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86 results for “Time of Flight”
Data for: "Continental-scale patterns in diel flight timing of high-altitude migratory insects"
<p>This dataset contains the proportional migratory insect intensity and traffic data used in Haest <em>et al.</em> (2024) to quantify patterns in diel flight periodicity of migratory insects between 50-500m above ground level during March-October 2021 using a network of seventeen vertical-looking radars across Europe. Please see the Materials and Methods section in Haest <em>et al.</em> (2024) for more details on the dataset. </p>
Non-refractory particulate sulfate and chloride data from a time of flight aerosol chemical speciation monitor around the Southern Ocean in the austral summer of 2016/17, during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE) campaign was conducted between 20th December 2016 and 19th March 2017. The time of flight aerosol chemical speciation monitor (ToF-ACSM, Aerodyne Research Inc.) was deployed. It is capable of providing 10-minute resolution chemical compositions of NR-PM1 (non-refractory particulate matter with aerodynamic diameter smaller than 1 µm), including sulphate, nitrate, ammonium and organics. Chloride is refractory and can only be measured qualitatively, that is relative changes in intensity are trustworthy while absolute concentrations are a clear underestimation, because most of the chloride is in refractory form as part of sea salt in the marine environment. Since this ACSM dataset was collected on the ship, the ship exhaust will occasionally interfere with the natural signal. Therefore data gaps exist. The overall concentrations of particulate organics, nitrate and ammonium remained low, mostly below detection limit, except during the polluted periods. Thus, we do not report these three components. Only sulphate can be retrieved as a quantitative variable from this dataset.</p> <p>This dataset provides limited information on the chemical composition of sub-micron non-refractory aerosol in the Southern Ocean and gives hints on potential sources. Chloride clearly reflects the contribution of sea salt to the aerosol population. This can be checked by relating the particulate chloride to wind speed (Landwehr et al., 2019; 10.5281/zenodo.3379590) and particles with large diameters (Schmale et al., 2019; 10.5281/zenodo.2636709). Particulate sulphate may originate from a variety of sources: sea salt (minor contribution), anthropogenic emissions and natural marine emissions of dimethylsulfide, which is converted to SO2 and sulphuric acid in the atmosphere and can subsequently partition into the particle phase via gas-phase or aqueous phase reactions (Schmale et al., 2019).</p> <p><strong>Dataset contents</strong></p> <ul> <li>raw_chl_SO4_mz_55_57_manual_with_flags.csv, data file, comma-separated values</li> <li>README.txt, metadata, text</li> <li>data_file_header.txt, metadata, text</li> <li>calibration_info.csv, metadata, comma-separated values</li> </ul>
Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes
<p><strong>Data for: First experimental time-of-flight-based proton radiography using low gain avalanche diodes</strong><br>The associated publication can be found on https://iopscience.iop.org/article/10.1088/1361-6560/ad3326.<br>All graphs inside the publication can be recreated with this dataset. Similar to the publication, the data for the timewalk and offset correction are only given for one sensor and one channel as they only serve a representative purpose. The raw data for all other channels can be shared upon request. Furthermore, as in the publication, the data for the water-equivalent-thickness (WET) calibration and proton radiography (pRAD) creation are given by the median and the interquartile range of the measured quantities of the individual graphs. Those data are also calibrated. If required, the raw, unprocessed data of each measurement can be shared upon request.<br><br>In the following, a description of the individual files and corresponding figures in the publication is given. If not specified otherwise, the physical units are given in brackets next to the name of the corresponding physical quantity (usually first line in file):<br><br></p> <ul> <li><em><strong>Figure 6:</strong></em> <ul> <li> RawToTspectrumrescaledLGAD3.txt: <ul> <li>Describes the re-scaled time-over-threshold (ToT) spectrum measured inside the third LGAD of the time-of-flight-based ion computed tomography (TOF-iCT) demonstrator using 800 MeV protons (Figure 6a). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence (counts[#]).