ライフサイエンスコーパス: data science

1 ltures to better retain and advance women in data science.

2 d at the core of artificial intelligence and data science.

3 ves an introduction to the principles of big-data science.

4 ion-making; and v) building more capacity in data science.

5 of tribological knowledge, manufacturing and data science.

6 tional statistics, information retrieval and data science.

7 them from chemistry, biology, modelling and data science.

8 s in biotechnology, and the growing power of data science.

9 demand for effective training in biological data science.

10 ting its usage in biomedical informatics and data science.

11 's Master's programs in Computer Science and Data Science.

12 ford Departments of Pathology and Biomedical Data Science.

13 y development in artificial intelligence and data science.

14 ectronics fabrication, electrophysiology and data science.

15 metabolomics is evolving into a field of big data science.

16 support their use in human population health data science.

17 he gap between experimental neuroscience and data science.

18 s medicine, digital health technologies, and data science.

19 and stability (PCS) framework for veridical data science.

20 testing is an essential component of modern data science.

21 cle, we ask why scientists should care about data science.

22 e recently transformed emerging areas within data science.

23 to train a national workforce in biomedical data science.

24 ese data types present new challenges to big data science.

25 Thus, Julia is popular in "Big Data" sciences.

26 What is data science?

27 identify its relative areas of focus within data science, a portfolio analysis from fiscal year 2008

28 , learners showed significant improvement in data science ability (Wave 1: t(47) = 10.18, p < .001, W

29 ered hypotheses that learners' self-reported data science ability and level of agreement with importa

30 underscore the robustness of improvement in data science ability.

31 Data science advances in behavioral signal processing an

32 logy (phage discovery, synthetic biology and data science/AI).

33 Data science allows the extraction of practical insights

34 ber of women acquiring skills and working in data science and (2) how to evolve organizations and pro

35 entred, distinctly public sector approach to data science and AI, in which these technologies do not

36 As data science and artificial intelligence continue to rap

37 Recent advances in data science and artificial intelligence have great pote

38 r secondary utilization as the deployment of data science and artificial intelligence in biology adva

39 f systems medicine and multiscale modelling, data science and computing, to provide their feedback in

40 which continues to expand the integration of data science and electrochemistry.

41 ealth record; 3) continued innovation in the data science and engineering methods required to identif

42 ine opportunities for further integration of data science and experimental chemistry to advance these

43 dated and tested using the best practices of data science and further analyzed to rationalize their p

44 a framework for addressing similarly complex data science and informatics challenges.

45 Genomics is a Big Data science and is going to get much bigger, very soon,

46 the first time, explores the integration of data science and machine learning for the classification

47 Recently, advances in wearable technologies, data science and machine learning have begun to transfor

48 The study utilized data science and machine learning methodologies to analy

49 There is a growing interest in utilizing data science and machine learning techniques to overcome

50 By integrating data science and machine learning techniques, the resear

51 Novel data science and marketing methods of smoking-cessation

52 pproximately 4000 applications for the MS in Data Science and MS in Computer Science programs at Ford

53 such databases depends on the discipline of data science and on the humble bricks and mortar that ma

54 nally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field

55 -authors discuss a forthcoming Collection on data science and social determinants of health.

56 Deep integration between data science and sustainability science in highly comple

57 , based on methods and concepts developed in data science and systems biology.

58 NHLBI's recent funding of research grants in data science and to identify its relative areas of focus

59 insight that can be gleaned from integrating data science and traditional physical organic techniques

60 e advances in precision medicine, biomedical data science and translational bioinformatics approaches

61 ulary inclusion through Observational Health Data Sciences and Informatics processes, or collaborate

62 in surgery, anaesthesia, and obstetric care, data science, and health indicators from high-, middle-,

63 ynthetic chemistry with process engineering, data science, and life cycle thinking will be critical t

64 cer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

65 cer Discoveries with Computational Research, Data Science, and Machine Learning/AI.

66 reduction is an indispensable part of modern data science, and many algorithms have been developed.

67 from the benefits of smart materials (SMs), data science, and nanosensors.

