Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
26
datasets available to search
ShareScore release 0.9.0
Dataset results
26 results for “Infrared observation”
Dataset for manuscript "Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations"
<p>This is the long-term satellite retrieval dataset of dust aerosol optical depth at 10 μm (DAOD<sub>10μm</sub>) and dust coarse mode effective diameter (D<sub>eff</sub>) based on collocated MODIS and CALIOP observations from July 2006 to August 2018. The full description is in the manuscript "<strong>Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations" </strong>by Zheng, Jianyu, et al. The readme file for the data is in "readme_dust_aod_size_product.txt". The variable list of Level-2 data is in "variable_list_L2.txt". The variable list of Level-3 data is in "variable_list_L3.txt".</p>
Data files for Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC)
<p>The files in this set are data obtained from the NIRAC airglow imager on the International Space Station. The files are named for a JGR paper by J. Hecht et al. entitled Atmospheric Gravity Wave and Instability Observations from the International Space Station using the Near InfraRed Airglow Camera (NIRAC). These files are for plots in Figures 5,7,10,11,17,18, and 19 in the submitted paper. The files are published here so as to be available for review. This paper should appear in JGR Atmospheres sometime in late 2023 or early 2024. The files that are text files are meant to be read with IDL as discussed in the readme file. </p>
Potential impact of assimilating visible and infrared satellite observations compared to radar reflectivity for convective‐scale NWP
<p>Contains</p> <ul> <li>raw_data <ul> <li>nature run initial conditions for the cases "random" and "warm-bubble"</li> <li>ensemble initial condition sounding profiles</li> </ul> </li> <li>evaluation_metrics <ul> <li>csv files of FSS, RMSE, MAE, ensemble spread, mean difference<br> to reproduce figures of FSS, RMSE and spread in the paper and more.<br> See the README.txt</li> </ul> </li> <li>figures <ul> <li>Map_VIS06 ... Simulated satellite images of visible reflectance for ensemble members and truth</li> <li>Map_VIS06_probability ... Map of ensemble probability for visible reflectance > 0.6 and the nature in red contours</li> </ul> </li> </ul> <p> </p>
Atmospheric observations of the water vapour continuum in the near-infrared windows - Data
<p>Dataset for our AMT paper (Atmospheric observations of the water vapour continuum in the near-infrared windows).</p> <p>Included is the best estimate of the continuum optical depth (18 September 2008). the ratio of this best estimate to the corresponding MT_CKD 3.2 optical depth from 18 September 2008, and the CAVIAR-field estimated continuum absorption coefficients using both MT_CKD and CAVIAR-lab. Other data are available from the corresponding author upon request.</p>
Reconciling the Infrared Catastrophe and Observations of SN 2011fe
<p>W7 at 300 and 1000d, and N100 at 1000d. Distance 10 Mpc assumed.</p>
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
Infrared Coagulator Ablation or Observation in Preventing Anal Cancer in HIV-Positive Patients With Anal Neoplasia
ClinicalTrials.gov study NCT01164722. IPD Sharing: NO. Countries: 1. Publications: 1.
Effects of nonlinear observation operators for visible and infrared radiances in ensemble data assimilation
<p><br>Description</p> <p>Dataset to accompany the first revision of the manuscript "Effects of nonlinear observation operators for visible and infrared radiances in ensemble data assimilation" for the QJRMS.</p> <p>Contains <br>- experiments (one folder per experiment)<br> each contains <br> - diagnostics (DART format) for each assimilation time<br> - obs_seq.final: assimilation output <br> - obs_seq.final-linear: contains linear posterior as "prior" ("evaluate" at +1s after analysis)<br> - obs_seq.final-evaluate: at analysis time (includes DART clamping), at 1s after analysis time: "nonlinear posterior"<br> - config file DART-WRF for each experiment (python format)</p> <p>- code<br> assim_tools_mod.f90: DART code modification to compute the linear posterior</p> <p>- other data: see v1 of this repository!<br> nature run initial conditions (WRF format)<br> forecast ensemble initial conditions (WRF format)<br> script to read "obs_seq.final"-files into pandas.DataFrame (python format)</p> <p><br>Experiment naming:<br>VIS ... visible reflectance 0.6 micrometer assimilation<br>WV73 ... infrared 7.3 micrometer assimilation<br>obs10 ... observation density: 1 observation per 10x10 km of domain area<br>obs30 ... 1 observation per 30x30 km<br>loc10 ... localization radius: 10 km<br>inf0 ... no prior nor posterior covariance inflation in the assimilation<br>sec0 ... no sampling error correction<br>reject ... not assimilating observations with a small first-guess departure <= 0.03</p> <p><br>Experiment names used in Figures</p> <p>Section 3.1<br>- Figure 2a: exp_v1.23_P2_rr+1_VIS_obsi211_loc20_inf0<br>- Figure 2b: exp_v1.23_P2_rr+1_VIS_obsi360_loc20_inf0</p> <p>Section 3.2.1 <br>- Figure 3: exp_v1.23_P2_rr+1_VIS_obs30_loc14_inf0</p> <p>Section 3.2.2<br>- Figure 4: exp_v1.23_P2_rr+1_VIS_obs10_loc20, exp_v1.23_P2_rr+1_VIS_obs10_loc20_reject</p> <p>Section 3.3<br>- Figures 5a,c: exp_v1.23_P2_rr+1_T2M_obs10_loc20_inf0_sec0<br>- Figures 5b,5d,7a: exp_v1.23_P2_rr+1_VIS_obs10_loc20_inf0_sec0<br>- Figure 6a,6b,7b: exp_v1.23_P2_rr+1_WV73_obs10_loc20_inf0_sec0</p> <p> </p>
