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 represents ethnic group (19 tick-box level) by dwelling tenure and by occupancy rating, 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.

All housing data in these tables do not include commual establishments.

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

For specific quality information about housing, 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

Occupancy rating of bedrooms: 0 or more

A household’s accommodation has an ideal number of bedrooms or more bedrooms than required (under-occupied)

Occupancy rating of bedrooms: -1 or less

A household’s accommodation has fewer bedrooms than required (overcrowded)

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)

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 data collection consists of semi-structured interviews designed to cover processes in five domains of integration (social, cultural, structural, civic and political, identity) with sections on life before and after marriage. The data deposited consists of the transcripts of the recorded semi-structured interviews with British Pakistani Muslim and British Indian Sikh spouses, and migrant Pakistani Muslim and migrant Indian Sikh spouses. This research explored the relationships between marriage migration and integration, focusing on the two largest UK ethnic groups involved in transnational marriages with partners from their parents’ or grandparents’ countries of origin: British Pakistani Muslims and British Indian Sikhs. Spouses constitute the largest category of migrant settlement in the UK. In Britain, as elsewhere in Europe, concern is increasingly expressed over the implications of marriage-related migration for integration. In some ethnic minority groups, significant numbers of children and grandchildren of former immigrants continue to marry partners from their ancestral homelands. Such marriages are presented as particularly problematic: a 'first generation' of spouses in every generation may inhibit processes of individual and group integration, impeding socio-economic participation and cultural change. New immigration restrictions likely to impact particularly on such groups have thus been justified on the grounds of promoting integration. The evidence base to underpin this concern is, however, surprisingly limited, and characterised by differing and often partial understandings of the contested and politicised concept of integration. This project combined analysis of relevant quantitative data sets, with qualitative research with the two largest ethnic groups involved (Indian Sikhs and Pakistani Muslims), to compare transnational ‘homeland’ marriages with intra-ethnic marriages within the UK. These findings will enhance understanding of the relationships between marriage-related migration and the complex processes glossed as integration, providing much needed new grounding for both policy and academic debates. The project employed mixed methods: analysis of existing survey data, semi-structured interviews, and focus groups. Data was collected between October 2013 and March 2015. Interview participants were recruited in Bradford, the Midlands, Bristol, Leeds and London.

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

amenity IN ('kindergarten','school','college','university') OR building IN ('kindergarten','school','college','university')

Features may have these attributes:

• addr:full
• addr:city
• source
• capacity:persons
• building
• amenity
• operator:type
• name

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.

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.

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

tags['aeroway'] IS NOT NULL OR tags['building'] = 'aerodrome' OR tags['emergency:helipad'] IS NOT NULL OR tags['emergency'] = 'landing_site'

Features may have these attributes:

• name
• name:en
• aeroway
• building
• emergency
• emergency:helipad
• operator:type
• capacity:persons
• 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.

Data from geophysical surveys carried out by the British Geological Survey in many countries in the Middle East, the Indian sub-continent and Indo-China for different agencies. The surveys range from regional gravity and airborne magnetic mapping to targetted surveys for mineral and water. Individual surveys do not yet have metadata entries: this entry describes a notional database that represents all geophysical surveys carried out within the region.

OpenStreetMap contains roughly 155 km of roads in this region. Based on AI-mapped estimates, this is approximately 91 % of the total road length in the dataset region. The average age of data for the region is 3 years ( Last edited 7 days ago ) and 9% of roads 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['highway'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• highway
• surface
• smoothness
• width
• lanes
• oneway
• bridge
• layer
• 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.

