This bundle contains microfilm scans of formatted outputs of all data acquired by the Apollo 15 Orbital Mass Spectrometer from lunar orbit during 30 July to 07 August 1971, along with relevant documentation.

This bundle contains microfilm scans of formatted outputs of all data acquired by the Apollo 16 Orbital Mass Spectrometer from lunar orbit during 20-24 April 1972, along with relevant documentation.

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 bundle contains the data collected by the Neutral Mass Spectrometer (NMS) instrument aboard the Lunar Atmosphere and Dust Environment Explorer (LADEE) satellite, along with the documents and other information necessary for the interpretation of that data.

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 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.

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.

The Mass Layoff Statistics (MLS) program collects reports on mass layoff actions that result in workers being separated from their jobs. Monthly mass layoff numbers are from establishments which have at least 50 initial claims for unemployment insurance (UI) filed against them during a 5-week period. Extended mass layoff numbers (issued quarterly) are from a subset of such establishments—where private sector nonfarm employers indicate that 50 or more workers were separated from their jobs for at least 31 days. MLS was eliminated in 2013 under sequestration. For more information and data visit: https://www.bls.gov/mls/

Europe Mass Transfer Technology market size will be USD 654.07 Million by 2023.

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.

Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching and retention time. We here show how retention times can be predicted from molecular structures. Two large, publicly available data sets were used for model training in machine learning: the Fiehn hydrophilic interaction liquid chromatography data set (HILIC) of 981 primary metabolites and biogenic amines,and the RIKEN plant specialized metabolome annotation (PlaSMA) database of 852 secondary metabolites that uses reversed-phase liquid chromatography (RPLC). Five different machine learning algorithms have been integrated into the Retip R package: the random forest, Bayesian-regularized neural network, XGBoost, light gradient-boosting machine (LightGBM), and Keras algorithms for building the retention time prediction models. A complete workflow for retention time prediction was developed in R. It can be freely downloaded from the GitHub repository (https://www.retip.app). Keras outperformed other machine learning algorithms in the test set with minimum overfitting, verified by small error differences between training, test, and validation sets. Keras yielded a mean absolute error of 0.78 min for HILIC and 0.57 min for RPLC. Retip is integrated into the mass spectrometry software tools MS-DIAL and MS-FINDER, allowing a complete compound annotation workflow. In a test application on mouse blood plasma samples, we found a 68% reduction in the number of candidate structures when searching all isomers in MS-FINDER compound identification software. Retention time prediction increases the identification rate in liquid chromatography and subsequently leads to an improved biological interpretation of metabolomics data.

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.

Massachusetts water features, including lakes, ponds, rivers, streams and wetlands. From USGS hydrography. For full metadata and links to download free data please visit https://www.mass.gov/info-details/massgis-data-massdep-hydrography-125000.

The PUMA data were submitted by IKI in two unique forms, a GROUPS FITS file and undocumented binary file. In one original form, the data in this set were received as 4,027 separate FITS files, one for each spectrum. These files have been reformatted and combined into four tables, one for each of the PUMA instrument operating modes. Within each table, the spectra are in order of original FITS file number. This order is assumed to be chronological, based on time given by the BLISI clock, in sec.

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.

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.

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a standard technology in metabolomics. In particular, label-free quantification based on LC-MS is easily amenable to large-scale studies and thus well suited to clinical metabolomics. Large-scale studies, however, require automated processing of the large and complex LC-MS datasets. We present a novel algorithm for the detection of mass traces and their aggregation into features (i.e. all signals caused by the same analyte species) that is computationally efficient and sensitive and that leads to reproducible quantification results. The algorithm is based on a sensitive detection of mass traces, which are then assembled into features based on mass-to-charge spacing, co-elution information, and a support vector machine–based classifier able to identify potential metabolite isotope patterns. The algorithm is not limited to metabolites but is applicable to a wide range of small molecules (e.g. lipidomics, peptidomics), as well as to other separation technologies. We assessed the algorithm's robustness with regard to varying noise levels on synthetic data and then validated the approach on experimental data investigating human plasma samples. We obtained excellent results in a fully automated data-processing pipeline with respect to both accuracy and reproducibility. Relative to state-of-the art algorithms, ours demonstrated increased precision and recall of the method. The algorithm is available as part of the open-source software package OpenMS and runs on all major operating systems. Plasma data is reported in the current study MTBLS234.Simulated data is reported in MTBLS235.

