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1,773 results for “Supplementary data,”
Supplementary data
<p>Supplementary data including: </p> <ol> <li>Wetland environmental data <strong>(wetlands_env_cha)</strong></li> <li>All hydrological parameter results tested for cluster analysis <strong>(hyd_params_all)</strong></li> <li>Final hydrological parameter results used in cluster analysis<strong> (hyd_params_final)</strong></li> <li>Final hydrological parameter results averaged according to archetype<strong> (cluster_means)</strong></li> <li>Plots of water extent v. summed daily precipitation in the upstream basin<strong> (precipitation_plots)</strong></li> <li>Discharge station number, regulation data and distance to wetland and RMSE/NRMSE for 23 wetlands validated using discharge data<strong> (validation_discharge)</strong></li> <li>RMSE, NRMSE and plots for wetlands validated using manual delineated data<strong> (validation_gt)</strong></li> <li>RMSE, NRMSE and plots forr wetlands validates using Dynamic World dataset<strong> (validation_dynamic_world)</strong></li> <li>Monthly water extent for all wetlands for period 2020-2023 <strong>(all_area_monthly)</strong></li> <li>All water extent values for all wetlands between 2020-2023 derived from available Sentinel-1 SAR tiles<strong> {wetland_name}_raw </strong></li> </ol>
Parameters of extremal micropolar materials and procedures for FE simulation (supplementary data)
<p>These are supporting data and related procedures of the article "Extremal micropolar materials for elastic wave cloaking" to be submitted.</p>
Supplementary material from "Utilising citizen science data to rapidly assess changing associations between wild birds and avian influenza outbreaks in poultry."
<p>Stephen H. Vickers, Jayna Raghwani, Ashley C. Banyard, Ian H. Brown, Guillaume Fournie and Sarah C. Hill </p>
Supplementary Material for Healthcare costs following falls and cataract surgery in older adults using Australian linked health data from 2012–2019
<p><strong>Note</strong>: The material contained herein is supplementary to the article:</p><p>Huang-Lung J, Chun Ho K, Lung T, Palagyi A, McCluskey P, White AJR, Boufous S, Keay L. Healthcare costs following falls and cataract surgery in older adults using Australian linked health data from 2012–2019. Public Health Res Pract. November 2023; Online early publication. https://doi.org/10.17061/phrp33342311</p><p> </p><p><strong>Contents</strong></p><p>Appendix Table A1. Details of linked administrative health data extracted for this study. </p><p>Appendix Table A2. ICD-10-AM codes for identification of fall-related hospital admissions in this study. As multiple diagnosis codes can be recorded for each hospital admission, all diagnosis codes associated with the episode of care were considered. </p><p>Appendix Table A3. SNOMED CT codes associated with fall-related emergency department presentations observed in this study. The SNOMED CT codes were searched in the CSIRO Shrimp database, version dated 31/10/21 (http://ontoserver.csiro.au/shrimp/). </p><p>Appendix Table A4. Results from the multivariable generalised linear model assessing factors associated with higher healthcare costs in study participants. </p><p>Appendix Table A5. Mean monthly healthcare costs according to cataract surgeries, sex and faller status groups, estimated from the generalised linear model using marginal means, with co-variates appearing in the model fixed at the following mean values: age = 75.5 and medications = 4.5.</p><p>Appendix Box 1. Data cleaning methods. </p><p>Figure A1. Flowchart of the study cohort. </p>
Supplementary Data and Videos for "Problems of classifying predator-induced prey immobility: an unexpected case of post-contact freezing"
<p> </p> <p>The data file and five videos below accompany our paper "Problems of classifying predator-induced prey immobility: an unexpected case of post-contact freezing", published in <em>Web Ecology</em> <strong>24,</strong> 35-40 (2024), https://doi.org/10.5194/we-24-35-2024.</p> <p> </p> <p><strong>Video 1.</strong> <em>Pachyoliva semistriata</em> freezes after making contact with <em>Agaronia propatula</em> (I).</p> <p><strong>Video 2.</strong> <em>Pachyoliva semistriata</em> freezes after making contact with <em>Agaronia propatula</em> (II). Surface water flows against the direction of movement of <em>Agaronia</em>.</p> <p><strong>Video 3.</strong> <em>Pachyoliva semistriata</em> freezes after making contact with <em>Agaronia propatula</em> (III). Surface water flows in the direction of movement of <em>Agaronia</em>.</p> <p><strong>Video 4.</strong> Responses of <em>Pachyoliva semistriata</em> when making contact with an obstacle; control experiments.</p> <p><strong>Video 5.</strong> <em>Pachyoliva semistriata</em> freezes when making contact with an obstacle and sensing <em>Agaronia</em> odours.</p> <p><strong>Data File 1.</strong> Numerical data used in creating Figure 2.</p>
Supplementary Data for 'Machine learning detection of SARS-CoV-2 high-risk variants'
Open the record for dataset details and reuse information.
