New open source tools for MARSIS: providing access to SEG-Y data format for 3D analysis.

2021 ◽  
Author(s):  
Giacomo Nodjoumi ◽  
Luca Guallini ◽  
Roberto Orosei ◽  
Luca Penasa ◽  
Angelo Pio Rossi
Keyword(s):  
Open Source ◽  
Planetary Science ◽  
Seismic Interpretation ◽  
3D Analysis ◽  
The European Union ◽  
Data Types ◽  
Data Formats ◽  
Multiple Data ◽  
Wide Range ◽  
Data Record

<p>The objective of this work is to present a new Free and Open-Source Software (FOSS) to read and convert to multiple data formats data acquired by the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) instrument on board Mars Express (MEX) orbiting Mars since 2005.</p><p>MARSIS is an orbital synthetic aperture radar sounder that operates with dual-frequency between 1.3 and 5.5 MHz and wavelengths between 230 and 55 m for subsurface sounding. The Experiment Data Record (EDR) and Reduced Data Record (RDR) datasets are available for download on public access platforms such as the Planetary Science Archive fo ESA and the PDS-NASA Orbital Data Explorer (ODE).</p><p>These datasets have been widely used for different research, focused to study the subsurface of the red planet up to a depth of a few kilometres, and especially for studying ice caps and looking for subsurface ice and water deposits, producing relevant results. (Lauro et al., 2020; Orosei et al., 2020)</p><p>The Python tool presented here is capable of reading common data types used to distribute MARSIS dataset and then converting into multiple data formats. Users can interactively configure data source, destination, pre-processing and type of outputs among:</p><ul><li>Geopackages: for GIS software, is a single self-contained file containing a layer in which are stored all parameters for each file processed.</li> <li>Numpy array dump: for fast reading and analysis of original data for both frequencies.</li> <li>PNG images: for fast inspections, created for each frequency, and saved. Image pre-processing filters, such as image-denoising, standardization and normalization, can be selected by user.</li> <li>SEG-Y: for analysing data with seismic interpretation and processing software, see e.g. OpendTect, consist of a SEG-Y file for each frequency.</li> </ul><p>SEG-Y capability is the most relevant feature, since is not present in any of other FOSS tool and give to researchers the possibility to visualize radargrams in advanced software, specific for seismic interpretation and analysis, making it possible to interpret the data in a fully three-dimensional environment.</p><p>This tool, available on zenodo (Nodjoumi, 2021), has been developed completely in Python 3, relying only on open-source libraries, compatible with principal operating systems and with parallel processing capabilities, granting easy scalability and usability across a wide range of computing machines. It is also highly customizable since it can be expanded adding processing steps before export or new types of output. An additional module to ingest data directly into PostgreSQL/PostGIS and a module to interact directly with ACT-REACT interface of data platforms are under development.</p><p>Acknowledgments:</p><p>This study is within the Europlanet 2024 RI, and it has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871149. </p><p>References:</p><p>Lauro, S. E. et al. (2020) ‘Multiple subglacial water bodies below the south pole of Mars unveiled by new MARSIS data’, doi: 10.1038/s41550-020-1200-6.</p><p>Nodjoumi, G. (2021) 'MARSIS-xDR-READER', doi: 10.5281/zenodo.4436199</p><p>Orosei, R. et al. (2020) ‘The global search for liquid water on mars from orbit: Current and future perspectives’, doi: 10.3390/life10080120.</p>

scholarly journals RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

10.2196/11734 ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. e11734 ◽  
Author(s):  
Yatharth Ranjan ◽  
Zulqarnain Rashid ◽  
Callum Stewart ◽  
Pauline Conde ◽  
Mark Begale ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Open Source ◽  
Mobile Health ◽  
Large Scale ◽  
Streaming Data ◽  
Security And Privacy ◽  
The European Union ◽  
Wide Range ◽  
Remote Assessment

Background With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

Muxstep: an open-source C ++ multiplex HMM library for making inferences on multiple data types

