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14,239 results for “STRUCTURE”
Data from: Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones
<p>This vcf file contains 7.023.689 SNPs and 870 honey bee samples, as described in the paper "Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones" by Wragg et al., available at https://doi.org/10.1101/2021.09.20.460798 as preprint.</p> <p>Eight hundred and seventy haploid drone samples from several honey bee subspecies hybrids were sequenced and aligned to the HAv3.1 reference genome. Sequence read alignment and genotyping quality filters were used to obtain a selection of 7.023.689 high-quality SNPs. The file Diversity_Study_629_Samples.txt corresponds to the 629 unique samples that were used for the diversity study described in the paper and can be used to recreate the restricted diversity dataset using bcftools or an equivalent software.</p> <p>Having sequenced haploid drones, heterozygous SNPs resulting from duplicated regions could be filtered out and the data is phased.</p>
Discoba protein sequences for protein structure predictions
<p>Comprehensive database of Discoba protein sequences, gathered for the purpose of improving protein structure predictions of Discoba species (including <em>Trypanosoma </em>and <em>Leishmania</em>) by AlphaFold and RoseTTAFold. Originally gathered for use with: https://github.com/zephyris/discoba_alphafold</p>
Enhanced 3D velocity structure, seismicity relocation and basement characterization of Changning shale gas and salt mining regions in Sichuan Basin
<p>This repository contains the datasets and results of the joint inversion-based Vp/Vs model consistency constrained double difference seismic tomography carried out for the manuscript titled “Enhanced 3D velocity structure, seismicity relocation and basement characterization of Changning shale gas and salt mining regions in Sichuan Basin.” Included are the following: column descriptions of data files, catalog earthquake information (CX_event.dat), relocated events after inversion (CX_tomoDDMC.reloc), inverted Vp model (CX_Vpmodel.dat), inverted Vs model (CX_Vsmodel.dat) and inverted Vp/Vs model (CX_VpVsmodel.dat). Please consult the manual for tomoDD by Zhang and Thurber (2003) for detailed formats of these files. In addition, an averaged velocity model (MOD_averaged) computed based on the inversion results is included, and the converged model (Vp_model_reinverted) resulting from the reinversion, as well as basement structure data for Figure 14.</p>
Database of Uniaxial Cyclic and Tensile Coupon Tests for Structural Metallic Materials
<p><strong>Database of Uniaxial Cyclic and Tensile Coupon Tests for Structural Metallic Materials</strong></p> <p> </p> <p><strong>Background</strong></p> <p>This dataset contains data from monotonic and cyclic loading experiments on structural metallic materials. The materials are primarily structural steels and one iron-based shape memory alloy is also included. Summary files are included that provide an overview of the database and data from the individual experiments is also included.</p> <p>The files included in the database are outlined below and the format of the files is briefly described. Additional information regarding the formatting can be found through the post-processing library (https://github.com/ahartloper/rlmtp/tree/master/protocols).</p> <p><strong>Usage</strong></p> <ul> <li>The data is licensed through the Creative Commons Attribution 4.0 International.</li> <li>If you have used our data and are publishing your work, we ask that you please reference both: <ol> <li>this database through its DOI, and</li> <li>any publication that is associated with the experiments. See the Overall_Summary and Database_References files for the associated publication references.</li> </ol> </li> </ul> <p><strong>Included Files</strong></p> <ul> <li>Overall_Summary_2022-08-25_v1-0-0.csv: summarises the specimen information for all experiments in the database.</li> <li>Summarized_Mechanical_Props_Campaign_2022-08-25_v1-0-0.csv: summarises the average initial yield stress and average initial elastic modulus per campaign.</li> <li>Unreduced_Data-#_v1-0-0.zip: contain the original (not downsampled) data <ul> <li>Where # is one of: 1, 2, 3, 4, 5, 6. The unreduced data is broken into separate archives because of upload limitations to Zenodo. Together they provide all the experimental data.