Many upcoming and proposed missions to ocean worlds such as Europa, Enceladus, and Titan aim to evaluate their habitability and the existence of potential life on these moons. These missions will suffer from communication challenges and technology limitations. We review and investigate the applicability of data science and unsupervised machine learning (ML) techniques on isotope ratio mass spectrometry data (IRMS) from volatile laboratory analogs of Europa and Enceladus seawaters as a case study for development of new strategies for icy ocean world missions. Our driving science goal is to determine whether the mass spectra of volatile gases could contain information about the composition of the seawater and potential biosignatures. We implement data science and ML techniques to investigate what inherent information the spectra contain and determine whether a data science pipeline could be designed to quickly analyze data from future ocean worlds missions. In this study, we focus on the exploratory data analysis (EDA) step in the analytics pipeline. This is a crucial unsupervised learning step that allows us to understand the data in depth before subsequent steps such as predictive/supervised learning. EDA identifies and characterizes recurring patterns, significant correlation structure, and helps determine which variables are redundant and which contribute to significant variation in the lower dimensional space. In addition, EDA helps to identify irregularities such as outliers that might be due to poor data quality. We compared dimensionality reduction methods Uniform Manifold Approximation and Projection (UMAP) and Principal Component Analysis (PCA) for transforming our data from a high-dimensional space to a lower dimension, and we compared clustering algorithms for identifying data-driven groups (“clusters”) in the ocean worlds analog IRMS data and mapping these clusters to experimental conditions such as seawater composition and CO2 concentration. Such data analysis and characterization efforts are the first steps toward the longer-term science autonomy goal where similar automated ML tools could be used onboard a spacecraft to prioritize data transmissions for bandwidth-limited outer Solar System missions.

The Exploratory Data Analysis (EDA) tools market is experiencing robust growth, driven by the increasing volume and complexity of data across various industries. The market, estimated at $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $5 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of big data analytics and business intelligence initiatives across large enterprises and SMEs is creating a significant demand for efficient EDA tools. Secondly, the growing need for faster, more insightful data analysis to support better decision-making is driving the preference for user-friendly graphical EDA tools over traditional non-graphical methods. Furthermore, advancements in artificial intelligence and machine learning are seamlessly integrating into EDA tools, enhancing their capabilities and broadening their appeal. The market segmentation reveals a significant portion held by large enterprises, reflecting their greater resources and data handling needs. However, the SME segment is rapidly gaining traction, driven by the increasing affordability and accessibility of cloud-based EDA solutions. Geographically, North America currently dominates the market, but regions like Asia-Pacific are exhibiting high growth potential due to increasing digitalization and technological advancements. Despite this positive outlook, certain restraints remain. The high initial investment cost associated with implementing advanced EDA solutions can be a barrier for some SMEs. Additionally, the need for skilled professionals to effectively utilize these tools can create a challenge for organizations. However, the ongoing development of user-friendly interfaces and the availability of training resources are actively mitigating these limitations. The competitive landscape is characterized by a mix of established players like IBM and emerging innovative companies offering specialized solutions. Continuous innovation in areas like automated data preparation and advanced visualization techniques will further shape the future of the EDA tools market, ensuring its sustained growth trajectory.

