Have a personal or library account? Click to login
The History and Future of Data Citation in Practice Cover

The History and Future of Data Citation in Practice

Open Access
|Nov 2019

References

  1. 1Aalbersberg, IJ, et al. 2018. Making science transparent by default; introducing the TOP statement. DOI: 10.31219/osf.io/sm78t
  2. 2Allen, L, et al. 2014. Publishing: Credit where credit is due. Nature, 508: 312313. DOI: 10.1038/508312a
  3. 3Alliez, P, et al. 2019. Attributing and referencing (research) software: Best practices and outlook from Inria. Computing in Science & Engineering. https://arxiv.org/abs/1905.11123.
  4. 4Altman, M and King, G. 2007. A proposed standard for the scholarly citation of quantitative data. D-Lib Magazine, 13. http://dlib.org/dlib/march07/altman/03altman.html accessed 2019-07-25.
  5. 5Baker, KS and Yarmey, L. 2009. Data stewardship: Environmental Data Curation and a Web-of-Repositories. International Journal of Digital Curation, 4. DOI: 10.2218/ijdc.v4i2.90
  6. 6Ball, A and Duke, M. 2015. How to Cite Datasets and Link to Publications. Edinburgh: Digital Curation Centre. http://www.dcc.ac.uk/resources/how-guides accessed 2019-02-06.
  7. 7Bechhofer, S, et al. 2010. Research objects: Towards exchange and reuse of digital knowledge. The Future of the Web for Collaborative Science (FWCS 2010). https://eprints.soton.ac.uk/268555/ accessed 2019-07-28.
  8. 8Bechhofer, S, et al. 2011. Why linked data is not enough for scientists. Future Generation Computer Systems. DOI: 10.1016/j.future.2011.08.004
  9. 9Bernknopf, R, et al. 2016. The cost-effectiveness of satellite Earth observations to inform a post-wildfire response. Working Paper, 1916. https://media.rff.org/documents/Valuables_Wildfires.pdf accessed 2019-07-28.
  10. 10Berquist, CR, Jr. 1999. Digital map production and publication by geological survey organizations: A proposal for authorship and citation guidelines. U.S. Geological Survey Open-File Report, 99386. https://pubs.usgs.gov/of/1999/of99-386/berquist.html accessed 2019-03-02.
  11. 11Bizer, C, Heath, T and Berners-Lee, T. 2009. Linked data – the story so far. International Journal on Semantic Web and Information Systems, 5: 122. DOI: 10.4018/jswis.2009081901
  12. 12Bolikowski, L, Nowiński, A and Sylwestrzak, W. 2015. A system for distributed minting and management of persistent identifiers. International Journal of Digital Curation, 10: 280286. DOI: 10.2218/ijdc.v10i1.368
  13. 13Borgman, C. 2015. Big Data, Little Data, No Data. Boston: MIT Press. DOI: 10.7551/mitpress/9963.001.0001
  14. 14Borgman, C. 2016. Data citation as a bibliometric oxymoron. In: Theories of Informetrics and Scholarly Communication, Sugimoto, CR (ed.), 93115. Berlin & Boston: Walter de Gruyter GmbH & Co KG. https://escholarship.org/content/qt8w36p9zf/qt8w36p9zf.pdf accessed 2019-07-26.
  15. 15Brinckman, A, et al. 2019. Computing environments for reproducibility: Capturing the “whole tale”. Future Generation Computer Systems, 94: 854867. DOI: 10.1016/j.future.2017.12.029
  16. 16Buneman, P, Davidson, S and Frew, J. 2016. Why data citation is a computational problem. Commun ACM, 59: 5057. DOI: 10.1145/2893181
  17. 17Burton, A, et al. 2017. The Scholix framework for interoperability in data-literature information exchange. D-Lib Magazine, 23. DOI: 10.1045/january2017-burton
  18. 18Callaghan, S, et al. 2009. Overlay journals and data publishing in the meteorological sciences. Ariadne. http://www.ariadne.ac.uk/issue60/callaghan-et-al/ accessed 2011-11-27.
  19. 19Chard, K, et al. 2019. Implementing computational reproducibility in the Whole Tale environment. Proceedings of the 2nd International Workshop on Practical Reproducible Evaluation of Computer Systems – P-RECS ‘19. DOI: 10.1145/3322790.3330594
  20. 20Cooke, R and Golub, A. 2019. Market-based methods for monetizing uncertainty reduction: A case study. Working Paper, 1915. https://media.rff.org/documents/WP_Cooke_Golub_4.pdf accessed 2019-07-28.
