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2,326 results for “clusters”
Voyage en cluster : qu'en est-il de sa promesse relationnelle ?
<p>Dataset, R script et guides d'entretien mobilisés pour l'article d'Estelle Vallier, 2018, <a href="https://arcs.episciences.org/9235">Voyage en cluster : qu'en est-il de sa promesse relationnelle ?</a>, 2018, ARCS.</p>
Simulation data on the growth of atmospheric molecular clusters and particles
<p>This data set contains output data from cluster population simulations performed with Atmospheric Cluster Dynamics Code (ACDC) model, which simulates the formation of clusters from atmospheric vapors and the growth of these clusters by further molecular and cluster-cluster collisions. The data can be used for investigating the formation and growth of atmospheric particles from inorganic and organic vapors.</p> <p>The data is output of a computational process model, and hence does not represent a specific time period or location. Simulation sets are calculated for a one or two-component system containing a quasi-unary inorganic compound representing a mixture of sulfuric acid and ammonia (SA) and/or oxidized organic vapors corresponding to a low volatility organic compound (LVOC) and an extremely-low volatility organic compound (ELVOC). The external conditions in the simulations correspond to those in the CLOUD (Cosmics Leaving Outdoor Droplets) chamber at temperature of 5 C°.</p> <p>Data are provided for 14 simulations.</p> <p><strong>References</strong></p> <p>Kontkanen J, Stolzenburg D, Olenius T, Yan C, Dada L, Ahonen L, Simon M, Lehtipalo K, Riipinen I (2022) What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?. Environ. sci. Atmos. <a href="https://doi:10.1039/d1ea00103e">https://doi:10.1039/d1ea00103e</a> </p> <p>Olenius T, Riipinen I (2017) Molecular-resolution simulations of new particle formation: Evaluation of common assumptions made in describing nucleation in aerosol dynamics models. Aerosol Sci. Tech. 51:397 – 408. <a href="https://doi.org/10.1080/02786826.2016.1262530">https://doi.org/10.1080/02786826.2016.1262530</a></p> <p>Olenius T, Atmospheric Cluster Dynamics Code. <a href="https://github.com/tolenius/ACDC">https://github.com/tolenius/ACDC</a> </p> <p>McGrath MJ et al. (2012) Atmospheric Cluster Dynamics Code: a flexible method for solution of the birth-death equations. Atmos. Chem. Phys. 12:2345 – 2355. <a href="https://doi.org/10.5194/acp-12-2345-2012">https://doi.org/10.5194/acp-12-2345-2012</a></p> <p><strong>Data description</strong></p> <p>The data is in the form of text files. The provided data files (total compressed size ~10GB) correspond to simulation output from the ACDC model. Simulation sets are shown in the table below and further described in Kontkanen et al. (2022). For the interpretation of the model output, the interested user is referred to the manual of ACDC model (<a href="https://github.com/tolenius/ACDC">https://github.com/tolenius/ACDC</a>). </p> <table align="left"> <tbody> <tr> <td> <p>Simulation set</p> </td> <td> <p>Model compounds</p> </td> <td> <p>Vapor concentrations (cm<sup>-3</sup>)</p> </td> <td> <p>Method to retrieve evaporation rates</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>SA</p> </td> <td> <p><em>C</em><sub>SA </sub>= 8.0*10<sup>6</sup>, 2.0*10<sup>7</sup>, 4.7*10<sup>7</sup>, 1.1*10<sup>8</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>SA</p> </td> <td> <p><em>C</em><sub>SA </sub>= 2.0*10<sup>7</sup>, 4.7*10<sup>7</sup>, 1.1*10<sup>8</sup></p> </td> <td> <p>QC data and Kelvin eq.<br> <em>(non-classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>LVOC</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 5.0*10<sup>7</sup>, 1*10<sup>8</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>LVOC,<br> ELVOC</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 5.0*10<sup>7</sup>, 1*10<sup>8</sup><br> <em>C</em><sub>ELVOC </sub>= 1.0 *10<sup>7</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>LVOC,<br> SA</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 2.0*10<sup>7</sup>, 5.0*10<sup>7</sup>, 1*10<sup>8</sup><br> <em>C</em><sub>SA </sub>= 8.0*10<sup>6</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> </tbody> </table> <p> </p>
A role for myosin II cluster and membrane energy in cortex rupture for Dictyostelium discoideum cells
