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809 results for “Network Analysis”
BioTAGME: A comprehensive platform for biological knowledge network analysis
<p><strong>A Knowledge Graph containing logical or physical relationships among biological elements</strong>. <br> This Network was built through BioTAGME, a system that combines TAGME, an entity-annotation framework based on Wikipedia corpus with a network-based inference methodology (i.e., DT-Hybrid).<br> <em>BioTAGME</em> exploits several Biological Ontologies as "ground truth" of significant bio-entities, such as <em>DisGeNET, DrugBank, STRING</em> and many more. <br> We Deployed <em>BioTAGME</em> on <em>PubMed</em>, where the aim was on extracting biological entities and their relations from titles and abstracts.<br> Biological entities are the <em>nodes</em> of our graph, while <em>edges</em> are the relations between them.<br> Edges are of three categories:</p> <ol> <li>Literature edges: interactions derived from publications.</li> <li> STRING: protein-protein associations stored in the STRING database.</li> <li>BioTAGME: interactions predicted by our tool.</li> </ol> <p> </p> <p>The <strong>network</strong> is released (<strong>BiotagmeNetwork.zip</strong>) in a neo4j compatible format. Such archive contains:</p> <ol> <li><strong>Edges.csv and Nodes.csv</strong> that contain the nodes and edges of our network, rispectively.</li> <li><strong>Name_Aliases.csv</strong>: contains the synonyms list of each biological entity.</li> <li><strong>Wiki_Titles.csv</strong>: contains the wikipedia pages title associated with the annotated entities.</li> <li><strong>wid1_wid2_pmid.csv</strong>: contains the associations between pairs of entities and pubmed id</li> <li><strong>BioIDs_WikiIDs.csv</strong>: contains the association between the biological entities annotated by BioTAGME, and the wikipedia pages that are associated. </li> </ol>
Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in Sorghum bicolor
<p>Supplementery materials of journal article "Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in <em>Sorghum bicolor</em>"</p>
R_JAGS code for estimation and analysis of species-area-relationship (SAR) parameters from NEON (National Ecological Observatory Network) data on plant surveys
<p><span>Invasive species science is heavily geared toward the invasive agent. </span>However, management to protect native species also requires a proactive approach focused on understanding the features affecting community vulnerability to invasion impacts<span>. </span><span>Vulnerability </span><span>is likely the result of </span><span>factors acting across spatial scales, from </span><span>local to regional, and it is the combined effects of these factors that will determine the magnitude of vulnerability.</span><span> We introduce an analytical framework that quantifies the scale-dependent impact of biological invasions from the shape of the native species-area-relationship (SAR). We leverage newly available, biogeographically extensive vegetation data from the US National Ecological Observatory Network to assess plant community vulnerability to invasion impact as a function of factors acting across scales. We analyzed more than 1000 SARs widely distributed across the USA along environmental gradients and under different levels of invasion. </span>Results show that a decrease in native richness is consistently associated with invasive species cover<span>, but it is only at relatively high levels of invasion that native richness is compromised. After accounting for variation in baseline ecosystem diversity, net primary productivity, and human modification, ecoregions that are colder and wetter seem to be most vulnerable to losses of native plant species at the local level, while warmer and wetter areas seem most susceptible at the landscape level. We also document how the combined effects of cross-scale factors result in a heterogenous spatial pattern of vulnerability. </span><span>This pattern </span><span>cannot be predicted by analyses at any single scale, underscoring the importance of accounting for factors acting across scales. Simultaneously assessing differences in vulnerability between distinct plant communities at local, landscape and regional scales provided outputs that can be used to inform policy and management aimed at reducing vulnerability to the impact of plant invasions.</span></p>
SUBATOMIC analysis of Homo sapiens integrated multi-edge networks
