This dataset from Dimensions.ai contains all published articles, preprints, clinical trials, grants and research datasets that are related to COVID-19. This growing collection of research information now amounts to hundreds of thousands of items, and it is the only dataset of its kind. You can find an overview of the content in this interactive Data Studio dashboard: https://reports.dimensions.ai/covid-19/ The full metadata includes the researchers and organizations involved in the research, as well as abstracts, open access status, research categories and much more. You may wish to use the Dimensions web application to explore the dataset: https://covid-19.dimensions.ai/. This dataset is for researchers, universities, pharmaceutical & biotech companies, politicians, clinicians, journalists, and anyone else who wishes to explore the impact of the current COVID-19 pandemic. It is updated daily, and free for anyone to access. Please share this information with anyone you think would benefit from it. If you have any suggestions as to how we can improve our search terms to maximise the volume of research related to COVID-19, please contact us at support@dimensions.ai. About Dimensions: Dimensions is the largest database of research insight in the world. It contains a comprehensive collection of linked data related to the global research and innovation ecosystem, all in a single platform. This includes hundreds of millions of publications, preprints, grants, patents, clinical trials, datasets, researchers and organizations. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. This Covid-19 dataset is a subset of the full database. The full Dimensions database is also available on BigQuery, via subscription. Please visit www.dimensions.ai/bigquery to gain access.Más información

Academic Research Databases Market Outlook

The global academic research databases market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 6.2 billion by 2032, growing at a CAGR of 6.5% during the forecast period. The increasing demand for digital resources in academic and research institutions, along with the growing emphasis on online learning and resource accessibility, are key factors driving market growth.

One significant growth factor for the academic research databases market is the exponential increase in academic research activity worldwide. With the surge in the number of higher education institutions and research facilities, the demand for comprehensive and easily accessible databases has skyrocketed. These databases provide a centralized platform for researchers to access a wide array of scholarly articles, data sets, and other pertinent information, streamlining the research process and enhancing the quality of scholarly work.

Another driving force behind the market's expansion is the continuous technological advancements in database management and search functionalities. Modern academic research databases are equipped with sophisticated search algorithms, artificial intelligence, and machine learning capabilities that enable users to efficiently locate relevant information. These advancements not only improve user experience but also significantly reduce the time and effort required to conduct comprehensive literature reviews and gather data.

The increasing prevalence of interdisciplinary research is also contributing to the growth of the academic research databases market. Researchers today often work at the intersection of multiple disciplines, necessitating access to a diverse range of subject-specific databases. The availability of comprehensive databases that cover various fields such as science, technology, medicine, social sciences, and humanities supports this trend by providing researchers with the resources they need to explore and integrate knowledge from different domains.

From a regional perspective, North America holds the largest share of the academic research databases market, driven by the high concentration of leading academic and research institutions and substantial investments in research and development. Europe follows closely, with significant contributions from countries like the UK, Germany, and France. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid expansion of higher education infrastructure and increasing government support for research activities. Latin America and the Middle East & Africa, though smaller in market size, are also projected to experience steady growth due to rising academic and research initiatives in these regions.

Database Type Analysis

The academic research databases market is segmented by database type into bibliographic, full-text, numeric, multimedia, and others. Bibliographic databases, which include indexes and abstracts of research articles, play a crucial role in helping researchers locate relevant literature. These databases have been foundational in academic research, providing essential references and citation tracking that are pivotal for scholarly work. Their significance remains high due to the increasing volume of academic publications and the need for comprehensive literature searches.

Full-text databases provide complete access to research articles, journals, and other scholarly materials, making them indispensable for researchers who require in-depth study materials. The convenience of accessing entire articles, rather than just abstracts or summaries, significantly enhances the research process. Full-text databases are particularly valuable in fields such as medicine, where access to full clinical study reports, reviews, and case studies is critical for evidence-based practice.

Numeric databases, which offer access to statistical and numerical data, are essential for researchers in fields like economics, social sciences, and the natural sciences. These databases provide valuable data sets that can be used for quantitative analysis, modeling, and empirical research. The increasing emphasis on data-driven research and the availability of large data sets are propelling the demand for numeric databases.

