Metadata Interoperability Research Articles

EDNAqua-Plan—Standardisation Overview for eDNA Sequencing of Aquatic Organisms and the Downstream Data Ecosystem

The eDNAqua-Plan project stands as a beacon of innovation in the biomonitoring of marine and freshwater ecosystems, propelled by the urgent need to integrate DNA-based approaches in aquatic bioassessment and monitoring frameworks. The broad utilisation of cutting-edge environmental DNA (eDNA) and DNA barcoding methodologies is dependent on complete, reliable, and accessible reference DNA sequence data (Rimet et al. 2021). Complete and interoperable metadata is crucial to allow a broad reuse of (e)DNA data and analysis outputs, and for a broader uptake of results by end users. The eDNAqua-Plan project aims to address key limitations to the routine implementation of eDNA-based monitoring methods in Europe by developing plans for federated DNA barcode reference libraries and eDNA data repositories to support DNA-based environmental monitoring. This will ensure a sustainable and reliable infrastructure to underpin its broad use, thereby paving the way for more effective conservation and management strategies. The project is working towards creating a comprehensive overview of standardisation efforts and data workflows, through collaborations with other projects, initiatives and infrastructures for aquatic monitoring across the European Union (EU) and associated countries. We are analysing existing archives (e.g., International Nucleotide Sequence Database Collaboration (INSDC), Barcode of Life Data System (BOLD), Global Biodiversity Information Facility (GBIF), Ocean Biodiversity Information System (OBIS)), portals, and papers to determine current and best practices through the use of questionnaires, manual evaluation of repositories, and machine learning methods (LLMs). This includes an overview of the usage of existing metadata and data standards (e.g., Minimum Information about any (X) Sequence Specifications from the Genomics Standards Consortium (GSC), Darwin Core standard). The results are being integrated by a team of experts in marine and freshwater biomonitoring. With a diverse consortium comprising 18 partner institutions from 11 countries and one international institute, eDNAqua-Plan brings together experts in marine and freshwater monitoring, eDNA analysis, and data science. The collective effort by this consortium will lay the groundwork for the creation of a digital ecosystem of eDNA repositories and an integrated reference library of marine and freshwater species, adhering to FAIR (Findable, Accessible, Interoperable, and Reusable) principles.

Global Burden of Animal Diseases informatics strategy, data quality and model interoperability.

The estimation of the global burden of animal diseases requires the integration of multidisciplinary models: economic, statistical, mathematical and conceptual. The output of one model often serves as input for another; therefore, consistency of the model components is critical. The Global Burden of Animal Diseases (GBADs) Informatics team aims to strengthen the scientific foundations of modelling by creating tools that address challenges related to reproducibility, as well as model, data and metadata interoperability. Aligning with these aims, several tools are under development: a) GBADs'Trusted Animal Information Portal (TAIL) is a data acquisition platform that enhances the discoverability of data and literature and improves the user experience of acquiring data. TAIL leverages advanced semantic enrichment techniques (natural language processing and ontologies) and graph databases to provide users with a comprehensive repository of livestock data and literature resources. b) The interoperability of GBADs'models is being improved through the development of an R-based modelling package and standardisation of parameter formats. This initiative aims to foster reproducibility, facilitate data sharing and enable seamless collaboration among stakeholders. c) The GBADs Knowledge Engine is being built to foster an inclusive and dynamic user community by offering data in multiple formats and providing user-friendly mechanisms to garner feedback from the community. These initiatives are critical in addressing complex challenges in animal health and underscore the importance of combining scientific rigour with user-friendly interfaces to empower global efforts in safeguarding animal populations and public health.

