来自杂志 Scientific data 的文献。
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1.
颜林林
(2024-08-18 05:49):
#paper doi:10.1038/s41597-024-03701-6, Scientific data, 2024, ChineseMPD: A Semantic Segmentation Dataset of Chinese Martial Arts Classic Movie Props. 只做数据清洗和整理,提供公开的数据集,也是可以发表文章的,Scientific Data杂志上就大量收录此类文章。这篇文章分享的数据很有意思,是来自大批量的中国武侠电影,通过语义分割算法,从中识别出枪、剑、棍、刀、钩、箭等武侠道具,动用了包括11名本科生在内的21人,历时半年,进行人工标注和审核,填补了现有语义分割数据集在动作电影道具方面的研究空白。数据集以CC BY 4.0许可发布,可供非商业用途的重新分发、修改、调整和构建作品,下载地址:https://www.scidb.cn/en/anonymous/SlpaelFy
IF:5.800Q1
Scientific data,
2024-Aug-14.
DOI: 10.1038/s41597-024-03701-6
PMID: 39143093
PMCID:PMC11325024
Abstract:
Recent advances in computer vision and deep learning techniques have facilitated significant progress in video scene understanding, thus helping film and television practitioners achieve accurate video editing. However, so far, …
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Recent advances in computer vision and deep learning techniques have facilitated significant progress in video scene understanding, thus helping film and television practitioners achieve accurate video editing. However, so far, publicly available semantic segmentation datasets are mostly limited to indoor scenes, city streets, and natural images, often ignoring example objects in action movies, which is a research gap that needs to be urgently filled. In this paper, we introduce a large-scale, high-precision semantic segmentation dataset of props in Chinese martial arts movie clips, named ChineseMPD. Specifically, this dataset first establishes segmentation rules and general review criteria for audiovisual data, and then provides semantic segmentation annotations for six weapon props (Gun, Sword, Stick, Knife, Hook, and Arrow) with a summary of 32,992 objects.To the best of our knowledge, this dataset is the largest semantic segmentation dataset for movie props to date. ChineseMPD dataset not only significantly expands the application of traditional tasks of computer vision such as object detection and scene understanding, but also opens up new avenues for interdisciplinary research.
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2.
小年
(2023-09-01 00:00):
#paper https://doi.org/10.1038/sdata.2018.157 A tissue-based draft map of the murine MHC class I immunopeptidome 文章介绍了一个关于小鼠MHC I类免疫肽组的组织图谱,其中包含了19种正常组织中呈现给CD8+ T细胞的肽段的全面概述。方法总结如下:
1. 得到质谱下机数据。
2. 使用Msconvert将原始质谱数据转换为mzML格式。
3. 使用Comet、MS-GF+和XTandem等数据库搜索引擎,将mzML格式的数据与小鼠蛋白质数据库进行比对,以鉴定肽段。
4. 使用Prophets进行统计验证,以确定鉴定的肽段的置信度。
5. 使用GibbsCluster v.1对鉴定的肽段进行聚类分析,以确定具有相似质谱特征的肽段。
6. 使用NetMHC v.4对鉴定的肽段进行注释分析,以确定其长度和预测MHC结合亲和力。
7. 根据统计验证和注释分析的结果,筛选出高置信度的MHC相关肽段。
8. 使用高置信度的MHC相关肽段,构建高质量的H2D b/K b特异性肽段光谱和测定库。
9. 将构建的肽段光谱和测定库共享到SysteMHC Atlas和SWATH Atlas中,以便其他研究人员可以使用和分析这些数据。
10. 最终的结果呈现,包括肽段光谱和测定库的构建、MHC相关肽段的注释和筛选等。
Abstract:
The large array of peptides presented to CD8+ T cells by major histocompatibility complex (MHC) class I molecules is referred to as the MHC class I immunopeptidome. Although the MHC …
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The large array of peptides presented to CD8+ T cells by major histocompatibility complex (MHC) class I molecules is referred to as the MHC class I immunopeptidome. Although the MHC class I immunopeptidome is ubiquitous in mammals and represents a critical component of the immune system, very little is known, in any species, about its composition across most tissues and organs in vivo. We applied mass spectrometry (MS) technologies to draft the first tissue-based atlas of the murine MHC class I immunopeptidome in health. Peptides were extracted from 19 normal tissues from C57BL/6 mice and prepared for MS injections, resulting in a total number of 28,448 high-confidence H2D/K-associated peptides identified and annotated in the atlas. This atlas provides initial qualitative data to explore the tissue-specificity of the immunopeptidome and serves as a guide to identify potential tumor-associated antigens from various cancer models. Our data were shared via PRIDE (PXD008733), SysteMHC Atlas (SYSMHC00018) and SWATH Atlas. We anticipate that this unique dataset will be expanded in the future and will find wide applications in basic and translational immunology.
