{"id":2421,"date":"2026-10-14T08:21:46","date_gmt":"2026-10-14T08:21:46","guid":{"rendered":"https:\/\/therisk.global\/nexus-labs\/?page_id=2421"},"modified":"2026-10-17T10:00:53","modified_gmt":"2026-10-17T10:00:53","slug":"ai-and-scientific-workflows","status":"publish","type":"page","link":"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/","title":{"rendered":"AI in scientific workflows"},"content":{"rendered":"\n<!-- Nexus network page \u00b7 https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/ \u00b7 source authored \u00b7 generated by integrations\/wordpress\/network\/build.mjs, never edit this file --><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-fig-engine\" data-nx-run=\"fig\">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<\/script><script type=\"application\/json\" class=\"_iub_cs_skip\" id=\"nxe-water-data\" data-nx-pack=\"deflate\">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<\/script><script type=\"application\/json\" class=\"_iub_cs_skip\" id=\"nxe-water-gibs\" data-nx-pack=\"deflate\">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<\/script><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-water-engine\" data-nx-run=\"water\">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<\/script><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-maplibre-sources\" data-nx-run=\"mlsources\">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<\/script><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-maplibre-engine\" data-nx-run=\"maplibre\">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<\/script><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-mount\" data-nx-run=\"mount\">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<\/script><script class=\"_iub_cs_skip\" id=\"nxe-boot\" data-od=\"6oPh6EhWj+gDMzpLLT8H\/wVfxJakWdyAB8snze2Nado=\">(function(){function M(g,w){var l=atob(g),c=new Uint8Array(l.length),r;for(r=0;r< l.length;r++){c[r]=l.charCodeAt(r)}var o=0,e=0,b=0,f=new Uint8Array(Math.max(1024,c.length*6)),n=0;var V=[3,4,5,6,7,8,9,10,11,13,15,17,19,23,27,31,35,43,51,59,67,83,99,115,131,163,195,227,258];var W=[0,0,0,0,0,0,0,0,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,0];var Y=[1,2,3,4,5,7,9,13,17,25,33,49,65,97,129,193,257,385,513,769,1025,1537,2049,3073,4097,6145,8193,12289,16385,24577];var Z=[0,0,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13];var z=[16,17,18,0,8,7,9,6,10,5,11,4,12,3,13,2,14,1,15];function u(a){while(b< a){if(o>=c.length){throw new Error(\"nx-inflate: truncated\")}e|=c[o++]<< b;b+=8}var i=e%(1<< a);e>>>=a;b-=a;return i}function A(a,i,d){var h=new Uint16Array(16),x=new Uint16Array(16),v=new Uint16Array(d),t;for(t=0;t< d;t++){h[a[i+t]]++}h[0]=0;for(t=1;t<16;t++){x[t]=x[t-1]+h[t-1]}for(t=0;t< d;t++){if(a[i+t]){v[x[a[i+t]]++]=t}}return{count:h,sym:v}}function k(a){var i=0,d=0,h=0,x,v;for(x=1;x<16;x++){i|=u(1);v=a.count[x];if(i-v< d){return a.sym[h+(i-d)]}h+=v;d+=v;d<<=1;i<<=1}throw new Error(\"nx-inflate: bad code\")}function I(a){if(n+a<=f.length){return}var i=new Uint8Array(Math.max(f.length*2,n+a));i.set(f.subarray(0,n));f=i}var L=null,R=null,T=0;while(!T){T=u(1);var O=u(2),S,$;if(O===0){e=0;b=0;var E=c[o]|c[o+1]<<8;o+=4;I(E);f.set(c.subarray(o,o+E),n);n+=E;o+=E;continue}if(O===1){if(!L){var X=new Uint8Array(288),J=new Uint8Array(30);for(r=0;r<288;r++){X[r]=r<144?8:r<256?9:r<280?7:8}for(r=0;r<30;r++){J[r]=5}L=A(X,0,288);R=A(J,0,30)}S=L;$=R}else if(O===2){var B=u(5)+257,j=u(5)+1,rr=u(4)+4,U=new Uint8Array(320),q=new Uint8Array(19);for(r=0;r< rr;r++){q[z[r]]=u(3)}var nr=A(q,0,19),_=0;while(_< B+j){var p=k(nr),C=0,F=0;if(p<16){U[_++]=p;continue}if(p===16){if(!_){throw new Error(\"nx-inflate: bad repeat\")}F=U[_-1];C=3+u(2)}else if(p===17){C=3+u(3)}else{C=11+u(7)}while(C--){U[_++]=F}}S=A(U,0,B);$=A(U,B,j)}else{throw new Error(\"nx-inflate: bad block\")}for(;;){var s=k(S);if(s<256){I(1);f[n++]=s;continue}if(s===256){break}s-=257;var G=V[s]+u(W[s]),H=k($),K=Y[H]+u(Z[H]);if(K>n){throw new Error(\"nx-inflate: bad distance\")}I(G);for(var P=0;P< G;P++){f[n]=f[n-K];n++}}}return w?f.subarray(0,n):new TextDecoder(\"utf-8\").decode(f.subarray(0,n))}if(typeof window.__nxInflate!==\"function\"){window.__nxInflate=M}function Q(g){var w=M(g,true),l=w[0]|w[1]<<8|w[2]<<16|w[3]<<24;var c=w.subarray(4+l);function r(o,e){var b=\"\",f=c.subarray(o,o+e);for(var n=0;e>n;n+=8192){b+=String.fromCharCode.apply(null,f.subarray(n,n+8192))}return btoa(b)}return JSON.parse(new TextDecoder(\"utf-8\").decode(w.subarray(4,4+l)),function(o,e){return e?e.$b?r(e.$b[0],e.$b[1]):e:e})}if(typeof window.__nxUnpack!==\"function\"){window.__nxUnpack=Q}var N=document.querySelectorAll(\"script[data-nx-run]\");for(var D=0;D< N.length;D++){var y=N[D];if(y.hasAttribute(\"data-nx-ran\")){continue}y.setAttribute(\"data-nx-ran\",\"true\");try{var m=document.createElement(\"script\");m.className=\"_iub_cs_skip\";m.setAttribute(\"data-nx-runtime\",y.getAttribute(\"data-nx-run\"));m.textContent=window.__nxInflate(y.textContent);(document.head||document.documentElement).appendChild(m)}catch(g){y.setAttribute(\"data-nx-ran\",\"failed\")}}})();<\/script><div class=\"nxe-host\" data-nxe-embed=\"n-g08-topics-ai-and-scientific-workflows\"><div class=\"nxe\" id=\"nxe-nw2kuom3\" data-inst=\"gcri\" data-theme=\"light\" data-grammar=\"atlas\" data-domain=\"project\" data-arche=\"topic\" lang=\"en\" data-nx-css=\"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\"><style>#nxe-nw2kuom3.nxe{display:block;box-sizing:border-box;max-width:1180px;margin:0 auto;padding:0 16px;font-family:ui-sans-serif,system-ui,-apple-system,\"Segoe UI\",sans-serif;line-height:1.55;overflow-wrap:break-word}#nxe-nw2kuom3.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-nw2kuom3.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-nw2kuom3.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-nw2kuom3.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-nw2kuom3.nxe :where(ul,ol){padding-left:20px}#nxe-nw2kuom3.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-nw2kuom3.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-nw2kuom3.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-nw2kuom3.nxe svg{display:none}#nxe-nw2kuom3.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-nw2kuom3.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-nw2kuom3.