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617 results for “IR”
GNSS-IR data for "Real-time water levels using GNSS-IR: a potential tool for flood monitoring"
<p>Organised SNR data used for GNSS-IR analysis in the article "Real-time water levels using GNSS-IR: a potential tool for flood monitoring" by David Purnell, Natalya Gomez, William Minarik and Gregory Langston.</p><p> </p><p>The directories 'rv3s' and 'sjdlr' contain SNR data corresponding to sites Trois-Rivières and Saint-Joseph-de-la-Rive, respectively.</p><p> </p><p>Software for processing the data can be found at: https://github.com/purnelldj/gnssir_rt</p><p> </p><p>SNR data is given as text files in the format specified here except for columns 4+:</p><p>https://gnssrefl.readthedocs.io/en/latest/pages/file_structure.html#the-snr-data-format</p><p>columns:</p><p>1. sat PRN with an offset such that GLONASS satellites are between 100-200 and Galileo are between 200-300</p><p>2. satellite elevation (degrees)</p><p>3. azi is satellite azimuth (degrees)</p><p>4. GPS time (seconds since 1980)</p><p>5. L1 SNR</p>
Orbit positions for the DS sources in the IRS 13 cluster close to Sgr A*
<p>This data is related to the manuscript entiteld "The Evaporating Massive Embedded Stellar Cluster IRS 13 Close to Sgr A*. II. Kinematic structure", published in the ApJ in 2024.</p> <p>We use the Keplerian elements and the data published in "The Evaporating Massive Embedded Stellar Cluster IRS 13 Close to Sgr A*. I. Detection of a Rich Population of Dusty Objects in the IRS 13 Cluster" and "The Evaporating Massive Embedded Stellar Cluster IRS 13 Close to Sgr A*. II. Kinematic structure" to create the orbital solutions published in this data set.</p> <h2>Organization of the data:</h2> <h3>Text files</h3> <p>All .txt files are the output of the fitted Keplerian approximation of the related source. The first column indicates the date, the second and third one the RA and DEC position in arcseconds, respectively. Please note that these positions are projected on the sky with respect to Sgr A<em>. </em>Since all sources are located<em> "to the right" </em>and <em>"below"</em> Sgr A, the related positions contain a minus. </p> <h3>Image files</h3> <p>The related .png files show the plotted .txt files together with the data points published in the manuscript entitled "The Evaporating Massive Embedded Stellar Cluster IRS 13 Close to Sgr A*. I. Detection of a Rich Population of Dusty Objects in the IRS 13 Cluster".</p> <h3>Statistics and Uncertainties</h3> <p>In addition, we publish the outcome of the Markow-Chain-Monte-Carlo (MCMC) simulations as .pdf files. These files indicate the uncertainty range and statistical robustness of the analysis presented in the manuscript entitled "The Evaporating Massive Embedded Stellar Cluster IRS 13 Close to Sgr A*. II. Kinematic structure". Due to the file number restrictions, we uploaded a .rar file that contains all pdf files.</p> <p> </p> <p> </p> <p> </p> <p> </p>
AFM-IR images and spectra of commercial polymers
<p>This data set contains photothermal IR images (AFM-IR) and spectra of a commercial polymers taken with a Bruker nanoIR3. A Daylight solutions MIRcat-QT external cavity quantum cascade laser. Fourier transform infrared (FTIR) spectra were recorded with Bruker Tensor 35 spectrometer using a "Platinum ATR" (Bruker) attenuated total reflection sampling accessory with a diamond ATR element.</p> <p> </p> <p>The AFM-IR raw data files (".axz") are gzipped XML files that can be opened with the <a href="https://github.com/GeorgRamer/anasys-python-tools">anasyspythontools</a> python library. FTIR files (".txt") are CSV files. The first column represents wavenumbers, the second column represents absorption.</p>
Metadata of "Solvent-free Reactions for the Synthesis of Indolenine-based Squaraines and Croconaines: Comparison of Thermal Heating, Mechanochemical Milling, and IR Irradiation"
<p>Metadata of "Solvent-free Reactions for the Synthesis of Indolenine-based Squaraines and Croconaines: Comparison of Thermal Heating, Mechanochemical Milling, and IR Irradiation"</p>
ir_metadata: An Extensible Metadata Schema for Information Retrieval Experiments
