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The Brazilian Soil Spectral Library (VIS-NIR-SWIR-MIR) Database: Open Access
<p><strong>Abstract:</strong></p> <p>NEW VERSION V.002 (Some Lat Long Coordinates added).</p> <p>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.</p> <p><strong>BSSL Description and Usefulness</strong></p> <p>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.</p> <p>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.</p> <p>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.</p> <p><strong>Coverage</strong></p> <p>The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a <em>.xlsx</em> 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.</p> <p><strong>Accessing original data source</strong></p> <p>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:</p> <p>a) Refer to sheet two for name and code-based searches;</p> <p>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;</p> <p>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.</p> <p><strong>Proceeding to data analysis</strong></p> <p>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).</p> <p>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.</p> <p>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;</p> <p>b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm;</p> <p>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;</p> <p>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;</p> <p>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;</p> <p>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</p> <p>g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°.</p> <p>The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface.</p> <p>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°.</p> <p>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.</p> <p>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.</p> <p>The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm<sup>-1</sup> to 600 cm<sup>-1</sup>, with a resolution of 2 cm<sup>-1</sup>. 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.</p> <p> </p> <p><strong>Dataset characterization</strong></p> <p>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:</p> <p> </p> <p><strong>Sheet 1. BSSL_Soil_Attributes_Dataset</strong></p> <p>Column 1. <strong>ID_unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data;</p> <p>Column 3. <strong>Vis_NIR_SWIR_availability</strong>: availability of spectral data in visible, near-infrared, and shortwave infrared ranges;</p> <p>Column 4. <strong>MIR_availability</strong>: availability of spectral data in the middle infrared range;</p> <p>Column 5. <strong>Sampling</strong>: type of soil sampling;</p> <p>Column 6. <strong>Depth_cm</strong>: soil surface layer depth in centimeters; </p> <p>Column 7. <strong>Lat</strong>: Latitude; </p> <p>Column 8. <strong>Lat</strong>: Longitude; </p> <p>Column 9. <strong>Region</strong>: Brazilian geographical region of samples' source;</p> <p>Column 10. <strong>Municipality</strong>: Brazilian municipality of samples' source;</p> <p>Column 11. <strong>State</strong>: Brazilian Federation Unit of samples' source;</p> <p>Column 12. <strong>Vegetation</strong>: type of vegetal covering;</p> <p>Column 13. <strong>Biome</strong>: groupings of ecosystems that share similar characteristics and span different regions;</p> <p>Column 14. <strong>Geology</strong>: type of rock matter from local soil sampling;</p> <p>Column 15. <strong>Sand_gkg</strong>: Content of the soil fraction with grain size between 2 and 0.053 mm, expressed in grams per kilogram;</p> <p>Column 16. <strong>Clay_gkg</strong>: Content of soil fraction with grain size smaller than 0.002 mm, expressed in grams per kilogram;</p> <p>Column 17. <strong>SOM_gkg</strong>: Soil organic matter content, expressed in grams per kilogram;</p> <p>Column 18. <strong>pH_H2O</strong>: Soil hydrogen ion potential measured in water;</p> <p>Column 19. <strong>Ca_mmolkg</strong>: Exchangeable calcium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 20. <strong>Mg_mmolkg</strong>: Exchangeable magnesium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 21. <strong>Na_mmolkg</strong>: Exchangeable sodium content in the soil, expressed in millimoles per kilogram; and</p> <p>Column 22. <strong>CEC_Ph7_mmolkg</strong>: Cation exchange capacity of the soil at neutral pH, expressed in millimoles per kilogram.