References
- 1Cai, L and Zhu, Y. 2015. The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 4(0): 2. DOI: 10.5334/dsj-2015-002
- 2Callaghan, S. 2023. Two journals and a pandemic: Reflections on being a data science editor-in-chief. Data Science Journal, 22(1): 14. DOI: 10.5334/dsj-2023-014
- 3Carroll, S R, Garba, I, Figueroa-Rodríguez, O L, Holbrook, J, Lovett, R, Materechera, S, et al. 2020. The CARE Principles for Indigenous Data Governance. Data Science Journal, 19(1): 43. DOI: 10.5334/dsj-2020-043
- 4Hey, T, Tansley, S and Tolle, K. (eds.) 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research.
- 5Mayernik, M S. 2023. Data science as an interdiscipline: Historical parallels from information science. Data Science Journal, 22:
16 , 1–18. DOI: 10.5334/dsj-2023-016 - 6Poirier, L. 2023. Attending to the cultures of data science work. Data Science Journal, 22: 6, 1–7. DOI: 10.5334/dsj-2023-006
- 7Rumble, J. 2023. Thoughts on starting the CODATA Data Science Journal. Data Science Journal, 22(1): 13. DOI: 10.5334/dsj-2023-013
- 8Smith, F J. 2023. The launch of the Data Science Journal in 2002. Data Science Journal, 22(1): 11. DOI: 10.5334/dsj-2023-11
- 9Zhang, L. 2023. Looking back to the future: A glimpse at twenty years of data science. Data Science Journal, 22(1): 7. DOI: 10.5334/dsj-2023-007