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1,773 results for “Supplementary data,”
FIG. 4. — A in From fin rays to DNA: supplementary morphological and molecular data to identify Mormyrus subundulatus Roberts, 1989 (Pisces: Mormyridae) from the Bandama River in Côte d'Ivoire
FIG. 4. — A, Distribution of Mormyrus subundulatus Roberts, 1989 according to available data; B, the Bandama River in the type locality is impacted by the Taabo dam just upstream; C, preserved stream habitat downstream the type locality where M. subundulatus still lives. This part of the River will be lost after the impoundment of another big dam, planned for the next few years; D, aerial view (GoogleEarth) of the Tano River in the type locality (red dot); E, upstream, showing the important buildup of soil and mud due to mining activities. The Tano River does not seem to host suitable habitat for M. subundulatus anymore, at least around the historical locality.
FIG. 3 in From fin rays to DNA: supplementary morphological and molecular data to identify Mormyrus subundulatus Roberts, 1989 (Pisces: Mormyridae) from the Bandama River in Côte d'Ivoire
FIG. 3. — Distribution of the dorsal fin rays counts for the specimens of M. subundulatus Roberts, 1989 examined by us (blue bars) and for the specimens of M. rume Valenciennes, 1847 (data from Lévêque and Bigorne, 1985; orange bars). Yellow bars: specimens presumably from the Sassandra River population. (1) position of paratype CAS-SU63507 from the Tano River in Ghana (red bar); (2) position of specimen MNHN-IC-2018-0558, for which genetic data confirms the identification as M. subundulatus.
FIG. 2 in From fin rays to DNA: supplementary morphological and molecular data to identify Mormyrus subundulatus Roberts, 1989 (Pisces: Mormyridae) from the Bandama River in Côte d'Ivoire
FIG. 2. — Bayesian phylogenetic analyses base on Cyt b gene fragment (A) and COI gene fragment (B). Numbers on branch node indicate posterior probability values, only node with posterior probability above 85 are represented. Nodes with probability> 98 are identified with *. Numbers on specimens indicate the GenBank accession number of the sequence or, when many specimens shared the same haplotype, the haplotype names in Table 1.
Challenges of sampling and how phylogenetic comparative methods help: Supplementary data
<p>Supplementary data and results files for the paper:</p> <p>Macklin-Cordes, Jayden L. & Erich R. Round (2022). Challenges of sampling and how phylogenetic comparative methods help: With a case study of the Pama-Nyungan laminal contrast. <em>Linguistic Typology</em> (advance online publication). <a href="https://doi.org/10.1515/lingty-2021-0025">https://doi.org/10.1515/lingty-2021-0025</a></p>
Supplementary Table for Earth observation data-driven cropland soil monitoring: A review
<p>Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.</p>
Supplementary datasets, data analysis code, and R tutorials for: Phylogenetic analysis of adaptation in comparative physiology and biomechanics: overview and a case study of thermal physiology in treefrogs
<p>Comparative phylogenetic studies of adaptation are uncommon in biomechanics and physiology. Such studies require collecting data from many species, a challenge when data collection is experimentally intensive. Moreover, researchers struggle to employ the most biologically appropriate phylogenetic tools for identifying adaptive evolution. Here, we detail an established but greatly underutilized phylogenetic comparative framework—the Ornstein-Uhlenbeck process—that explicitly models long-term adaptation. We discuss challenges in implementing and interpreting the model, and we outline potential solutions. We demonstrate use of the model through studying the evolution of thermal physiology in treefrogs. Frogs of the family Hylidae have twice colonized the temperate zone from the tropics, and such colonization likely involved a fundamental change in physiology due to colder and more seasonal temperatures. However, which traits changed to allow colonization is unclear. We measured cold-temperature tolerance and characterized thermal performance curves in jumping for twelve species of treefrogs distributed from the Neotropics to temperate North America. We then conducted phylogenetic comparative analyses to examine how tolerances and performance curves evolved and to test whether that evolution was adaptive. We found that tolerance to low temperatures increased with the transition to the temperate zone. In contrast, jumping well at colder temperatures was unrelated to biogeography and thus did not adapt during dispersal. Overall, our paper shows how comparative phylogenetic methods can be leveraged in biomechanics and physiology to test the evolutionary drivers of variation among species.</p>
