Data Analysis is the process that supports decision-making and informs arguments in empirical studies. Descriptive statistics, Exploratory Data Analysis (EDA), and Confirmatory Data Analysis (CDA) are the approaches that compose Data Analysis (Xia & Gong; 2014). An Exploratory Data Analysis (EDA) comprises a set of statistical and data mining procedures to describe data. We ran EDA to provide statistical facts and inform conclusions. The mined facts allow attaining arguments that would influence the Systematic Literature Review of DL4SE.

The Systematic Literature Review of DL4SE requires formal statistical modeling to refine the answers for the proposed research questions and formulate new hypotheses to be addressed in the future. Hence, we introduce DL4SE-DA, a set of statistical processes and data mining pipelines that uncover hidden relationships among Deep Learning reported literature in Software Engineering. Such hidden relationships are collected and analyzed to illustrate the state-of-the-art of DL techniques employed in the software engineering context.

Our DL4SE-DA is a simplified version of the classical Knowledge Discovery in Databases, or KDD (Fayyad, et al; 1996). The KDD process extracts knowledge from a DL4SE structured database. This structured database was the product of multiple iterations of data gathering and collection from the inspected literature. The KDD involves five stages:

Selection. This stage was led by the taxonomy process explained in section xx of the paper. After collecting all the papers and creating the taxonomies, we organize the data into 35 features or attributes that you find in the repository. In fact, we manually engineered features from the DL4SE papers. Some of the features are venue, year published, type of paper, metrics, data-scale, type of tuning, learning algorithm, SE data, and so on.

Preprocessing. The preprocessing applied was transforming the features into the correct type (nominal), removing outliers (papers that do not belong to the DL4SE), and re-inspecting the papers to extract missing information produced by the normalization process. For instance, we normalize the feature “metrics” into “MRR”, “ROC or AUC”, “BLEU Score”, “Accuracy”, “Precision”, “Recall”, “F1 Measure”, and “Other Metrics”. “Other Metrics” refers to unconventional metrics found during the extraction. Similarly, the same normalization was applied to other features like “SE Data” and “Reproducibility Types”. This separation into more detailed classes contributes to a better understanding and classification of the paper by the data mining tasks or methods.

Transformation. In this stage, we omitted to use any data transformation method except for the clustering analysis. We performed a Principal Component Analysis to reduce 35 features into 2 components for visualization purposes. Furthermore, PCA also allowed us to identify the number of clusters that exhibit the maximum reduction in variance. In other words, it helped us to identify the number of clusters to be used when tuning the explainable models.

Data Mining. In this stage, we used three distinct data mining tasks: Correlation Analysis, Association Rule Learning, and Clustering. We decided that the goal of the KDD process should be oriented to uncover hidden relationships on the extracted features (Correlations and Association Rules) and to categorize the DL4SE papers for a better segmentation of the state-of-the-art (Clustering). A clear explanation is provided in the subsection “Data Mining Tasks for the SLR od DL4SE”. 5.Interpretation/Evaluation. We used the Knowledge Discover to automatically find patterns in our papers that resemble “actionable knowledge”. This actionable knowledge was generated by conducting a reasoning process on the data mining outcomes. This reasoning process produces an argument support analysis (see this link).

We used RapidMiner as our software tool to conduct the data analysis. The procedures and pipelines were published in our repository.

Overview of the most meaningful Association Rules. Rectangles are both Premises and Conclusions. An arrow connecting a Premise with a Conclusion implies that given some premise, the conclusion is associated. E.g., Given that an author used Supervised Learning, we can conclude that their approach is irreproducible with a certain Support and Confidence.

Support = Number of occurrences this statement is true divided by the amount of statements Confidence = The support of the statement divided by the number of occurrences of the premise

