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The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 365 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles. This dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 392 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network. Collectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms. FILE FORMATS 1. article_list.csv - Unicode CSV 2. author_list.csv - Unicode CSV 3. Chinese_author_name_reference.csv - Unicode CSV 4. inclusion_net_edges.csv - Unicode CSV 5. review_article_details.csv - Unicode CSV 6. supplementary_reference_list.pdf - PDF 7. README.txt - text file 8. systematic_review_inclusion_criteria.csv - Unicode CSV UPDATES IN THIS VERSION COMPARED TO V3 (Clarke, Caitlin; Lischwe Mueller, Natalie; Joshi, Manasi Ballal; Fu, Yuanxi; Schneider, Jodi (2023): The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4614455_V3) - We added a new file systematic_review_inclusion_criteria.csv. 

Abstract Although systematic reviews are intended to provide trusted scientific knowledge to meet the needs of decision-makers, their reliability can be threatened by bias and irreproducibility. To help decision-makers assess the risks in systematic reviews that they intend to use as the foundation of their action, we designed and tested a new approach to analyzing the evidence selection of a review: its coverage of the primary literature and its comparison to other reviews. Our approach could also help anyone using or producing reviews understand diversity or convergence in evidence selection. The basis of our approach is a new network construct called the inclusion network, which has two types of nodes: primary study reports (PSRs, the evidence) and systematic review reports (SRRs). The approach assesses risks in a given systematic review (the target SRR) by first constructing an inclusion network of the target SRR and other systematic reviews studying similar research questions (the companion SRRs) and then applying a three-step assessment process that utilizes visualizations, quantitative network metrics, and time series analysis. This paper introduces our approach and demonstrates it in two case studies. We identified the following risks: missing potentially relevant evidence, epistemic division in the scientific community, and recent instability in evidence selection standards. We also compare our inclusion network approach to knowledge assessment approaches based on another influential network construct, the claim-specific citation network, discuss current limitations of the inclusion network approach, and present directions for future work. 

{"Abstract":["The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 365 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles.\r\n\r\nThis dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 392 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network.\r\nCollectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms.\r\n\r\nFILE FORMATS\r\n1.\tarticle_list.csv - Unicode CSV\r\n2.\tauthor_list.csv - Unicode CSV\r\n3.\tChinese_author_name_reference.csv - Unicode CSV\r\n4.\tinclusion_net_edges.csv - Unicode CSV\r\n5.\treview_article_details.csv - Unicode CSV\r\n6.\tsupplementary_reference_list.pdf - PDF\r\n7. README.txt - text file\r\n\r\nUPDATES IN THIS VERSION COMPARED TO V2<\/b> (Clarke, Caitlin; Lischwe Mueller, Natalie; Joshi, Manasi Ballal; Fu, Yuanxi; Schneider, Jodi (2022): The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4614455_V2)\r\n- We updated file article_list.csv to fill in a missing value: row 389, article_id 440. We filled in the "date" column as 2016-08, which was missing before."]} 

Sharing, reuse, and synthesis of knowledge is central to the research process. These core functions are in theory served by the system of monographs, abstracts, and papers in journals and proceedings, with citation indices and search databases that comprise the core of our formal scholarly communication infrastructure; yet, converging lines of empirical and anecdotal evidence suggest that this system does not adequately act as infrastructure for synthesis. Emerging developments in new institutions for science, along with new technical infrastructures and tooling for decentralized knowledge work, offer new opportunities to prototype new technical infrastructures on top of a different installed base than the publish or perish, neoliberal academy. This workshop aims to integrate these developments and communities with CSCW’s deep roots in knowledge infrastructures and collaborative and distributed sensemaking, with new developments in science institutions and tooling, to stimulate and accelerate progress towards prototyping new scholarly communication infrastructures that are actually optimized for sharing, reusing, and synthesizing knowledge. 

The goal of the Digital Infrastructures for Scholarly Content Objects (DISCO) workshop is to raise awareness of quality issues, improved discovery, and re-use challenges in digital infrastructures for scholarly content, and to collect potential solutions among an audience of diverse expertise.Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries 

{"Abstract":["The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 365 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles.\r\n\r\nThis dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 392 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network.\r\nCollectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms.\r\n\r\nFILE FORMATS\r\n1.\tarticle_list.csv - Unicode CSV\r\n2.\tauthor_list.csv - Unicode CSV\r\n3.\tChinese_author_name_reference.csv - Unicode CSV\r\n4.\tinclusion_net_edges.csv - Unicode CSV\r\n5.\treview_article_details.csv - Unicode CSV\r\n6.\tsupplementary_reference_list.pdf - PDF\r\n7. README.txt - text file\r\n\r\nUPDATES IN THIS VERSION COMPARED TO V1(Clarke, Caitlin; Lischwe Mueller, Natalie; Joshi, Manasi Ballal; Fu, Yuanxi; Schneider, Jodi (2022): The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4614455_V1)\r\nIn V1, we did not upload the file "article_list.csv." We uploaded the missing file in this version, and everything else remains the same."]} 

{"Abstract":["The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 364 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles.\r\n\r\nThis dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 391 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network.\r\nCollectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms.\r\n\r\nFILE FORMATS\r\n1.\tarticle_list.csv - Unicode CSV\r\n2.\tauthor_list.csv - Unicode CSV\r\n3.\tChinese_author_name_reference.csv - Unicode CSV\r\n4.\tinclusion_net_edges.csv - Unicode CSV\r\n5.\treview_article_details.csv - Unicode CSV\r\n6.\tsupplementary_reference_list.pdf - PDF\r\n7. README.txt - text file"]}