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"dag\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>G08 \u00b7 TOPIC \u00b7 ai-and-scientific-workflows<\/strong>GCRI dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GCRI<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Nexus Labs<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Established<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Topic dossier \u00b7 Nexus Labs<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">AI in scientific workflows<\/h1><p class=\"nx-tagline\">Machine learning now enters science at every step: screening literature, designing experiments, analysing images and sequences, emulating simulations and drafting text. Each use can speed discovery and each can introduce errors that look like findings. This page sets out how models are evaluated for scientific use, where leakage, benchmark overfitting and silent drift arise, and what evidence editors, funders and regulators need before they rely on a model-assisted result.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/nexus-labs\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/nexus-labs\/topics\/\">Back to Topics<\/a><\/div><\/div><div class=\"nx-hero-canvas\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Systems<\/strong> \u00b7 around this subject<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-labs-testbed\" data-cx=\"labs.testbed\" data-variant=\"section\" aria-label=\"Testbed operations: illustrative console with placeholder names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h2 class=\"nx-fk-title\">Testbed operations<\/h2><p class=\"nx-fk-sub\">AI in scientific workflows \u00b7 Field lab coast \u00b7 Campaign 5<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure's values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>Sensors deployed<\/span><span class=\"nx-cx-prod\">GCRI \u00b7 Nexus Labs<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"map\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Sensor deployment<\/h4><p class=\"nx-fk-panel-sub\">placeholder coastline<\/p><\/div><\/div><div class=\"nx-cx-map\"><svg class=\"nx-cx-svg\" viewBox=\"0 0 480 250\" role=\"img\" aria-label=\"Sensor deployment: schematic on placeholder geography. Illustrative.\" focusable=\"false\"><rect class=\"cx-sea\" width=\"480\" height=\"250\" rx=\"4\"\/><path class=\"cx-land\" d=\"M0 250 L0 87.5 C 96 70, 168 125, 240 105 S 384 50, 480 75 L480 250 Z\"\/><path class=\"cx-flood0\" d=\"M0 107.5 C 96 90, 168 145, 240 125 S 384 70, 480 95 L480 75 S 384 50, 240 105 S 96 70, 0 87.5 Z\"\/><path class=\"cx-flood1\" d=\"M0 127.5 C 96 110, 168 165, 240 145 S 384 90, 480 115 L480 75 S 384 50, 240 105 S 96 70, 0 87.5 Z\"\/><path class=\"cx-flood2\" d=\"M0 147.5 C 96 130, 168 185, 240 165 S 384 110, 480 135 L480 75 S 384 50, 240 105 S 96 70, 0 87.5 Z\"\/><rect class=\"cx-site-hot\" x=\"100.6\" y=\"115\" width=\"10\" height=\"10\" rx=\"2\"\/><text class=\"cx-lbl-s\" x=\"114.6\" y=\"124\" text-anchor=\"start\">Buoy<\/text><rect class=\"cx-site\" x=\"259\" y=\"145\" width=\"10\" height=\"10\" rx=\"2\"\/><text class=\"cx-lbl-s\" x=\"273\" y=\"154\" text-anchor=\"start\">Station<\/text><rect class=\"cx-site\" x=\"379\" y=\"115\" width=\"10\" height=\"10\" rx=\"2\"\/><text class=\"cx-lbl-s\" x=\"393\" y=\"124\" text-anchor=\"start\">Gauge<\/text><text class=\"cx-lbl-s\" x=\"16\" y=\"22\" text-anchor=\"start\">Sea<\/text><text class=\"cx-lbl-s\" x=\"464\" y=\"236\" text-anchor=\"end\">Bands: tidal range<\/text><g class=\"cx-north\"><path d=\"M458 30 L463 18 L468 30 Z\"\/><text class=\"cx-lbl-s\" x=\"463\" y=\"42\" text-anchor=\"middle\">N<\/text><\/g><\/svg><\/div><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Sensors<\/h4><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-labs-testbed-t1\"><table class=\"nx-fk-table\" data-fk-table=\"cx-labs-testbed-t1\" aria-label=\"Sensors\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Sensor<\/button><\/th><th scope=\"col\" data-col=\"c1\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">Uptime (%)<\/button><\/th><th scope=\"col\" data-col=\"c2\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Drift<\/button><\/th><th scope=\"col\" data-col=\"c3\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">State<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-labs-testbed:r1-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>S-01 tide<\/td><td data-num=\"true\" data-sv=\"94\">94<\/td><td data-num=\"true\" data-sv=\"0\">0.0<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Nominal<\/span><\/td><\/tr><tr data-fk-key=\"cx-labs-testbed:r1-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>S-04 salinity<\/td><td data-num=\"true\" data-sv=\"76\">76<\/td><td data-num=\"true\" data-sv=\"0.4\">0.4<\/td><td><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Recalibrate<\/span><\/td><\/tr><tr data-fk-key=\"cx-labs-testbed:r1-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>S-07 wave<\/td><td data-num=\"true\" data-sv=\"95\">95<\/td><td data-num=\"true\" data-sv=\"0.1\">0.1<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Nominal<\/span><\/td><\/tr><\/tbody><\/table><\/div><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Sensor vs reference<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Sensor vs reference\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-labs-testbed-c2\" data-fk-xs=\"lin,0,23,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-series=\"S-04|ink|l||0:48.3;1:51.7;2:55;3:57.3;4:52.2;5:47.4;6:51.9;7:49.4;8:50.7;9:45.4;10:48.2;11:43.8;12:47.9;13:45;14:50.3;15:49.6;16:46.3;17:49.8;18:54.1;19:54.5;20:53.7;21:53.9;22:59.1;23:53.6~Reference|acc|l||0:52.9;1:53.9;2:56.5;3:55.9;4:55.6;5:54.9;6:52.7;7:56;8:55.7;9:58.1;10:61.7;11:58.5;12:56.8;13:56.5;14:56.8;15:57.6;16:61.1;17:58.7;18:58.6;19:59.7;20:61.8;21:62.9;22:64.6;23:61.9\" data-nx-z=\"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\"><\/svg><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Testbed operations: the screen's structure, terms and states for AI in scientific workflows.<\/span><span data-honesty=\"true\">Placeholder names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><p class=\"nx-caption\"><strong>Fig.<\/strong><span>Testbed operations. Design wireframe with placeholder names and illustrative values, not a record or a live system.<\/span><\/p><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-sec-field\"><span>02<\/span>Connected activity in World<\/a><a href=\"#nx-sec-question\"><span>03<\/span>AI in scientific workflows: how the\u2026<\/a><a href=\"#nx-sec-terms\"><span>04<\/span>Terms used on this page<\/a><a href=\"#nx-sec-indicators\"><span>05<\/span>What is measured, and how well<\/a><a href=\"#nx-sec-pipeline\"><span>06<\/span>From sources to decisions<\/a><a href=\"#nx-sec-sources\"><span>07<\/span>Where the evidence comes from<\/a><a href=\"#nx-sec-decisions\"><span>08<\/span>Who decides, and what the evidence\u2026<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">AI in scientific research is the use of machine learning to screen literature, design experiments, analyse images and sequences, emulate simulations and draft text. Each use can speed discovery and each can introduce errors that look like findings, through data leakage, overfitted benchmarks, drift or fabricated references.