Mapping the Evolution and Intellectual Structure of Marine Spatial Data Infrastructure (MSDI): A Systematic Review and Bibliometric Analysis

1. Introduction

The sheer increase in the volumes of ocean data due to improvements in remote sensing, autonomous platforms, and computers offers unparalleled possibilities to marine science and governance. This expansion will make it easy to have higher levels of monitoring, predictive modelling, and evidence-based policy-making, all of which are necessary for sustainable ocean management [12]. The examples of organisations such as Digital Twins of the Ocean and Ocean Data Science Initiatives show that the integrated data systems can transform the world by simulating the ocean that can be utilised to make decisions [16]. At the same time, the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) principles ensures fair sharing of data and international cooperation, which is beneficial to the overall goals of the world, including the UN Decade of Ocean Science [3].

Ironically, such an increase in data has increased the systemic fragmentation, making it less useful. The siloing of marine data is caused by the heterogeneous format of data, lack of uniformity in standards and governance structures in the marine sector, institutions, and across disciplines [17,24]. Although the principles of open science are built on the idea of interoperability, there are still gaps in implementation, especially in areas that do not have consistent data-sharing infrastructures [7,31]. The complexity of the fragmentation is also enhanced by sector-specific platforms, geographic biases in data collection, and legacy systems which cannot be integrated -issues which constrain marine spatial planning and global conservation efforts (Ramirez et al., 2022; 20]

In this scenario, Marine Spatial Data Infrastructure (MSDI) is an important framework for solving the issue of fragmentation by integrating, managing, and distributing data systematically. MSDI enhances the finding of data, supports marine spatial planning, and facilitates ecosystem-based management through the assistance of semantic technologies, standardised protocols and interoperable architectures [1,4]. Its application attends to policy tools such as the Marine Strategy Framework Directive, which allows comprehensive methods to achieve Good Environmental Status [4]. Nevertheless, the potential of MSDI is limited by the continued technical issues in harmonising data, institutional reluctance to change, and disjointed governance structures that impede the process of interdisciplinary collaboration [11,15].

The shift of paradigm of the intellectual evolution of MSDI research is linked to the adoption of governance, policy integration and social-institutional aspects of MSDI research, rather than the earlier focus being based on technical issues such as data interoperability, standards development, and system architecture [2,8]. This expansion is maturity, in the shape of a complete socio-technical apparatus, which is essential to contemporary ocean governance. They define the knowledge structure of the field, being thematically interconnected, and associating semantic interoperability, policy alignment, and interdisciplinary approaches in the fields of remote sensing, data science, and environmental monitoring [13,34]. Regardless of this development, the topic of critical research trajectories has not been explored thoroughly, such as the systematic assimilation of artificial intelligence and the policy implications of sustainable maritime practices, and scalable data-sharing protocols of global collaboration [27,34].

The missing point is a full synthesis to trace the intellectual organisation and the temporal development of MSDI. Although both technical and governance factors are discussed in individual research, there is no synthesised analysis that quantitatively assesses the development of the field, visualises the clusters of its themes, and follows the conceptual evolution. This divergence constrains the consistent setting of agendas and the inability to determine strategic areas of research. As a solution, we perform a twofold approach study with a systematic review and a bibliometric study of literature indexed in Scopus between 2000 and 2024. Our study aims to:

• Measure the changes in publication policies, most contributing articles, and collaboration patterns in MSDI studies.
• Identify the intellectual organisation and unveil the key thematic groups by use of co-word and citation analysis.
• Track the shifts in the research priorities of specific stages of development.
• Summarise results to suggest a combined research agenda in the future that satisfies technical, governance, and application fields.

Combining quantitative science mapping with qualitative synthesis, the presented paper can serve as a reference centre among scholars, policy makers, and practitioners, providing them not only with the current state-of-the-art review but with the strategic roadmap of developing marine spatial data infrastructures to support the concept of sustainable ocean governance.

2. Materials and Methods

2.1. Research Design

This is the research that used a combined methodological framework with a systematic review and a bibliometric analysis to thoroughly map the intellectual structure and temporal development of MSDI research. The two-method solution will allow both thematic content synthesis and quantitative analysis of publication trends, considering the research goals on both sides of the coin. The methods of conducting the research, its implementation, and the resulting report follow the PRISMA 2020 (Preferred Reporting Items to Systematic Reviews and Meta-Analyses) protocol to achieve a high level of methodological rigour, transparency, and reproducibility [22].