Multimedia databases, which include audio, video, and other multimedia content, are gaining traction in academic research. These databases are particularly useful in disciplines such a

Dimensions is the largest database of research insight in the world. It represents the most comprehensive collection of linked data related to the global research and innovation ecosystem available in a single platform. Because Dimensions maps the entire research lifecycle, you can follow academic and industry research from early stage funding, through to output and on to social and economic impact. Businesses, governments, universities, investors, funders and researchers around the world use Dimensions to inform their research strategy and make evidence-based decisions on the R&D and innovation landscape. With Dimensions on Google BigQuery, you can seamlessly combine Dimensions data with your own private and external datasets; integrate with Business Intelligence and data visualization tools; and analyze billions of data points in seconds to create the actionable insights your organization needs. Examples of usage: Competitive intelligence Horizon-scanning & emerging trends Innovation landscape mapping Academic & industry partnerships and collaboration networks Key Opinion Leader (KOL) identification Recruitment & talent Performance & benchmarking Tracking funding dollar flows and citation patterns Literature gap analysis Marketing and communication strategy Social and economic impact of research About the data: Dimensions is updated daily and constantly growing. It contains over 112m linked research publications, 1.3bn+ citations, 5.6m+ grants worth $1.7trillion+ in funding, 41m+ patents, 600k+ clinical trials, 100k+ organizations, 65m+ disambiguated researchers and more. The data is normalized, linked, and ready for analysis. Dimensions is available as a subscription offering. For more information, please visit www.dimensions.ai/bigquery and a member of our team will be in touch shortly. If you would like to try our data for free, please select "try sample" to see our openly available Covid-19 data.En savoir plus

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

Database Management System Market Outlook

According to our latest research, the global Database Management System (DBMS) market size reached USD 79.3 billion in 2024, demonstrating robust expansion with a CAGR of 13.2% from 2025 to 2033, and is forecasted to attain USD 236.8 billion by 2033. The market’s rapid growth is primarily driven by the exponential increase in data generation across industries, the rising adoption of cloud-based solutions, and the growing need for real-time data analytics and security. As organizations increasingly recognize the strategic value of data, DBMS solutions are becoming indispensable for efficient data storage, access, and management.

A major growth factor propelling the Database Management System market is the surge in digital transformation initiatives across both public and private sectors. Industries such as BFSI, healthcare, retail, and manufacturing are generating vast volumes of structured and unstructured data, necessitating sophisticated DBMS platforms for effective data handling. The proliferation of IoT devices, social media, and e-commerce platforms has further amplified the need for scalable and secure database solutions that can process diverse data types in real time. Additionally, the integration of artificial intelligence and machine learning with DBMS is enabling organizations to derive actionable insights, automate routine processes, and improve decision-making, thereby fueling market demand.

Another key driver is the shift towards cloud-based database management systems, which offer unparalleled flexibility, scalability, and cost efficiency compared to traditional on-premises solutions. Cloud DBMS platforms are particularly attractive to small and medium enterprises (SMEs) that lack the resources for extensive IT infrastructure investments, allowing them to leverage enterprise-grade data management capabilities on a subscription basis. Furthermore, with the advent of hybrid and multi-cloud environments, organizations can now optimize their data architecture for performance, redundancy, and compliance, further accelerating the adoption of cloud DBMS solutions globally.

Regulatory compliance and data security concerns are also catalyzing the growth of the Database Management System market. Governments and industry bodies worldwide are introducing stringent regulations around data privacy, storage, and access, compelling organizations to upgrade their database infrastructure. Advanced DBMS solutions now incorporate robust encryption, granular access controls, and automated compliance monitoring, ensuring that sensitive data is protected and regulatory obligations are met. This heightened focus on data governance is prompting enterprises to invest in next-generation DBMS technologies, thereby expanding the market’s growth trajectory.

Regionally, North America continues to dominate the Database Management System market owing to its advanced IT infrastructure, strong presence of leading market players, and early adoption of emerging technologies. Europe follows closely, driven by stringent data protection regulations and increasing digitalization across industries. The Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, burgeoning IT and telecom sectors, and a rising number of SMEs embracing cloud-based solutions. Latin America and the Middle East & Africa are also experiencing steady growth, supported by expanding internet penetration and government-led digital initiatives. This regional diversity ensures that the DBMS market remains dynamic and resilient to global economic fluctuations.