Supplementary Material for 'Benchmarking Explanatory Models for Inertia Forecasting using Public Data of the Nordic Area'
<p>This data set is supplementary material for the paper 'Benchmarking Explanatory Models for Inertia Forecasting using Public Data of the Nordic Area' by Jemima Sophie Graham, Evelyn Heylen, and Fei Teng.</p> <p>This data set is intended for day-ahead inertia forecasting in the Nordic (Eastern Denmark, Finland, Norway, Sweden). It contains hourly data for the inertial energy (MVAs), day-ahead national demand forecast (MW), day-ahead wind power forecast (MW), day-ahead solar power forecast (MW), and interconnection flow (MW) in the Nordic between January 2016 and August 2020. </p>
Supplementary data for: Collaborative Program Comprehension via Software Visualization in Extended Reality
<p>Supplementary videos and images for: Collaborative Program Comprehension via Software Visualization in Extended Reality</p> <p>Videos are additionally hosted on: <a href="https://www.youtube.com/channel/UCijDvGaoZqH0LiFW3DSNH1w">https://www.youtube.com/channel/UCijDvGaoZqH0LiFW3DSNH1w</a></p>
Supplementary data for: "Historical glacier change on Svalbard predicts doubling of mass loss by 2100"
<p>Supplementary datasets for:</p> <p>Geyman, E.C., van Pelt, W.J.J., Maloof, A.C., Faste Aas, H., and Kohler, J., 2022. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature.</p> <p>Abstract:</p> <p>The melting of glaciers and ice caps accounts for about one-third of current sea-level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal climate shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Here we link historical and modern glacier observations to predict that twenty-first century glacier thinning rates will more than double those from 1936 to 2010. Making use of an archive of historical aerial imagery from 1936 and 1938, we use structure-from-motion photogrammetry to reconstruct the three-dimensional geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a more than 70-year timespan, enabling us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.28 m yr<sup>-1</sup> of water equivalent. Finally, we design a space-for-time substitution8 to combine our historical glacier observations with climate projections and make first-order predictions of twenty-first century glacier change across Svalbard.</p> <p> </p> <p>Dataset description: </p> <p><br> This dataset contains the digital elevation models (DEMs), elevation change maps, point clouds, orthophotos, and vector outlines of glacier extents based on the Norwegian Polar Institute's collection of 5,507 high-oblique aerial images captured over Svalbard in 1936/1938. The photographs were analyzed through structure-from-motion (SfM) photogrammetry to generate 3D models. We also provide an .xlsx spreadsheet containing glacier-by-glacier statistics of ice loss and climate fields. Note that all of the raster and point cloud files listed below have been georeferenced in Metashape using the ground control points (GCPs) illustrated in Main Text, Fig. 2e, but have not undergone the co-registration and bias-correction following the methods of Nuth & Kaab (2011), which was done on a glacier-by-glacier basis. However, the glacier change budgets in the .xlsx file [#5 below] do reflect the values from the glacier-by-glacier co-registered and bias-corrected DEMs. See below for descriptions of each dataset (each number below corresponds to a different zipped folder).