Research Data Management in Environmental Studies: Scoping Review and Bibliometrics Analysis

1. Introduction

Research data management is ‘a set of practices for handling and organising research data to make it easier to find, understand, and use’ (Higman, Bangert and Jones, 2019). RDM is ‘a process that involves various key stakeholders, such as researchers, executive management, and staff’ (Ashiq, Usmani and Naeem, 2022). The implementation of RDM is ‘tailored to the research cycle itself and involves all activities, including planning, managing, processing, organising, analysing, preserving, accessing, reusing, and creating data’ (Higman, Bangert and Jones, 2019; Ashiq, Usmani and Naeem, 2022). RDM aims to facilitate efficient research processes, ensure the accuracy, reliability, and replicability of research data, and ensure the security of research resources (Higman, Bangert and Jones, 2019) to ensure that researchers’ data collection processes are organised, understandable, and transparent (Surkis and Read, 2015) so that research becomes efficient, sustainable, highly qualitative, and provides the maximum impact and reach including publication and accessibility (Finkel et al., 2020); and to save research time (Ashiq, Usmani and Naeem, 2022).

In environmental research, it is essential to do RDM well, given that the data generated can be extensive, have various formats, come from various sources, be used for various purposes, and be utilised by various professions and occupations. Finkel et al. (2020) stated, ‘RDM is essential for long-term environmental research collaborations because of its role in efficiency, continuity, quality, and maximum impact, as well as providing access to valuable long-term data sets’. In addition, Beagrie (2003) mentioned that data serves as a tool in monitoring and enforcing compliance with environmental regulations or standards and policies and assisting in informed decision-making.

The ‘current state’ of RDM research in environmental studies is still unclear, even though RDM is a suitable data management approach to implement in an organisation, particularly a research institution. To get the ‘current state’ of research, reviewing the previous literature on the topic and how it relates to other topics is necessary. A literature review aims to analyse and synthesise the work done in a particular area and identify what we know and do not know about the research question posed (Bradbury-Jones et al., 2022). For this reason, this research will attempt to search, collect, analyse, and reveal the latest data on RDM studies or research related to environmental studies based on previous literature using the scoping review framework proposed by Bradbury-Jones et al. (2022). Alongside the scoping review, this research analysis is supported by statistical tools, specifically bibliometrics, to identify diverse patterns in the literature related to RDM studies within environmental studies. Bibliometrics analysis identifies a set of literature, usually associated with a publication dataset on particular subject areas (Napitupulu and Yakub, 2021). The technique of combining scoping review and bibliometric analysis is called ScoRBA, which was introduced by Wijaya et al. (2023).

From the background and problem formulation regarding the study of RDM in environmental studies, the research questions that will be tried to be answered in this study are:

2. Methods

2.4. Charting the data

Based on the study selection using a search strategy adapted from the PRISMA stage on November 22^nd, 2023, against five databases and one journal, there are 248 articles on RDM in the environmental studies (Figure 1). The data will be charted or used in other forms of visualisation with the help of Bibliometrix through the R Studio program and VOSviewer 1.6.19 to answer the research questions. The article’s metadata set in RIS format was input into VOSviewer. Before inputting data into VOSviewer and Bibliometrix, data cleaning was carried out on keywords as follows: (1) indicates keywords that have the same meaning; (2) sort all the keywords that have been marked from A to Z; (3) uniformise the keywords that will be used, and (4) re-insert the uniformed keywords into excel format. This stage needed to improve the quality of keywords, as stated by Bjarkefur et al., 2020 that a structured workflow for preparing newly acquired data for analysis is essential for efficient, transparent, and reproducible data work.

Figure 1

3. Results

3.1. The growth of RDM literature in environmental studies

Articles on RDM in environmental studies were found in 1985, but after that, nothing was published until 1999. From 2000 to 2010, RDM articles were published almost every year, although the number fluctuated. From 2011 to the following years, there was a significant increase. Article production peaked in 2020 and 2021 (n = 31), then decreased slightly in the following two years (Figure 2). However, the number of productions was still relatively high compared to the previous period. Apart from these fluctuations, the importance of RDM as a topic has generally increased since 2012.

Figure 2

3.2. Main themes and trends of research data management in environmental studies

3.2.1. Keyword Analysis

The themes of RDM in environmental studies can be seen from the keywords in the literature. The most common theme is RDM itself, with 114 occurrences. The second most common occurrence is data management, with 90 occurrences. Other keyword occurrences, such as information management, research data, metadata, data sharing, and information processing, can be seen in Figure 3.

