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Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\",\n  dpi = 300\n)\nlibrary(dplyr)\n```\n\n# bibliometrix\n\n## An R-tool for comprehensive science mapping analysis\n\n[![bibliometrix: An R-tool for comprehensive science mapping\nanalysis.](https://www.bibliometrix.org/JOI-badge.svg)](https://doi.org/10.1016/j.joi.2017.08.007)\n[![Project Status: Active - The project has reached a stable, usable\nstate and is being actively\ndeveloped.](http://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n[![R-CMD-check](https://github.com/massimoaria/bibliometrix/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/massimoaria/bibliometrix/actions/workflows/R-CMD-check.yaml)\n[![cran\nversion](http://www.r-pkg.org/badges/version/bibliometrix)](https://cran.r-project.org/package=bibliometrix)\n[![rstudio mirror\ndownloads](https://cranlogs.r-pkg.org/badges/bibliometrix)](https://github.com/r-hub/cranlogs.app)\n`r badger::badge_cran_download(\"bibliometrix\", \"grand-total\")`\n\n\u003cp align=\"center\"\u003e\n\n\u003cimg src=\"https://raw.githubusercontent.com/massimoaria/bibliometrix/master/inst/biblioshiny/www/logoAI.jpg\" width=\"400\"/\u003e\n\n\u003c/p\u003e\n\n## Overview\n\n**bibliometrix** provides a comprehensive set of tools for quantitative\nresearch in bibliometrics and scientometrics.\n\nBibliometrics applies quantitative analysis and statistics to scientific\npublications and their citation patterns. It has become essential across\nall scientific fields for evaluating growth, maturity, leading authors,\nconceptual and intellectual maps, and emerging trends within research\ncommunities.\n\nToday, bibliometrics is widely used in research performance evaluation\nby universities, government laboratories, policymakers, research\ndirectors, information specialists, librarians, and scholars themselves.\n\n**bibliometrix** supports scholars in three key phases of analysis:\n\n-   **Data importing and conversion** to R format from major\n    bibliographic databases;\n\n-   **Bibliometric analysis** of publication datasets, including\n    descriptive statistics, author productivity, and source impact;\n\n-   **Building and visualizing matrices** for co-citation, coupling,\n    collaboration, and co-word analysis. These matrices serve as input\n    for network analysis, multiple correspondence analysis, and other\n    data reduction techniques.\n\n## biblioshiny\n\n**bibliometrix** includes **biblioshiny: bibliometrix for no-coders**\n\n**biblioshiny** is a *shiny web application providing an intuitive\ninterface for bibliometrix*.\n\nIt enables scholars to easily access the main features of bibliometrix\nthrough an interactive workflow:\n\n### Data Management\n\n-   **Import and convert** data from multiple bibliographic databases\n    (Web of Science, Scopus, PubMed, OpenAlex, Cochrane CDSR, Lens.org)\n\n-   **Filter data** by various criteria including publication years,\n    journals, countries, citation counts, and custom journal rankings\n\n-   **Merge collections** from different databases\n\n-   **API Integration** for direct data retrieval from OpenAlex and\n    PubMed\n\n### Analytics and Visualization\n\n-   **Three-level metrics** for comprehensive analysis:\n\n    -   **Sources**: journal performance, impact metrics, Bradford's law\n\n    -   **Authors**: productivity analysis, h-index, collaboration\n        patterns, author profiles with biographical information\n\n    -   **Documents**: citation analysis, most relevant papers,\n        reference publication year spectroscopy (RPYS)\n\n### Knowledge Structure Analysis\n\n-   **Conceptual Structure**: analyzing the topics and themes through\n    co-word analysis, thematic mapping, and thematic evolution\n\n-   **Intellectual Structure**: examining the citation networks through\n    co-citation analysis, historiograph, and document coupling\n\n-   **Social Structure**: exploring collaboration patterns through\n    co-authorship networks at author, institution, and country levels\n\n### Advanced Features\n\n-   **Biblio AI**: An integrated AI