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1,169 results for “Infrared”
Nonperturbative infrared finiteness in super-renormalisable scalar quantum field theory -- data release
<p>This submission contains the Markov-Chain Monte Carlo data required to reproduce central results of the paper "Nonperturbative infrared finiteness in super-renormalisable scalar quantum field theory" (<a href="https://arxiv.org/abs/2009.14768">https://arxiv.org/abs/2009.14768</a>). </p> <p>The Python code required to read and analyse the data can be found under https://github.com/andreasjuettner/Finite-Size-Scaling-Analysis (the relevant release is attached to 10.5281/zenodo.4290508).</p> <p>For any questions please get in touch: juettner@soton.ac.uk.</p>
Reproduction package for the paper "Mapping the spectral index of Cas A: evidence for flattening from radio to infrared"
<p>This is a basic reproduction package for the paper "Mapping the spectral index of Cas A: evidence for flattening from radio to infrared" by V. Domček et al. (2021). It provides raw, intermediate and final data sets, including figures and scripts to allow the reproduction of the work performed in this paper. It also lists software used and data archives containing the public observational data. </p> <p>An open access version of the paper can be found at https://arxiv.org/abs/2005.12677</p>
Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership. SPCM Atlas Dataset.
<p>The SPCM (SFiNCs Possible Cluster Member) Atlas dataset accompanies the article entitled ``Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership,'' by Getman, Broos, Kuhn, Feigelson, Richert, Ota, Bate, and Garmire, to appear in The Astrophysical Journal Supplement Series. The paper is also available on-line on astro-ph at: https://arxiv.org/abs/1612.05282 . SPCM Atlas is a collection of 25 PDF files. Four pdf files are associated with the SFiNCs star forming region (SFR) Cep OB3b, and 21 pdf files are associated with the remaining 21 SFiNCs SFRs. Full description of SPCM Atlas is given in the Appendix B section of the article. This upload is superseded by a new version, http://doi.org/10.5281/zenodo.345398 .</p>
A Novel Model Hierarchy Isolates the Limited Effect of Supercooled Liquid Cloud Optics on Infrared Radiation
<p>This dataset contains data used in and resulting from an upcoming paper. For further detail on methodology and experiments, see that paper.</p> <h2>Supercooled liquid water optics</h2> <h3>Complex refractive indices (CRIs)</h3> <ul> <li>Water_DW_300.txt</li> <li>water_RFN_240K.txt</li> <li>water_RFN_253K.txt</li> <li>water_RFN_263K.txt</li> <li>water_RFN_273K.txt</li> </ul> <p>Water_DW_300.txt is sourced from Downing & Williams 1975 (https://doi.org/10.1029/JC080i012p01656). water_RFN_240K.txt, water_RFN_253K.txt, water_RFN_263K.txt, and water_RFN_273K.txt are sourced from Rowe et al. 2020 (https://doi.org/10.1029/2020JD032624).</p> <h3>CESM lookup tables of liquid water optics</h3> <ul> <li>CESM_CRI_RFN_240K.nc</li> <li>CESM_CRI_RFN_253K.nc</li> <li>CESM_CRI_RFN_263K.nc</li> <li>CESM_CRI_RFN_273K.nc</li> </ul> <p>These optics sets were created from the corresponding Rowe et al. 2020 CRI.</p> <p> </p> <h2>SCAM output</h2> <p>History files for the four MPACE SCAM runs.</p> <ul> <li>Control: tutorial.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>240K optics: cri240K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>263K optics: cri263K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>273K optics: cri273K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980-2018 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980-2018 experiment. For the variable FLDS (downwelling longwave flux at the surface), each optics set has a mean, count (n), and standard deviation file. These statistics are calculated over the 39 years of the model run and across all 3 ensemble members. </p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>B1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the B1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850 data</h2> <p>Data used to create graphs shown in PAPER from the F1850 experiment. For each optics run there is a mean, count (n), and standard deviation file. These statistics are calculated over the 40 years of the model run for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.std.All_data.non_filtered.nc</li> </ul>
Reproduction package for "A strong H− opacity signal in the near-infrared emission spectrum of the ultra-hot Jupiter KELT-9b"