</li> <li>We recommend you un-zip all the folders and place them in one "Unreduced_Data" directory similar to the "Clean_Data"</li> <li>The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.</li> <li>There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the unreduced data.</li> <li>The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.</li> </ul> </li> <li>Clean_Data_v1-0-0.zip: contains all the downsampled data <ul> <li>The experimental data is provided through .csv files for each test that contain the processed data. The experiments are organised by experimental campaign and named by load protocol and specimen. A .pdf file accompanies each test showing the stress-strain graph.</li> <li>There is a "db_tag_clean_data_map.csv" file that is used to map the database summary with the clean data.</li> <li>The computed yield stresses and elastic moduli are stored in the "yield_stress" directory.</li> </ul> </li> <li>Database_References_v1-0-0.bib <ul> <li>Contains a bibtex reference for many of the experiments in the database. Corresponds to the "citekey" entry in the summary files. </li> </ul> </li> </ul> <p> </p> <p><strong>File Format: Downsampled Data</strong></p> <p>These are the "LP_<N>_Specimen_<M>_processed_data.csv" files in the "Clean_Data" directory. The <N> is the load protocol designation and the <M> is the specimen number for that load protocol and material source. Each file contains the following columns:</p> <ul> <li>The header of the first column is empty: the first column corresponds to the index of the sample point in the original (unreduced) data</li> <li>Time[s]: time in seconds since the start of the test</li> <li>e_true: true strain</li> <li>Sigma_true: true stress in MPa</li> <li>(optional) Temperature[C]: the surface temperature in degC</li> </ul> <p>These data files can be easily loaded using the pandas library in Python through:</p> <pre><code class="language-python">import pandas data = pandas.read_csv(data_file, index_col=0)</code></pre> <p>The data is formatted so it can be used directly in RESSPyLab (https://github.com/AlbanoCastroSousa/RESSPyLab). Note that the column names "e_true" and "Sigma_true" were kept for backwards compatibility reasons with RESSPyLab.</p> <p> </p> <p><strong>File Format: Unreduced Data</strong></p> <p>These are the "LP_<N>_Specimen_<M>_processed_data.csv" files in the "Unreduced_Data" directory. The <N> is the load protocol designation and the <M> is the specimen number for that load protocol and material source. Each file contains the following columns:</p> <ul> <li>The first column is the index of each data point</li> <li>S/No: sample number recorded by the DAQ</li> <li>System Date: Date and time of sample</li> <li>Time[s]: time in seconds since the start of the test</li> <li>C_1_Force[kN]: load cell force</li> <li>C_1_Déform1[mm]: extensometer displacement</li> <li>C_1_Déplacement[mm]: cross-head displacement</li> <li>Eng_Stress[MPa]: engineering stress</li> <li>Eng_Strain[]: engineering strain</li> <li>e_true: true strain</li> <li>Sigma_true: true stress in MPa</li> <li>(optional) Temperature[C]: specimen surface temperature in degC</li> </ul> <p>The data can be loaded and used similarly to the downsampled data.</p> <p> </p> <p><strong>File Format: Overall_Summary</strong></p> <p>The overall summary file provides data on all the test specimens in the database. The columns include:</p> <ul> <li>hidden_index: internal reference ID</li> <li>grade: material grade</li> <li>spec: specifications for the material</li> <li>source: base material for the test specimen</li> <li>id: internal name for the specimen</li> <li>lp: load protocol</li> <li>size: type of specimen (M8, M12, M20)</li> <li>gage_length__mm_: unreduced section length in mm</li> <li>avg_reduced_dia__mm_: average measured diameter for the reduced section in mm</li> <li>avg_fractured_dia_top__mm_: average measured diameter of the top fracture surface in mm</li> <li>avg_fractured_dia_bot__mm_: average measured diameter of the bottom fracture surface in mm</li> <li>fy_n__mpa_: nominal yield stress</li> <li>fu_n__mpa_: nominal ultimate stress</li> <li>t_a__deg_c_: ambient temperature in degC</li> <li>date: date of test</li> <li>investigator: person(s) who conducted the test</li> <li>location: laboratory where test was conducted</li> <li>machine: setup used to conduct