</p> <p> </p> <p><strong>Sheet 2. BSSL_Vis_NIR_SWIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 2153. <strong>350 – 2500</strong>: Reflectance in 2151 spectral bands in nanometers from visible and near-infrared to shortwave infrared range (350 – 2500 nm).</p> <p> </p> <p><strong>Sheet 3. BSSL_MIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique:</strong> Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner_code:</strong> Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 683. <strong>4000 – 600:</strong> Reflectance in 681 spectral bands in centimeters in the middle infrared range (4000 – 600 cm<sup>-1</sup>).</p> <p> </p> <p><strong>Sheet 4. Contributors</strong></p> <p>Column 1. <strong>Owner_code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data, which identifies and links it to datasets;</p> <p>Column 2. <strong>Owner</strong>: Name of the collaborator who agreed to the availability of the data;</p> <p>Column 3. <strong>E-mail</strong>: Contact the e-mail of the owner for more information or a data request;</p> <p>Column 4. <strong>Institution</strong>: Contributor's affiliation;</p> <p>Column 5. <strong>Samples NIR</strong>: Number of Vis-NIR-SWIR samples sent to the BSSL collection;</p> <p>Column 6. <strong>Samples MIR</strong>: Number of MIR samples sent to the BSSL collection;</p> <p> </p> <p><strong>Sheet 5. Metadata</strong></p> <p>Column 1. <strong>Material and Methods</strong>: Description of procedures performed for soil data analyses</p> <p> </p> <p><strong>Expectation and Social Relevance</strong></p> <p>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.</p>
Spectral Library of European Pegmatites, Pegmatite Minerals and Pegmatite Host-Rocks – The Greenpeg Database
<p>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.</p>
Reference data to the low-wavenumber Raman spectral database of pharamceutical excipients
<p>Supplementary LF-785 and FT-Raman data of all the excipient samples included in the database. More information available in the following paper: <a href="https://doi.org/10.1016/j.vibspec.2020.103021">https://doi.org/10.1016/j.vibspec.2020.103021</a></p> <p>This version also contains all spectra in the .spc file format.</p>
ISDB: In Silico Spectral Database (of Natural Products)
<h1><strong>ISDB: In Silico DataBase (of Natural Products)</strong></h1> <h2><strong>Background</strong></h2> <p>The ISDB repository contains in silico fragmented spectra of natural products, generated using <strong>cfm-predict 4</strong> <a href="https://doi.org/10.1021/acs.analchem.1c01465" target="_new" rel="noopener">(DOI: 10.1021/acs.analchem.1c01465)</a>.</p> <p>The initial database preparation and its application for dereplication were first described in:<br><strong>Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication</strong> <a href="https://doi.org/10.1021/acs.analchem.5b04804" target="_new" rel="noopener">(DOI: 10.1021/acs.analchem.5b04804)</a>.</p> <h2><strong>Content</strong></h2> <p>Previous versions of ISDB contained predicted spectra for compounds aggregated within the <strong>LOTUS Initiative</strong> <a href="https://doi.org/10.7554/eLife.70780" target="_new" rel="noopener">(DOI: 10.7554/eLife.70780)</a> and included only "merged" spectra.</p> <p>This version expands beyond taxon-restricted compounds and includes:</p> <ul> <li>Individual in silico spectra predictions (<strong>~1 million</strong>)</li> <li><strong>Substructure annotations</strong></li> <li>Data for compounds from <strong>Wikidata</strong> and <strong>LOTUS</strong></li> </ul> <h2><strong>File Structure</strong></h2> <h3><strong><code>.zip</code> Files</strong></h3> <p>Each <code>.zip</code> file contains:</p> <ul> <li>All original outputs from <strong>cfm-predict 4</strong></li> <li>Substructure annotations</li> </ul> <h3><strong>Wikidata vs LOTUS Files</strong></h3> <ul> <li><strong>Wikidata files</strong>: Contain spectra for nearly all compounds found in Wikidata, obtained using this query: <a href="https://w.wiki/Cvdo" target="_new" rel="noopener">https://w.wiki/Cvdo</a>.