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14,239 results for “STRUCTURE”
Modifications of the plant-pollinator network structure and species' roles along a gradient of urbanization
<p>This file includes data and codes used in the article titled: " Modifications of the plant-pollinator network structure and species’ roles along a gradient of urbanization".</p> <p>Data include plant-pollinator interactions sampled in each site (1-12) at each sampling event (6 events) in the three urbanization classes (low, medium, high). Each row is a single insect pollinator X plant interaction. Full species names and abbreviations used in figures in the Supplementary Information are reported.<br> The data file is .txt with tab-separated values.</p>
Research Data Supporting "Understanding Structural and Electronic Properties of Bismuth Trihalides and Related Compounds"
<p>Research Data Supporting "Understanding Structural and Electronic Properties of Bismuth Trihalides and Related Compounds"</p> <p>DOI: 10.1021/acs.inorgchem.9b03214</p>
Underlying data for "Interpretation of Hydrogen-Deuterium Exchange Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles"
<p>This dataset contains code, data, and figures used in the article "Interpretation of Hydrogen-Deuterium Exchange Data<br> by Maximum-Entropy Reweighting of Simulated Structural Ensembles".</p> <p>Contents:</p> <p>code/* - Underlying code used to analyze molecular dynamics trajectories and calculate predicted HDX-MS data, used to reweight structural ensembles to best fit target HDX-MS data, and used to structurally cluster simulation frames after reweighting</p> <p>data/* - Simulation trajectories of the TeaA protein, along with two sub-trajectories corresponding to only 'closed' or 'open' TeaA frames, and predicted HDX-MS deuterated fractions used as target data in simulation reweighting. Also simulation trajectories of the LeuT protein, in either 'outward-facing' or 'inward-facing' conformational states embedded in a DMPC bilayer, and experimental HDX-MS deuterated fractions used as target data in simulation reweighting</p> <p>figures/* - Underlying data and scripts used to create all figures and movies used in the article.</p> <p>Where appropriate, README files include instructions for regenerating data used in the article, and details of the Python packages used to run Python scripts are available in conda_environment.yml</p>
Graphs and Attributes used for the attribute-structure correlation pattern mining
<p>## SCPM: An implementation of an algorithm for structural correlation pattern mining.</p> <p>The structural correlation measures how a set of attributes induces dense subgraphs in an attributed graph. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Structural correlation pattern mining is useful to analyze how different attribute sets are correlated to dense subgraphs in several real-life attributed graphs.</p> <p>**Relevant Publications**</p> <p>* Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Structural correlation pattern mining for large graphs. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs (MLG '10).</p> <p>* Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Mining Attribute-structure Correlated Patterns in Large Attributed Graphs. In Proceedings of the VLDB Endowment (PVLDB '12).</p> <p>* Arlei Silva. Structural correlation pattern mining for large graphs. M.Sc Thesis, Computer Science Department, Universidade Federal de Minas Gerais, 2011.</p> <p>* Arlei Silva, Wagner Meira Jr. Structural correlation pattern mining for large graphs. Thesis and Dissertation Contest of the Brazilian Computer Society (CTD'12).</p> <p><br> ## HOW TO</p> <p>cd to trunk and run make<br> see README in trunk</p> <p><br> ## Datasets:</p> <p>### Description:</p> <p>#### ATTRIBUTE FILE:</p> <p>Format: Lists the attributes of each vertex from the graph.</p> <p> <VERTEX_ID>,<ATTRIBUTE_ID>,<ATTRIBUTE_ID>...,<ATTRIBUTE_ID></p> <p> Example: <br> 1,A,C <br> 2,A <br> 3,A,C,D <br> 4,A,D <br> 5,A,E <br> 6,A,B,C <br> 7,A,B,E <br> 8,A,B <br> 9,A,B <br> 10,A,B,D <br> 11,A,B</p> <p>#### GRAPH FILE:</p> <p>Format: Lists the neighbors of each vertex from the graph (adjacency list). Although the graph is undirected, each edge must be included in both directions.</p> <p> <VERTEX_ID>,<NEIGHBOR_ID>,<NEIGHBOR_ID>...,<NEIGHBOR_ID> <br> <br> Example: <br> 1,4 <br> 2,3 <br> 3,2,4,5,6,7 <br> 4,1,3,5,6 <br> 5,3,4,6 <br> 6,3,4,5,7,8,9,10 <br> 7,3,6,8,11 <br> 8,6,7,9,10,11 <br> 9,6,8,10,11 <br> 10,6,8,9,11 <br> 11,7,8,9,10</p> <p>### REAL DATASETS</p> <p>Lastfm:</p> <p>attributes: attrLastFm.csv.tar.bz2</p> <p>network: graphLastFm.csv.tar.gz</p> <p>DBLP:</p> <p>attributes: newAttrDBLP.csv.tar.bz2</p> <p>network: newGraphDBLP.csv.tar.bz2</p> <p>CITESEER:</p> <p>attributes: attrCiteseer.csv.tar.bz2</p> <p>network: graphCiteseer.csv.tar.bz2</p>
