{"id":13599,"date":"2026-07-10T11:39:43","date_gmt":"2026-07-10T09:39:43","guid":{"rendered":"https:\/\/www.uni.lu\/c2dh-en\/?post_type=research-projects&#038;p=13599"},"modified":"2026-07-13T08:34:15","modified_gmt":"2026-07-13T06:34:15","slug":"dinoh","status":"publish","type":"research-projects","link":"https:\/\/www.uni.lu\/c2dh-en\/research-projects\/life\/dinoh\/","title":{"rendered":"DINOH \u2014 a Digital, AI-assisted Infrastructure for Oral History"},"content":{"rendered":"<div class=\"section py-0 static\">\n<section class=\"py-0 wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><!-- SecondaryNavigationMirror --><div class=\"js-secondary-nav--sticky wp-block-unilux-blocks-secondary-navigation\" id=\"2c67a266-7505-405d-972d-3918908101e1\">\n    <nav id=\"secondary-navigation\" role=\"navigation\" aria-label=\"Secondary menu\" class=\"secondary-nav\">\n        <button class=\"secondary-nav__button btn btn-solid--primary w-full\" aria-expanded=\"false\" aria-controls=\"secondary-navigation__list\" title=\"The selected page is DINOH \u2014 a Digital, AI-assisted Infrastructure for Oral History\" type=\"button\">\n            <span class=\"btn__name mr-auto\">DINOH \u2014 a Digital, AI-assisted Infrastructure for Oral History<\/span>\n            <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n                <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-down\" \/>\n            <\/svg>\n        <\/button>\n\n        <ul id=\"secondary-navigation__list\" class=\"secondary-nav__list\">\n            <li class=\"secondary-nav__item\">\n    <a href=\"https:\/\/www.uni.lu\/c2dh-en\/research-projects\/life\/\" target=\"\" rel=\"noreferrer noopener\" class=\"secondary-nav__link \">\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n        <\/svg>\n        <span class=\"secondary-nav__name\">Overview<\/span>\n    <\/a>\n<\/li>\n<li class=\"secondary-nav__item\">\n    <a href=\"https:\/\/www.uni.lu\/c2dh-en\/research-projects\/life\/oral-history-research-at-the-c2dh\/\" target=\"\" rel=\"noreferrer noopener\" class=\"secondary-nav__link \">\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n        <\/svg>\n        <span class=\"secondary-nav__name\">Oral History Research at the C\u00b2DH <\/span>\n    <\/a>\n<\/li>\n<li class=\"secondary-nav__item\">\n    <a href=\"https:\/\/www.uni.lu\/c2dh-en\/research-projects\/life\/interview-collections\/\" target=\"\" rel=\"noreferrer noopener\" class=\"secondary-nav__link \">\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n        <\/svg>\n        <span class=\"secondary-nav__name\">Interview collections<\/span>\n    <\/a>\n<\/li>\n<li class=\"secondary-nav__item\">\n    <a href=\"https:\/\/www.uni.lu\/c2dh-en\/research-projects\/life\/dinoh\/\" target=\"\" rel=\"noreferrer noopener\" class=\"secondary-nav__link isactived\" aria-current=\"page\">\n        <svg aria-hidden=\"true\" focusable=\"false\" class=\"icon icon-outline icon--xs\">\n            <use xlink:href=\"https:\/\/www.uni.lu\/wp-content\/themes\/unilux-theme\/assets\/images\/icons\/icons-outline.svg#icon--arrow-right\" \/>\n        <\/svg>\n        <span class=\"secondary-nav__name\">DINOH \u2014 a Digital, AI-assisted Infrastructure for Oral History<\/span>\n    <\/a>\n<\/li>\n        <\/ul>\n    <\/nav>\n<\/div>\n<!-- end SecondaryNavigationMirror --><\/div><\/section>\n\n\n\n<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\">\n<h2 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"dinoh-a-digital-ai-assisted-infrastructure-for-oral-history\"\n    >\nDINOH \u2014 a Digital, AI-assisted Infrastructure for Oral History<\/h2>\n\n\n\n<p>Within oral history research, recorded life stories are gathered across the Greater Region and its borders. To study them at scale, DINOH provides an AI-assisted pipeline infrastructure in which AI helps read and code interviews \u2014 and in which data-protection rules are built into the pipeline and made independently verifiable, rather than left to good intentions. The aim is simple: make oral histories easier to analyse and reuse, while keeping the people in them protected.