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Dataset supporting the paper "Transmitting Stepwise Rotation among Three Molecule-Gear on the Au(111) Surface. J.Phys.Chem.Lett. 11 6892 (2020)"
<p>Dataset corresponding to figure 3 of the paper "Transmitting Stepwise Rotation among Three Molecule-Gear on the Au(111) Surface. J.Phys.Chem.Lett. 11 6892 (2020), DOI: <a href="https://doi.org/10.1021/acs.jpclett.0c01747">10.1021/acs.jpclett.0c01747</a>"</p> <p>List of files:<br> There are two folders corresponding to brominated and debrominated structures:</p> <ul> <li>.siesta files: STM simulated images in WsXM format (<a href="http://www.wsxm.eu/">http://www.wsxm.eu/</a>) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> </ul>
Three-dimensional thermal structure of East Asian continental lithosphere
<p>This data set includes 3 supplement data files and 1 readme file for 3D thermal structure of East Asian continental lithosphere. </p>
Data and Workflow to: Three-dimensional buoyant hydraulic fracture growth: constant release from a point source (Möri and Lecampion, (2022))
<p>This upload contains the relevant scripts, notebooks, and datasets to reproduce the numerically obtained results of the Journal article "Three-dimensional buoyant hydraulic fracture growth: constant release from a point source" by Möri and Lecampion, (2022).</p>
Hyperspectral X-ray CT datasets of three chemical phantoms
<p><strong>General Data description:</strong></p> <p>The following are hyperspectral (energy-resolved) X-ray CT datasets for a set of chemical phantom samples, each containing multiple phases of an aqueous contrast agent at different concentrations. All scans were acquired with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction of each dataset. The phantom samples were produced as they each offer a distinct spectral marker which, when measured by an energy-sensitive detector, may be used as a form of calibration for spectral analysis. The phantoms were for the common contrast agents of I<sub>2</sub>KI, BaSO<sub>4</sub> and PTA.</p> <p><strong>File descriptions:</strong></p> <p>Contained are four MATLAB (.mat) data files, as well as three text (.txt) metadata files.</p> <p>Iodine_Phantom_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the I<sub>2</sub>KI phantom. The concentrations for the iodine phases were 25, 50, 76 and 101 mg/ml of aqueous I<sub>3</sub><sup>-</sup> ions respectively.</p> <p>Barium_Phantom_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the BaSO<sub>4</sub> phantom. The concentrations for the BaSO<sub>4</sub> phases were 100, 200 and 400 mg/ml of BaSO<sub>4</sub> respectively.</p> <p>Tungsten_Phantom_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the PTA phantom. The concentrations for the PTA phases were 50, 100 and 200 mg/ml of PTA respectively.</p> <p>Iodine_phantom_sinogram.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the I<sub>2</sub>KI phantom. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied, as well as a centre-of-rotation correction.</p> <p>Barium_phantom_sinogram.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the BaSO<sub>4</sub> phantom. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied, as well as a centre-of-rotation correction.</p> <p>Tungsten_phantom_sinogram.mat contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the PTA phantom. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied, as well as a centre-of-rotation correction.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p>
Supporting data for publication: The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences.
