Have a personal or library account? Click to login
Incorporating RDA Outputs in the Design of a European Research Infrastructure for Natural Science Collections Cover

Incorporating RDA Outputs in the Design of a European Research Infrastructure for Natural Science Collections

Open Access
|Dec 2020

References

  1. 1Addink, W, et al. 2020. Advancing the Catalogue of the World’s Natural History Collections – 10 recommendations from DiSSCo. DOI: 10.5281/zenodo.3949839
  2. 2Agosti, D, et al. 2019. Biodiversity Literature Repository (BLR), a repository for FAIR data and publications. Biodiversity Information Science and Standards. DOI: 10.3897/biss.3.37197
  3. 3Bechhofer, S, et al. 2013. Why linked data is not enough for scientists. Future Generation Computer Systems, 29(2): 599611. DOI: 10.1016/j.future.2011.08.004
  4. 4Besnard, G, et al. 2016. Valuing museum specimens: high-throughput DNA sequencing on historical collections of New Guinea crowned pigeons (Goura). Biological Journal of the Linnean Society, 117(1): 7182. DOI: 10.1111/bij.12494
  5. 5Blagoderov, V, Kitching, IJ, Livermore, L, Simonsen, TJ and Smith, VS. 2012. No specimen left behind: industrial scale digitization of natural history collections. ZooKeys, 209: 133. DOI: 10.3897/zookeys.209.3178
  6. 6Cazenave, N, Béchard, L and Rouchon O. 2019. Digitisation infrastructure design for EUDAT/CINES. ICEDIG Deliverable 6.2. DOI: 10.5281/zenodo.3364533
  7. 7Chapman, A, et al. 2020. Developing standards for improved data quality and for selecting fit for use biodiversity data. Biodiversity Information Science and Standards, 4: e50889. DOI: 10.3897/biss.4.50889
  8. 8Cocks, N, Livermore, L, Smith, V and Woodburn, M. 2020. Technical capacities of digitisation centres within ICEDIG participating institutions. Research Ideas and Outcomes, 6: e55522. DOI: 10.3897/rio.6.e55522
  9. 9Cook, JA, et al. 2020. Integrating Biodiversity Infrastructure into Pathogen Discovery and Mitigation of Emerging Infectious Diseases. BioScience, 70(7): 531534. DOI: 10.1093/biosci/biaa064
  10. 10Copas, K, Noesgaard, D and Schigel, D. 2019. Crediting the reuse and impact of free, FAIR and open biodiversity data through DOI citations and event tracking. AGUFM, 2019: IN21A06.
  11. 11De Smedt, K, Koureas, D and Wittenburg, P, 2020. FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units. Publications, 8(2): 21. DOI: 10.3390/publications8020021
  12. 12Díaz, S, et al. 2016. The global spectrum of plant form and function. Nature, 529(7585): 167171. DOI: 10.1038/nature16489
  13. 13DiSSCo DMP. 2019. Provisional Data Management Plan for the DiSSCo infrastructure. DOI: 10.5281/zenodo.3532937
  14. 14European Commission. 2018. Turning FAIR into reality. Final Report and Action Plan from the European Commission Expert Group on FAIR data. Luxembourg Publication Office of the European Union, Luxembourg, 78. DOI: 10.2777/1524
  15. 15Grobe, P, et al. 2019. From Data to Knowledge: A semantic knowledge graph application for curating specimen data. Biodiversity Information Science and Standards. DOI: 10.3897/biss.3.37412
  16. 16Groom, Q, Dillen, M, Hardy, H, Phillips, S, Willemse, L and Wu, Z, 2019. Improved standardization of transcribed digital specimen data. Database, 2019. DOI: 10.1093/database/baz129
  17. 17Güntsch, A, et al. 2017. Actionable, long-term stable and semantic web compatible identifiers for access to biological collection objects. Database, 2017. DOI: 10.1093/database/bax003
  18. 18Guralnick, RP, et al. 2015. Community next steps for making globally unique identifiers work for biocollections data. ZooKeys, 494: 133. DOI: 10.3897/zookeys.494.9352
