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57
datasets available to search
ShareScore release 0.9.0
Dataset results
57 results for “novel hybrid”
Data from: Recent non-hybrid origin of sunflower ecotypes in a novel habitat
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Data from: Hybridization between genetically modified Atlantic salmon and wild brown trout reveals novel ecological interactions
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Novel hybrid finds a peri-urban niche: Allen’s Hummingbirds in southern California
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Supplementary material 2 from: Abdullah AH, Alarareh AK, Al-Sha'er MA, Habashneh AY, Awwadi FF, Bardaweel SK (2024) Docking, synthesis, and anticancer assessment of novel quinoline-amidrazone hybrids. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e117192
Scanned IR, 1H NMR and 13C NMR spectra of all new compounds
Supplementary material 1 from: Abdullah AH, Alarareh AK, Al-Sha'er MA, Habashneh AY, Awwadi FF, Bardaweel SK (2024) Docking, synthesis, and anticancer assessment of novel quinoline-amidrazone hybrids. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e117192
Docking, synthesis, and anticancer assessment of novel quinoline-amidrazone hybrids
Figure 7 from: Abdullah AH, Alarareh AK, Al-Sha'er MA, Habashneh AY, Awwadi FF, Bardaweel SK (2024) Docking, synthesis, and anticancer assessment of novel quinoline-amidrazone hybrids. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e117192
Figure 7 A. Diagram of receptor-ligand interaction between compound (10d) and c-Abl kinase (PDB code: 1IEP); B. 3D diagram of receptor-ligand interaction between compound (10d) c-Abl kinase enzyme (PDB code: 1IEP).
Figure 4 from: Abdullah AH, Alarareh AK, Al-Sha'er MA, Habashneh AY, Awwadi FF, Bardaweel SK (2024) Docking, synthesis, and anticancer assessment of novel quinoline-amidrazone hybrids. Pharmacia 71: 1-12. https://doi.org/10.3897/pharmacia.71.e117192
Figure 4 A. The co-crystallized pyrimidine ligand (1IEP); B. The co-crystallized pose and the docked pose of the co-crystallized ligand with RMSD = 2.10 Å; C. The binding site of the c-Abl-kinase protein (PDB code: 1IEP, resolution: 2.10 Å). Blue is for the co-crystal compounds, and red is for the highest Libdock score compound (Libdock score = 169.76).
Supplementary material 1 from: Al Tall Y, Al-Nassar B, Abualhaijaa A, Sabi SH, Almaaytah A (2023) The design and functional characterization of a novel hybrid antimicrobial peptide from Esculentin-1a and melittin. Pharmacia 70(1): 161-170. https://doi.org/10.3897/pharmacia.70.e97116
RP-HPLC analysis Chromatogram of BKR1 peptide
Fall Risk Assessment Using Hybrid Machine Learning and Deep Learning Approaches and a Novel Posturography
ClinicalTrials.gov study NCT05308563. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Integrated genome and transcriptome sequencing identifies a novel case of hybrid and aggressive prostate cancer
GEO Series GSE34649. Homo sapiens. 5 samples. Type: Genome variation profiling by genome tiling array.
A novel hybrid two-component system Lvr mediates virulence and global regulation in pathogenic Leptospira
GEO Series GSE79107. Leptospira interrogans serovar Manilae. 6 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome profile analysis of young floral buds of fertile and sterile plants from the self-pollinated offspring of the hybrid between novel restorer line NR1 and Nsa CMS line in Brassica napus
GEO Series GSE42513. Brassica napus. 2 samples. Type: Expression profiling by high throughput sequencing.
Seeking novel mammalian genes using transcript profiling and genotyping of a human radiation hybrid panel
GEO Series GSE19003. Homo sapiens; Cricetulus griseus. 269 samples. Type: Expression profiling by array; Genome variation profiling by array.
Subtractive Hybridization Reveals Novel Genes Associated with D. melanogaster Early Embryogenesis.
GEO Series GSE15000. Drosophila melanogaster. 5 samples. Type: Expression profiling by array.
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 1)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 2)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 3)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (Kinect Part 4)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 2)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</p>
MuSHRoom: Multi-Sensor Hybrid Room Dataset for Joint 3D Reconstruction and Novel View Synthesis (iPhone Part 1)
<p>Metaverse technologies demand accurate, real-time, and immersive modeling on consumer-grade hardware for both non-human perception (e.g., drone/robot/autonomous car navigation) and immersive technologies like AR/VR, requiring both structural accuracy and photorealism. However, there exists a knowledge gap in how to apply geometric reconstruction and photorealism modeling (novel view synthesis) in a unified framework.</p><p>To address this gap and promote the development of robust and immersive modeling and rendering with consumer-grade devices, first, we propose a real-world Multi-Sensor Hybrid Room Dataset (MuSHRoom). Our dataset presents exciting challenges and requires state-of-the-art methods to be cost-effective, robust to noisy data and devices, and can jointly learn 3D reconstruction and novel view synthesis, instead of treating them as separate tasks, making them ideal for real-world applications. Second, we benchmark several famous pipelines on our dataset for joint 3D mesh reconstruction and novel view synthesis. Finally, in order to further improve the overall performance, we propose a new method that achieves a good trade-off between the two tasks. Our dataset and benchmark show great potential in promoting the improvements for fusing 3D reconstruction and high-quality rendering in a robust and computationally efficient end-to-end fashion.</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.