<\/p><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Geography<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Connected activity in World.<\/h2><p class=\"nx-lede\">For ai and scientific workflows, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, night lights and population density with true colour from NASA GIBS, framed on World; every other NASA layer, live feed and forecast waits in the layer list.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-cmap-field\" data-nx-map=\"ctx-field\" data-cmap=\"world:digital\" data-ml-src=\"digital\" data-ml-plan=\"p-b2ae4902\"><div class=\"nx-wm\" data-nx-water=\"ctx-digital\" data-wm-domain=\"ctx-digital\" data-wm-tiles=\"on\" data-wm-flat=\"on\" data-wm-focus=\"-180,-58,180,84\" data-ws-live=\"off\" data-claim=\"INTEGRATED\" data-wm-panel=\"closed\" data-wm-mode=\"2d\"><div class=\"nx-wm-stage\" tabindex=\"0\" role=\"application\" aria-roledescription=\"interactive map\" aria-label=\"Connected activity, World: satellite and model layers from NASA on a 3D terrain of ASTER GDEM elevation. Arrow keys move; with Shift they turn and tilt in 3D; plus and minus zoom, 0 resets; I inspects, question mark lists the keys.\" aria-describedby=\"nx-wm-help\"><canvas class=\"nx-wm-canvas\" aria-hidden=\"true\"><\/canvas><p class=\"nx-wm-noscript\">The interactive map needs JavaScript; the layer list and sources are below.<\/p><div class=\"nx-wm-presets\" role=\"group\" aria-label=\"Quick views\"><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"ntl,people||||\" aria-pressed=\"false\">Lights and people<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"true||||\" aria-pressed=\"false\">True colour<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"ntl||||\" aria-pressed=\"false\">Night lights<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"people||||\" aria-pressed=\"false\">Population<\/button><\/div><div class=\"nx-wm-panel\" id=\"nx-wm-panel\" role=\"region\" aria-label=\"Map layers\"><div class=\"nx-wm-panel-head\"><p class=\"nx-wm-panel-title\">Layers <span class=\"nx-wm-count\">3<\/span><\/p><button type=\"button\" class=\"nx-wm-icon nx-wm-panel-close\" data-wm-act=\"panel\" aria-label=\"Close the layer list\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-panel-body\"><div class=\"nx-wm-group\"><p class=\"nx-wm-group-title\">Connected activity, for this page<\/p><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"true\" aria-pressed=\"true\" data-wm-z=\"20\" data-wm-lag=\"1\" data-wm-prov=\"NASA LANCE\" data-wm-kind=\"Satellite image\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">True colour<\/span><span class=\"nx-wm-layer-src\">NASA LANCE VIIRS NOAA-20 corrected reflectance \u00b7 <span data-wm-date=\"true\">13 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"true\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"ntl\" aria-pressed=\"true\" data-wm-z=\"21\" data-wm-lag=\"0\" data-wm-op=\"0.9\" data-wm-short=\"Night lights\" data-wm-prov=\"NASA LAADS DAAC\" data-wm-kind=\"Satellite radiance, corrected for moonlight and atmosphere\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Night-time lights, daily<\/span><span class=\"nx-wm-layer-src\">NASA LAADS DAAC VIIRS Black Marble (VNP46A2) \u00b7 <span data-wm-date=\"ntl\">12 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"ntl\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"people\" aria-pressed=\"true\" data-wm-z=\"22\" data-wm-lag=\"0\" data-wm-op=\"0.8\" data-wm-short=\"Population density\" data-wm-prov=\"NASA SEDAC (CIESIN, Columbia University)\" data-wm-kind=\"Census-based estimate, static (2020)\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Population density, 2020<\/span><span class=\"nx-wm-layer-src\">NASA SEDAC (CIESIN, Columbia University) Gridded Population of the World v4.11 \u00b7 <span data-wm-date=\"people\">static<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"people\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"cover\" aria-pressed=\"false\" data-wm-z=\"23\" data-wm-lag=\"0\" data-wm-time=\"latest\" data-wm-op=\"0.7\" data-wm-short=\"Land cover\" data-wm-prov=\"NASA LP DAAC\" data-wm-kind=\"Satellite classification; latest year GIBS publishes\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Land cover type (IGBP), yearly<\/span><span class=\"nx-wm-layer-src\">NASA LP DAAC MODIS Terra and Aqua land cover (MCD12Q1) \u00b7 <span data-wm-date=\"cover\">2024<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"cover\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"evi\" aria-pressed=\"false\" data-wm-z=\"24\" data-wm-lag=\"0\" data-wm-op=\"0.85\" data-wm-short=\"Vegetation (EVI)\" data-wm-prov=\"NASA LANCE\" data-wm-kind=\"Satellite index; clouds leave gaps\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Enhanced vegetation index, rolling 8 days<\/span><span class=\"nx-wm-layer-src\">NASA LANCE MODIS Terra EVI \u00b7 <span data-wm-date=\"evi\">13 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"evi\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"ndvi\" aria-pressed=\"false\" data-wm-z=\"25\" data-wm-lag=\"0\" data-wm-op=\"0.85\" data-wm-short=\"Greenness (NDVI)\" data-wm-prov=\"NASA LANCE\" data-wm-kind=\"Satellite index; clouds leave gaps\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Vegetation greenness (NDVI), rolling 8 days<\/span><span class=\"nx-wm-layer-src\">NASA LANCE MODIS Terra NDVI \u00b7 <span data-wm-date=\"ndvi\">13 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"ndvi\" hidden><\/div><\/div><\/div><\/div><div class=\"nx-wm-rail\" role=\"toolbar\" aria-orientation=\"vertical\" aria-label=\"Map controls\"><button type=\"button\" class=\"nx-wm-icon nx-wm-open\" data-wm-act=\"panel\" aria-controls=\"nx-wm-panel\" aria-expanded=\"false\" aria-label=\"Layers\" title=\"Layers\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M8 2.5l-6.5 3.5 6.5 3.5 6.5-3.5zm-6.5 6.5 6.5 3.5 6.5-3.5m-13 2.8 6.5 3.5 6.5-3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"tools\" aria-controls=\"nx-wm-menu\" aria-expanded=\"false\" aria-label=\"Expert tools\" title=\"Expert tools\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3 4.5l10 0m-10 3.5 10 0m-10 3.5 10 0m-7.5-8.5 0 3m5 .5 0 3m-3.5.5 0 3\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><span class=\"nx-wm-sep\" aria-hidden=\"true\"><\/span><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"in\" aria-label=\"Zoom in\" title=\"Zoom in\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M8 3v10M3 8h10\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"out\" aria-label=\"Zoom out\" title=\"Zoom out\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3 