Connecting eScience to Information Science: scientific big metadata and its functionalities

Marques, Luana Faria Sales; Sayão, Luís Fernando

doi:10.20396/rdbci.v21i00.8673740/32407

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Article • Rev. Digit. Bibl. and Cienc. Inf. 21 • 2023 • https://doi.org/10.20396/rdbci.v21i00.8673740/32407 copy

Connecting eScience to Information Science: scientific big metadata and its functionalities

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Introduction:

In the eScience environment, digital research objects are characterized by having a complex and long-life cycle, which depends on different disciplinary contexts and perspectives of (re)use. This lifecycle starts before the start of research and extends beyond the end of the project, along this journey, various types of metadata are added to objects, assigned by different actors, including those generated automatically by scientific instruments and workflow tools, in a continuous process of adding value to datasets and other research objects. In this context, digital research objects are accompanied by a wide range of metadata - with many functions and properties - that often surpass the data themselves in volume and even in importance, configuring a “scientific big metadata” that is difficult to organize and manage. Objective: Systematically present the functions of new metadata to support metadata management and the construction of disciplinary schemes. Methodology: Underlying the construction of the proposal, four axes provide methodological support to the study: historical, pragmatic, standardization, and epistemological. Results: As a result, a model is proposed for schematizing the various elements of metadata based on their functionalities, based on the assumption of the connection between eScience and Information Science established by big metadata. Conclusion: It is concluded that big metadata creates a connection between eScience and CI, and that in addition to the need to curate research objects, specific FAIR management of metadata is also necessary.

KEYWORDS:
Information Science; E-science; Big metadata; Metadata management; Research data; Metadata functions; Research digital object.

V	BIG SCIENTIFIC METADATA
VOLUME	The large volume of metadata used to describe and record scientific processes, as well as the numerous functionalities they perform during the research lifecycle, confirm the existence of big metadata. Sometimes, the volume of metadata is less than or equal to the extent of the data it describes; other times, due to the complexity of data lifecycle activities, the metadata exceeds the size of the data being described or tracked.
SPEED	In the context of scientific experiments, metadata is generated by automatic processes using instruments, remote sensors, codes or high-speed workflow tools. Automated platforms for the collection/generation, organization and analysis of data and metadata are increasingly present on laboratory benches. There are, however, metadata elements created by intellectual means by researchers, computer scientists and information professionals.
VARIETY	The variety of metadata reflects the wide diversity of formats, cycles, models, structures and types of metadata present in the scientific world. There is a clear but natural inequality in the ecosystem of metadata that records the various procedures, systems and processes of laboratories. For example: descriptive, administrative, technical, structural, preservation and processing metadata, which go beyond description and perform various functions such as retrieval, access and interoperability. The demand for specific metadata applied to disciplinary domains intensifies this variety, which is amplified by the extensive and diverse types of data and metadata life cycles, which even using the same metadata standard/schema have different implementation practices.
VERACITY	The veracity of big scientific metadata is established by good practices for marking metadata as a way of adding value to data and other research objects; strongly contributing to veracity are the provenance of metadata schemes, their level of standardization, the connection with scientific processes, with the practices and idiosyncrasies of disciplinary communities, the completeness of metadata elements and the precision of terminological tools - vocabularies, taxonomies, ontologies.
VALUE	The ultimate value of big metadata lies in its support for the proper interpretation of data and its reuse in different spaces of meaning, over time and space, by human beings and computer agents. This also creates value for the data. The value of big metadata is also associated with its role as the primary raw material for analysis methods, i.e. metadata is considered not as a representation of content, but as the very object of analysis. The value of metadata is also associated with its quality, measured by parameters such as: granularity, timeliness, accuracy, completeness and provenance (meta-metadata).

