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324 results for “Test Case”
A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase I – The benchmark case
<p>This data set ("Data files.zip") contains the wind tunnel measurement data from Phase I of the Blind test on wind turbine wake modelling based on wind tunnel experiments organised during the TWEET-IE project (www.tweet-ie.eu).</p> <p>This updated version <strong>replaces</strong> the older versions 1.0.0 (https://doi.org/10.5281/zenodo.10566401), 1.1.0 (https://doi.org/10.5281/zenodo.11370112), 2.0 (https://doi.org/10.5281/zenodo.12188194) and 2.1 (https://doi.org/ 10.5281/zenodo.13918935). In comparison to the previous version 2.1 the data documentation has been updated to follow the template of the TWEET-IE project documents, indicating the Grant Agreement Number with the European Union and the Call Topic of the project.</p> <p>All tests were conducted in the closed-loop, low-speed boundary layer wind tunnel of the Chair of Aerodynamics and Fluid Mechanics at Technische Universität München (TUM). The experiments concerned two wind turbines, aligned with the flow, one downstream of the other, at a distance of 5 diameters. For Phase I, no control was applied to the wind turbine models, which were operating at constant RPM. The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1M. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.pdf"). </p> <p>File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.</p>
Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects
<p>The dataset contains analyzed projects and modules for the paper "Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects". The contents are the following:</p> <ul> <li><a href="/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/manual-studied-modules.csv?versionId=8258b59c-3f88-487b-a4a3-007cb362a44a">manual-studied-modules.csv</a>: Manually analyzed Maven modules mentioned in Section 5.2</li> <li><a href="https://zenodo.org/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/projects.zip">projects.zip</a>: Instrumented and Mutated Github Projects. Projects list applied mutation changes, and their dynamic and static call graph.</li> </ul>
SPLEEN - High Speed Turbine Cascade – Test Case Database - PIV Measurements
<p>This is an open-access database of Particle Image Velocimetry (PIV) measurements of the flow in the <strong>high-speed low-pressure turbine cascade SPLEEN C1</strong>. </p><p>This database complements aerodynamics measurements reported in the database "SPLEEN - High Speed Turbine Cascade – Test Case Database" which can be found at <a href="https://zenodo.org/doi/10.5281/zenodo.7264761">10.5281/zenodo.7264761</a>. </p><p>The data have been collected at the <strong>von Karman Institute for Fluid Dynamics</strong> during 2022 within the H2020 Clean Sky 2 project <strong>SPLEEN </strong>– Secondary and Leakage Flow Effects in High-Speed Low-Pressure Turbines.</p><p>This database contains documents that describe the experimental setup, instrumentation, measurement uncertainties, geometries, and the dataset structure related to the SPLEEN C1 PIV measurements.</p><p>The database contains experimental data of the test campaign conducted on the linear cascade codenamed SPLEEN C1 in the VKI high-speed wind tunnel S1/C.</p><p>PIV measurements were performed on the upstream blade-to-blade plane at cascade midspan, passage blade-to-blade plane at cascade midspan, and on a cascade outlet axial plane located 50% of the airfoil axial chord downstream the cascade trailing edge, near the cascade endwall (0-18% of the cascade span).</p><p>The turbine cascade geometry is representative of designs of high-speed low-pressure turbines for next-generation geared turbofan engines.</p><p>The measurement datasets describe the flow through the turbine cascade tested at on- and off-design conditions (cascade exit Reynolds and Mach numbers), with and without a turbulence grid located upstream of the cascade. The database includes measurements of flow velocity, flow angles, Mach numbers, and turbulence quantities.</p><p>The PIV measurements were performed on the <strong>SPLEEN C1 turbine cascade</strong> equipped <strong>WITH a REFERENCE CAVITY ENDWALL</strong> (Cavity Aref), <strong>WITHOUT WAKE GENERATOR</strong> (steady inlet flow), <strong>WITH and WITHOUT TURBULENCE GRID</strong> (different inlet turbulence level).</p><p>The project has received Funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program under the grant agreement 820883.</p>
Does Unit-Tested Code Crash? A Case Study of Eclipse
