Find research datasets worth reusing

opencc-by-4.0Jun 2019View details →

STOP-IT Real-Time Sensor Data Protection (RSDP)

Sensors, and other devices, generate large amounts of data, which can be used for different purposes; for example, controlling the proper functioning of a critical infrastructure, performing predictive maintenance actions or making decisions that improve the productivity of an industrial plant. However, the use or analysis of erroneous or corrupt data can cause catastrophic situations. For this reason, it is very important to be able to guarantee the integrity of the data generated by sensors, or other devices, which will be used to perform relevant actions for a critical infrastructure, industrial plant, etc. The RSDP tool provides exactly that service, it checks the integrity of the data that has been previously stored in the system, and this can be guaranteed thanks to the use of Blockchain, or DLT, technologies.

opencc-by-4.0Apr 2019View details →

Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework

[This repository contains the source data for the manuscript "Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework" https://nature-research-under-consideration.nature.com/users/37265-nature-communications/posts/47951-a-real-time-optical-and-electronic-chemical-sensor-based-on-a-amino-enone-linked-triazine-containing-2d-covalent-organic-framework] Fully-aromatic, two-dimensional covalent organic frameworks (2D COFs) are hailed as candidates for electronic and optical devices, yet to-date few applications emerged that make genuine use of their rational, predictive design principles and permanent pore structure. Here, we present a 2D COF made up of chemoresistant β-amino enone bridges and Lewis-basic triazine moieties that exhibits a dramatic real-time response in the visible spectrum and an increase in bulk conductivity by two orders of magnitude to a chemical trigger - corrosive HCl vapours. The optical and electronic response is fully reversible using a chemical switch (NH3 vapours) or physical triggers (temperature or vacuum). These findings demonstrate a useful application of fully-aromatic 2D COFs as real-time responsive chemosensors and switches.

opencc-by-4.0May 2019View details →

A Global Database of Historic and Real-time Flood Events based on Social Media

Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10.000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE are detected. All results are publicly available on <a href="http://www.globalfloodmonitor.org">www.globalfloodmonitor.org</a>.

