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69 results for “traceability”
Design, construction and traceable calibration of a phosphor-based fibre-optic thermometer from 0 °C to 650 °C
<p>Dataset associated with publication DOI <a href="https://doi.org/10.1088/1361-6501/abee53">https://doi.org/10.1088/1361-6501/abee53</a></p>
Dataset of "Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code"
<p>Dataset of "Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code"</p>
Data underlying: "Method to traceably determine the refractive index by measuring the angle of minimum deviation"
<p>Datasets of refractive index measurements of fused silica measurements at 405 nm, 436 nm, 546 nm and 579 nm, published in "Method to traceable determine the refractive index by measuring the angle of minimum deviation".</p> <p> </p>
Prompting Creative Requirements via Traceable and Adversarial Examples in Deep Learning
<p>File A: Datasets (.txt)</p> <p> A1: Webex</p> <p> A2: Zoom</p> <p> A3: Teams</p> <p> A4: Word</p> <p> A5: PowerPoint</p> <p> A6: Excel</p> <p>File B: Python Code (Both ours and baseline)</p> <p> B1: pert_class.ipynb</p> <p> B2: Baseline.ipynb</p> <p>File C: Trend of Adversarial Shifts (Graphs)</p> <p> C1: Webex Adversarial Shifts</p> <p> C2: Zoom Adversarial Shifts</p> <p> C3: Teams Adversarial Shifts</p> <p> C4: Word Adversarial Shifts</p> <p> C5: Powerpoint Adversarial Shifts</p> <p> C6: Excel Adversarial Shifts</p> <p>File D: Trend of Non-Adversarial Shifts (Graphs)</p> <p> D1: Webex Non-Adversarial Shifts</p> <p> D2: Zoom Non-Adversarial Shifts</p> <p> D3: Teams Non-Adversarial Shifts</p> <p> D4: Word Non-Adversarial Shifts</p> <p> D5: Powerpoint Non-Adversarial Shifts</p> <p> D6: Excel Non-Adversarial Shifts</p> <p> </p> <p> </p>
Supplementary Material - All Eyes on Traceability
<p>This PDF presents the interview questions used for the semi-structured interview of the paper "All Eyes on Traceability: An Interview Study on Industry Practices and Eye Tracking Potential".</p>
Online Appendix of "Why Don't We Trace? A Study on the Barriers to Software Traceability in Practice"
<p>This is the online appendix of the paper "Why Don't We Trace? A Study on the Barriers to Software Traceability in Practice", authored by Marcela Ruiz, Jimmy Hu, and Fabiano Dalpiaz.</p> <p>This contains five files:</p> <ul> <li>Software traceability survey.pdf - a PDF version of the survey that was used in the paper to collect information about traceability practices and the challenges experienced by practitioners;</li> <li>Software_traceability_InterviewProtocols.pdf - the interview protocol that was used to complement the survey with more in-depth information;</li> <li>Survey_Demographics_Final_V2.0.xlsx - demographic information about the participants in our study;</li> <li>Survey_SPSS.xlsx - the raw data used for the analysis via SPSS, and the results obtained through SPSS;</li> <li>Survey_StackedBar_Final_V1.4.xlsx - the Excel sheet we used to generate the stacked bar charts shown in the paper.</li> </ul>
Supplementary Material - All Eyes on Traceability: An Interview Study on Industry Practices and Eye Tracking Potential
<p>This dataset is the supplementary material for the paper "All Eyes on Traceability: An Interview Study on Industry Practices and Eye Tracking Potential" accepted at RE '23.</p> <p>The PDF titled "Interview-Questions" presents the interview questions used for the semi-structured interview of the paper.<br> The PDF titled "Interview-Questions_ReplicatorVersion" presents the interview questions enriched with comments on the qualitative and quantitative extent of the expected interviewee's responses and the category codes assigned to them during the analysis process.<br> The PDF "Eye-Tracking-Traceability-Explanatory-Slide" contains the Slide used in part 4 of the interview.<br> The spreadsheet "ArtifactsLinked.xlsx" was used to determine the number of interviewees who mentioned particular pairings of artifact types as currently being linked or ideally linked. This is the raw data for Fig. 5 in the paper.</p>
Data from: Outlier SNPs enable food traceability of the southern rock lobster, Jasus edwardsii
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Monitoring and traceability of genetically modified soybean event GTS 40-3-2 during soybean protein concentrate and isolate preparation
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Improving Traceability Recovery Between Bug Reports and Manual Test Cases
<p>Vídeo backup da apresentação SBES 2020.</p> <p>Artigo: Improving Traceability Recovery Between Bug Report and Manual Test Cases.</p>
Data from: Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods
Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber (Apostichopus japonicus) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictors selection (OPS) algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by the principal component analysis combined with Mahalanobis distance (PCA-MD) and Scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square errors of prediction (RMSEP) was 0.45, and correlation coefficients (R2) was 0.90. The correct classification rates of 100% were obtained both in identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China.
