This lesson helps students know some of the options for how to graph grouped continuous data (such as those involved in doing a t-test or ANOVA) and how to choose the best option.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

Construction of regulatory networks using cross-sectional expression profiling of genes is desired, but challenging. The Directed Acyclic Graph (DAG) provides a general framework to infer causal effects from observational data. However, most existing DAG methods assume that all nodes follow the same type of distribution, which prohibit a joint modeling of continuous gene expression and categorical variables. We present a new mixed DAG (mDAG) algorithm to infer the regulatory pathway from mixed observational data containing both continuous variables (e.g. expression of genes) and categorical variables (e.g. categorical phenotypes or single nucleotide polymorphisms). Our method can identify upstream causal factors and downstream effectors closely linked to a variable and generate hypotheses for causal direction of regulatory pathways. We propose a new permutation method to test the conditional independence of variables of mixed types, which is the key for mDAG. We also utilize an L1 regularization in mDAG to ensure it can recover a large sparse DAG with limited sample size. We demonstrate through extensive simulations that mDAG outperforms two well-known methods in recovering the true underlying DAG. We apply mDAG to a cross-sectional immunological study of Chlamydia trachomatis infection and successfully infer the regularity network of cytokines. We also apply mDAG to a large cohort study, generating sensible mechanistic hypotheses underlying plasma adiponectin level. The R package mDAG is publicly available from CRAN at https://CRAN.R-project.org/package=mDAG.

Description. Real-world data used to test our implementation of the average continuous Straightness, and associated results.

Source code. The source code is available on GitHub: https://github.com/CompNet/SpatialMeasures

Citation. If you use these data, please cite the following article:

V. Labatut, “Continuous Average Straightness in Spatial Graphs,” Journal of Complex Networks, 6(2):269–296, 2018. ⟨hal-01571212⟩ DOI: 10.1093/comnet/cnx033

@Article{Labatut2018, author = {Labatut, Vincent}, title = {Continuous Average Straightness in Spatial Graphs}, journal = {Journal of Complex Networks}, year = {2018}, volume = {6}, number = {2}, pages = {269-296}, doi = {10.1093/comnet/cnx033},}

United States - Producer Price Index by Commodity for Pulp, Paper, and Allied Products: Custom Continuous Business Forms was 328.90000 Index Dec 1983=100 in September of 2018, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity for Pulp, Paper, and Allied Products: Custom Continuous Business Forms reached a record high of 329.80000 in July of 2018 and a record low of 100.00000 in December of 1983. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity for Pulp, Paper, and Allied Products: Custom Continuous Business Forms - last updated from the United States Federal Reserve on November of 2025.

Distance-geometric graph representation for 3D graphs, utilizing continuous-filter convolutional layers with filter-generating networks.

The dataset for "A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms" can be divided into training and validation sets at any desired ratio.

The code has been published on GitHub: Lightning_Detection_Location or DOI 10.5281/zenodo.13350849

This data release includes water-quality data collected at 38 sites in central and eastern Massachusetts from April 2018 through May 2019 by the U.S. Geological Survey to support the implementation of site-dependent aluminum criteria for Massachusetts waters. Samples of effluent and receiving surface waters were collected monthly at four wastewater-treatment facilities (WWTFs) and seven water-treatment facilities (WTFs) (see SWQ_data_and_instantaneous_CMC_CCC_values.txt). The measured properties and constituents include pH, hardness, and filtered (dissolved) organic carbon, which are required inputs to the U.S. Environmental Protection Agency's Aluminum Criteria Calculator version 2.0. Outputs from the Aluminum Criteria Calculator are also provided in that file; these outputs consist of acute (Criterion Maximum Concentration, CMC) and chronic (Criterion Continuous Concentration, CCC) instantaneous water-quality values for total recoverable aluminum, calculated for monthly samples at selected ambient sites near each of the 11 facilities. Quality-control data from blank, replicate, and spike samples are provided (see SWQ_QC_data.txt). In addition to data tables, the data release includes time-series graphs of the discrete water-quality data (see SWQ_plot_discrete_all.zip). For pH, time-series graphs also are provided showing pH from the discrete monthly water-quality samples as well as near-continuous pH measured at one surface-water site at each facility (see SWQ_plot_contin_discrete_pH.zip). The near-continuous pH data, along with all of the discrete water-quality data except the quality-control data, are also available online from the U.S. Geological Survey's National Water Information System (NWIS) database (https://nwis.waterdata.usgs.gov/nwis).

