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952 results for “Noise”
Birdsong NOIZEUS: Bioacoustics noise reduction benchmark dataset
<p>------------------------------------------------------------------------<br>Birdsong noizeus dataset<br>------------------------------------------------------------------------</p> <p>Authors: Tim Sainburg & Asaf Zorea<br>Year: 2024</p> <p>------------------------------------------------------------------------<br>General information<br>------------------------------------------------------------------------<br>- There are 5 recordings from each of 14 individuals (European starlings) recorded in an acoustically isolated chamber. <br>- For each song, we apply noise at 5 SNR levels (0dB, 5dB, 10dB, 15dB). <br>- There are 8 noise types, each taken from a single noise clip from the "Soundscapes from around the world" dataset.<br>- They are "rain", "town", "wind", "waterfall", "insect", "swamp" "frogscape", "forest"<br>- Each soundscape contains multiple noise sources. <br>- Noise levels were estimated using the pyloudnorm software (Steinmetz et al., 2021)<br>- Audio is provided as waveforms at 44100 samplerate</p> <p>------------------------------------------------------------------------<br>Data format<br>------------------------------------------------------------------------</p> <p>- clean<br> - {bird_name}_{timestamp}.wav<br>- noisy<br> - {snr}dB<br> - {bird_name}_{timestamp}_{noise_category}_{snr}.wav<br>- noise_sample<br> - {snr}dB<br> - {bird_name}_{timestamp}_{noise_category}_{snr}.wav</p> <p>`clean` contains the original clean audio.<br>`noisy` contains the song+noise<br>`noise_sample` contains a 1-second sample of noise only. </p> <p>Timestamp is in the format YYYY-MM-DD_HH-MM-SS-MILLISECONDS and refers to the time that the song was recorded. </p> <p>------------------------------------------------------------------------<br>Data sources<br>------------------------------------------------------------------------</p> <p>Birdsong<br>---------<br>Birdsong are acoustically isolated songs from 14 European Starlings<br>https://zenodo.org/records/3237218</p> <p>Citation: <br>Arneodo, Z., Sainburg, T., Jeanne, J., & Gentner, T. (2019). An acoustically isolated European starling song library (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3237218</p> <p>This dataset is available under the following license:<br>- Creative Commons Attribution 4.0 International (https://creativecommons.org/licenses/by/4.0/legalcode)</p> <p><br>Noise<br>-----<br>Noise are taken from the Xeno-canto - "Soundscapes from around the world" dataset<br>https://www.gbif.org/dataset/ff571aeb-46bf-45c4-ad2c-af4d68315765</p> <p>Citation:<br>Vellinga W (2024). Xeno-canto - Soundscapes from around the world. Xeno-canto Foundation for Nature Sounds. Occurrence dataset https://doi.org/10.15468/9u3zaq accessed via GBIF.org on 2024-10-17.</p> <p>We sampled 8 soundscapes from this dataset:<br> 1. Rain https://www.gbif.org/occurrence/4523646364<br> - Virginia<br> - 457s<br> - rain, Recording of the feeders in the backyard with a light rain falling on the leaf litter, while a freight train passes by ~1/2 mile away.<br> 2. Town https://xeno-canto.org/696263<br> - 2:22<br> - the closer habitat are greater trees and coniferes ..smaller bushes ...some other Krautgärten ... traffic-noise is to hear..as airplanes,too.<br> - insects, birds, bells, traffic?, airplane<br> - Germany<br> 3. Wind https://xeno-canto.org/911773<br> - 5:29<br> - Very windy day, grassland to shrubland habitat<br> - Wisconsin<br> 4. Waterfall https://xeno-canto.org/406993<br> - 24:02<br> - sound scenes captured in a river forest with zarzas, hiedras and other matorrales at the edge of a small waterfall.<br> - Spain<br> 5. Insect https://xeno-canto.org/454914<br> - 5:45<br> - Australia<br> - cicadias, birds,<br> 6. Swanp https://xeno-canto.org/909875<br> - 2:24<br> - Swampy area along dirt road/trail. Species Include: American Bullfrog, Cricket Frogs, American Crow, Prothonotary Warbler, Red-winged Blackbird, Indigo Bunting, Northern Cardinal<br> 7. Frogscape https://xeno-canto.org/718213<br> - 4:42 <br> - European Tree Frog Hyla arborea Green Frog Pelophlyax sp.<br> - Austrial <br> 8. Forest https://xeno-canto.org/900744 <br> - 3:04<br> - Sweden<br> - A beautiful choir with frogs, toads, geese and ducks to enjoy in the darkness a foggy night.<br> <br> <br> <br>All noise recordings have one of the following licensesL<br>- Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/)<br>- Creative Commons Attribution-ShareAlike 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)</p> <p><br>Additional information about the noise dataset</p> <p><br>------------------------------------------------------------------------<br>citations<br>------------------------------------------------------------------------</p> <p>Steinmetz, C. J., & Reiss, J. (2021, May). pyloudnorm: A simple yet flexible loudness meter in python. In Audio Engineering Society Convention 150. Audio Engineering Society.</p> <p>Arneodo, Z., Sainburg, T., Jeanne, J., & Gentner, T. (2019). An acoustically isolated European starling song library (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3237218</p> <p>Vellinga W (2024). Xeno-canto - Soundscapes from around the world. Xeno-canto Foundation for Nature Sounds. Occurrence dataset https://doi.org/10.15468/9u3zaq accessed via GBIF.org on 2024-10-17.</p>
Seismic noise recorded at Solfatara Volcano in April 2007
<p>Seismic noise recorded during a seismic survey carried out at Solfatara Volcano in the period 2-6 April 2007. Five circular seismic arrays were deployed inside the crater; an other seismic station was installed on the eastern rim for a hardrock reference. Details on the experiment, as well as data description and station coordinates are reported in: Petrosino, S., Damiano, N., Cusano, P., Veneruso, M., Zaccarelli, L., Torello, V., & Del Pezzo, E. (2008). Seismic noise at Solfatara Volcano (Campi Flegrei, Italy): acquisition techniques and first results. <em>Quaderni di Geofisica</em>.</p> <p>Shallow crustal structure of Solfatara volcano, inferred from dataset analysis has been published in: Petrosino, S., Damiano, N., Cusano, P., Di Vito, M. A., de Vita, S., & Del Pezzo, E. (2012). Subsurface structure of the Solfatara volcano (Campi Flegrei caldera, Italy) as deduced from joint seismic‐noise array, volcanological and morphostructural analysis. <em>Geochemistry, Geophysics, Geosystems</em>, <em>13</em>(7).</p>
Deep-time convergent evolution in animal communication presented by shared adaptations for coping with noise in lizards and other animals