Figure 3

3.2.2. Thematic Maps

A more precise visualisation can use thematic maps, which are strategic diagrams used to find the main emerging themes (trends) and future research topics of the RDM in environmental studies. This study has thirteen theme clusters in different quadrants (Figure 4). The bottom right quadrant, the study’s basic theme, is cluster one, which contains the life cycle, data life cycle, and decision making, and cluster six, which contains the themes of research data, data sharing, and data curation. There is also cluster three, placed between basic and influencing themes, which contains research data management, data management, and information management. The upper right quadrant, namely the driving and influencing themes in RDM topics in environmental studies, is cluster five, containing information processing, human, and software. Next is cluster four: data acquisition, biodiversity, and climate change. Cluster seven has the theme of data handling, quality control, and climatology. The last one is cluster nine, which is placed between influencing and specific themes containing the themes of artificial intelligence, digital transformation, and open scholarship.

Figure 4

The upper left quadrant is a specific theme; the higher the position, the more frequently the themes appear or have many publications, namely cluster 12, which contains archaeological data, agricultural data, and biomedical data, and cluster two, which contains biodiversity informatics, data assimilation, and data protection. Finally, the bottom left quadrant is the emerging or declining themes: Cluster 11, academic libraries, South Africa, bibliometrics, and Cluster 10, agricultural productivity and technology. Next is cluster eight, which contains research data sharing, Tanzania, and agricultural research. The last is cluster 13, containing the conceptual model (Figure 4). Based on the keyword search in the lower left quadrant conducted on Scopus on May 29^th, 2024, it is known that the keyword ‘conceptual model’ is an emerging theme because the last publications in 2024 and 2023, although not yet cited and the number is only six publications.

3.2.3. The PAGER reporting

As the final stage of the scoping review, the following reports the search results for articles on emerging themes in RDM topics in environmental studies. As stated in the Methods chapter, the reporting uses the PAGER method of Arksey & O’Malley (2005).

This study generated 108 out of 1460 keywords from 248 selected articles. Selected keywords appeared four times simultaneously (co-occurrence), hoping the relationship between the keywords that appeared was strong. As a result, VOSviewer displays four clusters (Figure 5), with each cluster having a different focus of study (Table 4).

Figure 5

From the cluster in Table 3 and Figure 5, PAGER reporting was conducted (Table 4) along with literature sources derived from the corresponding keywords mentioned in the methods section (Appendix 4–7).

Table 3

Distribution of research themes and keywords across RDM clusters in Environmental Studies.

CLUSTER	THEME	DESCRIPTION
Red	Data management	The red cluster consists of 41 keywords, focusing on “Data Management” on environmental studies by looking at the 10 highest co-occurrences of data management, information management, metadata, digital storage, ecology, open data, fair data, information system, and research. In this cluster, the relationship between RDM and the environmental fields is most visible compared to the others. The relationship can be seen in ecology, biodiversity, climate change, ecosystems, plants, climatology, earth science, environmental management, and environmental science (Appendix 1).
Green	Information processing and biomedicine	The green cluster encompasses 26 keywords discussing RDM in information processing that focus on biology and medicine research, as can be seen from the keywords information processing, human, software, medical research, big data, bioinformatics, biomedical research, biology, biomedicine, computational biology, and human experiments (Appendix 1).
Blue	RDM practices	The blue cluster consists of 25 keywords discussing various RDM activities that can be seen in the 10 highest co-occurrence keywords: data sharing, data curation, data repository, data preservation, digital preservation, fair principles, research management, big data, data management plan, institutional repositories, and data reuse (Appendix 1).
Yellow	RDM and supporting system	The yellow cluster amounted to 16 keywords, which indicated the supporting system of RDM that involves librarians in academic libraries supporting research through RDM services. The keywords supporting the statement are research data management, research data, academic libraries, librarian, open access, universities, collaboration, bibliometrics, library services, and research support (Appendix 1).

Table 4

PAGER analysis results for RDM in environmental studies.

