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29,145 results for “Association”
Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"
<div> <div> <div> <div> <p><strong>Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a></strong></p> <p><strong><span>Suppl. File 1: Protein Family Clusters.</span></strong></p> <p><strong><span>Suppl. File 2: Cluster GO enrichments/depletions. </span></strong></p> <p><strong><span>Suppl. File 3: The raw ID-CBR data with annotations. </span></strong></p> <p><strong><span>Suppl. File 4: ­ID-CBR Cluster membership.</span></strong></p> <p><strong><span>Each file has an explanatory header. </span></strong></p> <p> </p> </div> </div> </div> </div>
GWAS Summary Statistics for Publication: Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data
<p>This dataset contains summary statistics for genome-wide association studies (GWAS) conducted on genomic features derived from off-target reads in whole-exome sequencing (WES) data. The study utilized tools like Seeing Beyond the Target (SBT) and ImReP to construct novel phenotypic features from unmapped reads in ~50,000 participants in the UK Biobank. Features include mitochondrial DNA (mtDNA) copy number, ribosomal DNA (rDNA) copy number (5S, 18S, 28S), immune repertoire metrics (e.g., T-cell receptor alpha diversity), and microvial genome load (viral and fungal).</p> <p>Summary statistics can be used for replication studies, meta-analyses, or further exploration of these phenotypes.</p>
Flood Hazard Maps and Associated Data for Case Study: Funding rules that promote equity in climate adaptation outcomes
<p>Inundation grids for multiple return periods and multiple scenarios. Please see the underlying study for more details about the methods. The data here can be reproduced following the code and instructions at this repository: https://github.com/CoRE-Lab-UCF/Pollack_et_al_2024/tree/main. Also available here: https://doi.org/10.5281/zenodo.14515896. </p>
Data associated with the article 'Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis'
<p>The efficacy of oral language comprehension interventions varies, but the reasons for this variation have received little attention. A meta-analysis was conducted to examine intervention factors associated with the efficacy (as expressed with effect sizes) of oral language comprehension interventions in children under the age of 18 with or at risk for (Developmental) Language Disorder, (D)LD.</p> <p>The meta-analysis article together with this additional material comprise the content needed for a thorough understanding and replication of the results.</p> <p>This dataset is based on two systematic scoping reviews on oral language comprehension interventions (Tarvainen et al., 2020, 2021). Further information from the sourced articles was extracted for this study titled ‘Intervention factors associated with efficacy, when targeting oral language comprehension of children with or at risk for (Developmental) Language Disorder: A meta-analysis’. </p> <p>In the future, we hope that this data is used with a growing body of oral language comprehension interventions to conduct further and more detailed examinations of intervention factors associated with efficacy.</p> <p>References:</p> <p>Tarvainen, S., Launonen, K., & Stolt, S. (2021). Oral language comprehension interventions in school-age children and adolescents with developmental language disorder: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>6</em>, 1–24. https://doi.org/10.1177/23969415211010423</p> <p>Tarvainen, S., Stolt, S., & Launonen, K. (2020). Oral language comprehension interventions in 1–8-year-old children with language disorders or difficulties: A systematic scoping review. <em>Autism & Developmental Language Impairments</em>, <em>5</em>, 1–24. https://doi.org/10.1177/2396941520946</p> <p> </p>
Data set associated to the manuscript entitled Carbon emissions from inland waters may be underestimated: evidence from European river networks fragmented by drying by López-Rojo et. al
<p>CO2 and CH4 emissions and several associated environmental variables were taken in 6 European drying river networks, in 20 river reaches per river network. The field work was carried across 3 sampling campaigns in 2021, coinciding with 3 hydrological seasons (pre-dry, dry and post-rewetting) to encompass most of the hydrological variability. Each time, measures were taken in the habitats available (flowing water, dry riverbeds, isolated pools).</p>
Data and code associated with "The Observed Availability of Data and Code in Earth Science and Artificial Intelligence"