<p>We applied SUBATOMIC (https://github.com/CBIGR/SUBATOMIC/) to analyze a composite <em>H. sapiens</em> network containing transcription factor-target gene, miRNA-target gene, protein-protein, homologous and co-functional interactions from three different databases. We derived and annotated 5586 modules with diverse topological, regulatory and functional properties.</p>
Extraction and Analysis of Fictional Character Networks: A Survey
<p><strong>Description. </strong>Resources used in our survey of character network extraction and analysis methods. This version of the resources does not correspond to the published paper, but to a later update used in the (longer) arXiv/HAL version of the paper. The resources used in the original published paper are provided in v1.0.0.</p> <p><strong>Updates. </strong>The latest version of the data (especially the bibliographic table) is available on a GitHub page, which is easier to update: <a href="https://compnet.github.io/CharNetReview/">https://compnet.github.io/CharNetReview/</a>. This Zenodo repository is no longer updated.</p> <p><strong>Citation. </strong>If you use these data or figures, please cite the following paper:</p> <ul> <li>V. Labatut and X. Bost, “<em>Extraction and Analysis of Fictional Character Networks: A Survey</em>,” ACM Computing Surveys 52(5):89, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02173918">hal-02173918</a>⟩ DOI: <a href="http://doi.org/10.1145/3344548">10.1145/3344548</a></li> </ul> <p><br><code>@Article{Labatut2019,</code><br><code> author = {Labatut, Vincent and Bost, Xavier},</code><br><code> title = {Extraction and Analysis of Fictional Character Networks: A Survey},</code><br><code> journal = {ACM Computing Surveys},</code><br><code> year = {2019},</code><br><code> volume = {52},</code><br><code> number = {5},</code><br><code> pages = {89},</code><br><code> doi = {10.1145/3344548},</code><br><code>}</code></p>
Social Network analysis on European countries involved in agroecology research
<p>All the 124 (68 European and 56 Transnational) agroecology research projects identified in the mapping activities carried out by the task 1.3 of the AE4EU project were used to perform a weighted social network analysis (SNA) having the participating countries as nodes and collaborations in projects as edges.</p> <p>This dataset contains data related to this SNA and consists of two sheets:</p> <ul> <li><strong>Indexes</strong> where values of some measures for each identified country in the social network analysis are reported (number of European agroecological research projects coordinated by the country; number of transnational agroecological research projects coordinated by the country; Degree Centrality; Closeness Centrality)</li> <li><strong>Edge_weights</strong> where the weights for each edge between two countries are provided according to the times two countries cooperated together for a European or a transnational project.</li> </ul>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (inputs, outputs, analysis)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt: contains raw data that are used for analysis, also conatin folder for GaMD testing.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. GaMD-testing :</p> <p> 1. Input file of GaMD used to run testing and output gamd.log files for multiple run of σOP 1.2 - 1.4 and σOD 2.5.</p> <p> 4. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant: contains raw data that are used for analysis.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant: contains raw data that are used for analysis.</li> </ul> <p><br> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>03_TT_analysis.tar.gz - TransportTools: contains config file and all the raw data from all set and subset of reclustered (using in-house python script) caver calculations used for running TT.</li> </ul> <p> 1. Caver input data for comparison between 500ns, 1 us, 2.5 us and 5us between LinB-Wt and it’s mutants.<br> 2. TransportTools log file.<br> 3. Main statistics result of comparative analysis.</p> <ul> <li>04_reweighting.tar.gz: directory contains reweighted .csv files after running in-house reweighting protocol.<br> <br> 1. GaMD log files from each simulation of LinB-Wt and it’s mutants.<br> 2. CSV files from TT result folder.<br> 3. Result *.csv file contained reweighted tunnel properties in folder reweighted_filtered_new.</li> <li>05_caverdock.tar.gz: contains raw data for caverdock calculations uisng 100 best tunnels with four ligands 2-bromoethanol (be), 1,2-dibromoethane (dbe), Bromide ion (br-) and water (h2o).</li> </ul> <p> 1. Top 100 tunnels present in tunnel folder for all three tunnels ST, p1b and p3 with subdirectory containing three variants and four ligand, whichare used for running caverdock.<br> 2. Ligand *.pdbqt file and receptor *.pdbqt are present in each 100 tunnel folder of respective caverdock calculation.<br> 3. Inside each variant and each ligand, there is respective result of migration analysis with energy barrier calculation of respective tunnels *energy_barriers-new.log* and further simplied *.csv files that was used for preparing figure in manuscript.</p> <p> </p>