Supplementary Information: Video data files: Gray et al. Caught on camera: Ocelot, Leopardus pardalis (Mammalia: Felidae) predation on foam nests of Savage's thin-toed frog, Leptodactylus savagei (Amphibia: Leptodactylidae)
<p>Supplementary Information: Original camera trap footage for Gray, Ibáñez, Barrios & Potvin: Caught on camera: Ocelot, <i>Leopardus pardalis </i>(Mammalia: Felidae) predation on foam nests of Savage's thin-toed frog, <i>Leptodactylus savagei</i> (Amphibia: Leptodactylidae). </p><p> </p>
Supplementary data for "Transient wave activity in snow avalanches is controlled by entrainment and topography"
<p>We are releasing the data of Figs. 2,4&5 in our manuscript submitted to Communications Earth & Environment.</p>
Supplementary Data - Haplotype-based inference of recent effective population size in modern and ancient DNA samples
<p>This repository contains the simulated data analyzed in our manuscript titled "Haplotype-based Inference of Recent Effective Population Size in Modern and Ancient DNA Samples".The data is split into 7 datasets. </p><p><strong>- demographies.tar.gz</strong>: the simulated demographic models.<br><strong>- modern_data.tar.gz:</strong> simulated SNP-araray data. The genotypes were simulated under 4 demographic histories (see demographies.tar.gz) for 256 samples and under 10 different random seeds (Replicate 1-10). Additionally, Replicates 11 and 12 include simulations at larger sample sizes. Note that the results at lower sample sizes can be obtained by keeping the first N samples of the simulated files.<br><strong>- true_ibd.tar.gz :</strong> This dataset contains the IBD segments from the simulated modern_data (ground truth from ARGON simulator).<br><strong>- ancient_data.tar.gz</strong> dataset contains simulated aDNA data. The dataset contains data simulated at different coverages (MISSING_$M, where $M = exp(-coverage)), sample sizes, demographic models, and random seeds. Split into three parts. See below for instructions on how to reconstruct the dataset. <br><strong>- structure.tar.gz</strong> and<strong> admixture.tar.gz :</strong> contain the data simulated under more complex demographic histories involving 2 isolated populations (structure) or a single population undergoing a recent admixture event (admixtuer.tar.gz). The manuscript provides more details about the demographic histories.<br>- <strong>imputed.tar.gz</strong> dataset contains simulated imputed aDNA data. Each region was simulated independently. Each folder corresponds to a chromosome arm and contains:<br> - data.anc_array.npy: list of the SNPs included in the analysis (simulating a 1240k array)<br> - data.glimpse.vcf.gz: phased ancient population data as phased by GLIMPSE v1<br> - data.glimpse.vcf.gz.csi: index for the above VCF file<br> - data.imputed.vcf.gz: imputed ancient population data (unphased, including dosages and genotype posteriors)<br> - data.imputed.vcf.gz.csi: index file for the above VCF<br> - data.map.gz: genetic map for GLIMPSE (tab-separated format: pos chr cM)<br> - data.ref.tsv.gz: file used for calculating genotype likelihoods with BCFtools mpileup command to use as input for GLIMPSE (format: chromosome position ref_allele,alt_allele)<br> - data.ref.tsv.gz.tbi: index for the above file<br> - data.ref.vcf.gz: simulated sequencing data from the reference panel<br> - data.ref.vcf.gz.csi: index file for the above VCF<br> - data.target_ground_truth.vcf.gz: simulated sequencing data for the ancient population (ground truth)<br> - datalist.txt: list of genotype likelihood files for each target individual (to be used for merging into a single file)<br> - data.temp.map.gz: genetic map for data creation with msprime simulator (format: chr position rate(cM/Mb) cM)<br> - data.tree -> msprime simulator output containing all samples, both reference panel samples and ancient population samples<br> - data.vcf.gz: VCF containing ground truth sequencing data, phased genotypes for reference panel samples and for ancient population samples<br> - dataref.fa.fai: index for reference fasta file used during reads creation</p><p> </p><p>Note that some of the datasets have been split into multiple parts, for example admixed.tar.gz has been split into three different parts admixed.tar.gz-part-aa, admixed.tar.gz-part-ab, and admixed.tar.gz-part-ac<br>You can get the data by typing:</p><p>cat admixed.tar.gz.part-* > admixed.tar.gz</p><p><br> </p>