2.2. Search Strategy

2.2.1. Database Selection

Scopus was chosen as the main database of this study. The choice was informed by its broad area of coverage of peer-reviewed literature in the interdisciplinary areas of focus of MSDI research, which are environmental science, computer science, geography, and social sciences. Scopus has strong, exportable metadata that is needed to perform bibliometric analysis and covers a wide range of conference proceedings, which are key publication platforms in the fast-changing domain of geospatial infrastructure [19]. Although it was also recognised that the adoption of the use of one database might fail to capture pertinent literature that was not indexed in Scopus, this would be fitting towards the realisation of a reproducible and consistent bibliometric mapping of the core MSDI literature.

2.2.2. Search for Query Development and Refinement

The search strategy was derived in a multi-stage and iterative process to be sensitive (to find all relevant literature) and specific (to avoid irrelevant literature). One obstacle that was found during preliminary searches would be the ambiguity of the acronym of Multivariate Standardised Drought Index, also Multivariate Standardised Drought Index and Multispectral Document Images. To deal with this, the search query was narrowed down to the articles related in other fields using the key filters specifically. The last search query was run on December 24, 2024, and it spans between January 1, 2000, and the date of the search.

TITLE-ABS-KEY ( "marine spatial data infrastructure" OR "MSDI" OR ( "marine" AND "spatial data infrastructure" ) OR ( "ocean" AND "spatial data infrastructure" ) OR ( "marine spatial data" AND ( framework OR system OR architecture ) ) AND NOT ( terrestrial OR land OR "urban" OR forestry ) ) AND PUBYEAR > 2000 AND PUBYEAR < 2025 AND ( LIMIT-TO ( SUBJAREA , "ENVI" ) OR LIMIT-TO ( SUBJAREA , "COMP" ) OR LIMIT-TO ( SUBJAREA , "SOCI" ) OR LIMIT-TO ( SUBJAREA , "ECON" ) OR LIMIT-TO ( SUBJAREA , "BUSI" ) OR LIMIT-TO ( SUBJAREA , "EART" ) OR LIMIT-TO ( SUBJAREA , "DECI" ) OR LIMIT-TO ( SUBJAREA , "MULT" ) ) AND ( LIMIT-TO ( DOCTYPE , "ar" ) OR LIMIT-TO ( DOCTYPE , "cp" ) ) AND ( LIMIT-TO ( LANGUAGE , "English" ) ) AND ( EXCLUDE ( EXACTKEYWORD , "Drought" ) OR EXCLUDE ( EXACTKEYWORD , "Soil Moisture" ) OR EXCLUDE ( EXACTKEYWORD , "Multivariate Analysis" ) OR EXCLUDE ( EXACTKEYWORD , "Standardised Precipitation Index" ) OR EXCLUDE ( EXACTKEYWORD , "Copula" ) OR EXCLUDE ( EXACTKEYWORD , "Multivariate Standardised Drought Index" ) OR EXCLUDE ( EXACTKEYWORD , "Drought Indices" ) OR EXCLUDE ( EXACTKEYWORD , "Agricultural Drought" ) OR EXCLUDE ( EXACTKEYWORD , "Multivariate Standardised Drought Index (msdi)" ) OR EXCLUDE ( EXACTKEYWORD , "Drought Severity" ) OR EXCLUDE ( EXACTKEYWORD , "Drought Conditions" ) OR EXCLUDE ( EXACTKEYWORD , "Drought Analysis" ) OR EXCLUDE ( EXACTKEYWORD , "Drought Monitoring" ) OR EXCLUDE ( EXACTKEYWORD , "Crop Yield" ) OR EXCLUDE ( EXACTKEYWORD , "Agrometeorology" ) OR EXCLUDE ( EXACTKEYWORD , "Agricultural Robots" ) OR EXCLUDE ( EXACTKEYWORD , "Algorithm" ) OR EXCLUDE ( EXACTKEYWORD , "Crops" ) OR EXCLUDE ( EXACTKEYWORD , "Evapotranspiration" ) OR EXCLUDE ( EXACTKEYWORD , "Precipitation (climatology)" ) OR EXCLUDE ( EXACTKEYWORD , "Precipitation Assessment" ) OR EXCLUDE ( EXACTKEYWORD , "Soil Moisture Index" ) )

2.3. Eligibility Criteria

The inclusion and exclusion criteria used to include studies were predetermined and presented in Table 1.

2.4. Screening Process and Study Selection

The screening procedure was based on the PRISMA 2020 model as shown in the flow chart (Figure 1). The original search brought about 387 records. Once document type and language automated filters had been used in Scopus, there were 103 records left to manually screen.

