2.1. Community Informatics

CI refers to the use of information and communication technology (ICT) to promote community development and participation. It emphasizes accessing and utilizing data to address local issues while creating opportunities for social, economic, and cultural development. CI enhances opportunities for communities to participate in decision making and local development through accessible information sources and digital tools for data management. Additionally, it connects community members, fosters transparency in data governance, and improves access to critical information for decision-making to achieve sustainable local development (Grisales Bohorquez et al., 2022; Gurstein, 2007, p. 11; Mehra et al., 2017).

CI is a field of study focusing on the relationship between ICT design and local communities, as well as implementing ICT projects within communities. It highlights using ICT to develop economical, social, and culturally appropriate and sustainable technological solutions. The field emphasizes the needs and local data practices involved in driving positive social change alongside community stakeholders (Davis et al., 2016; Ozuomba et al., 2013; Stillman & Linger, 2009).

Bunch (1993) categorized CI into two main types: (1) survival information: data related to essential aspects of living, such as health, housing, and political rights, and (2) citizen action information: information that facilitates participation in social, political, and economic processes within the community. The roles of CIS include strengthening social capital and expanding interactions within communities through the development and use of ICT applications that meet the needs of community members. These systems also foster networks that help sustain local resources in a sustainable manner (Gurstein, 2007, p. 74-77). CIS plays a crucial role in enhancing quality of life and providing access to essential services, such as healthcare, education, and economic opportunities (Eze et al., 2021; Lippeveld, 2017).

Community data or community profiles are analyses and reports based on data collected through community surveys. They provide an overview of the community’s population, including various aspects such as community geographical area; population and household numbers; age and gender distribution; birth and death rates; income data and economic status; social/community needs; access to services; knowledge, attitudes, and beliefs; and understanding and attitudes of the population toward significant health issues impacting the community (OpenLearn Create, n.d.). Community profiles are seen as a comprehensive approach to gathering information about communities, particularly regarding community needs. They serve as a foundation for driving effective community change and development (van der Waldt, 2019).

From a review of literature related to the use of community data in the development of information systems, it was found that the types of data utilized vary based on the objectives of each system’s development. These objectives significantly influence the types and characteristics of required data. Additionally, the unique characteristics of each community, including its specific needs and challenges, determine the appropriate data to use, ensuring alignment with the reality and requirements of community users. Community data has been applied in developing models for studying the context of community development, cultural heritage conservation projects, and as a guideline for data experts and technology developers aiming to design technologies for local communities (Huang et al., 2024). Furthermore, it serves as a medium for disseminating information from LAOs, enabling both public and private organizations to utilize the data effectively. Table 1 (Bunch, 1993; Department of Local Administration, n.d.; Library of Congress, 2006; Lopez, 2015; Ozuomba et al., 2013; van der Waldt, 2019) illustrates examples of community data from various concepts, research, and foundational information that have been employed in developing information systems.

From the analysis of community data based on concepts, research, and foundational information from agencies utilizing community data for the development of information systems, community data can be classified into five groups as follows: (1) statistical data–This category includes population statistics, such as population size, gender distribution, age, marital status, education, employment, health, and housing (Bunch, 1993; van der Waldt, 2019); (2) socio-economic data–This category encompasses general information (geographical factors, community history, community structure, livelihoods, local institutions, and individuals), infrastructure and public utilities (education, access to public services, medical and public health services, disaster prevention and mitigation, community facilities, volunteer organizations, and recreational facilities), social information (religion, ethnic groups), economic information (average household income), environmental information, and financial and fiscal data (Bunch, 1993; Department of Local Administration, n.d.; 2019; Library of Congress, 2006; Ozuomba et al., 2013; van der Waldt, 2019); (3) local issues–This data reflects problems or critical issues in the community, such as unemployment, poverty, lack of educational opportunities, access to healthcare services, infrastructure problems, and transportation. These factors impact the quality of life of residents in the area. Such data can be obtained from reports by government and private agencies, as well as from surveys of local residents’ opinions (Bunch, 1993); (4) residents’ viewpoints and community needs–This data includes community members’ opinions about local situations and issues. It can be gathered through interviews, public meetings, opinion surveys, local media (e.g., letters to the editor or community newspapers), and discussions on social media platforms. It also includes other communication sources that reflect the views and needs of community members. This information is crucial for understanding the real needs of the community (Bunch, 1993; Ozuomba et al., 2013); and (5) activity and network information–This category involves online activities, social networks, shared content, and tourism sites (Library of Congress, 2006; Lopez, 2015).