assistant to help interpret results,\n    generate insights, and provide context-aware recommendations\n\n-   **Life Cycle Analysis**: Track and visualize the evolution of\n    research topics, authors, and documents over time\n\n-   **Animated Networks**: Dynamic visualization of diachronic networks\n    showing temporal evolution\n\n-   **Content Analysis**: Advanced text mining using natural language\n    processing for deeper content exploration\n\n-   **Citation Matching**: Intelligent algorithm to match and reconcile\n    citations across different databases\n\n-   **Interactive Reports**: Generate comprehensive Excel reports\n    combining multiple analyses\n\n### How to use biblioshiny\n\nTo launch the application, simply run:\n\n```{r eval=FALSE}\nlibrary(bibliometrix)\nbiblioshiny()\n```\n\nFor detailed tutorials and guides, visit the bibliometrix website:\n\u003chttps://www.bibliometrix.org/\u003e\n\n## How to cite bibliometrix\n\nIf you use this package for your research, please cite it as:\n\nAria, M. \u0026 Cuccurullo, C. (2017) **bibliometrix: An R-tool for\ncomprehensive science mapping analysis**, *Journal of Informetrics*,\n11(4), pp 959-975, Elsevier, DOI: 10.1016/j.joi.2017.08.007\n\n## Community\n\nOfficial website: \u003chttps://www.bibliometrix.org\u003e\n\nCRAN page: \u003chttps://cran.r-project.org/package=bibliometrix\u003e\n\nGitHub repository: \u003chttps://github.com/massimoaria/bibliometrix\u003e\n\n### Tutorials\n\nIntroduction to bibliometrix:\n\u003chttps://www.bibliometrix.org/vignettes/Introduction_to_bibliometrix.html\u003e\n\nData importing and converting:\n\u003chttps://www.bibliometrix.org/vignettes/Data-Importing-and-Converting.html\u003e\n\n## Installation\n\nInstall the stable version from CRAN:\n\n```{r eval=FALSE}\ninstall.packages(\"bibliometrix\")\n```\n\nOr install the development version from GitHub:\n\n```{r eval=FALSE}\nif (!require(\"pak\", quietly=TRUE)) install.packages(\"pak\")\npak::pkg_install(\"massimoaria/bibliometrix\")\n```\n\nLoad `bibliometrix`:\n\n```{r echo=TRUE}\nlibrary(bibliometrix)\n```\n\n## Data loading and converting\n\nExport files from bibliographic databases can be imported into R using\nthe function **convert2df**:\n\n**convert2df**(*file*, *dbsource*, *format*)\n\nThe argument *file* is a character vector containing the names of export\nfiles downloaded from SCOPUS, Clarivate Analytics WoS, OpenAlex, Digital\nScience Dimensions, PubMed, Lens.org, or Cochrane CDSR. The *file*\nargument can also contain JSON/XML objects downloaded using OpenAlex,\nDigital Science Dimensions, or PubMed APIs (through the packages\n*openalexR*, *dimensionsR*, and *pubmedR*).\n\nExample:\n\n```{r eval=FALSE}\nfile \u003c- c(\"file1.txt\", \"file2.txt\", ...)\n```\n\n```{r Data loading and Converting}\n## An example from bibliometrix vignettes\n\nfile \u003c- c(\"https://www.bibliometrix.org/datasets/management1.txt\",\"https://www.bibliometrix.org/datasets/management2.txt\")\n\nM \u003c- convert2df(file = file, dbsource = \"wos\", format = \"plaintext\")\n\ndata(management, package = \"bibliometrixData\")\nM \u003c- management\n```\n\nThe **convert2df** function creates a bibliographic data frame where\nrows correspond to manuscripts and columns to metadata fields.\n\nEach manuscript contains multiple elements including authors' names,\ntitle, keywords, abstract, and other bibliographic information. All\nthese elements constitute the metadata of a document.\n\nData frame columns are named using the standard Clarivate Analytics WoS\nField Tag codes: [(Field Tags\ndocumentation)](https://www.bibliometrix.org/documents/Field_Tags_bibliometrix.pdf)\n\n## Checking metadata completeness\n\nAfter importing a bibliographic data frame, you can assess the\ncompleteness of metadata using **missingData()**:\n\n**missingData**(*M*)\n\nThe argument *M* is a bibliographic data frame obtained by the\n**convert2df** function.\n\n```{r Completeness of metadata}\n## An example from bibliometrix vignettes\n\ncom \u003c- missingData(M)\n\ncom$mandatoryTags\n```\n\n## Bibliometric Analysis\n\nThe function **biblioAnalysis** performs a comprehensive bibliometric\nanalysis of a dataset, calculating main bibliometric measures.