Simon Van Buggenhout , Gergo Ignacz , Scout Caspers , Robin Dhondt , Marie Lenaerts , Nathalie Lenaerts , Sareh Rezaei Hosseinabadi , Ines Nulens , Guy Koeckelberghs , Yi Ren , Ryan P. Lively , Murielle Rabiller-Baudry , Ki Min Lim , Nazlee Ghazali , Joaquin Coronas , Milan Abel , Matthias Wessling , Mirko Skiborowski , Adam Oxley , Seok Ju Han , Rhea Verbeke
{"title":"有机介质纳滤的开放和 FAIR 数据:统一方法","authors":"Simon Van Buggenhout , Gergo Ignacz , Scout Caspers , Robin Dhondt , Marie Lenaerts , Nathalie Lenaerts , Sareh Rezaei Hosseinabadi , Ines Nulens , Guy Koeckelberghs , Yi Ren , Ryan P. Lively , Murielle Rabiller-Baudry , Ki Min Lim , Nazlee Ghazali , Joaquin Coronas , Milan Abel , Matthias Wessling , Mirko Skiborowski , Adam Oxley , Seok Ju Han , Rhea Verbeke","doi":"10.1016/j.memsci.2024.123356","DOIUrl":null,"url":null,"abstract":"<div><div>Organic solvent nanofiltration (OSN), also called solvent-resistant nanofiltration (SRNF), has emerged as a promising technology for the removal of impurities, recovery of solutes, and the regeneration of solvents in various industries, such as the pharmaceutical and the (petro)chemical industries. Despite the widespread use of OSN/SRNF, the presence of scattered, non-standardized data, and the absence of openly accessible data pose critical challenges to the development of new membrane materials and processes, their comparison to the state-of-the-art materials, and their fundamental understanding. To overcome these hurdles, data from peer-reviewed research articles and commercial datasheets were curated via a standardized procedure to obtain an extensive dataset on the membrane materials, synthesis parameters, operational conditions, physicochemical properties, and performance of OSN/SRNF membranes. Thanks to a truly impressive joint effort of the OSN/SRNF community, the dataset contains, as per April 2024, 5006 unique membrane filtrations from 294 publications for 42 solvents under several process parameters. This findable, accessible, interoperable, reproducible, and open (FAIR/O) dataset is available on both the OSN Database and the newly inaugurated Open Membrane Database for SRNF (OMD4SRNF). These databases provide multiple visualization and data exploration tools. Here, the standardized procedure applied to curate the data and the functionality of the databases are outlined, as well as the online user interface to deposit new data by external users on the OMD4SRNF. This community-led project has been supported by all the co-authors of this work. Most importantly, they additionally agreed to systematically deposit their future peer-reviewed data on OSN/SRNF into the databases. We thereby pave the road for FAIR/O data in the field of OSN/SRNF to increase transparency, enable more accurate data analysis, and foster collaboration and innovation.</div></div>","PeriodicalId":368,"journal":{"name":"Journal of Membrane Science","volume":"713 ","pages":"Article 123356"},"PeriodicalIF":8.4000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Open and FAIR data for nanofiltration in organic media: A unified approach\",\"authors\":\"Simon Van Buggenhout , Gergo Ignacz , Scout Caspers , Robin Dhondt , Marie Lenaerts , Nathalie Lenaerts , Sareh Rezaei Hosseinabadi , Ines Nulens , Guy Koeckelberghs , Yi Ren , Ryan P. Lively , Murielle Rabiller-Baudry , Ki Min Lim , Nazlee Ghazali , Joaquin Coronas , Milan Abel , Matthias Wessling , Mirko Skiborowski , Adam Oxley , Seok Ju Han , Rhea Verbeke\",\"doi\":\"10.1016/j.memsci.2024.123356\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Organic solvent nanofiltration (OSN), also called solvent-resistant nanofiltration (SRNF), has emerged as a promising technology for the removal of impurities, recovery of solutes, and the regeneration of solvents in various industries, such as the pharmaceutical and the (petro)chemical industries. Despite the widespread use of OSN/SRNF, the presence of scattered, non-standardized data, and the absence of openly accessible data pose critical challenges to the development of new membrane materials and processes, their comparison to the state-of-the-art materials, and their fundamental understanding. To overcome these hurdles, data from peer-reviewed research articles and commercial datasheets were curated via a standardized procedure to obtain an extensive dataset on the membrane materials, synthesis parameters, operational