2.2. Cultural Capital Information

Cultural capital is a concept used to study the foundation of communities built upon strength. The French sociologist Bourdieu (1986) defines cultural capital as resources accumulated within individuals, objects, and institutions, shaped and transmitted through educational systems. The result of this accumulation is “taste,” which helps to create social distinctions and sustain social classes. Griswold (2004) expands on Bourdieu’s concept, stating that culture can be viewed as a type of capital that can be accumulated and converted into economic capital. It influences perceptions, cultural tastes, and lifestyles. The Department of Cultural Promotion (2019) explains that cultural capital refers to the inheritance of cultural wisdom, which can be used to add value in creative industries, transforming it into goods or services with added value. The UNESCO Institute for Statistics (n.d.) categorizes cultural heritage into two types: (1) tangible cultural heritage, which includes movable cultural heritage, such as paintings, sculptures, coins, and manuscripts; immovable cultural heritage, such as monuments, archaeological sites, and other structures; underwater cultural heritage, such as shipwrecks, underwater ruins, and submerged cities; and (2) intangible cultural heritage, which includes oral traditions, performing arts, rituals, and other cultural expressions.

Cultural heritage information (CHI) refers to all information related to cultural heritage, including data on the heritage itself, its management, preservation, relocation, and the administration of institutions responsible for such heritage. CHI consists of resources with evaluated significance, such as ancient documents, photographs, and historical maps, which require proper storage and preservation to be passed on to future generations. CHI is classified into three main categories: (1) collection information, which involves data about heritage items such as museum objects and documents; (2) technical information, which includes data on technical activities such as conservation and exhibitions; and (3) administrative information, which encompasses data on the management of institutions responsible for heritage, such as policies and visitor statistics. This information is crucial for preserving and transmitting cultural heritage from one generation to the next (Chaichuay, 2018).

Currently, institutions providing cultural information services, such as libraries, archives, and museums, emphasize the development of information services to improve the efficiency of access, sharing, and analysis of cultural data. These efforts aim to facilitate the effective use of such data for education, research, and conservation purposes. Efforts to enhance these services take various forms, particularly in the development and application of metadata. For example, Salse et al. (2022) studied metadata structures in museums and university collections across 23 institutions in Spain and Europe. They found that museums employed a variety of metadata standards, including Categories for the Description of Works of Art (CDWA), SPECTRUM, ICCD, MuseumDat, VRA Core, and LIDO. However, many museums still use custom metadata structures to meet the specific needs of their organizations. Similarly, a study by Pandey and Kumar (2023) examined metadata element sets used for digital art objects in Indian museums. Among the five museums studied, art objects were categorized into 13 categories, with each category employing different metadata standards to describe the data. It is evident that libraries, archives, and museums adopt diverse metadata standards and preserve various types of resources, making it impractical to use a single metadata standard for all resource types. However, having robust metadata is crucial for dissemination. Therefore, organizations must select metadata standards appropriate to the resources they manage and prioritize their effective implementation.

From a review of literature on cultural data management, there have been efforts to develop metadata capable of comprehensively and thoroughly describing cultural heritage according to its type. Based on the classification of cultural heritage by UNESCO, cultural heritage can be categorized into two types: tangible cultural heritage and intangible cultural heritage, as outlined in Table 2 (Chansanam & Tuamsuk, 2022; Giannoulakis et al., 2018; Kwiecien et al., 2021; Nachom, 2018; Trust, 2024; Ye, 2023).

2.3. Datasets

Datasets refer to collections of data organized into a structured format for practical use. These datasets often consist of interrelated data presented in an orderly manner, such as tables with rows and columns or files containing data in various formats, including text, numbers, images, or audio. Datasets can take many forms, such as tables, text files (e.g., CSV, JSON), or databases. A dataset typically includes multiple rows (records) and columns (attributes or fields) that store related information. In the fields of data science and data analysis, datasets serve as critical resources for researchers and analysts to process, summarize, predict, and create data analysis models (databricks, n.d.; Kelleher & Tierney, 2018).