\n\n**biblioAnalysis**(*M*, *sep = \";\"*)\n\nThe argument *sep* indicates the field separator character used in the\ndata frame.\n\n```{r biblioAnalysis}\nresults \u003c- biblioAnalysis(M, sep = \";\")\n```\n\nThe **biblioAnalysis** function returns an object of class\n\"bibliometrix\" containing:\n\n-   Annual scientific production\n-   Most productive authors\n-   Most cited manuscripts\n-   Most productive countries\n-   Total citations per country\\\n-   Most relevant sources (journals)\n-   Most frequent keywords\n\nUse the generic function **summary** to display main results:\n\n```{r summary generic function}\nS \u003c- summary(object = results, k = 10, pause = FALSE)\n```\n\nThe *k* parameter specifies the number of rows to display in each table\n(top k authors, sources, etc.). The *pause* parameter controls whether\nto pause between tables.\n\nBasic visualizations can be generated using the **plot** function:\n\n```{r plot generic function, fig.width=7}\nplot(x = results, k = 10, pause = FALSE)\n```\n\n## Author Bio Cards\n\nThe **AuthorBio** function retrieves and displays biographical\ninformation about authors using OpenAlex data:\n\n```{r eval=TRUE}\n# Example: Get biographical information for an author\nauthorInfo \u003c- authorBio(author_position = 1, \n                        doi = \"10.1016/j.joi.2017.08.007\")\n\ndplyr::glimpse(authorInfo)\n```\n\nThis function provides comprehensive author profiles including:\n\n-   Institutional affiliations\n-   Research areas and topics\n-   Publication history\n-   Citation metrics\n-   Co-author networks\n\n## Citation Matching\n\nThe **applyCitationMatching** function implements an intelligent\nalgorithm to match and reconcile citations across different databases:\n\n```{r eval=FALSE}\n# Apply citation matching to improve reference consistency\nresults \u003c- applyCitationMatching(management, threshold = 0.85)\n```\n\nThis feature is particularly useful when:\n\n-   Merging datasets from multiple databases\n-   Identifying duplicate or variant citations\n-   Building accurate citation networks\n-   Conducting historiographic analysis\n\n## Life Cycle Analysis\n\nThe **lifeCycle** function analyzes the temporal evolution of research\ntopics, identifying different phases in their development:\n\n```{r eval=TRUE}\n# Perform life cycle analysis\ndata \u003c- M %\u003e% group_by(PY) %\u003e% count()\nLC \u003c- lifeCycle(data, forecast_years = 20, plot = TRUE, verbose = FALSE)\n\nprint(LC$parameters)\n\nprint(LC$metrics)\n```\n\nThe \\*\\*Life Cycle of Scientific Production\\* function implements a\nlogistic growth model to analyze the temporal dynamics of research\ntopics. This approach, grounded in the theory of scientific paradigms\nand innovation diffusion, allows researchers to identify the current\ndevelopmental stage of a field, predict future trends, and estimate when\na topic will reach maturity or saturation.\n\nBy fitting a logistic curve to the annual publication counts in your\ncollection, this analysis reveals whether a research area is in its\nemergence phase, rapid growth phase, maturity phase, or decline phase.\n\n## Bibliographic network matrices\n\nManuscript attributes are interconnected through the publications\nthemselves: authors link to journals, keywords to publication dates, and\nreferences create citation networks.\n\nThese connections form bipartite networks that can be represented as\nrectangular matrices (Manuscripts × Attributes).\n\nAdditionally, scientific publications regularly cite other works,\ngenerating co-citation and coupling networks that reveal the\nintellectual structure of research fields.