test</li> <li>pid_force_k_p, pid_force_t_i, pid_force_t_d: PID parameters for force control</li> <li>pid_disp_k_p, pid_disp_t_i, pid_disp_t_d: PID parameters for displacement control</li> <li>pid_extenso_k_p, pid_extenso_t_i, pid_extenso_t_d: PID parameters for extensometer control</li> <li>citekey: reference corresponding to the Database_References.bib file</li> <li>yield_stress__mpa_: computed yield stress in MPa</li> <li>elastic_modulus__mpa_: computed elastic modulus in MPa</li> <li>fracture_strain: computed average true strain across the fracture surface</li> <li>c,si,mn,p,s,n,cu,mo,ni,cr,v,nb,ti,al,b,zr,sn,ca,h,fe: chemical compositions in units of %mass</li> <li>file: file name of corresponding clean (downsampled) stress-strain data</li> </ul> <p> </p> <p><strong>File Format: </strong><strong>Summarized_Mechanical_Props_Campaign</strong></p> <p>Meant to be loaded in Python as a pandas DataFrame with multi-indexing, e.g.,</p> <pre><code class="language-python">tab1 = pd.read_csv('Summarized_Mechanical_Props_Campaign_' + date + version + '.csv', index_col=[0, 1, 2, 3], skipinitialspace=True, header=[0, 1], keep_default_na=False, na_values='')</code></pre> <ul> <li>citekey: reference in "Campaign_References.bib".</li> <li>Grade: material grade.</li> <li>Spec.: specifications (e.g., J2+N).</li> <li>Yield Stress [MPa]: initial yield stress in MPa <ul> <li>size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign</li> </ul> </li> <li>Elastic Modulus [MPa]: initial elastic modulus in MPa <ul> <li>size, count, mean, coefvar: number of experiments in campaign, number of experiments in mean, mean value for campaign, coefficient of variation for campaign</li> </ul> </li> </ul> <p> </p> <p><strong>Caveats</strong></p> <ul> <li>The files in the following directories were tested before the protocol was established. Therefore, only the true stress-strain is available for each: <ul> <li>A500</li> <li>A992_Gr50</li> <li>BCP325</li> <li>BCR295</li> <li>HYP400</li> <li>S460NL</li> <li>S690QL/25mm</li> <li>S355J2_Plates/S355J2_N_25mm and S355J2_N_50mm</li> </ul> </li> </ul>
Data release for "Things that might Go bump in the night: Assessing structure in the binary black hole mass spectrum"
<p>Data release accompanying "Things that might go bump in the night: Assessing structure in the binary black hole mass spectrum"</p> <p>Included are:</p> <ul> <li>500 mock catalogs containing 69 events each, in netCDF4 format (can be found in `with_z_evo_lalprior_69_evs_prod_mock_PE.tar.gz`)</li> <li>A corresponding injection set using O3 sensitivity (`with_z_evo_lalprior_69_evs_prod_injections.h5`)</li> <li>Files containing hyperposterior samples resulting from a Power Law + Spline fit to 100 of the 69-event mock catalogs (`PowerLawSpline_69evs_20knots_2t100_*_result.json`)</li> <li>Files containing hyperposterior samples resulting from a smoothed power law fit to 100 of the 69-event mock catalogs (`Truncated_69evs_*_result.json`)</li> </ul> <p>Code using these files to create all plots in the paper can be found at https://git.ligo.org/amanda.farah/bump-significance</p> <p>Code used to create the mock catalogs can be found at https://git.ligo.org/amanda.farah/mock-PE</p>
Data from: Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide
<p><strong>Summary</strong></p> <p>This dataset accompanies the publication "<strong>Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide</strong>" published in Zoo Biology. It contains anonymised data from 540 captive flamingo populations, and includes the four species: <em>Phoeniconaias minor, Phoenicopterus chilensis, Phoenicopterus roseus</em> and<em> Phoenicopterus ruber</em>. Data were sourced from the Zoological Information Management System (ZIMS), operated by Species360 (https://www.species360.org/). ZIMS is the largest real-time database of comprehensive and standardized information spanning more than 1,200 zoological collections globally, and provides the number of institutions currently managing each flamingo species and both their current and historic population sizes. These data were used to investigate the relationship between reproductive success and both flock size, and structure, on a global scale.