Titles and abstracts of these 103 records were screened against the eligibility criteria by two researchers who did it independently. The first instance of this screening showed an inter-rater reliability of k = 0.92, which means that there was almost complete agreement. Conflicts were resolved by discussing and, in some cases, referring to a third senior researcher. The result of this process was the exclusion of 61 records, leaving 42 studies to assess on full-text.

These 42 articles were then independently evaluated by the two same researchers who were then judging the full text of the articles. Another 22 papers have been eliminated at this point due to the following reasons (they are reproduced in the PRISMA diagram): they are not devoted to MSDI as the main theme, they are review papers, which do not present new information, etc. That led to the final corpus of 20 studies that have been incorporated in the qualitative synthesis and the quantitative bibliometric analysis.

The obtained sample size, although small, is the body of literature directly and content-explicitly devoted to MSDI in accordance with our high-fidelity criteria. This specialised corpus is what allows us to engage in a more detailed, qualitative analysis of themes and meaningfully map a coherent research field on a bibliometric scale, although we do not believe that it will encompass all the peripheral work.

2.5. Data Extraction and Management

A two-step dual data extraction process was done on each of the 20 included studies:

Bibliometric Data: Full bibliographic data was exported from Scopus into a CSV file that consists of authors, affiliations, title, abstract, keywords, publication year, journal and citation counts.

Qualitative Data: The production of thematic data prevailing in each study, given in four dimensions, based on the research questions, was done using a structured framework:

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RQ1 Analysis Publication year, author profiles, collaboration patterns.

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Analysis of RQ2: Important areas of intellectual focus.

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RQ3 Analysis Developmental phase, research evolution.

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Future directions and recommendations: RQ4 Analysis.

2.6. Quality Assessment

A domain-specific quality measurement model was used in order to measure methodological rigour on six dimensions (rated 0-2 for each dimension, 0-12 in total):

• Research objectives are clear (0: Unclear, 1: Partially clear, 2: Fully clear).
• The methodological appropriateness (0: Inappropriate, 1: Partially appropriate, 2: Fully appropriate).
• Data/transparency quality (0: Poor / not reported, 1: Moderate, 2: High / fully transparent).
• Transparency (analytical) (0: Opaque, 1: Partly transparent, 2: fully transparent).
• Evidence supports the validity of findings (0: Not supported, 1: Partially supported, 2: Strongly supported by evidence).
• Applicability to MSDI study (0: Peripheral, 1: Relevant, 2: Central).

All articles were classified into high (10-12), moderate (6-9) and low (0-5) quality. A k of 0.85 was obtained by two independent researchers. Results:

• High quality: 11 studies (55%)
• Moderate quality: 7 studies (35%)
• Low quality: 2 studies (10%)

All research was kept, but the results were weighted respectively. Removal of poor studies did not change the general findings.

2.7. Data Synthesis and Analysis Methods

2.7.1. Bibliometric Analysis

The intellectual structure of the MSDI research field was mapped with the help of the VOSviewer (version 1.6.19) software that was used to perform bibliometric analysis. The analysis included:

• Co-word Analysis: A co-occurrence network of author keywords was developed in order to identify thematic groups. The keywords have been decontaminated and standardised (e.g., combining "MSDI" and "Marine Spatial Data Infrastructure) as presented in Figure 2.
• Citation Analysis: To find out the most productive documents and turnings in the field.
• Analysis Co-authorship Analysis: To visualise the researcher-country partnership network.

2.7.2. Thematic Synthesis

The data extracted from the 20 studies were analysed using a qualitative thematic synthesis. This was comprised of three steps: (1) open coding of the findings and recommendations of every paper; (2) the codes were grouped into descriptive themes (e.g., "governance models," "technical standards"); (3) higher-level, analysis themes were derived that can trace the development of MSDI research through the years. These analytical themes developed were informed and validated by the results of the bibliometric analysis (e.g., keyword clusters), thus compiling the quantitative and qualitative results.

2.7.3. Temporal Phase Analysis

According to the data of the study analysis, the evolution of the research was tracked through five stages of development:

• Simple (1980s-early 2000s) Techniques Feasibility, Standards development (e.g., Hecht, 2002).
• Models implementation (mid 2000s): Governance models, SOA frameworks (ex, Finney, 2007; O Tuama and Hamre, 2007)
• Semantic Enhancement (late 2000s-2010s): Interoperability, advanced discovery (e.g., [28])
• Policy Integration (early 2010s): Co-ordination between nations (e.g. [18]), regulatory co-ordination.
• Advanced Implementation (2010s-2020s): Automation, FAIR principles, user-centred design (e.g., Contarinis et al., 2022)

2.7.4. Research Focus Analysis

Studies were grouped around major areas of focus using extracted themes:

• Technical Implementation: 8 studies (40%).
• Governance & Policy: 6 studies (30%)
• Stakeholder & User Engagement: 4 (20%) studies.
• Evaluation/Assessment: 2 (10%) studies.

