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57 results for “Spectral library”
Data for "On optimizing mass spectral library search and spectral alignment by weighting low-intensity peaks and m/z frequency"
<p>Data for <a href="https://github.com/enveda/weighting-spectral-similarity#on-optimizing-mass-spectral-library-search-and-spectral-alignment-by-weighting-low-intensity-peaks-and-mz-frequency">On optimizing mass spectral library search and spectral alignment by weighting low-intensity peaks and m/z frequency</a>.</p>
Exposomics Spectral Library
<p><strong>Title: </strong>Repurposing Public Metabolomics Datasets for Construction of an Exposomics Spectral Library</p> <p><strong>Introduction</strong></p> <p>Publicly archived metabolomics datasets from diverse human biosamples provides an opportunity to repurpose the shared datasets for further exploratory analysis into human health. Though, most of the times the endogenous metabolome is implicated in disease research as biomarkers, the growing role of exposome in human health underscores the need for identification of chemical exposures in human samples. In this regard, I explored the possibility of finding previously unreported exposomal compounds (i.e., anthropogenic, industrial, dietary, and microbial chemicals) from the true unknowns in these studied datasets. Using in silico spectral library matching followed by molecular structure prediction approaches, the aim of this study is to recognize the exposome, and minimize the gap between potential number of true exposomic substances in biosamples.</p> <p><strong>Methods</strong></p> <p>Raw metabolomics (GC-MS) datasets were downloaded from Metabolomics Workbench, GNPS, and MetaboLights using key words- ‘human, GC-MS, serum, plasma, muscle, liver, kidney’. The vendor formatted mass spectrometry datasets were converted to .mzML formats using MSConvertGUI (ProteoWizad) for data processing and spectral library (GOLM, MoNA, Fiehnlib, MassBank) matching using MS-DIAL. For EI-MS spectral annotation, the identity was confirmed by the presence of [M−CH<sub>3</sub>]<sup>+</sup>, [M+H]<sup>+</sup>, [M+C<sub>2</sub>H<sub>5</sub>]<sup>+</sup> and [M+C<sub>3</sub>H<sub>5</sub>]<sup>+</sup> and using Global Natural Products Social (GNPS) molecular networking. Exposomal metabolites were separated from the rest based on identifiers at the Blood Exposome DB. True unassigned spectra were further interrogated using MS-FINDER for structural prediction. Exported spectra in .msp and .txt formats were pooled into a single file for free public download and use.</p> <p><strong>Preliminary data</strong></p> <p>The pooled GC-MS datasets (50) obtained from the three repositories were from multiple human samples, multiple vendors, and were generated using multiple mass analyzers (single and triple quads, ToFs, and Orbitraps). The .mzML files were processed for data preprocessing such as deconvolution, peak picking, and peak alignment followed by compound identification using MS-DIAL and GNPS tools. Processing parameters for the datasets were optimized individually in a study-specific manner. Altogether, the data resulted in spectral assignment of approx. 400 compounds of endogenous origin, associated with a KEGG and HMDB identifier relating to generic metabolic pathways, using only open source spectral libraries. Given extremely limited overlap between spectral libraries, I used a pooled spectral library generated from all available open source spectral data. Further, 350 unassigned spectra (displaying insufficient matching scores for an assignment, i.e., < 500; with S/N >25 in each dataset) were interrogated using MS-FINDER and Global Natural Products Social (GNPS) molecular networking approach (both cosine score, > 0.5; balance score, > 0.9) that resulted in annotation of 250 exposomic compounds. Using ClassyFire the exposomal compounds (InChIs) were assigned a hierarchical chemical classification which indicated diverse origin of these compounds ranging from medications, industrial chemicals, pollutants to phytochemicals of dietary origin. The assigned spectra were individually manually curated and then compiled as a single file available as the ‘Exposomics Spectral Library’ to public as .txt and .msp file formats for free use and is available: 10.5281/zenodo.3755855.</p>
