Asal Mehditabrizi, Behnam Tahmasbi, Saeed Saleh Namadi, Cinzia Cirillo
This study evaluates the accessibility of public EV charging stations in the Washington metropolitan area using a comprehensive measure that accounts for both destination-based and en route charging opportunities. By incorporating the full spectrum of daily travel patterns into the accessibility evaluation, our methodology offers a more realistic measure of charging opportunities than destination-based methods that prioritize proximity to residential locations. Results from spatial autocorrelation analysis indicate that conventional accessibility assessments often overestimate the availability of infrastructure in central urban areas and underestimate it in peripheral commuting zones, potentially leading to misallocated resources. By highlighting significant clusters of high-access and low-access areas, our approach identifies spatial inequalities in infrastructure distribution and provides insights into areas requiring targeted interventions. This study underscores the importance of incorporating daily mobility patterns into urban planning to ensure equitable access to EV charging infrastructure and suggests a framework that other regions could adopt to enhance sustainable transportation networks and support equitable urban development.
{"title":"Integrating En Route and Home Proximity in EV Charging Accessibility: A Spatial Analysis in the Washington Metropolitan Area","authors":"Asal Mehditabrizi, Behnam Tahmasbi, Saeed Saleh Namadi, Cinzia Cirillo","doi":"arxiv-2409.08287","DOIUrl":"https://doi.org/arxiv-2409.08287","url":null,"abstract":"This study evaluates the accessibility of public EV charging stations in the\u0000Washington metropolitan area using a comprehensive measure that accounts for\u0000both destination-based and en route charging opportunities. By incorporating\u0000the full spectrum of daily travel patterns into the accessibility evaluation,\u0000our methodology offers a more realistic measure of charging opportunities than\u0000destination-based methods that prioritize proximity to residential locations.\u0000Results from spatial autocorrelation analysis indicate that conventional\u0000accessibility assessments often overestimate the availability of infrastructure\u0000in central urban areas and underestimate it in peripheral commuting zones,\u0000potentially leading to misallocated resources. By highlighting significant\u0000clusters of high-access and low-access areas, our approach identifies spatial\u0000inequalities in infrastructure distribution and provides insights into areas\u0000requiring targeted interventions. This study underscores the importance of\u0000incorporating daily mobility patterns into urban planning to ensure equitable\u0000access to EV charging infrastructure and suggests a framework that other\u0000regions could adopt to enhance sustainable transportation networks and support\u0000equitable urban development.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
According to Eurostat estimates, the overall number of farms in Europe declined of about 3 million units between 2010 and 2020. Parallel, the agricultural standard output increased from 304 billion to nearly 360 billion over the same period. Such evidence, legitimately leads to questions about how the structure (e.g., type of production and average size) of farms has changed and whether this change has been uniform or heterogeneous within Europe. In this paper, we aim at investigating the phenomenon of market concentration in the European agricultural and livestock farming industry from 2010 to 2020 at the regional level by exploiting the spatio-temporal dynamics of the Gini concentration index for the land owned by the European farmers and for their standard output. In particular, we are interested in exploring the variability within-and-between regions with regard to land and production size to assess if the European agricultural market suffered from an increasingly concentration of power in fewer but larger farm holding. The extensive mapping provided by this study may allow a fine spatial-scale socio-economic and political assessment of the European agricultural market integration process, its recent and future trends in the complex and uncertain post-COVID context and the restructuring of international relations due to crises and the green energy transition.