ARMOR and NALMA data corresponding to "Observations of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Dataset includes dual-polarization C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR) data in Raw and quality-controlled Universal Format (UF) from a selected period on 10 April 2009 as well as the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) data in American Standard Code for Information Interchange (ASCII) format from selected period on 10 April 2009. </p> <p>The ARMOR is located at the Huntsville International Airport in Huntsville, Alabama at 34.64597, -86.77131, 200 m MSL. A set of 15 radar sampling volumes between 1712 UTC and 1821 UTC on 10 April 2009 are included in the dataset. Each of the raw and corrected UF files contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (°), and total power (dBZ) data. The corrected UF files additionally contain horizontal reflectivity and differential reflectivity data corrected for attenuation and differential attenuation following the methods of Bringi et al. (2001). The corrected files also contain estimated differential propagation phase (°) and computed specific differential phase (° km<sup>-1</sup>) data (Hubbert and Bringi 1995). </p> <p> </p> <p>ARMOR file naming conventions are as follows: </p> <p> </p> <p>RAW_NA_000_125_20090410171216.gz</p> <p>RAW: file format</p> <p>125: can scan type, where 125 indicates a full or sector volume plan position indicator </p> <p>20090410171216: date and time in the order of year, month, day, hour, minute, and second</p> <p> </p> <p>ARMOR_20090410171216_qc1.uf.gz</p> <p>ARMOR: radar name</p> <p>20090410171216: date and time in the order of year (YYYY), month (MM), day (DD), hour (HH), minute (MM), and second (SS)</p> <p>qc1: denotes ARMOR processed data</p> <p>uf: denotes the file format </p> <p> </p> <p>NALMA data consist of undecimated VHF source-level lightning measurements in hourly files. The center of the network is located at 34.72461, -86.64533. The network consisted of 11 sensors distributed throughout north Alabama and south-central Tennessee. Information about contributing stations is available in the header of each hourly file, including the station location, status, and the number of sources detected by each station. Further network-specific information documented by Koshak et al. (2004) while Rison et al. (1999) discuss LMA characteristics.</p> <p>Source data include information about the time the source was detected (UTC seconds of the day), latitude and longitude (decimal degrees), altitude (m), reduced chi<sup>2</sup> value associated with post-processing (unitless), power (dBW), and a network mask indicating the detecting stations (unitless). The format is (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). </p> <p> </p> <p>Hourly file naming conventions are as follows:</p> <p> </p> <p>LYLOUT_090410_160000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>090410: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>160000: time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>Acknowledgments: </p> <p>Data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p>
Genome-wide structure and function modeling of SARS-COV-2
<p>Homology models and function annotation for all proteins in the SARS-CoV-2 genome. For a description of each file, follow <a href="https://zhanglab.ccmb.med.umich.edu/COVID-19/">this link</a>. </p>
cldf-datasets/apics: The "Atlas of Pidgin and Creole Language Structures Online" as CLDF dataset
<p>Cite as</p> <blockquote> <p>Michaelis, Susanne Maria & Maurer, Philippe & Haspelmath, Martin & Huber, Magnus (eds.) 2013. Atlas of Pidgin and Creole Language Structures Online. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at <a href="https://apics-online.info">https://apics-online.info</a>)</p> </blockquote>
Dynamics of SARS-CoV-2 spike protein in open and closed states and identification of key structural perturbations upon mutations
<p>The SARS-Cov-2 spike protein resides on the exterior surface of the coronavirus, and therefore, acts as the first point of contact that mediates cell attachment and fusion. During this process, it undergoes dramatic conformational changes upon host receptor binding. We are leveraging high-performance computing to identify these structural perturbations in wildtype and mutant spike protein models. The files contain structures from molecular dynamics simulations of closed SARS-Cov-2 spike protein embedded in POPC membrane.</p>