Component Analysis

The Database Management System market is distinctly segmented by component into software and services, each playing a critical role in the overall ecosystem. The software segment, which encompasses both relational and non-relational DBMS platforms, forms the backbone of the market and accounts for the majority of revenue share. This dominance is attributed to the conti

Artificial Intelligence (AI) Training Dataset Market Outlook

According to our latest research, the global Artificial Intelligence (AI) Training Dataset market size reached USD 3.15 billion in 2024, reflecting robust industry momentum. The market is expanding at a notable CAGR of 20.8% and is forecasted to attain USD 20.92 billion by 2033. This impressive growth is primarily attributed to the surging demand for high-quality, annotated datasets to fuel machine learning and deep learning models across diverse industry verticals. The proliferation of AI-driven applications, coupled with rapid advancements in data labeling technologies, is further accelerating the adoption and expansion of the AI training dataset market globally.

One of the most significant growth factors propelling the AI training dataset market is the exponential rise in data-driven AI applications across industries such as healthcare, automotive, retail, and finance. As organizations increasingly rely on AI-powered solutions for automation, predictive analytics, and personalized customer experiences, the need for large, diverse, and accurately labeled datasets has become critical. Enhanced data annotation techniques, including manual, semi-automated, and fully automated methods, are enabling organizations to generate high-quality datasets at scale, which is essential for training sophisticated AI models. The integration of AI in edge devices, smart sensors, and IoT platforms is further amplifying the demand for specialized datasets tailored for unique use cases, thereby fueling market growth.

Another key driver is the ongoing innovation in machine learning and deep learning algorithms, which require vast and varied training data to achieve optimal performance. The increasing complexity of AI models, especially in areas such as computer vision, natural language processing, and autonomous systems, necessitates the availability of comprehensive datasets that accurately represent real-world scenarios. Companies are investing heavily in data collection, annotation, and curation services to ensure their AI solutions can generalize effectively and deliver reliable outcomes. Additionally, the rise of synthetic data generation and data augmentation techniques is helping address challenges related to data scarcity, privacy, and bias, further supporting the expansion of the AI training dataset market.

The market is also benefiting from the growing emphasis on ethical AI and regulatory compliance, particularly in data-sensitive sectors like healthcare, finance, and government. Organizations are prioritizing the use of high-quality, unbiased, and diverse datasets to mitigate algorithmic bias and ensure transparency in AI decision-making processes. This focus on responsible AI development is driving demand for curated datasets that adhere to strict quality and privacy standards. Moreover, the emergence of data marketplaces and collaborative data-sharing initiatives is making it easier for organizations to access and exchange valuable training data, fostering innovation and accelerating AI adoption across multiple domains.

From a regional perspective, North America currently dominates the AI training dataset market, accounting for the largest revenue share in 2024, driven by significant investments in AI research, a mature technology ecosystem, and the presence of leading AI companies and data annotation service providers. Europe and Asia Pacific are also witnessing rapid growth, with increasing government support for AI initiatives, expanding digital infrastructure, and a rising number of AI startups. While North America sets the pace in terms of technological innovation, Asia Pacific is expected to exhibit the highest CAGR during the forecast period, fueled by the digital transformation of emerging economies and the proliferation of AI applications across various industry sectors.

Data Type Analysis

The AI training dataset market is segmented by data type into Text, Image/Video, Audio, and Others, each playing a crucial role in powering different AI applications. Text da

The Academic Database market plays a vital role in the modern educational and research landscape, providing organizations with the essential tools they need to access, manage, and analyze vast sets of information. These databases serve as repositories that supply researchers, educators, and students with critical da

At CompanyData.com (BoldData), we provide verified company data sourced directly from official trade registers. Our global IT company dataset gives you access to 6 million IT businesses worldwide, including software firms, tech consultancies, system integrators, SaaS providers, and other IT service companies. Every record is sourced from authoritative local registries, ensuring unmatched accuracy, coverage, and compliance.

