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024	7		_a41688 _2local
210	1	0	_aData-Enabled Discov. Appl.
245	1	0	_aData-Enabled Discovery and Applications _h[electronic resource] / _cedited by Alan A. Barhorst, Gang Qi.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer.
300			_bonline resource.
520			_aData-Enabled Discovery and Applications is now closed for submissions. Springer will continue to host an archive of all articles published in Data-Enabled Discovery and Applications  and it will remain fully searchable via the Springer website. Please consider submitting to the topical collection of same name as part of the journal SN Applied Sciences. (https://www.springer.com/journal/42452/updates/18212646) This journal creates new avenues of dissemination for researchers using the increased availability of extensive data sets to quantitatively model real-world systems, and seek new discoveries, innovations and applications, often across the boundaries of traditional disciplines. Especially sought are contributions investigating novel research strategies so as to enable scientific discoveries, engineering innovations, or advances in medical diagnosis, prognosis, and treatment. Data-Enabled Discovery and Applications establishes a forum to share methods and best practices–in particular from fields involving large data harvesting such as computational science, engineering and biomedicine–for uncovering patterns and associations embedded in the data. It further provides a common platform for the publication of tools, measurements, and methodologies for investigating large data sets relevant for specific applicability to widely varying fields of endeavor. Papers in this journal will typically maintain a balance between general solution techniques and problem-specific results, promote challenges to traditional solutions, and foster a fruitful exchange of ideas among disparate fields. The journal will not be concerned with the information technological aspects of the underlying datasets, i.e. the study of efficient and effective techniques and systems for data collection, management and mining. The journal will also not be a venue for discipline-specific discussion. Authors are expected to present results in a manner that highlights the data-centric tools created or cross-utilized to solve discipline-specific problems. Suggestions for broader applications of their work should also be provided.
650		0	_aComputational intelligence.
650		0	_aMathematical physics.
650		0	_aComputer simulation.
650		0	_aMedical informatics.
650		0	_aBiotechnology.
650		0	_aQuantitative research.
650	1	4	_aComputational Intelligence.
650	2	4	_aTheoretical, Mathematical and Computational Physics.
650	2	4	_aComputer Modelling.
650	2	4	_aHealth Informatics.
650	2	4	_aBiotechnology.
650	2	4	_aData Analysis and Big Data.
700	1		_aBarhorst, Alan A. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aQi, Gang. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
856	4	0	_uhttp://link.springer.com/journal/41688 _zHybrid
999			_c4282 _d4282