In modern data-driven science, reproducibility and reusability are key
challenges. Scientists are well skilled in the process from data to
publication. Although some publication channels require source code and data to
be made accessible, rerunning and verifying experiments is usually hard due to
a lack of standards. Therefore, reusing existing scientific data processing
code from state-of-the-art research is hard as well. This is why we introduce
CLAIMED, which has a proven track record in scientific research for addressing
the repeatability and reusability issues in modern data-driven science. CLAIMED
is a framework to build reusable operators and scalable scientific workflows by
supporting the scientist to draw from previous work by re-composing workflows
from existing libraries of coarse-grained scientific operators. Although
various implementations exist, CLAIMED is programming language, scientific
library, and execution environment agnostic.

科学家们往往在进行数据处理之后将其发表，然而，由于缺乏标准，重新运行和验证实验通常很困难。因此，提出了 CLAIMED 框架，它支持科学家从先前的工作中汲取经验，通过重新组合来自现有库的大粒度科学操作构建可重用的操作和可伸缩的科学工作流。

CLAIMED—— 用于快速科学研究加速发现的开源粗粒度算子构建框架

CLAIMED -- the open source framework for building coarse-grained  operators for accelerated discovery in science

Data-driven science is an emerging paradigm where scientific discoveries
depend on the execution of computational AI models against rich,
discipline-specific datasets. With modern machine learning frameworks, anyone
can develop and execute computational models that reveal concepts hidden in the
data that could enable scientific applications. For important and widely used
datasets, computing the performance of every computational model that can run
against a dataset is cost prohibitive in terms of cloud resources. Benchmarking
approaches used in practice use representative datasets to infer performance
without actually executing models. While practicable, these approaches limit
extensive dataset profiling to a few datasets and introduce bias that favors
models suited for representative datasets. As a result, each dataset's unique
characteristics are left unexplored and subpar models are selected based on
inference from generalized datasets. This necessitates a new paradigm that
introduces dataset profiling into the model selection process. To demonstrate
the need for dataset-specific profiling, we answer two questions:(1) Can
scientific datasets significantly permute the rank order of computational
models compared to widely used representative datasets? (2) If so, could
lightweight model execution improve benchmarking accuracy? Taken together, the
answers to these questions lay the foundation for a new dataset-aware
benchmarking paradigm.

数据驱动科学是一种新兴的范例，其中科学发现取决于针对具体学科的丰富数据集执行计算 AI 模型。通过现代机器学习框架，任何人都可以开发和执行计算模型，揭示隐藏在数据中的概念，从而可能支持科学应用。然而，在实际应用中，收集并计算每个可以运行的计算模型的性能代价昂贵。由于使用代表性数据集来推断性能的基准测试方法具有局限性，每个数据集都具有独特的特征，这需要引入数据集配置文件来作为模型选择过程的一部分，以选择最佳模型进行优化。