<p>Convergence in communication appears rare compared to other forms of adaptation. This is puzzling, given communication is acutely dependent on the environment and expected to converge in form when animals communicate in similar habitats. We uncover deep-time convergence in territorial communication between two groups of tropical lizards separated by over 140 million years of evolution: the Southeast Asian Draco and Caribbean Anolis. These groups have repeatedly converged in multiple aspects of display along common environmental gradients. Robot playbacks to free-ranging lizards confirmed the most prominent convergence in display is adaptive, as it improves signal detection. We then provide evidence from a sample of the literature to further show convergent adaptation among highly divergent animal groups is almost certainly widespread in nature. Signal evolution is therefore curbed towards the same set of adaptive solutions, especially when animals are challenged with the problem of communicating effectively in noisy environments.</p>
Seismic noise recorded at Bagnolifutura area (Campi Flegrei) in 2012
<p>In 2012 two seismic surveys were carried out in the area of Bagnolifutura (Campi Flegrei, Naples), with the aim of characterizing the properties of the seismic noise. During the first survey, which was conducted from 2 to 4 April, seven broadband three-component seismometers were installed in two different array configurations. The second survey started on November 26 and ended on December 5. During this period, seismic noie was recorded by seven broadband and one short-period three-component sensors.</p>
Syntheses of aircraft noise obtained by computational methods
<p>In ANIMA WP4, where focus is put on toolset development, a benchmark on three partners’ auralization tools was performed.</p> <p>These tools are used to reproduce the sound of an aircraft flyover from either physical modelling of noise, a prediction based on measurement or a combination of both. As the chosen methodologies and the modelling hypotheses are different between partners, a benchmark was performed to assess the impact of these strategies on the produced sound synthesis.</p> <p>The realism of each auralization was evaluated through comparison to experimental recordings. For propriety reasons, only the synthesized sounds are available here, and can be compared between each other.</p> <p>Two of the three tools were further used in the WP3 task dedicated to Virtual Reality, see "<a href="https://doi.org/10.5281/zenodo.5517218">Virtual reality simulated aircraft flyovers: Influence of the landscape on the overall pleasantness of the environment</a>"</p> <p>The sounds represent three flight configurations, one landing, and two take-offs with different engine speeds. Two aircraft are considered, one single-aisle and one double-aisle aircraft. The synthesis is performed at a receiver position below the aircraft trajectory.</p> <p>For more information, please contact:</p> <ul> <li><a href="mailto:Ingrid.legriffon@onera.fr">Ingrid.legriffon@onera.fr</a> (ONERA)</li> <li><a href="mailto:isabelle.boullet@airbus.com">isabelle.boullet@airbus.com</a> (Airbus Aviation)</li> <li><a href="mailto:jean-michel.boiteux@safrangroup.com">jean-michel.boiteux@safrangroup.com</a> (Safran Aircraft Engine)</li> </ul> <p> </p>
A characterization of cardiac-induced noise in R2* maps of the brain
<p>A subset of data used in the original publication for computations of the results. These data contain 5D k-space data (kx/ky/kz/cardiac phase/TE) from one of the participants and can be used to perform the cardiac-induced noise characterization and retrieve the results from the paper.</p>
Data for: Distinguishing intrinsic photon correlations from external noise with frequency-resolved homodyne detection
<p>Data for the publication C. Lüders, M. Aßmann, Distinguishing intrinsic photon correlations from external noise with frequency-resolved homodyne detection, Scientific Reports 10 (1), 1-11 (2020).</p> <p>In this work, we apply homodyne detection to investigate the frequency-resolved photon statistics of a cw light field emitted by a driven-dissipative semiconductor system in real time. We demonstrate that studying the frequency dependence of the photon number noise allows us to distinguish intrinsic noise properties of the emitter from external noise sources such as mechanical noise while maintaining a sub-picosecond temporal resolution. We further show that performing postselection on the recorded data opens up the possibility to study rare events in the dynamics of the emitter. By doing so, we demonstrate that in rare instances, additional external noise may actually result in reduced photon number noise in the emission.</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB-Geschäftszeichen TRR142/3-2022 – Projektnummer 231447078, Project A04.</p>
Data and Results for: Comparing Apples with Apples: Robust Detection Limits for Exoplanet High-Contrast Imaging in the Presence of non-Gaussian Noise
<p>This collection of data and results contains everything needed to reproduce the results in the paper:</p> <p>Comparing Apples with Apples: Robust Detection Limits for Exoplanet \\ High-Contrast Imaging in the Presence of non-Gaussian Noise</p> <p>The <a href="/api/files/53bfc05e-f632-443a-9c07-590b9bf860e1/apples_root_dir.zip?versionId=d41f6d1e-ef44-497c-ae2a-a5b291a8c9b9">apples_root_dir.zip</a> is further needed to run the examples of the python package Applefy.</p>
A Model-Independent Determination of Red Noise in Pulsar Timing Arrivals
<p>Data files for Reyes & Bernido, submitted, 2023, A Model-Independent Determination of Red Noise in Pulsar Timing Arrivals. </p> <p>In this work, we analyze the pulsar timing data from the North American Nanohertz Observatory for Gravitational Waves (NANOGrav; Arzoumanian et al 2018). For 23 pulsars with 820 MHz data, we show that an evaluation of the mean square deviation (MSD) and probability distribution (PDF) of timing residuals can provide a straightforward way of determining the presence of red noise. The model-free method presented could complement the normally more sophisticated model-dependent way of determining red noise in timing residuals.</p> <p>Data available here:</p> <p>- ts.zip - uniform time-series of timing residuals for the 23 pulsars</p> <p>- msd.zip - mean square deviation vs. lag time for the 23 pulsars</p> <p>- pdf.zip - probability distributions for lag times equal to 30, 150, 300, 900, and 1200 days for the 23 pulsars</p>
Datasets and R source code of manuscript "No evidence for an effect of chronic boat noise on the fitness of reared water fleas"
<p>Datasets and R source code of manuscript "No evidence for an effect of chronic boat noise on the fitness of reared water fleas"</p> <p>Experiments : exposition of Daphnia magna to boatnoise or silence along all their life. Measure of survival and clonal reproduction.</p>
EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise'.
