This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

Data Mining Tools Market Outlook

According to our latest research, the global Data Mining Tools market size reached USD 1.93 billion in 2024, reflecting robust industry momentum. The market is expected to grow at a CAGR of 12.7% from 2025 to 2033, reaching a projected value of USD 5.69 billion by 2033. This growth is primarily driven by the increasing adoption of advanced analytics across diverse industries, rapid digital transformation, and the necessity for actionable insights from massive data volumes.

One of the pivotal growth factors propelling the Data Mining Tools market is the exponential rise in data generation, particularly through digital channels, IoT devices, and enterprise applications. Organizations across sectors are leveraging data mining tools to extract meaningful patterns, trends, and correlations from structured and unstructured data. The need for improved decision-making, operational efficiency, and competitive advantage has made data mining an essential component of modern business strategies. Furthermore, advancements in artificial intelligence and machine learning are enhancing the capabilities of these tools, enabling predictive analytics, anomaly detection, and automation of complex analytical tasks, which further fuels market expansion.

Another significant driver is the growing demand for customer-centric solutions in industries such as retail, BFSI, and healthcare. Data mining tools are increasingly being used for customer relationship management, targeted marketing, fraud detection, and risk management. By analyzing customer behavior and preferences, organizations can personalize their offerings, optimize marketing campaigns, and mitigate risks. The integration of data mining tools with cloud platforms and big data technologies has also simplified deployment and scalability, making these solutions accessible to small and medium-sized enterprises (SMEs) as well as large organizations. This democratization of advanced analytics is creating new growth avenues for vendors and service providers.

The regulatory landscape and the increasing emphasis on data privacy and security are also shaping the development and adoption of Data Mining Tools. Compliance with frameworks such as GDPR, HIPAA, and CCPA necessitates robust data governance and transparent analytics processes. Vendors are responding by incorporating features like data masking, encryption, and audit trails into their solutions, thereby enhancing trust and adoption among regulated industries. Additionally, the emergence of industry-specific data mining applications, such as fraud detection in BFSI and predictive diagnostics in healthcare, is expanding the addressable market and fostering innovation.

From a regional perspective, North America currently dominates the Data Mining Tools market owing to the early adoption of advanced analytics, strong presence of leading technology vendors, and high investments in digital transformation. However, the Asia Pacific region is emerging as a lucrative market, driven by rapid industrialization, expansion of IT infrastructure, and growing awareness of data-driven decision-making in countries like China, India, and Japan. Europe, with its focus on data privacy and digital innovation, also represents a significant market share, while Latin America and the Middle East & Africa are witnessing steady growth as organizations in these regions modernize their operations and adopt cloud-based analytics solutions.

Component Analysis

The Component segment of the Data Mining Tools market is bifurcated into Software and Services. Software remains the dominant segment, accounting for the majority of the market share in 2024. This dominance is attributed to the continuous evolution of data mining algorithms, the proliferation of user-friendly graphical interfaces, and the integration of advanced analytics capabilities such as machine learning, artificial intelligence, and natural language pro

Question Paper Solutions of chapter Validation strategies of Data Mining, 6th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

Data Mining Tools Market size was valued at USD 915.42 Million in 2024 and is projected to reach USD 2171.21 Million by 2032, growing at a CAGR of 11.40% from 2026 to 2032.• Big Data Explosion: Exponential growth in data generation from IoT devices, social media, mobile applications, and digital transactions is creating massive datasets requiring advanced mining tools for analysis. Organizations need sophisticated solutions to extract meaningful insights from structured and unstructured data sources for competitive advantage.• Digital Transformation Initiatives: Accelerating digital transformation across industries is driving demand for data mining tools that enable data-driven decision making and business intelligence. Companies are investing in analytics capabilities to optimize operations, improve customer experiences, and develop new revenue streams through data monetization strategies.

Data Mining Software Market Outlook

According to our latest research, the global Data Mining Software market size in 2024 stands at USD 12.7 billion. This market is experiencing robust expansion, driven by the growing demand for actionable insights across industries, and is expected to reach USD 38.1 billion by 2033, registering a remarkable CAGR of 13.1% during the forecast period. The proliferation of big data, increasing adoption of artificial intelligence, and the need for advanced analytics are the primary growth factors propelling the market forward.

