India Primary Energy Consumption per Capita data was reported at 7,129.110 kWh/Person in 2022. This records an increase from the previous number of 6,809.693 kWh/Person for 2021. India Primary Energy Consumption per Capita data is updated yearly, averaging 2,870.515 kWh/Person from Dec 1965 (Median) to 2022, with 58 observations. The data reached an all-time high of 7,129.110 kWh/Person in 2022 and a record low of 1,238.620 kWh/Person in 1965. India Primary Energy Consumption per Capita data remains active status in CEIC and is reported by Our World in Data. The data is categorized under Global Database’s India – Table IN.OWID.ESG: Environmental: CO2 and Greenhouse Gas Emissions: Annual.

OverviewThe dataset contains fully annotated electric transmission and distribution infrastructure for approximately 321 sq km of high resolution satellite and aerial imagery from around the world. The imagery and associated infrastructure annotations span 14 cities and 5 continents, and were selected to represent diversity in human settlement density (i.e. rural vs urban), terrain type, and development index. This dataset may be of particular interest to those looking to train machine learning algorithms to automatically identify energy infrastructure in satellite imagery or for those working on domain adaptation for computer vision. Automated algorithms for identifying electricity infrastructure in satellite imagery may assist policy makers identify the best pathway to electrification for unelectrified areas.Data SourcesThis dataset contains data sourced from the LINZ Data Service licensed for reuse under CC BY 4.0. This dataset also contained extracts from the SpaceNet dataset:SpaceNet on Amazon Web Services (AWS). “Datasets.” The SpaceNet Catalog. Last modified April 30, 2018 (link below).Other imagery data included in this dataset are from the Connecticut Department of Energy and Environmental Protection and the U.S. Geological Survey. Links to each of the imagery data sources are provided below as well as the link to the annotation tool and the github repository that provides tools for using these data.AcknowledgementsThis dataset was created as part of the Duke University Data+ project, "Energy Infrastructure Map of the World" (link below) in collaboration with the Information Initiative at Duke and the Duke University Energy Initiative.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Energy, IL, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Energy did not include 6, or 7-person households. Across the different household sizes in Energy the mean income is $73,613, and the standard deviation is $39,303. The coefficient of variation (CV) is 53.39%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $34,642. It then further decreased to 33,907 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/energy-il-median-household-income-by-household-size.jpeg" alt="Energy, IL median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Energy median household income. You can refer the same here

Global primary energy consumption has increased dramatically in recent years and is projected to continue to increase until 2045. Only hydropower and renewable energy consumption are expected to increase between 2045 and 2050 and reach 30 percent of the global energy consumption. Energy consumption by country The distribution of energy consumption globally is disproportionately high among some countries. China, the United States, and India were by far the largest consumers of primary energy globally. On a per capita basis, it was Qatar, Singapore, the United Arab Emirates, and Iceland to have the highest per capita energy consumption. Renewable energy consumption Over the last two decades, renewable energy consumption has increased to reach over 90 exajoules in 2023. Among all countries globally, China had the largest installed renewable energy capacity as of that year, followed by the United States.

This category of planning priorities in the CEC 2023 Land-Use Screens provides an estimate of terrestrial landscape condition based on the extent to which human impacts such as agriculture, urban development, natural resource extraction, and invasive species have disrupted the landscape across the State of California. It is based on the open-source logic modeling framework Environmental Evaluation Modeling System (EEMS) developed by Conservation Biology Institute (CBI). This multicriteria evaluation model result, last updated in 2016 and resolved at 1-kilometer square, spans values ranging from -1 to 1. The higher end of the spectrum indicates areas that are relatively intact based on the more than 30 input variables, and values in the lower end of the spectrum indicate where these human impacts to disturb the landscape and ecological function are relatively high.¹

In the adapted version of the CBI Terrestrial Landscape Intactness given here, the dataset is partitioned into high and low categories based on the mean. Values of the dataset that lie above 0.3 are considered highly intact and are used as an exclusion. Values of the dataset that are less than or equal to 0.3 are allowed to remain in consideration for resource potential. Applying the partition at the mean allows for lands that are relatively more intact than disturbed to be considered for resource potential. The high category of landscape intactness given by this dataset is used as an exclusion in both the Core and SB 100 Terrestrial Climate Resilience Study screens.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

More information about this layer and its use in electric system planning is available in the Land Use Screens Staff Report in the CEC Energy Planning Library.

