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Spectral Vegetation Indices from Harmonized Landsat and Sentinel-2 Data for Harvard Forest 2015-2020
The goal of this work is to exploit time series of remotely sensed data sets with ground observations to improve our understanding of how seasonal variation in canopy and environmental conditions affect the relationship between vegetation indices and leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (fAPAR). Using three different common vegetation indices (EVI2, NDVI, NIRV), we can estimate LAI, fAPAR, and daily absorbed photosynthetically active radiation (APAR) using a semi-empirical model.
Time-series of high-frequency profiles of fluorescence-based phytoplankton spectral groups in Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2014-2025
Depth profiles of fluorescence-based phytoplankton biomass were sampled using a bbe Moldaenke FluoroProbe (Schwentinental, Germany) during 2014 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of depth profiles of fluorescence-based phytoplankton biomass measured at the deepest site of each reservoir adjacent to the dam, except in Falling Creek Reservoir, where depth profiles were also taken at four upstream sites ranging from the riverine to the lacustrine zone during 2016-2019 and 2024-2025. Casts were taken approximately weekly from May-October and monthly from November-April. Casts were collected at Beaverdam and Falling Creek Reservoirs during all years (2014-2025); casts were collected at Carvins Cove Reservoir during 2014-2016, 2018-2023, and 2025; casts were collected at Spring Hollow Reservoir during 2014-2016 and 2019; and casts were collected at Gatewood Reservoir in 2015-2016. A sensor maintenance log and quality assurance/quality control analysis script accompanies the data package.
Supraglacial features of debris covered glaciers in the Himalaya from Landsat-8 spectral umixing and Pleiades
<p>This dataset contains the spectral unmixing output files for the debris covered glacier surfaces based on Landsat-8 OLI imagery and Pleiades imagery of 2015. Files are provided for two domains, the Khumbu reference region of Nepal and the greater Himalaya region (76.3 to 92.6° W and 26.3 to 34.2° N), which covers covering most area from Himachal/Jammu and Kashmir border to Bhutan Himalaya. </p> <ul> <li>Landsat surface reflectance : Himalaya_L8_6S_surface_reflectance_scenes_2015 .zip <ul> <li>Contains surface reflectance images of Landsat-8 OLI scenes mostly from 2015 (two images are from 2014 and 2016 due to clouds in 2015) </li> <li>Collection 1 Level 1 (L1TP)</li> <li>Atmospherically and topographically corrected using the ARCSI routine, supplied in .kea format. These can be converted to GeoTifs using the GDAL command.</li> <li>Naming structure: LS8_yyyymmdd_latYYlongXXXX_rRRpPPP_vmsk_topshad_rad_srefdem_stdsref.kea</li> <li>Projection is UTM (zones depending on the image), from the original Landsat L1TP files</li> <li>The file naming convention, which is a standard output from ARCSI routine, include the image date ("yyyy" = year, mm = "month", "dd" = day), latitude ("YY") and longitude ("XXXX") of the image center, path/row ("PPP" = path, "RRR" = row), and the output products generated by ARCSI ("rad" = radiation, "topshad" = topographic shadows, "srefdem" indicates the use of elevation data, "stdsref" = standardized surface reflectance)</li> </ul> </li> <li>Fractional maps for the Khumbu: LS8_20150930_r41p140_frac_files. zip <ul> <li>Raster format (GeoTiffs) </li> <li>Non-normalized fractional water, light and dark debris and vegetation maps for the Khumbu reference image (Sept 30, 2015, path 140 row 40)</li> <li>Output from the linear mixing model routine used to produce binary maps of surfaces with values ranging from 0 to 1 (0% to 100% pixel coverage)</li> </ul> </li> <li>Binary surface maps for the Himalaya: Himalaya_L8_raw_binary_surface_maps.zip <ul> <li>Vector format (ArcGIS shapefiles)</li> <li>Raw, unprocessed binary maps of ponds, vegetation debris, ice and clouds over the debris covered glacier tongues in the Himalaya around the year 2015 (binary files) </li> <li>Derived from tresholding the fractional maps using a variable threshold (see publication)</li> <li>Maps in this pre-release version have not been manually corrected for misclassified areas due to confusion of classes, and the ice and cloud classes are not highly accurate</li> <li>These are not the final coverages of these surfaces over the domain and should not be used as such</li> <li>The supraglacial pond maps will undergo manual corrections and the datasets will be updated on this page</li> </ul> </li> <li>Dataset for analysis, glacier-by-glacier: Himalaya_SDC_LS_for_analysis_gt1km2_with_frac_and_debris_attributes.txt <ul> <li>original data from the SupraGlacial Debris Cover dataset (Sherler et al 2018)</li> <li>updated with the preliminary fractional cover of each surface (in %) on a glacier-by-glacier basis</li> <li>contains only debris covered tongues >1 km2 </li> <li>debris covered attributes were calculated from the ALOS Global Digital Surface Model (AW3D30 DEM) for each debris covered tongue <ul> <li>DC_area_km2 = recalculated debris covered area</li> <li>DCmin = minimum debris cover elevation (meters)</li> <li>DCmax = maximum debris cover elevation (meters)</li> <li>DCrange = altitudinal range (meters)</li> <li>DCmed = median elevation (meters)</li> <li>SLmean = mean slope (degrees)</li> <li>SLrange = slope range (degrees)</li> <li>SLmin = min slope (degrees)</li> <li>SLmax = max slope (degrees)</li> </ul> </li> </ul> </li> </ul>
Mineral spectral refractive index and bulk optical property dataset for aerosol studies
<p>Version 1.3, updated 11/15/2024.</p> <p>Added a file with 27 regional dust sample mineral composition information 'NewRegionalSamples.xlsx',</p> <p>along with the refractive index data.</p> <p>All refractive index files here have 127 rows (wavelengths) and 27 columns (samples)</p> <p>'kall27_coarse.dat' is the imaginary part of the coarse mode. </p> <p>'kall27_fine.dat' is the imaginary part of the fine mode.</p> <p>'nall27_coarse.dat' is the real part of the coarse mode.</p> <p>'nall27_fine.dat' is the real part of the fine mode.</p> <p>Version 1.2, updated 04/23/2024.<br>Major changes: <br>Changed all the data file names to new format: "mix"+{property name}+{number}, rearranged the number of mixing samples</p> <p>Updated all the bulk optical property data. This version use constant values of standard deviation in the lognormal size distribution settings for the coarse mode and the fine mode respectively.</p> <p>The phase matrices are separated from the other bulk properties due to their large file sizes. The readme file is updated correspondingly. The information of scattering angles (498 angles in total) is uploaded as "TAMUdust2020_Angle.dat".</p> <p>Added supplemental file data in 'Supplemental.tar.gz'.</p> <p>Additional refractive indices are zipped in 'AdditionalRefInd.tar.gz'</p> <p>Version 1.1, updated 03/14/2024.<br>Major changes: <br>Added mixed bulk properties for "0 (99%coarse+1%fine)" and "11 (2.0 µm coarse+ 0.4 µm fine)";<br>Added "reff.dat" in the 'BulkProperties.tar.gz'. The data include four columns: fine mode fraction, bulk projected area <A>, bulk volume <V>, effective radius r_eff. The information is for mixed sample number 0 to 11, each corresponds to one row.<br>Added refractive indices for chlorite, mica, smectite, pyroxene, vermiculite and pyroxenes. These groups can be applied in some other models.</p> <p>Version 1.0, uploaded 01/02/2024.</p> <p>This database include supplemental data and files for the publication of this paper:</p> <p>Sensitivities of Spectral Optical Properties of Dust Aerosols to their Mineralogical and Microphysical Properties. Yuheng Zhang, M. Saito, P. Yang, G. L. Schuster, and C. R. Trepte, J. Geophys. Res. Atmos. 2024.</p> <p> </p> <p>*****************************************</p> <p>The supplemental data include:</p> <p>1) 'GroupRefInd.tar.gz' Mineral (group) refractive index files.<br>E. g., 1All_Illite.dat contains the complex refractive index files of illite group. Format (from left to right columns): Wavelength (unit: µm), Real part (n), Imaginary part (k), standard deviation of n, standard deviation of k.</p> <p>The file 'fine_log.dat' includes the mean and standard deviation values of n and k for all the generated fine mode dust samples at 11,044 wavelengths from 0.2 to 50 micron.</p> <p>The file 'fine_log127.dat' only includes the values at 127 wavelengths from 0.2 to 50 micron (defined in 'swav.txt' and 'lwav.txt'), and is used for the bulk property computations.</p> <p>The files 'coarse_log.dat' and 'coarse_log127.dat' are for the coarse mode dust samples.</p> <p>2) 'CompositionFraction.xlsx': Mineral composition data sources/references and composition data (mean and standard deviation values of each group).<br>'Vlog_coarse.dat': Randomly generated VOLUME FRACTION of 9 mineral groups for the coarse mode dust. Left to right: Illite, Kaolinite, Montmorillonite (Other clays), Quartz, Feldspar, Carbonate, Gypsum (Sulphate), Hematite, Goethite.</p> <p>'Vlog_fine.dat': For the fine mode dust.</p> <p>3) 'RefSources.xlsx': The data source references of mineral refractive indices. We didn't include the olivine, other silicates, soot and titanium-rich minerals in the paper, but the refractive indices are available for those who are interested. Chlorite, Mica and Vermiculite group are mentioned in some studies, and we included the refractive indices for these minerals as well.</p> <p>4) 'DustSamples.tar.gz' Dust sample refractive index files.<br>The files are enclosed in four folders: fine_sw/ fine_lw/ coarse_sw/ coarse_lw/.</p> <p>fine: fine mode. coarse: coarse mode.</p> <p>'sw' means shortwave (< 4 µm, in total 76 wavelengths defined in 'swav.txt') while 'lw' means longwave (>= 4 µm, in total 51 wavelengths defined in 'lwav.txt').</p> <p>All files start with 'rdn', which means that they are computed based on randomly generated composition (data given in sheet 2 of 'CompositionFraction.xlsx').</p> <p>The four digit number after 'rdn' is the index of each dust sample. In total, there are 5,000 samples. The sample composition is the same for the same sample index in the same size mode (fine/coarse). Data file format (from left to right columns): real part, imaginary part.</p> <p>5) 'BulkProperties.tar.gz' Bulk property files (excluding phase matrices)<br>'mixqx.dat' files format (from left to right columns): Extinction efficiency (Qext), Scattering efficiency (Qsca), Backscattering efficiency (Qbck), and Asymmetry coefficient (Qasy). To obtain asymmetry factor, use Qasy/Qsca.</p> <p>'mixbkx.dat' files format (from left to right columns): P11(pi) P12(pi) P22(pi) P33(pi) P34(pi) P44(pi).</p> <p>'x' refers to the number at the end of the file name. It can be 100 ~ 112, each represents a setting of coarse and fine mode effective radius and volume fraction (see details in "reff.dat")</p> <p>'reff.dat' contains the effective radius information of the mixture. It has 7 columns: File number "x", Fine mode volume fraction, Fine mode effective radius (µm), Coarse mode effective radius (µm), Bulk projected area (µm^2), Bulk volume (µm^3), Bulk effective radius (µm).</p> <p>6) 'PhaseMatrices.tar.gz' Phase matrices data<br>'mixphswx.dat' files contain phase matrix results at 532 nm (shortwave). From left to right: P11, P12, P22, P33, P34, P44.</p> <p>'mixphlwx.dat' files contain phase matrix results at 10.5 µm (longwave).</p> <p>There are 635,000 rows in each data file. 635,000 rows = 127 wavelengths * 5,000 samples. Row 1~127 is sample 1, row 128~254 is sample 2, etc.. Suggest to use matlab function 'reshape(property, 127, 5000)' for each column when processing the data.</p> <p>7) 'Supplemental.tar.gz'</p> <p>We also include data files mentioned in the supplemental file of the paper. The adjusted source data files of the nine mineral groups are included.</p> <p>The supplemental bulk property files are named based on the figure number.</p> <p>8) 'AdditionalRefInd.tar.gz'</p> <p>We also include additional refractive indices for chlorite, smectite, vermiculite, mica, dolomite, titanium-rich minerals, pyroxenes and soot. These data can be useful in other models.</p> <p>For more detailed information and datasets, please contact: Yuheng Zhang, yuheng98@tamu.edu or yuhengz98@qq.com.</p>
