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9,152 results for “Networking”
DATA SUPPORTING RESEARCH ON THE DEVELOPMENT OF PALEONTOLOGY IN BRAZIL (NETWORK VISUALIZATIONS)
<p>The documents made available present the data set that was processed in Lucas George Wendt's dissertation, presented in 2024 in the Postgraduate Program in Information Science (PPGCIN) of the Federal University of Rio Grande do Sul (UFRGS). The study is entitled: Brazilian Paleontology: a scientometric analysis based on the Lattes Curriculum. The abstract is as follows. This research sought to carry out a scientometric analysis of Paleontology in Brazil based on data collected in the Lattes Curriculum. The general objective of this dissertation is to analyze the scientific field of Paleontology diachronically and through a scientometric study - which will be explained based on the personal information of the researchers collected in their profiles and the scientific literature produced and registered in the Lattes Curriculum of the Lattes Platform. The literature review presented the concepts of Information Science, the area that, in this study, seeks to understand Paleontology through its research instruments; Scientific Communication, the main subject analyzed in this study; Metric Information Studies, the theoretical-methodological framework used in this research; Scientometrics, the theoretical scope used to understand in greater depth the constitution of the field of national Paleontology. Finally, references were also presented that help in the understanding of Paleontology in its national, South American, North American and European contexts. The research used a mixed approach of qualitative and quantitative elements. The data were generated from the CVs of researchers registered on the Lattes Platform, collected using the Brapci Bibliometric Tools tool and analyzed in specific software for metric analysis. To achieve the research objectives, data from 1,465 researcher profiles were analyzed. Regarding the full articles published in journals, 43,333 articles were considered valid. Regarding the keywords of the articles, 91,922 keywords were analyzed for word clouds and 84,771 for relationship networks. Of the academic orientations, 1,182 profiles generated 51,400 valid orientations. The aspect of the current employment relationship had 1,256 profiles considered. Regarding academic backgrounds, 1,465 profiles generated 4,556 academic backgrounds analyzed. The main contribution of this study is the realization of an unprecedented mapping of the panorama of Paleontology in Brazil, since there are no other studies that establish the same relationships that this research sought to establish. Regarding the results, based on the data collected and analyzed, the general metric indicators linked to the scientific production associated with Brazilian Paleontology were presented based on the information collected in the Lattes Curriculum; the directions of research in Paleontology that currently constitute this field in Brazil were mapped, as well as their thematic associations with other fields of knowledge; the training of PhD researchers who work with Paleontology or who have their production associated with Paleontology in terms of their academic training was characterized; and where the scientific knowledge in Paleontology or associated with Paleontology is produced was identified. The results of this study are relevant to understanding Brazilian Paleontology, highlighting its national orientation in fossil studies, doctoral training in local institutions and predominant activity in national organizations. These elements are important to consolidate Brazilian paleontological science globally. Regarding interdisciplinary relations, a clear proximity between Paleontology and Geosciences is observed, influenced by the history and current dynamics of the field. The study is available in full at this link: https://lume.ufrgs.br/handle/10183/278682.</p>
GNN Models and results for the paper "Band-gap regression with architecture-optimized message-passing neural networks"
<p>Contains files with model parameters for random search and reference models, as well as the converted AFLOW dataset, in graphs form. Corresponds to results in <a href="https://arxiv.org/pdf/2309.06348.pdf">https://arxiv.org/pdf/2309.06348.pdf</a>.</p> <p>Model predictions along with AUID identifiers are located in result_combined.zip, band gap (egap) and formation energy (ef) predictions are from the PaiNN ensemble, band gap classification is done by MPEU model.</p> <p>New results include PaiNN NAS models.</p> <p>Compatible source code can be found at <a href="https://github.com/tisabe/jraph_MPEU/tree/v1.0.0">jraph_MPEU GitHub repository</a>.</p>
Clock Synchronization Accuracy over Mobile Networks
<pre><code><strong>Dataset: Clock Synchronization Accuracy over Mobile Networks</strong> This dataset evaluates time synchronization accuracy between two devices operating in different mobile network environments and setups. The synchronization is achieved using Precision Time Protocol (PTP) version 2 (PTPv2) over the User Plane within the mobile network. The dataset includes two types of clock offset data: <strong> Calculated Offset:</strong> This data is extracted from the PTPd software logs on the Slave device. Specifically, the "Offset From Master" section in the log files provides the value (in seconds) representing how much the Slave device's internal clock was adjusted to align with the Master clock. However, this value does not represent the exact offset from the Master clock. Instead, it is derived from the PTPv2 synchronization process and reflects the clock adjustment calculated during synchronization. <strong>Pulse-Per-Second (PPS) Offset:</strong> The PPS offset represents the most accurate measurement of clock offset between two devices. It is obtained using an Analog Discovery 2 oscilloscope, which directly compares the PPS signals generated by the Network Interface Card (NIC) timers of both devices. This method provides a high-precision measurement of the synchronization accuracy. <strong>Dataset Structure</strong> Each folder in the dataset contains: Clock offset data: Organized by type (Calculated or PPS). Setup schematics: Detailed diagrams illustrating the complete clock synchronization setup used for data collection. <strong> Additional Information</strong> The data in the folder titled "Clock offset (Pulse-per-second offset) over private 5G SA mobile network" is further described in the following scientific publication: <strong>"Clock Synchronization and Network Delay Evaluation over a Private 5G Standalone Network"</strong> Authors: Marcis Kalnins, Artis Rusins, Atis Elsts.</code></pre>
