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Single-cycle, 643-mW average power THz source based on tilted pulse front in lithium niobate
<p>This data set is associated with the aforementioned paper.</p> <p>The data and the Jupyter notebooks (Python) to reproduce the figures in this paper can be downloaded below. To run a Jupyter notebook as a beginner, it is easiest to download and install anaconda, a Python environment that comes with many packages preinstalled and also offers Jupyter lab/notebook. It is available at <a href="https://www.anaconda.com/download" target="_blank" rel="noopener">https://www.anaconda.com/download</a>.</p> <h2>Fig.01</h2> <p><strong>Fig.01_literature_lithium_niobate_sources.csv</strong> contains a summary table of the last decades of published THz power values obtained with lithium niobate in the tilted pulse front geometry. The accompanying jupyter notebook allows to reproduce the figure that was used in the paper.</p> <h2>Fig.03</h2> <p>Each individual data frame (df), which is saved as an HDF file in the .zip file, contains a "power curve" measurement (i.e. measured THz power as a function of the applied pump power). Whenever a parameter is changed, all positions, angles, THz power and cryostat parameters are saved.</p> <ul> <li><strong>x1 </strong>is the position of the last mirror before the transmission grating (parallel to the pump beam direction before the crystal) in [mm]</li> <li><strong>x2 </strong>is the position of the first imaging lens in direction of the pump beam propagation direction before the crystal in [mm]</li> <li><strong>x3 </strong>is the position of the second imaging lens in the direction of the pump beam propagation direction before the crystal in [mm]</li> <li><strong>x4 </strong>is the position of the cryostat in the direction of the pump beam before reaching the crystal in [mm]</li> <li><strong>y0 </strong>is the position of the cryostat in the perpendicular direction of the pump beam before reaching the crystal in [mm]</li> <li><strong>α0 </strong>is the angle of the lambda/2 waveplate that allows the pump power to be varied at the crystal in [°]</li> <li><strong>α1 </strong>is the angle of the last mirror before the grating in [°]</li> <li><strong>α2 </strong>is the angle of the transmission grating in [°]</li> <li><strong>thz_power_W </strong>is the obtained power obtained from the Ophir 3A-P-THz power meter in [W]</li> <li><strong>temperature_setpoint_K </strong>is the LakeShore cryostat controller setpoint in [K]</li> <li><strong>temperature_K </strong>is the temperature read from the sensor on the cooling finger (above the crystal) in [K]</li> <li><strong>heater_output </strong>is the amount of power in [%] delivered to the resistive heating element inside the cryostat. 100% corresponds to about 50 W. Its value is controlled by an internal PID loop of the cryostat controller, which tries to stabilize <strong>temperature_K </strong>to <strong>temperature_setpoint_K</strong></li> <li><strong>pump_power </strong>is the average laser power reaching the crystal in [W]. It was calibrated before obtaining the data set by characterizing the lambda/2 waveplate angle <strong>α0</strong> to the value of an NIR power meter just before the cryostat.