Application of spectral library prediction for parallel reaction monitoring of viral peptides_DDA_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_NCE_data
<p><strong>Project description: </strong></p> <p>A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1,529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Additionally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab.</p> <p> </p> <p><strong>Sample processing protocol:</strong></p> <p>A total of 1,569 crude synthetic viral peptides were ordered in six pools from JPT (Berlin, Germany). Synthetic peptides were separated on a 200 cm μPAC™ column (PharmaFluidics) by using an EASY-nLC1200 system (Thermo Fisher Scientific) equipped with a μPAC™ trapping column (PharmaFluidics). The flow rate was set to 300 nL/min and a stepped linear 160 min gradient was applied: 3-10% B in 22 min, 10-33%B in 95 min, 33-49% B in 23 min, 49-80% B in 10 min and 80% B for 10 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive Plus (Thermo Fisher Scientific) operated in Full MS/dd-MS2 or unscheduled PRM mode. For MS/dd-MS2 the following parameters were used. MS1 resolution was 70.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z. The analysis parameters in PRM mode were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 17.500 with an AGC target of 10<sup>6</sup>, max. injection time of 55 ms and an isolation window of 1.4 m/z.</p> <p>Potential SARS-CoV-2 target peptides belonging to the N protein were identified by DDA of SARS-CoV-2 infected Calu-3 cells. Peptides were diluted in 0.1% TFA (0.2 µg/µL) and 5 µL were separated on a 50 cm μPAC™ column (PharmaFluidics) using an EASY-nLC1200 system (Thermo Fisher Scientific). The flow rate was set to 800 nL/min and a stepped 30 min gradient was applied: 6-11% B in 2:58 min, 11-30% B in 17:10 min, 30-35% B in 2:41 min, 35-47% B in 3:11 min, 47-80% B for 0:10 min, 80% B for 1:50 min, 80-0% B in 0:10 min and 100% A for 1:50 min. Solvent A was 0.1% (v/v) formic acid (FA) in water, solvent B consisted of 80% (v/v) acetonitrile in 0.1% (v/v) FA. The column temperature was set to 50 °C. The Q Exactive HF (Thermo Fisher Scientific) operated in Full MS/dd-MS2 (Top20) using the following parameters. MS1 resolution was 60.000 with an AGC target of 3x10<sup>6</sup>, max. injection time of 20 ms and a scan range of 300-1650 m/z. MS2 resolution was 17.500 with an AGC target of 10<sup>5</sup>, max. injection time of 50 ms and an isolation window of 2 m/z.</p> <p> </p> <p>To simulate a SARS-CoV-2 positive patient sample, we spiked cell-culture derived virus in a negative oropharyngeal swab and targeted the N protein by PRM. LC parameters were identical to DDA analysis of SARS-CoV-2 infected Calu-3 cells. The PRM parameters of the The Q Exactive HF (Thermo Fisher Scientific) were set as follows. MS1 parameters were identical to DDA. MS2 resolution was 45.000 with an AGC target of 10<sup>6</sup>, max. injection time of 100 ms and an isolation window of 1.4 m/z.</p> <p> </p> <p> </p> <p><strong>Data processing protocol:</strong></p> <p>DDA Raw files were searched with MaxQuant against the respective virus database (UniProt) with a peptide FDR of 1%. Detailed MaxQuant parameters can be found in the parameters.txt files of the according results. MaxQuant .msms output files were used to generate spectral libraries with BiblioSpec implemented in the Skyline environment using a cut-off score of 0.95. Peptide identification of PRM runs was done in Skyline using the top 6 fragment ions of the DDA spectral library or according Prosit derived library (Prosit_2020_intensity_model).</p>
Empirical Stellar Templates Associated with "An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra"
