ライフサイエンスコーパス: raw data

1 in the model ("direct boundary model" of the raw data).

2 ble to reproduce bioinformatic analyses from raw data.

3 dies and a mean peak area RSD of <15% in the raw data.

4 eling experiments from LC-high-resolution MS raw data.

5 hat likely caused deleterious effects on the raw data.

6 and 10% more proteins quantified on the same raw data.

7 t be selected from an overwhelming amount of raw data.

8 of a hundred smaller than using gzip on the raw data.

9 d samples by generating MD5 fingerprints for raw data.

10 methodology would be applied to the obtained raw data.

11 that imaging time we reframed the list-mode raw data.

12 tive statistical method from the same set of raw data.

13 EPA were for raw data.

14 viding a useful visual summary of underlying raw data.

15 ly two IQA requests to the U.S. EPA were for raw data.

16 asured using accelerometry after reanalyzing raw data.

17 only used summaries of the profiles based on raw data.

18 be achieved by postacquisition processing of raw data.

19 ngth and step frequency were determined from raw data.

20 he temporal order may not be apparent in the raw data.

21 and offering a simple way of visualizing the raw data.

22 rdant, even though the methods used the same raw data.

23 ta may miss spatial artefacts present in the raw data.

24 quality assessment to be carried out on the raw data.

25 ilable; otherwise, they were calculated from raw data.

26 dentifying the physical imperfections in the raw data.

27 gression results and do not directly reflect raw data.

28 eliminate background noise and artifacts in raw data.

29 echnological limits, and other biases in the raw data.

30 mino compounds in thousands of mass peaks in raw data.

31 genomes directly from proteomic and genomic raw data.

32 respondents (89%) reported downloading their raw data.

33 vector of informative statistics summarizing raw data.

34 and promise to flood current databases with raw data.

35 squares regression (PLSR) model based on the raw data.

36 ll as its ability to easily process ChIA-PET raw data.

37 Accordingly, we conducted a survey on raw data access and third-party tool usage among 1,137 D

38 The methodology, which is based on raw data acquisition followed by image processing, is he

39 comes of single analyses; however, comparing raw data across multiple experiments should enhance both

40 all stages of DNA sequencing data analysis: raw data, alignment, and variant detection.

41 Access to the raw data allows for a more detailed quality assessment a

42 ibility for the developers of algorithms for raw data analysis.

43 s to be born, which averaged 0.41 HAZ in the raw data and 0.34 HAZ after correction for age misreport

44 ificant reduction in required storage of the raw data and a significant speed up in its ability to qu

45 es' upper thermal tolerance limits, both for raw data and after accounting for the effects of phyloge

46 Two authors independently abstracted raw data and assessed methodological quality.

47 biomolecular process in question; analyzing raw data and assessing the results; and reporting data a

48 k by the absence of sustainable archives for raw data and derivative visualizations.

49 Outcomes were presented as raw data and descriptive statistics (means +/- standard

50 Raw data and differences from baseline were analysed in

51 is to fully automate the workflow to process raw data and ensure the quality of measurements in large

52 solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an

53 This pattern held for raw data and for phylogenetically independent contrasts.

54 of peptide isotopic envelopes in the HDX MS raw data and HDsite for residue-level resolution.

55 loped prioritization method enables reducing raw data and including identification of prioritized unk

56 rch, it should provide sufficient underlying raw data and information about methods to enable reanaly

57 rch, it should provide sufficient underlying raw data and information about methods to enable reanaly

58 reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the arte

59 f the pipeline used to generate high quality raw data and mitigate the need for batch correction are

60 We have scrutinized Stern's raw data and observe that his automated song pulse-detec

61 iscuss recent developments in ways of seeing raw data and presenting the results of statistical model

62 ntific community, including public access to raw data and protocols, the conduct of replication studi

63 We analyzed raw data and reconstructed images, including no correcti

64 lemented in the tool to: (1) read and import raw data and spectral libraries; (2) perform GC-SIM-MS d

65 using an open science approach, sharing both raw data and stimuli.

