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2,107 results for “Spectrum”
JWST spectrum of galaxy COSMOS-11142 from the Blue Jay survey.
<p>Spectroscopic and photometric data for galaxy COSMOS-11142, studied in Belli et al. (2024).</p> <ul> <li>The JWST/NIRSpec spectroscopy is stored as a FITS table which includes wavelength (in angstrom), calibrated flux, uncertainty, and best-fit model (in erg/(s cm2 A)).</li> <li>The JWST and HST photometry is stored as a FITS table which includes the name of each filter, the effective wavelength (in angstrom), the observed flux and its uncertainty (in microJy).</li> </ul>
AERPAW helikite spectrum monitoring at Packapalooza festival in Aug 2022
<p>In this dataset, we conducted a spectrum monitoring experiment in an urban environment. The helikite flies up to an altitude of 400 feet throughout the day from noon to 9 p.m. during NC State's Packapalooza festival in August, 2022. The spectrum is swept up to 6 GHz. Every sweep takes around 1 minute, while after every 4 measurements, the 5th measurement takes close to 5 minutes due to another data collection activity running in parallel. The photo of a flying Helikite at the experiment site is shown in the dataset image. The helikite goes up and stays at an altitude of around 400 feet and goes down around an altitude of around 70 feet four times during the whole measurement period.</p>
AERPAW helikite spectrum measurements at Lake Wheeler site in May 2022
<p>The AERPAW helikite flew up to 500 feet altitude, at increments of 10 meters, while waiting for 5 minutes in between altitude changes at the Lake Wheeler site on May 2022. The USRP B205mini continuously sweeps the spectrum up to 6 GHz while the helikite is at a fixed altitude, and power measurements at each frequency band are logged. The results are post-processed in Matlab to observe the spectrum occupancy at different bands and the effect of spectrum sensing altitude on the occupancy results.</p> <p>Similar datasets for August 2022 and August 2023 are in separate Dryad deposits. </p>
IXS spectrum at different Pressure
<p><span>Here is displayed the inelastic x-ray spectrum for different momentum transfers (Q) values at 65.0 GPa, 72.2 GPa, and 79.6 GPa.</span></p>
Intersectionality Spectrum - when DEI initiatives target gender equity
<p>alt-text:</p> <p>Intersectionality spectrum with different categories of intersectionality along the x-axis and the degree of difficulty shown as a bar graph on the y-axis. There is a dotted vertical line that separate visiable spectrum from invisible spectrum. There is an orange box that says “Most DEI initiatives target gender equity” and there are three orange arrows pointing to the white women symbol. This is because they benefit the most, and other non-white women do not, even though other non-white women have a higher degree of difficulty.</p>
Intersectionality Spectrum - triage like a hospital
<p>alt-text:</p> <p>Intersectionality spectrum with different categories of intersectionality along the x-axis and the degree of difficulty shown as a bar graph on the y-axis. It shows 3 arrows pointing down on the bars that have the highest degree of difficulty to signify that we need to prioritise support to those who need it most because they have been discriinated the most. It has one green arrow pointing down to those with smaller degrees of difficulty to signify we still need to help those people as well, but with less intensity or frequency. This is similar to how a hospital should triage patients, in that we need to look after the sickest people first.</p>
Lattice investigations of the chimera baryon spectrum in the Sp(4) gauge theory---Data Release
<p>This release contains the analysis workflow used to prepare the publication <a href="https://arxiv.org/abs/2311.14663" target="_blank" rel="noopener">Lattice investigations of the chimera baryon spectrum in the Sp(4) gauge theory</a>.</p> <p>A Python code for performing the analysis and generating the plots and tables is <a href="https://doi.org/10.5281/zenodo.10929539" target="_blank" rel="noopener">uploaded to Zenodo</a>. See the README therein for details on running the code.</p> <p>For details on the data formats, see the relevant README.md files.</p> <h2>Content of directories and files:</h2> <ul> <li>README.md: This contains general information on the content of the release.</li> <li>raw_data.zip: This compressed file contains all the raw data utilized in the research outlined in arXiv:2311.14663. These data were crucial in generating the results showcased in the paper.