This dataset is built for professionals who need reliable, structured insights into the global technology sector. Each company profile includes firmographic details such as legal entity name, registration number, business structure, size, revenue range, and industry classification (NACE/SIC). In addition, you'll find direct contact information for decision-makers—emails, mobile numbers, job titles, and department roles—helping you connect with the right people instantly.

Whether you're validating suppliers for compliance, identifying high-potential leads for sales, enriching your CRM data, or building AI models with clean and segmented business intelligence, our IT dataset is designed to support a wide range of critical use cases. From global enterprises to fast-scaling startups, our data empowers businesses to move faster and smarter.

We offer multiple delivery methods tailored to your needs. Choose from custom bulk files, access data through our self-service platform, integrate it directly into your systems via real-time API, or let us enrich your existing database with missing fields and decision-maker insights.

With a database spanning 380 million companies globally, deep IT sector segmentation, and proven expertise in sourcing from local trade registers, CompanyData.com (BoldData) helps your team identify opportunities, ensure compliance, and scale efficiently—wherever your growth takes you.

In-Memory Database Market size was valued at USD 9.84 Billion in 2024 and is projected to reach USD 35.52 Billion by 2031, growing at a CAGR of 19.20% during the forecast period 2024-2031.

Global In-Memory Database Market Drivers

Demand for Real-Time Analytics: Companies are depending more and more on real-time data to make prompt, well-informed choices. Because they speed up data processing, in-memory databases are crucial for real-time analytics applications. Growth of Big Data and IoT: Large volumes of data are generated by the spread of big data and the Internet of Things (IoT), which must be quickly processed and analyzed. Large data volumes can be handled by in-memory databases more effectively than by conventional disk-based databases. Both Scalability and Performance Requirements: Databases that can scale to accommodate growing data loads without sacrificing performance are essential for growing enterprises. Growing businesses can benefit from the great scalability and performance of in-memory databases. Developments in Memory Technologies: As memory technologies like RAM and flash memory continue to progress, in-memory databases are becoming more widely available and reasonably priced for a greater variety of uses. Quicker Decision-Making Is Required: Businesses must act fast in the current competitive environment in order to stay ahead. Decision-making processes can go more quickly because to in-memory databases' faster data access and processing speeds. Demand for Real-Time Personalization: To improve consumer experiences, real-time personalization is becoming more and more necessary as e-commerce and online services expand in popularity. Large volumes of client data may be instantly analyzed by in-memory databases, allowing them to provide tailored content and recommendations.

Success.ai’s Manufacturing Company Data and B2B Contact Data for Global Manufacturing Professionals empowers businesses to connect with key decision-makers in the manufacturing industry worldwide. With access to over 170 million verified professional profiles, this dataset includes critical contact information for executives, managers, engineers, and other professionals in manufacturing, supply chain, and production roles. Whether you're targeting plant managers, operations executives, or procurement officers, Success.ai ensures accurate and effective outreach.

Why Choose Success.ai’s Manufacturing Professionals Data?

1. Comprehensive Contact Information:
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for manufacturing leaders globally.

3. AI-validated data ensures 99% accuracy and up-to-date contact details for your outreach.

4. Global Reach Across Manufacturing Functions:

5. Includes profiles of manufacturing executives, plant managers, procurement specialists, engineers, and more.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East.

7. Continuously Updated Datasets:

8. Real-time data updates guarantee you always have the latest information about manufacturing professionals.

9. Ethical and Compliant:

10. Adheres to GDPR, CCPA, and other global privacy regulations for ethical use of data.

Data Highlights: - 170M+ Verified Professional Profiles: Includes manufacturing professionals from diverse industries. - 50M Work Emails: Verified and AI-validated for seamless communication. - 30M Company Profiles: Rich insights to support detailed targeting. - 700M Global Professional Profiles: Enriched data for broad business objectives.

Key Features of the Dataset:

1. Manufacturing Decision-Maker Profiles: Identify and connect with top-tier manufacturing professionals including operations leaders, plant managers, procurement officers, and senior executives.

2. Advanced Filters for Precision Targeting: Filter data by industry, company size, location, and specific job roles for optimal outreach.

3. AI-Driven Enrichment: Profiles enriched with actionable data to facilitate personalized engagement and higher success rates in campaigns.