</li> <li><strong>LOTUS files</strong>: Contain spectra only for compounds with a "found in taxon" (<code>P703</code>) statement, obtained using this query: <a href="https://w.wiki/D35x" target="_new" rel="noopener">https://w.wiki/D35x</a>.</li> </ul> <h3><strong>Energy Levels: <code>energyAll</code> vs <code>energySum</code></strong></h3> <ul> <li><strong><code>energyAll</code> files</strong>: Include spectra for three individual energy levels plus a summed spectrum (4 spectra per polarity per compound).</li> <li><strong><code>energySum</code> files</strong>: Contain only the summed spectrum (1 spectrum per polarity per compound).</li> </ul> <h2><strong>Related Resources</strong></h2> <ul> <li><strong>Building scripts</strong>: <a href="https://github.com/mandelbrot-project/spectral_lib_builder" target="_new" rel="noopener">https://github.com/mandelbrot-project/spectral_lib_builder</a></li> <li><strong>Matching scripts</strong>: <a href="https://github.com/mandelbrot-project/spectral_lib_matcher" target="_new" rel="noopener">https://github.com/mandelbrot-project/spectral_lib_matcher</a></li> </ul>
The CAPA Apple Quality Grading Multi-Spectral Image Database
<p>The CAPA Apple Quality Grading Multi-Spectral Image Database consists of multispectral (450nm, 500nm, 750nm, and 800nm) images of health and defected apples of bi-color, manual segmentations of defected regions, and expert evaluations of the apples into 4 quality categories. The defect types consist of bruise, rot, flesh damage, frost damage, russet, etc. The database can be used for academic or research purposes with the aim of computer vision based apple quality inspection.</p> <p>The CAPA Apple Quality Grading Multi-Spectral Image Database is a propriety of ULG (Gembloux Agro-Bio Tech) - Belgium, and cannot be used without the consent of the ULG (Gembloux Agro-Bio Tech), Belgium. <br> For consent, contact<br> Devrim Unay, İzmir University of Economics, Turkey: unaydevrim@gmail.com<br> OR<br> Marie-France Destain, Gembloux Agro-Bio Tech, Belgium: mfdestain@ulg.ac.be</p> <p><br> In disseminating results using this database, <br> 1. the author should indicate in the manuscript that it was acquired by ULG (Gembloux Agro-Bio Tech), Belgium.<br> 2. cite the following article Kleynen, O., Leemans, V., & Destain, M.-F. (2005). Development of a multi-spectral vision system for the detection of defects on apples. Journal of Food Engineering, 69(1), 41-49.</p> <p>Relevant publications:<br> Kleynen et al., 2003 O. Kleynen, V. Leemans and M.F. Destain, Selection of the most efficient wavelength bands for ‘Jonagold’ apple sorting. Postharv. Biol. Technol., 30 (2003), pp. 221–232.<br> Leemans and Destain, 2004 V. Leemans and M.F. Destain, A real-time grading method of apples based on features extracted from defects. J. Food Eng., 61 (2004), pp. 83–89.<br> Leemans et al., 2002 V. Leemans, H. Magein and M.F. Destain, On-line fruit grading according to their external quality using machine vision. Biosyst. Eng., 83 (2002), pp. 397–404.<br> Unay and Gosselin, 2006 D. Unay and B. Gosselin, Automatic defect detection of ‘Jonagold’ apples on multi-spectral images: A comparative study. Postharv. Biol. Technol., 42 (2006), pp. 271–279.<br> Unay and Gosselin, 2007 D. Unay and B. Gosselin, Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition. J. Food Eng., 78 (2007), pp. 597–605.<br> Unay et al., 2011 Unay, D., Gosselin, B., Kleynen, O, Leemans, V., Destain, M.-F., Debeir, O, “Automatic Grading of Bi-Colored Apples by Multispectral Machine Vision”, Computers and Electronics in Agriculture, 75(1), 204-212, 2011.<br> </p>
Camera Spectral Sensitivity Database - Jiang et al. (2013)