[1] Degagne, R., J. Brice, M. Gough, T. Sheehan, and J. Strittholt. 2016. “Terrestrial Landscape Intactness 1 kilometer, California.” Conservation Biology Institute.https://databasin.org/datasets/e3ee00e8d94a4de58082fdbc91248a65/

The current literature on energy access highlights energy deprivation on a regional or country basis, but frequently neglects those outside of national energy agendas such as refugees and displaced people. To fill this gap and to help inform future analysis, we used an end-use accounting model for energy consumption for cooking and lighting by displaced populations to create this dataset. The data includes three high-level scenarios for improving access to energy for cooking and lighting. There is a strong human, economic, and environmental case to be made for improving energy access for refugees and displaced people, and for recognising energy as a core concern within humanitarian relief efforts.

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

The information in this repository pertains to the Kenya case study for EDeMOS. EDeMOS is an innovative, open-source methodology designed for analyzing electricity consumption at a spatially detailed level in any geography.

References.

• A high-resolution gridded dataset to assess electrification in sub-Saharan Africa

Falchetta, G., Pachauri, S., Parkinson, S. et al. A high-resolution gridded dataset to assess electrification in sub-Saharan Africa. Sci Data 6, 110 (2019). https://doi.org/10.1038/s41597-019-0122-6.

• Gridded global Gross Domestic Product and Human Development Index datasets over 1990–2015

Kummu, M., Taka, M. & Guillaume, J. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015. Sci Data 5, 180004 (2018). https://doi.org/10.1038/sdata.2018.4

• High-Resolution Electricity Access. set_lightscore_sy_xxxx.tif: Predicted likelihood that a settlement is electrified (0 to 1)

Brian Min, Zachary P. O'Keeffe, Babatunde Abidoye, Kwawu Mensan Gaba, Trevor Monroe, Benjamin P. Stewart, Kim Baugh, Bruno Sánchez-Andrade Nuño, “Lost in the Dark: A Survey of Energy Poverty from Space,” Joule (2024), https://doi.org/10.1016/j.joule.2024.05.001.

• Relative Wealth Index (RWI)

Samapriya Roy, Swetnam, T., & Saah, A. (2025). samapriya/awesome-gee-community-datasets: Community Catalog (3.2.0). Zenodo. https://doi.org/10.5281/zenodo.14757583.

• Demographic and Health Surveys (DHS). DHS

ICF. Kenya: Standard DHS, 2022 Dataset. The DHS Program website. Funded by USAID. http://www.dhsprogram.com. [Accessed 01, 31, 2025].

other

• Kenya Population and Housing Census 2019. KNBS, 2019.

This dataset contains high time resolution MEPA rate channel data. MEPA is a particle telescope with an ION head and a TOF head. The TOF head can measure species and energy, while the ION head only measures the energy of the ions, which are mostly protons. In fact, the counts in the ION head are all assumed to be protons up to 1830 keV. The ION head has 10 energy channels, and so the first 8 channels, that are all below 1830 keV, are assumed to be all protons, and the 2 channels above this are assumed to be all alphas. The TOF head has 9 energy channels that are generic, ions of any species are counted, and some species specific channels for protons, helium, oxygen, and iron. The AMPTE data was divided into records, with each record holding data from 4 spins. In any record, all the TOF species channels are always present, but only one of either a, the 10 ION head channels or b, 9 TOF generic channels are present. The majority of records have the ION head channels. The AMPTE spacecraft had a spin period of about 6 seconds. The exact spin period varies slightly and is included in the data. MEPA data is sectored into 32 directions per spin. Nearly all channels are reported as sectored values, but to conserve telemetry, many channels are only read out every other spin, or every fourth spin. In this data, all values are summed so that they are reported every fourth spin. Note that in the original AMPTE datesets, there was a timing problem which required that 19.75 seconds, one Major Frame of telemetry, be added to time values extracted from the processing system. This correction has already been made in the particle data in this dataset.