conditions, physicochemical properties, and performance of OSN/SRNF membranes. Thanks to a truly impressive joint effort of the OSN/SRNF community, the dataset contains, as per April 2024, 5006 unique membrane filtrations from 294 publications for 42 solvents under several process parameters. This findable, accessible, interoperable, reproducible, and open (FAIR/O) dataset is available on both the OSN Database and the newly inaugurated Open Membrane Database for SRNF (OMD4SRNF). These databases provide multiple visualization and data exploration tools. Here, the standardized procedure applied to curate the data and the functionality of the databases are outlined, as well as the online user interface to deposit new data by external users on the OMD4SRNF. This community-led project has been supported by all the co-authors of this work. Most importantly, they additionally agreed to systematically deposit their future peer-reviewed data on OSN/SRNF into the databases. We thereby pave the road for FAIR/O data in the field of OSN/SRNF to increase transparency, enable more accurate data analysis, and foster collaboration and innovation.</div></div>\",\"PeriodicalId\":368,\"journal\":{\"name\":\"Journal of Membrane Science\",\"volume\":\"713 \",\"pages\":\"Article 123356\"},\"PeriodicalIF\":8.4000,\"publicationDate\":\"2024-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Membrane Science\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0376738824009505\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Membrane Science","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0376738824009505","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Open and FAIR data for nanofiltration in organic media: A unified approach
Organic solvent nanofiltration (OSN), also called solvent-resistant nanofiltration (SRNF), has emerged as a promising technology for the removal of impurities, recovery of solutes, and the regeneration of solvents in various industries, such as the pharmaceutical and the (petro)chemical industries. Despite the widespread use of OSN/SRNF, the presence of scattered, non-standardized data, and the absence of openly accessible data pose critical challenges to the development of new membrane materials and processes, their comparison to the state-of-the-art materials, and their fundamental understanding. To overcome these hurdles, data from peer-reviewed research articles and commercial datasheets were curated via a standardized procedure to obtain an extensive dataset on the membrane materials, synthesis parameters, operational conditions, physicochemical properties, and performance of OSN/SRNF membranes. Thanks to a truly impressive joint effort of the OSN/SRNF community, the dataset contains, as per April 2024, 5006 unique membrane filtrations from 294 publications for 42 solvents under several process parameters. This findable, accessible, interoperable, reproducible, and open (FAIR/O) dataset is available on both the OSN Database and the newly inaugurated Open Membrane Database for SRNF (OMD4SRNF). These databases provide multiple visualization and data exploration tools. Here, the standardized procedure applied to curate the data and the functionality of the databases are outlined, as well as the online user interface to deposit new data by external users on the OMD4SRNF. This community-led project has been supported by all the co-authors of this work. Most importantly, they additionally agreed to systematically deposit their future peer-reviewed data on OSN/SRNF into the databases. We thereby pave the road for FAIR/O data in the field of OSN/SRNF to increase transparency, enable more accurate data analysis, and foster collaboration and innovation.
期刊介绍:
The Journal of Membrane Science is a publication that focuses on membrane systems and is aimed at academic and industrial chemists, chemical engineers, materials scientists, and membranologists. It publishes original research and reviews on various aspects of membrane transport, membrane formation/structure, fouling, module/process design, and processes/applications. The journal primarily focuses on the structure, function, and performance of non-biological membranes but also includes papers that relate to biological membranes. The Journal of Membrane Science publishes Full Text Papers, State-of-the-Art Reviews, Letters to the Editor, and Perspectives.