cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc105msl
title: CCDC 928939: Experimental Crystal Structure Determination

description: Related Article: Chao-Wei Zhao, Jian-Ping Ma, Qi-Kui Liu, Yang Yu, Peng Wang, Yan-An Li, Kui Wang, Yu-Bin Dong|2013|Green Chemistry|15|3150|doi:10.1039/C3GC41154K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105kg6
title: CCDC 928867: Experimental Crystal Structure Determination

description: Related Article: Kerstin Freitag,Hung Banh,Chelladurai Ganesamoorthy,Christian Gemel,Rudiger W. Seidel,Roland A. Fischer|2013|Dalton Trans.|42|10540|doi:10.1039/C3DT51230D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105kf5
title: CCDC 928866: Experimental Crystal Structure Determination

description: Related Article: Kerstin Freitag,Hung Banh,Chelladurai Ganesamoorthy,Christian Gemel,Rudiger W. Seidel,Roland A. Fischer|2013|Dalton Trans.|42|10540|doi:10.1039/C3DT51230D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105ksj
title: CCDC 928877: Experimental Crystal Structure Determination

description: Related Article: Eleonora Conterosito,Gianluca Croce,Luca Palin,Cinzia Pagano,Luana Perioli,Davide Viterbo,Enrico Boccaleri,Geo Paul,Marco Milanesio|2013|Phys.Chem.Chem.Phys.(PCCP)|15|13418|doi:10.1039/C3CP51235E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105krh
title: CCDC 928876: Experimental Crystal Structure Determination

description: Related Article: Eleonora Conterosito,Gianluca Croce,Luca Palin,Cinzia Pagano,Luana Perioli,Davide Viterbo,Enrico Boccaleri,Geo Paul,Marco Milanesio|2013|Phys.Chem.Chem.Phys.(PCCP)|15|13418|doi:10.1039/C3CP51235E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc105kxn
title: CCDC 928881: Experimental Crystal Structure Determination

description: Related Article: Norio Sakai, Kota Enomoto, Mitsumasa Takayanagi, Takeo Konakahara, Yohei Ogiwara|2016|Tetrahedron Lett.|57|2175|doi:10.1016/j.tetlet.2016.04.007
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105h6w
title: CCDC 928797: Experimental Crystal Structure Determination

description: Related Article: Tomoki Yoneda, Atsuhiro Osuka|2013|Chem.-Eur.J.|19|7314|doi:10.1002/chem.201301030
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105h7x
title: CCDC 928798: Experimental Crystal Structure Determination

description: Related Article: Tomoki Yoneda, Atsuhiro Osuka|2013|Chem.-Eur.J.|19|7314|doi:10.1002/chem.201301030
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105h4t
title: CCDC 928795: Experimental Crystal Structure Determination

description: Related Article: Tomoki Yoneda, Atsuhiro Osuka|2013|Chem.-Eur.J.|19|7314|doi:10.1002/chem.201301030
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105h5v
title: CCDC 928796: Experimental Crystal Structure Determination

description: Related Article: Tomoki Yoneda, Atsuhiro Osuka|2013|Chem.-Eur.J.|19|7314|doi:10.1002/chem.201301030
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105hf3
title: CCDC 928804: Experimental Crystal Structure Determination

description: Related Article: Jing Lu, Qian Sun, Jun-Ling Li, Wen Gu, Jin-Lei Tian, Xin Liu,Shi-Ping Yan|2013|J.Coord.Chem.|66|3280|doi:10.1080/00958972.2013.832228
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105hg4
title: CCDC 928805: Experimental Crystal Structure Determination

description: Related Article: Jing Lu, Qian Sun, Jun-Ling Li, Wen Gu, Jin-Lei Tian, Xin Liu,Shi-Ping Yan|2013|J.Coord.Chem.|66|3280|doi:10.1080/00958972.2013.832228
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105l3w
title: CCDC 928887: Experimental Crystal Structure Determination

description: Related Article: Sébastien Bivaud, Sébastien Goeb, Jean-Yves Balandier, Marcos Chas, Magali Allain, Marc Sallé|2014|Eur.J.Inorg.Chem.|2014|2440|doi:10.1002/ejic.201400060
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105l4x
title: CCDC 928888: Experimental Crystal Structure Determination

description: Related Article: Sébastien Bivaud, Sébastien Goeb, Jean-Yves Balandier, Marcos Chas, Magali Allain, Marc Sallé|2014|Eur.J.Inorg.Chem.|2014|2440|doi:10.1002/ejic.201400060
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105l5y
title: CCDC 928889: Experimental Crystal Structure Determination

description: Related Article: Sébastien Bivaud, Sébastien Goeb, Jean-Yves Balandier, Marcos Chas, Magali Allain, Marc Sallé|2014|Eur.J.Inorg.Chem.|2014|2440|doi:10.1002/ejic.201400060
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105lj9
title: CCDC 928900: Experimental Crystal Structure Determination

