This dataset countains the number of applications withdrawn, rejected and accepted for entry into the United Kingdom by Indian citizens. This includes all visa types and the data is quarterly.

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

tags['place'] IN ('isolated_dwelling', 'town', 'village', 'hamlet', 'city') OR tags['landuse'] IN ('residential')

Features may have these attributes:

• name
• name:en
• place
• landuse
• population
• is_in
• 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 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 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.

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 dataset is about book subjects. It has 2 rows and is filtered where the books is An Indian summer of steam : railway travel in the United Kingdom and abroad 1962-2013. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

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.

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

tags['amenity'] IN ('mobile_money_agent','bureau_de_change','bank','microfinance','atm','sacco','money_transfer','post_office')

Features may have these attributes:

• name
• name:en
• amenity
• operator
• network
• 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.

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)

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 July of 2025.

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 data set consisting of initial conditions, boundary conditions and forcing profiles for the Single Column Model (SCM) version of the European Centre for Medium-range Weather Forecasts (ECMWF) model, the Integrated Forecasting System (IFS). The IFS SCM is freely available through the OpenIFS project, on application to ECMWF for a licence. The data were produced and tested for IFS CY40R1, but will be suitable for earlier model cycles, and also for future versions assuming no new boundary fields are required by a later model. The data are archived as single time-stamp maps in netCDF files. If the data are extracted at any lat-lon location and the desired timestamps concatenated (e.g. using netCDF operators), the resultant file is in the correct format for input into the IFS SCM.

The data covers the Tropical Indian Ocean/Warm Pool domain spanning 20S-20N, 42-181E. The data are available every 15 minutes from 6 April 2009 0100 UTC for a period of ten days. The total number of grid points over which an SCM can be run is 480 in the longitudinal direction, and 142 latitudinally. With over 68,000 independent grid points available for evaluation of SCM simulations, robust statistics of bias can be estimated over a wide range of boundary and climatic conditions.

The initial conditions and forcing profiles were derived by coarse-graining high resolution (4 km) simulations produced as part of the NERC Cascade project, dataset ID xfhfc (also available on CEDA). The Cascade dataset is archived once an hour. The dataset was linearly interpolated in time to produce the 15-minute resolution required by the SCM. The resolution of the coarse-grained data corresponds to the IFS T639 reduced gaussian grid (approx 32 km). The boundary conditions are as used in the operational IFS at resolution T639. The coarse graining procedure by which the data were produced is detailed in Christensen, H. M., Dawson, A. and Holloway, C. E., 'Forcing Single Column Models using High-resolution Model Simulations', in review, Journal of Advances in Modeling Earth Systems (JAMES).

For full details of the parent Cascade simulation, see Holloway et al (2012). In brief, the simulations were produced using the limited-area setup of the MetUM version 7.1 (Davies et al, 2005). The model is semi-Lagrangian and non-hydrostatic. Initial conditions were specified from the ECMWF operational analysis. A 12 km parametrised convection run was first produced over a domain 1 degree larger in each direction, with lateral boundary conditions relaxed to the ECMWF operational analysis. The 4 km run was forced using lateral boundary conditions computed from the 12 km parametrised run, via a nudged rim of 8 model grid points. The model has 70 terrain-following hybrid levels in the vertical, with vertical resolution ranging from tens of metres in the boundary layer, to 250 m in the free troposphere, and with model top at 40 km. The time step was 30 s.

The Cascade dataset did not include archived soil variables, though surface sensible and latent heat fluxes were archived. When using the dataset, it is therefore recommended that the IFS land surface scheme be deactivated and the SCM forced using the surface fluxes instead. The first day of Cascade data exhibited evidence of spin-up. It is therefore recommended that the first day be discarded, and the data used from April 7 - April 16.

The software used to produce this dataset are freely available to interested users; 1. "cg-cascade"; NCL software to produce OpenIFS forcing fields from a high-resolution MetUM simulation and necessary ECMWF boundary files. https://github.com/aopp-pred/cg-cascade Furthermore, software to facilitate the use of this dataset are also available, consisting of; 2. "scmtiles"; Python software to deploy many independent SCMs over a domain. https://github.com/aopp-pred/scmtiles 3. "openifs-scmtiles"; Python software to deploy the OpenIFS SCM using scmtiles. https://github.com/aopp-pred/openifs-scmtiles

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.

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

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.

Lithuania LT: Foreign Direct Investment Position: 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 Position: Inward: Total: British Indian Ocean Territory (Eurostat) data is updated yearly, averaging 0.000 EUR mn from Dec 2015 (Median) to 2023, with 9 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 Position: 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 Position: 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.

OVERVIEW This dataset contains data from a survey of low income households in four cities across south India. This fileset includes a guidance document on how the data was collected and how to interpret and use the data. The survey data was collected between April-June 2019. A team of 11 survey enumerators and researchers were involved in the data collection which was collected through a collaboration between the University of Cambridge and the Indian Institute for Human Settlements. Data collection for this project received ethical approval from both the Department of Engineering, University of Cambridge and Indian Institute for Human Settlements. This anonymised dataset is being released to allow full use by others.

DATASET CONTENTS This dataset contains the following files: - Indian_Low_Income_Household_Energy_Survey_Codebook.pdf - south_indian_household_energy_survey_19.csv - south_indian_household_energy_survey_19.Rda - README.txt Data contained in the csv files is the same as data contained in the Rda file.

HOW TO USE All csv files can be opened using any appropriate software. Rdata script files must be opened and run using R. We recommend using RStudio and R version 3.5.1 (“Feather Spray”) or later.

This survey followed the same methodology and as an earlier survey of low-income households in Bangalore, India. The dataset from this earlier survey can be found at: https://doi.org/10.17863/CAM.59870

This dataset was used as external validation dataset for a microsimulation of cooking fuel use in India cities. Code for the microsimulation model can be found in the following GitHub repository: github.com/anetobradley/urban_energy_microsimulation_india