{"datasetrec":{"DasID":8542,"Acronym":null,"StandardTitle":"RGB datasets for machine learning-based microplastic analysis - update","OrigTitle":null,"OrigTitleLangID":null,"OrigTitleLangCode":null,"OrigTitleLang":null,"OrigTitleLangNL":null,"VersionName":null,"ContactEmail":null,"VersionDate":null,"VersionDay":null,"VersionMonth":null,"VersionYear":null,"SizeReference":null,"EngAbstract":"This dataset features RGB statistics extracted from Nile red-stained particles, serving to train a 'Plastic Detection Model' and a 'Polymer Identification Model' using supervised machine learning techniques. By accurately discerning between plastic and natural particles, and distinguishing between different plastic polymers, the models facilitate efficient microplastic detection and identification.","EngDescr":"Datasets containing RGB statistics extracted from photographed fluorescent reference particles stained with Nile red. The most abundantly produced plastic polymers worldwide as well as natural materials with high prevalence in the marine environment were considered for these datasets. The spectral data was used to construct two supervised machine learning models, i.e. a \u2018Plastic Detection Model\u2019 (PDM) and a \u2018Polymer Identification Model\u2019 (PIM), based on random forest algorithms. The PDM allows to accurately distinguish plastic from natural particles, while the PIM allows to distinguish different plastic polymers, in a cost- and time-efficient way. The datasets contain Red, Green and Blue (RGB) statistics extracted from Nile red-stained reference particles (50-1200 \u03bcm) photographed under three different microscope filters (blue, green and UV). Four different datasets can be found, two for each model (PDM vs. PIM), based on photographs acquired with two different types of microscope (Leica DM 1000 fluorescence microscope vs. Leica M205 FA fluorescence stereomicroscope). The datasets represent an updated version of earlier published RGB datasets, now containing 135 \u2013 200 particles per polymer category (PIM), and 420-500 per particle type (plastic\/organic).","OrigAbstract":null,"OrigDescr":null,"Comments":null,"ReleaseDate":null,"ReleaseDate0":null,"OrigDescrLang":null,"EmbargoDate":null,"OrigDescrLangNL":null,"OrigLangCode":null,"OrigLangCodeExtended":null,"OrigLangID":null,"DescrCompFlag":0,"DescrTransFlag":0,"Citation":"Meyers, N.; De Witte, B.; Janssen, C.; Everaert, G.; Flanders Marine Institute (VLIZ); Flanders Research Institute for Agriculture, Fisheries and Food (ILVO); Ghent University Laboratory for Environmental Toxicology (GhEnToxLab): Belgium; (2024): RGB datasets for machine learning-based microplastic analysis - update. Marine Data Archive.","AccessConstraints":null,"UDate":"2024-04-05","CDate":"2024-04-05","CurrencyDate":null,"RevisionDate":null,"DateLastModified":{"date":"2026-04-14 01:43:53.753124","timezone_type":1,"timezone":"+02:00"},"CheckedFlag":0,"PublicFlag":1,"VlizCoreFlag":1,"MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"StatusID":1,"DasType":"Data","DasTypeID":1,"DasOrigin":"Research: lab experiment","Progress":"Completed","AccessConstraint":"Attribution (CC BY)","AccConstrEN":"Attribution (CC BY)","AccConstrDisplay":"\"Creative<\/a> This dataset is licensed under a Creative Commons Attribution 4.0 International License<\/a>.","License":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","AccConstrDescription":"This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.","Lineage":null,"AccConID":21},"dois":[{"DOIID":978,"PublicationYear":2024,"DOI":"10.14284\/665","Citation":"Meyers, N.; De Witte, B.; Janssen, C.; Everaert, G.; Flanders Marine Institute (VLIZ); Flanders Research Institute for Agriculture, Fisheries and Food (ILVO); Ghent University Laboratory for Environmental Toxicology (GhEnToxLab): Belgium; (2024): RGB datasets for machine learning-based microplastic analysis - update. Marine Data Archive.","MDAID":"VLIZ_00000311_66100b787008b891104529","Moratorium":0,"MDAURL":"https:\/\/mda.vliz.be\/directlink.php?fid=VLIZ_00000311_66100b787008b891104529","CurrentDOI":1,"Active":1,"URL":null,"downloadURL":"https:\/\/mda.vliz.be\/download.php?file=VLIZ_00000311_66100b787008b891104529"}],"spcols":null,"keywords":[{"ThesaurusTerm":"Automated detection","ThesTypID":0,"ThesType":null,"Code":null,"Description":null,"OrigThesTerm":"Automated detection","DutchTerm":null,"URI":null,"DasKeywordDescr":null},{"ThesaurusTerm":"Fluorescent