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Dataset results
270 results for “near-infrared”
Synthetic JWST NIRCam, Euclid NISP, and Roman WFI Near-Infrared Photometry for 800+ Ultracool Dwarfs
<p>JWST NIRCam, Euclid NISP, and Roman WFI photometry (apparent magnitudes) for 800+ ultracool dwarfs synthesized using near-IR SpeX prism spectra. This is a supplementary data product to <a href="https://iopscience.iop.org/article/10.3847/2515-5172/acf864">Sanghi et al. 2023 (RNAAS, 7, 194)</a> and <a href="https://ui.adsabs.harvard.edu/abs/2024RNAAS...8..137S/abstract">Sanghi et al. 2024 (RNAAS, 8, 137)</a>. JWST photometry is presented in the Vega magnitude system and Euclid and Roman photometry are presented in the AB magnitude system.</p> <p>For research that benefits from this compilation, please cite <a href="https://iopscience.iop.org/article/10.3847/2515-5172/acf864">Sanghi et al. 2023 (RNAAS, 7, 194)</a> for JWST photometry, <a href="https://ui.adsabs.harvard.edu/abs/2024RNAAS...8..137S/abstract">Sanghi et al. 2024 (RNAAS, 8, 137)</a> for Euclid and Roman photometry, and include the following acknowledgment:</p> <p>"This work has benefitted from The UltracoolSheet, maintained by Will Best, Trent Dupuy, Michael Liu, Aniket Sanghi, Rob Siverd, and Zhoujian Zhang, and developed from compilations by <a href="https://ui.adsabs.harvard.edu/abs/2012ApJS..201...19D/abstract">Dupuy & Liu (2012, ApJS, 201, 19)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2013Sci...341.1492D/abstract">Dupuy & Kraus (2013, Science, 341, 1492)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...833...96L/abstract">Liu et al. (2016, ApJ, 833, 96)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2018ApJS..234....1B/abstract">Best et al. (2018, ApJS, 234, 1</a>), <a href="https://ui.adsabs.harvard.edu/abs/2021AJ....161...42B">Best et al. (2021, AJ, 161, 42)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2023ApJ...959...63S/abstract">Sanghi et al. (2023, ApJ, 959, 63)</a>, and <a href="https://ui.adsabs.harvard.edu/abs/2023AJ....166..103S/abstract">Schneider et al. (2023, AJ, 166, 103)</a>."</p> <p>Contact <a href="mailto:asanghi@caltech.edu">asanghi@caltech.edu</a> regarding questions.</p>
Products and Models for "A benchmark JWST near-infrared spectrum for the exoplanet WASP-39 b"
<p>Publication Here: https://www.nature.com/articles/s41550-024-02292-x<br><br>Observing exoplanets through transmission spectroscopy supplies detailed information on their atmospheric composition, physics, and chemistry. Prior to <em>JWST,</em> these observations were limited to a narrow wavelength range across the near-ultraviolet to near-infrared, alongside broadband photometry at longer wavelengths. To understand more complex properties of exoplanet atmospheres, improved wavelength coverage and resolution are necessary to robustly quantify the influence of a broader range of absorbing molecular species. Here we show a combined analysis of <em>JWST</em> transmission spectroscopy across four different instrumental modes spanning 0.5–5.2 micron using Early Release Science observations of the Saturn-mass exoplanet WASP-39b. Our uniform analysis constrains the orbital and stellar parameters within sub-percent precision, including matching the precision obtained by the most precise asteroseismology measurements of stellar density to-date. Leveraging the advantages of a uniform light curve analysis, we improve the agreement between the transmission spectra of all modes, except for the NIRSpec PRISM, which is affected by partial saturation of the detector. Together, these collected data constitute the most comprehensive transmission spectrum of an exoplanet to date, providing unparalleled access to atmospheric absorbers including Na, K, H2O, CO, CO2, and SO2.</p>
Experiments for detection of Plasmodium berghei infected Anopheles stephensi mosquitoes using near-infrared spectroscopy
