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.
2,394
datasets available to search
ShareScore release 0.9.0
Dataset results
2,394 results for “MS”
Ramped Pyrolysis Oxidation (RPO) coupled radiocarbon (14C-DOC) and stable carbon (13C-DOC), high-resolution molecular composition (FT-ICR MS), and biodegradable dissolved organic carbon (BDOC) of groundwater, river water, and lagoon water in northeast Alaska, 2017
Supra-permafrost groundwater (SPGW), river water, and lagoon water were sampled near Kaktovik, AK to assess the reactivity and origin of dissolved organic matter (DOM) across interconnected hydrologic systems during late summer. Water samples were collected on August 17th 2017 from SPGW along the beach of Jago Lagoon (Jago GW), surface water from the Jago River’s main channel above tidal influence (Jago R), and from the water column of Kaktovik Lagoon at 2–3 m depth (KA LW). Measurements were made from grab samples for river and lagoon water, and from a composite sample for SPGW gathered from 10 individual shoreline locations. Data include dissolved organic carbon concentration (DOC, mg C L-1), Ramped Pyrolysis Oxidation (RPO) derived fraction compositions of 13C-DOC (δ13C ‰), 14C-DOC (in fraction modern), and method/instrumental error in the 14C and 13C results, and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) molecular composition and summarized compound classes. Biodegradable DOC (BDOC) bottle experiments were performed using all three sample types, where DOC concentration was subsequently measured at 2, 7, 14, and 28 days. FT-ICR MS composition was subsequently measured at the 28-day timepoint to track changes in molecular formulae and compound class relative abundance following biodegradation. Data from RPO serial thermal oxidation include temperature and normalized CO2 profiles for each background sample. Thermal-oxidation profiles of CO2 were transformed into non-parametric activation energy (E) distributions using an inverse model. Model output includes C mass of oxidized CO2 (µg C), Tmax (K), Emax (kJ mol-1), Emean (kJ mol-1), Estd (kJ mol-1), and p(0,E)max of user-defined sample fractions. FT-ICR MS results include a summary table of the relative abundance of compound classes (e.g., unsaturated phenolic, polyphenolic, aliphatic, condensed aromatics, peptide-like) and elemental groupings (e.g., CHO-type, CHON-type, CHOS-type, CHON
Dataset for Towards improved online dissolution evaluation of Pt-alloy PEMFC electrocatalysts via electrochemical flow cell - ICP-MS setup upgrades
<p>Experimental data comprises raw data from ICP-MS (Inductively coupled plasma mass spectrometry) (i.e. time dependence of signal intensity for Co59 and Pt195) for different cell geometry and operating parameters. <br>Model data comprise of time- and space-dependent values of Pt ions concentration in the modelling cell and local velocity vectors.</p>
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>
MS and NMR data of in situ Captured Marine Exometabolites
<p>This folder contains the raw data pertaining to the article <i><strong>In Situ</strong></i> <strong>Capture and Real Time Enrichment of Marine Chemical Diversity </strong></p><p><a href="https://doi.org/10.1021/acscentsci.3c00661">https://doi.org/10.1021/acscentsci.3c00661</a></p><p>Data are organized in folders corresponding to each figure. Briefly, this folder contains the raw mass spectrometry (MS) data, the cytoscape files of the full molecular network (Fig3), the xcel spreadsheets of annotated MS spectra related to each investigated specialized exometabolites from the Mediterranean sponges <i>Aplysina cavernicola </i>(AC, Fig4), <i>Spongia officinalis </i>(SO, Fig5)<i>, </i>and <i>Agelas oroides </i>(AO, Fig6)<i>, </i>the raw 1H NMR data from each sponge exometabolite (EM) extract with their corresponding crude extract (CR).