</li> </ul> </li> <li>ToTspectrumrescaledLocMaxLGAD3.txt <ul> <li>Describes the re-scaled ToT spectrum measured inside the third LGAD of the TOF-iCT demonstrator using only the local ToT maxima inside each 4D-cluster. The spectrum was obtained using 800 MeV protons (Figure 6b). The first column gives the channel number on the LGAD (channelnr[#]), the second column, the ToT value measured in this channel (ToT[ps]) and the third channel, the corresponding occurrence (counts[#]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 7:</strong></em> <ul> <li>offsetpraecalib.txt: <ul> <li>Describes the raw, uncalibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7a). The first column represents the detector channel nr in LGAD3, the second column the raw, uncalibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li>offsetpraecalib.txt: <ul> <li>Describes the time walk and offset-calibrated time difference spectrum in LGAD3 measured between all channels on LGAD3 and a central reference channel on LGAD4 (figure 7b). The first column represents the detector channel nr in LGAD3, the second column the calibrated time difference between LGAD3 and LGAD4 (TDiff[ns]) and the third column the number of corresponding counts (counts[#]).</li> </ul> </li> <li> praetwdata.txt: <ul> <li>Describes the ToT dependence of the measured time difference between LGAD1 and LGAD2 using the raw ToT of channel 31 in LGAD1 (figure 7c). The first column represents the raw, unscaled and uncalibrated ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column, the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> <li>posttwdata.txt <ul> <li>Describes the time walk-calibrated ToT vs TDiff spectrum using the measured time difference between LGAD1 and LGAD2 and the ToT of channel 31 in LGAD1 (figure 7d). The first column represents the ToT in LGAD 1 (ToT[ns]), the second column the measured time difference (TDiff[ns]) and the last column the number of corresponding counts (counts[#]). A ToT cut on the reference channel on LGAD2 has been applied in advance to guarantee a correlation between only true particle hits in the second sensor.</li> </ul> </li> </ul> </li> <li><em><strong>Figure 8:</strong></em> <ul> <li>tofinaridata.txt: <ul> <li>Describes the measured TOF in air through the scanner w.r.t the TOF measured at 800MeV, i.e. the median TOF value at 800MeV was subtracted from all data points (Figure 8a). The first column describes the beam energy (beamenergy[MeV]), the second column the first quartile of the measured TOF per pixel (TOFperpixelQ1[ps]), the second column the median TOF per pixel (TOFperpixelQ2[ps]) and the last column the third quartile of the measured TOF per pixel (TOFperpixelQ3[ps]).</li> </ul> </li> <li>tofinairtheodata.txt: <ul> <li>Describes the theoretical TOF in air through the scanner w.r.t the theoretical TOF at 800MeV, i.e. the theoretical TOF value at 800MeV was subtracted from all data points (Figure 8a).</li> </ul> </li> <li>intrinsictimeresolution.txt: <ul> <li>Describes the energy dependence of the intrinsic time resolution per channel measured inside LGAD1 (figure 8b). The first column represents the primary beam energy (beamenergy[MeV), the second column the corresponding energy loss in MIPs (relativeenergylossi[MIP]), the third column the first quartile of the intrinsic time resolution per LGAD channel (timeresperpixelQ1[ps]), the fourth column the median of the intrinsic time resolution per LGAD channel and the last column the third quartile of the intrinsic time resolution per LGAD channel (timeresperpixelmedian[ps],timeresperpixelQ3[ps]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 9:</strong></em> <ul> <li>wetcalib.txt <ul> <li>Describes the measured TOF increase per pixel w.r.t to the TOF in air (i.e. without a phantom) for a given WET and primary beam energy. The first column represents the WET of the irradiated sample (WET[mm]), the second column the used beam energy (beamenergy[MeV]), the third column the first quartile of the measured TOF distribution (TOFperpixelQ1[ps]), the fourth column the median (TOFperpixelQ2[ps]) and the sixth column the third quartile (TOFperpixelQ3[ps]).</li> <li>For each energy, a fifth-order polynomial was used to fit the WET and the TOF increase (Delta TOF(E)~sum_i a_i*(WET_i )^i, with i in [0,5] ). The fit parameters are given in the following for each beam energy:<br> <ul> <li>83 MeV: a_i=[-4.70496227e-02,4.64323118e-01, -2.71391535e-02,4.23655842e-03, -1.13034255e-04,1.23725678e-06]</li> <li>100.4 MeV: a_i=[-3.28976022e-02,-3.68818468e-02,1.96339858e-02,7.31585040e-04, -4.38697681e-05 ,7.52163384e-07]</li> </ul> </li> </ul> </li> </ul> </li> <li><em><strong>Figure 10:</strong></em> <ul> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 83 MeV (Figure 10a). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> <li>wetsperpixel83MeV.txt <ul> <li>Describes the proton radiography (pCR) for 100.4 MeV (Figure 10b). The first column represents the x position of the pixel (x[mm]), the second column the y position of the pixel (y[mm]) and the last column the corresponding WET (WET[mm]).</li> </ul> </li> </ul> </li> <li><em><strong>Figure 11:</strong></em> <ul> <li>wetdistrdata83MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 83 MeV protons (Figure 11a). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> <li>wetdistrdata100MeV.txt <ul> <li>Describes the measured TOF per pixel inside the ROI for 100.4 MeV protons (Figure 11b). The first column represents the lower boundary of each WET bin (WETlowerbinboundary[mm]), the second column the upper boundary of each WET bin (WETupperbinboundary[mm) and the last column the corresponding counts per bin (counts[#]).</li> </ul> </li> </ul> </li> </ul>
Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach
<p>This is the dataset used in the analysis "Identification of Southeast Asian <em>Anopheles </em>mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach". It consists in 3584 raw mass spectra (mzXML file format) of the head of 359 <em>Anopheles </em>mosquito specimens collected in Karen (Kayin state) in Myanmar between 2020 and 2022 and associated metadata (Rdata file format) including sample information (taxonomy.Rdata) and spectra information (metadata.Rdata).</p>
THz driven field emission: energy and time-of-flight spectra of ions (DATASET)
<p>We present an experimental and numerical study of ion field evaporation from LaB6 nanotips using single-cycle terahertz (THz) transients and a static bias voltage. Varying the amplitude and phase of the THz pulses and the value of the<br> bias, we explore the THz-induced reshaping of the ions energy and their time-of-flight spectra. These results prove that short THz transient of about 1 ps can induce ionization and emission of ions from LaB6 samples by a field effect: the THz<br> transient acts as an ultra-short electrical pulse. Moreover, comparing numerical and experimental results, we prove that the response time of surface atoms to the THz transient is shorter than 1 ps, corresponding to the vibration times of acoustic phonons<br> in LaB6.</p> <p>In the following dataset, you can find data from THz-APT obtained from LaB6 sample and results of simulation of ions under THz Field done with Lorentz</p>
Time-of-flight neutron tomography
<p>This dataset contains the wavelength-resolved neutron tomography of a contrast sample imaged with the time-of-flight (TOF) transmission imaging method at the IMAT beamline at the ISIS pulsed neutron source. The sample is made of several polycrystalline materials: nickel, iron, titanium, lead, copper and aluminium</p> <p>The data are pre-processed for the detector event overlap correction, binned in the TOF axis and cropped. The datasets can be used for replicating the tomographic reconstruction as described in (Carminati et al 2020, https://doi.org/10.1107/S1600576720000151), or for further analysis.</p>
Imaging of Organic Samples with Megaelectron Volt Time-of-Flight Secondary Ion Mass Spectrometry Capillary Microprobe
<p>Time-of-flight Secondary Ion Mass Spectrometry (TOF SIMS) with MeV primary ions offers a fine balance between secondary ion yield for molecules in the mass range from 100 to 1000 Da and beam spot size, both of which are critical for imaging applications of organic samples. Using conically shaped glass capillaries with an exit diameter of a few micrometers, a high energy heavy primary beam can be collimated to less than 10 μm. In this work, imaging capabilities of such a setup are presented for some organic samples (leucine-evaporated mesh, fly wing section, ink deposited on paper). Lateral resolution measurement and molecular distributions of selected mass peaks are shown. The negative influence of the beam halo, an unavoidable characteristic of primary beam collimation with a conical capillary, is also discussed. A new start trigger for TOF measurements based on the detection of secondary electrons released by the primary ion is presented. This method is applicable for a continuous primary ion beam, and for thick targets that are not transparent to the primary ion beam. The solution preserves the good mass resolution of the thin target setup, where the detection of primary ions with a PIN diode is used for a start trigger, reduces the background, and enables a wide range of samples to be analyzed.</p>
