About this dataset

Context

The dataset presents median household incomes for various household sizes in England, AR, 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, England did not include 5, 6, or 7-person households. Across the different household sizes in England the mean income is $64,018, and the standard deviation is $32,785. The coefficient of variation (CV) is 51.21%. 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 1-person households, with an income of $20,006. It then further increased to $59,740 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/england-ar-median-household-income-by-household-size.jpeg" alt="England, AR 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 England median household income. You can refer the same here

The Health Survey for England, 2000-2001: Small Area Estimation Teaching Dataset was prepared as a resource for those interested in learning introductory small area estimation techniques. It was first presented as part of a workshop entitled 'Introducing small area estimation techniques and applying them to the Health Survey for England using Stata'. The data are accompanied by a guide that includes a practical case study enabling users to derive estimates of disability for districts in the absence of survey estimates. This is achieved using various models that combine information from ESDS government surveys with other aggregate data that are reliably available for sub-national areas. Analysis is undertaken using Stata statistical software; all relevant syntax is provided in the accompanying '.do' files.

The data files included in this teaching resource contain HSE variables and data from the Census and Mid-year population estimates and projections that were developed originally by the National Statistical agencies, as follows:

• The main data file, 'hse_data.dta', is a reduced version of the HSE for 2000 and 2001. In order to combine data from two years of the HSE in a consistent way some changes have been made to the weights in each year. Additionally, some recoding of the limiting long term illness (LLTI), disability and the age variable has also been undertaken.
• File 'practical_1_task_5_data.dta' contains population counts and model mobility disability rates (estimated during practical 1) distinguishing single year of age and sex for the six case study districts.
• File 'practical_2_data.dta' contains the aggregate data required for Practical 2, including age- and sex-specific rates of LLTI (Census) for six UK case study districts, age- and sex-specific rates of mobility disability for England (HSE), and population counts for the six districts.
• File 'pop_data_practical_3.dta' contains population counts for the six districts (by age, sex and LLTI status) required for practical 3

The original HSEs for 2000 and 2001 are held at the UK Data Archive under SNs 4628 and 4912 respectively. Full details of the recoding of HSE variables and how the aggregate data was produced can be found in the data documentation.

This unrestricted access data collection is freely available to download under an Open Government Licence from the UK Data Service. Note that the files should be unzipped/saved to the C: drive of the computer to be used; all syntax assumes files are saved at this location.

About this dataset

Context

The dataset presents median household incomes for various household sizes in English, IN, 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, English did not include 3, 5, or 7-person households. Across the different household sizes in English the mean income is $45,508, and the standard deviation is $28,721. The coefficient of variation (CV) is 63.11%. 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 1-person households, with an income of $27,551. It then further decreased to 17,565 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/english-in-median-household-income-by-household-size.jpeg" alt="English, IN 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 English median household income. You can refer the same here

Tiny English

A collection of short texts that have been curated for long-term human value. The texts in this dataset have been filtered from the falcon-refinedweb and minipile datasets to ensure better quality and tiny in size. The tiny-en dataset is concise and small in size, yet highly diverse, making it an excellent resource for training natural language processing models. Despite its compact size, the dataset offers a wide range of content that has been carefully selected for… See the full description on the dataset page: https://huggingface.co/datasets/nampdn-ai/mini-en.

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

Snapshot img

Graph Database Market Size 2025-2029

The graph database market size is forecast to increase by USD 11.24 billion at a CAGR of 29% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing popularity of open knowledge networks and the rising demand for low-latency query processing. These trends reflect the growing importance of real-time data analytics and the need for more complex data relationships to be managed effectively. However, the market also faces challenges, including the lack of standardization and programming flexibility. These obstacles require innovative solutions from market participants to ensure interoperability and ease of use for businesses looking to adopt graph databases.
Companies seeking to capitalize on market opportunities must focus on addressing these challenges while also offering advanced features and strong performance to differentiate themselves. Effective navigation of these dynamics will be crucial for success in the evolving graph database landscape. Compliance requirements and data privacy regulations drive the need for security access control and data anonymization methods. Graph databases are deployed in both on-premises data centers and cloud regions, providing flexibility for businesses with varying IT infrastructures.