PATTERN	ADVANCES
Data management	Description	Sources
	Data management and FAIR principles	(Hampton et al., 2015; Onyancha, 2016; Ghaffar et al., 2020; Mayer et al., 2021; Arend et al., 2022)
	Integration and interoperability	(Arend et al., 2022; Gadelha et al., 2022; Felden et al., 2023; Williams et al., 2023)
	Open science and data sharing	(Hampton et al., 2015; Aubin et al., 2020; Brinkhaus et al., 2023; Felden et al., 2023)
	Technological innovation and adoption	(Knapp, 2008; Curry et al., 2019; Liggins and Noble, 2021; Blumberg et al., 2022)
	Collaboration and infrastructure	(Hampton et al., 2015; Aubin et al., 2020; Felden et al., 2023)
	Sustainability and cost efficiency	(Curry et al., 2019; Ghaffar et al., 2020)
	Indigenous perspectives and diverse criteria	(Elshall et al., 2022; Liggins et al., 2022)
Information processing and biomedicine	Training and professional development	(Attwood et al., 2019; Read et al., 2019; Ashley Thomas and Martin, 2020)
	Collaboration and knowledge sharing	(Attwood et al., 2019; A Thomas and Martin, 2020)
	RDM practices	(Schmidt et al., 2022; Zhou, Xu and Kogut, 2023)
	Data sharing and open science	(Schmidt et al., 2022; Zhou, Xu and Kogut, 2023)
	User-centered design and engagement	(Read et al., 2019; Ashley Thomas and Martin, 2020)
RDM practices	RDM practices and strategies	(Suhr et al., 2020; Feser et al., 2022; Roche et al., 2022; Gehlot et al., 2023; Rehnert and Takors, 2023)
	FAIR principles and data reusability	(Suhr et al., 2020; Chamanara et al., 2021; Kuhn Cuellar et al., 2022; Praetzellis et al., 2023)
	Collaboration and interdisciplinary practices	(Atwood et al., 2015; Kouper, Scheidt and Plale, 2021; Rehnert and Takors, 2023)
	Technological advancements and tools	(Zielinski, Hay and Millar, 2019; Chamanara et al., 2021; Feser et al., 2022; Kuhn Cuellar et al., 2022)
	Standardisation and interoperability	(Bowers, McPhillips and Ludäscher, 2008; Feser et al., 2022; Kuhn Cuellar et al., 2022)
	Data sharing and open science	(Murillo, 2022; Roche et al., 2022; Rehnert and Takors, 2023)
RDM and supporting system	RDM practices and services	(Persaud et al., 2021; Ismail et al., 2022; Jiang et al., 2023; Weil et al., 2023)
	Open science and data sharing	(Beck et al., 2020; Brandt et al., 2021; Sherbinin et al., 2021; Katabalwa, Bates and Abbott, 2021)
	Collaboration and integration	(Persaud et al., 2021; Gossler et al., 2022; Jiang et al., 2023; Weil et al., 2023)
	Policy development and support services	(Kennan, Corrall and Afzal, 2014; Tripathi, Shukla and Sonkar, 2017; Katabalwa, Bates and Abbott, 2021)
	Technological innovations and tools	(Berkley et al., 2001; Andreu-Perez et al., 2015; Wachtler et al., 2021; Musen et al., 2022)
	Data interoperability and reuse	(Berkley et al., 2001; Sherbinin et al., 2021; Weil et al., 2023)
Data management	GAPS
	Description	Sources
	Challenges in data sharing and fair compliance	(Mayer et al., 2021; Gadelha et al., 2022; Brinkhaus et al., 2023; Williams et al., 2023)
	Interoperability and integration issues	(Aubin et al., 2020; Ghaffar et al., 2020; Gadelha et al., 2021)
	Training and resource gaps	(Liggins and Noble, 2021; Arend et al., 2022; Liggins et al., 2022)
	Standardisation and metadata issues	(Mayer et al., 2021; Arend et al., 2022; Williams et al., 2023)
	Workflow and system limitations	(Ghaffar et al., 2020; Blumberg et al., 2022)
	Cultural and knowledge gaps	(Liggins and Noble, 2021, 2021)
	Data quality and completeness	(Gadelha et al., 2021; Elshall et al., 2022)
Information processing and biomedicine	Training and awareness gaps	(Read et al., 2019; Zhou, Xu and Kogut, 2023)
	Engagement and usability challenges	(Read et al., 2019; A Thomas and Martin, 2020)
RDM practices	Challenges in RDM	(Chamanara et al., 2021; Feser et al., 2022; Kuhn Cuellar et al., 2022; Rehnert and Takors, 2023)
	Adoption and implementation of advanced tools and practices	(Feser et al., 2022; Kuhn Cuellar et al., 2022; Praetzellis et al., 2023)
	Data sharing and reusability	(Murillo, 2022; Roche et al., 2022; Rehnert and Takors, 2023)
	Training and curriculum gaps	(Kouper, Scheidt and Plale, 2021; Gehlot et al., 2023)
	Sustainability and infrastructure limitations	(Chamanara et al., 2021; Feser et al., 2022)
RDM and supporting system	Training and capacity building in RDM	(Suhr et al., 2020; Katabalwa et.al., 2021; Ismail et al., 2022)
	Adoption and implementation of RDM practices	(Katabalwa, Bates and Abbott, 2021; Persaud et al., 2021)
	Challenges in open science and data sharing	(Beck et al., 2020; Donaldson and Koepke, 2022; Weil et al., 2023)
	Integration and interoperability issues	(Denker et al., 2021; Persaud et al., 2021; Gossler et al., 2022)
	Metadata and data quality concerns	(Donaldson and Koepke, 2022; Beckett et al., 2023)