68 ccuracy and efficiency of ChatGPT in medical data science applications but also offers valuable insig

69 cal analysis of multiomics data and enhances data science applications of multiple omics datasets.

70 g language, the de facto standard for modern data science applications, and is widely available under

71 ivacy, and ownership are pressing issues for data science applications, in general, and are especiall

72 morrhage control, allows the benchmarking of data science applications, including object detection, p

73 ubiquitous across quantitative research and data-science applications.

74 Data clustering is a core data science approach widely used and referenced in the

75 We adopt a behavioural data science approach, merging psychological schema theo

76 st structure and stereoselectivity through a data science approach.

77 We use a data-science approach exploiting 15 diverse datasets, in

78 ig data from a variety of sources means that data science approaches are becoming pervasive.

79 We review statistical considerations and new data science approaches needed to scrutinize the policy

80 in exposures and health outcomes; and (iii) data science approaches that can elucidate direct and in

81 lenging chemical reactions, although current data science approaches to deal with highly skewed data

82 approved prodrugs using cheminformatics and data science approaches to reveal trends in prodrug deve

83 cription of anatomical locations, and modern data science approaches using standardized brain regions

84 t bridges sophisticated graph analytics from data science approaches with the underlying cell biologi

85 ith these tools, when combined with advanced data science approaches, can be used to generate novel b

86 multidimensional datasets that benefit from data science approaches, including dimensionality reduct

87 odel skill, in turn, can benefit from modern data science approaches.

88 On these data, we applied advanced data-science approaches to derive low-dimensional geomet

89 model the developing placenta and integrated data-science approaches to study longer-term outcomes.

90 rther identified opportunities for potential data science areas in which NHLBI could foster research

91 NIH institutes/centers highlighted relative data science areas of emphasis and further identified op

92 achine learning (AutoML) systems are helpful data science assistants designed to scan data for novel

93 merging opportunities, new applications, and data science-assisted materials discovery.

94 tric nephrology, critical care, pharmacy and data science, at which the use of digital health for ris

95 We describe experimental, computational, and data science-based evidence that identifies the specific

96 ndeavor was the development of an end-to-end data science-based workflow to select a set of coupling

97 This research combines long-term monitoring data, science-based assessment methods, and novel data a

98 posed method-based tool, we tested on a 2018 Data Science Bowl (DSB) competition dataset, three users

99 ee top-performing algorithms from the Kaggle Data Science Bowl 2017 public competition: grt123, Julia

100 The 2018 Data Science Bowl attracted 3,891 teams worldwide to mak

101 computing, evolvable computing, and complex data science calls for determining the optimal amount an

102 Data science can be incorporated into every stage of a s

103 Here we describe how data science can be used to generate hypotheses, to desi

104 Ligand parametrization and AI based data science can potentially help predict the facile for

105 However, this revolution in data science cannot replace established microbiology pra

106 with increased biological understanding and data sciences capability should herald a fruitful post-M

107 ation with the MGH & BWH Center for Clinical Data Science (CCDS).

108 British Heart Foundation Data Science Centre (Health Data Research UK).

109 l approaches and tools for the highly valued data science challenge of mining knowledge graphs.

110 m design, but their effectiveness in solving data science challenges in real-world settings remains p

111 duate students used LLMs to solve biomedical data science challenges on Kaggle, focusing on tabular d

112 ghts-together with neuroscience, biology and data science-closer.

113 In this study, FL facilitated rapid data science collaboration without data exchange and gen

114 g as one of the priorities under their Joint Data Science Collaboration.

115 The OSM and data science community are invited to build upon our app

116 ck of COVID-19 patient data has hindered the data science community in developing models to aid in th

117 medical literature on multimorbidity and the data science community with an interest on exploratory a

118 Engineering Attribution Challenge, a public data-science competition to advance GEA techniques.

119 arried out on its own or embedded within any data science course.

120 emiology developments in biology and cognate data science disciplines.

121 In this era of data science-driven bioinformatics, machine learning res

122 By applying data science-driven hotspot analysis and machine learnin

123 ation, resulting in a paradigm shift towards data science-driven liquid biopsies in oncology.

