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4 results for “Conformance Checking”
Dataset: Stochastic Conformance Checking Based On Expected Sub-Trace Frequency
<p>This contains the dataset and experimental results for the submission "Stochastic Conformance Checking Based On Expected Sub-Trace Frequency"</p>
Artificial datasets for "Online Conformance Checking Using Behavioural Patterns"
<p>Dataset containing artificial datasets for the stress test of the online conformance prototype and for the correlation of the results of the online conformance checker with state of the art technique.</p> <p><strong>Stress test log</strong></p> <p>We randomly generated a BPMN model containing 64 activities and 26 gateways. The model was then used to simulate an event stream of 2 million events.</p> <p><strong>Correlation logs</strong></p> <p>We generated 12 random process models with number of activities according to a triangular distribution with lower bound 10, mode 20, and upper bound 30. We did not include duplicate labels, a probability of 0.2 for addition of silent activities, moreover, the probability of control-flow operator insertion was: 0.45 for sequence, 0.2 for parallel and xor-split operators, 0.05 for an inclusive-or operator and 0.1 for loop constructs. Incremental noise levels (both on a trace- and event-level) were introduced in the logs. Probability of trace- and event-level noises ranged from 0.1 to 0.5 with steps of 0.1.</p>
ArchPython: architecture conformance checking for Python systems
<p>Dynamically typed languages provide several resources for developers, such as dynamic invocations and constructs. However, such resources combined with short deadlines, conflicts in requirements, or technical difficulties may increase the number of code decisions that go against the planned architecture, leading to the phenomenon known asarchitectural erosion. Even though Python is the 3rd most used programming language, there is no tool that allows developers to monitor the architecture of their systems. This is possibly justified by the complexity to infer types since the same variable can assume different types at run time. Facing such challenge, this article proposes ArchPython, the first complete architectural conformance and visualization tool for Python systems. In a nutshell, developers specify the architecture of their systems in a simple and natural way using JSON files and ArchPython takes care of the rest. Automatically, the tool infers types ( Jedi + propagation heuristics) and detects architectural violations (divergences, absences, and even alerts). In addition to a JSON-based textual report, the tool also provides two ways of visualizing architectural violations (graph and DSM).</p>
We want to check chromotin conformation changes and chromotin accesiblility. We also want to compare gene profile with and without indusilam resistant tumors in mouse genetic model
GEO Series GSE293865. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
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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.