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3.
他者
(2023-08-20 15:47):
#paper doi:10.1038/s41597-023-02449-9 Nature Scientific Data,2023,Release of cognitive and multimodal MRI data including real-world tasks and hippocampal subfield segmentations 这篇研究介绍了一个包含217名健康成年人(平均年龄 29 岁,范围 20-41;109 名女性,108 名男性)的数据集,数据集包含3T的MRI数据(包括多参数图检查组织微结构、扩散加权MRI、T2加权高分辨部分容积结构MRI扫描、全脑静息态功能MRI扫描和部分容积高分辨静息态功能MRI扫描)和大量认知评估问卷和行为实验。该数据集对认知和临床心理学的研究者,尤其是关注海马脑区的研究者有用,其中的认知测试和问卷也具有一定的参考意义。所有数据均可在 Dryad 上免费获取。
Abstract:
We share data from N = 217 healthy adults (mean age 29 years, range 20-41; 109 females, 108 males) who underwent extensive cognitive assessment and neuroimaging to examine the neural …
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We share data from N = 217 healthy adults (mean age 29 years, range 20-41; 109 females, 108 males) who underwent extensive cognitive assessment and neuroimaging to examine the neural basis of individual differences, with a particular focus on a brain structure called the hippocampus. Cognitive data were collected using a wide array of questionnaires, naturalistic tests that examined imagination, autobiographical memory recall and spatial navigation, traditional laboratory-based tests such as recalling word pairs, and comprehensive characterisation of the strategies used to perform the cognitive tests. 3 Tesla MRI data were also acquired and include multi-parameter mapping to examine tissue microstructure, diffusion-weighted MRI, T2-weighted high-resolution partial volume structural MRI scans (with the masks of hippocampal subfields manually segmented from these scans), whole brain resting state functional MRI scans and partial volume high resolution resting state functional MRI scans. This rich dataset will be of value to cognitive and clinical neuroscientists researching individual differences, real-world cognition, brain-behaviour associations, hippocampal subfields and more. All data are freely available on Dryad.
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4.
颜林林
(2022-06-30 00:17):
#paper doi:10.1038/s41597-022-01450-y Scientific Data, 2022, HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening. 《Nature》子刊《Scientific Data》确实是宝藏。这篇来自匈牙利的论文,就分享了一组很有用的数据。取材了200张H&E染色的结直肠癌的肿瘤组织切片,使用40倍高分辨率扫描全片,然后由病理医生进行标注,从中切分出多个不同类别的图像块,可用于后续结直肠癌的各类病理图像分析研究。值得夸赞的是,从样本采集到数据处理,整个过程有详细描述,数据处理代码、带标注的原始图像、处理后的带分类信息的图像块,全部都开放供直接下载使用。
代码地址:
https://github.com/qbeer/qupath-binarymask-extension
https://github.com/patbaa/crc_data_paper
原始图像数据:
https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=91357370
处理后数据:
https://figshare.com/articles/dataset/patches_and_local_annotations_slide_200_zoom_124x124_um2/19500266
Abstract:
Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam's decision making. Highly-trained pathologists are needed for careful microscopic analysis of …
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Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam's decision making. Highly-trained pathologists are needed for careful microscopic analysis of the slides produced from tissue taken from biopsy. This is a time-consuming process. A reliable decision support system would assist healthcare systems that often suffer from a shortage of pathologists. Recent advances in digital pathology allow for high-resolution digitalization of pathological slides. Digital slide scanners combined with modern computer vision models, such as convolutional neural networks, can help pathologists in their everyday work, resulting in shortened diagnosis times. In this study, 200 digital whole-slide images are published which were collected via hematoxylin-eosin stained colorectal biopsy. Alongside the whole-slide images, detailed region level annotations are also provided for ten relevant pathological classes. The 200 digital slides, after pre-processing, resulted in 101,389 patches. A single patch is a 512 × 512 pixel image, covering 248 × 248 μm tissue area. Versions at higher resolution are available as well. Hopefully, HunCRC, this widely accessible dataset will aid future colorectal cancer computer-aided diagnosis and research.
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