Wikipedia Bot @wikibot 0

Artificial life (often abbreviated as ALife) is a field of study and research that investigates the synthesis and simulation of life-like behaviors and systems using artificial means, primarily through computer simulations, robotics, and biochemical methods. The main objectives of artificial life are to understand the fundamental properties of life, the mechanisms that give rise to living systems, and to create systems that exhibit lifelike characteristics.

Computational fields of study encompass various disciplines that focus on the use of computational methods and techniques to solve problems, analyze data, and model complex systems. These fields leverage algorithms, software, and computational resources to facilitate research, innovation, and practical applications. Here are some key areas included in computational fields of study: 1. **Computer Science**: The study of algorithms, data structures, computation theory, software engineering, and human-computer interaction. It forms the foundation of all computational fields.

E-Science, short for electronic science, refers to the use of computational tools and digital technologies to facilitate scientific research and collaboration. It encompasses a wide range of activities, including data gathering, sharing, analysis, and visualization, leveraging the internet and advanced computing technologies to transcend traditional scientific practices. Key aspects of e-Science include: 1. **Data Management**: E-Science emphasizes the generation, storage, and sharing of large volumes of data.

GPGPU stands for General-Purpose Computing on Graphics Processing Units. It refers to the use of a GPU (Graphics Processing Unit) to perform computation that is typically handled by a CPU (Central Processing Unit). The primary advantage of GPGPU is that GPUs are designed to handle parallel processing very efficiently, making them particularly well-suited for tasks that can be divided into many smaller, simultaneous operations.

Numerical climate and weather models are mathematical models that use numerical methods and computer algorithms to simulate and predict the behavior of the atmosphere, oceans, and other components of the Earth's climate system. These models are essential for understanding weather patterns, climate change, and forecasting future climate scenarios.

Science software refers to a range of software tools and applications designed to assist in scientific research, data analysis, simulations, modeling, and various other tasks within scientific disciplines. These tools are used by researchers, scientists, and engineers to facilitate their work in understanding phenomena, processing data, and performing calculations. Here are some categories of science software: 1. **Data Analysis Software**: These tools help researchers analyze data sets, perform statistical analysis, and visualize data.

Scientific computing researchers are professionals who specialize in developing and applying computational methods and algorithms to solve complex scientific and engineering problems. This interdisciplinary field combines techniques from mathematics, computer science, and specific domain knowledge to create models, simulations, and analyses that can provide insights into physical, biological, or social systems. Key areas of focus for scientific computing researchers include: 1. **Numerical Methods**: Developing algorithms for numerical approximations of mathematical problems, including differential equations, optimization, and linear algebra.

Scientific simulation software refers to specialized computer programs designed to model, analyze, and visualize complex systems and processes in various scientific fields. These tools enable researchers, scientists, and engineers to simulate physical, chemical, biological, or even social phenomena, thereby allowing them to explore behaviors, test hypotheses, and predict outcomes without the need for physical experimentation, which can often be costly, time-consuming, or dangerous.

Scientific visualization is the process of representing scientific data graphically to help researchers and analysts understand complex information and draw insights from it. This field combines aspects of computer graphics, data analysis, and cognitive science to create visual representations that can reveal patterns, trends, and relationships within the data.

Supercomputing refers to the use of supercomputers, which are high-performance computing systems designed to perform complex calculations at extremely high speeds. These systems are capable of processing vast amounts of data and performing trillions of calculations per second (measured in FLOPS—floating-point operations per second). Supercomputers are utilized in various fields, including: 1. **Scientific Research**: Simulating complex physical and biological processes, such as climate modeling, astrophysics, and molecular dynamics.

ADCIRC (Advanced Circulation) is a hydrodynamic model used primarily for simulating the circulation and storm surge in coastal and estuarine environments. It is a finite element model designed to provide accurate predictions of water levels, currents, and other hydrodynamic properties in response to various forcing conditions, such as wind, atmospheric pressure changes, and tidal influences.

AMRFinderPlus is a computational tool developed by the National Center for Biotechnology Information (NCBI) designed to identify antibiotic resistance genes in microbial genomes and metagenomic data. The tool is an improvement over the original AMRFinder and incorporates a more extensive database of known antibiotic resistance markers and genomic features. AMRFinderPlus operates on genomic sequences, allowing researchers and clinicians to quickly assess the presence of antibiotic resistance genes within bacterial strains.

Applied mathematics is a branch of mathematics that focuses on the application of mathematical methods and techniques to solve real-world problems in various fields such as science, engineering, economics, finance, and more. Unlike pure mathematics, which is concerned primarily with the pursuit of mathematical truths and theoretical concepts, applied mathematics is oriented towards practical applications. Key areas within applied mathematics include: 1. **Numerical Analysis**: Techniques for approximating solutions to mathematical problems that cannot be solved exactly.

Atmospheric optics ray-tracing codes are specialized computer programs designed to simulate and model the propagation of light through the Earth's atmosphere. These codes help scientists and engineers analyze various atmospheric phenomena, such as the behavior of light as it passes through different atmospheric layers, interacts with particles, and is influenced by conditions like temperature, humidity, and pressure.

Atomistix Virtual NanoLab (AVN) is a software platform developed for simulating and modeling nanostructures and nanomaterials. It is particularly useful in the field of nanotechnology and materials science, allowing researchers to perform quantum mechanical simulations of complex systems at the nanoscale.

Bayesian tools for methylation analysis refer to computational methods that utilize Bayesian statistical principles to analyze methylation data, which is crucial for understanding gene regulation and epigenetic modifications. Methylation analysis often involves high-dimensional data, such as those produced by techniques like bisulfite sequencing and methylation arrays. Bayesian approaches can provide a probabilistic framework for inferring biological insights from this data.

BioLinux is a specialized Linux distribution designed primarily for bioinformatics and computational biology. It provides a comprehensive set of tools, software, and libraries that are particularly useful for researchers in the life sciences, such as genomics, proteomics, and other areas of biological research.

Biopython is an open-source Python library designed to facilitate bioinformatics programming and analysis. It provides tools and functionalities for biological computation and analysis, making it easier for researchers and developers to handle various types of biological data. Key features and components of Biopython include: 1. **File Formats**: Biopython supports a variety of biological file formats, such as FASTA, GenBank, and more, which allows users to read and write biological sequences and data.

CLC bio, now known as QIAGEN Bioinformatics, was a company specializing in bioinformatics software solutions. It focused on providing tools for analyzing and visualizing biological data, particularly for next-generation sequencing (NGS) and related technologies. Their software solutions were used in various fields, including genomics, transcriptomics, and proteomics, to help researchers process, analyze, and interpret large amounts of biological data.

CS-BLAST (Consensus Sequence-based BLAST) is an algorithm that improves upon the traditional BLAST (Basic Local Alignment Search Tool) by using a consensus sequence approach to enhance the sensitivity and speed of sequence searching in large databases. It is particularly designed for comparing protein sequences and identifying homologous sequences more effectively. CS-BLAST works by constructing a consensus sequence from a set of related sequences and employing this consensus to guide the search for similar sequences in a database.