[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123440-en":3,"doc-seo-123440-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123440,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Determining Research Priorities Using Machine Learning","Exploratory research evaluates whether machine learning techniques applied to publicly available professional astronomy texts can strengthen strategic planning. Using Latent Dirichlet Allocation (LDA) on content drawn from astronomy journal papers, the study infers high-priority research areas. Although topic models are difficult to interpret directly, the inferred topics correlate with meaningful keywords and specific scientific papers that enable human understanding. Strong correlations also emerge between results from the 1998–2010 corpus and the DS2010 Decadal Survey frontier panel report, with further links to Decadal Survey whitepapers. Predictive metrics are derived to indicate which modeled content may become highly cited, helping planners identify important papers.","Determining Research Priorities Using Machine Learning  \nBrian A. Thomasa , Harley Thronsonb , Anthony Buonomoa , Louis Barbierc  \na Heliophysics Science Division, NASA Goddard Space Flight Center, 8800 Greenbelt Rd., Greenbelt, MD 20771, USA b NASA (retired), 617 Tivoli Passage, Alexandria, VA 22314, USAc Office of the Chief Scientist, NASA Headquarters, 300 E Street SW, Washington, DC 20546, USA  \nAbstract  \nWe summarize our exploratory investigation into whether Machine Learning (ML) techniques applied to publicly available professional text can substantially augment strategic planning for astronomy. We find that an approach based on Latent Dirichlet Allocation (LDA) using content drawn from astronomy journal papers can be used to infer high-priority research areas. While the LDA models are challenging to interpret, we find that they may be strongly associated with meaningful keywords and scientific papers which allow for human interpretation of the topic models.  \nSignificant correlation is found between the results of applying these models to the previous decade of astronomical research (“1998−2010” corpus) and the contents of the science frontier panel report which contains high-priority research areas identified by the 2010 National Academies’ Astronomy and Astrophysics Decadal Survey (“DS2010” corpus) . Significant correlations also exist between model results of the 1998−2010 corpus and the submitted whitepapers to the Decadal Survey (“whitepapers” corpus) . Importantly, we derive predictive metrics based on these results which can provide leading indicators of which content modeled by the topic models will become highly cited in the future. Using these identified metrics and the associations between papers and topic models it is possible to identify important papers for planners to consider.  \nA preliminary version of our work was presented by Thronson et al. (2021) and Thomas et al. (2022) .  \nKeywords: Machine Learning, Strategic Planning, Astronomy Research  \n1. Introduction  \nOne of the most critical planning activities in the sciences is identifying credible priorities for investment. A highly regarded process of scientific prioritization is the National Academies’Decadal Surveys. Among the principal challenges faced by this process is the Survey panelists’ necessity to assess a very large – and rapidly growing – amount of relevant information, specifically many tens of thousands of published research papers in journals (Santamaria (2018)) . The potential input materials have thus increased greatly over the years in both variety and quantity, while the basic processes of the Surveys and other strategic planning activities have changed relatively little. The primary approach for the Surveys over the past halfcentury remains the same (Dressler et al. (2015)): a central steering committee of a couple dozen members supported by large specialty panels. This leads to the primary motivation of our work: How do we substantially improve the current very labor-intensive process of identifying the highest-priority science without adding additional personnel?  \nAdvances in Artificial Intelligence (AI) over the past decade have been impressive. Increasingly powerful AI techniques have been developed which can comb through large corpora  \ninteresting, our goal was instead to avoid such questions as “the nature of discovery” and instead undertake an assessment of the empirical determination of science priorities. That is, assess the outcome rather than the process of science prioritization and discovery.  \nThere have recently been a few examples relevant to the determination of science prioritization that are somewhat similar to that which we describe here. For example, Zelnio (2020) reports the successful use of Machine Learning (ML) to evaluate research literature for promising technologies and Krenn and Zelinger (2020) demonstrate a method used to predict future trends in quantum physics. Tshitoyan et al. (2019) demonstrate","cbCaioO2XugBnzHI","https://ap.wps.com/l/cbCaioO2XugBnzHI","pdf",1482375,1,11,"English","en",105,"# Introduction\n## Topic Modeling\n## Predictive Metrics","[{\"question\":\"How does the study use machine learning to identify astronomy research priorities?\",\"answer\":\"The work applies Latent Dirichlet Allocation (LDA) to public astronomy journal paper content to infer topic models that correspond to high-priority research areas.\"},{\"question\":\"What evidence supports that the inferred priorities are meaningful?\",\"answer\":\"Results from the 1998–2010 corpus show significant correlations with the DS2010 Decadal Survey science frontier panel report and also align with the submitted Decadal Survey whitepapers.\"},{\"question\":\"How can the model outputs help future decision-making?\",\"answer\":\"The study derives predictive metrics from the topic-model results to provide leading indicators of which future content is likely to become highly cited, enabling planners to consider key papers.\"}]","Determining Research Priorities Using Machine Learning | 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does the study use machine learning to identify astronomy research priorities?","Question",{"text":75,"@type":76},"The work applies Latent Dirichlet Allocation (LDA) to public astronomy journal paper content to infer topic models that correspond to high-priority research areas.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What evidence supports that the inferred priorities are meaningful?",{"text":80,"@type":76},"Results from the 1998–2010 corpus show significant correlations with the DS2010 Decadal Survey science frontier panel report and also align with the submitted Decadal Survey whitepapers.",{"name":82,"@type":73,"acceptedAnswer":83},"How can the model outputs help future decision-making?",{"text":84,"@type":76},"The study derives predictive metrics from the topic-model results to provide leading indicators of which future content is likely to become highly cited, enabling planners to consider key 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