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1,773 results for “Supplementary data,”
Supplementary material 1 from: Ampai N, Rujirawan A, Yodthong S, Termprayoon K, Stuart BL, Wood Jr PL, Aowphol A (2022) Hidden diversity of rock geckos within the Cnemaspis siamensis species group (Gekkonidae, Squamata): genetic and morphological data from southern Thailand reveal two new insular species and verify the phylogenetic affinities of C. chanardi and C. kamolnorranathi. ZooKeys 1125: 115-158. https://doi.org/10.3897/zookeys.1125.94060
Table S1
Gel-PDA+GG+CNC hydrogel-supplementary data
<p>adhesion and injection of Gel-PDA+GG+CNC hydrogel</p>
Supplementary material 5 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
MaxEnt Supplementary Info
Supplementary material 4 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Moran's I Correlograms
Supplementary material 3 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Sampled PUD occurrence
Supplementary material 2 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
InVEST model configuration
Supplementary material 1 from: Neill AM, O`Donoghue C, Stout JC (2023) Spatial analysis of cultural ecosystem services using data from social media: A guide to model selection for research and practice. One Ecosystem 8: e95685. https://doi.org/10.3897/oneeco.8.e95685
Sites used for validation
Supplementary material 1 from: Zhang M, Zhang X-H, Shi S, Chen B-H (2023) Lithocarpus dahuensis (Fagaceae), a new species from Fujian Province based on morphology and genomic data. PhytoKeys 222: 1-18. https://doi.org/10.3897/phytokeys.222.99370
Supplementary data
Supplementary material 1 from: Li X, Ni J-B, Xia N-H (2023) The identity of Sasa oblongula C.H.Hu (Poaceae, Bambusoideae, Arundinarieae): evidence from morphology and molecular data. PhytoKeys 226: 17-32. https://doi.org/10.3897/phytokeys.226.101221
SNP matrix
Supplementary data: "Physics-informed machine learning for power grid frequency modelling"
<p>This repository contains result files for the paper "Physics-informed machine learning for power grid frequency modelling" <a href="https://doi.org/10.48550/arXiv.2211.01481">(Preprint)</a>. The code for producing the processed data and the results is <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">available at github</a>.</p> <p><strong>Results</strong></p> <p>The result folder comprises the results of hyper-parameter optimisation, scaling variation and interpretation via SHAP. In particular, it contains these sub-folders and files:</p> <ul> <li><em>tuning </em>: Results of hyper-parameter tuning.</li> <li><em>best_model </em>: Weights of the trained model with best hyper-parameters.</li> <li><em>best_model_<scaling-variation> </em>: Weights of the trained models with best hyper-parameters but with a variation of the parameter scaling.</li> <li><em>fixed_model_hps.pkl </em>: Hyper-parameters that are not optimised.</li> <li><em>shap_values_<parameter>_long.h5</em> : SHAP values for the prediction of the system parameters.</li> </ul>
Supplementary data to the paper: Automatic Extraction of Anthropometric Features for the Individualization of the Pinna-Related Transfer Function in the Median Plane
<p>Supplementary research data to the paper (rejected):</p> <blockquote> <p>Davide Fantini, Federico Avanzini, Stavros Ntalampiras and Giorgio Presti (2023) "Automatic Extraction of Anthropometric Features for the Individualization of the Pinna-Related Transfer Function in the Median Plane"</p> </blockquote> <p>The repository includes the research data generated and analyzed in the abovementioned paper describing a method for PRTF individualization. In particular, the following data are included:</p> <ul> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/README.md">README.md</a>: instructions for the data</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/pinna_range_img.mat">pinna_range_img.mat</a>: pinna range images extracted from the 3D head meshes of the <a href="https://depositonce.tu-berlin.de/items/dc2a3076-a291-417e-97f0-7697e332c960">HUTUBS dataset</a></li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/landmarks.mat">landmarks.mat</a>: landmarks coordinates both manually annotated and automatically placed with ASM</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/anthropometry.mat">anthropometry.mat</a>: anthropometric