The Knowledge Graph Technology market is experiencing robust growth, driven by the increasing need for enhanced data interoperability, improved data analysis capabilities, and the rising adoption of artificial intelligence (AI) and machine learning (ML) across various industries. The market's expansion is fueled by the advantages of knowledge graphs in improving decision-making processes, streamlining operations, and fostering innovation. Specific applications, such as semantic search, personalized recommendations, and fraud detection, are witnessing significant traction. While precise market size figures are unavailable, a conservative estimate places the 2025 market value at $5 billion, with a Compound Annual Growth Rate (CAGR) of 25% projected through 2033. This growth trajectory is supported by the escalating demand for efficient data management solutions in sectors like healthcare, finance, and retail, where knowledge graphs can significantly enhance operational efficiency and strategic decision-making. Technological advancements, particularly in graph database technologies and semantic web technologies, further bolster market expansion. However, the market faces challenges such as the complexity of knowledge graph implementation, the need for specialized expertise, and data integration issues across disparate sources. Despite these challenges, the long-term outlook for knowledge graph technology remains positive, driven by continuous technological innovations and the growing recognition of its transformative potential across diverse sectors. The segmentation of the Knowledge Graph Technology market reveals significant opportunities within various application areas and technology types. Application-wise, semantic search and recommendation engines are currently leading the market, while emerging applications in areas such as risk management and supply chain optimization are poised for rapid growth in the coming years. In terms of technology types, ontology engineering and graph databases are experiencing high demand. Regionally, North America and Europe currently dominate the market due to early adoption and established technological infrastructure. However, the Asia-Pacific region is projected to witness significant growth, spurred by increasing digitalization and investments in AI and ML initiatives. Competitive landscape analysis reveals a mix of established technology providers and emerging startups, creating a dynamic and competitive ecosystem. The continuous evolution of technologies and the expansion into new applications will continue to shape the market's growth and trajectory over the forecast period.

Service Dependency Graph Market Outlook

According to our latest research, the global Service Dependency Graph market size in 2024 stands at USD 1.27 billion, reflecting robust demand across multiple industries. The market is expected to expand at a CAGR of 20.3% from 2025 to 2033, reaching a projected value of USD 7.89 billion by 2033. This remarkable growth is driven by the increasing complexity of IT environments, the surge in adoption of cloud-native architectures, and the growing need for enhanced visibility into application dependencies to ensure operational resilience and efficient incident management.

One of the primary growth factors for the Service Dependency Graph market is the rapid digital transformation initiatives undertaken by enterprises globally. As organizations migrate to hybrid and multi-cloud environments, the IT landscape becomes more intricate, making it challenging to track and manage interdependencies among microservices, applications, and infrastructure components. Service dependency graphs provide a visual and analytical representation of these relationships, enabling IT teams to proactively identify potential points of failure, optimize resource allocation, and accelerate root cause analysis during outages. The adoption of DevOps and agile methodologies further amplifies the need for dynamic, real-time mapping of dependencies, as continuous integration and deployment cycles require up-to-date visibility into the service ecosystem.

Another significant driver is the heightened focus on security and compliance within regulated industries such as BFSI, healthcare, and telecommunications. Service dependency graphs play a critical role in risk management by uncovering hidden dependencies that may introduce vulnerabilities or compliance gaps. With the proliferation of stringent data protection regulations like GDPR, HIPAA, and PCI DSS, organizations are leveraging dependency mapping tools to ensure that sensitive data flows are monitored, access control policies are enforced, and audit trails are maintained. This not only mitigates the risk of data breaches but also simplifies compliance reporting and remediation efforts, making service dependency graphs an indispensable tool in modern IT governance frameworks.

Moreover, the rise of artificial intelligence and machine learning within IT operations (AIOps) is fueling the integration of service dependency graphs into advanced analytics platforms. By correlating dependency data with telemetry, logs, and event streams, organizations can automate anomaly detection, predict service degradations, and orchestrate self-healing workflows. This shift towards autonomous operations is particularly pronounced in large enterprises and service providers, where downtime and performance issues have direct financial and reputational impacts. As a result, investment in service dependency graph solutions is accelerating, with vendors continuously enhancing their offerings to support real-time data ingestion, scalability, and seamless integration with ITSM, monitoring, and orchestration tools.

