{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T17:34:58Z","timestamp":1777916098384,"version":"3.51.4"},"reference-count":69,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","award":["UID\/00319\/Centro"],"award-info":[{"award-number":["UID\/00319\/Centro"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>This study aims to identify and categorize barriers to the success of Data Science (DS) projects through a systematic literature review combined with quantitative methods of analysis. PRISMA is used to conduct a literature review to identify the barriers in the existing literature. With techniques from bibliometrics and network science, the barriers are hierarchically clustered using the Jaccard distance as a measure of dissimilarity. The review identified 27 barriers to the success of DS projects from 26 studies. These barriers were grouped into six thematic clusters: people, data and technology, management, economic, project, and external barriers. The barrier \u201cinsufficient skills\u201d is the most frequently cited in the literature and the most frequently considered critical. From the quantitative analysis, the barriers \u201cinsufficient skills\u201d, \u201cpoor data quality\u201d, \u201cdata privacy and security\u201d, \u201clack of support from top management\u201d, \u201cinsufficient funding\u201d, \u201cinsufficient ROI or justification\u201d, \u201cgovernment policies and regulation\u201d, and \u201cinadequate, immature or inconsistent methodology\u201d were identified as the most central in their cluster.<\/jats:p>","DOI":"10.3390\/data10080132","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T13:18:14Z","timestamp":1755695894000},"page":"132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Data Science Project Barriers\u2014A Systematic Review"],"prefix":"10.3390","volume":"10","author":[{"given":"Natan","family":"Labarr\u00e8re","sequence":"first","affiliation":[{"name":"Algoritmi Research Center\/LASI (Associate Laboratory for Intelligent Systems), Department of Production and Systems, School of Engineering, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4772-4404","authenticated-orcid":false,"given":"Lino","family":"Costa","sequence":"additional","affiliation":[{"name":"Algoritmi Research Center\/LASI (Associate Laboratory for Intelligent Systems), Department of Production and Systems, School of Engineering, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7991-0132","authenticated-orcid":false,"given":"Rui M.","family":"Lima","sequence":"additional","affiliation":[{"name":"Algoritmi Research Center\/LASI (Associate Laboratory for Intelligent Systems), Department of Production and Systems, School of Engineering, University of Minho, 4800-058 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"ref_1","unstructured":"(2022, January 28). The World Bank GDP Growth (Annual %). Available online: https:\/\/data.worldbank.org\/indicator\/NY.GDP.MKTP.KD.ZG."},{"key":"ref_2","unstructured":"(2022, November 20). Statista Research Department Total Data Volume Worldwide 2010\u20132025. Available online: https:\/\/www.statista.com\/statistics\/871513\/worldwide-data-created\/."},{"key":"ref_3","unstructured":"Arthur, C. (The Guardian, 2013). Tech Giants May Be Huge, but Nothing Matches Big Data, The Guardian."},{"key":"ref_4","unstructured":"(2022, January 28). Statista Research Department Biggest Companies in the World by Market Cap 2021. Available online: https:\/\/www.statista.com\/statistics\/263264\/top-companies-in-the-world-by-market-capitalization\/."},{"key":"ref_5","unstructured":"(2022, January 28). BusinessWire Global $243 Billion Big Data Market Trajectory & Analytics to 2027. Available online: https:\/\/www.businesswire.com\/news\/home\/20201208005685\/en\/Global-243-Billion-Big-Data-Market-Trajectory-Analytics-to-2027-Age-of-Analytics-Provides-the-Cornerstone-for-the-Disruptive-Growth-Proliferation-of-Big-Data-Technologies---ResearchAndMarkets.com."},{"key":"ref_6","unstructured":"Davenport, T.H., and Patil, D.J. (Harvard Business Review, 2012). Data Scientist: The Sexiest Job of the 21st Century, Harvard Business Review."},{"key":"ref_7","unstructured":"(2022, January 28). LinkedIn US Jobs on the Rise Report. Available online: https:\/\/business.linkedin.com\/talent-solutions\/resources\/talent-acquisition\/jobs-on-the-rise-us."},{"key":"ref_8","unstructured":"NewVantage Partners LLC (2022, January 28). Big Data and AI Executive Survey 2021: Executive Summary of Findings. Available online: https:\/\/www.newvantage.com\/_files\/ugd\/e5361a_d59b4629443945a0b0661d494abb5233.pdf."},{"key":"ref_9","unstructured":"(2022, January 28). Capgemini Consulting Cracking the Data Conundrum: How Successful Companies Make Big Data Operational 2014. Available online: https:\/\/www.capgemini.com\/gb-en\/wp-content\/uploads\/sites\/3\/2019\/01\/Cracking-the-Data-Conundrum-How-Successful-Companies-Make-Big-Data-Operational.pdf."