</p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Svalbard-wide datasets [all georeferenced Svalbard-wide datasets are in the coordinate system UTM 33N]: </strong></p> <p><br> 1. Svalbard-wide 1936 DEM (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>2. Svalbard-wide 1936 orthophotomosaic (20 m resolution) [georeferenced .tif file] </p> <p>3. Svalbard-wide dh (1936-2010) (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>4. Shapefile of 1936 glacier extents [ESRI .shp file] </p> <p>5. Glacier-by-glacier statistics [.xlsx file] </p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Regional-datasets: </strong></p> <p><em>Due to file size limitations, the high-resolution (5 m) datasets are split into the 8 regions illustrated in Main Text, Fig. 2d: </em></p> <p><em>Zone 1 - South Spitsbergen</em></p> <p><em>Zone 2 - Barentsoya-Edgeoya</em></p> <p><em>Zone 3 - Austfonna</em></p> <p><em>Zone 4 - Vestfonna</em></p> <p><em>Zone 5 - Northeast Spitsbergen</em></p> <p><em>Zone 6 - Central Spitsbergen</em></p> <p><em>Zone 7 - Northwest Spitsbergen</em></p> <p><em>Zone 8 - North Spitsbergen</em></p> <p><br> 6. Regional 1936 DEMs (5 m resolution) [georeferenced .tif files] </p> <p>7. Regional dh (1936-2010) (5 m resolution) [georeferenced .tif files] </p> <p>8. Local 1936 orthomosaics (5 m resolution) [georeferenced .tif files] </p> <p>9. Unprocessed point clouds [.laz files]. These files represent the raw 3D point clouds (x,y,z) generated in Agisoft Metashape for each of the 17 local models described in Extended Data Figure 3.</p> <p>10. Thumbnail-sized copies of the 5,507 historical aerial images (1936 and 1938) analyzed in this study, along with a .csv file labeling the approximate location of each photograph.</p>
Supplementary Material 1: Original dataset collected during the tracking and mark-release-recapture study and R script used to analyse the data
<p>The original dataset collected in northern Serbia during butterfly behavioural study on two species, <em>Phengaris teleius</em> and <em>Polyommatus icarus</em>. The dataset is provided in two separate CSV files for mark-release-recapture study and for butterfly tracking study. In addition, R script used to preopare the dataset and fit the models is given.</p>
Supplementary Data for "Sequencing the Pandemic: Rapid and High-Throughput Processing and Analysis of COVID-19 Clinical Samples for 21st Century Public Health"
<p>Supplementary material for F1000 methods manuscript. Includes raw sequencing metrics for two COVID sequencing methodologies, as well as a complete cost breakdown for each methodology.</p>
ExoCAM: A 3D Climate Model for Exoplanet Atmospheres :: Model data and supplementary figures and analysis
<p>This repository contains 3D GCM model output data from the paper, "ExoCAM: A 3D Climate Model for Exoplanet Atmospheres", which is published in the Planetary Science Journal: Trapppist Habitable Atmospheres Intercomparison Special Issue. The model data includes mean climate states for the standard THAI simulations of TRAPPIST-1e, simulations using an upgraded radiative transfer, along with a large variety sensitivity experiments considering common tuning parameters of sub-grid scale cloud and convection physics. In total 43 simulations are included. Also included here are a variety of multi-panel contour plots showing basic results from all simulations as supplemental figures.</p> <p>https://iopscience.iop.org/article/10.3847/PSJ/ac3f3d</p>
Supplementary Data: A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors
<p>This dataset is the supplementary dataset for the case study used in the journal article (<a href="https://doi-org.tudelft.idm.oclc.org/10.1016/j.apenergy.2022.118572">https://doi.org/10.1016/j.apenergy.2022.118572</a>):</p> <p>A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors</p> <p> </p> <p>Before using the dataset, please</p> <p>1. refer to the article for the details of the data used in the case study,</p> <p>2. read README.txt for the structure of the dataset.</p> <p> </p> <p>Please also kindly cite the journal article when using the dataset.</p>
Supplementary Data for "Interplay of river and tidal forcings promotes loops in coastal channel networks"
<p>This dataset contains supplemental data required to reproduce the results of the paper <em>Interplay of river and tidal forcings promotes loops in coastal channel networks</em> (in review at Geophysical Research Letters). We provide raw and extracted channel network data for 19 river deltas/coastal marsh sites. For each site, the following files are provided:</p> <p><strong>XXX_base.tif</strong> : the raw binary mask of the river channel network<br> <strong>XXX_clipper.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : polygon(s) used to clip the raw mask<br> <strong>XXX_clipped.tif</strong> : the binary mask of the river channel network after being clipped by XXX_clipper.shp<br> <strong>XXX_filled.tif</strong> : the binary mask after filling islands via the method specified in the paper<br> <strong>XXX_inlet_nodes.