What will be the Size of the Graph Database Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

In the dynamic market, security and data management are increasingly prioritized. Authorization mechanisms and encryption techniques ensure data access control and confidentiality. Query optimization strategies and indexing enhance query performance, while data anonymization methods protect sensitive information. Fault tolerance mechanisms and data governance frameworks maintain data availability and compliance with regulations. Data quality assessment and consistency checks address data integrity issues, and authentication protocols secure concurrent graph updates. This model is particularly well-suited for applications in social networks, recommendation engines, and business processes that require real-time analytics and visualization.

Graph database tuning and monitoring optimize hardware resource usage and detect performance bottlenecks. Data recovery procedures and replication methods ensure data availability during disasters and maintain data consistency. Data version control and concurrent graph updates address versioning and conflict resolution challenges. Data anomaly detection and consistency checks maintain data accuracy and reliability. Distributed transactions and data recovery procedures ensure data consistency across nodes in a distributed graph database system.

How is this Graph Database Industry segmented?

The graph database industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Large enterprises
  SMEs


Type

  RDF
  LPG


Solution

  Native graph database
  Knowledge graph engines
  Graph processing engines
  Graph extension


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By End-user Insights

The Large enterprises segment is estimated to witness significant growth during the forecast period. In today's business landscape, large enterprises are turning to graph databases to manage intricate data relationships and improve decision-making processes. Graph databases offer unique advantages over traditional relational databases, enabling superior agility in modeling and querying interconnected data. These systems are particularly valuable for applications such as fraud detection, supply chain optimization, customer 360 views, and network analysis. Graph databases provide the scalability and performance required to handle large, dynamic datasets and uncover hidden patterns and insights in real time. Their support for advanced analytics and AI-driven applications further bolsters their role in enterprise digital transformation strategies. Additionally, their flexibility and integration capabilities make them well-suited for deployment in hybrid and multi-cloud environments.

Graph databases offer various features that cater to diverse business needs. Data lineage tracking ensures accountability and transparency, while graph analytics engines provide advanced insights. Graph database benchmarking helps organizations evaluate performance, and relationship property indexing streamlines data access. Node relationship management facilitates complex data modeling, an

Numbers of enterprises and local units produced from a snapshot of the Inter-Departmental Business Register (IDBR) taken on 8 March 2024.

About this dataset

Context

The dataset presents median household incomes for various household sizes in North English, IA, 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, North English did not include 5, 6, or 7-person households. Across the different household sizes in North English the mean income is $60,348, and the standard deviation is $26,382. The coefficient of variation (CV) is 43.72%. 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 1-person households, with an income of $29,950. It then further increased to $85,272 for 4-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/north-english-ia-median-household-income-by-household-size.jpeg" alt="North English, IA 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 North English median household income. You can refer the same here

AONBs are designated areas where protection is afforded to protect and manage the areas for visitors and local residents. Under the Countryside and Rights of Way Act 2000, Natural England has the power to designate areas of outstanding natural beauty (AONBs) in England that are outside national parks and that are considered to have such natural beauty it is desirable they are conserved and enhanced; issue a variation order to change an existing AONB boundary. It also holds a duty to give advice on developments taking place in an AONB; take into account the conservation and enhancement of AONBs in its work. Full metadata can be viewed on data.gov.uk.

Abstract copyright UK Data Service and data collection copyright owner. To provide quantitative estimates of the principal demographic and social characteristics of emigrants to the USA from the UK, and to test the usefulness of the passenger lists of American ports for this purpose. Main Topics: Variables Age, sex, occupation, nationality, type and size of migrating household; type of vessel, class of accomodation, data and port of arrival and departure, destination and place of last residence (where available). Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. Simple random sample one-in-five of ships entering five US ports Compilation or synthesis of existing material

Snapshot img

AI Training Dataset Market Size 2025-2029

The AI training dataset market size is forecast to increase by USD 7.33 billion at a CAGR of 29% between 2024 and 2029.