</p> <p>This dataset also contains climatic data provided by WorldClim, which were used to assess the influence of climatic variables on captive flamingo reproductive success globally. The WorldClim database averages 19 different climatic variables derived from monthly temperature and rainfall values at a 1 km spatial resolution for the period 1970-2000. Using geographic coordinates (latitude and longitude) we calculated several climatic metrics for each institution. </p> <p> </p> <p><strong>Description of the Dataset</strong></p> <p>One file is provided for each species (<em>P. minor, P. chilensis, P. roseus </em>and <em>P. ruber</em>) as a csv file. Each file contains the following 15 columns:</p> <ul> <li><strong>Institution Code: </strong>An anonymous code used to identify individual zoological institutions. </li> <li><strong>Country: </strong>The country where the institution is located.</li> <li><strong>Year: </strong>Current year (<em>t</em>).</li> <li><strong>Flock Size:</strong> Flock size in year <em>t.</em></li> <li><strong>Males: </strong>The number of males in the flock in year <em>t.</em> </li> <li><strong>Females:</strong> The number of females in the flock in year <em>t.</em></li> <li><strong>Unsexed:</strong> The number of unsexed individuals in the flock in year <em>t.</em></li> <li><strong>Proportion of Females: </strong>The proportion of the flock made up of female individuals in year <em>t</em>. </li> <li><strong>Proportion of Unsexed:</strong> The proportion of the flock made up of unsexed individuals in year <em>t.</em></li> <li><strong>Hatches:</strong> Number of birds hatched in year <em>t.</em></li> <li><strong>Proportion of Additions:</strong> The proportion of the flock in year <em>t</em> made up of additions from year <em>t-1</em> (not including new birds hatched into the flock).</li> <li><strong>MAP: </strong>Mean annual precipitation (mm).</li> <li><strong>MAT: </strong>Mean annual temperature (°C).</li> <li><strong>MAP Var: </strong>Mean annual variation in precipitation (MAP coefficient of variation).</li> <li><strong>MAT Var: </strong>Mean annual variation in temperature (MAT standard deviation).</li> </ul> <p>Note: Mean Annual Temperature (MAT) is provided by WorldClim as °C multiplied by 10, and similarly mean annual variation in temperature as MAT standard deviation multiplied by 100. In the corresponding publication, both were divided (by 10 and 100 respectively) prior to modelling to avoid confusion in the units used.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge and thank all Species360 member institutions for their continued support and data input. The research which data refers to was funded by the Irish Research Council Laureate Awards 2017/2018 IRCLA/2017/60 to Y.M.B. Additionally, S.Q.S. received funding from the International Max Planck Research School for Organismal Biology. The Species360 Conservation Science Alliance would like to thank their sponsors: the World Association of Zoos and Aquariums, Wildlife Reserves of Singapore, and Copenhagen Zoo. </p> <p> </p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. Similarly, data contained within ZIMS are based on submitted records from individual institutions, and are not subject to editorial verification, potentially permitting errors or failure to update species holdings etc. Despite this, ZIMS represents the only global database of zoo collection composition records, and as a result, is used by the IUCN, Convention on International Trade in Endangered Species (CITES), the Wildlife Trade Monitoring Network (TRAFFIC), United States Fish and Wildlife Service (USFWS) and Department for Environment, Food and Rural Affairs (DEFRA). </p> <p> </p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the corresponding publication:</p> <p>Mooney, A., Teare, J. A., Staerk, J.,Smeele, S. Q., Rose, P., Edell, R. H., King, C. E., Conrad, L., & Buckley, Y. M. (2023). Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide.<em> Zoo Biology</em>, 1–14. <a href="https://doi.org/10.1002/zoo.21753">https://doi.org/10.1002/zoo.21753</a></p> <p> </p> <p> </p>
Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat
<p>SNPs obtained by UNEAK pipeline for <em>Habromys schmidlyi </em>and <em>Reithrodontomys microdon</em>. </p> <p>Pleae cite as: </p> <p>Colunga-Salas P., T Marines-Macías, G Hernández-Canchola, S Barbosa, C Ramírez, JB Searle, L León-Paniagua. 2022. <strong>Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat</strong>. Mammalian Reasearch. Doi: 10.1007/s13364-022-00667-x</p>
Quantitative electronic structure and work-function changes of liquid water induced by solute - data