<p> </p> <p><strong>Experiments for detection of <em>Plasmodium berghei</em> infected <em>Anopheles stephensi</em> mosquitoes using near-infrared spectroscopy</strong></p> <p>This dataset contains near-infrared spectroscopy (NIRS) measurements on <em>Plasmodium berghei</em> infected <em>Anopheles stephensi</em> mosquitoes reared in the lab together with either oocyst counts or sporozoite counts, correponding to the two experiments undertaken:</p> <ul> <li>Experiment 1 (oocysts), file "NIRSdata2017_Lab_AnSteph_PlasmBerg_oocysts.txt"</li> <li>Experiment 2 (sporozoites), file "NIRSdata2017_Lab_AnSteph_PlasmBerg_sporozoites.txt"</li> </ul> <p>For further details on the experimental setup see: P.M. Esperança, A.M. Blagborough, D.F. Da, F.E. Dowell, T.S. Churcher (2018) "Detection of <em>Plasmodium berghei</em> infected <em>Anopheles stephensi</em> using near-infrared spectroscopy". <em>Parasites and Vector</em>, <strong>11</strong>:377. <a href="https://doi.org/10.1186/s13071-018-2960-z">https://doi.org/10.1186/s13071-018-2960-z</a>.</p> <p>The structure of the data files is as follows:</p> <ul> <li>column 1 (<strong>Scan_ID</strong>): scan identifier</li> <li>column 2 (<strong>Mosquito_ID</strong>): mosquito identifier</li> <li>column 3 (<strong>Replication</strong>): replication identifier</li> <li>column 4 (<strong>Oocysts</strong> or <strong>Sporozoites</strong>): response variable <ul> <li>for the Experiment 1, the oocyst count<em> </em>on a level-scale</li> <li>for the Experiment 2, the sporozoite count on a log-scale: 0 (no sporozoites), 1 (1–10), 2 (11–100), 3 (101–1000), 4 (>1000)</li> </ul> </li> <li>columns 5 to 2155 (<strong>x350</strong> to <strong>x2500</strong>): NIRS absorbance measurements for wavelengths in the range 350 to 2500 nanometers</li> </ul> <p> </p>
Fig. 2. 1–10 in Near-infrared spectroscopy and microstructure of the scales of Sabethes (Sabethes) albiprivus (Diptera: Culicidae)
Fig. 2. 1–10: Specimens with scales that reflect blue, purple and green; 11–14: Specimens with scales that reflect golden and silver. The scale bars of the dorsal view images (1, 3, 5, 7, 9, 11, 13) represents 1 mm and of the side views of the abdomen (2, 6, 8, 10, 12, 14) indicate 0.5 mm.
Fig. 1 in Near-infrared spectroscopy and microstructure of the scales of Sabethes (Sabethes) albiprivus (Diptera: Culicidae)
Fig. 1. Scanning electron micrograph of a scale on the antepronotum of Sabethes albiprivus. The rectangle indicates where the images were obtained at 10,000× magnification. Legends: ap, apex; lr, longitudinal ridge; pe, pedicel.
Plasmonic Heating by Indium Tin Oxide Nanoparticles: Enabling Decoupled Near-Infrared Theranostics.
<p>Dataset corresponding to the experimental results shown in the figures 2 to 5 (four in total) within the original research work entitled "Plasmonic Heating by Indium Tin Oxide Nanoparticles: Enabling Decoupled Near-Infrared Theranostics".</p>
Data release for paper "The Araucaria Project: Deep near-infrared photometric maps of Local and Sculptor Group galaxies. I. Carina, Fornax, Sculptor"
<p>Deep near-infrared J- and K-band photometry of three Local Group dwarf spheroidal galaxies: Fornax, Carina, and Sculptor, is made available for the community. Until now, these data have only been used by the Araucaria Project to determine distances using the tip of the red giant and RR Lyrae stars. Now, we present the entire data collection in a form of a database, consisting of accurate J- and K-band magnitudes, sky coordinates, ellipticity measurements, and timestamps of observations, complemented by stars' loci in their reference images. Depth of our photometry reaches about 22 mag at 5 sigma level, and is comparable to NIR surveys, like the UKIRT Infrared Deep Sky Survey (UKIDSS) or the VISTA Hemisphere Survey (VHS), and small overlap with VHS and no overlap with UKIDSS makes our database a unique source of quality photometry.</p> <p>Data release consists of:</p> <ul> <li>databases in a form of text files for Carina, Fornax and Sculptor galaxies<br> (db_Car.txt, db_For.txt , db_Scu.txt)</li> <li>completeness tables and plots for every field in Carina, Fornax and Sculptor galaxies<br> (compl_Car.pdf, compl_Car.txt, compl_Scu.pdf, compl_Scu.txt, compl_For.pdf, compl_For.txt)</li> <li>explanatory file for each galaxy<br> (info_Car.txt, info_Scu.txt, info_For.txt)</li> <li>FITS images of scientific quality of all analyzed fields in Carina, Fornax and Sculptor galaxies, archived in tar.gz files</li> </ul>
Dataset used in "Comment on "Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam" by Kim-Anh Nguyen, Yuei-An Liou, Ha-Phuong Tran, Phi-Phung Hoang and Thanh-Hung Nguyen"