Physics-informed neural networks (PINNs) with unsaturated water flow models for inverse analysis of soil hydraulic parameters of layered soil profiles
<p>Information about the spatial distribution of soil hydraulic parameters is necessary for the accurate prediction of soil water flow and coupled movement of chemicals and heat at the field scale using a process-based model. Physics-informed neural networks (PINNs), which can provide physical constraints in deep learning to obtain a mesh-free solution, can be used to inversely estimate the soil hydraulic parameters from less and noisy training data. Previous studies using PINNs have successfully estimated soil hydraulic parameters for homogeneous soil but estimating such parameters of layered soil profiles where the interface depth and the parameters are unknown still has some difficulties. The objective of this study was to develop PINNs to inversely estimate the distribution of soil hydraulic parameters, such as saturated hydraulic conductivity and <em>α</em> and <em>n</em>, of the Mualem-van Genuchten model directly within layered soil profiles by predicting changes in pressure head from training data based on simulation results at given depths during infiltration. The impact of factors affecting PINNs performance, such as the weights assigned to each component of the loss function, the time range used in error computations, and the number of samples used to assess physical constraint was investigated. By assigning a larger weight to the physical constraint and excluding the earlier stage of infiltration in the loss function, the changes in pressure head and the three soil hydraulic parameter distributions within the layered soil profiles were successfully estimated. The developed PINNs can be further applied to more complex soils and can be improved.</p>
Impacts of centralized control on mixed traffic network performance: A strategic games analysis
<p>This dataset contains the data that were used to assess the proposed framework within the context of the case study in the paper entitled "Impacts of centralized control on mdaixed traffic network per-formance: A strategic games analysis".</p>
Internet AS-Graph Edgelist for Network Analysis
<p>This edgelist serves network analyses.</p>
An Analysis of Dependency Network Evolution in PyPI
<p>Retrieved from Libraries.io.</p>
Sea duck network analysis dataset
<p>This data file consists of habitat centroids used to construct network models for sea ducks in Eastern North America and is associated with the manuscript "Spatially-explicit network analysis reveals multi-species annual-cycle movement patterns of sea ducks" published in Ecological Applications. Columns are organized as follows:</p> <p>id - unique identifier</p> <p>species - species from which the centroid was obtained (BLSC = black scoter, COEI = common eider, LTDU = long-tailed duck, SUSC = surf scoter, WWSC = white-winged scoter)</p> <p>stage - period of the annual cycle to which the centroid belongs (W = winter, B = breeding, S = spring staging, M = fall staging and molt, WM = winter migration, BM = breeding migration, MM = molt migration, SM = spring migration)</p> <p>site - position of centroid within season (i.e., W1 = first site occupied during winter, W2 = second site occupied, etc.)</p> <p>cycle - number of annual cycles following transmitter attachment (1 = first cycle after attachment, 2 = second cycle after attachment, etc.)</p> <p>sex - sex of individual (M = male, F = female)</p> <p>age - age of individual (HY = hatch year, SY = second year, TY = third year, ASY = after second year, ATY = after third year, AHY = after hatch year</p> <p>capture_reg - general area where individual was captured</p> <p>capture_subreg - specific region within capture region where individual was captured</p> <p>lon - longitude of centroid</p> <p>lat - latitude of centroid</p> <p>duration - number of days spent at centroid</p> <p>start - date of arrival at centroid</p> <p>end - date of departure from centroid</p> <p>jstart - Julian date of arrival at centroid</p> <p>jend - Julian date of departure from centroid</p> <p>season - season of annual cycle in which centroid occurred (W = winter, F = fall, B = breeding, S = spring)</p> <p>year - calendar year in which centroid