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007			cr nn 008mamaa
008			230411s2017 sz | s |||| 0|eng d
020			_a9783031023064
024	7		_a10.1007/978-3-031-02306-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ180.55.M67 _b2017 EB
100	1		_aSeadle, Michael S., _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688037 _d1950-
245	1	0	_aQuantifying Research Integrity _cby Michael Seadle
250			_a1st edition 2017
264		1	_aCham _bSpringer International Publishing _c2017
300			_a1 recurso en línea (XIX, 121 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Information Concepts Retrieval and Services _x1947-9468
505	0		_aPreface -- Acknowledgments -- Introduction -- State of the Art -- Quantifying Plagiarism -- Quantifying Data Falsification -- Quantifying Image Manipulation -- Applying the Metrics -- Bibliography -- Author's Biography.
520			_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.
988			_aSynthesis Collection of Technology_2017
650		7	_2embne _9138985 _aInvestigación _xAspectos morales
776	0	8	_iPrinted edition: _z9783031011788
776	0	8	_iPrinted edition: _z9783031034343
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02306-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI