Find research datasets worth reusing

opencc-by-4.0Apr 2023View details →

Thermal infrared emissivity spectral library of silicates measured under the Mercury simulated environment

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 "Thermal Infrared Spectroscopy (7-14 µm) of Silicates under Simulated Mercury Daytime Surface Conditions and their Detection: Supporting MERTIS onboard the BepiColombo Mission".

opencc-by-4.0Oct 2024View details →

Vis-NIR Soil Spectral Library of the Hungarian Soil Degradation Observation System

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: pHKCl, soil organic matter (SOM), calcium carbonate (CaCO3), total salt content (TSC), total nitrogen (Ntotal), soluble phosphorus (P2O5-AL), soluble potassium (K2O-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. 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. <table> <tbody> <tr> <td> Column name </td> <td> Description </td> <td> Method </td> <td> Unit </td> </tr> <tr> <td> SAMPLE_TDR_ID </td> <td> Original TDR IDs </td> <td> - </td> <td> - </td> </tr> <tr> <td> SAMPLING_DATE </td> <td> Date of sampling </td> <td> - </td> <td> YYYY-MM-DD </td> </tr> <tr> <td> NORTHING_EOV </td> <td> Northing coordinate of sampling area centroids </td> <td> - </td> <td> m </td> </tr> <tr> <td> EASTING_EOV </td> <td> Easting coordinate of sampling area centroids </td> <td> - </td> <td> m </td> </tr> <tr> <td> LON_WGS84 </td> <td> Longitude of sampling area centroids </td> <td> - </td> <td> ° </td> </tr> <tr> <td> LAT_WGS84 </td> <td> Latitude of sampling area centroids </td> <td> - </td> <td> ° </td> </tr> <tr> <td> pH_KCl </td> <td> pH </td> <td> Potentiometer (MSZ–08 0206-2: 1978)40 </td> <td> - </td> </tr> <tr> <td> SOM </td> <td> Soil organic matter </td> <td> E4/E6 ratio41,42 (MSZ–08-0452:1980)43 </td> <td> % </td> </tr> <tr> <td> CaCO3 </td> <td> Calcium carbonate </td> <td> Scheibler type calcimeter (MSZ–08 0206-2:1978)40 </td> <td> % </td> </tr> <tr> <td> TSC </td> <td> Total salt content </td> <td> EC-TDS electrode (MSZ–08-0206-2:1978)40 </td> <td> w/w % </td> </tr> <tr> <td> TN </td> <td> Total nitrogen </td> <td> Kjeldahl method44 (ISO 11261:1995)45 </td> <td> mg kg-1 </td> </tr> <tr> <td> P2O5_AL </td> <td> Soluble phosphorus </td> <td> AL extract atomic adsorption spectrophotometry (MSZ 20135:1999)46 </td> <td> mg kg-1 </td> </tr> <tr> <td> K2O_AL </td> <td> Soluble potassium </td> <td> AL extract, flame photometer (MSZ 20135:1999)46 </td> <td> mg kg-1 </td> </tr> <tr> <td> PLI </td> <td> Plasticity index according to Hungarian standard </td> <td> Yarn test of Arany (MSZ–08 0205-2:1978)47 </td> <td> - </td> </tr> <tr> <td> PROFILE_LEVEL </td> <td> Soil profile depth level </td> <td> - </td> <td> - </td> </tr> <tr> <td> SPC350:2500 </td> <td> spectral reflectance in the range of 350 and 2500 nm </td> <td> ASD FieldSpec 4 spectroradiometer </td> <td> - </td> </tr> </tbody> </table> Table 1. Summary of included attributes and data set structure with laboratory test methods applied on the soil samples.   For more details / to cite this dataset please use: 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. Sci Data 12, 363 (2025). https://doi.org/10.1038/s41597-025-04667-9

opencc-by-4.0Jan 2023View details →

Near-infrared (NIR) soil spectral library using the NeoSpectra Handheld NIR Analyzer by Si-Ware