<p><strong>Source URL</strong>: <a href="http://www.gujinwei.org/research/camspec/db.html">http://www.gujinwei.org/research/camspec/db.html</a><br> <strong>Source DOI</strong>: 10.1109/WACV.2013.6475015</p> <p>Camera spectral sensitivity functions relate scene radiance with captured RGB triplets. They are important for many computer vision tasks that use color information, such as multispectral imaging, and color constancy.</p> <p>We create a database of 28 cameras covering a variety of types. The database contains the spectral sensitivity functions for 28 cameras, including professional DSLRs, point-and-shoot, industrial and mobile phone camera. We use a spectrometer PR655 from Photo Research Inc., a light source and monochrometer combined with an integrating sphere to do the measurement. Each measurement starts from wavelength 400nm to 720nm in an interval of 10nm. Measured Sensitivities are normalized to 1 for RGB channels seperately. The database is in the form of a text file. Each entry starts with camera name and follows by measured spectral sensitivities in red, green and blue channel.</p>
Spectral Sensitivity Database - Zhao et al. (2009)
<p><strong>Source URL</strong>: <a href="https://web.archive.org/web/20031123133629/http://colour.derby.ac.uk:80/colour/info/catweb/">http://web.archive.org/web/20150831124417/http://www.cvl.iis.u-tokyo.ac.jp/~rei/research/cs/zhao/database.html</a></p> <p>Spectral sensitivity measurement:</p> <p><strong>Equipment 1</strong>: Spectrometer (Photo Research, PR-655)<br> <strong>Equipment 2</strong>: Monochromator (Edmund Optics, Model C)<br> <strong>Wavelength range</strong>: 400nm-700nm<br> <strong>Interval</strong>: 4nm<br> <strong>Data file format</strong>: Wavelength Red Green Blue</p> <p>Spectral sensitivity database:</p> <p>1. SONY DXC 930<br> 2. KODAK DCS 420<br> 3. NIKON D1X<br> 4. SONY DXC 9000<br> 5. CANON 10D<br> 6. NIKON D70<br> 7. KODAK DCS 460<br> 8. CANON 400D<br> 9. CANON 5D<br> 10. CANON 5D Mark 2<br> 11. Ladybug2<br> 12. KODAK DCS 200</p>
Stellar chromospheric activity spectral database of solar-type stars based on the LAMOST Low-Resolution Spectroscopic Survey(disused)
<p>A stellar chromospheric activity spectral database of solar-type stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains 1,330,654 high-quality LRS spectra of solar-type stars with <em>T</em><sub>eff</sub> ranging from 4800 K to 6800 K. We measure the mean fluxes at line cores of the Ca II H&K lines using a 1 Å rectangular bandpass as well as a 1.09 Å full width at half maximum (FWHM) triangular bandpass, and the mean fluxes of two 20 Å pseudo-continuum bands on the two sides of the lines. Three chromospheric activity indexes, <em>S</em><sub>rec</sub> using the rectangular bandpass, and <em>S</em><sub>tri</sub> and <em>S</em><sub>MWL</sub> using the triangular bandpass, are evaluated based on the measured fluxes. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H&K lines for all the spectra in the database. This database with more than one million high-quality LAMOST LRS spectra and basal chromospheric activity parameters can be further used for investigating activity characteristics of solar-type stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv</p> <p>(2) Library of Spectrum Diagrams<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-199.zip (49 subfolders)<br> spectrum_diagrams_200-249.zip (47 subfolders)<br> spectrum_diagrams_250-299.zip (45 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-399.zip (39 subfolders)<br> spectrum_diagrams_400-449.zip (45 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p> <p> </p>
Stellar chromospheric activity spectral database of solar-type stars based on the LAMOST Low-Resolution Spectroscopic Survey (disused)
