Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools. Here we have developed massPix - an R package for analysing and interpreting data from MSI of lipids in tissue. MassPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications. MassPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries. Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering. Mouse cerebellum was analysed using matrix assisted laser desorption ionisation (MALDI) MSI. The resulting MSI dataset forms the test data for massPix.

Replication data for the publication: "rSIREM: an R package for MALDI spectral deconvolution" by Del Castillo Pérez et al. The deposited data are SALDI-MSI data of three consectutive thin tissue sections from mouse cerebellum measured at the different mass resolutions at the same instrument (MALDI-MSI: Spectroglyph Injector - Orbitrap Exploris). The paper describes a new R package (rSIREM) to computationally improve the mass resolution of an MSI post-measurement. The developed R package (https://github.com/EdelCastillo/rSirem ) applies a statistical treatment on the concentration of spatial images obtained by separately considering each of the m/z over all the pixels. A representative scalar is associated with each image, obtained by applying a new measure (SIREM) to it, derived from Shannon's entropy. The perturbations of this measure, when considering a sequence of consecutive images, reveal the existence of overlap, if it exists. This information serves as a seed to initialize the EM algorithm in the Gaussian Mixture Model context. The efficiency of the method has been verified using three independent procedures.

In the field of proteomics, generating biologically relevant results from mass spectrometry (MS) signals remains a challenging task. This is partly due to the fact that the computational strategies for converting MS signals into biologically interpretable data depend heavily on the MS acquisition method. Additionally, the processing and the analysis of these data vary depending on whether the proteomic experiment was performed with or without labeling, and with or without fractionation. Several R packages have been developed for processing and analyzing MS data, but they only incorporate identification and quantification data; none of them takes into account other invaluable information collected during MS runs. To address this limitation, we introduce MCQR, an alternative R package for the in-depth exploration, processing, and analysis of quantitative proteomics data generated from either data-dependent or data-independent acquisition methods. MCQR leverages experimental retention time measurements for quality control, data filtering, and processing. Its modular architecture offers flexibility to accommodate various types of proteomics experiments, including label-free, label-based, fractionated, or those enriched for specific post-translational modifications. Its functions, designed as simple building blocks, are user-friendly, making it easy to test parameters and methods, and to construct customized analysis scenarios. These unique features position MCQR as a comprehensive toolbox, perfectly suited to the specific needs of MS-based proteomics experiments.

The repository contains three mzML and four imzML mass spectrometry datasets,

The mzML data are compiled in a single directory 'mzML' and zipped:

• Col_1.mzML is a liquid chromatography (LC) ESI MS dataset from an Arabidopsis extraction published in: Sotelo-Silveira, M., Chauvin, A.-L., Marsch-Martínez, N., Winkler, R. & De Folter, S. Metabolic fingerprinting of Arabidopsis thaliana accessions. Frontiers in Plant Science 6, 1–13 (2015). https://doi.org/10.3389/fpls.2015.00365.
• Cytochrome_C.mzML is an electrospray mass spectrometry (ESI MS) dataset of Cytochrome C. The data were discussed in: Winkler, R. ESIprot: a universal tool for charge state determination and molecular weight calculation of proteins from electrospray ionization mass spectrometry data. Rapid Communications in Mass Spectrometry 24, 285- 294 (2010). https://doi.org/10.1002/rcm.4384.
• T9_A1.mzML is a low-temperature plasma (LTP) MS dataset of the interaction between Arabidopsis and Trichoderma, published in 1. Torres-Ortega, R. et al. In Vivo Low-Temperature Plasma Ionization Mass Spectrometry (LTP-MS) Reveals Regulation of 6-Pentyl-2H-Pyran-2-One (6-PP) as a Physiological Variable during Plant-Fungal Interaction. Metabolites 12, 1231 (2022). https://doi.org/10.3390/metabo12121231.

The imzML mass spectrometry imaging data are zipped individually:

• imzML_AP_SMALDI.zip contains an AP-SMALDI mass spectrometry imaging data set of mouse urinary bladder slides, published by Römpp A, Guenther S, Schober Y, Schulz O, Takats Z, Kummer W, Spengler B., ProteomeXchange dataset PXD001283. 2014., and available from https://www.ebi.ac.uk/pride/archive/projects/PXD001283; Publication: Römpp A, Guenther S, Schober Y, Schulz O, Takats Z, Kummer W, Spengler B; Histology by mass spectrometry: label-free tissue characterization obtained from high-accuracy bioanalytical imaging., Angew Chem Int Ed Engl, 49, 22, 3834-8 (2014). https://doi.org/10.1002/anie.200905559, PubMed: 20397170.
• imzML_DESI.zip is a DESI mass spectrometry imaging data set of human colorectal cancer tissue by Oetjen J, Veselkov K, Watrous J, McKenzie JS, Becker M, Hauberg-Lotte L, Kobarg JH, Strittmatter N, Mróz AK, Hoffmann F, Trede D, Palmer A, Schiffler S, Steinhorst K, Aichler M, Goldin R, Guntinas-Lichius O, von Eggeling F, Thiele H, Maedler K, Walch A, Maass P, Dorrestein PC, Takats Z, Alexandrov T. 2015. Benchmark datasets for 3D MALDI-and DESI-imaging mass spectrometry. GigaScience 4(1):2105 https://doi.org/10.1186/s13742-015-0059-4.
• imzML_LA-ESI.zip is an LA-ESI mass spectrometry imaging data set of an Arabidopsis thaliana leaf by Zheng, Z., Bartels, B., & Svatoš, A. (2020). Laser Ablation Electrospray Ionization Mass Spectrometry Imaging (LAESI MSI) of Arabidopsis thaliana leaf [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3678473.
• imzML_LTP.zip was generated by low-temperature plasma ionization ambient mass spectrometry imaging of a chili fruit, published by Maldonado-Torres M, López-Hernández Jé F, Jiménez-Sandoval P, Winkler R. 2014. Plug and play' assembly of a low-temperature plasma ionization mass spectrometry imaging (LTP-MSI) system. Journal of Proteomics 102C:60–65 https://doi.org/10.1016/j.jprot.2014.03.003; Mauricio Maldonado-Torres, José Fabricio López-Hernández, Pedro Jiménez-Sandoval, & Robert Winkler. (2017). Low-temperature plasma mass spectrometry imaging (LTP-MSI) of Chili pepper [Data set]. In Journal of proteomics (Vol. 102, pp. 60–65). Zenodo. https://doi.org/10.5281/zenodo.484496.

All these datasets are publicly available from different repositories; however, If you reuse them, please attribute the original authors!

R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.

This dataset contains aerosol gravimetric analysis of mass, using 2-stage multi-jet cascade impactors, taken aboard the Ron Brown ship during the ACE-Asia field project. This dataset contains the tab delimited (.acf) data files. Data can also be downloaded in a netCDF format.

This data release contains extended estimates of daily groundwater levels and monthly percentiles at 27 short-term monitoring wells in Massachusetts. The Maintenance of Variance Extension Type 1 (MOVE.1) regression method was used to extend short-term groundwater levels at wells with less than 10 years of continuous data. This method uses groundwater level data from a correlated long-term monitoring well (index well) to estimate the groundwater level record for the short-term monitoring well. MOVE.1 regressions are used widely throughout the hydrologic community to extend flow records from streamgaging stations but are less commonly used to extend groundwater records at wells. The data in this data release document the results of the MOVE.1 regressions to estimate groundwater levels and compute updated monthly percentiles for select wells used in the groundwater index in the Massachusetts Drought Management Plan (2019). The U.S. Geological Survey (USGS) groundwater identification site numbers and groundwater level data are available via the USGS National Water Information System (NWIS) database (available at https://waterdata.usgs.gov/nwis). Groundwater levels provided are in depth to water level, in feet below land surface datum. This data release accompanies a USGS scientific investigations report that describes the methods and results in detail (Ahearn and Crozier, 2024). Reference: Massachusetts Executive Office of Energy and Environmental Affairs and Massachusetts Emergency Management Agency, 2019, Massachusetts drought management plan: Executive Office of Energy and Environmental Affairs, 115 p., accessed September 2022, at https://www.mass.gov/doc/massachusetts-drought-management-plan The following are included in the data release: (1) R input file that lists the final site pairings (R_Input_MOVE1_Site_List.csv) (2) R script that performs the MOVE.1 and produces outputs for evaluation purposes (MOVE1_R_code.R) (3) MOVE.1 model outputs (MOVE1_Models.zip) (4) Estimates of daily groundwater levels using the MOVE.1 regression technique (MOVE1_Estimated_Record_Tables.zip) (5) Plots showing time series of estimated daily groundwater levels from the MOVE.1 technique (MOVE1_Estimated_Record_Plots.zip) (6) Plots showing time series of estimated daily groundwater levels from the MOVE.1 technique zoomed into the period of observed daily groundwater levels for the short-term site (Zoomed_MOVE1_Estimated_Record_Plots.zip) (7) Plots showing residuals (Residuals_WL_Plots.zip) (8) Monthly percentile table for 27 study wells (GWL_Percentiles_All_Study_Wells.csv)

This dataset is about: (Appendix 2) Compilation of mass accumulation rates from deep sea sediments during the Holocene. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.726364 for more information.

