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76 results for “GC-MS”
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 35 known metabolites(all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in one Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and one organism part (annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable STATO terms. The measurements over these metabolites, which were made in 2 distinct experiments, were extracted from: a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018 a supplementary material table available as a pdf from 'Biosynthesis of monoterpene scent compounds in roses' by Magnard et al, Science 03 Jul 2015 identified by the following doi: https://doi.org/10.1126/science.aab0696. This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR)and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.It is associated to the following project: https://github.com/proccaserra/rose2018ng-notebook with all the necessaryinformation, executable code and tutorials in the form of Jupyter notebooks.</p>
Frictionless Tabular Data Package for GC-MS data from the 'Rose Genome' article published in Nature genetics, June, 2018
<p>This dataset, in the form of a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. </p> <p>The data was extracted from a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018. This supplementary material table was deposited to Zenodo and is identified by the following doi: <a href="https://doi.org/10.5281/zenodo.2598799">https://doi.org/10.5281/zenodo.2598799</a></p> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. </p> <p>The data were extracted from:</p> <ul> <li>a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018</li> <li>a supplementary material table available as a pdf from "Biosynthesis of monoterpene scent compounds in roses" by Magnard et al, Science 03 Jul 2015 identified by the following doi: <a href="https://doi.org/10.1126/science.aab0696">https://doi.org/10.1126/science.aab0696</a></li> </ul> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p> <p> </p>
Frictionless Tabular data package for GC-MS data from Rose Genome article published in Nature genetics, June, 2018
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxId) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The data was extracted from a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018. This dataset is used to demonstrate how to make data Findeable, Accessible, Discoverable and Interoperable(FAIR) and how Tabular Data Package representations can be easily mobilized for re-analysis and data science. It is associated to the following project available from github at: https://github.com/proccaserra/rose2018ng-notebook with all necessary information and Jupyter notebooks.</p>
Archaeological bitumen from Tell Abraq - GC-MS & d13C data
<p>This dataset belongs to a research that was carried out on bitumen excavated at Tell Abraq, a Bronze Age period site located in the United Arab Emirates.</p> <p>Several bitumen samples from various contexts were sampled and subjected to both GC-MS and Stable Carbon Isotope Analysis. <br> This dataset holds:<br> -Measured d13C values<br> -GC-MS Raw Data (registered by Agilent Software)<br> -Peak surfaces and molecular ratios (both .xlsx and .csv format, both are identical)<br> -Photos linked to the samples</p>
RDF Linked Data representation of GC-MS data from the 'Rose Genome' article published in Nature genetics, June, 2018
<p>This dataset corresponds to the RDF Linked Data representation of the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. Most of the semantics resources belong to the <a href="http://obofoundry.org">OBO foundry</a>.</p> <p>The transformation to RDF was performed on a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holding the data extracted from a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018. This supplementary material table was deposited to Zenodo and is identified by the following doi: <a href="https://doi.org/10.5281/zenodo.2598799">https://doi.org/10.5281/zenodo.2598799</a></p> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p>
Galaxy Training Material for Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)