66 based on an intent-to-treat approach, using raw data and the blood pressure categories of prehyperte

67 because NP databases are not searchable with raw data and the NP community has no way to share data o

68 Careful examination of the raw data and the use of masses for predicted metabolites

69 oefficient values (CCV) of the mass spectral raw data and their variation was developed and used to a

70 r significant retention time shifting in the raw data and then demonstrate subsequent corrections of

71 pectratype analyzers to SpA, which saves the raw data and user-defined supplementary covariates to a

72 construct the annotated mitogenome from HTS raw data and will facilitate large scale ecological and

73 hesis of findings, increased availability of raw data, and a focus on good study design, all of which

74 as between normal and cancer cells; download raw data; and generate heatmaps; and finally, use its in

75 Programs, parameter sets and raw data are available online at.

76 Raw data are available via ProteomeXchange with identifi

77 Cross species comparative mapping raw data are collected and the processed information is

78 ult of the mathematical process by which the raw data are converted into Kubelka-Munk units, and we d

79 Raw data are deposited on SRA, accession numbers: brain

80 by the age of the implant, although when the raw data are examined, some trends are seen.

81 Because hundreds of gigabytes (GB) of raw data are generated from a GWAS, the samples are typi

82 the direct-to-consumer (DTC) context, where raw data are often made available to customers, the use

83 es to extracting spike times and labels from raw data are time consuming, lack standardization, and i

84 d searches; all source references, including raw data, are clearly described and hyperlinked.

85 ntegrating the statistical properties of the raw data as well as information of dense objects gained

86 of searches for arbitrary k-mers within the raw data as well as the ability to reconstitute arbitrar

87 These values represent the quality of the raw data, as no normalization or feature-specific intens

88 d not obviate rights under the IQA to obtain raw data at a later point.

89 The 96 Eyes acquires raw data at a rate of 0.7 frame per second (all wells) a

90 ighlight limitations inherent with a lack of raw data availability, insufficient clinical/histopathol

91 ible than a system that attempts to make all raw data available proactively.

92 Raw data-based iterative reconstruction yielded equivale

93 GC(2)MS also directly corrects the raw data baseline.

94 ld similarly have to include a balance among raw data, basic feature detection results, sufficiency i

95 spectroscopic data involves the denoising of raw data before any further processing.

96 es of individuals who might choose to access raw data before such return becomes routine.

97 structure fits two sets of crystallographic raw data best.

98 We progressively degraded the raw data by addition of noise and examined the ability o

99 ock analysis) and decomposition of the GC-MS raw data by PARADISe were applied to evaluate the influe

100 Artificial image noise was added to the CT raw data by using a dedicated software platform.

101 The exponential component of the raw data can be extracted and defined as the "extracted

102 thod, we have shown that the high-resolution raw data can be fully utilized without applying any arbi

103 Files and raw data can be imported and associated with the biologi

104 le use unless the resulting large volumes of raw data can be reliably translated into actual behaviou

105 monstrate that variances calculated from the raw data can be used as inverse weights in the DE analys

106 Errors in the raw data can lead to insertion or deletion errors (indel

107 spike times of the recorded neurons from the raw data captured from the probes.

108 ilitate computational analyses, M3D provides raw data (CEL file) and normalized data downloads of eac

109 ated in the radar-driven prototype where the raw data collected at the radar receiving channel shows

110 from the level of physical manipulation and raw data collection to automated recognition and data pr

111 Depending on the PET raw-data compression, the average correlation between MR

112 pected from whole-organism DNA sampling, our raw data contained reads from nontarget genomes.

113 s a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment,

114 y provided a full protocol and none made all raw data directly available.

115 samples while keeping all interpretation of raw data directly in the hands of the analyst-saving gre

116 nology-specific biases and produces distinct raw-data distributions, researchers have experienced dif

117 atical approach was developed and applied to raw data exported after the chromatographic course, in o

118 Statistical analysis was performed on raw data extracted from the TIC analysis.

119 ading necessary materials for converting the raw data files (*.CEL) for comparative analysis.

120 The raw data files for the samples were assembled into a sin

121 ML and mzData it can be difficult to extract raw data files in a form suitable for batch processing a

122 For this purpose, the same raw data files were processed with the software packages

123 these parameters are embedded in instrument raw data files, an opportunity exists to capture this me

124 apidly extracting semiquantitative data from raw data files, which allows for more rapid biological i

125 tem or directly from an MRI scanner, or from raw data files.

126 perimental protocols, related literature and raw data files.

127 ibuted workflow processes and open access to raw data for analysis by numerous laboratories.

128 e incidence of CVE post-TAVR while providing raw data for predictors of interest was performed.