German image spectral library of urban surface materials
<p>The German image spectral library consists of 5102 labelled image spectra of urban surface materials covering the spectral wavelength range between 455 nm and 2449 nm. The spectra have been extracted from high resolution imaging spectroscopy data (HyMap) acquired over the German cities of Dresden (18/05/1999, 01/08/2000, 20/07/2003), Potsdam (18/05/1999) and Munich (17/06/2007, 25/06/2007). This image data package ensures the collection of the most typical urban surface materials including their variations due to different illumination, alteration, observation conditions, regional specifications and data processing characteristics.</p> <p>The collection was done in two main steps: (1) manual collection of spectrally pure urban surface material pixels from the Dresden and Potsdam data sets including additional information, such as the results of field investigations, a field spectral library and color infrared aerial imagery (Heiden et al., 2007 ) and subsequent reduction for redundant pixel spectra; (2) spectral dissimilarity analysis to include and label meaningful unknow spectra from the Munich data set (Jilge et al. 2017 ). </p> <p>The image spectra are labelled based on three sets of spectra labels: one for EAGLE land cover (EAGLE_LCC, consult the “Explanatory Documentation of the EAGLE Concept” from the Copernicus Land website) , one for generalized material groupings (GENLIB_LCH_BuC_MG) and one for more detailed artificial material type (GENLIB_LCH_BuC_AMT).</p> <p>While every effort was made to ensure accurate information, this data set is presented "as is" without warranties of any kind. The authors accept no liability or responsibility to any person as a consequence of any reliance upon the data presented here. The user assumes all responsibility and risk for the use of this data.</p>
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
Mast-borne spectral reflectance measurements of boreal landscape during spring
<p>This dataset contains mast-borne spectral reflectance measurements (350-2500 nm / 350-1000 nm) measured with an ASD Field Spec Pro JR spectroradiometer and digital images of the measurement areas from the time of the measurements. The measurement targets are a boreal sparse pine forest and a forest opening located at the premises of the Arctic Space Centre of the Finnish Meteorological Institute in Sodankylä, northern Finland (N67.361833, E26.634154, WGS84).</p> <p>The dataset covers spring time periods during years 2010-2018 from the dry snow period until some time after the snow disappearance. The temporal coverage vary from year to year depending on the mounting date and due to technical problems. Measurements have been conducted every 30 min during fixed day-time period and based on set weather threshold values.</p> <p>The spectral reflectance data are organized in yearly CSV files the metadata information attached in the file header. Accordingly, the digital images from the measurement areas are organized in yearly folders and packed into zip files.</p> <p>For this version a data example plot (Data_example_mast.png) was added to have a quick visualisation of the sort of the data available.</p> <p>For further information contact Henna-Reetta Hannula (henna-reetta.hannula@fmi.fi) or Kirsikka Heinilä (kirsikka.heinila@ymparisto.fi)</p>
Thermal infrared emissivity spectral library of silicates measured under the Mercury simulated environment
<p>This is the thermal emissivity spectral library of silicates measured as a function of temperature under Mercury simulated environment. Data is measured at the Planetary Spectroscopy Laboratory (PSL), Institute of Planetary Research, German Aerospace Center (DLR), Berlin. The spectral library will be used for mineral identification of Mercury surface using MERTIS datasets. The manuscript related to this work is submitted to Icarus on the title "<strong>Thermal Infrared Spectroscopy (7-14 µm) of Silicates under Simulated Mercury Daytime Surface Conditions and their Detection: Supporting MERTIS onboard the BepiColombo Mission".</strong></p>
Solar spectral irradiance measurements above and in-canopy (SLOCS and CloudRoots Amazonia, 2022)
<p> </p> <p><strong>Shedding Light On CloudRoots</strong></p> <p>Solar spectral irradiance measurements made with the sensors produced within the Shedding Light On Cloud Shadows (SLOCS) project, deployed at the CloudRoots Amazonia 2022 campaign. </p> <p><strong>Dataset contents</strong></p> <ul> <li>Level 0 (raw): the raw data as it comes from the instruments</li> <li>Level 1 (L1): data in NetCDF format with metadata, quality control, homogenized factory calibration (counts bin-1 dt-1)</li> <li>Level 2 (L2): calibrated L1 data in W m-2 nm-1</li> <li>extras: this folder includes reference calibration spectra and data quality quicklooks</li> </ul> <p>Data is available at 1 Hz (resampled) and 10 Hz (native) resolution. 10 Hz resolution is compressed using NetCDF compression with gzip level 5 (uncompressed is 1.13 GB per date).</p> <p><strong>Data quality and uncertainty<br></strong></p> <p>Please note this dataset is in version 0.1.0, meaning you should use the dataset with caution. Not all unphysical data may have been flagged as such, and spectral calibration is an estimate based on a simple modelled spectrum. This modelled spectrum is a standard tropical atmosphere without aerosols, and is not run with observed profiles except an ERA5 estimate of total column water vapour. Please refer to 'extras' for technical validation of the spectral calibration method, and LibRadtran input/output files.</p> <p>A production (1.0) version will be released as soon data is fully validated.