parameters automatically extracted from both manually annotated and ASM-fitted landmarks</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/img_features.mat">img_features.mat</a>: image features pinna cavities extracted from both manually annotated and ASM-fitted landmarks</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/grnn_models.mat">grnn_models.mat</a>: Generalized Regression Neural Network (GRNN) models trained from both HUTUBS anthropometry and the proposed pinna features</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/predicted_dtf.mat">predicted_dtf.mat</a>: Directional Transfer Function (DTF) sets predicted from both HUTUBS anthropometry and the proposed pinna features</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/anthropometry_documentation.pdf">anthropometry_documentation.pdf</a>: documentation of the pinna anthropometric parameters</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/auditory_model_complete_elevation_range.pdf">auditory_model_complete_elevation_range.pdf</a>: auditory model evaluation in the complete elevation range</li> </ul> <p>The data are provided in the Matlab file format MAT. Nevertheless, the MAT files can be read with other programming languages, such as Python (<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">scipy.io.loadmat</a>).</p> <p>A GitHub repository to automatically extract the pinna landmarks and features as described in the paper is available <a href="https://github.com/DavideFantini/pinna-landmarks-fitting-and-anthropometry-extraction" target="_blank" rel="noopener">here</a>.</p>
Sick of ticks? Towards a comprehensive understanding of rarely diagnosed Ixodes ricinus-borne diseases - Supplementary data
<p>All supplementary data for the various chapters of this thesis can be found in the online publications or in the files below.</p>
Chapter 5 Supplementary Data Sheets
<p>Chapter 5 Supplementary Data Sheets</p>
Supplementary data for: PETROGENESIS OF A NEW TYPE OF INTRUSIVE SHERGOTTITE: OLIVINE-GABBRO NORTHWEST AFRICA 13227
<p>3D petrography videos, mineral chemistry data of pyroxene, olivine, maskelynite, Fe-Cr-Ti oxides, and phosphates, as well as bulk rock trace element data for olivine-gabbroic shergottite NWA 13227.</p> <p> </p> <p>For videos, the coloration of phases is as follows: orange = pyroxene, green = olivine, blue = maskelynite, and yellow = accessory phases. Videos S2 and S4 were created using the "Edge Contrast" function in Dragonfly (ORS, 2020), which allows for the edges of the minerals to be emphasized while the grains themselves are semi-transparent. </p>
Supplementary material 1 from: Hubancheva A, Bozicevic V, Morinière J, Goerlitz HR (2023) DNA metabarcoding data from faecal samples of the lesser (Myotis blythii) and the greater (Myotis myotis) mouse-eared bats from Bulgaria. Metabarcoding and Metagenomics 7: e106844. https://doi.org/10.3897/mbmg.7.106844
Metabarcoding data from M. myotis and M. blythii from Bulgaria
Supplementary data
<p>Supplementary data for research article submission at Polymers</p>
Supplementary material 1 from: Wei Z, Ren G (2023) Two new species of the genus Laena (Coleoptera, Tenebrionidae, Lagriinae) from northern Sichuan in China based on morphological and molecular data. ZooKeys 1173: 71-83. https://doi.org/10.3897/zookeys.1173.103125
Supplementary information
Supplementary material 1 from: Ruland KT, Montague T, Helwi P (2023) Impact of hail-netting on Vitis vinifera L. canopy microclimate, leaf gas exchange, fruit quality, and yield in a semi-arid environment. Viticulture Data Journal 555: e108805. https://doi.org/10.3897/vdj.555.e108805
Hail-netting on vines
Supplementary material 3 from: Ruland KT, Montague T, Helwi P (2023) Impact of hail-netting on Vitis vinifera L. canopy microclimate, leaf gas exchange, fruit quality, and yield in a semi-arid environment. Viticulture Data Journal 555: e108805. https://doi.org/10.3897/vdj.555.e108805
Compact canopy under hail-netting
Supplementary material 2 from: Ruland KT, Montague T, Helwi P (2023) Impact of hail-netting on Vitis vinifera L. canopy microclimate, leaf gas exchange, fruit quality, and yield in a semi-arid environment. Viticulture Data Journal 555: e108805. https://doi.org/10.3897/vdj.555.e108805
Li-Cor-6400 machines below hail-netting
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.