000			05051cam a2200709 i 4500
001			36661
003			MED
005			20250911110837.0
008			230713s2024 njua b 001 0 eng
010			_a 2023018370
020			_a9780691222752 _qpaperback ; _qalkaline paper
020			_a0691222754 _qpaperback ; _qalkaline paper
020			_a9780691222738 _qhardcover ; _qalkaline paper
020			_a0691222738 _qhardcover ; _qalkaline paper
020			_z9780691222745 _qelectronic book
035			_a(OCoLC)1391973987 _z(OCoLC)1457961771
040			_aDNLM/DLC _beng _erda _cDLC _dOCLCO _dYDX _dOCLCO _dCDN _dOCLCO _dOCLCF _dOCLCO _dNDD _dDLC _dIG# _dOCLCL
042			_apcc
050	0	4	_aRC349.D52 _bR65 2024
060	0	0	_aWL 26.5
082	0	0	_a616.8/04754 _223/eng/20230801
084			_aSCI089000 _aCOM021030 _2bisacsh
100	1		_aRokem, Ariel, _d1977- _eauthor.
245	1	0	_aData science for neuroimaging : _ban introduction / _cAriel Rokem and Tal Yarkoni.
264		1	_aPrinceton : _bPrinceton University Press, _c[2024]
300			_axiv, 377 pages : _billustrations ; _c26 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aIncludes bibliographical references (pages 371-373) and index.
504			_aIncludes bibliographical references (pages 371-373) and index.
505	0		_aThe unix operating system -- Version control -- Computational environments and computational containers -- A brief introduction to python -- The python environment -- Sharing code with others -- The scientific python ecosystem -- Manipulating tabular data with pandas -- Visualizing data with python -- Data science tools for neuroimaging -- Reading neuroimaging data with NiBabel -- Using NiBabel to align different measurements -- Image processing -- Image registration -- The core concepts of machine learning -- The Scikit-learn package -- Overfitting -- Validation -- Model selection -- Deep learning.
520			_a"Like many other research fields, over the last two decades neuroscience has turned towards data-driven discovery, a change which has dramatically reshaped the field. Through large collaborative projects and concerted data collection and data sharing efforts, the field is gaining access to large and heterogeneous data sets, at scales that have never been possible before. While these data present tremendous opportunities, their effective management, storage, and analysis presents serious challenges for many researchers. The tools and techniques of data science - a field which draws on software engineering, statistics, and machine learning to increase the efficiency and reproducibility of data extraction and analysis - have much to offer neuroscientists, but unfortunately these concepts are not taught within the standard neuroscience curriculum. This book offers an introduction to contemporary data science and its application in neuroimaging research. Taking a "hands-on" approach, the book explains common methods and approaches in a reader-friendly style, and includes numerous applications to openly available neuroscience datasets, including extensive code examples in Python. In contrast to most other neuroimaging-focused books, which place heavy emphasis on the process of acquiring and statistically analyzing neuroimaging data, the focus of this book is on developing and managing scalable and reproducible data analysis pipelines, broadly relevant skills that will readily translate to students' own research questions. Throughout, there is an emphasis on best-practices in data sharing and reporting, including how to apply principles of fairness, accountability, and transparency in neuroscience applications"-- _cProvided by publisher.
650		0	_aNervous system _xImaging _xData processing.
650		0	_aNeurosciences _xResearch _xMethodology.
650		0	_aProgramming languages (Electronic computers)
650		0	_aData sets.
650		0	_aMachine learning.
650		0	_aBrain _xImaging.
650	1	2	_aData Science _xmethods
650	1	2	_aNeuroimaging
650	2	2	_aProgramming Languages
650	2	2	_aDatasets as Topic
650		2	_aMachine Learning
650		6	_aNeuro-imagerie.
650		6	_aLangages de programmation.
650		6	_aJeux de donn�ees.
650		6	_aSyst�eme nerveux _xImagerie _xInformatique.
650		6	_aNeurosciences _xRecherche _xM�ethodologie.
650		6	_aApprentissage automatique.
650		6	_aCerveau _xImagerie.
650		7	_aSCIENCE / Life Sciences / Neuroscience. _2bisacsh
650		7	_aCOMPUTERS / Data Science / Data Analytics. _2bisacsh
650		7	_aBrain _xImaging _2fast
650		7	_aData sets _2fast
650		7	_aMachine learning _2fast
650		7	_aProgramming languages (Electronic computers) _2fast
700	1		_aYarkoni, Tal, _eauthor.
776	0	8	_iOnline version: _aRokem, Ariel, 1977- _tData science for neuroimaging _dPrinceton : Princeton University Press, [2024] _z9780691222745 _w(DLC) 2023018371
942			_2lcc _cBK _n0
948			_h
999			_c36661 _d36661