Recent advances in native mass spectrometry (MS) and denatured intact protein MS have made these techniques essential for biotherapeutic characterization. As MS analysis has increased in throughput and scale, new data analysis workflows are needed to provide rapid quantitation from large datasets. Here, we describe the UniDec processing pipeline (UPP) for the analysis of batched biotherapeutic intact MS data. UPP is built into the UniDec software package, which provides fast processing, deconvolution, and peak detection. The user and programming interfaces for UPP read a spreadsheet that contains the data file names, deconvolution parameters, and quantitation settings. After iterating through the spreadsheet and analyzing each file, it returns a spreadsheet of results and HTML reports. We demonstrate the use of UPP to measure the correct pairing percentage on a set of bispecific antibody data and to measure drug-to-antibody ratios from antibody–drug conjugates. Moreover, because the software is free and open-source, users can easily build on this platform to create customized workflows and calculations. Thus, UPP provides a flexible workflow that can be deployed in diverse settings and for a wide range of biotherapeutic applications.

As per Cognitive Market Research's latest published report, the Global Mass Transfer Technology market size was $2,277.18 Million in 2024 and is forecasted to reach $3,455.15 Million by 2030. The Mass Transfer Technology Industry's Compound Annual Growth Rate will be 7.00% from 2024 to 2031. Market Dynamics of the Mass Transfer Technology Market

What are the key driving factors for the Mass Transfer Technology market?

The Growing Petrochemical Industry is Driving the Market for Mass Transfer Technology

The growth of the petrochemical industry has been a significant driver for the global mass transfer technology sector for several reasons. As a major producer of vital chemicals and materials utilized in a variety of industries, including plastics, pharmaceuticals, agriculture, and manufacturing, the petrochemical industry is crucial to the global economy. The demand for petrochemical goods has been rising gradually as the world population continues to rise and industrialization grows. This has led to an increased demand for more economical and efficient methods of producing these chemicals. For instance, in 2021, the global production capacity of petrochemicals was close to 2.3 billion metric tons. It is anticipated to increase greatly by 2030, led by Iran, China, and India.

The petrochemical sector depends extensively on mass transfer technology, an essential component of chemical engineering. Procedures like distillation, absorption, and extraction, include the separation and purification of chemical components within intricate mixtures. The manufacturing of many petrochemical products, including gasoline, plastics, and fertilizers, depends on these separation processes. The need for mass transfer technology and solutions that can improve these operations increases along with the petrochemical sector.

Growing Healthcare and Pharmaceutical Industry Are Driving the Market for Mass Transfer Technology.

The growing healthcare and pharmaceutical industry have been a significant driver for the mass transfer technology market in recent years. The healthcare and pharmaceutical sector is continuously expanding due to several factors. For instance, around 30% of the world’s data volume is been generated by healthcare industry and healthcare expenditure in UK grew by 15.7%. The demand for pharmaceuticals, biopharmaceuticals, and healthcare services has increased as a result of population growth, aging demographics, and the rising prevalence of chronic diseases. Pharmaceutical companies are therefore obligated to produce a variety of medications effectively, economically, and in accordance with strict quality and safety standards. For instance, Koch Modular, an US based company, delivers cost effective solvent recovery and wastewater stripping mass transfer system to all the leading pharmaceutical companies. The following design aspects specific to the pharmaceutical sector and the processing of active pharmaceutical ingredients (API) are areas in which Koch Modular has experience:

• ICH Q7, 3-A and USP standards compliance • Process Validation • Sanitary equipment, tubing and fittings • Polishing and passivation

Similarly, GAB Neumann GmbH, provides mass transfer technology to various application including pharmaceutical industry which includes absorbers, columns, quenches, hydrochloric acid recovery units, sulfuric acid dilution units and well as vacuum jet pumps as a result, advanced separation and purifying methods including chromatography, crystallization, and filtering must be used. These methods significantly rely on mass transfer technology.

Market Restraints for the Mass Transfer Technology Market

High Initial Investment is Restraint for the Mass Transfer Technology Market.

The market for mass transfer technology is greatly restricted by the high initial investment cost for a number of reasons. For mass transfer operations like distillation, absorption, and extraction, significant upfront funding is needed to build the required infrastructure, buy specialized equipment, and pay for related costs. This poses difficulties for both long-standing industry participants and recent industry startups who want to use this technology for a variety of applications. For instance, refining crude oil into useful products like gasoline, diesel, and petrochemical feeds...