The accelerating digitization across sectors is a key factor fueling the growth of the Data Mining Software market. Organizations are generating and collecting vast amounts of data at unprecedented rates, requiring sophisticated tools to extract meaningful patterns and actionable intelligence. The rise of Internet of Things (IoT) devices, social media platforms, and connected infrastructure has further intensified the need for robust data mining solutions. Businesses are leveraging data mining software to enhance decision-making, optimize operations, and gain a competitive edge. The integration of machine learning and artificial intelligence algorithms into data mining tools is enabling organizations to automate complex analytical tasks, uncover hidden trends, and predict future outcomes with greater accuracy. As enterprises continue to recognize the value of data-driven strategies, the demand for advanced data mining software is poised for sustained growth.

Another significant factor contributing to the market’s expansion is the increasing regulatory pressure on data management and security. Regulatory frameworks such as GDPR, HIPAA, and CCPA are compelling organizations to adopt comprehensive data management practices, which include advanced data mining software for compliance monitoring and risk assessment. These regulations are driving investments in software that can efficiently process, analyze, and secure large data sets while ensuring transparency and accountability. Additionally, the surge in cyber threats and data breaches has heightened the importance of robust analytics solutions for anomaly detection, fraud prevention, and real-time threat intelligence. As a result, sectors such as BFSI, healthcare, and government are prioritizing the deployment of data mining solutions to safeguard sensitive information and maintain regulatory compliance.

The growing emphasis on customer-centric strategies is also playing a pivotal role in the expansion of the Data Mining Software market. Organizations across retail, telecommunications, and financial services are utilizing data mining tools to personalize customer experiences, enhance marketing campaigns, and improve customer retention rates. By analyzing customer behavior, preferences, and feedback, businesses can tailor their offerings and communication strategies to meet evolving consumer demands. The ability to derive granular insights from vast customer data sets enables companies to innovate rapidly and stay ahead of market trends. Furthermore, the integration of data mining with customer relationship management (CRM) and enterprise resource planning (ERP) systems is streamlining business processes and fostering a culture of data-driven decision-making.

From a regional perspective, North America currently dominates the Data Mining Software market, supported by a mature technological infrastructure, high adoption of cloud-based analytics, and a strong presence of leading software vendors. Europe follows closely, driven by stringent data privacy regulations and increasing investments in digital transformation initiatives. The Asia Pacific region is emerging as a high-growth market, fueled by rapid industrialization, expanding IT sectors, and the proliferation of digital services across economies such as China, India, and Japan. Latin America and the Middle East & Africa are also witnessing increasing adoption, particularly in sectors like banking, telecommunications, and government, as organizations seek to harness the power of data for strategic growth.

This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

Retrofitting is an essential element of any comprehensive strategy for improving residential energy efficiency. The residential retrofit market is still developing, and program managers must develop innovative strategies to increase uptake and promote economies of scale. Residential retrofitting remains a challenging proposition to sell to homeowners, because awareness levels are low and financial incentives are lacking. The U.S. Department of Energy's Building America research team, Alliance for Residential Building Innovation (ARBI), implemented a project to increase residential retrofits in Davis, California. The project used a neighborhood-focused strategy for implementation and a low-cost retrofit program that focused on upgraded attic insulation and duct sealing. ARBI worked with a community partner, the not-for-profit Cool Davis Initiative, as well as selected area contractors to implement a strategy that sought to capitalize on the strong local expertise of partners and the unique aspects of the Davis, California, community. Working with community partners also allowed ARBI to collect and analyze data about effective messaging tactics for community-based retrofit programs. ARBI expected this project, called Retrofit Your Attic, to achieve higher uptake than other retrofit projects, because it emphasized a low-cost, one-measure retrofit program. However, this was not the case. The program used a strategy that focused on attics-including air sealing, duct sealing, and attic insulation-as a low-cost entry for homeowners to complete home retrofits. The price was kept below $4,000 after incentives; both contractors in the program offered the same price. The program completed only five retrofits. Interestingly, none of those homeowners used the one-measure strategy. All five homeowners were concerned about cost, comfort, and energy savings and included additional measures in their retrofits. The low-cost, one-measure strategy did not increase the uptake among homeowners, even in a well-educated, affluent community such as Davis. This project has two primary components. One is to complete attic retrofits on a community scale in the hot-dry climate on Davis, CA. Sufficient data will be collected on these projects to include them in the BAFDR. Additionally, ARBI is working with contractors to obtain building and utility data from a large set of retrofit projects in CA (hot-dry). These projects are to be uploaded into the BAFDR.