description: Related Article: Nitin T. Patil, Ashok Konala, Sudha Sravanti, Ashita Singh, Ramesh Ummanni, Balasubramanian Sridhar|2013|Chem.Commun.|49|10109|doi:10.1039/C3CC45289A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc105hh5
title: CCDC 928806: Experimental Crystal Structure Determination

description: Related Article: Lina Chang, Tao Guo, Ziyi Wang, Shaozhong Wang, Zhu-Jun Yao|2017|J.Org.Chem.|82|1567|doi:10.1021/acs.joc.6b02760
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105lh8
title: CCDC 928899: Experimental Crystal Structure Determination

description: Related Article: Nitin T. Patil, Ashok Konala, Sudha Sravanti, Ashita Singh, Ramesh Ummanni, Balasubramanian Sridhar|2013|Chem.Commun.|49|10109|doi:10.1039/C3CC45289A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/cc105lnf
title: CCDC 928904: Experimental Crystal Structure Determination

description: Related Article: Fu Fu, Chunchao Zhao, Hongyan Zhang, xiaojun lv, Yong Chen|2014|J.Photochem.Photobiol.A:Chem.|282|41|doi:10.1016/j.jphotochem.2014.03.009
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21979/n9/xvtpw9
title: Robust-Wide: Robust Watermarking against Instruction-driven Image Editing

description: Instruction-driven image editing allows users to quickly edit an image according to text instructions in a forward pass. Nevertheless, malicious users can easily exploit this technique to create fake images, which could cause a crisis of trust and harm the rights of the original image owners. Watermarking is a common solution to trace such malicious behavior. Unfortunately, instruction-driven image editing can significantly change the watermarked image at the semantic level, making current state-of-the-art watermarking methods ineffective. To remedy it, we propose Robust-Wide, the first robust watermarking methodology against instruction-driven image editing. Specifically, we follow the classic structure of deep robust watermarking, consisting of the encoder, noise layer, and decoder. To achieve robustness against semantic distortions, we introduce a novel Partial Instruction-driven Denoising Sampling Guidance (PIDSG) module, which consists of a large variety of instruction injections and substantial modifications of images at different semantic levels. With PIDSG, the encoder tends to embed the watermark into more robust and semantic-aware areas, which remains in existence even after severe image editing. Experiments demonstrate that Robust-Wide can effectively extract the watermark from the edited image with a low bit error rate of nearly 2.6% for 64-bit watermark messages. Meanwhile, it only induces a neglectable influence on the visual quality and editability of the original images. Moreover, Robust-Wide holds general robustness against different sampling configurations and other popular image editing methods such as ControlNet-InstructPix2Pix, MagicBrush, Inpainting, and DDIM Inversion. Codes and models are available at https://github.com/hurunyi/Robust-Wide.
DOI: 10.21979/n9/phg7nv
title: Hybrid Mamba for Few-Shot Segmentation

description: Many few-shot segmentation (FSS) methods use cross attention to fuse support foreground (FG) into query features, regardless of the quadratic complexity. A recent advance Mamba can also well capture intra-sequence dependencies, yet the complexity is only linear. Hence, we aim to devise a cross (attention-like) Mamba to capture inter-sequence dependencies for FSS. A simple idea is to scan on support features to selectively compress them into the hidden state, which is then used as the initial hidden state to sequentially scan query features. Nevertheless, it suffers from (1) support forgetting issue: query features will also gradually be compressed when scanning on them, so the support features in hidden state keep reducing, and many query pixels cannot fuse sufficient support features; (2) intra-class gap issue: query FG is essentially more similar to itself rather than to support FG, i.e., query may prefer not to fuse support features but their own ones from the hidden state, yet the success of FSS relies on the effective use of support information. To tackle them, we design a hybrid Mamba network (HMNet), including (1) a support recapped Mamba to periodically recap the support features when scanning query, so the hidden state can always contain rich support information; (2) a query intercepted Mamba to forbid the mutual interactions among query pixels, and encourage them to fuse more support features from the hidden state. Consequently, the support information is better utilized, leading to better performance. Extensive experiments have been conducted on two public benchmarks, showing the superiority of HMNet.
DOI: 10.5281/zenodo.17309696
title: Data set for dissertation chapter 4
DOI: 10.5281/zenodo.17309697
title: Data set for dissertation chapter 4
DOI: 10.5281/zenodo.17309723
title: Data set for dissertation chapter 5
DOI: 10.5281/zenodo.17309722
title: Data set for dissertation chapter 5
DOI: 10.21979/n9/jmcw1w
title: FreeInit: Bridging Initialization Gap in Video Diffusion Models