Dataset of "Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code"
<p>Dataset of Inferring Fine-grained Traceability Links between Javadoc Comments and JUnit Test Code</p>
Research on a Machine Interpretable and Traceable Digital Representation Method for SI
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NOVAFOODIES SeaFood +Algae + Biomasses Value Chain information and Technologies for traceability
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Using DNA origami nanorulers as traceable distance measurement standards and nanoscopic benchmark structures
<p>In recent years, DNA origami nanorulers for superresolution (SR) fluorescence microscopy have been developed from fundamental proof-of-principle experiments to commercially available test structures. The self-assembled nanostructures allow placing a defined number of fluorescent dye molecules in defined geometries in the nanometer range. Besides the unprecedented control over matter on the nanoscale, robust DNA origami nanorulers are reproducibly obtained in high yields. The distances between their fluorescent marks can be easily analysed yielding intermark distance histograms from many identical structures. Thus, DNA origami nanorulers have become excellent reference and training structures for superresolution microscopy. In this work, we go one step further and develop a calibration process for the measured distances between the fluorescent marks on DNA origami nanorulers. The superresolution technique DNA-PAINT is used to achieve nanometrological traceability of nanoruler distances following the guide to the expression of uncertainty in measurement (GUM). We further show two examples how these nanorulers are used to evaluate the performance of TIRF microscopes that are capable of single-molecule localization microscopy (SMLM).</p> <p>Here we show the raw data the publication is based on.</p>
Metrological generation of SI-traceable gas-phase standards and reference materials for (semi-) volatile organic compounds
<p>EN 16516 sets specifications for the determination of emissions into indoor air from construction products. Reliable, accurate and SI-traceable measurement results of the emissions are the key to consumer protection. Such measurement results can be obtained by using metrologically traceable reference materials. Gas-phase standards of volatile organic compounds (VOCs) in air can be prepared by a variety of dynamic methods according to the ISO 6145 series. However, these methods are not always applicable for semi-volatile organic compounds (SVOCs) due to their high boiling point and low vapour pressure. Therefore, a novel dynamic gas mixture generation system has been developed. With this system gas-phase standards with trace level VOCs and SVOCs in air can be prepared between 10 nmol mol<sup>-1</sup> and 1000 nmol mol<sup>-1</sup>. The VOCs and SVOCs in this study have normal boiling points ranging from 146 °C to 343 °C. Metrologically traceable reference materials of the gas-phase standard were obtained by sampling of the VOC gas-phase standard into Tenax TA® sorbent material in SilcoNert® coated stainless steel tubes. Accurately known masses between 10 ng and 1000 ng per VOC were sampled. These reference materials were used to validate the dynamic system. Furthermore, the storage and stability periods of the VOCs in the reference materials were determined as these are crucial characteristics to obtain accurate and SI-traceable reference materials. In a Round Robin Test (RRT), the reference materials were used with the aim of demonstrating the feasibility of providing SI-traceable standard reference values for SVOCs for interlaboratory comparison purposes. Based on the results from the validation, the storage and stability studies and the RRT, gas-phase standards and reference materials of VOCs and SVOCs with relative expanded uncertainties between 5 % and 12 % (<em>k</em> = 2) have been developed. These reference standards can be used as calibrants, reference materials or quality control materials for the analysis of VOC emissions.</p> <p>In this repository data from the validation, the storage and stability studies and the RRT are published which is used for the manuscript "Metrological generation of SI-traceable gas-phase standards and reference materials for (semi-) volatile organic compounds" published in Measurement Science and Technology.