opencc-by-4.0Aug 2024View details →

Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography

This repository contains all data and code underlying the publication: J. de Wit et al. "Revealing real-time 3D in vivo pathogen dynamics in plants by label-free optical coherence tomography" in Nature Communications (2024) (https://doi.org/10.1038/s41467-024-52594-x) --------------Code description------------------ The set of scripts is largely organized around the figures. For each (sub)figure, also from supplementary materials, that involves data and plotting, there is a script that generates the plot from data that can be found in the different zip files that are present in the Zenodo repository under https://doi.org/10.5281/zenodo.11428245. The scripts use the data that is contained in the ZIP folders. The ZIP folders are organized by experiment (Experiment 1, including contrast optimization; Experiment 2), one for the other data (OtherData, the validation for with Trypan blue, and the Arabidopsis, Radish and nematode) and one as a smaller dataset to explain the method on a single B-scan (Example_Bscan_dynamicOCT). IMPORTANT: The folder where the ZIP files are unzipped should be put in the file 'basepath.txt', such that the data can be automatically loaded. Besides the figures that mention 'MakeFig...' there are a few more scripts: <ul> <li>pointcloud_generation_experiment1.py: this file makes the point clouds from the dynamic OCT images as described in Fig 2b. The resulting data is saved as maximum intensity projections and axial sums(forming the basis for Fig S4, S6 and S7) and as voxel counts (forming the basis of Fig.2c and Fig S5)</li> <li>pointcloud_generation_timelapses.py: this file does the segmentation for experiment 2 and saves the maximum intensity projections and axial sums of the different stages in the segmentation (forming the basis of Fig3a,d,e and FigS9a,b), and saves the point clouds of the data. These point clouds were refined manually in CloudCompare as described in methods. These segmented point clouds are contained in the data zip folder of experiment 2.</li> <li>StatisticalTests.R: This R file calculates the statistical tests for Fig.2cd and Fig.S5d. Here the path is not automatically updated, and should be manually set. The input file is contained in "Experiment1/SegmentationData/segmentationdata_samples.csv" and the output of the file is "D:/DataZenodo/Experiment1/SegmentationData/data_combined_Rstats_output.csv"</li> <li>example_dynamic_Bscan.py: This script gives an example of the dynamic OCT processing as proposed in this paper. First it shows the process from an OCT interference spectrum to a B-scan. Then it loads 100 B-scans and applies dynamic OCT, including normalization with histograms. Finally it gives a dynamic B-scan and plots this against the average normal OCT image. This script can be used with only the zip folder "Example_Bscan_dynamicOCT", which reduces the amount of data needed to download/unzip.</li> </ul> The list of other script files to load the data and generate the figures (guiding to the path of uncropped figures) is: <ul> <li>MakeFig1bce_Fig2e.py</li> <li>MakeFig1d.py</li> <li>MakeFig1agraphs_FigureS1.py</li> <li>MakeFig3acde_S9ab.py</li> <li>MakeFigS2_determine_dynamic_range_experiment1.py</li> <li>MakeFigS8.py</li> <li>MakeFigureS4-S6-S7.py</li> <li>MakeFigureS5.py</li> <li>MakeHistFig2b_makeFigS3b-e.py</li> <li>MakePlotsFig2ab.py</li> <li>MakePlotsFig2cd.py</li> </ul> Code was all run in Python 3 using Anaconda Spyder. Moreover, the zip file with the code contains the folder 'figures' with all subfigures. Some of them are automatically saved from the scripts, others (like photos, icons, but also the Trypan blue microscopy figure) are added in the respective folder. The are logically organized by figure number. --------------Dataset Description----------------- As mentioned above, the data is organized in four zip folders for both experiments, the other data (validation with Trypan blue, other plant-pathogens) and one for the dynamic OCT B-scan example. The data contain the following: Experiment 1:  <ul> <li>DynamicOCTimages whose subfolders (organized by date) contain a folder per volume dataset in experiment 1 with a z-stack of .tif files that form the imaged volume. The lateral sampling is 3 um and the axial sampling is 1.37 um. </li> <li>ContrastOptimization: This folder contains  <ul> <li>Bscans_with_segmentation: segmented B-scans for contrast optimization (Fig S3)</li> <li>Bscan_figS1_fig1: The B-scans and segementation for Figure S1.</li> <li>histogramdata_dynamicrange: The histograms, bins and deducted reference data for determining the dynamic range per color channel for experiment 1 (Fig S2)</li> <li>logcompressed_3value_dOCT_example: An example data stack for obtaining histograms (see script MakeFigS2_determine_dynamic_range_experiment1.py)</li> <li>overlaps_threshold-100-98-95-92-90-85-80-75-70-65-60-55-50-45perc_red-1_2_blue_-3_0_green1_filt.npy: A file with intermediate data for the contrast optimization, which can also be generated with the script "MakeHistFig2b_makeFigS3b-e.py"</li> </ul> </li> <li>SegmentationData: This folder contains  <ul> <li>MIP_segmentation_stages: maximum intensityp projections and axial sums for all images at different stages in the segmentation (basis for Fig S4,6,7)</li> <li>processed_masks and StackMasks: the manually obtained masks (segmented in StackMasks, made into masks in the folder 'processed_masks') for filtering out stomata, veins and artefacts.</li> <li>Unmasked_axialsum_th34_formanualsegmentation: This folder contains the images of Fig.S4 and were used for the segmentation (we addes a small offset, such that the in segmentation we could set it to 0 and have a unique mask). </li> <li>overview_samples_bremiayn.csv: A dataframe with the data for all the samples in experiment 1 that is used as input for the segmentation. It also contains the result of the manual check whether it has infection (Fig2c, left).</li> <li>segmentationdata_samples.csv: This supplements the file of overview_samples_bremiayn.csv with the results from the segmentation and is output to script "pointcloud_generation_experiment1.py". It forms the basis of Fig.2a-c, and FigS5, as well as for the R-script to do the statistical testing.</li> <li>qPCR_dOCT.csv: This script contains the qPCR data and is input to Fig2d. </li> </ul> </li> </ul> Experiment 2: <ul> <li>DynamicOCTimages: This contains the z-stacks of .tif files of the volumes for experiment 2 (and one extra, where a z-slice is used in Fig.1b, bottom). Sampling step size is here again 3 um in lateral direction and 1.37 um in axial direction.</li> <li>.npy files with the histograms (with same bins as Experiment 1), maxvalues and reference values for the dynamic range calculation.</li> <li>segmentation_data: this folder contains: <ul> <li>quantification_volume_disc160_33_10.csv and quantification_volume_disc160_33_10.xlsx: data from the manually segmented point clouds that form the basis of Fig.3c.</li> <li>timelapse_sampleoverview.csv: overview of the samples that is used as input in the file "pointcloud_generation_timelapses.py"</li> <li>pointclouds: Folder with segmented point clouds for the three leaf discs. These files could  be loaded in CloudCompare.</li> <li>overviewMIPs: folder with overview maximum intensity projections for the different steps in segmentation, which also forms the input of Fig.3a, FigS9ab.</li> <li>rawpointclouds: folder with the automatically generated point clouds from file pointcloud_generation_timelapses.py which were imported into CloudCompare as the basis for the segmented point clouds.</li> </ul> </li> </ul> OtherData: This folder contains the z-stacks of dynamic OCT tif images for Arabidopsis (here both a normal contrast and one that has been increased to only contain the original 0-180 range); nematodes, radish (called radijs_test_PP_py_0002), spores for Fig1c (SporesImaging) and the dynamic OCT image of Fig1d.  example_Bscan_dynamicOCT: This folder contains data to run the script example_dynamic_Bscan.py to show the dynamic OCT imaging process from raw OCT spectra. <ul> <li>raw_spectra_exampleframe: contains interference spectra, a reference spectrum and interpolation grid to show how to get from a raw OCT spectrum to a normal single B-scan.</li> <li>abs_images: contains 100 subsequent B-scans that can be used to generate a dynamic OCT image as done in example_dynamic_Bscan.py</li> </ul>