<p>This dataset contains the training data for the <strong>Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)</strong> GTN tutorial. It includes 3 GC-[EI+]-HRMS files from seminal plasma samples, the RECETOX Metabolome HR-[EI+]-MS library collected from mostly endogoenous compounds from MetaSci Human Metabolite Library, reference alkanes, sample metadata table, and preprocessed XCMS object.</p>
GC-MS data set for Generation of a chromosome-scale genome assembly of the insect-repellant terpenoid-producing Lamiaceae species, Callicarpa americana
<p>RAW GC/MS data set for characterization of class II terpene synthases from <em>Callicarpa americana </em></p>
Non-target screening of organic compounds in offshore produced water by GC×GC-MS (associated data)
<p>Associated data for the manuscript titled "<em>Non-target screening of organic compounds in offshore produced water by GC×GC-MS</em>"</p> <p>Preprint doi://10.26434/chemrxiv.13317938</p> <p> </p>
GC-MS raw data_Figure 6E_Lysophosphatidic Acid Shifts Metabolic and Transcriptional Landscapes to Induce a Distinct Cellular State in Human Pluripotent Stem Cells
<p><strong>Sample name</strong></p> <p>hESCs (H1 cells) were given treatments for two days and then collected for GC-MS analysis.</p> <p>E8: E8 medium</p> <p>AX: E8 + 1.6% AlbuMAX;</p> <p>BSA: E8 + 1% albumin;</p> <p>BSA+hCDL: E8 + 1% albumin + 0.1% hCDL;</p> <p>LPA+BSA: E8 + 1 μM LPA + 1% albumin;</p> <p>LPA+BSA+hCDL: E8 + 1 μM LPA + 1% albumin + 0.1% hCDL</p> <p>STD: standard lipids mixture used as reference</p> <p><strong>Extraction and Methylation</strong></p> <p>Sample preparation was conducted according to the previously reported method (Araujo et al., 2008) with the modification. Briefly, spent medium was removed, and cells were rinsed with 1 mL/well 0.9% (w/v) saline twice. Then 0.5 mL/well -80°C 80% methanol was added to quench the metabolism. Five wells of cells (from 6-well plate) were scrapped off into a glass screw-cap tube. Then 4 mL heptadecanoate containing chloroform (4 μg/mL, internal standard for fatty acids) was added into the tube. Vortex, and then centrifuge at 2000 rpm for 5 min. Cellular debris was carefully removed, and nitrogen blow the solution till dry. Add 1.5 mL hexane and 1.5 mL 14% boron trifluoride (BF<sub>3</sub>)/methanol solution. Seal the tube with nitrogen gas, heat it at 100°C for 1 h using MK200-2 dry bath incubator (Aosheng), and then cool down to room temperature. Add 1 mL water into the tube, vortex and then centrifuge at 3000 rpm for 10 min. The upper layer was transferred into a new 1.5-mL eppendorf tube and evaporated by nitrogen gas. The residue was re-dissolved in 100 μL hexane for GC-MS analysis.</p> <p><strong>GC-MS method</strong></p> <p>Samples were analyzed using an Agilent GC-MS system (Agilent) consisting of a 6890 gas chromatography and a 5973 mass spectrometer. Fatty acid methyl esters were separated by an Omegawax™ 250 fused silica capillary column (30 m × 0.25 mm i.d., 0.25 μm film thickness, Supelco, Bellefonte, PA). The optimized oven temperature program was: initial temperature set at 180°C and held for 3 min; ramped to 206°C at 2°C/min and held at 206°C for 25 min, then, ramped to 240°C at 10°C/min and held for 5 min. Overall, the total run time was 50 min. Carrier gas was high-purity helium at a flow rate of 1.5 mL/min. Injector temperature was set at 250°C. Injection volume was 2 μL with a split ratio of 1:15. The mass spectrometer was operated in electron-impact (EI) mode at 70 eV ionization energy. The temperatures of quadrupole and ionization source were set at 150°C and 280°C, respectively. The spectra from 3 to 50 min were acquired with the <em>m/z</em> range of 35–550 at a scan rate of 0.34 s per scan.</p>
Raw data of compounds extracted by GC-MS from each population replicate's of I. uriae ticks from Iceland.
<p>Raw data representing all the compounds extracted by GC-MS from each population replicate’s of <em>I. uriae</em> ticks from three sites in Iceland. Each replicate contain a pool of 10 living flat female ticks.</p> <p>Site: name of the site where ticks were collected.</p> <p>Host: name of the host bird.</p> <p>Replicate: number of the replicate (1 to 4).</p> <p>Peak: number of the detected peaks correponding to extracted compounds.</p> <p>Retention Time: time elapsed between sample introduction and the maximum signal of the given compound.</p> <p>Area: area under the curves of each detected coumpounds on the chromatogram.</p>
Chromatograms obtained by GC-MS of the four essential oils (Mentha pulegium, Origanum vulgare, Rosmarinus officinalis, Myrtus communis).
