatable system to image wide field of view in the brain.</p><p><strong>Data Description:</strong></p><p>Microbubbles have been detected, localized, and tracking with 3D ultrasound imaging <i>in vivo</i> in a rat brain with skull removal.</p><p>Individual microbubble trajectories are described in 4 columns vectores: <strong>[z, x, y, time]</strong> for each position of the path. Space positions are given in [mm], and times are given in [ms]. Trajectories data are stored in .mat files (<strong>tracks_0xx.mat </strong>and zipped inside <strong>tracks.zip</strong>) as cell arrays.</p><p>Tracks can be binned inside a volumetric grid with the sample code (<strong>ULM_rendering.m</strong>).</p><p><strong>Reference to be cited: </strong>Chavignon, Heiles, Hingot, Orset, Vivien and Couture.</p><p><i>Deep and complex vascular anatomy in the rat brain described with Ultrasound Localization Microscopy in 3D.</i><br> </p>
Simulations of METTL3/METTL14 in complex with SAM+ADE or SAH+m6ADE
<p>The current dataset contains a set of MD simulation trajectories for the METTLL3/METTL14 heterodimer in complex with the set of (co-)substrates or (co-)products SAM+ADE or SAH+m6ADE respectively. Each compressed file contains 16 independent trajectories of 500 ns each. The trajectories are written in .xtc format, and therefore a .pdb file is needed to read them. The parameters file ("md_input.mdp") is also provided. The current dataset also contains the PLUMED file that was used for the DFTB3/MM metadynamics simulations ("plumed.dat").</p>
Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media
<p>Dataset associated to the publication</p><p>"Spatial structure, chemotaxis and quorum sensing shape bacterial biomass accumulation in complex porous media"</p><p>By</p><p>David Scheidweiler, Ankur Deep Bordoloi, Wenqiao Jiao, Vladimir Sentchilo, Monica Bollani, Audam Chhun, Philipp Engel and Pietro de Anna</p><p>Folder named "Figure_X" contains the original raw data, analysed data and source data for each plot within figure "X" on the manuscript and supplementary information.</p><p>We do not provide raw data for each replica as one flow&growth experiment consists in 50 large images for a total of about 12 GB per dataset. Thus, we provide here the original data for the Wild Type experiment and the control D-luxS mutant. The data for the replicas and other control experiment can be available upon request.</p><p>We provide Matlab scripts to read and analyze the original images.</p>
Analysis of a complex role of trees in street canyon using LES model (experiment: Terronska)
<h1>README</h1> <p>This is a companion dataset to the paper <em>Analysis of a complex role of trees in street canyon using LES</em> model by <em>Řezníček et al.</em>, to be submitted to <em><span>Quarterly</span> <span>Journal</span> of the Royal Meteorological Society</em>. All the supplementary data needed for the reproduction of the experiment described in the manuscript are provided on this ZENODO repository. The supplementary data includes the following:</p> <p>1. <em>01_palm_source_code.zip</em> contains the source code for the current version of the PALM model used for this experiment</p> <p>2. <em>02_inputs-configs.zip</em> which contains:</p> <ul> <li>static driver files (for cases 01 = full-trees, 02 = half-trees, 03 = no-strees)</li> <li>dynamic driver files (for different winds directions W = west, SW = southwest, S = south and stratifications C = convective, N = neutral + stable)</li> <li>configuration files for the first PALM run (p3d), and the configuration files for the restart runs (p3dr) for each of the performed simulations</li> <li>the files with N02 are apllied for child domain</li> </ul> <p>3. 03_maps-GIS contains maps in gis or png format with one hour averages outputs: </p> <ul> <li>the cases are terC/N_W/SW/S_01/02/03 for the stratifications, wind direcrions and trees-scenario (see the legend above)</li> <li>abs for absolute values, diff for differences from no-tree scenario, 01h = 1 hour average</li> <li>variables are bio_UTCI - universal thermal climate index [deg C], kc_PM10 = PM10 concentration in 2m or 10m height [<span>μ</span>/m^3], theta_2m = temperature in 2m [deg C], wspeed_10m = wind-speed in 10m, tsurf = surface temperature [deg C], rad_sw_in = incoming shortwave radiation flux [W/m^2] and rad_lw_out = outgoing longwave radiation flux [W/m^2]</li> </ul> <p>4. 