According to Cognitive Market Research, the global Data Mining Software market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Focus on Customer Satisfaction to Drive Data Mining Software Market Growth

In today’s hyper-competitive and digitally connected marketplace, customer satisfaction has emerged as a critical factor for business sustainability and growth. The growing focus on enhancing customer satisfaction is proving to be a significant driver in the expansion of the data mining software market. Organizations are increasingly leveraging data mining tools to sift through vast volumes of customer data—ranging from transactional records and website activity to social media engagement and call center logs—to uncover insights that directly influence customer experience strategies. Data mining software empowers companies to analyze customer behavior patterns, identify dissatisfaction triggers, and predict future preferences. Through techniques such as classification, clustering, and association rule mining, businesses can break down large datasets to understand what customers want, what they are likely to purchase next, and how they feel about the brand. These insights not only help in refining customer service but also in shaping product development, pricing strategies, and promotional campaigns. For instance, Netflix uses data mining to recommend personalized content by analyzing a user's viewing history, ratings, and preferences. This has led to increased user engagement and retention, highlighting how a deep understanding of customer preferences—made possible through data mining—can translate into competitive advantage. Moreover, companies are increasingly using these tools to create highly targeted and customer-specific marketing campaigns. By mining data from e-commerce transactions, browsing behavior, and demographic profiles, brands can tailor their offerings and communications to suit individual customer segments. For Instance Amazon continuously mines customer purchasing and browsing data to deliver personalized product recommendations, tailored promotions, and timely follow-ups. This not only enhances customer satisfaction but also significantly boosts conversion rates and average order value. According to a report by McKinsey, personalization can deliver five to eight times the ROI on marketing spend and lift sales by 10% or more—a powerful incentive for companies to adopt data mining software as part of their customer experience toolkit. (Source: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/personalizing-at-scale#/) The utility of data mining tools extends beyond e-commerce and streaming platforms. In the banking and financial services industry, for example, institutions use data mining to analyze customer feedback, call center transcripts, and usage data to detect pain points and improve service delivery. Bank of America, for instance, utilizes data mining and predictive analytics to monitor customer interactions and provide proactive service suggestions or fraud alerts, significantly improving user satisfaction and trust. (Source: https://futuredigitalfinance.wbresearch.com/blog/bank-of-americas-erica-client-interactions-future-ai-in-banking) Similarly, telecom companies like Vodafone use data mining to understand customer churn behavior and implement retention strategies based on insights drawn from service usage patterns and complaint histories. In addition to p...

These are artificially made beginner data mining datasets for learning purposes.

Case study:

• FEELS LIKE HOME is an interior design company, which has about 100 000 registered customers and provide services for more than 200 000 clients annually.
• The range of the products can be divided in 5 major classes: Decor accessories, Furniture, Textiles, Lighting and Art with an option to purchase Limited Edition versions for an extra charge. These goods can be distributed by 3 channels: Physical stores, yearly catalogs and the companies’ website.
• FEELS LIKE HOME has been doing a great job during recent years, achieving decent profits and revenues, but the future remains volatile. In order to solve the problem of instability the company is planning to launch new marketing program, especially to improve the accuracy of marketing campaigns.

The aim of FeelsLikeHome_Campaign dataset is to create project is in which you build a predictive model (using a sample of 2500 clients’ data) forecasting the highest profit from the next marketing campaign, which will indicate the customers who will be the most likely to accept the offer.

The aim of FeelsLikeHome_Cluster dataset is to create project in which you split company’s customer base on homogenous clusters (using 5000 clients’ data) and propose draft marketing strategies for these groups based on customer behavior and information about their profile.

FeelsLikeHome_Score dataset can be used to calculate total profit from marketing campaign and for producing a list of sorted customers by the probability of the dependent variable in predictive model problem.

Performance of the “Training Data Set” using the classification algorithm J48.

