We present ChemPager, a freely available tool for systematically evaluating chemical syntheses. By processing and visualizing chemical data, the impact of past changes is uncovered and future work guided. The tool calculates commonly used metrics such as process mass intensity (PMI), Volume–Time Output, and production costs. Also, a set of scores is introduced aiming to measure crucial but elusive characteristics such as process robustness, design, and safety. Our tool employs a hierarchical data layout built on common software for data entry (Excel, Google Sheets, etc.) and visualization (Spotfire). With all project data being stored in one place, cross-project comparison and data aggregation becomes possible as well as cross-linking with other data sources or visualizations.

Contains Reddit Cross Post Data gathered using PRAW.

The Dataset is mostly clean.

Useful for Social Network Analytics problems and learning. Still a W.I.P, more data to be gathered and added!

Thank you.

About this dataset

Context

The dataset tabulates the median household income in Cross City. It can be utilized to understand the trend in median household income and to analyze the income distribution in Cross City by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Cross City, FL Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Cross City, FL: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Cross City, FL
• Cross City, FL households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Cross City median household income. You can refer the same here

Big Data Analytics in BFSI Market Outlook

As per our latest research, the global Big Data Analytics in BFSI market size reached USD 22.7 billion in 2024, driven by the increasing digital transformation initiatives and the accelerating adoption of advanced analytics across financial institutions. The market is expected to grow at a robust CAGR of 14.8% during the forecast period, reaching an estimated USD 62.5 billion by 2033. The rapid proliferation of digital banking, heightened focus on fraud detection, and the need for personalized customer experiences are among the primary growth drivers for the Big Data Analytics in BFSI market.

The exponential growth of data generated by financial transactions, customer interactions, and regulatory requirements has created an urgent need for advanced analytics solutions in the BFSI sector. Financial institutions are leveraging Big Data Analytics to gain actionable insights, optimize operations, and enhance decision-making processes. The integration of artificial intelligence and machine learning with Big Data Analytics platforms is enabling BFSI organizations to automate risk assessment, predict customer behavior, and streamline compliance procedures. Furthermore, the surge in digital payment platforms and online banking services has resulted in an unprecedented volume of structured and unstructured data, further necessitating robust analytics solutions to ensure data-driven strategies and operational efficiency.

Another significant growth factor is the increasing threat of cyberattacks and financial fraud. As digital channels become more prevalent, BFSI organizations face sophisticated threats that require advanced analytics for real-time detection and mitigation. Big Data Analytics empowers financial institutions to monitor vast datasets, identify unusual patterns, and respond proactively to potential security breaches. Additionally, regulatory bodies are imposing stringent data management and compliance standards, compelling BFSI firms to adopt analytics solutions that ensure transparency, auditability, and adherence to global regulations. This regulatory push, combined with the competitive need to offer innovative, customer-centric services, is fueling sustained investment in Big Data Analytics across the BFSI landscape.

The growing emphasis on customer-centricity is also propelling the adoption of Big Data Analytics in the BFSI sector. Financial institutions are increasingly utilizing analytics to understand customer preferences, segment markets, and personalize product offerings. This not only enhances customer satisfaction and loyalty but also drives cross-selling and upselling opportunities. The ability to analyze diverse data sources, including social media, transaction histories, and customer feedback, allows BFSI organizations to predict customer needs and deliver targeted solutions. As a result, Big Data Analytics is becoming an indispensable tool for BFSI enterprises aiming to differentiate themselves in an intensely competitive market.

From a regional perspective, North America remains the largest market for Big Data Analytics in BFSI, accounting for over 38% of global revenue in 2024. This dominance is attributed to the presence of major financial institutions, early adoption of advanced technologies, and a mature regulatory environment. However, the Asia Pacific region is witnessing the fastest growth, with a CAGR exceeding 17% during the forecast period, driven by rapid digitization, expanding banking infrastructure, and increasing investments in analytics solutions by emerging economies such as China and India.

