ライフサイエンスコーパス: data quality

1 cilitates comparative assessment of tool and data quality.

2 and numeric anchors can combine to influence data quality.

3 , depending on sample complexity and initial data quality.

4 studies were analysed by study location and data quality.

5 as used as a parameter for the assessment of data quality.

6 reasing throughput while maintaining similar data quality.

7 QC, a program which provides key measures of data quality.

8 omplete, will monitor, evaluate, and improve data quality.

9 nts of a given reflection are used to assess data quality.

10 erreporting of safety incidents and variable data quality.

11 ortunities for process changes that improved data quality.

12 rgy transfer (FRET), significantly improving data quality.

13 ple times without significantly compromising data quality.

14 in each group were excluded because of poor data quality.

15 ssing is a critical element that impacts the data quality.

16 high degree of replication and reported high data quality.

17 ased optical biosensors while retaining high data quality.

18 on and observing significant improvements in data quality.

19 menting procedures that will broadly enhance data quality.

20 riate data, taking particular care to ensure data quality.

21 ratories by providing a means for evaluating data quality.

22 rences that provide a robust means to assess data quality.

23 from hybridized data sets to further improve data quality.

24 hat are more distinctive because of improved data quality.

25 to demonstrate the effect of replication on data quality.

26 igh-throughput analysis without compromising data quality.

27 ents 100-fold while maintaining or improving data quality.

28 aural cues may stimulate recall and improve data quality.

29 be possible without concomitantly comprising data quality.

30 used for sample delivery greatly affects the data quality.

31 work is essential to assure food composition data quality.

32 f complete features, limit of detection, and data quality.

33 or non-uniform coverage, reducing sequencing data quality.

34 C is a useful tool for controlling scRNA-seq data quality.

35 of the massive volume of data and imperfect data quality.

36 ction) may create substantial variability in data quality.

37 ecifies procedures for continually assessing data quality; (6) it maintains strict controls for prote

38 rs use reliability data only as a metric for data quality, a more thorough approach can also quantita

39 ng throughput, reducing waste, and improving data quality, a universal 96-well filter plate format wa

40 illance systems are simplicity, flexibility, data quality, acceptability, sensitivity, predictive val

41 The benchmark comparisons of data quality across platforms will also serve as a refer

42 The high data quality allowed the correct identification of inter

43 The results of the data quality analyses support the usefulness of vaccinat

44 VSD study populations (n = 1,224-2,577) for data quality analyses.

45 f genome sequencing projects, in addition to data quality analysis and data format conversions; (ii)

46 ographic peaks across all samples to confirm data quality and (ii) for a given sample set, integrates

47 l Center for Genome Resources was to improve data quality and accessibility.

48 e array design characteristics for improving data quality and analysis.

49 diagnostic statistics and images to evaluate data quality and array processing.

50 Recent improvements in data quality and availability, including satellite-deriv

51 n working towards improving food composition data quality and availability, including the development

52 gated by sex is complicated by problems with data quality and availability.

53 potential, enhanced the database profile and data quality and broadened the inter-relation of HbVar w

54 studies and gene level studies by improving data quality and by providing data access capabilities t

55 Data quality and changes in classification are not likel

56 mation was evaluated for 332 botulism cases; data quality and completeness were variable.

57 ngitis in the region, despite limitations in data quality and completeness.

58 Adjusting for background noise improves PBM data quality and concordance with in vivo TF binding dat

59 rotein fused to histone 2B provides enhanced data quality and content over assays conducted without t

60 Data quality and correlations with trial outcomes were a

61 Data quality and currency are fostered in CollecTF by ad

62 within the same flow cell, thereby improving data quality and extending the kinetic range of the inst

63 r tools for accessing public data, assessing data quality and for data analysis.

64 The data quality and gap analyses for the generated inventor

65 Overall, poor data quality and gaps in knowledge limit the ability to

66 quality and are highly dependent on the read data quality and genome complexity.

