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1,773 results for “Supplementary data,”
AliSim: Ultrafast and Realistic Sequence Alignment Simulator for Phylogenetics - Supplementary Data
<p>This supplementary data contains testing scripts, input/output data for validating and benchmarking AliSim.</p>
Supplementary Data for Paper "The Effect of Display Pixel Density on Minimum Legible Size of Fundamental Cartographic Symbols"
<p>This is the result data of the user study described in the paper "The Effect of Display Pixel Density on Minimum Legible Size of Fundamental Cartographic Symbols".</p> <p>Author information and further metadata will be added after anonymous peer review.</p> <p>27 participants, 4 displays, 6 tasks. Note that the data contains the station ID (A-D), which maps to Displays 1-4 as described in the paper: A - D2; B - D4; C - D1; D - D3;</p> <p>File description:</p> <ul> <li>all_aggregated_users.csv: Thresholds for each task and station, for each participant. One row per participant, with fields for each station/task combination (27 rows).</li> <li>all_aggregated_thresholds.csv: Thresholds for each participant, task and station. One row per threshold value, thresholds for task #2 for participants #1-3 have been discarded, due to an error in the experiment configuration (see paper). (27 x 4 x 6 - 3 x 4 = 636 rows)</li> </ul>
Supplementary data_Wan et al_2021GB007072
<p>Dataset for the manuscript: Phytoplankton-nitrifier interactions control the geographic distribution of nitrite in the upper ocean (2021GB007072)</p>
Supplementary data for "Detrainment and braking of snow avalanches interacting with forests"
<p>We are releasing the data as the supplementary information for our manuscript “Detrainment and braking of snow avalanches interacting with forests”.</p> <p>Figure 5. Data of Fig. 5. Evolution of the detrainment mass per unit of area of snow with the velocity (regular staggered forest, e= 8m).</p> <p>Figure 6. Data of Fig. 6. Evolution of the detrainment mass observed for the one tree arrangement and the fits with the tree diameter for 2 flow regimes: Case 1 and Case 2 with respectively a front velocity of12.5 m/s and 10.6 m/s, ‘maximum’ refer to the maximum mass stored behind the tree and ‘final’ refer to the final mass stored. A slope of 30◦, and a top wedge angle of 60° (from measurements Feistl et al. (2014)) is used for the theoretical model (Eq. 8)</p> <p>Figure 8. Data of Fig. 8. Evolution of the detrainment mass per unit of area with the tree diameter for different types of snow and front velocity (regular staggered forest, e = 8 m)</p> <p>Figure 9. Data of Fig. 9. Evolution of the detrainment mass per unit of area with the forest density for different front velocities (regular staggered forest, snow properties: case 2, d = 1 m)</p> <p>Figure 10. Data of Fig. 10. Evolution of the detrainment mass predicted with the model (Eq. 11) and with the observation, r<sup>2</sup>=0.9926</p> <p>Figure 11. Data of Fig. 11. Temporal evolution of the kinetic and potential energy without forest and with a regular staggered forest (Case 2, v<sub>0 </sub>= 6 m/s)</p> <p>Figure 12. Data of Fig. 12. Temporal evolution of the detrainment energy and dissipation due to the forest (Case 2, v<sub>0 </sub>= 6 m/s)</p> <p>Figure 13. Data of Fig. 13. Energy detrainment and dissipation for different snow properties (1: case 1, 2: case 2, 3: case 3 with M=1) and for 3 types of forest structure (regular aligned, regular staggered and random)</p>
Supplementary data for "Timely vaccine strain selection and genomic surveillance improves evolutionary forecast accuracy of seasonal influenza A/H3N2"
<p>Supplementary materials associated with the manuscript by Huddleston and Bedford titled "Timely vaccine strain selection and genomic surveillance improves evolutionary forecast accuracy of seasonal influenza A/H3N2".</p>
Supplementary data for: New paromomyids (Mammalia, Primates) from the Paleocene of southwestern Alberta, Canada, and an analysis of paromomyid interrelationships