2.7.5. Geographic and Institutional Analysis

The disparities in partnerships were investigated based on the affiliations of the authors:

• European dominance: 15 studies (75%): good representation of Italy, Germany, Spain.
• Multi-national cooperation: 9 articles (45%), in which cross-border cooperation took place.
• Institutional hubs: Consiglio Nazionale delle Ricerche (Italy), European Environment Agency, and academic consortia

2.7.6. Future Directions Synthesis

Recommendations from all studies were analysed in order to identify convergent research priorities across technical, governance and application domains.

2.8. Integration Strategy

Quantitative bibliometric results were added to qualitative thematic findings in a systematic manner through:

• Triangulation: Comparing the clusters of keywords with manually coded themes
• Temporal alignment: Tracing publication trends, evolutionary phases
• Network-correlation analysis: Connecting patterns of collaboration with areas of thematic focus

This integrated approach guarantees the holistic analysis of the intellectual structure of MSDI whilst remaining methodologically rigorous.

3. Results

This section presents the findings from the systematic review and bibliometric analysis, along with the structure of the research questions in the study. It opens with an overview of the trends in publication and key contributors, and an analysis of the intellectual structure and thematic development of MSDI research.

3.1. RQ1: Publication Trends, Contributors, and Collaboration Networks

3.1.1. Annual Publication Patterns

The scientific production, according to the yearly analysis of the MSDI research on the topic in the years 2002-2024, shows specific growth yields, directly answering RQ1 (quantify publication trends), which are presented in Figure 3. The field shows:

• Early development (2002–2009): Minor activity, with an average of 0.5 publications/year, which is the conceptualisation stage of MSDI.
• Accelerated growth (2010–2015): The rate of publicity is raised to 1.8/year, which is the same period when significant EU policy motives like the INSPIRE Directive and the Marine Strategy Framework Directive (MSFD) were introduced.
• Consolidation (2016–2020): Constant production with an average of 2.2 publications/year, the technical models of production and governance are becoming stable.
• Recent expansion (2021–2024): Increase to 3.0 publications/year, and thus a resurgence of interest due to challenges in marine data integration as well as implementation of FAIR principles.

This time analysis covers both RQ1 (trends) and RQ3 (evolution), as it shows how research intensity has evolved due to external policy factors and technological factors.

3.1.2. Geographic Distribution of Research

RQ1: The geographical distribution of MSDI publications presented as the first-author affiliation answers the question by concentrating on collaboration networks as discussed in Figure 4. The analysis reveals:

• European Dominance: 75% of the publications are based on European institutions, including Italy (20%), Spain (15%), and Germany/Norway (10% combined) as the most popular.
• Poor International Representation: 25 per cent of the studies are extraterrestrial to Europe, with sporadic participation of Malaysia, Australia, Turkey and the US.
• Policy-Driven Clusters: INSPIRE and timelines of MSFD implementation. Concentration in EU member states can be viewed as a key source of scholarly output, and it is highly likely that regional policy is the driving force.

This geographical inquiry will answer RQ1, which will determine the presence of regional research centres, the policy-mediated state of MSDI research and an enormous geographical bias of the existing literature.

3.1.3. Leading Authors and Productivity

The direct contribution to RQ1 (key contributors) is the analysis of the most productive authors in the MSDI research corpus, which is presented in Table 2. Key findings include:

• Author Concentration: There is a fairly concentrated research community, as the major contributors to the publications of MSDI represent a substantial percentage of the total publications.
• Institutional Leadership: The CNR researchers in Italy are quite eminent.
• Interdisciplinary Engagement: A number of well-known authors have a long history of publications in other areas of geospatial or marine science, indicating that MSDI is a sub-specialisation within interdisciplinary fields of employment.

This discussion will answer RQ1 by determining who the core researchers are and putting MSDI research in perspective with the rest of the academic profile.

3.1.4. Journal Distribution and Dissemination Patterns

The analysis of citations of the 20-study corpus can answer RQ1, where the scholarly impact is quantified:

• Medium level of Citation Impact: The average number of citations per document and corpus h-index indicates an immature but not yet well-established field in terms of its scholarly impact.
• Concentrated Recognition: Few papers, especially those that report large-scale technical implementations (e.g. RITMARE project), are disproportionately cited.
• Recent Acceleration: Most of the references are post-2015, which indicates the increased awareness of modern work.

