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10.1073/pnas.1509757112 http://scihub22266oqcxt.onion/10.1073/pnas.1509757112 C4664375!4664375 !26554009     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26554009 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Proc+Natl+Acad+Sci+U+S+A 2015 ; 112 (47 ): 14569-74 Nephropedia Template TP {{tp|p=26554009 |t=2015. Choosing experiments to accelerate collective discovery |pdf=|usr=}}{{26554009 }} gab.com Text Choosing experiments to accelerate collective discovery JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=26554009 #FOAvip A scientist's choice of research problem affects his or her personal career trajectory. Scientists' combined choices affect the direction and efficiency of scientific discovery as a whole. In this paper, we infer preferences that shape problem selection from patterns of published findings and then quantify their efficiency. We represent research problems as links between scientific entities in a knowledge network. We then build a generative model of discovery informed by qualitative research on scientific problem selection. We map salient features from this literature to key network properties: an entity's importance corresponds to its degree centrality, and a problem's difficulty corresponds to the network distance it spans. Drawing on millions of papers and patents published over 30 years, we use this model to infer the typical research strategy used to explore chemical relationships in biomedicine. This strategy generates conservative research choices focused on building up knowledge around important molecules. These choices become more conservative over time. The observed strategy is efficient for initial exploration of the network and supports scientific careers that require steady output, but is inefficient for science as a whole. Through supercomputer experiments on a sample of the network, we study thousands of alternatives and identify strategies much more efficient at exploring mature knowledge networks. We find that increased risk-taking and the publication of experimental failures would substantially improve the speed of discovery. We consider institutional shifts in grant making, evaluation, and publication that would help realize these efficiencies. Twit Text FOAVip Choosing experiments to accelerate collective discovery ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=26554009 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26554009 #FOAvip English Wikipedia <ref>{{Cite pmid|26554009 }}</ref> Choosing experiments to accelerate collective discovery #MMPMID26554009 Rzhetsky A ; Foster JG ; Foster IT ; Evans JA Proc Natl Acad Sci U S A 2015[Nov]; 112 (47 ): 14569-74 PMID26554009 show ga A scientist's choice of research problem affects his or her personal career trajectory. Scientists' combined choices affect the direction and efficiency of scientific discovery as a whole. In this paper, we infer preferences that shape problem selection from patterns of published findings and then quantify their efficiency. We represent research problems as links between scientific entities in a knowledge network. We then build a generative model of discovery informed by qualitative research on scientific problem selection. We map salient features from this literature to key network properties: an entity's importance corresponds to its degree centrality, and a problem's difficulty corresponds to the network distance it spans. Drawing on millions of papers and patents published over 30 years, we use this model to infer the typical research strategy used to explore chemical relationships in biomedicine. This strategy generates conservative research choices focused on building up knowledge around important molecules. These choices become more conservative over time. The observed strategy is efficient for initial exploration of the network and supports scientific careers that require steady output, but is inefficient for science as a whole. Through supercomputer experiments on a sample of the network, we study thousands of alternatives and identify strategies much more efficient at exploring mature knowledge networks. We find that increased risk-taking and the publication of experimental failures would substantially improve the speed of discovery. We consider institutional shifts in grant making, evaluation, and publication that would help realize these efficiencies. |*Research [MESH] |*Science [MESH] |Humans [MESH] |Publications [MESH] |Qualitative Research [MESH]Choosing experiments to accelerate collective discovery (epmc).pdf DeepDyve Pubget Overpricing