Metadata application profile as a mechanism for semantic interoperability in FAIR and open data publishing

Application profiles, also known as metadata application profiles, are customised collections of vocabularies adapted from various namespaces and tailored for specific local applications. These profiles act as constrainers and explainers for the (meta)data. Semantic interoperability is the ability of computer systems to exchange data in a mutually understandable manner, facilitating data sharing across diverse platforms and applications without compromising its meaning. As a critical component of semantic interoperability, application profiles enforce semantics to (meta)data, enhancing its openness, interoperability, and reusability. This study assesses the feasibility of representing a comprehensive application profile in a format aligned with the semantic web, ensuring interoperability between profiles and datasets. Dublin Core Description Set Profiles (DSP) is adapted as the modeling framework for metadata application profiles, steering the associated datasets toward RDF compliance. The research outcomes include “Yet Another Metadata Application Profiles” (YAMA) as a preprocessor grounded in the DSP framework for developing and managing metadata application profiles. YAMA facilitates the generation of various standard formats of application profiles, ensuring they are represented in human-readable documentation, machine-actionable forms, and even data validation languages. A data mapping extension to YAMA is proposed to ensure the semantic interoperability of open data, bridging non-RDF data structures to RDF, thus enabling the publication of 5-star open data. This ensures smooth dataset integration and the creation of linkable, semantically rich open datasets. The work emphasizes the pivotal role of application profiles in fortifying the semantic interoperability of (meta)data, thereby elevating dataset openness.

PEPhub: a database, web interface, and API for editing, sharing, and validating biological sample metadata.

As biological data increase, we need additional infrastructure to share them and promote interoperability. While major effort has been put into sharing data, relatively less emphasis is placed on sharing metadata. Yet, sharing metadata is also important and in some ways has a wider scope than sharing data themselves. Here, we present PEPhub, an approach to improve sharing and interoperability of biological metadata. PEPhub provides an API, natural-language search, and user-friendly web-based sharing and editing of sample metadata tables. We used PEPhub to process more than 100,000 published biological research projects and index them with fast semantic natural-language search. PEPhub thus provides a fast and user-friendly way to finding existing biological research data or to share new data. https://pephub.databio.org.

FAIR-EuMon: a FAIR-enabling resource for biodiversity monitoring schemes.

Within the scope of the Helmholtz Metadata Collaboration (HMC), the ADVANCE project - Advanced metadata standards for biodiversity survey and monitoring data: supporting of research and conservation - aimed at supporting rich metadata generation with interoperable metadata standards and semantic artefacts that facilitate data access, integration and reuse across terrestrial, freshwater and marine realms. HMC's mission is to facilitate the discovery, access, machine-readability, and reuse of research data across and beyond the Helmholtz Association. We revised, adapted and expanded existing metadata schemas, vocabularies and thesauri to build a FAIR metadata schema and a metadata entry form built on it for users to provide their metadata instances focused on biodiversity monitoring data. The schema is FAIR because it is both machine-interpretable and follows domain-relevant community standards. This report provides a general overview of the project results and instructions on how to access, re-use and complete the metadata form.

Making Schemas and Mappings Available and FAIR: A metadata and schema crosswalk registry from the FAIRCORE4EOSC project

Community standards like the Darwin Core (Darwin Core Task Group 2009) together with semantic artefacts (controlled vocabularies, ontologies, thesauri, and other knowledge organisation systems) are key building blocks for the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. 2016), specifically as emphasized in the Interoperability principle I2 “(Meta)data use vocabularies that follow FAIR principles”. However, most of these artefacts are actually not FAIR themselves (Le Franc et al. 2020). To address this, the FAIRCORE4EOSC project (2022-25) is developing a Metadata Schema and Crosswalk Registry (MSCR) that will allow registered users and communities to create, register and version schemas and crosswalks that all have persistent identifiers (PIDs). The published content can then be searched, browsed and downloaded without restrictions. The MSCR will also provide an API to facilitate the transformation of data from one schema to another via registered crosswalks. It will provide projects and individual researchers with the possibility to manage their metadata schemas and/or relevant metadata schema crosswalks. The schema and crosswalks will be shared with the community for reuse and extension supported by a proper versioning mechanism. The registry tool will facilitate better interoperability between resource catalogues and information systems using different (metadata) schemas and encourage organisations and especially researchers to share their metadata interoperability by publishing the metadata crosswalks used in their workflows, which are currently not visible (FAIRification). By providing an easy-to-use graphical user interface (GUI) for creating crosswalks, the GUI will attract users currently relying on project-specific solutions.