Photon time-of-flight histograms measured with a photon-counting diffuse LiDAR on Crook Glacier and Collier Glacier, Oregon
<p>This data set contains photon time-of-flight histograms measured in September 2021 on Crook Glacier, Oregon, and two sites on Collier Glacier, Oregon (USA). Each data file is associated with a single measurement using a photon-counting diffuse LiDAR. The files contain a header with geo-location (WGS84) and instrument settings as well as the raw count numbers and integration time for each temporal bin. The given arrival times represent the center of each temporal bin. The color naming scheme of the file names represents the used laser wavelength (blue=405nm, green=520nm, red=640nm), the last number in each filename represents the distance between laser and detector (i.e. 1.8m at 520nm for file "green5_1.8.txt").</p> <p>The data is organized in folders for each site plus an additional folder containing Matlab-code needed for data evaluation. The code uses this folder structure for relative path referencing. Data is evaluated using ExampleDataEvalV2.m, which employs the other three files as helper functions. The helper function ReadTofHisto.m reads the raw data from the measurement files and provides a named structure with the header information.</p> <p>If you wish to use this data set please contact Markus Allgaier at markusa@uoregon.edu with a description of the work and any questions so that we may offer guidance in regards to the best usage of our dataset. When using the data set within a publication, please cite:</p> <p>Markus ALLGAIER, Matthew G. COOPER, Anders E. CARLSON, Sarah W. COOLEY, Jonathan C. RYAN, Brian J. SMITH, "Direct measurement of optical properties of glacier ice using a photon-counting diffuse LiDAR", in preparation (2022)</p>
Data from: Operando Proton Transfer Reaction-Time of Flight-Mass Spectrometry of Carbon Dioxide Reduction Electrocatalysis
<p>Seven top-level folders</p> <p>GC-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of GC-PTR-TOF-MS data</p> <p>LSV-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under linear sweep voltammetry</p> <p>MSCP-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under multi-step chronopotentiometry</p> <p>PTR-TOF-MS-Calibration<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS calibration data</p> <p>SEM<br> - Raw images from scanning electron microscope</p> <p>Stability<br> - Raw data of electrochemical stability</p> <p>TEM<br> - Raw images from transmission electron microscopy</p>
Vertical profiles and integrated time series of bird density and flight speed vector (19.09.2016-10.10.2016)
<p><strong>Description</strong></p> <p>This dataset contains the vertical profiles and integrated time series of bird density and flight speed (NS and EW) used in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>]. Data are stored in a JavaScript Object Notation (JSON) file for each radar, with the following structure:</p> <pre><code>{ "name" : "bejab", //code name of the radar (http://eumetnet.eu/wp-content/themes/aeron-child/observations-programme/current-activities/opera/database/OPERA_Database/index.html) "lat" : 51.1917, //Latitude "lon" : 3.0642, //Longitude "height" : 50, //Height of the radar antenna [m] a.s.l. "maxrange" : 25, //Maximum range [km] used for profile "alt" : [100, 300,...], "time" : ["19-Sep-2016 00:00:00", "19-Sep-2016 00:05:00",...], "dens" : [[...],...], //Vertical profile of bird density [1/km3] "u" : [[...],...], //Vertical profile of bird flight speed in East(+)/West(-) [m/s] "v" : [[...],...], //Vertical profile of bird flight speed in North(+)/South(-) [m/s] "denss" : [...], //Integrated profile of bird density [1/km2] "us" : [...], //Integrated profile of bird flight speed in East(+)/West(-) [m/s] "vs" : [...], //Integrated profile of bird flight speed in North(+)/South(-) [m/s] }</code></pre> <p> </p> <p><strong>Procedure</strong></p> <p>The raw data are downloaded on the <a href="http://enram.github.io/data-repository/">ENRAM repository</a>,( see Dokter (2011) and (2019) for more details) and processed according to the procedure described below.</p> <ol> <li>Of the 84 radars contributing data during the study period, 11 radars are discarded because of their poor quality due to S-band radar type, poor processing or large gaps (temporal or altitude cut). The same radars were removed in Nilsson et al. (2019).In addition, the 4 radars from Bulgaria and Portugal were excluded because of their geographic isolation.