Component Analysis

The Big Data Analytics in BFSI market is segmented by component into Software and Services. The software segment comprises analytics platforms, data management tools, visualization software, and advanced AI-powered solutions. In 2024, the software segment accounted for the largest share

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D

About this dataset

Context

The dataset presents median household incomes for various household sizes in Cross, Wisconsin, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Cross town did not include 1, 5, 6, or 7-person households. Across the different household sizes in Cross town the mean income is $100,435, and the standard deviation is $19,172. The coefficient of variation (CV) is 19.09%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 2-person households, with an income of $79,717. It then further increased to $117,549 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/cross-wi-median-household-income-by-household-size.jpeg" alt="Cross, Wisconsin median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cross town median household income. You can refer the same here

Supplemental Table S9: Input and output weightings, breakpoint analysis, and bi-cross validation results for eight sources

An understanding of the spatial dimension of economic and social activity requires methods that can separate out the relationship between spatial units that is due to the effect of common factors from that which is purely spatial even in an abstract sense. The same applies to the empirical analysis of networks in general. We use cross-unit averages to extract common factors (viewed as a source of strong cross-sectional dependence) and compare the results with the principal components approach widely used in the literature. We then apply multiple testing procedures to the de-factored observations in order to determine significant bilateral correlations (signifying connections) between spatial units and compare this to an approach that just uses distance to determine units that are neighbours. We apply these methods to real house price changes at the level of Metropolitan Statistical Areas in the USA, and estimate a heterogeneous spatio-temporal model for the de-factored real house price changes and obtain significant evidence of spatial connections, both positive and negative.

US Chronic Disease Indicators

124 Cross-Cutting Data Points

By Health [source]

About this dataset

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The first step is becoming familiar with the columns. The columns include YearStart, YearEnd, LocationAbbr, LocationDesc, DataSource, Topic, Question, Response, DataValueUnit,, DataValueType,, DataValueAlt,, DataValueFootnoteSymbol,, DatavalueFootnote,, StratificationCategory1,, Stratification1,, StratificationCategory2,, Stratification2,, StratificationCategory3 and GeoLocation. Each column contains information describing different aspects of the same data such as geographical location or response rates to certain questions regarding chronic diseases.

Once you have become familiar with each column's purpose and what it contains it is time to start analyzing the data. Depending on your research interest there are various ways in which you can use this dataset including comparing different categories of responses or regions along with their underlying characteristics such as age group or gender etc.. You can also examine other trends in terms of prevalence for certain diseases or changes over time for a particular region or disease type by exploring both year start and year end columns together.

Lastly don't forget that all these trends may be supplemented further by exploring associated footnotes symbols which indicate potential confounding factors and understanding how they relate to your research question which can then enable more focused interpretations of your results!

Research Ideas

• Producing state-level health profiles that characterize the burden of chronic diseases in a particular community or region.
• Identifying areas with higher prevalence of specific chronic disease risk factors and possible interventions to target such areas.
• Creating interactive maps to visualize changes in chronic disease prevalence over time by region, demographics, and other variables

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:----------------------------|:------------------------------------------------------------------------| | YearStart | The year the data was collected. (Integer) | | YearEnd | The year the data was collected. (Integer) | | LocationAbbr | The abbreviation of the location where the data was collected. (String) | | LocationDesc | The description of the location where the data was collected. (String) | | DataSource | The source of the data. (String) | | Topic | The topic of the data. (String) | | Question | The question the data is answering. (String) | | Response | The response to the question. (String) | | DataValueUnit | The unit of the data value. (String) | | DataValueType | The type of the data value. (String) | | DataValueAlt | An alternative data value. (Float) | | DataValueFootnoteSymbol | A footnote symbol for the data value. (String) | | DatavalueFootnote | A footnote for the data value. (String) | | StratificationCategory1 | The first stratification category. (String) | | Stratification1 | The first stratification. (String) | | StratificationCategory2 | The second stratification category. (String) | | Stratification2 | The second stratification. (String) | | StratificationCategory3 | The third stratification category. (String) | | Stratification3 | The third stratification. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

Cross-Domain Multi-INT Data Fusion Platforms Market Outlook

According to our latest research, the global Cross-Domain Multi-INT Data Fusion Platforms market size reached USD 4.87 billion in 2024, reflecting robust demand from defense, security, and commercial sectors. The market is projected to expand at a CAGR of 13.2% from 2025 to 2033, culminating in a forecasted value of USD 14.45 billion by 2033. This dynamic growth is being driven by the increasing need for integrated intelligence solutions that streamline and enhance situational awareness across multiple domains and intelligence sources.