The market is witnessing significant growth, driven by the proliferation and increasing complexity of foundational AI models. As AI applications expand across industries, the demand for high-quality, diverse, and representative training datasets is escalating. This trend is leading companies to invest heavily in acquiring and curating datasets, shifting their focus from data quantity to data quality. However, this strategic shift presents challenges. Navigating data privacy, security, and copyright complexities is becoming increasingly important. Deep learning algorithms and serverless functions are emerging technologies that are gaining traction in the market.
Companies must invest in robust infrastructure and expertise to effectively manage, preprocess, and label their datasets for optimal AI model performance. By addressing these challenges and capitalizing on the opportunities presented by the growing demand for high-quality training datasets, companies can gain a competitive edge in the AI market. Ensuring compliance with regulations and protecting sensitive information is crucial to avoid potential legal and reputational risks. Simultaneously, generative AI is becoming increasingly pervasive as a co-developer and application component, further expanding the market's potential.

What will be the Size of the AI Training Dataset Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

In the dynamic market, classification accuracy and data labeling accuracy are paramount for businesses seeking to optimize their machine learning models. Data mining algorithms and computer vision algorithms are employed to extract valuable insights from raw data, while inference latency and model training time are critical factors for efficient model deployment. Model selection criteria, such as AUC score evaluation and precision and recall, are essential for assessing the performance of various machine learning libraries and deep learning frameworks. Regularization techniques, hyperparameter tuning, and loss function optimization are integral to enhancing model complexity analysis and regression performance.

Time series forecasting and cross validation strategy are essential for businesses seeking to make data-driven decisions based on historical trends. Neural network architecture and natural language processing are advanced techniques that can significantly improve model accuracy and monitoring tools are necessary for anomaly detection methods and model retraining schedules. Resource utilization and model deployment strategy are crucial considerations for businesses looking to optimize their AI investments. Gradient descent methods and backpropagation algorithm are fundamental techniques for optimizing model performance, while statistical modeling techniques and F1 score calculation offer additional insights for model evaluation.

How is this AI Training Dataset Industry segmented?

The AI training dataset industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service Type

  Text
  Image or video
  Audio


Deployment

  On-premises
  Cloud


Type

  Unstructured data
  Structured data
  Semi-structured data


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Service Type Insights

The Text segment is estimated to witness significant growth during the forecast period. The cloud-based data storage market is experiencing significant growth due to the increasing demand for large volumes of diverse, high-quality data for artificial intelligence (AI) training, particularly in the field of natural language processing and large language models (LLMs). The importance of this market segment lies in the vast quantities of data required for pre-training, instruction fine-tuning, and safety alignment. Pre-training datasets, which can consist of petabytes of information sourced from the public web and supplemented with digitized books, academic papers, and code repositories, form the foundation. However, the true value and differentiation come from subsequent stages. Natural language processing, intelligent task routing, and computer vision integration are also key features that enhance the capabilities of these platforms.

Model deployment workflows and scalable data infrastructure are essential components of the

SUMMARYTo be viewed on combination with the dataset ‘Area of accessible green and blue space per 1000 population (England)’ and its associated metadata.This dataset identifies administrative areas for which Public Right of Way (PRoW) data was not available. While some gaps in the PRoW data will have been partially filled in by the OS MasterMap Highways Network Paths dataset, due to overlap between the two, some gaps will still remain. The area of accessible green/blue space in the areas highlighted by this dataset could be slightly under represented in the ‘Area of accessible green and blue space per 1000 population (England)’ dataset.COPYRIGHT NOTICEProduced by Ribble Rivers Trust. Contains Ordnance Survey data © Crown copyright and database right 2020. Contains public sector information licensed under the Open Government Licence v3.0.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Thanks to ahmedheakl/arzen-llm-speech-ds as this dataset was built upon it ✨

The dataset was constructed using ahmed's dataset and different videos from the youtube. The scraped data doubled the initial dataset size after deduplication and cleaning.

  Citation

If you use this dataset, please cite it as follows: @misc{rashad2024arabic, author = {Mohamed Rashad}, title = {arabic-english-code-switching}, year = {2024}, publisher = {Hugging Face}, url =… See the full description on the dataset page: https://huggingface.co/datasets/MohamedRashad/arabic-english-code-switching.