</li> <li><strong>repetition_rate</strong> is the repetition rate of the laser in [Hz]</li> </ul> <p>As an example, below is one line (for one pump power) of such a data frame:</p> <table> <tbody> <tr> <td> </td> <th>x1</th> <th>x2</th> <th>x3</th> <th>x4</th> <th>y0</th> <th>α0</th> <th>α1</th> <th>α2</th> <th>thz_power_W</th> <th>temperature_setpoint_K</th> <th>temperature_K</th> <th>heater_output</th> <th>pump_power</th> <th>repetition_rate</th> </tr> <tr> <td>0</td> <td>-12.000005</td> <td>2.500039</td> <td>9.100015</td> <td>-5.0</td> <td>-2.0</td> <td>35.905660</td> <td>25.68</td> <td>-23.3</td> <td>0.006000</td> <td>80.0</td> <td>79.883</td> <td>4.4</td> <td>20.0</td> <td>40000.0</td> </tr> </tbody> </table> <p>10 of such power curves were obtained at 100 kHz and 40 kHz and can be found in the respective zip-file.</p> <p> </p> <p><strong>Literature_Power_Efficiency.zip</strong> contains digitzed power and efficiency values from the following references:</p> <ol> <li>X. Wu, D. Kong, S. Hao, et al., "Generation of 13.9-mJ Terahertz Radiation from Lithium Niobate Materials," Advanced Materials 35, 2208947 (2023).</li> <li> <p>P. L. Kramer, M. K. R. Windeler, K. Mecseki, et al., "Enabling high repetition rate nonlinear THz science with a kilowatt-class sub-100 fs laser source," Opt. Express 28, 16951 (2020).</p> </li> <li> <p>T. Kroh, T. Rohwer, D. Zhang, et al., "Parameter sensitivities in tilted-pulse-front based terahertz setups and their implications for high-energy terahertz source design and optimization," Opt. Express, OE 30, 24186–24206 (2022).</p> </li> <li> <p>B. Zhang, Z. Ma, J. Ma, et al., "1.4-mJ High Energy Terahertz Radiation from Lithium Niobates," Laser & Photonics Reviews 15, 2000295 (2021).</p> </li> </ol> <p> </p> <h2>Fig.04</h2> <p><strong>EOS_dfs.p</strong> is a pickle file, contain electro-optic sampling traces, which are already averaged for various pump powers at 40 kHz repetition rate.</p>
Consumer Front Plant Trait Sampling in Two Virginia Coast Salt Marshes, 2018
A consumer front forms when dense aggregations of herbivores form at the edge of a resource. The front then propagates through the ecosystem in search of additional resources. In U.S. Atlantic salt marshes, the purple marsh crab, Sesarma reticulatum, creates consumer fronts as it grazes the smooth cordgrass, Spartina alterniflora. Sesarma fronts typically form at the heads of tidal creeks and create distinct zonation between the low marsh, tall-form Spartina zones and the high marsh, short-form Spartina zones, with a denuded band of mudflat in between. Over time, Sesarma consumer fronts are moving directionally inland towards the short-form zone and away from the tall-form zone. This movement inland allows for tall-form Spartina to revegetate, preventing further marsh loss. However, it remains unknown why these consumer fronts are moving inland. To test the hypothesis that plant traits (i.e., nutritional quality, palatability) are driving the Sesarma consumer front inland, we collected Spartina from consumer fronts at 8 unique creekheads across two marsh systems on the Eastern Shore of Virginia (4 consumer fronts at Upper Phillips Creek and 4 at Upshur Creek). Spartina was collected from 15 replicate quadrats (0.0625m^2) from the tall-form low marsh zones (TSA) and from the short-form low marsh zones (SSA) at each creekhead. The short-form zone was delineated into two additional zones, an interior (SSA-I) and an exterior (SSA-E), to assess if there were any differences in plant traits between Spartina being actively grazed (SSA-E, adjacent to consumer front) and those that have not been grazed (SSA-I, 2 meters from consumer front). Collected Spartina plants were then processed for a series of plant traits that can influence herbivore preference.