<p>Data and code associated with "The Observed Availability of Data and Code in Earth Science <br>and Artificial Intelligence" by Erin A. Jones, Brandon McClung, Hadi Fawad, and Amy McGovern.</p> <p>Instructions: To reproduce figures, download all associated Python and CSV files and place<br> in a single directory.<br> Run BAMS_plot.py as you would run Python code on your system.</p> <p>Code:<br>BAMS_plot.py: Python code for categorizing data availability statements based on given data<br> documented below and creating figures 1-3. </p> <p> Code was originally developed for Python 3.11.7 and run in the Spyder <br> (version 5.4.3) IDE.<br> <br> Libraries utilized:<br> numpy (version 1.26.4) <br> pandas (version 2.1.4)<br> matplotlib (version 3.8.0)<br> <br> For additional documentation, please see code file.</p> <p>Data:<br>ASDC_AIES.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence for the Earth Systems (AIES)<br>ASDC_AI_in_Geo.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence in Geosciences (AI in Geo.)<br>ASDC_AIJ.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence (AIJ)<br>ASDC_MWR.csv: CSV file containing relevant availability statement data for Monthly <br> Weather Review (MWR)<br><br></p> <p><br>Data documentation:<br>All CSV files contain the same format of information for each journal. The CSV files above are <br>needed for the BAMS_plot.py code attached.</p> <p>Records were analyzed based on the criteria below.</p> <p> Records:<br> 1) Title of paper<br> The title of the examined journal article.<br> 2) Article DOI (or URL)<br> A link to the examined journal article. For AIES, AI in Geo., MWR, the DOI is <br> generally given. For AIJ, the URL is given.<br> 3) Journal name<br> The name of the journal where the examined article is published. Either a full<br> journal name (e.g., Monthly Weather Review), or the acronym used in the <br> associated paper (e.g., AIES) is used.<br> 4) Year of publication<br> The year the article was posted online/in print.<br> 5) Is there an ASDC?<br> If the article contains an availability statement in any form, "yes" is <br> recorded. Otherwise, "no" is recorded.<br> 6) Justification for non-open data?<br> If an availability statement contains some justification for why data is not <br> openly available, the justification is summarized and recorded as one of the <br> following options: 1) Dataset too large, 2) Licensing/Proprietary, 3) Can be <br> obtained from other entities, 4) Sensitive information, 5) Available at later <br> date. If the statement indicates any data is not openly available and no <br> justification is provided, or if no statement is provided is provided "None" <br> is recorded. If the statement indicates openly available data or no data <br> produced, "N/A" is recorded.<br> 7) All data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access data associated with the article are given and there is no <br> indication that any data is not openly available; "n" is recorded if no means <br> to access data are given or there is some indication that some or all data is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 8) At least some data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access data associated with the article are given; "n" is <br> recorded if no means to access data are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 9) All code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access code associated with the article are given and there is no <br> indication that any code is not openly available; "n" is recorded if no means <br> to access code are given or there is some indication that some or all code is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 10) At least some code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access code associated with the article are given; "n" is <br> recorded if no means to access code are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 11) All data available upon request<br> If there is an availability statement indicating data is produced and no data <br> is openly available, "y" is recorded if any data is available upon request to <br> the authors of the examined journal article (not a request to any other <br> entity); "n" is recorded if no data is available upon request to the authors <br> of the examined journal article. If there is no availability statement, any <br> data is openly available, or no data is produced, the record is left blank.<br> 12) At least some data available upon request<br> If there is an availability statement indicating data is produced and not all <br> data is openly available, "y" is recorded if all data is available upon <br> request to the authors of the examined journal article (not a request to any <br> other entity); "n" is recorded if not all data is available upon request to <br> the authors of the examined journal article. If there is no availability <br> statement, all data is openly available, or no data is produced, the record<br> is left blank.