<p>This is the dataset of empirical stellar templates associated with the publication "An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra", which has been accepted for publication in the Astrophysical Journal Supplements. </p>
Spectral library of plant species from Montesinho Natural Park in Portugal
<p>The database contains the average spectra for leaves of each species, collected in different grids. Additionally, there is at least one photographic record for each species and the calculation of different Vegetation Indices (VIs), namely: Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Renormalized Difference Vegetation Index (RDVI), Greenness Index (GI), and Structure Insensitive Pigment Index (SIPI). A detailed description regarding the database, its content, and the measurement instrument can be found in the database report and in a data paper (In Prep). It showcases some of the most representative and relevant vascular plant species within a conservation area in Portugal - the Montesinho Natural Park (MNP), namely: <em>Alnus glutinosa</em> (L.) Gaertn, <em>Castanea sativa</em> Mill., <em>Cistus ladanifer</em> L., <em>Crataegus monogyna</em> Jacq., <em>Frangula alnus</em> Mill., <em>Fraxinus angustifolia</em> Vahl, <em>Quercus pyrenaica</em> Willd., <em>Quercus rotundifolia</em> Lam., <em>Trifolium repens</em> L., <em>Arbutus unedo</em> L. <em>Dactylis glomerata</em> L., <em>Genista falcata</em> Brot., <em>Cytisus multiflorus</em> (L*Hér.) Sweet, <em>Erica arborea</em> L., and <em>Acacia dealbata</em> Link. Samples were collected <em>in situ</em> on June 21st and 22nd, 2023.</p>
FTIR spectral library of the major components of archaeological sediments
<p>FTIR spectra of the major components of archaeological sediments. Spectra were collected using a Thermo Scientific Nicolet iS5 spectrometer equipped with an iD1 transmission compartment in the 4000-400 cm-1 range at 4 cm-1 resolution, and in 32 scans. Spectra were collected with the same instrument and settings in ATR mode using an iD7 ATR diamond crystal compartment. Spectra are available in .spa and .csv formats.</p>
CROPSPECPK: Spectral Library for Seasonal Crops
<p><span>CROPSPECPK is a comprehensive spectral library specifically developed for the accurate identification and monitoring of seasonal crops in Pakistan. There is a growing need for precise and localized tools that can support sustainable farming practices. CROPSPECPK addresses this need by providing a detailed repository of spectral signatures for three crop stages, including wheat, rice, cotton, maize, sugarcane, fodder, sesame, mustard, carrot, sorghum, spinach, and other vegetables. The spectral library was built using an Apogee Spectroradiometer. CROPSPECPK is designed to support a wide range of applications, including crop classification, health monitoring, yield prediction, and change detection. It is particularly valuable for researchers, agronomists, and policymakers who require accurate, real-time data to make decisions about agricultural planning and resource allocation. CROPSPECPK enhances the ability to monitor crop conditions, optimize agricultural practices, and contribute to the broader goal of achieving sustainable agriculture in Pakistan. This is the initial step towards a bright agricultural future in Pakistan.</span></p>
MultiMS2 spectral library - mzml positive and negative
<p>Dataset containing direct-injection mass spectrometry (DI-MS) data acquired on different.</p> <p>Data was acquired in positive and negative polarities, at different collision energies, using CID or EAD fragmentation.</p> <p>Data was first converted to profile <em>.mzML</em> using: </p> <pre><code>docker run -it --rm \</code><br><code>-v .:/data \</code><br><code>proteowizard/pwiz-skyline-i-agree-to-the-vendor-licenses \</code><br><code>wine msconvert "*.wiff" \</code><br><code>--ignoreUnknownInstrumentError</code></pre> <p>And then centroided using CentroidR (v.0.0.9001) (<a href="https://doi.org/10.5281/zenodo.17250308" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.17250308</a>) using default parameters.</p>