One of the primary growth factors for the Cross-Domain Multi-INT Data Fusion Platforms market is the escalating complexity and volume of intelligence data generated from various sources such as SIGINT, GEOINT, HUMINT, MASINT, and OSINT. As modern threats become more sophisticated and asymmetric, defense and security agencies require advanced platforms capable of synthesizing vast, heterogeneous datasets into actionable intelligence. The adoption of artificial intelligence and machine learning within these platforms further enhances their ability to identify patterns, anomalies, and potential threats in real-time, enabling timely and informed decision-making. This technological evolution is a significant catalyst for market expansion, particularly as governments and enterprises worldwide invest in upgrading their intelligence infrastructure.

Furthermore, the proliferation of cyber threats, geopolitical tensions, and the growing emphasis on homeland security are compelling both public and private organizations to deploy cross-domain data fusion solutions. These platforms not only enable a unified view of multi-source intelligence but also support collaboration among multiple agencies and stakeholders. The demand for interoperability, real-time analytics, and cross-domain situational awareness is rising, as organizations seek to break down silos and achieve a holistic understanding of evolving threats. As a result, the market is witnessing substantial investments in R&D and strategic collaborations aimed at enhancing the capabilities, scalability, and security of data fusion platforms.

Another notable growth driver is the increasing adoption of cloud-based deployment models, which offer scalability, flexibility, and cost-efficiency. Cloud-based multi-INT data fusion solutions are particularly attractive to commercial enterprises and smaller government agencies that require robust intelligence capabilities without the overhead of maintaining complex on-premises infrastructure. Additionally, the integration of advanced analytics, big data technologies, and secure communication protocols within these platforms is expanding their application scope beyond traditional defense and security domains, fueling adoption in sectors such as critical infrastructure protection, law enforcement, and cybersecurity.

Regionally, North America remains the largest market for Cross-Domain Multi-INT Data Fusion Platforms, driven by significant investments from the U.S. Department of Defense, intelligence agencies, and commercial security providers. Europe and Asia Pacific are also experiencing rapid growth, fueled by increasing security concerns, modernization initiatives, and the need for advanced intelligence solutions. The Middle East & Africa and Latin America, while smaller in market size, are expected to witness steady adoption due to rising regional security threats and the gradual modernization of intelligence and security infrastructure.

Component Analysis

The Cross-Domain Multi-INT Data Fusion Platforms market is segmented by component into software, hardware, and services, each playing a pivotal role in the deployment and operation of advanced intelligence solutions. Software represents the largest share of the market, as it forms the core of data fusion processes, integrating diverse intelligence sources, managing workflows, and providing advanced analytics and visualization cap

Cross-Domain Multi-INT Data Fusion Platforms Market Outlook

According to our latest research, the global Cross-Domain Multi-INT Data Fusion Platforms market size reached USD 4.82 billion in 2024, and it is expected to grow at a robust CAGR of 13.7% during the forecast period, reaching USD 14.23 billion by 2033. The primary growth factor propelling this market is the rising demand for advanced intelligence integration capabilities across defense, security, and commercial sectors, driven by the escalating complexity of modern threats and the expanding volume of heterogeneous data sources.

The growth trajectory of the Cross-Domain Multi-INT Data Fusion Platforms market is underpinned by the increasing need for actionable intelligence in real-time scenarios. Modern defense and security operations are confronted with a deluge of data from various intelligence sources—signals, geospatial, human, measurement, and open-source. The ability to synthesize these diverse data streams into a unified, actionable intelligence output is becoming mission-critical. Organizations are investing heavily in multi-INT platforms that can bridge traditional silos, enhance situational awareness, and enable faster, more informed decision-making. This is particularly vital in contexts such as counter-terrorism, border security, and cyber defense, where timely and accurate intelligence can be the difference between success and failure.