CATEGORY	FUNCTION
REPRESENTATION	It codifies and contributes to organizing the knowledge of a disciplinary domain in a way that is relevant to the research field and familiar to the research community.
	It transforms data objects into information objects (CCSDS, 2012CCSDS - CONSULTATIVE COMMITTEE FOR SPACE DATA SYSTEM. Reference Model for an Open Archival Information System (OAIS). Washington, DC: CCSDS, 2012. (Recommended Practice CCSDS 650.0-M-2. Magenta book). Disponível em: https://public.ccsds.org/pubs/650x0m2.pdf. Acesso em: 30 set. 2019. https://public.ccsds.org/pubs/650x0m2.pd... ) or epistemic objects (Rheinberger, 1977RHEINBERGER, H-J. Toward a history of epistemic things: Synthesizing proteins in the test tube. California: Stanford University Press, 1977.).
	It shows what needs to be represented in the research object, to the standard required by users (HARVEY, 2010HARVEY, R. Digital Curation: a how-to-do-it manual. New York, NY: Neal-Schuman Publishers, 2010.).
DESCRIPTION	It identifies the research object uniquely, globally and persistently (considering the persistent identifier as part of the elements of the metadata schema).
	Locates the object of research.
	Clarifies what the object of research is.
	It records bibliographic information about the research object, allowing it to be referenced and cited according to the relevant standards; it gives credit to the various authors of the objects.
	It points out the properties and technical structures of the elements that make up the digital research object.
MANAGEMENT	Supports the complete management of the research object's life cycle.
MANAGEMENT	Records the provenance of the metadata schema (informed by the meta metadata) and the degree of management applied to the current schema.
RECOVERY	It supports the formulation of queries with an appropriate level of granularity and precision, considering disciplinary characteristics.
	Supports findability and access to digital objects.
	Supports the selection and evaluation of retrieved objects.
	Maintains reliable links to the object.
CATEGORY	FUNCTION
RELATIONSHIP	Provides links from the research object to other related objects (journal articles, software, datasets, etc.) to make visible the ecosystem where the object is located and its relationship with other research objects.
INTERPRETABILITY	It expands the level of interpretability of research objects in different spaces of meaning for humans and computer agents, now and in the future.
	It facilitates the reuse of research objects by their creators and by other researchers.
	It supports interlocution with other collections and with different systems by automated means.
PROVENANCE AND CONTEXT	Records the history of the research object (provenance, traceability and lineage).
PROVENANCE AND CONTEXT	It reports on the processes, parameters, variables, methodologies, codes and instruments that were relevant to collecting/generating, processing and analyzing the research object.
QUALITY	Indicates the quality assurance and control actions applied to the objects (e.g. flags for missing or discrepant data, etc.).
INTEROPERABILITY	Enables machine actionability of search objects.
PEER REVIEW	It informs reviewers of conventional articles and data articles about the processes of obtaining, processing, analysis, quality levels and the potential for reproducibility of research objects.
PRESERVATION	It supports long-term preservation strategies.
	Inform the technical dependencies of the object of research.
	It provides semantic and structural representation information that allows interoperability with the future. Reconfiguration of the object in the future.
	It is part of the archiving information packages (AIP/OAIS).
TRUST	It supports the presumption of authenticity and reliability of research objects.
TRUST	Records the actions applied to guarantee the integrity of objects, such as hashing and chercksun.
PERMISSIONS	Announces the licenses associated with the object.
	Tells you how sensitive the object is.
	Informs about access permissions.
	It points out the actions that can be carried out on the object of research.
COPYRIGHT	Informs about the intellectual property rights associated with the object.
CATEGORY	FUNCTION
ANNOTATION	It includes collaborative comments from researchers other than the authors.
ADMINISTRATION	Identifies the people/teams who operate the data lifecycle: who collects, who indexes, who ensures physical security, etc.
	Identify the stakeholders involved, for example: funders, partner institutions.
	Identifies who is responsible for the management and preservation of research objects.

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[1] Conflicts of interest: The authors certify that they have no commercial or associative interest that represents a conflict of interest in relation to the manuscript.