67 Similar to estimates of DNA sequence data quality and genome size early in the Human Genome P

68 e temporal variations, allowing us to assess data quality and homogeneity over the entire record, and

69 It generally is assumed that improvements in data quality and implementation of sophisticated tractog

70 Pooling affects data quality and inference, but the exact effects are no

71 me Project, estimates of protein interaction data quality and interactome size are crucial to establi

72 rray probes, which is a fundamental issue in data quality and interpretation.

73 esolution is generally explained in terms of data quality and methodological issues, such as characte

74 Important advances in data quality and methodologies have allowed for better i

75 Four reviewers independently assessed data quality and methodology.

76 c bias in mass measurement adversely affects data quality and negates the advantages of high precisio

77 In this way, problems arising from data quality and only partially compatible framework and

78 to producing dense high-quality RH maps are data quality and panel size, not computation.

79 us metazoan gene superalignments in terms of data quality and quantity.

80 lation scanning profile can be used to check data quality and refine the analysis of DNA copy number

81 cancer registries that are heterogeneous in data quality and registration methodology; many registri

82 et constraints, comprehensive assessments of data quality and reliability, including masking of medic

83 well accepted by cancer patients, with good data quality and reliability.

84 We show that the handoff timing affects the data quality and repeatability of the electropherograms,

85 Problems in data quality and reporting appear unable to account for

86 MS based techniques offer superior data quality and reproducibility, but WB offers greater

87 trategy significantly improves bioanalytical data quality and saves time, costs, and resources by avo

88 The AUSCAN subscales were assessed for data quality and scaling properties.

89 ore fundamental ones about the assessment of data quality and the design guidelines of taxon sampling

90 Depending on data quality and the exact target region, we find betwee

91 tion (PDR) metrics were used to evaluate the data quality and the pretreatment effects.

92 otin linkage to PEG-coated AFM tips enhanced data quality and throughput.

93 f standardized methods are needed to improve data quality and to encourage the use of passive samplin

94 nts are examined to reveal the influences on data quality and to guide further developments.

95 has been under continued curation to enhance data quality and to increase breadth of pathway coverage

96 showed how the method can be used to examine data quality and to obtain robust quantification of prot

97 Assessment of data quality and validity included consideration of venu

98 Data quality and validity included publication or presen

99 Data quality and validity included the venue of the publ

100 Data quality and validity were assessed by each author i

101 Data quality and validity were independently assessed by

102 elines were used to abstract data and assess data quality and validity.

103 orrect weighting factors on curve stability, data quality, and assay performance were thoroughly inve

104 t-flow, mass spectral data acquisition rate, data quality, and liquid microjunction sampling area.

105 ble to incompleteness of flowcharts, limited data quality, and model assumptions.

106 ng systems in terms of patients' acceptance, data quality, and reliability.

107 er from the perspective of response rate and data quality, and that clarity and ease of administratio

108 Two reviewers assessed study data, quality, and applicability.

109 The Geospatial Data Quality API is part of the VertNet set of APIs.

110 label, and both high representation and good data quality are crucial in registry studies.

111 as been structured to check three particular data quality aspects: (i) data formatting, (ii) quality

112 This protocol assumes that data quality assessment and control has been performed,

113 This protocol details the steps for data quality assessment and control that are typically c

114 ic data quality that must be regarded during data quality assessment and how these impacts can be des

115 Data quality assessment is an essential part of the anal

116 or pre-processing, expression estimation and data quality assessment of high-throughput sequencing tr

117 encompasses significant aspects ranging from data quality assessment, assay characterization includin

118 cing depth, data and metadata reporting, and data quality assessment.

119 wnstream analysis along with web reports for data quality assessment.

120 eps are summarized from sample extraction to data quality assessment.

121 ata preprocessing, peak detection and visual data quality assessment.

122 Greater dissemination of data quality assessments, sensitivity analyses, and meth

123 from array data, the inherent challenges in data quality associated with most hybridization techniqu

124 Data quality assurance should start with deciding in adv

125 ation and labeling error is an indispensable data quality assurance step.

126 form peak quantification, peak alignment and data quality assurance.