},{"key":"ref_10","unstructured":"White, A. (2022, January 28). Our Top Data and Analytics Predicts for 2019. Gartner Blog Network 2019, Available online: https:\/\/blogs.gartner.com\/andrew_white\/2019\/01\/03\/our-top-data-and-analytics-predicts-for-2019\/."},{"key":"ref_11","unstructured":"Fleming, O., Fountaine, T., Henke, N., and Saleh, T. (2022, January 28). Getting Your Organization\u2019s Advanced Analytics Program Right. Available online: https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/ten-red-flags-signaling-your-analytics-program-will-fail."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kelleher, J.D., and Tierney, B. (2018). Data Science, The MIT Press.","DOI":"10.7551\/mitpress\/11140.001.0001"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1214\/ss\/1009213726","article-title":"Statistical Modeling: The Two Cultures","volume":"16","author":"Breiman","year":"2001","journal-title":"Stat. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s41019-016-0022-0","article-title":"Big Data Reduction Methods: A Survey","volume":"1","author":"Liew","year":"2016","journal-title":"Data Sci. Eng."},{"key":"ref_15","unstructured":"Brown, M.S. (2014). Transforming Unstructured Data into Useful Information. Big Data, Mining, and Analytics, Auerbach Publications."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"7776","DOI":"10.1109\/ACCESS.2017.2696365","article-title":"Machine Learning with Big Data: Challenges and Approaches","volume":"5","author":"Grolinger","year":"2017","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Krasteva, I., and Ilieva, S. (2020, January 10\u201313). Adopting Agile Software Development Methodologies in Big Data Projects\u2014A Systematic Literature Review of Experience Reports. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378118"},{"key":"ref_18","unstructured":"Project Management Institute (PMI) (2021). The Standard for Project Management and a Guide to the Project Management Body of Knowledge (PMBOK Guide), Project Management Institute. [7th ed.]."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1109\/TII.2016.2547584","article-title":"A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn","volume":"12","author":"Bi","year":"2016","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1287\/opre.2018.1739","article-title":"A Model-Based Embedding Technique for Segmenting Customers","volume":"66","author":"Jagabathula","year":"2018","journal-title":"Oper. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Thennakoon, A., Bhagyani, C., Premadasa, S., Mihiranga, S., and Kuruwitaarachchi, N. (2019, January 10\u201311). Real-Time Credit Card Fraud Detection Using Machine Learning. Proceedings of the 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.","DOI":"10.1109\/CONFLUENCE.2019.8776942"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mittal, S., and Tyagi, S. (2019, January 10\u201311). Performance Evaluation of Machine Learning Algorithms for Credit Card Fraud Detection. Proceedings of the 2019 9th International Conference on Cloud Computing, Data Science & Engineering, Noida, India.","DOI":"10.1109\/CONFLUENCE.2019.8776925"},{"key":"ref_23","first-page":"74","article-title":"Predicting Business Failure under the Existence of Fraudulent Financial Reporting","volume":"16","author":"Liou","year":"2008","journal-title":"Int. J. Account. Inf. Manag."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1109\/TKDE.2016.2571686","article-title":"Mining Suspicious Tax Evasion Groups in Big Data","volume":"28","author":"Tian","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103368","DOI":"10.1016\/j.compind.2020.103368","article-title":"Big Data Analytics: Implementation Challenges in Indian Manufacturing Supply Chains","volume":"125","author":"Raut","year":"2021","journal-title":"Comput. Ind."},{"key":"ref_26","first-page":"285","article-title":"Factors Activating Big Data Adoption by Korean Firms","volume":"61","author":"Park","year":"2021","journal-title":"J. Comput. Inf. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1108\/IJLM-01-2021-0002","article-title":"Are Artificial Intelligence and Machine Learning Suitable to Tackle the COVID-19 Impacts? An Agriculture Supply Chain Perspective","volume":"34","author":"Nayal","year":"2021","journal-title":"Int. J. Logist. Manag."},{"key":"ref_28","unstructured":"Piatetsky, G. (2023, May 07). CRISP-DM, Still the Top Methodology for Analytics, Data Mining, or Data Science Projects. KDnuggets 2014, Available online: https:\/\/www.kdnuggets.com\/2014\/10\/crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"100183","DOI":"10.1016\/j.bdr.2020.100183","article-title":"Data Science Methodologies: Current Challenges and Future Approaches","volume":"24","author":"Martinez","year":"2021","journal-title":"Big Data Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.procir.2021.11.091","article-title":"Concept for Enabling Customer-Oriented Data Analytics via Integration of Production Process Improvement Methods and Data Science Methods","volume":"104","author":"Morlock","year":"2021","journal-title":"Procedia CIRP"},{"key":"ref_31","first-page":"185","article-title":"A Qualitative Examination of Major Barriers in Implementation of Reverse Logistics within the South Australian Construction Sector","volume":"16","author":"Rameezdeen","year":"2016","journal-title":"Int. J. Constr. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1177\/0739456X17723971","article-title":"Guidance on Conducting a Systematic Literature Review","volume":"39","author":"Xiao","year":"2019","journal-title":"J. Plan. Educ. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e1230","DOI":"10.1002\/cl2.1230","article-title":"PRISMA2020: An R Package and Shiny App for Producing PRISMA 2020-compliant Flow Diagrams, with Interactivity for Optimised Digital Transparency and Open Synthesis","volume":"18","author":"Haddaway","year":"2022","journal-title":"Campbell Syst. Rev."},{"key":"ref_35","first-page":"256","article-title":"Are Enterprises Ready for Big Data Analytics? A Survey-Based Approach","volume":"25","author":"Brock","year":"2017","journal-title":"Int. J. Bus. Inf. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2720","DOI":"10.1002\/asi.23873","article-title":"Predicting Data Science Sociotechnical Execution Challenges by Categorizing Data Science Projects","volume":"68","author":"Saltz","year":"2017","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bernardi, L., Mavridis, T., and Estevez, P. (2019, January 4\u20138). 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.Com. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330744"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e12151","DOI":"10.1002\/isd2.12151","article-title":"The Barriers to Big Data Adoption in Developing Economies","volume":"87","author":"Alalawneh","year":"2021","journal-title":"Electron. J. Inf. Syst. Dev. Ctries."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1146\/annurev.psych.52.1.59","article-title":"Meta-Analysis: Recent Developments in Quantitative Methods for Literature Reviews","volume":"52","author":"Rosenthal","year":"2001","journal-title":"Annu. Rev. Psychol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1002\/aris.2007.1440410119","article-title":"Network Science","volume":"41","author":"Sanyal","year":"2007","journal-title":"Annu. Rev. Inf. Sci. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/nphys2162","article-title":"Communities, Modules and Large-Scale Structure in Networks","volume":"8","author":"Newman","year":"2012","journal-title":"Nat. Phys."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1111\/j.1469-8137.1912.tb05611.x","article-title":"The Distribution of the Flora in the Alpine Zone.1","volume":"11","author":"Jaccard","year":"1912","journal-title":"New Phytol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.patrec.2018.12.007","article-title":"A Note on the Triangle Inequality for the Jaccard Distance","volume":"120","author":"Kosub","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/BF02289263","article-title":"Who Belongs in the Family?","volume":"18","author":"Thorndike","year":"1953","journal-title":"Psychometrika"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_46","unstructured":"Riveros, C., Salas, J., and Skibski, O. (2021). How to Choose the Root: Centrality Measures over Tree Structures. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/MITP.2022.3156956","article-title":"Evaluating the Effect of Human Factors on Big Data Analytics and Cloud of Things Adoption in the Manufacturing Micro, Small, and Medium Enterprises","volume":"24","author":"Kavre","year":"2022","journal-title":"IT Prof."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"101624","DOI":"10.1016\/j.giq.2021.101624","article-title":"Implementing Challenges of Artificial Intelligence: Evidence from Public Manufacturing Sector of an Emerging Economy","volume":"39","author":"Sharma","year":"2022","journal-title":"Gov. Inf. Q."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1108\/BIJ-03-2022-0161","article-title":"Modelling the Relationships between the Barriers to Implementing Machine Learning for Accident Analysis: The Indian Petroleum Industry","volume":"30","author":"Gangadhari","year":"2022","journal-title":"Benchmarking"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s10799-021-00333-9","article-title":"Framework for Implementing Big Data Analytics in Indian Manufacturing: ISM-MICMAC and Fuzzy-AHP Approach","volume":"22","author":"Gupta","year":"2021","journal-title":"Inf. Technol. Manag."},{"key":"ref_51","first-page":"273","article-title":"Key Challenges in Big Data Startups: An Exploratory Study in Iran","volume":"14","author":"Bahrami","year":"2021","journal-title":"Iran. J. Manag. Stud."