In a large network of computers, wireless sensors, or mobile devices, each of the components (hence, peers) has some data about the global status of the system. Many of the functions of the system, such as routing decisions, search strategies, data cleansing, and the assignment of mutual trust, depend on the global status. Therefore, it is essential that the system be able to detect, and react to, changes in its global status. Computing global predicates in such systems is usually very costly. Mainly because of their scale, and in some cases (e.g., sensor networks) also because of the high cost of communication. The cost further increases when the data changes rapidly (due to state changes, node failure, etc.) and computation has to follow these changes. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient local algorithm which detect when the L2 norm of the average data surpasses a threshold. Then, we use this algorithm as a feedback loop for the monitoring of complex predicates on the data – such as the data’s k-means clustering. The efficiency of the L2 algorithm guarantees that so long as the clustering results represent the data (i.e., the data is stationary) few resources are required. When the data undergoes an epoch change – a change in the underlying distribution – and the model no longer represents it, the feedback loop indicates this and the model is rebuilt. Furthermore, the existence of a feedback loop allows using approximate and “best-effort ” methods for constructing the model; if an ill-fit model is built the feedback loop would indicate so, and the model would be rebuilt.

In a large network of computers, wireless sensors, or mobile devices, each of the components (hence, peers) has some data about the global status of the system. Many of the functions of the system, such as routing decisions, search strategies, data cleansing, and the assignment of mutual trust, depend on the global status. Therefore, it is essential that the system be able to detect, and react to, changes in its global status. Computing global predicates in such systems is usually very costly. Mainly because of their scale, and in some cases (e.g., sensor networks) also because of the high cost of communication. The cost further increases when the data changes rapidly (due to state changes, node failure, etc.) and computation has to follow these changes. In this paper we describe a two step approach for dealing with these costs. First, we describe a highly efficient local algorithm which detect when the L2 norm of the average data surpasses a threshold. Then, we use this algorithm as a feedback loop for the monitoring of complex predicates on the data – such as the data’s k-means clustering. The efficiency of the L2 algorithm guarantees that so long as the clustering results represent the data (i.e., the data is stationary) few resources are required. When the data undergoes an epoch change – a change in the underlying distribution – and the model no longer represents it, the feedback loop indicates this and the model is rebuilt. Furthermore, the existence of a feedback loop allows using approximate and “best-effort ” methods for constructing the model; if an ill-fit model is built the feedback loop would indicate so, and the model would be rebuilt.

The technological development in the new economic era has brought challenges to enterprises. Enterprises need to use massive and effective consumption information to provide customers with high-quality customized services. Big data technology has strong mining ability. The relevant theories of computer data mining technology are summarized to optimize the marketing strategy of enterprises. The application of data mining in precision marketing services is analyzed. Extreme Gradient Boosting (XGBoost) has shown strong advantages in machine learning algorithms. In order to help enterprises to analyze customer data quickly and accurately, the characteristics of XGBoost feedback are used to reverse the main factors that can affect customer activation cards, and effective analysis is carried out for these factors. The data obtained from the analysis points out the direction of effective marketing for potential customers to be activated. Finally, the performance of XGBoost is compared with the other three methods. The characteristics that affect the top 7 prediction results are tested for differences. The results show that: (1) the accuracy and recall rate of the proposed model are higher than other algorithms, and the performance is the best. (2) The significance p values of the features included in the test are all less than 0.001. The data shows that there is a very significant difference between the proposed features and the results of activation or not. The contributions of this paper are mainly reflected in two aspects. 1. Four precision marketing strategies based on big data mining are designed to provide scientific support for enterprise decision-making. 2. The improvement of the connection rate and stickiness between enterprises and customers has played a huge driving role in overall customer marketing.