<p>Paromomyidae are one of several families of plesiadapiforms that flourished during the Paleocene in North America soon after the extinction of non-avian dinosaurs some 66 million years ago. Although they are often among the best-represented plesiadapiforms in mammalian faunas in both North America and Europe, the early history of paromomyids is poorly understood, and their fossil record at higher latitudes is comparatively depauperate. We report here on the discovery of two new species of paromomyids from Paleocene deposits in southwestern Alberta:<strong> <em>Edworthia greggi</em> new species</strong> is the second known species of the basal paromomyid <em>Edworthia</em> Fox, Scott, and Rankin, whereas <strong><em>I</em><em>gnacius glenbowensis</em> new species</strong> is among the most abundantly represented species of <em>Ignacius</em> Matthew and Granger. These new discoveries document for the first time parts of the upper dentition of <em>Edworthia</em>, and the new species of<em> Ignacius</em> represents the first new, pre-Clarkforkian species of the genus to be described in nearly one hundred years. A comprehensive phylogenetic analysis of nearly all known paromomyid taxa (including the new species described herein) recovered both species of <em>Edworthia</em> near the base of the paromomyid tree in a polytomy with <em>Paromomys depressidens</em>, and a paraphyletic <em>Ignacius</em>. The new paromomyids from Alberta not only increase the known taxonomic diversity of <em>Edworthia</em> and <em>Ignacius</em>, but also add significantly to knowledge of the dental anatomy of these poorly known genera, and further add to a uniquely Canadian complement of Paleocene plesiadapiforms.</p>
bazsimarkus/tropomi-emission-source: Supplementary data
<p>These are the configuration files and the ground truth files with labels that were used to test the algorithms in the paper "Automatic Retrieval of Volcanic SO2 Emission Source from TROPOMI Products".</p>
Supplementary data for "Less extreme and earlier outbursts of ice-dammed lakes since 1900"
<p>This respository contains supplementary and source data for the study for<em><strong> Less extreme and earlier outbursts of ice-dammed lakes since 1900</strong></em> by Georg Veh and co-authors.</p> <p> </p> <p>We investigate trends in the peak discharge <em>Q</em><sub>p</sub>, flood volume <em>V</em><sub>0</sub>, timing <em>doy</em> (day of year) and elevation <em>Z</em> of glacier lake outburst floods (GLOFs), and focus on ice-dammed lakes. We use Bayesian hierarchical models, implemented in the package <em>brms</em> in in the statistical programming software <em>R</em>. More information on these data, including detailed scripts to process them, are available at available at <a href="https://github.com/geveh/IceDamFailures">https://github.com/geveh/IceDamFailures</a>, and archived at 10.5281/zenodo.7326865.</p> <p>We provide the following data:</p> <p><strong>1 The GLOF database</strong></p> <ul> <li><em>glofdatabase_2022_05_30.ods:</em> OpenOffice table with all reported GLOFs. Compiliation as of May 30, 2022</li> <li><em>Parameter_Readme.ods:</em> Readme file describing all parameters (i.e. columns) in glofdatabase_2022_05_30.ods</li> </ul> <p> </p> <p><strong>2 Data on glacier elevation changes</strong></p> <ul> <li><em>dh_pergla_cut.7z:</em> zipped csv tables of cumulative elevation change (in m) for glaciers with repeat GLOFs between 2000 and 2019</li> </ul> <p> </p> <p><strong>3 Preprocessed GLOF data as R objects</strong></p> <ul> <li>all_glofs_tibble.RDS: R-object with a preprocessed table of all reported GLOFs</li> <li>all_glofs_qp_tibble.RDS: R-object in table format of lakes with repeat GLOFs and reported peak discharge <em>Q</em><sub>p</sub></li> <li>all_glofs_V0_tibble.RDS: R-object in table format of lakes with repeat GLOFs and reported flood volume <em>V</em><sub>0</sub></li> <li>glofs_ice_with_z.RDS: R-object of first reported GLOF from a given lake and its elevation <em>Z</em></li> </ul> <p><br> <strong>4 Output from Bayesian hierarchial models</strong></p> <p><em><strong>a) Regional models</strong></em></p> <ul> <li>qp_models.RDS: R-object with regional quantile regression models of <em>Q</em><sub>p</sub> versus time for the 50th and 90th for 4 time periods</li> <li>V0_models.RDS: R-object with regional quantile regression models of <em>V</em><sub>0</sub> versus time for the 50th and 90th for 4 time periods</li> <li>doy_trends_per_region.RDS: R-object with regression models of <em>doy</em> versus time for all dated GLOFs in the six regions</li> <li>Z_trends_per_region.RDS: R-object with a hierarchical regression models of elevation <em>Z</em> versus time for dated GLOFs in the six regions between 1900 and 2021</li> <li>Regional_glacier_and_melt_volumes.rds: R-object containing the total volume of glacier volume and volume change between 2000 and 