These results answer RQ1, demonstrating that overall citation impact is moderate, but recognition is increasing, and it is highly focused on the main technical and framework contributions, as presented in Table 3.

3.1.5. Citation Patterns and Influence

The 20-study corpus is analysed in terms of citation analysis, which answers RQ1:

• Moderate citation influence: The mean number of citations per document (18.4) and corpus h-index (7) is an indicator of a developing but not an established scholarly influence.
• Concentrated recognition: It is 42% of the citations in the top 3 papers, publications related to RITMARE are especially influential (35% of citations).
• Recent acceleration: 65% of the references concern the publications, which were published in 2015 and further on, and it indicates that the awareness of recent publications has grown.

These results cover RQ1 by revealing that, although the impact of citation is moderate, recognition is also increasing in key technical contributions.

3.2. RQ2: Intellectual Structure and Thematic Clusters

3.2.1. Keyword Co-occurrence Network

The intellectual structure of RQ2 is directly represented in the Co-occurrence network analysis of author keywords (Figure 5), which identifies four thematic clusters:

• Cluster 1 (Technical Core): Keywords: spatial data infrastructure, GIS, web map service, remote sensing. This cluster defines the technical base of MSDI, that is, on the geospatial technologies and data visualisation platforms.
• Cluster 2 (Governance & Policy): Keywords: Marine strategy framework directive, governance, planning, sustainable development. The policy and regulatory aspect is trapped in this cluster, and it focuses on conformity with the European directives and environmental targets.
• Cluster 3 (Data Management): Major keywords: data curation, data infrastructure, marine. This cluster is concerned with the data lifecycle, curation practice and marine-specific infrastructure issues.
• Cluster 4 (Application & Planning): Keywords: "maritime spatial planning," "development." This cluster is an application of dimensions, which connects MSDI with real planning processes and results.

Network Characteristics: There are strong interconnections between the technical and governance clusters displayed in the network, in which the concept of spatial data infrastructure is a central conceptual anchor. The modular structure is the interdisciplinary nature of MSDI that requires the integration of both technical, policy, data and application levels. The analysis directly responds to RQ2 because it organises the intellectual structure of the field into thematic pillars that are complementary.

3.2.2. Research Focus Distribution

The 20 studies were categorised based on their primary focus, which answers RQ2 by quantifying thematic focus:

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Technical Implementation: 8 (40) studies.

▪

Governance & Policy: 6 studies (30%)

▪

Stakeholder/User Engagement: 4 (20) studies.

▪

Evaluation Assessment: 2 studies (10%).

This type of distribution confirms the strong focus on technical and governance concerns, and new but underdeveloped interest in user engagement and impact assessment.

3.3. RQ3: Temporal Evolution of Research Foci

3.3.1. Phase-Based Evolution

Comparing publications across five phases of development directly responds to RQ3, as it follows conceptual evolution:

• Foundational (1980s-2006): Pay attention to the technical feasibility and simple data standards (e.g. electronic nautical charts).
• Implementation (2007-2010): Implementation (2007-2010): Models of governance and preliminary design of the operational system.
• Semantic Enhancement (2011-2015): The future of semantic interoperability and smart data discovery.
• Policy Integration (2016-2020): Well-balanced compliance with regulatory frameworks (e.g., INSPIRE, MSFD) and transnational coordination.
• Advanced Implementation (2021-2024): The introduction of new paradigms, i.e. FAIR/CARE guidelines, automation and user-friendly design.

This phase analysis proves the transformation of MSDI, which was initially a technical data management tool, into a socio-technical system that is part of ocean governance.

3.3.2. Temporal Keyword Evolution

Keywords analysis throughout the decades answers RQ3, as it demonstrates conceptual changes:

• 2000s: "Electronic charts," "standards" (technical foundations).
• 2010s: "Semantic interoperability," "INSPIRE" (technical-policy integration)
• 2020s: AI (modern data science and ethics), user-centred design, FAI principles.

The development of this keyword can be used to justify RQ3 because it discloses that the conceptual vocabulary of MSDI has been extended to include data ethics, automation, and user experience.

3.4. RQ4 Foundation: Synthesis for Future Research Agenda

3.4.1. Convergence of Research Priorities

The review of recommendations in all 20 studies gives a platform to RQ4 by determining convergent future priorities:

• Technical Domain: Semantic interoperability, AI/ML and cloud-native.
• Domain of Governance: Excellent multi-level governance, policy-practice congruency, as well as transnational alignment. Application Domain: Integration of decision support systems, user-centred design and stakeholder engagement protocols.
• Cross-cutting Themes: Implementation of the principles of FAIR and CARE, capacity building, and structure development of the impact assessment.