began</p> <p>to - node in which centroid is grouped</p> <p>from - node in which previous centroid is grouped (i.e., node in which indiviual was located before moving to present node)</p> <p>to_sea - season of annual cycle in which centroid occurred</p> <p>from_sea - season of annual cycle in which previous centroid occurred</p> <p>type - movement type to centroid; the first letter represents the season (coded as in "season" column), and the second represents the nature of the movement (WD = dispersal within a season, M = migration among seasons)</p> <p>type2 - same as "type", but with dispersal movements coded by the stage in which they occur (W = winter, B = breeding, SM = spring migration, WM = winter migration)</p> <p>count_ind_sp - total number of tracked individuals of the species represented by centroid</p> <p>weight - base centroid weight (all centroids equal, deployments excluded)</p> <p>wt_sp - species-adjusted centroid weight: for centroid <em>x</em> in species <em>i</em>, weight<em><sub>x</sub></em> = (<em>N </em>centroids<em><sub>total</sub></em>) × (<em>N </em>centroids<em><sub>i</sub></em>)<sup>-1</sup></p> <p>wt_dur - duration-adjusted centroid weight: for centroid <em>x</em>, weight<em><sub>x</sub></em> = (days at centroid location) × 365<sup>-1</sup></p> <p>wt_sp_sex - species-and-sex-adjusted centroid weight: for centroid <em>x</em>, species <em>i</em>, and sex <em>s</em>, weight<em><sub>x</sub></em> = (<em>N </em>centroids<em><sub>total</sub></em>) × (<em>N </em>individuals<em><sub>is</sub></em>)<sup>-1</sup></p> <p>wt_ind - individual-adjusted centroid weight: for centroid <em>x</em> in individual<em> j</em>, weight<em><sub>x</sub></em> = (<em>N </em>centroids<em><sub>j</sub></em>)<sup>-1</sup></p> <p>wt_ind_sp - individual-and-species-adjusted centroid weight: for centroid x, individual <em>j</em>, and species <em>i</em>, weight<em><sub>x</sub></em> = (<em>N </em>centroids<em><sub>j</sub></em>)<sup>-1</sup> × (<em>N </em>individuals<em><sub>i</sub></em>)<sup>-1</sup></p>
Integrated analysis of miRNA landscape and cellular networking pathways in stage-specific prostate cancer
<p><strong>Figure S1.</strong> Heat map of miRNA-microarray. Expression of miRNAs differentially expressed and assessed in microarray analysis of RNA isolated from four different cell lines of prostate cancer (LNCaP, PC3, DU145, 22Rv1) compared with control cell line of prostate cancer (PrEc). The red color depicts high and green color showed a lower level of expression at p value <0.01.</p> <p><strong>Figure S2A.</strong> Drug resistance by drug efflux. The internetworking relationship with miRNAs and plasma membrane protein P-glycoprotein (Pgp-plasma membrane protein). The miRNA-130a and miR-181a are downregulated in this pathway (green color) and linked with Pg and BCRP (breast cancer resistant protein). miR-133a and miR379 are involved in regulating the expression of MRP2. While miR-298, miR27a, miR331-5p and miR-130a are involved in regulating the expression of poly-glycoprotein (P-gp).</p> <p><strong>Figure S2B.</strong> Epithelial mesenchymal transition pathway. The miR-200b was downregulated during early-stage prostate cancer and was involved in inhibiting Jagged-2 (JAG2), one of the NOTCH ligands.</p> <p><strong>Figure S2C.</strong> Adipogenesis pathway. The expression of miR-326 was upregulated during the metastatic stage of prostate cancer, (red color) and involved in regulating the expression of CCAT/enhancer binding protein β (C/EBPβ), directly linked with the nuclear hormone receptor peroxisome proliferator-activated receptor-gamma (PPAR-γ).</p> <p><strong>Figure S2D.</strong> Bone metamorphosis signaling pathway. The expression of miR-140, miR-145, and miR-155 was upregulated, along with miR-140 and miR-145 were associated with modulation of gene SOX9, and miR-155 was involved in regulating the gene FOXO3A.</p> <p><strong>Figure S2E.</strong> Th1 pathway. In this pathway, the expression of miR-146a showed a higher level of expression (red color) and modulated the expression of NF-κB signaling.</p> <p><strong>Figure S2F.</strong> Th1 and Th2 pathway. In this pathway, the expression of miR-146a showed a lower level of expression (green color) and may modulate the expression of NF-κB signaling.</p> <p><strong>Table S1.</strong> List of differentially expressed miRNAs derived from LNCaP cells lines statistically significant as P < 0.001.</p> <p><strong>Table S2.</strong> List of differentially expressed miRNAs derived from PC3 cells lines statistically significant as P < 0.001.</p> <p><strong>Table S3.</strong> List of differentially expressed miRNAs derived from DU145 cells lines statistically significant as P < 0.001.</p> <p><strong>Table S4.</strong> List of differentially expressed miRNAs derived from 22Rv1 cells lines statistically significant as P < 0.001.</p>