</li> <li><span>data.h5: An HDF5 file housing the correlators derived from the raw data through the processing code, <code>generate/transform_h5.py</code>.</span></li> <li><span>metadata.zip: This archive furnishes essential metadata such as ensemble information, fitting intervals, and smearing parameters crucial for extracting masses.</span></li> <li><span>F_meson.csv: Presents the fundamental meson masses extracted via the <code>analysis/analysis_F.py</code> script.</span></li> <li><span>AS_meson.csv: Presents the antisymmetric meson masses extracted via the <code>analysis/analysis_AS.py</code> script.</span></li> <li><span>CB_mass.csv: Presents the chimera baryon masses extracted via the <code>analysis/analysis_CB.py</code> script.</span></li> <li><span>FIT_mass.csv: Offers the AIC scan results conducted through the <code>analysis/analysis_AIC.py</code> script.</span></li> <li><span>FIT_cross_fixAS.csv and FIT_cross_fixF.csv: These files provide cross-check results computed by the</span> <span><code>analysis/analysis_cross.py</code> script, specifically for fixing antisymmetric and fundamental masses, respectively.</span></li> </ul>
Audio files for spectrum analysis demonstration
<div>This data set consists of 6 real-world audio files (in .wav format, 48000Hz, mono) that are carefully crafted as examples for spectral analysis (i.e for teaching or as sample test data for algorithms).</div> <div> </div> <div>The files have clear discernible sound with an added true random background ambient noise (composed of: distant fan noise + distant street traffic + close harddisk clicking noise). The sound is clearly discernible by a human, despite the noise. </div> <div> </div> <div>- There are some musical sounds (the note G3 on several instruments; fundamental frequency 196Hz) on a tubular bell, classical piano, trumpet, violin. The sounds of the instrument was generated from MIDI banks with FluidSynth software, played on a loudspeaker and re-recorded with an analogical microphone (with the ambient noises). Audio processing was performed with Tenacity software;</div> <div>- Sample from human speech (wovel “o”), with the same processing as above; </div> <div>- The “Noise” file is purely digitally generated (white noise).</div> <div> </div> <div> </div> <div>Each audio set is composed of three files: </div> <div>- The audio file (*.wav), each sampled at 48000 Hz, Mono. </div> <div>- an amplitude file (*_amplitude.csv, corresponding linear amplitudes recorded by the microphone of the .wav file). Numeric format in simple text format (.csv) with labeled column names. </div> <div>-a spectrum file (*_spectrum.csv, frequency/amplitude(dB) ) with the results of a FFT (Fast Fourier Transform). Numeric format in simple text format (.csv) with labeled column names. </div> <div> </div> <div>Detailed description of each set is provided below.</div> <div> </div> <div> <ul> <li><strong>Bell_G3.wav</strong></li> <li>Bell_G3_amplitude.csv:<br>Length processed: 113851 samples 2.37190 seconds.<br>Sample Rate: 48000 Hz. <br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 113851 samples, 2.37190 seconds.<br>Peak amplitude: 0.59001 (linear) -4.58276 dB. <br>Unweighted RMS: -19.87796 dB.<br>DC offset: 0.00069 linear, -63.18732 dB.</li> <li>Bell_G3_spectrum.csv:<br>FFT transform (Hz / dB)</li> </ul> </div> <div> <ul> <li><strong>Noise.wav</strong></li> <li>Noise_amplitude.csv:<br>Length processed: 31765 samples 0.66177 seconds.<br>Sample Rate: 48000 Hz. <br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 31765 samples, 0.66177 seconds.<br>Peak amplitude: 0.52797 (linear) -5.54788 dB.<br>Unweighted RMS: -16.61153 dB.<br>DC offset: -0.00013 linear, -77.53051 dB</li> <li>Noise_spectrum.csv)<br>FFT transform (Hz / dB)</li> </ul> </div> <div> <ul> <li><strong>Piano_G3.wav</strong></li> <li>Piano_G3_amplitude.csv:<br>Sample Rate: 48000 Hz.<br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 133063 samples, 2.77215 seconds.<br>Peak amplitude: 0.41070 (linear) -7.72946 dB.<br>Unweighted RMS: -23.95101 dB.<br>DC offset: 0.00030 linear, -70.58058 dB.</li> <li>Piano_G3_spectrum.csv:<br>FFT transform (Hz / dB)</li> </ul> </div> <div> <ul> <li><strong>Trumpet_G3.wav</strong></li> <li>Trumpet_G3_amplitude.csv:<br>Sample Rate: 48000 Hz.<br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 66931 samples, 1.39440 seconds.<br>Peak amplitude: 0.29551 (linear) -10.58853 dB. <br>Unweighted RMS: -22.00611 dB.<br>DC offset: 0.00076 linear, -62.39740 dB.</li> <li>Trumpet_G3_spectrum.csv:<br>FFT transform (Hz / dB)</li> </ul> </div> <div> <ul> <li><strong>Violin_G3.wav</strong></li> <li>Violin_G3_amplitude.csv:<br>Sample Rate: 48000 Hz.