ライフサイエンスコーパス: data management

1 for meta-analysis, visualization and better data management).

2 ons have different formats and approaches to data management.

3 for on-site determination of UA in blood and data management.

4 icient trial execution, site monitoring, and data management.

5 d well for the intended application in image data management.

6 for sequence analysis, data submission, and data management.

7 ies, focusing specifically on authorship and data management.

8 lete review of all data, and provides facile data management.

9 object-relational schema for more efficient data management.

10 maintenance and implementation of efficient data management.

11 blems of instrument accuracy, precision, and data management.

12 oks fit into the broader context of research data management.

13 tion of omics analysis outputs and efficient data management.

14 ble (FAIR) Guiding Principles for scientific data management.

15 out digital image analysis, acquisition, and data management.

16 ent data collection, storage, retrieval, and data management.

17 NVivo version 14 software was used for data management.

18 of operation, accessibility, and systematic data management.

19 losures, devices and systems, and scientific data management.

20 mlessly integrates with smartphones for easy data management.

21 in reaction design, execution, analysis, and data management.

22 stems, field trials, mutant collections, and data management.

23 nd user accounts may be generated for easier data management.

24 seq data can result in challenges related to data management, access to sufficient computational reso

25 ies Program Coordinating Center provided the data management, administrative, and statistical support

26 High-performance graphics and data management allows users to simultaneously visualise

27 to long-term improvements in administrative data management, alternatives for measuring routine immu

28 tegrated microbial genomes (IMG) system is a data management, analysis and annotation platform for al

29 The IAE integrates data management, analysis and visualization in a user-fr

30 scientists with cost-effective and efficient data management, analysis, and interpretation.

31 recently emerged as a powerful solution for data management, analysis, and sharing.

32 mplexity, however, is a serious challenge in data management, analysis, and sharing.

33 NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive to

34 TG database and UI application, we addressed data management and accessibility concerns despite its g

35 tegrated user interface (UI) to improve GWTG data management and accessibility.

36 ated in 2003, through the application of the Data management and Alimenta nutritional software.

37 importance of a dedicated database, allowing data management and analysis and can be used to tailor t

38 reported frequently, and yet the significant data management and analysis challenges presented by the

39 infrastructure capable of supporting growing data management and analysis environments is an increasi

40 blishes the foundation for building an F-SAA data management and analysis framework, enabling more co

41 increasingly data-driven, and dependent upon data management and analysis methods that facilitate rig

42 n-source Python library that streamlines the data management and analysis of functional imaging data.

43 ated microbial genomes (IMG) system is a new data management and analysis platform for microbial geno

44 IMG/M is a data management and analysis system for microbial commun

45 y is a web-based Affymetrix expression array data management and analysis system for researchers who

46 this paper IMG/M, an experimental metagenome data management and analysis system that is based on the

47 conducted in the context of a comprehensive data management and analysis system.

48 nables users to track and perform microarray data management and analysis tasks through a single easy

49 eloped an open source software framework for data management and analysis to describe trends and vari

50 n protocols, develop sustainable systems for data management and analysis to monitor MACV impact, and

51 Data management and analysis were conducted from April 1

52 The data management and analysis were performed from October

53 ls provide a stable and modular platform for data management and analysis.

54 llaborative neuroscience requires systematic data management and analysis.

55 st database integration facilitate efficient data management and analysis.

56 erated; however, they pose new challenges to data management and analysis.

57 ata sets, posing a challenge for traditional data management and analysis.

58 Effective data management and analytical approaches are essential

59 o share these data bring challenges for both data management and annotation and highlights the need f

60 ard, an intuitive graphic user interface for data management and bioinformatics analysis.

61 sets that they generate poses challenges for data management and computer-aided analysis.

62 cian in the Early Detection Research Network data management and coordinating center.

63 bioinformatics tools for the integration of data management and data analysis.

64 ware builds upon existing infrastructure for data management and data processing.

65 se modeling code tightly integrated with the data management and databasing aspects of HTS data proce

66 ources based on a decentralized approach for data management and dynamic conservation.

67 The keys are investing in data management and in internal AI talent who can create

68 onsibility, and ethics (CARE) frameworks for data management and include the use of standardized data

69 formatics (NCICB) has developed a Java based data management and information system called caCORE.

70 by the research community through a central data management and integrated bioinformatics hub.

71 Efficient tools for data management and integration are essential for many a

72 cided to develop a robust infrastructure for data management and integration that supports advanced b

73 data, presenting new challenges in effective data management and integration.

