Vedant, S., Silber, E. A. (2025) An Exploratory Data Mining Study of Re-entry Events: Foundations for Multi-Modality Sensing and Data Fusion, Sandia National Laboratories report

This resource collects teaching materials that are originally created for the in-person course 'GEOSC/GEOG 497 – Data Mining in Environmental Sciences' at Penn State University (co-taught by Tao Wen, Susan Brantley, and Alan Taylor) and then refined/revised by Tao Wen to be used in the online teaching module 'Data Science in Earth and Environmental Sciences' hosted on the NSF-sponsored HydroLearn platform.

This resource includes both R Notebooks and Python Jupyter Notebooks to teach the basics of R and Python coding, data analysis and data visualization, as well as building machine learning models in both programming languages by using authentic research data and questions. All of these R/Python scripts can be executed either on the CUAHSI JupyterHub or on your local machine.

This resource is shared under the CC-BY license. Please contact the creator Tao Wen at Syracuse University (twen08@syr.edu) for any questions you have about this resource. If you identify any errors in the files, please contact the creator.

This zip file contains data used to create figures and tables, describing the results of the paper "Reconstruction of magnetospheric storm-time dynamics using cylindrical basis functions and multi-mission data mining", by N. A. Tsyganenko, V. A. Andreeva, and M. I. Sitnov.

Additional file 2. Python code use to query the pipeline, build and compare the classifiers.

Additional file 1. A copy of the generic pipeline (github.com/mcdougallab/pipeline) that has been adapted for COVID-19 immune signatures.

LORE PMKB-CV

• Knowledge graph (LLM-ORE)
  • 70M relations between 8k Diseases (MeSH) and 18k Genes (NCBI, human protein coding) curated by LLMs reading PubMed
  • Data format: (D_id, G_id, PMID, relation) csv file
• Semantic embedding (LLM-EMB)
  • 2.5M DG vectors created by LLMs reading the knowledge graph
  • Data format: (D_id, G_id, vector) pkl file
• DG pathogenicity scores (ML-Ranker)
  • 3.1M DG scores predicted by pretrained models
  • Features, training annotations, pretrained models are also provided
• Curated key semantics taxonomy
  • A manually curated taxonomy of 105 semantic tags about DG pathogenicity in the knowledge graph
  • Use the github LORE Key-Semantics module to use the taxonomy as tags and add them to the knowledge graph

Source project

• https://github.com/ailabstw/LORE
  • Tools for running LLM-ORE relation extraction, LLM-EMB embedding, ML-Ranker prediction, Key-Semantics curation on custom datasets
• https://doi.org/10.1093/bib/bbaf070
  • Research article describing the LORE framework, analyses, experiments, and details of the PMKB-CV dataset

Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas was 5.80389 % Chg. at Annual Rate in June of 2025, according to the United States Federal Reserve. Historically, Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas reached a record high of 77.24099 in July of 1992 and a record low of -46.80217 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas - last updated from the United States Federal Reserve on August of 2025.

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Data Science Platform Market Size 2025-2029

The data science platform market size is forecast to increase by USD 763.9 million, at a CAGR of 40.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This fusion enables organizations to derive deeper insights from their data, fueling business innovation and decision-making. Another trend shaping the market is the emergence of containerization and microservices in data science platforms. This approach offers enhanced flexibility, scalability, and efficiency, making it an attractive choice for businesses seeking to streamline their data science operations. However, the market also faces challenges. Data privacy and security remain critical concerns, with the increasing volume and complexity of data posing significant risks. Ensuring robust data security and privacy measures is essential for companies to maintain customer trust and comply with regulatory requirements. Additionally, managing the complexity of data science platforms and ensuring seamless integration with existing systems can be a daunting task, requiring significant investment in resources and expertise. Companies must navigate these challenges effectively to capitalize on the market's opportunities and stay competitive in the rapidly evolving data landscape.