<p>Blebs, pressure driven protrusions of the cell membrane, facilitate the movement of eukaryotic cells such as the soil amoeba <em>Dictyostelium discoideum</em>, white blood cells and cancer cells. Blebs initiate when the cell membrane separates from the underlying cortex. A local rupture of the cortex, has been suggested as a mechanism by which blebs are initiated. However, much clarity is still needed about how cells inherently regulate rupture of the cortex in locations where blebs are expected to form. In this work, we examine the role of membrane energy and the motor protein myosin II (myosin) in facilitating the cell driven rupture of the cortex. We perform under-agarose chemotaxis experiments, using <em>Dictyostelium discoideum</em> cells, to visualize the dynamics of myosin and calculate changes in membrane energy in the blebbing region. To facilitate a rapid detection of blebs and analysis of the energy and myosin distribution at the cell front, we introduce an autonomous bleb detection algorithm that takes in discrete cell boundaries and returns the coordinate location of blebs with its shape characteristics. We are able to identify by microscopy naturally occurring gaps in the cortex prior to membrane detachment at sites of bleb nucleation. These gaps form at positions calculated to have high membrane energy, and are associated with areas of myosin enrichment. Myosin is also shown to accumulate in the cortex prior to bleb initiation and just before the complete disassembly of the cortex. Together our findings provide direct spatial and temporal evidence to support cortex rupture as an intrinsic bleb initiation mechanism and suggests that myosin clusters are associated with regions of high membrane energy where its contractile activity leads to a rupture of the cortex at points of maximal energy.</p>
Supporting dataset for: "Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering"
<p>This dataset was used in the meta-analysis and hierarchical clustering published in "Plasma essential amino acid concentration and profile are associated with performance of lactating dairy cows as revealed through meta-analysis and hierarchical clustering" in the Journal of Dairy Science. We searched Web of Science and Google Scholar databases through March 2020 with the terms “plasma EAA,” “milk urea” or “blood urea,” and “dairy” or lactating dairy”. To be included in our study, the papers must have met the following selection criteria: (1) been published in English in a peer-reviewed journal; (2) reported dietary ingredients on a DM basis and at minimum dietary CP concentration; (3) used treatments based on diet changes (e.g., no infusion trials were included); (4) reported DMI, lactation performance, and milk components yield; (5) reported all individual [EAA]p (excluding Trp); and (6) reported blood urea-N or plasma urea-N. Infusion studies were excluded to avoid possible effects of method of EAA supply (e.g., infusion vs. feeding) and to narrow the scope of application. The final dataset included 22 studies and 96 dietary treatments. For a more complete description of the methods, please refer to the published paper. </p>
REALISE CCUS webinar #3: The Cork Cluster study
<p>This webinar provides an overview of findings from a recent study of the Cork industrial cluster in Ireland. Participants will hear details of the industries involved: ESB Aghada and BGE Whitegate generation stations and Irving Oil Whitegate Refinery. There will be details of the role of carbon capture and storage (CCS) and how this might be configured, from the point of CO<sub>2</sub> capture to transport and long-term geological storage. Cost-benefit analysis will also be considered and there will be a discussion of different approaches to public engagement and best practices that influence the social acceptability of CCS projects.</p> <p>This free online event will be chaired by Inna Kim of SINTEF (REALISE Project Coordinator) and features presentations by Paul Murphy of Ervia (REALISE Work Package 3 Lead) and Paola Velasco-Herrejón of University College Cork (REALISE Work Package 4). There will be a 20-minute Q&A.</p>
Uncovering a miltiradiene biosynthetic gene cluster in the Lamiaceae reveals a dynamic evolutionary trajectory
