[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119840-en":3,"doc-seo-119840-105":30,"detail-sidebar-cat-0-en-105":82},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119840,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","FAIR Digital Object Application Case for Composing Machine Learning Training Data - Research Ideas and Outcomes 8 - Conference Abstract","The application case for implementing and using the FAIR Digital Object (FAIR DO) concept targets streamlined access to label information needed for composing Machine Learning (ML) training data. Domain-specific datasets often use non-identical label terms, forcing costly relabeling and manual integration. FAIR DOs provide machine-interpretable and actionable representations via globally unique Persistent Identifiers, mandatory metadata, and typed object descriptions. PID Kernel Information Profiles support validation and decision-making, while cross-references enable label retrieval. A dedicated PID-aware client resolves dataset and label FAIR DOs, retrieves label documents, and maps label terms for automated relabeling.","Research Ideas and Outcomes 8: e94113  \ndoi: 10.3897/rio.8.e94113  \nConference Abstract  \nFAIR Digital Object Application Case for Composing Machine Learning Training Data  \nNicolas Blumenröhr‡, Thomas Jejkal‡, Andreas Pfeil‡, Rainer Stotzka‡  \n‡ Karlsruhe Institute of Technology, Karlsruhe, Germany  \n\n| Corresponding author: Nicolas Blumenröhr ([nicolas.blumenroehr@kit.edu](nicolas.blumenroehr@kit.edu)) |  |  |\n| --- | --- | --- |\n|  | Reviewable | \u003Cbr>v 1 |\n| Received: 26 Aug 2022 | Published: 12 Oct 2022\u003Cbr>Citation: Blumenröhr N, Jejkal T, Pfeil A, Stotzka R (2022) FAIR Digital Object Application Case for Composing Machine Learning Training Data. Research Ideas and Outcomes 8: e94113 . [https://doi.org/10.3897/rio.8.e94113](https://doi.org/10.3897/rio.8.e94113) |  |  |\n\nAbstract  \nThe application case for implementing and using the FAIR Digital Object (FAIR DO) concept (Schultes and Wittenburg 2019), aims to simplify the access to label information for composing Machine Learning (ML) (Awad and Khanna 2015) training data.  \nData sets curated by different domain experts usually have non-identical label terms. This prevents images with similar labels from being easily assigned to the same category. Therefore, using them collectively for application as training data in ML comes with the cost of laborious relabeling. The data needs to be machine-interpretable and -actionable to automate this process. This is enabled by applying the FAIR DO concept. A FAIR DO is a representation of scientific data and requires at least a globally unique Persistent Identifier (PID) (Schultes and Wittenburg 2019), mandatory metadata, and a digital object type.  \nStoring typed information in the PID record demands a prior selection of that information. This includes mandatory metadata and a digital object type to enable machine interpretability and subsequent actionability. The information provided in the PID record refers to its PID Kernel Information Profile (PIDKIP), defined or selected by the creator of the FAIR DO. A PIDKIP is a standard that facilitates the definition and validation of the mandatory metadata attributes in the PID record. This information acts as a basis for a machine to decide if the digital object is reusable for a particular application. Part of that is also the digital object type, which enables a machine to work with the data represented by the FAIR DO. If more information is required, the data itself or other associated FAIR DOs need to be accessed through references in the PID record.  \n© Blumenröhr N et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \n2 Blumenröhr N et al  \nSpecifying the granularity of the data representation, and the granularity of the metadata in the information record is not a fixed task but depends on the objective. Here, the FAIR DO concept is used for representing image data sets with their label metadata. Each data set contains multiple images, which refer to the same label term. One data set associated with a particular label is represented as one FAIR DO. A type that provides information about this entity covers the packaged format of the images and the image format itself. Further information about the label term and other metadata associated with the data set is provided or accessed through references in the PID record. For the PIDKIP, the Helmholtz KIP was chosen, following the RDA Working Group recommendations on PID Kernel Information (RDA 2013) . This profile includes mandatory metadata attributes, used for machine-actionable decisions required for relabeling. Information about the data labels isnot directly provided in its PID record, but in another PID record of an associated image label FAIR DO. This one represents a metadata document, containing label information about the data set","cbCaipAzLSELaZq5","https://ap.wps.com/l/cbCaipAzLSELaZq5","pdf",114805,1,4,"English","en",105,"# Abstract\n## FAIR DO concept and motivation\n## PID records, metadata granularity, and PIDKIP\n## Automated relabeling workflow","[{\"question\":\"How does the described process automate relabeling across image datasets?\",\"answer\":\"A specialized client resolves the PID for a dataset FAIR DO, validates usability for ML training data composition, then follows referenced PIDs to access the related label FAIR DO and its label information. A compatible tool maps dataset label terms to corresponding terms from other datasets.\"}]","FAIR Digital Object Application Case for Composing Machine Learning Training Data - Research Ideas and Outcomes 8 - Conference Abstract | PDF",1785726577,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":28},"fair-digital-object-application-case-for-composing-machine-learning-training-data-research-ideas-and-outcomes-8-conference-abstract","",{"@graph":36,"@context":76},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/fair-digital-object-application-case-for-composing-machine-learning-training-data-research-ideas-and-outcomes-8-conference-abstract/119840/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"How does the described process automate relabeling across image datasets?","Question",{"text":74,"@type":75},"A specialized client resolves the PID for a dataset FAIR DO, validates usability for ML training data composition, then follows referenced PIDs to access the related label FAIR DO and its label information. A compatible tool maps dataset label terms to corresponding terms from other datasets.","Answer","https://schema.org",{"og:url":52,"og:type":78,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":80,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":83},[84,88,92,96,101,106,111,114,119,122,125],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":29,"slug":124},"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":97,"slug":128},19,"General","general"]