Array
(
    [0] => stdClass Object
        (
            [journal] => stdClass Object
                (
                    [id_jnl] => 87
                )

        )

    [1] => stdClass Object
        (
            [section] => stdClass Object
                (
                    [section] => 281
                )

        )

    [2] => stdClass Object
        (
            [title] => Array
                (
                    [0] => BibFusion: A Python package to integrate, deduplicate, and harmonize exported bibliographic records from Scopus and Web of Science for bibliometric analysis@en
                    [1] => BibFusion: paquete en Python para integrar, desduplicar y armonizar registros bibliográficos exportados de Scopus y Web of Science para análisis bibliométrico@es
                )

        )

    [3] => stdClass Object
        (
            [abstract] => Array
                (
                    [0] => Objective. The study presented BibFusion, a Python software package that harmonizes bibliographic exports from Scopus and Web of Science into a single, traceable, analysis-ready corpus for bibliometric and scientometric research.
Design/Methodology/Approach. BibFusion was capable of ingesting Scopus CSV and WoS TXT files, applying systematic normalization (e.g., ASCII/uppercase standardization of titles and SR keys, affiliation parsing with country extraction), and optionally enriching records via DOI‑based resolution against OpenAlex to recover persistent identifiers (e.g., OpenAlex work IDs, ORCID when available, and OpenAlex author IDs). Cross-database integration employed a DOI-first deduplication cascade with a conservative fallback (title–year–first author) in the event that a DOI is absent. The authors were disambiguated through a canonical PersonID hierarchy (ORCID → OpenAlexAuthorID → normalized name). Citation strings were cleaned and remapped to ensure the preservation of consistent citation links, and journal/Scimago information was consolidated using ISSN/EISSN rules.
Results. In a demonstration on an entrepreneurial marketing query, BibFusion consolidated 436 source records into 253 unique main works and materialized a unified corpus of 8,569 articles. The resulting dataset demonstrated high levels of identifier and geographic completeness, and it provided an analysis-ready citation layer.
Conclusions/Value. BibFusion offers a reusable, auditable integration workflow that has been demonstrated to reduce duplicate inflation and metadata fragmentation. This workflow facilitates the explicit determination of merge decisions and residual uncertainty, thereby ensuring transparency in downstream analyses.@en
                    [1] => Objetivo. Presentamos BibFusion, un paquete de software en Python que armoniza exportaciones bibliográficas de Scopus y Web of Science en un corpus único, trazable y listo para el análisis, orientado a la investigación bibliométrica y cienciométrica.
Diseño/Metodología/Enfoque. BibFusion ingiere archivos CSV de Scopus y TXT de Web of Science, aplica una normalización sistemática (p. ej., estandarización ASCII y en mayúsculas de títulos y claves SR; análisis de afiliaciones con extracción de país) y, de forma opcional, enriquece los registros mediante resolución basada en DOI contra OpenAlex para recuperar identificadores persistentes (p. ej., IDs de obras en OpenAlex, ORCID cuando está disponible e IDs de autores en OpenAlex). La integración entre bases utiliza una cascada de desduplicación con prioridad al DOI y un respaldo conservador (título–año–primer autor) cuando el DOI falta. Los autores se desambiguan mediante una jerarquía canónica de PersonID (ORCID → OpenAlexAuthorID → nombre normalizado). Las cadenas de citación se depuran y se remapean para preservar vínculos de citación consistentes y la información de revistas/SCImago se consolida mediante reglas basadas en ISSN/EISSN.
Resultados/Discusión. En una demostración sobre una consulta de marketing emprendedor, BibFusion consolida 436 registros de origen en 253 obras principales únicas y materializa un corpus unificado de 8.569 artículos. El conjunto de datos resultante logra una alta completitud de identificadores y de información geográfica y proporciona una capa de citaciones lista para análisis; los indicadores completos de control de calidad se reportan en las Tablas 2–3.
Conclusiones/Valor. BibFusion ofrece un flujo de trabajo de integración reutilizable y auditable que reduce la inflación por duplicados y la fragmentación de metadatos, al tiempo que hace explícitas las decisiones de fusión y la incertidumbre residual para habilitar análisis posteriores transparentes.@es
                )

        )

    [4] => stdClass Object
        (
            [author] => Array
                (
                    [0] => Angelo Britto
                    [1] => Sebastian Robledo
                    [2] => Martha Zuluaga
                )

        )

    [5] => stdClass Object
        (
            [subject] => Array
                (
                    [0] => Bibliometrics@en
                    [1] => Scientometrics@en
                    [2] => Cross-database integration@en
                    [3] => Scopus@en
                    [4] => Web of science@en
                    [5] => Metadata preprocessing@en
                    [6] => Author disambiguation@en
                    [7] => Citation networks@en
                    [8] => Reproducible research@en
                    [9] => Bibliometría@es
                    [10] => Cienciometría@es
                    [11] => Integración entre bases de datos@es
                    [12] => Scopus@es
                    [13] => Web of science@es
                    [14] => Preprocesamiento de metadatos@es
                    [15] => Desambiguación de autores@es
                    [16] => Redes de citas@es
                    [17] => Investigación reproducible@es
                )

        )

    [6] => stdClass Object
        (
            [source] => stdClass Object
                (
                    [vol] => 6
                    [nr] => 
                    [year] => 2026
                    [theme] => 
                )

        )

    [7] => stdClass Object
        (
            [datePub] => Array
                (
                    [0] => 2026-02-14
                )

        )

    [8] => stdClass Object
        (
            [DOI] => Array
                (
                    [0] => stdClass Object
                        (
                            [type] => DOI
                            [value] => Array
                                (
                                    [0] => 10.47909/ijsmc.342
                                )

                        )

                )

        )

    [9] => stdClass Object
        (
            [http] => Array
                (
                    [0] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://ijsmc.pro-metrics.org/index.php/i/article/view/342
                                )

                        )

                    [1] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://ijsmc.pro-metrics.org/index.php/i/article/view/342/205
                                )

                        )

                    [2] => stdClass Object
                        (
                            [type] => HTTP
                            [value] => Array
                                (
                                    [0] => https://ijsmc.pro-metrics.org/index.php/i/article/view/342/206
                                )

                        )

                )

        )

    [10] => stdClass Object
        (
            [language] => Array
                (
                    [0] => en
                    [1] => es
                )

        )

    [11] => stdClass Object
        (
            [license] => Array
                (
                    [0] => Copr
                    [1] => by-nc/4.0
                )

        )

)