On-Demand Processing for Remote Sensing Big Data Analysis

A fuzzy grade-of-membership (GoM) clustering algorithm is applied to analysis of remote sensing data, in particular, the type of data used in climatic classification. The methodology is applied to a cloud product data subset derived from NASA’s International Satellite Cloud Climatology Project, which includes remotely sensed global monthly average surface temperature and precipitation data for land and coastal regions for the year 1984. GoM partitions for this case are similar to those of vector quantization and fuzzy c-means clustering algorithms, which is significant given the striking differences between the algorithms. The GoM clustering approach is shown to provide an alternative means of interpreting large heterogeneous datasets for exploratory analysis, which broadens the application base by admitting categorical data.

Structured, efficient, and secure storage of experimental data and associated meta-information constitutes one of the most pressing technical challenges in modern neuroscience, and does so particularly in electrophysiology. The German INCF Node aims to provide open-source solutions for this domain that support the scientific data management and analysis workflow, and thus facilitate future data access and reproducible research. G-Node provides a data management system, accessible through an application interface, that is based on a combination of standardized data representation and flexible data annotation to account for the variety of experimental paradigms in electrophysiology. The G-Node Python Library exposes these services to the Python environment, enabling researchers to organize and access their experimental data using their familiar tools while gaining the advantages that a centralized storage entails. The library provides powerful query features, including data slicing and selection by metadata, as well as fine-grained permission control for collaboration and data sharing. Here we demonstrate key actions in working with experimental neuroscience data, such as building a metadata structure, organizing recorded data in datasets, annotating data, or selecting data regions of interest, that can be automated to large degree using the library. Compliant with existing de-facto standards, the G-Node Python Library is compatible with many Python tools in the field of neurophysiology and thus enables seamless integration of data organization into the scientific data workflow.

A workflow for data analysis is introduced to synthesize flux regulation maps of a Metabolic P system from time series of data observed in laboratory. The procedure is successfully tested on a significant case study, the photosynthetic phenomenon called NPQ, which determines plant accommodation to environmental light. A previously introduced MP model of such a photosynthetic process has been improved, by providing an MP system with a simpler regulative network that reproduces the observed behaviors of the natural system. Two regression techniques were employed to find out the regulation maps, and interesting experimental results came out in the context of their residual analysis for model validation.

Host-microbiome interactions are crucial in maintaining physiological homeostasis and influencing disease progression. While traditional microbiome research, focused on microbial diversity and abundance, provided essential frameworks, the integration of multi-omics approaches established a far more comprehensive, systems-level understanding of microbiome functionality. However, significant methodological challenges in multi-omics data integration remain, including inconsistent sample coverage, heterogeneous data formats, and complex downstream analytical workflows. These challenges impact the reproducibility and reliability of results and underscore the critical requirement for developing standardized, systematic workflows. Thus, we developed the EasyMultiProfiler (EMP) workflow, a streamlined and efficient analytical framework for multi-omics data analysis. EMP provides a comprehensive infrastructure based on the SummarizedExperiment and MultiAssayExperiment classes, creating a unified framework for storing and analyzing omics data. The framework's architecture comprises five interconnected functional modules: data extraction, preparation, support, analysis, and visualization. These modules are smoothly integrated into natural language-style analytical workflows, offering users an efficient and standardized solution for multi-omics analysis. EMP addresses critical challenges in multi-omics data analysis, including data integration, workflow standardization, and result reproducibility. The platform's modular architecture and intuitive interface provide researchers and clinicians with a robust, flexible workflow to systematically extract biologically relevant insights from complex multi-omics datasets.

DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts.

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Both interactive visualization and computational analysis methods are useful for data studies and an integration of both approaches is promising to successfully combine the benefits of both methodologies. In interactive data exploration and analysis workflows, we need successful means to quantitatively externalize results from data studies, amounting to a particular challenge for the usually qualitative visual data analysis. In this paper, we propose a hybrid approach in order to quantitatively externalize valuable findings from interactive visual data exploration and analysis, based on local linear regression models. The models are built on user-selected subsets of the data, and we provide a way of keeping track of these models and comparing them. As an additional benefit, we also provide the user with the numeric model coefficients. Once the models are available, they can be used in subsequent steps of the workflow. A model-based optimization can then be performed, for example, or more complex models can be reconstructed using an inversion of the local models. We study two datasets to exemplify the proposed approach, a meteorological data set for illustration purposes and a simulation ensemble from the automotive industry as an actual case study.