{"title":"Inequality and Concentration in Farmland Production and Size: Regional Analysis for the European Union from 2010 to 2020","authors":"Simone Boccaletti, Paolo Maranzano, Miguel Viegas","doi":"arxiv-2409.00111","DOIUrl":"https://doi.org/arxiv-2409.00111","url":null,"abstract":"According to Eurostat estimates, the overall number of farms in Europe\u0000declined of about 3 million units between 2010 and 2020. Parallel, the\u0000agricultural standard output increased from 304 billion to nearly 360 billion\u0000over the same period. Such evidence, legitimately leads to questions about how\u0000the structure (e.g., type of production and average size) of farms has changed\u0000and whether this change has been uniform or heterogeneous within Europe. In\u0000this paper, we aim at investigating the phenomenon of market concentration in\u0000the European agricultural and livestock farming industry from 2010 to 2020 at\u0000the regional level by exploiting the spatio-temporal dynamics of the Gini\u0000concentration index for the land owned by the European farmers and for their\u0000standard output. In particular, we are interested in exploring the variability\u0000within-and-between regions with regard to land and production size to assess if\u0000the European agricultural market suffered from an increasingly concentration of\u0000power in fewer but larger farm holding. The extensive mapping provided by this\u0000study may allow a fine spatial-scale socio-economic and political assessment of\u0000the European agricultural market integration process, its recent and future\u0000trends in the complex and uncertain post-COVID context and the restructuring of\u0000international relations due to crises and the green energy transition.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding the complex nature of spatial information is crucial for problem solving in social and environmental sciences. This study investigates how the underlying patterns of spatial data can significantly influence the outcomes of spatial predictions. Recognizing unique characteristics of spatial data, such as spatial dependence and spatial heterogeneity, we delve into the fundamental differences and similarities between spatial and non-geospatial prediction models. Through the analysis of six different datasets of environment and socio-economic variables, comparing geospatial models with non-geospatial models, our research highlights the pervasive nature of spatial dependence beyond geographical boundaries. This innovative approach not only recognizes spatial dependence in geographic spaces defined by latitude and longitude but also identifies its presence in non-geographic, attribute-based dimensions. Our findings reveal the pervasive influence of spatial dependence on prediction outcomes across various domains, and spatial dependence significantly influences prediction performance across all spaces. Our findings suggest that the strongest spatial dependence is typically found in geographic space for environment variables, a trend that does not uniformly apply to socio-economic variables. This investigation not only advances the theoretical framework for spatial data analysis, but also proposes new methodologies for accurately capturing and expressing spatial dependence under complex conditions. Our research extends spatial analysis to non-geographic dimensions such as social networks and gene expression patterns, emphasizing the role of spatial dependence in improving prediction accuracy, thereby supporting interdisciplinary applications across fields such as geographic information science, environmental science, economics, sociology, and bioinformatics.
{"title":"Pervasive impact of spatial dependence on predictability","authors":"Peng Luo, Yongze Song, Wenwen Li, Liqiu Meng","doi":"arxiv-2408.14722","DOIUrl":"https://doi.org/arxiv-2408.14722","url":null,"abstract":"Understanding the complex nature of spatial information is crucial for\u0000problem solving in social and environmental sciences. This study investigates\u0000how the underlying patterns of spatial data can significantly influence the\u0000outcomes of spatial predictions. Recognizing unique characteristics of spatial\u0000data, such as spatial dependence and spatial heterogeneity, we delve into the\u0000fundamental differences and similarities between spatial and non-geospatial\u0000prediction models. Through the analysis of six different datasets of\u0000environment and socio-economic variables, comparing geospatial models with\u0000non-geospatial models, our research highlights the pervasive nature of spatial\u0000dependence beyond geographical boundaries. This innovative approach not only\u0000recognizes spatial dependence in geographic spaces defined by latitude and\u0000longitude but also identifies its presence in non-geographic, attribute-based\u0000dimensions. Our findings reveal the pervasive influence of spatial dependence\u0000on prediction outcomes across various domains, and spatial dependence\u0000significantly influences prediction performance across all spaces. Our findings\u0000suggest that the strongest spatial dependence is typically found in geographic\u0000space for environment variables, a trend that does not uniformly apply to\u0000socio-economic variables. This investigation not only advances the theoretical\u0000framework for spatial data analysis, but also proposes new methodologies for\u0000accurately capturing and expressing spatial dependence under complex\u0000conditions. Our research extends spatial analysis to non-geographic dimensions\u0000such as social networks and gene expression patterns, emphasizing the role of\u0000spatial dependence in improving prediction accuracy, thereby supporting\u0000interdisciplinary applications across fields such as geographic information\u0000science, environmental science, economics, sociology, and bioinformatics.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Investigating how different ingredients are combined in popular dishes is crucial to reveal the fundamental principles behind the formation of food preferences. Here, we use data from food repositories and network analysis to characterize worldwide cuisines. In our framework, each cuisine is represented as a network, where nodes correspond to ingredient types and weighted links describe how frequently pairs of ingredient types appear together in recipes. The networks of ingredient combinations reveal cuisine-specific patterns, highlighting similarities and differences in gastronomic preferences across different world regions. We find that popular ingredients, recurrent combinations, and the way they are organized within the backbone of the network provide a unique fingerprint for each cuisine. Hence, we demonstrate that networks of ingredient combinations are able to cluster global cuisines into meaningful geo-cultural groups, and can also be used to train models to uniquely identify a cuisine from a subset of its recipes. Our study advances our understanding of food combinations and helps uncover the geography of taste, paving the way for the creation of new and innovative recipes.
{"title":"The networks of ingredient combination in cuisines around the world","authors":"Claudio Caprioli, Saumitra Kulkarni, Federico Battiston, Iacopo Iacopini, Andrea Santoro, Vito Latora","doi":"arxiv-2408.15162","DOIUrl":"https://doi.org/arxiv-2408.15162","url":null,"abstract":"Investigating how different ingredients are combined in popular dishes is\u0000crucial to reveal the fundamental principles behind the formation of food\u0000preferences. Here, we use data from food repositories and network analysis to\u0000characterize worldwide cuisines. In our framework, each cuisine is represented\u0000as a network, where nodes correspond to ingredient types and weighted links\u0000describe how frequently pairs of ingredient types appear together in recipes.\u0000The networks of ingredient combinations reveal cuisine-specific patterns,\u0000highlighting similarities and differences in gastronomic preferences across\u0000different world regions. We find that popular ingredients, recurrent\u0000combinations, and the way they are organized within the backbone of the network\u0000provide a unique fingerprint for each cuisine. Hence, we demonstrate that\u0000networks of ingredient combinations are able to cluster global cuisines into\u0000meaningful geo-cultural groups, and can also be used to train models to\u0000uniquely identify a cuisine from a subset of its recipes. Our study advances\u0000our understanding of food combinations and helps uncover the geography of\u0000taste, paving the way for the creation of new and innovative recipes.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Misinformation poses a significant challenge studied extensively by researchers, yet acquiring data to identify primary sharers is costly and challenging. To address this, we propose a low-barrier approach to differentiate social media users who are more likely to share misinformation from those who are less likely. Leveraging insights from previous studies, we demonstrate that easy-access online social network metrics -- average daily tweet count, and account age -- can be leveraged to help identify potential low factuality content spreaders on X (previously known as Twitter). We find that higher tweet frequency is positively associated with low factuality in shared content, while account age is negatively associated with it. We also find that some of the effects, namely the effect of the number of accounts followed and the number of tweets produced, differ depending on the number of followers a user has. Our findings show that relying on these easy-access social network metrics could serve as a low-barrier approach for initial identification of users who are more likely to spread misinformation, and therefore contribute to combating misinformation effectively on social media platforms.
{"title":"Easy-access online social media metrics can effectively identify misinformation sharing users","authors":"Júlia Számely, Alessandro Galeazzi, Júlia Koltai, Elisa Omodei","doi":"arxiv-2408.15186","DOIUrl":"https://doi.org/arxiv-2408.15186","url":null,"abstract":"Misinformation poses a significant challenge studied extensively by\u0000researchers, yet acquiring data to identify primary sharers is costly and\u0000challenging. To address this, we propose a low-barrier approach to\u0000differentiate social media users who are more likely to share misinformation\u0000from those who are less likely. Leveraging insights from previous studies, we\u0000demonstrate that easy-access online social network metrics -- average daily\u0000tweet count, and account age -- can be leveraged to help identify potential low\u0000factuality content spreaders on X (previously known as Twitter). We find that\u0000higher tweet frequency is positively associated with low factuality in shared\u0000content, while account age is negatively associated with it. We also find that\u0000some of the effects, namely the effect of the number of accounts followed and\u0000the number of tweets produced, differ depending on the number of followers a\u0000user has. Our findings show that relying on these easy-access social network\u0000metrics could serve as a low-barrier approach for initial identification of\u0000users who are more likely to spread misinformation, and therefore contribute to\u0000combating misinformation effectively on social media platforms.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sebastian Musslick, Laura K. Bartlett, Suyog H. Chandramouli, Marina Dubova, Fernand Gobet, Thomas L. Griffiths, Jessica Hullman, Ross D. King, J. Nathan Kutz, Christopher G. Lucas, Suhas Mahesh, Franco Pestilli, Sabina J. Sloman, William R. Holmes
Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.