Over the last ten years, social media has become a crucial data source for businesses and researchers, providing a space where people can express their opinions and emotions. To analyze this data and classify emotions and their polarity in texts, natural language processing (NLP) techniques such as emotion analysis (EA) and sentiment analysis (SA) are employed. However, the effectiveness of these tasks using machine learning (ML) and deep learning (DL) methods depends on large labeled datasets, which are scarce in languages like Spanish. To address this challenge, researchers use data augmentation (DA) techniques to artificially expand small datasets. This study aims to investigate whether DA techniques can improve classification results using ML and DL algorithms for sentiment and emotion analysis of Spanish texts. Various text manipulation techniques were applied, including transformations, paraphrasing (back-translation), and text generation using generative adversarial networks, to small datasets such as song lyrics, social media comments, headlines from national newspapers in Chile, and survey responses from higher education students. The findings show that the Convolutional Neural Network (CNN) classifier achieved the most significant improvement, with an 18% increase using the Generative Adversarial Networks for Sentiment Text (SentiGan) on the Aggressiveness (Seriousness) dataset. Additionally, the same classifier model showed an 11% improvement using the Easy Data Augmentation (EDA) on the Gender-Based Violence dataset. The performance of the Bidirectional Encoder Representations from Transformers (BETO) also improved by 10% on the back-translation augmented version of the October 18 dataset, and by 4% on the EDA augmented version of the Teaching survey dataset. These results suggest that data augmentation techniques enhance performance by transforming text and adapting it to the specific characteristics of the dataset. Through experimentation with various augmentation techniques, this research provides valuable insights into the analysis of subjectivity in Spanish texts and offers guidance for selecting algorithms and techniques based on dataset features.

Dataset

This dataset was created by Francis Brempong

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by ashar ali kamil

Contents

Despite exploratory data analysis (EDA) is a powerful approach for uncovering insights from unfamiliar datasets, existing EDA tools face challenges in assisting users to assess the progress of exploration and synthesize coherent insights from isolated findings. To address these challenges, we present FactExplorer, a novel fact-based EDA system that shifts the analysis focus from raw data to data facts. FactExplorer employs a hybrid logical-visual representation, providing users with a comprehensive overview of all potential facts at the outset of their exploration. Moreover, FactExplorer introduces fact-mining techniques, including topic-based drill-down and transition path search capabilities. These features facilitate in-depth analysis of facts and enhance the understanding of interconnections between specific facts. Finally, we present a usage scenario and conduct a user study to assess the effectiveness of FactExplorer. The results indicate that FactExplorer facilitates the understanding of isolated findings and enables users to steer a thorough and effective EDA.

The size of the EDA Tools Market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 8.46% during the forecast period.EDA tools include a suite of software applications for electronic system design and analysis. They are usually applied in the design of integrated circuits and printed circuit boards. These tools speed up several steps in the design process from conceptual to final physical implementation.EDAs play a crucial role in the semiconductor industry. According to the engineers, they come in handy in designing such very complex chips with billion transistors. They help in circuit design, simulation, verification, and layout. For instance, simulation tools allow engineers to predict the behavior of a circuit before it is produced, thus saving time and resources. Verification tools allow the correctness of the design, and physical design tools optimize the lay out of the circuit on the chip. The increasing complexity of electronic systems, along with the demand for more efficient and faster designs, and the advent of emerging technologies such as 5G and AI, drives the EDA market. As semiconductor technology advances further, so will EDA tools stay at the vanguard of innovations and pick up the pace of the development of cutting-edge electronic products. Recent developments include: July 2022 - Future Facilities' acquisition by Cadence Design Systems, Inc. has been finalized, the company announced. The inclusion of Future Facilities technologies and experience bolsters Cadence's approach to intelligent system design and expands its capabilities in computational fluid dynamics (CFD) and multiphysics system analysis. Leading technology companies can make wise business decisions about data center design, operations, and lifecycle management and lessen their carbon footprint thanks to Future Facilities' electronics cooling analysis and energy performance optimization solutions for data center design and operation using physics-based 3D digital twins., April 2022 - The Silicon Integration Initiative (Si2) Technology Interoperability Trajectory Advisory Council (TITAN), a thought leadership forum dedicated to accelerating ecosystem collaboration with technology interoperability for silicon-to-system success, has welcomed Keysight Technologies, Inc. as a new member. Keysight's vertical market expertise in providing software-centric solutions that target radio frequency and microwave applications offers an essential perspective to TITAN as Si2 expands into systems., May 2021 - Siemens Digital Industries Software acquired Fractal Technologies, a provider of production signoff-quality IP validation solutions based in the U.S. and the Netherlands. With this acquisition, Siemens' electronic design automation (EDA) customers can more quickly and easily validate internal and external IP, and libraries used in their integrated circuit (IC) designs to improve the overall quality and speed time-to-market. Siemens plans to add Fractal's technology to the Xcelerator portfolio as part of its suite of EDA IC verification offerings., May 2021- Keysight Technologies Inc. acquired Quantum Benchmark, a leader in error diagnostics, error suppression, and performance validation software for quantum computing. Quantum Benchmark provides software solutions for improving and validating quantum computing hardware capabilities by identifying and overcoming the unique error challenges required for high-impact quantum computing.. Key drivers for this market are: Booming Automotive, IoT, and AI Sectors, Upcoming Trend of EDA Toolsets Equipped with Machine Learning Capabilities. Potential restraints include: Moore's Law about to be Proven Faulty. Notable trends are: IC Physical Design and Verification Segment to Grow Significantly.