opencc-by-4.0Aug 2020View details →

Real-time ZTD and gradients from 20 IGS stations; year 2019

This dataset contains multi-GNSS real-time products, i.e. ECEF coordinates, zenith total delay (ZTD), and horizontal gradients, together with their uncertainties estimated every 1 minute over the entire year 2019 for 20 IGS stations: ALGO, BIK0, CAS1, CHPG, CPVG, FAA1, JPLM, KERG, KZN2, LHAZZ, LMMF, NKLG, NNOR, PNGM, POVE, REYK, RGDG, ULAD, WROC, YEL2. Two subsets are available that correspond to two processing strategies, i.e. common and advanced. A detailed description, validation and comparison of both strategies can be found in: https://link.springer.com/article/10.1007/s10291-020-01014-w Data are stored in standard Matlab MAT files. Each file contains a single table array (Matlab format) with the complete set of estimated parameters for a single station. Table columns are labeled and self-explanatory. A comma-delimited text file can be obtained using in-build Matlab function "writetable.m".

opencc-by-4.0Mar 2019View details →

Online Real-Time Delphi Survey for the research project "MENARA" - Compilation of all Comments to Closed and Open Questions

Looking into the Futures: Delphi Survey about the MENA region In order to get a more realistic overview of the situation and trends, of the potentials, problems and potentials of the countries of the MENA region a Real Time Delphi survey was conducted. This is an important tool of modern future research. It was managed by the IZT- Institute for Future Studies in Berlin. A group of 139 experts and researchers from different institutes and organizations were invited to participate at the Online Real-Time Delphi Survey (RTD) about possible and likely futures of the MENA region. The experts were asked to answer questions and provide their opinions on twelve topics such as social unrest, youth unemployment, urbanization, gender equality, security etc. In this dataset all comments to the closed and the open questions are compiled. The output was one of the basic material used for the creation of future regional scenarios for mid-term (2025) and long-term (2050) time horizons. Focus scenarios were produced in order to exemplify selected characteristic and important future options, in terms of chances and risks (e.g. energy futures).

Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches

Raw experimental data associated with the manuscript "Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches", by Sosa-Carrillo and colleagues, bioRxiv, 10.1101/2022.11.02.514931. The code to analyze this raw data and generate the figures for the manuscript is available on GitLab at https://gitlab.inria.fr/InBio/Public/yeastcybersecretion.