<p>Data set pertaining to the article "Quantitative electronic structure and work-function changes of liquid water induced by solute" | Physical Chemistry Chemical Physics, 24, 1310 (2022).</p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07 using the NXmpes user contributed format suggested by the Fairmat consortium, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> A few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file.<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are shown:<br> 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br> 2. As-measured data ('raw').</p> <p>Files with extension .txt are comma-separated ascii-files.<br> The following files are provided:</p> <p>Photoemission data pertaining to solute measurements using the cut-off as energy reference:<br> NaI_data.h5<br> tbai_data.h5</p> <p>Biased spectra were typically recorded in the following order:<br> [cut-off (fine), cut-off (coarse), (valence band)*(N repeats)]*(M repeats)<br> To avoid the saving of overly complex hdf5-files, these data were saved in a different order, namely:<br> [cut-off (fine)*(M repeats), cut-off (coarse)*(M repeats), (valence band)*(N*M repeats)].</p> <p>Numeric representations of the traces shown in the article's figures:<br> Figure_1a-data.txt<br> Figure_1b-data.txt<br> Figure_2a-data.txt<br> Figure_2b-data.txt<br> Figure_2c-data.txt<br> Figure_3-data.txt<br> Figure_4-data.txt<br> Figure_5a-data.txt<br> Figure_5b-data.txt<br> Figure_6a-data.txt<br> Figure_6b-data.txt<br> Figure_6c-data.txt<br> Figure_7_diff_spectra-data.txt<br> Figure_8-data.txt</p> <p>Traces shown in several figures are included only in the data file pertaining to the figure in which they occur first.</p> <p> </p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Bibliographic Data from the Computational Methods Applied to Earthen Historical Structures Review
<p>This database contains all the bibliographic information about the 293 records found after applying the Search Strategy used for the Computational Methods Applied to Earthen Historical Structures Review. Such strategy consisted on using relevant keywords grouped into three different search queries within ”TITLE-ABS-KEY”, for the years 2019-2023:</p> <ol> <li>(”earthen heritage” OR ”earthen historical building*” OR ”earthen historical structure*” OR ”earthen architect*” OR ”earthen monument*”).</li> <li>(adobe OR ”rammed earth” OR cob ) AND (”computational method*” OR ”numerical analy*”).</li> <li>(adobe OR ”rammed earth” OR cob ) AND (fem OR dem OR la OR ”finite element” OR ”discrete element” OR ”limit analysis”).</li> </ol> <p>The search was conducted on April 7, 2023.</p>
LIPID MAPS® Structure Database (LMSD) formatted for MetFrag
<p>This repository contains the LIPID MAPS® Structure Database (<a href="https://www.lipidmaps.org/databases/lmsd/overview">LMSD</a>) formatted for use in <a href="https://msbi.ipb-halle.de/MetFrag/">MetFrag</a> (and other workflows).</p> <p><em>LIPID MAPS® Lipidomics Gateway is a free, comprehensive website for researchers interested in lipid biology. Use <a href="https://www.lipidmaps.org"> https://www.lipidmaps.org</a> to stay abreast of developments each month from across the field, and explore the rich information collections, tools and resources from the LIPID Metabolites And Pathways Strategy (LIPID MAPS®) Consortium. </em><br> </p> <p>The workflow used to create this file (by B. Talavera Andújar) can be found here: <a href="https://gitlab.lcsb.uni.lu/eci/simple-utilities/sdf2csv">https://gitlab.lcsb.uni.lu/eci/simple-utilities/sdf2csv</a></p> <p><strong>Reference:</strong> LMSD: LIPID MAPS® structure database, Sud M., Fahy E., Cotter D., Brown A., Dennis E., Glass C., Murphy R., Raetz C., Russell D., and Subramaniam S., Nucleic Acids Research, 2006, DOI: <a href="https://doi.org/10.1093/nar/gkl838"> 10.1093/nar/gkl838 </a></p>
Unveiling the atomistic and electronic structure of NiII–NO adduct in a MOF-based catalyst by EPR spectroscopy and quantum chemical modelling