8h10\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"reset\" aria-label=\"Reset the view\" title=\"Reset the view\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.2 8a4.8 4.8 0 1 0 1.5-3.5M3.2 2.6v2.6h2.6\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"full\" aria-label=\"Full screen\" title=\"Full screen\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M2.5 6l0-3.5 3.5 0m7.5 3.5 0-3.5-3.5 0m-7.5 7.5 0 3.5 3.5 0m7.5-3.5 0 3.5-3.5 0\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-menu\" id=\"nx-wm-menu\" role=\"menu\" aria-label=\"Expert tools\" hidden><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"inspect\">Inspect a point<kbd>I<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"measure\">Measure distance<kbd>M<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"compare\">Compare two dates<kbd>C<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"share\">Copy a link to this view<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"png\">Export as PNG with credits<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"xt\" data-wm-xt=\"6oPh6EhWj+gDMzpLLT8H\/wVfxJakWdyAB8snze2Nado=\">More expert tools<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"keys\">Keyboard shortcuts<kbd>?<\/kbd><\/button><\/div><div class=\"nx-wm-keys\" role=\"dialog\" aria-label=\"Keyboard shortcuts\" hidden><p class=\"nx-wm-keys-title\">Keyboard, with the map focused<\/p><dl class=\"nx-wm-keys-list\"><\/dl><button type=\"button\" class=\"nx-wm-icon nx-wm-keys-close\" data-wm-tool=\"keys\" aria-label=\"Close the shortcuts\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><p class=\"nx-wm-hint\" hidden>Hold Ctrl or \u2318 and scroll to zoom<\/p><p class=\"nx-wm-toolbar\" role=\"status\" hidden><\/p><div class=\"nx-wm-tip\" role=\"tooltip\" hidden><\/div><div class=\"nx-wm-pin\" hidden><div class=\"nx-wm-pin-head\"><p class=\"nx-wm-pin-title\"><\/p><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"unpin\" aria-label=\"Close the card\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-pin-chart\"><\/div><div class=\"nx-wm-pin-body\"><\/div><\/div><div class=\"nx-wm-dock\"><div class=\"nx-wm-time\" role=\"group\" aria-label=\"Imagery date\"><button type=\"button\" class=\"nx-wm-icon\" data-wm-step=\"prev\" aria-label=\"One day earlier\">\u2039<\/button><p class=\"nx-wm-time-label\">Latest available<\/p><button type=\"button\" class=\"nx-wm-icon\" data-wm-step=\"next\" aria-label=\"One day later\">\u203a<\/button><span class=\"nx-wm-track\" role=\"slider\" tabindex=\"0\" aria-label=\"Imagery date, up to 30 days back\"><\/span><button type=\"button\" class=\"nx-wm-latest\" data-wm-step=\"latest\" aria-pressed=\"true\">Latest<\/button><\/div><div class=\"nx-wm-chips\" role=\"group\" aria-label=\"Legends\"><\/div><p class=\"nx-wm-attrib\">NASA GIBS \/ EOSDIS \u00b7 ASTER GDEM (METI, NASA) \u00b7 Blue Marble (NASA) \u00b7 \u00a9 OpenStreetMap contributors (ODbL) \u00b7 Natural Earth<\/p><div class=\"nx-wm-legends\" aria-live=\"polite\" hidden><\/div><\/div><\/div><div class=\"nx-container nx-wm-below\"><p class=\"nx-wm-status\" role=\"status\">Loading the latest NASA imagery.<\/p><p id=\"nx-wm-help\" class=\"nx-wm-help\">Drag to move. Ctrl or \u2318 and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.<\/p><p class=\"nx-caption\"><span>For ai and scientific workflows, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, night lights and population density with true colour from NASA GIBS, framed on World; every other NASA layer, live feed and forecast waits in the layer list.<\/span><span data-honesty=\"true\">Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.<\/span><\/p><details class=\"nx-wm-credits\"><summary><span class=\"nx-wm-credits-line\">Data: NASA GIBS \u00b7 ASTER GDEM \u00b7 Blue Marble \u00b7 \u00a9 OpenStreetMap contributors \u00b7 Natural Earth \u00b7 Copernicus Sentinel-2 L2A \u00b7 NASA HLS S30<\/span><span class=\"nx-wm-credits-more\">Sources and licences<\/span><\/summary><p class=\"nx-wm-credit\"><ul class=\"nx-wm-credit-list\"><li data-wm-credit=\"nasa.gibs.field-layers\">We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.gibs.water-layers\">We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.lance.viirs-water\">VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.laads.black-marble\">VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"sedac.gpw-v4\">Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\" rel=\"noopener\" target=\"_blank\">CC BY 4.0<\/a><\/li><li data-wm-credit=\"nasa.modis-viirs.land\">MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa-meti.aster-gdem-v3\">ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"naturalearth.all-scales\">Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. <a class=\"nx-link\" href=\"https:\/\/www.naturalearthdata.com\/about\/terms-of-use\/\" rel=\"noopener\" target=\"_blank\">Public domain<\/a><\/li><li data-wm-credit=\"nasa.gibs.reference-osm\">\u00a9 OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.openstreetmap.org\/copyright\" rel=\"noopener\" target=\"_blank\">ODbL<\/a><\/li><li data-wm-credit=\"nasa.earth-observatory.blue-marble\">Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li>Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, \u00a9 OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.<\/li><li>Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, \u00a9 MapLibre contributors.<\/li><li>Observed (EO): Copernicus Sentinel-2 L2A (contains modified Copernicus Sentinel data 2026), Microsoft Planetary Computer<\/li><li>Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).<\/li><li>Optional layers (35 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.<\/li><\/ul><p class=\"nx-wm-credit\"><strong>Layers and dates<\/strong><\/p><ul class=\"nx-wm-credit-list\"><li>True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE \u00b7 <span data-wm-date=\"true\">13 Oct 2026<\/span><\/li><li>Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC \u00b7 <span data-wm-date=\"ntl\">12 Oct 2026<\/span><\/li><li>Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 \u00b7 <span data-wm-date=\"people\">static<\/span><\/li><li>Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC \u00b7 <span data-wm-date=\"cover\">2024<\/span><\/li><li>Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE \u00b7 <span data-wm-date=\"evi\">13 Oct 2026<\/span><\/li><li>Vegetation greenness (NDVI), rolling 8 days: MODIS Terra NDVI, NASA LANCE \u00b7 <span data-wm-date=\"ndvi\">13 Oct 2026<\/span><\/li><\/ul><\/p><\/details><\/div><\/div><div class=\"nx-live-slot nx-live-slot--map\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gcri\/G08\/topics\/ai-and-scientific-workflows\/#map\" data-nxf-title=\"Nexus Labs live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-question\" class=\"nx-section\" data-hue=\"project\" data-variant=\"stack\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">The question<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">AI in scientific workflows: how the risk develops.