China Electricity Consumption: per Capita: Average data was reported at 6,257.000 kWh in 2022. This records an increase from the previous number of 6,032.000 kWh for 2021. China Electricity Consumption: per Capita: Average data is updated yearly, averaging 1,066.997 kWh from Dec 1978 (Median) to 2022, with 45 observations. The data reached an all-time high of 6,257.000 kWh in 2022 and a record low of 261.265 kWh in 1978. China Electricity Consumption: per Capita: Average data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCB: Electricity Summary.

The Odyssey Gamma Ray Spectrometer (GRS) Experiment Data Records (EDRs) are raw, uncalibrated spectra and ancillary data acquired by the Gamma Ray Subsystem -- the Gamma Sensor Head (GSH), the Neutron Spectrometer (NS), and the High Energy Neutron Detector (HEND).

This table shows the consumption of energy of households. Households are one or more people sharing the same living space, who provide their own everyday needs in a private, non-commercial way. Excludes transport. The consumption of energy is broken down by energy commodity, like for example petroleum products, natural gas and electricity.

Data available: From 1975 up to and including 2016

Status of the figures: All figures up to and including 2014 are definite. 2015 and 2016 figures are revised provisional.

Changes as of 22 December 2017: None, this table has been stopped. For more information see section 3.

Changes as of 21 June 2017 In the previous version the position of the decimal point for the variable “Energy consumption in private households” was wrong, leading to figures that were one hundred times too high. This has been corrected.

Changes as of 21 December 2016: Definite figures of 2014 and revised provisional figures have been added. For the years 1990 until 1994 the figures have been synchronized with the Energy Balance Sheet; Supply and Consumption. These data were published earlier in 2016. The revision for household consumption was given in by new insights gathered through the client files of network companies. After this revision, a trend shift occurs between 1989 and 1990.

When will new figures be published? Not applicable.

SPSS data file and questionnaires from a High Street survey conducted as part of the TRANSFER project. A total of 138 members of the general public (mix of genders and ages) were sampled in Sheffield (n = 111), Stockport (n = 19) and Manchester (n = 8), United Kingdom, in June/July 2014. Respondents completed either a questionnaire about purchasing 'green' energy tariffs (n = 61) or 'sustainable' clothing (n =77). The questionnaires were the same except for the target product (i.e. energy vs. clothing). The first section comprised items relating to respondents' interest, involvement, usage and consideration of the environmental impacts relating to the target product; and the importance of different factors (e.g. advertising) when purchasing the target product. The second section was based upon the Theory of Planned Behaviour and assessed respondents' attitudes, subjective norms, perceived behavioural control, personal norms and intentions regarding the purchase of the target product. An assessment of respondents' present engagement in a number of pro-environmental behaviours was also taken. The third section asked respondents to comment on the perceived motivations/actions of clothing/energy companies regarding the promotion of sustainability; and assessed the respondents ecological worldview using a short-form New Ecological Paradigm (NEP) scale. The fourth section comprised two free response items where respondents were invited to compete two sentences regarding shopping for the target product. Age, Gender and Bill-payer status were also recorded.Energy and fashion retailers face the common challenge of encouraging the reduced consumption of saleable products in order to promote sustainability, while simultaneously maintaining financial prosperity. The TRading Approaches to Nurturing Sustainable consumption in Fashion and Energy Retail (TRANSFER) project was designed to facilitate knowledge exchange between these retail sectors and other stakeholders. The aims of TRANSFER were twofold: (1) to bring together representatives of the energy and fashion retail sectors, with academic experts and other stakeholders, to exchange best practice around the promotion of sustainable consumption to consumers; and (2) to investigate how efforts to promote sustainable consumption within these sectors is received and responded to by consumers. These aims were achieved through a series of participatory knowledge exchange and public engagement activities (including commercial partner workshops, public focus groups and a public exhibition) coordinated by a trans-disciplinary team of academics from the University of Sheffield and the London College of Fashion. Drawing on theory from the disciplines of psychology, management and fashion; this project affords better understanding of how initiatives intended to promote conscientious consumption of fashion and energy can be successfully implemented in order to have maximum, beneficial impact on the attitudes and behaviour of consumers. A team of either two, three or four experimenters approached members of the general public in public spaces, shopping malls and high streets in Sheffield, Stockport and Manchester (June/July 2014). Prospective respondents where briefly introduced to the aims of the project (see information sheets) and were asked if they would like to participate by completing a short survey. Consenting participants were handed either a questionnaire relating to the purchase of 'green' energy tariffs or sustainable clothing products. The survey was completed in the presence of the experimenter(s) and comprised four principal sections. The first section comprised items relating to respondents' interest, involvement, usage and consideration of the environmental impacts relating to the target product; and the importance of different factors (e.g. advertising) when purchasing the target product. The second section was based upon the Theory of Planned Behaviour and assessed respondents' attitudes, subjective norms, perceived behavioural control, personal norms and intentions regarding the purchase of the target product. An assessment of respondents' present engagement in a number of pro-environmental behaviours was also taken. The third section asked respondents to comment on the perceived motivations/actions of clothing/energy companies regarding the promotion of sustainability; and assessed the respondents ecological worldview using a short-form (6-item) New Ecological Paradigm (NEP) scale. The fourth section comprised two free response items where respondents were invited to compete two sentences regarding shopping for the target product. Age, Gender and Bill-payer status were also recorded.