colouration","ThesTypID":0,"ThesType":null,"Code":null,"Description":null,"OrigThesTerm":"Fluorescent colouration","DutchTerm":null,"URI":null,"DasKeywordDescr":null},{"ThesaurusTerm":"Machine learning","ThesTypID":2,"ThesType":"CSA Technology Research Database Master Thesaurus","Code":null,"Description":null,"OrigThesTerm":"Machine learning","DutchTerm":null,"URI":null,"DasKeywordDescr":null},{"ThesaurusTerm":"Microplastics","ThesTypID":0,"ThesType":null,"Code":null,"Description":null,"OrigThesTerm":"Microplastics","DutchTerm":"Microplastics","URI":null,"DasKeywordDescr":null},{"ThesaurusTerm":"Nile red staining","ThesTypID":0,"ThesType":null,"Code":null,"Description":null,"OrigThesTerm":"Nile red staining","DutchTerm":null,"URI":null,"DasKeywordDescr":null},{"ThesaurusTerm":"Random forest models","ThesTypID":0,"ThesType":null,"Code":null,"Description":null,"OrigThesTerm":"Random forest models","DutchTerm":null,"URI":null,"DasKeywordDescr":null},{"ThesaurusTerm":"RGB colour data","ThesTypID":0,"ThesType":null,"Code":null,"Description":null,"OrigThesTerm":"RGB colour data","DutchTerm":null,"URI":null,"DasKeywordDescr":null}],"parents":null,"children":[{"DasID":7856,"Acronym":null,"EngAbstract":"Dataset containing RGB-statistics extracted from photographed fluorescent reference particles stained with Nile red. The most abundantly produced plastic polymers worldwide as well as natural materials with high prevalence in the marine environment were considered for this dataset. The spectral data was used to construct a supervised machine learning model that allows to accurately distinguish plastic from natural particles in a cost- and time-efficient way.","License":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","StandardTitle":"RGB-statistics derived from Nile red-stained reference plastics for the construction of the PDM (Plastics Detection Model)","doi":null,"vlizDoi":"10.14284\/512"},{"DasID":7855,"Acronym":null,"EngAbstract":"Dataset containing RGB-statistics extracted from photographed fluorescent reference plastics stained with Nile red. The most abundantly produced plastic polymers worldwide considered for this dataset. The spectral data was used to construct a supervised machine learning model that allows to accurately identify the polymer types microplastics belong to, in a cost- and time-efficient way.","License":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","StandardTitle":"RGB-statistics derived from Nile red-stained reference plastics for the construction of the PIM (Polymer Identification Model)","doi":null,"vlizDoi":"10.14284\/511"}],"othrel":null,"othrelrev":null,"ownerships":[{"OrderNr":null,"Surname":"Meyers","Firstname":"Nelle","Initials":"N.","PerPublicFlag":1,"AdrID":168059,"Email":"nelle.meyers@vliz.be","InsPublicFlag":1,"Acronym":"VLIZ","OrigNameLangCode":"en","OrigNameLangID":15,"FullOrigName":"Flanders Marine Institute","InsOwnerCNT":3,"PersID":30367,"InsID":36,"FullInstitute":"Vlaams Instituut voor de Zee","RoleID":24,"Role":"Contact","OrigName":"Flanders Marine Institute","StandardName":"Vlaams Instituut voor de Zee","FullAcronym":"VLIZ","ORC":"https:\/\/orcid.org\/0000-0001-9205-6260","ROR":"https:\/\/ror.org\/0496vr396"},{"OrderNr":null,"Surname":"Meyers","Firstname":"Nelle","Initials":"N.","PerPublicFlag":1,"AdrID":null,"Email":null,"InsPublicFlag":1,"Acronym":"GhEnToxLab","OrigNameLangCode":"en","OrigNameLangID":15,"FullOrigName":"Ghent University; 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ANDROMEDA portfolio of microplastics analyses protocols. ANDROMEDA Deliverable 5.5. JPI Oceans ANDROMEDA project: [s.l.]. 88 pp.","RR":"De Witte, B. et al.<\/i><\/b> (2024). ANDROMEDA portfolio of microplastics analyses protocols. ANDROMEDA Deliverable 5.5. JPI Oceans ANDROMEDA project: [s.l.]. 88 pp.","4":"ANDROMEDA portfolio of microplastics analyses protocols","StT":"ANDROMEDA portfolio of microplastics analyses protocols","5":"De Witte, B.; Power, O.-P.; Fitzgerald, E.; Kopke, K.","RSA":"De Witte, B.; Power, O.-P.; Fitzgerald, E.; Kopke, K.","6":"Gebaseerd op deze dataset","DutchTerm":"Gebaseerd op deze dataset","7":null,"AnaDate":null,"8":2024,"MonDate":2024,"9":". ANDROMEDA Deliverable 5.5. JPI Oceans ANDROMEDA project: [s.l.]. 88 pp.","":". ANDROMEDA Deliverable 5.5. JPI Oceans ANDROMEDA project: [s.l.]. 88 pp.","10":null,"doi":null}],"urls":null,"pictures":null,"urlmaps":null,"spatreps":null,"fileformats":null,"resmessage":"","complete":1}