<p>The repository contains the unprocessed EEG data recorded for the publications [1, 2]. For convenience, the onsets of the EEG data provided here are time-aligned with the onsets of the audio books in the 'audiobooks' folder, and the EEG data are provided in HDF5 format. Please refer to the original version of this dataset for more details.</p> <p>More details, as well as the original data files, are available at the original repository <a href="https://doi.org/10.5281/zenodo.7086209">here</a>.</p> <p>Examples of using these data (preprocessing, fitting linear models) can be found <a href="https://github.com/Mike-boop/trf-examples">here</a>.</p> <p>The English conditions (clean, lb, mb, hb, fM, fW) comprised a single recording session. The Dutch conditions (cleanDutch, lbDutch, mbDutch, hbDutch) comprised a separate recording session. You see which participants took part in each session in session_info.json.</p> <p>Please note some details about the stimulus presentation for the various listening conditions:</p> <ul> <li>English speech-in-babble-noise (lb, mb, hb): babble noise was played by itself for one second before the audiobook track began. The babble noise was also played for one second after the audiobook track ended. Therefore, you should discard the first second and the last second from these trial during your analysis.</li> <li>Dutch speech-in-babble-noise (lbDutch, mbDutch, hbDutch): the story (narrated in Dutch) was played by itself for one second before the babble noise track began. Then, the babble noise was increased linearly in amplitude for one second. Therefore, you should discard the first two seconds from these trials during your analysis.</li> <li>Dutch in quiet, and Dutch-in-babble-noise (cleanDutch, lbDutch, mbDutch, hbDutch): some English sentences were embedded in the Dutch narratives in order to encourage attention. You should crop these from your analysis. The onsets and offsets of the English sentences (in samples, at 44100Hz) are provided in the audiobooks/*Dutch/english_onsets_info.json files.</li> <li>Competing-speakers conditions (fM, fW): sometimes the attended track is longer than the unattended track, or vice-versa. The onsets of both tracks are aligned. You should crop the trial to the length of the shortest track for your analysis.</li> </ul> <p>If you use this data, please cite the original publications, as well as this repository [1,2,3].</p> <p>[1] Etard O, Kegler M, Braiman C, Forte A E and Reichenbach T. “Decoding of selective attention to continuous speech from the human auditory brainstem response” 2019. <em>NeuroImage</em> <strong>200</strong> 1–11</p> <p>[2] Etard O and Reichenbach T. “Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise” 2019. <em>J. Neurosci.</em> <strong>39</strong> 5750–9</p> <p>[3] Etard O and Reichenbach T. "EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise". Doi: 10.5281/zenodo.7086208</p>
Swiss Dwellings: A large dataset of apartment models including aggregated geolocation-based simulation results covering viewshed, natural light, traffic noise, centrality and geometric analysis
<p><strong>Introduction</strong></p> <p>This dataset contains detailed data on over 45,000 apartments (370,000 rooms) in ~3,100 buildings including their geometries, room typology as well as their visual, acoustical, topological, and daylight characteristics. Additionally, we have included location-specific characteristics for the buildings, including climatic data and points of interest within walking distance.</p> <p><strong>Changelog</strong></p> <ul> <li><strong>v3.0.0 (2023-03-31):</strong> <ul> <li>Updated the dataset increasing the total number of apartments to 45176 and incorporating fixes to some of the sites. The update includes re-digitized apartments and thus alters some ID values.</li> </ul> </li> <li><strong>v2.2.1 (2023-03-10):</strong> <ul> <li>A file, <code>location_ratings.csv</code>, has been included to provide ratings of the locations in which the buildings are situated. The ratings, provided by <a href="https://en.fpre.ch/">Fahrländer Partner AG</a>, give insights into the living situation at the buildings' addresses. Details for the different dimensions are provided below.</li> <li>The file <code>location.csv</code> has been updated to include the minimum and maximum temperatures for the locations in which the buildings are situated.</li> </ul> </li> <li><strong>v2.1.0 (2022-12-23)</strong>: <ul> <li>A file, <code>locations.csv</code>, has been included to provide information on the climatic and infrastructural characteristics of the locations in which each building is situated</li> </ul> </li> <li><strong>v2.0.0 (2022-10-17):</strong> <ul> <li>Additional to the residential units, we also include the commercial and public parts (such as staircases) of the models. The field <code>unit_usage</code> describes whether an area belongs to a commercial, residential, janitor or public part of the building</li> <li>Added the fields <code>elevation</code> and <code>height</code> to <em>geometries.csv</em> to describe the elevation above the terrain surface and the height of objects.</li> <li>Added the field <code>plan_id</code> which allows identifying which floors are based on the same floor plan (in some cases multiple floors of a building share the same floor plan</li> <li>Improved the ordering of fields in the CSV files (instead of alphabetic order)</li> <li>Minor changes to individual sites</li> </ul> </li> </ul> <p><strong>Procurement</strong></p> <p>The data is sourced from commercial clients of <a href="https://www.archilyse.com/">Archilyse AG</a> specializing on the digitization and analysis of buildings. The existing building plans of clients are converted into a geo-referenced, semantically annotated representation and undergo a manual Q/A process to ensure the accuracy of the data and to ensure a maximum 5%-deviation in the apartments' areas (validated with a median deviation of 1.2%).</p> <p><strong>Geometries</strong></p> <p>The dataset contains a file <code>geometries.csv</code> which contains the geometries of all areas, walls, railings, columns, windows, doors and features (sinks, bathtubs, etc.) of an apartment.</p> <p>In total, the datasets contain the 2D geometry of ~1.7 million separators (walls, railings), ~715,000 openings (windows, doors), ca. 520,000 areas (rooms, bathrooms, kitchens, etc.), and ~315,000 features (sinks, toilets, bathtubs, etc.).