124 As the next step, a data science-driven strategy was also used to explore a

125 This data science-driven workflow presents an illustrative ex

126 A data-science-driven approach to substrate scope evaluati

127 Herein we report a data-science-driven workflow to evaluate how these facto

128 Due to the rapid development of data science (DS), promising progress has been made in t

129 esign and seamless interface with the Python data science ecosystem, stands as a critical tool for ad

130 ta science, we surveyed organizers of ad hoc data science education efforts to understand how organiz

131 ally intensive disciplines, their support of data science education has significantly helped in copin

132 for collecting and sharing relevant data on data science education programs.

133 wever, researchers report struggling to find data science education that meets their needs, despite t

134 This first-of-its-kind work offers a data science enabled approach to identify market-level r

135 The reaction scope was evaluated using a data science enabled boronic acid chemical space to asse

136 tical ingredients, highlighting the power of data science-enabled approaches in reagent development.

137 Data sciences, ethical, legal and social implications re

138 ML, radiology residents may seek a formative data science experience.

139 ness), and specialties (both informatics and data science experts and the usual radiology clinical gr

140 dling COVID-19 like situations by leveraging data science experts in choosing the right tools.

141 eview is to introduce clinicians who are not data science experts to key concepts in machine learning

142 With increasing demand for training in data science, extracurricular or "ad hoc" education effo

143 Six hundred thirty data science-focused extramural research grants supporte

144 article unpacks the challenges presented by data science for development and humanitarianism.

145 livery, advanced electronic engineering, and data science for personalized disease management and rem

146 bringing this emerging field of single-cell data science forward.

147 nalytics and hardware-accelerated SQL into a data science framework to allow the scientific community

148 We present a data science framework to integrate pharmacological and

149 To answer, we discuss data science from three perspectives: statistical, compu

150 and Adolescents Trust Fund; Development and Data Science grant; and the Yemen Emergency Health and N

151 Data science has attracted a lot of attention, promising

152 Over the past few years, surgical data science has attracted substantial interest from the

153 of the potent new toolbox provided by modern data science has been slow, primarily because it is ofte

154 Data science has great potential as a low-cost, high-ret

155 Big Data science has significantly furthered our understandi

156 In all of its aspects, data science has the potential to narrow the gender gap

157 a new discipline, known as "big single-cell data science," has emerged.

158 Advancements in surgical data science have allowed health systems to identify pri

159 ncements in epidemiology, biostatistics, and data science have strengthened the research world's abil

160 s to build ethical governance frameworks for data science health research in Africa and the opportuni

161 Data science health research promises tremendous benefit

162 To evolve data science in a way that promotes gender diversity, we

163 s concise review examines the role of AI and data science in critical care, with a focus on their con

164 are intended to guide the responsible use of data science in critical care, with emphasis on ethics a

165 s Oration, I speak to the potential value of data science in drug delivery with particular focus on p

166 The possibilities for data science in mental health research are substantial.

167 iscuss current and potential applications of data science in mental health research using the UK Clin

168 e established fields of genomics and spatial data science in R, thereby enabling independent develope

169 roaden the application of bioinformatics and data science in research.

170 to support open petabyte-scale Earth system data science in the cloud by onboarding additional NOAA

171 se of artificial intelligence algorithms and data science in the diagnosis, classification, and treat

172 Fortunately, methods in data science including artificial intelligence / machine

173 The CRDC is a cloud-based data science infrastructure that eliminates the need for

174 The Shared Data Science Infrastructure, Boa(g), provides researcher

175 DC) aims to establish a national cloud-based data science infrastructure.

176 Shared data science infrastructures like Boa(g) is needed to ef

177 ndemic and consider the implications of such data science initiatives.

178 e, and an HD-X11 wireless telemetry monitor (Data Sciences International) was implanted that enabled

179 ds and training mechanisms to integrate "big data" science into the practice of epidemiology; 3) crea

180 The emergent field of data science is a critical driver for innovation in all

181 all three components is the essence of what data science is about.