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. 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. 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. 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. 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). The repository contains: <ul> <li>Neospectra_database_column_names.csv: 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>Neospectra_WoodwellKSSL_MIR.csv: the equivalent MIR spectra of neospectra samples fetched from the KSSL database and formatted to the OSSL specifications.</li> <li>Neospectra_WoodwellKSSL_soil+site+NIR.csv: 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> log..caco3_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+CaCO3). </li> <li> clay_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for clay. </li> <li> log..k.ex_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+Ex. K). </li> <li> ph.h2o_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for pH. </li> <li> sand_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for sand. </li> <li> silt_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for silt. </li> <li> log..soc_model_nir.neospectra_cubist_AVG_ossl_na_v1.2.qs: Cubist average NIR model for log(1+SOC). </li> <li> log..bd_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs:  Cubist replicates NIR model for log(1+BD). </li> <li> log..caco3_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+CaCO3). </li> <li> clay_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for clay. </li> <li> log..k.ex_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+Ex. K). </li> <li> ph.h2o_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for pH. </li> <li> sand_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for sand. </li> <li> silt_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for silt. </li> <li> log..soc_model_nir.neospectra_cubist_REPS_ossl_na_v1.2.qs: Cubist replicates NIR model for log(1+SOC). </li> </ul> </li> </ul>

opencc-by-4.0Aug 2021View details →

Spectral library of vegetation from Mediterranean woodlands

Site description: All reflectance measurements have been collected in Mediterranean oak woodland at Herdade da Machoqueira do Grou, located 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 (Quercus suber L.) with a tree density of 177 tree ha-1 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 Cistus salvifolius (cistus) and the Ulex airensis (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).   Reflectance measurements: 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 cm2. 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.   File description:  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.

opencc-by-4.0Oct 2024View details →

GNPS Drug Library spectral files and metadata

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.  Two MS/MS reference libraries:  <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> Two metadata files: <ul> <li>GNPS_Drug_Library_Metadata_Drugs.csv: Controlled-vocabulary pharmacologic metadata on the drugs. </li> <li>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.</li> </ul> Publication: https://www.biorxiv.org/content/10.1101/2024.10.07.617109v1

opencc-by-4.0Dec 2021View details →

Open Soil Spectral Library (training data and calibration models)

Open Soil Spectral Library contains training MIR (91,631) and VisNIR (65,063) spectral scans + soil calibration data (>60,000 unique locations) and calibration models. Key data set: <ul> <li>ossl_all_L1_v1.2.qs: soil laboratory, site and spectra information;</li> </ul> Important note: The data set spatially over-represents USA and European Union, with little training data in Asia, South America and Australia, hence calibration models reflect primarily soils of USA and Europe. To use the models and data please install <a href="https://hub.docker.com/r/opengeohub/r-geo">R and required packages</a>. Read more about the <a href="https://github.com/traversc/qs">QS data format</a> and how to convert it to CSV or similar. Modeling steps are explained in detail in: <a href="https://github.com/soilspectroscopy/ossl-models">https://github.com/soilspectroscopy/ossl-models</a>. To visualize database please use: <a href="https://explorer.soilspectroscopy.org/">https://explorer.soilspectroscopy.org/</a> Complete OSSL documentation can be found at: <a href="https://soilspectroscopy.github.io/ossl-manual/">https://soilspectroscopy.github.io/ossl-manual/</a> <a href="https://soilspectroscopy.org/">Soil Spectroscopy for the Global Good</a> is a Coordinated Innovation Network funded by USDA NIFA Food and Agriculture Cyberinformatics Tools Program (<a href="https://nifa.usda.gov/press-release/nifa-invests-over-7-million-big-data-artificial-intelligence-and-other">Award #2020-67021-32467</a>). Input datasets are property of the <a href="https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/research">USDA NRCS National Soil Survey Center – Kellogg Soil Survey Laboratory</a>, <a href="https://www.worldagroforestry.org/">ICRAF-World Agroforestry</a>, <a href="https://www.isric.org/">ISRIC-World Soil Information</a>, the <a href="http://africasoils.net/services/data/soil-databases/">Africa Soil Information Service</a> funded by the Bill and Melinda Gates Foundation, the <a href="https://esdac.jrc.ec.europa.eu/">European Soil Data Centre</a>, the <a href="https://www.neonscience.org/">National Ecological Observatory Network</a>, and <a href="https://sae.ethz.ch/">ETH Zurich</a>.  For more advanced uses of the soil spectral libraries we advise to contact the original data producers especially to get help with using, extending and improving the original SSL data.