<p><span>This repository contains the supplementary data used in the publication Roche et al., 2024 (The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences), including (1) the seismicity catalogues from Cahuilla, Yellowstone and West Bohemia, modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016), and (2) the pictures series used to build isochrone contour maps.</span></p> <p><span><span>1.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The seismicity catalogues from Cahuilla, Yellowstone and West Bohemia are modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016). The catalogues include the hypocentre location, relative time, and magnitude for non-filtered and filtered data. General information on each catalogue and filtering and modifications can be found in the associated publication.</span></p> <p><span> Dataset list:</span></p> <ul> <li><span>Cahuilla Catalogues (modified from Ross et al., 2019): </span></li> <ul> <li><span>Original data: File name: VR_sup_0021_Cah_All</span></li> <li><span>Filtered data: File name: VR_sup_0022_Cah_Filter</span></li> </ul> <li><span>Bohemia 2008 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0023_Boh_08_All</span></li> <li><span>Filtered data: File name: VR_sup_0024_Boh_08_Filter</span></li> </ul> <li><span>Bohemia 2014 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0025_Boh_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0026_Boh_14_Filter</span></li> </ul> <li><span>Yellowstone Catalogs (modified from Shelly et al., 2013): </span></li> <ul> <li><span>Original data: File name: VR_sup_0027_Yell_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0028_Yell_14_Filter</span></li> </ul> </ul> <p><span>The files are text files tab-delimited, with the following headers:</span></p> <ul> <li><span>Index: 1 by default</span></li> <li><span>Easting(m): hypocenter Easting in meters </span></li> <li><span>Northing(m): hypocenter Northing in meters </span></li> <li><span>Depth(m): hypocenter depth in meters </span></li> <li><span>Mw: magnitude</span></li> <li><span>Relative Time(s): date of the origin time in the format </span></li> </ul> <p><span><span>2.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The pictures series are images of seismicity at a regular time interval for each studied step.</span></p> <p><span>Dataset list:</span></p> <ul> <li><span>Step C1: File name: VR-sup-0012-Pictures_C1.</span></li> <li><span>Step C2: File name: VR-sup-0013-Pictures_C2.</span></li> <li><span>Step C3: File name: VR-sup-0014-Pictures_C3.</span></li> <li><span>Step C4: File name: VR-sup-0015-Pictures_C4.</span></li> <li><span>Step Y1: File name: VR-sup-0016-Pictures _Y1.</span></li> <li><span>Step B1I: File name: VR-sup-0017-Pictures _B1I.</span></li> <li><span>Step B1II: File name: VR-sup-0018-Pictures _B1II.</span></li> <li><span>Step B2: File name: VR-sup-0019-Pictures _B2.</span></li> <li><span>Step B3: File name: VR-sup-0020-Pictures _B3.</span></li> </ul> <p><span>Each file contains a series of pictures in JPEG format. For each picture, events in the overlying and underlying segments are indicated in blue and red. The full circles represent the events occurring during the last interval. The empty circles represent the events occurring in the previous intervals.</span></p> <p><span>If you find these data useful in your research, please cite Roche et al. (2024), as well as the relevant papers Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016).</span></p>
Statistical analysis and dataset for: Three-dimensional body reconstruction enables quantification of liquid consumption in small invertebrates
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.14.599002).</p> <p><em><strong>Abstract</strong></em></p> <p>Quantifying feeding patterns provides valuable insights into animal behaviour. However, small invertebrates often consume incredibly small amounts of food. This renders traditional methods, such as weighing individuals before and after food acquisition, either inaccurate or prohibitively expensive. Here, we present a non-invasive method to quantify food consumption of small invertebrates whose body expands during feeding. Using the markerless pose estimation software DeepLabCut, we three-dimensionally track the body of Argentine ants, <em>Linepithema humile</em>. Using these extracted markers, we developed an algorithm which computationally reconstructs the ant’s body, directly measuring volumetric change over time. Moreover, we provide measures of accuracy and quantify the ant’s feeding response to a range of sucrose concentrations, as well as a gradient of caffeine-laced sucrose solutions. Small invertebrates are often prolific invasive species and disease vectors, causing significant ecological and economical damage. Understanding their feeding behaviour could be an important step towards effective control strategies.</p> <p> </p> <ul> <li><strong>VolEst_C1_volume_calculation_multiprocessing.py</strong>: Takes as input H5 3D DeepLabCut files, calculates the gaster volume at every frame using seven different methods and outputs these as CSV files.