The global market for Engineering Design Automation (EDA) in the automotive industry is projected to reach a value of USD 3.7 billion by 2033, expanding at a CAGR of 5.8% during the forecast period (2025-2033). The growth of the market is primarily driven by the increasing adoption of advanced driver-assistance systems (ADAS) and autonomous vehicles, which require sophisticated software and electronic components. Moreover, the growing demand for lightweight and fuel-efficient vehicles is also contributing to the adoption of EDA tools, as they enable engineers to optimize vehicle designs for better performance and efficiency. The key trends shaping the automotive EDA market include the increasing adoption of cloud-based EDA solutions, the growing popularity of Model-Based Design (MBD) methodologies, and the integration of EDA tools with other software applications. The adoption of cloud-based EDA solutions is gaining traction as it offers several advantages, such as improved accessibility, scalability, and cost-effectiveness. MBD methodologies are also becoming increasingly popular as they enable engineers to create virtual prototypes of vehicles, which can be used to evaluate design performance and identify potential issues early in the design process. The integration of EDA tools with other software applications, such as computer-aided design (CAD) and product lifecycle management (PLM) systems, is also enhancing the overall efficiency of the vehicle design process.

The market for EDA (Electronic Design Automation) for Semiconductor Front End Design is expected to grow significantly in the coming years, driven by the increasing demand for complex and advanced semiconductor devices. The growing adoption of artificial intelligence (AI) and machine learning (ML) techniques in EDA tools is also expected to contribute to market growth. The increasing complexity of semiconductor design processes is driving the demand for advanced EDA tools that can help engineers design and verify complex chips efficiently. The growing adoption of advanced packaging technologies, such as chiplets and 3D ICs, is also creating opportunities for EDA vendors. The market for EDA for Semiconductor Front End Design is highly competitive, with a number of established players. The key players in the market include Siemens Mentor, Synopsys, Cadence, Ansys, Agnisys, AMIQ EDA, Breker, CLIOSOFT, Semifore, Concept Engineering, MunEDA, Defacto Technologies, Empyrean Technology, Hejian Industrial Software Group Co., Ltd., Robei, Tango Intelligence, Xinhuazhang Technology Co., Ltd., HyperSilicon Co., Ltd, S2C Limited, Freetech Intelligent Systems, Arcas, and others. These players offer a range of EDA tools and services to meet the needs of semiconductor designers.

Dataset

This dataset was created by Pranjal Bahore

Contents

The complete dataset used in the analysis comprises 36 samples, each described by 11 numeric features and 1 target. The attributes considered were caspase 3/7 activity, Mitotracker red CMXRos area and intensity (3 h and 24 h incubations with both compounds), Mitosox oxidation (3 h incubation with the referred compounds) and oxidation rate, DCFDA fluorescence (3 h and 24 h incubations with either compound) and oxidation rate, and DQ BSA hydrolysis. The target of each instance corresponds to one of the 9 possible classes (4 samples per class): Control, 6.25, 12.5, 25 and 50 µM for 6-OHDA and 0.03, 0.06, 0.125 and 0.25 µM for rotenone. The dataset is balanced, it does not contain any missing values and data was standardized across features. The small number of samples prevented a full and strong statistical analysis of the results. Nevertheless, it allowed the identification of relevant hidden patterns and trends.