Database of ASTI-Network Rome weather stations
<p>The database includes hourly data of the main meteorological parameters measured in the area of Rome (Italy) by 17 of the weather stations belonging to the ASTI-Network. The dataset also includes hourly data of Urban Heat Island (UHI) intensity, calculated using the imperviousness method [1], covering the time period of two summers (June, July, August, JJA) 2019 and 2020.</p> <p>The data are sampled by the sensors as 5-minute averages (except for precipitation, which is cumulative) and are subjected to a filtering process before being averaged to hourly resolution. The filtering process includes the following sequential steps: interference filter, climatic filter, temporal variation filter (of which spikes are a particular case), and spatial filter. Details on the implemented algorithms are provided in [1].</p> <p>The final hourly data are calculated taking the right edge, and the time zone adopted is Central European Time (UTC+1).</p> <p>The parameters available in the dataset include atmospheric pressure (relative and absolute), air temperature, relative humidity, wind speed, wind direction, wind gusts, rain rate, cumulative precipitation, dew point, and, where available, global radiation and UV index. More details about the variables and dataset formatting are provided in the file <em>‘README.txt’</em>.</p> <p>The file <em>‘Hourly_UHI.csv’</em> contains the UHI intensity, calculated using a linear fit between the measured temperatures (T) and the associated imperviousness (IMP, %) [1], for each time step, resulting in hourly resolution. Assuming the linear relationship T(IMP) = mIMP + q, the UHI intensity is given by 100m, and the corresponding column in the dataset is named <em>‘DT=100m’</em>. Additional columns provide statistical parameters and the average values across all stations for temperature, wind speed, rain rate, and cumulative precipitation. Further details are available in the file <em>‘README.txt’</em>.</p> <p>The file <em>‘metadata.csv’</em> contains metadata for each weather station, such as ID, coordinates, altitude, and associated imperviousness. Figure <em>'asti_network_sat.png' </em>shows their spatial distribution on 2d map.</p> <p>This dataset forms the basis of the paper <em>“Measuring the urban heat island of Rome through a dense weather station network and remote sensing imperviousness data”</em> published in December 2022 in the journal <em>Urban Climate</em> (<a href="https://doi.org/10.1016/j.uclim.2022.101355" target="_blank" rel="noopener">doi.org/10.1016/j.uclim.2022.101355</a>). It was also used for validating the numerical model developed within the LIFE-ASTI project.</p> <p>The ASTI-Network measurement network consists of rooftop weather stations and distributed throughout the city of Rome. It includes amateur stations from the Meteo Lazio network (<a href="https://meteoregionelazio.it" target="_blank" rel="noopener">meteoregionelazio.it</a>) and eight stations funded by the EU LIFE-ASTI project <em>"Implementation of a forecasting system for urban heat island effect for the development of urban adaptation strategy"</em> (LIFE17 CCA/GR/000108) and installed by the CNR-ISAC research team based in Rome.</p> <p><strong>Bibliography</strong></p> <p>1. Cecilia, A., Casasanta, G., Petenko, I., Conidi, A., Argentini, S. Measuring the urban heat island of Rome through a dense weather station network and remote sensing imperviousness data, Urban Climate 47 (01 2022).</p>
Support Data to "Interface flexibility controls the nucleation and growth of supramolecular networks"
<p><strong>Overview</strong></p> <p>This repository contains the inputs and support data for the publication "Interface Flexibility Controls the Nucleation and Growth of Supramolecular Networks," which is currently under review in Nature Chemistry.</p> <p><strong>Folder Structure</strong><br>Each subfolder is named after the primary method used to obtain the data. The internal structure may vary depending on whether it was more convenient to organize the data by the figure they were used for or by the structures analyzed. Each folder includes a detailed README.md file providing further information.<br><br><strong>Notes<br></strong>In the second version we added the data for Figure S27, which was not uploaded before due oversight.</p>
BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph
<p>BOCK is a knowledge graph integrating oligogenic disease information (originally from the Oligogenic Disease Database (Natchtegael et al. 2022)) together with multiple biological networks and ontologies.</p> <p>Compared to more generic knowledge graphs, we selected specifically networks relevant to understand the molecular mechanisms of epistasis, placing genes as the central entities, and focused on trusted resources describing a large set of human genes and their interactions.</p> <p>All entities in the KG are linked to their source database entry via an URI (Uniform Resource Identifier) to facilitate integrations within larger bioinformatics linked data repositories.</p> <p>BOCK 2.0 integrates recent versions of the used ontologies and databases, as well as additional pathway-specific (The Reactome Pathway Knowledgebase 2024, Milacic et al.) and tissue-specific information (COXPRESdb v8, Obayashi et al.). Additionally the database used for the coexpression relation between genes, has been replaced by COXPRESdb v8.</p> <p>We provide BOCK 2.0 in three formats:</p> <ol> <li><strong>GraphML (Graph Markup Language)</strong>: a network format enabling the fast import of the KG by multiple libraries (e.g networkx) and tools (e.g Cytoscape).</li> <li><strong>XML (Extensible Markup Language)</strong>: a text-encoding system that is human-readable and compatible with many systems.</li> <li><strong>Neo4J import files</strong>: tab-separated files that can be easily imported into Neo4J using the neo4j-admin utils.</li> </ol> <p> </p>
Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions
<p>This is the online data repository accompanying the following manuscript:<br><strong>Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions</strong></p> <p><em>Giulio Pergola<sup>1,2,3,*</sup>, Madhur Parihar<sup>1</sup>, Leonardo Sportelli<sup>1,2</sup>, Rahul Bharadwaj<sup>1</sup>, Christopher Borcuk<sup>2</sup>, Eugenia Radulescu<sup>1</sup>, Loredana Bellantuono<sup>2,5</sup>, Giuseppe Blasi<sup>2,4</sup>, Qiang Chen<sup>1</sup>, Joel E. Kleinman<sup>1,3</sup>, Yanhong Wang<sup>1</sup>, Srinidhi Rao Sripathy<sup>1</sup>, Brady J. Maher<sup>1,3,7</sup>, Alfonso Monaco<sup>5,9</sup>, Fabiana Rossi<sup>1,2</sup>, Joo Heon Shin<sup>1</sup>, Thomas M. Hyde<sup>1,3,6</sup>, Alessandro Bertolino<sup>2,4,*</sup>, Daniel R. Weinberger<sup>1,7,8,*</sup></em></p> <p> </p> <p><strong>Affiliations:</strong></p> <p><em>1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)<br>2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy<br>3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy<br>5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy<br>6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy</em></p> <p> </p> <p><strong>Abstract:</strong></p> <p><em>Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.</em></p> <p> </p> <p><strong>Citation:</strong> <em>Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812</em></p> <p> </p> <p><strong>Data Files:<br>DLPFC hit.genes_kb_200__online.version.zip: </strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.<br><strong>DLPFC hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>DLPFC all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes<br><strong>DLPFC all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP hit.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only<br><strong>HP hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes<br><strong>HP all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>Modulewise SCZ enrichment(1.0).xlsx:</strong><br>Excel file contains module level SCZ enrichment results for all networks<br><strong>wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:</strong><br>Excel file contains WGCNA output for sliding window networks<br><strong>wide_form_WGCNA(v3.7.1)_final.xlsx:</strong><br>Excel file contains WGCNA output for our generated networks and from previously published networks<br><strong>libdnetworks(NC).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.<br><strong>libdnetworks(SCZ).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.<br><strong>sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.<br><strong>sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.<br><strong>stemcell.preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.<br><strong>SCZ.ref.list.sciadv.ade2812.rds</strong>: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.</p> <p> </p> <p>Accompanying code can be found at: <a href="https://github.com/LieberInstitute/Brain_WGCNA">https://github.com/LieberInstitute/Brain_WGCNA</a><br>Data from this repository is also available at: <a href="https://nets.libd.org/age_wgcna/">https://nets.libd.org/age_wgcna/</a></p> <p> </p> <p>For any data inquiries please contact:<br><strong>Giulio Pergola: </strong><a href="mailto:Giulio.Pergola@libd.org"><strong>Giulio.Pergola@libd.org</strong></a></p> <p> </p>
A collection of draft gene regulatory networks and perturbation transcriptomics data
<p>These are collections of previously published gene regulatory networks and perturbation transcriptomics data analyzed in our manuscript "A systematic comparison of computational methods for expression forecasting". For more information and related code, see https://github.com/ekernf01/perturbation_benchmarking . </p>
Temporal Analysis of Knowledge Networks
<p>This dataset contains 64 topic networks extracted from the English Wikipedia's internal link network. Every topic network contains 19 snapshots, which are taken on March 1st of each year from 2002 to 2020. More detailed information are available at: https://github.com/XikunHuang/TAKN.</p>
Data for the Article: Cross-validation of a semantic segmentation network for natural history collection specimens
<p>This deposit contains six datasets which were used for testing and validating a semantic segmentation network. The purpose was to evaluate the suitability of the segmentation network for use in the processing of images from Natural History Collections.</p>
Archival Datasets for SuperNova Artificial Inference by Lstm neural networks (SNAIL)
<p>The spectral-observation dataset (enclosed in the file archival_spec_observations.tar.gz) is comprised of 3091 observed spectra from 361 SNe Ia, largely contributed from CfA (Blondin et al. 2012), BSNIP (Silverman et al. 2012), CSP (Folatelli et al. 2013) and Supernova Polarimetry Program (Wang & Wheeler 2008; Cikota et al. 2019a; Yang et al. 2020).</p> <p>The spectral-template dataset (enclosed in the file archival_spec_templates.tar.gz) includes 361 spectral templates, each of them (covering -15 to +33d with wavelength from 3800 to 7200 A) was generated from the available spectroscopic observations of an individual SN via a LSTM neural network model.</p> <p>The auxiliary photometry dataset (enclosed in the file archival_phot_observations.tar.gz) provides the B & V light curves of these SNe (in total, 196 available SNe Ia), that were used to calibrate the synthetic B-V color of the observed spectra.</p> <p>In additional, the two master catalogs give the detailed information about the 361 SNe and their spectroscopic observations, respectively. </p> <p>These datasets are associated to the paper "Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks" (Hu et al. 2022, ApJ, accepted).</p>
Supplementary material for the paper EXTREME ULTRAVIOLET AND X-RAY DRIVEN PHOTOCHEMISTRY OF GASEOUS EXOPLANETS - Chemical network details
