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Open reproducible scientometric research with Alexandria3k.

ABSTRACT: Considerable scientific work involves locating, analyzing, systematizing, and synthesizing other publications, often with the help of online scientific publication databases and search engines. However, use of online sources suffers from a lack of repeatability and transparency, as well as from technical restrictions. Alexandria3k is a Python software package and an associated command-line tool that can populate embedded relational databases with slices from the complete set of several open publication metadata sets. These can then be employed for reproducible processing and analysis through versatile and performant queries. We demonstrate the software's utility by visualizing the evolution of publications in diverse scientific fields and relationships among them, by outlining scientometric facts associated with COVID-19 research, and by replicating commonly-used bibliometric measures and findings regarding scientific productivity, impact, and disruption.

SUBMITTER: Spinellis D

PROVIDER: S-EPMC10688655 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Open reproducible scientometric research with Alexandria3k.

Spinellis Diomidis D

PloS one 20231130 11

Considerable scientific work involves locating, analyzing, systematizing, and synthesizing other publications, often with the help of online scientific publication databases and search engines. However, use of online sources suffers from a lack of repeatability and transparency, as well as from technical restrictions. Alexandria3k is a Python software package and an associated command-line tool that can populate embedded relational databases with slices from the complete set of several open publ ...[more]

PMID: 38032908

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Project description:BackgroundThousands of research articles on neuroblastoma have been published over the past few decades; however, the heterogeneity and variable quality of scholarly data may challenge scientists or clinicians to survey all of the available information. Hence, holistic measurement and analyzation of neuroblastoma-related literature with the help of sophisticated mathematical tools could provide deep insights into global research performance and the collaborative architectonical structure within the neuroblastoma scientific community. In this scientometric study, we aim to determine the extent of the scientific output related to neuroblastoma research between 1980 and 2018.MethodsWe applied novel scientometric tools, including Bibliometrix R package, biblioshiny, VOSviewer, and CiteSpace IV for comprehensive science mapping analysis of extensive bibliographic metadata, which was retrieved from the Web of ScienceTM Core Collection database.ResultsWe demonstrate the enormous proliferation of neuroblastoma research during last the 38 years, including 12,435 documents published in 1828 academic journals by 36,908 authors from 86 different countries. These documents received a total of 316,017 citations with an average citation per document of 28.35 ± 7.7. We determine the proportion of highly cited and never cited papers, "occasional" and prolific authors and journals. Further, we show 12 (13.9%) of 86 countries were responsible for 80.4% of neuroblastoma-related research output.ConclusionsThese findings are crucial for researchers, clinicians, journal editors, and others working in neuroblastoma research to understand the strengths and potential gaps in the current literature and to plan future investments in data collection and science policy. This first scientometric study of global neuroblastoma research performance provides valuable insight into the scientific landscape, co-authorship network architecture, international collaboration, and interaction within the neuroblastoma community.

Project description:Currently, there is a growing interest in ensuring the transparency and reproducibility of the published scientific literature. According to a previous evaluation of 441 biomedical journals articles published in 2000-2014, the biomedical literature largely lacked transparency in important dimensions. Here, we surveyed a random sample of 149 biomedical articles published between 2015 and 2017 and determined the proportion reporting sources of public and/or private funding and conflicts of interests, sharing protocols and raw data, and undergoing rigorous independent replication and reproducibility checks. We also investigated what can be learned about reproducibility and transparency indicators from open access data provided on PubMed. The majority of the 149 studies disclosed some information regarding funding (103, 69.1% [95% confidence interval, 61.0% to 76.3%]) or conflicts of interest (97, 65.1% [56.8% to 72.6%]). Among the 104 articles with empirical data in which protocols or data sharing would be pertinent, 19 (18.3% [11.6% to 27.3%]) discussed publicly available data; only one (1.0% [0.1% to 6.0%]) included a link to a full study protocol. Among the 97 articles in which replication in studies with different data would be pertinent, there were five replication efforts (5.2% [1.9% to 12.2%]). Although clinical trial identification numbers and funding details were often provided on PubMed, only two of the articles without a full text article in PubMed Central that discussed publicly available data at the full text level also contained information related to data sharing on PubMed; none had a conflicts of interest statement on PubMed. Our evaluation suggests that although there have been improvements over the last few years in certain key indicators of reproducibility and transparency, opportunities exist to improve reproducible research practices across the biomedical literature and to make features related to reproducibility more readily visible in PubMed.

Dataset Information

Open reproducible scientometric research with Alexandria3k.

Publications

Open reproducible scientometric research with Alexandria3k.

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