  19. 19Hardisty, A., et al. 2020. Conceptual design blueprint for the DiSSCo digitization infrastructure-DELIVERABLE D8. 1. Research Ideas and Outcomes, 6. DOI: 10.3897/rio.6.e54280
  20. 20Hardisty, A and Roberts, D. 2013. A decadal view of biodiversity informatics: challenges and priorities. BMC ecology, 13(1): 16. DOI: 10.1186/1472-6785-13-16
  21. 21Hedrick, BP, et al. 2020. Digitization and the future of natural history collections. BioScience, 70(3): 243251. DOI: 10.1093/biosci/biz163
  22. 22Hobern, D, et al. 2019. Connecting data and expertise: a new alliance for biodiversity knowledge. Biodiversity data journal, 7. DOI: 10.3897/BDJ.7.e33679.suppl10
  23. 23Kahn, R and Wilensky, R, 2006. A framework for distributed digital object services. International Journal on Digital Libraries, 6(2): 115123. DOI: 10.1007/s00799-005-0128-x
  24. 24Kays, R, et al. 2020. An empirical evaluation of camera trap study design: How many, how long and when?. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.13370
  25. 25Kays, R, McShea, WJ and Wikelski, M. 2020. Born-digital biodiversity data: Millions and billions. Diversity and Distributions, 26(5): 644648. DOI: 10.1111/ddi.12993
  26. 26Lannom, L, Koureas, D and Hardisty, AR. 2020. FAIR data and services in biodiversity science and geoscience. Data Intelligence, 2(1–2): 122130. DOI: 10.1162/dint_a_00034
  27. 27Lendemer, J, et al. 2020. The extended specimen network: A strategy to enhance US biodiversity collections, promote research and education. BioScience, 70(1): 2330. DOI: 10.1093/biosci/biz165
  28. 28Leonelli, S, 2016. Data-centric biology: A philosophical study. University of Chicago Press. DOI: 10.7208/chicago/9780226416502.001.0001
  29. 29Lewis, KP, Vander, Wal, E and Fifield, DA. 2018. Wildlife biology, big data, and reproducible research. Wildlife Society Bulletin, 42(1): 172179. DOI: 10.1002/wsb.847
  30. 30Lister, AM and Climate Change Research Group. 2011. Natural history collections as sources of long-term datasets. Trends in ecology & evolution, 26(4): 153154. DOI: 10.1016/j.tree.2010.12.009
  31. 31Livermore, L and Cubey, R. 2019. Specimen Data Refinery: A landscape analysis on machine learning, computer vision and automated approaches to capture specimen metadata. Biodiversity Information Science and Standards. DOI: 10.3897/biss.3.37647
  32. 32Martin, P, Chen, Y, Hardisty, A, Jeffery, K and Zhao, Z. 2017. Computational Challenges in Global Environmental Research Infrastructures. In: Terrestrial Ecosystem Research Infrastructures: Challenges and Opportunities, Chabbi, A and Loescher, HW (eds.). CRC Press ISBN 9781498751315. DOI: 10.1201/9781315368252
  33. 33Mons, B, et al. 2017. Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services & Use, 37(1): 4956. DOI: 10.3233/ISU-170824
  34. 34Nachman, MW. 2013. Genomics and museum specimens. Molecular Ecology, 22(24): 59665968. DOI: 10.1111/mec.12563
  35. 35Nelson, G and Ellis, S. 2019. The history and impact of digitization and digital data mobilization on biodiversity research. Philosophical Transactions of the Royal Society B, 374(1763): 20170391. DOI: 10.1098/rstb.2017.0391
  36. 36Nieva de la Hidalga, A, Hardisty, A, Martin, P, Magagna, B and Zhao, Z. 2020. The ENVRI Reference Model. In Towards Interoperable research infrastructures for environmental and earth sciences – A reference model guided approach for common challenges, Zhao, Z and Hellstrom, M (eds.). LNCS 12003, 6181. DOI: 10.1007/978-3-030-52829-4_4
  37. 37Page, R. 2016. Towards a biodiversity knowledge graph. Research Ideas and Outcomes, 2. DOI: 10.3897/rio.2.e8767