</p> <p>Lower-end uncertainty can be estimated by looking at the sensor to sensor spread at wavelength level during the calibration measurements. In the calibration phase, all sensors were co-located and homogenized at wavelength level. The 13:50 to 14:10 UTC time on August 7 is the reference frame for spectral calibration. </p> <p>Other sources of uncertainty are difficult to quantify due to measurements taking place in a very heteregeneous forest. These uncertainties relate primarily to the less-than-perfect placement of sensors on the towers in comparison to the reference calibration phase. </p> <p>Sensor 18 is only available in raw data or calibrated data. Precalibration (homogenizing) is not possible given its deviating spectral filter set compared to the others (sensor version 3b vs. 3a). </p> <p><strong>Technical information</strong></p> <ul> <li>The NetCDF files comply with CF1.7 where applicable.</li> <li>Metadata include sensor location (altitude relative to ground and sea level, lat, lon). </li> <li>Code for processing raw data to NetCDF available at <a href="../records/10159129">https://zenodo.org/records/10159129</a></li> <li>Calibration of raw sensor units to spectral irradiance is done using a reference clear-sky spectrum simulated with LibRadtran. Settings and output is included in "extras".</li> </ul> <p><strong>More information</strong></p> <ul> <li><a href="https://chiel.ghost.io/slocs">SLOCS project homepage</a></li> <li><a href="https://cloudroots.wur.nl/">CloudRoots project homepage</a></li> <li>2022 campaign reference paper is in preparation</li> <li>See 'related works' for the instrument reference paper </li> </ul>
Sublimation and infrared spectral properties of ammonium cyanide
<p>Data from</p> <p>Perry A. Gerakines, Yukiko Y. Yarnall, Reggie L. Hudson,<br>Sublimation and infrared spectral properties of ammonium cyanide,<br>Icarus,<br>Volume 413,<br>2024,<br>116007,<br>ISSN 0019-1035,<br>https://doi.org/10.1016/j.icarus.2024.116007.<br>(https://www.sciencedirect.com/science/article/pii/S0019103524000654)<br>Abstract: The ammonium ion (NH4+) has been suggested to be present in interstellar ices and has been observed on the surfaces of planetary bodies using infrared (IR) spectroscopy as the primary means of identification. Evidence for several ammonium salts has also been found in the dust and surface ices of comet 67P/Churyumov-Gerasimenko. Here we present a laboratory study of ammonium cyanide (NH4CN) and report on several properties of this compound, measured with higher accuracy than in previous reports, including its IR band strengths and optical constants for use in quantifying its abundance in interstellar and planetary ices. We also report the first measurements since 1882 of NH4CN vapor pressures, sublimation fluxes, and sublimation enthalpy measured at temperatures relevant to subliming cometary ices (134–155 K). The density and refractive index of NH4CN at 125 K and the sublimation enthalpy and vapor pressures of NH3 at ~100 K are also reported.</p> <p><br>Keywords: Ices; IR spectroscopy; Comets; Infrared observations</p> <p>This work was funded by the NASA Astrophysics Research and Analysis (APRA) and Planetary Data Archiving, Restoration, and Tools (PDART) Programs, as well as NASA's Planetary Science Division Internal Scientist Funding Program through the Fundamental Laboratory Research (FLaRe) work package at the NASA Goddard Space Flight Center.</p>
Spectral and Chemical Dataset for Ripeness Monitoring in cv. Tempranillo Grapes Using a Multispectral Sensor
<p><strong><span>The dataset consists of 1010 samples of Tempranillo grape berries, offering a comprehensive record of spectral and chemical measurements that serve as a valuable resource for evaluating berry ripeness and sugar content (ºBrix). Each row in the dataset corresponds to a single berry, and the columns include a unique identifier (ID), the date of sampling (spanning 21 different days during the ripening period in 2024), expressed as Day of the Year (DOY) from DOY 210 to DOY 284. The dataset also includes measurements for nine spectral bands which represent the reflectance values recorded by the sensor (F1–F8 and NIR), a dedicated channel to detect ambient light flicker (CLEAR), and the sugar content (</span><span>°Bx</span><span>), ranging from 4.8 to 45 </span><span>°Bx</span><span>, encompassing all maturity stages from early ripeness to over-ripeness. The dataset is structured so that rows correspond to individual berries, and columns represent the measured variables, enabling statistical and machine learning analyses</span></strong></p> <p><strong><span>Center wavelength (λp) (F1: 415 nm, F2: 445 nm, F3: 480nm, F4: 515nm, F5: 555nm, F6: 590nm, F7: 630nm, F8: 680nm)</span></strong></p>
Vis-NIR Soil Spectral Library of the Hungarian Soil Degradation Observation System
<p>Since soil spectroscopy is considered to be a fast, simple, accurate and non-destructive analytical method, its application can be integrated with wet analysis as an alternative. Therefore, development of national-level soil spectral libraries containing information about all soil types represented in a country is continuously increasing to serve as a basis for calibrated predictive models capable of assessing physical and chemical parameters of soils at multiple spatial scales. In this article, we present a database containing laboratory and visible-near infrared spectral data of legacy soil samples from the Hungarian Soil Degradation Observation System (HSDOS). The published data set includes the following parameters measured in 5,490 soil samples: pH<sub>KCl</sub>, soil organic matter (SOM), calcium carbonate (CaCO<sub>3</sub>), total salt content (TSC), total nitrogen (N<sub>total</sub>), soluble phosphorus (P<sub>2</sub>O<sub>5</sub>-AL), soluble potassium (K<sub>2</sub>O-AL), plasticity index according to Hungarian standard (PLI), soil profile depth and reflectance data between 350 and 2,500 nm wavelength. The presented database can be a complement for further soil related research on continental, national or regional scales to support sustainable soil management.</p> <p>Uploaded CSV file contains variables for general information, soil parameters and reflectance data of spectral bands between 350-2,500 nm. Details about variables can be found in Table 1.