This is a collection of data on men and women in the IT sector in India and the UK. The data includes quantitative survey undertaken with 155 IT firms in India; 400 IT workers in India and the UK divided across the following cohorts: migrant and non-migrant, in India and the UK, men and women. The deposited data also includes 86 interviews with migrant and non-migrant IT workers in India and the UK. This data explores the nature of the IT industry, its gendered formations, experiences of migration and future plans. The use of a comparative methodology in understanding gender issues in the IT sector makes it unique.The global Information Technology (IT) sector is characterised by low participation of women and the UK is no exception. In response, UK organizations (e.g. Women in Technology), committees (e.g. BCS Women) and campaigns (e.g. Computer Clubs for Girls) have been set up to address the problem and increase the small and falling number of women in IT education, training and employment. To complement and provide an evidence base for future interventions this project adopted a new approach by considering the problem from two unexplored angles simultaneously. First, India, in comparison with most OECD countries, has a much higher proportion of women working as IT specialists; the project compared the experiences of IT workers in India and the UK to see what the UK can learn from the Indian case. Secondly, the research explored the insights of migrant women and men who moved between UK and India and had experience of both work cultures in order to obtain new insights into gender norms in each country as well as best practice. The project answered the following questions: a) What are the gender differences in the labour market among migrant and non-migrant workers in the IT sector in India and the UK?; b) What processes have led to different gendered patterns of workplace experiences among migrant and non-migrant workers in the IT sector in India and the UK?; c) What is the role of firms, industry and national regulations and cultures in creating barriers and opportunities for migrant and non-migrant men and women's career entry and progression and labour markets? Data collection consisted of questionnaire surveys and interviews. A. Quantitative data: This data was collected through a market survey firm, KANTAR IMRB based in India. The company was employed to run two surveys. 1. a company level survey, undertaken with HR managers in 156 IT sector companies in India across nine cities. The responses to the company survey came from mid to senior level HR professionals; The sample had the following characteristics: 156 firms were surveyed; these included small (5000 employees) organisations. 2. a survey of 417 individuals working in the sector. This survey was organised around three variables: gender (male, female), migration status (migrant and non-migrant), and country of fieldwork (UK and India). This resulted in four cohorts: non-migrant IT workers in the UK and India; Indian migrant women and men in the UK and UK women and men who are visiting India. The respondents were all middle level IT workers with 10-15 years work experience in the sector. Non-probability sampling techniques were used to recruit the respondents through panels of IT sector firms and individuals in India and the UK. B. Qualitative data: Parallel to the application of these quantitative methods, we conducted semi structured interviews with employees working in the Indian and UK locations of selected multinational companies of which 86 are being submitted here. They were recruited through HR managers of participating firms.

This dataset contains tracks and intensities of Indian monsoon low-pressure systems (LPSs), as identified in all ensemble members of eleven models of the Subseasonal-to-Seasonal (S2S) prediction project during a common reforecast period of May–October 1999–2010. Track details of LPSs identified in the ERA-Interim and MERRA-2 reanalysis datasets during June–September 1999–2010. The temporal resolution of all S2S models is daily (0000 UTC), whereas that of ERA-Interim and MERRA-2 are six-hourly and three-hourly respectively. LPSs were tracked using a feature-tracking algorithm (Hunt et al., 2016; 2018), which is based on identifying and linking track points featuring 850 hPa relative vorticity maximum. Non-LPSs (e.g., heat lows) were eliminated from the dataset using a temperature-pressure filter. A full description of S2S models used in the dataset, and the tracking as well as post-tracking process is described in the paper: https://doi.org/10.1175/WAF-D-20-0081.1

Files:

  1. S2S models 

bom_lps: contains track details of LPSs identified in all ensemble members of the Bureau of Meteorology model

cma_lps: contains track details of LPSs identified in all ensemble members of the China Meteorological Administration model

cnrm_lps: contains track details of LPSs identified in all ensemble members of the Météo France/Centre National de Recherche Meteorologiques model

eccc_lps: contains track details of LPSs identified in all ensemble members of the Environment and Climate Change Canada model

ecmwf_lps: contains track details of LPSs identified in all ensemble members of the European Centre for Medium-Range Weather Forecasts model

hmcr_lps: contains track details of LPSs identified in all ensemble members of the Hydrometeorological Centre of Russia model

isac-cnr_lps: contains track details of LPSs identified in all ensemble members of the Institute of Atmospheric Sciences and Climate of the National Research Council model

jma_lps: contains track details of LPSs identified in all ensemble members of the Japan Meteorological Agency model

kma_lps: contains track details of LPSs identified in all ensemble members of the Korea Meteorological Administration model

ncep_lps: contains track details of LPSs identified in all ensemble members of the National Centers for Environmental Prediction model

ukmo_lps: contains track details of LPSs identified in all ensemble members of the UK Met Office model

  Columns: 

candidate_id: a random identity number for each LPS

hindcast: the reforecast date of a hindcast file from which an LPS was identified

lat: the latitude of an LPS at a given time step

lon: the longitude of an LPS at a given time step

lead: the forecast lead time, calculated as the difference between the LPS date and reforecast date of the hindcast from which it was identified

time: a time stamp showing when an LPS was present

vort: the 850 hPa relative vorticity at the centre of an LPS at a given time step

member: the ensemble member from which an LPS was identified; the control run is indicated by a zero (0)

 2. Reanalysis datasets 

era-interim_lps: contains track details of LPSs identified in the ERA-Interim reanalysis dataset.

merra-2_lps: contains track details of LPSs identified in the MERRA-2 reanalysis dataset.

