Time-Series Matrix (TSMx): A visualization tool for plotting multiscale temporal trends TSMx is an R script that was developed to facilitate multi-temporal-scale visualizations of time-series data. The script requires only a two-column CSV of years and values to plot the slope of the linear regression line for all possible year combinations from the supplied temporal range. The outputs include a time-series matrix showing slope direction based on the linear regression, slope values plotted with colors indicating magnitude, and results of a Mann-Kendall test. The start year is indicated on the y-axis and the end year is indicated on the x-axis. In the example below, the cell in the top-right corner is the direction of the slope for the temporal range 2001–2019. The red line corresponds with the temporal range 2010–2019 and an arrow is drawn from the cell that represents that range. One cell is highlighted with a black border to demonstrate how to read the chart—that cell represents the slope for the temporal range 2004–2014. This publication entry also includes an excel template that produces the same visualizations without a need to interact with any code, though minor modifications will need to be made to accommodate year ranges other than what is provided. TSMx for R was developed by Georgios Boumis; TSMx was originally conceptualized and created by Brad G. Peter in Microsoft Excel. Please refer to the associated publication: Peter, B.G., Messina, J.P., Breeze, V., Fung, C.Y., Kapoor, A. and Fan, P., 2024. Perspectives on modifiable spatiotemporal unit problems in remote sensing of agriculture: evaluating rice production in Vietnam and tools for analysis. Frontiers in Remote Sensing, 5, p.1042624. https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624 TSMx sample chart from the supplied Excel template. Data represent the productivity of rice agriculture in Vietnam as measured via EVI (enhanced vegetation index) from the NASA MODIS data product (MOD13Q1.V006). TSMx R script: # import packages library(dplyr) library(readr) library(ggplot2) library(tibble) library(tidyr) library(forcats) library(Kendall) options(warn = -1) # disable warnings # read data (.csv file with "Year" and "Value" columns) data <- read_csv("EVI.csv") # prepare row/column names for output matrices years <- data %>% pull("Year") r.names <- years[-length(years)] c.names <- years[-1] years <- years[-length(years)] # initialize output matrices sign.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) pval.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) slope.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) # function to return remaining years given a start year getRemain <- function(start.year) { years <- data %>% pull("Year") start.ind <- which(data[["Year"]] == start.year) + 1 remain <- years[start.ind:length(years)] return (remain) } # function to subset data for a start/end year combination splitData <- function(end.year, start.year) { keep <- which(data[['Year']] >= start.year & data[['Year']] <= end.year) batch <- data[keep,] return(batch) } # function to fit linear regression and return slope direction fitReg <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(sign(slope)) } # function to fit linear regression and return slope magnitude fitRegv2 <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(slope) } # function to implement Mann-Kendall (MK) trend test and return significance # the test is implemented only for n>=8 getMann <- function(batch) { if (nrow(batch) >= 8) { mk <- MannKendall(batch[['Value']]) pval <- mk[['sl']] } else { pval <- NA } return(pval) } # function to return slope direction for all combinations given a start year getSign <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) signs <- lapply(combs, fitReg) return(signs) } # function to return MK significance for all combinations given a start year getPval <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) pvals <- lapply(combs, getMann) return(pvals) } # function to return slope magnitude for all combinations given a start year getMagn <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) magns <- lapply(combs, fitRegv2) return(magns) } # retrieve slope direction, MK significance, and slope magnitude signs <- lapply(years, getSign) pvals <- lapply(years, getPval) magns <- lapply(years, getMagn) # fill-in output matrices dimension <- nrow(sign.matrix) for (i in 1:dimension) { sign.matrix[i, i:dimension] <- unlist(signs[i]) pval.matrix[i, i:dimension] <- unlist(pvals[i]) slope.matrix[i, i:dimension] <- unlist(magns[i]) } sign.matrix <-...

The code will run on an installation of R with the add on packages lattice, dplyr, lattice, and latticeExtra. The output is a graph (Fig. 2) and a table showing likelihood ratios of run chart rules for identification of non-random variation in simulated run charts of different length with or without a shift in process mean. (R)

The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

This database collates 3552 development indicators from different studies with data by country and year, including single year and multiple year time series. The data is presented as charts, the data can be downloaded from linked project pages/references for each set, and the data for each presented graph is available as a CSV file as well as a visual download of the graph (both available via the download link under each chart).