</p> <table> <tbody> <tr> <td> <p>Column name</p> </td> <td> <p>Description</p> </td> <td> <p>Method</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>SAMPLE_TDR_ID</p> </td> <td> <p>Original TDR IDs</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>SAMPLING_DATE</p> </td> <td> <p>Date of sampling</p> </td> <td> <p>-</p> </td> <td> <p>YYYY-MM-DD</p> </td> </tr> <tr> <td> <p>NORTHING_EOV</p> </td> <td> <p>Northing coordinate of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>m</p> </td> </tr> <tr> <td> <p>EASTING_EOV</p> </td> <td> <p>Easting coordinate of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>m</p> </td> </tr> <tr> <td> <p>LON_WGS84</p> </td> <td> <p>Longitude of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>LAT_WGS84</p> </td> <td> <p>Latitude of sampling area centroids</p> </td> <td> <p>-</p> </td> <td> <p>°</p> </td> </tr> <tr> <td> <p>pH_KCl</p> </td> <td> <p>pH</p> </td> <td> <p>Potentiometer (MSZ–08 0206-2: 1978)<sup>40</sup></p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>SOM</p> </td> <td> <p>Soil organic matter</p> </td> <td> <p>E4/E6 ratio<sup>41</sup><sup>,</sup><sup>42</sup> (MSZ–08-0452:1980)<sup>43</sup></p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>CaCO3</p> </td> <td> <p>Calcium carbonate</p> </td> <td> <p>Scheibler type calcimeter (MSZ–08 0206-2:1978)<sup>40</sup></p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>TSC</p> </td> <td> <p>Total salt content</p> </td> <td> <p>EC-TDS electrode (MSZ–08-0206-2:1978)<sup>40</sup></p> </td> <td> <p>w/w %</p> </td> </tr> <tr> <td> <p>TN</p> </td> <td> <p>Total nitrogen</p> </td> <td> <p>Kjeldahl method<sup>44</sup> (ISO 11261:1995)<sup>45</sup></p> </td> <td> <p>mg kg<sup>-1</sup></p> </td> </tr> <tr> <td> <p>P2O5_AL</p> </td> <td> <p>Soluble phosphorus</p> </td> <td> <p>AL extract atomic adsorption spectrophotometry (MSZ 20135:1999)<sup>46</sup></p> </td> <td> <p>mg kg<sup>-1</sup></p> </td> </tr> <tr> <td> <p>K2O_AL</p> </td> <td> <p>Soluble potassium</p> </td> <td> <p>AL extract, flame photometer (MSZ 20135:1999)<sup>46</sup></p> </td> <td> <p>mg kg<sup>-1</sup></p> </td> </tr> <tr> <td> <p>PLI</p> </td> <td> <p>Plasticity index according to Hungarian standard</p> </td> <td> <p>Yarn test of Arany (MSZ–08 0205-2:1978)<sup>47</sup></p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>PROFILE_LEVEL</p> </td> <td> <p>Soil profile depth level</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>SPC350:2500</p> </td> <td> <p>spectral reflectance in the range of 350 and 2500 nm</p> </td> <td> <p>ASD FieldSpec 4 spectroradiometer</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p>Table 1. Summary of included attributes and data set structure with laboratory test methods applied on the soil samples.</p> <p> </p> <p><strong>For more details / to cite this dataset please use:</strong></p> <p>Mészáros, J., Kovács, Zs., László, P., Vass-Meyndt, Sz., Koós, S., Pirkó, B., Szűcs-Vásárhelyi, N., Bakacsi, Zs., Laborczi, A., Balog, K., & Pásztor, L. (2024). Vis-NIR soil spectral library of the Hungarian Soil Degradation Observation System. <em>Sci Data</em> <strong>12</strong>, 363 (2025). https://doi.org/10.1038/s41597-025-04667-9</p>
Portable/on site devices (i.e. Specim IQ) spectral data on spice/pepper
<p>The dataset from the analysis of spices with portable/on-site devices (i.e. Specim IQ) based on light spectroscopy. Measurements are taken for the authentication of spice (i.e. pepper) using an on-site/portable/handheld device as part of WP3 (Task 3.1): Implementation of innovations in food authenticity. Measurements (reflectance values) are averaged per sample and only the final average spectral data is provided in the Excel sheets. The data is useful for anyone working with spectral data and its use for the authentication of spices.</p>
Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data
<p>Live fuel moisture content (LFMC), defined as the mass of water in the foliage and small twigs relative to its total dry mass, is a key factor affecting fire potential and determining wildfire danger and activity. Fuel moisture is directly related to the amount of energy needed to evaporate water before ignition. Consequently, high moisture values reduce, or even inhibit, ignitability and subsequent fire spread.</p> <p>To cover the absence of a specific model to estimate LFMC for the Mediterranean Basin at the sub-continental scale, we built an empirical model based on Random Forests (LFMC<sub>RF</sub>) and combining MODIS spectral bands, vegetation indices, land surface temperature, and the day of year as predictors. The details on the modeling and validation methods, and the accuracy of the estimates are in the related publication <strong><a href="https://doi.org/10.3390/rs14133162">Cunill Camprubí et al., 2022</a></strong>.</p> <p>This dataset contains a collection of weekly LFMC maps from February 2000 to December 2021. The maps cover the Mediterranean and part of the Temperate biomes of the Mediterranean Basin. File <em>mapping_area_LFMC-RF_W-1.0.png</em> shows the target mapping areas.</p> <p>Metadata:</p> <ul> <li>Spectral Information: MODIS MCD43A4 C.6</li> <li>Land Surface Temperature: MODIS MOD11A2 C.6</li> <li>Land Cover Mask: MODIS MCD12Q1 C.6</li> <li>Coordinate Reference System: Native MODIS Sinusoidal</li> <li>Temporal Resolution: Weekly (W)</li> <li>Spatial Resolution: ~500 m</li> <li>File Format: NetCDF v.4</li> <li>Scale Factor: 0.01</li> </ul> <p>Fundings:</p> <p>The study was funded by the MICINN (RTI2018-094691-B-C31), European Union’s Horizon 2020-Research and Innovation Framework Programme under grant agreement no. 101003890 project FirEUrisk, the National Natural Science Foundation of China (U20A20179, 31850410483), and the talent proposals in Sichuan Province (2020JDRC0065) from Southwest University of Science and Technology (18ZX7131).</p>
Near-infrared (NIR) soil spectral library using the NeoSpectra Handheld NIR Analyzer by Si-Ware
