This dataset represents ethnic group (19 tick-box level) by highest level qualification, for England and Wales combined. The data are also broken down by age and by sex.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity, or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021. This dataset shows population counts for usual residents aged 16+ Some people aged 16 years old will not have completed key stage 4 yet on census day, and so did not have the opportunity to record any qualifications on the census.

These estimates are not comparable to Department of Education figures on highest level of attainment because they include qualifications obtained outside England and Wales.

For quality information in general, please read more from here.

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

No qualifications

No qualifications

Level 1

Level 1 and entry level qualifications: 1 to 4 GCSEs grade A* to C , Any GCSEs at other grades, O levels or CSEs (any grades), 1 AS level, NVQ level 1, Foundation GNVQ, Basic or Essential Skills

Level 2

5 or more GCSEs (A* to C or 9 to 4), O levels (passes), CSEs (grade 1), School Certification, 1 A level, 2 to 3 AS levels, VCEs, Intermediate or Higher Diploma, Welsh Baccalaureate Intermediate Diploma, NVQ level 2, Intermediate GNVQ, City and Guilds Craft, BTEC First or General Diploma, RSA Diploma

Apprenticeship

Apprenticeship

Level 3

2 or more A levels or VCEs, 4 or more AS levels, Higher School Certificate, Progression or Advanced Diploma, Welsh Baccalaureate Advance Diploma, NVQ level 3; Advanced GNVQ, City and Guilds Advanced Craft, ONC, OND, BTEC National, RSA Advanced Diploma

Level 4 +

Degree (BA, BSc), higher degree (MA, PhD, PGCE), NVQ level 4 to 5, HNC, HND, RSA Higher Diploma, BTEC Higher level, professional qualifications (for example, teaching, nursing, accountancy)

Other

Vocational or work-related qualifications, other qualifications achieved in England or Wales, qualifications achieved outside England or Wales (equivalent not stated or unknown)

Abstract copyright UK Data Service and data collection copyright owner.The purpose of this survey was to study non-white people aged 15 and over, whose families originate from India, Pakistan and Bangladesh, or the East Indies, with reference to their housing, employment and educational characteristics, their awareness and experience of racial discrimination. Comparative data were also collected for white men aged 16 and over, using the same questionnaire but with questions omitted when not applicable. Main Topics: Attitudinal/Behavioural Questions Immigration: reasons; advantages of Britain/previous country; whether definite job arranged prior to arrival. Residence: number of rooms occupied; whether house was multi-occupied; amenities (whether shared); number of addresses in past five years. Tenure: 1. If owned: whether singly or jointly; mortgage/loan details; leasehold/freehold (date of expiry). 2. If rented: rent and rates details; council/private ownership; race of landlord. Council house tenants were asked how they obtained their housing. Reasons for leaving previous residence: A. Personal experience of mortgage/loan refusal, type of organisation which refused, year of application. B. Personal experience of refusal of rented accommodation, number of refusals, details of last refusal. In both A and B, respondents were asked to give the organisation's reasons for refusal and their personal opinion of reasons, with an explanation. Details of housing and financial facilities provided by the Council, entitlement/receipt of rent rebates and/or allowances, whether respondent has made an application to the council (length of time on waiting list). Occupation: hours worked per week, position, responsibility, qualifications, nature of firm, number of employees, source of information about job, promotion prospects, job satisfaction. In addition, respondents were asked whether they had visited the employment exchange or were receiving/had received benefits since 1964. Respondents were asked to relate experiences of unfair treatment with regard to promotion or application for jobs, and whether they thought there were firms giving equal opportunities to Asians and whites. Whether respondent believed employers discriminated against them - reasons. Details of previous refusals. Trade union membership and existence of unions at workplace. Whether unemployed women had ever considered working (reasons). Working women with children were asked about child care facilities (hours, cost, satisfaction, etc.) Asian women were asked whether religion or family custom restricted their lives in terms of work, going out, company. Desired change was explored. All respondents asked whether situation in Britain had improved for Asians over past five years - reasons. Knowledge of government bodies on race relations/Race Relations Board and its functions/Community Relations Commission and its functions was tested. Whether voted at previous general election. Whether on voting list. Background Variables Age, sex, place of birth, previous countries of residence, date of arrival in Britain, age on arrival in Britain. Number of persons in household, household status. Age finished full-time education, examination and qualification details, further study, school attended by children. Employment status, income, ownership of consumer durables. Residence: type, age, external conditions. Fluency in English, language of interview. Sampling area. Religion, church/mosque/temple attendance.

