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4,325 results for “real”
Real data supporting HTTomolib/GPU libraries
<p>This is an archive containing real data for testing methods of <a href="https://github.com/DiamondLightSource/httomolib">HTTomoLib</a> and <a href="https://github.com/DiamondLightSource/httomolibgpu">HTTomoLibGPU</a> packages. Also see Python scripts and images. </p> <div> <h2>Data description:</h2> </div> <h3>I12 Beamline data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><strong>i12_dataset1.npz</strong> (119647.nxs)</td> <td>180-degrees scan of a sandstone rock</td> <td> <p>Data dimensions <code>[1801, 50, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[50, 50, 2560]</code> and darks are <code>[20, 50, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>The resolution of the sample is well-defined and the sample is fully within the field of view (FOV). This provides some freedom for experimentation using various preprocesing and reconstruction methods.</td> </tr> <tr> <td><strong>i12_dataset2.npz </strong>(146344.nxs)</td> <td>180-degrees scan</td> <td> <p>Data dimensions <code>[2050, 50, 2560]</code> as <code>[angles, detY, detX]</code>, flats and darks are <code>[51, 50, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td> <p>The sample is larger than FoV and noisy. VoCentering method to find the centre of rotation can be tested on this data.</p> </td> </tr> <tr> <td> <p><strong>i12_dataset3.npz </strong>(119647.nxs)</p> </td> <td>180-degrees scan of a sandstone rock</td> <td> <p>projections, darks and flats are <code>[2050, 2560]</code> as <code>[detY, detX]</code>. Data type is <code>uint16</code>.</p> </td> <td>This data is suitable to test the centering method based on the phase cross-correlation</td> </tr> <tr> <td><strong>i12_dataset4.npz </strong>(<a href="https://zenodo.org/records/1443568">68067.nxs</a>)</td> <td>180-degrees scan</td> <td>Data dimensions <code>[2050, 30, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[50, 30, 2560]</code> and darks are <code>[20, 30, 2560].</code> Data type is <code>uint16</code>.</td> <td>This data is well suitable for testing stripe (ring) removal artefacts and is a part of this <a href="https://zenodo.org/records/1443568">dataset</a>. </td> </tr> <tr> <td><strong>i12_dataset5.npz </strong>(162289.nxs)</td> <td>360-degrees scan</td> <td>Data dimensions <code>[3601, 15, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[101, 15, 2560]</code> and darks are <code>[101, 15, 2560].</code> Data type is <code>uint16</code>.</td> <td>360 degrees scan with the left rotation. Testing 360 to 180 conversion methods.</td> </tr> <tr> <td><strong>i12_dataset6.npz </strong>(164301.nxs)</td> <td>180-degrees scan, bio-sample</td> <td>Data dimensions <code>[1801, 50, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[50, 50, 2560]</code> and darks are <code>[20, 50, 2560]</code>. Data type is <code>uint16</code>.</td> <td>Biological sample of a very poor contrast. Phase contrast method needs to be applied to boost the contrast up and then reconstruction with the increased padding of the detector. Iterative methods can be tested. </td> </tr> </tbody> </table> <h3>I13 Beamline data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td> <p><strong>i13_dataset1.npz </strong>(179623.nxs)</p> </td> <td>360-degrees scan of some biological sample</td> <td> <p>Projection data dimensions <code>[6001, 10, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[40, 10, 2560]</code> and darks are <code>[40, 10, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>This is a 360-degrees scan than needs to be converted into traditional 180-degrees data. The stitched data enlarges the FoV of the detector while reduced the angular dimension.</td> </tr> <tr> <td><strong>i13_dataset2.npz</strong> (179468.nxs)</td> <td>180-degrees scan of some biological sample.</td> <td> <p>Projection data dimensions <code>[2501, 10, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[40, 10, 2560]</code> and darks are <code>[40, 10, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>A well defined resolution for this biological sample.</td> </tr> <tr> <td><strong>i13_dataset3.npz </strong>(180622.nxs)</td> <td>360-degrees scan</td> <td> <p>Projection data dimensions <code>[6001, 3, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[40, 3, 2560]</code> and darks are <code>[40, 3, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>This is a 360-degrees scan than needs to be converted into traditional 180-degrees data. The stitched data enlarges the FoV of the detector, while reduced the angular dimension. </td> </tr> </tbody> </table> <h3>K11 Beamline data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><strong>k11_dataset1.npz </strong>(k11-37101)<strong><br></strong></td> <td>180-degrees scan of some soil/rock sample.</td> <td>Projection data dimensions <code>[4001, 10, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[100, 10, 2560]</code> and darks are <code>[100, 10, 2560]</code>. Data type is <code>uint16</code>.</td> <td>The sample is within the FOV.</td> </tr> <tr> <td><strong>k11_dataset2.npz </strong>(k11-38750)</td> <td>180-degrees scan of some soil/rock sample.</td> <td>Projection data dimensions <code>[521, 25, 2560]</code> as <code>[angles, detY, detX]</code>, flats <code>[10, 25, 2560]</code> and darks are <code>[10, 25, 2560]</code>. Data type is <code>uint16</code>.</td> <td>The sample is larger than the FOV and <span>acquired with a</span> very quick (1ms) exposure. Great for testing with iterative methods. </td> </tr> </tbody> </table> <h3>Simulated data</h3> <table> <tbody> <tr> <td><strong>Dataset file</strong></td> <td><strong>Information</strong></td> <td><strong>Data charachteristics</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td><strong>geant4_dataset1.npz</strong></td> <td>Geant4 simulated tomographic dataset in 180-degrees parallel-beam. </td> <td> <p>Data dimensions <code>[360, 540, 640]</code> as <code>[angles, detY, detX]</code>, flats and darks are <code>[20, 50, 2560]</code>. Data type is <code>uint16</code>.</p> </td> <td>The data is perfectly centered and within FOV. Various reconstruction methods can be tested, direct and iterative.</td> </tr> <tr> <td><strong>synth_tomophantom1.npz</strong></td> <td> <p><a href="https://github.com/dkazanc/TomoPhantom">TomoPhantom</a> simulated data of 3d Shepp-Logan with noise and stripe artefacts in 180-degrees parallel beam</p> </td> <td> <p>Data dimensions <code>[400, 314, 600]</code> as <code>[detY, angles, detX]</code>, data type is <code>float32</code>.</p> </td> <td>The data is perfectly centered and normalised with negative log taken. Quite a significant number of stripe artefacts present.</td> </tr> </tbody> </table>
Replication Data for "How Closely are Common Mutation Operators Coupled to Real Faults?"
<p># Replication Data for "How Closely are Common Mutation Operators Coupled to Real Faults?"</p> <p>## Overview</p> <p>In mutation testing, faulty versions of a program are generated through automated modifications of source code. These mutants are used to assess and improve test suite quality, under the assumption that detection of mutants is indicative of a test suite's ability to detect real faults - i.e., that mutants and faults have a semantic relationship. Improving the effectiveness - in both cost and quality - of mutation testing may lie in better understanding this relationship, in particular with regard to how individual mutation operators (types) couple to real faults. </p> <p>In this study, we examine coupling between 32,002 mutants produced by 31 mutation operators and 144 real faults, using a scale based on number of failing tests and reasons for failure. Ultimately, we observed that 9.92% of the mutants are strongly coupled to real faults, and 51.03% of the faults have at least one strongly coupled mutant. We identify and examine mutation operators with the highest median coupling, as well as the operators that tend to produce non-compiling mutants, undetected mutants, and mutants that cause the most tests to fail outside of the tests that detect the actual fault. We also examine how coupling could be used to filter the set of operators employed, leading to potentially significant cost savings during mutation testing. Our findings could lead to improvements in how mutation testing is applied, improved implementation of specific mutation operators, and inspiration for new mutation operators. </p> <p>## Data Contained in This Package</p> <p>- mutant_data.csv</p> <p>This dataset contains the coupling results for all mutants considered in our experiments. It contains the following attributes for each mutant:</p> <p>-- Project name from Defects4J<br> -- Fault number from Defects4J<br> -- Mutation ID<br> -- Mutation operator<br> -- Number of trigger tests for the fault (tests that detect the real fault)<br> -- Number of failing test cases for the mutant (-1 indicates a compilation error)<br> -- The number of failing trigger tests for the mutant<br> -- The number of trigger tests that fail for the same reason the tests failed for the real fault.<br> -- The number of failing non-trigger tests.<br> -- The categorization of coupling. In order: Compile Error, Not Detected, No Substitution, Partial Test Substitution + Additional Tests Fail, Partial Test Substitution, Partial Substitution + Additional Tests Fail, Partial Substitution, Test Substitution + Additional Tests Fail, Test Substitution, Strong Substitution + Additional Tests Fail, Strong Substitution. </p> <p>- mutant_logs/{Project}/{Project}{Fault Number}output.txt</p> <p>The raw output log that resulted from executing test cases for each mutant for each case example used from Defects4J. Used to generate the dataset discussed above. Scripting for generating the dataset is also included.</p>
Mp4-Version of the supplementary Movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"
<p>Videos in 'mp4'-format of the 8 supplementary movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"</p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</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/W6EH5l80NmU</p>
REAL dataset attached to the paper "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis"
<p>REAL dataset to replicate the results in "Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis", accepted to IEEE/RSJ IROS 2022.</p> <p>In order to fully reproduce the experiments, download also the SYNTHETIC dataset. </p> <p>To automatically download the REAL and SYNTHETIC dataset, run the script provided at the link below.</p> <p>Code to replicate the results available at: https://github.com/hsp-iit/prosthetic-grasping-experiments</p>
Health Record Hiccups - 5526 real-world time series with change points labelled by crowd-sourced visual inspection
<p>5526 real-world time series with labels for the location of all abrupt changes in level, variability, trend, presence/absence of data points, and irregular outliers. The time series were produced from a range of electronic health record data extracts from a large UK hospital group. Values in each data field were aggregated by day/week/month, and numeric summary values calculated for each timepoint from the (often non-numeric) data by applying simple functions (e.g. number of values present, percentage of missing values, number of distinct values, median value). Labels were produced by visual inspection of time series plots from ~2000 volunteers, via the Health Record Hiccups project on the Zooniverse platform (https://www.zooniverse.org/projects/phuongquan/health-record-hiccups). Volunteers drew a vertical line on the image wherever they saw a change point (green line if they were certain, yellow line if they were unsure). Consensus labels per image were calculated using density based clustering with noise (R v3.6.3, dbscan v1.1-5), and converted back to a date.</p>