Exploratory data analysis, information gain, hierarchical clustering, and supervised predictive modeling were performed using Orange Data Mining version 3.25.1 [41]. Hierarchical clustering was performed using the Euclidean distance metric and weighted linkage. Cluster maps were plotted to relate the features with higher mutual information (in rows) with instances (in columns), with the color of each cell representing the normalized level of a particular feature in a specific instance. The information is grouped both in rows and in columns by a two-way hierarchical clustering method using the Euclidean distances and average linkage. Stratified cross-validation was used to train the supervised decision tree. A set of preliminary empirical experiments were performed to choose the best parameters for each algorithm, and we verified that, within moderate variations, there were no significant changes in the outcome. The following settings were adopted for the decision tree algorithm: minimum number of samples in leaves: 2; minimum number of samples required to split an internal node: 5; stop splitting when majority reaches: 95%; criterion: gain ratio. The performance of the supervised model was assessed using accuracy, precision, recall, F-measure and area under the ROC curve (AUC) metrics.

According to Cognitive Market Research, The Global EDA Market size will be USD 14.9 billion in 2023 and will grow at a compound annual growth rate (CAGR) of 10.50% from 2023 to 2030.

The demand for the EDA Market is rising due to the rise in outdoor and adventure activities.
Changing consumer lifestyle trends are higher in the EDA market.
The cat segment held the highest EDA Market revenue share in 2023.
North American EDA will continue to lead, whereas the European EDA Market will experience the most substantial growth until 2030.

Supply Chain and Risk Analysis to Provide Viable Market Output

The industry is facing supply chain and logistics disruptions. EDA tools have been instrumental in analyzing supply chain data, identifying vulnerabilities, predicting risks, and developing disruption mitigation strategies. Consumer behavior has undergone drastic changes due to blockages and restrictions. EDA helps companies analyze changing trends in buying behavior, online shopping preferences, and demand patterns, enabling organizations to adjust their marketing and sales strategies accordingly.

Health and Pharmaceutical Research to Propel Market Growth.

EDA tools have played a key role in analyzing large amounts of data related to vaccine development, drug trials, patient records and epidemiological studies. These tools have helped researchers process and interpret complex medical data, leading to advances in the development of treatments and vaccines. The pandemic has created challenges in data collection, especially in sectors affected by lockdowns or blackouts. Rapidly changing conditions and incomplete data sets make effective EDA difficult due to data quality issues. The economic uncertainty caused by the pandemic has led to budget cuts in some sectors, impacting investment in new technologies. Some organizations have limited budgets that limit their ability to adopt or update EDA tools.

Market Dynamics of the EDA

Privacy and Data Security Issues to Restrict Market Growth.

With the focus on data privacy regulations such as GDPR, CCPA, etc., organizations need to ensure compliance when handling sensitive data. These compliance requirements may limit the scope of the EDA by limiting the availability and use of certain data sets for information analysis. EDA often requires data analysts or data scientists who are skilled in statistical analysis and data visualization tools. A lack of professionals with these specialized skills can hinder an organization's ability to use EDA tools effectively, limiting adoption. Advanced EDA techniques can involve complex algorithms and statistical techniques that are difficult for non-technical users to understand. Interpreting results and deriving actionable insights from EDA results pose challenges that affect applicability to a wider audience.

Key Opportunity of market.

Growing miniaturization in various industries can be an opportunity.