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20230706_01_CW_Xband </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20230706_02_HYSCORE </strong>and <strong>PARACAT_WP4_20230706_03_ENDOR </strong>folders include X-band HYSCORE and ENDOR data; original data are in DTA/DSC and txt formats.</li> <li>Files in <strong>PARACAT_WP4_20230706_ 04_MATLAB</strong> and<strong> PARACAT_WP4_20230706_ 05_Modelling</strong> folders include matlab and computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> <li>File <strong>PARACAT_WP4_20230706_ 06_Origin</strong> include origin plotted data</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>MFU– </strong>MFU-4l:NO<sub>2</sub> MOF material</li> <li>NiNO – NO adsorbed MFU-4l:NO<sub>2</sub> MOF</li> <li>@10K – measured at 10 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Optimized structures of the stationary points on the potential energy surface of the OH(2Π) + C2H4 reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> potential energy surface published in our article “OH(<sup>2</sup>Π) + C<sub>2</sub>H<sub>4</sub> Reaction: A Combined Crossed Molecular Beam and Theoretical Study” (P<em>hys. Chem. A</em> 2023, 127, 21, 4609–4623), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c08662">https://doi.org/10.1021/acs.jpca.2c08662</a>.</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized structures of the stationary points on the potential energy surface of the O(3P, 1D) + HCCCN(X1Σ+) reaction
<p>This Zip file contains the cartesian coordinates of optimized stationary points of the O(<sup>3</sup>P, <sup>1</sup>D) + HCCCN(X<sup>1</sup>Σ<sup>+</sup>) potential energy surface published in our article “Reactions O(<sup>3</sup>P, <sup>1</sup>D) + HCCCN(X<sup>1</sup>Σ<sup>+</sup>) (Cyanoacetylene): Crossed-Beam and Theoretical Studies and Implications for the Chemistry of Extraterrestrial Environments” (<em>J. Phys. Chem. A</em> 2023, 127, 3, 685–703), that can be found in <a href="https://doi.org/10.1021/acs.jpca.2c07708">https://doi.org/10.1021/acs.jpca.2c07708</a>.</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized structures of the stationary points on the potential energy surface of the dissociation of the CH3OH˙+ cation
<p>This Zip file contains the optimized stationary points structures of the potential energy surface (PES) for the dissociation of the CH3OH˙+ cation.</p> <p>The PES has been published in our paper “Fragmentation of interstellar methanol by collisions with He˙<sup>+</sup>: an experimental and computational study” (<em><strong>Phys. Chem. Chem. Phys.</strong></em>, 2022, <strong>24</strong>, 22437-22452), that can be found in https://doi.org/10.1039/D2CP02458F .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01 and the structures were optimized at ωB97X-D/aug-cc-pVTZ level of theory.</p>
Supplementary CIF files for "Shedding Light on the Enigmatic TcO2 ⋅ xH2O Structure with Density Functional Theory and EXAFS Spectroscopy"
<p>Optimized geometries from the paper "Shedding Light on the Enigmatic TcO2 ⋅ <em>x</em>H2O Structure with Density Functional Theory and EXAFS Spectroscopy" (<a href="https://doi.org/10.1002/chem.202202235">https://doi.org/10.1002/chem.202202235</a>), provided in CIF format.</p> <p>All structures were fully optimized (lattice vectors and atomic coordinates) using AMS/BAND (<a href="https://www.scm.com/">https://www.scm.com/</a>) with the PBE density functional, scalar relativistic effects (ZORA), and numerical atomic orbitals (NAOs) augmented with a triple-zeta polarized (TZP) set of Slater-type basis functions. For the chains, D3 dispersion corrections were also included.</p> <p> </p>
Integrative structure determination of PTBP1-viral IRES complex in solution
<p>Ensemble structure model of the RNA-binding protein PTBP1 in complex with the internal ribosome entry site (IRES) of encephalomyocarditis virus (EMCV) RNA and data underlying these models.</p> <ul> <li>Main ensemble based on all restraints (corresponding to Figure 2 in the associated paper)</li> <li>Ensemble obtained with only DEER distance distribution restraints corresponding to Figure S7(A) in the Supplementary Material of the associated paper</li> <li>Validation ensemble obtained with all restraints after removing the conformers of the main ensemble from the raw ensemble corresponding to Figure S7(B) inthe Supplementary Material of the associated paper</li> <li>Ensemble obtianed with all restraints by fitting populations with a non-negative linear least squares (NNLLSQ) approach corresponding to Figure S8(A) in ths Supplementary Material of the associated paper</li> <li>Primary DEER-EPR data underlying site-to-site distance distributions for 35 spin-label pairs and corresponding distanace distributions</li> <li>Small-angle