Goodwill-and-Other-Intagible-Assets Time Series for Hwa Create Corp Ltd. Hwa Create Corporation researches and develops, manufactures, and sells satellite navigation, and radar and communication products and technologies. The company provides avionics buses, unmanned aerial vehicle, communication, satellite navigation, radar and electronic system, and simulation and test products; and aerospace unmanned vehicle system and precisely guided weapon, radar, and electromagnetic counter measurement, VR and visualization simulation and emergency communication solutions. It serves the aviation, aerospace, ship, weapon, high-end civil equipment, electronics, electric power, and high-speed rail industries. The company was founded in 2001 and is headquartered in Beijing, China.

This data supports the assertions made in a paper (same name as this project) which surveyed 3 Second Language Acquisition journals (Modern Language Journal, Language Learning, and Studies in Second Language Acquisition) from the time of their inception to 2011/2012. The raw data used for calculations about the number of graphics and which type of graphics were published is included in the attached Excel file.

Inflation Rate in the United States remained unchanged at 2.70 percent in July. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_description
company_sic_code4 company_sic_code4_description
company_sic_code6
company_sic_code6_description
company_sic_code8
company_sic_code8_description company_parent_company
company_parent_company_location company_public_private company_subsidiary_company company_residential_business_code company_revenue_at_side_code company_revenue_range
company_revenue company_sales_volume
company_small_business company_stock_ticker company_year_founded company_minorityowned
company_female_owned_or_operated company_franchise_code company_dma company_dma_name
company_hq_address
company_hq_city company_hq_duns company_hq_state
company_hq_zip5 company_hq_zip4 co...

The Charts extension for CKAN enhances the platform's data visualization capabilities, allowing users to create, manage, and share charts that are linked to CKAN datasets. It allows users to create interactive and visually appealing chart representations of data directly within the CKAN environment, providing essential data analysis tools. This streamlines the process of visualizing data for a more intuitive and accessible experience. Key Features: Chart Creation: Enables users to create charts directly from CKAN datasets. Chart Editing: Allows users to modify and customize existing charts. Chart Embedding: Provides the ability to embed created charts into other web applications or platforms for wider dissemination. Chart Sharing: Supports sharing of chart visualizations with other users or groups within or outside the CKAN ecosystem. Multiple Chart Types: Supports a variety of common chart types, including bar charts, line charts, and pie charts. Further chart types are not mentioned explicitly, but it is implied the extension can be extended as well. Technical Integration: The extension integrates with CKAN primarily as a plugin. To enable the Charts extension, the chartsview and chartsbuilderview plugins must be added to the CKAN configuration file. The documentation also mentions the need to set CHARTS_FIELDS when autogenerating documentation for chart types fields, which implies a level of customization and extensibility for different chart types. It requires proper initialization of the CKAN instance and relies on validators and helpers, emphasizing the need for a correctly configured CKAN environment. Benefits & Impact: The primary benefit of the CKAN Charts extension is the enhancement of data analysis and presentation capabilities within CKAN. By providing tools to create, manage, and share charts, the extension makes it easier for users to understand and communicate insights from their data, fostering better data-driven decision-making. Also the documentation for chart types can be autogenerated.

Gold rose to 3,349.50 USD/t.oz on August 18, 2025, up 0.40% from the previous day. Over the past month, Gold's price has fallen 1.45%, but it is still 33.78% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on August of 2025.

Graph and download economic data for Personal Saving Rate (PSAVERT) from Jan 1959 to Jun 2025 about savings, personal, rate, and USA.