\n\n### biblioNetwork function\n\nThe **biblioNetwork** function calculates the most frequently used\nbibliometric networks from a bibliographic data frame:\n\n**Analysis types:** - **Coupling**: Documents sharing references -\n**Co-citation**: References cited together - **Co-occurrences**:\nKeywords or terms appearing together\\\n- **Collaboration**: Co-authorship patterns\n\n**Network levels:** - Authors - References - Sources (journals) -\nCountries - Universities - Keywords (Author keywords or Keywords Plus) -\nTitles - Abstracts\n\nExample - classical co-citation network:\n\n```{r eval=FALSE}\nNetMatrix \u003c- biblioNetwork(M, analysis = \"co-citation\", network = \"references\", sep = \";\")\n```\n\n## Visualizing bibliographic networks\n\nBibliographic networks can be visualized and analyzed using the\n**networkPlot** function, which offers multiple layout algorithms and\ncustomization options.\n\nThe main argument *type* specifies the network layout: circle,\nkamada-kawai, fruchterman-reingold, mds, etc.\n\n### Country Scientific Collaboration\n\n```{r Country collaboration, fig.height=7, fig.width=7, warning=FALSE}\n# Create a country collaboration network\n\nM \u003c- metaTagExtraction(M, Field = \"AU_CO\", sep = \";\")\nNetMatrix \u003c- biblioNetwork(M, analysis = \"collaboration\", network = \"countries\", sep = \";\")\n\n# Plot the network\nnet \u003c- networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = \"Country Collaboration\", \n                   type = \"circle\", size = TRUE, remove.multiple = FALSE, labelsize = 0.8)\n```\n\nThis visualization reveals international research collaborations,\nhighlighting countries with strong scientific partnerships.\n\n### Co-Citation Network\n\n```{r Co-citation network, fig.height=7, fig.width=7, warning=FALSE}\n# Create a co-citation network\n\nNetMatrix \u003c- biblioNetwork(M, analysis = \"co-citation\", network = \"references\", n = 30, sep = \";\")\n\n# Plot the network\nnet \u003c- networkPlot(NetMatrix, Title = \"Co-Citation Network\", type = \"fruchterman\", \n                   size = TRUE, remove.multiple = FALSE, labelsize = 0.7, edgesize = 5)\n```\n\nCo-citation analysis identifies the intellectual foundations of a\nresearch field by revealing which references are frequently cited\ntogether.\n\n### Keyword Co-occurrences\n\n```{r Keyword co-occurrences, fig.height=7, fig.width=7, warning=FALSE}\n# Create keyword co-occurrences network\n\nNetMatrix \u003c- biblioNetwork(M, analysis = \"co-occurrences\", network = \"keywords\", sep = \";\")\n\n# Plot the network\nnet \u003c- networkPlot(NetMatrix, normalize = \"association\", weighted = TRUE, n = 30, \n                   Title = \"Keyword Co-occurrences\", type = \"fruchterman\", \n                   size = TRUE, edgesize = 5, labelsize = 0.7)\n```\n\nKeyword co-occurrence networks reveal the conceptual structure of a\nresearch field, identifying main themes and their relationships.\n\n### Thematic Map\n\nThe thematicMap function creates a **strategic diagram** based on\nco-word network analysis and clustering. It plots themes in a\ntwo-dimensional space according to their centrality (measure of\nimportance) and density (measure of development).\n\nThis visualization helps identify:\n\n\\- Motor Themes (well-developed and central),\n\n\\- Niche Themes (well-developed but peripheral),\n\n\\- Emerging or Declining Themes (weakly developed), and\n\n\\- Basic Themes (important but not well-developed).\n\nThe methodology is based on Cobo et al. (2011) co-word analysis\napproach.\n\n```{r fig.height=7, fig.width=7}\n# Create a Thematic Map\nthematicMapResults \u003c- thematicMap(M, field = \"DE\", n = 250, minfreq = 5, \n                                  stemming = FALSE, size = 0.3, n.labels = 3, \n                                  repel = TRUE, cluster=\"louvain\")\nplot(thematicMapResults$map)\nplot(thematicMapResults$net$graph)\n```\n\n### Thematic Evolution\n\nThe thematicEvolution function analyzes how themes evolve over time by\ndividing the collection into multiple time periods and tracking thematic\nchanges across them. It performs a thematic map analysis for each period\nand measures the conceptual relationships between themes in consecutive\nperiods using inclusion indexes and stability measures. This\nlongitudinal analysis reveals emerging topics, declining themes, stable\nresearch areas, and thematic transformations.\n\nThe function produces an interactive Sankey-like diagram showing\nthematic flows between periods, along with strategic maps for each time\nslice.