Metadata authoring model

describing information about context and provenance of disciplinary research objects

Autores

DOI:

https://doi.org/10.5433/1981-8920.2023v28n4p1

Palavras-chave:

Autoria de metadados, Objeto digital de pesquisa, Linhagem dos dados, Contextualização dos dados

Resumo

In the field of research object management, there are a large number of standardized metadata schemas available, but in general they do not address the fragmentation and interdisciplinarity of contemporary science.
Problem: There are rich discipline-oriented metadata schemas in some key areas, but in other most cases they need to be constructed. Therefore, a major challenge for research objects to achieve an adequate level of FAIRification is that they are described by metadata schemas that have functionalities and qualities that support research reproducibility and data reuse.
Objective: To address this complexity, the goals of this research was to define the functionalities and quality levels of metadata standards required for FAIR research data management.
Methodology: This is a theoretical and exploratory research based on the concept of epistemic/technical/informational research object, considering four axes: historical, epistemological, standardization and application.
Results: As a result, a metadata authoring model was proposed that focused on recording the context and origin of research objects.
Conclusion: In conclusion, the paper reaffirms the urgent need to develop disciplinary metadata schemes that not only meet the specific needs of the domains, but also ensure interdisciplinary integration and efficient data retrieval, promoting more robust, accessible and collaborative science.

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Biografia do Autor

Luís Fernando Sayão , National Nuclear Energy Commission

Doutor em Ciência da Informação pela Universidade Federal do Rio de Janeiro (UFRJ).  Docente do Programa de Pós-Graduação em Ciência da Informação do Instituto Brasileiro de Informação em Ciência e Tecnologia (IBICT/UFRJ), do Programa de Pós-Graduação em Biblioteconomia da Universidade Federal do Estado do Rio de Janeiro (UNIRIO) e do Programa de Pós-Graduação em Memória e Acervos da Fundação Casa de Rui Barbosa. Atua na Comissão Nacional de Energia Nuclear (CNEN), Rio de Janeiro, Brasil.

Luana Farias Sales, Instituto Brasileiro de Informação em Ciência e Tecnologia

Doutora em Ciência da Informação pela Universidade Federal do Rio de Janeiro (UFRJ). Docente do Programa de Pós-Graduação em Ciência da Informação do Instituto Brasileiro de Informação em Ciência e Tecnologia (IBICT) e do Programa de Pós-Graduação em Biblioteconomia da Universidade Federal do Estado do Rio de Janeiro (UNIRIO). Analista em Ciência e Tecnologia do Instituto Brasileiro de Informação em Ciência e Tecnologia do Rio de Janeiro (IBICT), Rio de Janeiro, Brasil.

Referências

BATISTA, Dominique; GONZALEZ-BELTRAN, Alejandra; SANSONE, Susanna-Assunta; ROCCA-SERRA, Philippe. Machine actionable metadata model. Scientific Data, [S. l.], v. 9, n. 1, 2022. Disponível em: https://www.nature.com/articles/s41597-022-01707-6. Acesso em: 20 fev. 2023. DOI: https://doi.org/10.1038/s41597-022-01707-6

BORGMAN, Christine L. Big data, little data, no data: scholarship in the networked world. London: The MIT Press, 2015. DOI: https://doi.org/10.7551/mitpress/9963.001.0001

BOSE, Rajendra; FREW, James. Lineage Retrieval for Scientific Data Processing. ACM Computing Survey, [S. l.], v. 37, n. 1, 2005. DOI: https://doi.org/10.1145/1057977.1057978

BRUCE, Thomas; HILLMANN, Diane I. The continuum of metadata quality: defining, expressing, exploitation. In: HILLMANN, Diane I.; WESTBROOKS, Elaine L. (ed.). Metadata in Practice. Chicago: ALA Editions, 2004. Disponível em: https://ecommons.cornell.edu/handle/1813/7895. Acesso em: 16 fev. 2023.

BURKE, Peter. Uma história social do conhecimento: de Gutenberg a Diderot. Rio de Janeiro: Zahar, 2003.

CHIU, Chen; BLAKE, Mara; BOEHM, Reid; FEARON Dave. Metadata for effective research data management. Center of Open Science. 2019. Disponível em: https://osf.io/7q4cu. Acesso em: 06 dez. 2022.

CHOUDHURY, Sayeed; COWLES, Esme; CROFT, Holly; ESTLUND, Karen; FARY, Michael; FAUSTINO, Gracy; HAUSER, Thomas; LINTON, Anne; LYNCH, Clifford; MENARD, Karen; MINOR, David; MONACO, Gregory E.; NOONAN, Daniel; SHREEVES, Sarah; ULATE, David; WATERS, Natalie. Research Data Curation: A framework for an Institution Approach. Louisville, CO: ECAR, 2018. Disponível em: https://www.researchgate.net/publication/325093683_Research_Data_Curation_A_Framework_for_an_Institution-Wide_Services_Approach. Acesso em: 16 fev. 2023.

