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Dataset results
617 results for “IRS”
IR analysis of ([Hmim][OTf])3Zn - NCN project OPUS, grant no. 2020/37/B/ST8/00693.
<p>Dataset contains results obtained during the NCN project OPUS, grant no. 2020/37/B/ST8/00693. The file presents IR analysis of ([Hmim][OTf])3Zn.</p>
Ir and NMR for article Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (in vitro) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors
<p>this is IR and NMR data for article titled <strong>Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (<em>in vitro</em>) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors </strong></p> <p> </p>
IR data of the compounds published in "Synthesis and Reactivity of Molybdenum and Tungsten Alkyne Complexes Containing 6-Methylpyridine-2-thiolate Ligands"
<p>Here, the uploaded data are associated with the manuscript "Synthesis and Reactivity of Molybdenum and Tungsten Alkyne Complexes Containing 6-Methylpyridine-2-thiolate Ligands," published in Helvetica Chimica Acta under the following DOI: https://doi.org/10.1002/hlca.202100137<br>The .dpt files represent IR spectra of the compounds published in the manuscript. The labeling used consists of two parts, e.g., 1b – ME162, where 1b represents the label of the compound as presented in the manuscript, and ME162 represents the crystallographic label found in the supplementary information (SI).</p>
Ozone 3D field from IRS assimilation run - June 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the assimilation run for June 2019. One file per day including hourly fields.</p>
FT-IR spectra in triplicate for Pd EnCat™ 30 recycling
<p>FT-IR spectra in triplicate for Pd EnCat™ 30. The spectra were obtained for the pure catalyst and after 1, 10, 20, and 30 Suzuki coupling reactions to monitor its structural changes during recycling.</p>
Dataset for "High power single crystal KTA optical parametric amplifier for efficient 1.4–3.5 µm mid-IR radiation generation"
<p>The dataset represents the experimental data for publication "High power single crystal KTA optical parametric amplifier for efficient 1.4–3.5 µm mid-IR radiation generation".</p>
On-chip phonon-enhanced IR near-field detection of molecular vibrations
<p>This dataset contains the source data and raw interferograms associated with our manuscript, <em>'On-chip phonon-enhanced IR near-field detection of molecular vibrations</em>' by A. Bylinkin et al. The source data, provided in an .xlsx file, includes the datasets used to generate the figures in both the main text and the Supplementary Information. The raw interferogram dataset, located in the <em>'Interferograms</em>' folder, was used to calculate the experimental spectra shown in Fig. 3b, f, and Suppl. Fig. 13. This dataset was acquired using a NeaSNOM microscope (attocube AG).</p> <p>The data processing procedure for calculating spectra from the interferograms is described in the Methods section of our manuscript.</p>
Neptune Spitzer IRS
<p>Reduced Neptune data from the Spitzer Space Telescope Infrared Spectrograph (IRS)</p> <p>SL, SH and LH (SL also available in separate orders 1, 2 and 3)</p> <p>05/2006 = v2 (1 longitude)</p> <p>11/2005 = v1 (3 longitudes)</p> <p>SL_v1L1 = 14784512, SL_v1L2 = 14784768, SL_v1L3 = 15911424,</p> <p>11/2004 cy1 (cycle 1)</p> <p>cy1L1 = 11073536, cy1L2 = 11073792, cy1L3 = 11074048, cy1L4 = 11074560</p> <p>05/2004 (as in Meadows et al 2008)</p> <p>meadL1 = 4525056, meadL2 = 4525312, meadL3 = 4525568</p> <p>Raw data can be accessed at the Spitzer Heritage Archive: https://sha.ipac.caltech.edu/applications/Spitzer/SHA/ and can be searched by ‘Program’. Data from 05/2004 has ID 71, 11/2004 has ID 3534, 11/2005 has ID 20500, and 05/2006 has ID 20500.</p>
Anechoic and IR Convolution-based Auralization Data Compilation Ensemble (AIRCADE)
<p><strong>AIRCADE</strong> is a data-compilation ensemble, primarily intended to serve as a resource for researchers in the field of dereverberation, particularly for data-driven approaches. It comprises <strong><a href="https://zenodo.org/record/1188976#.ZDhNTHbMJPY">speech and song samples</a></strong>, together with <strong><a href="https://zenodo.org/record/3371780#.ZDhOC3bMJPZ">acoustic guitar sounds</a></strong>, with original annotations pertinent to emotion recognition and Music Information Retrieval (MIR). Moreover, it includes a selection of <strong><a href="https://www.openair.hosted.york.ac.uk/">Impulse Response (IR) samples</a></strong> with varying Reverberation Time (RT) values, providing a wide range of conditions for evaluation. This data-compilation can be used together with provided Python scripts (available on <strong><a href="http://github.com/TulioChiodi/AIRCADE">GitHub</a></strong>), for generating auralized data ensembles in different sizes: <em>tiny</em>, <em>small</em>, <em>medium</em> and <em>large</em>. Additionally, the provided metadata annotations also allow for further analysis and investigation of the performance of dereverberation algorithms under different conditions. All data is licensed under <strong><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode">Creative Commons Attribution 4.0 International License</a></strong>.