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Using persistent identifiers and type-based actionability, FAIR DOs support machine-actionable decisions and automate relabelling. The approach saves substantial time for scientists while enabling automated processing workflows for SEM-derived image and label datasets.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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is composing machine learning training data from heterogeneous sources laborious?","Question",{"text":63,"@type":64},"It requires manual relabelling of data into uniform categories, which takes significant effort and time across heterogeneous sources.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How do FAIR DOs help make images and labels easier to locate and access?",{"text":68,"@type":64},"Representing images and their labels as FAIR Digital Objects enables easier discovery and access to both the objects and their label metadata.",{"name":70,"@type":61,"acceptedAnswer":71},"What role do persistent identifiers (PIDs) play in the proposed workflow?",{"text":72,"@type":64},"PIDs record the FAIR DO metadata, resolve object identities, and provide machine-actionable links between image FAIR DOs and corresponding label FAIR DOs to enable automated relabelling.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},125560,1785899859,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & 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Learning Training Data  \nNicolas Blumenröhr, Thomas Jejkal, Andreas Pfeil, Rainer Stotzka  \n Composing Machine Learning (ML) training data sets from heterogeneous sources is laborious due to their relabelling into uniform categories.  \n\n|  |\n| --- |\n| \u003Cbr>Different Laboratories create SEM (1)  data sets, labelled heterogeneously.\u003Cbr>\u003Cbr>\u003Cbr>Composing training data set for ML.\u003Cbr>\u003Cbr>Laborious manual data\u003Cbr>relabelling.\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>gy \u003Cbr>biolo\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>cal \u003Cbr>biologi\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>Label access\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>Label location\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>Label structure\u003Cbr>\u003Cbr> |\n\nConclusions  \n• Images and their labels represented as FAIR DOs can be located and accessed easier.  \n• FAIR DOs enable machine actionable decisions on the data.  \n• This supports automated relabelling and saves a lot of time for the scientist.  \nTo automate this task, the FAIR Digital Object (FAIR DO) concept can be used.  \nComposing ML training data from SEM (1)  data sets represented as FAIR DOs.   \nData relabelling using PID-and type-based   computer actionability for FAIR DOs.   \nFAIR DO (2) of SEM (1) image  \nPID information  record   \nProfile for PID  \nPersistent Identifier (PID)  \n\n| PID record of an image FAIR DO |  |  |  |\n| --- | --- | --- | --- |\n| DO Type | DO Location | Metadata | … |\n| 21.11152/c700 9b0f-3cbd- 41fa-9e42-cecf0d1f3552 | [https://b2share.euda](https://b2share.euda)[t.eu/api/files/5fc88ad](t.eu/api/files/5fc88ad)[ ](t.eu/api/files/5fc88ad)[5-2f13-483c-8b80-](5-2f13-483c-8b80-)a5862c91dbbb/Biological.tar\\#L7 3f9aed 9a89cf9755a6c29b2 7b069addf.jpg | 21.11152/219ca65d-a534- 4f44-aec5-a98be36f9f56 | … |\n\nresolves to  \nFAIR DO (2) of label metadata  \nPID  \nresolves to  \npoints to  \n\n| PID record of a label FAIR DO |  |  |\n| --- | --- | --- |\n| DO Type | DO Location | … |\n| 21.11152/de2d965b-8941-46f1-b0f0- 94e2ca41c18c | [http://mm3.datamanager.kit](http://mm3.datamanager.kit)\u003Cbr>[.edu:8040/api/v1/metadata](.edu:8040/api/v1/metadata/)[/](.edu:8040/api/v1/metadata/)[ ](.edu:8040/api/v1/metadata/)[mldata_biology?version=1](mldata_biology?version=1) | … |\n\nrecord  \nLook up the full FAIR DO PID record  \ndefines  \n(1) Scanning Electron Microscopy (SEM) data set, provided [by R. Aversa et. al](by R. Aversa et. al). [http://doi.org/10.23728/b2share.19cc2afd23e34b92b36a1dfd0113a89f](http://doi.org/10.23728/b2share.19cc2afd23e34b92b36a1dfd0113a89f)  \n[nicolas.blumenroehr@kit.edu](nicolas.blumenroehr@kit.edu)  \n(2) Introduction to PIDs and FAIR DOs: [https://kit-data-manager.github.io/fairdo-cookbook/about.html](https://kit-data-manager.github.io/fairdo-cookbook/about.html)","cbCaivMV1VweqwEt","https://ap.wps.com/l/cbCaivMV1VweqwEt","pdf",3670769,"English","# Conclusions\n## FAIR Digital Objects (FAIR DO) approach\n## PID and type-based actionability\n## Example: PID records for image and label metadata","[{\"question\":\"Why is composing machine learning training data from heterogeneous sources laborious?\",\"answer\":\"It requires manual relabelling of data into uniform categories, which takes significant effort and time across heterogeneous sources.\"},{\"question\":\"How do FAIR DOs help make images and labels easier to locate and access?\",\"answer\":\"Representing images and their labels as FAIR Digital Objects enables easier discovery and access to both the objects and their label metadata.\"},{\"question\":\"What role do persistent identifiers (PIDs) play in the proposed workflow?\",\"answer\":\"PIDs record the FAIR DO metadata, resolve object identities, and provide machine-actionable links between image FAIR DOs and corresponding label FAIR DOs to enable automated relabelling.\"}]","FAIR DO Application Case for Composing Machine Learning Training Data - Steinbuch Centre for Computing (SCC) Data Exploitation Methods (DEM) | PDF"]