Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4,480
datasets available to search
ShareScore release 0.9.0
Dataset results
4,480 results for “hybrid”
FIGURE 9. H. melpomene meriana Turner, 1967 in Alternative facts: a reconsideration of putatively natural interspecific hybrid specimens in the genus Heliconius (Lepidoptera: Nymphalidae)
FIGURE 9. H. melpomene meriana Turner, 1967 "typical" form (dorsal, ventral). French Guiana. (Image source: https:/ /cliniquevetodax.com/Heliconius/pages/melpomene%20meriana.html).
FIGURE 20. H. pardalinus radiosus Butler, 1873 in Alternative facts: a reconsideration of putatively natural interspecific hybrid specimens in the genus Heliconius (Lepidoptera: Nymphalidae)
FIGURE 20. H. pardalinus radiosus Butler, 1873 (dorsal). Brazil: Amazonas, Villa Bella [Bolivia:Pando?]. Neukirchen coll. (FLMNH). (image source: https://cliniquevetodax.com/Heliconius/pages/pardalinus%20radiosus.html)
FIGURE 17. H. melpomene thelxiopeia Staudinger, 1897 in Alternative facts: a reconsideration of putatively natural interspecific hybrid specimens in the genus Heliconius (Lepidoptera: Nymphalidae)
FIGURE 17. H. melpomene thelxiopeia Staudinger, 1897 (dorsal, ventral). French Guiana: Rivière Comte, leg. C. Chazal. Note the reduction of ray elements on the HWV, and rounded proximal edges of yellow spots on FW (image source: https://cliniquevetodax.com/Heliconius/pages/melpomene%20thelxiopeia.html).
FIGURE 14. H. melpomene malleti Lamas, 1988 in Alternative facts: a reconsideration of putatively natural interspecific hybrid specimens in the genus Heliconius (Lepidoptera: Nymphalidae)
FIGURE 14. H. melpomene malleti Lamas, 1988 (dorsal, ventral). Peru: Loreto. (image source: https:// cliniquevetodax.com/Heliconius/pages/melpomene%20maletti.html)
FIGURE 6. H in Alternative facts: a reconsideration of putatively natural interspecific hybrid specimens in the genus Heliconius (Lepidoptera: Nymphalidae)
FIGURE 6. H. melpomene melpomene (Linnaeus, 1758) "typical" form (dorsal, ventral). French Guiana: Route Vidal. (image source: https://cliniquevetodax.com/Heliconius/pages/melpomene%20melpomene.html)
FIGURE 5. H. numata superioris Butler, 1875 in Alternative facts: a reconsideration of putatively natural interspecific hybrid specimens in the genus Heliconius (Lepidoptera: Nymphalidae)
FIGURE 5. H. numata superioris Butler, 1875 "typical" form (dorsal, ventral). Brazil: Río Tocantins. (image source: https://cliniquevetodax.com/Heliconius/pages/numata%20superioris.html).
FIGURE 13 in Alternative facts: a reconsideration of putatively natural interspecific hybrid specimens in the genus Heliconius (Lepidoptera: Nymphalidae)
FIGURE 13. Hybrid #11 (dorsal; ventral). Peru: Loreto, Río Itaya [near Iquitos]. 1997. Interpreted by Mallet et al. (2007) to be an F1 hybrid of H. numata aurora (Fig. 11) and H. melpomene malleti (Fig. 14), based on wing shape and the FW yellow band. This specimen, apparently collected near Iquitos, Peru by an unnamed third party is in the Neukirchen collection, purchased by the University of Florida's McGuire Center for Lepidoptera and Biodiversity Research (FLMNH). The white marginal dots, the absence of red basal dots, and the v-shaped distal ends of the red rays on the VHW all suggest that a more likely cross would be H. numata x H. elevatus (e. g., H. elevatus pseudocupidineus, Fig. 3). While still interspecific, such a hybrid would be between very closely-related species, rather than more distantlyrelated members of separate clades.