<p><span>The spatial organization of genes within plant genomes can drive evolution of specialized metabolic pathways. In this study we investigated the origin and subsequent evolution of a diterpenoid biosynthetic gene cluster (BGC) present throughout the Lamiaceae (mint) family. Terpenoids are important specialized metabolites in plants with </span><span>diverse</span><span> adaptive functions that enable environmental interactions, such as chemical defense. Based on core genes found in the BGCs of all species examined across the Lamiaceae, we predict a simplified version of this cluster evolved in an early Lamiaceae ancestor. The current composition of the extant BGCs highlights the dynamic nature of its evolution. We elucidate the terpene backbones made by the </span><span>Callicarpa americana</span><span> BGC enzymes, including miltiradiene and the novel terpene (+)-kaurene, and show oxidization activities of BGC cytochrome P450s. Our work reveals the fluid nature of BGC assembly and the importance of genome structure in contributing to the origin of novel metabolites.</span></p>
Reanalysis accounting for clustering and nesting overturns conclusions in: "Watching TV Cooking Programs: Effects on Actual Food Intake Among Children"
<p>Stata code to reproduce results from Folkvord F, Anschütz D, Geurts M. Watching TV cooking programs: effects on actual food intake among children. <em>J Nutr Educ Behav</em>. 2020;52(1):3-9.</p>
Subtyping of common complex diseases and disorders by integrating heterogeneous data. Identifying clusters among women with lower urinary tract symptoms in the LURN study
<p>We present a methodology for subtyping of persons with a common clinical symptom complex by integrating heterogeneous continuous and categorical data. We illustrate it by clustering women with lower urinary tract symptoms (LUTS), who represent a heterogeneous cohort with overlapping symptoms and multifactorial etiology. Data collected in the Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN), a multi-center observational study, included self-reported urinary and non-urinary symptoms, bladder diaries, and physical examination data for 545 women. Heterogeneity in these multidimensional data required thorough and non-trivial preprocessing, including scaling by controls and weighting to mitigate data redundancy, while the various data types (continuous and categorical) required novel methodology using a weighted Tanimoto indices approach. Data domains only available on a subset of the cohort were integrated using a semi-supervised clustering approach. Novel contrast criterion for determination of the optimal number of clusters in consensus clustering was introduced and compared with existing criteria. Distinctiveness of the clusters was confirmed by using multiple criteria for cluster quality, and by testing for significantly different variables in pairwise comparisons of the clusters. Cluster dynamics were explored by analyzing longitudinal data at 3- and 12-month follow-up. Five clusters of women with LUTS were identified using the developed methodology. None of the clusters could be characterized by a single symptom, but rather by a distinct combination of symptoms with various levels of severity. Targeted proteomics of serum samples demonstrated that differentially abundant proteins and affected pathways are different across the clusters. The clinical relevance of the identified clusters is discussed and compared with the current conventional approaches to the evaluation of LUTS patients. The rationale and thought process are described for the selection of procedures for data preprocessing, clustering, and cluster evaluation. Suggestions are provided for minimum reporting requirements in publications utilizing clustering methodology with multiple heterogeneous data domains.</p>
Space of Optimal Solutions of the Correlation Clustering Problem for Complete Signed Graphs
<p><strong>Description. </strong>This is the data used in the experiments of the following paper:</p> <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs,” <em>Journal of Complex Networks </em>8(6):cnaa025, 2020. DOI: <a href="http://doi.org/10.1093/comnet/cnaa025">10.1093/comnet/cnaa025</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02994011">hal-02994011</a>⟩</li> </ul> <p>This dataset contains:</p> <ul> <li>Plot files used in the article;</li> <li>Input signed networks;</li> <li>All optimal solutions (i.e. optimal solution space) of the corresponding networks;</li> <li>Evaluation files.</li> </ul> <p><strong>Source code. </strong>The code source is accessible on GitHub: <a href="https://github.com/CompNet/Sosocc">https://github.com/CompNet/Sosocc</a></p> <p><strong>Citation. </strong>If you use the data or source code, please cite the above article.