</p> <p>Each row contains:</p> <ul> <li><code>apartment_id</code>: The ID of the apartment (for features, areas), <em>note</em>: an apartment id is only unique per site</li> <li><code>site_id</code>: The ID of the site</li> <li><code>building_id</code>: The ID of the building</li> <li><code>floor_id</code>: The ID of the floor</li> <li><code>plan_id</code>: The ID of the plan on which the floor is based, multiple floors of a building might be based on the same plan</li> <li><code>unit_id</code>: The ID of the unit in which the element is spatially contained (for features, areas)</li> <li><code>area_id</code>: The ID of the area in which the element is spatially contained (for features)</li> <li><code>unit_usage</code>: The usage of the unit, possible values are: RESIDENTIAL, COMMERCIAL, PUBLIC, JANITOR</li> <li><code>entity_type</code>: The entity type (<em>area, separator, opening, feature</em>)</li> <li><code>entity_subtype</code>: The entity’s sub-type (e.g. <em>WALL</em>)</li> <li><code>geometry</code>: The element’s geometry as a <a href="https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry">WKT</a> geometry in meters. The geometry is given in the site’s local coordinate system. I.e. the position between elements of the same site are correct in respect to each other. The +y direction points northwards, the +x direction points eastwards.</li> <li><code>elevation</code>: The object's elevation above the terrain surface in meters. We assume one terrain baseline per building, thus all walls in a given floor share the same elevation value. However, windows in particular might start at different elevations and have differing heights.</li> <li><code>height</code>: The height of the entity in meters, <em>note</em>: In many cases, a default height is assumed</li> </ul> <p>An example:</p> <table> <thead> <tr> <th scope="col">column</th> <th scope="col"> </th> </tr> </thead> <tbody> <tr> <td>apartment_id</td> <td> <p>d4438f2129b30290845ce7eef98a5ba7</p> </td> </tr> <tr> <td>site_id</td> <td>127</td> </tr> <tr> <td>building_id</td> <td>164</td> </tr> <tr> <td>plan_id</td> <td>492</td> </tr> <tr> <td>floor_id</td> <td>861</td> </tr> <tr> <td>unit_id</td> <td>63777</td> </tr> <tr> <td>area_id</td> <td>767676</td> </tr> <tr> <td>unit_usage</td> <td>RESIDENTIAL</td> </tr> <tr> <td>entity_type</td> <td>area</td> </tr> <tr> <td>entity_subtype</td> <td>LIVING_ROOM</td> </tr> <tr> <td>geometry</td> <td> <p>POLYGON ((-6.1501158933490139 -4.8490786654693...</p> </td> </tr> <tr> <td>elevation</td> <td>0</td> </tr> <tr> <td>height</td> <td>2.6</td> </tr> </tbody> </table> <p><strong>Simulations</strong></p> <p>Besides the geometrical model, we also provide simulation data on the visual, acoustic, solar, layout, and connectivity-related characteristics of the apartments. The file <code>simulations.csv</code> contains the simulation data aggregated on a per-area basis. Each row contains the identifier columns <code>area_id</code>, <code>unit_id</code>, <code>apartment_id</code>, <code>floor_id</code>, <code>building_id</code>, <code>site_id</code> as defined above as well as 367 simulation columns. Each simulation column is formatted as:</p> <pre><code><simulation_category>_<simulation_dimensions>_<aggregation_function></code></pre> <p>For instance. the column <code>view_buildings_median</code> describes the amount of building surface that can be seen from any point in a given room. The aggregation methods vary per simulation category and are described in detail below.</p> <p><strong>Layout</strong></p> <p>The <em>layout</em> features represent simple features based on the geometry and composition of a room, the dataset provides the following information in an unaggregated form.</p> <p>Area Basics / Geometry</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_area_type</td> <td>The area’s area type</td> </tr> <tr> <td>layout_net_area</td> <td>The area’s share of the apartment’s net area (e.g. 0 for a balcony)</td> </tr> <tr> <td>layout_area</td> <td>The area’s actual area</td> </tr> <tr> <td>layout_perimeter</td> <td>The area’s perimeter</td> </tr> <tr> <td>layout_compactness</td> <td>The area’s compactness (the Polsby–Popper score)</td> </tr> <tr> <td>layout_room_count</td> <td>The area’s share to the apartment’s room count</td> </tr> <tr> <td>layout_is_navigable</td> <td>True if the area is navigable by a wheelchair</td> </tr> </tbody> </table> <p>Area Features</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_has_sink</td> <td>True if the area has a sink</td> </tr> <tr> <td>layout_has_shower</td> <td>True if the area has a shower</td> </tr> <tr> <td>layout_has_bathtub</td> <td>True if the area has a bathtub</td> </tr> <tr> <td>layout_has_toilet</td> <td>True if the area has a toilet</td> </tr> <tr> <td>layout_has_stairs</td> <td>True if the area has stairs</td> </tr> <tr> <td>layout_has_entrance_door</td> <td>True if the area is directly leading to an exit of the apartment</td> </tr> </tbody> </table> <p>Area Windows / Doors</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_number_of_doors</td> <td>The number of doors directly leading to the area</td> </tr> <tr> <td>layout_number_of_windows</td> <td>The number of windows of the area</td> </tr> <tr> <td>layout_door_perimeter</td> <td>The sum of all door lengths directly leading to the area</td> </tr> <tr> <td>layout_window_perimeter</td> <td>The sum of all window lengths of the area</td> </tr> </tbody> </table> <p>Area Walls / Railings</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_open_perimeter</td> <td>The sum of all of the boundaries of the area that are neither walls nor railings</td> </tr> <tr> <td>layout_railing_perimeter</td> <td>The sum of all of the boundaries of the area that are railings</td> </tr> <tr> <td>layout_mean_walllengths</td> <td>The mean length of the area’s sides</td> </tr> <tr> <td>layout_std_walllengths</td> <td>The standard deviation of the lengths of the area’s sides</td> </tr> </tbody> </table> <p>Area Adjacency</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>layout_connects_to_bathroom</td> <td>True if the area connects to a bathroom</td> </tr> <tr> <td>layout_connects_to_private_outdoor</td> <td>True if the area connects to an outside area that is private to the apartment</td> </tr> </tbody> </table> <p><strong>View</strong></p> <p>The views from an object help to understand the impact of the surroundings on the object. The view simulation calculates the visible amount of buildings, greenery, water, etc. on each individual hexagon from the analyzed object. The values are expressed in steradians (sr) and represent the amount a particular object category occupies in the spherical field of view.</p> <p>Each of the following dimensions is provided using the room-wise aggregations' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, the column <code>view_greenery_p20</code> describes the amount of greenery that can be seen from at least 20% of the positions in the area.