IQ Papers

This section lists selected papers from the literature - published by IQC members as well as others addressing information quality. They are shown in reverse chronological order.

Peng, G., Downs, R.R., Lacagnina, C., Ramapriyan, H., Ivánová, I., Moroni, D., Wei, Y., Larnicol, G., Wyborn, L., Goldberg, M., Schulz, J., Bastrakova, I., Ganske, A., Bastin, L., Khalsa, S.J.S., Wu, M., Shie, C.-L., Ritchey, N., Jones, D., Habermann, T., Lief, C., Maggio, I., Albani, M., Stall, S., Zhou, L., Drévillon, M., Champion, S., Hou, C.S., Doblas-Reyes, F., Lehnert, K., Robinson, E. and Bugbee, K., 2021. Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets. Data Science Journal, 20(1), p.19. DOI: http://doi.org/10.5334/dsj-2021-019
Downs, R.R., Ramapriyan, H.K., Peng, G., Wei, Y., 2021. Perspectives on Citizen Science Data Quality. Perspective, Frontiers in Climate, Section on Climate Risk Management, Special Issue: Open Citizen Science Data and Methods. 3:615032. https://doi.org/10.3389/fclim.2021.615032
Moroni, D.F., Ramapriyan, H., Peng, G., Hobbs, J., Goldstein, J., Downs, R., et al. 2019. Understanding the Various Perspectives of Earth Science Observational Data Uncertainty. ESIP. Report. https://doi.org/10.6084/m9.figshare.10271450.v1
Ramapriyan, H K, Peng G, Moroni D, Shie C-L, Ensuring and Improving Information Quality for Earth Science Data and Products. D-Lib Magazine, 23 (7/8), July/August 2017, DOI:https://doi.org/10.1045/july2017-ramapriyan
Labouseur, A G and Matheus, C C 2017 An introduction to dynamic data quality challenges. ACM Journal of Data and Information Quality (JDIQ), 8(2), January 2017. DOI:http://dx.doi.org/10.1145/2998575
Shankaranarayanan G and Blake R 2017 From content to context: The evolution and growth of data quality research. Journal of Data and Information Quality (JDIQ) 8(2), January 2017. DOI: http://doi.org/10.1145/2996198
Neumaier S, Umbrich J and Polleres S 2016 Automated quality assessment of metadata across open data portals. ACM Journal of Data and Information Quality (JDIQ), 8(1), October 2016, DOI: http://doi.org/10.1145/2964909.
Hampapuram Ramapriyan, Ge Peng, David Moroni, Chung-Lin Shie, Ensuring and Improving Information Quality for Earth Science Data and Products – Role of the ESIP Information Quality Cluster, SciDataCon 2016, Denver, CO, September 11-13, 2016. File:SciDataCon 2016 on ESIP Information Quality Cluster.docx
Peng, G., Ritchey, N. A., Casey, K. S., Kearns, E. J., Privette, J. L., Saunders, D., Jones, P., Maycock, T., & Ansari, S. (2016). Scientific stewardship in the open data and big data era — Roles and responsibilities of stewards and other major product stakeholders. D-Lib Magazine, 22(5/6). doi:10.1045/may2016-peng
Peng G et al. 2015 A unified framework for measuring stewardship practices applied to digital environmental datasets, Data Science Journal, 13:231, DOI: http://doi.org/10.2481/dsj.14-049.
Schneider D P et al. 2013, Climate Data Guide Spurs Discovery and Understanding, Eos Trans. AGU, 94(13):121. DOI: http://doi.org/10.1002/2013EO130001
Bates, J J and Privette, J L 2012 A maturity model for assessing the completeness of climate data records, EOS, Transactions of the AGU, 93 (44):441, DOI:http://doi.org/10.1029/2012EO440006
Embury, Suzanne M, Paolo Missier, Sandra Sampaio, and R Mark Greenwood. “Incorporating Domain-Specific Information Quality Constraints into Database Queries.” Journal of Data and Information Quality (JDIQ) 1, no. 2 (2009): 1-31. DOI:http://doi.org/10.1145/1577840.1577846.
Klein, A and Lehner, W 2009 Representing Data Quality in Sensor Data Streaming Environments, ACM Journal of Data and Information Quality (JDIQ), 1(2), September 2009. DOI:http://doi.org/10.1145/1577840.1577845.
Li X 2009 A Bayesian Approach for Estimating and Replacing Missing Categorical Data, ACM Journal of Data and Information Quality (JDIQ), 1(1), June 2009. DOI: http://dx.doi.org/10.1145/1515693.1515695.
Madnick S E et al. 2009 Overview and framework for data and information quality research. ACM Journal of Data and Information Quality (JDIQ), 8(1), June 2009. DOI: http://dx.doi.org/10.1145/1515693.1516680.
Weber K, Otto B and Osterle H et al. 2009. One Size Does Not Fit All---A Contingency Approach to Data Governance, Journal of Data and Information Quality (JDIQ), 1(1), June 2009. DOI: http://doi.org/10.1145/1515693.1515696
Preece, A., Missier, P., Embury, S., Jin, B., and Greenwood, M. (2008). An ontology-based approach to handling information quality in e-Science. Concurrency and Computation: Practice and Experience 20, 3, 253-264. DOI=10.1002/cpe.v20:3 http://dx.doi.org/10.1002/cpe.v20:3
Lee, Y W et al. 2002: AIMQ: a methodology for information quality assessment, Information & Management, 40, 133-146. DOI:http://doi.org/10.1016/S0378-7206(02)00043-5
Miller, H. 1996, The Multiple Dimensions of Information Quality. Information Systems Management. 13(2):79. doi: http://dx.doi.org/10.1080/10580539608906992
Wang R Y and Strong D M 1996 Beyond accuracy: What data quality means to consumers. Journal of Management Information Systems 12(4):5. DOI: http://doi.org/10.1080/07421222.1996.11518099

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