127 nd instrument drift, significantly improving data quality at each region of interest.

128 Our method has the potential to improve data quality at reduced costs.

129 form to facilitate comparisons and maintain data quality based on an international panel of SJS/TEN

130 The first approach consists of assessing data quality based on systematic characterization of MFA

131 Guaranteeing data quality becomes increasingly important as microarra

132 However, when we carefully match data quality between groups, all these effects disappear

133 sizes and/or fail to address differences in data quality between those with autism spectrum disorder

134 e standardization of data representation and data quality by facilitating the capture of the minimum

135 sensitivity by at least 4-fold and improves data quality by minimizing formation of a deleterious by

136 t the algorithm is robust to fluctuations in data quality by successfully clustering data with a desi

137 consistency was observed, illustrating that data quality can be improved by selecting arrays that me

138 ematic bias in data collection, case-mix, or data quality can explain a divergence in performance of

139 , with unconstrained annotation, can improve data quality; care should be taken when exploring such p

140 TU identification, such as data preparation, data quality check and RNA-read mapping.

141 anization of global databases; and rounds of data quality checking.

142 early in life and from validation and other data quality checks of such measurements.

143 pression easily accessible to users and with data quality close to that achievable at ambient.

144 ws for signal averaging, resulting in higher data quality, collision energy optimization, slower scan

145 Data quality concerns and complex batch effects in metab

146 Hence, data quality confidence appears to improve even while fr

147 are evaluated and categorized using proposed data quality (confidence) criteria derived from the stan

148 To improve data quality, consistency and use, MOPED includes metada

149 creating a need for user-friendly tools for data quality control (QC) and analysis.

150 essed included questions of study design and data quality control (QC), genotype imputation to augmen

151 Data quality control and analyses were done centrally wi

152 cs and reports, providing a simple way to do data quality control and assurance.

153 e iPlant image, including tools for ChIP-seq data quality control and evaluation adapted from the hum

154 grating bioinformatics databases, performing data quality control and sample selection.

155 tomates data downloading, data organization, data quality control assesment, differential gene expres

156 bundled with the tools needed for MALD, from data quality control through mapping of and visualizatio

157 O through diverse examples, ranging from NGS data quality control to characterization of enhancer reg

158 After genetic data quality control, 680 lung transplant recipients wer

159 for assay development, sensitivity regimes, data quality control, analysis, and ranking.

160 rease the speed and efficiency of performing data quality control, annotation, and association analys

161 Following data quality control, we analysed 22 single nucleotide p

162 There has been heavy focus on performing raw data quality control.

163 ce back key identification steps and perform data quality control.

164 Hardy-Weinberg equilibrium (HWE) testing for data quality control.

165 es, they also present serious challenges for data quality control.

166 edure starts with instructions for extensive data quality control.

167 ools for enabling smooth, secure transfer of data, quality control information, and analyses between

168 It provides comprehensive access to sequence data, quality control results, annotations, and many oth

169 nterpretation; archiving and distribution of data; quality control; and imaging-associated risks and

170 a systematic predictive procedure to produce data-quality crystals of bovine chymotrypsinogen A and u

171 s (e.g., data processing, data availability, data quality, data costs) and the specific challenges of

172 Despite this evolution, the design and data quality derived from imaging within clinical trials

173 an visualise experimental annotation, assess data quality, download and share data via a web-based ex

174 DP-SPME significantly improved precision and data quality due to decreased intersample variation.

175 requency and B0 shim changes, and effects on data quality during real-time-corrected three-dimensiona

176 an alternative approach to the evaluation of data quality-examination of the reliability of reports o

177 hich has been used to provide information on data quality, expansion of the secondary metabolism node

178 have benefited from statistical measures of data quality, extracting biologically relevant pathways

179 Methodologic characteristics and overall data quality for all 200 highly cited studies were asses

180 data calculated without ISs exhibited a poor data quality for both QCs and patients' concentrations.