<p>Up-to-date information on soil properties and the ability to track changes in soil properties over time are critical for improving multiple decisions on soil security at various scales, ranging from global climate change modeling and policy to national level environmental and development planning, to farm and field level resource management. Diffuse reflectance infrared spectroscopy has become an indispensable laboratory tool for the rapid estimation of numerous soil properties to support various soil mapping, soil monitoring, and soil testing applications. Recent advances in hardware technology have enabled the development of handheld sensors with similar performance specifications as laboratory-grade near-infrared (NIR) spectrometers.</p> <p>Here, we've compiled a hand-held NIR spectral library (1350-2550 nm) using the NeoSpectra Handheld NIR Analyzer developed by <a href="https://www.si-ware.com/">Si-Ware</a>. Each scanner is fitted with Fourier-Transform technology based on the semiconductor Micro Electromechanical Systems (MEMS) manufacturing technique, promising accuracy, and consistency between devices.</p> <p>This library includes 2,106 distinct mineral soil samples scanned across 9 of these portable low-cost NIR spectrometers (indicated by serial no). 2,016 of these soil samples were selected to represent the diversity of mineral soils found in the United States, and 90 samples were selected across Ghana, Kenya, and Nigeria. 519 of the US samples were selected and scanned by <a href="https://www.woodwellclimate.org/">Woodwell Climate Research Center</a>. These samples were queried from the <a href="https://ncsslabdatamart.sc.egov.usda.gov/">USDA NRCS NSSC-KSSL Soil Archives</a> as having a complete set of eight measured properties (TC, OC, TN, CEC, pH, clay, sand, and silt). They were stratified based on the major horizon and taxonomic order, omitting the categories with less than 500 samples. Three percent of each stratum (i.e., a combination of major horizon and taxonomic order) was then randomly selected as the final subset retrieved from KSSL's physical soil archive as 2-mm sieved samples. The remaining 1,604 US samples were queried from the USDA NRCS NSSC-KSSL Soil Archives by the <a href="https://www.unl.edu/">University of Nebraska - Lincoln</a> to meet the following criteria: Lower depth <= 30 cm, pH range 4.0 to 9.5, Organic carbon <10%, Greater than lower detection limits, Actual physical samples available in the archive, Samples collected and analyzed from 2001 onwards, Samples having complete analyses for high-priority properties (Sand, Silt, Clay, CEC, Exchangeable Ca, Exchangeable Mg, Exchangeable K, Exchangeable Na, CaCO3, OC, TN), & MIR scanned.</p> <p>All samples were scanned dry 2mm sieved. ~20g of sample was added to a plastic weighing boat where the NeoSpectra scanner would be placed down to make direct contact with the soil surface. The scanner was gently moved across the surface of the sample as 6 replicate scans were taken. These replicates were then averaged so that there is one spectra per sample per scanner in the resulting database.</p> <p>A subset of 1,976 US topsoil samples was used to create Cubist models for 8 soil properties including bulk density (BD, <2mm fraction, 1/3 Bar, units in grams per cubic centimeter), calcium carbonate (CaCO3, <2mm fraction, units in weight percent), clay content (percent), buffered ammonium-acetate exchangeable potassium (Ex. K, units in centimoles of charge per kilogram of soil), pH, sand content (percent), silt content (percent), and estimated organic carbon (SOC, estimated after inorganic carbon removal, units in weight percent). Two strategies were evaluated for handling scanner-to-scanner variability: averaging scans per sample (avg) versus retaining replicate scans across all scanners (reps) during model building. Cubist avg models and cubist reps models are provided here for the 8 soil properties outlined in “.qs” file format and can be opened and worked with in the R programming language. The subset of 1,976 samples has also been provided here for reproducibility (1976_NSlibrary_withmetadata.csv).</p> <p>The repository contains:</p> <ul> <li><em>Neospectra_database_column_names.csv</em>: describes the variables (columns) of site and soil data, and the range of near-infrared (NIR, 1350-2550 nm) and mid-infrared (MIR, 600-4000 cm-1) spectra. The CSV is composed of the file name, column name, type, example, and description with measurement unit.</li> <li><em>Neospectra_WoodwellKSSL_MIR.csv</em>: the equivalent MIR spectra of neospectra samples fetched from the KSSL database and formatted to the OSSL specifications.</li> <li><em>Neospectra_WoodwellKSSL_soil+site+NIR.csv</em>: soil, site, and Neospectra's NIR. Each row contains one replicated spectra of a given scanner (6 repeats per scanner per soil sample). Soil and site info is filled within the same soil sample.</li> <li>1976_NSlibrary_withmetadata.csv: data matrix for reproducible model calibration.</li> <li>Models: <ul> <li>log..bd_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+BD).</li> <li> <p>log..caco3_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+CaCO3).</p> </li> <li> <p>clay_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for clay.</p> </li> <li> <p>log..k.ex_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+Ex. K).</p> </li> <li> <p>ph.h2o_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for pH.</p> </li> <li> <p>sand_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for sand.</p> </li> <li> <p>silt_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for silt.</p> </li> <li> <p>log..soc_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+SOC).</p> </li> <li> <p>log..bd_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+BD).</p> </li> <li> <p>log..caco3_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+CaCO3).</p> </li> <li> <p>clay_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for clay.</p> </li> <li> <p>log..k.ex_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+Ex. K).</p> </li> <li> <p>ph.h2o_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for pH.</p> </li> <li> <p>sand_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for sand.</p> </li> <li> <p>silt_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for silt.</p> </li> <li> <p>log..soc_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+SOC).</p> </li> </ul> </li> </ul>