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>view_buildings</td> <td>The amount of visible buildings</td> </tr> <tr> <td>view_greenery</td> <td>The amount of visible greenery</td> </tr> <tr> <td>view_ground</td> <td>The amount of visible ground</td> </tr> <tr> <td>view_isovist</td> <td>The amount of visible isovist</td> </tr> <tr> <td>view_mountains_class_2</td> <td>The amount of visible mountains of UN mountain class 2</td> </tr> <tr> <td>view_mountains_class_3</td> <td>The amount of visible mountains of UN mountain class 3</td> </tr> <tr> <td>view_mountains_class_4</td> <td>The amount of visible mountains of UN mountain class 4</td> </tr> <tr> <td>view_mountains_class_5</td> <td>The amount of visible mountains of UN mountain class 5</td> </tr> <tr> <td>view_mountains_class_6</td> <td>The amount of visible mountains of UN mountain class 6</td> </tr> <tr> <td>view_railway_tracks</td> <td>The amount of visible railway_tracks</td> </tr> <tr> <td>view_site</td> <td>The amount of visible site</td> </tr> <tr> <td>view_sky</td> <td>The amount of visible sky</td> </tr> <tr> <td>view_tertiary_streets</td> <td>The amount of visible tertiary_streets</td> </tr> <tr> <td>view_secondary_streets</td> <td>The amount of visible secondary_streets</td> </tr> <tr> <td>view_primary_streets</td> <td>The amount of visible primary_streets</td> </tr> <tr> <td>view_pedestrians</td> <td>The amount of visible pedestrians</td> </tr> <tr> <td>view_highways</td> <td>The amount of visible highways</td> </tr> <tr> <td>view_water</td> <td>The amount of visible water</td> </tr> </tbody> </table> <p><strong>Sun</strong></p> <p>Sun simulations help to understand the impact of solar radiation on the object. The outcome of the sun simulations helps to identify surfaces that have great solar potential. Sun simulations are defined by the amount of solar radiation on each individual hexagon from the analyzed object. The sun simulation not only includes direct sun but also considers scattered light. The sun simulation values are given in Kilolux (klx). Simulations are performed for the days of the summer solstice, winter solstice, and the vernal equinox.</p> <p>Each of the following dimensions is provided using the room-wise aggregations' <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, column <code>sun_201806211200_median</code> describes the median amount of direct daylight received on the positions in the area.</p> <p>Vernal Equinox</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201803210800</td> <td>Daylight at 08:00 on 21st of March</td> </tr> <tr> <td>sun_201803211000</td> <td>Daylight at 10:00 on 21st of March</td> </tr> <tr> <td>sun_201803211200</td> <td>Daylight at 12:00 on 21st of March</td> </tr> <tr> <td>sun_201803211400</td> <td>Daylight at 14:00 on 21st of March</td> </tr> <tr> <td>sun_201803211600</td> <td>Daylight at 16:00 on 21st of March</td> </tr> <tr> <td>sun_201803211800</td> <td>Daylight at 18:00 on 21st of March</td> </tr> </tbody> </table> <p>Summer Solstice</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201806210600</td> <td>Daylight at 06:00 on 21st of June</td> </tr> <tr> <td>sun_201806210800</td> <td>Daylight at 08:00 on 21st of June</td> </tr> <tr> <td>sun_201806211000</td> <td>Daylight at 10:00 on 21st of June</td> </tr> <tr> <td>sun_201806211200</td> <td>Daylight at 12:00 on 21st of June</td> </tr> <tr> <td>sun_201806211400</td> <td>Daylight at 14:00 on 21st of June</td> </tr> <tr> <td>sun_201806211600</td> <td>Daylight at 16:00 on 21st of June</td> </tr> <tr> <td>sun_201806211800</td> <td>Daylight at 18:00 on 21st of June</td> </tr> <tr> <td>sun_201806212000</td> <td>Daylight at 20:00 on 21st of June</td> </tr> </tbody> </table> <p>Winter Solstice</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>sun_201812211000</td> <td>Daylight at 10:00 on 21st of December</td> </tr> <tr> <td>sun_201812211200</td> <td>Daylight at 12:00 on 21st of December</td> </tr> <tr> <td>sun_201812211400</td> <td>Daylight at 14:00 on 21st of December</td> </tr> <tr> <td>sun_201812211600</td> <td>Daylight at 16:00 on 21st of December</td> </tr> </tbody> </table> <p><strong>Noise / Window Noise</strong></p> <p>Noise level and the distribution of elements from an area help to understand how an object is exposed to the acoustics of this area. The acoustic simulation calculates the noise intensity on each individual hexagon from the analyzed object considering traffic and train noise datasets. Adjacent buildings are considered noise-blocking elements. The values are expressed in dBA (decibels).</p> <p>Window Noise</p> <p>The noise per window of a given area is aggregated via <code>min</code> and <code>max</code>. For instance, <code>window_noise_train_day_max</code> represents the maximum amount of noise received on any window of the area.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>window_noise_traffic_day</td> <td>The amount of noise received on the area’s windows from daytime car traffic</td> </tr> <tr> <td>window_noise_traffic_night</td> <td>The amount of noise received on the area’s windows from night-time car traffic</td> </tr> <tr> <td>window_noise_train_day</td> <td>The amount of noise received on the area’s windows from daytime train traffic</td> </tr> <tr> <td>window_noise_train_night</td> <td>The amount of noise received on the area’s windows from night-time train traffic</td> </tr> </tbody> </table> <p>Area-Wise Noise</p> <p>The area-wise noise describes the amount of noise received from a noise source aggregated over the whole area in an unaggregated form. For instance, <code>noise_traffic_night</code> describes the dBA of noise received in the area from car traffic at night when propagating noise from all windows.</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>noise_traffic_day</td> <td>The amount of noise received in the area from daytime car traffic</td> </tr> <tr> <td>noise_traffic_night</td> <td>The amount of noise received in the area from night-time car traffic</td> </tr> <tr> <td>noise_train_day</td> <td>The amount of noise received in the area from daytime train traffic</td> </tr> <tr> <td>noise_train_night</td> <td>The amount of noise received in the area from night-time train traffic</td> </tr> </tbody> </table> <p><br> <strong>Connectivity</strong></p> <p>Centrality simulations help to analyze a floor plan, whether it’s a shopping mall and you want to identify prominent areas in order to select the most prominent spot or it’s an interior design circulation path and you want to determine open floor plan areas. Centrality simulations are done using topological measures that score grid cells by their importance as a part of a grid cell network.</p> <p>The distances and centralities are aggregated via <em>min</em>, <em>max</em>, <em>mean</em>, <em>std</em>, <em>median</em>, <em>p20,</em> and <em>p80</em>. For instance, <code>connectivity_balcony_distance_min</code> describes the shortest distance to the next balcony from the point closest to the balcony in the area.