From Networked to Isolated: Observing Water Hydrogen Bonds in Concentrated Electrolytes with Two Dimensional Infrared Spectroscopy
<p>The files contain the data that are shown in the figures of the Main Text and of the Supplementary Materials of the research article:</p> <p>From Networked to Isolated: Observing Water Hydrogen Bonds in Concentrated Electrolytes with Two Dimensional Infrared Spectroscopy</p> <p>Nicholas H. C. Lewis, Bogdan Dereka, Yong Zhang, Edward J. Maginn, and Andrei Tokmakoff</p> <p>J. Phys. Chem. B, (2022)</p>
Joint Use of Far-Infrared and Mid-Infrared Observation for Sounding Retrievals: Learning from the Past for Upcoming Far-Infrared Missions
<p>Dataset and software (Matlab) used for the analysis of synoptic weather pattern, information content and retrieval estimates</p>
Soil moisture observations from shortwave infrared channels reveal tornado tracks: A case in December 10-11, 2021 tornado outbreak
<p>MODIS dataset used for the publication titled 'Soil moisture observations from shortwave infrared channels reveal tornado tracks: A case in December 10-11, 2021 tornado outbreak' on Geophysical Research Letters.</p>
Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations (Data)
<p>README file for LBL-ERA5, LBL-GCM, and band datasets used in:<br> Raghuraman et al., 2023, Geophysical Research Letters,<br> "Greenhouse gas forcing and climate feedback signatures identified in hyperspectral infrared satellite observations "</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p> </p> <p>LBL-ERA5</p> <p>6 files (3 experiments with olr and olr_clr output separately):</p> <p>2003-2021.GFDL.all-2010-o3_*.nc - varying WMGHG,Ts,T,q,clouds, fixed o3</p> <p>2003-2021.GFDL.fo3_*.nc - varying WMGHG and fixed Ts,T,q,clouds,surface albedo,o3</p> <p>2003-2021.GFDL.ff_*.nc - fixed Ts,T,q,clouds,surface albedo, varying o3</p> <p> </p> <p>LBL-GCM (clear-sky only)</p> <p>4 AM4 files:</p> <p>AM4piclim-control.nc (for ERF)</p> <p>AM4piclim-4xCO2_1xCO2IRF.nc (for ERF)</p> <p>AM4piclim-control_STRAT.nc (for SARF)</p> <p>AM4piclim-4xCO2_1xCO2IRF_STRAT.nc (for SARF)</p> <p>4 CM3/AM3 files:</p> <p>CTLAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>EXPAM3CM3_P1_APR_allctm_E1_RFM_1CM_A2_WN3_1xCO2N.nc (for \lambda)</p> <p>CTLAM3CM3_P1_2003C.nc (for IRF)</p> <p>CTLAM3CM3_P1_2021C.nc (for IRF)</p> <p> </p> <p>Band-AM4/MERRA-Total: GFDL AM4 with prescribed SSTs and sea-ice (AMIP) and nudged with MERRA winds</p> <p>2 files:</p> <p>atmos.200001-202112.olr.nc - all-sky OLR</p> <p>atmos.200001-202112.olr_clr.nc - clear-sky OLR</p> <p> </p> <p>Observational and reanalysis files used in paper (AIRS, CERES EBAF and SSF, GISTEMP, ERA5 input data) can be downloaded from their respective websites (see paper's "Open Research" section).</p> <p> </p>
Observe the Effects of Far Infrared Eye Masks on Acupoints Around the Eyes in Patients With Dry Eye Syndrome
ClinicalTrials.gov study NCT07060131. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A new dataset of rain cell generated from observations of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner and microwave imager
<p>This new dataset (M.TRMM-1B01-1B11-2A25-PMD-Rain) contains orbit-level data with 5 km spatial resolution and 0.25 km vertical resolution. It is produced by merging TRMM PR, VIRS and TMI measurements at PR pixel resolution combined with rain cell identification. The near-surface rain rate, profiles of rain rate and precipitation reflectivity factor, visible and infrared signals and microwave signals can be obtained in the dataset. The dataset provides new important data for in-depth research on the structural characteristics of rain cells and supports the study of precipitation mechanisms.</p>
Dataset for "Impact of geostationary interferometric infrared sounder observations from long- and middle-wave bands on weather forecasts with a locally cloud-resolving global model"
Open the record for dataset details and reuse information.
SolSysELTs2022 Part II: Mid-infrared spectroscopic observation of distant and faint comets with ELT/METIS
<p>Contributed talk: presentation and video recording</p>
Significance of Ultrasound Combined with Near-Infrared Spectroscopy in Monitoring Transfusion-Associated Intestinal Injury in Extremely Preterm Infants: a Study Protocol for a Prospective, Observation
ClinicalTrials.gov study NCT06857812. IPD Sharing: YES. Countries: 0. Publications: 0.
The First Evaluation of Newly Calibrated MODIS Infrared Water Vapour Observations in the Iqaluit, Arctic
<p>This dataset has not been published yet. This dataset corresponds to the GRL submission. It will be updated based on the GRL publication</p>
Effects of Repetitive Transcranial Magnetic Stimulation on Patients With Dysphagia in Stroke Observed Based on Functional Near-infrared Spectroscopic
ClinicalTrials.gov study NCT06213597. IPD Sharing: NO. Countries: 1. Publications: 0.
A Clinical Prospective Observational Study of Near Infrared Spectroscopy to Detect Subcranial Hematoma
ClinicalTrials.gov study NCT06404645. IPD Sharing: NO. Countries: 1. Publications: 0.
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.