Centralized scientific communities are less likely to generate replicable results
Centralized scientific communities are less likely to generate replicable results
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DOI：
10.7554/elife.43094
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发表时间：
2019-07-02
期刊：
影响因子：
7.7
通讯作者：
Evans, James A.
中科院分区：
文献类型：
--
作者：
Danchev, Valentin;Rzhetsky, Andrey;Evans, James A.
人们对几个科学领域的实验结果的可靠性表示关切，但这些问题尚未得到大规模的评估。在这里，我们确定了一个大样本的已发表的药物-基因相互作用的索赔策展在比较毒理学数据库（例如，苯并（a）芘降低SLC 22 A3的表达），并通过将它们与LINCS L1000程序的高通量实验相关联来评估这些索赔。我们的样本包括3363篇科学文章中关于51,292个药物-基因相互作用声明的60,159个支持性发现和4253个反对性发现。我们发现，在一篇论文中报告的声明重复频率比预期高19.0%（95%置信区间[CI]，16.9-21.2%），而在多篇论文中报告的声明重复频率比预期高45.5%（95% CI，21.8-74.2%）。我们还分析了与两个或两个以上已发表的研究结果相互作用的子样本（2 493项索赔; 6 272项支持调查结果; 339项反对调查结果; 1282篇研究文章），并表明，集中的科学社区，使用类似的方法，并涉及共同的作者谁贡献了许多文章，传播较少的可复制的索赔比分散的社区，使用更多样化的方法，包含更多独立的团队。我们的研究结果表明，促进分散合作的政策将如何增加生物医学研究中科学发现的稳健性。
Concerns have been expressed about the robustness of experimental findings in several areas of science, but these matters have not been evaluated at scale. Here we identify a large sample of published drug-gene interaction claims curated in the Comparative Toxicogenomics Database (for example, benzo(a)pyrene decreases expression of SLC22A3) and evaluate these claims by connecting them with high-throughput experiments from the LINCS L1000 program. Our sample included 60,159 supporting findings and 4253 opposing findings about 51,292 drug-gene interaction claims in 3363 scientific articles. We show that claims reported in a single paper replicate 19.0% (95% confidence interval [CI], 16.9-21.2%) more frequently than expected, while claims reported in multiple papers replicate 45.5% (95% CI, 21.8-74.2%) more frequently than expected. We also analyze the subsample of interactions with two or more published findings (2493 claims; 6272 supporting findings; 339 opposing findings; 1282 research articles), and show that centralized scientific communities, which use similar methods and involve shared authors who contribute to many articles, propagate less replicable claims than decentralized communities, which use more diverse methods and contain more independent teams. Our findings suggest how policies that foster decentralized collaboration will increase the robustness of scientific findings in biomedical research.