IQ Papers

From Earth Science Information Partners (ESIP)

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:
  • 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.
  • 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.
  • 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:
  • 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:
  • 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:
  • 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:
  • 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:
  • Schneider D P et al. 2013, Climate Data Guide Spurs Discovery and Understanding, Eos Trans. AGU, 94(13):121. DOI:
  • 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:
  • 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:
  • 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:
  • 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:
  • 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:
  • 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:
  • 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
  • Lee, Y W et al. 2002: AIMQ: a methodology for information quality assessment, Information & Management, 40, 133-146. DOI:
  • Miller, H. 1996, The Multiple Dimensions of Information Quality. Information Systems Management. 13(2):79. doi:
  • 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:


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