  Columns: 

time: a time stamp showing when an LPS was present

lon: the longitude of an LPS at a given time step

lat: the latitude of an LPS at a given time step

candidate_id: a random identity number for each LPS

vort: the 850 hPa relative vorticity at the centre of an LPS at a given time step

For further details, contact Akshay Deoras (deorasakshay@gmail.com).

This dataset is about book subjects. It has 4 rows 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. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Other-Appropriated-Reserves Time Series for JPMorgan Indian Inv Trust. JPMorgan Indian Investment Trust plc is a closed-ended equity mutual fund launched and managed by JPMorgan Funds Limited. It is co-managed by JPMorgan Asset Management (UK) Limited. The fund invests in the public equity markets of India. It seeks to invest in stocks of companies operating across diversified sectors. The fund primarily invests in value and growth stocks of companies. It benchmarks the performance of its portfolios against the MSCI India Index. JPMorgan Indian Investment Trust plc was formed on May 1, 1994 and is domiciled in the United Kingdom.

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.

Resource has no data rows! No conflict and disaster population movement (flows) data recorded for British Indian Ocean Territory in the last 180 days.

Internally displaced persons are defined according to the 1998 Guiding Principles (https://www.internal-displacement.org/publications/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.

The IDMC's Event data, sourced from the Internal Displacement Updates (IDU), offers initial assessments of internal displacements reported within the last 180 days. This dataset provides provisional information that is continually updated on a daily basis, reflecting the availability of data on new displacements arising from conflicts and disasters. The finalized, carefully curated, and validated estimates are then made accessible through the Global Internal Displacement Database (GIDD), accessible at https://www.internal-displacement.org/database/displacement-data. The IDU dataset comprises preliminary estimates aggregated from various publishers or sources.

This dataset contains radar-derived measurements of cell-top height, size, 2 km reflectivity, and cell latitude and longitude from all convective cells between 14 May and 30 September 2016, where radar is available. The data was collected as part of the NERC/MoES Interaction of Convective Organization and Monsoon Precipitation, Atmosphere, Surface and Sea (INCOMPASS) field campaign.

The seven sites analysed here represent four different Indian climate regions, allowing the study of the spatiotemporal development of convection during the 2016 monsoon season at high (1 km) resolution. Variation in these different cell statistics are found over timescales of variability such as the diurnal cycle, active-break periods, and monsoon progression.

The data were collected as part of the INCOMPASS field campaign May-July 2016, funded by Natural Environmental Research Council (NERC) (NE/L01386X/1). The aim of the project was to improve the skill of rainfall prediction in operational weather and climate models by way of better understanding and representation of interactions between the land surface, boundary layer, convection, the large-scale environment and monsoon variability on a range of scales.

India Imports from United Kingdom was US$6.53 Billion during 2024, according to the United Nations COMTRADE database on international trade. India Imports from United Kingdom - data, historical chart and statistics - was last updated on September of 2025.

The dataset comprises physical, biogeochemical and biological oceanographic, surface meteorological and benthic measurements. Hydrographic profiles including temperature, salinity, fluorescence, transmissance and suspended sediment concentration were collected at numerous stations, while surface hydrographic (fluorescence, transmissance, sea surface temperature, salinity) and meteorological (irradiance, air temperature, humidity, wind speed/direction) data were collected across the survey areas. Sediment, pore water and water column samples were also collected for biogeochemical analysis, as were biological samples for the purposes of species classification and abundance analyses. The data were collected across the Indian Ocean, Arabian Sea and Pakistan margin areas between March and October 2003. Data collection was undertaken by the RRS Charles Darwin during four cruises: CD145 (12 March 2003 to 9 April 2003), CD146 (12 April 2003 to 30 May 2003), CD150 (22 August 2003 to 15 September 2003) and CD151 (17 September 2003 to 20 October 2003). Conductivity-temperature-depth (CTD) profilers with auxiliary sensors, benthic samplers and nets were deployed from the ship, while underway sensors provided continuous surface ocean, meteorological and bathymetric data. The study was designed to investigate an oxygen-minimum zone (OMZ) in the northern Arabian Sea. Chief Investigators include Gregory L Cowie (University of Edinburgh School of GeoSciences) and Brian J Bett (Southampton Oceanography Centre), while other institutions including the Dunstaffnage Marine Laboratory, University of Liverpool and Netherlands Institute of Ecology were also involved in the research. Data management is being undertaken by BODC. Some of the data are still undergoing processing at BODC and further data are expected from originators in the future.