<p>Supplementary material for the paper</p> <p>EXTREME ULTRAVIOLET AND X-RAY DRIVEN PHOTOCHEMISTRY OF GASEOUS EXOPLANETS</p> <p>by Locci et al. 2021, submitted to PSJ (R1 version)</p> <p>This document contains the complete list of the chemical reactions included in the model: bimolecular reactions (neutral-neutral and ion-neutral) in Table 1, termolecular reactions in Table 2, thermodissociative reactions in Table 3, reverse reactions in Table 4, and finally photochemical reactions in Table 5.</p>
Database Search Results for Resource Management in Converged Optical and MillimeterWave Radio Networks Review
<p><strong>Paper Selection Procedure</strong></p> <p>In order to conduct the survey titled "Resource Management in Converged Optical and MillimeterWave Radio Networks: A Review", the authors reviewed works published in the literature with a focus on those that cover most of the identified optimization requirements for converged optical fronthaul and mmWave wireless access networks. The research method is based on the research steps given in "The PRISMA 2020 statement"[1]. The selection procedure is also illustrated in "Database Search Flow Chart.png" figure.</p> <p>The first step was the selection of the papers. We completed this step by making database searches in the ACM, Elsevier (Science Direct), IEEE, IET, MDPI, Optical Society (OSA), Springer, Taylor & Francis, and Wiley online library databases with keywords ``resource allocation AND converged mmWave fiber wireless (FiWi)'', ``resource management AND converged mmWave fiber wireless (FiWi)'', and ``resource allocation AND converged fiber wireless (FiWi)''. The searches in all databases were completed in May 2021. The resulting collection was screened, to exclude non-scientific texts, book chapters, out of context papers, and survey papers. The remaining 189 papers found in our database search are provided in the excel file titled "FiWi Resource Allocation Database Search.xlsx".</p> <p>Among these papers, our selection criteria was created to present the works that are most relevant to the target network architecture, providing novel implementation solutions to the requirements of the optimization objective. The criteria selected for our eligibility step can be summarized as follows:</p> <ul> <li>The study provided a sound research approach and published after a scholarly review process;</li> <li>The study had a resource management optimization objective for mmWave networks;</li> <li>The study explained the system model and proposed a well-defined optimization algorithm;</li> <li>The effects of the algorithm on a performance metric was reported and the different aspects of the performance metric was analyzed with different evaluation criteria.</li> </ul> <p>This review is limited to the focus scope on converged optical and mmWave radio network solutions and by the databases taken into consideration. The prioritization of the works that address a well-defined optimization algorithm led to the omission of relevant papers. We did not include works that do not clearly define a resource management objective, i.e., a study that focuses on the the hardware implementation aspects of optical and mmWave radio networks with no resource management perspective. We manually excluded all studies that do not match these criteria with a simple scoring system, in which a point is deducted from an eligible paper for each missing criterion. The initial screening process and the data collection steps were carried out by the first author and the final inclusion decision was made by all the reviewers for the studies with the highest scores. After this screening process, we identified 37 papers that focused on at least one of the resource management objectives of throughput maximization, delay minimization, energy-efficiency, and virtualized resource allocation. The papers that have joint objectives are classified under their main optimization focus of that paper. The list of the selected papers are provided in "FiWi Resource Allocation Papers Selected for Review.xlsx" file. Our target in this review is to understand the recent optimization techniques used in resource allocation for converged optical fronthaul and radio mmWave access network implementations, therefore we focused our search to the works completed in the last five years (between 2016 and 2021), and approximately 95% of the selected papers fit under this category.</p> <p><strong>Overview of the data collected from selected papers</strong></p> <p>In this section, we provide answers to the three following questions with the data collected from the eligible studies:</p> <ul> <li>Question 1: Which algorithms are used more often in performance optimization in converged mmWave networks?</li> <li>Question 2: Which performance metrics are determined to show that the optimization method achieves the objective?</li> <li>Question 3: Which criteria are used to evaluate the solution method?</li> </ul> <p>Regarding the first question, the figure titled "Distribution of Optimization Algorithms in Selected Papers" shows the distribution of the optimization algorithms used by the selected papers. The distribution of the main performance metrics according to the resource optimization objectives is given in Table 1 (Distribution of Evaluation Criteria) and the evaluation criteria to test the performances of the selected papers are grouped in Table 2 (Distribution of Main Performance Metrics Depending on Optimization Objectives), which shows how many times each criterion is used together with how many of the resource management objectives use these criterion.</p> <p><strong>References: </strong></p> <p>[1] Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;Brennan, S.E.; Chou, R.; Glanville, J.; Grimshaw, J.M.; Hróbjartsson, A.; Lalu, M.M.; Li, T.; Loder, E.W.; Mayo-Wilson, E.;McDonald, S.; McGuinness, L.A.; Stewart, L.A.; Thomas, J.; Tricco, A.C.; Welch, V.A.; Whiting, P.; Moher, D. The PRISMA 2020statement: an updated guideline for reporting systematic reviews.Systematic Reviews2021,10. doi:10.1186/s13643-021-01626-4.</p>
Dataset: Connectivity and rigidity percolation of cytoskeletal networks.