<p>This dataset accompanies our work that introduces a metadata schema for TREC run files based on the PRIMAD model. PRIMAD considers essential components of computational experiments that possibly can affect reproducibility on a conceptual level. We propose to align the metadata annotations to the PRIMAD components. In order to demonstrate the potential of metadata annotations, we curated a dataset with run files derived from experiments with different instantiations of PRIMAD components and annotated these with the corresponding metadata. With this work, we hope to stimulate IR researchers to annotate run files and improve the reuse value of experimental artifacts even further.</p> <p> </p> <p>This archive contains the following data:</p> <ul> <li> <p><strong>demo.tar.xz</strong> : Selected annotated runs files that are used in the Colab demonstration.</p> </li> <li> <p><strong>metadata.zip</strong> : YAML files containing only the metadata annotations for each run.</p> </li> <li> <p><strong>runs.zip</strong> : The entire set of run files with annotations.</p> </li> </ul> <p> </p> <p>The annotated runs result from the following experiments:</p> <ul> <li> <p>Grossman and Cormack @ TREC Common Core 2017 <a href="https://trec.nist.gov/pubs/trec26/papers/MRG_UWaterloo-CC.pdf">Paper</a> | <a href="https://trec.nist.gov/">Source</a></p> </li> <li> <p>Grossman and Cormack @ TREC Common Core 2018 <a href="https://trec.nist.gov/pubs/trec27/papers/MRG_UWaterloo-CC.pdf">Paper</a> | <a href="https://trec.nist.gov/">Source</a></p> </li> <li> <p>Yu et al. @ TREC Common Core 2018 <a href="https://trec.nist.gov/pubs/trec27/papers/h2oloo-CC.pdf">Paper</a> | <a href="https://github.com/castorini/Anserini/blob/master/docs/runbook-trec2018-h2oloo.md">Source</a></p> </li> <li> <p>Yu et al. @ ECIR 2019 <a href="https://link.springer.com/chapter/10.1007/978-3-030-15712-8_26">Paper</a> | <a href="https://github.com/castorini/anserini/blob/master/docs/runbook-ecir2019-ccrf.md">Source</a></p> </li> <li> <p>Breuer et al. @ SIGIR 2020 <a href="https://dl.acm.org/doi/10.1145/3397271.3401036">Paper</a> | <a href="https://zenodo.org/record/3856042">Source</a></p> </li> <li> <p>Breuer et al. @ CLEF 2021 <a href="https://link.springer.com/chapter/10.1007/978-3-030-85251-1_5">Paper</a> | <a href="https://zenodo.org/record/4105885">Source</a></p> </li> </ul>
Dataset - Direct-laser-written integrated mid-IR directional couplers in a BGG glass
<p>Dataset related to article:</p> <p>Direct-laser-written integrated mid-IR directional couplers in a BGG glass. Arthur Le Camus, Yannick Petit, Jean-Philippe Bérubé, Matthieu Bellec, Lionel Canioni, Réal Vallée. Optics Express. 2021, volume 29, issue 6, pp. 8531-8541</p> <p><a href="https://doi.org/10.1364/OE.409527">https://doi.org/10.1364/OE.409527</a></p> <p># Research materials<br> We provide here the data and scripts used to generate the figures in the main document</p> <p><br> ## Requirements<br> * All codes have been written using *python 3.7* and standard libraries (*numpy*, *matplotlib*, *scipy*).<br> * Note that pathnames work fine for Unix users but might be manually modified for Windows users.</p> <p><br> ## Data files description<br> All experimental data files are stored in the `./data` folder with `.npy` format, a standard binary file format in NumPy.<br> * `bgg-2p85.npy` and `silica-2p85.npy` contains the experimental data used to generate Fig. 3.<br> * `bgg-2p85-num.npy` contains numerical data used in Fig. 3 (dashed blue line).<br> * `bgg-4mm-exp.npy`/`bgg-4mm-num.npy` and `bgg-5mm-exp.npy`/`bgg-5mm-num.npy` contains the experimental/numerical data used to generate Fig. 4 left and right panels respectively. The `.npy` files are dictionnaries. The key `P_ref` corresponds to the grey curve in Fig. 4a. The keys `P_1_3` and `P_1_4`correspond to the output power in port 3 and port 4 respectively.</p> <p><br> ## Script files description<br> * Running the python scripts allow to generate the figures presented in the manuscript. The filenames are explicit.<br> * Fig. 4 has been slightly post-edited (add $y$-axis color and a) and b) labels) using Adobe Illustrator software.</p> <p> </p>
Theoretical and Experimental database for corannulene:water aggregates in a rare gas matrix. Structures and IR spectra.