<p>This is a basic reproduction package for the paper "A strong H− opacity signal</p><p>in the near-infrared emission spectrum of the ultra-hot Jupiter KELT-9b"</p><p>by [Jacobs, B.; Désert, J. -M.; Pino, L. et al. (2022)](https://doi.org/10.1051/0004-6361/202244533).</p><p> </p><p>Abstract:</p><p>We present the analysis of a spectroscopic secondary eclipse of the hottest transiting exoplanet detected to date, KELT-9b, obtained with the Wide Field Camera 3 aboard the <i>Hubble</i> Space Telescope. We complement these data with literature information on stellar pulsations and <i>Spitzer</i>/Infrared Array Camera and Transiting Exoplanet Survey Satellite eclipse depths of this target to obtain a broadband thermal emission spectrum. Our extracted spectrum exhibits a clear turnoff at 1.4 μm. This points to H− bound-free opacities shaping the spectrum. To interpret the spectrum, we perform grid retrievals of self-consistent 1D equilibrium chemistry forward models, varying the composition and energy budget. The model with solar metallicity and C/O ratio provides a poor fit because the H− signal is stronger than expected, requiring an excess of electrons. This pushes our retrievals toward high atmospheric metallicities ([M/H] = 1.98−0.21+0.19) and a C/O ratio that is subsolar by 2.4<i>σ</i>. We question the viability of forming such a high-metallicity planet, and therefore provide other scenarios to increase the electron density in this atmosphere. We also look at an alternative model in which we quench TiO and VO. This fit results in an atmosphere with a slightly subsolar metallicity and subsolar C/O ratio ([M/H] = −0.22−0.13+0.17, log (C/O) = −0.34−0.34+0.19). However, the required TiO abundances are disputed by recent high-resolution measurements of the same planet.</p>
Reproduction package for "Probing reflection from aerosols with the near-infrared dayside spectrum of WASP-80b"
<p>This is a basic reproduction package for the paper "Probing reflection from</p><p>aerosols with the near-infrared dayside spectrum of WASP-80b"</p><p>by [Jacobs, B.; Désert, J. -M.; Gao P. et al. (2023)](https://doi.org/10.3847/2041-8213/acfee9).</p><p>Abstract:</p><p>The presence of aerosols is intimately linked to the global energy budget and the composition of a planet's atmospheres. Their ability to reflect incoming light prevents energy from being deposited into the atmosphere, and they shape spectra of exoplanets. We observed five near-infrared secondary eclipses of WASP-80b</p><p>with the Wide Field Camera 3 (WFC3) aboard the Hubble Space Telescope to provide constraints on the presence and properties of atmospheric aerosols.</p><p>We detect a broadband eclipse depth of 34\pm10 ppm for WASP-80b. We detect a higher planetary flux than expected from thermal emission alone at 1.6 sigma, which hints toward the presence of reflecting aerosols on this planet's dayside, indicating a geometric albedo of A_g<0.33 at 3 sigma.</p><p>We paired the WFC3 data with Spitzer data and explored multiple atmospheric models with and without aerosols to interpret this spectrum.</p><p>Albeit consistent with a clear dayside atmosphere, we found a slight preference for near-solar metallicities and for dayside clouds over hazes. We exclude soot haze formation rates higher than 10^{-10.7} g cm^{-2} s^{-1} and tholin formation rates higher than 10^{-12.0} g cm^{-2} s^{-1} at 3 sigma.</p><p>We applied the same atmospheric models to a previously published WFC3/Spitzer transmission spectrum for this planet and found weak haze formation.</p><p>A single soot haze formation rate best fits both the dayside and the transmission spectra simultaneously. However, we emphasize that no models provide satisfactory fits in terms of the chi-square of both spectra simultaneously, indicating longitudinal dissimilarity in the atmosphere's aerosol composition.</p>
Top view of DR1/DR2 double riffle, each section contains a spawning ground made up of eight gravel-filled trays, a rest area. The "double riffle" was designed to accommodate two groups from 25 to 50 specimens of broodstock in strictly identical conditions. The spawning grounds are equipped with waterproof, motion-sensing cameras with infrared night vision, connected to a 1000 Gb recorder. The diurnal and nocturnal activities of the two groups can therefore be simultaneously recorded over a long period. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Top view of DR1/DR2 double riffle, each section contains a spawning ground made up of eight gravel-filled trays, a rest area. The "double riffle" was designed to accommodate two groups from 25 to 50 specimens of broodstock in strictly identical conditions. The spawning grounds are equipped with waterproof, motion-sensing cameras with infrared night vision, connected to a 1000 Gb recorder. The diurnal and nocturnal activities of the two groups can therefore be simultaneously recorded over a long period.
