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This article explores the factors influencing the future citations of conference papers. We concentrated on the explanatory power of early attention on conference papers for citations collected from Google Scholar and Scopus. The early attention data includes users' online activities in a conference support system: CN3. Bookmarks from the bibliographic management system, Citeulike, were used as a collateral source of early attention. To examine the chronological contributions of 13 factors on citations, a multiple sequential regression analysis was conducted for three timepoints of the publication cycle—paper submission, time of conferences, and months after conferences. Our results illustrate that online readers' early attention of Citeulike bookmarks were found to have the most influence on the future impact of the conference papers. The early attention records from CN3 made noteworthy improvements to explaining both the Google and Scopus citations as well. We also found that the type of papers the number of papers presented at a conference, and the best article award records were significant factors influencing future citations. However, the magnitude of the effects made by online readers' early attention from both sources appears to be larger than these three traditional factors. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

File sharing is an integral component of modern work. Files can be shared either using Group Information Management (GIM), where collaborators exploit a common repository (e.g., the cloud), or Personal Information Management (PIM), where files are sent via email attachments, and collaborators store files individually in personal collections. Given the recent prevalence of GIM, we compare the effects on retrieval for PIM versus GIM collections. We examine the effects of various theoretically motivated factors relating to collection size, properties of the target file, and user workload. In our study, 289 participants accessed 1,557 of their own shared files in a naturalistic setting. Results indicate that factors relating to collection size, file versions, and user workload negatively affect the retrieval of GIM more than PIM files, indicating that PIM is more scalable than GIM. Testing a very different population, we confirm previous findings that failure percentages of GIM are approximately double those of PIM. We discuss possible theoretical explanations, specifically how factors that hinder retrieval exacerbate the general GIM problem of retrieving files organized by other people. Overall, PIM's greater scalability has practical implications for fast‐growing organizations such as startups when choosing their sharing policies. [ABSTRACT FROM AUTHOR]

Scientifically significant sites are the source of, and long‐term repository for, considerable amounts of data—particularly in the natural sciences. However, the unique data practices of the researchers and resource managers at these sites have been relatively understudied. Through case studies of two scientifically significant sites (the hot springs at Yellowstone National Park and the fossil deposits at the La Brea Tar Pits), I developed rich descriptions of site‐based research and data curation, and high‐level data models of information classes needed to support integrative data reuse. Each framework treats the geospatial site and its changing natural characteristics as a distinct class of information; more commonly considered information classes such as observational and sampling data, and project metadata, are defined in relation to the site itself. This work contributes (a) case studies of the values and data needs for researchers and resource managers at scientifically significant sites, (b) an information framework to support integrative reuse at these sites, and (c) a discussion of data practices at scientifically significant sites. [ABSTRACT FROM AUTHOR]