Visualizations of the Network Analysis of Königsfelden Abbey
<p>Visualizations produced from networks regarding the production of charters in and for Königsfelden Abbey.</p> <p>A commented version of all networks is added as PDF file: modularity-and-networks.pdf.</p> <p>The referenced numbers of people can be found as PDF file: kf-personenliste.pdf.</p> <p>The data used, is published online: https://doi.org/10.5281/zenodo.632560</p>
Network analysis highlights increased generalisation and evenness of plant-pollinator interactions after conservation measures
<p><strong>DATASET used in the article entitled</strong> “Network analysis highlights increased generalisation and evenness of plant-pollinator interactions after conservation measures”.</p> <p>We supply weighted and binary matrices used for plant-pollinator network analyses, before and after the implementation of conservation measures.</p> <p>We also supply the list of plant and pollinator species recorded in this study.</p>
Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis
<p>This dataset contains the results of 2 related analyses, described in "Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis" (European Heart Journal Supplements, OUP). Link to the article: https://www.hal.inserm.fr/inserm-02310241</p> <p>1) In the directory "miRNAs_MARTHA_GWAS" : GWAS summary statistics for 162 circulating miRNAs in 344 VTE patients from the MARTHA cohort.</p> <p>Header for each summary file:</p> <p>Trait: miRNA id<br> chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> freq_A1: Frequency of reference allele<br> rsqr: Imputation quality defined by MACH<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)<br> z.score: Z-score</p> <p> </p> <p>2) In the directory "meta_analysis": Random effect meta-analysis combining the results of our GWAS on the MARTHA cohort, and the results from a similar analysis conducted by Nikpay et al. (doi: 10.1093/cvr/cvz030). Summary statistics of 142 microRNAs, common to both datasets, were processed (and combine 1054 samples).</p> <p>Header for each summary file:</p> <p>chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> N: Sample size<br> Q: Cochran's heterogeneity statistic<br> Q.p: p-value of Cochran's Q<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)</p> <p> </p>
Data from: Cross-sectional personal network analysis of adult smoking in rural areas
<p>This data package, titled <em>Data from: Cross-sectional personal network analysis of adult smoking in rural areas,</em> includes several files. First, there are annonymized raw data files in .rds file format (ego_data.rds & alter_data.rds). Second, there is the R code that allow the replication of various statistical analyses. Interested parts may consult the R code as .pdf file format (Supplementary_Material_R_Code.pdf), .Rmd file format (that can be run to create the .pdf file format) and the .R file format (that can be accesed with R and RStudio). Moreover, the labels files are useful for recreating the Supplementary Material pdf file. </p> <p>Readers should know that this dataset corresponds to the study (paper) <em>Cross-sectional personal network analysis of adult smoking in rural areas. </em></p> <p>The ego_data.rds file includes 20 variables by 76 observations (respondents) while the alter_data.rds file includes 46 variables by 1681 observations (social contacts). We collected this information by deploying a personal network analysis research design. Initially, we interviewed 83 respondents (dubbed <em>egos</em>). Due to missing data, we kept in the analysis 76 egos and dropped seven respondents. We recruited the respondents using a link-tracining sampling framework. We started from a number of six seeds. We interviewed the seeds then we asked them to recommend other people in the study. We continued in a referee-referral fashion until 83 interviews were completed. The study was performed in a small rural Romanian community (4124 residents): Lerești (Argeș county). </p> <p>Our study was carried out in accordance with the recommendations, relevant guidelines, and regulations (specifically, those provided by the Romanian Sociologists Society, i.e., the professional association of Romanian sociologists). The research was performed in accordance with the Declaration of Helsinki. The research protocol was approved by a named institutional/licensing committee. Specifically, the Ethics Committee of the Center for Innovation in Medicine (InoMed) reviewed and approved all these study procedures (EC-INOMED Decision No. D001/09-06-2023 and No. D001/19-01-2024). All participants gave written informed consent. The privacy rights of the study participants were observed. The authors did not have access to information that could identify participants. Face-to-face interviews were collected between September 13 – 23, 2023, in Lerești, Romania. After each interview, information that could identity the participants were anonymized. Before conducting the interview, we provided each participant with a dossier containing informative materials about the project's objectives, how the data would be analyzed and reported, and their participation rights (e.g., the right to withdraw from the project at any time, even after the interview was completed). All study participants gave their written informed consent prior to enrolment in the study.</p> <p>The variables in the ego_data.rds file are as follows:</p> <p>(1) "networkCanvasEgoUUID" (unique alpha numeric code for each observation); </p> <p>(2) "ego_age" (the age of each study participant); </p> <p>(3) "ego_age.cen" (the age of each study participant, centered); </p> <p>(4) "ego_educ_b" (the education of each ego, binary); </p> <p>(5) "ego_educ_f" (the education of each ego, educational achievement); </p> <p>(6) "ego_marital.s_f" (the marital status of each ego);</p> <p>(7) "ego_occupation.cat2_f" (the occupation of each ego); </p> <p>(8) "ego_occupation_b" (the occupation of each ego, unemployed vs employed); </p> <p>(9) "ego_relstatus_b" (whether the ego is in a relationship or not); </p> <p>(10) "ego_sex_f" (the sex of the ego assigned at birth; male & female); </p> <p>(11) "ego_sex_n" (the sex of the ego assigned at birth; 0 = male & 1 = female); </p> <p>(12) "ego_smk_status_b1" (smoking status: 1 smoking, 0 others);</p> <p>(13) "ego_smk_status_b2" (smoking status: 1 former smoker, 0 others); </p> <p>(14) "ego_smk_status_b3" (smoking status: 1 not a smoker, 0 others); </p> <p>(15) "ego_smkstatus_f" (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker); </p> <p>(16) "ego_smoking_3cat" (smoking status: non-smoker, former smoker, smoker);</p> <p>(17) "net.size" (number of social contacts, alters, that were elicited by an ego);</p> <p>(18) "net.components" (number of strong components in the personal network);</p> <p>(19) "net.deg.centralization" (personal network degree centralization);</p> <p>(20) "net.density" (personal network density). </p> <p>The variables in the alter_data.rds file are as follows:</p> <p>(1) "alter_age" (the age of the alter); </p> <p>(2) "alter_age.cen" (the age of the alter - centered); </p> <p>(3) "alter_btw" (alter's betweenness score); </p> <p>(4) "alter_btw.cen" (alter's betweenness score - centered); </p> <p>(5) "alter_deg" (alter's degree score); </p> <p>(6) "alter_deg.cen" (alter's degree score - centered); </p> <p>(7) "alter_educ_b" (alter's education); </p> <p>(8) "alter_educ_f" (alter's education); </p> <p>(9) "alter_marital.s_f" (alter's marital status); </p> <p>(10) "alter_relstatus_b" (alter's marital status - binary variable);</p> <p>(11) "alter_sex_f" (alter's sex assigned at birth);</p> <p>(12) "alter_sex_n" (alter's sex assigned at birth; 1 - female; 0 - male); </p> <p>(13) "alter_smk_status_b1" (alter's smoking status; 1 smoker, 0 others);</p> <p>(14) "alter_smk_status_b2" (alter's smoking status; 1 former smoker, 0 others);</p> <p>(15) "alter_smk_status_b3" (alter's smoking status; 1 non-smoker, 0 others);</p> <p>(16) "alter_smoking_3cat" (alter's smoking status: three categories - smoker, non-smoker, former smoker);</p> <p>(17) "assortativity_score_fsmoker" (assortativity score for alter, former smoker);</p> <p>(18) "assortativity_score_nsmoker" (assortativity score for alter, non-smoker);</p> <p>(19) "assortativity_score_smoker" (assortativity score for alter, smoker);</p> <p>(20) "ego.alter_meet_f" (ego's meeting frequency with alter); </p> <p>(21) "ego_alter_meet_b" (ego's meeting frequency with alter, binary variable);</p> <p>(22) "ego.alter_meet_n" (ego's meeting frequency with alter, numerical codes);</p> <p>(23) "alter_rel.w.ego_f" (type of alters in an ego's network);</p> <p>(24) "networkCanvasUUID" (alpha numeric code for alter);</p> <p>(25) "networkCanvasEgoUUID" (alpha numeric code for ego); </p> <p>(26) "ego_smkstatus_f" (smoking status: former smoker, never-smoker, non-smoker (smoked too little), occasional smoker, smoker); </p> <p>(27) "ego_smoking_3cat" (three categories, smoking status: former smoker, non-smoker, smoker);</p> <p>(28) "ego_type_fsmk" (former smoking egos by type of ego-alter