Strategic Use Cases:

1. Supply Chain and Procurement Outreach:
2. Reach professionals involved in purchasing, supply chain management, and vendor relations.

3. Perfect for suppliers of equipment, materials, and logistics services to target key decision-makers in manufacturing.

4. Lead Generation for Manufacturing Solutions:

5. Promote manufacturing software, automation tools, and process optimization solutions.

6. Connect with professionals in charge of manufacturing operations to present cost-saving and efficiency-driving solutions.

7. Market Research and Industry Insights:

8. Gather data for industry trends, connect with thought leaders, and conduct targeted research in the global manufacturing sector.

9. Engage with professionals to build relationships and gain insights into evolving manufacturing practices.

10. Targeted Marketing Campaigns:

11. Design email marketing campaigns or direct outreach strategies targeting manufacturing decision-makers.

12. Utilize accurate contact data to drive higher engagement and conversion rates in your campaigns.

Why Choose Success.ai?

1. Best Price Guarantee: Enjoy the highest quality datasets at the most competitive pricing.

2. Seamless Integration: Easily integrate data into your CRM systems using APIs or download in the preferred format.

3. Data Accuracy with AI Validation: All profiles in this dataset are verified for 99% accuracy, ensuring confidence in the data for marketing, outreach, and decision-making.

4. Customizable and Scalable Solutions: Tailor the dataset to specific manufacturing sectors or job functions for more targeted outreach.

APIs for Enhanced Functionality:

1. Data Enrichment API: Enhance your existing records with verified manufacturing contact data to improve engagement and targeting.

2. Lead Generation API: Automate the lead generation process for manufacturing-specific campaigns to increase efficiency and scale.

Leverage Success.ai’s B2B Contact Data for Manufacturing Professionals to connect with key decision-makers in the global manufacturing industry. With verified emails, phone numbers, and continuously updated profiles, this data ensures that your outreach and communication efforts are impactful and precise.

Contact Success.ai now to elevate your manufacturing industry strategies with verified, AI-validated contact data. And remember—no one beats us on price. Period.

Explore our public notebook content!

Meta Kaggle Code is an extension to our popular Meta Kaggle dataset. This extension contains all the raw source code from hundreds of thousands of public, Apache 2.0 licensed Python and R notebooks versions on Kaggle used to analyze Datasets, make submissions to Competitions, and more. This represents nearly a decade of data spanning a period of tremendous evolution in the ways ML work is done.

Why we’re releasing this dataset

By collecting all of this code created by Kaggle’s community in one dataset, we hope to make it easier for the world to research and share insights about trends in our industry. With the growing significance of AI-assisted development, we expect this data can also be used to fine-tune models for ML-specific code generation tasks.

Meta Kaggle for Code is also a continuation of our commitment to open data and research. This new dataset is a companion to Meta Kaggle which we originally released in 2016. On top of Meta Kaggle, our community has shared nearly 1,000 public code examples. Research papers written using Meta Kaggle have examined how data scientists collaboratively solve problems, analyzed overfitting in machine learning competitions, compared discussions between Kaggle and Stack Overflow communities, and more.

The best part is Meta Kaggle enriches Meta Kaggle for Code. By joining the datasets together, you can easily understand which competitions code was run against, the progression tier of the code’s author, how many votes a notebook had, what kinds of comments it received, and much, much more. We hope the new potential for uncovering deep insights into how ML code is written feels just as limitless to you as it does to us!

Sensitive data

While we have made an attempt to filter out notebooks containing potentially sensitive information published by Kaggle users, the dataset may still contain such information. Research, publications, applications, etc. relying on this data should only use or report on publicly available, non-sensitive information.

Joining with Meta Kaggle

The files contained here are a subset of the KernelVersions in Meta Kaggle. The file names match the ids in the KernelVersions csv file. Whereas Meta Kaggle contains data for all interactive and commit sessions, Meta Kaggle Code contains only data for commit sessions.

File organization

The files are organized into a two-level directory structure. Each top level folder contains up to 1 million files, e.g. - folder 123 contains all versions from 123,000,000 to 123,999,999. Each sub folder contains up to 1 thousand files, e.g. - 123/456 contains all versions from 123,456,000 to 123,456,999. In practice, each folder will have many fewer than 1 thousand files due to private and interactive sessions.

