951 to 960 of 76,898 Results
Apr 29, 2026 -
Final Data for: Controlling an altermagnetic spin density wave in the kagome magnet CsCr3Sb5
Unknown - 852 B -
MD5: 7b60de7d00556c8844dc1e37e0c22918
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Apr 29, 2026 -
Final Data for: Controlling an altermagnetic spin density wave in the kagome magnet CsCr3Sb5
Plain Text - 586.0 KB -
MD5: 85f4705fafba7b8b64ab39c794c642bc
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Apr 29, 2026 -
Final Data for: Controlling an altermagnetic spin density wave in the kagome magnet CsCr3Sb5
application/vnd.sun.xml.math - 8.0 MB -
MD5: 6ee33ed4b09a81fb9fb0791db8762e6a
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Apr 29, 2026 -
Final Data for: Controlling an altermagnetic spin density wave in the kagome magnet CsCr3Sb5
Unknown - 452 B -
MD5: 682176ff8037cbed951d115822801577
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Apr 29, 2026 -
Final Data for: Controlling an altermagnetic spin density wave in the kagome magnet CsCr3Sb5
Fixed Field Text Data - 25.1 KB -
MD5: 11242c3042252fd2f5432f90e5640a20
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Apr 29, 2026 - Mengda REN
Lo, Chih Hung; Ren, Mengda; Loi, Gavin Wen Zhao; Saipuljumri, Eka Norfaishanty; Indajang, Jonathan; Lim, Kah Leong; Zeng, Jialiu, 2026, "Replication Data for: Lysosome-Acidifying Nanoparticles Rescue A30P α-Synuclein Induced Neuronal Death in Cellular and Drosophila Models of Parkinson's Disease", https://doi.org/10.21979/N9/SBCIPP, DR-NTU (Data), V1, UNF:6:LEO4c+Jaez4Egr2Ht866VQ== [fileUNF]
Source data for Lo, C. H., et al. (2026). "Lysosome-Acidifying Nanoparticles Rescue A30P α-Synuclein Induced Neuronal Death in Cellular and Drosophila Models of Parkinson's Disease." Advanced Healthcare Materials n/a(n/a): e02906. https://doi.org/10.1002/adhm.202502906 |
Comma Separated Values - 2.2 KB -
MD5: 06ce46b5046e4883a6a3001c25f83e06
Fly DAM2 locomotor data. |
Tabular Data - 727 B - 3 Variables, 67 Observations - UNF:6:LEO4c+Jaez4Egr2Ht866VQ==
PAM TH neuron count data. |
Apr 28, 2026 - S-Lab for Advanced Intelligence
Xu, Yuanmu; Hou, Guanli; Hu, Jiangbei; Ren, Tenglong; Wang, Xiaokun; Zhang, Yalan; Ban, Xiaojuan; Qian, Chen; Hou, Fei; He, Ying, 2025, "PGA-NeuS: Physics and Geometry-Augmented Neural Implicit Surfaces for Rigid Bodies", https://doi.org/10.21979/N9/LTXKFL, DR-NTU (Data), V2
This paper tackles the challenges of physics-based simulation of rigid bodies in neural rendering, focusing on 3D model representation and collision handling. A synthetic and real-world dataset is also included in the paper. |
Compressed Archive - 462.9 MB -
MD5: bf5921041d638ae5ea5bbc8d236344b6
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