DATA METROLOGY FOR LIFE SCIENCES, MEDICINE AND PHARMACEUTICAL MANUFACTURING

Proceedings of the First International IMEKO TC6 Conference on Metrology and Digital Transformation - M4Dconf2022 Pub Date : 1900-01-01 DOI:10.21014/tc6-2022.025

P. M. Duncan, N. Smith, M. Romanchikova

{"title":"DATA METROLOGY FOR LIFE SCIENCES, MEDICINE AND PHARMACEUTICAL MANUFACTURING","authors":"P. M. Duncan, N. Smith, M. Romanchikova","doi":"10.21014/tc6-2022.025","DOIUrl":null,"url":null,"abstract":": In many disciplines, such as physics and engineering, the application of tools to support data metrology is encouraged and embedded in many processes and applications while in the life sciences, medicine and pharmaceutical manufacturing sectors these tools are often added as an afterthought, if considered at all. The use of data-driven decision making and the advent of machine learning in these industries has created an urgent demand for harmonised high-quality, instantly available, datasets across domains. The Findable, Accessible, Interoperable, Reproducible principles are designed to improve overall quality of research data. However, this alone does not guarantee that data is fit-for-purpose. Issues such as missing data and metadata, insufficient knowledge of measurement conditions or data provenance are well known and can be aided by applying metrological concepts to data preparation to increase confidence. This work presents the data metrology projects conducted by the National Physical Laboratory Data Science team in healthcare applications.","PeriodicalId":441252,"journal":{"name":"Proceedings of the First International IMEKO TC6 Conference on Metrology and Digital Transformation - M4Dconf2022","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the First International IMEKO TC6 Conference on Metrology and Digital Transformation - M4Dconf2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21014/tc6-2022.025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

: In many disciplines, such as physics and engineering, the application of tools to support data metrology is encouraged and embedded in many processes and applications while in the life sciences, medicine and pharmaceutical manufacturing sectors these tools are often added as an afterthought, if considered at all. The use of data-driven decision making and the advent of machine learning in these industries has created an urgent demand for harmonised high-quality, instantly available, datasets across domains. The Findable, Accessible, Interoperable, Reproducible principles are designed to improve overall quality of research data. However, this alone does not guarantee that data is fit-for-purpose. Issues such as missing data and metadata, insufficient knowledge of measurement conditions or data provenance are well known and can be aided by applying metrological concepts to data preparation to increase confidence. This work presents the data metrology projects conducted by the National Physical Laboratory Data Science team in healthcare applications.

查看原文本刊更多论文

用于生命科学、医学和制药制造的数据计量学

在许多学科中，如物理和工程，鼓励应用工具来支持数据计量，并将其嵌入到许多过程和应用中，而在生命科学、医学和制药制造领域，如果考虑的话，这些工具通常是在事后添加的。在这些行业中，数据驱动决策的使用和机器学习的出现产生了对跨领域协调的高质量、即时可用的数据集的迫切需求。可查找、可访问、可互操作、可重复原则旨在提高研究数据的整体质量。然而，仅凭这一点并不能保证数据符合目的。诸如缺少数据和元数据、对测量条件或数据来源的知识不足等问题是众所周知的，可以通过将计量概念应用于数据准备来增加信心。这项工作介绍了由国家物理实验室数据科学团队在医疗保健应用中进行的数据计量项目。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Proceedings of the First International IMEKO TC6 Conference on Metrology and Digital Transformation - M4Dconf2022

自引率

0.00%

发文量