Technological advancements are another significant driver fueling market expansion. The integration of artificial intelligence (AI), machine learning (ML), and advanced analytics into multi-INT fusion platforms has revolutionized the speed and accuracy of intelligence processing. These platforms are now capable of automating complex data correlation, anomaly detection, and predictive analytics, which were previously labor-intensive and error-prone. Furthermore, the proliferation of cloud computing and edge processing has enabled scalable and flexible deployment of these solutions, making them accessible to a broader range of end-users, from national defense agencies to commercial entities seeking enhanced security and operational intelligence.

The growing sophistication of global security threats—such as hybrid warfare, cyber intrusions, and transnational crime—has compelled governments and organizations to rethink their intelligence architectures. Cross-domain multi-INT data fusion platforms offer a holistic approach to threat detection and response by consolidating intelligence from multiple domains and disciplines. This integrated approach not only improves threat visibility but also enhances collaboration among agencies and departments. As a result, investments in R&D, strategic partnerships, and cross-sector collaborations are accelerating, further catalyzing market growth. The market is also witnessing increased adoption in non-traditional sectors such as critical infrastructure, financial services, and transportation, where advanced threat detection and situational awareness are becoming business imperatives.

From a regional perspective, North America currently dominates the Cross-Domain Multi-INT Data Fusion Platforms market, accounting for over 38% of the global revenue in 2024. This leadership is attributed to the presence of major defense contractors, robust government spending on intelligence modernization, and an innovative technology ecosystem. However, Asia Pacific is emerging as the fastest-growing region, with a projected CAGR of 16.1% over the forecast period, driven by escalating regional security challenges, increased defense budgets, and rapid digital transformation initiatives. Europe also remains a significant market, propelled by collaborative security frameworks and investments in cross-border intelligence sharing.

Component Analysis

The Cross-Domain Multi-INT Data Fusion Platforms market by component is segmented into Software, Hardware, and Services. The software segment currently holds the largest market share, reflecting the critical role of advanced analytics, AI, and data management tools in multi-INT fusion. Software platforms enable the seamless integration, correlation, and visualization of data from disparate intelligence sources, offering end-users a unified operational picture. The continuous evolution of software capabilities—such as real-time data processing, predictive analytics, and intuitiv

Using static analysis to analyze large source bases for errors can be difficult because the correctness rules in the system are often numerous and undocumented. In order to check those systems, a human would have to enumerate those rules at the cost of weeks or years of effort. Engler et. al [7] introduced techniques to infer many of those rules from the source itself, saving much of the human effort. This thesis leverages some of those techniques to cross check implementations for consistency. If there are multiple implementations of the same interface (i.e. device drivers all implement open, close, read, write), we can analyze the actions of those implementations to infer the correct behavior. For example, if most of the implementations check the first argument of open for NULL before dereferencing it, it is likely that all of the implementations should check that argument for NULL before dereferencing it. The implementations missing the checks are assumed to be in error. To demonstrate the flexibility and capability of our technique, we use static analysis to infer the rules for four classes of bugs and apply them to the Linux kernel. We found dozens of errors with relatively little user effort in spite of the kernel's size and lack of documentation.

About this dataset

Context

The dataset tabulates the Cross town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Cross town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Cross town was 374, a 0.27% increase year-by-year from 2022. Previously, in 2022, Cross town population was 373, an increase of 1.08% compared to a population of 369 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Cross town increased by 19. In this period, the peak population was 387 in the year 2007. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Cross town is shown in this column.
• Year on Year Change: This column displays the change in Cross town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Cross town Population by Year. You can refer the same here

The QoG Institute is an independent research institute within the Department of Political Science at the University of Gothenburg. The main objective of our research is to address the theoretical and empirical problem of how political institutions of high quality can be created and maintained.