</p><ul><li>All MS2 data were acquired on a Bruker Impact II qTOF (ESI positive, collision energy 20-40eV) also deposited here : MSV000091465</li><li>SIRIUS software and CANOPUS were used to further annotate the chemodiversity of captured marine EMs</li><li>All NMR data were acquired on a BRUKER avance II+ instrument (600 MHz, cryoprobe) in CD<i>3</i>OD</li></ul><p>-------------------------</p><p><strong>References related to in silico MS annotation tools:</strong></p><ul><li>Kai Dührkop, Louis-Félix Nothias, Markus Fleischauer, Raphael Reher, Marcus Ludwig, Martin A. Hoffmann, Daniel Petras, William H. Gerwick, Juho Rousu, Pieter C. Dorrestein and Sebastian Böcker <i>Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra</i>. Nature Biotechnology, 2020. https://doi.org/10.1038/s41587-020-0740-8</li><li>Yannick Djoumbou Feunang, Roman Eisner, Craig Knox, Leonid Chepelev, Janna Hastings, Gareth Owen, Eoin Fahy, Christoph Steinbeck, Shankar Subramanian, Evan Bolton, Russell Greiner, David S. Wishart <i>ClassyFire: automated chemical classification with a comprehensive, computable taxonomy </i>J Cheminf, 8, 2016. https://doi.org/10.1186/s13321-016-0174-y</li><li>Kim, Hyun Woo and Wang, Mingxun and Leber, Christopher A. and Nothias, Louis-Félix and Reher, Raphael and Kang, Kyo Bin and van der Hooft, Justin J. J. and Dorrestein, Pieter C. and Gerwick, William H. and Cottrell, Garrison W. NPClassifier:<i> A Deep Neural Network-Based Structural Classification Tool for Natural Products. </i>Journal of Natural Products, 84, 2021. https://doi.org/10.1021/acs.jnatprod.1c00399</li></ul>
S70 | EISUSGCEIMS | Environmental Institute GC-EI-MS suspect list
<p>This is the collection associated with list S70 EISUSGCEIMS Environmental Institute GC-EI-MS suspect list on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>GC-EI-MS suspect list of Environmental Institute. Provided by Peter Oswald, Nikiforos Alygizakis, Martina Oswaldova, Jaroslav Slobodnik. Dataset DOI: <a href="https://doi.org/10.5281/zenodo.3894827">10.5281/zenodo.3894827</a>.</p>
Oxidation of Ammonia/Methanol Mixtures in a plug-flow reactor with TOF-MS at 373-973K
<p>TOF-mass spectrometric measurements with a plug-flow reactor for ammonia/methanol gas mixtures (neat, 10% and 20% methanol in ammonia for equivalence ratios 1 and 2)</p> <ul> <li>temperature range: 373-973 K</li> <li>pressure: 3 bar</li> <li>dilution: 98 %</li> </ul> <p>Dataset described, analyzed and discussed in: A. Welp, C. Rudolph, B.R. Giri, K.P. Shrestha, R. Verma, F. Mauss, and B. Atakan, Oxidation of Ammonia Methanol Blends: An Experimental and Kinetic Modeling Study, 2025, accepted for publication in Combustion and Flame.</p>
Targeted and untargeted LC-MS copepodamide datasets for marine and freshwater copepods
<p>This repository contains the datasets, analysis code and output generated and used in the scientific article titled "Mass spectroscopy reveals compositional differences in copepodamides from limnic and marine copepods" published in Scientific Reports (https://doi.org/10.1038/s41598-024-53247-1)<em>.</em></p> <p>Detailed information about the datasets are available in the README.txt.</p> <p>The source dataset created from the sampling effort, with targeted liquid chromatography coupled mass spectrometry (LC-MS) data, taxonomic information of individual copepods, their length measurements, estimated biomass etc is available in <em><strong>Masterfile_targeted_data_final.xlsx</strong></em>.</p> <p>The source dataset for precursor LC-MS scan data is available in <em><strong>Precursor_data_Deisotoped.xlsx</strong></em>. </p> <p>The resulting analysis data frames (last sheet in each .xlsx file) are available as separate .csv files (<strong>Arnoldt_targeted_analysis_data.csv</strong> & <strong>Arnoldt_targeted_analysis_data.xlsx</strong>). These files are denoted "Supplementary Data. 2" and "Supplementary Data. 1" respectively in the main article. Data files <strong>Chromatography.csv</strong> and <strong>zooplankton_composition_bulk.csv</strong> are used to create chromatograph line plots (Figures 3a & 3b in the article) and one of the supplementary figures (S1), respectively.</p> <p>A R-markdown file (<strong>Arnoldt_R_Code</strong><em><strong>.Rmd</strong></em>) with the code to analyse and visualise all data, and its html-output file (<strong>Arnoldt_R_Code_Output</strong><em><strong>.html</strong></em>) are also available here. The markdown files uses the four csv-files described in the paragraph above to generate all analyses and figures. The output (.html) file is denoted "Supplementary Code" in the main article.</p>
MALDI MS data and metadata from "A biocodicological analysis of the medieval library and archive from Orval Abbey, Belgium"
<p>See <a href="https://doi.org/10.1098/rsos.210210">Ruffini-Ronzani et al</a>.</p>
Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach"
<p>Raw data for the article "Visualisation and quantification of flooding phenomena in gas diffusion electrodes used for electrochemical CO2 reduction: A combined EDX/ICP–MS approach", published in Journal of Catalysis 2022 408:1–8, doi: <a href="https://doi.org/10.1016/j.jcat.2022.02.014">10.1016/j.jcat.2022.02.014</a></p> <p>Folder names describe the type of data content.</p>
