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Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets Cover

Call to Action for Global Access to and Harmonization of Quality Information of Individual Earth Science Datasets

Data Science Journal
Volume 20 (2021): Issue 1
By: Ge Peng,  Robert R. Downs,  Carlo Lacagnina,  Hampapuram Ramapriyan,  Ivana Ivánová,  David Moroni,  Yaxing Wei,  Gilles Larnicol,  Lesley Wyborn,  Mitch Goldberg,  Jörg Schulz,  Irina Bastrakova,  Anette Ganske,  Lucy Bastin,  Siri Jodha S. Khalsa,  Mingfang Wu,  Chung-Lin Shie,  Nancy Ritchey,  Dave Jones,  Ted Habermann,  Christina Lief,  Iolanda Maggio,  Mirko Albani,  Shelley Stall,  Lihang Zhou,  Marie Drévillon,  Sarah Champion,  C. Sophie Hou,  Francisco Doblas-Reyes,  Kerstin Lehnert,  Erin Robinson and  Kaylin Bugbee  
Open Access
|May 2021

References

  1. 1Australia FAIR Access Working Group. 2017. Policy Statement on FAIR Access to Australia’s Research Outputs. Version: Jan 2017. Available at: https://www.fair-access.net.au/fair-statement.
    Back to article
  2. 2Baker, KS, Duerr, RE and Parsons, MA. 2016. Scientific Knowledge Mobilization: Co-evolution of Data Products and Designated Communities. International Journal of Digital Curation, 10(2): 110–135. DOI: 10.2218/ijdc.v10i2.346
    Back to article
  3. 3Barsi, Á, Kugler, Z, Juhász, A, Szabó, G, Batini, C, Abdulmuttalib, H, Huang, G and Shen, H. 2019. Remote sensing data quality model: from data sources to lifecycle phases. International Journal of Image and Data Fusion, 10(4): 280–99. DOI: 10.1080/19479832.2019.1625977
    Back to article
  4. 4Borda, A, Gray, K and Fu, Y. 2020. Research data management in health and biomedical citizen science: practices and prospects. JAMIA Open, 3(1): 113–25. DOI: 10.1093/jamiaopen/ooz052
    Back to article
  5. 5Breck, E, Polyzotis, N, Roy, S, Whang, SE and Zinkevich, M. 2019. Data Validation For Machine Learning. Proceedings of the 2nd SysML Conference, Palo Alto, CA, USA.
    Back to article
  6. 6Callahan, T, Barnard, J, Helmkamp, L, Maertens, J, Kahn, M. 2017. Reporting data quality assessment results: identifying individual and organizational barriers and solutions. eGEMs, 5(1). DOI: 10.5334/egems.214
    Back to article
  7. 7Canali, S. 2020. Towards a Contextual Approach to Data Quality. Data. 5(4): 90. DOI: 10.3390/data5040090
    Back to article
  8. 8Digital Science, Fane, B, Ayris, P, Hahnel, M, Hrynaszkiewicz, G, Baynes, G and others. 2019. The State of Open Data Report 2019. Digital Science. Report. DOI: 10.6084/m9.figshare.9980783
    Back to article
  9. 9CODATA. 2019. The Beijing Declaration on Research Data. Version: 7 November 2019. Available at: http://www.codata.org/uploads/Beijing%20Declaration-19-11-07-FINAL.pdf.
    Back to article
  10. 10Coetzee, S. 2018. Implementing Geospatial Data Quality Standards – Motivators and Barriers, 2nd International Workshop on Spatial Data Quality, Valletta, Malta 6–7 February 2018, https://eurogeographics.org/wp-content/uploads/2018/06/4-SDQ2018_Coetzee_V1e.pdf.
    Back to article
  11. 11European Commission. 2018. Turning FAIR into reality – Final Report and Action Plan from the European Commission Expert Group on FAIR data, European Commission: Brussels. DOI: 10.2777/1524
    Back to article
  12. 12European Commission. 2020. Recommendations on FAIR Metrics for EOSC, European Commission: Brussels. DOI: 10.2777/70791
    Back to article
  13. 13European Commission and PwC EU Services. 2018. Cost-benefit analysis for FAIR research data: Cost of not having FAIR research data. Version: March 2018. Available at: https://op.europa.eu/en/publication-detail/-/publication/d375368c-1a0a-11e9-8d04-01aa75ed71a1/language-en.