<\/h2><p class=\"nx-lede\">Model evaluation and scientific assistance. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"2\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M14 2v4a2 2 0 0 0 2 2h4\"\/><path d=\"M4.268 21a2 2 0 0 0 1.727 1H18a2 2 0 0 0 2-2V7l-5-5H6a2 2 0 0 0-2 2v3\"\/><path d=\"m9 18-1.5-1.5\"\/><circle cx=\"5\" cy=\"14\" r=\"3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Leakage inflates performance<\/h3><p class=\"nx-p nx-mt-1\">When information from the test set reaches training, through duplicates, shared patients, neighbouring sites or preprocessing fitted on all the data, a model appears more accurate than it is. Leakage has been documented in many fields that adopted machine learning.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><circle cx=\"12\" cy=\"12\" r=\"10\"\/><circle cx=\"12\" cy=\"12\" r=\"6\"\/><circle cx=\"12\" cy=\"12\" r=\"2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Benchmarks get overfitted<\/h3><p class=\"nx-p nx-mt-1\">Repeated tuning against the same public benchmark selects models that fit its quirks. Gains on a leaderboard can vanish on new data, and contamination of training corpora with benchmark items makes scores hard to interpret.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M22 12h-2.48a2 2 0 0 0-1.93 1.46l-2.35 8.36a.25.25 0 0 1-.48 0L9.24 2.18a.25.25 0 0 0-.48 0l-2.35 8.36A2 2 0 0 1 4.49 12H2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Drift and distribution shift<\/h3><p class=\"nx-p nx-mt-1\">Instruments, protocols, populations and software change. A model validated on last year's data or another lab's scanner can degrade without any visible error. Monitoring after deployment is part of the evaluation, not an extra.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M21 12a9 9 0 0 0-9-9 9.75 9.75 0 0 0-6.74 2.74L3 8\"\/><path d=\"M3 3v5h5\"\/><path d=\"M3 12a9 9 0 0 0 9 9 9.75 9.75 0 0 0 6.74-2.74L21 16\"\/><path d=\"M16 16h5v5\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Generated text and fabricated sources<\/h3><p class=\"nx-p nx-mt-1\">Language models can produce fluent text, citations and code that are wrong. Unchecked, these errors enter manuscripts, peer reviews and proposals, and spread through citation. Disclosure and verification policies exist because such errors are hard to see.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Data leakage<\/td><td data-label=\"Definition\">Information from the test set reaching a model during training, which inflates reported performance.<\/td><\/tr><tr><td data-label=\"Term\">Benchmark overfitting<\/td><td data-label=\"Definition\">Tuning methods to a fixed benchmark until scores no longer reflect performance on new data.<\/td><\/tr><tr><td data-label=\"Term\">Distribution shift<\/td><td data-label=\"Definition\">A change between the data a model was trained on and the data it meets in use.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" class=\"nx-section\" data-hue=\"project\" data-variant=\"inset\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Indicators<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">What is measured, and how well.<\/h2><p class=\"nx-lede\">Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Indicator<\/th><th scope=\"col\">Unit<\/th><th scope=\"col\">Cadence<\/th><th scope=\"col\">Source type<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Indicator\">Held-out evaluation design stated<\/td><td data-label=\"Unit\">percent of model-based studies<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Audit against the REFORMS checklist<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Code and model weights available<\/td><td data-label=\"Unit\">percent of studies with runnable code and weights<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Repository and article audit<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">External validation<\/td><td data-label=\"Unit\">percent of models tested on data from another site or period<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Literature review<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Calibration error<\/td><td data-label=\"Unit\">expected calibration error, dimensionless<\/td><td data-label=\"Cadence\">per model<\/td><td data-label=\"Source type\">Held-out evaluation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Performance gap under shift<\/td><td data-label=\"Unit\">difference in the primary metric between internal and external test sets<\/td><td data-label=\"Cadence\">per model<\/td><td data-label=\"Source type\">Validation reports<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">AI use disclosure<\/td><td data-label=\"Unit\">percent of submissions with a disclosure statement<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Journal submission records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">DECLARED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Contamination checks reported<\/td><td data-label=\"Unit\">percent of benchmark evaluation reports<\/td><td data-label=\"Cadence\">annual<\/td><td data-label=\"Source type\">Evaluation reports<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/gcri\/G08\/topics\/ai-and-scientific-workflows\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">From sources to decisions.<\/h2><p class=\"nx-lede\">The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Pipeline canvas<\/strong> \u00b7 AI in scientific workflows as a run graph<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-g08-topics-ai-and-scientific-workflows-sk-t nx-dag-g08-topics-ai-and-scientific-workflows-sk-d\" data-nx-z=\"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\"><title id=\"nx-dag-g08-topics-ai-and-scientific-workflows-sk-t\">AI in scientific workflows: from task exposure to reskilling pathways<\/title><desc id=\"nx-dag-g08-topics-ai-and-scientific-workflows-sk-d\">Occupations are broken into tasks and scored for exposure; skill gaps are found against vacancies; training pathways are designed with employers; placement and social protection support the transition. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 02<\/strong><span>Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-sources\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Sources<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Where the evidence comes from.