182 We demonstrate that data science is already being widely applied in mental h

183 Environmental data science is an emerging field that can be harnessed

184 Data science is assuming a pivotal role in guiding react

185 We provide examples of how data science is changing the way we conduct experiments,

186 mentalists work as a unified team, and where data science is incorporated into the experimental desig

187 Data science is likely to lead to major changes in cardi

188 s (big data) and computational capabilities, data science is quickly becoming a key component of the

189 A major challenge in contemporary data science is the development of statistically accurat

190 ol, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics

191 For this, we have developed the Integrated Data Science Laboratory for Metabolomics and Exposomics-

192 Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics-

193 oducible, and transparent results across the data science life cycle.

194 It is well studied for its applications in data sciences, life sciences, social sciences and techno

195 Now, new computational tools in data science, machine learning, and artificial intellige

196 type of useful model: an illustration of how data science, machine learning, and many-model thinking

197 ing imaging system design, image processing, data science, machine learning, computer vision, percept

198 portant for bioinformatic pipelines, such as data science, machine learning, or artificial intelligen

199 s of 19 researchers across computer science, data science, mathematics, social sciences, and biomedic

200 se theory, an unsupervised psychometrics and data science method, to develop a customized PFAS exposu

201 Focusing on data science methodologies, we conduct a detailed study

202 both expanding omics resources and evolving data science methodology to accelerate the translation o

203 sets for evaluating new machine learning and data science methods aggregated in one location.

204 Here we show how stereological and data science methods can be combined to quantitatively r

205 helps introduce public health researchers to data science methods that scale to the size and complexi

206 Herein, we describe the use of data science methods to connect catalyst and substrate s

207 consider adopting these skills and applying data science methods to their own problems.

208 ation research empowered by state-of-the-art data science methods, and may pave the way for the devel

209 Such recordings can be analyzed with data science methods, but it is not immediately clear wh

210 the application of quantitative image-based data science methods.

211 ta scientists; developing chemistry-specific data science methods; integrating algorithms, software a

212 nstances, their generation pipelines and the data science models currently in use.

213 L may be used to build and validate surgical data science models.

214 sciplines of chemistry, including catalysis, data science, nanoscience, interfacial phenomena and dyn

215 of high-throughput experimentation (HTE) and data science offers a promising solution for the optimiz

216 esis generation in observational, biomedical data science often starts with computing an association

217 In data science, ontologies are systems that represent rela

218 ssional marketing (IQVIA Institute for Human Data Science, Open Payments Data [Centers for Medicare &

219 ng, blood, and sleep cohorts to leverage new data science opportunities and encourage broad research

220 The authors piloted an elective Data Science Pathway (DSP) for 4th-year residents at the

221 ts in the MH space from a bioinformatics and data science perspective, summarized existing knowledge

222 e discuss our labs' efforts to implement NWB data science pipelines.

223 ltimately, the CHILD Cohort Study provides a data science platform designed to enable a deep understa

224 A holistic data science platform triangulating insights from struct

225 Translational bioinformatics and data science play a crucial role in biomarker discovery

226 ed in coping with the speed of innovation in data science practice and formal curricula.

227 and prognostic purposes and help propel fair data science practices in the exploration of complex dis

228 g., regenerative medicine, omics technology, data science, precision medicine, and mobile health), fi

229 ution in single-cell biology and pose unique data science problems.

230 lity and ameliorate inefficient solutions to data science problems.

231 eep disease areas as well as the subtypes of data science projects funded by NHLBI.

232 l these approaches through robust NHS-linked data science projects, such as the UK Biobank, Generatio

233 mical systems, combined with techniques from data science, provides an effective means for extracting

234 partnerships, train public health experts in data science, reduce biases related to digital data (gat

235 nd reliable methodology was used to identify data science research grants by utilizing several NIH da

236 Information was also collected for data science research grants funded by other NIH institu

237 ovement of surgical instruments for surgical data science research.

238 impact of African genomics on human health, data science skills and awareness of Africa's rich genet

239 e students increasingly need programming and data science skills to be competitive in the modern work

240 scaling analyses for HPC-acknowledging that data science skills will be required to build a deeper u

241 ng it easy to use for users without advanced data science skills.

242 e gap between needed and existing biomedical data science skills.

