Over the last six decades, significant progress has been made in studying poverty. Poverty research is an important issue for ensuring the sustainable development and governance of the world, especially the lower-income regions. More attention from multiple dimensions shall be paid to poverty research. However, apart from a few research publications, less research has investigated their citations using the most widely used approach for citation mapping—Scientometric analysis. Therefore, this research utilized 319 published papers on poverty research from 1964 to 20 February 2022, from the Scopus database to conduct bibliometric and social network analysis. The bibliometrix package in R and the VOSviewer program were used to perform data analysis and visualization. Theme mapping, trend themes, bibliometric coupling, and co-occurrence networks were utilized to discover potential study areas for existing and future trends. The findings reveal that poverty research has increased by 10.18% each year since 2006. Additionally, the results indicate the most influential sections of the research based on the most often mentioned subjects, papers, authors, and keywords. The findings indicate that future studies should focus on the poverty line, social policies, and living standards. The contributions of the paper may provide a reference to the understanding of poverty research through bibliometric analysis and promote poverty research in theory and practice.

Objective. The objective of this study was to develop and validate KKU-BiblioMerge V.1.0, a bibliometric tool designed to address the limitations of single-source data in bibliometric analysis by integrating data from multiple databases, specifically Scopus and Web of Science (WoS). Design/Methodology/Approach. The tool was developed using the R Shiny framework and incorporated key functions for data deduplication, field mapping, and integrity checks to ensure effective dataset merging. The performance of KKU-BiblioMerge was assessed by testing its ability to import, merge, and export bibliometric data, focusing on the efficiency and accuracy of consolidating records from Scopus and WoS. Findings. The KKU-BiblioMerge application effectively processed and integrated 686 initial documents, eliminating 24.49% duplicate records to produce a final dataset of 518 unique entries. The tool demonstrated strong data consistency and high accuracy in field mapping, offering reliable cross-platform integration of bibliometric data compared to tools such as VOSviewer and Biblioshiny. Originality/Value. KKU-BiblioMerge V.1.0 was a user-friendly, robust solution for multi-database bibliometric analysis. It enabled a more comprehensive and unbiased understanding of research landscapes. Its capability to integrate diverse datasets laid a foundation for advancing bibliometric software, broadening the scope and accuracy of analyses across scientific domains.

Purpose: Provenance information is crucial for consistent maintenance of metadata schemas over time. The purpose of this paper is to propose a provenance model named DSP-PROV to keep track of structural changes of metadata schemas. Design/methodology/approach: The DSP-PROV model is developed through applying the general provenance description standard PROV of the World Wide Web Consortium to the Dublin Core Application Profile. Metadata Application Profile of Digital Public Library of America is selected as a case study to apply the DSP-PROV model. Finally, this paper evaluates the proposed model by comparison between formal provenance description in DSP-PROV and semi-formal change log description in English. Findings: Formal provenance description in the DSP-PROV model has advantages over semi-formal provenance description in English to keep metadata schemas consistent over time. Research limitations/implications: The DSP-PROV model is applicable to keep track of the structural changes of metadata schema over time. Provenance description of other features of metadata schema such as vocabulary and encoding syntax are not covered. Originality/value: This study proposes a simple model for provenance description of structural features of metadata schemas based on a few standards widely accepted on the Web and shows the advantage of the proposed model to conventional semi-formal provenance description.

Purpose: The purpose of this paper is to discuss provenance description of metadata terms and metadata vocabularies as a set of metadata terms. Provenance is crucial information to keep track of changes of metadata terms and metadata vocabularies for their consistent maintenance. Design/methodology/approach: The W3C PROV standard for general provenance description and Resource Description Framework (RDF) are adopted as the base models to formally define provenance description for metadata vocabularies. Findings: This paper defines a few primitive change types of metadata terms, and a provenance description model of the metadata terms based on the primitive change types. We also provide examples of provenance description in RDF graphs to show the proposed model. Research limitations: The model proposed in this paper is defined based on a few primitive relationships (e.g. addition, deletion, and replacement) between pre-version and post-version of a metadata term. The model is simplified and the practical changes of metadata terms can be more complicated than the primitive relationships discussed in the model. Practical implications: Formal provenance description of metadata vocabularies can improve maintainability of metadata vocabularies over time. Conventional maintenance of metadata terms is the maintenance of documents of terms. The proposed model enables effective and automated tracking of change history of metadata vocabularies using simple formal description scheme defined based on widely-used standards. Originality/value: Changes in metadata vocabularies may cause inconsistencies in the longterm use of metadata. This paper proposes a simple and formal scheme of provenance description of metadata vocabularies. The proposed model works as the basis of automated maintenance of metadata terms and their vocabularies and is applicable to various types of changes.

Background: In the context of global open science trends, medical open-access repositories (OARs) promote transparency in research and facilitate the sharing of scientific data. The increase in scientific output necessitates a robust infrastructure to enhance OARs in China.Objectives: This study aimed to evaluate medical open-access repositories (OARs) in China that are indexed in re3data.org and OpenDOAR.org. The study analyzed data classification, descriptions, retrieval, and the utilization of selected repositories.Methods: This study ascertained the current status of the Chinese medical OARs by visiting their respective websites and attempted to identify the disciplinary orientation of each OAR. A content analysis approach was utilized to achieve this study’s objective. Twelve Chinese medical open-access repositories were selected from re3data.org and OpenDOAR.org to examine how their information is organized. The data were collected manually from May 1 to 30, 2023, and analyzed using various quantitative techniques to understand the current status of medical scientific repositories in China.Results: Based on the results, this study proposed the following recommendations: (1) implement multi-dimensional data classification, (2) use persistent data identifiers, (3) formalize the description metadata, (4) enhance advanced retrieval and result set filtering functions, and (5) optimize the preview and interaction features of data repositories.Conclusion: The scope of this study is restricted to the medical open-access repositories in China as listed on re3data.org and OpenDOAR.org. Therefore, the results of this study are only generalizable within China. The primary focus of research output in China is on medical open-access repositories. This study is essential for assessing China’s current status in research data management within the medical field and its distribution infrastructure in global open science trends.