S31 | WRTMSD | Wiley Registry of Tandem Mass Spectral Data, MSforID
<p>This is the collection associated with list S31 WRTMSD on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S31</p> <p>WRTMSD</p> <p><strong>Wiley Registry of Tandem Mass Spectral Data, MSforID</strong></p> <p>WRTMSD <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/WRTMSD_wDTXSIDs_24012019.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/WRTMSD_wDTXSIDs_24012019.xlsx">XLSX</a> (24/01/2019)</p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/wrtmsd">WRTMSD List</a></p> <p>WRTMSD <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/210119Update/WRTMSD_InChIKeys.txt">InChIKeys </a>(24/01/2019)</p> <p>The "Wiley Registry of Tandem Mass Spectral Data, MSforID" contains high-quality tandem MSacquired on a QqTOF instrument, developed by Herbert Oberacher (Medical University of Innsbruck, Austria). More information at <a href="http://www.msforid.com/">www.msforid.com</a> </p>
Spectral library of vegetation from Mediterranean woodlands
<p>Site description:</p> <p>All reflectance measurements have been collected in Mediterranean oak woodland at <em>Herdade </em>da <em>Machoqueira do Grou</em>, located<em> </em>in Central Portugal (39° 08′ 18.9″ N, 9° 19′ 56.22″ W, 165-m height). The site is characterized by a Mediterranean climate with mild winters and hot dry summers. The average annual precipitation recorded at the climate station of Santarém (39° 12′ N, 8° 44′ W) for the period 1981–2010 was 652 mm, and mean daily temperature was 17°C (<a href="http://www.ipma.pt/pt/oclima/normais.clima/">www.ipma.pt/pt/oclima/normais.clima/</a>). Detailed meteorological measurements of radiation, temperature, and air humidity are also publicly available (Cerasoli et al., 2020). The soil is a cambisol (FAO) with 81% sand, 5% clay, and 14% silt. The tree layer is represented exclusively by cork oak trees (<em>Quercus suber</em> L.) with a tree density of 177 tree ha<sup>-1</sup> and leaf area index (LAI) of 1.5. The mean total tree height and height below the canopy are 7.9 and 3.1m respectively (Cerasoli et al., 2015). Tree canopy represents 36% of the soil cover fraction. The understorey is composed of a mixture of shrubs and herbaceous species. The site was plowed in 2013 (Correia et al., 2016), hence the cover fraction of shrubs changed across years. A field survey in 2017 estimated an 18% coverage of shrubs and 41% of herbaceous species, while the remaining 41% was represented by litter and bare soil (Heuschmidt et al., 2020). The most represented shrub species are <em>Cistus salvifolius</em> (cistus) and the <em>Ulex airensis</em> (ulex). In spite of occupying the same habitat, the two species have different growth habits and stress strategies. While the cistus is a semi-deciduous species with shallow roots, decreasing its canopy area during the summer period, the ulex has a deep root system and spine shaped leaves and shoots conferring high drought resistance (Correia et al., 2014). The herbaceous layer is composed of C3 species mainly grasses (44.5%) and legumes (28.7%) (Cerasoli et al., 2015).</p> <p> </p> <p>Reflectance measurements:</p> <p>All spectral observations were acquired with an ASD FieldSpec3 spectroradiometer (Malvern Panalytical, Boulder, USA) in the range of 350-2300nm. The visible and near-infrared region (350-1000nm) has a spectral resolution (full-width half maximum) of 3nm and a sampling interval of 1.4nm, while the mid infrared region (1000-2500nm) has a spectral resolution of 10nm and a sampling interval of 2.0nm. Canopy spectral data were collected by a fiber optic cable inserted into a pistol grip. A white reference of known reflectance (Spectralon panel, Labsphere, Inc., North Sutton, USA) was used to normalize for variation in atmospheric conditions and to convert the measurements into absolute reflectance. All targets were fully exposed to solar radiation at the time of the measurements. Measurements were performed on cork oak, cistus, and ulex canopies. Herbaceous plots were delimited by a 50X50 cm quadrat. Oak trees canopy measurements were done using a scaffold on the south side of the canopy. All canopy measurements were performed with a nadir view, a field of view angle of 25º, and a distance of about 90cm from the target, which resulted in a field of view of about 1256 cm<sup>2</sup>. All spectra were collected for 2 hours around solar noon, to minimize the effects of shadowing and solar zenith changes, with five replicates for each target, representing each the average of 25 spectra. All reflectance values in the range 1350-1400nm and 1800-1950nm were excluded, corresponding to the atmospheric water vapor absorption regions. A leaf clip including a white and a black standard was used for the measurement of the reflectance of cork oak leaf blades avoiding main veins.</p> <p> </p> <p>File description: </p> <p>The file "specveg_data_spectra" concerns all spectral data, the "specveg_metadata" covers the additional data of every single measured vegetation including photos (URL), and the "specveg_meta" describes all the existing variables.</p>
Spectral dataset of daylights and surface properties of natural objects measured in Japan