74 Software is also needed to simplify data management and make large-scale bioinformatics anal

75 upportive infrastructure for gene expression data management and makes extensive use of ontologies.

76 Such advances are important for transparent data management and mining in functional genomics and sy

77 packages have been developed to assist with data management and post-processing.

78 Microarray data management and processing (MAD) is a set of Windows

79 to address measles and rubella elimination, data management and quality, and strengthening routine i

80 for increased visibility and transparency in data management and reporting practices.

81 etadata standards, principles for microbiome data management and reporting, and the importance of sta

82 ey had a separate hospital budget to support data management and reporting, oversight of their ICUs,

83 ta pose significant challenges for effective data management and reuse.

84 facilities for sample handling and storage, data management and scrutiny, and laboratory quality con

85 tional Institutes of Health (NIH) Policy for Data Management and Sharing (DMS Policy) recognizes the

86 nclude a requirement for the submission of a Data Management and Sharing Plan (DMSP) with funding app

87 ld be given to structuring and standardising data management and sharing plans to help provide a simi

88 actice of data sharing, and the reporting of data management and sharing plans, in all reports of bio

89 omote data sharing in support of the new NIH Data Management and Sharing Policy.

90 design, intensive communication, experienced data management and statistical centers, sophisticated a

91 relatively well-addressed, areas such as EHR data management and study design showed room for improve

92 age acquisition, in computer science for the data management and the execution of processing pipeline

93 pe pre-phasing, imputation, post imputation, data management and the extension to other existing pipe

94 lity of an R-specific solution for efficient data management and versatile data reuse.

95 ing, registration, annotation, mining, image data management and visualization, are further summarize

96 We developed novel software solutions for data management and visualization, while incorporating n

97 essential documents, (2) essential data, (3) data management, and (4) trial resources, specifically a

98 could offer an improvement in clinical trial data management, and could bolster trust in the clinical

99 ablished to support bioinformatics analysis, data management, and data sharing among PHLs.

100 nity for a genome project, the importance of data management, and how to make the data and results Fi

101 nmet needs are training in data integration, data management, and scaling analyses for HPC-acknowledg

102 of an object-relational schema for efficient data management; and integration with PROSITE, profiles,

103 using 4 routine databases: Hospital patient data management, anesthesia database, local data of the

104 Broadly, we suggest that the data management approach of the Organ Procurement and Tr

105 review, we identify the gaps between current data management approaches and the need for new capacity

106 d the accompanying tools, infrastructure and data management approaches that are emerging in this spa

107 7.50 laboratory supplies/staff, and $1820.00 data management)-approximately $39 per enrolled patient

108 nsmission, data processing, and personalized data management are comprehensively discussed.

109 ing guidelines and standards for proper food data management are presented, as well as different use

110 is of biological sequences, and professional data management are used routinely in a modern universit

111 enefit from incorporating emerging skills in data management, artificial intelligence, and precision

112 is workflow and demonstrate how professional data management, as enabled with INTOB, marks a signific

113 tocols for specimen processing, testing, and data management at CHAMPS site laboratories.

114 by a lack of skills in technical aspects of data management by data generators and a lack of resourc

115 ing data analysis in the database simplifies data management by minimizing the movement of data from

116 The Statistical and Data Management Center (SDMC) provides the Antibacterial

117 ollaboration with the ARLG's Statistical and Data Management Center (SDMC), the LC has developed nove

118 VID-19 cases were obtained from the National Data Management Center at the Ethiopian Public Health In

119 were analyzed by the Alliance Statistics and Data Management Center during September 2021.

120 Clinical Operations Center, Statistical and Data Management Center, and Laboratory Center of the ARL

121 NVivo 8 was used to facilitate data management, coding and analysis.

122 Innovative solutions for data management, data sharing, and for discovering novel

123 Given big data and systematic data management, digital twins can be used for predictin

124 Accessioning, result data management, electronic data transfer, reporting, an

125 Data management encompasses activities including organiz

126 Proper data management ensures long-term preservation and acces

127 Features include the biological data management environment for improved data submission

128 The high rate of subject dropout and data management errors substantially reduced the trial's

129 Robust data management establishes credibility, fostering trust

130 Leadership Group (ARLG) with statistical and data management expertise to advance the ARLG research a

131 mplementing technology for communication and data management, facilitating the informed consent proce

132 and designing the web-based Food Composition Data Management (FCDM) software for FCDB building.