CIAMBRELLA, Massimo; MCMAHON, Kevin; FEKETE, József I. Metadata in geological disposal of radioactive waste: The RepMet Initiative. Albuquerque, NM: Sandia National Lab., 2017. Disponível em: https://www.osti.gov/servlets/purl/1479240. Acesso em: 16 fev. 2023.

CONLON, Mike. The objects of science and their representation in eScience. In: Changing the Conduct of Science in the Information Age. 2011 p. 57-58. Disponível em: https://www.nsf.gov/pubs/2011/oise11003/oise11003_10.pdf. Acesso em: 20 fev. 2023.

CONSULTATIVE COMMITTEE FOR SPACE DATA SYSTEM - CCSDS. Reference Model for an Open Archival Information System (OAIS). Washington, DC: CCSDS, 2012. Magenta book (CCSDS 650.0-M-2). Disponível em: https://public.ccsds.org/pubs/650x0m2.pdf. Acesso em: 20 fev. 2023.

DE ROURE, David; JENNINGS, Nicholas R.; SHADBOLT, Nigel R. The Semantic Grid: a future e-Science infrastructure. In: BERMAN, Fran; FOX, Geoffrey; HEY, Anthony J. G. (ed.). Grid Computing. Making the Global Infrastructure a Reality. Chichester, West-Sussex, UK: John Wiley & Sons, 2003. p. 437-470. DOI: https://doi.org/10.1002/0470867167.ch17

DE SMEDT, Koenraad; KOUREAS, Dimitris; WITTENBURG, Peter. FAIR Digital Objects for Science: From data pieces to actionable knowledge units. Publications, [S. l.], v. 8, n. 2, 2020. Disponível em: https://www.mdpi.com/2304-6775/8/2/21?type=check_update&version=2. Acesso em: 06 jan. 2023. DOI: https://doi.org/10.3390/publications8020021

GOODMAN, Alyssa; PEPE, Alberto; BLOCKER, Alexander W; BORGMAN, Christine L.; CRANMER, Kyle; CROSAS, Merce; STEFANO, Rosanne Di; GIL, Yolanda; GROTH, Paul; HEDSTROM, Margaret; HOGG, David W.; KASHYAP, Vinay; MAHABAL, Ashish; SIEMIGINOWSKA, Aneta; SLAVKOVIC, Aleksandra. Ten simple rules for the care and feeding of scientific data. PLoS Computer Biology, [S. l.], v. 10, n. 4, 2014. Disponível em: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3998871/. Acesso em: 29 jul. 2022. DOI: https://doi.org/10.1371/journal.pcbi.1003542

GRAY, Jim; SZALAY, Alexander S.; THAKAR, Ani R.; STOUGHTON, Christopher; VANDENBERG, Jan. Online scientific data curation, publication, and archiving. Redmont, WA: Microsoft Corporation, July 2002. Disponível em: https://arxiv.org/ftp/cs/papers/0208/0208012.pdf. Acesso em: 29 jul. 2022.

GRAY, Jim; LIU, David T.; NIETO-SANTISTEBAN, Maria; SZALAY, Alexander S.; DEWITT, David; HEBER, Gerd. Scientific data management in the coming decade. Redmont, WA: Microsoft Corporation, Jan. 2005. Disponível em: https://arxiv.org/ftp/cs/papers/0502/0502008.pdf. Acesso em: 19 jul. 2022.

GRAY, Jim; SZALAY, Alexander S. Where the rubber meets the sky: Bridging the gap between database and science. Redmont, WA: Microsoft Corporation, Oct. 2004. Disponível em: https://arxiv.org/abs/cs/0502011. Acesso em: 22 mar. 2023.

GREENBERG, Jane. Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata. Journal of Data and Information Science, [S. l.], v. 2, n. 3, p. 19-36, 2017. Disponível em: https://sciendo.com/pdf/10.1515/jdis-2017-0012. Acesso em: 06 dez. 2003. DOI: https://doi.org/10.1515/jdis-2017-0012

HARVEY, Ross. Digital Curation: A How-To-Do-It Manual. New York, NY: Neal-Schuman Publishers, 2010.

