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	<title>UniResearch Community | Tao Zhu | Favorites</title>
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				<title>Madelyn posted an update: Method Help
Many researchers only organise data after [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/961/</link>
				<pubDate>Mon, 07 Sep 2026 12:25:56 +0800</pubDate>

									<content:encoded><![CDATA[<p><strong>Method Help</strong></p>
<p>Many researchers only organise data after paper acceptance. Poor archival practice creates reproducibility risks. What is your step‑by‑step routine to archive raw and intermediate data while the project is ongoing?</p>
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									<slash:comments>4</slash:comments>
				
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				<title>Adam Evans posted an update: Resource Share
Clean metadata is critical [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/942/</link>
				<pubDate>Mon, 07 Sep 2026 07:10:31 +0800</pubDate>

									<content:encoded><![CDATA[<p><strong>Resource Share</strong></p>
<p>Clean metadata is critical for reproducibility, yet manually organising metadata is extremely time‑consuming. Are there lesser‑known lightweight open utilities you rely on for dataset metadata standardisation?</p>
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									<slash:comments>2</slash:comments>
				
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				<title>Feng Song posted an update: Research Experience Sharing

Digital or physical [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/807/</link>
				<pubDate>Tue, 01 Sep 2026 09:48:31 +0800</pubDate>

									<content:encoded><![CDATA[<p><strong>Research Experience Sharing</strong></p>
<p>Digital or physical lab notebooks are your core research asset. Record raw data, parameter settings and unexpected anomalies in real‑time instead of recalling information days later.</p>
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									<slash:comments>3</slash:comments>
				
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				<title>Zhen Xie posted an update: Reading top conference papers, model metrics keep [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/415/</link>
				<pubDate>Thu, 20 Aug 2026 10:18:42 +0800</pubDate>

									<content:encoded><![CDATA[<p>Reading top conference papers, model metrics keep climbing. But when moving these models toward real‑world inference, latency, cost and robustness often become show‑stoppers. Do you think conference evaluation frameworks should put more weight on deployment‑oriented metrics?</p>
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									<slash:comments>5</slash:comments>
				
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				<title>Nathan Phillips posted an update: I’ve noticed many early‑stage researchers spend weeks [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/409/</link>
				<pubDate>Thu, 20 Aug 2026 10:18:17 +0800</pubDate>

									<content:encoded><![CDATA[<p>I’ve noticed many early‑stage researchers spend weeks picking suitable benchmarks. Some public datasets contain hidden distribution bias that can mislead model performance conclusions. What’s your go‑to checklist before locking down your benchmark suite?</p>
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									<slash:comments>3</slash:comments>
				
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