<p>This database is linked to the article entitled "Water clusters in interaction with corannulene in a rare gas matrix: structures, stability and IR spectra" by Leboucher et al. submitted to "Photochem" on March 1st 2022.</p> <p>Theoretical results can be found in the following directories:<br> - Geoms which contains the DFTB/FF optimized structures reported in Figures 2 to 4 of the manuscript (the last column of the files must not be taken into account)<br> - IR_harm which contains the IR harmonic data for these structures (first column: wavenumbers in cm-1, second column: intensities in km/mol)<br> - Spect_10K which contains the dynamics spectra as reported in Figures 5 to 7</p> <p>Experimental results can be found in the directory Experimental, with spectra in two columns (first column: wavenumber in cm-1, second column: absorbance). Data have been corrected for atmospheric water vapor.</p> <p>In the directory Gas-phase are reported the results of gas-phase calculations at the DFT (M062X/d95v(dip) and DFTB levels performed for benchmark purpose.<br> - in DFT_opt are reported DFT optimized structures, similar to DFTB optimized structures<br> - in IR-spect, harmonic DFT and DFTB spectra<br> - the En_GP.pdf file reports energetic data for these systems.</p> <p> </p>
Nuclear excitation functions for medical isotope production: targeted radionuclide therapy via nat Ir(d, x)193mPt
<p><sup>193m</sup>Pt is a medically valuable Auger-emitting radionuclide, believed to have therapeutic potential, particularly when labeled to the chemotherapeutic drug cisplatin. One challenge to broader explorations of its clinical potential is the need for production routes with high specific activity. As part of a larger campaign to address gaps in reaction data for emerging medical radionuclides, this work seeks to characterize the <sup>nat</sup>Ir(d,x) reactions as a potential production pathway for <sup>193m</sup>Pt. A stacked target irradiation, consisting of natural iridium, iron, nickel, and copper foils, was performed using a 33 MeV deuteron beam at the Lawrence Berkeley National Laboratory 88-Inch Cyclotron. This measurement, along with previous experimental data, suggests an energy window between 11 to 18 MeV to maximize the production and radiopurity of <sup>193m</sup>Pt. This experiment has yielded cross sections for 42 reaction channels of deuteron-induced reactions from threshold to 30 MeV, including the first experimental results of <sup>nat</sup>Ir(d,x)<sup>188m1+g,190m1+g</sup>Ir (cumulative), <sup>nat</sup>Ni(d,x)<sup>56,57,58m,58g</sup>Co (independent), <sup>nat</sup>Ni(d,x)<sup>53</sup>Fe (cumulative), and <sup>nat</sup>Fe(d,x)<sup>48 V, 51 Cr</sup>(cumulative). The results were compared with literature data, the TENDL-2019 database, and default theoretical calculations from the TALYS-1.9, CoH-3.5.3, EMPIRE-3.2.3, and ALICE-2017 reaction modeling codes. This work presents another example of the lack of predictive capabilities for this set of modern nuclear-reaction modeling codes, and highlights unsatisfactory modeling in the A ≈ 190 mass region, which proved particularly difficult to model using CoH-3.5.3. Finally, this measurement has revealed weaknesses with the current evaluation of the <sup>nat</sup>Cu(d,x)<sup>63</sup>Zn deuteron monitor reaction.</p>
Data for Vibrational Couplings between Protein and Co-factor in Bacterial Phytochrome Agp1revealed by 2D-IR Spectroscopy
<p>2D-IR data for the bacteriophytochrome Agp1 in the Pr and Pfr states </p>
soundscape_IR: A source separation toolbox for exploring acoustic diversity in soundscapes