Infrared Spectroscopy for Diagnosing Superlattice Minibands in Magic-angle Twisted Bilayer Graphene
Open the record for dataset details and reuse information.
The NGDEEP NIRIS calibration files for 'The Next Generation Deep Extragalactic Exploratory Public Near-Infrared Slitless Survey Epoch 1 (NGDEEP-NISS1): Extra-Galactic Star-formation and Active Galactic Nuclei at 0.5 < z < 3.6
<p>GRISMCONF configurations files used in Pirzkal et al. 2024. These contain the full field calibrated solution for the dispersion solution, trace as well as wavelength calibration. They provide a mean to extract NIRISS WFSS spectra obtained using the F115W, F150W, or F200W to within an acccuracy better than 0.25 pixel over most of the field of view. Wavelength calibration of both grism was verified to be accurate to within 15A over most of the field of view. Details can be found in Appendix A of Pirzkal et al. 2024.</p>
Solution-processed PbS quantum dot infrared laser with room-temperature tuneable emission in the optical telecommunications window - Open Data
<p>This is a supplementary upload attached to the paper titled "Solution-processed PbS quantum dot infrared laser with room-temperature tuneable emission in the optical telecommunications window" 10.1038/s41566-021-00878-9.</p> <p><strong>Figures</strong></p> <p>All figure data from the publication can be obtained from the original MATLAB .fig files. If one does not have access to MATLAB the figures can be opened using the open source software GNU Octave.</p> <p><strong>FDFD Simulation</strong></p> <p>Also in the upload is the original matlab code used to perform the simulations presented in the paper.</p> <p>"FDFD_2D_Ez_Hz_DFB_laser_UPLOAD" - Variable gain FDFD solver is uploaded as .mat and .pdf files.</p> <p>To run the code the functions "Dgen" and "gen2xDFB" are required and the .mat files containing the refractive indices "PbS1520" and "Al2O3".</p> <p>Parameters to vary can be found in the "DASHBOARD" section of the code. The uploaded code solves for the out-of-plane electric field (Ez Mode).</p>
Data from: Non-invasive estimation of absorbed ionizing radiation dose in mice using Near-Infrared Spectroscopy (NIRS) and aquaphotomics
<p>Accurate measurement of ionizing radiation exposure, whether therapeutic or accidental, is of utmost importance in various scenarios. This paper presents a study that addresses this critical need by utilizing near-infrared (NIR) spectroscopy and aquaphotomics to estimate radiation dose exposure in mouse models subjected to X-ray irradiation. The analysis of NIR spectra acquired from the mouse abdomen enabled non-invasive estimation of radiation doses ranging from 0.5 to 6.5 Gy, immediately following the irradiation exposure. The findings were consistent with the impact of total body irradiation in mice, as evidenced by measures such as animal survival rate, alterations in body weight observed over a 30-day post-exposure period, and changes in hematocrit levels. The spectroscopic measurements were based on detecting changes in the molecular structure of body water after radiation exposure, utilizing the water spectral pattern as a multidimensional biomarker. While further validation in nonhuman primates is necessary, the findings demonstrate a simple, non-destructive, and rapid method that holds promise for the estimation of radiation exposure across a range of doses, applicable to both clinical applications and catastrophic radiation events. These advancements in radiation dose quantification have significant implications for the timely and precise assessment of radiation exposure in humans.</p>
Infrared thermography of turbulence patterns of operational wind turbine rotor blades supported with high-resolution photography: KI-VISIR Dataset
<h2>Abstract</h2> <p><span>With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.</span></p>
Use of infrared spectroscopy for a sampling study of waste wood samples in a panel board industry