</p> <p><br><code>@Article{Arinik2020,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs},</code><br><code> journal = {Journal of Complex Networks},</code><br><code> year = {2020},</code><br><code> volume = {8},</code><br><code> number = {6},</code><br><code> pages = {cnaa025},</code><br><code> doi = {10.1093/comnet/cnaa025},</code><br><code>}</code><br><br></p> <p>--------------------------------------------</p> <p><strong>Details.</strong></p> <p><br><strong># PLOT FILES</strong><br>* `<em>Figure1.zip</em>`: Figures showing that there might be many distinct optimal solutions of a small-sized network.<br>* `<em>Figure2.zip</em>`: Figures showing that distinct optimal solutions of a given network might be partition-wise very similar or different.<br>* `<em>Figure4: All Results.zip</em>`: Figure 4 in the article contains only a few plots regarding the results for space considerations. This zip file contains all plots, and it is organized by the values of `<em>l<sub>0</sub></em>`. In each `<em>l<sub>0</sub></em>` folder, the results are shown in three different perspectives:<br>--- Detected Imbalance Percentage vs Graph Order (i.e. number of vertices)<br>--- Prop mispl vs Graph order<br>--- Graph order vs Prop mispl<br>* `<em>workflow.pdf</em>`: The workflow of the methodology used in the article.<br>* `<em>Syrian network With All Solutions.pdf</em>`: Syrian network (on top) with core part information through node colors, and its optimal solutions in which node colors represent partition information (on bottom).<br> </p> <p><strong>#NETWORKS</strong><br>All networks are in `<em>Input Signed Networks.tar.gz</em>`.<br>Networks are generated through a simple random model (available in <em>https://github.com/CompNet/SignedBenchmark</em>) designed to produce complete (or uncomplete) unweighted networks with built-in modular structure.<br>There are 3 parameters used for the generation:</p> <ol> <li>number of nodes (`<em>n</em>`)</li> <li>initial number of modules (`<em>l<sub>0</sub></em>`)</li> <li>proportion of misplaced links, i.e. proportion of frustrated links, (`<em>q<sub>m</sub></em>`)</li> </ol> <p>Inside `<em>Input Signed Networks.tar.gz</em>`:<br>NETWORKS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>The number of nodes, the initial number of modules and the network density are given. The network density is always 1, since we treat only complete signed networks.<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>Proportion of misplaced links is given.<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>Proportion of negative links (`<em>q<sub>n</sub></em>`) is specified. `<em>q<sub>n</sub></em>` changes depending on `<em>n</em>` and `<em>l<sub>0</sub></em>`. Since only complete signed networks are studied, this parameter is automatically computed from the other input parameters.<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>Network numbers are shown.<br>In the end, thre are three file formats describing the same network content: GraphML (.graphml), Pajek NET (.net) or .G format.<br><br><strong># PARTITIONS</strong><br>All partition results are in `<em>Partition Results.tar.gz</em>`. Note that all optimal partitions of a signed network are obtained through an exact partitioning method. The code source is accessible here: <em>https://github.com/arinik9/ExCC</em><br>Inside `<em>Partition Results.tar.gz</em>`:<br><br>PARTITIONS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br>................|__"<em>ExCC-all</em>"<br>....................|__"<em>signed-unweighted</em>"<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>- The fifth hierarchy => the folders are named as follows: "<em>ExCC-all</em>"<br>The name of the partitioning method are shown. Since an exact partitioning method is used to obtain all distinct optimal solutions, it is named as "<em>ExCC-all</em>".<br>- The sixth hierarchy => the folders are named as follows: "<em>signed-unweighted</em>"<br>The type of signed networks are shown: signed and unweighted</p> <p>In the end, the partition results are located, and the file names are named as follows: <em>membership.txt</em>. Note that the first partition result number starts from zero.</p> <p> </p> <p><strong># EVALUATIONS</strong><br>Evaluation results related to our plots are in `<em>Evaluation Results.tar.gz</em>. Note that the hierarchy of this folder is the same as that of 'Partitions'. Inside `<em>Evaluation</em><em> Results.tar.gz</em>`:</p> <p>- `Best-k-for-kmedoids.csv`: It contains three columns. 1) the number of solution classes via kmedoids, 2) the best Silhouette score, 3) the best clustering in terms of Silhouette score, which represents solution classes.</p> <p>- `class-core-part-size-tresh=1.00.csv`. It indicates the proportion of core part size for each solution class.</p> <p>- `exec-time.csv`: It indicates the execution time in seconds.</p> <p>- `imbalance.csv`: It contains the information of imbalance as 1) count and 2) percentage</p> <p>- `nb-solution.csv`: It indicates the total number of solutions<br>--------------------------------------------</p> <p>Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</p>