HEY, Tony; TANSLEY, Stewart; TOLLE, Kristin. Jim Gray on eScience: A transformed scientific method. In: HEY, T.; TANSLEY, S.; TOLLE, K. (ed.). The fourth paradigm: Data-intensive scientific discovery. Redmond: Microsoft Research, 2009. p. xvii-xxxi. Disponível em: bit.ly/3Cv2f7e. Acesso em: 29 jul. 2022.

HIGGINS, Sarah. What are metadata standards? Edinburgh: Digital Curation Centre, 2007. Disponível em: https://www.dcc.ac.uk/guidance/briefing-papers/standards-watch-papers/what-are-metadata-standards. Acesso em: 06 dez. 2022.

HOBSBAWM, Eric. The Age of Extremes: The Short Twentieth Century, 1914-1991. London: Abacus, 1995.

HUNTER, Jane. Scientific Models: A user-oriented approach to the integration of scientific data and digital libraries. 2005. Disponível em: https://core.ac.uk/download/pdf/14984655.pdf. Acesso em: 20 mar. 2023.

KHAN, Robert, WILENSKY, Robert. A framework for distributed digital objects service. 1995. Disponível em: http://www.cnri.reston.va.us/home/cstr/arch/k-w.html. Acesso em: 06 dez. 2022.

KHAN, Robert, WILENSKY, Robert. A framework for distributed digital objects service. International Journal on Digital Libraries, [S. l.], v. 6, n. 2, p. 115-123, 2006. DOI: https://doi.org/10.1007/s00799-005-0128-x

KIRÁLY, Péter. Towards an extensible measurement of metadata quality. In: INTERNATIONAL CONFERENCE ON DIGITAL ACCESS TO TEXTUAL CULTURAL HERITAGE - DATeCH2017, 2., 2017, Göttingen, Germany. Proceedings […]. New York: ACM Digital Library, 2017. p. 111-115. DOI: https://doi.org/10.1145/3078081.3078109

KITCHIN, Rob. Big data, new epistemologies and paradigm shifts. Big Data & Society, [S. l.], v. 1, n. 1, 2014. Disponível em: https://journals.sagepub.com/doi/epub/10.1177/2053951714528481. Acesso em: 29 jul. 2022. DOI: https://doi.org/10.1177/2053951714528481

KUHN, Thomas S. The Structure of Scientific Revolutions. Chicago: University of Chicago Press, 1962.

LISCHER-KATZ, Zack. Studying the materiality of media archives in the age of digitization: Forensics, infrastructures and ecologies. First Monday, [S. l.], v. 22, n. 1-2, 2017. Disponível em: https://firstmonday.org/ojs/index.php/fm/article/view/7263/5769. Acesso em: 06 dez. 2022.

LÖFFLER, Felicitas; WESP, Valentin; KÖNING-RIES, Birgitta; KLAN, Friederike. Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs? PloS one, [S. l.], v. 16, n. 3, 2021. Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0246099. Acesso em: 06 dez. 2022. DOI: https://doi.org/10.1371/journal.pone.0246099

LORENTZ, Alissa. With Big Data, Context is a Big Issue. Wired. 2018. Disponível em: https://www.wired.com/insights/2013/04/with-big-data-context-is-a-big-issue/. Acesso em: 06 dez. 2022.

MAYERNIK, Matthew S.; DILAURO, Tim; DUERR, Ruth; METSGER, Elliot; THESSEN, Anne E.; CHOUDHURY, G. Sayeed. Data conservancy provenance, context, and lineage services: Key components for data preservation and curation. Data Science Journal, [S. l.], v. 12, p. 158-171, 2013. Disponível em: https://www.jstage.jst.go.jp/article/dsj/12/0/12_12-039/_article/-char/ja/. Acesso em: 28 fev. 2023. DOI: https://doi.org/10.2481/dsj.12-039

MONS, Barend; NEYLON, Cameron; VELTEROP, Jan; DUMONTIER, Michel; SANTOS, Luiz Olavo Bonino da Silva; WILKINSON, Mark D. Cloudy, increasingly FAIR: revisiting the FAIR Data guiding principle for the European Open Science. Information Service & Use, [S. l.], v. 37, n. 1, p. 49-56, 2017. Disponível em: https://content.iospress.com/articles/information-services-and-use/isu824. Acesso em: 22 mar. 2023. DOI: https://doi.org/10.3233/ISU-170824

NISO. A Framework of Guidance for Building Good Digital Collections. 3. ed. Bethesda, MD: NISO Press, 2007. Disponível em: https://www.niso.org/sites/default/files/2017-08/framework3.pdf. Acesso em: 16 fev. 2023.