Pre-trained models for segmentation and tracking of Coronal Bright Fronts from SDO AIA Base Difference images
<p>Here we present pretrained U-NET-based models followed by SDO AIA Base Difference(BD) validation set after intensity tresholding [-50;150] with predicted feature masks samples. <br>We provide a command-line Python utility for image segmentation using our CNNs designed to process images of solar eruptive phenomena. The https://gitlab.com/iahelio/helios_cnn repository includes regularly updated and newly published models. </p> <p>First model we present is designed to predict the likelihood of each pixel belonging to a certain class or feature in the solar image. A probabilistic output allows for a more nuanced interpretation of ambiguous region. The output can be converted into binary masks through thresholding. The range of values also gives insights into the model's confidence</p> <p>We also present sample segmentation results and the second model designed to produce binary masks.</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Front-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the front-looking orientation, where the installation angle of radar is 0 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Lab_240522_160943.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Front_160943.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesFront_160943.mat' contains the timestamped velocity for the frames that are synchronised with the frames of side-looking radar.</p> <p>(The dataset for the side-looking radar is stored in another repository with DOI: 10.5281/zenodo.14174138)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesFront_160943.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Front_240522_160943_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
Data for "Saturation of destratifying and restratifying instabilities during down front wind events: a case study in the Irminger Sea"
<p>This archive contains processed data used in the study "Saturation of destratifying and restratifying instabilities during down front wind events: a case study in the Irminger Sea".</p> <p>We are grateful for the financial support of the Natural Environment Research Council (grants NE/L002612/1 and NE/T013494/1).</p> <p>This work used the ARCHER2 UK National Supercomputing Service (https://www.archer2.ac.uk).</p> <p>We would also like to thank Andrew Coward for providing computational support.</p> <p>The results contain modified Copernicus Climate Change Service information 2020. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>The results contain modified GEBCO data produced by the GEBCO Compilation Group (2023) GEBCO 2023 Grid (doi:10.5285/f98b053b-0cbc-6c23-e053-6c86abc0af7b)</p>
Multi-Objective Design of Actuators: Pareto fronts
<p>These are the best-known Pareto fronts for the 20 MODAct benchmark problems. Files are text files where each row is a point and each column an objective.</p> <p>Associated publication is under review.</p>
Data supplement for "Bifurcations of front motion in passive and active Allen-Cahn-type equations"
<p>This dataset contains the data and source files for figures 5 and 7-10 in the following publication: </p> <p>F. Stegemerten, S.V. Gurevich, U. Thiele</p> <p><em>'Bifurcations of front motion in passive and active Allen–Cahn-type equations' </em></p> <p>published in 2020 in CHAOS.</p> <p>Please follow the instructions given in 'Readme.txt'.</p>
Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in 'winning' hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.
<p>Datasets associated with Agostini, S., Houlbreque, F., Biscéré, T., Harvey, B. P., Heitzman, J. M., Takimoto, R., et al. (2020). Greater mitochondrial energy production provides resistance to ocean acidification in ‘winning’ hermatypic corals. Front. Mar. Sci. 7. doi:10.3389/fmars.2020.600836.</p>
Ship logs from ARCTOS Barents Sea Polar Front 2021-05 cruise
<p>PolarFront 2021-05 ship logs. Original (ISO 8859-1 encoded) text files from the ship logger on Helmer Hanssen.</p>
Calving Front Dataset for Marine-Terminating Glaciers in Svalbard 1985-2023
