ライフサイエンスコーパス: data quality

1 ghput in automated cryo-EM without degrading data quality.

2 nt and data analysis parameters for improved data quality.

3 erreporting of safety incidents and variable data quality.

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

5 used for sample delivery greatly affects the data quality.

6 work is essential to assure food composition data quality.

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

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

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

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

11 ction) may create substantial variability in data quality.

12 cilitates comparative assessment of tool and data quality.

13 and numeric anchors can combine to influence data quality.

14 , depending on sample complexity and initial data quality.

15 minate among surveys with excellent and poor data quality.

16 studies were analysed by study location and data quality.

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

18 reasing throughput while maintaining similar data quality.

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

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

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

22 ortunities for process changes that improved data quality.

23 RE and identify design issues that impacted data quality.

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

25 ple times without significantly compromising data quality.

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

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

28 high degree of replication and reported high data quality.

29 ensures scientific integrity, and maintains data quality.

30 ased optical biosensors while retaining high data quality.

31 on and observing significant improvements in data quality.

32 menting procedures that will broadly enhance data quality.

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

34 ion of the original method without a loss in data quality.

35 d low inter-plate variability while ensuring data quality.

36 e impact of various data processing steps on data quality.

37 , training, and consistent implementation in data quality.

38 ated quality control procedures for ensuring data quality.

39 re commonly used to reduce noise and improve data quality.

40 higher input amount, so generally has better data quality.

41 rom multiple sources with various degrees of data quality.

42 ation, provided a useful basis for assessing data quality.

43 he data easily but also serves as a test for data quality.

44 learning across datasets remarkably improves data quality.

45 be possible without concomitantly comprising data quality.

46 rent protocols, and having different overall data qualities.

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

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

49 in data quantity and minimal differences in data quality across preservation conditions.

50 The high data quality allowed the correct identification of inter

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

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

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

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

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

56 ngly being recognized, including issues with data quality and biases caused by simplifying assumption

57 cal limitations with the potential to affect data quality and biological interpretation.

58 a of participation could be used to maximize data quality and breadth of participation across the lar

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

60 Data quality and changes in classification are not likel

61 the outcome adjudication processes, and the data quality and collection processes.

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

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

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

65 ltidisciplinary field, as well as to enhance data quality and consistency.

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

67 Data quality and currency are fostered in CollecTF by ad

68 eral result, we find that the combination of data quality and data quantity of the text data is playi

69 eep-learning methods offer major advances in data quality and detail by allowing researchers to autom

70 The data quality and gap analyses for the generated inventor

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

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

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

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

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

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

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

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

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

80 Four reviewers independently assessed data quality and methodology.

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

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

83 To improve data quality and predictions of host specificity, IMG/VR

84 orous examination of the factors influencing data quality and project efficacy.

85 brain, while facing the challenge of limited data quality and quantity.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

101 tion among data tracking agencies, improving data quality, and expanding proper data sharing.

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

103 ite factors, such as recruitment strategies, data quality, and local coding patterns.

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

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

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

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

108 is-to-trans ratio of contacts, and the broad data quality as reflected by the proportion of mappable

109 an research require careful consideration of data quality as well as the general research and reporti

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

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

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

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

114 Only the data quality assessment for height for age for children

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 Study selection, data quality assessment, and interpretation of results w

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

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

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

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

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

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

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

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

126 We derived 6 indicators of anthropometric data quality at the survey level, including 1) date of b

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

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

129 Guaranteeing data quality becomes increasingly important as microarra

130 protocols necessitates careful assessment of data quality before biological conclusions can be drawn.

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

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

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

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

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

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

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

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

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

140 Data quality concerns and complex batch effects in metab

141 source from the highest burden regions, but data-quality concerns remain.

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

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

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

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

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

147 Data quality control and analyses were done centrally wi

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

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

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

151 Traditional methods of data cleaning and data quality control are just not sufficient.

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

153 New, more strict methodologies of data quality control need to be developed and tested.

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

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

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

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

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

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

160 edure starts with instructions for extensive data quality control.