opencc-by-4.0Jul 2023View details →

The Brazilian Soil Spectral Library (VIS-NIR-SWIR-MIR) Database: Open Access

Abstract: NEW VERSION V.002 (Some Lat Long Coordinates added). Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use. BSSL Description and Usefulness The Brazilian Soil Spectral Library (BSSL), available at <a href="https://bibliotecaespectral.wixsite.com/english">https://bibliotecaespectral.wixsite.com/english</a>, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data. The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative. The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability. Coverage The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a .xlsx format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (22 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range. Accessing original data source Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors: a) Refer to sheet two for name and code-based searches; b) Visit the website <a href="https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes">https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes</a> or locate the contributors' list by Brazilian state; c) Visit the website of the Brazilian Soil Spectral Service – Braspecs <a href="http://www.besbbr.com.br/">http://www.besbbr.com.br/</a>, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found. Proceeding to data analysis We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011). The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition. a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh; b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm; c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings; d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample; e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm; f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°. The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface. We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°. Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard. For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory. The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm-1 to 600 cm-1, with a resolution of 2 cm-1. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate.   Dataset characterization The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows:   Sheet 1.       BSSL_Soil_Attributes_Dataset Column 1.    ID_unique: Sequential code assigned to every record; Column 2.    Owner code: Acronym assigned to each contributor who allowed access to their proprietary data; Column 3.    Vis_NIR_SWIR_availability: availability of spectral data in visible, near-infrared, and shortwave infrared ranges; Column 4.    MIR_availability: availability of spectral data in the middle infrared range; Column 5.    Sampling: type of soil sampling; Column 6.    Depth_cm: soil surface layer depth in centimeters;   Column 7.    Lat: Latitude;   Column 8.    Lat: Longitude;   Column 9.    Region: Brazilian geographical region of samples' source; Column 10.    Municipality: Brazilian municipality of samples' source; Column 11.   State: Brazilian Federation Unit of samples' source; Column 12. Vegetation: type of vegetal covering; Column 13. Biome: groupings of ecosystems that share similar characteristics and span different regions; Column 14. Geology: type of rock matter from local soil sampling; Column 15. Sand_gkg: Content of the soil fraction with grain size between 2 and 0.053 mm, expressed in grams per kilogram; Column 16. Clay_gkg: Content of soil fraction with grain size smaller than 0.002 mm, expressed in grams per kilogram; Column 17. SOM_gkg: Soil organic matter content, expressed in grams per kilogram; Column 18. pH_H2O: Soil hydrogen ion potential measured in water; Column 19. Ca_mmolkg: Exchangeable calcium content in the soil, expressed in millimoles per kilogram; Column 20. Mg_mmolkg: Exchangeable magnesium content in the soil, expressed in millimoles per kilogram; Column 21. Na_mmolkg: Exchangeable sodium content in the soil, expressed in millimoles per kilogram; and Column 22. CEC_Ph7_mmolkg: Cation exchange capacity of the soil at neutral pH, expressed in millimoles per kilogram.   Sheet 2.     BSSL_Vis_NIR_SWIR_Dataset Column 1. ID_Unique: Sequential code assigned to every record; Column 2. Owner code: Acronym assigned to each contributor who allowed access to their proprietary data; and Column 3 – 2153. 350 – 2500: Reflectance in 2151 spectral bands in nanometers from visible and near-infrared to shortwave infrared range (350 – 2500 nm).   Sheet 3.    BSSL_MIR_Dataset Column 1. ID_Unique: Sequential code assigned to every record; Column 2. Owner_code: Acronym assigned to each contributor who allowed access to their proprietary data; and Column 3 – 683. 4000 – 600: Reflectance in 681 spectral bands in centimeters in the middle infrared range (4000 – 600 cm-1).   Sheet 4.       Contributors Column 1. Owner_code: Acronym assigned to each contributor who allowed access to their proprietary data, which identifies and links it to datasets; Column 2. Owner: Name of the collaborator who agreed to the availability of the data; Column 3. E-mail: Contact the e-mail of the owner for more information or a data request; Column 4. Institution: Contributor's affiliation; Column 5. Samples NIR: Number of Vis-NIR-SWIR samples sent to the BSSL collection; Column 6. Samples MIR: Number of MIR samples sent to the BSSL collection;   Sheet 5.       Metadata Column 1. Material and Methods: Description of procedures performed for soil data analyses   Expectation and Social Relevance These data can impact various disciplines such as soil surveying, soil attribute mapping, soil analysis, soil mineralogy, soil management zones, precision agriculture, development of new datasets and scientific groups, and others. We expect this contribution to be valuable and useful to the soil research community in promoting this non-renewable natural resource's conservation and sustainable use.