relationship);</p> <p>(29) "ego_type_nsmk" (non smoking egos by type of ego-alter relationship);</p> <p>(30) "ego_type_smk" (smoking egos by type of ego-alter relationship);</p> <p>(31) "ego_sex_f" (ego's sex, binary);</p> <p>(32) "ego_sex_n" (ego's sex, numerical code, 1 female, 0 male); </p> <p>(33) "ego_educ_b" (ego's education, binary variable)</p> <p>(34) "ego_age" (ego's age)</p> <p>(35) "ego_age.cen" (ego's age, centered)</p> <p>(36) "ego_relstatus_b" (ego's marital status, binary variable)</p> <p>(37) "ego_occupation_b" (ego's employment status, binary variable)</p> <p>(38) "net.components" (number of strong components in the personal network)</p> <p>(39) "net.deg.centralization" (degree centralization score in the personal networ)</p> <p>(40) "net.density" (density score in the personal network)</p> <p>(41) "prop_fsmokers" (proportion of former smokers in the personal network - alters)</p> <p>(42) "prop_fsmokers.cen" (proportion of former smokers in the personal network, centered- alters)</p> <p>(43) "prop_nsmokers" (proportion of non-smokers in the personal network- alters)</p> <p>(44) "prop_nsmokers.cen" (proportion of non-smokers in the personal network, centered- alters)</p> <p>(45) "prop_smokers" (proportion of smokers in the personal network- alters)</p> <p>(46) "prop_smokers.cen" (proportion of smokers in the personal network, centered- alters)</p>
The efficacy of hemoglobin spray in wound management: a systematic review and network meta-analysis of comparative studies
<p>PRISMA flow</p>
Trade network dynamics and alien plant pest introductions: A global analysis
<p>This is a supplement to "Trade network dynamics and alien plant pest introductions: A global analysis", Diversity and Distributions (<a href="https://doi.org/10.1111/ddi.13963">https://doi.org/10.1111/ddi.13963</a>). These data were partially derived from the following resources available in the public domain: <a href="http://www.cepii.fr/CEPII/fr/welcome.asp">www.cepii.fr/CEPII/fr/welcome.asp</a>; Seebens et al. (2017) (dataset DOI: <a href="https://doi.org/10.12761/sgn.2016.01.022">https://doi.org/10.12761/sgn.2016.01.022</a>); Fenn-Moltu et al. 2022 (dataset DOI: <a href="https://doi.org/10.5061/dryad.8931zcrrq">https://doi.org/10.5061/dryad.8931zcrrq</a>).</p>
Diversity in citations to a single study: Supplementary data set for citation context network analysis
<p><strong>Introduction</strong></p> <p>This document describes the data set used for all analyses in 'Diversity in citations to a single study: A citation context network analysis of how evidence from a prospective cohort study was cited' accepted for publication in <em>Quantitative Science Studies</em> [1].</p> <p><strong>Data Collection</strong></p> <p>The data collection procedure has been fully described [1]. Concisely, the data set contains bibliometric data collected from Web of Science Core Collection via the University of Edinburgh’s Library subscription concerning all papers that cited a cohort study, Paul <em>et al.</em> [2], in the period <1985. This includes a full list of citing papers, and the citations between these papers. Additionally, it includes textual passages (citation contexts) from 343 citing papers, which were manually recovered from the full-text documents accessible via the University of Edinburgh’s Library subscription. These data have been cleaned, converted into network readable datasets, and are coded into particular classifications reflecting content, which are described fully in the supplied code book and within the manuscript [1]. </p> <p><strong>Data description</strong></p> <p>All relevant data can be found in the attached file 'Supplementary_material_Leng_QSS_2021.xlsx', which contains the following five workbooks:</p> <ul> <li><strong>“Overview”</strong> includes a list of the content of the workbooks.</li> <li><strong>“Code Book”</strong> contains the coding rules and definitions used for the classification of findings and paper titles.</li> <li><strong>“Node attribute list”</strong> includes a workbook containing all node attributes for the citation network, which includes Paul et al. [2] and its citing papers as of 1984. Highlighted in yellow at the bottom of this workbook is two papers that were discarded due to duplication - remove these if analysing this dataset in a network analysis. The columns refer to:</li> </ul> <ol> <li><em>Id</em>, the node identifier</li> <li><em>Label</em>, the formal citation of the paper to which data within this row corresponds. Citation is in the following format: last name of first author, year of publication, journal of publication, volume number, start page, and DOI (if available). </li> <li><em>Title</em>, the paper title for the paper in question.