</p> <p>The following files can be found in this repository:</p> <p>- The following files contain data from the validation.Variation1_day1, Variation1_day2, Variation1_day3, Variation2_day1, Variation2_day2, Variation2_day3, Variation3_day1, Variation3_day2, Variation3_day3, Variation4_day1 and Variation4_day2. During the validation 4 different variations have been used and these have been tested on 3 or 2 days. The data contain information about the settings to obtain the gas-phase standard, reference materials and spiked tubes and the analysis data. </p> <p>- The "ANOVA validation data" file contains the ANOVA calculations used to obtain the repeatability standard deviation and reproducibility standard deviation.</p> <p>- The figure "Chromatogram VOCs used for the validation" is a copy of a chromatogram</p> <p>- The file "Storage and stability studies data" contains formation about the settings to obtain the gas-phase standard, reference materials and spiked tubes and the analysis data. </p> <p>- The figure "Chromatogram VOCs used for the storage and stability studies" is a copy of a chromatogram.</p> <p>- The file "RRT data" contains information about the settings to obtain the gas-phase standard, reference materials and spiked tubes and the analysis data. </p> <p>- The file "Report Homogeneity RRT" is a report on the homogeneity study performed during the RRT.</p> <p>- The figure "Chromatogram VOCs used for the RRT" is a copy of a chromatogram.</p> <p>- The file "VSL-Tubes-results-RR18-a". The dataset contains the results of a round robin test which tested the proficiency to analyse volatile organic compounds (VOC) of laboratories dealing with the determination of emissions from building materials. For this analysis check the participants were asked to send own sampling tubes filled with the adsorbent Tenax TA<sup>®</sup>, which were loaded with a reference gas mixture containing the compounds: styrene [100-42-5], n-decane [124-18-5], R(+)limonene [5989-27-5], 1,2,4-trimethylbenzene [95-63-6], decamethylcyclopentasiloxane [541-02-6], dimethylphthalate [131-11-3], dibutylphthalate [84-74-2], naphthalene [91-20-3], n-hexadecane [544-76-3] and eicosane [112-95 8]. These tubes were sent back to the participants for immediate analysis. The list of compounds was disclosed in advance. For all statistical evaluations, the mean values of the laboratories were used instead of all single measurement values. <strong>Expert laboratories:</strong> Laboratories who had successfully participated in the three former round robin tests (2014; 2016; 2018) organized by BAM were defined as expert laboratories. Their reported data were used to calculate the reference mean (ref. mean) and the reference standard deviation (ref st. dev.). <strong>Reference mean:</strong> The reference mean is determined as the robust mean value using the Hampel estimator (see Section C.5.3 in ISO 13528) on the basis of the results of the expert laboratories. It is a weighted arithmetic mean, with lower weights for outlying values. <strong>Standard deviation for proficiency assessment:</strong> The reference standard deviation for proficiency assessment is determined as the robust reproducibility standard deviation according to the Q method (see Section C.5.2 in ISO 13528) based on the results of the expert laboratories.</p>
Fig. 1 in Traceability of fruits and vegetables
Fig. 1. Typical natural δ13C ratios according to the plant metabolism.
Data from: Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods
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Spatial single cell transcriptomic analysis of a lineage-traceable mouse model of DICER1 Syndrome informs tumor developmental hierarchy [RNA-Seq]
GEO Series GSE309820. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Traceability Dataset for Open Source Systems
<p>This dataset provides requirement-to-method traces for four systeme: (1) Chess, (2) Gantt, (3) iTrust, and (4) JHotDraw. </p> <p>You can find four subfolders corresponding to each system within Data.zip. Each subfolder contains four JSON files: </p> <p>1- requirements.JSON: this lists the requirements for each system.</p> <p>2-classes.JSON: This lists the Java classes within each system.</p> <p>3-methods.JSON: this lists the methods for each system along with the class that the method belongs to.</p> <p>4-traces.JSON: this lists the requirement-to-method tracing relationships between each method and each requirement for each system.</p> <p> </p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.