Determining the Similarity of Research Data by Using an Interoperable Metadata Extraction Method

Determining the similarity of research data is not a simple task, as the formats can differ widely depending on the domain. Especially, since many formats are represented as binary files, the raw comparison of these will not yield good results. This makes it hard to accurately tell how similar certain research work is by comparing the data. With the emergence of extracted interoperable metadata, a form to describe data has been provided which is independent of the data format. Therefore, this work tries to use this extracted interoperable metadata and create a method to determine the similarity of research data based on their metadata. The produced method utilizes domain knowledge about the extracted metadata and the way they are formulated. A baseline is created, and further methods are created to compare to. The results show that our method outperforms all other methods, especially the ones which are focused on comparing the research data itself, not the metadata. Since the results are promising, we propose further investigations against other datasets and possible use cases.

An Analysis of Crosswalks from Research Data Schemas to Schema.org

ABSTRACTThe increased number of data repositories has greatly increased the availability of open data. To enable broad discovery and access to research dataset, some data repositories have begun leveraging the web architecture by embedding structured metadata markup in dataset web landing pages using vocabularies from Schema.org and extensions. This paper aims to examine metadata interoperability for supporting global data discovery. Specifically, the paper reports a survey on which metadata schema has been adopted by participating data repositories, and presents an analysis of crosswalks from fourteen research data schemas to Schema.org. The analysis indicates most descriptive metadata are interoperable among the schemas, the most inconsistent mapping is the rights metadata, and a large gap exists in the structural metadata and controlled vocabularies to specify various property values. The analysis and collated crosswalks can serve as a reference for data repositories when they develop crosswalks from their own schemas to Schema.org, and provide the research data community a benchmark of structured metadata implementation.

An enhanced version of the PHIRI infrastructure: improving the technological solutions

Abstract The proof of concept tested by PHIRI consisted of the development of several research questions in multiple data hubs using a federated approach. It was possible to embed the use cases’ analytical pipelines in a portable standalone (i.e. docker image) and distribute it in different health data hubs and technological environments sources for execution. The tested solution has the advantage of not moving sensitive data out of the silos and thus protecting privacy - the code meets data and not the opposite. Some precious lessons provide guidance on how to further develop the PHIRI infrastructure. 1) A deep knowledge on what data is available in the different data hubs of a federation is key since the basis for the development of a research query is the construction of a data model that is common to all the nodes in the federation. In an eventual enhanced PHIRI infrastructure, a solution will be implementing a semantic information system that allows the exchange of metadata using federated and interoperable metadata catalogues based on Semantic RDF graph databases, compliant with the W3C DCAT metadata standard and exposing the end-points of the SPARQL querying language of the Web of linked-data. 2) Making available training samples mimicking real-world data within the docker image has been of high added-value for the development of the use cases’ analytical pipelines. In an eventual enhanced PHIRI infrastructure, a generalisation could consist of setting up a “knowledge hub” where synthetic data, twinning the population, data would allow any expert users to search and find data through federated queries and prepare and train their analytical pipelines; the “knowledge hub” would provide a computational environment (e.g. Jupyter as a service playground), the necessary tools (i.e. cookbooks and capacity building services) and training samples to answer research questions, with the advantage of using data that is anonymous by nature and open access.

Frictionless Data Standards

Getting insight from data is not always a straightforward process. Data is often hard to find, archived in difficult to use formats, poorly structured and/or incomplete. These issues create friction and make it difficult-to-use, publish and share data. The Frictionless Data initiative at Open Knowledge Foundation aims to reduce friction in working with data, with a goal to make it effortless to transport data among different tools and platforms for further analysis. The corresponding toolkit consists of a set of standards for data and metadata interoperability, accompanied by a collection of open source software that implement these standards, and a range of best practices for data management. In this article we are going to focus on data standards, why they are important, and, using the Frictionless Data standards as a base for the reflection, we will try to understand what makes - in our opinion- a standard successful.