    Back to article
  14. 14Eyring, V, Bony, S, Meehl, GA, Senior, CA, Stevens, B, Stouffer, RJ and Taylor, KE. 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) Experimental Design and Organization. Geoscientific Model Development, 9(5): 1937–1958. DOI: 10.5194/gmd-9-1937-2016
    Back to article
  15. 15Ferraro, R, Waliser, DE, Gleckler, P, Taylor, KE and Eyring, V. 2015. Evolving Obs4MIPs to Support Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Bull. Amer. Meteor. Soc., 96: ES131–ES133. DOI: 10.1175/BAMS-D-14-00216.1
    Back to article
  16. 16G20 Leaders. 2016. G20 Leaders’ Communique Hangzhou Summit. Version: 5 September 2016. Available at: https://ec.europa.eu/commission/presscorner/detail/en/STATEMENT_16_2967.
    Back to article
  17. 17Illari, P. 2014. IQ: Purpose and Dimensions. In The Philosophy of Information Quality; Floridi, L, Illari, P, Eds.; Springer: Berlin, Germany pp. 281–302. DOI: 10.1007/978-3-319-07121-3_14
    Back to article
  18. 18ISO 19115-1. 2014. Geographic Information—Metadata – Part 1: Fundamentals. Version: 2014–04. International Organization for Standardization. Geneva, Switzerland. Available at: https://www.iso.org/standard/53798.html.
    Back to article
  19. 19ISO 19157. 2013. Geographic information—Data quality. Version: 2013–1. International Organization for Standardization. Geneva, Switzerland. Available at: https://www.iso.org/standard/32575.html.
    Back to article
  20. 20Kahn, MG, Brown, JS, Chun, AT, Davidson, BN, Meeker, D, Ryan, PB, Schilling, LM, Weiskopf, NG, Williams, AE and Zozus, MN. 2015. Transparent reporting of data quality in distributed data networks. Egems, 3(1). DOI: 10.13063/2327-9214.1052
    Back to article
  21. 21Lacagnina, C, Peng, G, Downs, RR, Ramapriyan, H, Ivanova, I, Moroni, DF, Larnicol, G, Wei, Y, Bastin, L, Ritchey, NA, Wyborn, LA, Shie, C-L, Habermann, T, Ganske, A, Champion, SM, Wu, M, Bastrakova, I, Jones, D and Berg-Cross, G. 2021. Towards Developing Community Guidelines for Sharing and Reusing Quality Information of Earth Science Datasets. EGU General Assembly 2021, Virtual, 19–30 April 2021, EGU21–23. DOI: 10.5194/egusphere-egu21-23
    Back to article
  22. 22Lee, YW, Strong, DM, Khan, BK and Wang, RY. 2002. AIMQ: a methodology for information quality assessment, Information & Management, 40: 133–146. DOI: 10.1016/S0378-7206(02)00043-5
    Back to article
  23. 23Leonelli, S. 2017. Global Data Quality Assessment and the Situated Nature of “Best” Research Practices in Biology. Data Science Journal, 16: 32. DOI: 10.5334/dsj-2017-032
    Back to article
  24. 24High-Level Expert Group on Artificial Intelligence. 2018. Ethics guidelines for trustworthy AI. FUTURIUM – European Commission. Version: December 17, 2018. Available at: https://ec.europa.eu/futurium/en/ai-alliance-consultation.
    Back to article
  25. 25Lenhardt, W, Ahalt, S, Blanton, B, Christopherson, L and Idaszak, R. 2014. Data management lifecycle and software lifecycle management in the context of conducting science. Journal of Open Research Software, 2(1). DOI: 10.5334/jors.ax
    Back to article
  26. 26Maskey, M, Alemohammad, H, Murphy, KJ and Ramachandran, R. 2020. Advancing AI for Earth Science: A Data Systems Perspective. EOS, 101. DOI: 10.1029/2020EO151245
    Back to article
  27. 27Mons, B. 2018. Data Stewardship for open science: implementing FAIR principles. 1st Edition. Chapman and Hall/CRC Press, Taylor & Francis, New York. 244 pp. Available at: https://www.taylorfrancis.com/books/9781315380711. DOI: 10.1201/9781315380711-1
    Back to article
  28. 28Moroni, DF, Ramapriyan, H, Peng, G, Hobbs, J, Goldstein, JC, Downs, RR, Wolfe, R, Shie, C-L, Merchant, CJ, Bourassa, M, Matthews, JL, Cornillon, P, Bastin, L, Kehoe, K, Smith, B, Privette, JL, Subramanian, AC, Brown, O and Ivánová, I. 2019. Understanding the Various Perspectives of Earth Science Observational Data Uncertainty. Figshare. DOI: 10.6084/m9.figshare.10271450
    Back to article
  29. 29Peng, G. 2018. The state of assessing data stewardship maturity – an overview. Data Science Journal, 17. DOI: 10.5334/dsj-2018-007
    Back to article
  30. 30Peng, G, Lacagnina, C, Downs, RR, Ivanova, I, Moroni, DF, Ramapriyan, H, Wei, Y and Larnicol, G. 2020a. Laying the Groundwork for Developing International Community Guidelines to Effectively Share and Reuse Digital Data Quality Information – Case Statement, Workshop Summary Report, and Path Forward. Open Science Framework. DOI: 10.31219/osf.io/75b92
    Back to article
  31. 31Peng, G, Lacagnina, C, Downs, RR, Ramapriyan, H, Ivanova, I, Moroni, DF, Larnicol, G, Wei, Y, Bastin, L, Ritchey, NA, Wyborn, LA, Shie, C-L, Habermann, T, Ganske, A, Champion, SM, Wu, M, Bastrakova, I, Jones, D and Berg-Cross, G. 2020b. Towards Developing Community Guidelines for Sharing and Reuse of Digital Data Quality Information. AGU 2020 Fall Meeting. Abstract 674372. Available at: https://agu.confex.com/agu/fm20/meetingapp.cgi/Paper/674372.
    Back to article
  32. 32Peng, G, Lacagnina, C, Downs, RR, Ramapriyan, H, Ivanova, I, Moroni, DF, Larnicol, G, Wei, Y, Bastin, L, Ritchey, NA, Wyborn, LA, Shie, C-L, Habermann, T, Ganske, A, Champion, SM, Wu, M, Bastrakova, I, Jones, D, Hou, C-Y and Berg-Cross, G. 2021. An update on a community effort to promote global sharing of dataset quality information. ESIP 2021 Winter Meeting. Virtual.
    Back to article
  33. 33Press, G. 2016. Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says. Forbes. Version: March 23, 2016. Available at: https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/?sh=1ee368c06f63.
    Back to article
  34. 34Ramapriyan, H, Peng, G, Moroni, D and Shie, C-L. 2017. Ensuring and Improving Information Quality for Earth Science Data and Products. D-Lib Magazine, 23. DOI: 10.1045/july2017-ramapriyan
    Back to article
  35. 35Redman, CT. 1996. Data quality of the information age. Artech House, Boston. 303 pp.
    Back to article
  36. 36Shen, Y and Sanghavi, S. 2019. Learning with Bad Training Data via Iterative Trimmed Loss Minimization. Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, USA.
    Back to article
  37. 37Stockhause, M, Höck, H, Toussaint, F and Lautenschlager, M. 2012. Quality assessment concept of the World Data Center for Climate and its application to CMIP5 data. Geosci. Model Dev. 5. DOI: 10.5194/gmd-5-1023-2012
    Back to article
  38. 38Stockhause, M and Lautenschlager, M. 2017. CMIP6 Data Citation of Evolving Data. Data Science Journal, 16. DOI: 10.5334/dsj-2017-030
    Back to article
  39. 39Taylor, KE, Stouffer, RJ and Meehl, GA. 2012. An Overview of CMIP5 and the Experiment Design. Bulletin of the American Meteorological Society, 93(4): 485–498. DOI: 10.1175/BAMS-D-11-00094.1
    Back to article
  40. 40Tilmes, C, Privette, AP, Chen, J, Ramachandran, R, Bugbee, KM and Wolfe, RE. 2015a. Linking from observations to data to actionable science in the climate data initiative. Proc. 2015 IEEE Geosci. and Remote Sensing Symposium, 26–31 July 2015, Milan, Italy. DOI: 10.1109/IGARSS.2015.7326027
    Back to article
  41. 41Tilmes, C, Wolfe, RE, Duggan, B, Aulenbach, S, Goldstein, JC, Ma, X and Zednik, S. 2015b. Supporting trust with provenance of the findings of the national climate assessment. METHOD 2015: The 4th Intl. Workshop on Methods for Establishing Trust of (Open) Data. 11 Oct. 2015, Bethlehem, PA, USA. [Available at: http://www.few.vu.nl/~dceolin/method2015/papers/METHOD_2015_paper_2.pdf].
    Back to article
  42. 42UN-GGIM. 2018. Integrated Geospatial Information Framework Part 1. United Nations Committee of Experts for Global Geospatial Information Management. Available at: https://ggim.un.org/IGIF/part1.cshtml.
    Back to article
  43. 43UN-GGIM. 2019. Integrated Geospatial Information Framework Part 2. United Nations Committee of Experts for Global Geospatial Information Management. Available at: https://ggim.un.org/IGIF/part2.cshtml.
    Back to article
  44. 44U.S. Public Law 115-435. 2019. Foundations for Evidence-Based Policymaking Act of 2018. Title II OPEN Government Data Act. Version: 14 January 2019. 115th U.S. Congress. Available at: https://www.govinfo.gov/content/pkg/PLAW-115publ435/pdf/PLAW-115publ435.pdf.