181 We assessed data quality for completeness, diagnostic accuracy, miss

182 s to know its relationship to some metric of data quality for individual patient scans, such as noise

183 However, the HTS data quality for lead discovery, lead optimization, and

184 of length and clarity on response rates and data quality for two food frequency questionnaires (FFQs

185 es unique and independent evaluations of the data quality from different perspectives.

186 trics to rapidly and quantitatively evaluate data quality from structure probing experiments, demonst

187 ime and human resources but is essential for data quality; funders should support this step in future

188 Data quality has also been increased by improvements to

189 Data quality has been improved by extensive curation of

190 New criteria on data quality have also been included.

191 Although several frameworks for data quality have been proposed, general tools and measu

192 europsychological profile, eye movements, or data quality; however, they were specifically impaired i

193 Genome Resources (NCGR) has been to improve data quality, improve data collections, and provide new

194 dary oxidation in model peptides and improve data quality in examining the reactivity of peptides wit

195 The improvements in data quality in the legacy archive have been achieved la

196 ble patients, who met the CPRD threshold for data quality, in a GP practice defined by the CPRD as co

197 dized analytical methods for CECs to improve data quality, increase comparability between studies, an

198 We recommend the addition of a data-quality indicator of the per cent of all live prete

199 Data quality indicators were also assessed.

200 ntegrating both gene expression patterns and data quality information.

201 ule, ligand complex or sequence family using data-quality information from the wwPDB validation repor

202 formative, problems related to study design, data quality, integration, and reproducibility still nee

203 ch strategies include thorough assessment of data quality, interrupted time-series or policy gradient

204 Data quality is a critical issue in the analyses of DNA

205 The assessment of data quality is a major concern in microarray analysis.

206 ing accurate, scalable algorithms to improve data quality is an important computational challenge ass

207 Data quality is an important issue when managing food co

208 Specifically, AFM-based SMFS data quality is degraded by a commercial cantilever's li

209 Furthermore, the data quality is enhanced by the prediction of an estimat

210 However, improvement in data quality is essential.

211 umulatively, these comparisons indicate that data quality is essentially equivalent between the one-

212 With BeadArray technology, high data quality is generated from low sample input at reduc

213 ity on the same scale, and this reveals when data quality is limiting model improvement.

214 pecially in the equatorial region and if the data quality is not optimal.

215 ntifies the correct protein(s) even when the data quality is relatively low or when the sample consis

216 al time series model, accounting for various data quality issues and assessing the uncertainty in sex

217 Data quality issues are considered throughout the lifecy

218 To help address these data quality issues in the context of family-based assoc

219 o discover hidden data properties and detect data quality issues, e.g. data noise, inappropriately la

220 mates of under-five mortality rates, analyze data quality issues, note the relative effects of these

221 sed synthetic biology databases face similar data quality issues, we propose to visualize biobricks t

222 However, despite the limitations in data quality, it is clear that current capacity to treat

223 problems with such evaluations include poor data quality, lack of concurrent controls, inability to

224 illustrate that greater degradation of input data quality leads to greater variability in the results

225 However, poor data quality limits inferences and draws attention to th

226 and retaining only the datasets with highest data quality (measured soil bulk densities and external

227 Data quality measures for functional MR images included

228 Subjects include normalization, scaling and data quality measures, LOESS (local polynomial) smoothin

229 th a comprehensive and unbiased reference of data quality metrics.

230 ch makes proper allowance for the effects of data quality, model errors, and incompleteness.

231 We achieve the throughput and data quality necessary for genomic-scale structural anal

232 set of model parameters to determine whether data quality needs to be improved, and to enhance interp

233 w cellular and reagent consumption, and high data quality obtained with the microfluidic device, the

234 eat potential to increase the throughput and data quality of DNA sequencing.

235 management framework was developed to assure data quality of food composition data, incorporating sev

236 We found no difference in the data quality of nonexperts and experts.

237 d demographics, body mass index, physiologic data, quality of life, dyspnea, oxygen utilization, hemo

238 CC* also can be used to assess model and data quality on the same scale, and this reveals when da

239 omposition and may be improved by the marker data quality only within the limits of the population re

240 ing implementation challenges including poor data quality or lack of interest in change.