LOFAR Observation (MS file) from the Boötes Field and the Toothbrush cluster used in the paper: "Looking beyond pixels with continuous-space EstimAtion of Point sources"
<p>The dataset contains the measurement sets (MS file) of the LOFAR observations from the Boötes field and the Toothbrush cluster. The dataset was used in the experiments of the paper: </p> <blockquote> <p>LEAP: Looking beyond pixels with continuous-spaceEstimAtion of Point sources</p> <p>Pan, H., Simeoni, M., Hurley, P., Blu, T. & Vetterli, M. In: Astronomy & Astrophysics, in press, 2017</p> </blockquote> <p>The data was provided as a collaboration between ASTRON and IBM within the DOME project. The data was acquired for a LOFAR sky survey of the Boötes field:</p> <blockquote> <p>LOFAR 150-MHz observations of the Boötes field: Catalogue and Source Counts</p> <p>Williams, W. L. , Hardcastle, M. J. & 33 others In: Monthly Notices of the Royal Astronomical Society. 460, 3, p. 2385–2412</p> </blockquote> <p>and the Toothbrush cluster (RX J0603.3+4214):</p> <blockquote> <p>Simulating the toothbrush: evidence for a triple merger of galaxy clusters</p> <p>Brüggen, M., van Weeren, R. J., Röttgering, H. J. A. In: Monthly Notices of the Royal Astronomical Society: Letters. 425, 1, p. L76--L80</p> </blockquote> <p>In case of questions concerning the measurement set, please contact the original authors for details.</p> <p> </p> <p>We have also included the three catalogs used in the experiments, which are converted from their original FITS table to Numpy arrays:</p> <ul> <li>skycatalog.npz is the catalog of the Boötes field: https://academic.oup.com/mnras/article-lookup/doi/10.1093/mnras/stw1056</li> <li>TGSSADR1_7sigma_catalog.npz is the TGSS ADR1 source catalog: http://tgssadr.strw.leidenuniv.nl/catalogs/TGSSADR1_7sigma_catalog.fits</li> <li>NVSS_CATALOG.npz is the NRAO/VLA Sky Survey: ftp://nvss.cv.nrao.edu/pub/nvss/CATALOG/</li> </ul>
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>
BioDeep/metabolomics-report-standards: BioDeep LC-MS Metabolite Identification Demo Report
<p><em>A Metabolomics unknown feature identification report industry standards from <a href="http://www.bionovogene.com/">BioNovoGene</a> corporation.</em></p> <p>2019.08.16# at Suzhou, China</p> <p>There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, enables the reinterrogation and comparison of data by others, which is also could let us interpret the result in a more clearly way.</p> <p>This article is mainly address at the unknown metabolite identification in LC-MS experiment, and proposes the reporting standards related to the chemical analysis aspects of metabolomics experiments its metabolite identification.</p> <p>Some terms in this article that address to:</p> <ul> <li>feature, the term feature in this article is refer to a parent ion in LC-MS experiment result raw data. Where a parent ion feature is a peak in chromatography data, which is consist of mass to charge ratio in ms1 level and its retention time (with a range of lower bound and upper bound) in chromatography experiment result.</li> <li>annotation, the term annotation in this article is refer to the multidimensional information about the metabolite that assigned to a unknown feature, which such multidimensional information consist with the metabolite its cross reference id in different database, common name, basic chemical data like mass and formula composition and its molecule structure information, etc.</li> <li>alignment, the term alignment means a kind of operation that use to compare the similarity of the mass spectrum data between user sample and the reference standard library. Such similarity comparison result is the most important evidence that use for unknown feature its identification.</li> <li>score, the term score is a kind of numeric value that produced by the alignment comparison calculation. Literally, the higher score the alignment it produce, the better the result it is.</li> </ul> <p>Our metabolite identification report consist with two parts of data which present to our user:</p> <ol> <li>Report excel table that contains the raw sample information and the meta annotation information of the metabolite.</li> <li>Data visual plot for the mass spectrum alignment details.</li> </ol>
FTICR MS data for standards and mixtures for quantitative peak intensity investigation