  38. 38Patterson, D, Mozzherin, D, Shorthouse, DP and Thessen, A. 2016. Challenges with using names to link digital biodiversity information. Biodiversity Data Journal, 4. DOI: 10.3897/BDJ.4.e8080
  39. 39RDA DTR. 2015. Data Type Registries working group output. DOI: 10.15497/A5BCD108-ECC4-41BE-91A7-20112FF77458
  40. 40RDA DF&T. 2015. Data Foundation and Terminology Work Group Products. Research Data Alliance. DOI: 10.15497/06825049-8CA4-40BD-BCAF-DE9F0EA2FADF
  41. 41RDA DFIG. 2018. Data Fabric Interest Group; Summary of Virtual Layer Recommendations. Research Data Alliance. DOI: 10.15497/RDA00026
  42. 42RDA FAIR Data Maturity Model. 2020. FAIR Data Maturity Model: specification and guidelines. Research Data Alliance. DOI: 10.15497/RDA00050
  43. 43RDA PID KI. 2019. RDA Recommendation on PID Kernel Information. Research Data Alliance. DOI: 10.15497/RDA00031
  44. 44RDA PID Information Types. 2015. Final deliverable. Research Data Alliance. DOI: 10.15497/FDAA09D5-5ED0-403D-B97A-2675E1EBE786
  45. 45RDA Research Data Collections. 2017. Recommendation on Research Data Collections. Research Data Alliance. DOI: 10.15497/RDA00022
  46. 46RDA/TDWG Attribution Metadata. 2018. Final Recommendations. Research Data Alliance. DOI: 10.15497/RDA00029
  47. 47Schindel, DE and Cook, JA. 2018. The next generation of natural history collections. PLoS Biology, 16(7): e2006125. DOI: 10.1371/journal.pbio.2006125
  48. 48Senderov, V, et al. 2018. OpenBiodiv-O: ontology of the OpenBiodiv knowledge management system. Journal of biomedical semantics, 9(1): 5. DOI: 10.1186/s13326-017-0174-5
  49. 49Sharp, C. 2016. Overview of the digital object architecture (DOA). An Internet Society Information Paper, Internet Society. Retrieved from https://www.internetsociety.org/resources/doc/2016/overview-of-the-digital-object-architecture-doa/.
  50. 50Smith, V, et al. 2019. SYNTHESYS+ Abridged Grant Proposal. Research Ideas and Outcomes, 5: e46404. DOI: 10.3897/rio.5.e46404
  51. 51Sterner, B and Franz, NM. 2017. Taxonomy for humans or computers? Cognitive pragmatics for big data. Biological Theory, 12(2): 99111. DOI: 10.1007/s13752-017-0259-5
  52. 52Stocker, M, et al. 2018. Curating Scientific Information in Knowledge Infrastructures. Data Science Journal, 17(21): 116. DOI: 10.5334/dsj-2018-021
  53. 53Sun, S, Lannom, L and Boesch B. 2003. Handle System Overview, RFC 3650. DOI: 10.17487/rfc3650
  54. 54Sustkova, HP, Hettne, KM, Wittenburg, P, Jacobsen, A, Kuhn, T, Pergl, R, Slifka, J, McQuilton, P, Magagna, B, Sansone, SA and Stocker, M. 2020. FAIR convergence matrix: Optimizing the reuse of existing FAIR-related resources. Data Intelligence, 2(1–2): 158170. DOI: 10.1162/dint_a_00038
  55. 55Webster, MS. (ed.) 2017. The extended specimen: emerging frontiers in collections-based ornithological research. CRC Press.
  56. 56Weigel, T, et al. 2020. Making data and workflows findable for machines. Data Intelligence, 2(1–2): 4046. DOI: 10.1162/dint_a_00026
  57. 57Wilkinson, M, Dumontier, M, Aalbersberg, I, et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1): 19. DOI: 10.1038/sdata.2016.18
  58. 58Wittenburg, P, et al. 2019. Digital objects as drivers towards convergence in data infrastructures. Technical paper. DOI: 10.23728/b2share.b605d85809ca45679b110719b6c6cb11
  59. 59Wittenburg, P and Strawn, G. 2019. Commenting on “Digital Object” Aspects. DOI: 10.23728/b2share.2317b12321764f669c92ebbcf7518164
Language: English
Submitted on: Jul 17, 2020
Accepted on: Nov 17, 2020
Published on: Dec 14, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Sharif Islam, Alex Hardisty, Wouter Addink, Claus Weiland, Falko Glöckler, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.