Supplementary Data for "Synchronous asteroid breakup paused the Great Ordovician Biodiversification Event"
<p>Supplementary Data includes a detailed comparison of the YW2 borehole with the well-studied Puxi River section, the U-Pb isotopic data of the bentonite, and coupled carbon isotope data of the YW2 borehole. </p>
Supplementary data for: "Global growth, green goals: Shaping sustainable futures in international business education and research" published at BAR - Brazilian Administration Review
Open the record for dataset details and reuse information.
Data, scripts and supplementary materials for "Color polymorphism and conspicuousness do not increase speciation rates in Lacertids" (de Solan et al. 2023)
<p>This data_and_script file contain the phylogeny and coloration data, as well as the R script used in the article "Color polymorphism and conspicuousness do not increase speciation rates in Lacertids".</p><p>The other file contain the supplementary materials for the publication.</p>
Supplementary data as part of the article "Mechanism of Benzene Hydroxylation on Tri-Iron Oxo-Centered Cluster-Based Metal-Organic Frameworks" (https://doi.org/10.1021/acs.jpcc.3c06423)
<p>Cartesian coordinates in the *.XYZ format for all the structures optimized at the M06-L/def2-TZVP in the reactivity study as part of the article "Mechanism of Benzene Hydroxylation on Tri-Iron Oxo-Centered 2 Cluster-Based Metal-Organic Frameworks" (https://doi.org/10.1021/acs.jpcc.3c06423)</p>
BP-tracer: Supplementary Data 2 Version 2.0
<p>Supplementary Data 2 contains <i>1) </i>the raw results obtained from the Benchmark Part of the paper, encompassing BP-Tracer, Reads, Contigs, MAGs and Correlation methods. <i>2)</i> the raw results of BP-Tracer obtained from Real metagenomic datasets, including metagenomic datasets from clinical settings associated with pathogenic infection (urinary tract infections, UTIs), hospitalization (preterm infant using antibiotic), antibiotic exposure (healthy adults following antibiotic exposure) and the datasets from 7 different environments (Extended Data).</p>
Supplementary material 1 from: Jasso-Martínez JM, Brady SG, Kula RR (2023) Phylogenetic affinities of the non-cyclostome subfamilies Amicrocentrinae and Dirrhopinae (Hymenoptera, Braconidae) confirmed by ultraconserved element data. Journal of Hymenoptera Research 96: 1017-1030. https://doi.org/10.3897/jhr.96.111012
Maximum likelihood tree resulting from the 50% complete matrix without collapsed clades and with full names of the terminal taxa
Supplementary material 4 from: Luo T, Mao M-L, Lan C-T, Song L-X, Zhao X-R, Yu J, Wang X-L, Xiao N, Zhou J-J, Zhou J (2023) Four new hypogean species of the genus Triplophysa (Osteichthyes, Cypriniformes, Nemacheilidae) from Guizhou Province, Southwest China, based on molecular and morphological data. ZooKeys 1185: 43-81. https://doi.org/10.3897/zookeys.1185.105499
Comparison of the diagnostic features of the four new species described here with those selected for the 35 recognized hypogean species of the genus Triplophysa
Supplementary material 1 from: Luo T, Mao M-L, Lan C-T, Song L-X, Zhao X-R, Yu J, Wang X-L, Xiao N, Zhou J-J, Zhou J (2023) Four new hypogean species of the genus Triplophysa (Osteichthyes, Cypriniformes, Nemacheilidae) from Guizhou Province, Southwest China, based on molecular and morphological data. ZooKeys 1185: 43-81. https://doi.org/10.3897/zookeys.1185.105499