Overview    This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level. Key Definitions    Aggregation   The process of summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes.     AMR Meter Automatic meter reading (AMR) is the technology of automatically collecting consumption, diagnostic, and status data from a water meter remotely and periodically. Dataset   Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fields.  Data Zone Data zones are the key geography for the dissemination of small area statistics in Scotland Dumb Meter A dumb meter or analogue meter is read manually. It does not have any external connectivity. Granularity   Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours   ID   Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.    LSOA Lower Layer Super Output Areas (LSOA) are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales. Open Data Triage   The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.    Schema   Structure for organising and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.    Smart Meter A smart meter is an electronic device that records information and communicates it to the consumer and the supplier. It differs from automatic meter reading (AMR) in that it enables two-way communication between the meter and the supplier. Units   Standard measurements used to quantify and compare different physical quantities.  Water Meter Water metering is the practice of measuring water use. Water meters measure the volume of water used by residential and commercial building units that are supplied with water by a public water supply system. Data History    Data Origin    Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies. Data Triage Considerations    This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements. Identification of Critical Infrastructure  This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details. Commercial Risks and Anonymisation Individual Identification Risks There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information. Meter and Property Association Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial. Interpretation of Null Consumption Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions. Meter Re-reads The dataset must account for instances where meters are read multiple times for accuracy. Joint Supplies & Multiple Meters per Household Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation. Schema Consistency with the Energy Industry: In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above. After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection. Schema The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters. Aggregation to Mitigate Risks The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns. Data Freshness  Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data. Publish Frequency  Annually Data Triage Review Frequency    An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends. Data Specifications   For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include: Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption. Where it is necessary to estimate consumption, this is calculated based on actual meter readings. Meters of all types (smart, dumb, AMR) are included in this dataset. The dataset is updated and published annually. Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release. Context   Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns. The geographical data provided does not pinpoint locations of water meters within an LSOA. The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