<p>Svalbard has experienced increased climate variability as a result of global warming, leading to significant mass loss in its marine-terminating glaciers over recent decades. Nevertheless, the mechanisms driving this mass loss remain less understood, primarily due to a limited understanding of calving dynamics. Here we present a new high-resolution calving front dataset of 149 marine-terminating glaciers in Svalbard, comprising 124919 glacier calving front positions during the period of 1985-2023. This dataset was generated using a novel automated deep learning framework and multiple optical and SAR satellite images from Landsat, Terra-ASTER, Sentinel-2, and Sentinel-1 satellite missions.</p> <p>The information regarding the glacier calving front terminal traces, glacier centrelines, glacier domains, fjord masks and the along-centreline glacier calving front change time series is consolidated into a single Geopackage file named "Svalbard_Calving_Front_Product.gpkg." The specific file structure for this data file is detailed in Table 1, and the feature attribute table for the different data layers recorded in this data file can be found in Table 2.</p> <p>Furthermore, we have included spatial distribution map plots of the glacier calving front traces and line plots depicting the time series of calving front changes for each individual glacier. These plots are provided in .PNG file format and can be accessed within the Figures folder.</p> <p>Table 1. The layer structure of the Svalbard calving front data product.</p> <table> <tbody> <tr> <td> <p><strong>Layer Name</strong></p> </td> <td> <p><strong>Details</strong></p> </td> </tr> <tr> <td> <p>traces</p> </td> <td> <p>Line geometries recording the terminal traces of all the glaciers (EPSG:3995).</p> </td> </tr> <tr> <td> <p>centrelines</p> </td> <td> <p>Line geometries recording the glacier centrelines used in calving front change estimation (EPSG:3995).</p> </td> </tr> <tr> <td> <p>domains</p> </td> <td> <p>Polygon geometries recording the glacier domains (EPSG:3995).</p> </td> </tr> <tr> <td> <p>fjord_masks</p> </td> <td> <p>Polygon geometries recording the fjord masks (EPSG:3995).</p> </td> </tr> <tr> <td> <p>front_change_time_series</p> </td> <td> <p>Point geometries recording the along-centreline glacier calving front change time series (EPSG:4326).</p> </td> </tr> </tbody> </table> <p> </p> <p>Table 2. The feature attribute table of the data layer.</p> <table> <tbody> <tr> <td> <p><strong>Data Field</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Glacier</p> </td> <td> <p>The Randolph Glacier Inventory (RGI) version 6 (RGI Consortium, 2017) glacier id.</p> </td> </tr> <tr> <td> <p>Sensor</p> </td> <td> <p>The satellite platform used in mapping glacier calving front, including “Landsat”, “Terra-ASTER”, “Sentinel2” and “Sentinel1”.</p> </td> </tr> <tr> <td> <p>ImageId</p> </td> <td> <p>The image id of the satellite image used in mapping the glacier calving front.</p> </td> </tr> <tr> <td> <p>DateString</p> </td> <td> <p>The datetime string of the satellite image in the format of “YYYYMMDD”.</p> </td> </tr> <tr> <td> <p>CFL_Change</p> </td> <td> <p>The calving front location (CFL) changes in meters along the glacier centreline in relation to the earliest calving front location in the time series.</p> </td> </tr> <tr> <td> <p>glacier_lat</p> </td> <td> <p>The latitude of the glacier location (WGS84 coordinate system).</p> </td> </tr> <tr> <td> <p>glacier_lon</p> </td> <td> <p>The longitude of the glacier location (WGS84 coordinate system).</p> </td> </tr> </tbody> </table>
Front polylines extracted from DWD Maps
<p>Weather fronts extracted from surface analysis maps of the German Weather Service (Deutscher Wetterdienst, DWD) for the years 2015 to 2020</p> <p>Fronts are described by an identifier (warm, cold, occ) followed by several [lat, lon] coordinate pairs in degree [North, East]</p> <p>Stationary-Fronts are extracted as either cold or warm fronts</p> <p>e.g.</p> <p>warm [75.69211706913667, 29.558180185836815] [75.12348547753966, 26.89164117647018] ...</p> <p>No guarantee is given regarding correctness or completeness of extracted fronts</p>
A consistent discretization of the single-field two-phase momentum convection term for the unstructured finite volume Level Set / Front Tracking method - data
<p>Research data from the rhoLENT unstructured Level Set / Front Tracking method for simulating two-phase flows with large density ratios. </p>
