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1,773 results for “Supplementary data,”
Supplementary material and supplementary data files for: Handling logical character dependency in phylogenetic inference: Extensive performance testing of assumptions and solutions using simulated and empirical data
<p>Logical character dependency is a major conceptual and methodological problem in phylogenetic inference of morphological datasets, as it violates the assumption of character independence that is common to all phylogenetic methods. It is more frequently observed in higher-level phylogenies or in datasets characterizing major evolutionary transitions, as these represent parts of the tree of life where (primary) anatomical characters either originate or disappear entirely. As a result, secondary traits related to these primary characters become "inapplicable" across all sampled taxa in which that character is absent. Various solutions have been explored over the last three decades to handle character dependency, such as alternative character coding schemes and, more recently, new algorithmic implementations. However, the accuracy of the proposed solutions, or the impact of character dependency across distinct optimality criteria, has never been directly tested using standard performance measures. Here, we utilize simple and complex simulated morphological datasets analyzed under different maximum parsimony optimization procedures and Bayesian inference to test the accuracy of various coding and algorithmic solutions to character dependency. This is complemented by empirical analyses using a recoded dataset on palaeognathid birds. We find that in small, simulated datasets, absent coding performs better than other popular coding strategies available (contingent and multistate), whereas in more complex simulations (larger datasets controlled for different tree structure and character distribution models) contingent coding is favored more frequently. Under contingent coding, a recently proposed weighting algorithm produces the most accurate results for maximum parsimony. However, Bayesian inference outperforms all parsimony-based solutions to handle character dependency due to fundamental differences in their optimization procedures—a simple alternative that has been long overlooked. Yet, we show that the more primary characters bearing secondary (dependent) traits there are in a dataset, the harder it is to estimate the true phylogenetic tree, regardless of the optimality criterion, owing to a considerable expansion of the tree parameter space.</p>
Supplementary Data of "Introgression between highly divergent sea squirt genomes: an adaptive breakthrough?"
<p><strong>Datasets used in the analyses (see Table S5 for a detailed description).</strong></p> <p>► Dataset #1: phased SNPs with offspring.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle.vcf<br> ► Dataset #2: all SNPs with missing data.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_parents_oNA5.vcf<br> ► Dataset #3a: phased SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents.vcf<br> ► Dataset #3b: CDS version of “phased SNPs”.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents_SNP_CHRall.orf.cds<br> ► Dataset #3c: FASTA version of “phased SNPs”.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents_SNP_chr5.sub_${START}-${END}.fasta<br> ► Dataset #4: ancestry informative phased SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_phased_phasedBeagle_parents.frq.fixed.vcf<br> ► Dataset #5: all SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_parents_oNA.vcf<br> ► Dataset #6: all polarized SNPs.<br> joint_bwa_mem_mdup_IR_recal_variants_refine_HQ_denovo_Oad18ad31ad2.clean.biallelic.noindels_filter_parents_raw_refine_HQ_edwardsi_oNA3_polar.vcf<br> ► Dataset #7: unfiltered mapping files.<br> ciona_bwa-mapping_${IND}_bwa_mem_mdup_IR_chromosome5:700000-1500000_sorted_nodup.bam</p>
Supplementary data for Plutniak, S. 2022. "What makes the identity of a scientific method? A history of the 'Structural and analytical typology' in the growth of evolutionary and digital archaeology in southwestern Europe (1950s–2000s)", Journal of Paleolithic Archaeology, vol. 5, 10.
