References
- 1Aalbersberg, IJ, et al. 2018. Making science transparent by default; introducing the TOP statement. DOI: 10.31219/osf.io/sm78t
- 2Allen, L, et al. 2014. Publishing: Credit where credit is due. Nature, 508: 312–313. DOI: 10.1038/508312a
- 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 . - 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. - 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
- 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. - 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. - 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
- 9Bernknopf, R, et al. 2016. The cost-effectiveness of satellite Earth observations to inform a post-wildfire response. Working Paper, 19–16.
https://media.rff.org/documents/Valuables_Wildfires.pdf accessed 2019-07-28. - 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, 99–386.https://pubs.usgs.gov/of/1999/of99-386/berquist.html accessed 2019-03-02. - 11Bizer, C, Heath, T and Berners-Lee, T. 2009. Linked data – the story so far. International Journal on Semantic Web and Information Systems, 5: 1–22. DOI: 10.4018/jswis.2009081901
- 12Bolikowski, L, Nowiński, A and Sylwestrzak, W. 2015. A system for distributed minting and management of persistent identifiers. International Journal of Digital Curation, 10: 280–286. DOI: 10.2218/ijdc.v10i1.368
- 13Borgman, C. 2015. Big Data, Little Data, No Data. Boston: MIT Press. DOI: 10.7551/mitpress/9963.001.0001
- 14Borgman, C. 2016.
Data citation as a bibliometric oxymoron . In: Theories of Informetrics and Scholarly Communication, Sugimoto, CR (ed.), 93–115. Berlin & Boston: Walter de Gruyter GmbH & Co KG.https://escholarship.org/content/qt8w36p9zf/qt8w36p9zf.pdf accessed 2019-07-26. - 15Brinckman, A, et al. 2019. Computing environments for reproducibility: Capturing the “whole tale”. Future Generation Computer Systems, 94: 854–867. DOI: 10.1016/j.future.2017.12.029
- 16Buneman, P, Davidson, S and Frew, J. 2016. Why data citation is a computational problem. Commun ACM, 59: 50–57. DOI: 10.1145/2893181
- 17Burton, A, et al. 2017. The Scholix framework for interoperability in data-literature information exchange. D-Lib Magazine, 23. DOI: 10.1045/january2017-burton
- 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. - 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
- 20Cooke, R and Golub, A. 2019. Market-based methods for monetizing uncertainty reduction: A case study. Working Paper, 19–15.
https://media.rff.org/documents/WP_Cooke_Golub_4.pdf accessed 2019-07-28. - 21Costello, MJ. 2009. Motivating online publication of data. Bioscience, 59: 418–427. DOI: 10.1525/bio.2009.59.5.9
- 22Cousijn, H, et al. 2018. A data citation roadmap for scientific publishers. Sci Data, 5:
180259 . DOI: 10.1038/sdata.2018.259 - 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
- 24Crosas, M. 2014. The evolution of data citation: From principles to implementation. IASSIST Quarterly, 37: 62. DOI: 10.29173/iq504
- 25DCSG – Data Citation Synthesis Group. 2014. Joint Declaration of Data Citation Principles. DOI: 10.25490/a97f-egyk
- 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
- 27Donovan, C and Hanney, S. 2011. The payback framework explained. Research Evaluation, 20: 181–183. DOI: 10.3152/095820211X13118583635756
- 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
- 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
- 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
- 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
- 32Fenner, M, et al. 2018. Code of practice for research data usage metrics release 1. DOI: 10.7287/peerj.preprints.26505v1
- 33Guralnick, RP, et al. 2015. Community next steps for making globally unique identifiers work for biocollections data. Zookeys, 133–154. DOI: 10.3897/zookeys.494.9352
- 34Haak, LL, et al. 2012. Orcid: A system to uniquely identify researchers. Learned Publishing, 25: 259–264. DOI: 10.1087/20120404
- 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, 277–296. The MIT Press. DOI: 10.7551/mitpress/9780262151207.003.0016 - 36Hayes, PJ and Halpin, H. 2008. In defense of ambiguity. International Journal on Semantic Web and Information Systems, 4: 1–18. DOI: 10.4018/jswis.2008040101
- 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: 2137–2155. DOI: 10.1002/asi.23538
- 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
- 39Kahn, R and Wilensky, R. 1995. A framework for distributed digital object services.
