Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
57
datasets available to search
ShareScore release 0.9.0
Dataset results
57 results for “Spectral Library”
Dataset associated with: "An in silico infrared spectral library of molecular ions for metabolite identification"
<p>Dataset associated with the publication: "An in silico infrared spectral library of molecular ions for metabolite identification"</p> <p>Both the experimental and computational data are also available on the website of the human metabolome database (https://hmdb.ca/).</p>
Datasets for Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit
<p>This sdataset contains two zip files, one for the HLA and one for metaproteomics data. These contain the results from individual oktoberfest runs for the publication including the required msms.txt files and percolator outputs.</p>
Fig. 4 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds
Fig. 4. Evaluation of searching blocks against those of BMDMS-NP. Blocks in (A) and (C) are from NIST17 and those in (B) and (D) are from MoNA. Lines marked with 'x' in (C) and (D) show the performance after application of precursor m/z filter had been adopted prior to calculation of the similarity score between blocks.
Fig. 3 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds
Fig. 3. Overview of BMDMS-NP. (A) The MS/MS library was established by two types of spectrometers with several conditions. (B) BMDMS-NP consists of information about compound spectra. The program provided by GitHub helped to search and build an in-house library. (C) Spectrum search is performed using the concept of data block. The spectra of the library are grouped as shown by the bold lines in the table. The user can group input spectral data into a block and search against BMDMS-NP and get ranking and scores for the searches.
Fig. 1 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds
Fig. 1. Principal component analysis of molecular fingerprints between the 'natural product/for sale' subset of ZINC and BMDMS-NP. The graph describes that the structural diversity of BMDMS-NP is overlapped with that of ZINC. In order to compare intuitively, the ZINC points were randomly selected to be the same quantity as the BMDMS-NP points and the 95% confidence interval ellipses are added.
Fig. 2 in BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds
Fig. 2. Venn diagram representing the number of overlapped metabolites between libraries. The number of each set was determined using InChIKey as the compound identifier.
Application of spectral library prediction for parallel reaction monitoring of viral peptides_PRM_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>
MSnLib Mass spectral libraries (.mgf and .json)
<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 the automatically generated spectral libraries (MSnLib) within mzmine, acquired using a flow injection method on an Orbitrap ID-X instrument, for all compound libraries. There are multiple files for each compound library containing MS2 only or MSn in two data formats (.mgf or .json) for both polarities. </p> <p>MS2 contains next to all MS2 spectra all pseudo MS2 spectra (a full MSn tree merged into one spectrum per compound ion). MSn contains all individual MSn stages additionally. The first number for each file highlights the library building date.</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>TargetMolHTSNP: Subset of Natural Product Library for HTS, TargetMol (L6000), 2175 compounds</li> </ul> <p>Information regarding the SPECTYPE</p> <ul> <li>no SPECTYPE or SINGLE_BEST_SCAN: Best spectrum for each precursor and energy (highest TIC)</li> <li>'SAME_ENERGY' = Additionally, if a spectrum was acquired multiple times for a precursor with the same energy, they are merged into one spectrum only with the same energy (max. signal height used for each fragment signal).</li> <li>'ALL_ENERGIES' = merged spectrum of all used energies (in our case 3 for each precursor, using the merged (same energy) if available).</li> <li>'ALL_MSN_TO_PSEUDO_MS2' = mzmine merges all MSn into one pseudo MS2.</li> </ul> <p> </p> <p>MCEDIV and TargetmolNPHTS </p>
Berlin Urban Materials Spectral Library
<p>Berlin Urban Materials Spectral Library</p>
The critical role that spectral libraries play in capturing the metabolomics community knowledge
<p>Data and analysis notebooks related to the manuscript "The critical role that spectral libraries play in capturing the metabolomics community knowledge."</p>
Semi-supervised learning for sensitive open modification spectral library searching - dataset PXD009476
<p>This is the analysis results of ANN-SoLo + Rescoring as an integrated module. ANN-SoLo spectral library search engine is a tool for efficient open modification searching. ANN-SoLo uses a cascade search strategy to optimally identify both unmodified and modified peptides: in the first stage a standard search is performed to identify unmodified peptides, after which the remaining unidentified spectra are submitted to second stage during which an open search is performed to additionally identify modified peptides. We have augmented this approach by natively integrating PSM rescoring into ANN-SoLo using the mokapot Python framework for semi-supervised learning for peptide detection.</p> <p>The repository includes the results and search database used to analyze a human glycoproteomics dataset that was acquired from human kidney tissue, serum, and T cells to study O-linked glycosylation. Samples were trypsin-digested and separated into 24 fractions after enrichment of intact glycopeptides and release of O-linked glycopeptides. Next, the data was acquired on a Fusion Lumos mass spectrometer with an Easy-nLC 1200 system or a Q-Exactive HF mass spectrometer with a Waters NanoAcquity UPLC. From this dataset, four raw files derived from kidney tissue samples were retrieved from PRIDE (project PXD009476) and converted to MGF files using ThermoRawFileParser (version 1.7.2).</p> <p>The four files are available both in RAW and MGF format below. </p> <p> </p> <ul> <li>Arab, Issar, William E. Fondrie, Kris Laukens, and Wout Bittremieux. "<strong>Semisupervised Machine Learning for Sensitive Open Modification Spectral Library Searching</strong>." Journal of Proteome Research 22, no. 2 (2023): 585-593. <a title="DOI URL" href="https://doi.org/10.1021/acs.jproteome.2c00616">doi.org/10.1021/acs.jproteome.2c00616</a></li> </ul>
PTA SWIR and LWIR Spectral Libraries
<p>Spectral libraries that encompass the mineral endmembers identified most commonly in the high resolution images</p>
Global Hyperspectral Imaging Spectral-library of Agricultural crops for Central Asia V001
The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world. Crops include wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas, which together occupy about 65% of all global cropland areas. The GHISA spectral libraries were collected and collated using spaceborne, airborne (e.g., aircrafts and drones), and ground based hyperspectral imaging spectroscopy.The GHISA for Central Asia (GHISACASIA) Version 1 product provides dominant crop data (wheat, rice, corn, alfalfa, and cotton) in different growth stages across the Galaba and Kuva farm fields in the Syr Darya river basin in Central Asia. The GHISA hyperspectral library for the two irrigated areas was developed using Earth Observing-1 (EO-1) Hyperion hyperspectral data acquired in 2007 and ASD (Analytical Spectral Devices, Inc.) Spectroradiometer data acquired in 2006 and 2007. GHISACASIA is extracted from three Hyperion hyperspectral images and several thousands of field ASD Spectroradiometer data. Measurements were taken from 1,232 randomly chosen points scattered across the two farm sites throughout the growing season. All the processing algorithms are coded in Statistical Analysis System (SAS) format and available for download.Provided in the .xlsx files are the spectral library including image information, plot IDs, study area, instrument, Julian or acquisition date, and crop type labels for Central Asia sample fields.