</li> <li>The full vertical profile was discarded when rain was present at any altitude bin. A dedicated MATLAB GUI was used to visualise the data and manually set bird densities to “not-a-number” in such cases. </li> <li>Zones of high bird densities can sometimes be incorrectly eliminated in the raw data. To address this, Nilsson et al. (2019) excluded problematic time or height ranges from the data. Here, in order to keep as much data as possible, the data was manually edited to replace erroneous data either with “not-a-number”, or by cubic interpolation using the dedicated MATLAB GUI.</li> <li>Due to ground scattering,the lower altitude layers are sometimes contaminated by errors or excluded in the raw data. We vertically interpolated bird density by copying the first layer without error into to the lower ones. This approach is relatively conservative as bird migration intensity usually decreases with height in the absence of obstacles, and more so in autumn (Bruderer, 2018)</li> <li>The vertical profiles are vertically integrated from the radar altitude and up to 5000 m asl.</li> <li>The data recorded during daytime are excluded. Daytime is defined at each radar by the civil dawn and dusk (6° below horizon).</li> <li>Finally, the data of 10 radars with high temporal resolution (5-10minutes) was down-sampled to 15 minutes to preserve a balanced representation of each radar.</li> </ol> <p>The resulting cleaned vertical-integrated time series of nocturnal bird density can be viewed in vp_corrected.zip.</p> <p>More details and illustrations are available in Nussbaumer (2019) [open access: <a href="https://www.mdpi.com/2072-4292/11/19/2233">https://www.mdpi.com/2072-4292/11/19/2233</a>], </p> <p><strong>Acknowledgement</strong></p> <p>We acknowledge the <a href="http://eumetnet.eu/activities/observations-programme/current-activities/opera/">European Operational Program for Exchange of Weather Radar Information (EUMETNET/OPERA)</a> for providing access to European radar data, faciliated through a research-only license agreement between EUMETNET/OPERA members and <a href="http://enram.eu/">ENRAM</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Bruderer, B.; Liechti, F. Variation in density and height distribution of nocturnal migration in the south of israel. <em>Israel Journal of Zoology</em> <strong>1995</strong>, <em>41</em>, 477–487. <a href="http://doi.org/10.1080/00212210.1995.10688815">doi:10.1080/00212210.1995.10688815</a>.</p> <p>Dokter A. M. , F. Liechti, H. Stark, L. Delobbe, P. Tabary, and I. Holleman, “Bird migration flight altitudes studied by a network of operational weather radars,” <em>J. R. Soc. Interface</em>, vol. 8, no. 54, pp. 30–43, Jan. <strong>2011</strong>. <a href="http://doi.org/10.1098/rsif.2010.0116">doi:10.1098/rsif.2010.0116</a></p> <p>Dokter A. M. , P. Desmet, J. H. Spaaks, S. van Hoey, L. Veen, L. Verlinden, C. Nilsson, G. Haase, H. Leijnse, A. Farnsworth, W. Bouten, and J. Shamoun‐Baranes, “bioRad: biological analysis and visualization of weather radar data,” <em>Ecography </em>(Cop.)., vol. 42, no. 5, pp. 852–860, May <strong>2019</strong>. <a href="http://doi.org/10.1111/ecog.04028">doi: 10.1111/ecog.04028</a></p> <p>Nilsson, C.; Dokter, A.M.; Verlinden, L.; Shamoun-Baranes, J.; Schmid, B.; Desmet, P.; Bauer, S.; Chapman, J.; Alves, J.A.; Stepanian, P.M.; Sapir, N.;Wainwright, C.; Boos, M.; Górska, A.; Menz, M.H.M.; Rodrigues, P.; Leijnse, H.; Zehtindjiev, P.; Brabant, R.; Haase, G.; Weisshaupt, N.; Ciach, M.; Liechti, F. Revealing patterns of nocturnal migration using the European weather radar network. <em>Ecography </em><strong>2019</strong>, <em>42</em>, 876–886. <a href="http://doi.org/10.1111/ecog.04003">doi:10.1111/ecog.04003</a>.</p> <p>Nussbaumer R., L. Benoit, G. Mariethoz, F. Liechti, S. Bauer, and B. Schmid, “A Geostatistical Approach to Estimate High Resolution Nocturnal Bird Migration Densities from a Weather Radar Network,” <em>Remote Sens</em>., vol. 11, no. 19, p. 2233, Sep. <strong>2019</strong>. <a href="https://www.mdpi.com/2072-4292/11/19/2233">doi: 10.3390/rs11192233</a></p> <p> </p> <p> </p>
Data from: Timing of departure from natal areas by Golden Eagles is not constrained by acquisition of flight skills