<p>This is a spectral dataset of natural objects and daylights collected in Japan. </p> <p>We collected 359 natural objects and measured the reflectance of all objects and the transmittance of 75 leaves. We also measured daylights from dawn till dusk on four different days using a white plate placed (i) under the direct sun and (ii) under the casted shadow (in total 359 measurements). We also separately measured daylights at five different locations (including a sports ground, a space between tall buildings and a forest) with minimum time intervals to reveal the influence of surrounding environments on the spectral composition of daylights reaching the ground (in total 118 measurements).</p> <div> <div> <div> <p>If you use this dataset in your research, please cite the following publication.</p> </div> </div> </div> <div> <div> <div> </div> </div> </div> <div>Morimoto, T., Zhang, C., Fukuda, K., & Uchikawa, K. (2022). Spectral measurement of daylights and surface properties of natural objects in Japan. <em>Optics express</em>, <em>30</em>(3), 3183. https://doi.org/10.1364/OE.441063</div> <p> </p> <p>Dataset contains following Excel spread sheets and csv files:</p> <p><strong>(A) Surface properties of natural objects</strong></p> <p><strong> (A-1) Reflectance_ver1-2.xlsx and .csv</strong></p> <p><strong> (A-2) Transmittance_FrontSideUp_ver1-2.xlsx and .csv</strong></p> <p><strong> (A-2) Transmittance_BackSideUp_ver1-2.xlsx and .csv</strong></p> <p><strong>(B) Daylight measurements</strong></p> <p> <strong>(B-1) Daylight_TimeLapse_v1-2.xlsx and .csv</strong></p> <p> <strong>(B-2) Daylight_DifferentLocations_v1-2.xlsx and .csv</strong></p> <p> </p> <p>Data description</p> <p><strong>(A) Surface properties</strong></p> <p><strong>(A-1) Reflectance_ver1-2.xlsx and .csv</strong></p> <p>This file contains surface spectral reflectance data (380 - 780 nm, 5 nm step) of 359 natural objects, including 200 flowers, 113 leaves, 23 fruits, 6 vegetables, 8 barks, and 9 stones measured by a spectrophotometer (SR-2A, Topcon, Tokyo, Japan). Photos of all samples are included in the .xlsx file.</p> <p>For the analysis presented in the paper, we identified reflectance pairs that have a Pearson’s correlation coefficient across 401 spectral channels of more than 0.999 and removed one of reflectances from each pair. The column 'Used in analysis' indicates whether or not each sample is used for the analysis (TRUE indicates used and FALSE indicate not used).</p> <p>At the time of collection, we noted the scientific names of flowers, leaves and barks from a name board provided by the Tokyo Institute of Technology in which samples are collected. If not available, we used a smartphone software which automatically identifies the scientific name from an input image (<em>PictureThis - Plant Identifier</em> developed by Glority Global Group Ltd.). The names of 2 flowers and 9 stones whose name could not be identified through either method were left blank.</p> <p><strong>(A-2) Transmittance_FrontSideUp_v1-2.xlsx and .csv</strong></p> <p>This file contains surface spectral transmittance data (380 - 780 nm, 5 nm step) for 75 leaves measured by a spectrophotometer (SR-2A, Topcon, Tokyo, Japan). Photos of all samples are included in the .xlsx file.</p> <p>For this data, the transmittance was measured with the front-side of leaves up (the light was transmitted from the back side of the leaves). This is the data presented in the associated article.</p> <p><strong>(A-3) Transmittance_BackSideUp_v1-2.xlsx and .csv</strong></p> <p>Spectral transmittance data of the same leaves presented in (A-2).</p> <p>For this data, the transmittance was measured with the back-side of leaves up (the light was transmitted from the front side of the leaves).</p> <p> </p> <p><strong>(B) Daylight measurements</strong></p> <p><strong>(B-1) Daylight_TimeLapse_ver1-2.xlsx and .csv</strong></p> <p>This file contains daylight spectra from sunrise to sunset on four different days (2013/11/20, 2013/12/24, 2014/07/03 and 2014/10/27) measured by a spectrophotometer (SR-LEDW, Topcon, Tokyo, Japan) with a wavelength range from 380 nm to 780 nm with 1 nm step. We measured the reflected light from the white calibration plate placed either under a direct sunlight or under a casted shadow.</p> <p>The column 'Cloud cover' provides visual estimate of percentage of cloud cover across the sky at the time of each measurement. The column 'Red lamp' indicates whether an aircraft warning lamp at the measurement site was on (circle) or off (blank).</p> <p><strong>(B-2) Daylight_DifferentLocations_ver1-2.xlsx and .csv</strong></p> <p>This file includes daylight spectra measured at five different sites within the Suzukakedai Campus of Tokyo Institute of Technology with minimum time gap on 2014/07/08, using a spectroradiometer (IM-1000, Topcon) from 380 nm to 780 nm with 1 nm step. The instrument was oriented either towards the sun or towards the zenith sky. When the instrument was oriented to the sun, we measured spectra in two ways: (i) one using a black cylinder covering the photodetector and (ii) the other without using a cylinder.</p> <p>The column 'Cylinder' indicates whether the black cylinder was used (circle) or not (cross). The column 'Cloud cover' shows the visual estimate of percentage of cloud cover at the time of each measurement. The column 'Sun hidden in clouds' denotes whether the measurement was taken when the sun was covered by clouds (circle) or not (blank).</p>
GNPS Drug Library spectral files and metadata
<p>Global Natural Product Social Molecular Networking (GNPS) Drug Library: a centralized collection of reference spectra for drugs and their metabolites/analogs along with structured pharmacologic metadata including exposure source, pharmacologic class, therapeutic indication, and mechanism of action. </p> <p>Two MS/MS reference libraries: </p> <ul> <li>GNPS_Drug_Library_Spectra_Drugs_and_Metabolites.mgf: Reference spectra for drugs and drug metabolites collected from the GNPS Spectral Library and MSnLib.</li> <li>GNPS_Drug_Library_Spectra_Drug_Analogs.mgf: MS/MS spectra analogs of drugs in publicly accessible untargeted metabolomics data derived from spectral alignment strategies.</li> </ul> <p>Two metadata files:</p> <ul> <li><span>GNPS_Drug_Library_Metadata_Drugs.csv: Controlled-vocabulary pharmacologic metadata on the drugs. </span></li> <li><span>GNPS_Drug_Library_Metadata_Drug_Analogs.csv: Metadata on the drug analogs, including connections to parent drugs, mass offsets, and pharmacologic metadata based on the parent drugs.</span></li> </ul> <p><span>Publication:</span></p> <p><span>https://www.biorxiv.org/content/10.1101/2024.10.07.617109v1</span></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.