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69 results for “traceability”
Traceability Dataset for Open Source Systems
<p>This dataset provides requirement-to-method traces for four systems: (1) Chess, (2) Gantt, (3) iTrust, and (4) JHotDraw. </p> <p>All applications are open source: </p> <p>Chess: https://github.com/warpwe/java-chess.</p> <p>Gantt: https://sourceforge.net/projects/ganttproject.</p> <p>iTrust: https://sourceforge.net/projects/itrust.</p> <p>JHotDraw: https://sourceforge.net/projects/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>5-methodcalls.JSON: this lists the parsed method calls for each system. </p> <p>classes.JSON, methods.JSON, methodcalls.JSON all represent information obtained after parsing the source code using the open source library Spoon (http://spoon.gforge.inria.fr/).</p> <p> </p>
The causal loop diagram model of traceability system rental equipment in oil and gas supporting companies
Open the record for dataset details and reuse information.
Spatial single cell transcriptomic analysis of a lineage-traceable mouse model of DICER1 Syndrome informs tumor developmental hierarchy [Xenium]
GEO Series GSE289001. Mus musculus. 72 samples. Type: Other.
Spatial single cell transcriptomic analysis of a lineage-traceable mouse model of DICER1 Syndrome informs tumor developmental hierarchy [scRNA-seq]
GEO Series GSE288990. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Traceability Solutions for Supporting Intermingled-Bilingual Artifacts
<p>Bilingual software engineering dataset for issues and commits</p>
TCRs enable traceability of CAR-T cells but impair function in dual target cell contexts [car_pre_infusion]
GEO Series GSE299415. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
TCRs enable traceability of CAR-T cells but impair function in dual target cell contexts [car_post_infusion]
GEO Series GSE299416. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing; Other.
Traceability of statements to their source
<p>The spreadsheet contains statements extracted from two chapters of the ARINC 653 standard, namely partition management and health monitor. This spreadsheet allows the mapping between each statement and its exact paragraph from which it was extracted in the ARINC 653 part 1 document</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> </p> <p>File B: Python Code (Both ours and baseline)</p> <p> B1: pert_class.ipynb</p> <p> B2: Baseline.ipynb</p> <p> </p> <p>FIle C: Result Tables (.xlsx)</p> <p> C1: Table of perturbed outputs in Webex </p> <p> C2: Table of perturbed outputs in Zoom</p> <p> C3: Table of perturbed outputs in Teams</p> <p> C4: Table of perturbed outputs in Word</p> <p> C5: Table of perturbed outputs in PowerPoint</p> <p> C6: Table of perturbed outputs in Excel </p> <p> </p> <p>File D: Trend of Adversarial Shifts (Graphs)</p> <p> D1: Webex Adversarial Shifts</p> <p> D2: Zoom Adversarial Shifts</p> <p> D3: Teams Adversarial Shifts</p> <p> D4: Word Adversarial Shifts</p> <p> D5: Powerpoint Adversarial Shifts</p> <p> D6: Excel Adversarial Shifts</p> <p>File E: Trend of Non-Adversarial Shifts (Graphs)</p> <p> E1: Webex Non-Adversarial Shifts</p> <p> E2: Zoom Non-Adversarial Shifts</p> <p> E3: Teams Non-Adversarial Shifts</p> <p> E4: Word Non-Adversarial Shifts</p> <p> E5: Powerpoint Non-Adversarial Shifts</p> <p> E6: Excel Non-Adversarial Shifts</p> <p> </p> <p>File F: Adversarial Examples (.pdf)</p> <p> F1: Adversarial vs original in Webex</p> <p> F2: Adversarial vs original in Zoom</p> <p> F3: Adversarial vs original in Teams</p> <p> F4: Adversarial vs original in Word</p> <p> F5: Adversarial vs original in PowerPoint</p> <p> F6: Adversarial vs original in Excel</p> <p> </p> <p>File G: Questionnaire<br> <br> <br> <br> <br> </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.