UM experiments for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>NetCDF4 files containing UM vn 11.1 data used in Lambert et al., 2020, Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection, submitted to Journal of Advances in Modeling Earth Systems.</p> <p>Key:</p> <p>"summaryday.nc" contain eleven months of data in each year, excluding either February or March.</p> <p>"summarydat2.nc" contain one month of data in each year, either February or March.</p> <p>"last5" indicates that for this simulation only the last five years of data are available.</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"llcsemu" are simulations with the Lambert-Lewis emulator.</p> <p>"gremu" are simulations with the Gregory-Rowntree emulator.</p> <p>"llcsemu_llcs" is the test simulation wherein the LLCS emulator is run equatorward of 30 degrees and the original LLCS convection scheme is run poleward of 30 degrees.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>"30day" are one month simulations for July for which daily output are available. Other data are monthly mean only.</p> <p> </p>
The Cassandra retrotransposon landscape in sugar beet (Beta vulgaris): Recombination and re-shuffling leads to a high structural variability
<p>Here we provide supplementary data for our study of non-autonomous Cassandra terminal-repeat retrotransposons in miniature (TRIMs) in sugar beet and related genomes.</p> <p>Cassandra sequences are distributed across the plant kingdom and share a unique feature: conserved 5S rDNA promoter motifs within their long terminal repeats (LTRs). This dataset contains two multiple sequence alignments and a sequence list of tandemly-arranged (TA) Cassandra sequences in FASTA format. Alignments cover LTR and internal regions of all Amaranthaceae Cassandra (Ama-Cassandra) from our study. This includes Cassandra full-length sequences from <em>B. vulgaris</em> (Ama_Cassandra_Beet_full-length) and <em>C. quinoa</em> (Ama_Cassandra_Quinoa_full-length). Sequence names include information on host plant, subfamily classification, localisation (scaffold), start and stop position, a Lab-unique TE identifier and sequence orientation. For the tandemly-arranged Cassandra sequences from sugar beet, we provide a sequence list of twelve sequences (Ama_Cassandra_TA_Beet_list). Here, sequence names refer to TA copy number, host, localisation (scaffold), start and stop position, a Lab-unique TE identifier and sequence orientation.</p> <p>All sequences were identified in the recent genome assemblys of <em>B. vulgaris</em> (RefBeet1.2; Dohm <em>et al</em>. 2014) and <em>C. quinoa</em> (ASM168347v1; Jarvis <em>et al</em>., 2017).</p>
ARMOR and NALMA data corresponding to 2008 storms analyzed in "Examining conditions supporting the development of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Total lightning and dual-polarization Doppler velocity data are available from the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) and the C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR), respectively, over selected periods on 6 February 2008 and 11 April 2008. NALMA data are provided in American Standard Code for Information Interchange (ASCII) format and ARMOR data are provided in Raw and quality-controlled Universal Format (UF), where quality control methods are described below. </p> <p> </p> <p>The NALMA data are provided in hourly files which include undecimated point location (source-level) data corresponding to the detection of very high frequency (VHF) radiation emitted during the breakdown of lightning (Rison et al., 1999; Thomas et al., 2001). Source locations were reported from active sensors configured in an 11-sensor array distributed throughout North Alabama and South Central Tennessee, the center of which is located at 34.72641, -86.64533 (Koshak et al. 2004). Data files include information on the time that each source was detected (UTC seconds of the day), the latitude, longitude, and altitude of each source’s location (decimal degrees and m, respectively), the reduced chi<sup>2</sup> value associated with data processing (unitless), a station mask indicating which sensors contributed to the resolved location of each source (unitless). These data are provided in a line-by-line format of (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). The 2008 data files additionally include a header section that provides further information about each sensor in the network and its relative contribution to the dataset. </p> <p> </p> <p>The hourly fine naming conventions are as follows for the February 2008 data:</p> <p>LMA_NA_6.2_125_2008-02-06_10-00-00.dat.gz</p> <p>LMA_NA: LMA file designator