<p>1. Soundscapes contain rich acoustic information associated with animal behaviors, environmental characteristics, and human activities, providing opportunities for predicting biodiversity changes and associated drivers. However, assessing the diversity of animal vocalizations remains challenging due to the interference of environmental and anthropogenic noise. A tool for separating sound sources and delineating changes in acoustic signals is crucial for an effective assessment of acoustic diversity.</p> <p>2. We present soundscape_IR, an open-source Python toolbox dedicated to soundscape information retrieval in which non-negative matrix factorization is applied. This toolbox provides algorithms for supervised and unsupervised source separation (SS). It also enables the use of a snapshot recording for model training and subsequently applying adaptive and semi-supervised SS when target species produce sounds with varying features and when unseen sound sources are encountered.</p> <p>3. Our results demonstrated that SS could enhance the vocalizations of target species, characterize the complexity of vocal repertoires, and investigate the spatio-temporal divergence of soundscapes. In tropical forest soundscapes, the application of SS effectively detected the rutting vocalizations of sika deer and revealed a graded structure in their acoustic characteristics. In subtropical estuarine soundscapes, SS automated the process of identifying distinct biotic and abiotic sounds, and the result uncovered divergent sound compositions between inshore and offshore waters.</p> <p>4. Implementation of SS in soundscape analysis offers a promising method for streamlining the assessment of acoustic diversity in diverse environments. Future application of SS will open new directions to acoustically quantify ecological interactions across individual, species, and ecosystem levels.</p>
Microdata on vector abundance and IRS quality assurance (Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study)
<p>This repository contains the microdata on vector abundance and quality assurance of indoor residual spraying (IRS) that was used to estimate the impact of IRS on sandfly abundance and incidence of visceral leishmaniasis (VL) in India, as described in the paper "Estimating the impact of indoor residual spraying on sandfly abundance and incidence of visceral leishmaniasis in India from 2016 to 2022: an interrupted time-series analysis and modelling study" by Coffeng et al (<a href="https://doi.org/10.1016/S1473-3099(24)00420-1">https://doi.org/10.1016/S1473-3099(24)00420-1</a>). These data were collected as part of a BMGF-funded project led by dr. Michael Coleman at the Liverpool School for Tropical Medicine, as described in an earlier paper by Deb et al (<a href="https://doi.org/10.1371/journal.pntd.0009101">https://doi.org/10.1371/journal.pntd.0009101</a>).</p> <p>This repository does not include microdata on VL cases as these are owned by India's National Center for Vector Borne Disease Control (NCVBDC, <a href="https://ncvbdc.mohfw.gov.in/" target="_blank" rel="nofollow noreferrer noopener">https://ncvbdc.mohfw.gov.in/</a>).</p>
Spectral effects of regolith porosity in the mid-IR - Forsteritic olivine
<p>This dataset contains laboratory spectra of olivine in the mid-Infrared (MIR; 5-35 micron) wavelength region as described in Martin et al., 2022. </p> <p>Files are labeled accordingly: mineral_largest particle size_regolith porosity</p> <p>Example: The file labeled OLV1_45_10.txt contains spectra of olivine (OLV1 in the paper), with 20-45 micron particle sizes, has 10% regolith porosity. </p>
Spectral effects of regolith porosity in the Mid-IR - Pyroxene [part 2]
<p>This dataset is one of three that contains laboratory spectra of pyroxene in the mid-Infrared (MIR; 5-35 micron) wavelength region as described in Martin et al., 2023. Specifically, this dataset (part 2) has .txt files of HEN1, HEN2, and AEG spectra.</p> <p>Dataset part 1 (10.5281/zenodo.11398016) contains spectra of ENS, DIOP1, and DIOP2.</p> <p>Dataset part 3 (10.5281/zenodo.11402393) contains spectra of AUG and MXT.</p> <p>Files are labeled accordingly: mineral_largest particle size_regolith porosity</p> <p>Example: The file labeled AEG_45_10.txt contains spectra of aegirine (AEG in the paper), with 20-45 micron particle sizes, has 10% regolith porosity. </p>