</p> <p><strong>About the sizeable versions:</strong></p> <p>The data-compilation is hosted here at <strong><a href="https://zenodo.org/record/7818761#.ZD7ON3bMJPa">Zenodo</a></strong>, with an approximate total file size of 1.3 GB. For simplicity, all samples in our data-compilation were renamed, e.g., <em>guitar_0000</em>, <em>rir_0000</em>, <em>song_0000</em>, <em>speech_0000</em>, and so on. The ensemble versions are available in different sizes, from a <em>tiny</em> version, with limited data, to a <em>large</em> version, with almost 300,000 samples. This allows users to choose the most suitable version for their specific research needs. The following table illustrates the differences between all versions, detailing the number of song, speech, guitar, IR and auralized samples in each one, together with their respective total file size and duration.</p> <table align="center"> <caption>Number of anechoic, IR and resultant auralized data samples, together with their respective total duration and file size for each ensemble version</caption> <tbody> <tr> <td><strong>Version</strong></td> <td><strong>Tiny</strong></td> <td><strong>Small</strong></td> <td><strong>Medium</strong></td> <td><strong>Large</strong></td> </tr> <tr> <td>Song samples</td> <td>100</td> <td>500</td> <td>1,012</td> <td>1,012</td> </tr> <tr> <td>Speech samples</td> <td>100</td> <td>500</td> <td>1,012</td> <td>1,440</td> </tr> <tr> <td>Guitar samples</td> <td>100</td> <td>500</td> <td>1,012</td> <td>2,004</td> </tr> <tr> <td>IR samples</td> <td>5</td> <td>9</td> <td>33</td> <td>65</td> </tr> <tr> <td>Auralized samples</td> <td>1,500</td> <td>13,500</td> <td>100,188</td> <td>289,640</td> </tr> <tr> <td>Total duration</td> <td>3.2 h</td> <td>30.41 h</td> <td>221.77 h</td> <td>658.08 h</td> </tr> <tr> <td>Total file size (required)</td> <td>1.1 GB</td> <td>10.5 GB</td> <td>76.6 GB</td> <td>227.5 GB</td> </tr> </tbody> </table> <p>For more information, please refer to our data paper on <strong><a href="https://arxiv.org/abs/2304.09318">ArXiv</a></strong>.</p> <p><strong>Citation</strong>:</p> <p>If you find <strong>AIRCADE </strong>useful in your research, please cite:</p> <blockquote> <pre>@misc{chiodi2023aircade, title={AIRCADE: an Anechoic and IR Convolution-based Auralization Data-compilation Ensemble}, author={Túlio Chiodi and Arthur dos Santos and Pedro Martins and Bruno Masiero}, year={2023}, eprint={2304.09318}, archivePrefix={arXiv}, primaryClass={eess.AS} }</pre> </blockquote> <p><strong>Acknowledgement</strong>:</p> <p>This work was partially supported by the <strong><a href="https://fapesp.br/">São Paulo Research Foundation (FAPESP)</a></strong>, grants #2017/08120-6 and #2019/22795-1.</p>
Time/frequency-domain characterization of a mid-IR DFG frequency comb via two-photon and heterodyne detection: datasets
<p>This archive contains the datasets used for generating the plots shown in the paper entitled "Time/frequency-domain characterization of a mid-IR DFG frequency comb via two-photon and heterodyne detection", authored by Tecla Gabbrielli, Giacomo Insero, Michele De Regis, Nicola Corrias, Iacopo Galli, Davide Mazzotti, Paolo Bartolini, Jeong Hyun Huh, Carsten Cleff, Alexander Kastner, Ronald Holzwarth, Simone Borri, Luigi Consolino, Paolo De Natale, and Francesco Cappelli. </p> <p>A README file describing the contained data and how to elaborate them is also provided. </p> <p> </p>
Data for Transient 2D IR spectroscopy and multiscale simulations reveal vibrational couplings in the Cyanobacteriochrome Slr1393-g3
<p>Data used in the Manuscript Transient 2D IR spectroscopy and multiscale simulations reveal vibrational couplings in the Cyanobacteriochrome Slr1393-g3</p>
Data for: Direct numerical simulation surface layer and IR-based measurements
<p>This is the primary dataset used in a manuscript in the process of being submitted to JTECH. The bulk of the dataset is a direct numerical simulation (DNS) of an open channel flow with a shear-free surface. The DNS includes scalar, velocity, and divergence fields over 181 realizations. From the scalar field, another vector field was generated by a method called feature image velocimetry that successively cross-correlates the scalar fields to derive the underlying velocity field. The two velocity fields are then compared by their spectra, one from the DNS and the other derived from the scalar field.</p>