Data HistoryData OriginDomestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.Data Triage ConsiderationsThis section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.Identification of Critical InfrastructureThis aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.Individual Identification RisksThere is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.Meter and Property AssociationChallenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.Interpretation of Null ConsumptionInstances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.Meter Re-readsThe dataset must account for instances where meters are read multiple times for accuracy.Joint Supplies & Multiple Meters per HouseholdSpecial consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.Schema Consistency with the Energy Industry:In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset"s schema with the standards established within the energy industry. This decision was influenced by the energy sector"s experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.SchemaThe dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.Aggregation to Mitigate RisksThe dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.Data FreshnessUsers should be aware that this dataset reflects historical consumption patterns and does not represent real-time data.Publish FrequencyAnnuallyData Triage Review FrequencyAn annual review is conducted to ensure the dataset"s relevance and accuracy, with adjustments made based on specific requests or evolving data trends.Data SpecificationsFor the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include:Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption.Consumption for calendar year dates is calculated based on actual meter readings.​Meters of all types (smart, dumb, AMR) are included in this dataset.​The dataset is updated and published annually.​The dataset includes LSOAs with 10 or more meters. Any LSOAs with less than 10 meters have been excluded.​The dataset includes only meters that are currently shown as active.​The dataset excludes any meters where consumption is recorded as null.ContextUsers are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.​The geographical data provided does not pinpoint locations of water meters within an LSOA.​​The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.​This dataset has been calculated using actual read data. To align reads to calendar year, our approach uses a combination of previous and next usage readings, along with the number of days between these readings to calculate the total consumption.Supplementary Information​Below is a curated selection of links for additional reading, which provide a deeper understanding of this dataset:Ofwat guidance on water meters Data SchemaDATA_SOURCE: Company that provided the dataYEAR: The calendar year covered by the dataLSOA_CODE: LSOA or Data Zone converted code of the meter locationNUMBER_OF_METERS: Number of meters within an LSOATOTAL_CONSUMPTION: Total consumption within the LSOATOTAL_CONSUMPTION_UNITS: Units for total consumption

Overview

This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level.

Key Definitions

Aggregation

The process of summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes.

AMR Meter

Automatic meter reading (AMR) is the technology of automatically collecting consumption, diagnostic, and status data from a water meter remotely and periodically.

Dataset

Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fields.

Data Zone

Data zones are the key geography for the dissemination of small area statistics in Scotland

Dumb Meter

A dumb meter or analogue meter is read manually. It does not have any external connectivity.

Granularity

Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours

ID

Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.

LSOA

Lower Layer Super Output Areas (LSOA) are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales.

Open Data Triage

The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.

Schema

Structure for organising and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.

Smart Meter

A smart meter is an electronic device that records information and communicates it to the consumer and the supplier. It differs from automatic meter reading (AMR) in that it enables two-way communication between the meter and the supplier.

Units

Standard measurements used to quantify and compare different physical quantities.

Water Meter

Water metering is the practice of measuring water use. Water meters measure the volume of water used by residential and commercial building units that are supplied with water by a public water supply system.

Data History

Data Origin

Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.

Data Triage Considerations

This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.

Identification of Critical Infrastructure

This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.

Commercial Risks and Anonymisation

Individual Identification Risks

There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.

Meter and Property Association

Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.

Interpretation of Null Consumption

Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.

Meter Re-reads

The dataset must account for instances where meters are read multiple times for accuracy.

Joint Supplies & Multiple Meters per Household

Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.

Schema Consistency with the Energy Industry:

In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.

After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.

Schema

The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.

Aggregation to Mitigate Risks

The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.

Data Freshness

Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data.

Publish Frequency

Annually

Data Triage Review Frequency

An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.

Data Specifications

For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include:

·
Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption.

· Where it is necessary to estimate consumption, this is calculated based on actual meter readings.

· Meters of all types (smart, dumb, AMR) are included in this dataset.

·
The dataset is updated and published annually.

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Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release.

Context

Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.

The geographical data provided does not pinpoint locations of water meters within an LSOA.