{"title":"Automating the Practice of Science -- Opportunities, Challenges, and Implications","authors":"Sebastian Musslick, Laura K. Bartlett, Suyog H. Chandramouli, Marina Dubova, Fernand Gobet, Thomas L. Griffiths, Jessica Hullman, Ross D. King, J. Nathan Kutz, Christopher G. Lucas, Suhas Mahesh, Franco Pestilli, Sabina J. Sloman, William R. Holmes","doi":"arxiv-2409.05890","DOIUrl":"https://doi.org/arxiv-2409.05890","url":null,"abstract":"Automation transformed various aspects of our human civilization,\u0000revolutionizing industries and streamlining processes. In the domain of\u0000scientific inquiry, automated approaches emerged as powerful tools, holding\u0000promise for accelerating discovery, enhancing reproducibility, and overcoming\u0000the traditional impediments to scientific progress. This article evaluates the\u0000scope of automation within scientific practice and assesses recent approaches.\u0000Furthermore, it discusses different perspectives to the following questions:\u0000Where do the greatest opportunities lie for automation in scientific practice?;\u0000What are the current bottlenecks of automating scientific practice?; and What\u0000are significant ethical and practical consequences of automating scientific\u0000practice? By discussing the motivations behind automated science, analyzing the\u0000hurdles encountered, and examining its implications, this article invites\u0000researchers, policymakers, and stakeholders to navigate the rapidly evolving\u0000frontier of automated scientific practice.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As large language models (LLMs) continue to make significant strides, their better integration into agent-based simulations offers a transformational potential for understanding complex social systems. However, such integration is not trivial and poses numerous challenges. Based on this observation, in this paper, we explore architectures and methods to systematically develop LLM-augmented social simulations and discuss potential research directions in this field. We conclude that integrating LLMs with agent-based simulations offers a powerful toolset for researchers and scientists, allowing for more nuanced, realistic, and comprehensive models of complex systems and human behaviours.
{"title":"Modelisation a base d'Agent Augmentes par LLM pour les Simulations Sociales: Defis et Opportunites","authors":"Önder Gürcan","doi":"arxiv-2409.00100","DOIUrl":"https://doi.org/arxiv-2409.00100","url":null,"abstract":"As large language models (LLMs) continue to make significant strides, their\u0000better integration into agent-based simulations offers a transformational\u0000potential for understanding complex social systems. However, such integration\u0000is not trivial and poses numerous challenges. Based on this observation, in\u0000this paper, we explore architectures and methods to systematically develop\u0000LLM-augmented social simulations and discuss potential research directions in\u0000this field. We conclude that integrating LLMs with agent-based simulations\u0000offers a powerful toolset for researchers and scientists, allowing for more\u0000nuanced, realistic, and comprehensive models of complex systems and human\u0000behaviours.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reese AK Richardson, Jeonghyun Moon, Spencer S Hong, Luís A Nunes Amaral
Materials science and engineering (MSE) research has, for the most part, escaped the doubts raised about the reliability of the scientific literature by recent large-scale replication studies in psychology and cancer biology. However, users on post-publication peer review sites have recently identified dozens of articles where the make and model of the scanning electron microscope (SEM) listed in the text of the paper does not match the instrument's metadata visible in the images in the published article. In order to systematically investigate this potential risk to the MSE literature, we develop a semi-automated approach to scan published figures for this metadata and check it against the SEM instrument identified in the text. Starting from an exhaustive set of 1,067,102 articles published since 2010 in 50 journals with impact factors ranging from 2 to 24, we identify 11,314 articles for which SEM make and model can be identified in an image's metadata. For 21.2% of those articles, the image metadata does not match the SEM manufacturer or model listed in the text and, for another 24.7%, at least some of the instruments used in the study are not reported. Unexplained patterns common to many of these articles suggest the involvement of paper mills, organizations that mass-produce, sell authorship on, and publish fraudulent scientific manuscripts at scale.