Data file for paper: Rubio Garcia, Javier; Kucernak, Anthony; Zhao, Dong; Li, Danlei; Fahy, Kieran; Yufit, Vladimir; Brandon, Nigel; Gomez-Gonzalez, Miguel, "Hydrogen/manganese hybrid redox flow battery", Journal of Physics: Energy, 2018
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Rubio Garcia, Javier; Kucernak, Anthony; Zhao, Dong; Li, Danlei; Fahy, Kieran; Yufit, Vladimir; Brandon, Nigel; Gomez-Gonzalez, Miguel, "Hydrogen/manganese hybrid redox flow battery", Journal of Physics: Energy, 2018</p> <p>DOI: 10.1088/2515-7655/aaee17 </p> <p>Please cite the above reference if you wish to use this data</p>
FIGURE 4. Hybrid form Bythotrephes brevimanus x B in Morphological assessment of the North Eurasian interspecific hybrid forms of the genus Bythotrephes Leydig, 1860 (Crustacea: Cladocera: Cercopagididae)
FIGURE 4. Hybrid form Bythotrephes brevimanus x B. cederströmii, females (A–G, Iriklinskoe Reservoir; H–O—Lake Saimaa, Finland). A–F, H, L–O, postabdominal and caudal claws. G, I–K, bend of caudal process.
FIGURE 3. Hybrid form Bythotrephes brevimanus x B in Morphological assessment of the North Eurasian interspecific hybrid forms of the genus Bythotrephes Leydig, 1860 (Crustacea: Cladocera: Cercopagididae)
FIGURE 3. Hybrid form Bythotrephes brevimanus x B. cederströmii, females (A–F, Gor'kovskoe Reservoir; F–J— Verhnekamskoe Reservoir; K, L—the Gulf of Finland). A, brood pouch with resting eggs. B–L, postabdominal and caudal claws.
FIGURE 1. Hybrid form Bythotrephes brevimanus x B in Morphological assessment of the North Eurasian interspecific hybrid forms of the genus Bythotrephes Leydig, 1860 (Crustacea: Cladocera: Cercopagididae)
FIGURE 1. Hybrid form Bythotrephes brevimanus x B. cederströmii, females (Rybinskoe Reservoir, Central European Russia). A, general lateral view. B, dorsal seta on proximal part of antennal basipodite. C, setae of distal end of first endopodital segment of tl I. D, E, the same of second endopodital segment of tl I. F, distal part of protopodites of tl II and tl III, outer view. G, apical setae of caudal process. H–R, caudal process and postabdominal and caudal claws. S, caudal process with highly sclerotized parts.
FIGURE 6. Hybrid form Bythotrephes brevimanus x B in Morphological assessment of the North Eurasian interspecific hybrid forms of the genus Bythotrephes Leydig, 1860 (Crustacea: Cladocera: Cercopagididae)
FIGURE 6. Hybrid form Bythotrephes brevimanus x B. cederströmii, females (A–G). Bythotrephes sp. x B. cederströmii, females (H–M). (A–F, Lake Gusinoe; G, Lake Zaysan; H–M, Lake Borzu-Khol'). A–E, J, K, postabdominal and caudal claws. F, L, M, bend of caudal process. G, I, setae of distal end of second endopodital segment of tl I. H, the same of first endopodital segment of tl I.
FIGURE 1 in Redescription of Orthopristis ruber and Orthopristis scapularis (Haemulidae: Perciformes), with a hybridization zone off the Atlantic coast of South America
FIGURE 1. Body in lateral view, Orthopristis ruber (left column). A–B. Syntypes of (MNHN-IC-A-0457 and MNHN-IC-A- 7821). C. Live specimen (not preserved, São Paulo state, image of Gabriel Togni). D–E. Fresh specimens (not preserved, D. Santa Catarina state, image of Leonardo Machado, E. Espírito Santo state, image of João Gasparini). F. Fixed specimen (AZUSC 1812, São Paulo). Body in lateral view, Orthopristis scapularis (right column). G. Holotype of O. scapularis (ANSP 45084). H. Live specimen (not preserved, Ceará state). I-J-K. Fresh specimens (not preserved, I. Rio Grande do Norte state, image of Alfredo Carvalho, J. Venezuela, image of James van Tassell & Ross Robertson, K. Amapá state, image of Marceniuk). L. Fixed specimen (MPEG 34292, Alagoas state).
FIGURE 4 in Redescription of Orthopristis ruber and Orthopristis scapularis (Haemulidae: Perciformes), with a hybridization zone off the Atlantic coast of South America
FIGURE 4. The Atlantic coast of South America, showing the geographic distribution of Orthopristis scapularis (green), Orthopristis ruber (yellow), Orthopristis hybrid (red), locations with material, in zoological collections, not examined (dark blue), and localities with record in the literature (light blue). Some symbols represent more than one locality or a large number of specimens. Non-type specimens are represented with circles, and type specimens are indicated by square
FIGURE 2 in Redescription of Orthopristis ruber and Orthopristis scapularis (Haemulidae: Perciformes), with a hybridization zone off the Atlantic coast of South America
FIGURE 2. Neighbor-joining tree of the species of Orthopristis based on partial sequences of the cytochrome oxidase c subunit I. Numers near nodes represent bootstrap support.