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

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

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

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

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

166 Forty hospitals met data quality criteria across four electronic health reco

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

168 ial improvements and updates in the areas of data quality, data coverage, statistical algorithms and

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

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

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

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

173 with new and improved data sources, refining data quality, enhancing website usability, and increasin

174 y metrics along with visualizations for fast data quality evaluation.

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

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

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

178 andard (PCI-IS) assisted strategy to improve data quality for DBS-based metabolomics studies.

179 generate a composite index of anthropometric data quality for HAZ and WHZ separately.

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

181 ting black box technologies, assessing input data quality for training such models, and the risk of p

182 develop composite indices of anthropometric data quality for use in multisurvey analysis of child he

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

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

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

186 of access to data, combined with inadequate data quality, has led to difficulties linking environmen

187 New criteria on data quality have also been included.

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

189 ades of methodological advances and improved data quality have facilitated the contribution of modell

190 Data quality highlights the need for improvements in mon

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

192 to account for variations in anthropometric data quality in multisurvey epidemiologic analyses of ch

193 ach can be widely and easily used to improve data quality in proteomics data.

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

195 lactation and/or HMC; 3) considerations for data quality, including addressing sampling strategies a

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

197 Two reviewers assessed data quality independently using the Quality Assessment

198 Results were similar for the WHZ data quality index.

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

200 Data quality indicators were also assessed.

201 ntegrating both gene expression patterns and data quality information.

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

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

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

211 e groups using MULTI-seq barcode abundances, data quality is improved through doublet identification

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

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

214 n TE concentrations in the coal blend, where data quality is the weakest.

215 The output data quality is very important for downstream analysis,

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 ntify functional modules were limited by the data quality issues of high-throughput techniques.

220 has a number of limitations, including poor data quality issues that reflected bias in the report of

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

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

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

224 ess of sample ploidy calls and also detected data quality issues.

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

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

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

228 cused our efforts on data curation, improved data quality maintenance, new tool developments, and dat

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

230 Data quality measures for functional MR images included

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

232 CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations.

233 ns to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments

234 The workflows produce data quality metrics, visualization tracks, and aggregat

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

236 ets, and propose a comprehensive set of four data-quality metrics to address this issue.

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

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

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

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

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

242 microscopy are often limited by insufficient data quality or artifacts.

243 s have been limited by small data sets, poor data quality, or both.

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

245 est potential improvements in anthropometric data quality over time, there continues to be substantia

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

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

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

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

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

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

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

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

254 ranked from highest to lowest anthropometric data quality relative to other surveys using the composi

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

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

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

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

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

260 for varying portions of biomarker and intake data "qualities." RESULTS: With citrus intake, linear re

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

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

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

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

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

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

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

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

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

270 To assess data quality, the intra-individual response variability

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

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

273 k-space data, without a significant loss in data quality, thereby supporting clinical translation of

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

275 MenAfriNet improved data quality through use of standardized tools, procedur

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

277 data, including standardizing data, managing data quality, understanding data comparability, and ensu

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

279 A composite index of anthropometric data quality using a parsimonious set of individual indi

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

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

282 Data quality was assessed by repeated measurements of th

283 ble definitions were used and information on data quality was collected.

284 RICS data quality was constant over the nM-muM concentration

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

286 ular brightness E = 100 kHz and higher, RICS data quality was sufficient using as little as 20 images

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

288 As one means of verifying data quality, we have replicated genome-wide association

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

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

291 opharmaceuticals could benefit from improved data quality, which can subsequently lead to improved dr

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

293 which is concentrated in ten countries, and data quality, which varies across studies.

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

295 ely remove analytical errors and improve the data quality, which would make the DBS-based metabolomic

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

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

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

299 dardised than surveys, resulting in variable data quality, with good validity for the best performing

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