Regionally, North America dominates the Service Dependency Graph market in 2024, accounting for over 42% of global revenue, followed by Europe and Asia Pacific. The United States, in particular, benefits from a mature IT ecosystem, high cloud adoption rates, and a strong presence of leading technology vendors. Meanwhile, Asia Pacific is expected to witness the fastest growth during the forecast period, driven by rapid enterprise digitization, increasing investments in cloud infrastructure, and the emergence of regional cloud service providers. Latin America and the Middle East & Africa are also experiencing steady adoption, albeit at a slower pace, as organizations in these regions recognize the value of service dependency mapping for operational efficiency and risk mitigation.

As organizations continue to navigate the complexities of modern IT ecosystems, Dependency Health Monitoring has emerged as a critical practice to ensure the smooth operation of interconnected services. By continuously assessing the health and performance of dependencies, IT teams can proactively identify potential issues before they impact service delivery. This approach not only enhances operational resilience but also supports more effective incident management by p

Statistics of datasets used in the experiments.

DRAKO is a leader in providing Device Graph Data, focusing on understanding the relationships between consumer devices and identities. Our data allows businesses to create holistic profiles of users, track engagement across platforms, and measure the effectiveness of advertising efforts.

Device Graph Data is essential for accurate audience targeting, cross-device attribution, and understanding consumer journeys. By integrating data from multiple sources, we provide a unified view of user interactions, helping businesses make informed decisions.

Key Features: - Comprehensive device mapping to understand user behaviour across multiple platforms - Detailed Identity Graph Data for cross-device identification and engagement tracking - Integration with Connected TV Data for enhanced insights into video consumption habits - Mobile Attribution Data to measure the effectiveness of mobile campaigns - Customizable analytics to segment audiences based on device usage and demographics - Some ID types offered: AAID, idfa, Unified ID 2.0, AFAI, MSAI, RIDA, AAID_CTV, IDFA_CTV

Use Cases: - Cross-device marketing strategies - Attribution modelling and campaign performance measurement - Audience segmentation and targeting - Enhanced insights for Connected TV advertising - Comprehensive consumer journey mapping

Data Compliance: All of our Device Graph Data is sourced responsibly and adheres to industry standards for data privacy and protection. We ensure that user identities are handled with care, providing insights without compromising individual privacy.

Data Quality: DRAKO employs robust validation techniques to ensure the accuracy and reliability of our Device Graph Data. Our quality assurance processes include continuous monitoring and updates to maintain data integrity and relevance.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

This dataset consists of images and keyed data of daily precipitation strip charts for the country of El Salvador and have a period of record ranging from 1984 to 2010. The strip charts were rescued and imaged by the International Environmental Data Rescue Organization (IEDRO). These precipitation chart forms contain continuous ink traces representing the instantaneous measurement of rainfall amounts for a 24-hour period. The chart form background has a calibrated grid (usually in mm or inches) superimposed on the chart.

We present a Mathematica package, QSWalk, to simulate the time evaluation of Quantum Stochastic Walks (QSWs) on arbitrary directed and weighted graphs. QSWs are a generalization of continuous time quantum walks that incorporate both coherent and incoherent dynamics and as such, include both quantum walks and classical random walks as special cases. The incoherent component allows for quantum walks along directed graph edges. The dynamics of QSWs are expressed using the Lindblad formalism, originally developed for open quantum systems, which frames the problem in the language of density matrices. For a QSW on a graph of N vertices, we have a sparse superoperator in an N^2-dimensional space, which can be solved efficiently using the built-in MatrixExp function in Mathematica. We illustrate the use of the QSWalk package through several example case studies.

The dataset and replication package of the study "A continuous open source data collection platform for architectural technical debt assessment".

Abstract

Architectural decisions are the most important source of technical debt. In recent years, researchers spent an increasing amount of effort investigating this specific category of technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.

However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.

In response to this concern, researchers strive to expand the scope of their study by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.

To address this issue, this paper presents our initial attempt at tackling this problem and enabling researchers to study architectural smells at large scale, a well-known indicator of architectural technical debt. Specifically, we introduce a novel approach to data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets using Arcan, a tool for architectural smells detection (or any other tool).