neutron scattering (SANS) curves a two detector distances with corresponding resolution files and a small-angle x-ray scattering (SAXS) curve</li> <li>Restraint file for the ensemble fit with MMMx software, specifying the mean distances and standrad deviations of distance distributions that were also used for specifying lower and upper distance bounds in CYANA generation of the raw ensemble</li> <li>Source data for the figures in the associated paper</li> <li>Source data for the tables in the associated paper</li> </ul> <p>All ensembles are ZIP files containing single PDB files for all conformers and an ensemble specification that reports populations for all conformers.</p>
Bibliographic Data from the SoTL in Civil and Structural Engineering Systematic Review
<p>This database contains all the bibliographic information found after applying the Search Strategy used for the SoTL in Civil and Structural Engineering Systematic Review. The following electronic databases were searched:</p> <ul> <li>Scopus.</li> <li>Web of Science.</li> <li>OsloMet Library.</li> <li>Google Scholar (no bibliographic information is presented since this database does not allow to download such data).</li> </ul> <p>A total of 84 records were found in Scopus, 43 in Web of Science, and 55 in OsloMet Library. The search was conducted on September 1, 2023.</p> <p>The information is presented in .ris, .bib, and .csv format.</p>
Summer water chemistry, phytoplankton and zooplankton community composition, size structure, and biomass in a shallow, hypereutrophic reservoir in southwestern Iowa, USA (2019).
This data product contains data for Green Valley Lake, a hypereutrophic reservoir in southwest Iowa (USA) from the summer of 2019. We sampled and quantified zooplankton, phytoplankton, and nutrient concentrations (total N, total P, soluble reactive P, nitrate) in the lake weekly with the primary aim of assessing consumer nutrient cycling, specifically zooplankton nutrient cycling, in a hypereutrophic reservoir. Weekly plankton sampling included quantifying zooplankton and phytoplankton biomass, community composition, and size structure. Phytoplankton size was measured as the greatest axial linear distance which would be approached by a zooplankton grazer. Allometric equations from the literature were applied to the zooplankton size measurements to estimate zooplankton community excretion of N and P. We found that the estimated contribution of zooplankton excretion to the dissolved P pool was substantial in the spring. Further, we found evidence that zooplankton affected phytoplankton size distributions through selective grazing of smaller phytoplankton cells likely affecting nutrient uptake and storage by phytoplankton.
Regional and local variation in chemical, structural, and physical leaf traits for tree species in the northeastern United States, 2016-2023.
This dataset is a compilation of leaf trait measurements for 25 different Northern American tree species in the northeastern United States collected between 2016 and 2023 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains measurements for 2,006 samples across 18 chemical, physical, and structural traits. Measured traits include stable isotopes for carbon (C) and nitrogen (N), chlorophyll estimates, leaf and petiole dimensions, and leaf and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions has been included. Additional leaf physiological and optical traits have been measured concurrently on many of these samples and have been or will be published separately. This is a continuous dataset that will be updated on an as needed basis.
Structural Diversity from the NEON Discrete-Return LiDAR Point Cloud in 2013-2022
Structural diversity, characterizing the volumetric capacity and physical arrangement of biotic components in an ecosystem, controls critical ecosystem functions like light interception, hydrology, and microclimate. This product generates structural diversity metrics for the NEON sites, sourced from the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform (DP1.30003.001; collected in March 2023). Using R programming, we computed the metrics detailing height, heterogeneity, and density at 30 m, aligned to the Landsat grids, for 243 site years in 57 NEON sites from 2013 to 2022.
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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.