<p>The four selected bioactive EOs (Mentha pulegium, Origanum vulgare, Rosmarinus officinalis, Myrtus communis) was performed using gas chromatography (GC) (Agilent 7890A Series) coupled to mass spectrometry (MS) equipped with a multimode injector and a 123-BD11 column of dimension (15 m × 320 μm × 0.1 μm) at Moroccan Foundation for Advanced Science, Innovation and Research (MAScIR) Institute.</p> <p>This file showed the outputs of the chromatogram obtained by GC-MS of the four essential oils (Mentha pulegium, Origanum vulgare, Rosmarinus officinalis, Myrtus communis).</p>
GC-MS chromatogram of the hexane fraction of the ciceraritnum
<p>GC-MS chromatogram of the hexane fraction of the ciceraritnum<br> </p>
Dataset on the characterization of the flavor of two red wine varieties using sensory descriptive analysis, volatile organic compounds quantitative analysis by GC-MS and odorant composition by GC-MS-O
<p>The dataset contains data that were collected on 2 sets of 8 French red wines from two grape varieties, Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Gas Chromatography–Mass Spectrometry (GC-MS) and (iii) odorant composition obtained by Gas Chromatography–Mass Spectrometry–Olfactometry (GC-MS-O).</p> <p> </p> <p>The dataset is a Microsoft Excel Worksheet containing 8 sheets.</p> <p>- Sheet 1: Information</p> <p>Gives information about the sheets contained in this .xlsx file</p> <p>- Sheet 2: Experimental_factors</p> <p>Each row represents a wine</p> <p>Each column corresponds to an experimental factors of the wines (Grape variety, Vintage and Protected Designation of Origin)</p> <p>- Sheet 3: List_sensory_descriptors</p> <p>Lists the 33 sensory descriptors used for the sensory descriptive analysis of the wines</p> <p>- Sheet 4: Sensory_descriptive_analysis</p> <p>Each row represents a wine</p> <p>Each column corresponds to a condition (2640 columns)</p> <p>Senso_(ortho or retro)_(Panelist1 to Panelist 16)_(1 to 33 Sensory descriptors)_(1 to 3 repetitions for ortho and 1 to 2 repetitions for retro)</p> <p>For the ortho (orthonasal) measurements, there is 16 panelists, 33 sensory descriptors and 3 repetitions = 1584 columns</p> <p>For the retro (retronasal) measurements, there is 16 panelists, 33 sensory descriptors and 2 repetitions = 1056 columns</p> <p>Each cell contains a sensory measurement for the corresponding condition in the corresponding wine</p> <p>- Sheet 5: List_VOC</p> <p>Lists the 45 VOC quantified in the wines with their corresponding CAS number</p> <p>VOC: Volatil Organic Compounds</p> <p>- Sheet 6: VOC_quantification</p> <p>Each row represents a wine</p> <p>Each column corresponds to a VOC (45 columns)</p> <p>Each cell contains the quantification of the corresponding VOC in the corresponding wine</p> <p>- Sheet 7: List_GC-MS-O</p> <p>Lists the 49 odor-active compounds identified with their corresponding CAS number and the 34 compounds identified by their apex indice</p> <p>- Sheet 8: GC-MS-O</p> <p>Each row represents a wine</p> <p>Each column corresponds to an odor-active compound identified by its CAS number or by its Apex indice if the compound was not identify (81 odor-active compounds) + the number of judges who smelled the compound and its description (by 8 judges) = 9 columns per odor-active compound for a total of 729 columns</p>
GC-MS of 4 commercial perfumes
<p>GC-MS data from 4 commercial perfumes</p> <p>Four commercial perfumes were purchased and stored at 12 °C: Black opium from Yves Saint Laurent (France), Poison Girl from DIOR (France), 212 SEXY from Carolina Herrera (New york, USA) and 24 FAUBOURG from HERMES (France). The analysis of the four perfumes was performed using an Agilent 7890B GC, equipped with a Supelcowax 10 capillary column (30 m, 0.25 mm i.d., 0.25 µm film thickness) and coupled to a mass spectrometer 5977 Agilent Technologies. Helium was chosen as carrier gas at a flow rate of 1.3 mL/min. Column temperature was initially fixed at 40 °C for 2 min, then gradually increased to 240 °C at 3 °C/ min, and finally 240 °C for 5 min. For GC-MS detection an electron impact source was used with an ionization energy fixed at 70 eV. Data were acquired using full scan mode with a <em>m/z</em> range from 30 to 450. The perfumes were diluted 1:10 (v:v) with ethanol and 1.0 µL of the diluted samples was automatically injected in split mode (split ratio 100:1). Injector and detector temperatures were set at 250 and MS source at 230 °C, respectively. Agilent G1701EA MSD Productivity ChemStation Software Version E.02.02 was used to manage analysis.</p>
GC-MS metabolite profile of Pseudocercospora fijiensis exposed to thiabendazole
Open the record for dataset details and reuse information.