	User experience and automation	(Beck et al., 2020; Brandt et al., 2021)
Data management	EVIDENCE FOR PRACTICE
	Description	Sources
	Implementation of FAIR principles and data stewardship	(Arend et al., 2016, 2022; Mayer et al., 2021; Felden et al., 2023)
	Collaboration and community engagement	(Aubin et al., 2020; Liggins and Noble, 2021; Liggins et al., 2022)
	Open science and data sharing	(Hampton et al., 2015; Brinkhaus et al., 2023)
	Practical strategies and tools for data management	(Karasti, Baker and Halkola, 2006; Arend et al., 2014, 2016)
	Real-world applications and pilot projects	(Curry et al., 2019)
	Data quality and harmonisation	(Steffen et al., 2012; Felden et al., 2023)
	Integration of advanced technologies	(Avdis et al., 2018; Gadelha et al., 2021)
Information processing and biomedicine	Training and skill development	(Atwood et al., 2015; Attwood et al., 2019)
	Collaboration and community engagement	(Atwood et al., 2015)
	Practical applications of big data in healthcare	(Andreu-Perez et al., 2015)
	Metadata and data repositories	(Schmidt et al., 2022)
	sustainability and funding models	(Atwood et al., 2015)
RDM practices	RDM Strategies and Implementation	(Roche et al., 2022; Gehlot et al., 2023; Rehnert and Takors, 2023)
	Data sharing policies and practices	(Chamanara et al., 2021; Murillo, 2022; Roche et al., 2022)
	Tools and infrastructure for RDM	(Bowers, McPhillips and Ludäscher, 2008; Suhr et al., 2020; Feser et al., 2022)
	Convergence and standardisation of RDM practices	(Jones et al., 2020)
	Survey findings and insights	(Machina and Wild, 2013; Aydinoglu, Dogan and Taskin, 2017)
RDM and supporting system	RDM practices and integration	(Persaud et al., 2021; Jiang et al., 2023)
	Data quality and standardisation	(Berkley et al., 2001; Boden, Krassovski and Yang, 2013; Sherbinin et al., 2021)
	Tools and platforms for data management	(Brandt et al., 2021; Gossler et al., 2022; Weil et al., 2023)
	Data sharing and collaboration	(Katabalwa, Bates and Abbott, 2021; Persaud et al., 2021)
	Survey findings and insights	(Corrall, Kennan and Afzal, 2013; Kennan, Corrall and Afzal, 2014)
	Policy development and advocacy	(Pinfield, Cox and Smith, 2014)
Data management	RESEARCH RECOMMENDATION
	Description	Sources
	Training and capacity building	(Katabalwa, Bates and Abbott, 2021; Arend et al., 2022; Liggins et al., 2022)
	Implementation of fair principles and open science	(Ghaffar et al., 2020; Mayer et al., 2021; Brinkhaus et al., 2023)
	Data sharing and collaboration	(Onyancha, 2016; Aubin et al., 2020; Gadelha et al., 2021)
	Interoperability and integration	(Curry et al., 2019; Ghaffar et al., 2020; Williams et al., 2023)
	Scalability and infrastructure development	(Gadelha et al., 2021; Felden et al., 2023; Williams et al., 2023)
	Validation and performance assessment	(Curry et al., 2019; Elshall et al., 2022)
	Interdisciplinary research and holistic approaches	(Liggins and Noble, 2021)
Information processing and biomedicine	Training and education in RDM	(Read et al., 2019; A Thomas and Martin, 2020; Zhou, Xu and Kogut, 2023)
	Integration of tools and best practices	(Schmidt et al., 2022)
RDM practices	Importance of communication and collaboration in data management	(Chamanara et al., 2021; Gehlot et al., 2023; Praetzellis et al., 2023)
	Development and enhancement of data management tools and infrastructure	(Chamanara et al., 2021; Feser et al., 2022; Praetzellis et al., 2023)
	Evolution of data management plans (DMPs) and FAIR principles	(Gehlot et al., 2023; Praetzellis et al., 2023)
	Domain-specific expertise and training	(Kouper, Scheidt and Plale, 2021; Gehlot et al., 2023)
	Scalability and integration of data management systems	(Suhr et al., 2020; Kuhn Cuellar et al., 2022)
	Standardisation and best practices	(Roche et al., 2022)
	Long-term evaluation and impact assessment	(Kouper, Scheidt and Plale, 2021)
	Challenges in data sharing and resource availability	(Rehnert and Takors, 2023)
	Exploration of use cases and validation	(Suhr et al., 2020; Kuhn Cuellar et al., 2022)
RDM and supporting system	Collaboration and community engagement in data management	(Beck et al., 2020; Denker et al., 2021; Persaud et al., 2021)
	Development and refinement of data management tools and systems	(Gossler et al., 2022)
	Data sharing policies and practices	(Katabalwa, Bates and Abbott, 2021; Persaud et al., 2021)
	Integration and adaptability of data management systems	(Brandt et al., 2021)
	Data citation and annotation	(Weil et al., 2023)
	Role of librarians and user feedback in data management	(Donaldson and Koepke, 2022; Ismail et al., 2022)
	Open science and its impact	(Beck et al., 2020; Persaud et al., 2021)
	Standardisation and best practices	(Brandt et al., 2021; Musen et al., 2022)
	Refining techniques and processes	(Beckett et al., 2023)