FANTASIAThis repository contains the data related to image descriptors and sound associated with a selection of frames of the films Fantasia and Fantasia 2000 produced by DisneyAboutThis repository contains the data used in the article Automatic composition of descriptive music: A case study of the relationship between image and sound published in the 6th International Workshop on Computational Creativity, Concept Invention, and General Intelligence (C3GI). Data structure is explained in detail in the article. AbstractHuman beings establish relationships with the environment mainly through sight and hearing. This work focuses on the concept of descriptive music, which makes use of sound resources to narrate a story. The Fantasia film, produced by Walt Disney was used in the case study. One of its musical pieces is analyzed in order to obtain the relationship between image and music. This connection is subsequently used to create a descriptive musical composition from a new video. Naive Bayes, Support Vector Machine and Random Forest are the three classifiers studied for the model induction process. After an analysis of their performance, it was concluded that Random Forest provided the best solution; the produced musical composition had a considerably high descriptive quality. DataNutcracker_data.arff: Image descriptors and the most important sound of each frame from the fragment "The Nutcracker Suite" in film Fantasia. Data stored into ARFF format.Firebird_data.arff: Image descriptors of each frame from the fragment "The Firebird" in film Fantasia 2000. Data stored into ARFF format.Firebird_midi_prediction.csv: Frame number of the fragment "The Firebird" in film Fantasia 2000 and the sound predicted by the system encoded in MIDI. Data stored into CSV format.Firebird_prediction.mp3: Audio file with the synthesizing of the prediction data for the fragment "The Firebird" of film Fantasia 2000.LicenseData is available under MIT License. To make use of the data the article must be cited.

Background: As time evolved, traditional Chinese medicine (TCM) became integrated into the global medical system as complementary treatments. Some essential TCM herbs started to play a limited role in clinical practices because of Western medication development. For example, Fuzi (Aconiti Lateralis Radix Praeparata) is a toxic but indispensable TCM herb. Fuzi was mainly used in poor circulation and life-threatening conditions by history records. However, with various Western medication options for treating critical conditions currently, how is Fuzi used clinically and its indications in modern TCM are unclear. This study aimed to evaluate Fuzi and Fuzi-based formulas in modern clinical practices using artificial intelligence and data mining methods.Methods: This nationwide descriptive study with market basket analysis used a cohort selected from the Taiwan National Health Insurance database that contained one million national representatives between 2003 and 2010 used for our analysis. Descriptive statistics were performed to demonstrate the modern clinical indications of Fuzi. Market basket analysis was calculated by the Apriori algorithm to discover the association rules between Fuzi and other TCM herbs.Results: A total of 104,281 patients using 405,837 prescriptions of Fuzi and Fuzi-based formulas were identified. TCM doctors were found to use Fuzi in pulmonary (21.5%), gastrointestinal (17.3%), and rheumatologic (11.0%) diseases, but not commonly in cardiovascular diseases (7.4%). Long-term users of Fuzi and Fuzi-based formulas often had the following comorbidities diagnosed by Western doctors: osteoarthritis (31.0%), peptic ulcers (29.5%), hypertension (19.9%), and COPD (19.7%). Patients also used concurrent medications such as H2-receptor antagonists, nonsteroidal anti-inflammatory drugs, β-blockers, calcium channel blockers, and aspirin. Through market basket analysis, for the first time, we noticed many practical Fuzi-related herbal pairs such as Fuzi–Hsihsin (Asari Radix et Rhizoma)–Dahuang (Rhei Radix et Rhizoma) for neurologic diseases and headache.Conclusion: For the first time, big data analysis was applied to uncover the modern clinical indications of Fuzi in addition to traditional use. We provided necessary evidence on the scientific use of Fuzi in current TCM practices, and the Fuzi-related herbal pairs discovered in this study are helpful to the development of new botanical drugs.

Abstract Background Regarding to oral health, little has been advanced on how to improve quality within dental care. Objective The aim of this study was to identify the demographic factors affecting the satisfaction of users of the dental public service having the value of a strategic and high consistency methodology. Method The Data Mining was used to a secondary database, contemplating 91 features, segmental in 9 demographic factors, 17 facets, and 5 dominions. Descriptive statistics were extracted to a demographic data and the satisfaction of the users by facets and dominions, being discovered as from Decision Trees and Association Rules. Results the analysis of the results showed the relation between the demographic factor 'professional occupation' and satisfaction, in all of the dominions. The occupations of general assistant and home assistant with daily wage stood out in Association Rules to represent the lower level of satisfaction compared to the facets that were worse evaluated. Also, the factor ‘health unit's name’ showed relation with most of the investigated dominions. The difference between health units was even more evident through the Association Rule. Conclusion The Data Mining allowed to identify complementary relations to the user's perception about the public oral health services quality, constituting a safe tool to support the management of Brazilian public health and the basis of future plans.

Data Analytics Market size was valued at USD 68.83 Billion in 2024 and is projected to reach USD 482.73 Billion by 2032, growing at a CAGR of 30.41% during the forecast period 2026-2032. • Rapid Digital Transformation: The global push toward digital transformation is arguably the most significant driver of the data analytics market. As companies across all industries digitize their operations, from supply chains to customer service, they generate enormous volumes of data. This data, which includes everything from website clickstreams to sensor data from industrial machinery, holds immense value.• Explosion of Big Data: The explosion of big data is a powerful force pushing the market forward. Big data is characterized by its Volume, Velocity, and Variety (the 3 V's). The sheer volume of data, from social media interactions to financial transactions, is too large for traditional databases and analytical tools.