<p>Supplementary data for Plutniak, S. 2022. “What makes the Identity of a Scientific Method? A History of the ‘Structural and analytical typology’ in the Growth of Evolutionary and Digital Archaeology in Southwestern Europe (1950s–2000s)”, <em>Journal of Paleolithic Archaeology</em>. vol 5, 10. DOI: <a href="https://doi.org/10.1007/s41982-022-00119-7">10.1007/s41982-022-00119-7</a>.</p> <p> </p> <p> </p> <p> </p>
Supplementary Data for DISCAN
<p>Supplementary data for "The frequency of somatic mutations in cancer predicts the phenotypic relevance of germline mutations". This entry includes two data tables, uploaded as compressed text files:</p> <ul> <li><strong>features.csv.gz</strong> contains all the covariates used to train the models. </li> <li><strong>phenotype_probabilities.csv.gz</strong> contains the Random Forest probabilities for 1,361 phenotypes</li> </ul> <p>Gene identifiers are reported as Entrez ID.</p>
Supplementary data of endogenous starter culture and cheese metagenomes
<p>The first description of the virome composition in Brazilian artisanal Canastra cheese and the phage-bacterial interactions in this food system. Here you can access the contigs that compose the 16 metagenome-assembled genomes (MAG) explored in paper and their spacers sequences.</p>
Exoplanet atmosphere evolution: emulation with neural networks: supplementary data
<p>Supplementary data for 'Exoplanet atmosphere evolution: emulation with neural networks'. Includes MCMC chain for Bayesian Hierarchical Model (BHM) including samples of core mass for all planets, as well 5 hyper parameters (see paper for details). Additionally, a machine readable version of Table 1 is made available.</p>
Supplementary Simulation Data for "Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation"
<p>Additional simulation data for "Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation", which has been submitted as a preprint to ChemRxiv.</p>
Supplementary Data for "The history of Cenozoic carbonate flux in the Atlantic Ocean constrained by multiple regional carbonate compensation depth reconstructions"
<p>The files on this site accompany the paper:</p> <p>Dutkiewicz, A. And Müller, R.D., in review, The history of Cenozoic carbonate flux in the Atlantic Ocean constrained by multiple regional carbonate compensation depth reconstructions, Geochemistry, Geophysics, Geosystems.</p> <p>There are two zipped file archives:</p> <p>1) backtracked_sites.zip</p> <p>This archive contains two directories of backtrack site files, one for the North Atlantic and one for the South Atlantic.</p> <p>Each directory contains a set of files listing, by site:</p> <p>age(Ma), compacted_depth (observed)(mbsf), compacted_thickness (observed)(m), decompacted_thickness(m), decompacted_density(g/cm3), water_depth(m), tectonic_subsidence (since formation of crust)(m), decompacted_depth(mbsf) dynamic_topography(m) lithology</p> <p>The lithology classification follows the lithology classes defined in Muller et al. (2018).</p> <p>A second set of files contains:</p> <p>age(Ma), depth(mbsf), paleowaterdepth(m), dry_bulk_density(g/cm3), DLSR(m/my), carbonate(weight_%) CAR(mg/cm2/kyr)</p> <p>DLSR=decompacted linear sedimentation rate<br> CAR=carbonate accumulation rate</p> <p>2) regional_Cenozoic_carbonate_thickness_grids.zip</p> <p>This archive contains 3 folders with grids for modelled Cenozoic carbonate thicknesses for the South Atlantic, central North Atlantic and northern North Atlantic. They can be viewed with netcdf viewers like panoply, or plotted using the Generic Mapping Tools. The workflow for creating these grids can be found on GitHub:</p> <p>https://github.com/EarthByte/CarbonateSedimentThickness</p> <p><br> This site also contains a spreadsheet entitled "Dutkiewicz_Muller_G3_2022_model_data_summary.xlsx"</p> <p>It contains our model outputs including regional decompacted carbonate sediment volumes and thicknesses, depositional areas, carbonate carbon fluxes and carbonate compensation depths for the northern and central North Atlantic and South Atlantic.</p> <p>A video entitled "compacted_carb_thick_atlantic_66-0Ma.mp4" shows the Cenozoic evolution of carbonate sediment thickness in the Atlantic Ocean.</p> <p><br> References:</p> <p>Spasojevic, S., & Gurnis, M. (2012). Sea level and vertical motion of continents from dynamic earth models since the Late Cretaceous. AAPG bulletin, 96(11), 2037-2064. https://doi.org/10.1306/03261211121</p> <p>Müller, R. D., Cannon, J., Williams, S. and Dutkiewicz, A., 2018, PyBacktrack 1.0: A Tool for Reconstructing Paleobathymetry on Oceanic and Continental Crust, Geochemistry, Geophysics, Geosystems, 19, 1898-1909, https://doi.org/10.1029/2017GC007313.</p> <p><br> </p>
supplementary data about Extraction, Isolation and Structure elucidation of Two Phenolic acids from Aerial parts of Celery and Coriander.