<ul> <li>The "jacoco.xml" file contains the code coverage of Eclipse Project generated by JaCoCo</li> <li>The "incidents (stack traces).zip" file contains the stacktraces files (in JSON format) with the specific popular release and product that we used for our research.</li> <li>All The Eclipse stacktraces can be downloaded <a href="http://software-data.org/datasets/aeri-stacktraces/downloads/incidents_full.tar.bz2">here.</a></li> <li>The "output.csv" contains the data that we produced after combining JaCoCo Report and stacktraces.</li> </ul>
SPLEEN - High Speed Turbine Cascade – Test Case Database
<p>This is an open-access database of experimental data of the flow in a <strong>high-speed low-pressure turbine cascade</strong>. The data have been collected at the <strong>von Karman Institute for Fluid Dynamics</strong> in the period 2018-2022 within the H2020 Clean Sky 2 project <strong>SPLEEN</strong> – Secondary and Leakage Flow Effects in High-Speed Low-Pressure Turbines, a project in collaboration with Safran Aircraft Engines.</p> <p>This version contains documents that describe the experimental setup, instrumentation, measurement uncertainties, geometries, and database structure.</p> <p>The database contains experimental data of the test campaign conducted on the linear cascade codenamed SPLEEN C1 in the VKI high-speed wind tunnel S1/C.</p> <p>The turbine cascade geometry is representative of designs of high-speed low-pressure turbines for next-generation geared turbofan engines.</p> <p>The measurement datasets describe the <strong>flow through the turbine cascade tested at on- and off-design conditions</strong>, with and without a wake generator located upstream of the cascade, and with and without endwall cavity injection. The database includes measurements of pressure, temperature, flow angles, Mach numbers, pressure loss, unsteady blade and endwall pressure and quasi-shear-stress.</p> <p>The database includes measurement datasets performed on the turbine cascade <strong>WITH a FLAT ENDWALL </strong>(no cavity)<strong> WITHOUT WAKE GENERATOR (</strong>Steady inlet flow)<strong>, WITH a FLAT ENDWALL </strong>(no cavity) <strong>AND WAKE GENERATOR</strong> (unsteady inlet flow)<strong>, WITH ENDWALL CAVITY</strong> (purged cavity) <strong>AND WAKE GENERATOR</strong> (unsteady inlet flow)<strong>.</strong></p> <p>The project has received Funding from the Clean Sky 2 Joint Undertaking under the European Union's Horizon 2020 research and innovation program under the grant agreement 820883.</p>
Replication Package "Applying Test Case Prioritization to Software Microbenchmarks"
<p>Replication package for the paper "Applying Test Case Prioritization to Software Microbenchmarks" accepted for publication in Empirical Software Engineering.</p>
IEEE New England 39-bus test case: Dataset for the Transient Stability Assessment
<p>The <strong>dataset</strong> contains <strong>350</strong> <strong>features</strong> engineered from the phasor measurements (PMU-type) signals from the <strong>IEEE New England 39-bus power system</strong> test case network, which are generated from the 9360 systematic MATLAB®/Simulink electro-mechanical transients simulations. It was prepared to serve as a convenient and open database for experimenting with different types of <strong>machine learning</strong> techniques for <strong>transient stability assessment</strong> (TSA) of electrical power systems.</p> <p>Different load and generation levels of the New England 39-bus benchmark power system were systematically covered, as well as all three major types of short-circuit events (three-phase, two-phase and single-phase faults) in all parts of the network. The consumed power of the network was set to 80%, 90%, 100%, 110% and 120% of the basic system load levels. The short-circuits were located on the busbar or on the transmission line (TL). When they were located on a TL, it was assumed that they can occur at 20%, 40%, 60%, and 80% of the line length. Features were obtained directly from the time-domain signals at the pickup time (pre-fault value) and at the trip time (post-fault value) of the associated distance protection relays.</p> <p>This is a <strong>stochastic dataset</strong> of 3120 cases, created from the population of 9360 systematic simulations, which features a statistical distribution of different fault types, as follows: single-phase (70%), double-phase (20%) and three-phase faults (10%). It also features a <strong>class imbalance</strong>, with less than 20% of cases belonging to the unstable class. Dataset is a compressed CSV file.