<p>This upload contains raw (Bruker .d format) FTICR mass spectrometry data (direct infusion, negative mode ESI) for standards in different mixtures and matrices for the purposes of investigating the (non)quantitative nature of the data. <br>Processed data (Excel format), and Python scripts used for data processing are also included. </p> <p>Note - the Python scripts used CoreMS version prior to V2.0 for analysis - to re-run these scripts with a more recent release likely requires syntax updates. </p>
NMR and MS data of identified bromotyrosine alkaloids produced and released by Aplysina cavernicola
<p>This folder contains NMR and MS datasets of each identified bromotyrosine spiroisoxazoline pertaining to the publication<em> </em>entitled:</p> <p><strong>Diving into the molecular diversity of <em>Aplysina cavernicola’s </em>exo-metabolites: contribution of bromo-spiroisoxazoline alkaloids.</strong> <em>ACS Omega</em> 2022 <strong> <a href="https://pubs.acs.org/doi/10.1021/acsomega.2c05415"> </a></strong><a href="https://pubs.acs.org/doi/10.1021/acsomega.2c05415">https://doi.org/10.1021/acsomega.2c05415</a></p> <ul> <li>All NMR data were acquired in CD<sub>3</sub>OD at 600 MHz (Bruker Avance III, cryosonde TCI) using 2 mm NMR tubes</li> <li>All MS<sup>2</sup> data were acquired on a Bruker Impact II qTOF (ESI positive, collision energy 20-40eV)</li> </ul> <p>The compressed folder of the newly described Aplysine1 contains also raw data related to circular dichroism (CD) and infrared (IR) analyses, as well as quantum mechanical calculations of <sup>13</sup>C NMR shifts using GIAO NMR and DP4+ analyses.</p> <p>The Excel spreadsheet for DP4+ analyses were obtained from: Grimblat N et al. “Beyond DP4: An Improved Probability for the Stereochemical Assignment of Isomeric Compounds Using Quantum Chemical Calculations of NMR Shifts.” <em>The Journal of Organic Chemistry</em> 80, no. 24 (December 18, 2015): 12526–34. <a href="https://doi.org/10.1021/acs.joc.5b02396">https://doi.org/10.1021/acs.joc.5b02396</a>.</p> <p>All MS data are also made Freely available at the UCSD Center for Computational Mass Spectrometry database with the MassIVE identifier <a href="https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=4f6d3c00539a412a9c6d7fac0f7f2a81">MSV000089502</a> .</p> <p>NOTE: 3,5 dibromotyrosine was not identified neither in<em> Aplysina cavernicola </em>crude extract nor as exo-metabolites but was used for MS dereplication purposes.</p>
AFLP and MS-AFLP data for Spartina alterniflora and Borrichia frutescens collected from three habitats (i.e. low, medium, and high salt) within five sites, respectively, on Sapelo Island, GA in May 2011
Using amplified fragment length polymorphism (AFLP) and methylation sensitive (MS)-AFLP we assessed genetic and epigenetic variation in two salt marsh perennials, Spartina alterniflora and Borrichia frutescens, in Sapelo Island, Georgia. We sampled Apex (A), Cabretta (C), Hunt Camp (H), Lighthouse (L), and Marsh Landing (M) for S. alterniflora, and C, H, L, M, and Shell Hammock for B. frutescens due to site specific differences in species among the sites. We tested the hypothesis that populatation structure at the habitat level would be due to epigenetic loci and not genetic. The presence and absence AFLP bands and MS-AFLP methylation respresents a genome-wide snapsnot of variation within individuals. We used hierarchical AMOVAs, permutational MANOVA, Bayesian clustering (genetic only), Mantel and partial Mantel tests, and generalized linear models to assess the spatial structure of genetic and epigenetic variation among our two study organisms across five sites for each organism. (Note: genetic and habitat distance tables were normalized for database compatibility. These data must be formatted as a square dissimilarity matrix for input to the code files.)
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>
MALDI-TOF-MS spectra of historical whale skeletons from the Museum of Zoology, Strasbourg
<p>Spectra data from historical whale skeletons from the Museum. Samples were acid demineralized followed by gelatinization, digestion with trypsin, and peptide purification on C18 filters. They were run on on Bruker autoflex MALDI-TOF-MS. Mzml file formats for the raw data are provided here along with a file information csv file which provides the identification of the samples.<br> <br> For more information see the associated publciation.</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.