<p>supplementary data about Extraction, Isolation and Structure elucidation of Two Phenolic acids from Aerial parts of Celery and Coriander.</p> <p><br> caffiec acid nmr 2.pdf <br> supplementary data.docx</p> <p><a href="https://zenodo.org/api/files/52908924-99c6-4a2c-8047-ef7131714205/p%20coumaric%20acid%20nmr%202.pdf">p coumaric acid nmr 2.pdf</a></p>
Supplementary Data for: Shifts in food webs and niche stability shaped survivorship and extinction at the end-Cretaceous
<p>This compendium of files includes a list of statistical routines (Data S1) and cleaned fossil datasets from the Paleobiology Database (Data S2 to S3) for the <em>Science Avances</em> paper "<em>Shifts in food webs and niche stability shaped survivorship and extinction at the end-Cretaceous</em>". Data S1 and Data S2 can be opened in Microsoft Office and Data S3 can be imported in R using the <em>load()</em> command. Data S2 and S3 contain the same information (i.e., cleaned fossil datasets) but in different formats (<em>xlsx</em> vs. <em>RData</em>).</p> <p>Please, feel free to send an email to the maintainer Dr. Jorge García Girón (jogarg@unileon.es) if you face any trouble downloading, opening or using these files.</p>
Supplementary Data of Sexual Harassment Studies in Indonesia
<p>This metadata is a supplementary file to support research about sexual harassment in Indonesia (https://doi.org/10.1016/j.heliyon.2022.e10194). Metadata was developed by Nurbayani et al. (2022) in line with a data article to reveal several studies (bachelor's theses) at Islamic State Universities of Indonesia (UIN).</p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
<p>This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.</p>
Supplementary data for "ENGINEERED ADAPTATION MECHANISMS BETWEEN MARINE AND FRESHWATER ENVIRONMENTS IN FISH AFTER THE FLOOD" for the ICC 2023 in Cedarville, Ohio
<p>Supplementary data for "ENGINEERED ADAPTATION MECHANISMS BETWEEN MARINE AND FRESHWATER ENVIRONMENTS IN FISH AFTER THE FLOOD" for the ICC 2023 in Cedarville, Ohio.</p> <p>These include FishBase annotation, mtDNA sequence similarity matrixes, clustering, and statistics for nine fish orders:</p> <p>1. Acipenseriformes</p> <p>2. Angulliformes</p> <p>3. Beloniformes</p> <p>4. Characiformes</p> <p>5. Clupeiformes</p> <p>6. Cyprinodontiformes</p> <p>7. Elasmobranchii</p> <p>8. Pleuronectiformes</p> <p>9. Salmoniformes</p>
Supplementary Data: UFBoot2: Improving the Ultrafast Bootstrap Approximation
<p>Supplementary Data<br> UFBoot2: Improving the Ultrafast Bootstrap Approximation<br> doi: https://doi.org/10.1101/153916<br> http://www.biorxiv.org/content/early/2017/06/22/153916</p> <p>This record contains PANDIT based dataset and TreeBASE dataset (Nguyen et al. 2015) which are analyzed by different bootstrap methods in the study "UFBoot2: Improving the Ultrafast Bootstrap Approximation". The PANDIT based dataset (compressed in file data_pandit.tar.gz) is used to benchmark the accuracy of bootstrap estimates. The TreeBASE dataset (compressed in file data_treebase.tar.gz) is used to benchmark runtimes. </p> <p>After being uncompressed, the PANDIT based dataset comprises:</p> <ul> <li>5,690 numbered directories corresponding to 5,690 DNA MSAs simulated by Seq-Gen (Rambaut and Grass 1997), where the model parameters and true tree were inferred from the original MSAs downloaded from the PANDIT database (Whelan et al. 2006). Note that the numbering of these directories is not consecutive because we kept only MSAs that can be tested under the mild and severe model violations as defined in the UFBoot paper (Minh et al. 2013).</li> <li>In each numbered directory N, there are three files: (1) data.N contains the simulated MSA in PHYLIP format; (2) model.N contains the best-fit model detected from the corresponding original MSA; (3) tree.N contains the tree (in Newick format) inferred from the corresponding original MSA. tree.N and model.N are used by Seq-Gen to simulate the MSA in data.N.</li> </ul> <p>After being uncompressed, the TreeBASE dataset comprises 115 files corresponding to 115 MSAs. There are:</p> <ul> <li>70 DNA MSAs in PHYLIP format. These files follow the naming scheme dna_[number of sequences]_[number of sites].phy.</li> <li>45 protein MSAs in PHYLIP format. These files follow the naming scheme prot_[number of sequences]_[number of sites].phy.</li> </ul>
Supplementary material 1 from: Motloung R, Robertson M, Rouget M, Wilson J (2014) Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions. NeoBiota 20: 31-48. https://doi.org/10.3897/neobiota.20.5778
Current and potential distributions of sixteen species that are not widespread in southern Africa arranged on the basis of their suitable range size : a) Acacia paradoxa, b) A. cultriformis, c) A. falciformis, d) A. pendula, e) A. rubida, f) A. stricta, g) A. retinodes, h) A. fimbriata, i) A. aneura, j) A. viscidula, k) A. acuminata, l) A. adunca, m) A. binervata, n) A. schinoides, o) A. prominens, p) A. mangium. The grey shading indicates areas that SDMs have identified as suitable by SDMs while the white ones are unsuitable.