129 tion for meta-analyses and, if possible, the raw data for re-analyses.

130 ion leads to additional information, such as raw data for specific isotopic forms or for metabolites

131 The raw data for the 17 WT Arabidopsis thaliana datasets is

132 PM implantation after TAVR and that provided raw data for the predictors of interest.

133 In fact, the raw data for the standard curves were highly scattered a

134 This package also contains the raw data for the three example datasets used in this man

135 uitment into the study, and (e) inclusion of raw data (for true-positive, false-positive, true-negati

136 related, as they involve multiple use of the raw data, for example, yielding ~ 250 E estimates from ~

137 es, Z- and time stacks in a broad variety of raw-data formats, as well as movies and animations.

138 e obtain high-quality images of objects from raw data formed from an average of fewer than one detect

139 G, AND PATIENTS: Post-hoc analysis combining raw data from 4 prospective randomized trials (performed

140 e report the first post-processing of binary raw data from a high-resolution LSCI camera.

141 We show that direct analysis of raw data from a quantitative genotyping platform can det

142 We use raw data from a real completed clinical trial, simulate

143 , we present our experience of analysing the raw data from an Illumina spike-in experiment and offer

144 tic and is capable of post-processing binary raw data from any camera source to improve the sensitivi

145 Analysis of raw data from assays carried out on the diffractive micr

146 KairosMS imports raw data from common file types, processes it, and expor

147 The R/Bioconductor package beadarray allows raw data from Illumina experiments to be read and stored

148 QuaMeter can directly read raw data from instruments manufactured by different vend

149 ve algorithm, we build a pipeline to process raw data from L1000 assay into signatures that represent

150 strategy, in which statistical treatment of raw data from liquid chromatography-mass spectrometry (L

151 The Sequence Read Archive (SRA) contains raw data from many different types of sequence projects.

152 The raw data from many diverse proteomic experiments are mad

153 logical roles, locations, concentrations and raw data from metabolic experiments.

154 RNASEQR analyzes raw data from RNA-seq experiments effectively and output

155 There is increased demand for disclosure of raw data from studies used by the U.S. EPA in these revi

156 The raw data from such experiments are two-dimensional, but

157 ilable in the published report, we requested raw data from the authors.

158 PATIENTS AND METHODS Raw data from the Prostate Cancer Prevention Trial were

159 Raw data from the prostate DWI scans were retrospectivel

160 each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing

161 A combined analysis of raw data from these studies was performed to better asse

162 rmed by mapping the predicted sequences with raw data from total transcript sequence generated using

163 The paper presents the raw data from which our conclusions were drawn and discu

164 cipant data (IPD) meta-analyses that obtain "raw" data from studies rather than summary data typicall

165 RawConverter accepts RAW data generated by either data-dependent acquisition

166 ol, IsoMS, has been developed to process the raw data generated from one or multiple LC-MS runs by pe

167 The workflow covers all steps from raw data handling, feature selection, and compound ident

168 The mass spectrometric raw data has been deposited in PRIDE (PXD003737).

169 Once the raw data have been preprocessed, running CellNet takes o

170 e in miRDeepFinder include pre-processing of raw data, identifying conserved miRNAs, mining and class

171 iterature and additional analyses as well as raw data if appropriate.

172 Domains of expression from raw data images are spatially integrated into a set of s

173 ng down to approximately 20 nm resolution in raw data images as well as 15-19 nm diameter probing are

174 er are clearly improved in comparison to the raw data images for frequencies above 2000 cm(-1).

175 ramework for reproducible research, allowing raw data import, quality control, visualization, data pr

176 AutoTuner, obtains parameter estimates from raw data in a single step as opposed to many iterations.

177 zing storage and transfer of full-resolution raw data in dynamically varying scenes.

178 underlying data, performing better than the raw data in revealing important biological insights.

179 e conducted using the random effect model on raw data in the StatsDirect statistical program.

180 performance by at least 50% compared to the raw data; in most cases, it leads to a strategy very clo

181 Furthermore, the ability to incorporate raw data, including some metagenomic samples containing

182 of the actual pictures and difficulties with raw data interpretation.

183 h a user-friendly interface and (i) converts raw data into a format for visualization on a genome bro

184 In bioinformatics, we pre-process raw data into a format ready for answering medical and b

185 ogy itself and the algorithm used to convert raw data into expression estimates.