<br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 59252 samples, 1.23442 seconds.<br>Peak amplitude: 0.41633 (linear) -7.61119 dB.<br>Unweighted RMS: -18.36738 dB.<br>DC offset: 0.00021 linear, -73.46784 dB.</li> <li>Violin_G3_spectrum.csv:<br>FFT transform (Hz / dB)</li> </ul> </div> <div> <ul> <li><strong>Wovel_O.wav</strong></li> <li>Wovel_O_amplitude.csv<br>Sample Rate: 48000 Hz.<br>Sample values on linear scale. 1 channel (mono).<br>Length processed: 4975 samples, 0.10365 seconds.<br>Peak amplitude: 0.46174 (linear) -6.71214 dB. <br>Unweighted RMS: -14.88086 dB.<br>DC offset: 0.00009 linear, -80.54917 dB.</li> <li>Wovel_O_spectrum.csv:<br>FFT transform (Hz / dB)</li> </ul> </div> <div> </div> <div>These files are created by A. Iftime and released under Creative Commons Licence, 2024. </div> <div> </div> <div>You might cite the dataset as: </div> <div>“Audio files for spectrum analysis demonstration” [dataset] (2024), in “Medical Biophysics for 1st year medical students”, by Călinescu O., Babeș R., Iftime A., Băran I., Ionescu D., Ganea C., in publishing </div>
Light-activated molecular machines display broad spectrum antibacterial action
<p>TEM images of <em>E. coli</em> treated with 1% DMSO or 0.5x MIC of different visible light-activated molecular machines. Light-activated molecular machines (MM) are synthetic molecular structures that, following light activation, undergo successive unidirectional rotation that results in a drilling-like rapid motion that can thrust the motor through biological membranes. Transmission electron microscopy (TEM) images revealed that treatment of <em>E. coli</em> with 0.5x MIC of different MM (DL-654, DL-877, DL-878) followed by activation with 42.6 J cm<sup>-2</sup> of 405 nm light resulted in substantial changes in cell morphology including the detachment of the inner membrane from the cell wall, damage to peptidoglycan, distortion of the cell surface, and formation of outer membrane vesicles, denoting membrane and periplasmic stress, that were not evident in 1% DMSO-treated and irradiated cells. </p>
Signatures of interactions in the Andreev spectrum of nanowire Josephson junctions
<p>Data arXiv:2112.05625</p>
The symmetry spectrum in a hybridising, tropical group of rhododendrons
<p>Many diverse plant clades possess bilaterally symmetrical flowers and specialized pollination syndromes suggesting these traits may promote diversification. We examine the evolution of diverse floral morphologies and the association with diversification history in a species-rich tropical radiation of <em>Rhododendron</em>. We used restriction-site associated DNA sequencing on 114 taxa from <em>Rhododendron</em> sect. <em>Schistanthe</em> to reconstruct phylogenetic relationships, infer colonization of Southeast Asia and examine hybridization. We then captured and quantified floral variation using geometric morphometric analyses which we interpret in a phylogenetic context. We uncovered phylogenetic complexity caused by introgression within and between clades. Morphometric analyses revealed flower symmetry to be a morphological continuum without a clear transition from radial to bilateral symmetry. The largest radiation of tropical <em>Rhododendron</em> species is associated with an expansion into novel floral morphological space as species diversified in New Guinea about 6 million years ago. Our results showed that the recent radiation of tropical <em>Rhododendron</em> is a consequence of hybridization, genetic isolation caused by mountain building, and the evolution of floral novelty. Floral variation evolved via changes to multiple components of the corolla that are only recognized in geometric morphometrics with both front and side views of flowers.</p>
LILBID mass spectrum of water
<p>LILBID mass spectrum of water, shown in "Developing a Laser Induced Liquid Beam Ion Desorption Spectral Database as Reference for Spaceborne Mass Spectrometers" by Klenner et al (2022), published in Earth and Space Science.</p>
Global characterization of the ocean's internal gravity wave vertical wavenumber spectrum from Argo float profiles