description: Though diffusion-based video generation has witnessed rapid progress, the inference results of existing models still exhibit unsatisfactory temporal consistency and unnatural dynamics. In this paper, we delve deep into the noise initialization of video diffusion models, and discover an implicit training-inference gap that attributes to the unsatisfactory inference quality. Our key findings are: 1) the spatial-temporal frequency distribution of the initial noise at inference is intrinsically different from that for training, and 2) the denoising process is significantly influenced by the low-frequency components of the initial noise. Motivated by these observations, we propose a concise yet effective inference sampling strategy, FreeInit, which significantly improves temporal consistency of videos generated by diffusion models. Through iteratively refining the spatial-temporal low-frequency components of the initial latent during inference, FreeInit is able to compensate the initialization gap between training and inference, thus effectively improving the subject appearance and temporal consistency of generation results. Extensive experiments demonstrate that FreeInit consistently enhances the generation quality of various text-to-video diffusion models without additional training or fine-tuning.
DOI: 10.21979/n9/uz06zg
title: LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation

description: The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called LN3Diff to address this gap and enable fast, high-quality, and generic conditional 3D generation. Our approach harnesses a 3D-aware architecture and variational autoencoder (VAE) to encode the input image into a structured, compact, and 3D latent space. The latent is decoded by a transformer-based decoder into a high-capacity 3D neural field. Through training a diffusion model on this 3D-aware latent space, our method achieves state-of-the-art performance on ShapeNet for 3D generation and demonstrates superior performance in monocular 3D reconstruction and conditional 3D generation across various datasets. Moreover, it surpasses existing 3D diffusion methods in terms of inference speed, requiring no per-instance optimization. Our proposed LN3Diff presents a significant advancement in 3D generative modeling and holds promise for various applications in 3D vision and graphics tasks.
DOI: 10.6084/m9.figshare.30326470.v1
title: Three new ascomycetous fungi from freshwater habitats in China

description: During a survey of saprobic fungi in Jiangxi and Zhejiang Province, China, three fungal species were collected from decaying wood samples in freshwater habitats. We selected the internal transcribed spacers (ITS1-5.8S-ITS2), large subunit (28S), small subunit (18S), translation elongation factor-1 alpha (<i>tef1</i>), RNA polymerase Ⅰ subunit (<i>rpb1</i>) genes and RNA polymerase II subunit (<i>rpb2</i>) genes for phylogenetic analyses. Maximum likelihood and Bayesian analyses were performed to clarify the phylogenetic affinities of the species. These species are separated from the other species in respective genera based on dimensions in ascomata, asci, ascospores and DNA sequence data. Newly added species are compared with other similar species, and comprehensive descriptions and micrographs are provided.
DOI: 10.21979/n9/27jljb
title: LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation

description: 3D content creation has achieved significant progress in terms of both quality and speed. Although current feed-forward models can produce 3D objects in seconds, their resolution is constrained by the intensive computation required during training. In this paper, we introduce Large Multi-view Gaussian Model (LGM), a novel framework designed to generate high-resolution 3D models from text prompts or single-view images. Our key insights are two-fold: (1) 3D Representation: We propose multi-view Gaussian features as an efficient yet powerful representation, which can then be fused together for differentiable rendering. (2) 3D Backbone: We present an asymmetric U-Net as a high-throughput backbone operating on multi-view images, which can be produced from text or single-view image input by leveraging multi-view diffusion models. Extensive experiments demonstrate the high fidelity and efficiency of our approach. Notably, we maintain the fast speed to generate 3D objects within 5 seconds while boosting the training resolution to 512, thereby achieving high-resolution 3D content generation.
DOI: 10.6084/m9.figshare.30326530
title: Raw sequencing data and analysis files for the midgut transcriptome and microbiome of Bombyx mori exposed to manganese (Mn)

description: This dataset contains the raw sequencing data and key analysis files supporting the manuscript titled "Integrated analysis of midgut transcriptome and microbiome reveals manganese-induced dysbiosis driver metabolic disruption and developmental toxicity in <i>Bombyx mori</i>." The research investigates the toxicological effects of dietary manganese (Mn) on the economically important insect, <i>Bombyx mori</i> (Liangguang No. 2 strain). Fifth-instar larvae were fed an artificial diet supplemented with different concentrations of manganese sulfate (MnSO₄) for 48 hours, divided into three groups: a control group (MC, 0 g/kg Mn), a medium-dose group (MM, 15 g/kg Mn), and a high-dose group (MH, 30 g/kg Mn). The dataset is comprised of three main components. First, it includes the raw paired-end FASTQ (.fq) files from 16S rRNA gene (V4 region) amplicon sequencing of the midgut microbiome, containing 21 samples with 7 biological replicates per group (named MCS1-7, MMS1-7, and MHS1-7). Second, it contains the Wiggle format (.wig) files from the midgut tissue RNA-sequencing (RNA-seq) analysis, which represent the genome-wide read coverage depth for visualization purposes, consisting of 9 samples with 3 biological replicates per group (named MCT1-3, MMT1-3, and MHT1-3). Third, the dataset includes the reference genome annotation file (SilkDB 3.0) that was used for the alignment and analysis of the RNA-seq data. This collection of data serves as a valuable resource for studying the interactive responses of the host gut transcriptome and gut microbial community under heavy metal stress, contributing to a deeper understanding of the ecotoxicological mechanisms of manganese in <i>Bombyx mori</i>.