2016 ◽  
Vol 32 (16) ◽  
pp. 2562-2564 ◽  
Author(s):  
Petar Veličković ◽  
Pietro Liò
Keyword(s):  
Open Source ◽  
Data Types ◽  
Multiple Data

scholarly journals Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization

2021 ◽  
Author(s):  
Sehi L'Yi ◽  
Qianwen Wang ◽  
Fritz Lekschas ◽  
Nils Gehlenborg
Keyword(s):  
Data Visualization ◽  
Data Types ◽  
Interactive Rendering ◽  
Web Based ◽  
Multi Scale ◽  
Data Formats ◽  
Wide Range ◽  
Visualization Techniques ◽  
Diverse Data ◽  
Data Visualizations

The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling—a grammar for interactive and scalable genomics data visualization. Gosling balances expressiveness for comprehensive multi-scale genomics data visualizations with accessibility for domain scientists. Our accompanying JavaScript toolkit called Gosling.js provides scalable and interactive rendering. Gosling.js is built on top of an existing platform for web-based genomics data visualization to further simplify the visualization of common genomics data formats. We demonstrate the expressiveness of the grammar through a variety of real-world examples. Furthermore, we show how Gosling supports the design of novel genomics visualizations. An online editor and examples of Gosling.js and its source code are available at https://gosling.js.org.

scholarly journals Griottes: a generalist tool for network generation from segmented tissue images

2022 ◽  
Author(s):  
Gustave Ronteix ◽  
Valentin Bonnet ◽  
Sebastien Sart ◽  
Jeremie Sobel ◽  
Elric Esposito ◽  
...  
Keyword(s):  
Open Source ◽  
Relevant Information ◽  
Data Types ◽  
Biologically Relevant ◽  
Open Source Tool ◽  
Biological Images ◽  
Multi Scale ◽  
Wide Range ◽  
Microscopy Techniques ◽  
2D And 3D

Microscopy techniques and image segmentation algorithms have improved dramatically this decade, leading to an ever increasing amount of biological images and a greater reliance on imaging to investigate biological questions. This has created a need for methods to extract the relevant information on the behaviors of cells and their interactions, while reducing the amount of computing power required to organize this information. This task can be performed by using a network representation in which the cells and their properties are encoded in the nodes, while the neighborhood interactions are encoded by the links. Here we introduce Griottes, an open-source tool to build the "network twin" of 2D and 3D tissues from segmented microscopy images. We show how the library can provide a wide range of biologically relevant metrics on individual cells and their neighborhoods, with the objective of providing multi-scale biological insights. The library's capacities are demonstrated on different image and data types. This library is provided as an open-source tool that can be integrated into common image analysis workflows to increase their capacities.

scholarly journals Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling

2021 ◽  
Author(s):  
Gary Sutlieff ◽  
Lucy Berthoud ◽  
Mark Stinchcombe
Keyword(s):  
Satellite Data ◽  
Meteorological Data ◽  
Atmospheric Composition ◽  
Chemical Detection ◽  
List Type ◽  
Data Types ◽  
Wide Range ◽  
Future Technologies ◽  
The Impact ◽  
Near Future

Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field

scholarly journals Bioinsecticides based on plant essential oils: a short overview

2020 ◽  
Vol 75 (7-8) ◽  
pp. 179-182
Author(s):  
Murray B. Isman
Keyword(s):  
Essential Oils ◽  
Crop Protection ◽  
Large Body ◽  
Regulatory Agencies ◽  
The European Union ◽  
Plant Essential Oils ◽  
The Past ◽  
Wide Range ◽  
The Usa ◽  
Regulatory Challenges

AbstractInterest in the discovery and development of plant essential oils for use as bioinsecticides has grown enormously in the past 20 years. However, successful commercialization and utilization of crop protection products based on essential oils has thus far lagged far behind their promise based on this large body of research, most notably because with the exceptions of the USA and Australia, such products receive no special status from regulatory agencies that approve new pesticides for use. Essential oil-based insecticides have now been used in the USA for well over a decade, and more recently have seen use in the European Union (EU), Korea, and about a dozen other countries, with demonstrated efficacy against a wide range of pests and in numerous crop systems. For the most part these products are based on commodity essential oils developed as flavor and fragrance agents for the food and cosmetic industries, as there are formidable logistic, economic, and regulatory challenges to the use of many other essential oils that otherwise possess potentially useful bioactivity against pests. In spite of these limitations, the overall prospects for biopesticides, including those based on essential oils, are encouraging as the demand for sustainably-produced and/or organic food continues to increase worldwide.