Additional results for article "A new approach for the generation of real-time GNSS low-latitude ionospheric scintillation maps"

Complete set of interpolation error and correlation metrics for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options using the SSS cross-validation scheme for the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Complete table of interpolation errors and correlation.csv”. Complete set of scintillation maps for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options covering the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Scintillation maps for the 10-hour dataset.zip”. Comparison plots of the scintillation maps generated by the approaches GDA, IDW, RBF and GPR with the pre-processing options SAR, SMR and VQI for each of the  40 intervals of time of 16 minutes covering the 10-hour dataset - file “Set of maps for all 4 approaches with the SAR, SMR and VQI sets of options.zip”. Sequence of scintillation maps for the 8-hour dataset generated by the GPR(VQI) approach for the three time resolutions (1, 2, and 16-minute) - file “Scintillation maps for the 8-hour comparison dataset.zip”. Animations corresponding to the sequence of maps generated by the GPR(VQI) approach for the 8-hour dataset, and for the three time resolutions (1, 2, and 16-minute) - file “Animations of GPR(VQI) maps for the 8-hour comparison dataset.zip”.

opencc-by-4.0Feb 2023View details →

Datasets of the work named Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform

<pre>- 1_Motion_Simulator/     - IMU_results/         - 20211028101756.csv         - 20220114101543.csv         - 20220117000000.csv     - Rotary_Table/         - 15/             - rover_20220105.nav             - rover_20220105.obs             - solution_20220105_CAS.log         - 360/             - rover_20211221.nav             - rover_20211221.obs             - solution_20211221_CAS.log         - 360-15/             - rover_20220202_CAS.nav             - rover_20220202_CAS.obs             - solution_20220202_CAS.log     - Static_Tests/         - solution_SSRA00CAS0         - solution_SSRA00WHU0 - 2_GNSS_Signal_Simulator/     - platformmov_C1.xtd     - platformmov_C2.xtd     - platformmov_C3.xtd     - TestBetaNoneMov_C1     - TestBetaNoneMov_C1.nav     - TestBetaNoneMov_C1.obs     - TestBetaNoneMov_C1.ubx     - TestBetaNoneMov_C2     - TestBetaNoneMov_C2.nav     - TestBetaNoneMov_C2.obs     - TestBetaNoneMov_C2.ubx     - TestBetaNoneMov_C3     - TestBetaNoneMov_C3.nav     - TestBetaNoneMov_C3.obs     - TestBetaNoneMov_C3.ubx - 3_Test_Sea/     - 20220503000000.xlsx     - solution_28.nav     - solution_28.obs     - solution_28.ubx Background: {Journal Article using this dataset} 'Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform' Paper DOI: <a href="https://doi.org/10.3390/s23020925">https://doi.org/10.3390/s23020925</a> Abstract: a low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved. </pre>

opencc-by-4.0May 2022View details →

Digital Elevation Models, orthoimages and lava outlines of the 2021 Fagradalsfjall eruption: Results from near real-time photogrammetric monitoring

This repository contains the data behind the work described in Pedersen et al (in review), specifically the Digital Elevation Models (DEMs), orthoimages and lava outlines created as part of the near-real time monitoring of the Fagradalsfjall 2021 eruption (SW-Iceland). The processing of the data is explained in detail in the Supplement S2 of Pedersen et al (2022). The data derived from Pléiades surveys includes only the DEMs and the lava outlines. The Pléiades-based orthoimages are subject to license. Please contact the authors for further information about this. Convention for file naming: Data: DEM, Ortho, Outline YYYYMMDD_HHMM: Date of acquisition Platform used: Helicopter (HEL), Pléiades (PLE), Hasselblad A6D (A6D) Origin of elevations in DEMs: meters above ellipsoid (zmae) Ground Sampling Distance: 2x2m (DEM) and 30x30cm (Ortho) Cartographic projection: isn93 (see cartographic specifications for further details)   Cartographic specifications: Cartographic projection: ISN93/Lambert 1993 (EPSG: 3057, https://epsg.io/3057) Horizontal and vertical reference frame: The surveys after 18 April 2021 are in ISN2016/ISH2004, updated locally around the study area in April 2021 (after pre-eruptive deformations occurred). The rest of the surveys of late March and early April were created using several floating reference systems (see Supplement S3 for details), since no ground surveys were available during the first weeks of the data collection. The surveys of 23 March 2021, 31 March 2021 were re-procesed in Gouhier et al., 2022, using the survey done on 18 May 2021 as reference. Origin of elevations: Ellipsoid WGS84 Raster data format: GeoTIFF Raster compression system: ZSTD (http://facebook.github.io/zstd/) Vector data format: GeoPackage (https://www.geopackage.org/)

Source data for manuscript "Real-time microscopy of the relaxation of a glass".