<p>Oral presentation at the conference 'NIR Italia Online, waiting for Slovenia 2022' (24-25 February 2021). </p> <p>NIR Italia Symposium are biennial conferences on infrared spectroscopy. Due to the pandemic situation the Italian Society for Near Infrared Spectroscopy (SISNIR), in collaboration with the InnoRenew CoE and University of Primorska, has decided to organize an online National Symposium of NIR Spectroscopy, waiting for the opportunity to meet physically next year. </p> <p>The event is an important opportunity to virtually present works, and exchange ideas, opinions and future perspectives. This presentation perfectly fits with theme of NIR spectroscopy. Indeed, the presentation is about the use of NIR spectroscopy for assessing the best sampling procedure in order to describe a really heterogeneous material as waste wood is. </p>
Dataset: Planetary-scale waves seen in thermal infrared images of Venusian cloud top
<p>The data archive contains data used in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al.</p> <p>The contents of the directories are as follows.</p> <p>"data_used_in_figures" : The table data used in the figures are given in Excel and CSV. The table format is described in the data files. An image in NetCDF format is also included.</p> <p>"time_series_of_brightness_temperature_gradient" : The files contain the time series of the longitudinal gradient of Venusian cloud's brightness temperature measured by LIR onboard JAXA's Venus orbiter Akatsuki. The data were derived and analyzed in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al. The filename represents the latitude for each time series (For example, "10N" means 10 degrees north, and "EQ" means the equator). In all files, the first column gives the approximate elapsed time in days from 18 May 2017: the exact dates are given in the paper (Table S1 in the Supporting Information). The second column gives the longitudinal gradient of the brightness temperature in unit of K/degree.</p>
Development of a Low-Cost Method for Quantifying Microplastics in Soils and Compost Using Near-Infrared Spectroscopy
<p>Datasets and scripts for data evaluation</p>
Ultrafast Infrared Transient Absorption Spectroscopy of Gas-Phase Ni(CO)4 Photodissociation at 261 nm
<p>This is the data repository for the following publication:</p> <p>Neil C. Cole-Filipiak, Jan Tross, Paul Schrader, Laura M. McCaslin, and Krupa Ramasesha, "Ultrafast infrared transient absorption spectroscopy of gas-phase Ni(CO)4 photodissociation at 261 nm," J. Chem. Phys. 156, 144306 (2022), https://doi.org/10.1063/5.0080844.</p> <p><strong>Abstract:</strong></p> <p>We employ ultrafast mid-infrared transient absorption spectroscopy to probe the rapid loss of carbonyl ligands from gas-phase nickel tetracarbonyl following ultraviolet photoexcitation at 261 nm. Here, nickel tetracarbonyl undergoes prompt dissociation to produce nickel tricarbonyl in a singlet excited state; this electronically excited tricarbonyl loses another CO group over tens of picoseconds. Our results also suggest the presence of a parallel, concerted dissociation mechanism to produce nickel dicarbonyl in a triplet excited state, which likely dissociates to nickel monocarbonyl. Mechanisms for the formation of these photoproducts in multiple electronic excited states are theoretically predicted with one-dimensional cuts through the potential energy surfaces and computation of spin–orbit coupling constants using equation of motion coupled cluster methods (EOM-CC) and coupled cluster theory with single and double excitations (CCSD). Bond dissociation energies are calculated with CCSD, and anharmonic frequencies of ground and excited state species are computed using density functional theory (DFT) and time-dependent density functional theory (TD-DFT).</p> <p> </p> <p><strong>Experimental Data:</strong></p> <p>All data are saved as a .csv file. The first column contains frequencies (in cm<sup>-1</sup>) while the first row indexes each time delay (in ps). High-resolution transient spectra at select time delays are similarly structured. Each transient .csv file is labeled according to molecule, pump wavelength, file contents, pump laser power, pressure, and a date (<em>e.g.</em> NT261_trans_1mW_1.5torr_17Feb2021.csv).</p> <p> </p> <p><strong>Computational Data:</strong></p> <p>This data repository consists of 7 directories, which contain the data used in the main paper. Computational data published in the supplementary material may be requested from the corresponding authors.