\n\n```{r fig.height=7, fig.width=9}\n# Thematic Evolution\nTEResults \u003c- thematicEvolution(M, field = \"DE\", n = 250, \n                                              minFreq = 5, stemming = FALSE, \n                                              size = 0.3, n.labels = 1, \n                                              repel = TRUE, cluster=\"louvain\", \n                                              years = c(2004, 2008, 2015))\nplotThematicEvolution(TEResults$Nodes, TEResults$Edges, measure=\"weighted\")\n\n```\n\n#### 1985-2004\n\n```{r fig.height=9, fig.width=9}\n# 1985-2004\nplot(TEResults$TM[[1]]$map)\n\n```\n\n#### 2005-2008\n\n```{r fig.height=9, fig.width=9}\n\n# 2005-2008\nplot(TEResults$TM[[2]]$map)\n\n```\n\n#### 2009-2015\n\n```{r fig.height=9, fig.width=9}\n\n\n# 2009-2015\nplot(TEResults$TM[[3]]$map)\n\n```\n\n#### 2016-2020\n\n```{r fig.height=9, fig.width=9}\n\n# 2016-2020\nplot(TEResults$TM[[4]]$map)\n```\n\n## Co-Word Analysis: The conceptual structure of a field\n\nCo-word analysis maps the conceptual structure of a research domain by\nexamining word co-occurrences in a bibliographic collection.\n\nThe analysis employs dimensionality reduction techniques including: -\n**Multiple Correspondence Analysis (MCA)** - **Correspondence Analysis\n(CA)** - **Multidimensional Scaling (MDS)**\n\nThe **conceptualStructure** function performs CA or MCA to visualize the\nconceptual structure and uses K-means clustering to identify document\nclusters sharing common themes. Results are displayed on two-dimensional\nmaps.\n\nThe function includes natural language processing (NLP) routines (see\n**termExtraction**) to extract terms from titles and abstracts, and\nimplements Porter's stemming algorithm to reduce words to their root\nform.\n\n```{r Co-Word Analysis, fig.height=9, fig.width=9, warning=FALSE}\n# Conceptual Structure using keywords (method=\"MCA\")\n\nCS \u003c- conceptualStructure(M, field = \"ID\", method = \"MCA\", minDegree = 10, \n                          clust = 5, stemming = FALSE, labelsize = 15, \n                          documents = 20, graph = FALSE)\n\nplot(CS$graph_terms)\nplot(CS$graph_dendogram)\n```\n\nThis analysis helps identify: - Main research themes and sub-themes -\nRelationships between concepts - Evolution of research focus - Emerging\ntopics and declining areas\n\n## Referenced Publication Years Spectroscopy (RPYS)\n\nRPYS analysis examines the age distribution of cited references to\nidentify seminal works and breakthrough moments in a research field. The\n**rpys** function provides advanced capabilities for detecting:\n\n```{r eval=TRUE}\n# Perform RPYS analysis\nrpysResults \u003c- rpys(M, sep = \";\", timespan = NULL, graph = TRUE)\n\nprint(rpysResults$Sequences %\u003e% filter(Class!=\"\") %\u003e% group_by(Class) %\u003e% slice_max(order_by=Freq,n=3, with_ties = FALSE) %\u003e% arrange(desc(RPY),.by_group = TRUE), n=30)\n```\n\nThe analysis identifies four types of influential references:\n\n-   **Hot Papers**: Recently published works receiving immediate,\n    intense attention\n-   **Constant Performers**: Works consistently cited over extended\n    periods\n-   **Life Cycles**: Publications showing typical rise and fall patterns\n    in citation frequency\n-   **Sleeping Beauties**: Works initially overlooked but later\n    recognized as significant contributions\n\nRPYS helps researchers: - Identify foundational works in a field -\nDetect breakthrough moments and paradigm shifts - Understand citation\npatterns over time - Discover underappreciated but important\ncontributions\n\n## Historical Direct Citation Network\n\nThe historiograph, proposed by Eugene Garfield, represents a\nchronological network of the most relevant direct citations in a\nbibliographic collection.