Iratxe Puebla, Giorgio A Ascoli, Jeffrey Blume, John Chodacki, Joshua Finnell, David N Kennedy, Bernard Mair, Maryann E Martone, Jamie Wittenberg, Jean-Baptiste Poline
{"title":"在聘用、晋升和终身任职方面承认数据和软件贡献的十条简单规则。","authors":"Iratxe Puebla, Giorgio A Ascoli, Jeffrey Blume, John Chodacki, Joshua Finnell, David N Kennedy, Bernard Mair, Maryann E Martone, Jamie Wittenberg, Jean-Baptiste Poline","doi":"10.1371/journal.pcbi.1012296","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":20241,"journal":{"name":"PLoS Computational Biology","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309403/pdf/","citationCount":"0","resultStr":"{\"title\":\"Ten simple rules for recognizing data and software contributions in hiring, promotion, and tenure.\",\"authors\":\"Iratxe Puebla, Giorgio A Ascoli, Jeffrey Blume, John Chodacki, Joshua Finnell, David N Kennedy, Bernard Mair, Maryann E Martone, Jamie Wittenberg, Jean-Baptiste Poline\",\"doi\":\"10.1371/journal.pcbi.1012296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":20241,\"journal\":{\"name\":\"PLoS Computational Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11309403/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PLoS Computational Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1371/journal.pcbi.1012296\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/8/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PLoS Computational Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1371/journal.pcbi.1012296","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
期刊介绍:
PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery.
Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines.
Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights.
Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology.
Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.