<p>The Excel file provides all the data included in Tab.4 of Nguyen et al. 2020 plus reflectances extracted from the Landsat 8 OLI image acquired on 14 February 2017 and downloaded from the USGS Earth Explorer website. Observations on the number of pixels falling of water, land and mixed water/land surfaces are provided as well as water percentage cover estimated using regular spaced points. </p> <p>The dataset includes vector files (kml format) of the grids corresponding to the selected L8 pixels as well as the regularly spaced points generated within the selected pixels. These files can be imported in QGIS, Google Earth Pro and other free GIS software.</p>
Data From: Near-infrared co-illumination of fluorescent proteins reduces photobleaching and phototoxicity
<p>Data From: Near-infrared co-illumination of fluorescent proteins reduces photobleaching and phototoxicity</p>
Broadband Laser-Induced Luminescence Generated in the Visible and Near-Infrared Ranges from Nontransparent CsPbCl3:Yb3+ Perovskite Ceramics
<p>ABSTRACT</p> <p>All inorganic perovskites with the general formula ABX<sub>3</sub> (A = alkaline cation, B = lead ions, and X = halide anion) in the form of colloids have become very popular in recent years due to their extraordinary spectroscopic properties, which can be additionally modulated by changing their structural composition. Here, we report for the first time a novel study considering nontransparent CsPbCl<sub>3</sub>:10%Yb<sup>3+</sup> ceramics that exhibit broadband laser-induced emission (LIE) with warm color (CRI = 96) upon exposure to a high-power-density infrared beam. It was found that the analyzed phenomenon strongly depends on the excitation power density and the pressure around the sample. Moreover, threshold behavior was observed, characterized by a significant increase in luminescence intensity already from the excitation of 0.2 W. The investigated material exhibits extremely strong photoconductivity. During the spectroscopic measurements of LIE, it was found that the current value varies by more than 4 orders of magnitude. Because of the relatively low sample temperature during the experiment, a LIE generation mechanism based on the creation of ytterbium mixed valence ion pairs and intervalence charge transfer (IVCT) transitions was proposed.</p>
Pretty cool beetles: Can manipulation of visible and near-infrared sunlight prevent overheating?
Open the record for dataset details and reuse information.
Data from: Non-invasive estimation of absorbed ionizing radiation dose in mice using Near-Infrared Spectroscopy (NIRS) and aquaphotomics
Open the record for dataset details and reuse information.
Laboratory-based hyperspectral visible near-infrared reflectance spectral dataset of soil samples across a range of surface orientations
Open the record for dataset details and reuse information.
Spectral data presented in Hinrichs J L, Lucey P G. Temperature-dependent near-infrared spectral properties of minerals, meteorites, and lunar soil.
<p>In this dataset, we present the spectral data in paper: Hinrichs, J. L., & Lucey, P. G. (2002). Temperature-dependent near-infrared spectral properties of minerals, meteorites, and lunar soil. <em>Icarus</em>, <em>155</em>(1), 169-180.</p>
Data from: Bio-inspired imager improves sensitivity in near-infrared fluorescence image-guided surgery
Image-guided surgery can enhance cancer treatment by decreasing, and ideally eliminating, positive tumor margins and iatrogenic damage to healthy tissue. Current state-of-the-art near-infrared fluorescence imaging systems are bulky and costly, lack sensitivity under surgical illumination, and lack co-registration accuracy between multimodal images. As a result, an overwhelming majority of physicians still rely on their unaided eyes and palpation as the primary sensing modalities for distinguishing cancerous from healthy tissue. Here we introduce an innovative design, comprising an artificial multispectral sensor inspired by the Morpho butterfly's compound eye, which can significantly improve image-guided surgery. By monolithically integrating spectral tapetal filters with photodetectors, we have realized a single-chip multispectral imager with 1000× higher sensitivity and 7× better spatial co-registration accuracy compared to clinical imaging systems in current use. Preclinical and clinical data demonstrate that this technology seamlessly integrates into the surgical workflow while providing surgeons with real-time information on the location of cancerous tissue and sentinel lymph nodes. Due to its low manufacturing cost, our bio-inspired sensor will provide resource-limited hospitals with much-needed technology to enable more accurate value-based health care.