    Back to article
  45. 45Wang, RY and Strong, DM. 1996. Beyond Accuracy: What Data Quality Means to Consumers. Journal of Management Information Systems, 12(4). DOI: 10.1080/07421222.1996.11518099
    Back to article
  46. 46Wilkinson, MD, Dumontier, M, Aalbersberg, IJ, Appleton, G, Axton, M, Baak, A and others. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3: 160018. DOI: 10.1038/sdata.2016.18
    Back to article
  47. 47WMO. 1986. Guidelines On The Quality Control Of Surface Climatological Data. WMO/TD-No. 111. Geneva, Switzerland: World Meteorological Organization. Available at: https://library.wmo.int/doc_num.php?explnum_id=9205.
    Back to article
  48. 48WMO. 1991. Resolution 40 (Cg-XII) – WMO policy and practice for the exchange of meteorological and related data and products including guidelines on relationships in commercial meteorological activities. WMO-No. 827. Geneva, Switzerland: World Meteorological Organization. Available at: https://www.wmo.int/pages/prog/hwrp/documents/wmo_827_enCG-XII-Res40.pdf.
    Back to article
  49. 49WMO. 1999. Resolution 25 (Cg-XIII) – Exchange of Hydrological Data and Products. Geneva, Switzerland: World Meteorological Organization. Available at: https://www.wmo.int/pages/prog/hwrp/documents/Resolution_25.pdf.
    Back to article
  50. 50WMO. 2004. Guidelines on Quality Control Procedures for Data from Automatic Weather Stations. Expert Team on Surface Technology and Measurement Techniques, Geneva, Switzerland: World Meteorological Organization. Available at: https://www.wmo.int/pages/prog/www/IMOP/meetings/Surface/ET-STMT1_Geneva2004/Doc6.1(2).pdf.
    Back to article
  51. 51WMO. 2015. Resolution 60 (Cg-17) – WMO Policy for the International Exchange of Climate Data and Products to Support the Implementation of the Global Framework for Climate Services. Geneva, Switzerland: World Meteorological Organization. Available at: https://library.wmo.int/doc_num.php?explnum_id=4192.
    Back to article
  52. 52WMO. 2019a. Origin, impact and aftermath of WMO resolution 40. WMO-no 1244. Geneva, Switzerland: World Meteorological Organization. Available at: https://library.wmo.int/doc_num.php?explnum_id=10140.
    Back to article
  53. 53WMO. 2019b. WMO Guidelines on Surface Station Data Quality Assurance for Climate Applications. Draft: April 5, 2019. Geneva, Switzerland: World Meteorological Organization. Available at: https://www.wmo.int/pages/prog/wcp/wcdmp/hq-gdmfc/documents/QC_QAguidelines-April2019.pdf.
    Back to article
  54. 54WMO. 2019c. Manual on the high-quality global data management framework for climate. WMO-No. 1238. Geneva, Switzerland: World Meteorological Organization. 43 pp. Available at: https://library.wmo.int/doc_num.php?explnum_id=10197.
    Back to article
  55. 55WMO. 2019d. WMO data policy statement. Draft 1.0. Study Group on Data Issues and Policies. WMO Data Conference. 16–19 November 2020, Virtual. Available at: https://meetings.wmo.int/WMO-Data-Conference/Documents/Flyer%20for%20Res.42%2011%2015.pdf.
    Back to article
  56. 56W3C (World Wide Web Consortium). 2020. Data Catalog Vocabulary (DCAT), Version 2. Available at: https://www.w3.org/TR/vocab-dcat-2/#Class:Dataset.
    Back to article
DOI: https://doi.org/10.5334/dsj-2021-019 | Journal eISSN: 1683-1470
Journal RSS Feed
Language: English
Submitted on: Dec 14, 2020
Accepted on: Apr 18, 2021
Published on: May 4, 2021
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Data Quality,
Quality Dimension,
Earth Science Information,
Interoperability,
FAIR,
Stewardship

© 2021 Ge Peng, Robert R. Downs, Carlo Lacagnina, Hampapuram Ramapriyan, Ivana Ivánová, David Moroni, Yaxing Wei, Gilles Larnicol, Lesley Wyborn, Mitch Goldberg, Jörg Schulz, Irina Bastrakova, Anette Ganske, Lucy Bastin, Siri Jodha S. Khalsa, Mingfang Wu, Chung-Lin Shie, Nancy Ritchey, Dave Jones, Ted Habermann, Christina Lief, Iolanda Maggio, Mirko Albani, Shelley Stall, Lihang Zhou, Marie Drévillon, Sarah Champion, C. Sophie Hou, Francisco Doblas-Reyes, Kerstin Lehnert, Erin Robinson, Kaylin Bugbee, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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