  21. 21Costello, MJ. 2009. Motivating online publication of data. Bioscience, 59: 418427. DOI: 10.1525/bio.2009.59.5.9
  22. 22Cousijn, H, et al. 2018. A data citation roadmap for scientific publishers. Sci Data, 5: 180259. DOI: 10.1038/sdata.2018.259
  23. 23Cousijn, H, et al. 2019. Bringing citations and usage metrics together to make data count. Data Science Journal, 18. DOI: 10.5334/dsj-2019-009
  24. 24Crosas, M. 2014. The evolution of data citation: From principles to implementation. IASSIST Quarterly, 37: 62. DOI: 10.29173/iq504
  25. 25DCSG – Data Citation Synthesis Group. 2014. Joint Declaration of Data Citation Principles. DOI: 10.25490/a97f-egyk
  26. 26Di Cosmo, R, Gruenpeter, M and Zacchiroli, S. 2018. Identifiers for digital objects: The case of software source code preservation. Open Science Framework. DOI: 10.17605/OSF.IO/KDE56
  27. 27Donovan, C and Hanney, S. 2011. The payback framework explained. Research Evaluation, 20: 181183. DOI: 10.3152/095820211X13118583635756
  28. 28Downs, RR, Duerr, R, Hills, DJ and Ramapriyan, HK. 2015. Data stewardship in the Earth sciences. D-Lib Magazine, 21. DOI: 10.1045/july2015-downs
  29. 29EDPSC – ESIP Data Preservation and Stewardship Committee. 2019. Data Citation Guidelines for Earth Science Data, Version 2. Earth Science Information Partners. DOI: 10.6084/m9.figshare.8441816.v1
  30. 30ESSCC – ESIP Software and Services Citation Cluster. 2019. Software and Services Citation Guidelines and Examples. Ver. 1. Earth Science Information Partners. DOI: 10.6084/m9.figshare.7640426
  31. 31Fenner, M, et al. 2019. A data citation roadmap for scholarly data repositories. Scientific Data, 6(1) (1): 28. DOI: 10.1038/s41597-019-0031-8
  32. 32Fenner, M, et al. 2018. Code of practice for research data usage metrics release 1. DOI: 10.7287/peerj.preprints.26505v1
  33. 33Guralnick, RP, et al. 2015. Community next steps for making globally unique identifiers work for biocollections data. Zookeys, 133154. DOI: 10.3897/zookeys.494.9352
  34. 34Haak, LL, et al. 2012. Orcid: A system to uniquely identify researchers. Learned Publishing, 25: 259264. DOI: 10.1087/20120404
  35. 35Hackett, EJ, et al. 2008. Ecology transformed: The national center for ecological analysis and synthesis and the changing patterns of ecological research. In: Scientific Collaboration on the Internet, 277296. The MIT Press. DOI: 10.7551/mitpress/9780262151207.003.0016
  36. 36Hayes, PJ and Halpin, H. 2008. In defense of ambiguity. International Journal on Semantic Web and Information Systems, 4: 118. DOI: 10.4018/jswis.2008040101
  37. 37Howison, J and Bullard, J. 2016. Software in the scientific literature: Problems with seeing, finding, and using software mentioned in the biology literature. Journal of the Association for Information Science and Technology, 67: 21372155. DOI: 10.1002/asi.23538
  38. 38Kafkas, Ş, Kim, JH and McEntyre, JR. 2013. Database citation in full text biomedical articles. PLoS One, 8: e63184. DOI: 10.1371/journal.pone.0063184
  39. 39Kahn, R and Wilensky, R. 1995. A framework for distributed digital object services. http://handle.net/cnri.dlib/tn95-01 accessed 2019-07-20.
  40. 40Kahn, RE, et al. 2018. Digital Object Interface Protocol Specification, Ver. 2.0. DONA. https://www.dona.net/sites/default/files/2018-11/DOIPv2Spec_1.pdf accessed 2019-07-25.
  41. 41Katz, DS. 2014. Transitive credit as a means to address social and technological concerns stemming from citation and attribution of digital products. Journal of Open Research Software, 2: e20. DOI: 10.5334/jors.be
  42. 42Katz, DS and Chue Hong, NP. 2018. Software citation in theory and practice. Arxiv preprint. https://arxiv.org/pdf/1807.08149.pdf accessed 2018-12-06.