Improving spectral library search untargeted metabolomics identification by dynamic tolerance peak matching and false discovery estimation
<p><strong>The unprocessed and processed benchmarking data used in the paper of "Improving spectral library search untargeted metabolomics identification by dynamic tolerance peak matching and false discovery estimation"</strong></p>
The Ultracool RIZzo Spectral Library
<p>This library contains reduced optical spectra for ultracool objects (low-mass stars, brown dwarfs and giants) covering the R, I and Z bands in the visible, from a variety of references.<br /> <br /> Please always cite the references in the « Reference » column when using these spectra.<br /> <br /> When you use parts or all of this spectral library in a publication, we would appreciate that you acknowledge it in the following way :<br /> « This research has benefitted from the Ultracool RIZzo Spectral Library, maintained by Jonathan Gagné and Kelle Cruz ».</p>
All Spectra Associated with "An Empirical Template Library of Stellar Spectra for a Wide Range of Spectral Classes, Luminosity Classes, and Metallicities Using SDSS BOSS Spectra"
<p>Zip files of all the individual SDSS BOSS spectra. They are sorted by metallicity, luminosity class and spectral type, where each zip file contains all the original SDSS spectra to be co-added into each template. The fits files are in the following format: The primary HDU contains comments about the right ascension, declination and other useful object information. The first table extension contains the spectrum information (wavelength, flux, inverse variance, etc.) The second table extension contains other parameter information (object type, flags, and more). </p> <p> </p>
MsnLib multi-stage fragmentation mass spectral libraries - mzml positive and negative
<p>The data for <a href="https://doi.org/10.26434/chemrxiv-2024-l1tqh-v2">MSnLib</a> are divided into several Zenodo records due to size constraints. </p> <p>raw positive: <a href="https://doi.org/10.5281/zenodo.10966404">10966404</a><br>raw negative: <a href="https://doi.org/10.5281/zenodo.10967081">10967081</a><br>mzml positive and negative: <a href="https://doi.org/10.5281/zenodo.10966280">10966280</a><br>spectral libraries: <a href="https://doi.org/10.5281/zenodo.11163380">11163380</a></p> <p>This record includes zipped files containing the converted mzml positive and negative data, acquired using a flow injection method on an Orbitrap ID-X instrument, for all compound libraries. The different polartities are kept separated. </p> <p>9 Compound Libraries:</p> <ul> <li>Short Name: Full name, Provider (Catalog number), total compounds (not all detected during library building)</li> <li>MCEBIO: Bioactive Compound Library, MedChemExpress (HY-L001), 10,315 compounds</li> <li>MCESAF: 5k Scaffold Library, MedChemExpress, (HY-L902), 4998 compounds</li> <li>NIHNP: NIH NPAC ACONN collection of NP, NIH/NCATS, 3988 compounds</li> <li>OTAVAPEP: Alpha-helix Peptiomimetic Library, OTAVAchemicals (a-helix-Peptido), 1298 compounds</li> <li>ENAMDISC: Discovery Diversity Set -10, Enamine (DDS-10), 10,240 compounds</li> <li>ENAMMOL: Carboxylic Acid Fragment Library + Random, Enamine and Molport, 4378 compounds</li> <li>MCEDRUG: FDA-Approved Drug Library, MedChemExpress (HY-L022), 2610 compounds</li> <li>MCEDIV_50k_Sub: Subset of 50K Diversity Library, MedChemExpress (HY-L901), 20000 compounds</li> <li>TargetMolNPHTS: Subset of Natural Product Library for HTS, TargetMol (L6000), 2175 compounds</li> </ul>
GNPS nearest neighbor suspect spectral library
<p>Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of public MS/MS spectra. Annotations were propagated based on structural relation to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer’s brain phenotype. The nearest neighbor suspect spectral library is openly available through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.</p>
Supporting data for "The benefit of in silico predicted spectral libraries in data-independent acquisition data analysis workflows"
Open the record for dataset details and reuse information.