\n\nThe **histNetwork** function generates a chronological direct citation\nnetwork matrix that can be visualized using **histPlot**:\n\n```{r Historical Co-citation network, fig.height=9, fig.width=9, warning=FALSE}\n# Create a historical citation network\n\nhistResults \u003c- histNetwork(M, sep = \";\")\n\n# Plot a historical citation network\nnet \u003c- histPlot(histResults, n = 20, size = FALSE, label = \"short\")\n```\n\nThe historiograph reveals: - The chronological development of ideas in a\nfield - Key publications and their influence over time - Citation paths\nshowing knowledge flow - Temporal relationships between foundational\nworks\n\nThis visualization is particularly valuable for understanding how\nscientific knowledge evolves and builds upon previous research.\n\n## Main Authors' References\n\n### Core bibliometrix publication\n\nAria, M. \u0026 Cuccurullo, C. (2017). **bibliometrix: An R-tool for\ncomprehensive science mapping analysis**, *Journal of Informetrics*,\n11(4), pp 959-975, Elsevier, DOI: 10.1016/j.joi.2017.08.007\n(\u003chttps://doi.org/10.1016/j.joi.2017.08.007\u003e)\n\n### Recent methodological advances\n\nM. Aria, C. Cuccurullo, L. D'Aniello, M. Misuraca, M. Spano (2024).\n**Comparative science mapping: a novel conceptual structure analysis\nwith metadata**, *Scientometrics*.\n(\u003chttps://doi.org/10.1007/s11192-024-05161-6\u003e)\n\nAria, M., Le, T., Cuccurullo, C., Belfiore, A., \u0026 Choe, J. (2024).\n**openalexR: An R-Tool for Collecting Bibliometric Data from OpenAlex**.\n*The R Journal*, [DOI:\n10.32614/RJ-2023-089](https://doi.org/10.32614/RJ-2023-089).\n\n### Applications in various domains\n\nAria, M., D’Aniello, L., Grassia, M. G., Marino, M., Mazza, R., \u0026\nStavolo, A. (2024). **Mapping the evolution of gender dysphoria\nresearch: a comprehensive bibliometric study**. *Quality \u0026 Quantity*,\n58(6), 5351-5375.\n\nAria, M., Cuccurullo, C., D'Aniello, L., Misuraca, M., \u0026 Spano, M.\n(2022). **Thematic Analysis as a New Culturomic Tool: The Social Media\nCoverage on COVID-19 Pandemic in Italy**. *Sustainability*, 14(6), 3643,\n(\u003chttps://doi.org/10.3390/su14063643\u003e)\n\nAria M., Misuraca M., Spano M. (2020) **Mapping the evolution of social\nresearch and data science on 30 years of Social Indicators Research**,\n*Social Indicators Research*.\n(\u003chttps://doi.org/10.1007/s11205-020-02281-3\u003e)\n\nAria M., Alterisio A., Scandurra A, Pinelli C., D'Aniello B, (2021)\n**The scholar's best friend: research trends in dog cognitive and\nbehavioural studies**, *Animal Cognition*.\n(\u003chttps://doi.org/10.1007/s10071-020-01448-2\u003e)\n\nAngelelli, M., Ciavolino, E., Ringle, C. M., Sarstedt, M., \u0026 Aria, M.\n(2025). **Conceptual structure and thematic evolution in partial least\nsquares structural equation modeling research**. *Quality \u0026 Quantity*,\n1-46.\n\nBelfiore, A., Cuccurullo, C., \u0026 Aria, M. (2022). **IoT in healthcare: A\nscientometric analysis**. *Technological Forecasting and Social Change*,\n184, 122001. (\u003chttps://doi.org/10.1016/j.techfore.2022.122001\u003e)\n\nCiavolino, E., Aria, M., Cheah, J. H., \u0026 Roldán, J. L. (2022). **A tale\nof PLS structural equation modelling: episode I—a bibliometrix citation\nanalysis**. *Social Indicators Research*, 164(3), 1323-1348\n(\u003chttps://doi.org/10.1007/s11205-022-02994-7\u003e).\n\nD'Aniello, L., Spano, M., Cuccurullo, C., \u0026 Aria, M. (2022). **Academic\nHealth Centers' configurations, scientific productivity, and impact:\ninsights from the Italian setting**. *Health Policy*.\n(\u003chttps://doi.org/10.1016/j.healthpol.2022.09.007\u003e)\n\nSarto, F., Cuccurullo, C., \u0026 Aria, M. (2014). **Exploring healthcare\ngovernance literature: systematic review and paths for future\nresearch**. *Mecosan*\n(\u003chttps://www.francoangeli.it/Riviste/Scheda_Rivista.aspx?IDarticolo=52780\u0026lingua=en\u003e)\n\nScarano, A., Aria, M., Mauriello, F., Riccardi, M. R., \u0026 Montella, A.\n(2023). **Systematic literature review of 10 years of cyclist safety\nresearch**. *Accident Analysis \u0026 Prevention*, 184, 106996\n(\u003chttps://doi.org/10.1016/j.aap.2023.106996\u003e).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmassimoaria%2Fbibliometrix","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmassimoaria%2Fbibliometrix","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmassimoaria%2Fbibliometrix/lists"}