</li> <li><em>Publication_year</em>, the year of publication.</li> <li><em>Document_type, </em>the document type (e.g. review, article)</li> <li><em>WoS_ID</em>, the paper’s unique Web of Science accession number.</li> <li><em>Citation_context</em>, a column specifying whether citation context data is available from that paper</li> <li><em>Explanans</em>, the title explanans terms for that paper;</li> <li><em>Explanandum</em>, the explanandum terms for that paper.</li> <li><em>Combined_Title_Classification</em>, the combined terms used for fig 2 of the published manuscript.</li> <li><em>Serum_cholesterol_(SC)</em>, a column identifying papers that cited the serum cholesterol findings.</li> <li><em>Blood_Pressure_(BP), </em>a column identifying papers that cited the blood pressure findings.</li> <li><em>Coffee_(C),</em> a column identifying papers that cited the coffee findings.</li> <li><em>Diet_(D), </em>a column identifying papers that cited the dietary findings.</li> <li><em>Smoking_(S), </em>a column identifying papers that cited the smoking findings.</li> <li><em>Alcohol_(A), </em>a column identifying papers that cited the alcohol findings.</li> <li><em>Physical_Activity_(PA),</em> a column identifying papers that cited the physical activity findings.</li> <li><em>Body_Fatness (BF), </em>a column identifying papers that cited the body fatness findings.</li> <li><em>Indegree,</em> the number of within network citations to that paper, calculated for the network shown in Fig 4 of the manuscript.</li> <li><em>Outdegree</em>, the number of within network references of that paper as calculated for the network in Fig 4.</li> <li><em>Main_component</em>, a column specifying whether a node is contained in the largest weakly connect component as shown in Fig 4 of the manuscript.</li> <li><em>Cluster</em>, provides the cluster membership number as discussed within the manuscript (Fig 5).</li> </ol> <ul> <li><strong>“Edge list”</strong> includes a workbook including the edges for the network. The columns refer to:</li> </ul> <ol> <li><em>Source</em>, contains the node identifier of the citing paper.</li> <li><em>Target,</em> contains the node identifier of the cited paper.</li> </ol> <ul> <li><strong>“Citation context classification</strong>” includes a workbook containing the WoS accession number for the paper analysed, and any finding category discussed in that paper established via context analysis (see the code book for definitions). The columns refer to:</li> </ul> <ol> <li><em>Id</em>, the node identifier</li> <li><em>Finding_Class, </em>the findings discussed from Paul et al. within the body of the citing paper. </li> </ol> <ul> <li><strong> “Citation context data”</strong> includes a workbook containing the WoS accession number for papers in which citation context data was available, the citation context passages, the reference number or format of Paul et al. within the citing paper, and the finding categories discussed in those contexts (see code book for definitions). The columns refer to:</li> </ul> <ol> <li><em>Id</em>, the node identifier</li> <li><em>Citation_context</em>, the passage copied from the full text of the citing paper containing discussion of the findings of Paul et al.</li> <li><em>Reference_in_citing_article</em>, the reference number or format of Paul et al. within the citing paper.</li> <li><em>Finding_class, </em>the findings discussed from Paul et al. within the body of the citing paper. </li> </ol> <p><strong>Software recommended for analysis</strong></p> <p>For the analyses performed within the manuscript, Gephi version 0.9.2 was used [3], and both the edge and node lists are in a format that is easily read into this software. The Sci2 tool was used to parse data initially [4].</p> <p><strong>Notes</strong></p> <ol> <li>Leng, R. I. (Forthcoming). Diversity in citations to a single study: A citation context network analysis of how evidence from a prospective cohort study was cited. Quantitative Science Studies.</li> <li>Paul, O., Lepper, M. H., Phelan, W. H., Dupertuis, G. W., Macmillan, A., McKean, H., <em>et al.</em> (1963). A longitudinal study of coronary heart disease. <em>Circulation, </em><strong>28</strong>, 20-31. <a href="https://doi.org/10.1161/01.cir.28.1.20">https://doi.org/10.1161/01.cir.28.1.20</a>.</li> <li>Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.</li> <li>Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies. Stable URL: <a href="https://sci2.cns.iu.edu">https://sci2.cns.iu.edu</a></li> </ol>
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.