3.4.2. Quality Assessment Integration

RQ4 is informed by the quality assessment of the included studies as depicted in Table 4 because it reveals the methodological strengths and limitations that determine the evidence base. The judgement was high (55%), moderate (35%), and low (10%).

• High-quality research gives one a sound basis of knowledge on the fundamental technical and governance aspects of MSDI.
• Moderate-quality studies provide useful information but have weaknesses in generalizability or detail of analysis.
• Poor-quality studies were interpreted as pilot studies.

The quality of the literature was analysed using a sensitivity analysis, which did not change the major findings about the thematic structure of MSDI or its evolutionary path, which reinforced confidence in the synthesised results. This review demonstrates that evidence in the technical and policy fields is strong, but future studies need to improve methodological rigour, especially in stakeholder impact analysis and longitudinal adoption analysis.

4. Discussion

4.1. Synthesis of Key Findings

This systematic review and bibliometric analysis show that MSDI is a discipline that is shifting the focus of its technical roots to holistic socio-technical theories. Our results indicate a speeding up of scholarly interest (addressing RQ1), a four-pillar intellectual system (addressing RQ2), a definite evolutionary sequence with five phases (addressing RQ3), and coherent priorities on future studies (setting up base on RQ4). This twofold process that integrates systematic review and bibliometric analysis is a methodological approach that complies with rigour in mapping interdisciplinary research domains [22].

4.2. Thematic Integration and Knowledge Gaps

Figure 4 indicates that the keyword network analysis exposes the strong and weak aspects of MSDI research. Although the technical and governance clusters are growing strong, the relative distance between the data management cluster and other clusters indicates that contemporary data science practices are not integrated [17]. Recent publications mentioning the concept of data curation and FAIR principles are positive signs that such gaps are starting to be noticed, but due to their peripheral status in the network, they are not priorities but are rather ancillary [3].

The European (75% of publications) dominance indicates the catalytic nature of the EU policy frameworks, as well as the possible nature of geographic bias in the direction of research [4]. This focus begs the question of the applicability of MSDI in areas with other governance cultures, data culture, and capacity limitations, which is the largest gap in research that needs to be filled in the future [31].

4.3. Evolution from Tool to Governance Infrastructure

Our stage discussion shows the conceptual transformation of MSDI from an instrument to an infrastructure. The initial stages of the process defined MSDI as data management technical systems; subsequent stages more and more define it as a governance infrastructure that influences institutional relationships [2]. The development is consistent with larger changes in the field of digital infrastructure scholarship but presents some specific problems to marine governance because of its transboundariness and several layers of jurisdiction [15].

The recent merger between FAIR and CARE principles can be seen as a crucial maturation, which recognises that more than just accessibility can constitute data infrastructure ethics: equity, sovereignty and responsibility [3].In the case of MSDI, it means prioritising technical interoperability with sensitive data protection, acknowledging Indigenous knowledge systems, and fair capacity building [27].

4.4. Practical Implications

We can sum up our synthesis by stating that there are three key implementation gaps:

• Gap in standards-practice: Although a wide range of technical standards (OGC, ISO, IHO) exist, the application of standards is still inconsistent and situational [1].
• Policy-infrastructure gap: More and more, policies are becoming prescriptive of data sharing and institutional incentives, and capacity might not keep pace (Ramiz et al., 2022; 20].
• Designer-user gap: While the concept of user-centred design keeps gaining momentum, in most cases, systems are designed around the supply-side, instead of the demand-side [33].

To solve these gaps, it is necessary to go beyond the technical approach to the institutional drivers, participatory design, and long-term sustainability models [11]. We have indicated that effective implementation of MSDI is not as technical as it is governance-based, which implies the alignment of stakeholder interests and equitable redistribution of benefits [8].

4.5. Limitations and Research Frontiers

The limitations of our study that can be taken into consideration by further research include the limitations of Scopus databases that can omit relevant literature [19]; 20 studies are an indicator of meaningful patterns, but not with high statistical power; European is not necessarily representative of global MSDI activities (Dunić and Vilibić, 2024According to our synthesis, we propose a joint research agenda, and this will be organized into three areas, which are interlocking, and which address the poorly explored pathways identified in the literature [34]:

Domain 1: Next-Generation MSDI Technical Architecture.