Datasets and R scripts from González-Suárez, M; Gonzalez-Voyer, A; von Hardenberg, A; Santini, L (2021) The role of brain size on mammalian population densities Journal of Animal Ecology, 90: 653– 661. DOI: 10.1111/1365-2656.13397Additional details in the README.pdf file

SUMMARY OF FILES INCLUDED

• CSV datasets: total 21 files representing

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12 csv datasets from other sources (described below) with brain and body mass data in the zip file Brain and Mass data.

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Six csv datasets from other sources and compilations (described below) with population density and diet information

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Two csv files (Complete_dataset_published.csv, Brain_data_compilation_published.csv) produced during this study. Details of the files and the compilation protocol are provided in the README file.

• R scripts: three scripts that describe the protocols to: combine brain and body mass data (brain_data_compilation_published.Rmd), combine all sources (Joining_all_data_published.Rmd), and analyse and produce tables and figures presented in the manuscript (Data_analysesPPA_published.Rmd).

Supporting datasets and algorithms (R-based) for the manuscript entitled "Development of a Tool to Determine the Variability of Consensus Mass Spectra", including an R Markdown script to reproduce the manuscript's figures.

Chemical contamination is one of the major obstacles for mechanical recycling of plastics. In this article, we built and open-sourced an in-house MS/MS library containing more than 500 plastic-related chemicals and developed mspcompiler, an R package, for the compilation of various libraries. We then proposed a workflow to process untargeted screening data acquired by liquid chromatography high-resolution mass spectrometry. These tools were subsequently employed to data originating from recycled high-density polyethylene (rHDPE) obtained from milk bottles. A total of 83 compounds were identified, with 66 easily annotated by making use of our in-house MS/MS libraries and the mspcompiler R package. In silico fragmentation combined with data obtained from gas chromatography–mass spectrometry and lists of chemicals related to plastics were used to identify those remaining unknown. A pseudo-multiple reaction monitoring method was also applied to sensitively target and screen the identified chemicals in the samples. Quantification results demonstrated that a good sorting of postconsumer materials and a better recycling technology may be necessary for food contact applications. Removal or reduction of non-volatile substances, such as octocrylene and 2-ethylhexyl-4-methoxycinnamate, is still challenging but vital for the safe use of rHDPE as food contact materials.

This dataset is about: (Appendix 2) Compilation of mass accumulation rates from deep sea sediments during the last glacial maximum. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.726364 for more information.

Scripts for analysis of DI-qTOF data recorded for studies of refractory dissolved organic matter