Global Hyperspectral Imaging Spectral-library of Agricultural crops for Conterminous United States V001
The Global Hyperspectral Imaging Spectral-library of Agricultural crops (GHISA) is a comprehensive compilation, collation, harmonization, and standardization of hyperspectral signatures of agricultural crops of the world. This hyperspectral library of agricultural crops is developed for all major world crops and was collected by United States Geological Survey (USGS) and partnering volunteer agencies from around the world. Crops include wheat, rice, barley, corn, soybeans, cotton, sugarcane, potatoes, chickpeas, lentils, and pigeon peas, which together occupy about 65% of all global cropland areas. The GHISA spectral libraries were collected and collated using spaceborne, airborne (e.g., aircraft and drones), and ground based hyperspectral imaging spectroscopy. The GHISA for the Conterminous United States (GHISACONUS) Version 1 product provides dominant crop data in different growth stages for various agroecological zones (AEZs) of the United States. The GHISA hyperspectral library of the five major agricultural crops (e.g., winter wheat, rice, corn, soybeans, and cotton) for CONUS was developed using Earth Observing-1 (EO-1) Hyperion hyperspectral data acquired from 2008 through 2015 from different AEZs of CONUS using the United States Department of Agriculture (USDA) Cropland Data Layer ([CDL](https://nassgeodata.gmu.edu/CropScape/)) as reference data.GHISACONUS is comprised of seven AEZs throughout the United States covering the major agricultural crops in six different growth stages: emergence/very early vegetative (Emerge VEarly), early and mid vegetative (Early Mid), late vegetative (Late), critical, maturing/senescence (Mature Senesc), and harvest. The crop growth stage data were derived using crop calendars generated by the Center for Sustainability and the Global Environment ([SAGE](https://nelson.wisc.edu/sage/)), University of Wisconsin-Madison. Provided in the CSV file is the spectral library including image information, geographic coordinates, corresponding agroecological zone, crop type labels, and crop growth stage labels for the United States.
HEK293T FAIMS stepping proteomics spectral library
<p>This spectral library is from FAIMS CV stepping DDA injections of HEK293T. </p>
RELAB Spectral Library Bundle
The RELAB Spectral Library Bundle contains reflectance spectra and ancillary data acquired at the Reflectance Experiment Laboratory (RELAB) at Brown University.
2013 - London LUMA KSL SLUM Spectral Library of Urban Materials
<p>2013 - London LUMA KSL SLUM Spectral Library of Urban Materials</p> <ol> <li><strong>Site:</strong> KSL</li> <li>Country\City\Location: UK London King's College London K Strand Campus S Strand Building Lab L <strong>KSL</strong></li> <li>Inside Lab work</li> </ol> <p><strong>Manual/<strong>: https://urban-meteorology-reading.github.io/other%20files/LUMA_SLUM.pdf</strong></strong></p> <p><strong>Spectral Library of Impervious Urban Materials</strong></p> <ol> <li>The radiative response of impervious urban materials is a highly influential surface property, due to its impacts on the radiation balance of incoming and outgoing long- and short-wave fluxes. Information about the material composition can be determined with data resolved to the wavelength level. Spectral reflectance in the visible- to short-wave infrared (VIS-SWIR) region is widely used in remote sensing-based land cover classification and spectral long-wave infrared (LWIR) emissivity is required for the observation of surface temperatures. The Spectral Library of impervious Urban Materials (SLUM) available from the London Urban Micromet data Archive (LUMA) includes LWIR emissivity spectra of 74 samples of impervious surfaces derived using measurements made by a portable Fourier Transform InfraRed (FTIR) spectrometer and matching short-wave reflectance spectra observed for each urban sample.</li> <li>Data are provided in csv format for short-wave reflectance and long-wave emissivity.</li> <li>Documentation contains photos, meta information for all 74 samples and plots of the short-wave reflactance (300-2500 nm) and long-wave (8-14 um) emissivity spectra.</li> <li>Manual: https://urban-meteorology-reading.github.io/other%20files/LUMA_SLUM.pdf</li> <li><strong> Final Data</strong> provided in csv format for:</li> <li>short-wave reflectance: https://urban-meteorology-reading.github.io/other%20files/LUMA_SLUM_SW.csv</li> <li>long-wave emissivity: https://urban-meteorology-reading.github.io/other%20files/LUMA_SLUM_IR.csv</li> <li>Here the raw data are provided.</li> </ol>
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.