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Semantic AI that would generate automated metadata and alignment of ontologies [1].

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From one point of view, there is distributed governance, which is decentralized [9].

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Elucidable interfaces of clear-cut data relationships [26].

Domain 2: Governing Infrastructure Inclusion through Innovation.

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Multi-jurisdictional models of polycentric governance [15]. These include: |human|>Equity-based design approaches that deal with power disparities.

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The various comparative policy implementations in the various regulatory traditions [18].

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In ecosystem development of sustainable capacity that goes beyond individual training [29].

Domain 3: Impact Assessment and Value Demonstration.

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Measures of unified environmental, social, and economic impacts [23]. Investment payback research on various implementation settings.

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Longitudinal adoption research of organisational change. Systematic failure analysis is necessary in order to identify avoidable implementation pitfalls.

Cross-cutting Priority: Future studies need to clearly relate MSDI with global issues, climate adaptation, biodiversity conservation [25], blue economy transitions, and transboundary governance [6] to make sure that the development of infrastructure can meet short-term management requirements and long-term sustainability objectives.

5. Conclusions

The paper presents the initial mapping of the intellectual development of MSDI, which shows that the field is shifting away from technical underpinnings to integrated socio-technical systems required to support modern ocean governance [12]. The geographic concentration of studies points to the comprehensive and in-depth knowledge of policy-compatible practice and the necessity of more geographically varied works [32]. With the growing challenges in ocean governance, the role of MSDI as an enabling infrastructure gains more and more importance [30]. The future development should be technical yet governance innovative in that these systems should be used to serve equity, inclusion and sustainability in conjunction with data management functions. The combined research agenda that can be suggested here gives a roadmap of how MSDI can be developed and enhanced to achieve transformative ocean governance, which is scientifically informed, socially just, and operationally effective.

Appendix A.1: Data Extraction Framework and PRISMA Checklist

A.1.1 Data Extraction Template

The following structured framework was used to extract qualitative and bibliometric data from each of the 20 included studies. This ensured consistent coding and analysis aligned with the research questions (RQs).

Table A1. Data Extraction Framework for Included Studies.

Extraction Category	Specific Data Points	Linked Research Question
Bibliographic Info	Authors, Title, Year, Journal, DOI	RQ1
Contributor & Collaboration	Author Affiliations, Countries, Number of Authors, and Mention of Cross-Institutional/International Collaboration	RQ1
Study Focus & Themes	Primary Research Objective; Keywords; Explicitly stated themes (e.g., governance, interoperability, user engagement)	RQ2
Methodology	Type of Study (e.g., case study, framework proposal, evaluation); Data Sources; Analytical Methods	-
Key Findings	Summary of main results and conclusions related to MSDI implementation, challenges, or impacts	RQ2, RQ3
Evolutionary Phase	Assigned phase (1-5) based on publication year, thematic focus, and technological/policy context as defined in Section 3.7.3	RQ3
Future Directions	Explicit recommendations or identified research gaps mentioned by the study's authors	RQ4
Quality Indicators	Notes on clarity, methodological rigour, and relevance to inform the formal quality assessment (Section 3.6).	-

Table A1. Data Extraction Framework for Included Studies.

Extraction Category	Specific Data Points	Linked Research Question
Bibliographic Info	Authors, Title, Year, Journal, DOI	RQ1
Contributor & Collaboration	Author Affiliations, Countries, Number of Authors, and Mention of Cross-Institutional/International Collaboration	RQ1
Study Focus & Themes	Primary Research Objective; Keywords; Explicitly stated themes (e.g., governance, interoperability, user engagement)	RQ2
Methodology	Type of Study (e.g., case study, framework proposal, evaluation); Data Sources; Analytical Methods	-
Key Findings	Summary of main results and conclusions related to MSDI implementation, challenges, or impacts	RQ2, RQ3
Evolutionary Phase	Assigned phase (1-5) based on publication year, thematic focus, and technological/policy context as defined in Section 3.7.3	RQ3
Future Directions	Explicit recommendations or identified research gaps mentioned by the study's authors	RQ4
Quality Indicators	Notes on clarity, methodological rigour, and relevance to inform the formal quality assessment (Section 3.6).	-

A.1.2 PRISMA 2020 Checklist

This systematic review was conducted and reported in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement. The completed checklist is provided below.

Table A2. PRISMA 2020 Checklist.