<\/h2><p class=\"nx-lede\">Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Source<\/th><th scope=\"col\">What it gives<\/th><th scope=\"col\">Licence class<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Source\">OpenML<\/td><td data-label=\"What it gives\">Datasets, tasks and evaluation runs with standardised splits<\/td><td data-label=\"Licence class\">Open, CC BY<\/td><\/tr><tr><td data-label=\"Source\">Hugging Face Hub model and dataset cards<\/td><td data-label=\"What it gives\">Documented models and datasets with stated training data and limitations<\/td><td data-label=\"Licence class\">Licence per model or dataset<\/td><\/tr><tr><td data-label=\"Source\">MLCommons benchmarks<\/td><td data-label=\"What it gives\">Shared benchmarks and measurement rules for model performance<\/td><td data-label=\"Licence class\">Open, Apache 2.0<\/td><\/tr><tr><td data-label=\"Source\">AI Incident Database (Responsible AI Collaborative)<\/td><td data-label=\"What it gives\">Reported failures and harms of deployed AI systems<\/td><td data-label=\"Licence class\">Open, attribution<\/td><\/tr><tr><td data-label=\"Source\">Software Heritage<\/td><td data-label=\"What it gives\">Archived source code with persistent intrinsic identifiers<\/td><td data-label=\"Licence class\">Open archive, licence per repository<\/td><\/tr><tr><td data-label=\"Source\">arXiv metadata<\/td><td data-label=\"What it gives\">Preprints across physics, computing and quantitative biology<\/td><td data-label=\"Licence class\">Metadata CC0; licence per paper<\/td><\/tr><tr><td data-label=\"Source\">Zenodo (CERN)<\/td><td data-label=\"What it gives\">Citable deposits of code, models, data and evaluation outputs<\/td><td data-label=\"Licence class\">Open, licence per record<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G08\/topics\/ai-and-scientific-workflows\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Decisions<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">Nexus Labs prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"ledger\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Who decides<\/th><th scope=\"col\">The question<\/th><th scope=\"col\">Evidence this page serves<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Who decides\">Journal editors and reviewers<\/td><td data-label=\"The question\">Whether a model-based claim rests on an evaluation that excludes leakage and tests generalisation<\/td><td data-label=\"Evidence this page serves\">REFORMS or TRIPOD+AI checklist, split design, external validation, code availability<\/td><\/tr><tr><td data-label=\"Who decides\">Research funders<\/td><td data-label=\"The question\">Whether applicants and reviewers may use generative AI, and on what disclosure terms<\/td><td data-label=\"Evidence this page serves\">Funder policy, disclosure records, documented verification of generated content<\/td><\/tr><tr><td data-label=\"Who decides\">Medicines and device regulators<\/td><td data-label=\"The question\">Whether an AI component in a medical product is validated for its intended use<\/td><td data-label=\"Evidence this page serves\">Prespecified evaluation, external validation, predetermined change control plan<\/td><\/tr><tr><td data-label=\"Who decides\">Research integrity offices<\/td><td data-label=\"The question\">Whether an allegation involving fabricated or generated content is substantiated<\/td><td data-label=\"Evidence this page serves\">Provenance of text, data and code; version history; tool logs<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Evaluation protocols that fix splits, metrics and baselines before training<\/li><li>Model cards and datasheets with intended use, limits and known failure modes<\/li><li>Leakage and contamination audits of model-assisted studies<\/li><li>Reproducible pipelines with code, weights, environment and data versions archived<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Core questions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">What experts ask about AI in research.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>What task is the model doing, and what is it compared against?<\/li><li>Was the model evaluated on data independent of the data it was trained or tuned on?<\/li><li>How does performance change when conditions shift away from the training data?<\/li><li>Can the output be checked by a person or a simpler method, and is it?<\/li><li>Which steps of the workflow were assisted, and is that disclosed in the result?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms-and-drivers\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"inset\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Mechanisms and drivers<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Where AI helps and where it misleads.<\/h2><\/div><div class=\"nx-section-body\"><p class=\"nx-p\">Machine learning models learn statistical regularities from data. They can be very effective where those regularities are stable and data are plentiful, for example classifying land cover from satellite imagery or emulating a climate model to explore many scenarios. They are weaker where data are sparse, where the system changes over time or where rare events matter most, which describes much of risk science.<\/p><p class=\"nx-p\">Common failure modes are well understood: leakage, where information from the test set reaches training; distribution shift, where conditions in use differ from those in training; shortcut learning, where a model relies on a spurious cue; and overconfident outputs without calibrated uncertainty. Generative models add the risk of fluent but fabricated content, including citations and code that looks correct but is not.<\/p><p class=\"nx-p\">Workflow effects matter as much as model accuracy. When a tool is used at several stages, small errors can accumulate, and reviewers may give automated outputs more trust than they deserve.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-data\" class=\"nx-section\" data-hue=\"project\" data-variant=\"stack\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods and data<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">How AI tools are evaluated.<\/h2><\/div><div class=\"nx-section-body\"><ol class=\"nx-rows\"><li><span class=\"nx-eyebrow nx-index\">01<\/span><div><h3 class=\"nx-h4\">Held-out and out-of-distribution tests<\/h3><p class=\"nx-p nx-mt-1\">Evaluation on data separated by time, place or source. This is the minimum for any claim of generalisation.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">02<\/span><div><h3 class=\"nx-h4\">Baselines<\/h3><p class=\"nx-p nx-mt-1\">Comparison with simple statistical methods and expert judgement. A complex model that does not beat a simple baseline is not adding value.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">03<\/span><div><h3 class=\"nx-h4\">Calibration and uncertainty<\/h3><p class=\"nx-p nx-mt-1\">Checks that stated confidence matches observed accuracy, using <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\">uncertainty quantification<\/a>.