Event Abstract Back to Event Versatile format and tools for comprehensive data organization in neuroscience Christian J. Kellner1*, Adrian Stoewer1, Michael Sonntag1, Achilleas Koutsou1, Andrey Sobolev1, Jan Benda2, Thomas Wachtler1 and Jan Grewe2 1 Ludwig-Maximilians-Universität München, German Neuroinformatics Node, Germany 2 Universität Tübingen, Institut für Neurobiologie, Germany Managing neuroscience data requires the integration of information from multiple sources. Background information, or metadata, about the experiment is necessary to interpret the resulting data correctly. Storing such information consistently is an essential part of experimental research and depends crucially on available file formats. Many existing formats provide only limited support for linking metadata to the data. Here we present the NIX project[1], consisting of an open format and software tools to store and organize data and metadata. The format can store recorded or derived data together with the meta-information, including relationships between data items. Units and dimension information are stored alongside the data to make quick interpretation possible. The format further allows specifying regions of interest, such as areas in an image or events in a recorded signal. Efficient use of NIX features is facilitated by software libraries for different languages, including C++, Python, Matlab, and Java. Installable packages exist for different platforms, together with documentation, examples, and tutorials. Additionally the NixView program[2] can be used to explore, plot and export the stored data in a user friendly and convenient way. An I/O backend for NEO[3,4] has been implemented, creating a route for raw data in various proprietary formats to be converted to the open NIX format via the NEO data structures. Moreover, results of data analysis done with NEO, e.g. using the Elephant[5] toolkit, can be saved in the very same format. The NIX project thus supports comprehensive annotation and efficient organisation of neuroscience data, and the variety of libraries makes it easy to integrate access to data and metadata in the lab data collection and analysis workflow. Acknowledgements Supported by the Federal Ministry of Education and Research (BMBF Grant 01GQ1302) References [1] https://github.com/G-Node/nix [2] http://bendalab.github.io/NixView/ [3] http://neuralensemble.org/neo/ [4] https://github.com/G-Node/python-neo-nixio [5] http://neuralensemble.org/elephant/ Keywords: data format, metadata, Metadata Management, data organization, open source, Open Source Software, Open Science, Metadata Sharing Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016. Presentation Type: Demo Topic: Data systems Citation: Kellner CJ, Stoewer A, Sonntag M, Koutsou A, Sobolev A, Benda J, Wachtler T and Grewe J (2016). Versatile format and tools for comprehensive data organization in neuroscience. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00055 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 May 2016; Published Online: 18 Jul 2016. * Correspondence: Mr. Christian J Kellner, Ludwig-Maximilians-Universität München, German Neuroinformatics Node, Planegg-Martinsried, 82152, Germany, kellner@biologie.uni-muenchen.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Christian J Kellner Adrian Stoewer Michael Sonntag Achilleas Koutsou Andrey Sobolev Jan Benda Thomas Wachtler Jan Grewe Google Christian J Kellner Adrian Stoewer Michael Sonntag Achilleas Koutsou Andrey Sobolev Jan Benda Thomas Wachtler Jan Grewe Google Scholar Christian J Kellner Adrian Stoewer Michael Sonntag Achilleas Koutsou Andrey Sobolev Jan Benda Thomas Wachtler Jan Grewe PubMed Christian J Kellner Adrian Stoewer Michael Sonntag Achilleas Koutsou Andrey Sobolev Jan Benda Thomas Wachtler Jan Grewe Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Flight simulators can recreate aircraft flights for flight training and aircraft design. This paper reviews existing engineering flight simulators and introduces the development of a multi-purpose reconfigurable engineering flight simulator at Ryerson University. The multi-purpose engineering flight simulator named the Ryerson Fixed Base Simulator (RFBS) has been designed and built to teach and initiate research projects in the area of aircraft design, flight simulation, pilot training, and flight data analysis. It consists of three 46 inch high definition screens and six 22 inch touch screen panels to represent the instrument panel, the centre console, and the overhead panels of an actual aircraft flight deck. Several low-cost, commercial flight simulation software were tested and X-Plane was selected as the main flight simulation tool. This paper also introduces a research that utilizes the RFBS and a commercial flight analysis software. The objective of the research was to develop a flight data conversion methodology. A case study was commenced to verify the work flow of flight data generation and analysis with an example of hard-landing analysis.

