`scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It supports labelled n-dimensional (multidimensional) data, which is used in many scientific fields and in machine learning. At present, `scores` primarily supports the geoscience communities; in particular, the meteorological, climatological and oceanographic communities. `scores` not only includes common scores (e.g., Mean Absolute Error), it also includes novel scores not commonly found elsewhere (e.g., FIxed Risk Multicategorical (FIRM) score, Flip-Flop Index), complex scores (e.g., threshold-weighted continuous ranked probability score), and statistical tests (such as the Diebold Mariano test). It also contains isotonic regression which is becoming an increasingly important tool in forecast verification and can be used to generate stable reliability diagrams. Additionally, it provides pre-processing tools for preparing data for scores in a variety of formats including cumulative distribution functions (CDF). At the time of writing, `scores` includes over 50 metrics, statistical techniques and data processing tools. All of the scores and statistical techniques in this package have undergone a thorough scientific and software review. Every score has a companion Jupyter Notebook tutorial that demonstrates its use in practice. `scores` supports `xarray` datatypes, allowing it to work with Earth system data in a range of formats including NetCDF4, HDF5, Zarr and GRIB among others. `scores` uses Dask for scaling and performance. Support for `pandas` is being introduced. The `scores` software repository can be found at https://github.com/nci/scores/

翻译：`scores` 是一个包含用于预报、预测或模型验证、评估与优化的数学函数的Python软件包。它支持带标签的n维（多维）数据，此类数据广泛应用于众多科学领域及机器学习中。目前，`scores` 主要服务于地球科学领域，特别是气象、气候和海洋学界。`scores` 不仅包含常用评分指标（如平均绝对误差），还涵盖其他工具中不常见的创新性指标（如固定风险多分类评分、翻转指数）、复杂评分方法（如阈值加权连续分级概率评分）以及统计检验（如迪博尔德-马里亚诺检验）。该工具包还包含等渗回归方法，该方法正日益成为预报验证的重要工具，可用于生成稳定的可靠性图表。此外，它提供了多种数据预处理工具，能够将数据准备为适用于评分计算的格式，包括累积分布函数格式。截至本文撰写时，`scores` 已集成超过50种度量指标、统计技术和数据处理工具。本软件包中所有评分方法和统计技术均经过严格的科学与软件评审。每个评分指标都配有配套的Jupyter Notebook教程，演示其实际应用方法。`scores` 支持 `xarray` 数据类型，可处理包括NetCDF4、HDF5、Zarr和GRIB在内的多种地球系统数据格式。该工具包采用Dask实现计算扩展与性能优化，目前正在引入对 `pandas` 的支持。`scores` 软件代码库位于 https://github.com/nci/scores/