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.

This is a basic reproduction package for the paper "Constraining planetary mass-loss rates by simulating Parker wind profiles with Cloudy" by Linssen et al. (2022). It provides the data products necessary to reproduce the figures of the paper.

Zoom in on the map above and click your area of interest to determine which package(s) you require for download.

The Mass Points and Breaklines elevation data is a collection of 3D mass points and breakline features that were interpreted using softcopy photogrammetry from aerial photography. The elevation data is organized into tiles grouped into packages for download. See the detailed User Guides linked below for additional information.

Additional Documentation

OBM - DTM - Documentation (Word)
OBM - DTM - User Guide (Word)


GTA 2002 Mass Points and Breaklines - User Guide (Word)


SWOOP 2010 Mass Points and Breaklines - User Guide (Word)

Product Packages

OBM DTM Package North (SHP)
OBM DTM Package South (SHP)


GTA 2002 Mass Points and Breaklines Package NE (SHP)
GTA 2002 Mass Points and Breaklines Package NW (SHP)
GTA 2002 Mass Points and Breaklines Package SW (SHP)


SWOOP 2010 Mass Points and Breaklines Package (SHP)

Status Completed: Production of the data has been completed

Maintenance and Update Frequency As needed: Data is updated as deemed necessary

Contact

Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

This dataset contains geolocated airborne LiDAR point cloud measurements from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco during a pilot campaign over two weeks in October 2021, and two intensive operating periods (IOPs) in Fall 2022 and Spring 2023. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a high resolution LiDAR, used to characterize the properties of ocean surface topography. The sensor has a maximum pulse repetition rate of 400 kHz, with a +/- 30° cross-heading raster scan rate of 200 Hz. Level 1 LiDAR point clouds are available in .laz format.

This dataset is a collection of 3D mass points and breakline features that were interpreted using photogrammetry from aerial photography. The elevation data is organized into tiles grouped into packages for download. This data is for geospatial tech specialists, and is used by government, municipalities, conservation authorities and the private sector for land use planning and environmental analysis.

Leaf mass per area (LMA), leaf carbon and nitrogen content of vegetation (Petasites frigidus) within warming chambers and paired control plots. See related datasets for plant physiology, phenology and environmental conditions. The files included in this data package are in .csv format, and include 2 data files and 4 metadata files. These data were collected in 2017 as part of a series of single-season warming experiments on tundra vegetation on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. A different plant species was targeted each year, over four experimental years from 2017–2021. Each year, five warming chambers and paired ambient control plots were deployed from around the time of snowmelt in mid-June through to mid-September. Average seasonal warming of 3–4°C was achieved using Zero Power Warming (ZPW) chambers (Lewin et al, 2017). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

Chromatographic separation is often an important part of mass-spectrometry-based proteomic analysis. It reduces the complexity of the initial samples before they are introduced to mass-spectrometric detection and chromatographic characteristics (such as retention time) add analytical features to the analyte. The acquisition and analysis of chromatographic data are thus of great importance, and specialized software is used for the extraction of quantitative information in an efficient and optimized manner. However, occasionally, automatic peak picking and correct peak boundary setting is challenged by, for instance, aberration of peak shape, peak truncation, and peak tailing, and a manual review of a large number of peaks is frequently required. To support this part of the analysis, we present here a software tool, Peakfit, that fits acquired chromatographic data to the log-normal peak equation and reports the calculated peak parameters. The program is written in R and can easily be integrated into Skyline, a popular software packages that is frequently used for proteomic parallel reaction monitoring applications. The program is capable of processing large data sets (>10 000 peaks) and detecting sporadic outliers in peak boundary selection performed, for instance, in Skyline. In an example data set, available via ProteomeXchange with identifier PXD026875, we demonstrated the capability of the program to characterize chromatographic peaks and showed an example of its ability to objectively and reproducibly detect and solve problematic peak-picking situations.

This map displays the location of 75 acute care and 45 non-acute care hospitals in Massachusetts.Acute care hospitals are those licensed under MGL Chapter 111, section 51 and which contain a majority of medical-surgical, pediatric, obstetric, and maternity beds, as defined by the Massachusetts Department of Public Health (DPH). Read layer metadata.Non-acute hospitals in Massachusetts are typically identified as psychiatric, rehabilitation, and chronic care facilities, along with some non-acute specialty hospitals. Read layer metadata.Data sources: DPH, Office of Emergency Medical Services (OEMS), the Center for Health Information and Analysis (CHIA) and the state's Bureau of Hospitals.