2019 in 100-m elevation bins </li> </ul> <p><em><strong>b) Local models</strong></em></p> <ul> <li><em>V0_model_median_local.RDS:</em> R-object with regional quantile regression models of median <em>V</em><sub>0</sub> versus time</li> <li><em>qp_model_median_local.RDS:</em> R-object with local quantile regression models of median <em>Q</em><sub>p</sub> versus time</li> <li><em>doy_trends_per_glacier.RDS: </em>R-object with regression models of <em>doy</em> versus time for lakes with repeat GLOFs</li> <li><em>local_Qp_vs_dhdt_model.RDS:</em> R-Object containing a hierarchical model of local changes in <em>Q</em><sub>p</sub> versus glacier elevation change</li> <li><em>local_V0_vs_dhdt_model.RDS:</em> R-Object containing a hierarchical model of local changes in <em>V</em><sub>0</sub> versus glacier elevation change</li> </ul> <p> </p> <p><strong>4 GIS Data</strong></p> <p> </p> <ul> <li><em>Ice_dammed_lakes_Zenodo.7z: </em>zipped folder containing manually mapped outlines of ice-dammed lakes. </li> <li><em>Region_extents.7z:</em> zipped folder containing the outlines of the study regions.</li> </ul> <p> </p> <p><strong>5 Figures</strong></p> <ul> <li><em>Qp_local.pdf:</em> PDF figure showing temporal trends of median Qp for individual glacier lakes</li> <li><em>V0_local.pdf: </em>PDF figure showing temporal trends of median V0 for individual glacier lakes</li> <li><em>doy_local.pdf:</em> PDF figure showing temporal trends in GLOF timing for individual glacier lakes<br> </li> </ul> <ul> </ul>
Supplementary data of survey on self-help books reading attitudes and mental well-being among young adults
<p>Data was generated through SHRAS and WEMWBS survey questionnaires and statistical analysis. Responses were based on reading attitudes towards self-help books and mental well-being.</p>
Supplementary Data for 'Altered body iron distribution and microcytosis in mice deficient in iron regulatory protein 2 (IRP2).'
<p>Supplementary data for Galy, B., Ferring, D., Minana, B., Bell, O., Janser, H.G., Muckenthaler, M., Schümann, K. and Hentze, M.W., 2005. Altered body iron distribution and microcytosis in mice deficient in iron regulatory protein 2 (IRP2). <em>Blood</em>, <em>106</em>(7), pp.2580-2589.</p>
Exploring the Effects of LC Parameters on Retention Indices of Small Molecules using Amine Scaling - Supplementary Data
<p>In this document the retention times, calculated retention indices and calculated delta retention indices can be found that were used for the thesis on <strong>Exploring the Effects of LC Parameters on Retention Indices of Small Molecules using Amine Scaling</strong>, as well as the raw-data files of the performed measurements.</p>
High resolution solar irradiance variability climatology dataset part 2: classifications, supplementary data, and statistics
<p><strong>Dataset paper</strong></p> <p>See the official dataset description paper (preprint) over at <a href="https://essd.copernicus.org/preprints/essd-2022-456/">Earth System Science Data</a></p> <p><strong>Dataset description</strong></p> <p>High resolution surface solar irradiance series classification, cloud shadow and enhancement statistics, and satellite observations for studying intra-day surface solar irradiance variability.</p> <p><strong>Part 2 of 2</strong></p> <p>This dataset is the derived from the <a href="https://doi.org/10.5281/zenodo.7093164">1 Hz observational record of direct, diffuse, and global horizontal irradiance</a> measured by the Baseline Surface Radiation Network station at Cabauw, the Netherlands. More information about the observational site Cabauw can be found at the <a href="https://ruisdael-observatory.nl/cabauw/">Ruisdael Observatory website</a>.</p> <p><strong>Methodology</strong></p> <p>An extensive dataset description is currently being written for Earth System Science Data. In the mean time, a more condensed description is available in preprint at <a href="https://arxiv.org/abs/2209.10284">Arxiv</a>.</p> <p>Processing scripts are published at this <a href="https://zenodo.org/record/7099491">Zenodo release</a>.