EDITORIAL
Metadata as Data Intelligence
Jane Greenberg1, Mingfang Wu2, Wei Liu3, Fenghong Liu4,5†
1Metadata Research Center, College of Computing and Informatics, Drexel University, 3675 Market St 10th floor,Filadelfia, Pensilvania 19104, EE.UU
3Shanghai Library (Institute of Scientific and Technical Information of Shanghai), No. 1555 Huaihai Middle Road Xuhui District,
2Australian Research Data Commons, Melbourne, Victoria 3145, Australia
4Department of Library, Información, and Archive, School of Economic Management, University of Chinese Academy of Sciences,
33 Beisihuanxilu, Haidian District, Beijing 100190, Porcelana
5Data Intelligence, National Science Library, Academia China de Ciencias, 33 Beisihuanxilu, Haidian District, Beijing 100190, Porcelana
Shanghai 200031, Porcelana
Citación: Greenberg, J., Wu, M.F., Liu, w., et al.: Metadata as Data Intelligence. Data Intelligence 5 (2023). doi: 10.1162/
dint_e_00212
1. INTRODUCCIÓN
Metadata, as a type of data, describes content, provides context, documents transactions, and situates data.
Interest in metadata has steadily grown over the last several decades, motivated initially by the increase in
digital information, open access, early data sharing policies, and interoperability goals. This foundation has
accelerated in more recent times, due to the increase in research data management policies and advances
in AI. Specific to research data management, one of the larger factors has been the global adoption of the
FAIR (findable, accessible, interoperable, and reusable) data principles [1, 2], which are highly metadata-
driven. Además, researchers across nearly every domain are interested in leveraging metadata for
machine learning and other AI applications. The accelerated interest in metadata expands across other
communities as well. Por ejemplo, industry seeks metadata to meet company goals; and users of information
systems and social computing applications wish to know how their metadata is being used and demand
greater control of who has access to their data and metadata. All of these developments underscore
the fact that metadata is intelligent data, or what Riley has called value added data [3]. En general, este
intense and growing interest in metadata helps to frame the contributions included in this special issue of
Data Intelligence.
This special issue of Data Intelligence includes a collection of 14 original articles covering metadata
related research, práctica, and theory. The contributions as a whole, include work complied by over 50
authors from eleven countries, including Australia, Canada, Porcelana, Francia, Alemania, Italia, Los países bajos,
†
Autor correspondiente: Fenghong Liu (Correo electrónico: liufh@mail.las.ac.cn; ORCID: 0000-0002-3633-1464).
© 2023 Academia China de Ciencias. Publicado bajo una atribución Creative Commons 4.0 Internacional (CC POR 4.0)
licencia.
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Metadata as Data Intelligence
Portugal, España, the United Kingdoms, and the United States. Contributing authors are from range of
organizaciones, national research data programs, such as the Australian Research Data Commons, academic
and research institutions, such as the Chinese Academy of Sciences; government agencies and industry
such as the United States Bureau of Labor Statistics among other national agencies. Among some of the
overriding themes, contributions address FAIR principles in relation to metadata; metadata tools, práctica
or polices, innovative ideas, and theoretical approaches. The majority of contributions are arranged
under “research” and “practice and implementation”, and with three final contributions covering “vision
and theory”.
2. RESEARCH ARTICLES
Research in the study of any phenomena confirms a level of maturity. The rich collection of research
articles in this special issue confirm that metadata work has expanded well beyond scheme development
and implementation, and that metadata is a significant research topic [4, 5].
The series of research articles is initiated with a piece entitled, Improving Domain Repository
Connectivity [6]. This contribution explores the notion of a connectivity metric, which is applied it to
datasets collected and papers published by members of the UNAVCO community. The author explains that
“as community members contribute to multiple datasets and articles, identifiers for them, once found, poder
be used multiple times”. Identifiers found in DataCite and Crossref metadata that are shared through
UNAVCO DataCite metadata can increase connectivity from less than 10% to nearly 50% for people and
organizaciones.
Two of the research contributions interconnect with FAIR. Primero, FAIRification of Scanning Tunneling
Microscopy [7] focuses on data management practices and services for making FAIR compliant a scientific
archive of Scanning Tunneling Microscopy (STM) images. The authors report on a metadata database that
includes metadata extracted from instruments and each image, which have been enriched via human
annotation, machine learning techniques, and instrument metadata filtering. Además, the W3C PROV
standard was explored for STM image.
A second work, FAIR Data and Metadata: GNSS Precise Positioning User Perspective [8], presents an
analysis of current GNSS users’ requirements in various application sectors on the way data, metadata and
services are provided. We engaged with GNSS stakeholders to validate our findings and to gain understanding
on their perception of the FAIR principles. Authors indicate that results confirm FAIR GNSS data and services
are important for this community and have have had an impact standard compliant GNSS community
metadata enabling FAIR GNSS data and service delivery for both humans and the machines.