</p> <p><strong>List of feature names in the dataset:</strong></p> <ul> <li><em>WmGx</em> - rotor speed for each generator Gx, from G1 to G10,</li> <li><em>DThetaGx</em> - rotor angle deviation for each generator Gx, from G1 to G10,</li> <li><em>ThetaGx</em> - rotor mechanical angle for each generator Gx, from G1 to G10,</li> <li><em>VtGx</em> - stator voltage for each generator Gx, from G1 to G10,</li> <li><em>IdGx</em> - stator d-component current for each generator Gx, from G1 to G10,</li> <li><em>IqGx</em> - stator q-component current for each generator Gx, from G1 to G10,</li> <li><em>LAfvGx</em> - pre-fault power load angle for each generator Gx, from G1 to G10,</li> <li><em>LAlvGx</em> - post-fault power load angle for each generator Gx, from G1 to G10,</li> <li><em>PfvGx</em> - pre-falut value of the generator active power for each generator Gx, from G1 to G10,</li> <li><em>PlvGx</em> - post-falut value of the generator active power for each generator Gx, from G1 to G10,</li> <li><em>QfvGx</em> - pre-falut value of the generator reactive power for each generator Gx, from G1 to G10,</li> <li><em>QlvGx</em> - post-falut value of the generator reactive power for each generator Gx, from G1 to G10,</li> <li><em>VAfvBx</em> - pre-fault bus voltage magnitude in phase A for each bus Bx, from B1 to B39,</li> <li><em>VBfvBx</em> - pre-fault bus voltage magnitude in phase B for each bus Bx, from B1 to B39,</li> <li><em>VCfvBx</em> - pre-fault bus voltage magnitude in phase C for each bus Bx, from B1 to B39,</li> <li><em>VAlvBx</em> - post-fault bus voltage magnitude in phase A for each bus Bx, from B1 to B39,</li> <li><em>VBlvBx</em> - post-fault bus voltage magnitude in phase B for each bus Bx, from B1 to B39,</li> <li><em>VClvBx</em> - post-fault bus voltage magnitude in phase C for each bus Bx, from B1 to B39,</li> <li><em>Stability</em> - binary indicator (0/1) that determines if the power system was stable or unstable (0 - stable, 1 - unstable); this is the label variable.</li> </ul> <p><strong>License</strong>:<strong> </strong>Creative Commons CC-BY.</p> <p><strong>Disclaimer</strong>: This dataset is provided "as is", without any warranties of any kind.</p>
Enhanced Westermo dataset - Transformed and Modified for Test case Selection and Priorotization in the context of Continuous Integration and Reinforcement Learning.
<p><strong>Overview</strong></p> <p>This repository contains a modified version of the existing, recently published dataset, Westermo. The initial dataset was gathered at Westermo Network Technologies AB, located in Västerås, Sweden. It encompasses over <strong>1 Million verdicts</strong> obtained from testing embedded systems, collected over a span of more than <strong>500 consecutive days</strong> of nightly testing. The dataset has been transformed and tailored specifically to cater to the research community, particularly for addressing challenges such as regression test selection, identification of flaky tests, and visualization of test results. The original dataset can be accessed through the reference provided in <strong>[1]</strong>.</p> <p>The Westermo dataset offers valuable historical information regarding the execution of test cases and their corresponding results. It serves as a valuable resource for evaluating and comparing different Test case Selection and Prioritization (TSP) techniques, enabling researchers to identify test cases that are more likely to fail during subsequent executions. Test cases in the dataset are characterized by attributes such as execution duration, previous last execution time, and the results of their recent executions.</p> <p>This dataset offers valuable historical information regarding the execution of test cases and their corresponding results. It serves as a valuable resource for evaluating and comparing different test case prioritization and selection techniques, enabling researchers to identify test cases that are more likely to fail during subsequent executions. Test cases in the dataset are characterized by attributes such as execution duration, previous last execution time, and the results of their recent executions.</p> <table align="left"> <caption><strong>Table 1: Dataset Overview</strong></caption> <tbody> <tr> <td>Test Cases</td> <td>1855</td> </tr> <tr> <td>CI Cycles</td> <td>15,197</td> </tr> <tr> <td>Verdict</td> <td>1,036,818</td> </tr> <tr> <td>Failed</td> <td>5.03%</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>However, the diversity and multitude of the features in the dataset can be irrelevant to some TSP approaches. This led us to perform a dataset conversion, where we customized Westermo to have the same features from Paint Control and IOF/ROL, two widely used datasets in Reinforcement Learning based TSP approaches.