ライフサイエンスコーパス: data management

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

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

3 object-relational schema for more efficient data management.

4 maintenance and implementation of efficient data management.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

25 The data management and analysis were performed from October

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

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

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

29 Effective data management and analytical approaches are essential

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

62 This data management framework allows aggregation and import

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

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

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

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

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

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

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

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

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

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

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

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

75 A data management layer allows collaborative data analysis

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

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

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

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

80 Effective data management of this information is essential to effi

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

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

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

84 Formal data management plans represent a new emphasis in resear

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

86 Data management plans, stewardship, and sharing, impart

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

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

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

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

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

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

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

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

95 Data management requirements for the input and documenta

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

123 ta explosion is intractable without advanced data management systems.

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

125 These data management tools should integrate defined workflow

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

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

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

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

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

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

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

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

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