<p>A stellar chromospheric activity spectral database of solar-type stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains 1,330,654 high-quality LRS spectra of solar-type stars with effective temperature ranging from 4800 K to 6800 K. We measure the mean fluxes at line cores of the Ca II H&K lines using a 1 Å rectangular bandpass as well as a 1.09 Å full width at half maximum (FWHM) triangular bandpass, and the mean fluxes of two 20 Å pseudo-continuum bands on the two sides of the lines. Three chromospheric activity indexes, <em>S</em><sub>rec</sub> based on the 1 Å rectangular bandpass, and <em>S</em><sub>tri</sub> and <em>S</em><sub>MWL</sub> based on the 1.09 Å FWHM triangular bandpass, are evaluated from the measured fluxes. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H&K lines for all the spectra in the database. This database with more than one million high-quality LAMOST LRS spectra and basal chromospheric activity parameters can be further used for investigating activity characteristics of solar-type stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv</p> <p>(2) Library of Spectrum Diagrams<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-199.zip (49 subfolders)<br> spectrum_diagrams_200-249.zip (47 subfolders)<br> spectrum_diagrams_250-299.zip (45 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-399.zip (39 subfolders)<br> spectrum_diagrams_400-449.zip (45 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p>
Stellar chromospheric activity spectral database of solar-type stars based on the LAMOST Low-Resolution Spectroscopic Survey(disused)
<p>A stellar chromospheric activity spectral database of solar-type stars is constructed based on the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low-Resolution Spectroscopic Survey (LRS). The database contains 1,330,654 high-quality LRS spectra of solar-type stars with effective temperature ranging from 4800 K to 6800 K. We measure the mean fluxes at line cores of the Ca II H&K lines using a 1 Å rectangular bandpass as well as a 1.09 Å full width at half maximum (FWHM) triangular bandpass, and the mean fluxes of two 20 Å pseudo-continuum bands on the two sides of the lines. Three chromospheric activity indexes, <em>S</em><sub>rec</sub> based on the 1 Å rectangular bandpass, and <em>S</em><sub>tri</sub> and <em>S</em><sub>MWL</sub> based on the 1.09 Å FWHM triangular bandpass, are evaluated from the measured fluxes. The uncertainties of all the obtained parameters are estimated. We also produce spectrum diagrams of Ca II H&K lines for all the spectra in the database. This database with more than one million high-quality LAMOST LRS spectra and basal chromospheric activity parameters can be further used for investigating activity characteristics of solar-type stars and solar-stellar connection.</p> <p> </p> <p>The entity of the database is composed of (1) a catalog of spectral sample and activity parameters, and (2) a library of spectrum diagrams.</p> <p>(1) Catalog of Spectral Sample and Activity Parameters<br> CaIIHK_Sindex_LAMOST_DR7_LRS.csv</p> <p>(2) Library of Spectrum Diagrams<br> spectrum_diagrams_000-049.zip (46 subfolders)<br> spectrum_diagrams_050-099.zip (40 subfolders)<br> spectrum_diagrams_100-149.zip (47 subfolders)<br> spectrum_diagrams_150-199.zip (49 subfolders)<br> spectrum_diagrams_200-249.zip (47 subfolders)<br> spectrum_diagrams_250-299.zip (45 subfolders)<br> spectrum_diagrams_300-349.zip (41 subfolders)<br> spectrum_diagrams_350-399.zip (39 subfolders)<br> spectrum_diagrams_400-449.zip (45 subfolders)<br> spectrum_diagrams_450-499.zip (35 subfolders)<br> spectrum_diagrams_500-549.zip (27 subfolders)<br> spectrum_diagrams_550-599.zip (38 subfolders)<br> spectrum_diagrams_600-649.zip (32 subfolders)<br> spectrum_diagrams_650-699.zip (26 subfolders)<br> spectrum_diagrams_700-749.zip (28 subfolders)</p>
Spectral Databases (GNPS, HMDB, MassBank)
<p>GNPS saved at 2023-01-09 15:24:46</p> <p>HMDB saved at 2023-01-09 14:35:46 with release version Current Version (5.0)</p> <p>MassBank saved at 2022-01-09 15:10:52 with release version 2022.12 as mbankNIST.rda </p>
Spectral database of the subspecies of the Mycobacterium abscessus complex (MALDI-TOF Mass Spectrometry)