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Life Sciences Analytics Market Size 2025-2029

The life sciences analytics market size is valued to increase USD 26.37 billion, at a CAGR of 20.6% from 2024 to 2029. Growing integration of big data with healthcare analytics will drive the life sciences analytics market.

Major Market Trends & Insights

Asia dominated the market and accounted for a 37% growth during the forecast period.
By Deployment - Cloud segment was valued at USD 7.18 billion in 2023
By End-user - Pharmaceutical companies segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 277.25 million
Market Future Opportunities: USD 26365.00 million
CAGR from 2024 to 2029 : 20.6%

Market Summary

The market represents a dynamic and continually evolving landscape, driven by the increasing integration of big data with healthcare analytics. This market encompasses core technologies such as machine learning, artificial intelligence, and data mining, which are revolutionizing the way life sciences companies analyze and interpret complex data. Applications of life sciences analytics span various sectors, including drug discovery, clinical research, and population health management. Despite its transformative potential, the high implementation cost of life sciences analytics poses a significant challenge for market growth. However, the growing emphasis on value-based medicine and the increasing regulatory focus on data-driven decision-making present substantial opportunities for market expansion. For instance, according to a recent report, the global market for life sciences analytics is projected to account for over 30% of the total healthcare analytics market by 2025. This underscores the immense potential of this market and the ongoing efforts to harness its power to drive innovation and improve patient outcomes.

What will be the Size of the Life Sciences Analytics Market during the forecast period?

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How is the Life Sciences Analytics Market Segmented ?

The life sciences analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentCloudOn-premisesEnd-userPharmaceutical companiesBiotechnology companiesOthersTypeDescriptive analyticsPredictive analyticsPrescriptive analyticsDiagnostic analyticsGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.

In the dynamic and evolving landscape of life sciences analytics, cloud-based solutions have emerged as a game-changer, revolutionizing data management and analysis in the healthcare sector. According to recent reports, the number of biotech and pharmaceutical companies adopting cloud analytics has increased by 18%, enabling real-world evidence synthesis and disease pathway mapping for improved patient care. Furthermore, the integration of genomic data, proteomic data processing, and systems biology approaches has led to a 21% rise in target identification validation and clinical outcome assessment. Data security measures are paramount in this industry, with regulatory compliance software ensuring pharmacovigilance signal detection and biostatistical modeling to maintain the highest standards. Advanced analytics techniques, such as machine learning algorithms and predictive modeling, have driven a 25% surge in drug development informatics and precision medicine insights. Toxicogenomics applications and network biology analysis have also gained significant traction, contributing to a 27% increase in drug metabolism prediction and AI-driven drug discovery. The integration of high-throughput screening data, patient stratification methods, and translational bioinformatics has further enhanced the value of cloud-based life sciences analytics. Pharmacokinetics modeling and biomarker discovery platforms have seen a 29% growth in usage, providing valuable insights for drug repurposing identification and regulatory compliance. The ongoing unfolding of these trends underscores the importance of cloud computing infrastructure, next-generation sequencing, and omics data integration in the life sciences sector.

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The Cloud segment was valued at USD 7.18 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

Asia is estimated to contribute 37% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

Overall Quality Descriptive Statistics for the Dicode Collaboration Support Service.

This digital publication, GPR 2008-1, contains geophysical data and a digital elevation model that were produced from airborne geophysical surveys conducted in 2007 for part of the western Fortymile mining district, east-central Alaska. Aeromagnetic and electromagnetic data were acquired for 250 sq miles during the helicopter-based survey. Data provided in GPR 2008-1 include processed (1) linedata ASCII database, (2) gridded files of magnetic data, a calculated vertical magnetic gradient (first vertical derivative), apparent resistivity data, and a digital elevation model, (3) vector files of data contours and flight lines, and (4) the Contractor's descriptive project report. Data are described in more detail in the "GPR2008-1Readme.pdf" and "linedata/GPR2008-1-Linedata.txt" files included on the DVD.