</p> <p>Distances</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>connectivity_room_distance</td> <td>Distance to the next area of type ROOM</td> </tr> <tr> <td>connectivity_living_dining_distance</td> <td>Distance to the next area of type LIVING_DINING</td> </tr> <tr> <td>connectivity_bathroom_distance</td> <td>Distance to the next area of type BATHROOM</td> </tr> <tr> <td>connectivity_kitchen_distance</td> <td>Distance to the next area of type KITCHEN</td> </tr> <tr> <td>connectivity_balcony_distance</td> <td>Distance to the next area of type BALCONY</td> </tr> <tr> <td>connectivity_loggia_distance</td> <td>Distance to the next area of type LOGGIA</td> </tr> <tr> <td>connectivity_entrance_door_distance</td> <td>Distance to the next apartment exit</td> </tr> </tbody> </table> <p>Centralities</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>connectivity_eigen_centrality</td> <td>The Eigen-Centrality value</td> </tr> <tr> <td>connectivity_betweenness_centrality</td> <td>The Betweenness-Centrality value</td> </tr> <tr> <td>connectivity_closeness_centrality</td> <td>The Closeness-Centrality value</td> </tr> </tbody> </table> <p><strong>Location Properties</strong></p> <p>In addition to the apartment-related data, we also provide simulation data on the climatic, and infrastructural characteristics of the locations. The file <code>locations.csv</code> contains the simulation data aggregated on a per-building basis. Each row contains the identifier <code>building_id</code> corresponding to the building ids referenced in <code>geometries.csv</code> and <code>simulations.csv</code>.</p> <p><strong>Climate</strong></p> <p>The climate features represent 39 simple features based on the spatial climate analysis of Meteo Swiss as derived from <a href="https://www.meteoswiss.admin.ch/climate/the-climate-of-switzerland/spatial-climate-analyses.html.">MeteoSwiss</a>. Each column is formatted as <code>climate_<category>_<period>. </code>For instance, the column <code>climate_tnorm_january</code> describes the monthly mean temperature in degrees Celsius (from the norm period of 1991-2020) at the location of the building. The aggregation methods vary per simulation category and are described in detail below.</p> <p>Temperature Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_tnorm_year</td> <td>The yearly mean temperature in degrees Celsius of the current norm period from 1991 to 2020 (TnormY9120)</td> </tr> <tr> <td>climate_tnorm_january</td> <td>The monthly mean temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tnorm_februry</td> <td>The monthly mean temperature in February in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_tnorm_december</td> <td>The monthly mean temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tminnorm_january</td> <td>The monthly minimum temperature in January in degrees Celsius of the current norm period from 1991 to 2020 (TminnormM9120)</td> </tr> <tr> <td>...</td> <td> </td> </tr> <tr> <td>climate_tminnorm_december</td> <td>The monthly minimum temperature in December in degrees Celsius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>climate_tmaxnorm_january</td> <td>The monthly maximum temperature in January in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> <tr> <td>...</td> <td> </td> </tr> <tr> <td>climate_tmaxnorm_december</td> <td>The monthly maximum temperature in December in degrees Celcius of the current norm period from 1991 to 2020 (TnormM9120)</td> </tr> </tbody> </table> <p>Sunshine Duration Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_snorm_year</td> <td>The yearly mean relative sunshine duration in percent of the current norm period from 1991 to 2020 (SnormY9120). Relative sunshine duration (RSD) is the ratio between the effective sunshine duration and the duration maximally possible if no clouds were covering the sun. A period with sunshine is defined as a period when the direct solar irradiance exceeds 200 W/m²</td> </tr> <tr> <td>climate_snorm_january</td> <td>The monthly mean relative sunshine duration for January in percent of the current norm period</td> </tr> <tr> <td>climate_snorm_februry</td> <td>The monthly mean relative sunshine duration for February in percent of the current norm period</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_snorm_december</td> <td>The monthly mean relative sunshine duration for December in percent of the current norm period</td> </tr> </tbody> </table> <p>Precipitation Normals</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>climate_rnorm_year</td> <td>The yearly mean precipitation in mm of the current norm period (RnormY9120)</td> </tr> <tr> <td>climate_rnorm_january</td> <td>The monthly mean precipitation for January in mm of the current norm period (RnormM9120)</td> </tr> <tr> <td>climate_rnorm_februry</td> <td>The monthly mean precipitation for February in mm of the current norm period (RnormM9120)</td> </tr> <tr> <td>...</td> <td>...</td> </tr> <tr> <td>climate_rnorm_december</td> <td>The monthly mean precipitation for December mm of the current norm period (RnormM9120)</td> </tr> </tbody> </table> <p><strong>10-Minute Walkshed Infrastructure</strong></p> <p>Based on OpenStreetMap data and its tagging system we counted all 465 tags (key and value tuples as listed here: https://wiki.openstreetmap.org/wiki/Map_features) which can be reached within a 10-minute walk from the location of the building. Each column is formatted as <code>walkshed_<poi_category>_<poi_type>. </code>For instance, the column <code>walkshed_shop_coffee</code> describes the number of coffee shops located within 10 minutes of walking from the building.</p> <p>The following is an excerpt of support categories and their corresponding types.</p> <ul> <li><code>shop: antique, art, ...</code></li> <li><code>amenity: art, atm, ...</code></li> <li><code>tourism: alpine, attraction, ...</code></li> <li><code>leisure: amusement, beach, ...</code></li> <li><code>healthcare: clinic, dentist, ...</code></li> <li><code>historic: archaeological, battlefield, ...</code></li> <li><code>ariaelway: station</code></li> </ul> <p><strong>Location Ratings</strong></p> <p>The location ratings, provided by <a href="https://en.fpre.ch/">Fahrländer Partner AG</a>, give insights into the living situation at locations in which the buildings are situated. The file location_ratings.csv provides the following information:</p> <table> <thead> <tr> <th>dimension</th> <th>description</th> </tr> </thead> <tbody> <tr> <td>location_rating_MIKRAT_W</td> <td>Living situation - Overall (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_IMAGE_W</td> <td>Living situation - Image (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_DL_W</td> <td>Living situation - Service Quality (1.0=worst, 5.0=best)</td> </tr> <tr> <td>location_rating_FZ_W</td> <td> <p>Living situation - Leisure Quality (1.0=worst, 5.0=best)</p> </td> </tr> <tr> <td>location_rating_NASE_W_DOM</td> <td>The most dominant segment of demand:<br> <br> 1 Rural-traditional<br> 2 Modern worker<br> 3 Transitional-alternative<br> 4 Traditional middle class<br> 5 Liberal middle class<br> 6 Established alternative<br> 7 Upper middle class<br> 8 Professional elite<br> 9 Urban elite<br> 10 Unknown<br> <br> <a href="https://en.fpre.ch/marktdaten/nachfragersegmente/nachfragersegmente-im-wohnungsmarkt/">More Information</a></td> </tr> <tr> <td>location_rating_FGFRQZ</td> <td> <p>The mean number of pedestrians per hour throughout a day between 7 am and 8 pm of an average working day.<br> <br> 1 <50<br> 2 50-100<br> 3 100-200<br> 4 200-500<br> 5 >500</p> </td> </tr> </tbody> </table>
Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.