What will be the Size of the Data Science Platform Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for advanced analytics and artificial intelligence solutions across various sectors. Real-time analytics and classification models are at the forefront of this evolution, with APIs integrations enabling seamless implementation. Deep learning and model deployment are crucial components, powering applications such as fraud detection and customer segmentation. Data science platforms provide essential tools for data cleaning and data transformation, ensuring data integrity for big data analytics. Feature engineering and data visualization facilitate model training and evaluation, while data security and data governance ensure data privacy and compliance. Machine learning algorithms, including regression models and clustering models, are integral to predictive modeling and anomaly detection. Statistical analysis and time series analysis provide valuable insights, while ETL processes streamline data integration. Cloud computing enables scalability and cost savings, while risk management and algorithm selection optimize model performance. Natural language processing and sentiment analysis offer new opportunities for data storytelling and computer vision. Supply chain optimization and recommendation engines are among the latest applications of data science platforms, demonstrating their versatility and continuous value proposition. Data mining and data warehousing provide the foundation for these advanced analytics capabilities.

How is this Data Science Platform Industry segmented?

The data science platform 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. DeploymentOn-premisesCloudComponentPlatformServicesEnd-userBFSIRetail and e-commerceManufacturingMedia and entertainmentOthersSectorLarge enterprisesSMEsApplicationData PreparationData VisualizationMachine LearningPredictive AnalyticsData GovernanceOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the dynamic the market, businesses increasingly adopt solutions to gain real-time insights from their data, enabling them to make informed decisions. Classification models and deep learning algorithms are integral parts of these platforms, providing capabilities for fraud detection, customer segmentation, and predictive modeling. API integrations facilitate seamless data exchange between systems, while data security measures ensure the protection of valuable business information. Big data analytics and feature engineering are essential for deriving meaningful insights from vast datasets. Data transformation, data mining, and statistical analysis are crucial processes in data preparation and discovery. Machine learning models, including regression and clustering, are employed for model training and evaluation. Time series analysis and natural language processing are valuable tools for understanding trends and customer sen

A suction bucket foundation is a new type offering high construction efficiency, precise positioning, cost-effectiveness, and environmental friendliness. It has been extensively employed in marine resource development, particularly in offshore wind power and oil and gas extraction. It usually involves multiple suction bucket conduit rack platforms during offshore construction projects. Accurately predicting the sinking penetration resistance and determining the suction value is crucial during the construction of the suction bucket foundation, as it ensures the safe sinking of the platform foundation to the designated depth. This paper examines the feasibility of the suction bucket foundation’s sinking, sinking penetration resistance, suction value, and self-weight penetration depth, using the offshore wind farm guiding frame platform foundation project in Yangjiang, Guangdong Province, as a basis of analysis. The measured data is analyzed using the API specification static equilibrium analysis method, ABAQUS finite element analysis, and data mining techniques. The suction drum base platform’s sinking process was monitored for negative pressure and penetration resistance. These observed values were compared to theoretical and finite element calculations. Results demonstrated that the API specification’s theoretical calculations and finite element analyses effectively predict sinking penetration resistance, the suction force value, and the penetration depth for self-gravitational penetration. On-site engineering data fit these theoretical calculations, and finite element analyses well. The findings from this study have enriched the engineering application database of the suction drum foundation, providing a valuable reference for the design and construction of similar projects and establishing the groundwork for further promotion and application.

Flavonoid represents a significant class of secondary metabolites in Pu-erh tea with benefits to human health. For a rapid and complete discovery of such compounds, we established a data mining workflow that integrates software MS-DIAL, MS-FINDER, and molecular networking analysis. As a result, 181 flavonoids were tentatively annotated including 22 first found in Pu-erh tea, and two of them were potentially new molecules. The dynamic alteration of these flavonoids during Pu-erh fermentation was further investigated. They all showed a trend of first increasing and then decreasing. Moreover, statistical analysis showed that the first to third pile turnings of the fermentation process had a greater impact on the changes of flavonoids. Partial metabolic pathways were proposed. This study provides a quick and automatic strategy for flavonoid profiling. The temporal dimension of flavonoids during fermentation may serve as a theoretical basis for Pu-erh tea manufacturing technology and study on substance foundation.