</p> <p>This conversion required the combination of multiple variables and generating the target ones. When it comes to generating the “LastResults” and “Cycle” values, further analysis was required and the data handling needed an in-depth understanding of how the nightly testing was conducted. This led us to investigate what a CI cycle is in their context, and we followed their definition of a session, stating that “a session is when we run a suite of tests on one test system with a certain software version and testware version”. When splitting the data according to the 9 different systems used, we were able to generate 9 different sub-sets that fit the CI context.</p> <p> </p> <p><strong>File Format</strong></p> <p>The compressed .zip file contains 9 files, each one corresponding to each of the 9 systems. The datasets are available in CSV format, with the semicolon (;) serving as the delimiter. The columns included are represented in the table below along with their descriptions.</p> <table> <caption><strong>Table 2: Parameters of the dataset</strong></caption> <thead> <tr> <th scope="col">Column Name</th> <th scope="col">Content</th> </tr> </thead> <tbody> <tr> <td>Id</td> <td>Unique numeric identifier of the test execution </td> </tr> <tr> <td>Name</td> <td>Unique numeric identifier of the test case</td> </tr> <tr> <td>Duration</td> <td>Approximated runtime of the test case</td> </tr> <tr> <td>CalcPrio</td> <td>Priority of the test case, calculated by the prioritization algorithm (output column, initially 0)</td> </tr> <tr> <td>LastRun</td> <td>Previous last execution of the test case as date-time-string (Format: <em>YYYY-MM-DD HH:ii </em>)</td> </tr> <tr> <td>LastResults</td> <td>List of previous test results (Failed: 1, Passed: 0), ordered by ascending age. Lists are delimited by [ ].</td> </tr> <tr> <td>Verdict</td> <td> <p>Test verdict of this test execution (Failed: 1, Passed: 0)</p> </td> </tr> <tr> <td>Cycle</td> <td>The number of the CI cycle this test execution belongs to.</td> </tr> </tbody> </table> <p> </p> <p>The implications of this conversion are important as it can help the previous works to re-assess their approaches and have more data for training and testing, as well as opening a broader data spectrum for future researchers in this field to find ready-to-use, rich datasets, on which they could evaluate their approaches and contribute to the TSP community. This also addresses the limitations in the field discussed in the systematic literature review <strong>[2]</strong>, stating that future research on TSP techniques should focus on collecting data from more recent subjects in a CI context with varying failure rates and larger execution times, as reproducible studies with appropriate datasets are needed to develop a usable body of knowledge regarding TSP over time. We believe that this conversion of the Westermo dataset is our contribution to alleviating the gap for the RL-based approaches.</p> <p>The original dataset can be found <a href="https://sites.mdu.se/aidoart/results/open-source/test-results-dataset-westermo">here.</a></p>
PHASTA compressible test case for two processes
<p>Input files and execution instructions for a PHASTA compressible flow case using two processes. The expected results are included in the included README.md.</p>
The OPS-SAT case: test dataset
<p><i>The OPS-SAT case: test dataset</i> is the test split of the official dataset of <a href="https://kelvins.esa.int/opssat/home/"><strong>ESA's Kelvins</strong> <strong>"the OPS-SAT case" challenge</strong></a><strong>, </strong>created in collaboration<strong> with ESA's Φ-lab </strong>and <strong>ESA's OPS-SAT spacecraft operations team</strong>. It consists of<strong> 588 raw patches</strong> generated by cropping original acquisitions taken by the OPS-SAT cube-sat using its on-board camera. The patches have a resolution of <strong>200x200</strong> and are provided in <strong>.png</strong> format. Each patch was assigned through manual labeling to one of the following landcover classes: <strong>Agricultural, Cloud, Mountain, Natural, River, Sea_Ice, Snow, </strong>and <strong>Water.</strong></p><p>The competition's goal is, given a model of a neural network (in this case the <strong>EfficientNet-Lite0</strong>), to provide best possible network parameters to perform an <strong>on-board classification</strong> task. To this goal, competitors were free to use any data, including <a href="https://zenodo.org/records/6524750">The OPS-SAT case dataset</a>, which was released before the start of the competition.</p><p>The OPS-SAT case: test dataset was hidden from the competitors during the competition and was used to evaluate and rank their submissions. </p><p>More details about the competition setup and solution evaluation are on the <a href="https://kelvins.esa.int/opssat/home/">Kelvins competition platform</a>.</p>