材料科学与工程（MSE）研究在很大程度上避免了心理学和癌症生物学领域近期大规模复制研究对科学文献可靠性的质疑。然而，发表后同行评审网站的用户最近发现了数十篇文章，在这些文章中，论文正文中列出的扫描电子显微镜（SEM）的品牌和型号与发表文章图片中可见的仪器元数据不符。为了系统地研究 MSE 文献的这一潜在风险，我们开发了一种半自动方法来扫描已发表文章中的图片，以查找这些元数据，并与文中标明的 SEM 仪器进行核对。从 2010 年以来在 50 种期刊上发表的 1,067,102 篇文章（影响因子从 2 到 24 不等）的详尽集合开始，我们发现有 11,314 篇文章的图像元数据中可以识别出 SEM 制造商和型号。在这些文章中，有 21.2% 的图片元数据与文中列出的 SEM 制造商或型号不符，另有 24.7% 的文章至少没有报告研究中使用的部分仪器。在这些文章中，许多文章都有无法解释的共同模式，这表明有造纸厂参与其中，这些造纸厂是大规模生产、出售作者署名权和出版虚假科学手稿的组织。
{"title":"Widespread misidentification of SEM instruments in the peer-reviewed materials science and engineering literature","authors":"Reese AK Richardson, Jeonghyun Moon, Spencer S Hong, Luís A Nunes Amaral","doi":"arxiv-2409.00104","DOIUrl":"https://doi.org/arxiv-2409.00104","url":null,"abstract":"Materials science and engineering (MSE) research has, for the most part,\u0000escaped the doubts raised about the reliability of the scientific literature by\u0000recent large-scale replication studies in psychology and cancer biology.\u0000However, users on post-publication peer review sites have recently identified\u0000dozens of articles where the make and model of the scanning electron microscope\u0000(SEM) listed in the text of the paper does not match the instrument's metadata\u0000visible in the images in the published article. In order to systematically\u0000investigate this potential risk to the MSE literature, we develop a\u0000semi-automated approach to scan published figures for this metadata and check\u0000it against the SEM instrument identified in the text. Starting from an\u0000exhaustive set of 1,067,102 articles published since 2010 in 50 journals with\u0000impact factors ranging from 2 to 24, we identify 11,314 articles for which SEM\u0000make and model can be identified in an image's metadata. For 21.2% of those\u0000articles, the image metadata does not match the SEM manufacturer or model\u0000listed in the text and, for another 24.7%, at least some of the instruments\u0000used in the study are not reported. Unexplained patterns common to many of\u0000these articles suggest the involvement of paper mills, organizations that\u0000mass-produce, sell authorship on, and publish fraudulent scientific manuscripts\u0000at scale.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Malvika Sharan, Emma Karoune, Vicky Hellon, Cassandra Gould van Praag, Gabin Kayumbi, Arielle Bennett, Alexandra Araujo Alvarez, Anne Lee Steele, Sophia Batchelor, Arron Lacey, Kirstie Whitaker
This article discusses the professionalisation of community management roles in data science and AI research, referred to here as Research Community Managers (RCMs).
本文讨论了数据科学和人工智能研究中社区管理角色的专业化问题，在此称为研究社区管理者（RCMs）。
{"title":"Professionalising Community Management Roles in Interdisciplinary Research Projects","authors":"Malvika Sharan, Emma Karoune, Vicky Hellon, Cassandra Gould van Praag, Gabin Kayumbi, Arielle Bennett, Alexandra Araujo Alvarez, Anne Lee Steele, Sophia Batchelor, Arron Lacey, Kirstie Whitaker","doi":"arxiv-2409.00108","DOIUrl":"https://doi.org/arxiv-2409.00108","url":null,"abstract":"This article discusses the professionalisation of community management roles\u0000in data science and AI research, referred to here as Research Community\u0000Managers (RCMs).","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Fronzetti Colladon, A. L. Pisello, L. F. Cabeza
The demand for research supporting the development of new policy frameworks for energy saving and conservation has never been more critical. As climate change accelerates and its impacts become increasingly severe, the need for sustainable and resilient socioeconomic systems is increasingly pressing. In response to this global challenge, the ten articles of this special issue seek to explore how advances in Artificial Intelligence and Data Science can drive the energy transition and enhance environmental sustainability.
{"title":"Boosting the clean energy transition through data science","authors":"A. Fronzetti Colladon, A. L. Pisello, L. F. Cabeza","doi":"arxiv-2408.15211","DOIUrl":"https://doi.org/arxiv-2408.15211","url":null,"abstract":"The demand for research supporting the development of new policy frameworks\u0000for energy saving and conservation has never been more critical. As climate\u0000change accelerates and its impacts become increasingly severe, the need for\u0000sustainable and resilient socioeconomic systems is increasingly pressing. In\u0000response to this global challenge, the ten articles of this special issue seek\u0000to explore how advances in Artificial Intelligence and Data Science can drive\u0000the energy transition and enhance environmental sustainability.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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