133 emaining dedicated to the FAIR principles of data management (findability, accessibility, interoperab

134 mputerized patient records and prepare their data management for an information framework by (1) expa

135 ghlighted the fundamental importance of good data management for effective outbreak response, regardl

136 Big data management for information centralization (i.e. mak

137 This data management framework allows aggregation and import

138 hyderm is an open-source workflow system and data management framework that fulfils these needs by cr

139 rt concepts, approaches and technologies for data management from computing academia and industry to

140 In addition, we discuss a few concepts of data management from the perspective of an individual or

141 visualization capabilities and comprehensive data management functionality, DendroTweaks introduces a

142 The needs for data management, handling population structure and relat

143 to characterize the implications of censored data management, identify sources of uncertainty, and in

144 nd cohesive computational infrastructure for data management; identity management; collaboration tool

145 Issues include data management, image analysis, and result visualizatio

146 buted compute clusters and has been used for data management in a number of genome annotation and com

147 tured manner, GeOMe sets a gold standard for data management in biodiversity science.

148 Selected approaches to data management in PERCH may be extended to the planning

149 Eight data-management incidents, defined as compromises of any

150 erspectives as it explores emerging areas of data management, including federation, attribution and m

151 ific infrastructure supporting data sharing, data management, informatics, statistical methodology, a

152 g specific analysis calculations from common data management infrastructure enables us to optimize th

153 Investments in data management infrastructure often seek to catalyze ne

154 deep learning on a hybrid software-hardware data management infrastructure, enabling real-time autom

155 tting with limited budgets and computing and data management infrastructure.

156 User-driven learning has implications for data management, integration, and curation.

157 ss upcoming changes to GI identifiers, a new data management interface for BioProject, and advice for

158 We expect that this format will facilitate data management, interpretation and dissemination in pro

159 Effective data management is crucial for scientific integrity and

160 ralian software used for donor and recipient data management is currently being updated.

161 Sample and data management is now feasible through the application,

162 In experimental biomedicine, comprehensive data management is vital due to the typically intricate

163 A data management layer allows collaborative data analysis

164 hestrate those processing stages and (iii) a data management layer that tracks data as it moves throu

165 e platforms with system-level attributes for data management, machine learning, artificial intelligen

166 ger (BRM) v2.3 is a software environment for data management, mining, integration and functional anno

167 mately establish standardized frameworks for data management, model certification, and transparency,

168 ia an account management system and provides data management modules that enable collection, visualiz

169 To support the complex data management needs and workflows of several such biob

170 artificial intelligence (AI) is transforming data management, neurological education, and neurologica

171 is requires scalable and robust software for data management of large datasets.

172 n-source tool, QubiCSV facilitates efficient data management of quantum computing, providing data ver

173 Effective data management of this information is essential to effi

174 nsequences of CRVO may be guided by the CVOS data, management of the underlying cause of CRVO-the occ

175 t in novel hypothesis generation, as well as data management, online sharing and exploration.

176 untries, the administration of a centralized data management operation was a major challenge.

177 ital clinicians, and individuals involved in data management or analysis were masked to treatment all

178 ecause of the in-built capacity for improved data management, organ allocation processes will have th

179 Overall, after disclosure of FFR data, management plan based on CA alone was changed in 2

180 Data management plans (DMPs) are documents accompanying

181 Formal data management plans represent a new emphasis in resear

182 In this context or in the drafting of data management plans, common questions are (1) what are

183 Data management plans, stewardship, and sharing, impart

184 on the LabKey data platform, an open-source data management platform, which enables developers to ad

185 However, the lack of dedicated data management platforms presents a significant obstacl

186 ssing software with larger informatics-based data management platforms.

187 have unfulfilled roles in archive design and data management policy.

188 ed workflows, diverse analysis, and improved data management practices for greater accessibility and

189 ncreased findability of samples and improved data management practices support the goals of the ReSOL

190 ase study highlighted challenges for current data management practices that must be overcome to succe

191 Starting from a foundation of sound data management practices, we make specific recommendati

192 g national standards; (5) improving clinical data management practices; (6) establishing a clear comm

193 h practice; (ii) providing clear guidance on data-management practices; (iii) improving communication

194 plant registries are compounded by different data management processes at the United Network for Orga

195 e collected data are compounded by different data management processes at three US organizations that

196 demand efficient computational solutions for data management, processing and analysis.

197 ach, has always been challenging in terms of data management, processing, analysis and visualization,

198 These practices, which encompass data management, programming, collaborating with colleag

199 ly developed, each with variable outputs and data management protocols.

200 Research data management (RDM) is needed to assist experimental a

201 Data management requirements for the input and documenta

202 ferent scales to reduce acquisition time and data management requirements.