</p> <p><strong>Dataset contents</strong></p> <p>This dataset contains daily time series with the following data, from 2011-02 until 2020-12-31:</p> <ol> <li>Cloud shadow and cloud enhancement time series classifications (see methodology)</li> <li>Overcast, clear-sky and variable time series classifications (see methodology)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-mcclear/">CAMS McClear</a> for clear-sky global horizontal irradiance (version 3.5)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-mcclear/">CAMS McClear</a> atmospheric composition input (aerosols, ozone, and total column water vapour)</li> <li>Solar elevation and azimuth angles (calculated using <a href="https://github.com/pingswept/pysolar/releases/tag/0.10">PySolar</a>)</li> <li>Quality flags: non-official 1 Hz and official 1-minute (from <a href="https://doi.pangaea.de/10.1594/PANGAEA.940531">BSRN at PANGAEA</a>)</li> <li>Cabauw observatory<a href="https://dataplatform.knmi.nl/dataset/cesar-tower-meteo-lb1-t10-v1-2"> tower wind speed and direction</a></li> </ol> <p>Additional satellite data time series from 2014-01 until 2016-12:</p> <ol> <li>MSGCPP satellite data for an area over central Netherlands (<a href="https://essd.copernicus.org/articles/9/415/2017/">CLAAS2 source</a>)</li> <li>Post processed timeseries of cloud types over Cabauw derived from this MSGCPP satellite data</li> <li>A nubiscope + satellite derived validation dataset for overcast and clear-sky classifications</li> </ol> <p>Statistics files:</p> <ol> <li>Cloud shadow and cloud enhancement event detection and event statistics based on the time series for 2011-2020</li> <li>Daily radiation statistics for 2011-2020</li> </ol> <p>And finally, for all days there are quicklooks available that visualize the irradiance time series, classification, and if available satellite data.</p> <p><strong>Version History</strong></p> <p><em>New in v1.1</em><strong> </strong></p> <ul> <li>CAMS McClear updated from v3.1 to v3.5 (2011-2020)</li> <li>Fix incorrect dominant cloud type in CLAAS2 timeseries (`claas2_processed.zip`, 2014-2016)</li> <li>Update timeseries statistics and quicklooks with new clear-sky data (2011-2020)</li> <li>Added official quality flags (2011-2020)</li> <li>Added preprocessed validation dataset (2014-2016)</li> <li>Added nubiscope to quicklooks (2014-2016)</li> </ul>
Supplementary data to: Soil Moisture to Runoff (SM2R) A data-driven model for runoff estimation across poorly gauged Asian water towers based on soil moisture dynamics
<p>This data archive includes simulated monthly runoff anomaly using the Soil Moisture to Runoff model during 1981‒2020 over the representative drainage basin in each water tower. Please see Readme for more data information. For calculation details please see the publication.</p>
Supplementary Data to "Simulation of dendritic-eutectic growth with the phase-field method" by Seiz et al.
<p>Video files for several simulations conducted for the paper, showing more of the dynamic time evolution than possible in the paper itself.</p> <p> </p> <p>Update 24/04/2023: A few additional simulations were conducted to test for the applicability of the theory delineating the dendritic-eutectic regime from the eutectic regime. Videos of these plus some additional data is deposited at</p> <p> </p> <p>https://zenodo.org/record/7858461</p> <p> </p> <p><br> All videos show the Cu composition field, with the color ranging from 0.02 (pure black) to 0.33 (pure white).<br> Thus black represents the fcc Al crystal, whitish-grey the Al2Cu intermetallic phase and grey shades in between the liquid melt, with lighter shades being richer in Cu.<br> Excluding the complete directional solidification videos (full*webm), all videos show regions of 280x250um^2, with the far-field to the right being cut off to emphasize the structure.<br> <br> {close,far}_d+e.webm:<br> Complete simulations resulting in a eutectic structure either growing close to the dendrite tip or far from it, cropped to slightly above the solidification front.<br> The same speed v=160um/s and melt composition c_0=0.12 are used, but two different gradients: 99K/mm for close growth and 24.7K/mm for far growth; at the even smaller gradient the eutectic is no longer in the moving window.<br> <br> traveling_oscillation.webm:<br> Complete simulation resulting in a eutectic with traveling oscillations. (v=160um/s, c_0=0.13, G=6.18K/mm)<br> <br> jump_d+e_e.webm:<br> Jumps from v = 160um/s to 320um/s at simulation start in order to move from a dendritic-eutectic morphology to a eutectic morphology.<br> After a eutectic morphology is obtained, the jump is reversed (around 17s into the video) and only a coarsening of the eutectic is observed.<br> <br> jump_e_d+e.webm:<br> Jumps from v=320um/s to 20um/s at simulation start in order to move from a eutectic morphology to a dendritic-eutectic morphology.