</li> <li><strong>VolEst_C2_interactive_GUI.py</strong>: Given a folder with Volume CSV files, interactively plots the volume over time, 3D coordinates tracked by DeepLabCut and the frame of interest for both cameras.</li> <li><strong>VolEst_C3_linear_regression.py</strong>: Applies a linear regression to each feeding event tracked and provides measures of interest such as crop load and consumption rate.</li> <li><strong>VolEst_C4_statistical_analysis</strong>: Complete statistical analysis and code for the manuscript.</li> <li><strong>VolEst_D1_sucrose_density.csv</strong>: Data obtained to quantify the density of sucrose solutions of varying molarity.</li> <li><strong>VolEst_D2_accuracy_weight_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D3_accuracy_weight.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D4_accuracy_nanoliter_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D5_accuracy_nanoliter.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D6_sucrose_caffeine_consumption_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_D7_sucrose_caffeine_consumption.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_Camera_A-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera A.</li> <li><strong>VolEst_Camera_B-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera B.</li> <li><strong>VolEst_base.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> <li><strong>VolEst_platform.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> </ul>
Potential of the three-terminal heterojunction bipolar transistor solar cell for space applications
<p>In this video we repeat the presentation we gave at the European Space Power Conference (2019), in JeanLes Pins, describing the potential of the three-terminal heterojunction bipolar transistor solar cell for space applications. There is a paper published in the proceedings of the Conference were you can find more details.</p>
Three Annotated Anomaly Detection Datasets for Line-Scan Algorithms
<h1>Summary</h1> <p>This dataset contains two hyperspectral and one multispectral anomaly detection images, and their corresponding binary pixel masks. They were initially used for real-time anomaly detection in line-scanning, but they can be used for any anomaly detection task.</p> <p>They are in .npy file format (will add tiff or geotiff variants in the future), with the image datasets being in the order of (height, width, channels). The SNP dataset was collected using sentinelhub, and the Synthetic dataset was collected from AVIRIS. The Python code used to analyse these datasets can be found at: https://github.com/WiseGamgee/HyperAD</p> <h1>How to Get Started</h1> <p>All that is needed to load these datasets is Python (preferably 3.8+) and the NumPy package. Example code for loading the Beach Dataset if you put it in a folder called "data" with the python script is:</p> <pre><code>import numpy as np # Load image file hsi_array = np.load("data/beach_hsi.npy") n_pixels, n_lines, n_bands = hsi_array.shape print(f"This dataset has {n_pixels} pixels, {n_lines} lines, and {n_bands}.") # Load image mask mask_array = np.load("data/beach_mask.npy") m_pixels, m_lines = mask_array.shape print(f"The corresponding anomaly mask is {m_pixels} pixels by {m_lines} lines.")</code></pre> <h1>Citing the Datasets</h1> <p>If you use any of these datasets, please cite the following paper:</p> <pre><code>@article{garske2024erx,</code><br><code> title={ERX - a Fast Real-Time Anomaly Detection Algorithm for Hyperspectral Line-Scanning},</code><br><code> author={Garske, Samuel and Evans, Bradley and Artlett, Christopher and Wong, KC},</code><br><code> journal={arXiv preprint arXiv:2408.14947},</code><br><code> year={2024},</code><br><code>}</code></pre> <div> <pre>If you use the beach dataset please cite the following paper as well (original source):</pre> </div> <pre><code>@article{mao2022openhsi, title={OpenHSI: A complete open-source hyperspectral imaging solution for everyone}, author={Mao, Yiwei and Betters, Christopher H and Evans, Bradley and Artlett, Christopher P and Leon-Saval, Sergio G and Garske, Samuel and Cairns, Iver H and Cocks, Terry and Winter, Robert and Dell, Timothy}, journal={Remote Sensing}, volume={14}, number={9}, pages={2244}, year={2022}, publisher={MDPI} }</code></pre>
Variation and overlaps in non-breeding regions of three less-studied breeding populations of the Eurasian Reed Warbler: Figures and datasheet