To achieve said goal, the QoG Institute makes comparative data on QoG and its correlates publicly available. To accomplish this, we have compiled several datasets that draw on a number of freely available data sources, including aggregated individual-level data.

The QoG OECD Datasets focus exclusively on OECD member countries. They have a high data coverage in terms of geography and time. In the QoG OECD TS dataset, data from 1946 to 2021 is included and the unit of analysis is country-year (e.g., Sweden-1946, Sweden-1947, etc.).

In the QoG OECD Cross-Section dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data are available for a country for 2018, data for 2019 is included. If no data for 2019 exists, data for 2017 is included, and so on up to a maximum of +/- 3 years. In the QoG OECD Time-Series dataset, data from 1946 to 2021 are included and the unit of analysis is country-year (e.g. Sweden-1946, Sweden-1947 and so on).

The QoG OECD Datasets focus exclusively on OECD member countries. They have a high data coverage in terms of geography and time.

In the QoG OECD Cross-Section dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data are available for a country for 2018, data for 2019 is included. If no data for 2019 exists, data for 2017 is included, and so on up to a maximum of +/- 3 years.

Cross-Case Link Analysis Tools Market Outlook

According to our latest research, the global Cross-Case Link Analysis Tools market size reached USD 1.42 billion in 2024, driven by the increasing sophistication of criminal networks and the urgent need for advanced investigative solutions. The market is poised for robust expansion, with a projected CAGR of 13.9% from 2025 to 2033. By 2033, the market is forecasted to attain a value of USD 4.17 billion. This impressive growth trajectory is fueled by the escalating adoption of artificial intelligence, machine learning, and big data analytics in both public and private sectors to combat complex crimes and fraudulent activities more efficiently.

One of the primary growth factors for the Cross-Case Link Analysis Tools market is the mounting volume and complexity of data generated across various sectors, including law enforcement, financial institutions, and intelligence agencies. Investigative teams are increasingly challenged by disparate data silos and the need to correlate information across multiple cases and sources. Cross-case link analysis tools enable organizations to bridge these gaps by integrating structured and unstructured data, facilitating the rapid identification of hidden relationships, patterns, and anomalies. The integration of advanced analytics and visualization capabilities empowers investigators to make informed decisions, accelerate case resolution, and ultimately enhance public safety and organizational security.

Another significant driver is the rising incidence of cybercrime, financial fraud, and organized criminal activities on a global scale. As threat actors employ more sophisticated tactics, traditional investigative methods often fall short in detecting and disrupting complex schemes. Cross-case link analysis tools, leveraging technologies such as natural language processing and predictive analytics, provide a comprehensive view of interconnected entities, transactions, and events. This holistic approach is particularly valuable for financial crime detection, anti-money laundering (AML) initiatives, and cybersecurity operations, where timely insights can mean the difference between prevention and escalation. The increasing regulatory pressure on organizations to ensure compliance and transparency further amplifies the demand for these advanced solutions.

Additionally, the growing digital transformation in both public and private sectors is accelerating the adoption of cross-case link analysis tools. Government agencies, BFSI (Banking, Financial Services, and Insurance), healthcare, and retail organizations are investing heavily in digital infrastructure and data-driven technologies to enhance operational efficiency and security. The shift towards cloud-based deployment models and the proliferation of Software-as-a-Service (SaaS) offerings are making these tools more accessible, scalable, and cost-effective. As organizations recognize the strategic value of proactive threat detection and risk mitigation, the market for cross-case link analysis tools is expected to witness sustained momentum in the coming years.

From a regional perspective, North America currently dominates the Cross-Case Link Analysis Tools market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is underpinned by significant investments in law enforcement technology, a high prevalence of cybercrime and financial fraud, and the presence of leading technology vendors. However, Asia Pacific is anticipated to register the highest CAGR during the forecast period, driven by rapid urbanization, increasing digitalization, and rising security concerns across emerging economies. The Middle East & Africa and Latin America are also witnessing steady growth as governments and enterprises intensify their focus on crime prevention and data-driven investigations.