The Enterprise Data Warehouse (EDW) market is experiencing robust growth, projected to reach $14.40 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 30.08% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing volume and variety of data generated by businesses necessitate robust solutions for storage, processing, and analysis. Cloud-based deployments are gaining significant traction, offering scalability, cost-effectiveness, and accessibility. Furthermore, the growing adoption of advanced analytics techniques like machine learning and AI is driving demand for sophisticated EDW solutions capable of handling complex data sets and delivering actionable insights. The market is segmented by product type (information and analytical processing, data mining) and deployment (cloud-based, on-premises). While on-premises solutions still hold a market share, the cloud segment is witnessing significantly faster growth due to its inherent advantages. Key players like Snowflake, Amazon, and Microsoft are leading the charge, leveraging their existing cloud infrastructure and expertise in data management to capture market share. Competitive strategies focus on innovation in areas like data virtualization, enhanced security features, and integration with other enterprise applications. Industry risks include data security breaches, the complexity of data integration, and the need for skilled professionals to manage and utilize EDW systems effectively. The North American market currently dominates, followed by Europe and APAC regions, each showing strong growth potential. The forecast period (2025-2033) anticipates continued market expansion driven by ongoing digital transformation initiatives across various industries. The increasing adoption of big data analytics and the growing need for real-time business intelligence will further fuel market growth. Companies are investing heavily in upgrading their EDW infrastructure and adopting advanced analytical capabilities to gain a competitive edge. The competitive landscape is dynamic, with both established players and emerging startups vying for market share. Strategic partnerships, mergers, and acquisitions are expected to reshape the market landscape over the forecast period. The continued development of innovative solutions addressing the evolving needs of businesses will be crucial for success in this rapidly growing market. Regions like APAC show immense growth potential due to increasing digitization and data generation across emerging economies.

The global market for Business Analysis Tools and Software is experiencing robust growth, driven by the increasing need for data-driven decision-making across diverse industries. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and accessibility, the growing prevalence of big data requiring sophisticated analytical capabilities, and the increasing demand for improved operational efficiency and enhanced business intelligence. The competitive landscape is highly fragmented, with a mix of established players like IBM, SAP, and Oracle, alongside emerging innovative companies like Alteryx and ThoughtSpot. This competition fosters innovation and drives the development of more advanced features, such as predictive analytics, AI-powered insights, and integrated data visualization dashboards. The market is segmented by deployment (cloud, on-premise), functionality (data mining, predictive modeling, reporting & analytics), and industry verticals (finance, healthcare, retail). The overall market demonstrates a significant opportunity for growth, particularly in regions with burgeoning digital economies and a growing emphasis on data-driven strategies. While precise market figures are unavailable, based on observed industry trends and the involvement of major technology corporations, a reasonable estimate for the 2025 market size could be in the range of $50 billion. Assuming a conservative Compound Annual Growth Rate (CAGR) of 12% over the forecast period (2025-2033), the market is projected to exceed $150 billion by 2033. However, this projection is subject to fluctuations based on economic conditions, technological advancements, and evolving regulatory landscapes. Key restraints include the high initial investment costs associated with implementing sophisticated business analysis solutions and the need for skilled professionals to effectively manage and interpret the generated insights. The successful adoption of these tools will depend heavily on effective integration with existing IT infrastructure and the successful training and upskilling of the workforce.

Discover the booming open-source tools market! This comprehensive analysis reveals key trends, drivers, and restraints impacting growth from 2025-2033, covering applications like machine learning & data science across major regions. Explore market size, CAGR projections, and leading companies shaping the future of open-source technology.

Leveraging Data Mining, Active Learning, and Domain Adaptation for Efficient Discovery of Advanced Oxygen Evolution Electrocatalysts

This repository supports our Science Advances paper, “Leveraging Data Mining, Active Learning, and Domain Adaptation for Efficient Discovery of Advanced Oxygen Evolution Electrocatalysts,” (previous preprint on arXiv https://arxiv.org/abs/2407.04877) and serves as a roadmap to our data and code. The repository is organized into two main parts: Experimental Records, Raw Data, and Figures (released under CC0), Supplementay Notes (CC-BY) and the Machine Learning Scripts (released under the MIT License). All materials are available on Dryad (DOI: 10.5061/dryad.nk98sf83g) and GitHub (https://github.com/ruiding-uchicago/DASH).

Experimental Records, Raw Data, and Supplementary Figures

Overview: This repository contains experimental data—including raw electrochemica...

MS971: Data sharing agreement

Web multi omics knowledgebase based upon public, manually curated transcriptomic and cistromic datasets involving genetic and small molecule manipulations of cellular receptors, enzymes and transcription factors. Integrated omics knowledgebase for mammalian cellular signaling pathways. Web browser interface was designed to accommodate numerous routine data mining strategies. Datasets are biocurated versions of publically archived datasets and are formatted according to recommendations of the FORCE11 Joint Declaration on Data Citation Principles73, and are made available under Creative Commons CC 3.0 BY license. Original datasets are available.