<p>The post-fledging dependence period (PFDP), which extends from a fledgling’s first flight out of the nest to its departure from the parents’ territory, is crucial in the lifecycle of birds. During this period, juveniles develop their flight and foraging skills to become fully independent. Despite the importance of this life stage in basic bird ecology and conservation, it remains largely overlooked – notably its link with the acquisition of flight skills. In this study, we modeled the variation in seven proxies describing flight skills of 84 GPS-tracked Golden Eagle juveniles in France between 2016 and 2020. Juveniles had a long but highly variable PFDP, averaging 177.9 (±62.2) days after departure from the nest. This period is divided into two phases: a first phase of rapid increase in flight skills over the first 60 days after departure from the nest, followed by a plateau in which flight skills no longer develop until independence. These results suggest that the full development of flight skills is not a constraining factor during the PFDP and that it is advantageous for juveniles to choose to remain in their natal territory. We posit that parents’ tolerance of fledged juveniles is a type of parental care that may maximize their own fitness by improving the survival of their descendants. In future studies, it may be of interest to investigate the factors that may explain the high variability in the duration of this stage between individuals within the same population.</p>
3D metabolic profiles acquired by a Time-of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS)
<p>Dataset for "Spatially resolved 3D metabolomic profiling in tissues"</p> <p>The Dataset has 6 subdata of TOF-SIMS signal raw data:<br> * Inside germinal center dataset separate into 2<br> * Outside germinal center dataset<br> * Border germinal center dataset<br> * Unlabeled dataset separate into 2<br> * Labeled tonsil tissue dataset<br> * Unlabeled tonsil tissue dataset</p> <p>1 processed data for python numpy version: 3D metabolites npy Data.zip</p> <p>Each dataset is a folder that has 191 txt files corresponding to the signal of 189 mass channels, the sum of all signals and the rest of signals not captured. <br> In each folder, the signal corresponding to a channel is named: name - molecular compound<br> </p>
Deep Reinforcement Learning for END-To-END Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments
<p> </p> <p>Videos for the real flight tests and the simulation experiments </p>
Precise timing is ubiquitous, consistent and coordinated across a comprehensive, spike-resolved flight motor program
<p>Sequences of action potentials, or spikes, carry information in the number of spikes and their timing. Spike timing codes are critical in many sensory systems, but there is now growing evidence that millisecond-scale changes in timing also carry information in motor brain regions, descending decision-making circuits, and individual motor units. Across all the many signals that control a behavior how ubiquitous, consistent, and coordinated are spike timing codes? Assessing these open questions ideally involves recording across the whole motor program with spike-level resolution. To do this, we took advantage of the relatively few motor units controlling the wings of a hawk moth, <em>Manduca sexta</em>. We simultaneously recorded nearly every action potential from all major wing muscles and the resulting forces in tethered flight. We found that timing encodes more information about turning behavior than spike count in every motor unit, even though there is sufficient variation in count alone. Flight muscles vary broadly in function as well as in the number and timing of spikes. Nonetheless, each muscle with multiple spikes consistently blends spike timing and count information in a 3:1 ratio. Coding strategies are consistent. Finally, we assess the coordination of muscles using pairwise redundancy measured through interaction information. Surprisingly, not only are all muscle pairs coordinated, but all coordination is accomplished almost exclusively through spike timing, not spike count. Spike timing codes are ubiquitous, consistent, and essential for coordination. </p>
CHU Time of Flight Data, 8 April 2024 Eclipse - ET Stations
<p>See <a href="https://doi.org/10.5281/zenodo.13293306">10.5281/zenodo.13293306</a> for system documentation.<br>See <a href="https://doi.org/10.5281/zenodo.14004715">10.5281/zenodo.14004715</a> for python code to parse these files. </p>
Time-resolved ARPES RAW data of bulk WSe2 for a quantitative comparison of time-of-flight momentum microscopes and hemispherical analyzers: RAW MM data
<p>Time- and angle-resolved photoemission spectroscopy data of bulk WSe2 using a laser-based XUV source and a time-of-flight momentum microscope analyzer.</p> <p>The dataset here comprises part of the machine RAW data used to construct the processed data stored in the datasets at</p> <pre>https://doi.org/10.5281/zenodo.4067968</pre> <p>Analysis scripts and conversion tools into NeXus format can be found at</p> <p>https://github.com/nomad-coe/nomad-parser-nexus/tree/master/tests/data/tools/dataconverter/readers/mpes</p>
