000 06855nam a2200757 i 4500
001 7809449
003 IEEE
005 20200413152923.0
006 m eo d
007 cr cn |||m|||a
008 170125s2017 caua foab 000 0 eng d
020 _a9781627059671
_qebook
020 _z9781627056403
_qprint
024 7 _a10.2200/S00743ED1V01Y201611ICR053
_2doi
035 _a(CaBNVSL)swl00407068
035 _a(OCoLC)970006781
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQ180.55.M67
_bS426 2017
082 0 4 _a174.95
_223
100 1 _aSeadle, Michael S.,
_d1950-,
_eauthor.
245 1 0 _aQuantifying research integrity /
_cMichael Seadle.
264 1 _a[San Rafael, California] :
_bMorgan & Claypool,
_c2017.
300 _a1 PDF (xix, 121 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on information concepts, retrieval, and services,
_x1947-9468 ;
_v# 53
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader.
500 _aPart of: Synthesis digital library of engineering and computer science.
504 _aIncludes bibliographical references (pages 111-119).
505 0 _a1. Introduction -- 1.1 Overview -- 1.2 Context -- 1.3 Time -- 1.4 Images --
505 8 _a2. State of the art -- 2.1 Introduction -- 2.2 Legal issues -- 2.3 Ethics -- 2.3.1 Second-language students -- 2.3.2 Self-plagiarism -- 2.4 Prevention -- 2.4.1 Education -- 2.4.2 Detection as prevention -- 2.5 Detection tools -- 2.5.1 Plagiarism tools -- 2.5.2 iThenticate -- 2.5.3 Crowdsourcing -- 2.5.4 Image-manipulation tools -- 2.6 Replication --
505 8 _a3. Quantifying plagiarism -- 3.1 Overview -- 3.1.1 History -- 3.1.2 Definition -- 3.1.3 Pages and percents -- 3.1.4 Context, quotes, and references -- 3.1.5 Sentences, paragraphs, and other units -- 3.1.6 Self-plagiarism -- 3.2 In the humanities -- 3.2.1 Overview -- 3.2.2 Paragraph-length examples -- 3.2.3 Book-length examples -- 3.3 In the social sciences -- 3.3.1 Overview -- 3.3.2 Example 1 -- 3.3.3 Example 2 -- 3.4 In the natural sciences -- 3.4.1 Overview -- 3.4.2 Example 1 -- 3.4.3 Example 2 -- 3.5 Conclusion: plagiarism --
505 8 _a4. Quantifying data falsification -- 4.1 Introduction -- 4.2 Metadata -- 4.3 Humanities -- 4.3.1 Introduction -- 4.3.2 History -- 4.3.3 Art and art history -- 4.3.4 Ethnography -- 4.3.5 Literature -- 4.4 Social sciences -- 4.4.1 Introduction -- 4.4.2 Replication studies -- 4.4.3 Diederik Stapel -- 4.4.4 James Hunton -- 4.4.5 Database revisions -- 4.4.6 Data manipulation -- 4.5 Natural sciences -- 4.5.1 Introduction -- 4.5.2 Lab sciences -- 4.5.3 Medical sciences -- 4.5.4 Computing and statistics -- 4.5.5 Other non-lab sciences -- 4.6 Conclusion --
505 8 _a5. Quantifying image manipulation -- 5.1 Introduction -- 5.2 Digital imaging technology -- 5.2.1 Background -- 5.2.2 How a digital camera works -- 5.2.3 Raw format -- 5.2.4 Discovery analytics -- 5.2.5 Digital video -- 5.3 Arts and humanities -- 5.3.1 Introduction -- 5.3.2 Arts -- 5.3.3 Humanities -- 5.4 Social sciences and computing -- 5.4.1 Overview -- 5.4.2 Training and visualization -- 5.4.3 Standard manipulations -- 5.5 Biology -- 5.5.1 Legitimate manipulations -- 5.5.2 Illegitimate manipulations -- 5.6 Medicine -- 5.6.1 Limits -- 5.6.2 Case 1 -- 5.6.3 Case 2 -- 5.7 Other natural sciences -- 5.8 Detection tools and services -- 5.9 Conclusion --
505 8 _a6. Applying the metrics -- 6.1 Introduction -- 6.2 Detecting gray zones -- 6.3 Determining falsification -- 6.4 Prevention -- 6.5 Conclusion -- 6.6 HEADT Centre --
505 8 _aBibliography -- Author's biography.
506 1 _aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 _aCompendex
510 0 _aINSPEC
510 0 _aGoogle scholar
510 0 _aGoogle book search
520 3 _aInstitutions typically treat research integrity violations as black and white, right or wrong. The result is that the wide range of grayscale nuances that separate accident, carelessness, and bad practice from deliberate fraud and malpractice often get lost. This lecture looks at how to quantify the grayscale range in three kinds of research integrity violations: plagiarism, data falsification, and image manipulation. Quantification works best with plagiarism, because the essential one-to-one matching algorithms are well known and established tools for detecting when matches exist. Questions remain, however, of how many matching words of what kind in what location in which discipline constitute reasonable suspicion of fraudulent intent. Different disciplines take different perspectives on quantity and location. Quantification is harder with data falsification, because the original data are often not available, and because experimental replication remains surprisingly difficult. The same is true with image manipulation, where tools exist for detecting certain kinds of manipulations, but where the tools are also easily defeated. This lecture looks at how to prevent violations of research integrity from a pragmatic viewpoint, and at what steps can institutions and publishers take to discourage problems beyond the usual ethical admonitions. There are no simple answers, but two measures can help: the systematic use of detection tools and requiring original data and images. These alone do not suffice, but they represent a start. The scholarly community needs a better awareness of the complexity of research integrity decisions. Only an open and wide-spread international discussion can bring about a consensus on where the boundary lines are and when grayscale problems shade into black. One goal of this work is to move that discussion forward.
530 _aAlso available in print.
588 _aTitle from PDF title page (viewed on January 24, 2017).
650 0 _aResearch
_xMoral and ethical aspects.
650 0 _aIntegrity.
650 0 _aExperimental design.
653 _aresearch integrity
653 _aplagiarism
653 _adata falsification
653 _aimage manipulation
653 _agrayscale decisions
653 _aresearch fraud
653 _adetection tools
653 _aplagiarism tools
653 _aforensic droplets
653 _aRetraction Watch
653 _aOffice of Research Integrity
653 _aHEADT Centre
776 0 8 _iPrint version:
_z9781627056403
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on information concepts, retrieval, and services ;
_v# 53.
_x1947-9468
856 4 2 _3Abstract with links to resource
_uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7809449
999 _c562242
_d562242