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In-text citation's frequencies-based recommendations of relevant research papers.

ABSTRACT: From the past half of a century, identification of the relevant documents is deemed an active area of research due to the rapid increase of data on the web. The traditional models to retrieve relevant documents are based on bibliographic information such as Bibliographic coupling, Co-citations, and Direct citations. However, in the recent past, the scientific community has started to employ textual features to improve existing models' accuracy. In our previous study, we found that analysis of citations at a deep level (i.e., content level) can play a paramount role in finding more relevant documents than surface level (i.e., just bibliography details). We found that cited and citing papers have a high degree of relevancy when in-text citations frequency of the cited paper is more than five times in the citing paper's text. This paper is an extension of our previous study in terms of its evaluation of a comprehensive dataset. Moreover, the study results are also compared with other state-of-the-art approaches i.e., content, metadata, and bibliography. For evaluation, a user study is conducted on selected papers from 1,200 documents (comprise about 16,000 references) of an online journal, Journal of Computer Science (J.UCS). The evaluation results indicate that in-text citation frequency has attained higher precision in finding relevant papers than other state-of-the-art techniques such as content, bibliographic coupling, and metadata-based techniques. The use of in-text citation may help in enhancing the quality of existing information systems and digital libraries. Further, more sophisticated measure may be redefined be considering the use of in-text citations.

SUBMITTER: Shahid A

PROVIDER: S-EPMC8189020 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Research in computer systems often involves the engineering, implementation, and measurement of complex systems software and data. The availability of these artifacts is critical to the reproducibility and replicability of the research results, because system software often embodies numerous implicit assumptions and parameters that are not fully documented in the research article itself. Artifact availability has also been previously associated with higher paper impact, as measured by citations counts. And yet, the sharing of research artifacts is still not as common as warranted by its importance. The primary goal of this study is to provide an exploratory statistical analysis of the artifact-sharing rates and associated factors in the research field of computer systems. To this end, we explore a cross-sectional dataset of papers from 56 contemporaneous systems conferences. In addition to extensive data on the conferences, papers, and authors, this analyze dataset includes data on the release, ongoing availability, badging, and locations of research artifacts. We combine this manually curated dataset with citation counts to evaluate the relationships between different artifact properties and citation metrics. Additionally, we revisit previous observations from other fields on the relationships between artifact properties and various other characteristics of papers, authors, and venue and apply them to this field. The overall rate of artifact sharing we find in this dataset is approximately 30%, although it varies significantly with paper, author, and conference factors, and it is closer to 43% for conferences that actively evaluated artifact sharing. Approximately 20% of all shared artifacts are no longer accessible four years after publications, predominately when hosted on personal and academic websites. Our main finding is that papers with shared artifacts averaged approximately 75% more citations than papers with none. Even after controlling for numerous confounding covariates, the release of an artifact appears to increase the citations of a systems paper by some 34%. This metric is further boosted by the open availability of the paper's text.

Project description:BACKGROUND:There is a lack of knowledge regarding the frequency of disease associated polymorphisms in populations and population attributable risk for many populations remains unknown. Factors that could affect the association of the allele with disease, either positively or negatively, such as race, ethnicity, and gender, may not be possible to determine without population based allele frequencies.Here we used a panel of 51 polymorphisms previously associated with at least one disease and determined the allele frequencies within the entire Personalized Medicine Research Project population based cohort. We compared these allele frequencies to those in dbSNP and other data sources stratified by race. Differences in allele frequencies between self reported race, region of origin, and sex were determined. RESULTS:There were 19544 individuals who self reported a single racial category, 19027 or (97.4%) self reported white Caucasian, and 11205 (57.3%) individuals were female. Of the 11,208 (57%) individuals with an identifiable region of origin 8337 or (74.4%) were German.41 polymorphisms were significantly different between self reported race at the 0.05 level. Stratification of our Caucasian population by self reported region of origin revealed 19 polymorphisms that were significantly different (p = 0.05) between individuals of different origins. Further stratification of the population by gender revealed few significant differences in allele frequencies between the genders. CONCLUSIONS:This represents one of the largest population based allele frequency studies to date. Stratification by self reported race and region of origin revealed wide differences in allele frequencies not only by race but also by region of origin within a single racial group. We report allele frequencies for our Asian/Hmong and American Indian populations; these two minority groups are not typically selected for population allele frequency detection. Population wide allele frequencies are important for the design and implementation of studies and for determining the relevance of a disease associated polymorphism for a given population.

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In-text citation's frequencies-based recommendations of relevant research papers.

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