Time-Optimal Planning for Quadrotor Waypoint Flight
<p>Quadrotors are amongst the most agile flying robots. However, planning time-optimal trajectories at the actuation limit through multiple waypoints remains an open problem. This is crucial for applications such as inspection, delivery, search and rescue, and drone racing. Early works used polynomial trajectory formulations, which do not exploit the full actuator potential due to their inherent smoothness. Recent works resorted to numerical optimization, but require waypoints to be allocated as costs or constraints at specific discrete times. However, this time-allocation is a priori unknown and renders previous works incapable of producing truly time-optimal trajectories. To generate truly time-optimal trajectories, we propose a solution to the time allocation problem while exploiting the full quadrotor's actuator potential. We achieve this by introducing a formulation of progress along the trajectory, which enables the simultaneous optimization of the time-allocation and the trajectory itself. We compare our method against related approaches and validate it in real-world flights in one of the world's largest motion-capture systems, where we outperform human expert drone pilots in a drone-racing task.</p>
WWV Time of Flight Example Files
<p>These are sample files for time of flight measurement. </p> <p> </p> <p><strong>example_second.csv</strong></p> <p>The file example_second.csv includes one second's worth of data taken at a 512 /s sampling rate. The file encompasses 512 samples spaced linearly in time throughout that second. Each of the 512 entries in the file is one sample of the audio voltage taken by a 10-bit A/D converter, with values from 0 to 1023 mapped linearly to the range of minimum to maximum measurable voltage: i.e., for an audio range of -1.5 to 1.5 volts, a value of 0 is equivalent to a value of -1.5V, a value of 512 is equivalent to 0V, and a value of 1023 is equivalent to a value of 1.5V.</p> <p><strong>Second_ticks_20220908</strong></p> <p>This file contains one day's worth of ToF measurements. The date of recording is included in the filename. The metadata at the beginning of the file contains the receiving station callsign; the transmitter frequency; and the Maidenhead grid square of the receiving station. Each row of the .csv file contains the second tick measurements for one second of the UTC day. Only the first 24 ms after the start of the second are recorded. (At the location of the receiving station, AD8Y, used for these measurements, this is an adequate length of time to capture the second ticks; elsewhere, longer recording may be needed.) The values of each row then follow the same convention listed above. Ionospheric variability may be visualized by creating a heatmap of these values with a divergent colormap.</p> <p><strong>Second_ticks_20221008</strong></p> <p>This file contains one day's worth of ToF measurements which show a coronal mass ejection. </p>
Angle Dependent Spectral Reflectance Material Dataset based on 945 nm Time-of-Flight Camera Measurements
<p>The dataset contains angle dependent spectral reflectance measurements of materials in the infrared spectrum at a wavelength of 945 nm taken by a time-of-flight camera for an angle range of 0° to 80° in incremental steps of 10°, mounted on an adjustable angle measurement device.</p> <p>Each data file is structured in columns with two parameters: reflectance (%) and incidence angle (°).</p> <p>For each single material measurement a description file is attached in a separate ZIP-file.</p> <p>The extended dataset include new materials such as vehicle varnish and moss rubber. This new version further includes pictures for almost each of the measurements to depict the measurement setup and environmental conditions during the data acquisition. They are provided in a separate ZIP-file.</p> <p> </p> <p><strong>Note: </strong>Please always use the latest version!</p>
Multi-photon time-of-flight MLEM application for the positronium imaging in J-PET
<p>Data from the simulation for the J-PET detector with four sources, designed to test positronium imaging reconstruction algorithm.<br> Data generated with the J-PET Geant4 and analyzed with the J-PET Framework.<br> Data in form:<br> Deexcitation Hit2 X[cm], Deexcitation Hit2 Y[cm], Deexcitation Hit2 Z[cm], Deexcitation Hit2 time[ps],<br> Annihilation Hit1 X[cm], Annihilation Hit1 Y[cm], Annihilation Hit1 Z[cm], Annihilation Hit1 time[ps],<br> Annihilation Hit2 X[cm], Annihilation Hit2 Y[cm], Annihilation Hit2 Z[cm], Annihilation Hit2 time[ps]</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.