Optical constants of CO and CO2 in the IR ans THz ranges and code to calculate the opacity of icy interstellar grains
<p>Data set of optical constants of CO and CO2 in the IR and THz ranges (0.3–12.0 THz). These data are used to compute the opacity of coated materials in an astrophysical context in our accepted article for publication in A&A, 2022.</p> <p>The following link provides access to the "qabs" open access code and its source to calculate the opacity using the optical constants. The code computes Qabs, Qsca, and Qext from the refractive index or the dielectric constant using Mie's theory:</p> <p>https://bitbucket.org/tgrassi/compute_qabs/src/master/</p> <p>The link redirects you to an open-access environment where to execute the code properly.</p>
Data from: Can IR images of the water surface be used to quantify the energy spectrum and the turbulent kinetic energy dissipation rate?
Open the record for dataset details and reuse information.
Proteome Discoverer IR E. coli, D. radiodurans, and H. Sapiens result files
<p>File containing all of the Proteome Discoverer result files available with the publication "<strong>Ionizing radiation-induced proteomic oxidation in <em>Escherichia coli."</em></strong></p>
Data for "Nanosecond Protein Dynamics in a Red/Green Cyanobacteriochrome Revealed by Transient IR Spectroscopy "
<p>Raw data behind manuscript figures.</p>
Spectral data used in the paper "Intense Zonal Wind in the Martian Mesosphere During the 2018 Planet-Encircling Dust Event Observed by Ground-based IR Heterodyne Spectroscopy"
<p>This data contains the spectral data used in the paper "Intense Zonal Wind in the Martian Mesosphere During the 2018 Planet-Encircling Dust Event Observed by Ground-based IR Heterodyne Spectroscopy". </p> <p>"MILAHI_2018PEDE_Spectral_Data" is the spectral data and you can find the detailed information of the data in "README".</p> <p> </p> <p> </p>
The cultural transmission of causally (ir)relevant actions in the laboratory: Does modelling or verbal instruction lead to greater copying fidelity?
<p>This study examines the fidelity with which adults vs. children transmit a set of actions within diffusion chains (generations 1 to 3). The set consisted of causally relevant and causally irrelevant actions. Half of the adult and half of the child chains transmitted the actions via demonstration (next participant saw the previous perform the actions on video); the other half transmitted them verbally (next participant listened to audio file the previous participant describe his/her actions). We measured whether the actions were retained (i.e. re-produced by each next participant in the chain).</p>
Census of R Coronae Borealis stars I: Infrared light curves from Palomar Gattini IR
<p>Lightcurves and spectra presented in the paper "Census of R Coronae Borealis stars I: Infrared light curves from Palomar Gattini IR", Karambelkar et al. 2021.</p>
Supplementary material: Modeling and Compensating Temperature-dependent Non-uniformity Noise in IR Microbolometer Cameras
<p><strong>Abstract</strong>: Images rendered by uncooled microbolometer-based infrared (IR) cameras are severely degraded by the spatial non-uniformity (NU) noise. The NU noise imposes a fixed-pattern over the true images, and the intensity of the pattern changes with time due to the temperature instability of such cameras. In this paper, we present a novel model and a compensation algorithm for the spatial NU noise and its temperature-dependent variations. The model separates the NU noise into two components: a constant term, which corresponds to a set of NU parameters determining the spatial structure of the noise, and a dynamic term, which scales linearly with the fluctuations of the temperature surrounding the array of microbolometers. We use a black-body radiator and samples of the temperature surrounding the IR array to offline characterize both the constant and the temperature-dependent NU noise parameters. Next, the temperature-dependent variations are estimated online using both a spatially uniform Hammerstein-Wiener estimator and a pixelwise least mean squares (LMS) estimator. We compensate for the NU noise in IR images from two long-wave IR cameras. Results show an excellent non-uniformity correction performance and a root mean square error of less than 0.25◦C, when array’s temperature varies approximately 15◦C.</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.