Spectral effects of regolith porosity in the Mid-IR - Pyroxene [part 1]
<p>This dataset is one of three that contains laboratory spectra of pyroxene in the mid-Infrared (MIR; 5-35 micron) wavelength region as described in Martin et al., 2023. Specifically, this dataset (part 1) has .txt files of ENS, DIOP1, and DIOP2 spectra.</p> <p>Dataset part 2 (10.5281/zenodo.11402380) contains spectra of HEN1, HEN2, and AEG.</p> <p>Dataset part 3 (10.5281/zenodo.11402393) contains spectra of AUG and MXT.</p> <p>Files are labeled accordingly: mineral_largest particle size_regolith porosity</p> <p>Example: The file labeled DIOP1_45_10.txt contains spectra of diopside (DIOP1 in the paper), with 20-45 micron particle sizes, has 10% regolith porosity. </p>
Spectral effects of regolith porosity in the Mid-IR - Pyroxene [part 3]
<p>This dataset is one of three that contains laboratory spectra of pyroxene in the mid-Infrared (MIR; 5-35 micron) wavelength region as described in Martin et al., 2023. Specifically, this dataset (part 3) has .txt files of AUG and MXT spectra.</p> <p>Dataset part 1 (10.5281/zenodo.11398016) contains spectra of ENS, DIOP1, and DIOP2.</p> <p>Dataset part 2 (10.5281/zenodo.11402380) contains spectra of HEN1, HEN2, and AEG.</p> <p>Files are labeled accordingly: mineral_largest particle size_regolith porosity</p> <p>Example: The file labeled AUG_45_10.txt contains spectra of augite (AUG in the paper), with 20-45 micron particle sizes, has 10% regolith porosity. </p>
Sensing Reflector Height Variation on Seconds-Scale Using Low Earth Orbit Interferometric Reflectometry (LEO-IR)
<p><span>The simulated data (SNR, elevation and azimuth angles etc.) used in this study.</span></p> <p><span>File name format: arc number for GNSS (or LEO) <span>constellation</span> '_' PRN '_' 'seg'+arc number for a GNSS (or LEO) satellite</span></p> <p><span>File content format: 1[year] 2[month] 3[day] 4[hour] 5[minute] 6[seconds] 7[elevation angle] 8[azimuth angle] 9[chang rate of elevation angle] 10[SNR] 11[rh_ref] 12[rhdot_ref] 13[t_seconds]</span></p> <p> </p>
IR data of the compounds published in "Bioinspired Nucleophilic Attack on a Tungsten-Bound Acetylene: Formation of Cationic Carbyne and Alkenyl Complexes"
Open the record for dataset details and reuse information.
Multivariate curve resolution -alternating least squares augmented with partial least squares baseline correction applied to mid-IR laser spectra resolves protein denaturation by reducing rotational ambiguity
<p>This record contains mid-IR spectra of a titration of beta-lactoglobulin with two detergents. Spectra are stored in the form of Matlab .mat files. More details on the data and data processing can be found in the associated publication.</p><ul><li>F_smooth: Surfactant calibration spectra</li><li>initial_estimate_4c: Intitial estimate of spectra used for MCR-ALS</li><li>protein_spectra_new: Protein spectra obtained by subtracting the background surfactant titration spectra (surftitrationnewwvcorr) from the protein</li><li>titration spectra (prtntitrationnewwvcorr)</li><li>tit_concat_new_mcr: Input for standalone MCR-ALS</li><li>true_conc_sds_nis_pr_cal: Concentrations of SDS and C12E8 as well as protein used in training data for PLS</li></ul>
Ozone 3D field from IRS OSSE Control 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 control run for June 2019. One file per day including hourly fields, gathered in 3 tar files.</p>
Ozone 3D field from IRS assimilation run - July 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 July 2019. One file per day including hourly field, all days in one tar file.</p>
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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.