4. Discussion

The topic of RDM in environmental studies is divided into four clusters: the red cluster about data management, the green cluster about environment-related research fields, information processing in biomedicine, the blue cluster about RDM practices, and the yellow cluster about RDM with any supporting systems. Based on Tables 3, 4, and Appendix 4, the themes discussed in all clusters are, on average, almost the same so that this discussion will highlight PAGER as a whole regardless of the cluster.

The advancements widely discussed on RDM in environmental studies are RDM practices, Data sharing and open science, Collaboration and integration, FAIR principles and data reusability, Technological innovations and tools, and Standardisation and interoperability. These themes were labelled because the literature found in this study showed the number of scientific communities in the environmental field trying to develop various data repositories, websites, and databases by adopting and utilising technological innovations, collaboration and integration among researchers and funding agencies, FAIR principles compliance to implement and improve RDM practices. Noted repository-database-tools-projects that are the object of study are: PANGAEA, The Ocean Carbon and Acidification Data System, PLANTdataHUB, PalMod II, Tephra Data, The 2021 NFDI4BIOIMAGE, Adacta, Earth System Model, SynBio2Easy-on SBOL, The German Network for Bioinformatics Infrastructure (de.NBI), Kadi4Mat, SMM-CD and SMM-CD_NRP (Dunn et al., 2021), The Ira Moana Project, BEXIS2, Menoci, The Long Term Ecological Research (LTER), The ISCCP B1, PGP Repository, E!DAL, and GOBLET.

Furthermore, because they allow for increased transparency and data accessibility, open science and data sharing, methods have emerged as a crucial basis for improving ecological research and tackling global environmental concerns (Hampton et al., 2015). Cooperation among academics, institutions, and funding agencies is also essential for developing RDM infrastructure and innovation, including using cloud-based platforms and sophisticated data management tools (Persaud et al., 2021). However, in order to guarantee data quality and long-term sustainability, data standardisation and interoperability initiatives are essential (Thomas & Martin, 2020). Therefore, the basis for more comprehensive and long-lasting development of environmental research has been established by combining various data sources, embracing cutting-edge technology, and committing to sound data governance.

The advances of RDM in the environmental domain are stated based on the disclosure of evidence that has been done (evidence for practice) by various environmental organisations. In line with the advances, evidence for practice across all clusters also shows collaboration and community engagement, RDM practices and integration, data sharing policies and practices, and the beginning of using various tools and infrastructure for RDM. In order to create the infrastructure, tools, and common policies that facilitate data integration and sharing, cooperation and community involvement have been essential (Persaud et al., 2021). These organisations have also implemented strict RDM procedures, such as long-term preservation and data management strategies with metadata standards (Michener, 2015). Furthermore, open science methods and data sharing regulations have been widely embraced to promote cooperation and openness (Hampton et al., 2015). In addition, the support of advanced tools and infrastructure, such as smart energy and water management and IoT-enabled smart environment (Curry et al., 2019), Integration of genetic data (Liggins et al., 2022), development of a feature-constraint mesh generation algorithm within a GIS (Avdis et al., 2018). Thus, this evidence suggests that advances in RDM in the environmental field are driven by collaboration, standardised practices, data sharing policies and infrastructure innovations.