Standard deviation of responses for 'Life Satisfaction' in the First ONS Annual Experimental Subjective Wellbeing survey.

The Office for National Statistics has included the four subjective well-being questions below on the Annual Population Survey (APS), the largest of their household surveys.

• Overall, how satisfied are you with your life nowadays?
• Overall, to what extent do you feel the things you do in your life are worthwhile?
• Overall, how happy did you feel yesterday?
• Overall, how anxious did you feel yesterday?

This dataset presents results from the first of these questions, "Overall, how satisfied are you with your life nowadays?". Respondents answer these questions on an 11 point scale from 0 to 10 where 0 is ‘not at all’ and 10 is ‘completely’. The well-being questions were asked of adults aged 16 and older.

Well-being estimates for each unitary authority or county are derived using data from those respondents who live in that place. Responses are weighted to the estimated population of adults (aged 16 and older) as at end of September 2011.

The data cabinet also makes available the proportion of people in each county and unitary authority that answer with ‘low wellbeing’ values. For the ‘life satisfaction’ question answers in the range 0-6 are taken to be low wellbeing.

This dataset contains the standard deviation of the responses, alongside the corresponding sample size.

The ONS survey covers the whole of the UK, but this dataset only includes results for counties and unitary authorities in England, for consistency with other statistics available at this website.

At this stage the estimates are considered ‘experimental statistics’, published at an early stage to involve users in their development and to allow feedback. Feedback can be provided to the ONS via this email address.

The APS is a continuous household survey administered by the Office for National Statistics. It covers the UK, with the chief aim of providing between-census estimates of key social and labour market variables at a local area level. Apart from employment and unemployment, the topics covered in the survey include housing, ethnicity, religion, health and education. When a household is surveyed all adults (aged 16+) are asked the four subjective well-being questions.

The 12 month Subjective Well-being APS dataset is a sub-set of the general APS as the well-being questions are only asked of persons aged 16 and above, who gave a personal interview and proxy answers are not accepted. This reduces the size of the achieved sample to approximately 120,000 adult respondents in England.

The original data is available from the ONS website.

Detailed information on the APS and the Subjective Wellbeing dataset is available here.

As well as collecting data on well-being, the Office for National Statistics has published widely on the topic of wellbeing. Papers and further information can be found here.

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

Success.ai offers B2B marketing data across industries and geographies, tailored to fit your specific business needs. With our white-glove service, you’ll receive curated, ready-to-use company datasets without the hassle of managing data platforms yourself. Whether you’re looking for UK B2B data or global datasets, Success.ai ensures a seamless experience with the most accurate and up-to-date information in the market.

API Features:

• Real-Time Data Access: Our APIs ensure you can integrate and access the latest company data directly into your systems, providing real-time updates and seamless data flow.
• Scalable Integration: Designed to handle high-volume requests efficiently, our APIs can support extensive data operations, perfect for businesses of all sizes.
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Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.

Why Choose Success.ai?

• Best Price Guarantee: We offer industry-leading pricing and beat any competitor.
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• Comprehensive Data: Over 15 data points, including company size, industry, funding, and technologies used.
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• API Access: Our robust APIs and customizable data solutions provide the flexibility and scalability needed to adapt to changing market conditions and business needs.
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Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new...

Labour Force Survey (LFS) estimates including measures of uncertainty of the number of households by household size, for regions of England and also Scotland and Wales.

🍷 FineWeb

15 trillion tokens of the finest data the 🌐 web has to offer

  What is it?

The 🍷 FineWeb dataset consists of more than 18.5T tokens (originally 15T tokens) of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library. 🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb.

United Kingdom UK: Urban Land Area data was reported at 58,698.750 sq km in 2010. This stayed constant from the previous number of 58,698.750 sq km for 2000. United Kingdom UK: Urban Land Area data is updated yearly, averaging 58,698.750 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 58,698.750 sq km in 2010 and a record low of 58,698.750 sq km in 2010. United Kingdom UK: Urban Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

This dataset was compiled for the Regional Seabed Monitoring Plan (RSMP) baseline assessment reported in Cooper & Barry (2017).