<p>Oceanic internal gravity wave energy levels E (m^2/s^2), vertical wavenumber spectral slopes s, and vertical wavenumber scale m* (1/m) estimated by fitting the Garrett Munk model vertical wavenumber shape function to strain spectra obtained from Argo float hydrographic profiles based on the finestructure method, as discussed in Pollmann (2020): "Global Characterization of the Ocean’s Internal Wave Spectrum" (<em>Journal of Physical Oceanography</em> 50.7: 1871-1891). The paper and hence this dataset are a contribution to the Collaborative Research Centre TRR181 ‘Energy Transfers in Atmosphere and Ocean’ funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Projektnummer 274762653. The hydrographic profiles used in this study were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System.</p> <p>Please cite Pollmann (2020) when using this dataset.</p> <p>This dataset includes:</p> <p>a) energy density (m^2/s^2) binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>b) vertical wavenumber spectral slopes binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>c) vertical wavenumber scale m* (1/m) binned into 1°x1° horizontal bins and averaged into 3 depth bins (300-500 m, 500-1000 m, 1000-2000 m)</p> <p>d) latitude and longitude, defined such that, e.g., E(10,10) represents energy levels in the bin bounded by lat(10), lat(11) as well as lon(10), lon(11)</p>
Data from: Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders
<p>This dataset contains supporting data for the publication: Boudriot, E., Schworm, B., Slapakova, L. <em>et al.</em> Optical coherence tomography reveals retinal thinning in schizophrenia spectrum disorders. <em>Eur Arch Psychiatry Clin Neurosci</em> (2022). https://doi.org/10.1007/s00406-022-01455-z</p> <p> </p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Data used for the Walther et al (2017) Lya forest power spectrum measurement
<p>The Files "PS_dataset_*.hdf5" contain the masked datasets used in the Walther et al (2017) power spectrum analysis with or without metal masking.</p> <p>They also contain MCMC chains for performing the masking corrections.</p> <p>The README file gives basic usage instructions.</p>
Expanding the plant economics spectrum with root nitrogen reallocation
<p>Harnessing root nitrogen reallocation (RNR) for optimization of plant productivity commences with positioning RNR in root economics space about which we still know little. We conducted a global synthesis linking RNR to root traits, combined with a two-year <sup>15</sup>N-labelling field experiment to position RNR in plant economics spectrum under acidification. RNR correlated negatively with specific root length (SRL) and mycorrhizal colonization globally, suggesting that RNR is a conservative trait. Sedges, grasses and forbs coordinated root traits (e.g., SRL) from acquisitive to conservative and from low to high RNR reliance (and <em>vice versa</em> for their direct-root N uptake) in the <sup>15</sup>N-tracing experiment. Specifically, sedges and forbs exhibited the lowest and highest RNR that increased and decreased with acidification, respectively. Grasses cooperated well with mycorrhizas, showing moderate RNR and root traits. Our results demonstrated the significance of RNR in plant growth, and the necessity of considering RNR as a conservative trait.</p>
Figure 4 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 4. Scatter diagram load ratio versus ant size. The load ratio was calculated as follows: ant size (head width) + load size/ant size. Data are from random samples of returning foragers of a single M. wasmanni colony. One dot represents one observation (N = 776). Linear regression analysis revealed a low negative correlation (R² = 0.14, p = 0.0001) between ant size and load ratio. The larger the worker size class, the smaller the range in the load ratio. Residuals from regressions were approximately normally distributed around zero in all cases.
Figure 5 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 5. Mean foraging efficiency (in %) per day. Calculations were performed separately per size class and per season. Foraging efficiency varied considerably over the seasons and between size classes. Sample size represented by numbers in bars. Minor = minor-sized workers, Media = media-sized workers, Major = major-sized workers.
Figure 3 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 3. Scatter diagram load size versus ant size. Data are from random samples of returning foragers of a single M. wasmanni colony. One dot represents one observation (N = 776). Linear regression analysis revealed a very low positive correlation (R² = 0.02, p = 0.0001) between ant size and load size, indicating only a small tendency for majorsized workers to carry larger loads than minor-sized workers. Residuals from regressions were approximately normally distributed around zero in all cases.
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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.