{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T19:39:50Z","timestamp":1761766790964,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T00:00:00Z","timestamp":1602979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Databases such as research data management systems (RDMS) provide the research data in which information is to be searched for. They provide techniques with which even large amounts of data can be evaluated efficiently. This includes the management of research data and the optimization of access to this data, especially if it cannot be fully loaded into the main memory. They also provide methods for grouping and sorting and optimize requests that are made to them so that they can be processed efficiently even when accessing large amounts of data. Research data offer one thing above all: the opportunity to generate valuable knowledge. The quality of research data is of primary importance for this. Only flawless research data can deliver reliable, beneficial results and enable sound decision-making. Correct, complete and up-to-date research data are therefore essential for successful operational processes. Wrong decisions and inefficiencies in day-to-day operations are only the tip of the iceberg, since the problems with poor data quality span various areas and weaken entire university processes. Therefore, this paper addresses the problems of data quality in the context of RDMS and tries to shed light on the solution for ensuring data quality and to show a way to fix the dirty research data that arise during its integration before it has a negative impact on business success.<\/jats:p>","DOI":"10.3390\/bdcc4040029","type":"journal-article","created":{"date-parts":[[2020,10,18]],"date-time":"2020-10-18T21:26:06Z","timestamp":1603056366000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Treatment of Bad Big Data in Research Data Management (RDM) Systems"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5225-389X","authenticated-orcid":false,"given":"Otmane","family":"Azeroual","sequence":"first","affiliation":[{"name":"German Center for Higher Education Research and Science Studies (DZHW), Sch\u00fctzenstra\u00dfe 6a, 10117 Berlin, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"154","DOI":"10.3163\/1536-5050.103.3.011","article-title":"Research data management","volume":"103","author":"Surkis","year":"2015","journal-title":"J. Med. Libr. Assoc."},{"key":"ref_2","first-page":"1","article-title":"Research Data Management","volume":"62","author":"Heuer","year":"2020","journal-title":"It-Inf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.procs.2014.10.023","article-title":"Research Data Management in the curriculum: An interdisciplinary Approach","volume":"38","author":"Tammaro","year":"2014","journal-title":"Procedia Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10209-016-0475-y","article-title":"A comparison of research data management platforms: Architecture, flexible metadata and interoperability","volume":"16","author":"Amorim","year":"2017","journal-title":"Univ. Access Inf. Soc."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Azeroual, O. (2020). Data Wrangling in Database Systems: Purging of Dirty Data. Data, 5.","DOI":"10.3390\/data5020050"},{"key":"ref_6","unstructured":"Batini, C., Barone, D., Mastrella, M., Maurino, A., and Ruffini, C. (2007, January 9\u201311). A Framework and {A} Methodology for Data Quality Assessment and Monitoring. Proceedings of the 12th International Conference on Information Quality, Cambridge, MA, USA."},{"key":"ref_7","first-page":"232","article-title":"Metadata-based data quality assessment","volume":"46","author":"Aljumaili","year":"2016","journal-title":"VINE J. Inf. Knowl. Manag. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.im.2018.05.014","article-title":"A theoretical framework to improve the quality of manually acquired data","volume":"56","author":"Haegemans","year":"2019","journal-title":"Inf. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Pinfield, S., Cox, A.M., and Smith, J. (2014). Research Data Management and Libraries: Relationships, Activities, Drivers and Influences. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0114734"},{"key":"ref_10","first-page":"137","article-title":"Die digitale Forschungswelt als Gegenstand der Forschung","volume":"64","author":"Kindling","year":"2013","journal-title":"Inf.-Wiss. Prax."},{"key":"ref_11","unstructured":"OECD (2020, August 27). OECD Principles and Guidelines for Access to Research Data for Public Funding. Available online: http:\/\/www.oecd.org\/sti\/sci-tech\/38500813.pdf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2182","DOI":"10.1002\/asi.23781","article-title":"Developments in research data management in academic libraries: Towards an understanding of research data service maturity","volume":"68","author":"Cox","year":"2017","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1177\/0031721719834029","article-title":"Toward more effective data use in teaching","volume":"100","author":"McDonald","year":"2019","journal-title":"Phi Delta Kappan"},{"key":"ref_14","first-page":"84","article-title":"Providing Research Data Management (RDM) Services in Libraries: Preparedness, Roles, Challenges, and Training for RDM Practice","volume":"3","author":"Tang","year":"2019","journal-title":"Data Inf. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Azeroual, O., and Lewoniewski, W. (2020). How to Inspect and Measure Data Quality about Scientific Publications: Use Case of Wikipedia and CRIS Databases. Algorithms, 13.","DOI":"10.3390\/a13050107"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/07421222.1996.11518099","article-title":"Beyond Accuracy: What Data Quality means to Data Consumers","volume":"12","author":"Wang","year":"1996","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1145\/269012.269021","article-title":"Examining Data Quality","volume":"41","author":"Tayi","year":"1998","journal-title":"Commun. ACM"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/2.607057","article-title":"10 Potholes in the Road to Information Quality","volume":"30","author":"Lee","year":"1997","journal-title":"IEEE Comput."},{"key":"ref_19","first-page":"12","article-title":"Data Quality Measures and Data Cleansing for Research Information Systems","volume":"16","author":"Azeroual","year":"2018","journal-title":"J. Digit. Inf. Manag."},{"key":"ref_20","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","author":"Azeroual","year":"2018","journal-title":"Int. J. Inf. Manag."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/4\/4\/29\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:23:28Z","timestamp":1760178208000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/4\/4\/29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,18]]},"references-count":20,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["bdcc4040029"],"URL":"https:\/\/doi.org\/10.3390\/bdcc4040029","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2020,10,18]]}}}