Metadata Model Research Articles

ODM Clinical Data Generator: Syntactically Correct Clinical Data Based on Metadata Definition.

The Operational Data Model (ODM) is a data standard for interchanging clinical trial data. ODM contains the metadata definition of a study, i.e., case report forms, as well as the clinical data, i.e., the answers of the participants. The portal of medical data models is an infrastructure for creation, exchange, and analysis of medical metadata models. There, over 23000 metadata definitions can be downloaded in ODM format. Due to data protection law and privacy issues, clinical data is not contained in these files. Access to exemplary clinical test data in the desired metadata definition is necessary in order to evaluate systems claiming to support ODM or to evaluate if a planned statistical analysis can be performed with the defined data types. In this work, we present a web application, which generates syntactically correct clinical data in ODM format based on an uploaded ODM metadata definition. Data types and range constraints are taken into account. Data for up to one million participants can be generated in a reasonable amount of time. Thus, in combination with the portal of medical data models, a large number of ODM files including metadata definition and clinical data can be provided for testing of any ODM supporting system. The current version of the application can be tested at https://cdgen.uni-muenster.de and source code is available, under MIT license, at https://imigitlab.uni-muenster.de/published/odm-clinical-data-generator.

A user-centric metadata model to foster sharing and reuse of multidisciplinary datasets in environmental and life sciences

The recent technological advancements and emergence of the open data in environmental and life sciences are opening new research opportunities while creating new challenges around data management. They make available an unprecedented amount of data that can be exploited for studying complex phenomena. However, new challenges related to data management need to be addressed to ensure effective data sharing, discovery and reuse, especially when dealing with interdisciplinary research contexts. These issues are magnified in interdisciplinary context, by the fact that each discipline has its practices, e.g., specific formats and metadata standards. Moreover, the majority of current data management practices do not consider semantic heterogeneity existing among disciplines. For this reason, we introduce a flexible metadata model that describes the datasets of various disciplines using a common paradigm based on the observation concept. It provides a key vision for articulating the user point of view and underlying scientific domains. In this study, we therefore decide to mainly reuse the SOSA lightweight ontology (Sensor, Observation, Sample, and Actuator) to efficiently leverage others existing ontologies to improve datasets discovery and reuse coming from Earth and life observation. The main benefit of the proposed metadata model is that it extends the technical description, usually provided by existing metadata models, with the observation context description enabling the need of a user viewpoint. Moreover, following the FAIR principles, the metadata model specifies the semantics of its elements using ontologies and vocabularies, and reuses as much as possible ontological and terminological existing resources. We show the benefit and applicability of the model through a case study we identified as representative after interviewing researchers in environmental and life sciences.

Enhanced metadata modelling and extraction methods to acquire contextual pedagogical information from e-learning contents for personalised learning systems

To make online learning systems (OLSs) effective, it is important to make sure that the learners get the learning objects (LOs) according to their pedagogical suitability and requirements. To assess the suitability of an LO, sufficient information of it is required to be available. These information can be specified as metadata of the document. But there is a dearth of metadata defined for educational documents. Existing standard metadata models like IEEE LOM and others are promising but lack in capturing some crucial learning and pedagogical aspects of LOs. In this paper, we propose a new metadata model that has extended the IEEE LOM to provide an extensive set of metadata for LOs. The proposed metadata seem adequate to describe the contextual learning and pedagogical information of any text and web document based LO. But only specifying the metadata is not sufficient; they need to be extracted from a learning content automatically so that these information can be used by the learners and the OLSs and the educational recommendation systems. Automated extraction of metadata from e-learning contents is a non-trivial task. In view of that, we have provided extraction mechanisms for each of the specified metadata, separately. The experimental results show that the proposed extraction methods are quite accurate in identifying and retrieving the different educational metadata. The statistical inferences of the automated and manual extractions are found to have substantial similarities for each of the extracted metadata element.

Resource Management Technology and Application of Remote Network Education Based on Metadata

Based on the effective solution to the problem of poor supply and application of digital education resources. Aiming at the digital basic education resources involved in the field of basic education, this paper uses the methods of investigation and research, demand analysis, metadata model design, system design, practice verification, etc., based on the metadata system and web service of digital basic education resources We have completed the business process analysis of the shared service system, the overall design of the sharing platform, the design of the three-tier metadata structure, the collation and aggregation of digital resources, the realization and verification of platform technology. From the compilation, supplement and description of resource metadata specification, to the clustering, integration and cataloging of digital resources, and then to the realization of retrieval and service interface, this paper focuses on the effective integration of high-quality digital basic education resources in the process of network sharing. Through the establishment of a relatively complete set of digital education resources integration, convergence engineering methodology, to provide the best solution for teachers in the application of resources, maximize support for teachers’ work efficiency, and promote the improvement of curriculum teaching level.

