Metadata Model Research Articles

Metadata model for organizing digital archives of tangible and intangible cultural heritage, and linking cultural heritage information in digital space

Metadata model for organizing digital archives of tangible and intangible cultural heritage, and linking cultural heritage information in digital space

Metadata Schema to Facilitate Linked Data for 3D Digital Models of Cultural Heritage Collections: A University of South Florida Libraries Case Study

ABSTRACTThe University of South Florida Libraries house and provide access to a collection of cultural heritage and 3D digital models. In an effort to provide greater access to these collections, a linked data project has been implemented. A metadata schema for the 3D cultural heritage objects which uses linked data is an excellent way to share these collections with other repositories, thus gaining global exposure and access to these valuable resources. This article will share the process of building the 3D cultural heritage metadata model as well as an assessment of the model and recommendations for future linked data projects.

Enriching and enhancing moving images with Linked Data

PurposeThe purpose of this paper is to examine the current state of Linked Data (LD) in archival moving image description, and propose ways in which current metadata records can be enriched and enhanced by interlinking such metadata with relevant information found in other data sets.Design/methodology/approachSeveral possible metadata models for moving image production and archiving are considered, including models from records management, digital curation, and the recent BIBFRAME AV Modeling Study. This research also explores how mappings between archival moving image records and relevant external data sources might be drawn, and what gaps exist between current vocabularies and what is needed to record and make accessible the full lifecycle of archiving through production, use, and reuse.FindingsThe author notes several major impediments to implementation of LD for archival moving images. The various pieces of information about creators, places, and events found in moving image records are not easily connected to relevant information in other sources because they are often not semantically defined within the record and can be hidden in unstructured fields. Libraries, archives, and museums must work on aligning the various vocabularies and schemas of potential value for archival moving image description to enable interlinking between vocabularies currently in use and those which are used by external data sets. Alignment of vocabularies is often complicated by mismatches in granularity between vocabularies.Research limitations/implicationsThe focus is on how these models inform functional requirements for access and other archival activities, and how the field might benefit from having a common metadata model for critical archival descriptive activities.Practical implicationsBy having a shared model, archivists may more easily align current vocabularies and develop new vocabularies and schemas to address the needs of moving image data creators and scholars.Originality/valueMoving image archives, like other cultural institutions with significant heritage holdings, can benefit tremendously from investing in the semantic definition of information found in their information databases. While commercial entities such as search engines and data providers have already embraced the opportunities that semantic search provides for resource discovery, most non-commercial entities are just beginning to do so. Thus, this research addresses the benefits and challenges of enriching and enhancing archival moving image records with semantically defined information via LD.

A novel metadata management model to capture consent for record linkage in longitudinal research studies

ABSTRACTBackground: Informed consent is an important feature of longitudinal research studies as it enables the linking of the baseline participant information with administrative data. The lack of standardized models to capture consent elements can lead to substantial challenges. A structured approach to capturing consent-related metadata can address these. Objectives: a) Explore the state-of-the-art for recording consent; b) Identify key elements of consent required for record linkage; and c) Create and evaluate a novel metadata management model to capture consent-related metadata. Methods: The main methodological components of our work were: a) a systematic literature review and qualitative analysis of consent forms; b) the development and evaluation of a novel metadata model. Discussion: We qualitatively analyzed 61 manuscripts and 30 consent forms. We extracted data elements related to obtaining consent for linkage. We created a novel metadata management model for consent and evaluated it by comparison with the existing standards and by iteratively applying it to case studies. Conclusion: The developed model can facilitate the standardized recording of consent for linkage in longitudinal research studies and enable the linkage of external participant data. Furthermore, it can provide a structured way of recording consent-related metadata and facilitate the harmonization and streamlining of processes.

Development of a visual e-learning system for supporting the semantic organization and utilization of open learning content

In an open e-learning content management environment, relation metadata is of benefit to improve semantic organization and reusability of learning content. Although the suggested relations defined in the SCORM and the extended relations proposed in the past studies can describe semantic relationships, there are some new requirements of semantic organization and utilization of open learning content. Based on existing models, this paper presents an extended relation metadata model for open knowledge communities. In order to help users to author and utilize the semantic relation, the visual authoring system named web-based visual authoring system for relation metadata (WVAS-RM) in the Learning Cell Knowledge Community is designed and implemented to assist the construction and utilization of semantic relations of Learning Cells. The paper presents an empirical evaluation of the teachers’ and learners’ acceptance and satisfaction of the proposed system using the adapted Technology Acceptance Model and System Usability Scale. The semi-structured interviews are also carried out with participants including teachers and students. It is concluded that students and teachers feel confident and satisfied with the system.