Other-Stockholder-Equity Time Series for Jfrog Ltd. JFrog Ltd. provides software supply chain platform in the United States, Israel, India, and internationally. The company offers JFrog Artifactory, a package repository that allows teams and organizations to store, update, and manage their software packages; JFrog Curation that functions as a guardian outside the software development pipeline, controlling the admission of packages into an organization, primarily from open source or public repositories; JFrog Xray, which scans JFrog Artifactory to secure all software packages; JFrog Advanced Security, an optional add-on for select JFrog subscriptions; and JFrog Runtime Security, an optional add-on for select JFrog subscriptions to work with other JFrog Security solutions. It also provides JFrog ML, a platform-integrated solution designed for data science and MLOps teams to transform and store data, build, train, and deploy models, and monitor the entire Machine Learning pipeline as part of the JFrog Software Supply Chain Platform; JFrog Distribution that provides software package distribution; and JFrog Connect, a device management solution that allows companies to manage software updates and monitor performance in IoT device fleets. In addition, the company offers JFrog Pro that provides access to the universal version of JFrog Artifactory and ongoing updates, upgrades, and bug fixes; JFrog Pro X, a self-hosted-only subscription; JFrog Enterprise X, which offers cluster configuration, federated repositories, multi-region replication, larger enterprise-scale deployments, service-level agreement support, and deeper security; and JFrog Enterprise Plus, a full platform subscription option. It serves technology, financial services, retail, healthcare, and telecommunications organizations. JFrog Ltd. was incorporated in 2008 and is headquartered in Sunnyvale, California.

Organization Chart Software Market Outlook

The global organization chart software market size was estimated to be around USD 1.1 billion in 2023 and is projected to reach approximately USD 2.3 billion by 2032, growing at a CAGR of 8.5% during the forecast period. This growth is primarily driven by the increasing need for efficient organizational management and enhanced workflow in businesses across various industries.

One of the major growth factors for the organization chart software market is the rising complexity within organizational structures as companies expand and diversify. With businesses growing larger and more intricate, the need for a clear and concise visualization of organizational hierarchies has become essential. Organization chart software provides a solution by helping businesses map out their internal structures, thereby enhancing communication, decision-making, and operational efficiency.

Additionally, the widespread adoption of digital transformation initiatives across various industries is further propelling market growth. As organizations increasingly move towards digital platforms for their operations, there is a parallel need for advanced tools to manage and visualize their organizational structures. Organization chart software enables businesses to maintain updated and easily accessible organizational charts, which are crucial for smooth operational workflows and for onboarding new employees.

Another significant factor contributing to the growth of this market is the enhanced focus on employee performance and human resource management. Companies are recognizing the importance of having well-defined organizational structures that can support employee development and performance tracking. Organization chart software helps HR departments to better understand reporting relationships, manage talent pools, and identify potential succession planning opportunities, thus supporting overall strategic HR management.

Diagramming Software plays a crucial role in the realm of organization chart software, offering enhanced capabilities for visualizing complex organizational structures. These tools provide users with the ability to create detailed diagrams that accurately represent the hierarchy and relationships within an organization. By utilizing diagramming software, businesses can ensure that their organizational charts are not only accurate but also visually appealing, making it easier for stakeholders to understand and engage with the information presented. This is particularly important in today's fast-paced business environment, where clear and effective communication is key to maintaining operational efficiency and achieving strategic goals.

The regional outlook for the organization chart software market shows substantial growth across various regions. North America, in particular, is expected to dominate the market due to the high adoption rate of advanced software solutions and the presence of numerous large enterprises. The Asia Pacific region is also anticipated to witness significant growth, driven by the rapid digitalization and expansion of businesses in emerging economies like China and India. Europe is expected to follow suit, with steady growth attributable to the strong presence of multinational corporations and the ongoing digital transformation across the region.

Deployment Type Analysis

The organization chart software market can be segmented by deployment type into on-premises and cloud-based solutions. On-premises deployment involves installing software on local servers within the organization. This type of deployment provides businesses with greater control over their data and systems, which is particularly crucial for companies with stringent data security requirements. However, the initial investment and ongoing maintenance costs can be relatively high, which might be a barrier for smaller organizations.

On the other hand, cloud-based deployment offers a more flexible and cost-effective solution. Cloud-based organization chart software allows businesses to access their systems via the internet, thereby reducing the need for extensive IT infrastructure and maintenance. This deployment type is particularly advantageous for organizations with remote and distributed teams, as it enables easy access to updated organizational charts and data from any location. The rising popularity of cloud computing and the widespread implementation of remote work policies hav

Graph and download economic data for All Employees, Manufacturing (MANEMP) from Jan 1939 to Jul 2025 about headline figure, establishment survey, employment, manufacturing, and USA.