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in New Britain. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2012 and 2022, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In New Britain, the median household income for the households where the householder is White decreased by $7,273(13.87%), between 2012 and 2022. The median household income, in 2022 inflation-adjusted dollars, was $52,438 in 2012 and $45,165 in 2022.
• Black or African American: In New Britain, the median household income for the households where the householder is Black or African American increased by $19,271(39.03%), between 2012 and 2022. The median household income, in 2022 inflation-adjusted dollars, was $49,373 in 2012 and $68,644 in 2022.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

https://i.neilsberg.com/ch/new-britain-ct-median-household-income-by-race-trends.jpeg" alt="New Britain, CT median household income trends across races (2012-2022, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2022 1-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in New Britain.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• Please note: 2020 1-Year ACS estimates data was not reported by Census Bureau due to impact on survey collection and analysis during COVID-19, thus for large cities (population 65,000 and above) median household income data is not available.
• Please note: All incomes have been adjusted for inflation and are presented in 2022-inflation-adjusted dollars.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for New Britain median household income by race. You can refer the same here

The dataset was created as part of an ESRC-sponsored study, ‘British economic, social, and cultural interactions with Asia, 1760-1833’. It contains statistics relating to the trade and domestic finances of the monopolistic English East India Company primarily between 1755 and 1834, the year in which the Company ceased to function as a commercial organization. Until now quantitative data derived from original sources has only been available in time series for the Company’s trade and some aspects of its domestic finances for the years before 1760. But many of the details, patterns, and trends of trade and finance in the decades after 1760, a most important period when the Company fully embarked on the interlinked processes of military, political, and commercial expansion in Asia, have remained unclear. In creating this dataset, the aim was thus two-fold: i) to establish for the first time a set of statistics detailing the changing value, volume, and geographical structure of the East India Company’s overseas trade for the period when the Company began to exert imperial control over large parts of the Indian subcontinent; and ii) to generate select statistics relating to the Company’s domestic finances, thereby enabling analysis to be undertaken of a range of Company interactions with Britain’s economy and society.

This dataset is about book subjects and is filtered where the books is British fish, Indian style : 100 simple spicy recipes, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

This dataset is about book subjects and is filtered where the books is The British troops in the Indian mutiny, 1857-59, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['railway'] IN ('rail','station')

Features may have these attributes:

• name
• name:en
• railway
• ele
• operator:type
• layer
• addr:full
• addr:city
• source

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

India Exports to United Kingdom was US$12.48 Billion during 2023, according to the United Nations COMTRADE database on international trade. India Exports to United Kingdom - data, historical chart and statistics - was last updated on March of 2025.