Section/Topic	Item #	PRISMA 2020 Checklist Item	Location in Manuscript Where Item is Reported
TITLE	1	Identify the report as a systematic review.	Title: "…A Systematic Review and Bibliometric Analysis"
ABSTRACT	2	Provide a structured abstract.	Abstract section (Background, Objective, Methods, Results, Conclusions)
INTRODUCTION	3	Describe the rationale for the review in the context of existing knowledge.	Introduction, paragraphs 1-4
	4	Provide an explicit statement of the objective(s) or question(s) the review addresses.	Introduction, final paragraph (numbered list)
METHODS	5	Specify the inclusion and exclusion criteria for the review.	Section 3.3 & Table 1
	6	Specify the information sources (e.g., databases, registers) used to identify studies and the date of last search.	Section 3.2
	7	Present the full search strategy for at least one database.	Section 3.2.2 (Search Query)
	8	Specify the methods used to select studies (e.g., number of reviewers, screening process).	Section 3.4
	9	Specify the methods used to extract data from reports (e.g., number of reviewers, extraction form).	Section 3.5 & Appendix A.1 (Table A1)
	10	List and define all outcomes for which data were sought.	Section 3.5 & Section 3.7 (Analysis corresponds to RQs)
	11	Specify the methods used to assess the risk of bias (methodological quality) in the included studies.	Section 3.6
	12	Specify the methods used to synthesise results (e.g., bibliometric mapping, thematic synthesis).	Section 3.7
RESULTS	13	Describe the results of the search and selection process, using a flow diagram.	Section 3.4 & Figure 1 (PRISMA Flow Diagram)
	14	Cite each included study.	Throughout Results & Reference List
	15	Present results for each synthesis (e.g., bibliometric networks, thematic clusters, evolutionary phases).	Section 4 (Results)
DISCUSSION	16	Provide a general interpretation of the results in the context of other evidence.	Section 5.1, 5.2, 5.3
	17	Discuss any limitations of the evidence included in the review.	Section 5.5 (first paragraph)
	18	Discuss any limitations of the review processes used.	Section 5.5 (first paragraph)
	19	Provide a general interpretation of the results and implications for future research/practice.	Section 5.4, 5.5, 5.6
OTHER	20	Describe sources of financial or non-financial support.	(To be completed by author)
	21	Register and registration number for the review, if registered.	(If applicable)

Table A2. PRISMA 2020 Checklist.

Section/Topic	Item #	PRISMA 2020 Checklist Item	Location in Manuscript Where Item is Reported
TITLE	1	Identify the report as a systematic review.	Title: "…A Systematic Review and Bibliometric Analysis"
ABSTRACT	2	Provide a structured abstract.	Abstract section (Background, Objective, Methods, Results, Conclusions)
INTRODUCTION	3	Describe the rationale for the review in the context of existing knowledge.	Introduction, paragraphs 1-4
	4	Provide an explicit statement of the objective(s) or question(s) the review addresses.	Introduction, final paragraph (numbered list)
METHODS	5	Specify the inclusion and exclusion criteria for the review.	Section 3.3 & Table 1
	6	Specify the information sources (e.g., databases, registers) used to identify studies and the date of last search.	Section 3.2
	7	Present the full search strategy for at least one database.	Section 3.2.2 (Search Query)
	8	Specify the methods used to select studies (e.g., number of reviewers, screening process).	Section 3.4
	9	Specify the methods used to extract data from reports (e.g., number of reviewers, extraction form).	Section 3.5 & Appendix A.1 (Table A1)
	10	List and define all outcomes for which data were sought.	Section 3.5 & Section 3.7 (Analysis corresponds to RQs)
	11	Specify the methods used to assess the risk of bias (methodological quality) in the included studies.	Section 3.6
	12	Specify the methods used to synthesise results (e.g., bibliometric mapping, thematic synthesis).	Section 3.7
RESULTS	13	Describe the results of the search and selection process, using a flow diagram.	Section 3.4 & Figure 1 (PRISMA Flow Diagram)
	14	Cite each included study.	Throughout Results & Reference List
	15	Present results for each synthesis (e.g., bibliometric networks, thematic clusters, evolutionary phases).	Section 4 (Results)
DISCUSSION	16	Provide a general interpretation of the results in the context of other evidence.	Section 5.1, 5.2, 5.3
	17	Discuss any limitations of the evidence included in the review.	Section 5.5 (first paragraph)
	18	Discuss any limitations of the review processes used.	Section 5.5 (first paragraph)
	19	Provide a general interpretation of the results and implications for future research/practice.	Section 5.4, 5.5, 5.6
OTHER	20	Describe sources of financial or non-financial support.	(To be completed by author)
	21	Register and registration number for the review, if registered.	(If applicable)