243 s these datasets with the modern interactive data science software ecosystem in Python.

244 Bioframe extends the Python data science stack to use cases for computational genome

245 Session topics included "Data Science;" "Standards and Interoperability;" "Open S

246 interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning

247 ts" and "imports" between genomics and other data-science subdomains (e.g., astronomy).

248 lly benefit from rapidly evolving methods in data science, such as neural networks, that have shown t

249 Emerging data science techniques of predictive analytics expand t

250 tions of neuronal relationships derived from data science techniques to the detailed identification o

251 (dimensions) per sample, with the hope that data science techniques will be able to build accurate d

252 We also present our vision for how data science techniques will be an integral part of the

253 Using data science techniques, a virtual library of calculated

254 Further development of statistical and data science techniques, alongside public engagement and

255 Then, we describe the integration of data science techniques, including DFT featurization, di

256 a dependence test that juxtaposes disparate data science techniques, including k-nearest neighbors,

257 diagnosis, and treatment could benefit from data science that uses cohort studies, genomics, and rou

258 d device concepts combined with cutting-edge data science to boost bioelectronic performance and diag

259 re, we address this challenge using advanced data science to capture, process and analyze 52 clinical

260 for developing such orientations-and harness data science to map it across America.

261 Leveraging emerging opportunities in data science to open new frontiers in heart, lung, blood

262 However, employing data science to predict acute kidney injury might be mor

263 ntage of the possibilities offered by modern data science to solve problems in experimental chemistry

264 The potential for data science to spur health discoveries and catalyze inn

265 al live-cell imaging, mass spectrometry, and data science to systematically map the localization and

266 these foundational issues can enable AI and data science to transform healthcare systems across the

267 ines working with the phenotyping community: data science, to address the challenge of generating FAI

268 ed approach relies on Orange, an open-source data science toolbox that combines data visualization an

269 , as well as microfluidics, biochemistry and data science toolkits.

270 egrating High-Throughput Experimentation and Data Science tools and methods.

271 ble to connect metabolomics data with common data science tools and supports flexible experimental de

272 complex experimental data sets, and advanced data science tools are taking on central roles in neuros

273 creening strategy that integrates predictive data science tools for mapping excited-state properties

274 Specifically, chemical data science tools such as chemometric modeling are leve

275 opportunities, the information sources, the data science tools that use the information, and the app

276 vast quantities of data and the emergence of data science tools to plan and interpret these experimen

277 ting modern computational thermodynamics and data science tools to span complex multicomponent spaces

278 s that integrate computational chemistry and data science tools with high-throughput experimentation

279 Substrate scope was selected employing data science tools, and evaluation of these substrates r

280 This enabled us to employ data science tools, including machine learning, to analy

281 hile DL methods enjoy a plethora of advanced data science tools.

282 eness, providing not only another option for data science training but also a model for collecting an

283 We created a new data science training program focused on rigorous, repro

284 ues could be the first method encountered in data science training.

285 Data science uses computer science and statistics to ext

286 , we study a method of automating biomedical data science using a web-based AI platform to recommend

287 Students in statistics or data science usually learn early on that when the sample

288 n applicability outside machine learning and data science warrants a careful exposition.

289 each of the three is a critical component of data science, we argue that the effective combination of

290 electronic components, micro-composites, and data science, we propose a modular strategy for construc

291 and the role of different ad hoc formats for data science, we surveyed organizers of ad hoc data scie

292 n being the Founding Dean of a new School of Data Science, what we do suddenly looks different.

293 ary research with an integrative approach to data science, whereby basic scientists, clinicians, data

294 guage extensively used in bioinformatics and data science, which is particularly suitable for beginne

295 of smart cities, linking innovations in the data sciences with the goal of advancing human well-bein

296 Pachyderm enables efficient and sustainable data science workflows while maintaining reproducibility

297 write understandable Dockerfiles for typical data science workflows.

298 interface that integrates well with popular data science workflows.

299 f, which was addressed by the NIH's internal Data Science Workforce Development Center.

300 We describe the first African genomics data science workshop, implemented by the African Societ