203 Robust data management requires precise ontologies and standard

204 ware environment that provides the user with data management, retrieval and integration capabilities.

205 The trial was stopped early by the Data Management Safety Board because it surpassed the pr

206 is written in R, including wrappers for bash data management scripts and PLINK-1.9 to minimize comput

207 use of the Ocelot object database system for data management services for PGDBs.

208 marized; protocols, standards, and tools for data management, sharing, and integration are reviewed;

209 Together they represent a comprehensive data management solution for alignment data.

210 dern automated laboratories need substantial data management solutions to both store and make accessi

211 pared with existing clinical and preclinical data management solutions, the presented framework bette

212 g clinician investigators, biostatisticians, data management specialists, biomedical ethicists, and o

213 Additionally, with increasing amounts of data; management, storage and sharing challenges arise.

214 Better electronic data management strategies are needed, including the pri

215 of an annotation database and the associated data management subsystem that forms the software bus al

216 various open-source packages into a coherent data management suite to make quantitative multidimensio

217 describe the five main domains of function: data management, summary statistics, population stratifi

218 Therefore, a gap exists in providing data management support for a large set of non-technical

219 asibility and usefulness of an Environmental Data Management System (EDMS) using Open Data was evalua

220 ata were extracted from the clinical patient data management system and analysed using a specialised

221 We created a data management system called the Hormone Receptor Targe

222 nd July 2014 were extracted from the patient data management system database.

223 ch we have developed AntigenApp a laboratory data management system for nanobody generation and seque

224 d continuous integration, to create a modern data management system that automates the pipeline.

225 Online Database (GOLD) is a manually curated data management system that catalogs sequencing projects

226 bial Genomes (IMG) system is a comprehensive data management system that supports multidimensional co

227 To begin with, it provides an efficient data management system that users can upload single cell

228 ed labor, the availability of a computerized data management system, and the noninvasive, nonradiomet

229 te with examples a novel epidemic simulation data management system, epiDMS, that was developed to ad

230 s developed using the DNA microarray project/data management system, micro ArrayDB.

231 ent operational definitions from a Web-based data management system.

232 ata were entered into a prospective clinical data management system.

233 an online slide deck, and (3) a centralized data management system.

234 ge the work of others effectively within its data management system.

235 The lack of microarray data management systems and databases is still one of th

236 mia, hospitals, patient associations, health data management systems and industry.

237 nually, the Institute develops and maintains data management systems and specialized analytical capab

238 The JGI maintains extensive data management systems and specialized analytical capab

239 Existing data management systems can be harnessed to enable real-

240 domain and describe some of the software for data management systems currently available for plant re

241 rimental and reporting guidelines, efficient data management systems, sharing practices, and relevant

242 ta explosion is intractable without advanced data management systems.

243 The funder, site staff, data management team, and participants were masked.

244 tumor microenvironment models employing vast data management techniques.

245 ease in the utilization of IoT-enabled drone data management technology.

246 leic acid extraction, quality assurance, and data management to ensure comprehensive molecular testin

247 : Do not proceed aloneRule 8: Deploy optimal data management to ensure that the data shared is useful

248 ogical Observations (INTOB), a comprehensive data management tool that standardizes the collection of

249 , our SNP/Indel Variant Calling Pipeline and data management tool used for the analysis of whole geno

250 use access to alignment results and flexible data management tools (e.g. filtering, merging, sorting,

251 These data management tools should integrate defined workflow

252 is review details the fabrication of arrays, data management tools, and applications of microarrays t

253 fficult to deal with without the appropriate data management tools.

254 d forms, 5 subcommittees, and laboratory and data management training programs.

255 is a longstanding issue due to little formal data management training within the fields of ecology an

256 es a unique computational challenge, such as data management, trial simulations, statistical analyses

257 We discuss the examples of better rain-data management, urban pluvial flood-risk management and

258 users that facilitates research analysis and data management using BAM files.

259 ells with increased sensitivity and improved data managements, we developed an imaging flow cytometer

260 clinicians across sites and for centralized data management.Weighted descriptive analyses, intraclas

261 patient assessment, monitoring, analysis and data management were masked to group assignment.

262 of centralized metadata and distributed raw data management, which promotes effective data sharing.

263 full control over data use and their way of data management, while SPI-Birds creates tailored pipeli

264 tegrating remote HPC resources and efficient data management with ease of use for biological users.

265 atile and reproducible approach to effective data management within R.

266 liable and reproducible approach for genomic data management within the R environment to enhance the

267 an incentive for data contribution early in data management workflows and eliminates the additional