{"title":"广泛的数据集蠕动泵精度提高在制药环境。","authors":"Davide Privitera, Alessandro Mecocci, Sandro Bartolini","doi":"10.1038/s41597-025-05902-z","DOIUrl":null,"url":null,"abstract":"<p><p>We publish a comprehensive dataset of peristaltic pump dosing outputs in pharmaceutical manufacturing, where accuracy is crucial for drug quality and patient safety, consisting of 149,847 measurements spanning volumes from 0.1 to 2.0 ml. An industrial filling system was used to acquire data under controlled conditions, using calibrated weighing equipment. To the best of our knowledge, this is the first dataset documenting pump behavior across such a wide range of volumes. The dataset aims to constitute a solid tool that enables investigation from short-term precision to long-term stability standpoints, providing detailed insights into peristaltic pump behavior under various operating conditions. Additionally, the dataset incorporates compensation outcomes across multiple volumes, documenting both statistical and AI-based compensation strategies, thus exemplifying how the statistical behavior of the dosing can change in response to some compensation strategies aimed to improve dosing accuracy. This resource directly addresses pharmaceutical industry needs by supporting optimization of quality control systems and validation of novel compensation strategies.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1618"},"PeriodicalIF":6.9000,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500925/pdf/","citationCount":"0","resultStr":"{\"title\":\"Extensive Dataset for Peristaltic Pump Accuracy Enhancement in Pharmaceutical Environments.\",\"authors\":\"Davide Privitera, Alessandro Mecocci, Sandro Bartolini\",\"doi\":\"10.1038/s41597-025-05902-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We publish a comprehensive dataset of peristaltic pump dosing outputs in pharmaceutical manufacturing, where accuracy is crucial for drug quality and patient safety, consisting of 149,847 measurements spanning volumes from 0.1 to 2.0 ml. An industrial filling system was used to acquire data under controlled conditions, using calibrated weighing equipment. To the best of our knowledge, this is the first dataset documenting pump behavior across such a wide range of volumes. The dataset aims to constitute a solid tool that enables investigation from short-term precision to long-term stability standpoints, providing detailed insights into peristaltic pump behavior under various operating conditions. Additionally, the dataset incorporates compensation outcomes across multiple volumes, documenting both statistical and AI-based compensation strategies, thus exemplifying how the statistical behavior of the dosing can change in response to some compensation strategies aimed to improve dosing accuracy. This resource directly addresses pharmaceutical industry needs by supporting optimization of quality control systems and validation of novel compensation strategies.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"1618\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12500925/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-05902-z\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-05902-z","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Extensive Dataset for Peristaltic Pump Accuracy Enhancement in Pharmaceutical Environments.
We publish a comprehensive dataset of peristaltic pump dosing outputs in pharmaceutical manufacturing, where accuracy is crucial for drug quality and patient safety, consisting of 149,847 measurements spanning volumes from 0.1 to 2.0 ml. An industrial filling system was used to acquire data under controlled conditions, using calibrated weighing equipment. To the best of our knowledge, this is the first dataset documenting pump behavior across such a wide range of volumes. The dataset aims to constitute a solid tool that enables investigation from short-term precision to long-term stability standpoints, providing detailed insights into peristaltic pump behavior under various operating conditions. Additionally, the dataset incorporates compensation outcomes across multiple volumes, documenting both statistical and AI-based compensation strategies, thus exemplifying how the statistical behavior of the dosing can change in response to some compensation strategies aimed to improve dosing accuracy. This resource directly addresses pharmaceutical industry needs by supporting optimization of quality control systems and validation of novel compensation strategies.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.