<p>Dataset containing information for "Connectivity and rigidity percolation of cytoskeletal networks."</p> <p>File: Fig1A_MEDYAN_Unbranched_timeseries_motor_333_linker_1500_tmax_122.csv<br> Description:<br> Average MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, no branchers.<br> Columns:<br> Last_Timestep: Last time step of the simulations<br> N_Motors: Total number of motors in the simulation<br> N_Linkers: Total number of linkers in the simulation<br> Simulation: Number of simulations<br> M_p: Number of plus ends<br> M_m: Number of minus ends<br> M_c: Number of free binding sites<br> M_M: Number of free motors<br> M_L: Number of free linkers<br> M_pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> M_cMc: Number of bound motors<br> M_cLc: Number of bound linkers<br> M_G: Number of free G-actin<br> M_b: number of free branchers</p> <p>File: Fig1A_ODE_Unbranched_timeseries_motor_333_linker_1500_tmax_10000.csv<br> Description:<br> Chemical kinetics calculations for transient concentrations of motor, linker and brancher for equivalent MEDYAN simulations of a 1um3 box with 333 motors and 1500 linkers, no branchers.<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> cMc: Number of bound motors<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of minus ends<br> p: Number of plus ends<br> G: Number of free G-actin<br> M: Number of free motors</p> <p>File: Fig1A_Unbranched_MEDYAN.csv<br> Description:<br> Species concentrations in MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, no branchers.<br> The simulations can be found in the Simulations_Unbranched folder<br> Columns:<br> Last_Timestep: Measured timestep<br> N_Motors: Total number of motors in the simulation<br> N_Linkers: Total number of linkers in the simulation<br> chem_path: Path of the simulation<br> AD: Number of unbound G-actins<br> MD: Number of unbound motors<br> LD: Number of unbound linkers<br> FA: Number of bound F-actin monomers<br> PA: Number of plus ends<br> MA: Number of minus ends<br> LA: Number of bound linkers<br> MOA: Number of bound motors<br> Simulation: Simulation ID</p> <p>File: Fig1B_Branched_MEDYAN.csv<br> Description:<br> Species concentrations in MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, 300 branchers.<br> The simulations can be found in the Simulations_Branched folder<br> Columns:<br> Last_Timestep: Measured timestep<br> N_Motors: Number of motors<br> N_Linkers: Number of linkers<br> chem_path: Path of the simulation<br> AD: Number of unbound G-actins<br> BD: Number of unbound branchers<br> MD: Number of unbound motors<br> LD: Number of unbound linkers<br> FA: Number of bound F-actin monomers<br> PA: Number of plus ends<br> MA: Number of minus ends<br> LA: Number of bound linkers<br> MOA: Number of bound motors<br> BA: Number of bound branchers<br> Simulation: Simulation ID</p> <p>File: Fig1B_MEDYAN_Branched_timeseries_motor_333_linker_1500_tmax_122.csv<br> Description:<br> Average MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, 300 branchers.<br> Columns:<br> Last_Timestep: Last time step of the simulations<br> N_Motors: Total number of motors in the simulation<br> N_Linkers: Total number of linkers in the simulation<br> Simulation: Number of simulations<br> M_p: Number of plus ends<br> M_m: Number of minus ends<br> M_c: Number of free binding sites<br> M_M: Number of free motors<br> M_L: Number of free linkers<br> M_pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> M_cMc: Number of bound motors<br> M_cLc: Number of bound linkers<br> M_G: Number of free G-actin<br> M_B: total number of branchers<br> M_cBm: number of bound branchers<br> M_b: number of free branchers</p> <p>File: Fig1B_ODE_Branched_timeseries_motor_333_linker_1500_tmax_10000_v2.csv<br> Description:<br> Chemical kinetics calculations for transient concentrations of motor, linker and brancher for equivalent MEDYAN simulations of a 1um3 box with 333 motors and 1500 linkers, and 300 branchers.<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> p: Number of plus ends<br> cMc: Number of bound motors<br> cBm: Number of bound branchers<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of minus ends<br> M: Number of free motors<br> G: Number of free G-actin<br> B: Number of free branchers</p> <p>File: Fig1C_Ps_timeseries_unbranched.csv<br> Description:<br> Flory-Stockmayer results for unbranched chemical kinetics calculations<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> cMc: Number of bound motors<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of free minus ends<br> p: Number free of plus ends<br> G: Number of free G-actin<br> M: Number of free motors<br> P0: Probability that an F-actin monomer is connected to another one on its plus end<br> P1: Probability that an F-actin monomer is connected to another one on its minus end<br> P2: Probability that an F-actin monomer is connected to another one on its binding site<br> Ps: Probability that an F-actin monomer is not connected to an infinite cluster<br> Nb: Average number of bonds per F-actin monomer<br> Nn: Mean cluster size<br> Nw: Mean weighted cluster size<br> Ratio: Nw/Nn Ratio</p> <p>File: Fig1D_Ps_timeseries_branched.csv<br> Description:<br> Flory-Stockmayer results for branched chemical kinetics calculations<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> cMc: Number of bound motors<br> cBm: Number of bound branchers<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of free minus ends<br> p: Number free of plus ends<br> G: Number of free G-actin<br> M: Number of free motors<br> B: Number of free branchers<br> P0: Probability that an F-actin monomer is connected to another one on its plus end<br> P1: Probability that an F-actin monomer is connected to another one on its minus end<br> P2: Probability that an F-actin monomer is connected to another one on its binding site<br> Ps: Probability that an F-actin monomer is not connected to an infinite cluster<br> Nb: Average number of bonds per F-actin monomer<br> Nn: Mean cluster size<br> Nw: Mean weighted cluster size<br> Ratio: Nw/Nn Ratio<br> Qm: Probability that an F-actin monomer is not connected to an infinite cluster through the minus end<br> Qp: Probability that an F-actin monomer is not connected to an infinite cluster through the plus end<br> Qc: Probability that an F-actin monomer is not connected to an infinite cluster through the binding site</p> <p>File: Fig2_Two-step.csv<br> Description:<br> Representative steady state concentrations for a non-cooperative two-step model of linker binding.<br> Columns:<br> Fc: Concentration of free binding sites<br> FcL: Concentration of linkers bound to a single binding site<br> FcLFc: Concentration of linkers bound to a pair of binding sites<br> L: Concentration of unbound linkers<br> Fc0: Total concentration of binding sites<br> L0: Total concentration of linkers</p> <p>File: Fig3_two_step_heatmap.csv<br> Proportion of the concentration of crosslinks to the concentration of total binding sites as a function of the linker binding equilibrium constant<br> Description:<br> 2D matrix, where the columns indicate the linker binding constant multiplied by the total concentration of binding sites, the rows indicate the total concentration of linkers per binding site , and the value corresponds to the total number of linkers bound to two binding sites divided by the total concentration of binding sites.</p> <p>File: Fig5A_Ps_unbranched.csv<br> Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model without brancher.</p> <p><br> File: Fig5B Ps_branched.csv<br> Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model with brancher.<br> </p> <p>File: Fig6_Ps_Branched_var.csv<br> Description:<br> 2D matrix, where the columns indicate the proportion of branchers to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model without brancher or motors.</p> <p><br> File: Fig7B_Ps_unbranched_linkeronly.csv</p> <p>Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model without brancher. The clusters are defined here as F-actin monomers connected by linkers, and without including motor connections.</p> <p>File: Fig7D_Ps_branched_linkeronly.csv</p> <p>Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model with brancher. The clusters are defined here as F-actin monomers connected by linkers or branchers, and without including motor connections.</p> <p>File: Fig9_data.csv<br> Description:<br> Minimum motor concentration to reach rigidity percolation as a function of the linker concentration for systems with and without brancher, considering both linker and motor connections or just motor connections and for different values of linker rigidity. The motor and linker concentrations are measured as the proportion of linkers or motors to actin.<br> Columns:<br> L: linker concentration <br> "M (unbranched, linkers and motors, bcLc=0)"<br> "M (unbranched, linkers and motors, bcLc=1)"<br> "M (unbranched, linkers and motors, bcLc=2)"<br> "M (unbranched, linkers and motors, bcLc=3)"<br> "M (unbranched, linkers and motors, bcLc=4)"<br> "M (unbranched, linkers and motors, bcLc=5)"<br> "M (unbranched, linkers and motors, bcLc=6)"<br> "M (branched, linkers and motors, bcLc=0)"<br> "M (branched, linkers and motors, bcLc=1)"<br> "M (branched, linkers and motors, bcLc=2)"<br> "M (branched, linkers and motors, bcLc=3)"<br> "M (branched, linkers and motors, bcLc=4)"<br> "M (branched, linkers and motors, bcLc=5)"<br> "M (branched, linkers and motors, bcLc=6)"<br> "M (unbranched, linkers only, bcLc=0)"<br> "M (unbranched, linkers only, bcLc=1)"<br> "M (unbranched, linkers only, bcLc=2)"<br> "M (unbranched, linkers only, bcLc=3)"<br> "M (unbranched, linkers only, bcLc=4)"<br> "M (unbranched, linkers only, bcLc=5)"<br> "M (unbranched, linkers only, bcLc=6)"<br> "M (branched, linkers only, bcLc=0)"<br> "M (branched, linkers only, bcLc=1)"<br> "M (branched, linkers only, bcLc=2)"<br> "M (branched, linkers only, bcLc=3)"<br> "M (branched, linkers only, bcLc=4)"<br> "M (branched, linkers only, bcLc=5)"<br> "M (branched, linkers only, bcLc=6)"</p> <p>File: FigS1_data.csv<br> Description:<br> Connectivity percolation as a function of the probabilities that an F-actin monomer site is bound to another F-actin.