<br> 13) no data produced<br> If there is an availability statement that indicates that no data was<br> produced for the examined journal article, "y" is recorded. Otherwise, the<br> record is left blank.<br> 14) links work<br> If the availability statement contains one or more links to a data or code <br> repository, "y" is recorded if all links work; "n" is recorded if one or more <br> links do not work. If there is no availability statement or the statement <br> does not contain any links to a data or code repository, the record is left <br> blank. </p>
Genome-wide association analyses identify novel Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility
<p>The Brugada syndrome GWAS summary statistics</p> <p>Brugada syndrome is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of <em>SCN5A</em>, encoding the cardiac sodium channel Na<sub>V</sub>1.5, susceptibility genes remain largely unknown. We performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with Brugada syndrome and 10,001 controls.</p> <p> </p>
Dataset associated with paper "Topographic hotspots of Southern Ocean eddy upwelling"
<p><strong>Data repository for Yung, Morrison and Hogg (2022) <em>Topographic hotspots of Southern Ocean eddy upwelling</em>, submitted to Frontiers in Marine Science</strong></p> <p> </p> <p>This repository contains processed data, created using scripts available in the github repository <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code</a>.</p> <p> </p> <p>The data comes from the repeat year atmospheric forcing version of the ACCESS-OM2 modelling suite 0.1 degree model (see Kiss et al. (2020), http://www.cosima.org.au, model available at <a href="https://github.com/COSIMA/access-om2">https://github.com/COSIMA/access-om2</a>). The model was spun up for 270 years, and the next 10 years of output were used.</p> <p> </p> <p>Daily resolution data was used in the calculation of quantities, which results in a large amount of raw data (~4TB for Southern Ocean latitudes (35-70S), 10 years). Therefore, we only provide relevant processed data. Details of these calculations are available in the above github repository.</p> <p> </p> <p>Any quantities calculated along sea surface height contours are labelled with a letter. These are referred to in the following table.</p> <p> </p> <p>| Letter | SSH |</p> <p>| :---: | :---: |</p> <p>| A | -0.1m |</p> <p>| B | -0.2m |</p> <p>| C | -0.3m |</p> <p>| D | -0.4m |</p> <p>| E | -0.5m |</p> <p>| F | -0.6m |</p> <p>| G | -0.7m |</p> <p>| H | -0.8m |</p> <p>| I | -0.9m |</p> <p>| J | -1.0m |</p> <p>| K | -1.1m |</p> <p>| L | -1.2m |</p> <p>| M | -1.3m |</p> <p>| N | -1.4m |</p> <p>| O | -1.5m |</p> <p>| P | -0.15m|</p> <p>| Q | -0.25m|</p> <p>| R | -0.35m|</p> <p>| S | -0.45m|</p> <p>| T | -0.55m|</p> <p>| U | -0.65m|</p> <p>| V | -0.75m|</p> <p>| W | -0.85m|</p> <p>| X | -0.95m|</p> <p>| Y | -1.05m|</p> <p>| Z | -1.15m|</p> <p>| Z1 | -1.25m|</p> <p>| Z2 | -1.35m|</p> <p>| Z3 | -1.45m|</p> <p> </p> <p>There are two versions of the along-contour coordinates for each contour. The latlon named files are more useful for analysis, the other is used while computing transport across contours. These are made using <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/make_contour.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/make_contour.ipynb</a>.</p> <p> </p> <p>Distance along contour files contain the cumulative distance along the contour in 10^3 km from 80E (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Figure_Code/Fig7-upwelling_characteristics.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Figure_Code/Fig7-upwelling_characteristics.ipynb</a>). Dimensions are contour index, counting from 80E. There are also segment length files of each part of the contour.</p> <p> </p> <p>vh_eddy files contain the eddy transport across the contours, averaged over 10 years. These are calculated by taking the time mean of the residual transport (<span class="math-tex">\(\overline{vh}\)</span>), e.g. SO_L_vol_trans_across_contour_binned.nc, and subtracting the mean transport <span class="math-tex">\(\overline{v}\overline{h}\)</span>, calculated from the time mean isopycnal thicknesses along contours (e.g. SO_L_dzu_across_contour_binned) and the time mean velocity, <span class="math-tex">\(v = vh/h\)</span> (calculated in <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_and_bin_along_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_and_bin_along_contours.ipynb</a>). The full files are provided for the contour L (SSH=-1.2 m) as is provided in the paper manuscript Fig. 6. These eddy transports have dimensions of sigma1 and contour index (the two extra SO_L files have time too).