Summary statistics for association tests between human and Plasmodium falciparum genetic variants in 3,346 severe malaria cases from The Gambia and Kenya
<p>This dataset contains summary statistics for association tests between human and<br> <em>Plasmodium falciparum</em> malaria parasite genetic variants, using data from 3,346 severe malaria cases from The Gambia and Kenya. These results underlie the analysis described in our paper:</p> <p><strong>"Malaria protection due to sickle haemoglobin depends on parasite genotype"</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy Nguyen, Sónia M. Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor, David J. Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>:: <a href="https://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a><br> <br> The genotype data underlying these summary statistics has also been deposited on Zenodo<br> (<a href="https://zenodo.org/record/4973477">doi:10.5281/zenodo.4973477</a>). The <a href="https://www.well.ox.ac.uk/~gav/hptest)">HPTEST software</a> used to generate these results has also been deposited (<a href="https://doi.org/10.5281/zenodo.5685580">doi:10.5281/zenodo.5685580</a>). Please see the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a> for a full list of datasets which have been released with this manuscript.</p> <p><strong>Data contents.</strong></p> <p>The dataset consists of a single <a href="http://sqlite.org">sqlite database file</a> containing the results, and an accompanying README file in markdown and html format. Please see the README file for full details of the data contents.</p> <p> </p>
LEXIS Turbomachinary use case: T106 profile test case
<p>The test case for T106 profile in a linear cascade. Cascade rigs are a way to test in a simple way various turbomachinery blades. Blades are cylindrical, which means equal 2D sections from hub to tip wall, and repeated tangentially according to the specified pitch. In this case, end walls are linear, not cylindrical and not tapered. The dataset contains an</p> <ul> <li>excel file with <ul> <li>description of the airfoil (x, y coordinates in [mm])</li> <li>geometric pitch /chord data, aspect ratio (parallel end walls)</li> <li>entry boundary conditions <ul> <li>total pressure</li> <li>total temperature</li> <li>flow angle</li> </ul> </li> <li>outlet boundary condition <ul> <li>static pressure</li> </ul> </li> <li>Reynold number (120 000)</li> <li>Velocity at the exit (Mach = 0,59</li> </ul> </li> <li>OpenFOAM input files for <ul> <li>serial run (Allrun)</li> <li>parallel run (Allrun_parallel)</li> </ul> </li> <li>PDF file with the description of the case and obtained results</li> </ul>
ClinSpEn-CC (Clinical Cases) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Clinical Cases sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with three columns: document number, line number and English line. The direction of this sub-track is EN>ES. The clinical cases themselves include COVID-19 case reports as well as diverse content extracted from PubMed.</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
<p>Dataset for the paper "Predicting Patch Correctness Based on the Similarity of Failing Test Cases"</p> <p>Tool name: BATS</p>
LPSE data for ray-based CBET test cases
<p>This dataset contains field data from LPSE simulations for the purpose of validating ray-based CBET models. The input parameters required to replicate these results are given in the Physics of Plasmas paper "Validation of ray-based cross-beam energy transfer models." All of the data is stored in HDF5 files. Each file has three data sets: Ez, xAxis, and yAxis (the 1-D datset does not have yAxis).</p> <p>Here is an example of the Matlab code to open and plot one of the 2-D files:</p> <pre><code>filename = 'two_beam_at_caustic.h5'; hInfo = h5info(filename ); data = h5read(filename , '/Ez'); xAxis = h5read(filename , '/x_axis'); yAxis = h5read(filename , '/y_axis'); figure(1); clf; imagesc(yAxis, xAxis, data), colorbar, axis xy </code></pre> <p>Here is an example of the Python code to open and plot one of the 2-D files:</p> <pre><code class="language-python">import h5py import matplotlib.pyplot as plt filename = 'two_beam_at_caustic.h5' f = h5py.File(filename, 'r') Ez = list(f["Ez"]) x_axis = list(f["x_axis"]) y_axis = list(f["y_axis"]) plt.figure() plt.pcolormesh(x_axis,y_axis,Ez) plt.colorbar() plt.show() </code></pre> <p> </p>
Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University)