From Siloed to Reusable

In the past twenty-five years, cross-institutional communities have come together in the creation and use of open source software and open data standards to build digital collections (Madden, 2012). These librarians, developers, archivists, artists, and researchers recognize that the custom-built architectures and bespoke data structures of earlier digital collections development are unsustainable. Their collaborations have produced now-standard technologies such as Samvera, Fedora, GeoBlacklight, Islandora 8, as well as RDF, and JSON-LD among other open schemas. A core principle animating these efforts is reusability: data, schemas, and technologies in the open era must be coherent and flexible enough to be reused across multiple digital contexts. The authors of this paper show how reuse guided the migration of the Hopkins Digital Library from an outdated isolated system to a sustainable interconnected environment in GeoBlacklight, Islandora, with metadata based in Linked Open Data. Three areas of reuse focus this paper: the creation of robust interoperable metadata; the expansion of IIIF functionality to integrate the needs of the Hopkins Geoportal’s users; the development of a broadly re/usable data migration module focused on expanding a diverse community of invested users. In focusing on reusability as an organising principle of digital collections development, this case study shows how one digital curation team produced a platform that meets the changing and specific needs of an individual institution, on the one hand, and participated in and furthered the creative coherence of the open communities supporting the team’s work, on the other.

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

This study presents an in-depth study and analysis of IoT semantic association and decision-making using a partial differential fuzzy unsupervised approach. It focuses on a semantic annotation framework for device metadata and a knowledge base construction method to further improve the interoperability of IoT domain knowledge by building a unified IoT domain knowledge base and designing and implementing a semantic IoT knowledge management and application generation system. The main proposal is an IoT generic domain ontology, which reuses the existing excellent ontologies of IoT as much as possible, extracts the commonly used concepts of the domain and combines them, and provides a unified semantic template for IoT applications. On the other hand, by applying the entity linking technique to the extension of the knowledge base and linking the structured metadata of devices to the corresponding entities of the background knowledge base, the domain knowledge base can be made to share the rich background knowledge. At the same time, the interoperability of heterogeneous IoT metadata between applications is enhanced by unifying data and concepts from different device applications to the same background knowledge base through entity alignment techniques. The semantic representation of events applicable to IoT application scenarios is investigated, and an IoT event ontology for representing abstract events and event relationships in IoT is designed; next, a domain ontology with IoT sensing and control event representation capability is constructed based on the IoT event ontology, in which the typical domain ontology (SSN) that can be used for IoT applications is followed by the ontology reuse principle is improved and extended to support the description of event types and interevent relationships, and the IoT event model is associated with the improved IoT base ontology through an ontology alignment approach. Finally, the IoT sensing and control ontology are validated by semantic modeling of device composition, component relationships, and operational processes based on the IoT sensing and control ontology.

A data management infrastructure for the integration of imaging and omics data in life sciences

BackgroundAs technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing. In particular, systems supporting the findability, accessibility, interoperability, and reusability (FAIR) principles of scientific data management.ResultsWe propose a Service Oriented Architecture approach for integrated management and analysis of multi-omics and biomedical imaging data. Our architecture introduces an image management system into a FAIR-supporting, web-based platform for omics data management. Interoperable metadata models and middleware components implement the required data management operations. The resulting architecture allows for FAIR management of omics and imaging data, facilitating metadata queries from software applications. The applicability of the proposed architecture is demonstrated using two technical proofs of concept and a use case, aimed at molecular plant biology and clinical liver cancer research, which integrate various imaging and omics modalities.ConclusionsWe describe a data management architecture for integrated, FAIR-supporting management of omics and biomedical imaging data, and exemplify its applicability for basic biology research and clinical studies. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, including studies which leverage advanced machine learning methods, as the joint analysis of omics and imaging data, in conjunction with phenotypic metadata, becomes not only desirable but necessary to derive novel insights into biological processes.