Characterizing Measures for the Assessment of Cluster Analysis and Community Detection
<p><strong>Description. </strong>The dataset is constituted of:</p> <ul> <li>`figs.zip`: an archive containing the plot files;</li> <li>`data&results.zip`: an archive containing the necessary data to perform our analysis, as well as result files.</li> </ul> <p>These are the resources used in the following articles:</p> <ol> <li>N. Arınık, V. Labatut and R. Figueiredo, "Characterizing measures for the assessment of cluster analysis and community detection", Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques (MARAMI), 2020. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02993542">hal-02993542</a>⟩</li> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Characterizing and comparing external measures for the assessment of cluster analysis and community detection,” <em>IEEE Access </em>9:20255–20276, 2021. DOI: <a href="http://doi.org/10.1109/access.2021.3054621">10.1109/access.2021.3054621</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-03124118">hal-03124118</a>⟩</li> </ol> <p><strong>Source code. </strong>The associated source code is available on GitHub: <a href="https://github.com/CompNet/ExtMeasEval">https://github.com/CompNet/ExtMeasEval</a></p> <p><strong>Citation. </strong>If you use these data, please cite the paper [2].</p> <p><br><code>@Article{Arinik2021,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Characterizing and Comparing External Measures for the Assessment of Cluster Analysis and Community Detection},</code><br><code> journal = {IEEE Access},</code><br><code> year = {2021},</code><br><code> volume = {9},</code><br><code> pages = {20255-20276},</code><br><code> doi = {10.1109/access.2021.3054621},</code><br><code>}</code></p>
Data for: Repeatability of the 20th Century Earthquake Cluster in Mongolia: Paleoseismology along the Tsetserleg Fault (Mongolia)
<p>This dataset is associated to the article "Repeatability of the 20<sup>th</sup> Century Earthquake Cluster in Mongolia: Paleoseismology along the Tsetserleg Fault (Mongolia)" submitted to Journal of Geophysical Research: Solid Earth.</p> <p>It includes the following:</p> <ul> <li>Dataset S1 includes the output of the horizontal offset measurements performed with the LaDiCaOz Matlab GUI.</li> <li>Dataset S2 includes the drone DEMs.</li> </ul>
Task resource usage of Google Cluster Usage Trace dataset
<p>The dataset contains csv files. All csv files named by its cluster number and machine id. There are 2 features , time stamp and mean CPU usage. This data set is prepared from task resource usage (TRU) table of GCT dataset for the research purpose. Mainly TRU contains task information. Sum Average algorithm employed and calculate the machine information for every 5 minute time stamp.</p>
ArMoR Cluster: 5 research projects fight Antimicrobial Resistance in livestock farming
<p>Within Horizon Results Booster programme (HRB), 4 Horizon 2020 projects (AVANT, DISARM, HealthyLivestock and ROADMAP) and 1 BBSRC funded project (AMRILS) have formed the "ArMoR Cluster" to develop a conceptual framework to improve understanding of AMR in livestock systems.</p> <p>Supported by the European Commission, Horizon Dissemination Booster (HRB) contributes to an effective transfer of research and innovation project results to policy makers, industry and society by offering various services as dissemination, exploitation strategy and business plan development to projects.</p> <p>The video is available on YouTube: <strong><a href="https://www.youtube.com/watch?v=rnU35ytdEuM">https://www.youtube.com/watch?v=rnU35ytdEuM</a></strong></p> <p>For any further questions please contact us at:</p> <ul> <li><strong><a href="https://zenodo.org/record/avant@rtds-group.com">avant@rtds-group.com</a></strong> (project AVANT),</li> <li><strong><a href="https://zenodo.org/record/info@disarmproject.eu">info@disarmproject.eu</a></strong> (project DISARM),</li> <li><strong><a href="https://zenodo.org/record/healthylivestockproject@yahoo.com">healthylivestockproject@yahoo.com</a></strong> (project Healthy Livestock) or</li> <li><strong><a href="mailto:roadmap.communication@gmail.com">roadmap.communication@gmail.com</a></strong> (project ROADMAP). </li> </ul>