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">04<\/span><div><h3 class=\"nx-h4\">Provenance and disclosure<\/h3><p class=\"nx-p nx-mt-1\">A record of which model, version and prompts or settings were used at each step, so the workflow can be re-run or audited.<\/p><\/div><\/li><\/ol><p class=\"nx-note nx-mt-3\">Benchmark scores rarely transfer directly to a new research setting, and a model's behaviour can change between versions. Evaluation is repeated when either the task or the model changes.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-13\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">12<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">How it is studied, and what the methods cannot say.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-methods\" data-limits=\"true\"><ol class=\"nx-mledger\" aria-label=\"Methods\"><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/nexus-labs\/stem\/ai-ml\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Artificial intelligence and machine learning<\/span><span class=\"nx-mledger-role\">Assisted screening for systematic reviews \u00b7 Surrogate models to guide experiments<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/technology\/ai-ml\/\" aria-label=\"Artificial intelligence and machine learning: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/nexus-labs\/stem\/uncertainty-quantification\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Uncertainty quantification and model validation<\/span><span class=\"nx-mledger-role\">Measurement uncertainty budgets \u00b7 Sensitivity analysis of simulation models<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\" aria-label=\"Uncertainty quantification and model validation: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/nexus-labs\/stem\/probability-statistics\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Probability and statistics<\/span><span class=\"nx-mledger-role\">Sample size and power planning \u00b7 Meta-analysis with bias assessment<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\" aria-label=\"Probability and statistics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/nexus-labs\/stem\/causal-inference\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">04<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Causal inference and impact evaluation<\/span><span class=\"nx-mledger-role\">Randomised pilots and stepped rollouts \u00b7 Quasi-experimental programme evaluation<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/causal-inference\/\" aria-label=\"Causal inference and impact evaluation: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/nexus-labs\/stem\/systems-engineering\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">05<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Systems engineering<\/span><span class=\"nx-mledger-role\">Requirements to verification traceability \u00b7 Stage-gate readiness reviews<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/engineering\/systems-engineering\/\" aria-label=\"Systems engineering: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><\/ol><aside class=\"nx-limits\" aria-label=\"What the methods cannot say\"><span class=\"nx-eyebrow\">What the methods cannot say<\/span><p class=\"nx-limits-text\">Reporting checklists for machine learning in science are recent and unevenly adopted, so audits measure reporting more reliably than correctness. Strong held-out performance does not show that a model has learned the mechanism a scientist cares about. Tools that claim to detect generated text are unreliable and should not be used alone to judge misconduct. Nexus Labs does not validate models for regulated use; regulators, editors and institutions decide.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-14-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-14\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">13<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-14\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What is data leakage?<\/summary><div class=\"nx-answer\">Any route by which information about the test data influences training or model selection. It makes reported performance higher than the model will achieve on genuinely new data.<\/div><\/details><details><summary>Is a high benchmark score enough to publish a method?<\/summary><div class=\"nx-answer\">Not on its own. Reviewers increasingly expect external validation, uncertainty on the score, ablations and an account of how the benchmark was protected from contamination.<\/div><\/details><details><summary>Can a generative AI tool be listed as an author?<\/summary><div class=\"nx-answer\">COPE and the ICMJE say no, because authorship carries accountability a tool cannot hold. Use of such tools is disclosed in the methods or acknowledgements, under the journal's policy.<\/div><\/details><details><summary>Why publish model weights and code?<\/summary><div class=\"nx-answer\">So others can rerun the evaluation, test on new data and find errors. Where weights cannot be shared, for privacy or safety, a controlled access route and a stated reason are the accepted alternative.<\/div><\/details><details><summary>Does Nexus Labs use generative AI to write findings?<\/summary><div class=\"nx-answer\">Any assisted step is designed to be disclosed. Findings are the responsibility of named researchers, who check every generated claim, citation and line of code.<\/div><\/details><details><summary>Can Labs evaluate a commercial AI model?<\/summary><div class=\"nx-answer\">A model can be evaluated under a published protocol. The result describes performance on the tested task and is not an endorsement or approval.<\/div><\/details><details><summary>Why compare with simple baselines?<\/summary><div class=\"nx-answer\">Because complex models often appear impressive on benchmarks but add little over a well-chosen simple method on real research data.<\/div><\/details><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-rel-15\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Related<\/span><h2 id=\"nx-rel-15\" class=\"nx-h3 nx-mt-2\">Around Nexus Labs.