Elizabeth Bradley, James W C White, Joshua Garland
{"title":"深思熟虑的数据分析。","authors":"Elizabeth Bradley, James W C White, Joshua Garland","doi":"10.1063/5.0230739","DOIUrl":null,"url":null,"abstract":"<p><p>The acceleration in the field of data science is well known [see, e.g., D. Donoho, J. Comput. Graph. Stat. 26(4), 745-766 (2017) and references therein]. Improvements in technology for acquisition, storage, and processing have made unheard of amounts of data available to scientists; in parallel with that, the pace of methodological advance has also been rapid; with new techniques and packages becoming available, it seems, every day. With these affordances come many challenges, notably the volume and variety of the data [Fan et al., Natl. Sci. Rev. 1(2), 293-314 (2014)]. In this Perspective piece, we examine a different challenge-how to choose and use the right analysis method-and make an argument for the sharing of raw data.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Thoughtful data analysis.\",\"authors\":\"Elizabeth Bradley, James W C White, Joshua Garland\",\"doi\":\"10.1063/5.0230739\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The acceleration in the field of data science is well known [see, e.g., D. Donoho, J. Comput. Graph. Stat. 26(4), 745-766 (2017) and references therein]. Improvements in technology for acquisition, storage, and processing have made unheard of amounts of data available to scientists; in parallel with that, the pace of methodological advance has also been rapid; with new techniques and packages becoming available, it seems, every day. With these affordances come many challenges, notably the volume and variety of the data [Fan et al., Natl. Sci. Rev. 1(2), 293-314 (2014)]. In this Perspective piece, we examine a different challenge-how to choose and use the right analysis method-and make an argument for the sharing of raw data.</p>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0230739\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1063/5.0230739","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
The acceleration in the field of data science is well known [see, e.g., D. Donoho, J. Comput. Graph. Stat. 26(4), 745-766 (2017) and references therein]. Improvements in technology for acquisition, storage, and processing have made unheard of amounts of data available to scientists; in parallel with that, the pace of methodological advance has also been rapid; with new techniques and packages becoming available, it seems, every day. With these affordances come many challenges, notably the volume and variety of the data [Fan et al., Natl. Sci. Rev. 1(2), 293-314 (2014)]. In this Perspective piece, we examine a different challenge-how to choose and use the right analysis method-and make an argument for the sharing of raw data.
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