The ipynb files in this dataset hosted on Kaggle do not contain the output cells. If the outputs are required, the full set of ipynbs with the outputs embedded can be obtained from this public GCS bucket: kaggle-meta-kaggle-code-downloads. Note that this is a "requester pays" bucket. This means you will need a GCP account with billing enabled to download. Learn more here: https://cloud.google.com/storage/docs/requester-pays

Questions / Comments

We love feedback! Let us know in the Discussion tab.

Happy Kaggling!

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

BCC Research Market Report says AI Training Dataset market is projected to grow from $1.8 billion in 2022 to $6.9 billion in 2028, at a CAGR of 25.4%.

The Global Graph Database Market, valued at USD 3.12 billion in 2024, is projected to grow at a 23.56% CAGR from 2025-30, driven by AI tools and low-latency query processing.

Global real-world evidence solutions market worth at USD 2.11 Billion in 2024, is expected to surpass USD 8.17 Billion by 2034, CAGR of 16.2% from 2025 to 2034.

A global self-hosted Market Research dataset containing all administrative divisions, cities, addresses, and zip codes for 247 countries. All geospatial data is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Global Zip Code Database (Market Research data)

• Address capture and validation

• Map and visualization

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Data export methodology

Our map data packages are offered in variable formats, including .csv. All geographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Fully and accurately geocoded

• Administrative areas with a level range of 0-4

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

For additional insights, you can combine the map data with:

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Times

Why do companies choose our Market Research databases

• Enterprise-grade service

• Reduce integration time and cost by 30%

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Russia RU: Business Enterprise Researchers: Per Thousand Employment in Industry data was reported at 3.546 Per 1000 in 2020. This records a decrease from the previous number of 3.618 Per 1000 for 2019. Russia RU: Business Enterprise Researchers: Per Thousand Employment in Industry data is updated yearly, averaging 4.275 Per 1000 from Dec 1998 (Median) to 2020, with 23 observations. The data reached an all-time high of 6.298 Per 1000 in 1998 and a record low of 3.355 Per 1000 in 2018. Russia RU: Business Enterprise Researchers: Per Thousand Employment in Industry data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Russian Federation – Table RU.OECD.MSTI: Number of Researchers and Personnel on Research and Development: Non OECD Member: Annual. In response to Russia's large-scale aggression against Ukraine, the OECD Council decided on 8 March 2022 to immediately suspend the participation of Russia and Belarus in OECD bodies. In view of this decision, the OECD suspended its solicitation of official statistics on R&D from Russian authorities, leading to the absence of more recent R&D statistics for this country in the OECD database, while previously compiled data are still available.The business enterprise sector includes all organisations and enterprises whose main activity is connected with the production of goods and services for sale, including those owned by the state, and private non-profit institutions serving the above-mentioned organisations. In practice however, R&D performed in this sector is carried out mostly by industrial research institutes other than enterprises. This particularity reflects the traditional organisation of Russian R&D.Headcount data include full-time personnel only, and hence are underestimated, while data in full-time equivalents (FTE) are calculated on the basis of both full-time and part-time personnel. This explains why the FTE data are greater than the headcount data.New budgetary procedures introduced in 2005 have resulted in items previously classified as GBARD being attributed to other headings and have affected the coverage and breakdown by socio-economic objective.;

Definition of MSTI variables 'Value Added of Industry' and 'Industrial Employment':

R&D data are typically expressed as a percentage of GDP to allow cross-country comparisons. When compiling such indicators for the business enterprise sector, one may wish to exclude, from GDP measures, economic activities for which the Business R&D (BERD) is null or negligible by definition. By doing so, the adjusted denominator (GDP, or Value Added, excluding non-relevant industries) better correspond to the numerator (BERD) with which it is compared to.

The MSTI variable 'Value added in industry' is used to this end:

It is calculated as the total Gross Value Added (GVA) excluding 'real estate activities' (ISIC rev.4 68) where the 'imputed rent of owner-occupied dwellings', specific to the framework of the System of National Accounts, represents a significant share of total GVA and has no R&D counterpart. Moreover, the R&D performed by the community, social and personal services is mainly driven by R&D performers other than businesses.

