{"title":"揭示牲畜贸易趋势:生成式人工智能可视化初学者指南","authors":"Yoshiyasu Takefuji","doi":"10.1016/j.rvsc.2024.105435","DOIUrl":null,"url":null,"abstract":"<div><div>This tutorial, rooted in the context of livestock research, is designed to assist novice or non-programmers in visualizing trends in livestock exports between the US and Japan using Python and generative AI systems such as Microsoft's Copilot and Google's Gemini. The analysis of these trends plays a pivotal role in optimizing livestock production. The tutorial offers a thorough guide on preparing data using reliable federal datasets, generating Python code, and tackling potential issues such as overlapping data points. It effectively simplifies complex tasks into manageable steps and includes Python code in the appendices for easy reference. By enabling researchers to extract insights and make predictions from livestock data, this tutorial addresses a significant void in the existing literature. This innovative approach has the potential to transform the way researchers engage with and interpret livestock data, thereby making a substantial contribution to the field.</div></div>","PeriodicalId":21083,"journal":{"name":"Research in veterinary science","volume":"180 ","pages":"Article 105435"},"PeriodicalIF":2.2000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unveiling livestock trade trends: A beginner's guide to generative AI-powered visualization\",\"authors\":\"Yoshiyasu Takefuji\",\"doi\":\"10.1016/j.rvsc.2024.105435\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This tutorial, rooted in the context of livestock research, is designed to assist novice or non-programmers in visualizing trends in livestock exports between the US and Japan using Python and generative AI systems such as Microsoft's Copilot and Google's Gemini. The analysis of these trends plays a pivotal role in optimizing livestock production. The tutorial offers a thorough guide on preparing data using reliable federal datasets, generating Python code, and tackling potential issues such as overlapping data points. It effectively simplifies complex tasks into manageable steps and includes Python code in the appendices for easy reference. By enabling researchers to extract insights and make predictions from livestock data, this tutorial addresses a significant void in the existing literature. This innovative approach has the potential to transform the way researchers engage with and interpret livestock data, thereby making a substantial contribution to the field.</div></div>\",\"PeriodicalId\":21083,\"journal\":{\"name\":\"Research in veterinary science\",\"volume\":\"180 \",\"pages\":\"Article 105435\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research in veterinary science\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0034528824003023\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"VETERINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in veterinary science","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034528824003023","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"VETERINARY SCIENCES","Score":null,"Total":0}
Unveiling livestock trade trends: A beginner's guide to generative AI-powered visualization
This tutorial, rooted in the context of livestock research, is designed to assist novice or non-programmers in visualizing trends in livestock exports between the US and Japan using Python and generative AI systems such as Microsoft's Copilot and Google's Gemini. The analysis of these trends plays a pivotal role in optimizing livestock production. The tutorial offers a thorough guide on preparing data using reliable federal datasets, generating Python code, and tackling potential issues such as overlapping data points. It effectively simplifies complex tasks into manageable steps and includes Python code in the appendices for easy reference. By enabling researchers to extract insights and make predictions from livestock data, this tutorial addresses a significant void in the existing literature. This innovative approach has the potential to transform the way researchers engage with and interpret livestock data, thereby making a substantial contribution to the field.
期刊介绍:
Research in Veterinary Science is an International multi-disciplinary journal publishing original articles, reviews and short communications of a high scientific and ethical standard in all aspects of veterinary and biomedical research.
The primary aim of the journal is to inform veterinary and biomedical scientists of significant advances in veterinary and related research through prompt publication and dissemination. Secondly, the journal aims to provide a general multi-disciplinary forum for discussion and debate of news and issues concerning veterinary science. Thirdly, to promote the dissemination of knowledge to a broader range of professions, globally.
High quality papers on all species of animals are considered, particularly those considered to be of high scientific importance and originality, and with interdisciplinary interest. The journal encourages papers providing results that have clear implications for understanding disease pathogenesis and for the development of control measures or treatments, as well as those dealing with a comparative biomedical approach, which represents a substantial improvement to animal and human health.
Studies without a robust scientific hypothesis or that are preliminary, or of weak originality, as well as negative results, are not appropriate for the journal. Furthermore, observational approaches, case studies or field reports lacking an advancement in general knowledge do not fall within the scope of the journal.