The Hybrid Consensus Model Based on Blockchain Self-Executing Contract for Secure E-voting System
<p><strong>Data for Review</strong></p>
Finite element analysis results from simulation of fusion energy heat exchange component: hybrid CAD/IBSim model including a graphite foam interlayer
<p>Temperature profile data from a finite element analysis of a conceptual design for a fusion energy heat exchange component (monoblock). The mesh is a hybrid from a computer aided design (CAD) drawing for the pipe and armour and IBSim for the interlayer. The IBSim interlayer is generated directly from a 3D volumetric image of a graphite foam block (KFoam). The 3D image was generated with an X-ray tomography scan performed by Dr Llion Evans with Manchester X-ray Imaging Facility equipment, which was funded in part by the EPSRC (grants EP/F007906/1, EP/F001452/1 and EP/I02249X/1). Conversion of the data to FE mesh was achieved using ScanIP, part of the Simpleware suite of programmes, version 7 (Synopsys Inc., Mountain View, CA, USA).</p> <p>The mesh used for the analysis is available as a separate dataset:</p> <p><a href="https://doi.org/10.5281/zenodo.3522319">https://doi.org/10.5281/zenodo.3522319</a></p> <p>This data was used originally for the following publications (please cite if re-using the data):</p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Image based in silico characterisation of the effective thermal properties of a graphite foam”, Carbon, Vol. 143, pp. 542-558, 2018. <a href="https://doi.org/10.1016/j.carbon.2018.10.031">https://doi.org/10.1016/j.carbon.2018.10.031</a></p> <p>Ll.M. Evans, L. Margetts, P.D. Lee, C.A.M. Butler, E. Surrey, “Improving modelling of complex geometries in novel materials using 3D imaging”, Proceedings of NEA International Workshop on Structural Materials for Innovative Nuclear Systems, Manchester, UK, July 2016. <a href="https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf">https://www.oecd-nea.org/science/smins4/documents/P1-18_LlME_SMINS4_paper_reviewed.pdf</a></p>
Analysis of heritage stones and model wall paintings by pulsed laser excitation of Raman, laser-induced fluorescence and laser-induced breakdown spectroscopy signals with a hybrid system
<p>Laser based analysis of artworks benefits from the development of hybrid instruments where a single laser source serves to excite fluorescence, Raman and laser induced breakdown spectroscopy (LIBS) signals. Laser induced fluorescence (LIF) and Raman spectra provide information at the molecular level, while LIBS serves for identifying the elemental composition of the substrate under consideration. Studies using several excitation wavelengths on different types of materials and substrates help to develop and establish these hybrid systems for the conservation of artworks.</p>
Data for "Magnetic field independent sub-gap states in hybrid Rashba nanowires"
<p>Data for the publication "Magnetic field independent sub-gap states in hybrid Rashba nanowires"</p>
Hcropland30: A hybrid 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model
<p><strong>Hcropland30</strong><strong>:</strong><strong>A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model</strong></p> <p><strong>***Please note this dataset is undergoing peer review***</strong></p> <p><strong>Version</strong>: <strong>1.0</strong></p> <p><strong>Authors</strong>: Qiong Hu <sup>a, 1</sup>, Zhiwen Cai<sup> b, 1</sup>, Liangzhi You<sup> c, d</sup>, Steffen Fritz<sup> e</sup>, Xinyu Zhang<sup> c</sup>, He Yin<sup> f</sup>, Haodong Wei<sup>c</sup>, Jingya Yang<sup> g</sup>, Zexuan Li<sup> a</sup>, Qiangyi Yu<sup> g</sup>, Hao Wu<sup> a</sup>, Baodong Xu<sup> b *</sup>, Wenbin Wu<sup> g, *</sup></p> <p><em><sup>a</sup></em><em> Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China</em></p> <p><em><sup>b</sup></em><em> College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China</em></p> <p><em><sup>c</sup></em><em> Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China</em></p> <p><em><sup>d </sup></em><em>International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA</em></p> <p><em><sup>e </sup></em><em>Novel Data Ecosystems for sustainability Research Group, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria</em></p> <p><em><sup>f </sup></em><em>Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, USA</em></p> <p><a name="_Hlk166672711"></a><em><sup>g </sup></em><em>State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China</em></p> <p><strong> </strong></p> <p><strong>Introduction</strong></p> <p>We are pleased