The dataset comprises of 33,198 macrofaunal samples (83% with associated data on sediment particle size composition) covering large parts of the UK continental shelf. Whilst most samples come from existing datasets, also included are 2,500 new samples collected specifically for the purpose of this study. These new samples were collected during 2014-2016 from the main English aggregate dredging regions (Humber, Anglian, Thames, Eastern English Channel and South Coast) and at four individual, isolated extraction sites where the RSMP methodology is also being adopted (e.g. Area 457, North-West dredging region; Area 392, North-West dredging region; Area 376, Bristol Channel dredging region; Goodwin Sands, English Channel). This work was funded by the aggregates industry, and carried out by contractors on their behalf. Samples were collected in accordance with a detailed protocols document which included control measures to ensure the quality of faunal and sediment sample processing. Additional samples were acquired to fill in gaps in spatial coverage and to provide a contemporary baseline for sediment composition.

Sources of existing data include both government and industry, with contributions from the marine aggregate dredging, offshore wind, oil and gas, nuclear and port and harbour sectors. Samples have been collected over a period of 48 years from 1969 to 2016, although the vast majority (96%) were acquired since 2000. Samples have been collected during every month of the year, although there is a clear peak during summer months when weather conditions are generally more favourable for fieldwork.

The DOI includes multiple files for use with the R script that accompanies the paper: Cooper, K. M. & Barry, J. A big data approach to macrofaunal baseline assessment, monitoring and sustainable exploitation of the seabed. Scientific Reports 7, doi: 10.1038/s41598-017-11377-9 (2017). Files include:

1. C5922 FINAL SCRIPTV91.R
2. C5922DATASET13022017REDACTED.csv (Raw data)*
3. Dataset description.xlsx (Description of data in C5922DATASET13022017.csv)
4. PARTBAGG05022017.csv (Faunal Aggregation data)
5. EUROPE.shp (European Coastline)
6. EuropeLiteScoWal.shp (European Coastline with UK boundaries)
7. Aggregates_Licence_20151112.shp (Aggregates Licensed extraction areas)
8. Aggregates_Application_20150813.shp (Aggregates Application areas)
9. HUMBERLICANDAPP.shp (Licensed Extraction and Application Areas - Humber)
10. H_SIZ_PSD_POLYGONS_UNION_2014.shp (Humber SIZs)
11. H_492_PIZ_APP.shp (Area 492 Application Area)
12. ANGLIANLICANDAPP.shp (Licensed Extraction and Application Areas - Anglian)
13. A_SIZ_PSD_POLYGONS_UNION.shp (Anglian SIZs)
14. THAMESLICANDAPP.shp (Licensed Extraction and Application Areas - Thames)
15. T_SIZ_PSD_POLYGONS_UNION_REV_2014.shp (Thames SIZs)
16. T_501_1_2_SIZ_PSD.shp (Area 501 1/2 SIZ)
17. EECLICANDAPP.shp (Licensed Extraction and Application Areas-East Channel)
18. EC_SIZ_PSD_POLYGONS_UNION_REV.shp (East Channel SIZs)
19. SCOASTLICANDAPP.shp (Licensed Extraction and Application Areas - South Coast)
20. SC_SIZ_PSD_POLYGONS_UNION.shp (South Coast SIZs)
21. BRISTOLCHANNELLICANDAPP.shp (Licensed Extraction and Application Areas - Bristol Channel)
22. BC_SIZ2.shp (Bristol Channel/Severn Estuary SIZs)
23. NORTHWESTLICANDAPP.shp(Licensed Extraction and Application Areas - North West)
24. NW_392_SIZ_PSD_LICENCE_EXISTING.shp (Area 392 SIZ)
25. AREA_457_PSD.shp (Area 457 SIZ)
26. GOODWIN LICENCE FINAL POLYGON.shp (Goodwin Sands Extraction area)
27. GoodwinSIZ.shp (Goodwin Sands SIZ)
28. DEFRADEMKC8.shp (Seabed bathymetry)

*At the request of data owners, macrofaunal abundance and sediment particle size data have been redacted from 13 of the 777 surveys (1.7%) in the dataset. Note that metadata and derived variables are still included. Surveys with redacted data include:

SurveyName

1. TRIKNOOWF2008,
2. EAOWF (Owner: East Anglia Offshore Wind Limited),
3. Wight Barfleur_cSAC_infauna,
4. MPAFORTH2011,
5. Hinkely point 108 benthos survey (BEEMS-WP2),
6. Hinkely point 208 benthos survey (BEEMS-WP2),
7. Hinkely point 408 benthos survey (BEEMS-WP2),
8. Hinkely point 308 benthos survey (BEEMS-WP2),
9. BEEMS WP2 Hinkley Point Q2 2009,
10. BEEMS WP5 Hinkley Point Infauna,
11. Hinkley Point 510 benthic survey (WP2-BEEMS),
12. Hinkley Point benthos survey June 2011 (BEEMS-WP2),
13. Hinkley Point benthos survey Feb 2010 (BEEMS-WP2)

Cefas will only make redacted data available where the data requester can provide written permission from the relevant data owner(s) - see below. Note that it is the responsibility of the data requester to seek permission from the relevant data owners.

Data owners for the redacted surveys listed above are:

1. Triton Knoll Offshore Wind Farm Limited
2. East Anglia Offshore Wind Limited
3. Joint Nature Conservation Committee (JNCC)
4. Joint Nature Conservation Committee (JNCC)
5. EDF Energy
6. EDF Energy
7. EDF Energy
8. EDF Energy
9. EDF Energy
10. EDF Energy
11. EDF Energy
12. EDF Energy
13. EDF Energy

Description of the C5922DATASET13022017.csv/ C5922DATASET13022017REDACTED.csv (Raw data)

A variety of gear types have been used for sample collection including grabs (0.1m2 Hamon, 0.2m2 Hamon, 0.1m2 Day, 0.1m2 Van Veen and 0.1m2 Smith McIntrye) and cores. Of these various devices, 93% of samples were acquired using either a 0.1m2 Hamon grab or a 0.1m2 Day grab. Sieve sizes used in sample processing include 1mm and 0.5mm, reflecting the conventional preference for 1mm offshore and 0.5mm inshore (see Figure 2). Of the samples collected using either a 0.1m2 Hamon grab or a 0.1m2 Day grab, 88% were processed using a 1mm sieve.

Taxon names were standardised according to the WoRMS (World Register of Marine Species) list using the Taxon Match Tool (http://www.marinespecies.org/aphia.php?p=match). Of the initial 13,449 taxon names, only 4,248 remained after correction. The output from this tool also provides taxonomic aggregation information, allowing data to be analysed at different taxonomic levels - from species to phyla. The final dataset comprises of a single sheet comma-separated values (.csv) file. Colonials accounted for less than 20% of the total number of taxa and, where present, were given a value of 1 in the dataset. This component of the fauna was missing from 325 out of the 777 surveys, reflecting either a true absence, or simply that colonial taxa were ignored by the analyst. Sediment particle size data were provided as percentage weight by sieve mesh size, with the dataset including 99 different sieve sizes. Sediment samples have been processed using sieve, and a combination of sieve and laser diffraction techniques. Key metadata fields include: Sample coordinates (Latitude & Longitude), Survey Name, Gear, Date, Grab Sample Volume (litres) and Water Depth (m). A number of additional explanatory variables are also provided (salinity, temperature, chlorophyll a, Suspended particulate matter, Water depth, Wave Orbital Velocity, Average Current, Bed Stress). In total, the dataset dimensions are 33,198 rows (samples) x 13,588 columns (variables/factors), yielding a matrix of 451,094,424 individual data values.

A curated dataset of colloquial English phrases and their corresponding Hindi translations. This dataset focuses on informal language, including slang, idioms, and everyday expressions, making it ideal for training models that handle casual conversations. Dataset Details: Size:e.g., 500+ phrase pairs] Source: Collected from publicly available conversational datasets, social media, and crowdsourced contributions. Language Pair: English → Hindi Annotations: Each phrase pair is manually verified… See the full description on the dataset page: https://huggingface.co/datasets/bajpaideeksha/english-hindi-colloquial-dataset.