Component Analysis

The Component segment of the Cross-Case Link Analysis Tools market is bifurcated into Software and Services, each playing a pivotal role in delivering comprehensive investigative solutions. The software component encompasses advanced analytical platforms equipped with features such as data integration, visualization, link analysis, and predictive modeling. These platforms are designed to process vast volumes of structured and unstructured data, enabling investigators to uncover hidden relationships and patterns across multiple cases. The continuous evoluti

Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)–C(sp3) bond formation. While many of these methods typically employ aryl bromides as the C(sp2) coupling partner, a variety of aliphatic radical sources have been investigated. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because nonstandardized sets of aryl bromides are used in scope evaluation. Herein, we report a Ni/photoredox-catalyzed (deutero)methylation and alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. Reaction development, mechanistic studies, and late-stage derivatization of a biologically relevant aryl chloride, fenofibrate, are presented. Then, we describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing scope examples from published Ni/photoredox methods on this same chemical space, we identify areas of sparse coverage and high versus low average yields, enabling comparisons between prior art and this new method. Additionally, we demonstrate that the systematically selected scope of aryl bromides can be used to quantify population-wide reactivity trends and reveal sources of possible functional group incompatibility with supervised machine learning.

Comprehensive global lodging intelligence covering more than seven million hotel and short-term rental properties worldwide.

The Complete Lodging Dataset provides a full-market view of the global accommodation landscape by integrating data from hotel reservation systems, Online Travel Agencies (OTAs), and directly connected property management systems. It includes verified property identifiers, occupancy rates, ADR, RevPAR, pricing trends, and physical attributes across both traditional hotel inventory and short-term rental supply.

Sourced from real booking and reservation data and refined through proprietary normalization processes, this dataset ensures consistency and accuracy across all lodging types. Updated on a frequent cadence, it enables robust benchmarking, forecasting, and investment analysis across countries, cities, and submarkets.

Key Highlights: Extensive Global Coverage: More than 7 million verified hotel and short-term rental properties across 200+ countries.

Unified Market View: Combines professional rental data, OTA listings, and hotel system performance for complete supply visibility.

Comprehensive Metrics: Includes occupancy, ADR, RevPAR, booking patterns, and property-level attributes.

Standardized Data Structure: Harmonized schema for cross-market and cross-segment analysis.

Flexible Delivery: Available via secure API or downloadable datasets with customizable geography and temporal depth.

Use It To: Analyze total lodging supply and demand across regions and property types.

Benchmark market performance between hotels and short-term rentals.

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BackgroundElectroanatomic mapping systems are used to support electrophysiology research. Data exported from these systems is stored in proprietary formats which are challenging to access and storage-space inefficient. No previous work has made available an open-source platform for parsing and interrogating this data in a standardized format. We therefore sought to develop a standardized, open-source data structure and associated computer code to store electroanatomic mapping data in a space-efficient and easily accessible manner.MethodsA data structure was defined capturing the available anatomic and electrical data. OpenEP, implemented in MATLAB, was developed to parse and interrogate this data. Functions are provided for analysis of chamber geometry, activation mapping, conduction velocity mapping, voltage mapping, ablation sites, and electrograms as well as visualization and input/output functions. Performance benchmarking for data import and storage was performed. Data import and analysis validation was performed for chamber geometry, activation mapping, voltage mapping and ablation representation. Finally, systematic analysis of electrophysiology literature was performed to determine the suitability of OpenEP for contemporary electrophysiology research.ResultsThe average time to parse clinical datasets was 400 ± 162 s per patient. OpenEP data was two orders of magnitude smaller than compressed clinical data (OpenEP: 20.5 ± 8.7 Mb, vs clinical: 1.46 ± 0.77 Gb). OpenEP-derived geometry metrics were correlated with the same clinical metrics (Area: R2 = 0.7726, P < 0.0001; Volume: R2 = 0.5179, P < 0.0001). Investigating the cause of systematic bias in these correlations revealed OpenEP to outperform the clinical platform in recovering accurate values. Both activation and voltage mapping data created with OpenEP were correlated with clinical values (mean voltage R2 = 0.8708, P < 0.001; local activation time R2 = 0.8892, P < 0.0001). OpenEP provides the processing necessary for 87 of 92 qualitatively assessed analysis techniques (95%) and 119 of 136 quantitatively assessed analysis techniques (88%) in a contemporary cohort of mapping studies.ConclusionsWe present the OpenEP framework for evaluating electroanatomic mapping data. OpenEP provides the core functionality necessary to conduct electroanatomic mapping research. We demonstrate that OpenEP is both space-efficient and accurately representative of the original data. We show that OpenEP captures the majority of data required for contemporary electroanatomic mapping-based electrophysiology research and propose a roadmap for future development.