Bayesian Samples and Data Behind Figures: Comprehensive Bayesian Modeling of Tidal Circularization in Open Cluster Binaries part I
<p>Auxiliary data associated with the article <a href="https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.6145P/abstract">"Comprehensive Bayesian Modeling of Tidal Circularization in Open Cluster Binaries part I: M 35, NGC 6819, NGC 188" by Penev, K & Schussler, J</a></p> <p>The type of data corresponds to a particular filename format. Bayesian samples are in HDF5 format, directly as saved by the <a href="https://emcee.readthedocs.io/en/stable/index.html">emcee</a> sampler (see <a href="https://emcee.readthedocs.io/en/stable/user/backends/">https://emcee.readthedocs.io/en/stable/user/backends/</a>). All other files are in AAS-journal style machine readable tables format generated by <a href="https://github.com/cds-astro/cds.pyreadme">cdspyreadme</a> python library.</p> <p>Description of contents by filename format:</p> <pre><code><CLUSTER>_<BINARY ID>_.*.h5</code></pre> <p>Bayesian analysis samples constraining the tidal dissipation efficiency of the given binary. The values of the sampled system and tidal dissipation parameters are stored as blobs (<a href="https://emcee.readthedocs.io/en/stable/user/blobs/">https://emcee.readthedocs.io/en/stable/user/blobs/)</a></p> <pre><code><CLUSTER>_<BINARY ID>_lgQ_period.mrt</code></pre> <p>The 2.3%, 15.9%, 84.1%, and 97.7% quantiles of <span class="math-tex">\(\log_{10}Q_\star'\)</span> for the given binary as a function of tidal period</p> <pre><code><CLUSTER>_<BINARY ID>_burnin_period.mrt</code></pre> <p>The MCMC burn-in period before the 2.3%, 15.9%, 84.1%, and 97.7% quantiles of <span class="math-tex">\(\log_{10}Q_\star'\)</span> for the given binary are considered converged (see article text).</p> <pre><code><CLUSTER>_<BINARY ID>_cdfstd_period.mrt</code></pre> <p>The standard deviation of the <span class="math-tex">\(CDF(\log_{10}Q_\star')\)</span> for the given binary as a function of tidal period for each of the quantiles. The maximum likelihood value is the target percentile, i.e. one of: 2.3%, 15.9%, 84.1%, and 97.7%</p>
Text-fig. 1. Pistacia terrazasae sp. nov., a: UF 279-85025; b–i: UF 279-24545. a: Ring-porous wood with widely spaced solitary earlywood vessels; latewood vessels in radial multiples of 4 or more and in clusters, TS. b: Growth ring boundary, fiber walls thin to thick, TS. c: Simple perforation plates, alternate intervessel pits, helical thickenings in vessels, TLS. d: Multiseriate rays to 4-seriate, tyloses in vessels, helical thickenings throughout body of vessel element, and alternate intervessel pitting, TLS. e: Vessel-ray parenchyma pitting with reduced borders, oval in outline, RLS. f: Marginal row of upright cells, one inflated and crystalliferous, procumbent body cells, RLS. g: Multiseriate rays mostly 3-seriate, occasionally 4-seriate, uniseriate rays usually <10 cells tall, TLS. h: Ray with enlarged crystalliferous marginal cell, to left of C, TLS. i: Ray with canal, TLS. Scale bars: 200 µm in a, g; 100 µm in b, d, h; 50 µm in c, i; 20 µm in e, f. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 1. Pistacia terrazasae sp. nov., a: UF 279-85025; b–i: UF 279-24545. a: Ring-porous wood with widely spaced solitary earlywood vessels; latewood vessels in radial multiples of 4 or more and in clusters, TS. b: Growth ring boundary, fiber walls thin to thick, TS. c: Simple perforation plates, alternate intervessel pits, helical thickenings in vessels, TLS. d: Multiseriate rays to 4-seriate, tyloses in vessels, helical thickenings throughout body of vessel element, and alternate intervessel pitting, TLS. e: Vessel-ray parenchyma pitting with reduced borders, oval in outline, RLS. f: Marginal row of upright cells, one inflated and crystalliferous, procumbent body cells, RLS. g: Multiseriate rays mostly 3-seriate, occasionally 4-seriate, uniseriate rays usually <10 cells tall, TLS. h: Ray with enlarged crystalliferous marginal cell, to left of C, TLS. i: Ray with canal, TLS. Scale bars: 200 µm in a, g; 100 µm in b, d, h; 50 µm in c, i; 20 µm in e, f.