Automatic Content Recommendation and Aggregation According to SCORM

Although widely used, the SCORM metadata model for content aggregation is difficult to be used by educators, content developers and instructional designers. Particularly, the identification of contents related with each other, in large repositories, and their aggregation using metadata as defined in SCORM, has been demanding efforts of computer science researchers in pursuit of the automation of this process. Previous approaches have extended or altered the metadata defined by SCORM standard. In this paper, we present experimental results on our proposed methodology which employs ontologies, automatic annotation of metadata, information retrieval and text mining to recommend and aggregate related content, using the relation metadata category as defined by SCORM. We developed a computer system prototype which applies the proposed methodology on a sample of learning objects generating results to evaluate its efficacy. The results demonstrate that the proposed method is feasible and effective to produce the expected results.

MetaStore: an adaptive metadata management framework for heterogeneous metadata models

In this paper, we present MetaStore, a metadata management framework for scientific data repositories. Scientific experiments are generating a deluge of data, and the handling of associated metadata is critical, as it enables discovering, analyzing, reusing, and sharing of scientific data. Moreover, metadata produced by scientific experiments are heterogeneous and subject to frequent changes, demanding a flexible data model. Existing metadata management systems provide a broad range of features for handling scientific metadata. However, the principal limitation of these systems is their architecture design that is restricted towards either a single or at the most a few standard metadata models. Support for handling different types of metadata models, i.e., administrative, descriptive, structural, and provenance metadata, and including community-specific metadata models is not possible with these systems. To address this challenge, we present MetaStore, an adaptive metadata management framework based on a NoSQL database and an RDF triple store. MetaStore provides a set of core functionalities to handle heterogeneous metadata models by automatically generating the necessary software code (services) and on-the-fly extends the functionality of the framework. To handle dynamic metadata and to control metadata quality, MetaStore also provides an extended set of functionalities such as enabling annotation of images and text by integrating the Web Annotation Data Model, allowing communities to define discipline-specific vocabularies using Simple Knowledge Organization System, and providing advanced search and analytical capabilities by integrating the ElasticSearch. To maximize the utilization of the data models supported by NoSQL databases, MetaStore automatically segregates the different categories of metadata in their corresponding data models. Complex provenance graphs and dynamic metadata are modeled and stored in an RDF triple store, whereas the static metadata is stored in a NoSQL database. For enabling large-scale harvesting (sharing) of metadata using the METS standard over the OAI-PMH protocol, MetaStore is designed OAI-compliant. Finally, to show the practical usability of the MetaStore framework and that the requirements from the research communities have been realized, we describe our experience in the adoption of MetaStore for three communities.

Software Application Profile: Opal and Mica: open-source software solutions for epidemiological data management, harmonization and dissemination.

MotivationImproving the dissemination of information on existing epidemiological studies and facilitating the interoperability of study databases are essential to maximizing the use of resources and accelerating improvements in health. To address this, Maelstrom Research proposes Opal and Mica, two inter-operable open-source software packages providing out-of-the-box solutions for epidemiological data management, harmonization and dissemination.ImplementationOpal and Mica are two standalone but inter-operable web applications written in Java, JavaScript and PHP. They provide web services and modern user interfaces to access them.General featuresOpal allows users to import, manage, annotate and harmonize study data. Mica is used to build searchable web portals disseminating study and variable metadata. When used conjointly, Mica users can securely query and retrieve summary statistics on geographically dispersed Opal servers in real-time. Integration with the DataSHIELD approach allows conducting more complex federated analyses involving statistical models.AvailabilityOpal and Mica are open-source and freely available at [www.obiba.org] under a General Public License (GPL) version 3, and the metadata models and taxonomies that accompany them are available under a Creative Commons licence.