For around a decade, southern elephant seals (mirounga leonina) have been used to collect hydrographic (temperature & salinity) profiles in the Southern Ocean. CTD-SRDLs (Conductivity Temperature Depth –Satellite Relayed Data Loggers) attached to seals' heads in Antarctic and sub-Antarctic locations measure water property profiles during dives and transmit data using the ARGOS (Advanced Research & Global Observation Satellite) network (Fedak 2013). CTD-SRDLs are built by the Sea Mammal Research Unit (SMRU, University of St Andrews, UK); they include miniaturised CTD units made by Valeport Ltd. When seals are foraging at sea 2.5 profiles can be obtained daily, on average. Profiles average 500m depth, but can be 2000m in extreme cases (Boehme et al. 2009, Roquet et al. 2011). Deployment efforts have been very intensive in the Southern Indian Ocean, with biannual campaigns in the Kerguelen Islands since 2004 and many deployments in Davis and Casey Antarctic stations (Roquet et al., 2013) more recently. 207 CTD-SRDL tags have been deployed there, giving about 75,000 hydrographic profiles in the Kerguelen Plateau area. About two thirds of the dataset was obtained between 2011 & 2013 as a consequence of intensive Australian Antarctic station deployments. There is also regular data since 2004 from French and Franco-Australian Kerguelen Island deployments. Although not included here, many CTD-SRDL tags deployed in the Kerguelen Islands included a fluorimeter. Fluorescence profiles can be used as a proxy for chlorophyll content (Guinet et al. 2013, Blain et al. 2013). Seal-derived hydrographic data have been used successfully to improve understanding of elephant seal foraging strategies and their success (Biuw et al., 2007, Bailleul, 2007). They provide detailed hydrographic observations in places and seasons with virtually no other data sources (Roquet et al. 2009, Ohshima et al. 2013, Roquet et al. 2013). Hydrographic data available in this dataset were edited using an Argo-inspired procedure and then visually. Each CTD-SRDL dataset was adjusted using several delayed-mode techniques, including a temperature offset correction and a linear-in-pressure salinity correction - described in Roquet et al. (2011). Adjusted hydrographic data have estimated accuracies of about +/-0.03oC and +/-0.05 psu (practical salinity unit). The salinity accuracy depends largely on the distribution of CTD data for any given CTD-SRDL, which decides the quality of adjustment parameters. Adjustments are best when hydrographic profiles are available in the region between the Southern Antarctic Circumpolar Current Front and the Antarctic divergence (55oS-62oS latitude range in the Southern Indian Ocean). Several institutes provided funding for the associated programs and the logistics necessary for the fieldwork. The observatory MEMO (Mammifères Echantillonneurs du Milieu Marin), funded by CNRS institutes (INSU and INEE), carried out the French contribution to the study. The project received financial and logistical support from CNES (TOSCA program), the Institut Paul-Emile Victor (IPEV), the Total Foundation and ANR. MEMO is associated with the Coriolis centre, part of the SOERE consortium CTD02 (Coriolis-temps différé Observations Océaniques, PI: G. Reverdin), which distributes real-time and delayed-mode products. The Australian contribution came from the Australian Animal Tracking and Monitoring System, an Integrated Marine Observing System (IMOS) facility. The work was also supported by the Australian Government's Cooperative Research Centres Programme via the Antarctic Climate & Ecosystem Cooperative Research Centre. The University of Tasmania and Macquarie University's Animal Ethics Committees approved the animal handling. Both tagging programs are part of the MEOP (Marine Mammals Exploring the Oceans Pole to Pole) international consortium - an International Polar Year (IPY) project. Link: http://www.nature.com/articles/sdata201428

The SHAMSA* bibliographical database and digital collection has been developed as part of the European Research Council project Musical Transitions to European Colonialism in the Eastern Indian Ocean (MUSTECIO, Grant no. 263643, PI Katherine Butler Schofield, 2011–2015/16). The attached xlsx document, licensed as a CC-BY-NC resource, provides the bibliographical metadata of Version 1.0 of the database. It describes well over 300 major written sources c. 1700-1900 for the history and analysis of North Indian (Hindustani) music and dance in Mughal and British-colonial South Asia. About one third – well over 100 – of these sources are also currently held in digital copies in the Department of Music at King’s College London. The SHAMSA digital collection already constitutes the largest single repository of primary written sources on Indian music and dance in the world, and is planned to be a major ongoing resource for future researchers on Indian music, dance, and cultural history.

The sources of SHAMSA 1.0 were located and consulted Jan 2011– Dec 2015 by members of the Awadh Case Study of the ERC Musical Transitions project, including James Kippen, David Lunn, Allyn Miner, Katherine Butler Schofield, Margaret E Walker, and Richard David Williams. The initial collection has clearly defined limits: it 1) focusses on the Gangetic plains region between Delhi, Lucknow, and Calcutta; 2) during the timeframe c. 1700-1900 i.e. explicitly before the era of recorded sound (but with some key 17C and 20C outliers, and some materials from e.g. Hyderabad, Kashmir). 3) The linguistic focus of the collection is on works largely in Persian, Brajbhasha/Hindavi, Hindi, Urdu and Bengali, but with some works in Sanskrit, English, and other Northern Indian vernacular languages. Sources include music and dance treatises, biographical works (tazkiras), song collections, ethnographic works, department archives, encyclopedias, cosmographies, theatre scripts, moral and ethical tracts, histories, and a tiny handful of the large number of extant ragamala painting sets (for a comprehensive treatment of ragamala sets see e.g. Ebeling 1973 and the Ebeling digital image collection at Cornell).

It is critically important to note that this is by no means a complete collection of everything written on music and dance in Northern India in the period of transition from the Mughal to the British empires. We have been completely overwhelmed by the volume and richness of the materials we have uncovered for the history of music and dance before the period of recorded sound. This bibilography should be considered a mere starting point; we are already aware of a large number of sources, especially visual, that we have not yet included. Version 1.0 consists only of (largely) textual sources that at least one of the team members personally consulted 2011–15, and considered to include substantial and noteworthy musical and/or dance-related contents (very occasionally key sources are also included that we know about, but have been unable to locate yet despite our best efforts.)