04_cuts contains svg and png plots with vertical and horizontal (xy) cuts </p> <ul> <li>the cases are terC/N_W_01/02/03 for the stratifications and trees-scenario (see the legend above) and west winds</li> <li>jugp-ciirc = the vertical cut for (JugP) street (near the ciirc-CTU building), terr-street = the vertical cut for (Terr) street</li> </ul> <h1>PALM MODEL INSTALLATION AND USAGE GUIDE</h1> <h2>A. Installation</h2> <p>1. First, make sure to satisfy the Software Requirements. On Debian-based Linux Distributions, this can be achieved by the following command:</p> <p><code>sudo apt-get install gfortran g++ make cmake coreutils libopenmpi-dev openmpi-bin libnetcdff-dev netcdf-bin libfftw3-dev python3-pip python3-pyqt5 flex bison ncl-ncarg</code></p> <p>2. Also, some additional python dependencies are needed, which can be installed using pip. In case you want to use a virtual environment for these dependencies, please make sure to create one first. Afterwards, you can install the python dependencies by executing the following command:</p> <p><code>python3 -m pip install -r requirements.txt</code></p> <p>3. Now the PALM model system can be installed with the following commands (please replace with the desired installation directory):</p> <p><code>export install_prefix=""</code><br><code>bash install -p ${install_prefix}</code><br><code>export PATH=${install_prefix}/bin:${PATH}</code></p> <p>4. The following optional command permanently adds this installation to your bash environment:</p> <p><code>echo "export PATH=${install_prefix}/bin:\${PATH}" >> ~/.bashrc</code></p> <p>5. Type <code>bash install -h</code> to get all available options of the install script. During installation, the script calls the respective install script of all packages in this repository and installs them to the chosen directory. Therefore, it is not necessary to manually install any of the packages.</p> <p>You can test your installation with the following commands:</p> <p><code>palmtest --cases urban_environment_restart --cores 4</code></p> <h2>B. Usage</h2> <p>After a successful installation, the executables for all packages have been linked into the directory <code>/bin</code> and a default PALM configuration file can be found at <code>/.palm.config.default</code>. In case you have installed the python dependencies inside a virtual environment, that environment needs to be active whenever you wand to use PALM. For usage of each of the packages, please refer to their individual documentation. Next, you need to create your first PALM setup in order to start a simulation. To get a simple preconfigured setup and start your first PALM simulation, please execute the following sequence of commands:</p> <p><code>mkdir -p "${install_prefix}/JOBS/example_cbl/INPUT"</code><br><code>cp "packages/palm/model/tests/cases/example_cbl/INPUT/example_cbl_p3d" "${install_prefix}/JOBS/example_cbl/INPUT/"</code><br><code>cd ${install_prefix}</code><br><code>palmrun -r example_cbl -c default -a "d3#" -X 4 -v -z</code></p> <h1>ACKNOWLEDGEMENT</h1> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic<br>georisks.</p> <p>The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Data for "Nano onions based on an amphiphilic Au3(pyrazolate)3 complex"
<p>This upload contains raw data (NMR, DLS, Zeta Potential) files for the article: </p> <p><strong>Nano onions based on an amphiphilic Au<sub>3</sub>(pyrazolate)<sub>3</sub> complex</strong></p> <p>Nanoscale, 2024, Advance Article, <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4NR03901G">https://doi.org/10.1039/D4NR03901G</a></p>
Complex basis of hybrid female sterility and Haldane's rule in Heliconius butterflies: Z-linkage and epistasis - RADseq and RNAseq reads, sterility phenotypes and pedigree
<p>RADseq and RNAseq reads (.fastq files), and sterility phenotypes and pedigree (.xlsx) using for QTL mapping of Heliconius pardalinus sterility crosses in Rosser, N., Edelman, N.B., Queste, L.M., Nelson, M., Seixas, F., Dasmahapatra, K.K. and Mallet, J., 2021. Complex basis of hybrid female sterility and Haldane’s rule in Heliconius butterflies: Z-linkage and epistasis, accepted for publication in Molecular Ecology. Queries to Neil Rosser (neil.rosser@york.ac.uk). </p> <p> </p>
Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"
<p>Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields.</p> <p>Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample.</p> <p>Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.</p>
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