Matchms cleaned mass spectral library positive mode
<p>The library consists of the GNPS library, MassBank, MoNA and Brungs et al. 's library. The library was cleaned using matchms filtering. The settings for filtering and logging can be found here as well. </p>
Machms cleaned mass spectral library negative ionisation mode
<p>The library consists of the GNPS library, MassBank, MoNA and Brungs et al. 's library. The library was cleaned using matchms filtering. This library contains all negative ionisation mode ms2 spectra. The settings for filtering and logging can be found here as well. </p>
MsnLib multi-stage fragmentation mass spectral libraries - raw negative
<p>The data for <a href="https://doi.org/10.26434/chemrxiv-2024-l1tqh-v2">MSnLib</a> are divided into several Zenodo records due to size constraints. </p> <p>raw positive: <a href="https://doi.org/10.5281/zenodo.10966404">10966404</a><br>raw negative: <a href="https://doi.org/10.5281/zenodo.10967081">10967081</a><br>mzml positive and negative: <a href="https://doi.org/10.5281/zenodo.10966280">10966280</a><br>spectral libraries: <a href="https://doi.org/10.5281/zenodo.11163380">11163380</a></p> <p>This record includes zipped files containing the raw negative data, acquired using a flow injection method on an Orbitrap ID-X instrument, for all compound libraries.</p> <p>7 Compound Libraries:</p> <ul> <li>Short Name: Full name, Provider (Catalog number), total compounds (not all detected during library building)</li> <li>mce_bioactive: Bioactive Compound Library, MedChemExpress (HY-L001), 10,315 compounds</li> <li>mce_scaffold: 5k Scaffold Library, MedChemExpress, (HY-L902), 4998 compounds</li> <li>nih: NIH NPAC ACONN collection of NP, NIH/NCATS, 3988 compounds</li> <li>otavapep: Alpha-helix Peptiomimetic Library, OTAVAchemicals (a-helix-Peptido), 1298 compounds</li> <li>enamdisc: Discovery Diversity Set -10, Enamine (DDS-10), 10,240 compounds</li> <li>enammol: Carboxylic Acid Fragment Library + Random, Enamine and Molport, 4378 compounds</li> <li>mcedrug: FDA-Approved Drug Library, MedChemExpress (HY-L022), 2610 compounds</li> </ul>
MsnLib multi-stage fragmentation mass spectral libraries - raw positive
<p>The data for <a href="https://doi.org/10.26434/chemrxiv-2024-l1tqh-v2">MSnLib</a> are divided into several Zenodo records due to size constraints. </p> <p>raw positive: <a href="https://doi.org/10.5281/zenodo.10966404">10966404</a><br>raw negative: <a href="https://doi.org/10.5281/zenodo.10967081">10967081</a><br>mzml positive and negative: <a href="https://doi.org/10.5281/zenodo.10966280">10966280</a><br>spectral libraries: <a href="https://doi.org/10.5281/zenodo.11163380">11163380</a></p> <p>This record includes zipped files containing the raw positive data, acquired using a flow injection method on an Orbitrap ID-X instrument, for all compound libraries.</p> <p>7 Compound Libraries:</p> <ul> <li>Short Name: Full name, Provider (Catalog number), total compounds (not all detected during library building)</li> <li>mce_bioactive: Bioactive Compound Library, MedChemExpress (HY-L001), 10,315 compounds</li> <li>mce_scaffold: 5k Scaffold Library, MedChemExpress, (HY-L902), 4998 compounds</li> <li>nih: NIH NPAC ACONN collection of NP, NIH/NCATS, 3988 compounds</li> <li>otavapep: Alpha-helix Peptiomimetic Library, OTAVAchemicals (a-helix-Peptido), 1298 compounds</li> <li>enamdisc: Discovery Diversity Set -10, Enamine (DDS-10), 10,240 compounds</li> <li>enammol: Carboxylic Acid Fragment Library + Random, Enamine and Molport, 4378 compounds</li> <li>mcedrug: FDA-Approved Drug Library, MedChemExpress (HY-L022), 2610 compounds</li> </ul>
Walloon roof material (WaRM) spectral library
<p>The Walloon Roof Material spectral library (WaRM) was created in the framework of the CASMATTELE project to reflect regional peculiarities of roof materials employed in Wallonia, Belgium. This library accounts for 26 roof material spectra in the spectral range 350–2,500 nm. Spectra were acquired using an ASD FieldSpec3 Hi-Res spectrometer in laboratory and in situ conditions with a spectral sampling interval of 1 nm. Although building materials can vary regionally due to different available construction materials, the WaRM spectral library can certainly be used for wider applications.</p>
ScienceDex guides
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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.