241 The improvement in data quality over time was associated with an increased

242 Immediate correction improves data quality, particularly in patients who have difficul

243 ports of escaped farmed salmon requires high data quality, particularly since reports of farmed salmo

244 bortion, but challenges with measurement and data quality persist.

245 Poor data quality, plus factors such as the proportion of pat

246 the endogenous analytes provide the highest data quality (precision, accuracy).

247 The Data Quality Program consists of 3 main components: 1) a

248 ovascular Data Registry (NCDR) developed the Data Quality Program to meet the objectives of ensuring

249 ns that included spectral acquisition rates, data quality, proteome coverage, and biological depth.

250 rching conclusions are apparent: first, that data quality, rather than the assembler itself, has a dr

251 nsights into human disease pathogenesis, but data quality remains a major obstacle.

252 ncy for submitted data fields as part of the data quality report.

253 Program consists of 3 main components: 1) a data quality report; 2) a set of internal quality assura

254 irst step in this direction, we investigated data quality requirements for an information system to m

255 in terms of application, data treatment, and data quality requirements should dictate the selection o

256 ts categories as the scope classification of data quality requirements.

257 Five psychometric properties of the MSIS-29 (data quality, scaling assumptions, acceptability, reliab

258 on that makes it easy and efficient to gauge data quality, screen for transcripts with high-quality i

259 r selectivity, quantitative performance, and data quality since the same ion from different samples m

260 nd the routine monitoring of key measures of data quality such as the number of alignable reads, dupl

261 Several issues related to data quality, such as the existence of sequencing artifa

262 However, is the data quality sufficient to allow reuse and reanalysis?

263 onomic evaluation, outcome measures used and data quality support the need for further research.

264 ility concerns have required the development data quality tests for common systematic biases.

265 microbial dynamics in streams with improved data quality than prior studies, (2) advances a stochast

266 njunction with the VQSR tool, achieve higher data quality than when using VQSR alone.

267 sample, and with the potential for superior data quality, than the conventional methods used in most

268 ents have different impacts on the intrinsic data quality that must be regarded during data quality a

269 also providing a concomitant improvement in data quality that now makes feasible the identification

270 ion and using a variety of methods to assess data quality, the investigators address concerns about t

271 ance of using exploratory analyses to assess data quality, the strengths and limitations of commonly

272 with 3D-SIM data, and assess resolution and data quality through objective control parameters.

273 e monitored in real time and with sufficient data quality to allow numerical analysis of proteolysis

274 lots), which provide extra information about data quality unavailable in other microarray repositorie

275 se is expected to contribute to the improved data quality, usage, generation, publication and appreci

276 d independently by the 2 authors.We assessed data quality using the Jadad scoring system and used a r

277 More generally, enhancing data quality via an improved combination of time resolut

278 Data quality was assessed by repeated measurements of th

279 Data quality was based on venue of publication and relev

280 Data quality was determined by publication in peer-revie

281 Data quality was determined by publication in peer-revie

282 Data quality was determined by publication in peer-revie

283 Data quality was determined by publication in the peer-r

284 Data quality was determined by publication in the peer-r

285 Data quality was determined by publication in the peer-r

286 Data quality was determined by publication in the peer-r

287 Data quality was excellent, missing data were low (maxim

288 Based on almost 2 million genotypes, data quality was shown to be extremely high, with a 99.9

289 We discovered that data quality was significantly improved by imposing the

290 Data quality was variable, and worse in the ASD group, w

291 To improve the data quality, we used a custom-made nose cone to monitor

292 es for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field.

293 hat data processing significantly influences data quality, which provides an explanation for the conf

294 ized the role of clinical coders in ensuring data quality, which was at odds with the policy drive to

295 w improvement in statistical methodology and data quality will increase our understanding of SLE gene

296 could not observe systematic correlation of data quality with chemical similarity or proximity in re

297 ncipal limitation of our analysis is limited data quality, with cases not being entered into the data

298 there is a need for new methods that improve data quality without sacrificing throughput.