corresponding to the NALMA</p> <p>2008-02-06: year (YYYY)-month (MM)-day (DD)</p> <p>10-00-00: UTC time, (HH)-minute (MM)-second (SS)</p> <p> </p> <p>And for the April 2008 data:</p> <p>LYLOUT_080411_180000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>080411: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>180000: UTC time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>ARMOR data are provided as sets of 14 (14) sampling volumes corresponding to the 6 February 2008 (11 April 2008) periods between 1002 UTC and 1119 UTC (1844 UTC and 1952 UTC). Each RAW and processed UF file contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (º), and total power (dBZ) data. Horizontal reflectivity and differential reflectivity data were corrected for attenuation and differential attenuation, differential propagation phase (º) was estimated, and specific differential phase (º km<sup>-1</sup>) was calculated during post-processing (Hubbert and Bringi 1995, Bringi et al. 2001).</p> <p> </p> <p>Acknowledgments: </p> <p>NALMA data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p> <p>Thomas, R. J., Krehbiel, P. R., Hamlin, T., Harlin, J., & Shown, D. (2001). Observations of VHF source powers radiated by lightning. <em>Geophysical Research Letters</em>, <em>28</em>(1), 143–146. https://doi.org/10.1029/2000GL011464</p> <p> </p>
Supplementary Material Containing DFT Structure Files and Convergence Tests for our μSR study on Fe2O3
<p>Supplemental material for <em>Local Electronic Structure and Dynamics of Muon-Polaron Complexes in Fe<sub>2</sub>O<sub>3</sub></em></p> <p>M. H. Dehn,<sup>1,2,3</sup> J. K. Shenton,<sup>4,*</sup> D. J. Arseneau,<sup>3</sup> W. A. MacFarlane,<sup>2,3,5</sup> G. D. Morris,<sup>3</sup> A. Maigné,<sup>2</sup> N. A. Spaldin<sup>4</sup> and R. F. Kiefl<sup>1,2,3</sup></p> <p><sup>1</sup>Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada<br> <sup>2</sup>Stewart Blusson Quantum Matter Institute, University of British Columbia, Vancouver, BC V6T 1Z4, Canada<br> <sup>3</sup>Triumf, Vancouver, BC V6T 2A3, Canada<br> <sup>4</sup>Department of Materials, ETH Zurich, CH-8093 Zürich, Switzerland<br> <sup>5</sup>Department of Chemistry, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada<br> <sup>*</sup> For queries about the supplemental material in this repository contact <a href="mailto:john.shenton@mat.ethz.ch">J. Kane Shenton</a>.</p> <p>In these notebooks we provide supplemental material for our work on understanding the behaviour of muon-polaron complexes in Fe<sub>2</sub>O<sub>3</sub>.</p> <p>We provide VASP input and output files for each of the candidate muon stopping sites and states identified in the paper (also labelled as in the paper). We summarise the muon stopping sites and provide the code for analysing hyperfine tensors in the jupyter notebook: <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/Muon-site-summary.ipynb"><code>Muon-site-summary.ipynb</code></a>. There one can also find a summary of the <strong>computational details</strong> for the paper.</p> <p>We further provide <code>vasprun.xml</code> files for some of the tests of convergence with respect to plane-wave cutoff energy and k-point sampling density. These tests are summarised in the jupyter notebook: <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/Convergence_tests-ENCUT-KPOINTS.ipynb"><code>Convergence_tests-ENCUT-KPOINTS.ipynb</code></a>.</p> <p>A major source of uncertainty stems from the choice of Hubbard U<sub>eff</sub> correction. We varied U<sub>eff</sub> in the range 3 − 6 eV to gauge the impact of this parameter on the predicted energies and precession frequencies of the four charge-neutral muon-polaron complex states. This analysis is available in the notebook: <code><a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/muons_wrt_U.ipynb">muons_wrt_U.ipynb</a>.</code> Although the numerical values do vary as a function of U<sub>eff</sub>, the qualitative behaviour as well as the ordering of frequencies and energy differences presented in the paper (corresponding to U<sub>eff</sub> = 4 eV) remain robust throughout the range: 3 − 5 eV which is the range typically employed for Fe <em>d</em> states.</p> <p>Finally, in the notebook: <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/Separating_the_muon-polaron_complex.ipynb"><code>Separating_the_muon-polaron_complex.ipynb</code></a>, we analyse the separation of muon from the polaron in different configurations. Here again we provide the VASP input and output files as well as the code used to analyse these results.</p> <p>These jupyter notebooks may be previewed on <a href="https://github.com/Shenton-supplemental/Muons_in_Fe2O3">github</a> or via the <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3">jupyter notebook viewer</a>. The latter does a better job of rendering the inline LaTeX and is therefore preferred.</p> <p>Note that all of files are currently compressed to save space. These must be uncompressed before the notebooks will run. In each notebook there is a cell one can run to decompress the files needed for that particular notebook.</p>
Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms
<p>This repository contains a dataset for the research of domain generation algorithms (DGAs) and machine learning. More precisely, it targets dictionary-based DGAs.</p> <p><em>Constantinos Patsakis, Fran Casino: "Exploiting Statistical and Structural Features for the Detection of Domain Generation Algorithms", Journal of Information Security and Applications, 2021.</em></p> <p>Features ordered as in the shared dataset:</p> <ul> <li>Family: DGA that the domain belongs to</li> <li>SLD: SLD of the Domain</li> <li>L-LEN: The length of Domain</li> <li>L-DIG: The number of digits in Domain</li> <li>L-CON-MAX: The maximum number of consecutive consonants Domain</li> <li>R-CON-VOW: Number of consonants divided by L-LEN </li> <li>L-SYM: The number of special characters</li> <li>R-SYM-LEN: L-SYM divided by L-LEN</li> <li>R-Dom-3G: Ratio of benign grams in Dom-3G</li> <li>R-Dom-4G: Ratio of benign grams in Dom-4G</li> <li>R-Dom-5G: Ratio of benign grams in Dom-5G</li> <li>L-W2: Number of words with more than 2 characters in Domain</li> <li>L-W3: Number of words with more than 3 characters in Domain</li> <li>R-WS-LEN: Dom-WS divided by L-LEN</li> <li>R-WDS-LEN: Dom-WDS divided by L-LEN</li> <li>R-W2-LEN: Dom-W2 divided by L-LEN</li> <li>R-W3-LEN: Dom-W3 divided by L-LEN</li> <li>M2-Dom-Ws: 2-Chain Markov English grams applied to Dom-WS</li> <li>M2-Dom-WDS: 2-Chain Markov English grams applied Dom-WDS</li> <li>E-Dom-WS: Entropy of Dom-WS </li> <li>E-Dom-WDS: Entropy of Dom-WDS</li> <li>E-Dom-W2: Entropy of Dom-W2</li> <li>E-Dom-W3: Entropy of Dom-W3</li> </ul>
Representative Structures from Molecular Dynamics Simulations of the Inward Facing and Outward Facing States of LaINDY
<p>This upload is a supplementary data set for the following publication: <a href="https://doi.org/10.7554/eLife.61350">D. B. Sauer, N. Trebesch, J. J. Marden, N. Cocco, J. Song, A. Koide, S. Koide, E. Tajkhorshid, and D.-N. Wang. "Structural basis for the reaction cycle of DASS dicarboxylate transporters." <em>eLife</em>. <strong>9</strong>, e61350. DOI: 10.7554/eLife.61350</a>. Please see the main publication for the methods, analysis, and discussion associated with this data set.</p>
Dataset: Effects of high altitude reservoirs on the structure and function of lotic ecosystems: a case study in Italy
<p>Supporting data for "Effects of high altitude reservoirs on the structure and function of lotic ecosystems: a case study in Italy"</p> <p>Macroinvertebrate community composition</p> <p>Water chemical characteristics</p> <p>Daily mean water temperature and daily temperature variations</p> <p>Results of the leaf bags experiments</p>
Data for "Atomic structure of solute clusters in Al-Zn-Mg alloys"
<p>This dataset contains the data used in the publication entitled "<a href="https://www.sciencedirect.com/science/article/abs/pii/S1359645420310119"><strong>Atomic structure of solute clusters in Al-Zn-Mg alloys</strong></a>", published in Acta Materialia 17. December 2020.</p> <p>The data contained herein are:</p> <ul> <li>As-acquired transmission electron microscopy (TEM) images.</li> <li>Atom probe tomography data.</li> <li>All structural models used in density functional theory (DFT) calculations.</li> <li>Structures used for simulating scanning-TEM (STEM) images and nanobeam diffraction (NBD) patterns.</li> </ul> <p> </p> <p>The TEM images includes high angle annular dark field (HAADF) images and selected area diffraction patterns. These are given in .dm3/.dm4 files, and can be opened in e.g. the "<a href="https://www.gatan.com/products/tem-analysis/gatan-microscopy-suite-software">Gatan Microscopy Suite" </a>software. The images are also given as .tif images. The files are names after the "Figx_alloy_condition_xxx". "Figx" refers to the figure in the main article, "alloy" describes the alloy used and "condition" describes from what ageing condition. The uncorrected image series used for Fig. 6c (in the article) is included and requires the <a href="http://lewysjones.com/software/smart-align/">SmartAlign </a>plugin in the Gatan Microscopy Suite to analyse the dataset. SmartAlign allows for correcting rigid and non-rigid distortions in the STEM images in order to reduce effect of specimen drift and scan noise during acquisition. </p> <p>The ATP data is given as a .xlsx file. The data here is the processed data after applying the maximum separation algorithm. The data here is used to produce Figs. 2b and 2c in the paper. <br> <br> The structures used in the DFT calculations are given here as .cif files. These are separated into "Single_clusters" and "Stacked_clusters" and named according to Tabs. 1 and 2 in the Supplementary material of the paper.