{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T07:49:19Z","timestamp":1774338559642,"version":"3.50.1"},"reference-count":58,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T00:00:00Z","timestamp":1650499200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["EL"],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to provide a process-driven scientific data quality (DQ) monitoring framework by information product map (IP-Map) in identifying the root causes of poor DQ issues so as to assure the quality of scientific data.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>First, a general scientific data life cycle model is constructed based on eight classical models and 37 researchers\u2019 experience. Then, the IP-Map is constructed to visualize the scientific data manufacturing process. After that, the potential deficiencies that may arise and DQ issues are examined from the aspects of process and data stakeholders. Finally, the corresponding strategies for improving scientific DQ are put forward.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The scientific data manufacturing process and data stakeholders\u2019 responsibilities could be clearly visualized by the IP-Map. The proposed process-driven framework is helpful in clarifying the root causes of DQ vulnerabilities in scientific data.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title>\n<jats:p>As for the implications for researchers, the process-driven framework proposed in this paper provides a better understanding of scientific DQ issues during implementing a research project as well as providing a useful method to analyse those DQ issues based on IP-Map approach from the aspects of process and data stakeholders.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>The process-driven framework is beneficial for the research institutions, scientific data management centres and researchers to better manage the scientific data manufacturing process and solve the scientific DQ issues.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This research proposes a general scientific data life cycle model and further provides a process-driven scientific DQ monitoring framework for identifying the root causes of poor data issues from the aspects of process and stakeholders which have been ignored by existing information technology-driven solutions. This study is likely to lead to an improved approach to assuring the scientific DQ and is applicable in different research fields.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/el-08-2021-0157","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T01:11:22Z","timestamp":1650417082000},"page":"177-195","source":"Crossref","is-referenced-by-count":4,"title":["Process-driven quality improvement for scientific data based on information product map"],"prefix":"10.1108","volume":"40","author":[{"given":"Wei","family":"Zong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songtao","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxing","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanying","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2022,4,21]]},"reference":[{"issue":"1","key":"key2022051209585907200_ref001","first-page":"12","article-title":"Data quality measures and data cleansing for research information systems","volume":"16","year":"2018","journal-title":"Journal of Digital Information Management"},{"key":"key2022051209585907200_ref002","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ijinfomgt.2018.02.007","article-title":"Analyzing data quality issues in research information systems via data profiling","volume":"41","year":"2018","journal-title":"International Journal of Information Management"},{"issue":"3","key":"key2022051209585907200_ref003","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1007\/s11192-018-2735-5","article-title":"Data measurement in research information systems: Metrics for the evaluation of data quality","volume":"115","year":"2018","journal-title":"Scientometrics"},{"key":"key2022051209585907200_ref004","first-page":"29","article-title":"Text data mining and data quality management for research information systems in the context of open data and open science","year":"2018","journal-title":"Third International Colloquium on Open Access"},{"key":"key2022051209585907200_ref005","article-title":"Data and information quality: Dimensions","volume-title":"Principles and Techniques","year":"2016"},{"issue":"3","key":"key2022051209585907200_ref006","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.compind.2014.01.009","article-title":"Collaborative simulation and scientific big data analysis: Illustration for sustainability in natural hazards management and chemical process engineering","volume":"65","year":"2014","journal-title":"Computers in Industry"},{"issue":"17","key":"key2022051209585907200_ref007","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1056\/NEJMsb1616595","article-title":"Data authorship as an incentive to data sharing","volume":"376","year":"2017","journal-title":"New England Journal of Medicine"},{"key":"key2022051209585907200_ref008","volume-title":"Big Data, Little Data, No Data: Scholarship in the Networked World","year":"2015"},{"issue":"2","key":"key2022051209585907200_ref009","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3141248","article-title":"Validating data quality actions in scoring processes","volume":"9","year":"2018","journal-title":"Journal of Data and Information Quality"},{"key":"key2022051209585907200_ref010","unstructured":"Committee on Earth Observation Satellites (CEOS) (2021), \u201cData life cycle models and concepts\u201d, available at: http:\/\/ceos.org\/ourwork\/workinggroups\/wgiss\/documents\/ (accessed 18 March 2021)."},{"issue":"1","key":"key2022051209585907200_ref011","first-page":"103","article-title":"Leveraging internet of things and big data analytics initiatives in European and American firms: is data quality a way to extract business value?","volume":"57","year":"2020","journal-title":"Information and Management"},{"key":"key2022051209585907200_ref012","first-page":"1","article-title":"Enabling scientific data sharing and re-use","volume-title":"IEEE 8th International Conference on E-Science","year":"2012"},{"issue":"1","key":"key2022051209585907200_ref013","doi-asserted-by":"crossref","first-page":"25","DOI":"10.31937\/ijnmt.v4i1.534","article-title":"Designing information product (IP) maps on the process of data processing and academic information","volume":"4","year":"2017","journal-title":"International Journal of New Media Technology"},{"key":"key2022051209585907200_ref014","doi-asserted-by":"crossref","unstructured":"Faundeen, J.L., Burley, T.E., Carlino, J., Govoni, D.L., Henkel, H.S., Holl, S., Hutchison, V.B., Mart\u00edn, E., Montgomery, E.T., Ladino, C.C. and Tessler, S. 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