186 ocedures have been proposed for transforming raw data into genotype calls.

187 putational methodologies used to convert the raw data into inferences at the DNA level, and details t

188 M), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k

189 By converting the instrument raw data into mzXML format as its input data, MetSign pr

190 It allows individual researchers to condense raw data into spectral libraries, summarizing informatio

191 ormalization, binning, smoothing) to process raw data into visualizable tracks.

192 gating (DDG) using signals derived from PET raw data is a promising alternative to gating approaches

193 ly extract the relevant information from the raw data is a robust tool for evaluating the number and

194 equencing reads in color-space; that is, the raw data is a sequence of colors, where each color repre

195 Raw data is available at NCBI's SRA with accession numbe

196 The raw data is deposited to ProteomeXchange (PXD001907).

197 Storing raw data is infeasible because of its enormous size and

198 Since the raw data is inherently noisy, lossy compression has pote

199 Raw data is processed and mapped to genomic coordinates

200 ll the quality assessments and filtration of raw data is still lacking.

201 Raw data is stored in a generic database schema, Chado N

202 ess association for related phenotypes where raw data is unavailable or inappropriate for analysis us

203 ata-driven algorithm, that is, using the PET raw data itself, to address these limitations.

204 k nonlinear retention time correction at the raw data level.

205 ) identifies and eliminates batch effects at raw data level; (b) assembles individual CNV calls into

206 mode of centralized metadata and distributed raw data management, which promotes effective data shari

207 However, the raw data, microarray probe intensities, are heavily proc

208 Prior to any interpretation, raw data must be processed to remove noise and to align

209 o analyzing these information-rich datasets, raw data must undergo several computational processing s

210 ere, we demonstrate that the large volume of raw data obtained from real-time dPCR instruments can be

211 In untargeted proteomics and metabolomics, raw data obtained with an LC/MS instrument are processed

212 rofiles, we first performed a meta-analysis: raw data of 1539 microarrays and 705 NGS blood-borne miR

213 n the genotype calling algorithm BRLMM using raw data of 270 HapMap samples analyzed with the Affymet

214 with use of odds ratios calculated from the raw data of every trial.

215 To this end, we collected the raw data of publicly available immune-related scRNA-seq

216 here to reevaluate the analysis methods and raw data of published SH2-pTyr HTP experiments.

217 o an associated problem: the large volume of raw data often makes it challenging to analyze and integ

218 We aim to use raw data on HM markers at different genomic loci to (1)

219 d and EMBASE databases for studies reporting raw data on new-onset LBBB post-TAVR and the need for PP

220 s or entire compound classes, and can export raw data or graphics for off-line use.

221 the full space of parameters compatible with raw data or selected data features.

222 little is known about why individuals access raw data or what they do with the information received f

223 ametric two sample test method) based on the raw data outperformed limma based on the transformed dat

224 -50% further reduction in compressed size of raw data over the state-of-the-art lossless compressor w

225 large whole genome datasets with 100 GB+ of raw data per sample and to single-cell datasets with tho

226 several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration,

227 Users interact with raw data primarily in the form of extracted ion chromato

228 The resource supports archiving of raw data, processed data and metadata which are indexed,

229 The amounts of raw data produced are prodigious, and many computational

230 However, the quantity of raw data produced by this technology requires efficient

231 We make the images and raw data publicly available, providing an initial morpho

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

233 AM OFDM) stream with sidelobe filtering, the raw data rate expedites from 17.2 to 18.4 Gbps.

234 g a full contraction-relaxation cycle with a raw data rate of 200 mus/frame and to generate spatially

235 lobe filtering are respectively delivered at raw data rates of 16.4 and 18 Gbps with spectral-density

236 or determining hyperfine-coupling signs; and Raw-DATA (RD)-PESTRE, a PESTRE variant that gives a cont

237 ion (+/- 10 to 15 per thousand), however the raw data required daily calibration by TCE and/or DCE st

238 Logistic regressions of the raw data reveal that this nonrandom distribution stems p

239 Analysis of Perucca's raw data reveals that he was observing a convolution of

240 cesses automatically algebra combinations of raw data sequencing into a comprehensive final annotated

241 The raw data series are reduced to phase-insensitive summary

242 By using essentially the same raw data set as Collard and Wood, 65 quantitative cranio

243 The raw data set consisting of diffusion volumes were first

244 Each raw data set was reconstructed with bone and soft-tissue

245 Raw data sets were reconstructed by using filtered back

246 d related components, capable, from the same raw data sets, of enabling increased assay sensitivity a

247 rchiving protocols to minimize bias from the raw data sources.