http://handle.net/cnri.dlib/tn95-01 accessed 2019-07-20. - 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. - 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
- 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. - 43Klump, J, et al. 2006. Data publication in the open access initiative. Data Science Journal, 5: 79–83. DOI: 10.2481/dsj.5.79
- 44Klump, J, Huber, R and Diepenbroek, M. 2015. DOI for geoscience data-how early practices shape present perceptions. Earth Science Informatics, 1–14. DOI: 10.1007/s12145-015-0231-5
- 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
- 46Kratz, JE and Strasser, C. 2015a. Comment: Making data count. Sci Data, 2:
150039 . DOI: 10.1038/sdata.2015.39 - 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 - 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
- 49Lawrence, S, et al. 2001. Persistence of web references in scientific research. Computer, 34: 26–31. DOI: 10.1109/2.901164
- 50Ma, X, et al. 2017. Weaving a knowledge network for deep carbon science. Frontiers in Earth Science, 5. DOI: 10.3389/feart.2017.00036
- 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
- 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 - 53Mooney, H and Newton, MP. 2012. The anatomy of a data citation: Discovery, reuse, and credit. Journal of Librarianship & Scholarly Communication, 1: 1–16.
https://jlsc-pub.org/articles/abstract/10.7710/2162-3309.1035/ accessed 2017-10-03. DOI: 10.7710/2162-3309.1035 - 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. - 55Parsons, MA, et al. 2008. Managing permafrost data: Past approaches and future directions. Permafrost Ninth International Conference
29 June–3 July 2008 Proceedings, 1369–1374. DOI: 10.5281/zenodo.3519368 - 56Parsons, MA and Fox, PA. 2013. Is data publication the right metaphor? Data Science Journal, 12. DOI: 10.2481/dsj.WDS-042
- 57Parsons, MA and Fox, PA. 2018. Power and persistent identifiers. International Data Week 2018. DOI: 10.5281/zenodo.1495321
- 58Paskin, N. 2000. E-citations: Actionable identifiers and scholarly referencing. Learned Publishing, 13: 159–166. DOI: 10.1087/09531510050145308
- 59Peters, I, et al. 2016. Research data explored: An extended analysis of citations and altmetrics. Scientometrics, 107: 723–744. DOI: 10.1007/s11192-016-1887-4
- 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
- 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 - 62Silvello, G. 2018. Theory and practice of data citation. Journal of the Association for Information Science and Technology, 69: 6–20. DOI: 10.1002/asi.23917
- 63Smith, AM, Katz, DS, Niemeyer, KE, FORCE11 and SCWG. 2016. Software citation principles. PeerJ Computer Science, 2: e86. DOI: 10.7717/peerj-cs.86
- 64Stall, S, et al. 2018. Advancing FAIR data in Earth, space, and environmental science. Eos, 99. DOI: 10.1029/2018EO109301
- 65Stockhause, M and Lautenschlager, M. 2017. CMIP6 data citation of evolving data. Data Science Journal, 16. DOI: 10.5334/dsj-2017-030
- 66Stuart, D. 2017. Data bibliometrics: Metrics before norms. Online Information Review, 41: 428–435. DOI: 10.1108/OIR-01-2017-0008
- 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: CIDCR1–CIDCR75. DOI: 10.2481/dsj.OSOM13-043
- 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 - 69Wittenburg, P, Hellström, M, Zwölf, CM, Abroshan, H, et al. (eds.) 2017. Persistent identifiers: Consolidated assertions. Research Data Alliance. DOI: 10.15497/RDA00027
