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	<title>UniResearch Community | Peter Stewart | Favorites</title>
	<link>https://community.uniresearch.ai/members/peterstewart/activity/favorites/</link>
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	<description>Activity feed of Peter Stewart's saved posts.</description>
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				<title>Zhiqiang Peng posted an update: Resource Share
Finding clean, simple academic [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/1011/</link>
				<pubDate>Wed, 09 Sep 2026 08:30:01 +0800</pubDate>

									<content:encoded><![CDATA[<p><strong>Resource Share</strong></p>
<p>Finding clean, simple academic slide templates takes extra work. Could everyone share links to open‑source slide decks they reuse for conference presentations?</p>
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									<slash:comments>2</slash:comments>
				
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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>Min Pan posted an update: Full‑text reading for every paper is impossible when [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/832/</link>
				<pubDate>Wed, 02 Sep 2026 01:13:15 +0800</pubDate>

									<content:encoded><![CDATA[<p>Full‑text reading for every paper is impossible when writing a thesis. Learn to skim abstracts, conclusions and key figures first, then dive into full‑text for high‑relevance resources.</p>
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									<slash:comments>4</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>Edwards posted an update: Every researcher faces paper rejection. It can [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/658/</link>
				<pubDate>Thu, 27 Aug 2026 07:14:32 +0800</pubDate>

									<content:encoded><![CDATA[<p>Every researcher faces paper rejection. It can shake confidence and stall your project momentum. What is your practical process to recover and move forward after receiving rejection notices?</p>
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									<slash:comments>7</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>Chao Guo posted an update: I’ve been testing out UniResearch for aggregating and [&#133;]</title>
				<link>https://community.uniresearch.ai/news-feed/p/418/</link>
				<pubDate>Thu, 20 Aug 2026 10:18:59 +0800</pubDate>

									<content:encoded><![CDATA[<p>I’ve been testing out UniResearch for aggregating and synthesizing large paper sets lately. Being able to quickly cross‑compare findings across dozens of related works cuts down my reading time significantly. Curious who else has tried similar AI‑augmented research workflows. What pain points are you still hoping these tools can solve?</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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