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1939","DOI":"10.1108\/IMDS-02-2020-0066","article-title":"Unlocking Causal Relations of Barriers to Big Data Analytics in Manufacturing Firms","volume":"121","author":"Raut","year":"2021","journal-title":"Ind. Manag. Data Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s10479-020-03790-7","article-title":"Big Data Analytics in Sustainable Humanitarian Supply Chain: Barriers and Their Interactions","volume":"319","author":"Bag","year":"2020","journal-title":"Ann. Oper. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1080\/03088839.2019.1628318","article-title":"A Fuzzy Delphi-AHP-TOPSIS Framework to Identify Barriers in Big Data Analytics Adoption: Case of Maritime Organizations","volume":"46","author":"Zhang","year":"2019","journal-title":"Marit. Policy Manag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1016\/j.cie.2018.04.013","article-title":"Barriers to Big Data Analytics in Manufacturing Supply Chains: A Case Study from Bangladesh","volume":"128","author":"Moktadir","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/j.cie.2018.04.055","article-title":"Next Generation Smart Sustainable Auditing Systems Using Big Data Analytics: Understanding the Interaction of Critical Barriers","volume":"128","author":"Shukla","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2758","DOI":"10.1016\/j.jksuci.2020.11.024","article-title":"Big Data Analytics in Telecommunications: Governance, Architecture and Use Cases","volume":"34","author":"Kastouni","year":"2022","journal-title":"J. King Saud. Univ. Comput. Inf. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Aho, T., Kilamo, T., Lwakatare, L., Mikkonen, T., Sievi-Korte, O., and Yaman, S. (2021, January 15\u201318). Managing and Composing Teams in Data Science: An Empirical Study. Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA.","DOI":"10.1109\/BigData52589.2021.9671737"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1007\/s10845-021-01765-4","article-title":"Quality 4.0: A Review of Big Data Challenges in Manufacturing","volume":"32","author":"Escobar","year":"2021","journal-title":"J. Intell. Manuf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1108\/JKM-02-2020-0081","article-title":"Big Data for Small and Medium-Sized Enterprises (SME): A Knowledge Management Model","volume":"24","author":"Wang","year":"2020","journal-title":"J. Knowl. Manag."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Saltz, J.S., and Shamshurin, I. (2019, January 9\u201312). Achieving Agile Big Data Science: The Evolution of a Team\u2019s Agile Process Methodology. Proceedings of the 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9005493"},{"key":"ref_62","unstructured":"Jensen, M.H., Nielsen, P.A., and Persson, J.S. (2019, January 8\u201314). Managing Big Data Analytics Projects: The Challenges of Realizing Value. Proceedings of the 27th European Conference on Information Systems (ECIS), Muenster, Germany."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1109\/TSE.2017.2754374","article-title":"Data Scientists in Software Teams: State of the Art and Challenges","volume":"44","author":"Kim","year":"2018","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Barham, H., and Daim, T. (2018, January 20\u201322). Identifying Critical Issues in Smart City Big Data Project Implementation. Proceedings of the SCC \u201818: The 1st ACM\/EIGSCC Symposium on Smart Cities and Communities, Portland, OR, USA.","DOI":"10.1145\/3236461.3241967"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Barham, H. (2017, January 9\u201313). Achieving Competitive Advantage through Big Data: A Literature Review. Proceedings of the 2017 Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR, USA.","DOI":"10.23919\/PICMET.2017.8125459"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Becker, D.K. (2017, January 11\u201314). Predicting Outcomes for Big Data Projects: Big Data Project Dynamics (BDPD): Research in Progress. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8258186"},{"key":"ref_67","unstructured":"Chipidza, W., George, J., and Koch, H. (2016, January 11\u201314). Chartering Predictive Analytics: A Case Study. Proceedings of the 22nd Americas Conference on Information Systems, AMCIS 2016, San Diego, CA, USA."},{"key":"ref_68","unstructured":"Klosowski, T. (2022, November 20). The State of Consumer Data Privacy Laws in the US (And Why It Matters). Wirecutter, Available online: https:\/\/www.nytimes.com\/wirecutter\/blog\/state-of-privacy-laws-in-us\/."},{"key":"ref_69","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."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/8\/132\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:31:38Z","timestamp":1760034698000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/8\/132"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"references-count":69,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["data10080132"],"URL":"https:\/\/doi.org\/10.3390\/data10080132","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,20]]}}}