Requisite R files for the Proteo-SAFARI app

There is a total of 17 datasets to produce all the Figures in the article. There are mainly two different data files: GUP White Dwarf Mass-Radius (GUPWD_M-R) data and GUP White Dwarf Profile (GUPWD_Profile) data. The file GUPWD_M-R gives only the Mass-Radius relation with Radius (km) in the first column and Mass (solar mass) in the second. On the other hand GUPWD_Profile provides the complete profile with following columns. column 1: Dimensionless central Fermi Momentum $\xi_c$ column 2: Central Density $\rho_c$ ( Log10 [$\rho_c$ g cm$^{-3}$] ) column 3: Radius $R$ (km) column 4: Mass $M$ (solar mass) column 5: Square of fundamental frequency $\omega_0^2$ (sec$^{-2}$) ===================================================================================== Figure 1 (a) gives Mass-Radius (M-R) curves for $\beta_0=10^{42}$, $10^{41}$ and $10^{40}$. The filenames of the corresponding dataset are GUPWD_M-R[Beta0=E42].dat GUPWD_M-R[Beta0=E41].dat GUPWD_M-R[Beta0=E40].dat ===================================================================================== Figure 1 (b) gives Mass-Radius (M-R) curves for the high value of $\beta_0=10^{44}$ which is numerically obtained in the dataset with the filename GUPWD_M-R[Beta0=E44].dat. The Figure also plots analytically obtained M-R relation. For low $\xi_c$ values (inset), the mass-radius curve is given by the expression (3.9) and (3.12) in the article, the corresponding data is given in the file ''GUPWD_M-R_Asym_Low.dat''. Note that Mass-radius the curve is independent of the GUP parameter for low values of central Fermi momentum. For high $\xi_c$ values, the mass-radius curve is given by the expression (3.20), and it is a function of the value $\beta_0$. The corresponding data is given in the file ''GUPWD_M-R_Asym_High[Beta0=E44].dat'' for $\beta_0=10^{44}$. ===================================================================================== Figure 2 (a) plots Mass-Radius (M-R) curves for $\beta_0=6.3\times 10^{39}$, $6.0\times 10^{39}$, $5.38\times 10^{39}$, $5.0\times 10^{39}$ and $4.5\times 10^{39}$. The filenames of the corresponding dataset are GUPWD_M-R[Beta0=6.30E39].dat GUPWD_M-R[Beta0=6.00E39].dat GUPWD_Profile[Beta0=5.38E39].dat GUPWD_Profile[Beta0=5.00E39].dat GUPWD_M-R[Beta0=4.50E39].dat ===================================================================================== Figure 2 (b) plots Mass-Radius (M-R) curves for $\beta_0=10^{39}$, $\beta_0=10^{38}$ and $\beta_0=0$. The filenames of the corresponding dataset are GUPWD_M-R[Beta0=E39].dat GUPWD_Profile[Beta0=1.00E38].dat GUPWD_Profile[Beta0=0.0].dat ===================================================================================== Figure 3 plots the square of the eigenfrequency of the fundamental mode as the function of central density. The filenames for the corresponding dataset is GUPWD_Profile[Beta0=1.00E40].dat GUPWD_Profile[Beta0=5.60E39].dat GUPWD_Profile[Beta0=5.38E39].dat GUPWD_Profile[Beta0=5.00E39].dat GUPWD_Profile[Beta0=1.00E38].dat GUPWD_Profile[Beta0=0.0].dat The research article entitled Existence of Chandrasekhar's limit in generalized uncertainty white dwarfs'' by the same authors requires a numerical solution of the Einstein equation for spherically symmetric white dwarf stars. A single dataset in Figures (1) and (2) in the research article corresponds to solving Tolman Oppenheimer Volkoff (TOV) equations for a range central Fermi momenta with a particular choice of GUP parameter $\beta_0$. The first-order differential equations (see equations 3.3 and 3.4 in the article) are solved numerically with the aid of C programming using the fourth-order Runge-Kutta method with boundary conditions as described in the article. The dataset for the eigenfrequency of the fundamental mode is obtained from the dynamical instability scheme as described in the article. Integrations in equations 4.14-4.16 carried out employing the Trapezoidal method. This yields the eigenfrequency corresponding to a range of central Fermi momentum (or central density) for a particular choice of the GUP parameter $\beta_0$. The enclosed code and dataset correspond to the numerical solution of Tolman Oppenheimer Volkoff (TOV) equation (3.3) and (3.4) and the dynamical instability scheme given by equations 4.13 to 4.16 in the research articleExistence of Chandrasekhar's limit in generalized uncertainty white dwarfs'' by the same authors. The dataset is generated for a wide range of central Fermi momentum $xi_c$ supplemented by the equation of state (2.6) and (2.11) parametrized by the Fermi momentum $\xi$. For a given value of central Fermi momentum, the solution gives the total mass and radius of the white dwarfs. These solutions facilitate the evaluation of the integrals 4.14--4.16 giving the eigenfrequency of the fundamental mode in equation (4.13).

A major bottleneck of mass spectrometric metabolomic analysis is still the rapid detection and annotation of unknown m/z features across biological matrices. This kind of analysis is especially cumbersome for complex samples with hundreds to thousands of unknown features. Traditionally, the annotation was done manually imposing constraints in reproducibility and automatization. Furthermore, different analysis tools are typically used at different steps which requires parsing of data and changing of environments. We present here MetNet, implemented in the R programming language and available as an open-source package via the Bioconductor project. MetNet, which is compatible with the output of the xcms/CAMERA suite, uses the data-rich output of mass spectrometry metabolomics to putatively link features on their relation to other features in the data set. MetNet uses both structural and quantitative information on metabolomics data for network inference and enables the annotation of unknown analytes.

This dataset contains mass-to-charge ratio (m/z) and monoisotopic intensity peaks detected in the northern and southern Gulf of Mexico dissolved organic matter extracts using a Fourier transform ion cyclotron mass spectrometry (FTICR-MS). The instrument used was manufactured by Bruker Daltonics; Model: Solarix, 12T. The water samples were collected using a CTD-Niskin rosette aboard the R/V Justo Sierra cruise JS-0815 in the southern Gulf of Mexico on 2015-08-03 and aboard the R/V Weatherbird II cruise WB-0815 in the northern Gulf of Mexico on 2015-08-18. The R/V Justo Sierra cruise was led by chief scientist Dr. Steve Murawski and the R/V Weatherbird II cruise was led by chief scientist Dr. Dave Hollander. Mass-to-charge ratio (m/z) and monoisotopic intensity from a sediment core collected aboard the R/V Justo Sierra cruise JS-0815 in the southern Gulf of Mexico is available under GRIIDC UDI R4.x267.179:0006.

SOIREE Sediment Traps - Total Mass Flux

. Visit https://dataone.org/datasets/sha256%3A2c31a8029059075ca1ab79991d6de232cb2c8c83650aeb1534b5245273f1feb4 for complete metadata about this dataset.

This dataset is about: (Table 2) Accelerator mass spectrometry (AMS) radiocarbon dates and calibrated ages. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.819014 for more information.

Location codes that start with a “1” indicate the front of the body and codes that begin with a “2” indicate the back of the body.