</p> <p> </p> <p>vh_eddy_interp files contain the interpolated eddy transports at hotspots, for the density range 1032.2kg/m^3 <= sigma_1 <= 1032.5kg/m^3. Calculation method provided at <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Interpolation_between_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Interpolation_between_contours.ipynb</a>.</p> <p> </p> <p>We also provide files that summarise the transport in density and SSH space for hotspots and the circumpolar total. See <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/UpwellingArmDefn.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/UpwellingArmDefn.ipynb</a> for calculation details. The exact names and specifications are provided in the README.</p> <p> </p> <p>We provide 10 year mean files of the energy conversion and energy terms over the Southern Ocean latitude range. These are made by binning daily transports and layer thicknesses into sigma 1 bins over the Southern Ocean (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Binning_SouthernOcean_code.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Binning_SouthernOcean_code.ipynb</a>) and then calculating energy terms (<a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_MKE_EKE.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_MKE_EKE.ipynb</a>, <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_Energy_Conversion_Terms.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/Save_Energy_Conversion_Terms.ipynb</a>)</p> <p> </p> <p> </p> <p>The files named with contour_energies contain the EKE, Form stress and Reynolds stress averaged over 10 years but extracted along the same contours as eddy transport. <a href="https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/save_energy_terms_along_contours.ipynb">https://github.com/claireyung/Topographic_Hotspots_Upwelling-Paper_Code/blob/main/Analysis_Code/save_energy_terms_along_contours.ipynb</a></p> <p> </p> <p>We also provide 10 year averaged density binned transport, layer thickness and densities.</p> <p> </p> <p>Please refer to the README file for additional details.</p>
TBGA: A Large-Scale Gene-Disease Association Dataset for Biomedical Relation Extraction
<p>This repository contains the TBGA dataset. TBGA is a large-scale, semi-automatically annotated dataset for Gene-Disease Association (GDA) extraction. The dataset consists of three text files, corresponding to train, validation, and test sets, plus an additional JSON file containing the mapping between relation names and IDs. Each record in train, validation, or test files corresponds to a single GDA extracted from a sentence. Records are represented as JSON objects with the following structure:</p> <ul> <li><strong>text:</strong> sentence from which the GDA was extracted.</li> <li><strong>relation:</strong> relation name associated with the given GDA.</li> <li><strong>h: </strong>JSON object representing the gene entity, composed of: <ul> <li><strong>id: </strong>NCBI Entrez ID associated with the gene entity.</li> <li><strong>name:</strong> NCBI official gene symbol associated with the gene entity.</li> <li><strong>pos: </strong>list consisting of starting position and length of the gene mention within text.</li> </ul> </li> <li><strong>t:</strong> JSON object representing the disease entity, composed of: <ul> <li><strong>id: </strong>UMLS CUI associated with the disease entity.</li> <li><strong>name:</strong> UMLS preferred term associated with the disease entity.</li> <li><strong>pos:</strong> list consisting of starting position and length of the disease mention within text.</li> </ul> </li> </ul> <p>TBGA contains over 200,000 instances and 100,000 bags.<br> The zip file consists of one folder, named TBGA, containing the files corresponding to the dataset.</p> <p>If you use or extend our work, please cite the following: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04646-6#citeas<br> TBGA paper can be found at: <a href="https://rdcu.be/cKkY2">https://rdcu.be/cKkY2</a><br> TBGA code is available at: https://github.com/GDAMining/gda-extraction</p>
Dataset associated to Picone, A. et al., ACS Appl. Nano Mater. 2021, 4, 12, 12993–13000
<p>Dataset associated to paper published under the SINFONIA project</p> <p>Picone, A. et al., ACS Appl. Nano Mater. 2021, 4, 12, 12993–13000</p>
Dataset associated with Banks et al. (2022): "Impacts of the desiccation of the Aral Sea on the Central Asian dust life-cycle"