<br> <br> full_cropped*webm:<br> Complete directional solidification for different alloy compositions and processing conditions yielding different structures. Cropped to slightly above the final maximum position of any solid phase, showing a 970x500um^2 domain.<br> A scaling to 50% of the original resolution is performed as some players/browsers have trouble with large resolutions.<br> e: primarily eutectic (v=320um/s, G=24.7K/mm, c_0 = 0.12)<br> d+e: dendritic-eutectic (v=160um/s, G=24.7K/mm, c_0 = 0.12)<br> <br> full_d.webm:<br> Same as above, only non-cropped as the structure fills the entire simulation box (1500x500um^2). (v=320um/s, G=24.7K/mm, c_0 = 0.08)</p>
CO2 electroreduction experiments - supplementary data
<p>Set of raw data supporting our work on CO2 electroreduction.</p>
Supplementary data for a study of microtearing modes at low plasma β
<p>Supplementary data for the Plasma Physics and Controlled Fusion Publication `New linear stability parameter to describe low-β electromagnetic microinstabilities driven by passing electrons in axisymmetric toroidal geometry, M. R. Hardman, F. I. Parra, B. S. Patel, C. M. Roach, J. Ruiz Ruiz, M. Barnes, D. Dickinson, W. Dorland, J. F. Parisi, D. St-Onge, and H. Wilson'. The publication appears as an arXiv preprint with identifier arXiv:2208.10615.</p> <p>The dataset consists of GS2 input files for the study, and a python (3) script for generating a selection of the figures from raw GS2 output data. Details are given in the Readme. </p>
Supplementary Data: Global fits of vector-mediated s-channel simplified models for scalar and fermionic dark matter with GAMBIT
<p>This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of vector-mediated s-channel simplified models for scalar and fermionic dark matter with GAMBIT". The paper can be found at https://arxiv.org/abs/2209.13266.</p> <p>Samples have been created using GAMBIT and figures can be reproduced with pippi.</p>
Supplementary data for "Testing the efficacy of different molecular tools for parasite conservation genetics: a case study using horsehair worms (Phylum Nematomorpha)"
<p>Supplementary data for "Testing the efficacy of different molecular tools for parasite conservation genetics: a case study using horsehair worms (Phylum Nematomorpha)"</p> <p>alignments: alignments used for BEAST ("bayes") and PopArt ("popart"). The "popart" folder also has a traits file per each species.</p> <p>bayesian_plots: TSVs ("tsv") and PDF files ("ogs") generated by BEAST. The "tsv" folder also has the scripts for plotting the results in R.</p> <p>easysfs: scripts, population file and results from the VCF to SFS conversione done by easySFS.</p> <p>fineRADstructure: fineRADstructure input files and output PDF plots ("plots") for <em>C. formosanus</em> ipyrad and Stacks ("stacks") data. </p> <p>logs: logs for ipyrad, ModelTest, PGDspider, PopArt ("popart") and Stacks ("stacks"). The "popart" folder also have the generated networks in a TXT file. The "stacks" folder also has ODS files for calculating the amount of loci per each M/n fixed value.</p> <p>snapclust: STR files used with R for snapclust. Scripts included.</p> <p>stairway_plot: input (blueprint files) and outputs for Stairway Plot 2 analyses. The <em>C. formosanus</em> folder ("chordodes") also has scripts for R plotting.</p> <p>vcfs: VCF and HDF5 files used in this study. Also scripts for filtering/converting data and plotting the PCA with ipyrad (activate python first!) for <em>C. formosanus</em>.</p> <p>"acutogordius" = <em>A. taiwanensis</em><br> "chordodes" = <em>C. formosanus</em><br> "gordius" = <em>G. chiashanus</em></p>
Dryococelus australis genome assembly supplementary data
<p>We present a chromosome-scale genome assembly for the critically endangered Lord Howe Island stick insect <em>Dryococelus australis. </em>Contained in this repository are the original unfiltered annotation .gff file, the .fasta file of repeat families identified by RepeatScout, and the .gff file of repetitive elements throughout the genome assembly.</p>
Supplementary data for "Flat does not mean 2D: Using X-ray microtomography to study insect wings in 3D"
<p>Supplementary data for "Flat does not mean 2D: Using X-ray microtomography to study insect wings in 3D". Contain all 3D CT-scans used for figures and analyses</p>
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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.