With the age of highly advanced electronics, miniaturization has become a trend that enabled organizations across diverse sectors such as healthcare, consumer electronics, aerospace and defense, automotive and others to design miniature electronic devices. The devices incorporate miniaturized semiconductor components, e.g., surgical instruments and blood glucose meters in healthcare, fitness bands in wearable devices, automotive modules in the automotive sector, and intelligent baggage labels. Miniaturization has a number of advantages such as freeing space for other features and better batteries. The increased consciousness among consumers towards fitness is fueling the demand for smaller fitness devices such as smartwatches and fitness trackers. This is motivating companies to come up with innovative products with improved features, while researchers are concentrating on cost-effective and efficient product development through electronic design tools. Besides, use of portable equipment has gained immense popularity among media professionals because of the increasing demand for live reporting of different events like riots, accidents, sports, and political rallies. As a result of the inconvenience in the use of cumbersome TV production vans to access such events, demand for portable handheld equipment has risen. Such devices are simply portable and can be quickly moved to the event venue if carried in backpacks. Therefore, the need for compact devices across various indust...

Dataset

This dataset was created by Shubhangi Govil

Contents

This data consists of the incidents involving guns. Perform EDA to find out the hidden patterns. Columns: 1) Race: Race of individual 2) Date: Date of incident 3) Education 4) Police involvment

Please leave an upvote if you find this relevant. P.S. I am new and it will help immensely. :)

"Electrodermal Activity artifact correction BEnchmark" (EDABE) is a dataset for training and testing artifact recognition and correction models to automatically remove major artifacts in electrodermal activity (EDA) signals. It is the first public benchmark to compare methods.

EDABE contains a total of 74.46 h of EDA recording affected by hand and body motion artifacts from 43 subjects. It is divided into a training set with 33 subjects (56.27 h), and test set with 10 subjects (18.19 h). The data was collected using a Shimmer3 GSR+ Unit at 128 Hz.

The dataset is used to develop a fully automatic pipeline that emulates the manual correction done by the expert, providing a final clean signal. The paper that describe the pipeline is currently in a peer-review process.

Each file includes in the filename the user_id and the expert that correct the signal. In addition, the file includes the signal with the following variables:

time: timestamp of the signal. rawdata: raw data obtained by Shimmer3 GSR+ Unit. cleandata: reconstructed clean signal performed by a human expert. binarytarget: label of each sample as artefact or no artifact. signal_automatic: automatic cleaning of the signal performed by the automatic pipeline. predArtifacts: label predicted by the automatic cleaning pipeline. postProcessedPredArtifacts: label predicted by the automatic cleaning pipeline after postprocessing.

Predicted values and intervals for the double recommended dosage

The Electronic Design Automation (EDA) market for semiconductor chips is experiencing robust growth, driven by the increasing complexity of integrated circuits and the surging demand for advanced semiconductor technologies in various applications, including 5G, AI, and automotive electronics. The market size in 2025 is estimated at $12 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033. This growth is fueled by several key trends: the adoption of advanced process nodes, the increasing need for efficient design flows and verification methodologies, and the rise of specialized EDA tools for specific semiconductor applications like high-performance computing and edge AI. Key players like Synopsys, Cadence, and Mentor Graphics dominate the market, while emerging companies are focusing on niche segments and innovative solutions. However, challenges such as high development costs, the need for skilled professionals, and the complexities of integrating diverse EDA tools represent restraints to market expansion. The market is segmented based on various factors including design methodology (front-end, back-end), application (logic, memory, analog), and geographic region. Despite these restraints, the long-term outlook for the EDA market remains positive. The continued miniaturization of semiconductor chips, coupled with the growing demand for higher performance and power efficiency, will necessitate more sophisticated EDA tools. The adoption of artificial intelligence and machine learning in the EDA process is expected to significantly improve design efficiency and reduce time-to-market. Furthermore, the emergence of new semiconductor technologies, such as 3D-ICs and chiplets, will create new opportunities for EDA vendors. This will further propel market growth beyond the forecast period. The global nature of the semiconductor industry ensures continued expansion across various geographic regions, although North America and Asia are expected to maintain their dominant positions.