<p>This video is the seventh talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Unmet Clinical Needs and Case Studies in Blood Testing - Prof Bryant Lin and Dr. Kevin Chang (Stanford University)</p> <p>Bio: Bryant Lin, MD, MEng is a primary care physician, educator and researcher. The cornerstone of Dr. Lin's work is keeping medicine focused on humans - patients, providers, families and trainees - and not lost in technology and algorithms. His research and educational interests span (1) Developing and testing novel medical technologies, (2) Improving the health of Asian populations with Precision and Population Health, and (3) Increasing expression and interconnections in the Health Community with the Humanities and Arts. After receiving his undergraduate and master's degrees in Electrical Engineering and Computer Science from MIT, he completed his MD and internal Medicine training at Tufts University School of Medicine and Tufts Medical Center. He came to Stanford to serve as a Research Fellow in Cardiac Electrophysiology and Biodesign Fellow where he learned to identify unmet human-centered needs. Since completing his post-graduate training, he stayed at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine where he has invented and researched new medical technologies addressing unmet human-centered needs and started the Consultative Medicine Clinic evaluating patients with medical mysteries. He serves as the Training Director for the Joe and Linda Chlapaty DECIDE Center which has created a novel shared decision making tool for atrial fibrillation anticoagulation and is an investigator in several active clinical trials. Three years ago, he co-founded and currently co-directs, with Dr. Latha Palaniappan, the Center for Asian Health Research and Education (CARE) which aims to improve the health of Asians everywhere. Most recently, he has worked closely with the Medicine and the Muse leadership to help start the Stuck@Home concert series, the Stanford SoundWalk and the COVID Remembrance project. Dr. Lin has an active interest in storytelling and film-making. He co-directs an undergraduate seminar, MED 53Q “Storytelling in Medicine”, with Dr. Lauren Edwards and is working with a group of students on a documentary on end-of-life care at a JapaneseAmerican Senior Home in the Bay Area.</p> <p>Kevin Chang MD, MS, is a primary care physician. His focus in on patient care, population health and quality improvement, and medical education. He received his undergraduate degree and master's degree in biomedical engineering from Duke University and Stanford University respectively, and completed his MD at New York University, followed by his medical training at Stanford University. He has since stayed on at Stanford as clinical faculty in Primary Care and Population Health in the Department of Medicine, where he also serves as the co-director of the resident physician Internal Medicine clinic.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ozk1iJYC1yk</p>
iSamples export test case
<p>This is a collection of specimen metadata records retrieved from iSamples and being used for test purposes. </p>
FIGURE 16 in Testing hypothesis of skeletal unity using bone histology: The case of the sauropod remains from the Howe-Stephens and Howe Scott quarries (Morrison Formation, Wyoming, USA)
FIGURE 16. Detailed images of the EFS of both the SMA 0084 "Rich" samples. A, EFS of SMA 0084 "Rich" tibia, in which a cycle of faster growth is included (indicated by arrow). B, EFS of SMA 0084 "Rich" fibula, which also shows a cycle of faster growth (indicated by arrow). Images taken under plane-polarized light.
FIGURE 13 in Testing hypothesis of skeletal unity using bone histology: The case of the sauropod remains from the Howe-Stephens and Howe Scott quarries (Morrison Formation, Wyoming, USA)
FIGURE 13. Images of all forelimb samples taken from SMA 0011 "Max". A, humerus (r). B, humerus (l). C, ulna (l). D, radius (l). For all samples, the bone tissue types are indicated to the left, and the number and patterns of the visible growth cycles are indicated to the right. Abbreviations; D: Bone tissue type D, E: Bone tissue type E, F: Bone tissue type F, MC: Medullary cavity, RA: Remodeled area.
FIGURE 2 in Testing hypothesis of skeletal unity using bone histology: The case of the sauropod remains from the Howe-Stephens and Howe Scott quarries (Morrison Formation, Wyoming, USA)
FIGURE 2. Detailed quarry map of the Howe Scott Quarry with finds from excavation seasons 1995-2003. The color coding reflects the hypothesis of skeletal unity based on field observations (Ayer, 2000). Sauropods are indicated in red in the legend. The arrows indicate the sampled bones used in this study. The quarry map was made by E. Premru, and provided by the Sauriermuseum Aathal, Aathal, Switzerland. Note that SMA M16/12-3 is not marked on the map due to a coordinate inaccuracy, which makes it impossible to correctly locate bone SMA M16/12-3 on the quarry map.
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