<p>Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.</p> <p> </p> <p>List of files used for each dataset.</p> <p> </p> <p>Dataset 0 : MS2-CY5_Cyto543_560_woStim</p> <p> Images in the dataset :</p> <p> ROI001_XY1657814108_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI002_XY1657815441_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI003_XY1657814110_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI004_XY1657814111_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI005_XY1657814112_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI006_XY1657814113_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI007_XY1657814114_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI008_XY1657814115_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI009_XY1657814116_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI010_XY1657814117_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI011_XY1657814118_Z00_T0_merged.tif - Image Id Number: 10</p> <p> ROI012_XY1657814119_Z00_T0_merged.tif - Image Id Number: 11</p> <p> </p> <p>Datset 1 : MS2-CY5_Cyto543_560_18minTPL_5uM</p> <p> Images in the dataset :</p> <p> ROI001 - Position 1_XY1657818948_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI001 - Position 2_XY1657818949_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI001 - Position 4_XY1657818951_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI001 - Position 5_XY1657818952_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI001 - Position 6_XY1657818953_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI001 - Position 7_XY1657818954_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI001 - Position 8_XY1657818955_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI001 - Position 9_XY1657818956_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI001 - Position 10_XY1657818957_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI001 - Position 11_XY1657818958_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI001 - Position 12_XY1657818959_Z00_T0_merged.tif - Image Id Number: 10</p> <p> </p> <p>Dataset 2: MS2-CY5_Cyto543_560_5hTPL_5uM</p> <p> Images in the datset :</p> <p> ROI001_XY1657822809_Z00_T0_merged.tif - Image Id Number: 0</p> <p> ROI002_XY1657822933_Z00_T0_merged.tif - Image Id Number: 1</p> <p> ROI003_XY1657822934_Z00_T0_merged.tif - Image Id Number: 2</p> <p> ROI005_XY1657822936_Z00_T0_merged.tif - Image Id Number: 3</p> <p> ROI006_XY1657822937_Z00_T0_merged.tif - Image Id Number: 4</p> <p> ROI007_XY1657822938_Z00_T0_merged.tif - Image Id Number: 5</p> <p> ROI008_XY1657822939_Z00_T0_merged.tif - Image Id Number: 6</p> <p> ROI010_XY1657822941_Z00_T0_merged.tif - Image Id Number: 7</p> <p> ROI013_XY1657822944_Z00_T0_merged.tif - Image Id Number: 8</p> <p> ROI014_XY1657822945_Z00_T0_merged.tif - Image Id Number: 9</p> <p> ROI015_XY1657822946_Z00_T0_merged.tif - Image Id Number: 10</p> <p> ROI016_XY1657822947_Z00_T0_merged.tif - Image Id Number: 11</p> <p> ROI017_XY1657822948_Z00_T0_merged.tif - Image Id Number: 12</p> <p> ROI018_XY1657822949_Z00_T0_merged.tif - Image Id Number: 13</p> <p> </p> <p> </p>
Datasets and R source code of manuscript "From behaviour to complex communities: Resilience to anthropogenic noise in a fish-induced trophic cascade" by Emilie Rojas et al.
<p>Datasets and R source code of manuscript "From behaviour to complex communities: Resilience to anthropogenic noise in a fish-induced trophic cascade" by Emilie Rojas et al.</p>
Echo from noise: synthetically generated cardiac ultrasound data using semantic diffusion models
<p>This is the data repository for the paper: "Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation", available at: https://arxiv.org/abs/2305.05424. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise</p> <p> </p> <p>This is the first work to utilize Denoising Diffusion Probabilistic Models (DDPMs) for generating medical images using semantic label maps as a source image for conditioning the generated image.</p> <p>Each of the 400+50 CAMUS patients contributes with 4 labelled frames (ED and ES for 2 chamber and 4 chamber), totalling 1800 initial semantic maps, to which we added the sector label. These semantic maps then had five random deformations applied (a combination of random affine and elastic deformation) to produce, 9000 transformed semantic maps (8000 for training and 1000 for validation). </p> <p>Affine transformation ranges for rotation degrees, translate, scale and shear were: (-5, 5), (0, 0.05), (0.8, 1.05) and 5 respectively. This was implemented using the torchvision python package. Elastic deformation was implemented using the TorchIO package. The settings for number of control points and max displacement were (10, 10, 4) and (0, 30, 30) respectively.</p> <p>Using these 9000 semantic maps as input to the generative models, we produced 9000 synthetic ultrasound images.</p> <p>Each echo view folder contains 3 folders:</p> <p>1) annotations: augmented labels, with no sector label and no clipping due to sector</p> <p>2) images: semantic diffusion model inferenced images</p> <p>3) sector_annotations: label maps which contain ultrasound cone sector, which were used to generate corresponding semantic diffusion model images</p> <p>ema_0.9999_050000_2ch_ed_256.pt and ema_0.9999_050000_4ch_ed_256.pt are the saved checkpoints for the 2 and 4 chamber diffusion models respectively.</p> <p>The pretrained segmentation networks are provided within the <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/final_models.zip">final_models.zip</a> file.</p> <p>A diagram of image numbers is shown in <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/Data%20diagram.png">Data diagram.png</a></p>
The source data for "Inductively shunted transmon: A superconducting qubit with flux noise insensitive plasmon states and a protected fluxon decay exceeding 3 hours"
<p>The following folder contains all the raw data, analysis Mathematica notebook and ScQubits python codes used to generate the results in “Inductively shunted transmon: A superconducting qubit with flux noise insensitive plasmon states and a protected fluxon decay exceeding 3 hours” in nature communications. Please follow the instruction below for proper navigation through the data:</p> <p>Fig. 1 folder:</p> <ol> <li>Run “Color map of Matrix element Vs energy parameters” to generate “x.dat”, “y.dat”, ”M.dat”(respectively EJ/EL, EJ/EC and the matrix element of the first flux transition). <strong>Make sure to correct the address where these file should be saved</strong>.</li> <li>The mathematica notebook plots the dispersion in IST limit and the matrix element gray scale color map contours separately and the full image was constructed in illustrator later. The green dots on the dispersion plot represent the position of other qubits on the color map. </li> </ol> <p>Fig. 2&3 folder:</p> <ol> <li>The python file “paper figures” uses ScQubits to generate different studies in IST limit presented in Fig. 2&3 and generates the following files:</li> </ol> <p>Fig. 2a:</p> <p>“fluxonium.hdf5”: The spectrum of a typical fluxonium</p> <p>“fluxoniumME.hdf5”: The matrix element of all transition in “fluxonium.hdf5”</p> <p> </p> <p>Fig. 2d:</p> <p>“case1.hdf5”: The spectrum of fluxonium with EJ/EC=6.6</p> <p>“ME1.hdf5”: The matrix element of transition in “case1.hdf5”</p> <p>“case2.hdf5”: The spectrum of fluxonium with EJ/EC=13</p> <p>“ME2.hdf5”: The matrix element of transition in “case2.hdf5”</p> <p>.</p> <p>.