Enabling FAIR Discovery of Rare Disease Digital Resources

Rajaram Kaliyaperumal; Núria Queralt Rosinach; Kees Burger; Luiz Olavo Bonino Da Silva Santos; Marc Hanauer; Marco Roos

doi:10.3233/SHTI210101

Enabling FAIR Discovery of Rare Disease Digital Resources

Rajaram Kaliyaperumal^*, Núria Queralt Rosinach, Kees Burger, Luiz Olavo Bonino Da Silva Santos, Marc Hanauer, Marco Roos

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

92 Downloads (Pure)

Abstract

Background: Integration of heterogenous resources is key for Rare Disease research. Within the EJP RD, common Application Programming Interface specifications are proposed for discovery of resources and data records. This is not sufficient for automated processing between RD resources and meeting the FAIR principles. Objective: To design a solution to improve FAIR for machines for the EJP RD API specification. Methods: A FAIR Data Point is used to expose machine-actionable metadata of digital resources and it is configured to store its content to a semantic database to be FAIR at the source. Results: A solution was designed based on grlc server as middleware to implement the EJP RD API specification on top of the FDP. Conclusion: grlc reduces potential API implementation overhead faced by maintainers who use FAIR at the source.

Original language	English
Title of host publication	Navigating Healthcare Through Challenging Times - Proceedings of dHealth 2021 - Health Informatics Meets Digital Health
Editors	Dieter Hayn, Gunter Schreier, Martin Baumgartner
Publisher	IOS
Pages	144-146
Number of pages	3
ISBN (Electronic)	9781643681801
DOIs	https://doi.org/10.3233/SHTI210101
Publication status	Published - 7 May 2021
Event	15th Annual Conference on Health Informatics meets Digital Health, dHealth 2021 - Virtual, Online, Austria Duration: 11 May 2021 → 12 May 2021 Conference number: 15

Publication series

Name	Studies in Health Technology and Informatics
Volume	279
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	15th Annual Conference on Health Informatics meets Digital Health, dHealth 2021
Abbreviated title	dHealth 2021
Country/Territory	Austria
City	Virtual, Online
Period	11/05/21 → 12/05/21

Keywords

data management
FAIR
information storage and retrieval
metadata
rare disease
semantic web

Access to Document

10.3233/SHTI210101Licence: CC BY-NC

Kaliyaperumal-2021-Enabling-fair-discovery-of-rare-disFinal published version, 439 KBLicence: CC BY-NC

Cite this

Kaliyaperumal, R., Queralt Rosinach, N., Burger, K., Bonino Da Silva Santos, L. O., Hanauer, M., & Roos, M. (2021). Enabling FAIR Discovery of Rare Disease Digital Resources. In D. Hayn, G. Schreier, & M. Baumgartner (Eds.), Navigating Healthcare Through Challenging Times - Proceedings of dHealth 2021 - Health Informatics Meets Digital Health (pp. 144-146). (Studies in Health Technology and Informatics; Vol. 279). IOS. https://doi.org/10.3233/SHTI210101

Kaliyaperumal, Rajaram ; Queralt Rosinach, Núria ; Burger, Kees et al. / Enabling FAIR Discovery of Rare Disease Digital Resources. Navigating Healthcare Through Challenging Times - Proceedings of dHealth 2021 - Health Informatics Meets Digital Health. editor / Dieter Hayn ; Gunter Schreier ; Martin Baumgartner. IOS, 2021. pp. 144-146 (Studies in Health Technology and Informatics).

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abstract = "Background: Integration of heterogenous resources is key for Rare Disease research. Within the EJP RD, common Application Programming Interface specifications are proposed for discovery of resources and data records. This is not sufficient for automated processing between RD resources and meeting the FAIR principles. Objective: To design a solution to improve FAIR for machines for the EJP RD API specification. Methods: A FAIR Data Point is used to expose machine-actionable metadata of digital resources and it is configured to store its content to a semantic database to be FAIR at the source. Results: A solution was designed based on grlc server as middleware to implement the EJP RD API specification on top of the FDP. Conclusion: grlc reduces potential API implementation overhead faced by maintainers who use FAIR at the source.",

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Kaliyaperumal, R, Queralt Rosinach, N, Burger, K, Bonino Da Silva Santos, LO, Hanauer, M & Roos, M 2021, Enabling FAIR Discovery of Rare Disease Digital Resources. in D Hayn, G Schreier & M Baumgartner (eds), Navigating Healthcare Through Challenging Times - Proceedings of dHealth 2021 - Health Informatics Meets Digital Health. Studies in Health Technology and Informatics, vol. 279, IOS, pp. 144-146, 15th Annual Conference on Health Informatics meets Digital Health, dHealth 2021, Virtual, Online, Austria, 11/05/21. https://doi.org/10.3233/SHTI210101

Enabling FAIR Discovery of Rare Disease Digital Resources. / Kaliyaperumal, Rajaram; Queralt Rosinach, Núria; Burger, Kees et al.
Navigating Healthcare Through Challenging Times - Proceedings of dHealth 2021 - Health Informatics Meets Digital Health. ed. / Dieter Hayn; Gunter Schreier; Martin Baumgartner. IOS, 2021. p. 144-146 (Studies in Health Technology and Informatics; Vol. 279).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Kaliyaperumal, Rajaram

AU - Queralt Rosinach, Núria

AU - Burger, Kees

AU - Bonino Da Silva Santos, Luiz Olavo

AU - Hanauer, Marc

AU - Roos, Marco

N1 - Conference code: 15

PY - 2021/5/7

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AB - Background: Integration of heterogenous resources is key for Rare Disease research. Within the EJP RD, common Application Programming Interface specifications are proposed for discovery of resources and data records. This is not sufficient for automated processing between RD resources and meeting the FAIR principles. Objective: To design a solution to improve FAIR for machines for the EJP RD API specification. Methods: A FAIR Data Point is used to expose machine-actionable metadata of digital resources and it is configured to store its content to a semantic database to be FAIR at the source. Results: A solution was designed based on grlc server as middleware to implement the EJP RD API specification on top of the FDP. Conclusion: grlc reduces potential API implementation overhead faced by maintainers who use FAIR at the source.

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Kaliyaperumal R, Queralt Rosinach N, Burger K, Bonino Da Silva Santos LO, Hanauer M, Roos M. Enabling FAIR Discovery of Rare Disease Digital Resources. In Hayn D, Schreier G, Baumgartner M, editors, Navigating Healthcare Through Challenging Times - Proceedings of dHealth 2021 - Health Informatics Meets Digital Health. IOS. 2021. p. 144-146. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI210101

Enabling FAIR Discovery of Rare Disease Digital Resources

Abstract

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Keywords

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