Datasets in research and data analysis can be categorized into several types based on the characteristics of the records and the types of variables that make up the dataset (Auwattanamongkol, 2019, pp. 10-11). For example: (1) record data refers to a dataset consisting of individual records, each with attributes or characteristics (attributes) that have a defined number and type of data. Each record can be compared to a row in a database table, for instance, personal information, which may include variables such as ID number, name, address, etc. (2) A data matrix consists of records where all variables are numerical data (numeric). The dataset can be represented as a matrix, where each row represents one data instance or record. Each instance can be represented as a vector consisting of numerical values. The vector’s size depends on the number of variables in the record: for example, the proportion of ingredients in a food mixture. (3) Transactional data consists of a list of events or items that appear in each record, such as customer purchase data for each transaction. The number of events or items on each record may vary. (4) Document data consists of individual documents (e.g., text). Each record contains variables indicating specific features of the document, such as the frequency of certain words appearing in the document. These variables can help determine the content of the document. This type of data often is high and most of the variable values are zero (sparse high dimensional data). (5) Binary data consists of records where each variable can only have a value of 1 or 0, indicating whether a specific event or status has occurred. This type of data is often converted from transactional data, with many variables, most of which are zero. (6) Graph data consists of data in a graph structure, where each record represents information in the form of nodes and edges, for example, chemical molecular structure data or protein-related data. These structures tend to be irregular and unpredictable. (7) Sequence data consists of a series of events or items occurring in sequence, such as gene sequences in chromosomes or web page visits within a certain time frame. This type of data reflects the occurrence of events in meaningful order. And (8) time series data consists of data where the variables change over time, such as daily stock indices or daily temperatures throughout the year. This type of data is related to time periods and temporal changes (databricks, n.d.; Khan & Hanna, 2022).

The use of datasets in research is diverse and highly beneficial across many disciplines, such as medicine, marketing, education, finance, and social sciences. Utilizing datasets for analysis can help identify relationships, predict outcomes, and make data-driven decisions based on sound principles. Example include analyzing basic datasets to develop a system for linking family medicine data (Saensrisawat et al., 2024), or developing a dataset on patients with heart disease symptoms to train a model for predicting heart disease risk using various techniques, such as machine learning (Janosi et al., 1989). However, the use of datasets also comes with several limitations that may impact on the quality of analysis and data handling. These limitations may arise from incomplete or improperly stored data that does not meet the user’s needs, such as incorrect, incomplete, redundant, or inconsistent data (Auwattanamongkol, 2019; Little & Rubin, 2002). Users of datasets may apply different techniques to prepare the data for more efficient and accurate analysis or model creation—for instance, using a data dictionary to explain the details of each field in the dataset, such as field names, data types, or descriptions. A data dictionary is a set of names, definitions, and characteristics of data elements used or collected in a database, information system, or research project. It explains the meaning and purpose of data elements in the context of the project while providing guidelines for interpreting and displaying the data. Additionally, the data dictionary includes metadata to define the scope, nature, and rules for using data elements, which helps reduce inconsistencies within the project, sets common standards, ensures consistency in data collection and use among research team members, facilitates data analysis, and establishes standards for data management. This makes it easier for users or programmers to understand and manage the dataset effectively (UC Merced Library, n.d.).

The management of cultural data in Thailand, despite efforts from various agencies to create databases and collect information, still faces challenges. A case study by Boonto (2025) on the development of digital cultural heritage management in local museums in Chiang Mai aimed to examine the lessons learned from managing and digitizing cultural heritage data using the Navanurak and Museum Pool platforms. The study also aimed to explore the development of digital cultural heritage data management for local museums in Thailand. The findings revealed that the studied area has a strong community network and is effectively able to connect museums with cultural tourism in the community. However, the data stored in the system primarily focuses on museum objects. The planning for data management from the initial stages of museum establishment lacked a systematic approach to storing cultural heritage data, such as the absence of an object register. This has resulted in challenges in the management and digitization processes, as there is a lack of necessary data in both the overall context and the specific context of each object. Furthermore, the absence of a connection between the object data and local community data has led to information that does not fully reflect the realities of the area or carry cultural significance in an authentic way (Boonto, 2025).

Studies on the role of LAOs in cultural capital information management reveal that LAOs have legal responsibilities for promoting and preserving local cultural heritage. Cultural operations typically fall under the purview of educational personnel who may lack specialized expertise in cultural management, resulting in unsystematic cultural information management. Consequently, most cultural data remains scattered and outdated. The cultural capital in the studied areas can be categorized into tangible and intangible assets, as well as policy-related information and administrative data. The research indicates that while LAOs have formal mandates for cultural preservation, the implementation of systematic cultural information management remains challenging due to the absence of systematic practices for data management. Despite these limitations, local cultural information management practices show promise in fostering educational opportunities, enhancing community engagement, and contributing to sustainable cultural conservation efforts (Anontachai et al., 2025).