We know that there are many more written and visual sources for North Indian forms of music and dance beyond the geographical, temporal, and/or linguistic scope of the current version of this database – for example for Panjab, Rajasthan, Gujarat, Maharashtra, etc. – but even considering our core region and timeframe we keep uncovering more sources all the time, and aim to update the open access versions of the database periodically. We would be delighted to hear from anyone who has information about sources that are not yet in our database that we might be able to consult and include, or about any verifiable errors in the metadata that need correcting.

Well over one third of the works in the bibliographical file are already available to consult as digital copies in situ at King’s College London. The copyright statuses of these copies are exceedingly complex; but we aim to make as many of these available via Creative Commons licenses as and when we gain approval from the holders of the original documents to do so. Please do get in touch with Dr Katherine Schofield at King’s College London if you wish to consult the digital copies in the SHAMSA collection, or if you have suggestions of works whose metadata should be included in the bibliographical list. (Version 1.0 was completed 1 Jan 2016, and checked/exported 2 Oct 2018.)

*Deriving from the Persian word "shams", meaning "sun", a shamsa is both a ray of solar light often indicating the bestowal of special knowledge or enlightenment, and the technical term for an illuminated orb-like frontispiece in Islamicate manuscripts that often encloses the patron's name, titles, and/or portrait — see for example the beautiful shamsa for the Mughal emperor Shah Jahan that adorns the SHAMSA Community page (Metropolitan Museum of Art). In later lithographed works on music in Urdu, the title of the manuscript would often be enclosed in a shamsa. But the name SHAMSA also pays homage to the first Persian treatise on North Indian music written by an imperial hereditary musician, the Shams al-Aswat by Ras Baras Khan (1698), in which he named shams as the presiding star of the musical note Ma, the fourth scale degree (MUSTECIO 0131/British Library, I O Islamic 1746, f. 19r).

This dataset is about book subjects and is filtered where the books is A various universe : a study of the journals and memoirs of British men and women in the Indian subcontinent, 1765-1856, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

OpenStreetMap contains roughly 777 buildings in this region. Based on AI-mapped estimates, this is approximately 72% of the total buildings.The average age of data for this region is 4 years ( Last edited 22 days ago ) and 4% buildings were added or updated in the last 6 months. Read about what this summary means : indicators , metrics

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['building'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• building
• building:levels
• building:materials
• addr:full
• addr:housenumber
• addr:street
• addr:city
• office
• source

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

A weekly dataset providing the total number of reported political violence, civilian-targeting, and demonstration events in British Indian Ocean Territory. Note: These are aggregated data files organized by country-year and country-month. To access full event data, please register to use the Data Export Tool and API on the ACLED website.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.0001 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0 and 0.0 (in million kms), corressponding to 0.3477% and 28.0622% respectively of the total road length in the dataset region. 0.0001 million km or 71.5901% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0 million km of information (corressponding to 0.0% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['waterway'] IS NOT NULL OR tags['water'] IS NOT NULL OR tags['natural'] IN ('water','wetland','bay')

Features may have these attributes:

• name
• name:en
• waterway
• covered
• width
• depth
• layer
• blockage
• tunnel
• natural
• water
• source

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Lithuania LT: Foreign Direct Investment Income: Inward: Total: British Indian Ocean Territory (Eurostat) data was reported at 0.000 EUR mn in 2023. This stayed constant from the previous number of 0.000 EUR mn for 2022. Lithuania LT: Foreign Direct Investment Income: Inward: Total: British Indian Ocean Territory (Eurostat) data is updated yearly, averaging 0.000 EUR mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 0.000 EUR mn in 2023 and a record low of 0.000 EUR mn in 2023. Lithuania LT: Foreign Direct Investment Income: Inward: Total: British Indian Ocean Territory (Eurostat) 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 Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Income: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

The dataset comprises 23 hydrographic data profiles, collected by a conductivity-temperature-depth (CTD) sensor package, from across the Indian Ocean area specifically the Central Indian Ridge, during May and June of 2001. A complete list of all data parameters are described by the SeaDataNet Parameter Discovery Vocabulary (PDV) keywords assigned in this metadata record. The data were collected by the Southampton Oceanography Centre.