Source data for manuscript "Real-time microscopy of the relaxation of a glass" (DOI: 10.1038/s41567-023-02125-0), including: - AFM source images - data points for all plots in the manuscript

opencc-by-4.0Jul 2022View details →

Supplementary Movies and Source Data for: Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation

Supplementary Movies and raw data for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation": Source_Data.zip: Supplementary Code, Supplementary Data and Weka Analysis Lan_supplementary_movies_AVI.zip: Supplementary movies as AVI Lan_supplementary_movies_MP4.zip: Supplementary movies as MP4 Lan_raw_movies.zip: Raw TIFF stacks of the movies. Lan_supplementary_movies.zip: Old version of the movies.

opencc-by-4.0Aug 2023View details →

Data of the publication "Real-time broadening of bath-induced density profiles from closed-system correlation functions"

The Lindblad master equation is one of the main approaches to open quantum systems. While it has been widely applied in the context of condensed matter systems to study properties of steady states in the limit of long times, the actual route to such steady states has attracted less attention yet. Here, we investigate the nonequilibrium dynamics of spin chains with a local coupling to a single Lindblad bath and analyze the transport properties of the induced magnetization. Combining typicality and equilibration arguments with stochastic unraveling, we unveil for the case of weak driving that the dynamics in the open system can be constructed on the basis of correlation functions in the closed system, which establishes a connection between the Lindblad approach and linear response theory at finite times. In this way, we provide a particular example where closed and open approaches to quantum transport agree strictly. We demonstrate this fact numerically for the spin-1/2 XXZ chain at the isotropic point and in the easy-axis regime, where superdiffusive and diffusive scaling is observed, respectively.

Real-Time Sensor Network of Detroit Green Infrastructure: Datasets and Code

<ol> <li>MonitoredRainGardens_PaperTable.xlsx: Excel spreadsheet where the "Garden" sheet contains the 14 monitored green infrastructure (GI) sites with their design and physiographic features and the "Field Log" sheet contains the installation and field maintenance trips.</li> <li>WaterWells.xlsx: an Excel file containing all of the water wells in the Detroit region uses for interpolating groundwater levels.</li> <li>GI_GIS_Analysis.aparx: ArcGIS Pro project file which includes the 14 monitored GI sites and the GIS data for Detroit (percent imperviousness, elevation, slope, land use type, wells, interpolated groundwater levels, hydrologic soil group).</li> <li>Code.zip: Zip folder containing another folder titled "Code" which holds: (1) a folder titled "SensorData" containing 16 csv files with the raw pressure transducer data for the 16 monitored GI sites during the measurement period (including the two excluded sites); (2) a csv file titled "MonitoredRainGardens.csv" containing the 14 monitored green infrastructure (GI) sites with their design and physiographic features used in the correlation analysis; (3) a csv file titled "storm_constants.csv" which contain the computed decay constants for every storm in every GI during the measurement period;  (4) a csv file titled "GLWA_RainGaugesforStudy.csv" that contains rainfall from 9 rain gauges during the measurement period; (5) a Jupyter notebook titled "storm_constants_analysis.ipynb" which provides the code for calculating the decay constants for the monitored GI; (6) a Jupyter notebook titled "storm_constants.ipynb" which provides the code for analyzing the decay constants including the correlation analysis and surface plots; and (7) a Jupiter notebook titled "modeled_response.ipynb" which provides the code for plotting the drawdown curves based on the decay constant.</li> </ol>

opencc-by-4.0Mar 2020View details →

Station catalog in "Real-time earthquake location based on the Kalman filter formulation"

Station catalog used to locate earthquakes in Parkfield, California, in the manuscript entitled "Real-time earthquake location based on the Kalman filter formulation" submitted to Geophysical Research Letters

opencc-by-4.0Jun 2020View details →

Real-time measurement and source apportionment of elements in Delhi's atmosphere

Here we present semi-continuous and real-time measurements of elemental composition for PM2.5 and PM10 aerosols in Delhi, India, at a time resolution of 30 min to 1 h during two consecutive winters in 2018 and 2019, to identify the prevailing sources. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, brake wear, a S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-30 Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the pollutants enter Delhi via three distinct air corridors during winters including Nepal and Uttar Pradesh (India) in the east, and Pakistan, Punjab (India) and Haryana (India) in the north-west, during winter, when the national capital’s air quality is at its worst. This repository has excel file corresponding to each figures presented in main text published version.