to introduce a comprehensive global cropland mapping dataset (named Hcropland30) in 2020, meticulously curated to support a wide range of research and analysis applications related to agricultural land and environmental assessment. This dataset encompasses the entire globe, divided into 16,284 grids, each measuring an area of 1°×1°. Hcropland30 was produced by leveraging global land cover products and Landsat data based on a deep learning model. Initially, we established a hierarchal sampling strategy that used the simulated annealing method to identify the representative 1°×1° grids globally and the sparse point-level samples within these selected 1°×1°grids. Subsequently, we employed an ensemble learning technique to expand these sparse point-level samples into the densely pixel-wise labels, creating the area-level 1°×1° cropland labels. These area-level labels were then used to train a U-Net model for predicting global cropland distribution, followed by a comprehensive evaluation of the mapping accuracy.</p> <p> </p> <p><strong>Dataset</strong></p> <p><strong><em><u>1. Hcropland30</u></em></strong><strong>:</strong> A hybrid 30-m global cropland map in 2020</p> <p>****<strong>Data format</strong>: GeoTiff</p> <p>****<strong>Spatial resolution</strong>: 30 m</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Values</strong>: 1 denotes cropland and 0 denotes non-cropland</p> <p>The dataset has been uploaded in 16,284 tiles. The extent of each tile can be found in the file of “Grids.shp”. Each file is named according to the grid’s Id number. For example, “000015.tif” corresponds to the cropland mapping result for the 15-th 1°×1° grid. This systematic naming convention ensures easy identification and retrieval of the specific grid data.</p> <p><strong><em><u>2. </u></em></strong><strong><em><u>1°×1° </u></em></strong><strong><em><u>Grids</u></em></strong><strong>:</strong> This file contains all 16,284 1°×1° grids used in the dataset. The vector file includes 18 attribute fields, providing comprehensive metadata for each grid. These attributes are essential for users who need detailed information about each grid’s characteristics.</p> <p>****<strong>Data format</strong>: ESRI shapefile</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Attribute Fields</strong>:</p> <p><strong>Id:</strong> The grid’s ID number.</p> <p><strong>area:</strong> The area of the grid.</p> <p><strong>mode:</strong> Indicates the representative sample grid.</p> <p><strong>climate:</strong> The climate type the grid belongs to.</p> <p><strong>dem: </strong>Average DEM value of the grid.</p> <p><strong>ndvi_s1 to ndvi_s4:</strong> Average NDVI values for four seasons within the grid.</p> <p><strong>esa, esri, fcs30, fromglc, glad, globeland30:</strong> Proportion of cropland pixels of different publicly available cropland products.</p> <p><strong>inconsistent:</strong> Proportion of inconsistent pixels within the grid according to different public cropland products.</p> <p><strong>hcropland30:</strong> Proportion of cropland pixels of our Hcropland30 dataset.</p> <p><strong><em><u>3. Samples</u></em></strong>: The selected representative pixel-level samples, including 32,343 cropland and 67657 non-cropland samples. The category information of each sample was determined based on visual interpretation on Google Earth image and three-year NDVI time series curves from 2019-2021.</p> <p>****<strong>Data format</strong>: ESRI shapefile</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Attribute Fields</strong>:</p> <p><strong>type:</strong> 1 denotes cropland sample and 0 denotes non-cropland sample.</p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite the following paper:</p> <p>Hu, Q., Cai, Z., You, L., Fritz, S., Zhang, X., Yin, H., Wei, H., Yang, J., Li, Z., Yu, Q., Wu, H., Xu, B., Wu, W. (2024). Hcropland30: A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model, Remote Sensing of Environment, submitted.</p> <p><strong>License</strong></p> <p>The data is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).</p> <p><strong>Disclaimer</strong></p> <p>This dataset is provided as-is, without any warranty, express or implied. The dataset author is not</p> <p>responsible for any errors or omissions in the data, or for any consequences arising from the use</p> <p>of the data.</p> <p><strong>Contact</strong></p> <p>If you have any questions or feedback regarding the dataset, please contact the dataset author</p> <p>Qiong Hu (huqiong@ccnu.edu.cn)</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.