Antimicrobial resistance (AMR) is an urgent global one health challenge and irrational and over use of antimicrobials in livestock production and human medicine is contributing to this problem. This online questionnaire was part of a large interdisciplinary research project on the drivers for AMR and the role of livestock and poultry production in India (see www.liverpool.ac.uk/infection-and-global-health/research/darpi/) involving multiple UK and Indian partners, with this specific survey led by the University of Liverpool, University of Edinburgh and the Karnataka Veterinary, Animal and Fisheries Sciences University. This study was designed to help us understand how and what veterinary students are taught, their knowledge and attitudes around AMR and antimicrobial use. Participates were invited to take part in this study if they had recently graduated or were near to the completion of their veterinary course and will be a future prescriber and therefore represent a important stakeholder.The aim of this study is to first to map antimicrobial use (AMU) and the antimicrobial resistance (AMR) that is driven by inappropriate use, across the entire poultry meat supply chain from farm to table in India. The study provides [1] a unique opportunity to map AMU, [2] to understand entry points for development of AMR and [3] the contribution by inappropriate AMU in poultry, and [4] suggests potential solutions to address the huge AMR burden in India. AMR is a major global health risk, particularly in developing countries, threatening human and animal health. Contributing to this problem is the inappropriate use of antimicrobials in people and livestock production. India has a high burden of infectious disease, and bacteria from human clinical infections are becoming increasingly difficult to treat, with fewer treatment options available. Studies suggest that livestock may commonly carry resistant bacteria in their gut, with poultry and poultry meat also identified as a source of such bacteria. However, there is a complete lack of data on the scale of the problem, or on what antimicrobials are being used in poultry meat production, how they are used, and how this contributes to the carriage of AMR bacteria that may be a threat to human and animal health. Poultry meat is one of the main protein sources in Indian and is the fastest growing livestock sector. Increasingly, poultry meat in India is produced through more intensive integrated or semi-integrated farming systems where antimicrobials are used for various purposes, including for growth promotion, to prevent and treat disease. To date there have been no comprehensive studies on AMU or AMR through the poultry meat supply chain. Our interdisciplinary project aims to address these data gaps by studying the poultry meat food supply in its entirety to determine: behaviours that drive AMU and how these contribute to the selection and transmission of AMR, to inform better use; to design with farmers and other stakeholders interventions to reduce AMU/AMR, which are cost-effective and easy to implement; determine the economic impact from changing AMU practices, or using alternatives. The project will involve working closely with the poultry industry, policy makers and other stakeholders throughout to ensure the findings have impact. This project is timely in providing crucial data to inform antimicrobial stewardship: the trajectory of the Indian poultry industry is shifting towards intensive farming and AMU is predicted to rise substantially. Therefore, this is an opportunity to intervene through working closely with stakeholders to provide alternative strategies for sustainable AMU. The project also offers other benefits, with a strong social science component providing unique insights into behaviours driving AMU, as well as service design enabling visualization of AMU and AMR, and co-design strategies. Indian researchers will be trained in these methods, building capacity for social science in Indian agricultural and veterinary research that will have value long after the conclusion of this project. The study will be the first to map AMU and AMR in the entire poultry meat supply chain from farm to table in India. The study provides a unique opportunity to map AMU, understand entry points for development of AMR and the contribution by inappropriate AMU in poultry, and suggest potential solutions to address the huge AMR burden in India.

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['amenity'] = 'ferry_terminal' OR tags['building'] = 'ferry_terminal' OR tags['port'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• amenity
• building
• port
• operator:type
• addr:full
• addr:city
• source

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: British Indian Ocean Territory (Eurostat) data was reported at 0.000 USD mn in 2023. This stayed constant from the previous number of 0.000 USD mn for 2022. Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: British Indian Ocean Territory (Eurostat) data is updated yearly, averaging 0.000 USD mn from Dec 2013 (Median) to 2023, with 11 observations. The data reached an all-time high of 0.000 USD mn in 2023 and a record low of 0.000 USD mn in 2023. Czech Republic CZ: Foreign Direct Investment Position: Outward: USD: Total: British Indian Ocean Territory (Eurostat) 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 Czech Republic – Table CZ.OECD.FDI: Foreign Direct Investment Position: USD: by Region and Country: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is not applied in the recording of total inward and outward FDi transactions and positions. Such cases have never been observed. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the direct investor. Resident Special Purpose Entities (SPEs) do not exist or are not significant and are recorded as zero in the FDI database. Valuation method used for listed inward and outward equity positions: Own funds at book value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are completely covered. Collective investment institutions are covered as direct investment enterprises. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.