</p> <p>“case6.hdf5”: The spectrum of fluxonium with EJ/EC=200</p> <p>“ME6.hdf5”: The matrix element of transition in “case6.hdf5”</p> <p>“IST.hdf5”: The spectrum of the IST qubit</p> <p>“ISTME.hdf5”: The matrix element of transition in “IST.hdf5”</p> <p>“transmon.hdf5”: The spectrum of a transmon with the same EJ and EC as IST qubit</p> <p>Fig. 3a</p> <p>“Waveamp.hdf5”: The wave functions and eigenenergies of the IST qubit</p> <p>“WaveampT.hdf5”: The wave functions and eigenenergies of the transmon</p> <p> </p> <p>Fig. 3b:</p> <p>“ELcase1.hdf5”: The spectrum of IST qubit with EL=2 GHz</p> <p>“ELME1.hdf5”: The matrix element of transition in “ELcase1.hdf5”</p> <p>“ELcase2.hdf5”: The spectrum of IST qubit with EL=1.5 GHz</p> <p>“ELME2.hdf5”: The matrix element of transition in “ELcase2.hdf5”</p> <p>.</p> <p>.</p> <p>“ELcase6.hdf5”: The spectrum of IST qubit with EL=0.25 GHz</p> <p>“ELME6.hdf5”: The matrix element of transition in “ELcase6.hdf5”</p> <p> </p> <p>Fig. 3b inset:</p> <p>“WaveampEL.hdf5”contains the wave functions for El={2,1.5,1,0.75,0.5,0.25}GHz.</p> <p> </p> <p>Fig. 3c:</p> <p>“EC.hdf5” contains numerical simulation of an IST qubit with fixed EJ and Ec while EL is changing to calculate anharmonicity.</p> <ol> <li>The Mathematica notebook “Theory_figures” runs based on the files above and plot the result presented in the paper.</li> </ol> <p>Fig. 5 folder:</p> <ol> <li>Fig. 5a&b folder contains the raw data of spectroscopy of the IST qubit with different temperature and the Mathematica notebook “Tempsweeps_figa&b” simply plots the data. In the data set the I and Q quadrature as well as the amplitude and power of the signal coming back from cavity is provided.</li> <li>Fig. 5c folder contains several sweeps of both spectroscopy and resonator performed at fridge base temperature (7mK) labeled as “specge#.txt” and “Res_VNA_*.txt” respectively. The ScQubits python code “IST_Device” provides a fit for the data using the fit procedure explained in Supplementary Note 4 and generates the bare spectrum of the device saved in “Fit.h5”. The Mathematica notebook “spec_analysis” uses all spectroscopy data and the fit file to plot Fig. 5c.</li> </ol> <p>Fig. 6 folder: Contains all the raw data of T1 and T2 experiment at different flux positions across a flux quantum. The Mathematica notebook “T1&2” performs all the analysis presented in Fig. 6 for devices A, B and C.</p> <p>Fig. 7 folder:</p> <ol> <li>Fig. 7a: In this folder the we provide the raw data for fidelity experiment. The data is in the “*.mat” format and contains 40000 single shot I&Q bins collected with measurement band width of 2MHz and integration time of 500ns. The files names indicate whether the data was taken with qubit prepared in ground/excited state by having “_g_”/”_e_”. Following the state preparation condition, the measurement power at which the data was taken is indicated. The Mathematica notebook “fidelity_sweep” takes the data and extract the fidelities shown in Fig. 7a and the 2D histogram plots presented in Supplementary Figure 5d.</li> <li>Fig. 7b: The raw data for QND-ness experiment is presented in this folder. Each file contains 500 time traces of the two consecutive pulses applied to the resonator to study the non-QND effects of the IST qubit in high power. The Qubit preparation condition is apparent in the file name along with the power at which the measurement was performed. The Mathematica notebook “QND_ness” extracts the QND_ness and plots the results shown in Fig. 7b</li> </ol> <p> </p> <p>Fig. 8 folder:</p> <ol> <li>Fig. 8a: This folder contains the spectroscopy sweeps conditions by the fluxon state using a strong microwave pulse applied to the resonator. The Mathematica notebook “sweeps” plots the data.</li> <li>Fig. 8c: This folder contains the raw data for long fluxon decays collected using quantum machines (QM). In this experiment the fluxon excitation pulse was applied and repeated until a successful fluxon state is detected. Afterwards, the experiment enters monitoring stage where every 30s we check the fluxon state until a tunneling to fluxon ground state is detected. This event is logged and the QM repeats the fluxon excitation immediately followed by a monitoring stage and logging the time it took for tunneling to occur. The raw data of every 30 second monitoring stage is saved in files with “_raw_” in their labels. The files containing “_taus_” in their names have only the logged tunneling time events. The Mathematica notebook “qubit analysis” takes the data for three external flux bias and, by loading the “_taus_” files, reconstructs the quasi quantum jump traces and finally the decay traces presented in Fig. 8c.</li> </ol>
Noise Spectra and Stochastic Background Sensitivity Curve for the NG15-year Dataset
<p>This repository contains noise spectra for individual pulsars and stochastic gravitational wave background sensitivity curves for the NANOGrav 15-year data set analysis, highlighted in the paper <em>"The NANOGrav 15-Year Data Set: Detector Characterization and Noise Budget"</em> (DOI: <a href="https://iopscience.iop.org/article/10.3847/2041-8213/acda88">10.3847/2041-8213/acda88</a>). As in the paper, these spectra include the noise recovered from a common uncorrelated process analysis across the entire PTA. In other words, the spectra include the white noise, the power from the common process and any significant additional red noise in an individual pulsar.</p>
From Noise to Signal: Multi-layer Speckle Correlation with Applications in Visible Light Communication
<p>Dataset for journal article "Enhanced Secrecy in Optical Communication using Speckle from Multiple Scattering Layers"</p> <p>The basic publication is:<br> Alfredo Rates, Joris Vrehen, Bert Mulder, Wilbert L. IJzerman, and Willem L. Vos, "Enhanced Secrecy in Optical Communication using Speckle from Multiple Scattering Layers", Opt. Express <strong>31</strong>, 23897-23909 (2023).<br> <br> We have uploaded to the Zenodo database all data enabling everyone to reuse our data, and to reproduce all the figures of our paper.</p> <p>The upload contains the file "Metadata.txt" explaining the content of the upload.</p>
Noise Resistant Phase Imaging with Intensity Correlation
<p>Raw data for the arXiv:2301.11969 "Noise Resistant Phase Imaging with Intensity Correlation"</p> <p>Supported by the National Science Centre, Poland, grant number 2022/45/N/ST2/04249</p>
Data for "Two-stage, low noise quantum frequency conversion of single photons from silicon-vacancy centers in diamond to the telecom C-band"
<p>The silicon-vacancy center in diamond holds great promise as a qubit for quantum communication networks. However, since the optical transitions are located within the visible red spectral region, quantum frequency conversion to low-loss telecommunication wavelengths becomes a necessity for its use in long-range, fiber-linked networks. This work presents a highly efficient, low-noise quantum frequency conversion device for photons emitted by a silicon-vacancy (SiV) center in diamond to the telecom C-band. By using a two-stage difference-frequency mixing scheme SPDC noise is circumvented and Raman noise is minimized, resulting in a very low noise rate of 10.4(7) photons per second as well as an overall device efficiency of 35.6 %. By converting single photons from SiV centers we demonstrate the preservation of photon statistics upon conversion.</p>
ScienceDex guides
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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.