Editing Scientific misconduct (section)

===Exposure of fraudulent data===
With the advancement of the internet, there are now several tools available to aid in the detection of [[plagiarism]] and [[multiple publication]] within biomedical literature. One tool developed in 2006 by researchers in Dr. [[Harold Garner]]'s laboratory at the [[University of Texas Southwestern Medical Center at Dallas]] is [[Déjà vu]],<ref>{{cite web |url=http://dejavu.vbi.vt.edu/dejavu/ |title=Déjà vu: Medline duplicate publication database |website=dejavu.vbi.vt.edu |access-date=2013-08-04 |archive-date=2015-04-25 |archive-url=https://web.archive.org/web/20150425082932/http://dejavu.vbi.vt.edu/dejavu/ |url-status=dead }}</ref> an open-access database containing several thousand instances of duplicate publication. All of the entries in the database were discovered through the use of text data mining algorithm [[eTBLAST]], also created in Dr. Garner's laboratory. The creation of Déjà vu<ref>{{cite web |url=http://dejavu.vbi.vt.edu/dejavu |title=Deja vu: Medline duplicate publication database |website=dejavu.vbi.vt.edu |access-date=2013-08-04  |archive-url=https://web.archive.org/web/20140722061546/http://dejavu.vbi.vt.edu/dejavu/ |archive-date=2014-07-22 }}</ref> and the subsequent classification of several hundred articles contained therein have ignited much discussion in the scientific community concerning issues such as [[ethics|ethical behavior]], journal standards, and intellectual copyright. Studies within this database have been published in journals such as ''[[Nature (journal)|Nature]]'' and ''[[Science (journal)|Science]]'', among others.<ref>{{cite journal |author1=Errami M |author2=Garner HR |doi=10.1038/451397a |title=A tale of two citations |date=2008-01-23 |volume=451 |issue=7177 |journal=[[Nature (journal)|Nature]] |pages=397–399 |pmid=18216832|bibcode=2008Natur.451..397E |s2cid=4358525 |doi-access=free }}</ref><ref>{{cite journal |author1=Long TC |author2=Errami M |author3=George AC |author4=Sun Z |author5=Garner HR |doi=10.1126/science.1167408 |title=Scientific Integrity: Responding to Possible Plagiarism |journal=[[Science (journal)|Science]] |date=2009-03-06 |volume=323 |issue=5919 |pages=1293–1294 |pmid=19265004|s2cid=28467385 }}</ref>

Other tools which may be used to detect fraudulent data include [[error analysis (mathematics)|error analysis]]. Measurements generally have a small amount of error, and repeated measurements of the same item will generally result in slight differences in readings. These differences can be analyzed, and follow certain known mathematical and statistical properties. Should a set of data appear to be too faithful to the hypothesis, i.e., the amount of error that would normally be in such measurements does not appear, a conclusion can be drawn that the data may have been forged. Error analysis alone is typically not sufficient to prove that data have been falsified or fabricated, but it may provide the supporting evidence necessary to confirm suspicions of misconduct.