Finally, we present the publicly-available dataset resulting from the first three months of execution of the pipeline, that includes over 30,000 analysed commits and releases from over 10,000 open source GitHub projects written in 5 different programming languages and amounting to over a billion of lines of code analysed.

Graph and download economic data for Producer Price Index by Industry: Commercial Printing (Except Screen and Books): Stock Continuous Business Forms (DISCONTINUED) (PCU32311K32311K67) from Dec 1983 to Aug 2016 about book, printing, stocks, commercial, business, PPI, industry, inflation, price index, indexes, price, and USA.

Continuing Jobless Claims in the United States increased to 1960 thousand in the week ending November 15 of 2025 from 1953 thousand in the previous week. This dataset provides the latest reported value for - United States Continuing Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph Database Market Outlook

According to our latest research, the global graph database market size in 2024 stands at USD 2.92 billion, with a robust compound annual growth rate (CAGR) of 21.6% projected from 2025 to 2033. By the end of 2033, the market is expected to reach approximately USD 21.1 billion. The rapid expansion of this market is primarily driven by the rising need for advanced data analytics, real-time big data processing, and the growing adoption of artificial intelligence and machine learning across various industry verticals. As organizations continue to seek innovative solutions to manage complex and interconnected data, the demand for graph database technologies is accelerating at an unprecedented pace.

One of the most significant growth factors for the graph database market is the exponential increase in data complexity and volume. Traditional relational databases often struggle to efficiently handle highly connected data, which is becoming more prevalent in modern business environments. Graph databases excel at managing relationships between data points, making them ideal for applications such as fraud detection, social network analysis, and recommendation engines. The ability to visualize and query data relationships in real-time provides organizations with actionable insights, enabling faster and more informed decision-making. This capability is particularly valuable in sectors like BFSI, healthcare, and e-commerce, where understanding intricate data connections can lead to substantial competitive advantages.

Another key driver fueling market growth is the widespread digital transformation initiatives undertaken by enterprises worldwide. As businesses increasingly migrate to cloud-based infrastructures and adopt advanced analytics tools, the need for scalable and flexible database solutions becomes paramount. Graph databases offer seamless integration with cloud platforms, supporting both on-premises and cloud deployment models. This flexibility allows organizations to efficiently manage growing data workloads while ensuring security and compliance. Additionally, the proliferation of IoT devices and the surge in unstructured data generation further amplify the demand for graph database solutions, as they are uniquely equipped to handle dynamic and heterogeneous data sources.

The integration of artificial intelligence and machine learning with graph databases is also a pivotal growth factor. AI-driven analytics require robust data models capable of uncovering hidden patterns and relationships within vast datasets. Graph databases provide the foundational infrastructure for such applications, enabling advanced features like predictive analytics, anomaly detection, and personalized recommendations. As more organizations invest in AI-powered solutions to enhance customer experiences and operational efficiency, the adoption of graph database technologies is expected to surge. Furthermore, continuous advancements in graph processing algorithms and the emergence of open-source graph database platforms are lowering entry barriers, fostering innovation, and expanding the marketÂ’s reach.

Graph Analytics is becoming an essential component in the realm of graph databases, offering powerful tools to analyze and visualize complex data relationships. As organizations strive to extract deeper insights from their data, graph analytics enables them to uncover hidden patterns and trends that are not easily detectable with traditional analytics methods. This capability is particularly beneficial for sectors such as finance, healthcare, and retail, where understanding intricate connections can lead to more informed strategic decisions. By leveraging graph analytics, businesses can enhance their predictive modeling, optimize operations, and ultimately drive competitive advantage in a data-driven world.

From a regional perspective, North America currently dominates the graph database market, owing to the early adoption of advanced technologies and the presence of major industry players. However, the Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, driven by rapid digitalization, increasing investments in IT infrastructure, and the rising demand for data-driven decision-making across emerging economies. Europe also holds a significant share, supported by stringent dat

Graph and download economic data for Producer Price Index by Commodity for Pulp, Paper, and Allied Products: Custom Continuous Business Forms (WPU09450103) from Dec 1983 to Sep 2018 about paper, business, commodities, PPI, inflation, price index, indexes, price, and USA.