Comprehensive Multi-Domain Experiment Reproducibility Dataset (E1-E28)"

This dataset, titled "Multi-Domain Experiment Dataset for Evaluating Reproducibility Tools (E1-E28)," is curated to assess and benchmark the efficiency of various reproducibility frameworks. The dataset is designed for researchers and developers working on computational reproducibility, providing a comprehensive resource to test and compare the effectiveness of different tools in recreating scientific experiments. Content Overview: The dataset comprises 28 experiments (E1 to E28) sourced from diverse scientific domains, including computer science, medicine, artificial intelligence, and climate change. The experiments range from simple scripts to complex setups involving integrated databases and multiple programming languages. Experiments are collected from: Computer Science: Including the IEEE/ACM International Conference on Software Engineering (ICSE) 2022 and the International Conference on Very Large Databases (VLDB) 2021. Interdisciplinary Fields: Additional experiments from Zenodo, focusing on medicine, AI, and climate change.本数据集命名为《用于评估可复现性工具的多领域实验数据集（E1-E28）》，旨在对各类可复现性框架的效率开展评估与基准测试。本数据集面向从事计算可复现性研究的科研人员与开发者，提供一套全面的资源，用于测试并对比不同工具在复现科学实验方面的有效性。 内容概览： 本数据集包含28个实验（编号E1至E28），其来源覆盖计算机科学、医学、人工智能（Artificial Intelligence，以下简称AI）、气候变化等多个科学领域。这些实验涵盖了从简单脚本到集成数据库、多编程语言的复杂实验配置。 实验来源包括： 计算机科学领域：实验源自2022年IEEE/ACM国际软件工程会议（International Conference on Software Engineering，简称ICSE）与2021年超大型数据库国际会议（International Conference on Very Large Databases，简称VLDB）。 跨学科领域：额外实验取自Zenodo平台，涵盖医学、AI与气候变化方向。