GC-MS data corresponding to: Methanol-Based Esterification of Palm Oil Sludge – Preparation of Fatty Acids (Palmitic and Oleic) Ethyl Esters via Ethyl Acetate Transesterification by Javier Chaparro-Acosta and Juan-Manuel Urbina-González
<p>GC-MS data corresponding to:</p> <p>Methanol-Based Esterification of Palm Oil Sludge – Preparation of Fatty Acids (Palmitic and Oleic) Ethyl Esters via Ethyl Acetate Transesterification<br> by<br> Javier Chaparro-Acosta<sup>(1)</sup> and Juan-Manuel Urbina-González<sup>(2*)</sup></p> <p><sup>1</sup>Escuela de Ingeniería Química, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia<br> <sup>2</sup>Escuela de Química, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia<br> * Correspondence: jurbina@uis.edu.co<br> <br> <strong>Abstract</strong><br> Acid catalyzed Fischer esterification of fatty acids using methanol (as reagent and solvent) allow the preparation of long chain alkyl methyl esters. Transesterification of palm oil in basic media using methanol is also a known path to monoalkyl methyl ethers derived of fatty acids. In this work we report the Fischer esterification using methanol of a sample of local palm oil sludge (a fraction rich in fatty acids) and how during the extraction with ethyl acetate a transesterification reaction occurred, allowing the preparation of ethyl esters of oleic and palmitic acids as main compounds.</p> <p> </p>
Novel Insights into the Bioactive Metabolites of Macrocybe gigantea (Massee) Pegler & Lodge, a Wild Edible Macrofungi Using Gas Chromatography Mass Spectrometry (GC-MS) Combined with Chemoinformatics Approaches
<p><em>Macrocybe gigantea </em>(MG) is an edible mushroom and has multiple pharmacological activities such as antibacterial, antioxidant, and antitumor activities. However, only a few reports were available on the bioactive compounds and bioactivity of this mushroom. In this concern, the present study was aimed to explore the unique chemical diversity from the fruiting body of MG<em>. </em>The species identification was done accurately with morphological and molecular methods followed by mycochemical extraction in different solvent systems. The ethanolic extract of the fruiting body gave maximum yield and its Gas Chromatography-Mass Spectrometry (GC-MS) analysis was performed along with antibacterial activity and cell viability by MTT assay. The GC-MS analysis revealed 50 metabolites and further chemoinformatics analysis of these metabolites revealed their possible biological activities. In addition, the mushrooms' physico-chemical and mineral element analysis revealed the quality and authenticity of the species. Altogether, the current investigation gives a comprehensive overview of the bioactive metabolites of MG.</p>
Lichens as bioindicators of monitoring of the selective air pollution, Zaabrze (Poland) - chromatograms of GC-MS
<p>Detailed geochemical analyses were performed on 21 powdered samples after their extraction using ultrasound Elmasonic Easy with a dichloromethane (DCM) and methanol (MeOH) mixture (1:1 vol). Extracts were separated into aliphatic-, aromatic-, semipolar- and polar fractions by column chromatography. Silica-gel was first activated at 120 °C for 24 h, cooled, and poured into Pasteur pipettes. Foour eluents were used for fraction collection, namely, n-pentane for the aliphatic fraction, n-pentane and DCM (7:3) for the aromatic fraction, acetone and DCM (1:1) for the semipolar fraction, and DCM and methanol (1:1) for the polar fraction. The semipolar - and polar fraction was derivatized with MTBSTFA (N-tertbutyldimethylsilyl-N-methyltrifluoroacetamide). Samples were derivatized with MTBSTFA dissolved in super-dehydrated n-hexane, and heated at 70 °C for 3 h. The composition of the separated extracts was analyzed by gas chromatography–mass spectrometry (GC–MS) using an Agilent gas chromatograph 7890A coupled with a mass spectrometer 5975C XL MDS. A DB-5UI column was applied (60 m × 250 μm id, 0.25 μm stationary phase film), with He (purity of 99.9999%) as a carrier gas. The experimental conditions were as follows: injection volume of 1 μL; split/splitless mode; initial temperature of 45 ◦C (isothermal for 1 min); heating rate up to 100 ◦C at 20 ◦C/min, then 3 ◦C/min to 280 ◦C for 66.25 min. The mass spectrometer worked in electron ionization (EI) mode at 70 eV in full scan mode and scanned from 50 to 650 Da.<br> Dr Ewa Szram, employed at the Institute of Earth Sciences, Faculty of Natural Sciences, Silesian University in Katowice, carried out the project. This research was funded by the National Science Centre, Poland MINIATURA-6 2022/06/X/ST10/00338 “Lichens as bioindicators of monitoring of the selective air pollution”</p>
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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.