five

AmelHap pilot: filter1 data

收藏
Mendeley Data2024-05-10 更新2024-06-27 收录
下载链接:
https://zenodo.org/records/6563399
下载链接
链接失效反馈
资源简介:
Honey bee Apis mellifera drones are typically haploid, developing from an unfertilized egg, inheriting only their queen’s alleles and none from the many drones she mated with. Being haploid, the ordered combination or ‘phase’ of alleles is known, making drones a valuable haplotype resource. We collated whole genome sequence data for 688 drones, including 45 newly sequenced Scottish drones, which collectively represent 13 countries, 7 subspecies and various hybrids strains. After alignment to the reference assembly Amel_Hav3.1, and haploid variant calling, we identified 18.9M variants. Whole-genome sequencing data underpinning the dataset is available from the European Nucleotide Archive (ENA), https://www.ebi.ac.uk/ena, with the project accession codes: PRJEB16533, PRJNA311274, PRJNA363032, PRJNA516678, PRJNA544324, and PRJEB39369. Sequencing reads were aligned to the Amel_HAv3.1 reference genome using BWA-MEM v0.7.17. Reads were sorted with SAMtools v1.9 and duplicates marked (MarkDuplicates) with GATK v4.0.11.0. Variants for each sample were called using GATK’s HaplotypeCaller with the following non-default parameters --ERC GVCF, --sample-ploidy 1 and -A AlleleFraction. Joint variant calling was performed across all samples using GATK’s GenomicDBImport and GenotypeGVCFs with --sample-ploidy 1 and a window size of 2.5 Mb. This dataset is the result of applying filters to exclude variants with 'QD<20 || QD>40 || MQ < 50 || SOR >3' in the raw dataset, leaving 16.6M variants. The code used in filtering is outlined here: https://bitbucket.org/scriptBee/hapmap-pilot.
创建时间:
2023-06-28
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4099个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

学生课堂行为数据集 (SCB-dataset3)

学生课堂行为数据集(SCB-dataset3)由成都东软学院创建,包含5686张图像和45578个标签,重点关注六种行为:举手、阅读、写作、使用手机、低头和趴桌。数据集覆盖从幼儿园到大学的不同场景,通过YOLOv5、YOLOv7和YOLOv8算法评估,平均精度达到80.3%。该数据集旨在为学生行为检测研究提供坚实基础,解决教育领域中学生行为数据集的缺乏问题。

arXiv 收录

URPC系列数据集, S-URPC2019, UDD

URPC系列数据集包括URPC2017至URPC2020DL,主要用于水下目标的检测和分类。S-URPC2019专注于水下环境的特定检测任务。UDD数据集信息未在README中详细描述。

github 收录

UAVDT Dataset

The authors constructed a new UAVDT Dataset focused on complex scenarios with new level challenges. Selected from 10 hours raw videos, about 80, 000 representative frames are fully annotated with bounding boxes as well as up to 14 kinds of attributes (e.g., weather condition, flying altitude, camera view, vehicle category, and occlusion) for three fundamental computer vision tasks: object detection, single object tracking, and multiple object tracking.

datasetninja.com 收录

era5

ERA5数据集是基于Hersbach等人的研究,包含26个气候变量,数据采样间隔为每6小时一次,覆盖了整个月份的每天,适用于气候研究。

huggingface 收录

ScanNet v2

ScanNet 是一个 RGB-D 视频数据集,包含 1500 多次扫描中的 250 万个视图,并使用 3D 相机姿势、表面重建和实例级语义分割进行注释。为了收集这些数据,我们设计了一个易于使用且可扩展的 RGB-D 捕获系统,其中包括自动表面重建和众包语义注释。我们表明,使用这些数据有助于在几个 3D 场景理解任务上实现最先进的性能,包括 3D 对象分类、语义体素标记和 CAD 模型检索。

OpenDataLab 收录