Geospatial binding for transdisciplinary research in crop science: the GRASPgfs initiative

The paper retraces the GRASPgfs endeavor (Geospatial Resource for Agricultural Species and Pests with integrated workflow modelling to support Global Food Security) between multiple disciplines around a common objective of facilitating research and model simulations for sustainable food security. Within this endeavor, the geospatial media has been the enabler for multidisciplinary research in crop modelling. Geospatial genetic-trait variations and associations with environmental forecasting were the main focus of the GRASPgfs. Designing the platform achieving this objective generated a transdisciplinary vision of modelling and forecasting for sustainable agriculture. Based on interoperability principles, seamless access as well as sharing for data, metadata and processing models, the design is described in this paper. This geospatial binding facilitates and supports new types of hypotheses and analysis as illustrated in the paper with a landscape genetic case study (bambara groundnut) and a crop disease modelling (eyespot disease). The approach and the eGRASP platform are generic enough to accommodate further complexity into the integrated modelling that this geospatial binding enables.

Statistical metadata in knowledge discovery

Los metadatos representan el esquema semántico de los datos recolectados a lo largo de los años por una organización para aplicar el enfoque de inteligencia de negocios. Sin embargo, los metadatos normalmente recopilados no son suficientes para facilitar los procesos de descubrimiento de conocimiento porque están concebidos, principalmente, para la interoperabilidad entre sistemas de información. La investigación realizada en este estudio confirmó la necesidad de enriquecer los sistemas de almacenamiento de datos con metadatos significativos y estructurados con el fin de aumentar la productividad y la eficacia de cualquier investigación, incluida la gestión de datos y la analítica futura del negocio. Esta necesidad nos llevó a adoptar y ampliar el concepto de "metadatos estadísticos". Por lo tanto, nuestro modelo conceptual propuesto de metadatos estadísticos no sólo considera estándares reconocidos, sino que también representa otras propiedades adicionales. Esto significa que nuestro modelo conceptual permite mayores niveles de detalle sobre los datos y la calidad de los contenidos semánticos.

Optimizing the double inverted pendulum’s performance via the uniform neuro multiobjective genetic algorithm

An inverted pendulum is a sensitive system of highly coupled parameters, in laboratories, it is popular for modelling nonlinear systems such as mechanisms and control systems, and also for optimizing programmes before those programmes are applied in real situations. This study aims to find the optimum input setting for a double inverted pendulum (DIP), which requires an appropriate input to be able to stand and to achieve robust stability even when the system model is unknown. Such a DIP input could be widely applied in engineering fields for optimizing unknown systems with a limited budget. Previous studies have used various mathematical approaches to optimize settings for DIP, then have designed control algorithms or physical mathematical models. This study did not adopt a mathematical approach for the DIP controller because our DIP has five input parameters within its nondeterministic system model. This paper proposes a novel algorithm, named UniNeuro, that integrates neural networks (NNs) and a uniform design (UD) in a model formed by input and response to the experimental data (metamodel). We employed a hybrid UD multiobjective genetic algorithm (HUDMOGA) for obtaining the optimized setting input parameters. The UD was also embedded in the HUDMOGA for enriching the solution set, whereas each chromosome used for crossover, mutation, and generation of the UD was determined through a selection procedure and derived individually. Subsequently, we combined the Euclidean distance and Pareto front to improve the performance of the algorithm. Finally, DIP equipment was used to confirm the settings. The proposed algorithm can produce 9 alternative configured input parameter values to swing-up then standing in robust stability of the DIP from only 25 training data items and 20 optimized simulation results. In comparison to the full factorial design, this design can save considerable experiment time because the metamodel can be formed by only 25 experiments using the UD. Furthermore, the proposed algorithm can be applied to nonlinear systems with multiple constraints.

