NeurIPS Datasets and Benchmarks Track Datasets

NeurIPS Datasets and Benchmarks Track Datasets是由多伦多大学开发的数据集，旨在评估和改进机器学习领域的数据集开发实践。该数据集包含60个数据集，涵盖了从2021年到2023年发布的多个机器学习应用。数据集的创建过程遵循数据管理原则，强调文档化、透明度和伦理考虑。该数据集主要应用于机器学习模型的评估和改进，旨在提高数据集的可重用性和可重复性，促进标准化和负责任的研究。

The NeurIPS Datasets and Benchmarks Track Datasets, developed by the University of Toronto, is a dataset collection designed to evaluate and improve dataset development practices in the field of machine learning. It comprises 60 datasets spanning multiple machine learning applications published between 2021 and 2023. The development process follows core data management principles, with strong emphasis on documentation, transparency, and ethical considerations. Primarily intended for the evaluation and enhancement of machine learning models, this collection aims to boost dataset reusability and reproducibility, and promote standardized and responsible research practices.

搜集汇总

数据集介绍

构建方式

NeurIPS Datasets and Benchmarks Track Datasets 数据集的构建方式是通过对 NeurIPS 数据集和基准轨道中发布的 60 个数据集进行系统评估来实现的。研究团队开发了一个评估框架，该框架由一个评分标准和一个工具包组成，通过多阶段的迭代过程来确定适用于评估的评分标准元素及其相应的评估标准。该框架通过系统评估样本集中的数据集，分析当前机器学习数据集开发中的数据管理实践，并展示如何改进这些实践。

特点

该数据集的特点在于其专注于数据管理的严谨性和伦理考虑。通过引入数据管理原则，强调文档记录、透明度和伦理考虑，该数据集旨在提高数据集的质量、伦理性和人类监督。此外，数据集还展示了数据管理实践在机器学习领域的广泛应用，以及对数据管理实践的改进建议。

使用方法

使用该数据集的方法包括两个主要方面：首先，数据集创建者可以使用评分标准和相关资源，促进其在数据集创建过程中的批判性参与和反思。其次，现有数据集可以通过应用评分标准进行评估，以确定需要进一步文档记录的领域和可能引入偏差的区域。通过这些方法，数据集可以更有效地被发现、访问、伦理使用、一致评估和适当重用。

背景与挑战

背景概述

NeurIPS Datasets and Benchmarks Track Datasets, introduced in 2021 by the NeurIPS conference, aim to enhance the quality and ethicality of datasets in machine learning (ML) by addressing the challenges of datasets being used beyond their original scope. The track was established to encourage the development of new datasets, improve the quality of existing ones, and emphasize the critical role of data in ML. The initiative is led by researchers from the University of Toronto, including Eshta Bhardwaj, Harshit Gujral, Siyi Wu, Ciara Zogheib, Tegan Maharaj, and Christoph Becker. The core research question revolves around assessing and improving dataset development practices through a data curation lens, thereby influencing the quality and ethicality of datasets released in the ML community.

当前挑战

The primary challenge addressed by the NeurIPS Datasets and Benchmarks Track Datasets is the misuse of datasets outside their intended scope, which can lead to field-level overfitting and unanticipated ethical and privacy issues. The construction process faces significant hurdles in ensuring comprehensive documentation, including environmental footprint, ethical considerations, and data management. Additionally, the challenge of maintaining consistency and reliability in dataset evaluations, as well as the need for interdisciplinary collaboration to bridge the gap between data curation and ML practices, further complicates the development and assessment of these datasets.

常用场景

经典使用场景

NeurIPS Datasets and Benchmarks Track Datasets 数据集的经典使用场景主要集中在机器学习模型的训练和评估上。该数据集通过提供高质量、经过严格筛选的数据，帮助研究人员在各种机器学习任务中进行基准测试和模型性能评估。数据集的多样性和广泛性使其成为评估模型在不同领域和应用中的泛化能力的理想选择。

实际应用

在实际应用中，NeurIPS Datasets and Benchmarks Track Datasets 数据集被广泛用于开发和验证各种机器学习模型，包括但不限于自然语言处理、计算机视觉和推荐系统。企业、研究机构和教育机构利用这些数据集来提升其产品和服务的智能化水平，同时确保数据使用的伦理和环境可持续性。

衍生相关工作

基于该数据集，衍生出了许多相关的经典工作，包括数据集文档化框架的开发、数据集质量评估工具的创建以及数据集伦理和环境影响的研究。这些工作不仅推动了数据集开发的标准化，还为机器学习领域的伦理和环境责任提供了新的研究方向和实践指南。