Real-time Automatic Temperature Regulation During In Vivo MRI-guided Laser Induced Thermotherapy (MR-LITT)
<p>This dataset is associated with the article.</p> <p>For each example (in vitro/in vivo), two matrices are provided, each containing :</p> <p>- coefficients estimation curves ;</p> <p>- automatic regulation curves and recontructed data (thermometry, phase, module) of the MRI sequence.</p>
6TiSCH traces in real-world network deployment
<p><strong>Introduction</strong></p> <p>This data set includes the detailed logs of the 6TiSCH network created with the Contiki-NG OS running on LOG-a-TEC testbed, which is located in the park area of the Jožef Stefan Institute, Ljubljana. The data set can be used to analyze the operation of the 6TiSCH network in a real-world outdoor environment, to detect network anomalies or environmental changes, to classify network uptime, throughput and traffic volume, etc. The generated logs contain all metrics (RSSI, LQI, SQN, ASN, IPv6 address, frequency channel, timestamp) for in-depth analysis.<br> <br> <strong>Network setup</strong></p> <p>6TiSCH network was set up with 15 testbed devices which and operated for 5 days. To generate additional traffic, the devices sent a UDP packet (size 10B) to the root of the network at intervals of 10 seconds (+ random jitter). Besides application packets (UDP), the devices also exchanged packets for network maintenance - UDP packets can be identified by searching for a packet length.</p> <p><strong>Data set structure</strong></p> <p>The included folder contains the logs from all devices used. The suffix of the folder name represents the last 3 digits of the device's IP address. Each folder contains several files. The prefix of a file stands for the order of the collected logs - after 1 hour elapsed, the measurements are saved in a new file.</p> <p> </p>
Data for: Real time g(2) monitoring with 100 kHz sampling rate
<p>Data for the publication C. Lüders, J. Thewes, and M. Assmann, Real time g<sup>(2)</sup> monitoring with 100 kHz sampling rate, Opt. Express <strong>26</strong>, 24854-24863 (2018).</p> <p>We introduce a technique to determine photon correlations of optical light fields in real time. The method is based on ultrafast phase-randomized homodyne detection and allows us to follow the temporal evolution of the second-order correlation function <em>g</em><sup>(2)</sup>(0) of a light field. We demonstrate the capabilities of our approach by applying it to a laser diode operated in the threshold region. In particular, we are able to monitor the emission dynamics of the diode switching back and forth between lasing and spontaneous emission with a <em>g</em><sup>(2)</sup>(0)-sampling rate of 100 kHz.</p> <p>Funded by German Science Foundation (DFG) (AS 459/1-2).</p>
Finnish cooperatives active in the forestry and real estate sector
<p>58 cooperatives incorporated in Finland (osuuskunta / osk) active in the forestry or real estate business, with name and business ID (y-tunnus).</p> <p>The companies have been identified by searching all 200+ cooperatives in the agriculture or real estate field as listed on OpenCorporates and manually identifying those most likely to do business relevant for climate change and climate sink preservation (industry codes 02100, 02400, 68201, 68202, 68209 in Finland TOL 2008). Some extra notes were added manually, including an URL with more information.</p> <p>This is a first version of the dataset. Future versions may include more companies and more extracts from the trade register (PRH).</p>
Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium
<p>Dataset includes 2.5 hours real-world data, for one participant from each of the technical validation study cohorts: ongestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), healthy adult (HA), multiple sclerosis (MS), Parkinson’s (PD) and proximal femoral facture (PFF) linked to the manuscript entitled ‘Assessing real-world gait with digital technology? Validation, insights, and recommendations from the Mobilise-D consortium’. The attached documentation has 6 datasets and a README document.</p> <p>The MOBILISE-D project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA).</p> <p>Content on this publication reflects the author’s view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.</p>