<br> Columns:<br> ppm: probability that an F-actin monomer plus end is connected to another F-actin monomer minus end<br> pcc: probability that an F-actin monomer binding site is connected to another F-actin monomer binding site<br> Pcm: probability that an F-actin monomer binding site is connected to another F-actin monomer minus end<br> Qp: Probability that an F-actin monomer is not connected to an infinite cluster through the plus end<br> Qm: Probability that an F-actin monomer is not connected to an infinite cluster through the minus end<br> Qc: Probability that an F-actin monomer is not connected to an infinite cluster through the binding site<br> Ps: Probability that an F-actin monomer is not connected to an infinite cluster<br> Percolated: Whether the system is percolated or not.</p> <p>File: simulations.tar.gz<br> Description: Contains the MEDYAN simulations used for figure 1. Each folder contains an individual simulation, with the following files:<br> systeminput.txt: Contains the input for the system conditions and settings<br> chemistryinput.txt: Contains the input for the chemical species<br> chemistry.traj: Output trajectory containing number of species in the simulations<br> snapshot.traj: Output trajectory containing the coordinates of the species.<br> For more information please reference the MEDYAN user guide and reference:<br> K Popov, JE Komianos and GA Papoian (2016) MEDYAN: Mechanochemical Simulations of Contraction and Polarity Alignment in Actomyosin Networks. PLoS Comput Biol 12(4): e1004877. doi:10.1371/journal.pcbi.1004877</p>
Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."
<p>Data related to<br> ===========<br> title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.",<br> journal = "Computer Methods in Applied Mechanics and Engineering",<br> volume ="390",<br> year = "2022",<br> doi = "https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> ",<br> pages = "114476 ",<br> author = "Wu, Ling and Noels, Ludovic"</p> <p>We would be grateful if you could cite the paper in the case in which you are using the data</p> <p> </p> <p>The files replace version 1 whose zip was corrupted.</p> <p> </p>
Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network
<p>In this repository we provide raw and processed datasets for the article: “Genome-wide gene expression noise in <em>Escherichia coli </em>is condition-dependent and determined by propagation of noise through the regulatory network<strong>” </strong>by Arantxa Urchueguía, Luca Galbusera, Dany Chauvin, Gwendoline Bellement, Thomas Julou and Erik van Nimwegen.</p> <p>A preprint is available under the following DOI: <a href="https://doi.org/10.1101/795369">https://doi.org/10.1101/795369</a>. </p> <p>The repository consists of the following datasets: </p> <p><strong>1. preprocessed_datasets.zip(~22GB)</strong></p> <ul> <li>This dataset contains raw data from the flow cytometry experiments (FACS Canto II, BD Bioscience) in all measured conditions in RData format. Raw fcs files were processed with the tools described in the publication ''Using fluorescence flow cytometry data for single-cell gene expression analysis in bacteria" published here: <a href="https://doi.org/10.1371/journal.pone.0240233">https://doi.org/10.1371/journal.pone.0240233</a>. The tools themselves are available here: <a href="https://github.com/vanNimwegenLab/E-Flow">https://github.com/vanNimwegenLab/E-Flow</a>. Included in the files are the outputs of these processing tools together with all raw values that came directly from the flow cytometer. The file <em>directory_structure_in_preprocessed </em>contains information about how the files are organized.</li> </ul> <p><strong>2. info_files: </strong>This is a set of csv files containing detailed information about the experiments done to acquire the preprocessed_datasets as well as annotation files that we used to retrieve promoter information. </p> <p><strong>3. processed_datasets:</strong> These files correspond to the processed datasets from the raw Rdata files under 1 above. The processed data provide mean and variance estimates in fluorescence of E.coli promoters across the different growth conditions. Note that we discarded flow cytometry measurements from promoter/growth-condition combinations that contained abnormal fluorescence distributions (due to contamination) as well as measurements from reporters with annotation mismatches. The folder contains the following clean dataset files that were used in the paper:</p> <ul> <li><strong>FULL_dataset_mean_var_wreplicates:</strong> In this dataset we include the processed means and variances (in both logarithmic and linear scale) of all promoters in each condition. Included as well are replicate measurements for some conditions.. We also include the name and Blattner number of the gene immediately downstream of each promoter, the DNA sequence of each promoter, and regulatory information (number of unique inputs for transcription factors sites and their names) which we obtained from RegulonDB v 10.5 (<a href="https://doi.org/10.1093/nar/gky1077">https://doi.org/10.1093/nar/gky1077</a>). </li> <li><strong>dataset_with_noise_estimates: </strong>In this dataset we provide noise estimates for all promoters expressed above an expression threshold (mean GFP fluorescence at