River islands are considered one of the most important components of the development of the river environment, due to their function as a ground appearance that hinders the speed of the flow of the river stream and the distribution of that flow over the seasons of the year, and these islands are important in creating environmental balance, as their components of sand have a role in reducing pollutants due to the mechanism of denitrogenization as a weakening element for aquatic organisms, and their cadastral development is of great importance in the possibilities of creating development opportunities for the surrounding areas, which are still the mechanism of investment of these islands is still limited to a very high degree, and therefore the aim of the study is to identify and compare the engineering characteristics of one of the most important islands of the Euphrates River by measuring its areas and height from the river and comparing its cadastral development in all its engineering dimensions using remote sensing data and processing it in the GIS environment, and the results showed that the area of the island has developed between 1973, which amounted to 289.07 m2 to 2024 4,681.18 m2 to reach the final length of the island 3,587 meters The results showed that the island takes a shape close to a rectangle with a strip extension, where the values ​​according to the results of the aspect ratio reached (30.94), Its heights range between 32 - 58 meters above sea level, with a height difference of up to 2 meters from the riverbed. Its climatic effects are evident through the dense vegetation cover, which ranged according to NDVI between 1 - 0.92 in 1973 to 0.74-1 in 2024, and it has also become one of the most important lands that humans have begun to exploit for many purposes, including grazing. There is a need for further study on the development of river islands over time, and the importance of the impact of this study appears through alerting the community and farmers living in the region to the possibility of investing in these islands for many purposes, while being careful of the possibility of their exposure to sudden river floods.

ABSTRACTForest cover change can be detected with high precision using 3D geospatial data and semi-automatic analyses of Remote Sensing data. The aim of our study, performed in Gorce National Park in Poland, was to generate a land use land cover (LULC) map and use it to analyse forest cover change. The study area is a subalpine forest region that has been affected by bark beetle and wind disturbances. The Geographic Object-Based Image Analysis approach was used for classification, with Colour Infrared orthophotos and normalized Digital Surface Models generated using image-matching approach. Gathered results showed that dominating LULC class is coniferous forests (3380 ha; 47% of study area), when second largest class is deciduous forests (2204 ha; 30%). The dead Norway spruce stands (465.5 ha; 6.5%) showed significant increase comparing to 114.1 ha mapped in 1997.

The search for novel drugs that efficiently eliminate prokaryotic pathogens is one of the most urgent health topics of our time. Robust evaluation methods for monitoring the antibiotic stress response in prokaryotes are therefore necessary for developing respective screening strategies. Besides advantages of common in vitrotechniques, there is a growing demand for in vivoinformation based on imaging techniques that allow to screen antibiotic candidates in a dynamic manner. Gathering information from imaging data in a reproducible manner, robust data processing and analysis workflows demand advanced (semi-)automation and data management to increase reproducibility. Here we demonstrate a versatile and robust semi-automated image acquisition, processing and analysis workflow to investigate bacterial cell morphology in a quantitative manner. The presented workflow, A.D.I.C.T, covers aspects of experimental setup deployment, data acquisition and handling, image processing (e.g. ROI management, data transformation into binary images, background subtraction, filtering, projections) as well as statistical evaluation of the cellular stress response (e.g. shape measurement distributions, cell shape modeling, probability density evaluation of fluorescence imaging micrographs) towards antibiotic-induced stress, obtained from time-course experiments. The imaging workflow is based on regular brightfield images combined with live-cell imaging data gathered from bacteria, in our case from recombinant Shewanellacells, which are processed as binary images. The model organism expresses target proteins relevant for membrane-biogenesis that are functionally fused to respective fluorescent proteins. Data processing and analysis are based on customized scripts using ImageJ2/FIJI, Celltool and R packages that can be easily reproduced and adapted by users. Summing up, our approach aims at supporting life-scientists to establish their own imaging-pipeline in order to exploit their data as versatile as possible and in a reproducible manner.