Prueba piloto de implementación del estándar de catalogación RDA en Colombia

La Biblioteca Nacional de Colombia, como ente rector en la formulación, coordinación y ejecución de políticas, planes y programas relacionados con el procesamiento técnico del patrimonio bibliográfico y documental colombiano, lideró la prueba piloto de implementación del estándar de catalogación RDA en Colombia. Esta prueba piloto, cuenta actualmente con la participación de diferentes unidades de información del país como la Biblioteca Luis Ángel Arango, la Biblioteca Pública Piloto de Medellín, el Instituto Caro y Cuervo, el SENA, Corpoica, la Corporación Biblioteca Rafael Carrillo Luquez, la Fundación Universitaria de Ciencias de la Salud FUCS (Fundación Universitaria Ciencias de la Salud, la Universidad de Antioquia, la Universidad de “ El Bosque”, la Universidad del Valle, la Universidad Javeriana, la Universidad Nacional de Colombia (Manizales), la Universidad Pontificia Bolivariana y la Universidad Santo Tomás, todas ellas interesadas en estudiar la norma y establecer políticas de aplicación de forma consensuada, además de reconocer los cambios e impactos de su implementación. En este sentido, para la prueba piloto RDA se han efectuado reuniones periódicas, tanto presenciales como virtuales, con el objetivo de trabajar en la creación de registros RDA, los cuales son aplicables a todo tipo de recursos y contenidos, en especial a los del entorno digital. La experiencia de esta prueba piloto representa un referente para la implementación de la norma RDA en América Latina, dado que ha generado diversos productos: adaptación de software libre y configuración de los SIB (Sistema Integrado de Bibliotecas) de las entidades participantes para el modelo de metadatos, etiquetas y campos fijos MARC21 necesarios para la elaboración de registros RDA, elaboración de registros (obra, expresión, manifestación, autoridad: personal, corporativo y de familia), creación de un foro especializado para intercambiar, publicar, poner al servicio avances y, finalmente, elaborar manuales relacionados con la creación de registros. Considerando la importancia de esta prueba piloto para América Latina, este artículo presenta todos los aspectos relevantes de su desarrollo, los resultados obtenidos y los pasos a seguir en el futuro.

A Metadata Model Proposal for Simulation Based E-learning

<p class="Abstract">Existing E-learning standards and specifications present a great basis for the development of E-learning, on line and distance learning contents that are accessible, interoperable, durable and reusable. E-learning contents are supported by these standards as well as by the LMS (Learning Management System) or web technologies compliant to them. However, simulation based contents or learning objects are less integrated in the E-learning contents than other learning objects, partially because standards and specification don’t pay much attention to more specify them in term of metadata requirements.</p>The main objective of this paper is the elaboration of a new Metadata model on the basis of Learning Object Metadata (LOM) with a wider scope that could support more easily simulation objects, especially in experiential E-learning content in which simulation activity should be executed by the learner, monitored and tracked by the tutor completely on the LMS.

A Schema-Based Approach to Enable Data Integration on the Fly

On-the-fly data integration, i.e. at query time, happens mostly in tightly coupled, homogeneous environments where the partitioning of the data can be controlled or is known in advance. During the process of data fusion, the information is homogenized and data inconsistencies are hidden from the application. Beyond this, we propose in this paper the Nexus metadata model and a processing approach that support on-the-fly data integration in a loosely coupled federation of autonomous data providers, thereby advancing the status quo in terms of flexibility and expressive power. It is able to represent data and schema inconsistencies like multi-valued attributes and multi-typed objects. In an open environment, this best suites the application needs where the data processing infrastructure is not able to decide which attribute value is correct. The Nexus metadata model provides the foundation for integration schemata that are specific to a given application domain. The corresponding processing model provides four complementary query semantics in order to account for the subtleties of multi-valued and missing attributes. In this paper we show that this query semantics is sound, easy to implement, and it builds upon existing query processing techniques. Thus the Nexus metadata model provides a unique level of flexibility for on-the-fly data integration.

A Spatio-Temporal Enhanced Metadata Model for Interdisciplinary Instant Point Observations in Smart Cities

Due to the incomprehensive and inconsistent description of spatial and temporal information for city data observed by sensors in various fields, it is a great challenge to share the massive, multi-source and heterogeneous interdisciplinary instant point observation data resources. In this paper, a spatio-temporal enhanced metadata model for point observation data sharing was proposed. The proposed Data Meta-Model (DMM) focused on the spatio-temporal characteristics and formulated a ten-tuple information description structure to provide a unified and spatio-temporal enhanced description of the point observation data. To verify the feasibility of the point observation data sharing based on DMM, a prototype system was established, and the performance improvement of Sensor Observation Service (SOS) for the instant access and insertion of point observation data was realized through the proposed MongoSOS, which is a Not Only SQL (NoSQL) SOS based on the MongoDB database and has the capability of distributed storage. For example, the response time of the access and insertion for navigation and positioning data can be realized at the millisecond level. Case studies were conducted, including the gas concentrations monitoring for the gas leak emergency response and the smart city public vehicle monitoring based on BeiDou Navigation Satellite System (BDS) used for recording the dynamic observation information. The results demonstrated the versatility and extensibility of the DMM, and the spatio-temporal enhanced sharing for interdisciplinary instant point observations in smart cities.