Consequently, the following service industries are also excluded: ISIC rev.4 84 to 88 and 97 to 98. GVA data are presented at basic prices except for the People's Republic of China, Japan and New Zealand (expressed at producers' prices).In the same way, some indicators on R&D personnel in the business sector are expressed as a percentage of industrial employment. The latter corresponds to total employment excluding ISIC rev.4 68, 84 to 88 and 97 to 98.

Survey and interview data from a study on the views and experiences of early career researchers (postdoctoral researchers) around the support from research organisations, funding bodies and career services and how this offer might be improved in the future. This applied to those employed inside and outside of academia. The data result from an online survey of early career social scientists (N=1048), interviews with a subset of the respondents (N=35) and with experts (N=9). The findings informed the strategy for careers advice and support provided by the Economic and Social Research Council through Doctoral Training Partnerships and Centres for Doctoral Training and the creation of new funding strands for early career researchers. The last two generations have seen a remarkable world-wide transformation of higher education (HE) into a core social sector with continually expanding local and global reach. Most nations are moving towards, or have already become, 'high participation' HE systems in which the majority of people will be educated to tertiary level. In the UK HE is at the same time a pillar of science and the innovation system, a primary driver of productivity at work, a major employer and a mainstay of cities and regions, and a national export industry where 300,000 non-EU students generated over 7 billion in export-related earnings for the UK in 2012-13. In 2012, 60 per cent of UK school leavers were expected to graduate from tertiary education over the lifetime, 45 per cent at bachelor degree level, compared to OECD means of 53/39 per cent. Higher education and the scientific research associated with universities have never been more important to UK society and government. HE is large and inclusive with a key role in mediating the future. Yet it is poorly understood. Practice has moved ahead of social science. There has been no integrated research centre dedicated to this important part of the UK. The Centre for Engaged Global Higher Education (CEGHE), which has been funded initially for five years by the Economic and Social Research Council (ESRC), now fills that gap. On behalf of the ESRC CEGHE conducts and disseminates research on all aspects of higher education (HE), in order to enhance student learning and the contributions of Higher Education Institutions (HEIs) to their communities; develop the economic, social and global engagement of and impacts of UK HE; and provide data resources and advice for government and stakeholder organisations in HE in the four nations of the UK and worldwide. CEGHE is organised in three closely integrated research programmes that are focused respectively on global, national-system and local aspects of HE. CEGHE's team of researchers work on roblems and issues with broad application to the improvement of HE; develop new theories about and ways of researching HE and its social and economic contributions; and respond also to new issues as they arise, within the framework of its research programmes. An important part of CEGHE's work is the preparation and provision of data, briefings and advice to national and international policy makers, for HEIs themselves, and for UK organisations committed to fostering HE and its engagement with UK communities and stakeholders. CEGHE's seminars and conferences are open to the public and it is dedicated to disseminating its research findings on a broad basis through published papers, media articles and its website and social media platform. CEGHE is led by Professor Simon Marginson, one of the world's leading researchers on higher education matters with a special expertise in global and international aspects of the sector. It works with partner research universities in Sheffield, Lancaster, Ireland, Australia, South Africa, Netherlands, China, Hong Kong SAR, Japan and USA. Among the issues currently the subject of CEGHE research projects are inquiries into ways and means of measuring and enhancing HE's contribution to the public good, university-industry collaboration in research, the design of an optimal system of tuition loans, a survey of the effects of tuition debt on the life choices of graduates such as investment in housing and family formation, the effects of widening participation on social opportunities in HE especially for under-represented social groups, trends and developments in HE in Europe and East Asia and the implications for UK HE, the emergence of new HE providers in the private and FE sectors, the future academic workforce in the UK and the skills that will be needed, student learning and knowledge in science and engineering, and developments in online HE. Online survey of self-selecting early-career social scientists. Interviews of a sub-sample of respondents to the survey. Interviews with a selection of experts in relation to early career social scientists. detailed methods information is described in the attached report.

Global Database Software market size is expected to reach $311.05 billion by 2029 at 10.7%, segmented as by type, database operation management, database maintenance management