Another study by Nonthacumjane and Johansson (2023) on shaping local information in Thailand identifies key issues in the digitalization of local information within the Provincial University Library Network (PULINET). These include contradictions in the conceptualization of local information among group members and the definitions established by the group. Additionally, there is a misalignment between the conceptual view of local information as a dynamic resource and the actual digitalization activities, which focus on formal boundaries and prominent objects, thus undermining the diversity and contextual richness of local information. The study also highlights the exclusion of marginalized groups and expressions that are not deemed socially acceptable. Furthermore, these contradictions are not leveraged as opportunities for learning and development but are instead concealed and resisted, preventing the full potential of local information digitalization from being realized (Nonthacumjane & Johansson, 2023).

Building upon the aforementioned research, the review of relevant studies underscores key challenges in the management of cultural heritage and local information in Thailand. These challenges include the absence of systematic data storage, the fragmentation of data across different sources, and the lack of comprehensive data management frameworks that encompass both tangible and intangible cultural heritage. Furthermore, there is a notable deficiency in the diversity and contextual richness of local information. Developing datasets for a community cultural capital information system is essential for effectively linking museum data with community information. By constructing datasets that integrate cultural heritage and local administrative data while reflecting cultural diversity, these efforts can address the identified challenges and foster systematic data management, thereby facilitating sustainable cultural development and preservation at the local level.

In this research, the authors studied and analyzed standards for storing various types of data, including those used for managing community and cultural heritage data. This includes the CDWA standard for tangible cultural heritage data and research that developed metadata for intangible cultural heritage data, to analyze the necessary information for managing cultural heritage. They then developed a dataset using a data dictionary as a tool to manage and explain the data, such as field names, data types, data definitions, and related rules. This helps clarify the meaning of the data, reduces confusion, and ensures that data usage is organized and understood consistently.

2.4. Research Conceptual Framework

From an analysis of previous studies and literature, the researchers have established a research conceptual framework based on relevant concepts, theories, and related studies, including CI, cultural capital information, and datasets, as illustrated in Fig. 1 (Library of Congress, 2006; UNESCO, 2011).

Fig. 1

Research conceptual framework. MARC, machine readable cataloging; UNESCO, United Nations Educational, Scientific and Cultural Organization.

According to Fig. 1 (Library of Congress, 2006; UNESCO, 2011), CI is a concept that focuses on utilizing ICT to strengthen communities in areas of development, learning, and network connectivity. Cultural capital information, on the other hand, refers to data related to cultural capital, which reflects the knowledge, wisdom, beliefs, and identity of the community. When integrated with systematically collected datasets, this information can be analyzed to provide a clearer and more comprehensive understanding of the community’s overall picture and context. In developing the research conceptual framework, the researchers link the relationships found in the literature, beginning with the overarching concept of CI, proceeding in-depth into cultural capital information as a key data component that should be documented, employing datasets as tools for data collection, analysis, and transformation into new knowledge. These elements are interconnected in a systematic sequence, progressing from concept to practice and leading to the design of a clear and structured research conceptual framework.

3.1. Formulating of the Research Topic

The researchers identified a topic that is both pertinent and significant to the study’s objectives, emphasizing the analysis of key datasets essential for the development of a community cultural capital information system. The objective is to acquire datasets that are comprehensive, clear, and conducive to the efficient management of cultural capitals, thereby fostering sustainable development at both local and national levels. Based on the objectives of the study, the research questions can be identified as shown in Table 3.

3.2. Research Design

In designing the research process, the researchers established a framework and methodology that aligns with the objectives of the study, taking into account relevant elements such as theories, concepts, requirements, and related experiences. This approach ensures that the study is comprehensive and capable of addressing the research questions effectively. Additionally, the researchers defined a structured and standardized research framework and plan to ensure that the research process leads to accurate, reliable, and valid results. The details of the research design and process are summarized in Table 4.

3.3. Designing the Structure of Datasets to Identify and Define Key Data Categories

To fulfill the objective of identifying and defining the key data categories required for the development of a CIS focused on managing cultural capital, the researchers conducted a systematic process of dataset structuring. This process aimed to ensure that all essential data related to both community and cultural capital were clearly categorized and appropriately defined. The steps are outlined as follows.