<p>Non-breeding regions of Eurasian Reed Warbler, <em>Acrocephalus scirpaceus</em> breeding in Finland, Jordan, and Kazakhstan as predicted with hydrogen stable isotope analysis.</p> <p>file name corresponds to the following figure titles:</p> <p>"Fig 2": <strong><span>Figure 2:</span></strong><span> The predicted non-breeding regions of Eurasian Reed Warblers breeding in Finland, Jordan, and Kazakhstan.</span></p> <p><span><span>"Fig 3": <strong><span>Figure </span></strong><span>3: Predicted non-breeding regions for Eurasian Reed Warblers sampled at a breeding site in Finland assigned to isotopic clusters A and </span><span><span>B.</span></span></span></span></p> <p><span><span></span></span><span></span><span><span><span>"Fig 4": </span></span></span><strong><span>Figure 4:</span></strong><span> Predicted within population average non-breeding regions for Eurasian Reed Warblers assigned to clusters A-F breeding in Jordan </span><span>(see Table S1 for details).</span></p> <p><span>Also uploaded is a csv file containing the isotopic signature of the birds. The file name is "data_Acrocephalus_scirpaceus"</span></p>
Measurement of interturn short-circuits emulation on dual three-phase PMS motor
<p>The published data contains measurement records made on a special permanent magnet synchronous motor (PMSM). The motor has a stator with winding taps. The winding taps can be used to emulate various severities of interturn short-circuits.</p> <p>A detailed machine description and modeling can be found in the paper "Interturn short circuit modelling in dual three-phase PMSM" (https://dx.doi.org/10.1109/IECON49645.2022.9968364). </p> <p>The file name specifies the measurement conditions according to</p> <p>spd10-5000rpm_flt<strong>N</strong>z<strong>P</strong>_<strong>XX</strong>NM.mat</p> <p><strong>N</strong> - Number of shorted turns (0-healthy operation)</p> <p><strong>P</strong> - Fault phase ( U or V)</p> <p><strong>XX </strong>- Load torque generated by a dynamometer</p> <p> </p> <p>For example, the file spd10-5000rpm_flt1zu_25NM.mat contains data where one coil turn in phase U was shorted, and the machine was producing 25 Nm torque.</p> <p>Measurement was performed by the microcontroller and synchronised with the control algorithm. The sample rate is 10 kHz for this reason. Each data file contains measured currents in stator coordinates as well as currents and voltages (control value) in rotor (dq) coordinates. These variables are measured for each three-phase sub-system. Measured motor position, speed and required motor speed are also included. </p>
Comparative Study of Entomotoxicity of Three Medicinal Plant Extracts against Sitophilus oryzae
<p>The Sitophilus oryzae is the most widespread and destructive primary stored cereals and grain pest in the world. The major effect of Sitophilus oryzae on an infestation by the feeding activity of grubs and adults and increasing the secondary growth of pests by making conditions optimum for optimum and further infestation. Plant extracts Azadirachta indica, Osmium Sanctum, and Mentha piperita were evaluated for Entomotoxicity such as repellency, adulticidal and larvicidal effect against Sitophilus oryzae. The Entomotoxicity of plant extracts expressed in percentage and Repellency were also expressed in class repellency with class 1, class 2, Class 3, Class 4, and class 5.TheRepellency with 80% of Class 4, Adulticidaland larvicidal percentage with 100 % of Azadirachta Indica and Adulticidal highest Entomotoxicity effect than Osmium sanctum and Mentha piperita</p>
Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures
<p>The data of the gravitational wavefroms of core-collapse supernovae, which are used in Takiwaki, Kotake, and Foglizzo, (2021), Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 1, pp.966-985</p>
Data from a three-phase Delphi study used to investigate Knowledge Infrastructure for Research Data in Norway, KIRDN_Data; PhD project
<p>A modified three-phase Delphi study was used to explore the knowledge infrastructure for research data in Norway. The study includes different stakeholders involved in research data sharing. A Delphi study is characterised by the use of an expert panel to elicit opinions on a shared reality from different perspectives. Data collection is performed in several rounds with the intention of reaching consensus or solving an issue. </p> <p>A group of 24 experts took part in the study. The group consisted of policy-makers, representatives of national service providers, and researchers and research support staff from four Norwegian universities. The participants were invited based on their involvement in the development of policies, infrastructure or data-related research support. The research support staff were recruited to include representatives from different research support services at the universities, including libraries, research offices and IT departments. While the researchers were selected from based on their receival of EU funding with requirements of data management plans.</p> <p>Data were collected in three phases. The first phase, the ‘exploration phase’, was conducted using open interviews lasting approximately one hour in January/February 2018. The purpose of this phase was to obtain an initial overview of the panel members opinions’ on issues regarding research data management.