opencc-by-4.0Sep 2020View details →

Spectral library of laser-induced fluorescence (LiF) properties from Smithsonian rare-earth element (REE) orthophosphate standards

The spectral library presents a data set of laser-induced fluorescence (LiF) spectra from rare-earth element (REE) orthophosphates provided and distributed as reference material for microbeam analysis by the Smithsonian National Museum of Natural History (sample IDs: 16484 - NMNH 168499; Jarosewich and Boatner, 1991; Donovan et al., 2002 and 2003). The data set delivers high-resolution LiF spectra excited at three standard laser wavelengths (325 nm, 442 nm, 532 nm) recorded in the UV-visible to near-infrared spectral range (340 - 1080nm). Presented LiF spectra represent data from efficient signal excitation conditions and contain the diagnostic emission lines of individual REE including detailed information on splitting into sub-levels. The LiF spectral library data provides a reference for various applications in spectroscopy-based material composition analysis with the scope of REE identification. LiF as a tool can complement the merging technique of reflectance spectroscopy, because LiF is a particularly well suited method for REE detection and can be used to cross-validate results (e.g. Lorenz et al. 2019) The LiF library allows for transparent and reproducible result analysis in scientific studies and promotes further developments of efficient automated algorithms for REE identification and characterisation. This addresses especially the need for innovative, non-invasive techniques of raw material exploration (securing REE supply) and material stream characterisation (e.g. in e-waste recycling) or for manifold applications in other fields of geosciences (e.g. geology) and physics. references: Donovan, J., Hanchar, J., Picolli, P., Schrier, M., Boatner, L., Jarosewich, E., 2002. Contamination in the rare-earth element orthophosphate reference sam- ples. J. Res. National Institute of Standards and Technology 106, 693–701. doi:10.6028/jres.107.056.  Donovan, J., Hanchar, J., Piccoli, P., Schrier, M., Boatner, L., Jarosewich, E., 2003. A reexamination of the rare-earth element orthophosphate reference samples for electron microprobe analysis. Canadian Mineralogist 41, 221– 232. doi:10.2113/gscanmin.41.1.221.  Jarosewich, E., Boatner, L., 1991. Rare-earth element reference samples for electron microprobe analysis. Geostandards Newsletter 15, 397–399. doi:10. 1111/j.1751-908X.1991.tb00115.x.  Lorenz, S., Beyer, J., Fuchs, M., Seidel, P., Turner, D., Heitmann, J., Gloaguen, R., 2019. The Potential of Reflectance and Laser Induced Luminescence Spectroscopy for Near-Field Rare Earth Element Detection in Mineral Ex- ploration. Remote Sensing 11, 21. doi:10.3390/rs11010021.

opencc-by-4.0Oct 2022View details →

Spectral Libraries for Metabolome Annotation Workflow (MAW)

MassBank saved at 2022-09-12 10:28:52 with release version 2022.06 as mbankNIST.rda (MsBackendMsp) GNPS saved at 2022-09-12 13:37:42 as gnps.rda (MsBackendMsp) HMDB saved with the release version 4 as hmdb.rda (MsBackendHmdb) All .rda files can be reloaded into R session using the respective Backends. These databases were created for MAW version 1. hmdb_dframe_str.csv is downloaded from HMDB Downloads for structural information on HMDB IDs present in the HMDB version 4 spectral data.

opencc-by-4.0Sep 2024View details →

vPro-MS peptide spectral library for the identification of human-pathogenic viruses by untargeted proteomics

The viral proteomics workflow (vPro-MS) enables identification of human-pathogenic viruses from patient samples by untargeted proteomics. vPro-MS is based on an in-silico derived peptide library covering the human virome in <a href="https://www.uniprot.org/" rel="nofollow">UniProtKB</a> (331 viruses, 20,386 genomes, 121,977 peptides). vPro-MS is intended to identify human-pathogenic viruses from DiaNN (<a href="https://github.com/vdemichev/DiaNN">https://github.com/vdemichev/DiaNN</a>) outputs of either DIA or diaPASEF data. A scoring algorithm (vProID) assesses the confidence of virus identification and the results are finally summarized in a report table.  The vPro Peptide Library folder contains 3 peptide FASTA files (Contaminants.fasta, Human.fasta, vPro.Virus.fasta), which were used to predict the spectral library (vPro-lib.predicted.speclib). Please note, that the additional commands “--cut” and “--duplicate-proteins” are needed to reprocess the prediction in DiaNN. This spectral library should be used to identify peptide sequences from samples of human origin using DiaNN. Furthermore, the folder contains the metadata file of the viral peptide sequences (vPro.Peptide.Library.txt) and a summary file of the virus taxonomy covered by the library (Taxonomy.Summary.txt). The metadata file is used by the vPro script to identify viruses from the DiaNN main report.