<\/p>\n\n\n<figure class=\"wp-block-dev4-reusable-blocks-image  object-fit--contain\">\n    \n<img decoding=\"async\" class=\"wp-block-image unilux-custom-image-block\"\n                alt=\"DINOH pipline\"\n            src=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/7\/2026\/07\/10112754\/DINOH_pipeline_four-pillars-png.png\"\n                srcset=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/7\/2026\/07\/10112754\/DINOH_pipeline_four-pillars-png-300x171.png 300w, https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/7\/2026\/07\/10112754\/DINOH_pipeline_four-pillars-png-1024x583.png 1024w, https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/7\/2026\/07\/10112754\/DINOH_pipeline_four-pillars-png-768x437.png 768w, https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/7\/2026\/07\/10112754\/DINOH_pipeline_four-pillars-png-1536x875.png 1536w, https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/7\/2026\/07\/10112754\/DINOH_pipeline_four-pillars-png-2048x1167.png 2048w\"\n                style=\"object-position: 50.00% 50.00%; font-family: &quot;object-fit: contain; object-position: 50.00% 50.00%;&quot;; aspect-ratio: 16\/9; object-fit: contain; width: 100%;\"\n        loading=\"lazy\"\n\/>    <\/figure>\n\n<div class=\"wp-block-unilux-blocks-spacer is-spacer-size-sm\"><\/div>\n\n\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"access-is-conditional\"\n    >\nAccess is conditional<\/h3>\n\n\n\n<p>Access is not automatic. Before a researcher may use the pipeline, they must pass a data-protection (DPO) and research-ethics review and receive written approval. Nobody simply runs an interview through it \u2014 the review comes first.<\/p>\n\n\n\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"four-working-parts-admission-analysis-benchmark-provenance\"\n    >\nFour working parts: Admission, Analysis, Benchmark, Provenance<\/h3>\n\n\n\n<p>It works like an assembly line with a guard at the door. <strong>Admission<\/strong> is the guard: it classifies every dataset and checks it before it may be used. <strong>Analysis<\/strong> is the line itself \u2014 it splits interviews into segments and supports AI-assisted coding; in the public demonstration this coding is shown through a transparent, rule-based illustration, while integration of local, open-weight AI tools is gated and in progress. <strong>Benchmark<\/strong> is a test bench that measures how good the AI\u2019s suggestions are, using only synthetic data. And <strong>Provenance<\/strong> records how each interpretation was made, so findings can always be traced back.<\/p>\n\n\n\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"under-the-hood-how-the-four-parts-work-in-plain-terms-and-in-code\"\n    >\nUnder the hood \u2014 how the four parts work, in plain terms and in code<\/h3>\n\n\n\n<ul class=\"ulux-list\">\n<li class=\"ulux-list-item\"><strong>Admission \u2014 the door check, with GDPR built in.<\/strong> Before any interview is analysed, the system checks the dataset the way a data-protection review would: is the file complete and well-formed, are consent and purpose in order, and does processing stay on approved local\/EU infrastructure with its privacy status cleared? If anything is missing, the door stays shut \u2014 the default answer is \u201cno\u201d, and every refusal is logged with a reason. In engineering terms this is a <strong>default-deny gate<\/strong>: the legal rules are checked by code, before data is touched \u2014 which is exactly what lets anyone who must respect GDPR and legal status rely on it.<\/li>\n\n\n\n<li class=\"ulux-list-item\"><strong>Analysis \u2014 reading and coding, with receipts.<\/strong> The interview is split into time-stamped sections, each given thematic labels \u2014 and every label must point to the exact words that justify it. In engineering terms this is <strong>span-binding<\/strong>: a code with no anchor in the text is rejected. The local AI coding runs primarily on <strong>Mistral<\/strong>, an open-weight model held and run on University infrastructure \u2014 no interview data leaves the machine.<\/li>\n\n\n\n<li class=\"ulux-list-item\"><strong>Benchmark \u2014 the practice-run scoreboard.<\/strong> The AI\u2019s suggestions are compared against a human reference and scored \u2014 metadata by exact agreement, segmentation by two standard measures, <strong>WindowDiff<\/strong> and <strong>Pk<\/strong> \u2014 always language by language, and only on synthetic data.<\/li>\n\n\n\n<li class=\"ulux-list-item\"><strong>Provenance \u2014 the tamper-evident logbook.<\/strong> Every run records what went in and what produced it, sealed with a cryptographic fingerprint (<strong>SHA-256<\/strong>), so any result can be re-computed and checked rather than taken on trust.