An intelligent algorithm for autonomous scientific sampling with the VALKYRIE cryobot

2017 ◽  
Vol 17 (3) ◽  
pp. 247-257 ◽  
Author(s):  
Evan B. Clark ◽  
Nathan E. Bramall ◽  
Brent Christner ◽  
Chris Flesher ◽  
John Harman ◽  
...  
Keyword(s):  
Human Performance ◽  
Autonomous Underwater Vehicles ◽  
Field Performance ◽  
Planetary Science ◽  
Operating Environment ◽  
Intelligent Algorithm ◽  
Future State ◽  
Science Operations ◽  
Wide Range ◽  
Matanuska Glacier

AbstractThe development of algorithms for agile science and autonomous exploration has been pursued in contexts ranging from spacecraft to planetary rovers to unmanned aerial vehicles to autonomous underwater vehicles. In situations where time, mission resources and communications are limited and the future state of the operating environment is unknown, the capability of a vehicle to dynamically respond to changing circumstances without human guidance can substantially improve science return. Such capabilities are difficult to achieve in practice, however, because they require intelligent reasoning to utilize limited resources in an inherently uncertain environment. Here we discuss the development, characterization and field performance of two algorithms for autonomously collecting water samples on VALKYRIE (Very deep Autonomous Laser-powered Kilowatt-class Yo-yoing Robotic Ice Explorer), a glacier-penetrating cryobot deployed to the Matanuska Glacier, Alaska (Mission Control location: 61°42′09.3″N 147°37′23.2″W). We show performance on par with human performance across a wide range of mission morphologies using simulated mission data, and demonstrate the effectiveness of the algorithms at autonomously collecting samples with high relative cell concentration during field operation. The development of such algorithms will help enable autonomous science operations in environments where constant real-time human supervision is impractical, such as penetration of ice sheets on Earth and high-priority planetary science targets like Europa.

scholarly journals Imaging anisotropic layering with Bayesian inversion of multiple data types

2016 ◽  
Vol 206 (1) ◽  
pp. 605-629 ◽  
Author(s):  
T. Bodin ◽  
J. Leiva ◽  
B. Romanowicz ◽  
V. Maupin ◽  
H. Yuan
Keyword(s):  
Bayesian Inversion ◽  
Data Types ◽  
Multiple Data

scholarly journals Product formalisms for measures on spaces with binary tree structures: representation, visualization, and multiscale noise

2020 ◽  
Vol 8 ◽  
Author(s):  
Devasis Bassu ◽  
Peter W. Jones ◽  
Linda Ness ◽  
David Shallcross
Keyword(s):  
Data Science ◽  
Theoretical Foundation ◽  
Continuous Functions ◽  
Noise Model ◽  
Data Types ◽  
Data Set ◽  
Tree Structures ◽  
Simple Concept ◽  
Wide Range ◽  
Computer Scientists

Abstract In this paper, we present a theoretical foundation for a representation of a data set as a measure in a very large hierarchically parametrized family of positive measures, whose parameters can be computed explicitly (rather than estimated by optimization), and illustrate its applicability to a wide range of data types. The preprocessing step then consists of representing data sets as simple measures. The theoretical foundation consists of a dyadic product formula representation lemma, and a visualization theorem. We also define an additive multiscale noise model that can be used to sample from dyadic measures and a more general multiplicative multiscale noise model that can be used to perturb continuous functions, Borel measures, and dyadic measures. The first two results are based on theorems in [15, 3, 1]. The representation uses the very simple concept of a dyadic tree and hence is widely applicable, easily understood, and easily computed. Since the data sample is represented as a measure, subsequent analysis can exploit statistical and measure theoretic concepts and theories. Because the representation uses the very simple concept of a dyadic tree defined on the universe of a data set, and the parameters are simply and explicitly computable and easily interpretable and visualizable, we hope that this approach will be broadly useful to mathematicians, statisticians, and computer scientists who are intrigued by or involved in data science, including its mathematical foundations.

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