OCHOA, Xavier; DUVAL, Erik. Automatic evaluation of metadata quality in digital repositories. International Journal on Digital Libraries, [S. l.], v. 10, n. 2-3, 2009. Disponível em: https://www.academia.edu/2808304/Automatic_evaluation_of_metadata_quality_in_digital_repositories. Acesso em: 16 fev. 2023. DOI: https://doi.org/10.1007/s00799-009-0054-4

PREMIS Data Dictionary for Preservation Metadata. Version 3.0. June 2015. Disponível em: http://www.loc.gov/standards/premis/v3/premis-3-0-final.pdf. Acesso em: 15 fev. 2023.

RHEINBERGER, Hans-Jörg. Toward a History of Epistemic Things: Synthesizing proteins in the test tube. California: Stanford University Press, 1977.

SANTOS, Luiz Olavo Bonino da Silva (ed.). FAIR digital object framework documentation. 2021. Disponível em: https://fairdigitalobjectframework.org/. Acesso em: 06 jan. 2023.

SAYÃO, Luis Fernando; SALES, Luana Farias. Guia de Gestão de dados de pesquisa para bibliotecários e pesquisadores. Rio de Janeiro: CNEN, 2015. Disponível em: https://oasisbr.ibict.br/vufind/Record/IEN_b6a823ef451ba363fe2d3f83088db887. Acesso em: 20 mar. 2023.

SCHEMBERA, Björn; IGLEZAKIS, Dorothea. Metadata for Computational Engineering. International Journal of Metadata, Semantics and Ontologies, [S. l.], v. 14, n. 1, p. 26-38, 2020. Disponível em: https://www.inderscienceonline.com/doi/abs/10.1504/IJMSO.2020.107792. Acesso em: 06 dez. 2022. DOI: https://doi.org/10.1504/IJMSO.2020.10030004

SCHWARDEMANN, Ulrich. Digital objects – FAIR Digital Objects: Which services are required? Data Science Journal, [S. l.], v. 19, n. 1, 2020. Disponível em: https://datascience.codata.org/articles/10.5334/dsj-2020-015/. Acesso em: 20 mar. 2023. DOI: https://doi.org/10.5334/dsj-2020-015

TYBJERG, Karin. Exhibiting Epistemic Objects. Museum & Society, [S. l.], v. 15, n. 3, p. 269-286, 2017. Disponível em: https://pdfs.semanticscholar.org/a152/1b29555cb7982794a6c80a1e3504ba2d8782.pdf. Acesso em: 06 mar. 2023.

W3C WORKING GROUP. PROV-Overview: An Overview of the PROV Family of Documents. Apr. 2013. Note 30. Disponível em: https://www.w3.org/TR/prov-overview/.Acesso em: 16 fev. 2023.

WILKINSON, Mark D. DUMONTIER, Michel; AALBERSBERG, IJsbrand Jan; APPLETON, Gabrielle; AXTON, Myles; BAAK, Arie; BLOMBERG, Niklas; BOITEN, Jan-Willem; SANTOS, Luiz Bonino da Silva; BOURNE, Philip E.; BOUWMAN, Jildau; BROOKES, Anthony J.; CLARK, Tim; CROSAS, Mercè; DILLO, Ingrid; DUMON, Olivier; EDMUNDS, Scott; EVELO, Chris T.; FINKERS, Richard; GONZALEZ-BELTRAN, Alejandra; GRAY, Alasdair J. G.; GROTH, Paul; GOBLE, Carole; GRETHE, Jeffrey S.; HERINGA, Jaap; HOEN, Peter A.C ’t; HOOFT, Rob; KUHN, Tobias; KOK, Ruben; KOK, Joost; LUSHER, Scott J.; MARTONE, Maryann E.; MONS, Albert; PACKER, Abel L.; PERSSON, Bengt; ROCCA-SERRA, Philippe; ROOS, Marco; VAN SCHAIK, Rene; SANSONE, Susanna-Assunta; SCHULTES, Erik; SENGSTAG, Thierry; SLATER, Ted; STRAWN, George; SWERTZ, Morris A.; THOMPSON, Mark; VAN DER LEI, Johan; VAN MULLIGEN, Erik; VELTEROP, Jan; WAAGMEESTER, Andra; WITTENBURG, Peter; WOLSTENCROFT, Katherine; ZHAO, Jun; MONS, Barend.The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, [S. l.], v. 3, n. 1, 2016. Disponível em: https://www.nature.com/articles/sdata201618. Acesso em: 22 mar. 2023.

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Publicado

2024-09-28

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Sayão , L. F., & Sales, L. F. (2024). Metadata authoring model: describing information about context and provenance of disciplinary research objects. Informação & Informação, 28(4), 1–37. https://doi.org/10.5433/1981-8920.2023v28n4p1