This data package contains leaf mass data per unit area measured on a monthly basis from February to April, 2016, at the Bosque Protector San Lorenzo (PA-SLZ) and Parque Natural Metropolitano (PA-PNM) sites in Panama. Data from the Barro Colorado Island (PA-BCI) site are only available for March. This data was collected as part of the 2016 El Niño-Southern Oscillation (ENSO) campaign. Included in this data package are an Excel file with data (2016ENSO_Panama_LMA1) and two Excel files with associated metadata. Also included is a Word document (Metadata_description_2016_ENSO_Panama) with details such as data collection methods, equipment used, and site information. See related datasets for further sample details, leaf water potential, leaf spectra, gas exchange and leaf chemistry. VERSION 2 update. The identification of a species from the PNM site has been corrected as follows: the identification of the tree initially identified as Pseudosamanea guachapele (ALBIED) has been revised to Albizia adinocephala (ALBIAD). The updated data package includes revised data, metadata and protocol documents updated to reflect this change. This dataset was originally published on the NGEE Tropics Archive and is being mirrored on ESS-DIVE for long-term archival Acknowledgement: This research was supported as part of NGEE-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-SC0012704.

This dataset contains airborne visible imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes an IO Industries Flare 12M125-CL camera with 14mm lens mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the aircraft heading. The camera was synchronized to a coupled GPS/IMU system with images taken at 5hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the post-processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent spatial resolution. Level 1 images are available in TIFF format.

This dataset contains airborne DoppVis imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during the IOP1 campaign conducted approximately 300 km offshore of San Francisco in Fall 2022. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a Nikon D850 camera with a 14mm lens mounted with a 90 degree rotation and a 30 degree positive pitch angle during flight. The camera was synchronized to a coupled GPS/IMU system with images taken at 2hz. Raw images were calibrated for lens distortion and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to WGS84 datum with a UTM zone 10 projection (EPSG 32610) at 50cm resolution. Level 1 DoppVis images are available as GZIP flightlines containing individual TIFF images.

Data source is the Office of Tax and Revenue’s Computer-Assisted Mass Appraisal (CAMA) system. The CAMA system is used by the Assessment Division (AD) within the Real Property Tax Administration to value real estate for ad valorem real property tax purposes.The intent of this data is to provide a sale history for active properties listed among the District of Columbia’s real property tax assessment roll. This data is updated daily. The AD constantly maintains sale data, adding new data and updating existing data. Daily updates represent a “snapshot” at the time the data was extracted from the CAMA system, and data is always subject to change.

Tommybear1136/Mass dataset hosted on Hugging Face and contributed by the HF Datasets community

The GCMS Mass Spectrometer analyzed programmable flask packages from the NOAA Whole Air Sampler flown aboard the NSF/NCAR GV during HIPPO-4. This dataset contains GCMS-M2 data in NASA Ames format collected during HIPPO-4.

One unmanned ice station (UIS) has been deployed at the L3 site (85.13ºN, 135.68ºE) of the Distributed Network (DN) of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign on 10 October 2019. The measurement of the UIS ice unit lasted until 15 June 2020 when the buoy drifted to 82.28°N; while the ocean unit lasted until 28 September 2020 and finally failed at 74.09°N.

NOTICE: This dataset is currently undergoing maintenance to be repackaged as zip files of flight lines. The file count will decrease dramatically when new zip files are available.This dataset contains airborne longwave infrared (LWIR) imagery from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) during a pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The Modular Aerial Sensing System (MASS) is an airborne instrument package that is mounted on the DHC-6 Twin Otter aircraft which flies long duration detailed surveys of the field domain during deployments. MASS includes a FLIR SC6700 camera with 13mm lens was mounted nadir in the aircraft in an orientation so that the short edge of the image was parallel with the flight track. The camera was synchronized to a coupled GPS/IMU system with images collected at 50hz. Raw images were calibrated for lens distortion, vignetting, and boresight misalignment with the GPS/IMU. Images were georeferenced to the processed aircraft trajectory and exported with reference to the WGS84 datum with a UTM zone 10 projection (EPSG 32610) at an altitude-dependent resolution. Level 1 images are available in TIFF format.