<p>This dataset contains the COSMO-MUSCAT simulation output for the 'Dustbelt' (DUBLT) scenarios of Central Asian dust aerosol described by the paper "Impacts of the desiccation of the Aral Sea on the Central Asian dust life-cycle", written by Banks et al. and published in JGR in 2022 (<a href="https://doi.org/10.1029/2022JD036618">https://doi.org/10.1029/2022JD036618</a>).</p>
Supporting data for review article: The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective
<p>Supporting data and code for review article: Sokol N.W., Whalen E.D., Kallenbach C., Pett-Ridge J., Georgiou K. The Global Distribution, Formation, and Fate of Mineral-Associated Soil Organic Matter Under a Changing Climate – A Trait-Based Perspective. <em>Functional Ecology, </em>2022.</p> <p>We leveraged data from a global synthesis of soil fractionation measurements (DOI: 10.5281/zenodo.5987415). For this review article, we specifically focused on measurements of bulk and mineral-associated soil organic carbon concentrations (reported in units of gC/kg soil) and the proportion of bulk soil organic carbon that is mineral-associated (reported as a %). This subset also includes auxiliary data regarding climate and biome characteristics extracted from the synthesized papers; for more variables, see the original full dataset. Köppen-Geiger climate zones were extracted from a georeferenced global database (using R package 'kgc' v1.0.0.2) with site coordinates, where available. Three files are provided in this repository: (1) data file, (2) metadata file, and (3) code for manuscript figures and summary statistics.</p>
A Niclosamide-releasing hot-melt extruded catheter prevents Staphylococcus aureus experimental biomaterial-associated infection
<p>Biomaterial-associated infections are a major healthcare challenge as they are responsible for high disease burden in critically ill patients. In this study, we have developed drug-eluting antibacterial catheters to prevent catheter-related infections. Niclosamide (NIC), originally an antiparasitic drug, was incorporated into the polymeric matrix of thermoplastic polyurethane (TPU) via solvent casting, and catheters were fabricated using hot-melt extrusion technology. The mechanical and physicochemical properties of TPU polymers loaded with NIC were studied. NIC was released in a sustained manner from the catheters and exhibited <em>in vitro</em> antibacterial activity against <em>Staphylococcus aureus</em> and <em>Staphylococcus epidermidis</em>. Moreover, the antibacterial efficacy of NIC-loaded catheters was validated in an <em>in vivo</em> biomaterial-associated infection mouse model using a methicillin-susceptible and methicillin-resistant strain of <em>S. aureus. </em>The released NIC from the produced catheters reduced bacterial colonization of the catheter as well as of the surrounding tissue. In summary, the NIC-releasing hot-melt extruded catheters prevented implant colonization and reduced the bacterial colonization of peri-catheter tissue by methicillin sensitive as well as resistant <em>S. aureus</em> in a biomaterial-associated infection mouse model and has good prospects for preclinical development.</p>
Deciphering the Neurosensory Olfactory Pathway and Associated Neo-Immunometabolic Vulnerabilities Implicated in COVID-Associated Mucormycosis (CAM) and COVID-19 in a Diabetes Backdrop—A Novel Perspective
<p>Raw data files of transcriptomic profiling experiments, which form the basis for our publication (https://www.mdpi.com/2673-4540/3/1/13).</p>
UWB-IODA project: Datasets associated to WP2
<p>The signal acquisition is carried out using an IR-UWB sensor module Xethru X4, from Novelda, Norway, having the following parameters:<br> Output power −12.6 dBm<br> Center frequency 8.748 GHz<br> Pulse repetition frequency 400 MHz<br> Bandwidth (–10 dB) 2.95 GHz<br> Range resolution 6.4 mm<br> Beamwidth 65°<br> Staggered PRF sequence length 220 cycles<br> No. of antenna arrays per radar chip 1 Tx & 1 Rx</p> <p>The transmitted signals reflected by the human back contain the information related to the motion caused by the human heart beats. The backscattered signals are then stored in the PC through a micro-USB cable. The distance of the human to the radar sensor is kept between 0.5 and 1m due to the short distance between the driver and the seat. During the experiments, the subjects were resting for most of the time and also talking and moving slightly their bodies for certain time intervals.</p> <p>The five data files corresponds to the five subjects involved in the experiments, having the following characteristics:</p> <p>Gender Age (years) Height (cm) Weight (kg) <br> M 33 182 72<br> M 21 173 73<br> M 28 174 77<br> F 26 170 60<br> M 24 165 62</p> <p>All of these individuals were healthy and without any special health conditions or disabilities.</p>
Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues
<p>AlphaFold and RoseTTAFold models of domains of disease associated human proteins without structures/known homologues.</p> <p>Tables containing the model quality, model region, sequence alignment statistics, matched FunFam, associated GO terms for the FunFam, ddG of mutation, pathogenicity of mutation, if mutation is near a predicted functional site (conserved residue/ligand binding site/protein-protein interface)</p>
Second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'
<p>This is the second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'.</p> <p>The paper was published in Geophysical Research Letters. We provide the data that has been smoothed by moving filter and not. The data can be loaded by the <em>raster </em>package in <em>R.</em> Note that the unit is ppmv.</p> <p>Please note that both of these files must be in the same directory to open in <em>R</em> properly<em>.</em></p> <p>Please get in touch with the authors if you have any issues, email: xliu21@smail.nju.edu.cn or wanghk@nju.edu.cn</p>
Data associated with the Tectonics manuscript "Building a Young Mountain Range: Insight into the Growth of the Greater Caucasus Mountains from Detrital Zircon (U-Th)/He Thermochronology and 10Be Erosion Rates"
<p>U-Pb and U-Th/He ages of zircons from a suite of detrital catchments reported in the manuscript "Building a Young Mountain Range: Insight into the Growth of the Greater Caucasus Mountains from Detrital Zircon (U-Th)/He Thermochronology and 10Be Erosion Rates" submitted to Tectonics. Repository includes sample locations and DEMs of each sampled catchment.</p>
Code and data associated with: Searching the web builds fuller picture of arachnid trade
<p>Data and code used in the paper: Searching the web builds fuller picture of arachnid trade. Throughout the methods we have indicated the stage of analysis each data component was used and the code script connected. We have numbered to code and data supplements to reflect as closely as possible the order in which data generation and summary was undertaken. The following provide additional details linked to each of the data files.</p> <p>Data S1 - Website data: lang = language of the search engine used, ad hoc websites had language described after discovery; engine = the search engine used; page = the page on which the website appeared from the search engine; searchdate = search date in YYYY-mm-dd HH:MM:SS; link = link to the webpage, redacted to protect website identity; reviewdate = date revewied for arachnids being sold and search strategy; sells = whether the website sells arachnids (1 == sells); allow = whether the site explcicilt forbids automated searching (1 == allows, NA when search method was not fully automated, e.g., single page); type = the type of the website (e.g., trade, classified ads); order = whether arachnids where organised in a particular ways; target = a refined target URL to start search; method = the search method chosen, see methods for details; refine = any refinement or filter than could constrain the scope of the website to be searched; spages = the number of pages required to cycle through to cover the entire stock (also separated by ; if multiple cycles where needed or multiple single pages could be easily collected); prelimCheck = whether the website passed initial checks for arachnid selling; notes = any details that might need special attention during searches; webID = code used for subsequent data summary.</p> <p>Data S2 - Raw keyword searches outputs: species keywords. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (only applies to Data S3); webID = the website ID.</p> <p>Data S3 – Raw keyword searches outputs: genus keywords. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (multiple detections separated by ;); webID = the website ID.</p> <p>Data S4 - Raw keyword search outputs: temporal sample. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (multiple detections separated by ;); webID = the website ID; timestamp.parse = the timestamp extracted from the archived web page; year = a simplified timestamp including only the year.</p> <p>Data S5 - LEMIS data used. An arachnid filtered version of <sup>74,75</sup>.</p> <p>Data S6 - CITES trade database data used <sup>76</sup>.</p> <p>Data S7 - CITES appendices data used <sup>77</sup>.</p> <p>Data S8 - IUCN Redlist data used <sup>78</sup>.