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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-4.0Feb 2022View details →

Soil visible–near infrared (vis–NIR) spectra for the Biomes of Australian Soil Environments (BASE) soil microbial diversity database

Visible–near infrared spectra of 695 soil samples collected in the Biomes of Australian Soil Environments (BASE) soil microbial diversity project (Bissett et al., 2016). The spectra represent reflectance values from 2151 wavelengths that range from 350 nm to 2500 nm with a 1 nm interval. The dataset has unique sample identification numbers and the date of sampling, which can be related to the BASE (Australian Microbiome) database (https://data.bioplatforms.com/organization/australian-microbiome)

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-4.0Jun 2023View details →

Enantiopure J-Aggregate of Quaterrylene Bisimides for Strong Chiroptical NIR-Response

Data to report https://doi.org/10.1021/jacs.3c03367: Chiral polycyclic aromatic hydrocarbons can be tailored for next-generation photonic materials by carefully designing their molecular as well as supramolecular architectures. Hence, excitonic coupling can boost the chiroptical response in extended aggregates but is still challenging to achieve by pure self-assembly. Whereas most reports on these potential materials cover the UV and visible spectral range, systems in the near infrared are underdeveloped. We report a new quaterrylene bisimide derivative with a conformationally stable twisted π-backbone enabled by the sterical congestion of a fourfold bay-arylation. Rendering the π-subplanes accessible by small imide substituents allows for a slip-stacked chiral arrangement by kinetic self-assembly in low polarity solvents. The well dispersed solid-state aggregate reveals a sharp optical signature of strong J-type excitonic coupling in both, absorption (897 nm) as well as emission (912 nm) far in the NIR region and reaches absorption dissymmetry factors up to 1.1×10−2. The structural elucidation was achieved by AFM and single-crystal X-ray analysis which we combined to derive a structural model of a fourfold stranded enantiopure superhelix. We could deduce the role of phenyl substituents not only granting stable axial chirality but also guiding the chromophore into a chiral supramolecular arrangement needed for strong excitonic chirality.

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-4.0Mar 2024View details →

Room Temperature NIR Phosphorescence from C64 Nanographene Tetraimide by π-Stacking Complexation with Pt(II)Porphyrin

Additional data to report <a href="https://doi.org/10.1002/anie.202406353">https://doi.org/10.1002/anie.202406353</a>: Near Infrared (NIR) phosphorescence at room temperature is challenging to achieve for organic molecules due to a negligible spin-orbit coupling and a low energy gap leading to fast non-radiative transitions. Here, we show a supramolecular host–guest strategy to harvest the energy from the low-lying triplet state of C64 nanographene tetraimide 1. 1H NMR and X-ray analysis confirmed the 1:2 stoichiometric binding of a Pt(II)porphyrin on the two π-surfaces of 1. While the free 1 does not show emission in the NIR, the host–guest complex solution shows NIR phosphorescence at 77 K. Further, between 860–1100 nm room temperature NIR phosphorescence (λmax = 900 nm, τavg = 142 µs) was observed for a solid-state sample drop-casted from a preformed complex in solution. Theoretical calculations reveal a non-zero spin-orbit coupling between isoenergetic S1 and T3 of π-stacked [1·Pt(II)porphyrin] complex. External heavy atom induced spin-orbit coupling along with rigidification and protection from oxygen in the solid state promotes both the intersystem crossing from the first excited singlet state into the triplet manifold and the NIR phosphorescence from the lowest triplet state of 1.

openmit-licenseNov 2024View details →

NIR spectra display of flexible packaging

Display of NIR spectras of flexible packaging Analysis obtained with Lab Spectrometer Antaris II Coming from flexible packaging waste (french yellow bins)

opencc-by-4.0Nov 2022View details →

Doodleverse/Segmentation Zoo Res-UNet models for 4-class (water, whitewater, sediment and other) segmentation of Sentinel-2 and Landsat-7/8 5-band (RGB+NIR+SWIR) images of coasts.

These Residual-UNet model data are based on 5-band RGB+NIR+SWIR (red, green, blue, near-infrared, and short-wave infrared) images of coasts and associated labels.   Models have been created using Segmentation Gym* using the following dataset**: <a href="https://doi.org/10.5281/zenodo.7344571">https://doi.org/10.5281/zenodo.7344571 </a> Classes: {0=water, 1=whitewater, 2=sediment, 3=other} File descriptions For each model, there are 5 files with the same root name: 1. '.json' config file: this is the file that was used by Segmentation Gym* to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. It is a handy wee thing and mastering it means mastering the entire Doodleverse. 2. '.h5' weights file: this is the file that was created by the Segmentation Gym* function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym* function  `seg_images_in_folder.py`. Models may be ensembled. 3. '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model 4. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` 5. '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU   References *Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ** Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7344571">https://doi.org/10.5281/zenodo.7344571</a>

opencc-by-4.0Nov 2022View details →

Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)

Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) Description 579 images and 579 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. The 4 classes are 0=water, 1=whitewater, 2=sediment, 3=other These images and labels have been made using the Doodleverse software package, Doodler*. These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**. Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 4 classes. The label images are a subset of the following data release**** <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a> Imagery comes from the following 10 sand beach sites: <ol> <li>Duck, NC, Hatteras NC, USA</li> <li>Santa Cruz CA, USA</li> <li>Galveston TX, USA</li> <li>Truc Vert,France</li> <li>Sunset State Beach CA, USA</li> <li>Torrey Pines CA, USA</li> <li>Narrabeen, NSW, Australia</li> <li>Elwha WA, USA</li> <li>Ventura region, CA, USA</li> <li>Klamath region, CA USA</li> </ol> Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, NIR, and SWIR bands only File descriptions <ol> <li>classes.txt, a file containing the class names</li> <li>images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents</li> <li>nir.zip, a zipped folder containing the corresponding near-infrared (NIR) imagery</li> <li>swir.zip, a zipped folder containing the corresponding shortwave-infrared (SWIR) imagery</li> <li>labels.zip, a zipped folder containing the 1-band label images</li> <li>overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (blue=0=water, red=1=whitewater, yellow=2=sediment, green=3=other)</li> <li>resized_images.zip, RGB images resized to 512x512x3 pixels</li> <li>resized_nir.zip, NIR images resized to 512x512x3 pixels</li> <li>resized_swir.zip, SWIR images resized to 512x512x3 pixels</li> <li>resized_labels.zip, label images resized to 512x512 pixels</li> </ol> References *Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085<a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>. See <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler.</a> **Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a> ***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/P91NP87I">https://doi.org/10.5066/P91NP87I</a>. See <a href="https://coasttrain.github.io/CoastTrain/">https://coasttrain.github.io/CoastTrain/ </a>for more information **** Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7335647">https://doi.org/10.5281/zenodo.7335647</a>

opencc-by-4.0Jun 2023View details →

June 2023 Supplement Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other)

June 2023 Supplement of Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) Description Supplementary dataset to: Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647 This supplemental dataset consists of 283 RGB images and 283 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. Of these, 77 images-label pairs also have a corresponding NIR and SWIR satellite image. The 4 classes are 0=water, 1=whitewater, 2=sediment, 3=other These images and labels have been made using the Doodleverse software package, Doodler*. These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. NIR, SWIR, Red, Green, and Blue bands only File descriptions <ol> <li>classes.txt, a file containing the class names</li> <li>images.zip, a zipped folder containing the 3-band images of varying sizes and extents</li> <li>labels.zip, a zipped folder containing the 1-band label images</li> <li>overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (blue=0=water, red=1=whitewater, yellow=2=sediment, green=3=other)</li> <li>nir.zip</li> <li>swir.zip</li> </ol> References Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647 *Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085<a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>. See <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler.</a> **Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a>

opencc-by-4.0Sep 2020View details →

Instantaneous In Vivo Imaging of Acute Myocardial Infarct by NIR‐II Luminescent Nanodots

Fast and precise localization of ischemic tissues in the myocardium after an acute infarct is required by clinicians as the first step toward accurate and efficient treatment. Nowadays, diagnosis of a heart attack at early times is based on biochemical blood analysis (detection of cardiac enzymes) or by ultrasound‐assisted imaging. Alternative approaches are investigated to overcome the limitations of these classical techniques (time‐consuming procedures or low spatial resolution). As occurs in many other fields of biomedicine, cardiological preclinical imaging can also benefit from the fast development of nanotechnology. Indeed, bio‐functionalized near‐infrared‐emitting nanoparticles are herein used for in vivo imaging of the heart after an acute myocardial infarct. Taking advantage of the superior acquisition speed of near‐infrared fluorescence imaging, and of the efficient selective targeting of the near‐infrared‐emitting nanoparticles, in vivo images of the infarcted heart are obtained only a few minutes after the acute infarction event. This work opens an avenue toward cost‐effective, fast, and accurate in vivo imaging of the ischemic myocardium after an acute infarct.

opencc-by-4.0Mar 2024View details →

UVC Up-Conversion and Vis-NIR Luminescence Examined in SrO-CaO-MgO-SiO2 Glasses Doped with Pr

This work was supported by the National Science Centre, Poland, under grant number DEC-2021/41/B/ST5/03792 entitled: Phosphors for UVC LEDs: Self-Disinfecting Surfaces.

dryad40/100

Data from: Non-invasive estimation of absorbed ionizing radiation dose in mice using Near-Infrared Spectroscopy (NIRS) and aquaphotomics

Accurate measurement of ionizing radiation exposure, whether therapeutic or accidental, is of utmost importance in various scenarios. This paper presents a study that addresses this critical need by utilizing near-infrared (NIR) spectroscopy and aquaphotomics to estimate radiation dose exposure in mouse models subjected to X-ray irradiation. The analysis of NIR spectra acquired from the mouse abdomen enabled non-invasive estimation of radiation doses ranging from 0.5 to 6.5 Gy, immediately following the irradiation exposure. The findings were consistent with the impact of total body irradiation in mice, as evidenced by measures such as animal survival rate, alterations in body weight observed over a 30-day post-exposure period, and changes in hematocrit levels. The spectroscopic measurements were based on detecting changes in the molecular structure of body water after radiation exposure, utilizing the water spectral pattern as a multidimensional biomarker. While further validation in nonhuman primates is necessary, the findings demonstrate a simple, non-destructive, and rapid method that holds promise for the estimation of radiation exposure across a range of doses, applicable to both clinical applications and catastrophic radiation events. These advancements in radiation dose quantification have significant implications for the timely and precise assessment of radiation exposure in humans.

opencc-zeroApr 2024View details →

dryad40/100

NIRS data during sandplay and interview

Interactions between the client (Cl) and therapist (Th) evolve therapeutic relationships in psychotherapy. An interpersonal link or therapeutic space is implicitly developed wherein certain important elements are expressed and shared. However, neural basis of psychotherapy, especially of non-verbal modalities, have scarcely been explored. Therefore, we examined the neural backgrounds of such therapeutic alliances during sandplay, a powerful art/play therapy technique. Real-time and simultaneous measurement of hemodynamics was conducted in the prefrontal cortex (PFC) of Cl-Th pairs participating in sandplay and subsequent interview sessions through multichannel near-infrared spectroscopy. As sandplay is highly individualized, and no two sessions and products (sandtrays) are the same, we expected variation in interactive patterns in the Cl–Th pairs. Nevertheless, we observed a statistically significant correlation between the spatio-temporal patterns in signals produced by the homologous regions of the brains. During the sandplay condition, significant correlations were obtained in the lateral PFC and frontopolar (FP) regions in the real Cl-Th pairs. Furthermore, a significant correlation was observed in the FP region for the interview condition. The correlations found in our study were explained as a "remote" synchronization (i.e., unconnected peripheral oscillators synchronizing through a hub maintaining free desynchronized dynamics) between two subjects in a pair, possibly representing the neural foundation of empathy which arises commonly in sandplay therapy.

opencc-zeroDec 2021View details →

opencc-by-4.0Feb 2022View details →

NIR-Almond_Data_Freeze-drying_Lösel_Shakiba

The impact of freeze-drying on the origin determination of almonds by near-infrared (NIR) spectroscopy was investigated. This upload contains four data sets of the same 72 authentic almond samples from six different countries of origin, which have been measured with NIR without freeze-drying and after 3 hours, 24 hours and 48 hours of freeze-drying.

opencc-by-4.0Mar 2022View details →

deepNIR: Dataset for generating synthetic NIR images and improved fruit detection system using deep learning techniques

In this paper, we present datasets that can be utilised for synthetic near infrared (NIR) image and bounding box level fruit detection system. It is undeniable fact that high-caliber machine learning software frameworks such as Tensorflow or Pytorch and large scale dataset such as ImageNet and COCO, and accelerated GPU hardware support have pushed the limit of machine learning for more than decades. Among these breakthroughs quality dataset is one of important key building blocks that can lead to success in model generalisation and deployment for data-driven deep neural networks. Particularly, synthetic data generation such as generative adversarial networks often requires relatively larger scale data than other supervised approaches. In addition, posing constrains such as geometrical facial constrains in fake face generation or consistent and radiometrically calibrated reflectances from satellite imagery commonly yield better results. We share NIR+RGB dataset that are re-processed from other two public datasets (nirscene and SEN12MS) and our own novel sweetpepper dataset to be able to timely adopt to other following studies. We oversampled from original nirscene dataset at 10, 100, 200, and 400 ratios and total of 127k pair of images. For SEN12MS satellite multispectral dataset, we selected one largest subset; Summer (45k) and All seasons (180k). Our sweetpeppr dataset consists of 1,615 pairs of NIR+RGB images. We demonstrate these NIR+RGB datasets are sufficient to be used for synthetic NIR generation quantitatively and qualitatively. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweetpepper dataset respectively. We also release 11 fruits' bounding box annotations that can be exported as various formats using cloud service. 4 newly added fruits [blueberry, cherry, kiwi, and wheat] compounds 11 novel bounding box dastaset together with our previous work in deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances is 162k and all bounding box dataset is ready for use from cloud service. For evaluation of these dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.5:0.95] results of [min:0.49, max:0.812]. We hope these dataset is useful and serves as one of baseline for the following up studies.

opencc-by-4.0Sep 2022View details →

SALT3-NIR

A fully trained SALT model for Type Ia supernova light curve evolution from 2,000-20,000 Angstrom.

opencc-by-4.0Oct 2022View details →

NMR titration experiments that study binding of a NIR emitting osmium polypyridyl probe to cMYC and hTel G-quadruplex DNA

1D and 2D NMR spectra of cMYC and hTel G-quadruplex DNA and their complexes with Λ- and Δ-enantiomers of the osmium polypyridyl probe [Os(TAP)2(dppz)]2+. Spectra were recorded on a 600 MHz NMR spectrometer with 70 mM KCl, 20 or 25 mM K-phosphate buffer, pH 7, 298 K, in 90% H2O and 10% D2O at 25 °C.

opencc-by-4.0Dec 2016View details →

Fig. 3. Spatial 2D in Nir Raman Scattering For The Study Of Biochemical Features Of The Human Skin Epidermis And A Skin Surface Micro-Mapping In Vitro

Fig. 3. Spatial 2D image of the human epidermis surface: A) an optical image; B) mapping scheme; C) micro–Raman signal intensity map.