A Fourier analysis of the magnitudes and timing of the Phanerozoic mass extinctions (MEs) demonstrates that many of the periodicities claimed in other analyses are not statistically significant. Moreover we show that the periodicities associated with oscillations of the Solar System about the galactic plane are too irregular to give narrow peaks in the Fourier periodograms. This leads us to conclude that, apart from possibly a small number of major events, astronomical causes for MEs can largely be ruled out.

The dataset provided by Holling(1922) includes species and body masses of boreal forest birds. It is a commonly used dataset for discontinuity analysis. This dataset is not publicly accessible because: It is secondary data accessible online. It can be accessed through the following means: All data used are freely available and located in the appendix of Holling (1992):https://esajournals.onlinelibrary.wiley.com/doi/abs/10.2307/2937313. Format: Electronic text files. This dataset is associated with the following publication: Barichievy, C., D. Angeler, T. Eason, A. Garmestani, K. Nash, C. Stow, S. Sundstrom, and C. Allen. A method to detect discontinuities in census data. Ecology and Evolution. Wiley-Blackwell Publishing, Hoboken, NJ, USA, 8(19): 9614-9623, (2018).

The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for a wide range of phylogenetic Gaussian models. Taking advantage of it, we redesigned the R package mvSLOUCH. Here, we demonstrate how the new version of the package can be used to thoroughly examine the evolution and adaptation of traits in a large dataset of 1252 vascular plants through the use of multivariate Ornstein-Uhlenbeck processes. The results of our analysis demonstrate the ability of the modeling framework to distinguish between various alternative hypotheses regarding the evolution of functional traits in angiosperms., The compiled vascular plant dataset involves two key components: phenotypic data (plant_data.csv) and phylogenetic tree (plant_tree.txt), which consist of the following:  Ellenberg indicator values for nitrogen (Nutrients), leaf area (leaf.area in mm2), plant height (plant.height in m), seed mass (seed.mass in mg) and leaf mass (leaf.mass in mg).The Ellenberg indicator values are taken from Chytrý et al. (2018); leaf area, seed mass, and leaf mass are taken from  Carmona et al. (2021); and plant height from  the TRY database (Kattge et al. 2011). The dated phylogeny is the supertree of plants used by Carmona et al. (2021). The analyses were completed in R using the computing cluster FUN–K at the Biological and Chemical Research Center, University of Warsaw using one node with 48 threads. The exact output can depend on the random seed. However, in the script we have the option of rerunning the analyses as it was in the manuscript, i.e.the random seeds that were used to generate the resu..., , # Fast mvSLOUCH: multivariate Ornstein-Uhlenbeck-based models of trait evolution on large phylogenies

https://doi.org/10.5061/dryad.8w9ghx3vt

These are the data, R scripts and numerical results accompanying Bartoszek, Clarke, Fuentes Gonzalez, Mitov, Pienaar, Piwczynski, Puchalka, Spalik and Voje " Fast mvSLOUCH: multivariate Ornstein–Uhlenbeck-based models of trait evolution on large phylogenies". The two data files concern functional traits in vascular plants: plant height, seed mass, leaf area, leaf mass and their Ellenberg indicator values for nitrogen, and their phylogeny.

Description of the data and file structure

The compiled vascular plant dataset involves two key components: phenotypic data (plant_data.csv) and phylogenetic tree (plant_tree.txt), which consist of the following: Ellenberg indicator values for nitrogen (Nutrients), leaf area (leaf.area in mm2), plant height (plant.height in m), seed mass (seed.mass in mg) and lea...

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis. See more information about the full DIMSpec code base - as well as these data for demonstration purposes - at GitHub (https://github.com/usnistgov/dimspec) or view the full User Guide for DIMSpec (https://pages.nist.gov/dimspec/docs). Files of most interest contained here include the database file itself (dimspec_nist_pfas.sqlite) as well as an entity relationship diagram (ERD.png) and data dictionary (DIMSpec for PFAS_1.0.1.20230615_data_dictionary.json) to elucidate the database structure and assist in interpretation and use.