PMMA Pyrolysis Simulation – from Micro- to Real-Scale -- Dataset
<p>Dataset to the article "PMMA Pyrolysis Simulation – from Micro- to Real-Scale". It contains experiment data, FDS simulation and PROPTI input files, as well as the Jupyter Notebooks used to process the different data series.</p> <p> </p> <p>Published with the Fire Safety Journal: <a href="https://doi.org/10.1016/j.firesaf.2023.103926">https://doi.org/10.1016/j.firesaf.2023.103926</a><br> Preprint on arXiv: <a href="https://arxiv.org/abs/2303.17446">https://arxiv.org/abs/2303.17446</a><br> </p> <p>A video series explaining the different parts of this dataset is available on YouTube: <a href="https://www.youtube.com/playlist?list=PLWziITJoPnJKtVuaVudyO2Lz-OWaCE9Co">https://www.youtube.com/playlist?list=PLWziITJoPnJKtVuaVudyO2Lz-OWaCE9Co</a></p> <p>Recommended minimum download: "RunReports", "SmallerDirectories", "PMMA_MicroScale" and "PMMA_Cone_Aalto".</p> <p>Extract all files into the same directory, such that all the relative paths point to the correct locations.</p> <p>The data of the inverse modelling processes are stored in the "PMMA_MicroScale" and "PMMA_Cone_Aalto" archives.</p> <p>The "RunReports" archive contains all the data processing and analysis. This is, the notebooks to generate the plots from the IMP results, extracting the stoichiometry from the CHID.out files, writing the best material parameter sets into the parallel panel simulation templates and so forth.</p> <p>The data of the parallel panel simulations is distributed over a couple of *.rar archives, labelled "ParallelPanelTest_A" to "ParallelPanelTest_H". Their content should be extracted into a directory labelled "ParallelPanelTest", to allow the Python scripts to work as intended. Please refer to the video series for further information.</p> <p>The archive "SmallerDirectories" contains a couple of smaller assessments (compared to the parallel panel simulations), like the cone calorimeter GEOM to generate the flux map, flame height as well as the "GeneralInformation" directory. It is recommended to download this archive together with the "RunReports".</p> <p> </p> <p>Note: the data series labels used in the article differ from the ones used for the simulations.</p> <p>Simulations: Aalto_01, Aalto_02, Aalto_03, Aalto_04, Aalto_05, Aalto_06, Aalto_06b, Aalto_07</p> <p>Article: Cone_01, Cone_02, Cone_03, Cone_04, Cone_05, Cone_06, Cone_07, Cone_08</p> <p> </p> <p>Acknowledgments:<br> We gratefully acknowledge the computing time granted through JARA (project jjsc27) on the supercomputer JURECA at Forschungszentrum Jülich and through the project on the CoBra-system, funded by the German Federal Ministry of Education and Research with the grant number 13N15497. This research was partially funded by the German Federal Ministry of Education and Research with the grant number 13N15497.</p>
DATA7: A dataset that uses synthetic trajectories of vehicles and real cellular tower locations to simulate the workload of Edge nodes in the city of Pisa
<p><strong>Description</strong></p> <p>The dataset contains observations of vehicles in the range of edge nodes (cellular towers). The trajectories of vehicles are synthetically generated with <a href="https://www.eclipse.org/sumo/">SUMO</a>. The cellular tower positions have been taken from <a href="https://opencellid.org/">OpenCelliD</a>. The dataset is in the comma-separated values (CSV) format, and is around 220MB decompressed.</p> <p><br> The CSV contains the following fields:<br> * edge_id: unique identifier of the edge devices<br> * edge_lat: latitude coordinate of the edge device<br> * edge_lon: longitude coordinate of the edge device<br> * time: simulation step of the observation<br> * vehicle_id: unique identifier of the vehicle<br> * vehicle_lat: latitude coordinate of the vehicle<br> * vehicle_lon: longitude coordinate of the vehicle<br> * distance: geodesic distance in meters from the vehicle and the edge device</p>
Dataset of "Near-real-time diagnosis of electron optical phase aberrations in scanning transmission electron microscopy using an artificial neural network"
<p>Dataset containing the jupyter notebook used to construct the database of image, to model and train ANN and to analyze the experimental data. Furthermore there are also a reduced database of 100 images that can be utilized to test the ANN, the h5 file containing the ANN weigths and other supporting files.</p>
Measurements of type 3 non-uniqueness between 660 °C and 960 °C for 18SIB02 EMPIR project "Real-K"