Supplementary Materials "Isolated proton bunch acceleration by a petawatt laser pulse": 3D3V Input Files and Plot Data Figure 3
<p><strong>PIConGPU Simulation Input + Code</strong></p> <p>3D3V simulations are based on a pre-release of PIConGPU 0.2.0 [1]</p> <p>Directory: hilz-darmstadt-2707-3D<br> - branch with all applied & backported patches<br> - input files: code/examples/SphereDarmstadt/</p> <p>[1] DOI:10.5281/zenodo.168390</p> <p> </p> <p><strong>Data</strong></p> <p>data behind simulation plots in Fig 2c and Fig 3.</p>
Supplementary data: co-modulated auditory steady-state responses
<p><strong>Supplementary data: co-modulated auditory steady-state responses</strong></p> <p><em>Original publication:</em></p> <p>Guérit, F., Marozeau, J. and Epp, B (2017) "Linear combination of auditory steady-state responses evoked by co-modulated tones". J. Acous. Soc. Am. https://doi.org/10.1121/1.5007757</p> <p><strong>Data</strong></p> <p>This repository includes:</p> <ul> <li>raw .bdf files for each subject and condition</li> <li>corresponding calibrated stimuli</li> </ul> <p>For some recordings, external electrodes were used instead the whole cap:</p> <ul> <li>EXT3: FCz</li> <li>EXT4: Cz</li> <li>EXT6: P9 or P10 (cf original publication)</li> </ul> <p><strong>Triggering:</strong></p> <p>One trigger is sent at every repetition of the stimulus (every second). This is to compensate for the slow drift between the clocks of the Biosemi and of the sound card.</p>
Supplementary data: Modelling of future changes in seasonal snowpack and impacts on summer low flows in Alpine catchments
<p>The files in this record represent supplementary data for the article titled “Modelling of future changes in seasonal snowpack and impacts on summer low flows in Alpine catchments” in Water Resources Research. The files contain simulations of the HBV rainfall-runoff model for 14 alpine catchments in Switzerland. The model simulated different water balance components (such as runoff, snow water equivalent and evapotranspiration) for the reference period 1980-2009 and the three scenario periods (2020-2049, 2045-2074 and 2070-2099) using the A1B emission scenario.</p>
Supplementary data and code: Human–Wildlife Interactions Predict Febrile Illness in Park Landscapes of Western Uganda
<p>Data at code used for analyses.</p> <p>Salerno J, Ross N, Ghai R, et al (2017) Human–Wildlife Interactions Predict Febrile Illness in Park Landscapes of Western Uganda. EcoHealth. doi: 10.1007/s10393-017-1286-1<br> </p>
Supplementary Data for "How Strong is the Hydrogen Bond in Hybrid Perovskites?
<p>DFT optimised structures for the hybrid perovskites with the X organic cation and the Y anion:</p> <p>POSCAR-X-Y-D3</p> <p>NMRdata.zip with NMR data for the four Zn formate perovskites and the X organic cation:</p> <p>X.dx </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.