least as large as autofluorescence). Note that the noise estimate correspond to the difference between the promoter’s variance in log-expression and the minimal variance as a function of its mean expression (i.e. the so called noise floor was subtracted). Apart from the mean, variance, noise and promoter features (sequence, name of gene downstream, number of unique regulatory inputs and name of the TFs binding), we also include the parameters used for fitting the minimal noise, i.e. noise floor, in each of the conditions. </li> <li><strong>time_course_data_SI</strong>: This dataset contains mean and variance measurements of one of the plates of the library measured at different time points during growth in Minimal media 0.4M NaCl: 0h (just after dilution), 1h, 2h, 3h, 5h, 6.5h, 8.5h, 10h and 11h. </li> <li><strong>growth_curves_SI</strong>: Growth data (OD<sub>600</sub> as a function of time) for a subset of the promoters from the library across different growth conditions.</li> <li><strong>singlecell_areas_SI: </strong>Single-cell areas estimated using agar patches of cells growing in each condition. Each row of the table contains data for a single-cell. </li> <li><strong>synthetic_promoters_dataset: </strong>This dataset contains mean, variance and noise measurements of a set of constitutive promoters from <a href="https://doi.org/10.7554/eLife.05856.001">https://doi.org/10.7554/eLife.05856.001</a> across different conditions.</li> <li><strong>MARA_results:</strong> All transcription factor activities results explaining measured noise levels in each condition. This data has been obtained after performing Motif Activity Response Analysis on the noise levels of all measured promoters in each condition.</li> </ul>
Training material for flye genome assembly (Galaxy Training Network tutorial)
<p>Datasets are subsets of 3 public datasets (Mucor mucedo Fresen. NRRL 3635 Standard Draft genome sequencing with PacBio technology)</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336965[accn]</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336964[accn]</p> <p>https://www.ncbi.nlm.nih.gov/sra/SRX5336963[accn]</p> <p> </p> <p> </p>
Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network
<p>Supporting information, datasets, and R and JavaScript code for the the National Ecological Observatory Network’s Airborne Observation Platform (AOP) sampling design and publication, <em>"Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network"</em></p>
Classification of blood cells dynamics with convolutional and recurrent neural networks: a sickle cell disease case study
<p>The fraction of red blood cells (RBC) adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease (SCD). Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples(out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. These videos are of different durations (from 6 to more than 100 frames).</p> <p>This dataset contains four adult patients with SCD. They were enrolled in the study Drepaforme (approved by the institutional review board CPP Ouest 6 under the reference n°2018A00679-46) and were sampled weekly for several months. The movies were processed using in-house routines in Matlab (Matlab, R2016a) and RBC were detected individually and tracked over time. The database provided in this repository are already pre-processed sequences of tracked and centered RBC over time, each time step image being normalized to 31x31 pixels. Within the 32 experiments, the total number of sequences (or samples) is nearly 150 000. All sequences were semi-automatically labelled into 3 classes, depending on the dynamic of the cell: tank-treading, flipping and unreliable (140 000 are unreliable). The percentage of tank-treading cells with respect to all reliable cells (tank-treading+flipping) in every experiment is the final goal of this study.</p> <p>This dataset is very interesting to the community as it is a large database for cell dynamics classification: the class depends on the movement of the cell.</p> <p>An automatic processing of the database using a 2-stage deep learning model is available here https://github.com/icannos/redbloodcells_disease_classification</p> <p>For opening the data in python:</p> <p> from scipy.io import loadmat<br> x=loadmat('BG20191003shear10s01_Export.mat')</p> <p> * x['Norm_Tab'] is of size nb_samples x max_len_sequences x 31 x 31, where max_len_sequences is the length of the longest sequence of the series, typically ~150 to 180. The other sequences are padded with 31x31 zero matrices at the end in order to fill this maximal length.</p> <p> * x['Labels_Num'] is the corresponding label of each sequence, of size nb_samples. Label can be:<br> - 0 : "tank-treading" (or healthy)<br> - 1 : "flipping" (or tumbling, i.e. related to a SCD)<br> - 2 : "unreliable"</p>
Polymeric Property Prediction Using Recurrent Neural Networks with Optimizations
<p>Database of the Dielectric Polymers used for training and testing of the developed Recurrent Neural Network models (DOI:10.5281/zenodo.4280446)</p>
ScienceDex guides
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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.