<p>Dataset coming from type 3 non uniqueness investigation at INRiM, for project Real-K.</p> <p>The dataset has been collected by 5 test HT-SPRTs, inserted into a sodium heat pipe, calibrated at the fixed points of water, tin, zinc, silver and aluminium. The measurements have been performed at 760, 780, 800, 820, 840 and 860 °C.</p> <p>RPTW_Polito.xlsx are measurements taken on 6 probes at the triple point of water. </p> <p>MAP-HP-SODIO.txt contains measurements performed with 2 reference sensors in order to evaluate the uniformity among the HP wells.</p> <p>note-durante.xlsx contains notes taken during the "MAP-HP-SODIO" measurements</p> <p>760-SODI-2.txt contains the actual measurements of the 5 probes + 1 control probe (in channel 4).</p> <p>calibrations_2023.xlsx contains the calibration measurements and calculations of the test HT-SPRTs</p> <p> </p>
NOBEL-BOX: A Ship-Based Low-Cost Instrument for Real-Time Ocean Monitoring and Analysis
<p>This data is the measurement result obtained from the NOBEL-BOX instrument. The principle of NOBEL-BOX is to attach sensors in a container connected to a microcontroller and then measure directly. This data results from measurements using fresh water and sea water mixed to see the response from NOBEL BOX. Furthermore, data was also obtained from sea measurements in Pangandaran, West Java, Indonesia. These measurements include pH, water and water temperature, dissolved oxygen, TDS, and salinity. The use of this parameter is to see the condition of the sea so that it becomes a reference in mitigating and managing the ocean.</p>
The Role Of The Intraoperative Optical Coherence Tomography For Vitreoretinal Surgery In A Real-Life Setting
<p>Intraoperative coherence tomography represents a useful tool for managing several eye surgical challenges. Indeed, it provides surgeons with a previously unreachable source of information. The use of OCT revolutionized the diagnosis and treatment of vitreoretinal diseases and is currently an indispensable tool in this field. In this paper, we report our experience using Intraoperative coherence tomography in dealing with vitreoretinal diseases in a real-life setting.</p> <p> </p> <p> </p>
Real Driving Emissions Tests Records
<p>This repository contains six recordings of Real Driving Emissions (RDE) tests conducted with two cars. Two test satisfy the RDE constraints and two violate it. We provide a Readme file with more details for each record.<br> The data was recorded with the Android app LolaDrives and recordings are encoded in the portable car data format (PCDF).</p>
Dataset on comparison of use of problem list versus a real-world patient record
<p>This dataset contains information about performance of clinicians interacting with two versions of a patient record, where time spent and successful completion of the task are captured.</p><p>The research has been published in:</p><p><a href=" https://doi.org/10.1016/j.ijmedinf.2023.105264">Klappe ES, Heijmans J, Groen K, ter Schure J, Cornet R, de Keizer NF</a><br><a href=" https://doi.org/10.1016/j.ijmedinf.2023.105264">Correctly structured problem lists lead to better and faster clinical decision-making in electronic health records compared to non-curated problem lists: a single-blinded crossover randomized controlled trial </a><br><a href=" https://doi.org/10.1016/j.ijmedinf.2023.105264">International Journal of Medical Informatics, 2023;180:105264.</a></p><p> </p>
Associated data underlying the publication "Flipped Classroom Real-World Activities for Learning Open Computing Concepts"
<p>Various “open” concepts in Computing, such as open standards, open data, open licenses or system interoperability are becoming more important in the professional lives of software engineers. However, students usually do not receive a systematic education about these concepts ; rather they sporadically learn about a subset of these topics. This paper presents the revised version of the Open Computing course in the University of Zagreb, Faculty of Electrical Engineering and Computing (FER), which teaches a clear set of current topics focused on open data and correlating concepts. The new e-learning course is carried out using the “flipped classroom” educational method ; students construct their knowledge in a set of real-world mini-activities throughout the course, instead of passively learning from the official course resources. In this paper, we discuss our flipped classroom activities, their relation to revised Bloom’s taxonomy of educational objectives, and present the evaluation of the first course instance us- ing this model. Students’ feedback shows they welcome this change in approach, finding the course useful and interesting, and preferring this method to the traditional full lecture setting.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.