Text-fig. 2. SEM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Flowers in oblique lateral view showing remains of calyx and slightly semi-inferior ovary with elongated apical style (a); note larger openings in the floral tissue (asterisk) interpreted as schizogenous secretory cavities and the stomata-like secretory structures on the upper portion of the ovary (arrows) that are interpreted as nectariferous (b). c: Detail of ovary surface showing secretory stomata-like structures (arrows). d: Flower in lateral view showing fragmentary calyx and broken slightly semi-inferior ovary with secretory stomata-like structures; note the point of attachment of the central placenta (pl). e: Cluster of seeds removed from the ovary in (d) showing reticulate surface. f: Outer (abaxial) surface of calyx lobe showing the slightly pointed papillae and scattered, fine trichomes (arrows). g: Triaperturate pollen grains from the ovary surface. Specimens, Mira 100-S153146 (a, b), Mira 100-S170155 (c), Mira 100-S101266 (d, e), Mira 105-S100732 (f), Mira 100-S170125 (g). Scale bars = 600 µm (a, b, d), 300 µm (f), 100 µm (c, e), 10 µm (g). in Early Flowers Of Primuloid Ericales From The Late Cretaceous Of Portugal And Their Ecological And Phytogeographic Implications
Text-fig. 2. SEM images of Miranthus elegans gen. et sp. nov.; Mira locality, Portugal. a, b: Flowers in oblique lateral view showing remains of calyx and slightly semi-inferior ovary with elongated apical style (a); note larger openings in the floral tissue (asterisk) interpreted as schizogenous secretory cavities and the stomata-like secretory structures on the upper portion of the ovary (arrows) that are interpreted as nectariferous (b). c: Detail of ovary surface showing secretory stomata-like structures (arrows). d: Flower in lateral view showing fragmentary calyx and broken slightly semi-inferior ovary with secretory stomata-like structures; note the point of attachment of the central placenta (pl). e: Cluster of seeds removed from the ovary in (d) showing reticulate surface. f: Outer (abaxial) surface of calyx lobe showing the slightly pointed papillae and scattered, fine trichomes (arrows). g: Triaperturate pollen grains from the ovary surface. Specimens, Mira 100-S153146 (a, b), Mira 100-S170155 (c), Mira 100-S101266 (d, e), Mira 105-S100732 (f), Mira 100-S170125 (g). Scale bars = 600 µm (a, b, d), 300 µm (f), 100 µm (c, e), 10 µm (g).
Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks
<p>Classification dataset used in Kosiba et al. 2020 (10.1093/mnras/staa1723) consisting of candidate clusters in the XCLASS survey</p> <p>Training and testing images and corresponding labels low-z (clusters, 0<z<0.3), hi-z (clusters, z>0.3), nearby galaxy, point source (point, double source, star/AGN), and other (artefact, edge)</p>
Codes and data for: Clustering optimisation method for highly connected biological data
<p>Currently, data-driven discovery in biological sciences resides in finding segmentation strategies in multivariate data that produce sensible descriptions of the data. Clustering is but one of several approaches and sometimes falls short because of difficulties in assessing reasonable cutoffs, the number of clusters that need to be formed or that an approach fails to preserve topological properties of the original system in its clustered form. In this work, we show how a simple metric for connectivity clustering evaluation leads to an optimised segmentation of biological data.</p> <p>The novelty of the work resides in the creation of a simple optimisation method for clustering crowded data. The resulting clustering approach only relies on metrics derived from the inherent properties of the clustering. The new method facilitates knowledge for optimised clustering, which is easy to implement.<br>We discuss how the clustering optimisation strategy corresponds to the viable information content yielded by the final segmentation. We further elaborate on how the clustering results, in the optimal solution, corresponds to prior knowledge of three different data sets.</p> <p>This is the dataset and the codes required to conduct the above-mentioned analysis.</p>
Data for Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing
<p>Data for<em> Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing </em>(https://doi.org/10.5194/acp-2022-484)</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Figure 3: AntCo2 algorithm for graph clustering: on the left the output of the computation on a communication network; on the right the output on a regular grid
<p>Social and human developments are typical complex systems. Urban development<br> and dynamics are the perfect illustration of systems where spatial<br> emergence, self-organization and structural interaction between the system<br> and its components occur [3, 4, 5, 6]. In figure 4, we concentrate on the emergence<br> of organizational systems from geographical systems.</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.