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : locations of the inlet nodes; used by RivGraph<br> <strong>XXX_shoreline.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : location of the shoreline; used by RivGraph<br> <strong>XXX_links.json</strong> : GeoJSON file containing the geometries, connectivities, and widths of each link in the network<br> <strong>XXX_nodes.json</strong> : GeoJSON file containing the locations of each node of the network<br> <strong>process_XXX.py</strong> : the python script used to generate the above files</p> <p>All files listed below (except .py files) are georeferenced (i.e. can be opened with QGIS, ArcGIS or another GIS). Exceptions to the provided files include:</p> <p><strong>Barnstable</strong>: no "base.tif" is provided. Use "filled.tif".<br> <strong>GBM</strong>: some hand-cleaning was performed on "filled.tif".<br> <strong>Mackenize</strong>: "clipper.shp" is not provided, but "clipped.tif" is.<br> <strong>Mississippi</strong>: "clipper.shp" is not provided as the mask was made from a shapefile.</p> <p>In order to run process_XXX.py, the RivGraph package will need to be installed. Instructions<br> can be found at https://github.com/jonschwenk/RivGraph.</p>
Supplementary Data - Using landscape genomics to infer genomic regions involved in environmental adaptation of soybean genebank accessions
<p><strong>File: 50K_GenotypesEU_raw_UHOH_SoySNP50K.csv.tgz </strong></p> <p>Genotyping data of SoySNP50k SNP array of 170 European soybean varieties.</p> <p>The array includes 51.955 SNP markers.</p> <p>Genotypes of each variety are in columns and each row is a SNP marker. Naming of markers follows the annotation of the soybean genome.</p> <p><strong>File: EUvarieties_infos.csv </strong></p> <p>Description of European varieties</p> <p>Contains variety name, country of origin, EU region and maturity group assignment.</p> <p> </p> <p><strong>File: Supplementary_Data_Haupt_Schmid.xlsx</strong></p> <p>Additional data derived from data analysis. Description of data contained within file (Worksheet "Summary")</p> <p> </p>
Supplementary data to The vegetation of Chile and the EcoVeg approach in the context of the International Vegetation Classification project
<p>The rar file contains a map of Macrogroups of Chile in ESRI shapefile format. Macrogroups are hierarchically included in the categories of division and formation of IVC classification. These categories can also be displayed using the table associated with the shapefile. Likewise, Chilean zonal vegetation units of Luebert & Pliscoff (2017) are included, so the crosswalk for generating the map of Macrogroups based on the Chilean zonal vegetation units is fully documented.</p>
Supplementary Information: Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b. Chubb and Min, A&A (2022).
<p>Supplementary information containing additional figures of the journal article 'Exoplanet atmosphere retrievals in 3D using phase curve data with ARCiS: application to WASP-43b' by K. L. Chubb and M. Min, published in Astronomy & Astrophysics (2022).</p>
Supplementary data to the Baseline Methodological User Needs Analysis
<p>Supplementary data to the Baseline Methodological User Needs Analysis</p>
Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.
<p>Supplementary data for article "Small hydropower – small ecological footprint? A multi-annual environmental impact analysis using aquatic macroinvertebrates as bioindicators. Part 2: effects on functional diversity" by Scotti A., et al.:</p> <p><br> - Trait-based distances calculated for each pair of taxa;</p> <p>- CWM, CWM(LN) values, and their difference (CWMDIFF)</p> <p>Refer to the published articles for further details.</p>
Supplementary Data to *Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data*
<p>Supplementary code and data to <strong>Robust adaptive distance functions for approximate Bayesian inference on outlier-corrupted data</strong> by <strong>Y. Schaelte et al., 2021</strong>.</p> <p>The archive contains a <strong>README.rst </strong>for information on what is where and how to execute the study and generate the figures. The underlying code without the data can be found at the repository https://github.com/yannikschaelte/study_abc_rad, of which this archive is a snapshot.</p> <p> </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.