Raw data for "Deep mouse brain two-photon near-infrared fluorescence imaging using a superconducting nanowire single-photon detector array"
<p>Two-photon microscopy (2PM) has become an important tool in biology to study the structure and function of intact tissues in-vivo. However, adult mammalian tissues such as the mouse brain are highly scattering, thereby putting fundamental limits on the achievable imaging depth, which typically resides around 600-800um. In principle, shifting both the excitation as well as (fluorescence) emission light to the shortwave near-infrared (SWIR, 1000-1700 nm) region promises substantially deeper imaging in 2PM, yet has proven challenging in the past due to the limited availability of detectors and probes in this wavelength region. To overcome these limitations and fully capitalize on the SWIR region, in this work we introduce a novel array of superconducting nanowire single-photon detectors (SNSPDs) and associated custom detection electronics for the use in near-infrared 2PM. The SNSPD array exhibits high efficiency and dynamic range, as well as low dark-count rates over a wide wavelength range. Additionally, the electronics and software permit seamless integration into typical 2PM systems. Together with a fluorescent dye emitting at 1105 nm, we report imaging depth of > 1.1mm in the in-vivo mouse brain, limited only by available labeling density and laser power. Our work further establishes SWIR 2PM approaches and SNSPDs as promising technologies for deep tissue biological imaging. </p>
Data and Software for: Resolved Near-infrared Stellar Photometry from the Magellan Telescope for 13 Nearby Galaxies: JAGB Method Distances
<p><strong>12/17/24 Update: I was made aware that the MRT files had the J-band and H-band column header labels incorrectly switched (this occured when converting the CSV files to MRT). I have fixed this mistake. The CSV files were correct the entire time. No data have been changed. Apologies for any inconvinence this may have caused. </strong></p> <p> </p> <p>Data for Resolved Near-infrared Stellar Photometry from the Magellan Telescope for 13 Nearby Galaxies: JAGB Method Distances</p> <p>The files are provided in two formats. A CSV format with a single line header, and a MRT files (zipped) written in the machine-readable format used by AAS & CDS: <a href="https://journals.aas.org/mrt-overview/" target="_blank" rel="noopener noreferrer">https://journals.aas.org/mrt-overview/</a></p> <p>Column descriptions in photometry catalogs:</p> <p>X/Y: Image coordinates from Fourstar camera</p> <p>J, J_err: J-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>H, H_er: H-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>K, K_err: K-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>chi: chi value returned from DAOPHOT/ALLFRAME</p> <p>sharp: sharpness value returned from DAOPHOT/ALLFRAME</p> <p>ra/dec: RA/Decl </p> <p>RGC: semi-major axis distance</p> <p> </p> <p>Notes on versions: all catalogs from all versions contain the same information, style was just adjusted to adhere to AAS standards. </p> <p> </p>
Long-term Continuous Red and Near-infrared Channel Reflectance from MODIS, 2001-2023 (LCREF-MODIS)
<p><strong>Usage Notes</strong>:<br>This is the updated LCREF-MODIS dataset (v3.2) consists of BRDF-normalized MODIS red and near-infrared surface reflectance. The LCREF-MODIS product was used to calibrate and benchmark the AVHRR surface reflectance to produce a temporally consistent record of surface reflectance prior to the MODIS era. It was also used to generate LCSPP-MODIS (previously known as LCSIF-MODIS) as a benchmark.</p> <p><strong>Key updates in version 3.2 include:</strong></p> <ul> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension:</strong> to include observations from the year of 2023.</li> <li><strong>Snow mask: </strong>we note that all pixels marked with percent_snow >0 in the original MCD43C1.v061 have been removed. This conservative approach was applied to reduce bias during cross-calibration, since unlike MODIS, AVHRR does not have a reliable snow detection algorithm. Therefore, surface reflectance values in high latitude regions are almost entirely gap-filled and should never be used for analysis for both LCREF-AVHRR and LCREF-MODIS. We encourage users to use only QA=0 and QA=1 pixels for their analysis. Alternatively, users can use LCREF-MODIS from the previous version for high latitude regions (v3.1), which did not mask out snow-covered pixles. </li> </ul> <p>The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test. </p> <ul> <li>The LCREF-AVHRR v3.2 (1982-2023) is available at <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> </ul> <p>The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p> <p> </p>
Long-term Continuous Red and Near-infrared Channel Reflectance from AVHRR, 1982-2023 (LCREF-AVHRR)
<p><strong>Usage Notes</strong>:<br>This is the updated LCREF dataset (v3.2) consists of calibrated AVHRR surface reflectance record for the red and near-infrared channel. </p> <p><strong>Key updates in version 3.2 include:</strong></p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>The user can choose between LCREF-AVHRR and LCREF-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCREF-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCREF-AVHRR or use a blend dataset of LCREF-AVHRR and LCREF-MODIS as a sensitivity test. </p> <ul> <li>The LCREF-MODIS v3.2 (2001-2023) is available at <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a>.</li> </ul> <p>The LCREF-AVHRR dataset was used as the input to generate LCSPP-AVHRR (previously known as LCSIF-AVHRR), and it can also be used to derive temporally consistant records of NDVI, NIRv, kNDVI, and other vegetation indices based on red and NIR surface reflectance variables. The user can access LCSPP products at:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, which detailed the uses and limitations of the dataset. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Boosting the Near-Infrared Emission of Ag2S Nanoparticles by a Controllable Surface Treatment for Bioimaging Applications
<p>Dataset of https://pubs.acs.org/doi/10.1021/acsami.1c19344</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.