</p> <p>In the second phase, the ‘evaluation phase’, conducted in August/September 2018, participants answered a survey containing nine questions on topics such as data stewardship, DMPs, ethical aspects of data sharing and core functions in a research data infrastructure. The survey was designed to further explore issues and tensions uncovered in the first interviews. Several of the questions were formulated as statements that the participants were asked to agree or disagree upon. </p> <p>The third, ‘concluding phase’ was conducted using interviews in March/April 2019. These interviews lasted approximately 30 minutes and were based on results from the questionnaire as well as the first interview. Participants were asked whether they had thoughts on the preliminary findings of the study. </p> <p>Based on requests from some of the participants, the questions were sent to all participants prior to the data collection, in all three phases. The participants were also sent the transcripts from the interviews and were asked for permission to share the complete material or parts of the data material to which they contributed. </p>
A three-dimensional map of the Milky Way using 66,000 Mira variable stars
<p>We provide here full Table 1 from Iwanek, P., et al., 2023, "A three-dimensional map of the Milky Way using 66,000 Mira variable stars", ApJS (accepted for publication, DOI: 10.3847/1538-4365/acad7a), which contains mean magnitudes, distances, extinction values, and photometric chemical types for 65,981 Galactic Miras (Iwanek2023_Table1_GalMirasDist.txt file). The corner plot, i.e., the two-dimensional projections of the multi-dimensional posterior parameter spaces fitted to the Galactic Miras distribution is presented in Figure Iwanek2023_corner_plot.png.</p> <p> </p> <p>Patryk Iwanek is partially supported by Kartezjusz program No. POWR.03.02.00-00-I001/16-00, founded by the National Centre for Research and Development, Poland. Szymon Kozłowski acknowledges the support from the National Science Centre, Poland, via grant OPUS 2018/31/B/ST9/00334. </p> <p>This publication makes use of data products from WISE, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration (NASA). This work is based in part on archival data obtained with the Spitzer Space Telescope, which was operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA.</p>
Dataset Treatment of early childhood caries with three different topical fluoride treatments: a randomised clinical trial
<p>These files contain the data and the research script about the effectiveness of the 38% silver diamine fluoride (SDF, SDI Riva Star) and Tiefenfluorid (TF, Humanchemie GmbH), using two different application protocols in children ≤71 months of age with early childhood caries, with a focus on patient-centred outcomes including major complications (pain, abscess, extraction) and minor complications (lesion progression).</p> <p>This work was funded by European Regional Development Fund Postdoc Latvia 1.1.1.2/VIAA/3/19/543, Contract No 9.-14.5/27. Uribe was supported by European Union’s Horizon 2020 grant agreement 857287 for the Baltic Biomaterials Centre of Excellence.</p>
Dataset from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters"
<p>This repository contains several data from the paper "Eccentric black hole mergers via three-body interactions in young, globular and nuclear star clusters".</p> <p> </p> <p><strong>BBH_mergers_cat_*.dat</strong> contains the data for the BBH merger population produced by the three-body simulations. These data can be used to reproduce figures 4,5, and 8 of the paper. The file is organized in columns as:</p> <ul> <li>ID of the simulation.</li> <li>outcome of the simulation (12, merger triggered by a flyby event, 13 and 23 merger triggered after an exchange event in which the secondary (primary) BH is replaced by the intruder, 123 second generation BBH merger.</li> <li>mass of the primary BH in solar masses</li> <li>mass of the secondary BH in solar masses</li> <li>Chirp mass of the system in solar masses</li> <li>coalescence time since the beginning of the simulation in year (note that all the simulation with tcoal<1e5 yr have merged during the direct N-body simulation, while all the mergers that take place after this value are evolved with the equations by Peters 1964)</li> <li>eccentricity of the binary at 10 Hz in the detector frame</li> <li>tilt angle in radiant, defined as the angle between the orbital plane of the initial binary at the beginning of the simulation and the orbital plane of the final binary at the end of the simulation.</li> </ul> <p>The files named <strong>data_*.txt</strong> contains the masses, the position and the velocities at each timestep for the three simulations showed in fig.1 in the paper. The data are referred to the center-of-mass of the three-body system. The file is organized as follows:</p> <ul> <li>The first line of the file reports the masses in solar masses of the three BHs.