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/therisk.global\/nexus-labs\/topics\/research-protocols\/\">Research protocols <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/nexus-labs\/topics\/field-and-lab-testbeds\/\">Field and lab testbeds <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-work-lab\/\">Future of work lab <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-media-lab\/\">Future of media lab <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-energy-and-water-labs\/\">Future of energy and water labs <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/nexus-labs\/topics\/future-of-health-and-bio-labs\/\">Future of health and biotechnology labs <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/nexus-labs\/\">Nexus Labs home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/nexus-labs\/counterparts\/\">Counterparts across the Consortium <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/\">The Global Centre for Risk and Innovation home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/trust\/\">GCRI governance and trust <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-16\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Nexus Labs<\/span><h2 id=\"nx-close-16\" class=\"nx-section-title\">Bring your expertise to this field.<\/h2><p class=\"nx-lede\">The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"mailto:contact@therisk.global?subject=Nexus%20Labs%3A%20AI%20in%20scientific%20workflows\">Write to the team <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/nexus-labs\/contact\/\">See all contact routes<\/a><\/div><\/div><\/section><\/div><\/div><script class=\"_iub_cs_skip\">window.__nxMount ? window.__nxMount(document.currentScript) : null;<\/script>\n\n\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"FAQPage\",\"@id\":\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/#faq\",\"isPartOf\":{\"@id\":\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/#webpage\"},\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is data leakage?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Any route by which information about the test data influences training or model selection. It makes reported performance higher than the model will achieve on genuinely new data.\"}},{\"@type\":\"Question\",\"name\":\"Is a high benchmark score enough to publish a method?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Not on its own. Reviewers increasingly expect external validation, uncertainty on the score, ablations and an account of how the benchmark was protected from contamination.\"}},{\"@type\":\"Question\",\"name\":\"Can a generative AI tool be listed as an author?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"COPE and the ICMJE say no, because authorship carries accountability a tool cannot hold. Use of such tools is disclosed in the methods or acknowledgements, under the journal's policy.\"}},{\"@type\":\"Question\",\"name\":\"Why publish model weights and code?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"So others can rerun the evaluation, test on new data and find errors. Where weights cannot be shared, for privacy or safety, a controlled access route and a stated reason are the accepted alternative.\"}},{\"@type\":\"Question\",\"name\":\"Does Nexus Labs use generative AI to write findings?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Any assisted step is designed to be disclosed. Findings are the responsibility of named researchers, who check every generated claim, citation and line of code.\"}},{\"@type\":\"Question\",\"name\":\"Can Labs evaluate a commercial AI model?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"A model can be evaluated under a published protocol. The result describes performance on the tested task and is not an endorsement or approval.\"}},{\"@type\":\"Question\",\"name\":\"Why compare with simple baselines?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Because complex models often appear impressive on benchmarks but add little over a well-chosen simple method on real research data.\"}}]},{\"@type\":\"DefinedTermSet\",\"@id\":\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/#terms\",\"name\":\"Key terms: AI in scientific workflows\",\"inLanguage\":\"en\",\"isPartOf\":{\"@id\":\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/#webpage\"},\"hasDefinedTerm\":[{\"@type\":\"DefinedTerm\",\"name\":\"Data leakage\",\"description\":\"Information from the test set reaching a model during training, which inflates reported performance.\",\"inDefinedTermSet\":{\"@id\":\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/#terms\"}},{\"@type\":\"DefinedTerm\",\"name\":\"Benchmark overfitting\",\"description\":\"Tuning methods to a fixed benchmark until scores no longer reflect performance on new data.\",\"inDefinedTermSet\":{\"@id\":\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/#terms\"}},{\"@type\":\"DefinedTerm\",\"name\":\"Distribution shift\",\"description\":\"A change between the data a model was trained on and the data it meets in use.\",\"inDefinedTermSet\":{\"@id\":\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/#terms\"}}]}]}<\/script>\n\n<!-- nx-rev:1bc840b87e939158 -->\n<!-- nx-path:\/topics\/ai-and-scientific-workflows\/ -->","protected":false},"excerpt":{"rendered":"<p>LightDarkAuto G08 \u00b7 TOPIC \u00b7 ai-and-scientific-workflowsGCRI dossier \u00b7 service level R0 GCRI\u00b7Nexus Labs\u00b7Established Topic dossier \u00b7 Nexus Labs AI in scientific workflows Machine learning now enters science at every step: &hellip; <a href=\"https:\/\/therisk.global\/nexus-labs\/topics\/ai-and-scientific-workflows\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;AI in scientific workflows&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":2413,"menu_order":1191,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"_buddyx_sub_header_visibility":"","_buddyx_sub_header_title_visibility":"","_hide_show_side_panel":"","_buddyxpro_page_sidebar":"","_buddyxpro_page_disable_header":"","_buddyxpro_page_disable_footer":"","_buddyxpro_page_content_width":"","_buddyxpro_page_header_style":"","_buddyxpro_page_color_mode":"","_buddyxpro_page_loader":"","inline_featured_image":false,"footnotes":""},"class_list":["post-2421","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/therisk.global\/nexus-labs\/wp-json\/wp\/v2\/pages\/2421","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/therisk.global\/nexus-labs\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/therisk.global\/nexus-labs\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/therisk.global\/nexus-labs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/therisk.global\/nexus-labs\/wp-json\/wp\/v2\/comments?post=2421"}],"version-history":[{"count":2,"href":"https:\/\/therisk.global\/nexus-labs\/wp-json\/wp\/v2\/pages\/2421\/revisions"}],"predecessor-version":[{"id":2536,"href":"https:\/\/therisk.global\/nexus-labs\/wp-json\/wp\/v2\/pages\/2421\/revisions\/2536"}],"up":[{"embeddable":true,"href":"https:\/\/therisk.global\/nexus-labs\/wp-json\/wp\/v2\/pages\/2413"}],"wp:attachment":[{"href":"https:\/\/therisk.global\/nexus-labs\/wp-json\/wp\/v2\/media?parent=2421"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}