opencc-by-4.0Nov 2022View details →

Development of a spectral library for the discovery of altered genomic events in Mycobacterium avium associated with virulence using mass spectrometry-based proteogenomic analysis

Mycobacterium avium is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. M. avium has ~4,500 predicted protein-coding regions on an average, which can be helpful in discovering several variants at the proteome level. Many of them are potentially associated with virulence, thus identifying such proteins can be a helpful feature in the development of panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of M. avium with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using the protein database for Mycobacterium tuberculosis, and a genome six-frame translated database and a variant protein database of M. avium. A search of mass spectrometry data analysis against M. avium protein database resulted in the identification of 2,954 proteins. Further, proteogenomic analyses aided in the identification of 1,301 novel peptide sequences and correction of translation start sites for 15 proteins. At the end, we created a spectral library of M. avium proteins including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the M. avium proteome. Thus, we present a M. avium spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.

opencc-by-4.0May 2022View details →

Spectral Library of European Pegmatites, Pegmatite Minerals and Pegmatite Host-Rocks – The Greenpeg Database

Spectral signature, obtained through reflectance spectroscopy studies, of European pegmatites and minerals, as well of their host rocks. Samples include LCT- and NYF-type pegmatites and host rocks from pegmatite locations in Austria, Ireland, Norway, Portugal, and Spain. Sample preparation and spectral measurement were conducted in the Universidade do Porto – Faculdade de Ciências (UPORTO) laboratories. The database contains the reflectance spectra (raw and with continuum removed), sample photographs, and main absorption features automatically extracted by a Python routine. Whenever possible, spectral mineralogy was interpreted based on the continuum-removed spectra. A detailed description of the database, its content, the measuring instrument, and interoperability with GIS is found in the database report.

openmpl-2.0Jun 2013View details →

Spectral Library of Impervious Urban Materials

The radiative response of impervious urban materials is a highly influential surface property, due to its impacts on the radiation balance of incoming and outgoing long- and short-wave fluxes. Information about the material composition can be determined with data resolved to the wavelength level. Spectral reflectance in the visible- to short-wave infrared (VIS-SWIR) region is widely used in remote sensing-based land cover classification and spectral long-wave infrared (LWIR) emissivity is required for the observation of surface temperatures. The Spectral Library of impervious Urban Materials (SLUM) available from the London Urban Micromet data Archive (LUMA) includes LWIR emissivity spectra of 74 samples of impervious surfaces derived using measurements made by a portable Fourier Transform InfraRed (FTIR) spectrometer and matching short-wave reflectance spectra observed for each urban sample. The documentation (<a href="http://www.met.reading.ac.uk/micromet/documents/LUMA_SLUM.pdf">pdf</a>) contains photos, meta information for all 74 samples and plots of the short-wave reflactance (300-2500 nm) and long-wave (8-14 um) emissivity spectra. Data are provided in csv format for short-wave reflectance (LUMA_SLUM_SW.csv) and long-wave emissivity (LUMA_SLUM_LW.csv). Further details (including integrated broadband values of emissivity and albedo), methods and data analysis are presented in Kotthaus, S, TEL Smith, MJ Wooster, and CSB Grimmond 2014: Derivation of an urban materials spectral library through emittance and reflectance spectroscopy, ISPRS Journal of Photogrammetry and Remote Sensing, 94, 194–212. doi:10.1016/j.isprsjprs.2014.05.005 Please cite both this site and the paper above if you use this data

opencc-by-4.0Nov 2020View details →

Training dataset: Generation of a spectral library from HEK-Ecoli Spike-in mass spectrometry data

