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	<title>UniResearch Community | Raymond | 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>Ping Lu posted an update: Finding good public datasets for method validation takes [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/608/</link>
				<pubDate>Tue, 25 Aug 2026 09:43:34 +0800</pubDate>

									<content:encoded><![CDATA[<p>Finding good public datasets for method validation takes a lot of digging. What are your go‑to high‑quality open datasets for general‑purpose method benchmarking in your field?</p>
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									<slash:comments>4</slash:comments>
				
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				<title>Mark Taylor posted an update: Data collection is the foundation of all empirical [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/455/</link>
				<pubDate>Thu, 20 Aug 2026 14:22:25 +0800</pubDate>

									<content:encoded><![CDATA[<p>Data collection is the foundation of all empirical and computational research. However, many researchers waste months on invalid data screening, low‑quality data cleaning and inconsistent data standards. What efficient data collection and filtering workflows do you adopt to accelerate your research progress?</p>
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									<slash:comments>3</slash:comments>
				
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				<title>Xiulan Jiang posted an update: Experimental reproducibility is the basic principle [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/454/</link>
				<pubDate>Thu, 20 Aug 2026 14:22:19 +0800</pubDate>

									<content:encoded><![CDATA[<p>Experimental reproducibility is the basic principle of academic research, but many published papers have unrepeatable experimental results due to incomplete parameter records and hidden operation details. How can we strictly guarantee research reproducibility in daily experiments?</p>
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									<slash:comments>3</slash:comments>
				
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				<title>Dylan Rogers posted an update: Just wrapped up a major round of journal revisions. [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/416/</link>
				<pubDate>Thu, 20 Aug 2026 10:18:52 +0800</pubDate>

									<content:encoded><![CDATA[<p>Just wrapped up a major round of journal revisions. One lesson learned: address every single reviewer comment explicitly, even when you disagree. Polite, evidence‑backed rebuttals work far better than defensive responses. What’s your best revision advice for early‑career researchers?</p>
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									<slash:comments>3</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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