The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

Supplementary Information

1. Below is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.

2. Ofwat guidance on water meters

3. https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf

HEAD_TED Data Collections## Data InventoryThis collection comprises a set of files generated and utilized in our study. The following archives and tables are included:- [x] Raw_conformations.tar.gz- [x] Mininplace_conformations.tar.gz- [x] All_results_extended_tables.tar.gz- [x] PoseBusters_detailed_results.tar.gz- [x] LigBoundConf_TED_PoseCheck.csv- [x] GM-5K_mmff94_min.sdf.tar.gz- [x] GM-5K_raw.sdf.tar.gz- [x] GM-5K_collection.csv- [x] GM-1K_mmff94_min.sdf.tar.gz- [x] GM-1K_raw.sdf.tar.gz- [x] GM-1K_collection.csv- [x] DFT-5K.sdf.tar.gz- [x] DFT-5K.csv## Data File Descriptions### Pre- and Post-Optimization ConformationsThe Raw_conformations.tar.gz archive contains the initial, unoptimized molecular conformations generated by 5 models in .sdf format. Correspondingly, the Mininplace_conformations.tar.gz archive houses the same set of molecules following in situ refinement using OPLS3e force field with protein pocket fixed. Within each .sdf file, individual molecular entries are distinguished by unique identifiers embedded in the molecule headers. Each archive contains five independent .sdf files, with filenames corresponding to the different generative models employed.### Extended Results TablesThe All_results_extended_tables.tar.gz archive encompasses five independent .csv files, each named according to the generative model used. These tables provide a comprehensive overview of the generated molecules, with the following columns:- mol_id: Unique identifier extracted from the header of each molecule block in the respective .sdf file.- smiles: Simplified Molecular Input Line Entry System (SMILES) representation of the molecule.- model: The generative model responsible for producing the molecule.- pdb_id: The Protein Data Bank (PDB) identifier of the protein target pocket from which the binding site information was derived.- qed: Quantitative Estimate of Drug-likeness (QED) score, calculated using RDKit.- sas: Synthetic Accessibility (SA) score, calculated using RDKit.- mininplace_failure: A binary indicator of the success of the local optimization process within the binding pocket: - 0: Optimization was successful. - 1: Optimization failed.- raw_PoseBusters_ligand_protein_interaction_invalidity: A binary indicator assessing the validity of ligand-protein interactions in the unoptimized conformation, as determined by PoseBusters: - 0: Valid interaction geometry. - 1: Invalid interaction geometry.- raw_PoseBusters_ligand_conformation_invalidity: A binary indicator assessing the validity of the ligand's internal conformation in the unoptimized state, as determined by PoseBusters: - 0: Valid ligand conformation. - 1: Invalid ligand conformation.- min_PoseBusters_ligand_protein_interaction_invalidity: A binary indicator assessing the validity of ligand-protein interactions in the minimized conformation, as determined by PoseBusters: - 0: Valid interaction geometry. - 1: Invalid interaction geometry.- min_PoseBusters_ligand_conformation_invalidity: A binary indicator assessing the validity of the ligand's internal conformation in the minimized state, as determined by PoseBusters: - 0: Valid ligand conformation. - 1: Invalid ligand conformation.- HEAD_ligand_protein_interaction_invalidity: An indicator assessing the validity of ligand-protein interactions in the unoptimized conformation, as determined by HEAD: - 0: Valid interaction geometry. - 1: Invalid interaction geometry. - -1: Unsupported conformation that may contain elements out of {H, C, N, O, F, S, Cl} or encounter unexpected error during loading- HEAD_ligand_conformation_invalidity: An indicator assessing the validity of the ligand's internal conformation in the unoptimized state, as determined by HEAD: - 0: Valid ligand conformation. - 1: Invalid ligand conformation. - -1: Unsupported conformation that may contain elements out of {H, C, N, O, F, S, Cl} or encounter unexpected error during loading- TED_torsion_energy_irrationality: An indicator of the plausibility of the predicted torsion energy, as determined by TED: - 0: Torsion energy is considered rational. - 1: Torsion energy is considered irrational. - -1: The conformation is unsupported by the TED model.The original, detailed output files from the PoseBusters analysis are provided in the compressed archive PoseBusters_detailed_results.tar.gz. This archive contains 10 independent .csv files, detailing the results for all analyzed conformations, encompassing both the raw and the in situ optimized structures.### LigBoundConf Extension DataThe LigBoundConf_TED_PoseCheck.csv file contains data used for the comparative analysis of PoseCheck and TED on a set of optimized ligand-bound conformations. The columns are defined as follows:- mol_id: The ligand identifier as used in the original LigBoundConf publication.