</p> <p>The two structures used for simulating STEM-HAADF and NBD patterns are given in the folder "TEM_simulations". "Mg32Zn124D_94x94" was used for NBD and "Mg32Zn124D_X_Zn4" was used for HAADF-STEM. The stack used for Supplementary Fig. 7c is labeled "Mg32Zn124D_94x94_slab_1Allayerop.cif".</p> <p> </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>
Damage Monitoring of Structural Resins Loaded with Carbon Fillers: Experimental and Theoretical Study
<p>Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Simulation model, Dynamic Light Scattering, FTIR spectroscopy</p> <p>Thermogravimetric analysis, Differential Scanning Calorimetry, Electro-Temperature Measurement, </p> <p>Thermal Image Camera , Water sorption measurement, Transmission Electron Microscopy, Stress Strain</p>
Non-structural carbohydrates and photosynthesis in boreal Scots pine and dwarf shrubs, in field and laboratory.
<p>The manuscript entitled "Non-structural carbohydrates and photosynthesis in boreal Scots pine and dwarf shrubs" used two set of data: Field data and Laboratory data</p> <p>##### 1. FIELD DATA:<br> We measured photosynthesis and non-structural carbohydrate (NSC) content in adult Scots pine (Pinus sylvestris L.), in boreal conditions at Hyytiaälä SMEAR II station in Sourthen Finland. In the folder "Field Data", you will find automatic CO2 exchange measurements by shoot chambers, dynamic parameters for the light response of photosynthesis, and needles´ non-structural carbohydrate content (NSC) in 2008, 2009 and 2015. See the readme file in the folder for further information.</p> <p> </p> <p>#### 2. LABORATORY DATA</p> <p>We measured the relationship between photosynthesis and non-structural carbohydrate (NSC) content under stable laboratory conditions in three shrubs species:<br> i) evergreen lingonberry (Vaccinium vitis-idaea L.),<br> ii) evergreen heather (Calluna vulgaris (L.) Hull) and<br> iii) deciduous bilberry (Vaccinium myrtillus L.).<br> The plants grew in chambers where we measured the CO2 gas exchange and estimated photosynthesis. After CO2 gas exhcnage measurements we sampled the leaves for NSC analyses. See the readme file in the folder for further information.</p> <p> </p> <p> </p> <p> </p>
NoSyms: A neural network approach to detecting data structures in raw memory
<p>This data was used for a experiments with graph convolutional neural networks for memory forensics as part of a bachelor thesis (included as pdf).<br> <br> Abstract:<br> <br> This work presents a neural network based approach for data structure detection in raw memory that does not require an entirely matching description of the target data structure. Instead, it’s merely necessary to provide multiple descriptions of data structures similar to the target as training data in the form of debugging symbols. The core contribution of this work is a formal description and implementation of encoding data structure definitions as well as raw memory contents such that they can be processed by graph convolutional neural networks. A description and implementation of a neural network meant to detect data structures in the memory contents of a Linux Kernel demonstrates the practical applicability of the described approach.<br> <br> The Code is available on GitHub <a href="https://github.com/NiklasBeierl/nosyms">https://github.com/NiklasBeierl/nosyms</a>.<br> <br> nokaslr_dump is the qemu memory snapshot used to test the model.<br> nokaslr.raw is the "raw" form of the snapshot as produced by Volatility 3's layerwriter plugin.<br> symbols-training-data contains the Volatility symbol JSON files from which training data was derived.<br> nokaslr_pointers.csv lists the kernel space pointers in the snapshot and<br> nokaslr_tasks.csv lists task structs in the snapshot. Both were extracted via a Volatility plugins that are included in the GitHub Repo.<br> vmlinux-5.4.0-58-generic.json is the symbol file for the kernel the snapshot was taken from.<br> other-symbols.zip contains symbol files I generated vor various other kernels but did not end up using, use at your own discretion.</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.