Graph and download economic data for Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas (TX15238100A674FRBDAL) from 1991 to 2024 about contractors, logging, mining, payrolls, buildings, construction, TX, employment, rate, and USA.

Graph and download economic data for Change in Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas (TX15238100MC175FRBDAL) from Feb 1990 to Jul 2025 about contractors, logging, mining, payrolls, buildings, construction, TX, employment, and USA.

Change in Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas was 0.44330 Thous. of Persons in June of 2025, according to the United States Federal Reserve. Historically, Change in Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas reached a record high of 3.13976 in May of 2020 and a record low of -4.54229 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Change in Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas - last updated from the United States Federal Reserve on August of 2025.

This dataset is about companies. It has 7,196 rows and is filtered where the industry is Metals & Mining. It features 5 columns: city, country, industry, and foundation year.

Land cover is the visible, biophysical cover on the Earth’s surface including trees, shrubs, grasses, soils, exposed rocks and water bodies, as well as anthropogenic elements such as plantations, crops and built environments. Land cover changes for many reasons, including seasonal weather, severe weather events such as cyclones, floods and fires, and human activities such as mining, agriculture and urbanisation. Remote sensing data recorded over a period of time allows the observation of land cover dynamics. Classifying these responses provides a robust and repeatable way of characterising land cover types. These complement on ground survey where available.

United States Imports: NAICS: Mining data was reported at 12.156 USD bn in Sep 2018. This records a decrease from the previous number of 15.129 USD bn for Aug 2018. United States Imports: NAICS: Mining data is updated monthly, averaging 14.201 USD bn from Jan 2000 (Median) to Sep 2018, with 225 observations. The data reached an all-time high of 39.916 USD bn in Jul 2008 and a record low of 4.075 USD bn in Feb 2002. United States Imports: NAICS: Mining data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.JA008: Trade Statistics: Census Basis: NAICS: Imports.

Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas was 94.51289 Thous. of Persons in June of 2025, according to the United States Federal Reserve. Historically, Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas reached a record high of 96.49045 in August of 2023 and a record low of 33.59765 in January of 1991. Trading Economics provides the current actual value, an historical data chart and related indicators for Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Mining, Logging, and Construction: Foundation, Structure, and Building Exterior Contractors Payroll Employment in Texas (DISCONTINUED) (TX15238100A175FRBDAL) from 1990 to 2016 about contractors, logging, payrolls, mining, buildings, construction, TX, employment, and USA.

United States Exports: NAICS: Mining: Minerals & Ores data was reported at 1.639 USD bn in May 2018. This records a decrease from the previous number of 1.791 USD bn for Apr 2018. United States Exports: NAICS: Mining: Minerals & Ores data is updated monthly, averaging 1.003 USD bn from Jan 2000 (Median) to May 2018, with 221 observations. The data reached an all-time high of 2.541 USD bn in Aug 2011 and a record low of 224.600 USD mn in Feb 2003. United States Exports: NAICS: Mining: Minerals & Ores data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA007: Trade Statistics: Census Basis: NAICS: Exports.

United States Imports: NAICS: Mining: Oil & Gas data was reported at 11.572 USD bn in Sep 2018. This records a decrease from the previous number of 14.515 USD bn for Aug 2018. United States Imports: NAICS: Mining: Oil & Gas data is updated monthly, averaging 13.666 USD bn from Jan 2000 (Median) to Sep 2018, with 225 observations. The data reached an all-time high of 38.740 USD bn in Jul 2008 and a record low of 3.856 USD bn in Feb 2002. United States Imports: NAICS: Mining: Oil & Gas data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.JA008: Trade Statistics: Census Basis: NAICS: Imports.