The five raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/). HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in the following ratios (amount in µg): Sample    HEK    E.coli    MS method Sample1    2.5      0.00        DDA Sample2    2.5      0.05        DDA Sample3    2.5      0.15        DDA Sample4    2.5      0.40        DDA Sample5    2.5      0.80        DDA Additionally, iRT peptides were added and 1µg of each samples was measured with a Q-Exactive Plus mass spectrometer. Besides the five raw files, we uploaded two fasta files that serve as human and ecoli protein sequence databases, an transition list for the iRT peptides as well as an experimental design for the MaxQuant search. Additionally, we uploaded the Galaxy MaxQuant training result files: protein groups, peptides, mqpar, msms, evidence and PTXQC.

opencc-by-4.0Apr 2023View details →

GNPS Bile acid modifications MS/MS spectral library

Bile acids are important signaling molecules with impact on host health and metabolism. However, the diversity of bile acids furnished by both the host and microbes is incompletely characterized. To address this knowledge gap, we created a reusable resource of candidate tandem mass spectrometry (MS/MS) spectra by filtering approximately 1.2 billion publicly available MS/MS spectra from 2,706 untargeted metabolomics projects for bile acid-specific MS/MS ion patterns. Provided here are two MGFs/mzMLs consisting of 594,431 MS/MS spectra directly obtained from the public data on GNPS/MassIVE using MassQL queries designed for non-, mono-, di-, tri-, tetra- and penta-hydroxylated bile acids and MS/MS spectra from synthetic standards of 38 amino acids and 28 polyamines conjugated to bile acids. An annotation table (.tsv file) with delta masses and GNPS library matches is also provided. The MGFs/mzMLs can be selected as a group while running molecular networking jobs on GNPS (<a href="https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp">https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp</a>) and used in conjugation with the annotation table to gain unprecedented insights into the new biology of bile acids.

opencc-by-4.0Apr 2024View details →

An 8-(Diazomethyl) Quinoline Derivatized Acyl-CoA In Silico Mass Spectral Library Reveals the Landscape of Acyl-CoA in the Aging Mouse Organs (Data Supplement)

Data supplement for publication "An 8-(Diazomethyl) Quinoline Derivatized Acyl-CoA In Silico Mass Spectral Library Reveals the Landscape of Acyl-CoA in the Aging Mouse Organs (Data Supplement)" (2024) Jinhui Yu1&dagger;, Menghao Guo1,3&dagger;, Sha Li5, Jian Ni2, Yu-Qi Feng4,5*, Jun Ding1,2* 1. CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, PR China. 2. Renmin Hospital of Wuhan University, Wuhan University, 430072, Wuhan, P. R. China. 3. College of Life Sciences, Wuhan University, Wuhan 430072, China. 4. School of Bioengineering and Health, Wuhan Textile University, Wuhan 430200, China. 5. Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, 430071, China.   &dagger;The authors contribute equally to this work. * Corresponding author Email: <a href="mailto:dingjun@wbgcas.cn">dingjun@wbgcas.cn</a>, <a href="mailto:yqfeng@whu.edu.cn">yqfeng@whu.edu.cn</a> ---- Content: 1) Developement: XLS template sheet for development (can be used to adjust or create new library) 2) MSP library: spectra in NIST MSP format (use with MS-Dial or NIST MS Search) 3) NIST library: NIST23 compatible 8-DMQ-acyl-CoA library (use with NIST MS Search) 4) Reference spectra msp: 8-DMQ-acyl-CoA authentic MS/MS spectrum in NIST MSP format (generated by Thermo QE HF-X MS (HCD), for searching NIST MS-Search) Version 1.0 April 22 2024

opencc-by-4.0Nov 2024View details →

NIR/SWIR Spectral Library of Plastic-Substrate Mixtures

NIR/SWIR spectra of substrate-plastic mixtures at varying concentrations Plastics include polyethylene (PE), polyethylene terephthalate (PET), polylactic acid (PLA), polypropylene (PP), polyvinyl chloride (PVC), and styrene-butadiene rubber (SBR) Substrates included 3 soils (Bu5, W6, TG), crushed cement (C), oak leaf powder (V), and water (DIW) Concentrations of 0% (pure substrate), 0.15%, 1.5%, 5%, 15%, 50%, and 100% (pure plastic) Datasets are in .csv format for reflectance spectra, absorbance spectra, absorbance 1st derivative, and absorbance 2nd derivative. Reflectance spectra are also included as .hdr and .sli for ease of importing into ENVI or other hyperspectral image processing software. Additionally, raw ASD spectra and the python script for processing are included for custom spectral processing or analyses.