NExtSEEK: Extending SEEK for Active Management of Interoperable Metadata.

Data management is a critical challenge required to improve the rigor and reproducibility of large projects. Adhering to Findable, Accessible, Interoperable, and Reusable (FAIR) standards provides a baseline for meeting these requirements. Although many existing repositories handle data in a FAIR-compliant manner, there are limited tools in the public domain to handle the metadata burden required to connect data from multi-omic projects that span multiple institutions and are deposited in diverse repositories. One promising approach is the SEEK platform, which allows for diverse metadata and provides an established repository. SEEK is challenged by the assumption of single deposition events where a sample is immutable once entered in the database. This is structured for published data but presents a limitation for ongoing studies where multiple sequential events may occur in a single sample at different sites. To address this issue, we have created a modified wrapper around the SEEK platform that allows for active data management by establishing more discrete sample types that are mutable to permit the expansion of the types of metadata, allowing researchers to track additional information. The use of discrete nodes also converts assays from nodes to edges, creating a network model of the study and more accurately representing the experimental process. With these changes to SEEK, users are able to collect and organize the information that researchers need to improve reusability and reproducibility as well as make data and metadata available to the scientific community through public repositories.

Semantic Metadata Annotation Services in the Biomedical Domain—A Literature Review

For all research data collected, data descriptions and information about the corresponding variables are essential for data analysis and reuse. To enable cross-study comparisons and analyses, semantic interoperability of metadata is one of the most important requirements. In the area of clinical and epidemiological studies, data collection instruments such as case report forms (CRFs), data dictionaries and questionnaires are critical for metadata collection. Even though data collection instruments are often created in a digital form, they are mostly not machine readable; i.e., they are not semantically coded. As a result, the comparison between data collection instruments is complex. The German project NFDI4Health is dedicated to the development of national research data infrastructure for personal health data, and as such searches for ways to enhance semantic interoperability. Retrospective integration of semantic codes into study metadata is important, as ongoing or completed studies contain valuable information. However, this is labor intensive and should be eased by software. To understand the market and find out what techniques and technologies support retrospective semantic annotation/enrichment of metadata, we conducted a literature review. In NFDI4Health, we identified basic requirements for semantic metadata annotation software in the biomedical field and in the context of the FAIR principles. Ten relevant software systems were summarized and aligned with those requirements. We concluded that despite active research on semantic annotation systems, no system meets all requirements. Consequently, further research and software development in this area is needed, as interoperability of data dictionaries, questionnaires and data collection tools is key to reusing and combining results from independent research studies.

Smart Parking Systems: A Data-Oriented Taxonomy and a Metadata Model

Smart parking systems (SPS) represent an evolving and heterogeneous field of approaches and applications in parking management. One commonality is that all present systems deal with digital data related to the parking domain, such as data about parking infrastructure, parking demand, transactions, and similar. Data offerings of SPS seem to provide essential benefits for actors in parking space management, as long as they can be discovered and assessed efficiently. This paper presents mechanisms for the discovery and assessment of SPS data offerings. First, a taxonomy is developed via an inductive approach, based on a review of existing approaches to categorizing such data offerings. The taxonomy represents a hierarchical classification system, looking at functional, technical, and content perspectives of SPS data. This taxonomy is further integrated and formalized into a metadata model, allowing structured and harmonized descriptions about data offerings of individual SPS. The metadata model is built on established metadata frameworks, namely the Resource Description Framework (RDF). For reasons of reusability and interoperability, it also adopts existing metadata vocabularies from the domain of internet data catalogs. This work intends to make the data offerings of SPS assessable and comparable for potential SPS users, namely actors in parking space management. It also provides a foundation for integrating the various forms and technologies of current SPS deployments. Such integration is missing so far, according to some other authors, and is addressed in this work by an interoperable metadata approach.

ISO 21526 Conform Metadata Editor for FAIR Unicode SKOS Thesauri.