</p> <p>Data S9 - Compiled final dataset, with data deriving from WSC, Scorpion files, ITIS, WAM and the data collection process. speciesId = a numeric code, one per species; clade = the clade the species belongs to; family = the family the species belongs to; genus = the genus of the species; species = the species epithet; author = the species authority name; year = the species authority year; parentheses = whether parentheses are needed with the authority; distribution = WSC original distribution descriptions; invalid = whether the species is considered valid; source = the species source, either World Spider Catalogue, Scorpion files, ITIS or WAM; accName = the species binomial being used as our accepted name; allNames = the accepted species binomial and all synonyms; allGenera = the accepted genus, and all other genera the species has belonged to at one point; onlineTradeSnap = whether the species was detected via a match to the accName in the snapshot data; onlineTradeSnap_Any = whether the species was detected via any synonym in the snapshot data; onlineTradeSnap_genus = whether the genus was detected via a match to the genus in the snapshot data; onlineTradeSnap_genusAny = whether the genus was detected via any synonym in the snapshot data; onlineTradeTemp = whether the species was detected via a match to the accName in the temporal data; onlineTradeTemp_Any = whether the species was detected via any synonym in the temporal data; onlineTradeTemp_genus = whether the genus was detected via a match to the genus in the temporal data; onlineTradeTemp_genusAny = whether the genus was detected via any synonym in the temporal data; onlineTradeEither = whether the species was detected via a match to the accName in the temporal data or snapshot data; onlineTradeEither_Any = whether the species was detected via any synonym in the temporal data or snapshot data; LEMIStrade = whether the species was detected via a match to the accName in the LEMIS data; LEMIStrade_Any = whether the species was detected via any synonym in the LEMIS data; LEMIStrade_genus = whether the genus was detected via any synonym in the LEMIS data; LEMIStrade_genusAny = whether the genus was detected via any synonym in the LEMIS data; CITEStrade = whether the species was detected via a match to the accName in the CITES trade database data; CITEStrade_Any = whether the species was detected via any synonym in the CITES trade database data; CITEStrade_genus = whether the genus was detected via any synonym in the CITES trade database data; CITEStrade_genusAny = whether the genus was detected via any synonym in the CITES trade database data; CITESapp = the CITES appendix the species is listed under using an exact match to the accName; CITESapp_Any = the CITES appendix the species is listed under using any match to any of the species’ synonyms; redlist = the IUCN Redlist category the species is listed under using an exact match to the accName; redlist_Any = the IUCN Redlist category the species is listed under using any match to any of the species’ synonyms; extactMatchTraded = the species is detected in any of the trade sources via a match to the accName; anyMatchTraded = the species is detected in any of the trade sources via a match to any species’ synonym.</p> <p>Data S10 - Forum listings of “What species are you currently keeping” from an online fora posted between 9th September 2021 and 9th October 2021, to provide an idea of online discussions. Each user with a separate list is provided in a separate tab. Morph_collector is the same as poster1, but the potential cryptic species or morphs are noted separately to make them clearer.</p> <p>Data S11 – Distribution information for spiders. Only two columns used in summaries: accName = the accepted name used throughout summaries; NAME = the country name the spider occurs in.</p> <p>Data S12 - Distribution information for scorpions. species = the accepted name used throughout summaries; NAME = the country name the scorpions occurs in.</p> <p>Code S1 - Search URL Extract.R</p> <p>Code S2 - Retrieve web data.R</p> <p>Code S3 - Temporal Classified Ads.R</p> <p>Code S4 - Keyword Generation.R</p> <p>Code S5 - Keyword Search.R</p> <p>Code S6 - LEMIS filter and summary.R</p> <p>Code S7 - Compiling results.R</p> <p>Code S8 - Summary Figures.R</p> <p>Code S9 - Temporal Figures.R</p> <p>Code S10 - New description figure.R</p> <p>Code S11 - Term exploration.R</p> <p>Code S12 - LEMIS summary and mapping.R</p>
NanoString dataset for study: Impairment of cancer-associated fibroblasts promotes CD8+ T cell infiltration and enhances sensitivity to immune checkpoint blockade
<p>Pre-processed NanoString mRNA abundance data and associated sample sheet for study:</p> <p>Impairment of cancer-associated fibroblasts promotes CD8+ T cell infiltration and enhances sensitivity to immune checkpoint blockade</p>
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