DVT: A high-throughput paradigm for locomotion and social behavior analysis in adult Drosophila melanogaster

This dataset contains source data for fig generated in DVT manuscript. All involved DVT experiment results were also included and visulized by boxplot. Drosophila Video Tracking (DVT) was proposed as a high-throughput paradigm for quantifying spatiotemporal characteristics of adult locomotion and social behaviors. DVT includes standardized equipment and protocols to ensure experimental reproducibility and consistency. This enables us to output 74 unique behavior metrics, including two new social behaviors, acquaintance and average number of crowded flies. Key to utility of DVT is our new behavior analysis pipeline that eliminates the need to manually correct identify assignment errors in video footage, allowing more efficient analyses. Demonstrating the power of DVT, we successfully quantified differences in the behavior of wild-type flies exposed to distinct environmental factors and animals harboring gene variants.

本数据集包含某果蝇视频追踪（Drosophila Video Tracking，DVT）手稿中所用图表的源数据，同时收录全部相关DVT实验结果，并通过箱线图完成可视化展示。果蝇视频追踪（DVT）作为一种高通量范式，被提出用于量化成年果蝇运动与社会行为的时空特征。DVT包含标准化的实验设备与操作流程，以保障实验的可重复性与一致性。依托该系统，我们可输出74项独特的行为指标，其中包含两项新增社交行为指标：相识行为与拥挤果蝇平均数量。DVT实用价值的核心在于其全新的行为分析流程，该流程可免除手动校正视频素材中身份分配错误的步骤，从而实现更高效的行为分析。为验证DVT的应用效能，我们成功量化了暴露于不同环境因素下的野生型果蝇与携带基因变异的果蝇的行为差异。

搜集汇总

背景与挑战

背景概述

该数据集基于DVT（果蝇视频跟踪）方法，提供成年果蝇运动和社交行为分析的高通量源数据，包括74个独特行为指标和两个新社交行为指标。数据集采用标准化协议确保可重复性，并引入自动化分析流程以减少人工干预，有效支持环境因素和基因变异对果蝇行为影响的研究。