opencc-by-4.0Oct 2019View details →

Using Blender EEVEE for the Generation of Real Time Background Plate in Green Screen Movie Shots

This talk will introduce you to the use of blender EEVEE for green screen shots for a short movie. Green (and blue) screen shots are notoriously difficult to get the lighting condition right, since the image from the camera is dominated by the bright green background. It is very helpful on the set to see in real time the final composition of the scene with the proper background to make adjustments of the camera and the lighting position and the lighting intensity and color. For the shots a large volume mocap solution (Optitrack) was used to track the movie camera (Arri Alexa) and the transformation data was sent to Blender to animate the virtual camera. The previously laser scanned background was rendered in real time in EEVEE. To combine the camera image and the rendered image a dedicated live-keying hardware was used. The described set-up was used in connection with the research project Virtually Real – Aesthetics and Perception of Virtual Spaces in Film by the Zurich University of the Arts and the University of Bern, funded by the Swiss National Science Foundation.

opencc-by-4.0Feb 2020View details →

A versatile microfluidic platform measures hyphal interactions between Fusarium graminearum and Clonostachys rosea in real-time

Routinely, fungal-fungal interactions (FFIs) are studied on agar surfaces. However, this format restricts high-resolution dynamic imaging. To gain experimental access to FFIs at the hyphal level in real-time, we developed a microfluidic platform, a FFI device. This device utilises microchannel geometry to enhance the visibility of hyphal growth and provides control channels to allow comparisons between localised and systemic effects. We demonstrate its function by investigating the FFI between the biological control agent (BCA) Clonostachys rosea and the plant pathogen Fusarium graminearum. Microscope image analyses confirm the inhibitory effect of the necrotrophic BCA and we show that a loss of fluorescence in parasitised hyphae of GFP-tagged F. graminearum coincides with the detection of GFP in mycelium of C. rosea. The versatility of our device to operate under both water-saturated and nutrient-rich as well as dry and nutrient-deficient conditions, coupled with its spatio-temporal output, opens new opportunities to study relationships between fungi.

opencc-by-4.0Nov 2023View details →

Cine and real-time free-breathing CMR at rest and under exercise stress of healthy volunteers

The dataset consists of cine and real-time images from 15 healthy volunteers (7 males; 8 females). All images were acquired in supine position using a 32-channel cardiac surface receiver coil at 3 T (Skyra, Siemens Healthineers, Germany). Conventional imaging at rest included a balanced steady-state free precession (bSSFP) ECG-gated cine sequence to create a short-axis stack covering the entire heart including both ventricles and atria. Real-time CMR data acquisition was performed during free-breathing and without ECG-synchronization at rest and under two different levels of exercise stress. The dataset includes automatically created contours (comDL) using Medis (version 4.0.56.4, QMass® 8.1, Medical Imaging Systems, Leiden, Netherlands) for all images, as well as manually corrected (mc) contours based on the comDL contours for all cine and real-time measurements at rest and under exercise stress for end-diastolic (ED) and end-systolic phases (ES). The dataset also includes segmentation masks in NIfTI format for cine and real-time CMR at rest and under exercise stress created with nnU-Net (DOI:10.1038/s41592-020-01008-z) with freely available weights based trained on the dataset of the cardiac segmentation challenge "Automated Cardiac Segmentation Challenge" (ACDC) (DOI:10.1109/TMI.2018.2837502). To minimize the influence of respiratory motion on clinical measures, images in the ED and ES phase of the cardiac cycle during end-expiration were manually selected for each slice. The dataset includes indices for these images for real-time CMR measurements at rest and under exercise stress. For intra-observer variability, manually corrected contours for the derivation of the clinical measures were created three to six months after the initial segmentation. For inter-observer variability, manually corrected contours for the derivation of the clinical parameters were created for the first five volunteers by a second reader with experience in cardiac segmentation. Single images in the ED and ES phase during end-expiration were once again chosen from each slice. Image data is provided in a file format used by the BART toolbox.  DOI:10.5281/zenodo.7110562