<p><strong>Spectral database of the subspecies of the Mycobacterium abscessus complex (MALDI-TOF Mass Spectrometry)</strong></p> <ul> <li>This data set originates from a collection of 41 clinical strains of <em>Mycobacterium abscessus complex</em> corresponding to 1001 mass spectra: <ul> <li>25 strains of <em>Mycobacterium abscessus</em> subsp. <em>abscessus</em> (633 mass spectra)</li> <li>9 strains of <em>Mycobacterium abscessus</em> subsp. <em>massiliense</em> (204 mass spectra)</li> <li>7 strains of <em>Mycobacterium abscessus</em> subsp. <em>bolletii </em>(164 mass spectra)</li> </ul> </li> </ul> <p> </p> <ul> <li>Each strain has been characterized using molecular method (DNA/DNA hydridation, using GenoType NTM-DR (Hain Lifescience, Nehren, Germany) according to the manufacturer's instructions for identification and analyzed by MALDI-TOF mass spectrometry according MycoEx protocol (Bruker<sup>®</sup>). The mass spectra spectra were obtained according to the following steps :</li> </ul> <p> </p> <ol> <li>Each of the 41 strains was cultured in aerobic atmosphere at 37°C for 7 ± 2 days on blood agar (COH, bioMerieux<sup>®</sup>). Then, one colony was extracted according to the MycoEx protocol (Bruker<sup>®</sup>). For each of the extracts, 8 technical replicates were realized and analyzed by MALDITOF MS (Bruker<sup>®</sup>). Dried spots were overlaid with 1µL of MALDI matrix (α-HCCA).</li> <li> <p>Data acquisition was performed using a Microflex LT (Bruker<sup>®</sup> Daltonics) mass spectrometer equipped with a N2 laser (λ =377 nm). Instrument parameters used were as follows: a masse range between 200-20000 Da, ion source 1: 20 kV, ion source 2: 18.5 kV, Iens: 8.45 kV, pulsed ion extraction: 330 ns, laser frequency: 20.0 Hz. Spectra were obtained after 500 shots. Each spot was analyzed three times. In total 24 spectra were obtained for each extraction.</p> </li> <li> <p>Spectra acquired for each isolate were visualized and analyzed using Flex Analysis software (Bruker<sup>®</sup> Daltonics), and spectra with low quality peaks were removed. A minimum of 15 spectra per extraction was necessary to validate the extraction.</p> </li> </ol> <p><strong>This database is only intended for medical research. Please contact: medecine-drv@sorbonne-universite.fr for data access.</strong></p> <p>After access agreement, the three following files will be available :</p> <ul> <li>The MABSC_spectra.zip file contains the MS peak list data in a Matlab compatible format.</li> <li>The MABSC_metadata.pdf file contains the molecular identifications of strains.</li> <li>The MABSC_notes.txt file contains informations concerning contains informations on the method of obtaining the data.</li> </ul>
Spectral database of Streptococcus pneumoniae, S. mitis and S. pseudopneumoniae (MALDI-TOF Mass Spectrometry)
<p>A total of 80 strains, including 60 <strong>pneumococca</strong>l strains, 8 <strong><em>S. pseudopneumoniae</em> </strong>strains, and 12 <strong><em>S. mitis</em></strong> strains, were molecularly characterized and obtained from the National Reference Center for Pneumococci for the creation of the database. Each strain was tested a minimum of 20 and a maximum of 24 times using MALDI-TOF MS.</p> <p><br> MALDI-TOF MS acquisition and analysis. Mass spectra were acquired using a Microflex LT instrument (Bruker Daltonics). The standard parameters of the CE-IVD method recommended by the manufacturer were used. This instrument was equipped with an N2 laser (377 nm) using the following parameters: mass range, 2,000 to 20,000 Da; ion source 1, 20 kV; ion source 2, 18.15 kV; lens, 6 kV; pulsed ion extraction, 150 ns; laser frequency, 20 Hz. A manual external calibration standard (Bacterial Test Standard; Bruker Daltonics) was used for calibration. Data acquisition was performed using FlexControl (version 3.0; Bruker Daltonics). <strong>Eight deposits were performed for each isolate.</strong> The dried spots were coated with 1 µl of α-cyano-4-hydroxycinnamic acid (a-HCCA) in 50% acetonitrile-2.5% trifluoroacetic acid and <strong>each spot was analysed three times by MALDI-TOF MS</strong>.</p> <p><strong>A total of 1890 spectra were produced :</strong></p> <ul> <li><em>S. mitis</em> (n=264),</li> <li><em>S.pneumoniae</em> (n= 1434),</li> <li><em>S. pseudopneumoniae </em>(n=192)</li> </ul> <p><strong>Fore more details:</strong> please contact alexandre.godmer@aphp.fr</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.