Abstract

The files included in this database contain the conventional and the adjusted measures of official development assistance to a set of 133 countries between 1975 and 1995. The principal component of the data set is Effective Development Assistance (EDA), an aggregate measure of aid flows combining total grants and the grant equivalents of all official loans. EDA is computed on a loan-by-loan basis to reflect the financial cost the creditor incurs in making loans on concessional terms.

Three files are included in this data set: EDA.xls, BEDA.xls and MEDA.xls. BEDA.xls aggregates annual flows from all bilateral donors. MEDA.xls aggregates annual flows from all multilateral donors. EDA.xls shows the total from all official sources, which is equal to the sum of BEDA and MEDA for the same variables. The data set is organized by recipient country and year. A more detailed description of variables and files is included in the "readme" file.

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

The Electronic Design Automation (EDA) software market is experiencing robust growth, driven by the increasing complexity of integrated circuits (ICs) and the rising demand for advanced electronics across various sectors. The market, estimated at $12 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of around 8% from 2025 to 2033, reaching approximately $20 billion by 2033. This growth is fueled by several key factors, including the proliferation of 5G technology, the expansion of the Internet of Things (IoT), and the surging adoption of Artificial Intelligence (AI) and machine learning (ML) in design processes. Furthermore, the automotive industry's shift towards electric vehicles and autonomous driving systems is significantly boosting demand for sophisticated EDA tools. Key trends include the integration of cloud-based solutions for collaborative design and improved design efficiency, the increasing use of advanced simulation and verification techniques, and the development of specialized EDA tools for specific applications like high-performance computing (HPC) and RF/microwave design. However, market growth faces certain restraints. High initial investment costs for sophisticated EDA software and the need for specialized expertise can pose challenges for smaller companies. The intense competition among established players like Synopsys, Cadence, and Siemens also creates a dynamic and competitive landscape. Nevertheless, the long-term outlook for the EDA software market remains positive, underpinned by continuous technological advancements and the ever-growing demand for complex and efficient electronic systems across various industries. The market segmentation, while not explicitly provided, likely includes categories based on software type (e.g., IC design, PCB design, verification), application (e.g., automotive, consumer electronics, aerospace), and deployment model (e.g., cloud, on-premise). The regional breakdown likely shows strong concentration in North America and Europe, with emerging markets in Asia-Pacific demonstrating significant growth potential.

The Electronic Design Automation (EDA) market is experiencing robust growth, driven by the increasing complexity of electronic systems and the rising demand for advanced functionalities across various industries. The market size in 2025 is estimated at $843.9 million. While the provided CAGR is missing, considering the strong industry trends towards miniaturization, increased integration, and the rise of AI in design processes, a conservative estimate of a 7% Compound Annual Growth Rate (CAGR) from 2025 to 2033 seems plausible. This would project a market value exceeding $1.5 billion by 2033. Key drivers include the proliferation of 5G and IoT devices requiring sophisticated EDA tools for efficient design and verification, alongside advancements in Artificial Intelligence (AI) and Machine Learning (ML) enhancing automation capabilities within the design process. Emerging trends such as system-on-chip (SoC) design, the growing adoption of cloud-based EDA solutions, and the increasing focus on verification and validation methodologies contribute to the market's expansion. However, challenges such as the high cost of EDA software and the need for skilled professionals to operate these complex tools represent potential restraints to market growth. The EDA market's segmentation reveals significant opportunities across various applications. The Aerospace & Defense, Automotive, and Telecommunications sectors are major contributors due to the increasing need for highly reliable and sophisticated electronic systems. Within the segments, Computer Aided Engineering (CAE) and IC Physical Design & Verification are expected to dominate, driven by the continuous push for higher performance and integration density in chips and systems. Key players like Cadence Design Systems, Siemens (Mentor Graphics), and Synopsys are actively shaping market trends through innovations in software and services. The geographic distribution of the market reveals strong growth across North America and Asia Pacific, fueled by technological advancements and robust manufacturing capabilities in these regions. Overall, the EDA market presents a compelling investment opportunity for companies that can provide innovative solutions to meet the increasing demands of the electronics industry.