Grid Architecture and Metadata Model of Computer Education Resources Based on Network Teaching Mode

With the continuous progress of our modern science and information technology, network computer distance teaching has gradually become an indispensable part of our school education. In view of the major shortcomings of the current network physical education teaching system, such as the isolation of information resources and the difficulty of updating, the comprehensive research and application of network education information grid management technology and education metadata processing model are established. In the research and analysis, this paper selects 235 network teaching model involved in the sample survey, and through the survey of network teaching mode and traditional teaching mode multimedia, non-linear, independent comparative analysis, from the comparative results show that the network teaching mode is superior to the traditional teaching mode, not only can promote the development of computer teaching mode, but also can improve computer teaching effect. Through the investigation, it is found that both teachers and students have more than 85% recognition of the network teaching mode, which provides a guarantee for the network teaching mode.

Covid-19 knowledge graphs in Health Communication and Information

This commentary discusses recent developments in ‘knowledge graph’ technology over the course of the Covid-19 pandemic. Recently experiencing a surge in popularity, knowledge graphs are technologies that assist with data integration through structured metadata modeling. Researchers tag and collate vast amounts of diverse data using knowledge graphs, yet problems related to semantic drift and more salient issues related to the political economy of information and communication technologies persist. Researchers should anticipate that the semantics of these Covid-19 knowledge graphs can change over time. Equally important, researchers should also consider all stakeholders involved, including those stakeholders that might be excluded.

EU-Citizen.Science: A Platform for Mainstreaming Citizen Science and Open Science in Europe

Citizen Science (CS) is a prominent field of application for Open Science (OS), and the two have strong synergies, such as: advocating for the data and metadata generated through science to be made publicly available [ 1 ]; supporting more equitable collaboration between different types of scientists and citizens; and facilitating knowledge transfer to a wider range of audiences [ 2 ]. While primarily targeted at CS, the EU-Citizen. Science platform can also support OS. One of its key functions is to act as a knowledge hub to aggregate, disseminate and promote experience and know-how; for example, by profiling CS projects and collecting tools, resources and training materials relevant to both fields. To do this, the platform has developed an information architecture that incorporates the public participation in scientific research (PPSR)—Common Conceptual Model ① . This model consists of the Project Metadata Model, the Dataset Metadata Model and the Observation Data Model, which were specifically developed for CS initiatives. By implementing these, the platform will strengthen the interoperating arrangements that exist between other, similar platforms (e.g., BioCollect and SciStarter) to ensure that CS and OS continue to grow globally in terms of participants, impact and fields of application.

Public Sentiment Big Data Query Processing and Optimization with Unified Storage of Source and Meta Data

Public sentiment big data has the characteristics of mass, multi-source, heterogeneity and multi-mode. At present, it mostly adopts separate storage strategies and has low storage and query efficiency. To this end, a distributed storage model is proposed in which structured and unstructured data correspond and are stored uniformly. By establishing a unified storage framework for source metadata, the original source data and characteristic metadata are stored uniformly according to different storage methods. Based on the above storage architecture, a hierarchical index structure is proposed to improve the efficiency of big data query. The unified storage model of source metadata is compared with the common storage methods, and is in the leading position in terms of Block number and query processing efficiency.

Like a rainbow in the dark: metadata annotation for HPC applications in the age of dark data

The deluge of dark data is about to happen. Lacking data management capabilities, especially in the field of supercomputing, and missing data documentation (i.e., missing metadata annotation) constitute a major source of dark data. The present work contributes to addressing this challenge by presenting ExtractIng, a generic automated metadata extraction toolkit. Existing metadata information of simulation output files scattered through the file system, can be aggregated, parsed and converted to the EngMeta metadata model. Use cases from computational engineering are considered to demonstrate the viability of ExtractIng. The evaluation results show that the metadata extraction is simulation-code independent in the sense that it can handle data outputs from various fields of science, is easy to integrate into simulation workflows and compatible with a multitude of computational environments.