The search for novel drugs that efficiently eliminate prokaryotic pathogens is one of the most urgent health topics of our time. Robust evaluation methods for monitoring the antibiotic stress response in prokaryotes are therefore necessary for developing respective screening strategies. Besides advantages of common in vitrotechniques, there is a growing demand for in vivoinformation based on imaging techniques that allow to screen antibiotic candidates in a dynamic manner. Gathering information from imaging data in a reproducible manner, robust data processing and analysis workflows demand advanced (semi-)automation and data management to increase reproducibility. Here we demonstrate a versatile and robust semi-automated image acquisition, processing and analysis workflow to investigate bacterial cell morphology in a quantitative manner. The presented workflow, A.D.I.C.T, covers aspects of experimental setup deployment, data acquisition and handling, image processing (e.g. ROI management, data transformation into binary images, background subtraction, filtering, projections) as well as statistical evaluation of the cellular stress response (e.g. shape measurement distributions, cell shape modeling, probability density evaluation of fluorescence imaging micrographs) towards antibiotic-induced stress, obtained from time-course experiments. The imaging workflow is based on regular brightfield images combined with live-cell imaging data gathered from bacteria, in our case from recombinant Shewanellacells, which are processed as binary images. The model organism expresses target proteins relevant for membrane-biogenesis that are functionally fused to respective fluorescent proteins. Data processing and analysis are based on customized scripts using ImageJ2/FIJI, Celltool and R packages that can be easily reproduced and adapted by users. Summing up, our approach aims at supporting life-scientists to establish their own imaging-pipeline in order to exploit their data as versatile as possible and in a reproducible manner.

McDonald, Erickson, and McDonald (2000) and Murtaugh (2000) have presented two interesting views of analysis and interpretation of data from before-after control-impact (BACI) studies. In my opinion, the general message they convey to the reader is that analysis of data from a BACI design, in combination with professional expertise, can add useful evidence as long as one is prudent about the resulting interpretations. Whether scientists who analyze BACI data will indeed use caution in interpretation, particularly in making statements regarding cause and effect, remains to be seen. McDonald et al.'s (2000) study is application specific, taking the reader through an analysis of bird count data from the Exxon Valdez oil spill (EVOS). The authors prudently phrase their interpretation in terms of a weight of evidence argument rather than the much more difficult one of causation. I see the primary usefulness of this paper as that of encouraging scientists to use more complex models to realistically describe count data through time. (Using these models in data analysis has been considerably enhanced by the increasing availability of various types of computer software.) Particularly with biological data, a conventional first step is to try an additive model on untransformed data or an additive model on log-transformed data with the hope that the log transform restores the assumptions of variance homogeneity and normally distributed error terms. The presence of data recorded on the same units through time necessitates that the Huynh-Feldt condition (also known as sphericity) holds in order to use a conventional repeated measures approach. In case this sphericity condition does not hold, there are references to make conservative adjustments to the usual F-tests. (I do wonder just how many people would take the trouble to test these assumptions and do the proper corrections if such corrections were not already built into the

This study investigates the potential of the advanced AI model, ChatGPT, in providing innovative solutions and applications within Geomatics Engineering. ChatGPT enhances data accuracy, improves process efficiency, and supports project management by analyzing large geospatial datasets and interpreting complex information. It offers significant benefits to professionals and students, such as automating routine tasks, providing technical support, and contributing to education by developing users' skills. The case studies presented demonstrate tangible benefits in real-world Geomatics Engineering applications, including timely and budget-compliant project completion, improved accuracy in GIS and remote sensing data analysis, and increased efficiency. Additionally, the integration of ChatGPT has led to notable improvements in environmental monitoring and urban planning projects. In addition to its current applications, future research should focus on deeper integration of ChatGPT with existing technologies such as GIS and remote sensing systems. This will enable more sophisticated data analyses and foster the development of innovative projects in Geomatics Engineering. Furthermore, customizing ChatGPT to specific tasks within Geomatics, such as land use planning, topographic mapping, and boundary delineation, will lead to more precise and efficient solutions. As AI becomes more widespread in the industry, it is crucial to address data security and ethical concerns by establishing robust ethical frameworks that ensure responsible AI implementation and safeguard user data. These advancements will help maximize the potential of ChatGPT and similar AI models in transforming the future of Geomatics Engineering