IN02040 Inscription in front of the Changu Narayana Temple
<p>Gnoli, Raniero. <em>Nepalese Inscriptions in Gupta Characters</em>. Roma: Is. M. E. O. 1956. Plate 36, Inscription 34, pp. 46-47.</p>
Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"
<p>Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)".</p> <p>Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea.</p> <p>Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report.</p> <p>Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.</p> <p> </p> <p>Manuscript abstract:</p> <p>In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.</p> <p> </p> <p> </p>
Earthquake rupture front tracked by polarization azimuths: Codes and extra material
<p>Set of matlab codes to calculate the rupture front position and migration speed every second starting from a set of SAC files.</p> <p>delays_turkey_event.m needs as input SAC files and returns a set of figures displaying the rupture front position and migrations speed. It needs some ad-hoc functions that are contained in this repository. </p> <p>list_of_accelerometers.txt contains the list of instruments processed in the code. </p> <p>turkey_section.py plots the seismic section of a subset of instruments located on or close the East Anatolian Fault line slipped during the Mw 7.8 2023 Kahramanmaraş earthquake.</p> <p>For all details, see Palo and Zollo, Small-scale segmented fault rupture along the East Anatolian Fault during the 2023 Kahramanmaraş earthquake, <em>Commun Earth Environ, 2024. </em>Uploaded files .fig correspond to the source figures of the graphs included in this paper. </p> <p> </p>
IN02040 Inscription in front of the Changu Narayana Temple. Sanskrit XML file, draft epidoc edition
<p>IN02040 Inscription in front of the Changu Narayana Temple. Sanskrit XML file (without metadata). Draft epidoc edition to be incorporated into 'Siddham' archive</p>
RAKSILA 3D. Laser scanning survey of the street fronts and green areas in Raksila, Oulu (FINLAND)
<p>The video shows the preliminary results of the laser scanner survey of Raksila district in Oulu, Finland. Raksila is an important historical trace in the development of the urban planning of the city of Oulu. The district of Raksila is mainly a well-preserved residential Neighborhood characterized by a strong typicality.The general plan consists of a regular structure and a system of street fronts on the road are ordered and in an homogeneous profile. Despite this, Raksila still has no detailed and updated guidelines capable of managing all different types of interventions allowed (renovation, restoration, repair actions, possible modifications). For this reason, a laser scanner survey and detailed documentation have been created, through which all the elements and characteristics of the place have been defined and collected in sort of atlas and inventory reports. This new documentation is going to constitute the base for the definition of new guidelines, a practical support and analysis for future interventions that can be carried out in total respect of this heritage. This topic is inserted as case study for developing the Research Project n. 746215 entitled "Preserving Wooden Heritage". The project is financed by the European Commission with an Individual Marie S. Curie Fellowship assigned to PostDoctoral Researcher Sara Porzilli, who is working at the University of Oulu - Finland.</p>
Data set of detected atmospheric rivers, cyclones, and fronts within the region of 75°N – 82.5°N, 0°E – 30°E and at Ny-Ålesund (Svalbard) for 2017 – 2021
<p>This data set contains times when atmospheric rivers, cyclones, or fronts have been detected within the broader region of 75°N – 82.5°N, 0°E – 30°E and specifically at Ny-Ålesund, Svalbard (78.92308 °N, 11.92108 °E) for the years 2017 to 2021. To this end, the detection methods, as described in Lauer et al. (2023), have been applied to the hourly-resolved ERA5 reanalysis (Hersbach et al., 2020) data. </p> <p>Data set overview</p> <p>Each file contains the times (year, month, day, hour in UTC) when the corresponding weather system, i.e. atmospheric river, cyclone and front, has been detected within the region of 75°N – 82.5°N, 0°E – 30°E. The last column indicates if the weather system was located also over Ny-Ålesund Svalbard (78.92308 °N, 11.92108 °E). </p>