Despite the evidence for practices and advances that have been made, there are still many gaps in RDM in environmental studies. Some of the most discussed gaps are Training and capacity building in RDM, Challenges in data sharing and FAIR compliance, Interoperability and integration issues, Adoption and implementation of advanced tools and practices, and Data sharing and reusability. In environmental studies, gaps in RDM remain a significant challenge. Lack of training and capacity building in RDM is one of the primary problems, as many researchers cannot efficiently manage, distribute, and protect data (Tenopir et al., 2014). Data accessibility and metadata quality (Roche et al., 2022), inconsistent metadata, and inadequate infrastructure (Wilkinson et al., 2016) are the main reasons why data access and reuse are still hampered by issues with data sharing and FAIR principle compliance, even with the growing adoption of these principles. Interoperability is also essential because, in the absence of standardised protocols and tools, it can be challenging to integrate data from various sources and formats (Michener, 2015).

Furthermore, because there is a dearth of standardised data, interoperability and integration problems still impede environmental research. In addition, interoperability and integration issues continue to hamper environmental research due to the lack of standardised data formats and uniform metadata practices (Astell et al., 2018). On the other hand, adopting advanced tools and practices such as machine learning and cloud computing remains limited, often due to a lack of expertise, funding and infrastructure (Hampton et al., 2015).

From the gaps that occur in Table 4, future research in the environmental field needs to focus on several critical areas to address gaps in RDM are Interoperability and integration, Data sharing and collaboration, Training and capacity building, and implementation of FAIR principles and open science, Standardisation and best practices, Collaboration and community engagement, Validation and performance assessment. Apart from PAGER, future research themes recommended for future research can also be seen from thematic maps (3.2.2), namely in the lower right quadrant (basic theme). In this quadrant, some themes that still have opportunities to conduct various RDM research in environmental studies are those related to research data, data sharing, data curation, data life cycle, information management, and RDM itself. These themes are close to centrality, which means they have a high level of relevance but low density, which means they have not been explored much.

5. Conclusion

This research identifies trends and developments related to environmental studies in research data management (RDM). Through scoping review and bibliometric analysis of 248 papers from various databases, it was found that interest in RDM in environmental studies began to increase significantly since 2012. The most dominant keywords in this study are RDM, data management, and information management, which reflect the focus of the researchers. The main themes that are widely discussed include the application of FAIR principles, open data management, infrastructure development, and innovations in data sharing and data stewardship. On the other hand, the research also revealed areas that still need further exploration, such as the research data, data sharing, data curation, data life cycle, information management, and data management.

As such, this research provides an in-depth understanding of the development of RDM in environmental studies and highlights gaps and future research opportunities. The findings are expected to guide researchers and practitioners in developing strategies and innovations in environmental research data management.

The study, of course, has many limitations. First, this research used five databases and one journal with different literature search features, resulting in a non-uniform search strategy applied to each database and journal. This non-uniform search strategy suggests that all documents in each database and journal could not be entirely retrieved. The following limitation is that not all metadata in the five databases and one journal has the same level of accuracy, which caused limitations in mapping or visualising the bibliometric network, inconsistencies, and errors during analysis, including keywords. However, we tried to clean them up before putting them into the analysis tool. The final limitation is that the content and context of research that may be important for scoping reviews that seek to pinpoint knowledge gaps and guide future research paths can only be revealed partially through bibliometric analysis. The PAGER methodology came to ensure a more thorough scoping review of RDM research in the environmental field.

Data Accessibility Statement

Data supporting the findings of this study, including a list of publications analysed is available through the Figshare Repository at https://doi.org/10.6084/m9.figshare.28765952.

Additional File

The additional file for this article can be found as follows:

Competing Interests

The authors have no competing interests to declare.

Author Contributions

Conceptualisation, Rosini; data curation, Ida Fajar Priyanto; formal analysis & review, Mudiyati Rahmatunnisa and Sunardi; writing & editing draft, Rosini; all authors have read and agreed to the published version of the manuscript.