Another research piece includes, Research on Intelligent Organization and Application of Multi-source
Heterogeneous Knowledge Resources for Energy Internet [9] focuses on improving the informaionization
and intelligence of the energy Internet industry for enhancing the capability of knowledge services. El
authors propose methods to synthesis and transform the original multiple, heterogeneous knowledge
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Metadata as Data Intelligence
resources of the State Grid into a unified and well-organized knowledge system. The effectiveness of
the proposed methods are demonstrated with knowledge resources in the field of human resources of the
State Grid.
The collection of research articles also include, An analysis of crosswalks from research data schemas
to Schema.org [10]. This work presents an extensive analysis of crosswalks from fourteen research data
schemas to Schema.org. The analysis indicates that 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.
The research cluster also includes a contribution that underscores metadata as data intelligence with
attention to AI/ML methods. The work entitled Automated metadata annotation: What is and is not possible
with machine learning [11] presents three use cases that demonstrate the possibility of utilizing AI/ML
models in improving subject indexing of culture or data catalogs, and it requires bringing process, tecnología
and interdisciplinary team together to achieve quality of automated subject.
The last contribution in the research cluster: Provenance documentation to enable explainable and
trustworthy AI: A literature review [12] discusses the importance of capturing and providing provenance
information within the context of running AI/ML models, for making AI/ML results explainable, trustworthy
and reproducible by capturing provenance metadata about each step of the AI process (p.ej. datos, AI models,
software source code for data preparation and executing models).
3. PRACTICE AND IMPLEMENTATION
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The practice and implementation articles included in this special issue report on innovative approaches
and developing technologies. Three pieces contributions that target practices focus on FAIR principles. El
FAIR (datos)meta principles provide guidance to the documentation of metadata so that data can be Findable,
Accessible, Interoperable and Reusable, by both human and machine. Making metadata fair is a progressive
proceso, it requires to gauge gaps and set up goals for improvement, and methods and tools to assist the
proceso. Two of the FAIR contributions are about tools relating to data repositories “FAIR Data Point:
A FAIR-Oriented Approach for Metadata Publication” [13] and “The FAIR Data Point: Interfaces and
Toolings” [14] introduce the FAIR Data Point (FDP)—a general-purpose metadata repository that follows
the DCAT schema and has been implemented by following the FAIR principles. The first paper introduces
the software architecture, core components and features of the FAIR Data Point (FDP), and the second paper
describes interfaces and tools for implementing FDP and facilitating the uptakes and utilities of FDP. El
two papers will benefit those who want to adopt a metadata repository solution or existing metadata
repositories for extending their repository functionalities for publishing semantically-rich and machine-
actionable metadata by following the FAIR principles.
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Metadata as Data Intelligence
The third practice paper addressing FAIR, FAIREST: A Framework for Assessing Research Repositories [15],
introduces a set of metrics for assessing and selecting solutions for creating digital repositories for research
artifacts. The metrics are built on the FAIR principles, Compromiso, Social Connections and Trust (este
FAIREST). The paper also applied metrics for an assessment of 11 widespread solutions, with the goal to
provide an overview of the current landscape of research data repository solutions, identifying gaps and
research challenges to be addressed.
The last contribution in the practice and implementation contribution, Building Community Consensus
for Scientific Metadata with YAMZ [16], introduces YAMZ (Yet Another Metadata Zoo). YAMZ was developed
to help address the challenges with the formal metadata standardization process. The paper presents an
exploratory demonstration of YAMZ within an academic research lab, where there is a need to standardize
metadata to help with data management activities, but researchers lack of time and metadata expertise to
proceed through the formal standardization process.
4. VISIONARY AND THEORY
Three contributions cover vision and theory. The work, Achieving Transparency: A Metadata
Perspective [17], discusses what information should be captured in metadata (schema) and in consistent
way (technical specification) to ensure metadata quality and transparency of data; in order to communicate
better what data mean and why they should be trusted, within the context of providing datasets from the
government and government agencies.
The work Continuous Metadata in Continuous Integration, Stream Processing and Enterprise Data Ops [18]
argues that metadata is continuous in many real data context, thus one-off metadata collection may be
inadequate for future analysis. Based on the review of some current tools in specifying, capturing and
consuming metadata; the author suggests features and design patterns for future cloud native software,
which could enable streamed metadata to power real time data fusion or fine turn automated reasoning
through real time ontology updates.
Finalmente, a contribution entitled, Metadata as a Methodological Commons: From Aboutness Description
to Cognitive Modeling [19], discusses the requirement and feasibility for semantic coding and cognitive
metadata modeling, as the rise of huge volume of labeled data and ChatGPT, as well as the availability of
emerging technologies (e.g Web 3.0, AI/ML, knowledge graph).
5. CONCLUSIÓN
Metadata is often seen as a practical topic, not worthy of research and, todavía, it is a topic that is central
to data driven research [18]. This issue of Data Intelligence underscores the significance of metadata as
a research worthy and critical topic in advancing our data infrastructure and achieving greater ‘data
intelligence’. In closing, this issue presents a unique collection of articles and that confirms the importance
of metadata research, practice and implementation, and visionary and theory as we aim to collectively
advance our data infrastructure across all information sectors.
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