Intelligence Analysis Software Market Outlook

According to our latest research, the global intelligence analysis software market size stood at USD 6.4 billion in 2024, reflecting robust adoption across security, defense, and commercial sectors. The market is expected to grow at a CAGR of 13.8% during the forecast period, reaching USD 19.8 billion by 2033. This growth is primarily driven by escalating cyber threats, the increasing complexity of global security environments, and the growing demand for real-time data analytics in decision-making processes across public and private organizations. The integration of artificial intelligence (AI) and machine learning (ML) into intelligence analysis platforms is further accelerating market expansion, enabling organizations to process vast datasets and derive actionable insights with unprecedented speed and accuracy.

One of the most significant growth factors in the intelligence analysis software market is the intensification of global security threats, including cyberattacks, terrorism, and transnational crime. Organizations are under immense pressure to proactively identify and mitigate risks before they escalate. As a result, intelligence analysis solutions that can aggregate, correlate, and analyze data from multiple sources in real time have become indispensable. The adoption of advanced analytics, natural language processing, and predictive modeling is helping government agencies and enterprises detect patterns, anticipate threats, and respond with greater agility. Additionally, the proliferation of IoT devices and the expansion of digital infrastructure have exponentially increased the volume and complexity of data, necessitating sophisticated software tools for effective intelligence gathering and analysis.

Another key driver is the rapid digital transformation across both public and private sectors. Enterprises are increasingly leveraging intelligence analysis software to gain competitive advantage, enhance operational efficiency, and ensure regulatory compliance. In sectors such as finance, healthcare, and critical infrastructure, the ability to detect fraud, monitor transactions, and safeguard sensitive information is paramount. The rise of cloud computing and the availability of scalable, cost-effective SaaS solutions have democratized access to advanced intelligence tools, enabling even small and medium enterprises to benefit from cutting-edge analytics capabilities. Furthermore, the integration of AI-powered automation has reduced the manual burden on analysts, allowing organizations to focus on higher-value strategic activities.

The intelligence analysis software market is also benefiting from heightened government investments in national security and public safety. Many countries are prioritizing the modernization of their intelligence and law enforcement agencies, allocating substantial budgets for the procurement of advanced analytical platforms. Initiatives aimed at improving cross-agency information sharing, enhancing situational awareness, and supporting evidence-based policymaking are driving demand for interoperable and scalable intelligence solutions. The trend towards public-private partnerships in security and intelligence is further expanding the addressable market, as commercial organizations collaborate with government bodies to combat evolving threats and ensure resilience against emerging risks.

In this rapidly evolving landscape, the emergence of the All-Source Intelligence Platform is playing a transformative role. This platform integrates data from a multitude of sources, including open-source intelligence, signals intelligence, and human intelligence, to provide a comprehensive view of the threat environment. By leveraging advanced analytics and machine learning, the All-Source Intelligence Platform enables organizations to detect patterns and anomalies that would otherwise go unnoticed. This holistic approach is particularly valuable in addressing complex security challenges, as it allows for more informed decision-making and proactive threat mitigation. As the demand for real-time intelligence continues to grow, the adoption of all-source platforms is expected to accelerate, offering organizations a powerful tool to navigate the complexities of modern security landscapes.

Regionally, North America continues to dominate the intelligen