Metadata repositories are an indispensable component of data integration infrastructures and support semantic interoperability between knowledge organization systems. Standards for metadata representation like the ISO/IEC 11179 as well as the Resource Description Framework (RDF) and the Simple Knowledge Organization System (SKOS) by the World Wide Web Consortium were published to ensure metadata interoperability, maintainability and sustainability. The FAIR guidelines were composed to explicate those aspects in four principles divided in fifteen sub-principles. The ISO/IEC 21526 standard extends the 11179 standard for the domain of health care and mandates that SKOS be used for certain scenarios. In medical informatics, the composition of health care SKOS classification schemes is often managed by documentalists and data scientists. They use editors, which support them in producing comprehensive and valid metadata. Current metadata editors either do not properly support the SKOS resource annotations, require server applications or make use of additional databases for metadata storage. These characteristics are contrary to the application independency and versatility of raw Unicode SKOS files, e.g. the custom text arrangement, extensibility or copy & paste editing. We provide an application that adds navigation, auto completion and validity check capabilities on top of a regular Unicode text editor.

Pidgin Metadata Framework as a Mediator for Metadata Interoperability

Abstract A pidgin metadata framework based on the concept of pidgin metadata is proposed to complement the limitations of existing approaches to metadata interoperability and to achieve more reliable metadata interoperability. The framework consists of three layers, with a hierarchical structure, and reflects the semantic and structural characteristics of various metadata. Layer 1 performs both an external function, serving as an anchor for semantic association between metadata elements, and an internal function, providing semantic categories that can encompass detailed elements. Layer 2 is an arbitrary layer composed of substantial elements from existing metadata and performs a function in which different metadata elements describing the same or similar aspects of information resources are associated with the semantic categories of Layer 1. Layer 3 implements the semantic relationships between Layer 1 and Layer 2 through the Resource Description Framework syntax. With this structure, the pidgin metadata framework can establish the criteria for semantic connection between different elements and fully reflect the complexity and heterogeneity among various metadata. Additionally, it is expected to provide a bibliographic environment that can achieve more reliable metadata interoperability than existing approaches by securing the communication between metadata.

The need for addressing multilingualism, ambiguity and interoperability for visual resources management across metadata platforms

The digitization of visual resources and the creation of corresponding metadata that meets the criteria of clarity and interoperability, while also approaching the needs of the multilingual Web, are pressing concerns. Because visual resources make up a significant percentage of digital information, this paper focuses on the aforementioned concerns and proposes ways to address them, including swift progression and adoption of cohesive, multi-user, multilingual metadata standardization to improve digital access and to allow all descriptive image metadata to be approachable and translatable. We offer some recommendations such as those involved in visual resource management moving away from using primarily the English writing system based metadata schemas in order to provide flexible lexicon in non-Roman languages, which can easily be recognized and interpreted by both monolingual and multilingual users alike as well as facilitate digital metadata interoperability.

Reducing Consumer Uncertainty: Towards an Ontology for Geospatial User-Centric Metadata

With the increased use of geospatial datasets across heterogeneous user groups and domains, assessing fitness-for-use is emerging as an essential task. Users are presented with an increasing choice of data from various portals, repositories, and clearinghouses. Consequently, comparing the quality and evaluating fitness-for-use of different datasets presents major challenges for spatial data users. While standardization efforts have significantly improved metadata interoperability, the increasing choice of metadata standards and their focus on data production rather than potential data use and application, renders typical metadata documents insufficient for effectively communicating fitness-for-use. Thus, research has focused on the challenge of communicating fitness-for-use of geospatial data, proposing a more “user-centric” approach to geospatial metadata. We present the Geospatial User-Centric Metadata ontology (GUCM) for communicating fitness-for-use of spatial datasets to users in the spatial and other domains, to enable them to make informed data source selection decisions. GUCM enables metadata description for various components of a dataset in the context of different application domains. It captures producer-supplied and user-described metadata in structured format using concepts from domain-independent ontologies. This facilitates interoperability between spatial and nonspatial metadata on open data platforms and provides the means for searching/discovering spatial data based on user-specified quality and fitness-for-use criteria.