  43. 43Klump, J, et al. 2006. Data publication in the open access initiative. Data Science Journal, 5: 7983. DOI: 10.2481/dsj.5.79
  44. 44Klump, J, Huber, R and Diepenbroek, M. 2015. DOI for geoscience data-how early practices shape present perceptions. Earth Science Informatics, 114. DOI: 10.1007/s12145-015-0231-5
  45. 45Klump, J, Murphy, F, Weigel, T and Parsons, MA. 2017. 20 years of persistent identifiers–applications and future directions. Data Science Journal, 16. DOI: 10.5334/dsj-2017-052
  46. 46Kratz, JE and Strasser, C. 2015a. Comment: Making data count. Sci Data, 2: 150039. DOI: 10.1038/sdata.2015.39
  47. 47Kratz, JE and Strasser, C. 2015b. Researcher perspectives on publication and peer review of data. PLoS One, 10: e0117619. DOI: 10.1371/journal.pone.0117619
  48. 48Lawrence, B, et al. 2011. Citation and peer review of data: Moving towards formal data publication. International Journal of Digital Curation, 6. DOI: 10.2218/ijdc.v6i2.205
  49. 49Lawrence, S, et al. 2001. Persistence of web references in scientific research. Computer, 34: 2631. DOI: 10.1109/2.901164
  50. 50Ma, X, et al. 2017. Weaving a knowledge network for deep carbon science. Frontiers in Earth Science, 5. DOI: 10.3389/feart.2017.00036
  51. 51Mayernik, MS, Phillips, J and Nienhouse, E. 2016. Linking publications and data: Challenges, trends, and opportunities. D-Lib Magazine, 22. DOI: 10.1045/may2016-mayernik
  52. 52McMurry, JA, et al. 2017. Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data. PLoS Biol, 15: e2001414. DOI: 10.1371/journal.pbio.2001414
  53. 53Mooney, H and Newton, MP. 2012. The anatomy of a data citation: Discovery, reuse, and credit. Journal of Librarianship & Scholarly Communication, 1: 116. https://jlsc-pub.org/articles/abstract/10.7710/2162-3309.1035/ accessed 2017-10-03. DOI: 10.7710/2162-3309.1035
  54. 54NISO. 2016. Outputs of the NISO alternative assessment metrics project: A recommended practice of the National Information Standards Organization. NISO RP-25-2016. https://www.niso.org/publications/rp-25-2016-altmetrics accessed 2019-07-22.
  55. 55Parsons, MA, et al. 2008. Managing permafrost data: Past approaches and future directions. Permafrost Ninth International Conference 29 June–3 July 2008 Proceedings, 13691374. DOI: 10.5281/zenodo.3519368
  56. 56Parsons, MA and Fox, PA. 2013. Is data publication the right metaphor? Data Science Journal, 12. DOI: 10.2481/dsj.WDS-042
  57. 57Parsons, MA and Fox, PA. 2018. Power and persistent identifiers. International Data Week 2018. DOI: 10.5281/zenodo.1495321
  58. 58Paskin, N. 2000. E-citations: Actionable identifiers and scholarly referencing. Learned Publishing, 13: 159166. DOI: 10.1087/09531510050145308
  59. 59Peters, I, et al. 2016. Research data explored: An extended analysis of citations and altmetrics. Scientometrics, 107: 723744. DOI: 10.1007/s11192-016-1887-4
  60. 60Rauber, A, Asmi, A, van Uytvanck, D and Proell, S. 2015. Data Citation of Evolving Data: Recommendations of the Working Group on Data Citation (WGDC). Research Data Alliance. Accessed 2019-07-14. DOI: 10.15497/RDA00016
  61. 61Schopf, JM. 2012. Treating data like software: A case for production quality data. Proceedings of the Joint Conference on Digital Libraries, 11–14 June 2012. Washington DC. DOI: 10.1145/2232817.2232846
  62. 62Silvello, G. 2018. Theory and practice of data citation. Journal of the Association for Information Science and Technology, 69: 620. DOI: 10.1002/asi.23917
  63. 63Smith, AM, Katz, DS, Niemeyer, KE, FORCE11 and SCWG. 2016. Software citation principles. PeerJ Computer Science, 2: e86. DOI: 10.7717/peerj-cs.86
  64. 64Stall, S, et al. 2018. Advancing FAIR data in Earth, space, and environmental science. Eos, 99. DOI: 10.1029/2018EO109301
  65. 65Stockhause, M and Lautenschlager, M. 2017. CMIP6 data citation of evolving data. Data Science Journal, 16. DOI: 10.5334/dsj-2017-030
  66. 66Stuart, D. 2017. Data bibliometrics: Metrics before norms. Online Information Review, 41: 428435. DOI: 10.1108/OIR-01-2017-0008
  67. 67TGDCSP – Task Group on Data Citation Standards and Practices, CODATA-ICSTI. 2013. Out of cite, out of mind: The current state of practice, policy, and technology for the citation of data. Data Science Journal, 12: CIDCR1CIDCR75. DOI: 10.2481/dsj.OSOM13-043
  68. 68Wilkinson, MD, et al. 2016. The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3: 160018. DOI: 10.1038/sdata.2016.18
  69. 69Wittenburg, P, Hellström, M, Zwölf, CM, Abroshan, H, et al. (eds.) 2017. Persistent identifiers: Consolidated assertions. Research Data Alliance. DOI: 10.15497/RDA00027
Language: English
Submitted on: Jul 30, 2019
Accepted on: Oct 4, 2019
Published on: Nov 1, 2019
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Mark A. Parsons, Ruth E. Duerr, Matthew B. Jones, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.