</p> <p>Anharmonic frequencies and DFT energies can be obtained in the directory "VPT2", where the files are labelled nicoX_*_anharm.out, where X=3,2,1 (the compound) and * corresponds to the electronic state for which the calculation was performed.</p> <p>EOM-CC calculations of the spin-orbit coupling constants at the geometries reported are found within the "SOCC" directory using the naming convention nicoX_[]_so_*.out, where X=3,2, []=an indication of the geometry, and * corresponds to the electronic state for which the calculation was performed.</p> <p>Calculations of the minimum energy crossing points (MECPs) can be found in the directory "MECP" using the naming convention nicoX_min*.out, where X=4,3,2,1, and * corresponds to the two electronic states for which the MECP is calculated (e.g. s0s1).</p> <p>The following 4 directories contain all the EOM-CC output files needed to reproduce the curves from Figure 2: 4to3, 3to2, 4to2, and 2to1, corresponding to panels a, b, c, and d, respectively. The naming conventions for the files within these directories are X_yz.out, where X=the name of the directory, y=the value of the reaction coordiante, and z=s (singlet) or t (triplet).</p>
Dataset for 'Experimental Quantification of Gas Dispersion in 3D-Printed Logpile Structures Using a Noninvasive Infrared Transmission Technique'
<p>This dataset contains the infrared images of tracer flow that were taken in the investigations of transverse dispersion in 3D-printed logpile structures. Accompanying the files (which are labelled according to the convention of the camera software) is a Python script which can be used to link the images to the operating conditions at which they were obtained. Documentation of this script can be found in the file at the very top. <br> This dataset was used as basis for the journal article 'Experimental Quantification of Gas Dispersion in 3D-Printed Logpile Structures Using a Noninvasive Infrared Transmission Technique', published in ACS Engineering Au under DOI:<a href="https://doi.org/10.1021/acsengineeringau.1c00040">10.1021/acsengineeringau.1c00040</a>. This paper can also be found in this repository at https://zenodo.org/record/6517082</p> <p> </p>
Rapid assessment of lipidomics sample quality and quantity using attenuated total reflectance Fourier-transform infrared spectroscopy
<p>In this work, we aimed to develop a simple lipid quality and quantification method for biological lipid extracts, as a step in lipidomics workflows, with minimal sample requirement. We chose FTIR spectroscopy with an Attenuate Total Reflectance (ATR) sampling method as it requires just 1 microliter of MS-ready sample without additional sample preparation. We validated the proposed lipidomics sample quality control workflow using a set of plasma samples (n=107, with 3-4 technical replicates) with comparison to LC-MS-based lipidomics. The following file contains the resulting spectra acquired by ATR-FTIR spectrometry for these plasma samples, standard curves and contaminated samples used for method development. Spectrometer was ambient blanked and detector cleaned between each measurement. Lipid samples were extracted by butanol-methanol (3:1) precipitation, and dried directly onto the ATR-FTIR detector. Absorbance was measured between 4,000 and 650 cm-1 wavenumbers, at a resolution of 8cm-1. Each spectra has been baseline corrected (whole spectra).</p>
Carbon Nanotube Uptake in Cyanobacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics
<p>Dataset of the work entitled "Carbon Nanotube Uptake in Cyanobacteria for Near-infrared Imaging and Enhancing Bioelectricity Generation in Living Photovoltaics".</p>
Supplementary files for paper: "Design and fabrication of an electrostatic precipitator for infrared spectroscopy" in Atmospheric Measurement Techniques, 2022.
<ol> <li>File of absorbance spectra and hypothetical thickness for each sample.</li> <li>MATLAB function to perform clean crystal spectrum subtraction and baseline correction (described in the paper).</li> </ol>
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.