248 agrams and how to estimate HAT barriers from raw data, starting with the simplest reaction H + H2 and

249 pts that enable the user to go from a set of raw data, such as fastq files, to publication-ready resu

250 search is needed on the preprocessing of the raw data, such as the normalization step and filtering,

251 Analysis of the study methodology and raw data suggest that this estimate is statistically fla

252 atistical data about clinical trials but not raw data; this database may be a model for data from stu

253 d through the entire analysis workflow, from raw data through preprocessing (including a wide range o

254 ysis was performed by post-processing of the raw data (time intensity curve [TIC] analysis).

255 We urge that empirical studies publish raw data to allow evaluation of covariation in cross-stu

256 Covering the spectrum from raw data to assembled and functionally annotated genomes

257 include data on calibrations and sufficient raw data to assess precision and accuracy of the results

258 neural network applied to corneal topography raw data to classify examinations of 3 categories: norma

259 The pipelines go from raw data to CpG-level methylation estimates and can be r

260 de a comprehensive analysis of ChIP-seq from raw data to downstream analysis.

261 adversarial examples, which are created from raw data to fool the classifier such that it assigns the

262 mber of known and unknown compounds from the raw data to reduce investigator's bias and to vastly acc

263 r friendly complete pipeline from processing raw data to reporting analytic results; (ii) it detects

264 approach in order to allow resampling of the raw data to resolve the statistical weighting of coexist

265 We combined the available raw data to strengthen the current literature in compari

266 whole metagenome sequencing promises enough raw data to study those changes, existing tools are limi

267 It aims to fully encompass and simplify the "raw data-to-publication" pathway and make it reproducibl

268 products (tools and data) to facilitate the raw-data-to-knowledge process in health research.

269 from normalization, also, influences of used raw data types are demonstrated.

270 ully recover the recurrent CNV patterns from raw data under different scenarios.

271 repositories, the experiment annotation and raw data uploading can be very timeconsuming, especially

272 We processed raw data using a graphical-based algorithm by transformi

273 Processing of raw data using the XCMS software resulted in time-aligne

274 re the use of lossy compression for nanopore raw data using two state-of-the-art lossy time-series co

275 and visualize the results as overlays on the raw data via any web browser using a personal computer o

276 play, statistical analysis and access to the raw data via web services.

277 Raw data was only handled by coauthors with direct affil

278 Raw data was preprocessed using SAGE (GE) software.

279 207-559), and the ratio, calculated from the raw data, was ~6.25:1.

280 With use of raw data, we recalculated all participant assignments to

281 A) of 2001 provides an avenue for request of raw data, we reviewed all IQA requests to the U.S. EPA i

282 Raw data were analyzed using SAGE (GE) software.

283 The raw data were compiled, descriptive analyses were perfor

284 ures ranging from 5 to 37 degrees C, and the raw data were fit globally to derive a single set of rat

285 Raw data were grouped into nine dimensions of periodonta

286 nalysts and investigators with access to the raw data were masked to study group by coding the groups

287 Raw data were obtained from the authors and quality cont

288 Raw data were post-processed using custom-designed softw

289 Obtained raw data were processed using multivariate statistical a

290 alyzed in an LC-Orbitrap Elite platform, and raw data were processed using Proteome Discoverer 2.1.

291 EXTRACTION: All data were renormalized from raw data, when available, using consistent methods.

292 ethod that is applicable for analysis of the raw data where there are often more than a million rows

293 ess of spurious and deleted cut sites in the raw data, which are called Rmaps, make assembly and alig

294 prises a significant portion of the measured raw data, which can have serious implications for the in

295 complex mixtures, produces large amounts of raw data, which needs to be analyzed to identify molecul

296 iKit assembly is a reduced representation of raw data while retaining most of the original informatio

297 Using combined corneal topography raw data with a convolutional neural network is an effec

298 utomatic local thresholding of the resultant raw data with the Phansalkar method was analyzed with ge

299 ating signals and signals extracted from PET raw data with the sensitivity method, by applying princi

300 reliability and noise characteristics of the raw data, with important consequences for the power to d