Toolkit for intensive work with metadata in specialized information systems

We present a concept of the information system for intensive work with metadata in heterogeneous data store to modify dynamically both the data and the metadata models by user. This concept is illustrated by example of such information system for the project where the digital registrar is developing for the culture heritage of Old Russian paintings, namely Dionisius frescoes in the Cathedral of the Nativity of the Virgin at the Ferapontov Monastery painted in 1502.

System architecture of coastal remote sensing data mining and services based on cloud computing

Coastal remote sensing images have huge data which has features of the volume, variety, complexity and specialisation. The problem is how to effectively carry out data integration, fast extract and data mining of knowledge and information from these massive remote sensing data as it is far behind the requirements of coastal professional applications. In this paper, based on data mining, remote sensing theory, space information and cloud computing technology towards the goal of coastal zone remote sensing data integration and data mining service system, the meta-data model, data storage model, data mining framework, web service framework model, etc. are provided and designed. Finally, a prototype system of remote sensing data mining services in the cloud computing environment is designed and developed using system integration, and is demonstrated and verified by professional applications. It is valuable to serve the development of the coastal zone, monitoring, planning and integrated management and other fields.

A Critique of Imbalanced Data Learning Approaches for Big Data Analytics

Biomedical research becomes reliant on multi-disciplinary, multi-institutional collaboration, and data sharing is becoming increasingly important for researchers to reuse experiments, pool expertise and validate approaches. However, there are many hurdles for data sharing, including the unwillingness to share, lack of flexible data model for providing context information for shared data, difficulty to share syntactically and semantically consistent data across distributed institutions, and expensive cost to provide tools to share the data. In our work, we develop a web-based collaborative biomedical data sharing platform SciPort to support biomedical data sharing across distributed organisations. SciPort provides a generic metadata model for researchers to flexibly customise and organise the data. To enable convenient data sharing, SciPort provides a central server-based data sharing architecture, where data can be shared by one click through publishing metadata to the central server. To enable consistent data sharing, SciPort provides collaborative distributed schema management across distributed sites. To enable semantic consistency for data sharing, SciPort provides semantic tagging through controlled vocabularies. SciPort is lightweight and can be easily deployed for building data sharing communities for biomedical research.

Practices of metadata use in research information management systems

ABSTRACTThis poster reports on a study that examines the practices of metadata use in a research information management (RIM) system ResearchGate. Understanding these practices can help institutional repositories to better align their RIM metadata models with researchers' needs and priorities. The study identified three categories of RIM system users. The study's preliminary findings suggest that community members are more willing to share their personal information and provide full‐texts of their works on ResearchGate compared to readers and personal record managers.

A Semantic Metadata Enrichment Software Ecosystem (SMESE) Based on a Multi-Platform Metadata Model for Digital Libraries

Software industry has evolved to multi-product and multi-platform development based on a mix of proprietary and open source components. Such integration has occurred in software ecosystems through a software product line engineering (SPLE) process. However, metadata are underused in the SPLE and interoperability challenge. The proposed method is first, a semantic metadata enrichment software ecosystem (SMESE) to support multi-platform metadata driven applications, and second, based on mapping ontologies SMESE aggregates and enriches metadata to create a semantic master metadata catalogue (SMMC). The proposed SPLE process uses a component-based software development approach for integrating distributed content management enterprise applications, such as digital libraries. To perform interoperability between existing metadata models (such as Dublin Core, UNIMARC, MARC21, RDF/RDA and BIBFRAME), SMESE implements an ontology mapping model. SMESE consists of nine sub-systems: 1) Metadata initiatives & concordance rules; 2) Harvesting of web metadata & data; 3) Harvesting of authority metadata & data; 4) Rule-based semantic metadata external enrichment; 5) Rule-based semantic metadata internal enrichment; 6) Semantic metadata external & internal enrichment synchronization; 7) User interest-based gateway; 8) Semantic master catalogue. To conclude, this paper proposes a decision support process, called SPLE decision support process (SPLE-DSP) which is then used by SMESE to support dynamic reconfiguration. SPLE-DSP consists of a dynamic and optimized metadata-based reconfiguration model. SPLE-DSP takes into account runtime metadata-based variability functionalities, context-awareness and self-adaptation. It also presents the design and implementation of a working prototype of SMESE applied to a semantic digital library.