Boxplots of morphometrics based on 35 morphometric measurements distinguishing T. anlongensis sp. nov., T. cehengensis sp. nov., T. panzhouensis sp. nov., T. rongduensis sp. nov., T. baotianensis, and T. zhenfengensis
Supplementary material 5 from: Luo T, Mao M-L, Lan C-T, Song L-X, Zhao X-R, Yu J, Wang X-L, Xiao N, Zhou J-J, Zhou J (2023) Four new hypogean species of the genus Triplophysa (Osteichthyes, Cypriniformes, Nemacheilidae) from Guizhou Province, Southwest China, based on molecular and morphological data. ZooKeys 1185: 43-81. https://doi.org/10.3897/zookeys.1185.105499
Morphological comparison of T. cehengensis sp. nov. (TC), T. rongduensis sp. nov. (TR), T. panzhouensis sp. nov. (TP), T. anlongensis sp. nov. (TA), T. baotianensis (TB), and T. zhenfengensis (TZ)
Supplementary material 6 from: Luo T, Mao M-L, Lan C-T, Song L-X, Zhao X-R, Yu J, Wang X-L, Xiao N, Zhou J-J, Zhou J (2023) Four new hypogean species of the genus Triplophysa (Osteichthyes, Cypriniformes, Nemacheilidae) from Guizhou Province, Southwest China, based on molecular and morphological data. ZooKeys 1185: 43-81. https://doi.org/10.3897/zookeys.1185.105499
Results and percentage of variance explained by principal component and discriminant function analyses
Supplementary Data: Uncovering DFG-out sequence propensity determinants of kinases with machine learning
<h2>General description</h2><p>This submission accompanies the paper "Uncovering DFG-out sequence propensity determinants of kinases with machine learning" and covers:</p><ul><li><strong>Trained models (train/models_latest)</strong>. A subset of these is also published as a part of the <a href="https://github.com/edikedik/kinactive">KinActive tool</a>; each model is of `KinactiveClassifier` type and can be loaded using this tool as well via `kinactive.io.load()` function).</li><li><strong>Patched sequences (patched_seqs)</strong>. PDB sequences, with missing regions patched by UniProt sequences. This is a collection of <a href="https://github.com/edikedik/lXtractor">lXtractor</a> ChainSequence objects.</li><li><strong>All labels, variables, and datasets</strong> (datasets and labels).</li><li><strong>Initial chains and predictions for SwissProt proteins</strong> (SP_predictions).</li></ul><h2>Note on model abbreviations</h2><p>The main text emphasized datasets that yielded more interpretable results. These were constructed from domain sequences, labeled as apo, inactive, DFG-in, or DFG-out, and further divided into TK and STK subsets. We refer to these datasets with the abbreviation <strong>AAIO</strong> (Apo All In or Out) to distinguish them from additional datasets.</p><p>In addition to <strong>AAIO</strong>, we explored two alternative labeling strategies:</p><ul><li><strong>AHAO</strong> (Apo/Holo Any Out): This dataset includes sequences from ligand-bound entries. All sequences within 95\% identity clusters are labeled as DFG-out if the cluster contains at least one sequence in this state. All others are labeled as DFG-in.</li><li><strong>AAO</strong> (Apo Any Out): This dataset excludes sequences corresponding to ligand-bound entries but includes those with conflicting conformational tendencies within 95\% identity clusters.</li></ul><p>Each of these datasets had two versions:</p><ol><li>A seed version, denoted by a "*" symbol.</li><li>A version enriched with orthologous sequences (no special designation).</li></ol><p>Together with the <strong>TkST</strong> datasets (encompassing TK and STK labels) used for testing the methodology, a total of 14 datasets were used, and both <i>RF</i> and <i>XGB</i> models were applied to each, using the same initial settings, resulting in 28 different models. This additional information is provided for completeness.</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.