Sharpening emitter localization in front of a tuned mirror - NPC dataset
<p>This is a depository for two single molecule localisation microscopy datasets of nuclear pore complex (NPC) structures for single particle averaging. The data was published in: </p> <p>Heil, H.S., Schreiber, B., Götz, R. <em>et al.</em> Sharpening emitter localisation in front of a tuned mirror. <em>Light Sci Appl</em> <strong>7, </strong>99 (2018). https://doi.org/10.1038/s41377-018-0104-z</p> <p>Both datasets have two different levels of localisation precision as one is a conventional STORM experiment and the second a mirror-enhanced STORM experiment. A detailed description of the sample preparation and imaging conditions can be found in the related publication. In short the NPC structures are placed on the surface of a glas coverslip or nano-mirror coated coverslip by manual isolation and spreading of nuclear envelopes from xenopus laevis oocytes, fixed and stained by indirect immunolabeling. The primary antibody targets GP210, the secondary F(ab')<sub>2</sub> fragment is conjugated with Alexa Fluor 647. </p> <p>In this depository I'm providing the raw images data, localisation data and super-resolved reconstruction for the two experiments, as well as the localisation data and super-resolved reconstruction of single NPC rings. </p> <p>I'm also providing a MatLab script that allows to select single NPC positions in the super-resolved image and export the localization data of the single NPC ROI: <strong>P01_ImageAlignment_PickElements.m</strong></p> <p>Information about the dataset is also available here: <strong>NPC Image Alignment Dataset_Info.pdf.</strong></p> <p>Image parameters: 102 nm pixel size, EM Gain 100, Photoelectrons per A/D count: 15.01</p> <p>Column structure of the localisation text files: </p> <p>Id,Frame, x [nm], y [nm], sigma [nm], intensity [photon], offset [photon], bkgstd [photon], chi2, Uncertainty [nm], detections</p> <p>Files: </p> <ul> <li><strong>NPCData_glass_EPI.tif</strong></li> </ul> <p>-> NPC on glass coverslip, low power EPI illumination, widefield image, 20 ms exposure</p> <ul> <li><strong>NPCData_glass_STORM.tif</strong></li> </ul> <p>-> NPC on glass coverslip, high power EPI illumination, 5 ms exposure, 20000 frames</p> <ul> <li><strong>NPCData_glass_STORM_loc.csv</strong></li> </ul> <p>-> ThunderSTORM Localisation data of NPCData_glass_STORM.tif, parameters specified NPCData_glass_STORM_loc-protocol.txt</p> <ul> <li><strong>NPCData_glass_STORM_20xNormalizedGaussian.tif</strong></li> </ul> <p>-> 20x Nomalized Gaussian reconstruction of localization data from NPCData_glass_STORM.tif (ThunderSTORM), pixelsize 5.1 nm</p> <ul> <li><strong>NPCData_glass_STORM_singleRings.zip</strong></li> </ul> <p>-> Localisation data and 20x 20x Nomalized Gaussian reconstruction of single NPC ROIs picked out of the NPCData_glass_STORM dataset, ROI size is 240*240 nm<sup>2</sup></p> <ul> <li><strong>NPCData_nanomirror_EPI.tif</strong></li> </ul> <p>-> NPC on nanomirror coated coverslip, low power EPI illumination, widefield image, 20 ms exposure</p> <ul> <li><strong>NPCData_nanomirror_STORM.tif</strong></li> </ul> <p>-> NPC on nanomirror coated coverslip, high power EPI illumination, 5 ms exposure, 20000 frames</p> <ul> <li><strong>NPCData_nanomirror_STORM_loc.csv</strong></li> </ul> <p>-> ThunderSTORM Localisation data of NPCData_nanomirror_STORM.tif, parameters specified NPCData_nanomirror_STORM_loc-protocol.txt</p> <ul> <li><strong>NPCData_nanomirror_STORM_20xNormalizedGaussian.tif</strong></li> </ul> <p>-> 20x Nomalized Gaussian reconstruction of localisation data from NPCData_nanomirror_STORM.tif (ThunderSTORM), pixelsize 5.1 nm</p> <ul> <li><strong>NPCData_nanomirror_STORM_singleRings.zip</strong></li> </ul> <p>-> Localisation data and 20x 20x Nomalized Gaussian reconstruction of single NPC ROIs picked out of the NPCData_nanomirror_STORM dataset, ROI size is 240*240 nm<sup>2</sup></p>
Water depth observed in front of lake-terminating glaciers in Patagonia
<p>This is the dataset of water depth observed in front of O'Higgins, Upsala, Viedma, and Tyndall glaciers in southern Patagonia.</p> <p> </p> <p>Data format:</p> <p>Latitude [deg], Longitude [deg], Depth [m]</p>
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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.