On the Complexities of Federating Research Data Infrastructures

Federated Research Data Infrastructures aim to provide seamless access to research data along with services to facilitate the researchers in performing their data management tasks. During our research on Open Science (OS), we have built cross-disciplinary federated infrastructures for different types of (open) digital resources: Open Data (OD), Open Educational Resources (OER), and open access documents. In each case, our approach targeted only the resource “metadata”. Based on this experience, we identified some challenges that we had to overcome again and again: lack of (i) harvesters, (ii) common metadata models and (iii) metadata mapping tools. In this paper, we report on the challenges we faced in the federated infrastructure projects we were involved with. We structure the report based on the three challenges listed above.

Comparative Analysis of Metadata Models on e-Government Open Data Platforms

E-government open data platforms use metadata to provide an adequate environment for the consumption of data released on these platforms. Metadata enable the machine processing of information in a structured manner. Given that it reveals details and context about data, metadata allow interpretation in unique way and offer semantics for open government data. This paper presents an overview and analysis of four metadata models used in contemporary e-government open data platforms. We illustrate the strengths and limitations of these models through aspects and principles of open data defined in the paper by proposing metadata quality assessment model. Moreover, we examine how metadata structure is oriented toward the provision of valuable information assets needed for the utilization of open government data. Finally, we give an overview of available API support on the selected open data platforms for the provision and reuse of open data. The research results indicate that more attention should be paid to metadata to uncover relationships between datasets.

An Efficient Organization Method for Large-Scale and Long Time-Series Remote Sensing Data in a Cloud Computing Environment

Historical earth observation (EO) data have played an important role in long-term scientific and environmental monitoring. The effective organization of large-scale and long-term remote-sensing data to achieve efficient retrieval and access has become one of the important issues. However, inherent big data characteristics, such as a large scale, and asymmetrical temporal and spatial distributions, have caused problems with the efficiency of data retrieval and access. Therefore, this study proposes an efficient data organization method for use in a cloud-computing environment that has two aims. First, it addresses the problem of low retrieval efficiency. An asymmetrical index model for the image metadata is constructed that is based on a unified spatio-temporal grid coding; a prepartitioning mechanism under the HBase architecture is established to realize the uniform storage of the metadata with an asymmetrical spatiotemporal distribution and to avoid retrieval efficiency bottlenecks caused by a load imbalance. Second, it addresses low access efficiency. By dividing the remote-sensing image into tiles, a unified spatio-temporal code is established for each tile, and a consistent hash operation is performed; tiles with similar hash values are stored in the same or adjacent Hadoop Distributed File System nodes. In this way, tiles with temporal or spatial correlations can be gathered and stored, and lots of disk seeks can be avoided during retrieval, thereby greatly improving the data access efficiency. Comparative experiments showed that the data organization method can effectively improve the retrieval and access efficiencies of large-scale and long time-series remote-sensing data in a cloud-computing environment.

New Data Placement Strategy in the HADOOP Framework

Today, the data quantities generated and exchanged between information systems continues to increase. Storing and exploiting such quantities require can’t be done without bigdata systems with mechanisms capable of meeting technological challenges commonly grouped under the four Vs (Volume, Velocity, Variety and Veracity). These technologies include mainly the Distributed File System (DFS). Like Hadoop, which is based on HDFS, the main Big Data systems use a data distributed storage where a subsystem is responsible for subdividing data (data striping) and replicating it on a network of nodes called Grid. In the typical case of Hadoop, a Grid generally consists of many nodes, grouped in multiple Racks. The logic of distributing the stored data through the Grid respects a simple strategy that guarantees the durability of the data and a certain speed of writing. This strategy does not take into consideration neither the technical characteristics of nodes, nor the number of requests on the data, which means a considerable loss in processing capacity of the grid. In this work we proposed a new placement strategy based on exploitation analysis of new information integrated into the HDFS metadata model. A significant 20% improvement in overall processing time was reached through the simulations we conducted on Hadoop.

31] Construction of the tv program metadata model for the national archive: Focusing on the hierarchical structure of bangumi

31] Construction of the tv program metadata model for the national archive: Focusing on the hierarchical structure of bangumi

Quality of Metadata in Open Data Portals

During the last decade, numerous governmental, educational or cultural institutions have launched Open Data initiatives that have facilitated the access to large volumes of datasets on the web. The main way to disseminate this availability of data has been the deployment of Open Data catalogs exposing metadata of these datasets, which are easily indexed by web search engines. Open Source platforms have facilitated enormously the labor of institutions involved in Open Data initiatives, making the setup of Open Data portals almost a trivial task. However, few approaches have analyzed how precisely metadata describes the associated datasets. Taking into account the existing approaches for analyzing the quality of metadata in the Open Data context and other related domains, this work contributes to the state of the art by extending an ISO 19157 based method for checking the quality of geographic metadata to the context of Open Data metadata. Focusing on metadata models compliant with the Data Catalog Vocabulary proposed by W3C, the proposed extended method has been applied for the evaluation of the Open Data catalog of the Spanish Government. The results have been also compared with those obtained by the Metadata Quality Assessment methodology proposed at the European Data Portal.