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"a-traffic-light-for-data\"\n    >\nA traffic light for data<\/h3>\n\n\n\n<p>Not all data is equally sensitive, so every dataset is given a colour. <strong>Green<\/strong> is safely anonymous and can be used right away. <strong>Yellow<\/strong> still points to real people \u2014 even when names are replaced by codes (pseudonymised) \u2014 so it needs a formal data-protection review and a sign-off before any analysis. <strong>Red<\/strong> is too sensitive for this route (for example interviews with minors, or about health) and is set aside for separate, case-by-case handling.<\/p>\n\n\n\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"gates-that-default-to-no\"\n    >\nGates that default to \u201cno\u201d<\/h3>\n\n\n\n<p>Before any interview reaches the analysis engine, the run must first declare and attest how it will be processed \u2014 route, purpose and wording. Each record then passes three automatic checks: the file is well-formed, consent and purpose are in order, and processing stays on approved local\/EU infrastructure with its privacy status cleared. If anything required is missing, the gate simply refuses \u2014 in engineering terms it is <strong>default-deny<\/strong>: the answer is \u201cno\u201d unless everything is in order. Nothing slips through quietly; every refusal is recorded with a reason.<\/p>\n\n\n\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"multilingual-by-design\"\n    >\nMultilingual by design<\/h3>\n\n\n\n<p>The Greater Region is multilingual, and so is DINOH. The benchmark reports on seven languages \u2014 German, English, French, Italian, Spanish, Luxembourgish and Portuguese \u2014 each judged on its own terms and never merged into one score, so weaker performance in a smaller language cannot hide behind a stronger one.<\/p>\n\n\n\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"what-the-machine-does-and-what-the-human-in-the-loop-decides\"\n    >\nWhat the machine does \u2014 and what the human in the loop decides<\/h3>\n\n\n\n<p>The computer is strict, but it is not in charge. It applies the rules in code and prepares the material \u2014 validating records, pseudonymising, keeping everything on approved infrastructure and logging every step. The judgements that matter stay with a <strong>human in the loop<\/strong>: whether data is genuinely anonymous, on what legal basis it may be used, how to review the text for anything that could re-identify someone, and how to interpret and code the interviews. And depending on the qualitative-analysis (QDA) method, the researcher can inspect and revise not only the interpretations but the evidence paths that lead to them. Responsibility remains human and is made visible, not hidden behind the technology.<\/p>\n\n\n\n<h3 class=\"has-text-align-left wp-block-unilux-blocks-heading\"        id=\"openness-credit\"\n    >\nOpenness &amp; credit<\/h3>\n\n\n\n<p>The evaluation part is openly available and citable: <a href=\"https:\/\/github.com\/klausbehnamshad\/DINOH\" target=\"_blank\" rel=\"noreferrer noopener\">github.com\/klausbehnamshad\/DINOH<\/a> \u00b7 <a href=\"https:\/\/zenodo.org\/records\/21273367\" target=\"_blank\" rel=\"noreferrer noopener\">Zenodo DOI 10.5281\/zenodo.21273367<\/a>. The governance engine is described in the concept note. Infrastructure <strong>design &amp; engineering: <a href=\"https:\/\/www.uni.lu\/c2dh-en\/people\/klaus-behnam-shad\/\">Klaus Behnam Shad<\/a><\/strong> (DINOH PI \/ operator), developed within the LIFE project at the C\u00b2DH, 糖心Vlog. This tab explains methods and safeguards, and is not legal advice.<\/p>\n<\/div><\/section>\n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":57,"featured_media":0,"parent":9413,"menu_order":0,"template":"","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"LIFE","uluxPrePostTitle":"Research project","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"rpp_rp_identifier":"9410","rpp_taxonomies_in_sync":true},"research-project-status":[272],"research-project-type":[],"field-of-interest":[289],"organisation":[223,221],"authorship":[57],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>DINOH \u2014 a Digital, AI-assisted Infrastructure for Oral History - C\u00b2DH I 糖心Vlog<\/title>\n<meta name=\"description\" content=\"Within oral history research, recorded life stories are gathered across the Greater Region and its borders. 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