3.4. Evaluation and Refinement of the Dataset

In the final stage, the researchers evaluated the information derived from the previous stages to determine which data is essential for future development or research. This evaluation process involved additional interviews with experts to arrive at more definitive conclusions.

4.1. Community Datasets Required for Developing CIS

The analysis of the community data required for collection, as identified by six experts, reveals the essential data to be collected in common, as shown in Table 7.

Based on Table 7, the community data required for collection and utilization consists of the following categories.

1) Spatial information, including geographic data, economic data, social data (such as community groups and social organizations), and cultural data (local traditions and customs, religious activities and beliefs, and local wisdom holders).

2) Community organization information, comprising (1) core function organizations, which are the primary government agencies directly responsible for the area, such as LAOs (subdistrict administrative organizations, subdistrict municipalities, etc.), and (2) area function organizations related to the community, which may include public agencies, private organizations, civil society groups, and community-based organizations.

3) Community capital and potential information, including data on individuals, groups, agencies, organizations, local resources, and both physical and social infrastructure.

4) Information for development and construction purposes, such as data on building locations, surrounding environments, site accessibility, boundary definitions, budget allocation, and the specific needs of the community (e.g., for establishing community museums).

The analysis of cultural capital data required for collection and utilization, based on the perspectives of six experts, indicates that the necessary data to be collected are consistent across all experts, as shown in Table 8.

From Table 8, it was found that the cultural capital data required for collection and utilization consists of the following:

1) Tangible cultural capital, such as historical sites, monuments, and artifacts.

2) Intangible cultural capital, including traditions, local craftsmanship, and folk arts.

3) Information on individuals involved in cultural heritage, such as key cultural figures, experts, and successors.

4) Detailed in-depth information for reference purposes, including historical records from government sources, community legends, and oral histories from local wisdom keepers.

5) Natural heritage, such as landscapes, natural tourist attractions, and native plants and animals.

6) Information on cultural management and support, such as conservation, restoration, promotion, dissemination, financial assistance, and policy support.

7) Community valuation of cultural capital in various dimensions, which may include belief systems related to the cultural capital and its relationship with the community.

8) Potential benefits for extension and application, including commercial, professional, educational, or other uses.

Results of the development and evaluation on the necessity level of the 15 community datasets revealed that four datasets were rated as most necessary for the CIS for managing cultural capital, as follows: Individual data (e.g., community leaders/local scholars) ( $\overline{\text{χ}}$=4.69); Organizational data ( $\overline{\text{χ}}$=4.63); Area context data–community history ( $\overline{\text{χ}}$=4.50); and Community map data ( $\overline{\text{χ}}$=4.50). The remaining 11 datasets were rated as very necessary, including: Community location data ( $\overline{\text{χ}}$=4.48); Local project data ( $\overline{\text{χ}}$=4.42); Local personnel data ( $\overline{\text{χ}}$=4.36); LAO data ( $\overline{\text{χ}}$=4.31); Area context data–population size ( $\overline{\text{χ}}$=4.30); Activity data ( $\overline{\text{χ}}$=4.22); Program or service data ( $\overline{\text{χ}}$=4.20); Area context data–area size and administrative boundaries ( $\overline{\text{χ}}$=4.61); Community slogan data ( $\overline{\text{χ}}$=3.92); and Budget management data ( $\overline{\text{χ}}$=3.67), respectively. In total, the 15 community datasets are comprised of 117 data elements. The required community datasets are presented in Table 9.

4.2. Cultural Capital Datasets Required for Developing CIS

Results of the development and evaluation of the necessity level of the cultural capital datasets revealed that among the two main datasets, intangible cultural datasets were rated at the most necessary level ( $\overline{\text{χ}}$=4.4.64), while the tangible cultural dataset was rated at a very necessary level ( $\overline{\text{χ}}$=4.37) for the CIS for managing cultural capital. For the intangible cultural datasets, they are divided into five subsets. It is revealed that all five subsets were rated at the most necessary level, respectively, as follows: Traditional craftsmanship data ( $\overline{\text{χ}}$=4.74); Social practices, rituals, and festivals data ( $\overline{\text{χ}}$=4.73); Performing arts data ( $\overline{\text{χ}}$=4.61); Knowledge and practices related to nature and the universe data ( $\overline{\text{χ}}$=4.61); and Narrative data ( $\overline{\text{χ}}$=4.53). In total, the two cultural capital datasets comprise 124 data elements. The required community datasets are presented in Table 10.