Family of Apps includes Facebook, Instagram, Messenger, WhatsApp, and other services.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Jul 2025 about average, earnings, hours, establishment survey, wages, private, employment, and USA.

Graph Database Market Outlook

The global graph database market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 8.5 billion by 2032, growing at a CAGR of 21.2% from 2024 to 2032. The substantial growth of this market is driven primarily by increasing data complexity, advancements in data analytics technologies, and the rising need for more efficient database management systems.

One of the primary growth factors for the graph database market is the exponential increase in data generation. As organizations generate vast amounts of data from various sources such as social media, e-commerce platforms, and IoT devices, the need for sophisticated data management and analysis tools becomes paramount. Traditional relational databases struggle to handle the complexity and interconnectivity of this data, leading to a shift towards graph databases which excel in managing such intricate relationships.

Another significant driver is the growing adoption of artificial intelligence (AI) and machine learning (ML) technologies. These technologies rely heavily on connected data for predictive analytics and decision-making processes. Graph databases, with their inherent ability to model relationships between data points effectively, provide a robust foundation for AI and ML applications. This synergy between AI/ML and graph databases further accelerates market growth.

Additionally, the increasing prevalence of personalized customer experiences across industries like retail, finance, and healthcare is fueling demand for graph databases. Businesses are leveraging graph databases to analyze customer behaviors, preferences, and interactions in real-time, enabling them to offer tailored recommendations and services. This enhanced customer experience translates to higher customer satisfaction and retention, driving further adoption of graph databases.

From a regional perspective, North America currently holds the largest market share due to early adoption of advanced technologies and the presence of key market players. However, significant growth is also anticipated in the Asia-Pacific region, driven by rapid digital transformation, increasing investments in IT infrastructure, and growing awareness of the benefits of graph databases. Europe is also expected to witness steady growth, supported by stringent data management regulations and a strong focus on data privacy and security.

Component Analysis

The graph database market can be segmented into two primary components: software and services. The software segment holds the largest market share, driven by extensive adoption across various industries. Graph database software is designed to create, manage, and query graph databases, offering features such as scalability, high performance, and efficient handling of complex data relationships. The growth in this segment is propelled by continuous advancements and innovations in graph database technologies. Companies are increasingly investing in research and development to enhance the capabilities of their graph database software products, catering to the evolving needs of their customers.

On the other hand, the services segment is also witnessing substantial growth. This segment includes consulting, implementation, and support services provided by vendors to help organizations effectively deploy and manage graph databases. As businesses recognize the benefits of graph databases, the demand for expert services to ensure successful implementation and integration into existing systems is rising. Additionally, ongoing support and maintenance services are crucial for the smooth operation of graph databases, driving further growth in this segment.

The increasing complexity of data and the need for specialized expertise to manage and analyze it effectively are key factors contributing to the growth of the services segment. Organizations often lack the in-house skills required to harness the full potential of graph databases, prompting them to seek external assistance. This trend is particularly evident in large enterprises, where the scale and complexity of data necessitate robust support services.

Moreover, the services segment is benefiting from the growing trend of outsourcing IT functions. Many organizations are opting to outsource their database management needs to specialized service providers, allowing them to focus on their core business activities. This shift towards outsourcing is further bolstering the demand for graph database services, driving market growth.

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

This chart shows the trend in percentage of preterm births for Albany county. It also shows the 2017 objective. To view the chart for a different county, create a new chart under the "Visualize" tab. This chart is based on one of three datasets related to the Prevention Agenda Tracking Indicators county level data posted on this site. Each dataset consists of county level data for 68 health tracking indicators and sub-indicators for the Prevention Agenda 2013-2017: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Each dataset includes tracking indicators for the five Priority Areas of the Prevention Agenda 2013-2017. The most recent year dataset includes the most recent county level data for all indicators. The trend dataset includes the most recent county level data and historical data, where available. Each dataset also includes the Prevention Agenda 2017 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among socioeconomic groups. For more information, check out: http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/. The "About" tab contains additional details concerning this dataset.

Graph and download economic data for Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over (LES1252881600Q) from Q1 1979 to Q2 2025 about full-time, salaries, workers, earnings, 16 years +, wages, median, employment, real, and USA.