The Industrial Analytics market is experiencing robust growth, projected to reach $32.60 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 16.92% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of Industry 4.0 technologies, including the Internet of Things (IoT) and advanced sensors, generates massive amounts of data from industrial operations. Analyzing this data provides invaluable insights into optimizing processes, improving efficiency, reducing downtime, and enhancing predictive maintenance. Furthermore, the growing need for enhanced operational visibility and the pressure to improve resource allocation are strong drivers. The market is segmented across various deployment models (on-premises and cloud), components (software and services), analytics types (predictive, prescriptive, and descriptive), and end-user industries (construction, manufacturing, mining, transportation, and others). The cloud deployment model is witnessing faster growth due to its scalability, flexibility, and cost-effectiveness. Predictive analytics, offering the potential to anticipate equipment failures and optimize production schedules, is a particularly high-growth segment. The leading players in the Industrial Analytics market – including Cisco Systems, IBM, General Electric, Amazon Web Services, Oracle, and others – are continually innovating and expanding their offerings to meet the rising demand. Competition is fierce, driving continuous advancements in analytics capabilities and the development of user-friendly interfaces. While data security and integration challenges represent some restraints, the overall market outlook remains extremely positive. The North American market currently holds a significant share, driven by early adoption of Industry 4.0 technologies and strong investments in digital transformation initiatives. However, the Asia-Pacific region is expected to exhibit the fastest growth, fuelled by increasing industrialization and government support for digital initiatives. The continued maturation of AI and machine learning technologies will further accelerate market growth in the coming years, leading to more sophisticated and impactful analytics solutions. Recent developments include: November 2022-Fractal, a developer of artificial intelligence and advanced analytics solutions to Fortune 500 enterprises, announced the introduction of Asper.ai today. Asper.ai is a purpose-built linked AI solution for consumer goods, manufacturing, and retail, building on the company's existing AI capabilities., January 2022: Dunnhumby, the prominent player in Customer Data Science, announced a new strategic collaboration with SAP, the industry leader in business application software, to assist retailers in integrating sophisticated customer insights into their marketing and merchandising operations. The collaboration will enable businesses to make quicker, client-driven choices and provide a more individualized in-store and online shopping experience. As they prepare for the future of retail, retailers will be better able to transform customer data into unambiguous actions to streamline and improve routine business procedures.. Key drivers for this market are: Increasing Demand for Big-Data in Information Technology Sector, Rising Demand from the E-commerce Sector. Potential restraints include: Increasing Demand for Big-Data in Information Technology Sector, Rising Demand from the E-commerce Sector. Notable trends are: Manufacturing Sector to Dominate the Market Over the Forecast Period.

Descriptive statistics for selected variables.

Group descriptive statistics and network parameters.

This digital publication, GPR 2011-4, contains Fugro Airborne Surveys' analysis and interpretation of data produced from airborne geophysical surveys published by DGGS in 2011 (GPR 2011-2) of the Iditarod survey area. Fugro Airborne Surveys' frequency-domain DIGHEM V system was used for the EM data. GPR 2011-4 includes (1) Fugro's project report with interpretation and detailed EM Anomalies in tabular format, (2) Multi-channel stacked profiles in pdf format, and (3) interpretation and em anomalies are each provided as Geotiffs and maps. The document gpr2011_004_readme (.txt and .pdf) lists all files included in this publication. Other supporting files include gpr2011-4_browsegraphic.pdf, gpr2011-4_emanomaly_readme.pdf. The airborne data were acquired and processed under contract between the State of Alaska, Department of Natural Resources, Division of Geological & Geophysical Surveys (DGGS), and Fugro GeoServices, Inc. Fugro Airborne Surveys, the subcontractor, acquired and processed the data in 2010 and 2011.

Rejestr Obszarów Górniczych (ROG) stanowi szczegółową ewidencję obszarów i terenów górniczych wyznaczonych w Polsce. Prowadzony jest w księgach rejestrowych oraz w aktualizowanej na bieżąco, ogólnodostępnej bazie danych MIDAS (podsystem Rejestr Obszarów Górniczych). Informacje dotyczące obszarów i terenów górniczych gromadzone są w bazie ROG w postaci danych opisowych (nazwa, powierzchnia, położenie administracyjne, status – aktualny lub zniesiony, numer w rejestrze, nazwa złoża i rodzaj kopaliny) oraz danych przestrzennych (kontury obszarów i terenów górniczych).

Dodatkowo, baza ROG udostępnia szczegóły decyzji ustanawiających, zmieniających i likwidujących obszary i tereny górnicze (data wydania, wydawca, termin ważności) wraz z informacjami o przedsiębiorcach eksploatujących kopaliny w granicach wyznaczonych obszarów górniczych.