Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1080/08989621.2017.1327813 http://scihub22266oqcxt.onion/10.1080/08989621.2017.1327813 C6060414!6060414 !28481648     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28481648 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Account+Res 2017 ; 24 (6 ): 344-358 Nephropedia Template TP {{tp|p=28481648 |t=2017. Data-Intensive Science and Research Integrity |pdf=|usr=}}{{28481648 }} gab.com Text Data-Intensive Science and Research Integrity JO: Account Res http://www.kidney.de/pget.php?&tw=1&pmid=28481648 #FOAvip In this commentary, we consider questions related to research integrity in data-intensive science and argue that there is no need to create a distinct category of misconduct that applies to deception related to processing, analyzing, or interpreting data. The best way to promote integrity in data-intensive science is to maintain a firm commitment to epistemological and ethical values, such as honesty, openness, transparency, and objectivity, which apply to all types of research, and to promote education, policy development, and scholarly debate concerning appropriate uses of statistics. Twit Text FOAVip Data-Intensive Science and Research Integrity ????Account Res http://www.kidney.de/pget.php?&tw=1&pmid=28481648 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28481648 #FOAvip English Wikipedia <ref>{{Cite pmid|28481648 }}</ref> Data-Intensive Science and Research Integrity #MMPMID28481648 Resnik DB ; Elliott KC ; Soranno PA ; Smith EM Account Res 2017[]; 24 (6 ): 344-358 PMID28481648 show ga In this commentary, we consider questions related to research integrity in data-intensive science and argue that there is no need to create a distinct category of misconduct that applies to deception related to processing, analyzing, or interpreting data. The best way to promote integrity in data-intensive science is to maintain a firm commitment to epistemological and ethical values, such as honesty, openness, transparency, and objectivity, which apply to all types of research, and to promote education, policy development, and scholarly debate concerning appropriate uses of statistics. |*Ethics, Research [MESH] |*Scientific Misconduct [MESH] |Deception [MESH] |Humans [MESH]Data-Intensive Science and Research Integrity (epmc).pdf DeepDyve Pubget Overpricing