</li> <li>Column 0 reports the time in yr</li> <li>Colum 1-3 report the x,y,z position for the m1 BH in parsec</li> <li>Colum 4-6 report the x,y,z position for the m2 BH in parsec</li> <li>Colum 7-9 report the x,y,z position for the m3 BH in parsec</li> <li>Colum 10-12 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 13-15 report the x,y,z components of the velocities of the m1 BH in km/s</li> <li>Colum 16-18 report the x,y,z components of the velocities of the m1 BH in km/s</li> </ul> <p>Finally, <strong>outcomes_*.dat</strong> contains two columns:</p> <ul> <li>Column 0 reports the ID of the simulation</li> <li>Column 1 reports the outcome of the simulation as: 12 flyby (or merger after a flyby), 13 and 23 exchange (or merger after an exchange) in which the secondary (primary) BH is replaced by the intruder, 0 in the system is ionized in three single BHs, 3 if the system is still interacting at 1Myr, i.e. when we stop our simulation.</li> </ul> <p>This file might be useful to train a machine-lerning classificator, and can be used to reproduce Fig. 2 of the paper.</p> <p> </p> <p><strong>Contacts:</strong></p> <p>Marco Dall'Amico</p> <p>marco.dallamico@phd.studenti.unipd.it</p> <p>marco.dallamico@pd.infn.it</p>
Dataset for Broadband three-mode converter and multiplexer based on cascaded symmetric Y-junctions and subwavelength engineered MMI and phase shifters
<p>This dataset contains the raw data for the figures (Fig. 5, Fig. 6 and Fig. 7) in the publication entitled "Broadband three-mode converter and multiplexer based on cascaded symmetric Y-junctions and subwavelength engineered MMI and phase shifters" published by Optics and Laser Technology (DOI: 10.1016/j.optlastec.2023.109513). Datafiles are in .txt format.</p> <p>All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript. </p>
High frequency limnological sensor data from three lakes in the Pocono Mountains region, Pennsylvania USA, 2016-2024
This dataset publication provides access to eight years of high-frequency sensor data from three lakes: Giles, Lacawac, and Waynewood. These lakes are located in the Pocono Mountains region of Pennsylvania, USA and have been the site of long-term monitoring and research. Lake Giles is a relatively clear-water low dissolved organic matter oligotrophic lake in a largely protected watershed. Lake Lacawac has higher dissolved organic matter concentrations and is considered a dystrophic brown-water system; it is also in a highly-protected watershed. Lake Waynewood is a relatively productive eutrophic lake with a larger watershed that is mixed agricultural, forested, and residential use. High-frequency sensors were deployed on sensor lines at the deepest point in each lake. Measurements included temperature and dissolved oxygen through the water column, fluorescent dissolved organic matter at the surface and bottom, and chlorophyll fluorescence at the surface of each lake. These data are collected at a frequency of 10 to 30 minutes and are available in the data packages GilesHighFrequencyData.csv, LacawacHighFrequencyData.csv, and WaynewoodHighFrequencyData.csv. Data from weather stations located adjacent to each lake can be found in the data package PoconosWeatherStationData.csv. Additional long-term limnological data (four decades) for Lakes Giles, Lacawac, and Waynewood are available in the data package edi.186.8.
Monitoring of Microtus ochrogaster and Microtus pennsylvanicus populations in three different habitats in east-central Illinois, 1972 to 1997.
Populations of 2 species of arvicoline rodents, the prairie vole (Microtus ochrogaster) and meadow vole (Microtus pennsylvanicus), were monitored monthly from 1972-1997 in three distinct habitats: restored tallgrass prairie, bluegrass (Poa pratensis) and alfalfa (Medicago sativa). The study sites were located in the University of Illinois Biological Research Area (Phillips Tract) and Trelease Prairie. Tallgrass prairie was the original habitat of both species in Illinois. Bluegrass, an introduced species, represents the more common habitat in which the two species can be found today in Illinois. Alfalfa, an atypical habitat, provides an abundant source of high-quality food for both species. At each station, one wooden multiple-capture live-trap was placed. Every month, a two-day period of prebaiting was followed by a 3-day trapping session. The data include the species, individual identification, grid station, sex, reproductive status and body mass. Over the span of 25 years, three trapping sessions monthly were conducted to cover the three habitats, dedicating three weeks each month. Several papers have been based on these data.
The Jefferson Project 2021 water quality data from three vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2021, The Jefferson Project deployed three vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2021. These vertical profiler stations are named VP_AnthonysNose, VP_HarrisBay, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter or less depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data are transferred in near real-time to an off-site database for monitoring and review. The data provided here have undergone data correction by Jefferson Project researchers.
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.