A generic metadata management model for heterogeneous sources in a data warehouse

For more than 30 decades, data warehouses have been considered the only business intelligence storage system for enterprises. However, with the advent of big data, they have been modernized to support the variety and dynamics of data by adopting the data lake as a centralized data source for heterogeneous sources. Indeed, the data lake is characterized by its flexibility and performance when storing and analyzing data. However, the absence of schema on the data during ingestion increases the risk of the transformation of the data lake into a data swamp, so the use of metadata management is essential to exploit the data lake. In this paper, we will present a conceptual metadata management model for the data lake. Our solution will be based on a functional architecture of the data lake as well as on a set of features allowing the genericity of the metadata model. Furthermore, we will present a set of transformation rules, allowing us to translate our conceptual model into an owl ontology.

Documenting flooding areas calculation: a PROV approach

Flooding events related to waste-lake dam ruptures are one of the most threatening natural disasters in Brazil. They must be managed in advance by public institutions through the use of adequate hydrographic and environmental information. Although the Open Data paradigm offers an opportunity to share hydrographic data sets, their actual reuse is still low because of metadata quality. Our previous work highlighted a lack of detailed provenance information. The paper presents an Open Data approach to improve the release of hydrographic data sets. We discuss a methodology, based on W3C recommendations, for documenting the provenance of hydrographic data sets, considering the workflow activities related to the study of flood areas caused by the waste-lakes breakdowns. We provide an illustrative example that documents, through W3C PROV metadata model, the generation of flooding area maps by integrating land use classification, from Sentinel images, with hydrographic data sets produced by the Brazilian National Institute for Space Research.

Constructing a metadata model to capture the deconstruction of literary characters: Remediation of evil

This study explores the prospects of constructing a metadata Omeka model to capture the transformation of four classic evil characters through various media adaptations. The scope encompasses ancie...

A heterogeneous key performance indicator metadata model for air quality monitoring in sustainable cities

Due to heterogeneous and inconsistent key performance indicators (KPIs) for the quantitative evaluation of a sustainable city's operational status, it is a great challenge to share multidimensional, multi-source and heterogeneous indicators. We propose a heterogeneous KPI capability representation model (HKPM) in our study. Based on the Meta Object Facility architecture, a nine-tuple multi-hierarchical meta-model is formulated to define the metadata components. Nine specific representation element datasets for specific KPIs are proposed to represent the meta-model. Besides, the KPI classification based on Sustainable Development Goals (SDGs) has been accomplished to support HKPM instantiated in concrete application. Experiments are conducted with the multi-type KPIs to validate the feasibility of HKPM, as shown in public service and Air Quality Index KPI instantiation example. Furthermore, the KPIs can be characterized in different dimensions, which can be modelled in a stereoscopic manner, promoting a comprehensive perception of sustainable cities.

Europeana, EDM, and the Europeanisation of Cultural Heritage Institutions

Abstract Over the past two decades, the European Commission has mobilised cultural heritage to bolster a European identity. One of the main flagship initiatives promoted to this end has been Europeana, the most extensive digital cultural project financed by the EU. At the core of the project stands europeana.eu, a digital cultural portal aggregating metadata provided by national and local heritage institutions. Central in our analysis is the Europeana Data Model (EDM). Using standardised thesauri and vocabularies, EDM offers the possibility to create a semantic contextualisation for objects, allowing semantic operations on the metadata and their enrichment with Linked Open Data on the web. Due to its overarching nature, EDM cannot deliver the granularity that cultural heritage institutions need when documenting their resources. Nonetheless, heritage institutions accept to sacrifice accuracy to have their information represented in a Europewide collection. We study how this digital heritage infrastructure was designed to enact a sense of Europeanness amongst national and local institutions. Policy documents, ethnographic research and a systematic survey amongst the European heritage institutions enabled us to trace how a standardised European metadata structure plays a role in governing local and national heritage institutions. The EDM might enable heritage stakeholders to benefit from Europeana’s online exposure while enacting a European mindset. Ultimately, this study of the metadata model enriches the debate on the EU’s cultural heritage politics, which has not fully explored the role of the digital. At the same time, it also taps into debates about infrastructure and digital governmentality.