- LCSE: The Local Conformational Strain Energy value (in kcal/mol) as reported in the original LigBoundConf publication.- TED_torsion_energy_irrationality: As described in the "Extended Results Tables" section.- PoseCheck_strain_energy: The strain energy calculated by PoseCheck (units should be specified if known, e.g., kcal/mol).### GM-5K and GM-1K SubsetsThe GM-5K and GM-1K datasets represent subsets of 5,000 and 1,000 molecules, respectively, extracted from the larger collection. In addition to the original conformations, this release includes two new compressed .sdf archives containing conformations optimized using the MMFF94 force field: GM-5K_mmff94_min.sdf.tar.gz and GM-1K_mmff94_min.sdf.tar.gz. The molecule headers within these optimized .sdf files retain the identifiers corresponding to the raw conformations in the original datasets, facilitating direct comparison.Detailed DFT single-point energies (for both the original and MMFF94-optimized conformations), along with HEAD analysis results, are provided in the .csv files GM-1K_collection.csv and GM-5K_collection.csv.Besides the columns mentioned above, the other columns in the GM-1K_collection.csv file are explained as follows:- DFT_single_point_energy_raw: Single-point energy (in kcal/mol) of the unoptimized raw conformation calculated using Density Functional Theory (DFT).- DFT_single_point_energy_min: Single-point energy (in kcal/mol) of the MMFF94-optimized conformation calculated using Density Functional Theory (DFT).- dE: The energy difference ($ΔE=E_{raw}−E_{opt}$) in kcal/mol, representing the change in energy upon MMFF94 optimization.- HEAD_invalid_atoms: A list detailing any atoms flagged as invalid by HEAD, along with the associated high-energy response value (in kcal/mol). If no invalid atoms are detected, this field contains None. For example: [(2, 'C', 40.962)] indicates that carbon atom number 2 (indexed from 1) was flagged with a high-energy response of 40.962 kcal/mol (Note: this energy value is a relative indicator and may not represent the absolute energy).- information_entropy_label: A binary indicator where 1 signifies an invalid conformation detected solely by the information entropy approach; otherwise, 0.The GM-5K_collection.csv file includes all the columns mentioned above for GM-1K_collection.csv, as well as the following additional columns:- MM/GBSA:The calculated binding energy (in kcal/mol) of each molecule and its binding protein using the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) method. Amber ff14SB force field is used for protien and the General Amber Force Field 2 (GAFF2) is for organic molecules.- HEAD_E_bind: The calculated binding energy by HEAD approach of each ligand molecule and its binding protein, unit in (kcal/mol). $E_{bind} = E^{bound}_{complex}-E^{isolated}_{protein} - E^{isolated}_{ligand}$.- PoseCheck_num_clashes: Number of steric clashes detected in the binding pose by PoseCheck.- SMINA_docking_score: The docking score of the input binding pose by SMINA software, using 'score_only' tag.- DrugPose_score: The calcuated simlarity score by DrugPose approach, where a higher value indicates a more similar binding pose to the reference ligand.- DrugPose_ligand_protein_interaction_invalidity: An indicator assessing the validity of protein-ligand interaction detected solely using DrugPose by comparing its DrugPose_score with a threshold of 50: - 0: Valid interaction geometry. - 1: Invalid interaction geometry. - -1: Not supported.### DFT-5KThe conformations of DFT-5K dataset are provided in the DFT-5K.sdf.tar.gz archive. All molecules in this file have been optimized using the DFT method with appropriate constraints on specific dihedrals. Their single-point energies are re-calculated using a higher-level DFT method than used for optimization.Each torsion fragment in DFT-5K is represented by 24 conformations, grouped under the same name in the header, corresponding to different dihedral angle values ranging from -180° to 180° in 15° increments (-180° is equivalent to 180°). Each molecule block in the .sdf file includes the following properties:- TORSION_ATOMS: Indicese of atom quartet defining the specific dihedral, starting from 0.- DIHEDRAL_ANGLE: The degree of the dihedral being investigated.- DFT_SINGLE_POINT_ENERGY: Single-point energy (in kcal/mol) calculated using DFT method.In addition to the .sdf file, the dataset includes a DFT-5K.csv file containing the following columns:- DFT_id: The name in the header of each molecule block in .sdf file. Conformations with different dihedral angles of the same torsion fragment share the same DFT_id.- xTB_dih_relative_energies: A string of relative energies, joined by --, representing the energies of the conformations optimized with constraints and calculated using GFN2-xTB. The order of energies corresponds to dihedral angles from

This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level.Data HistoryData OriginDomestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.Data Triage ConsiderationsThis section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.Identification of Critical InfrastructureThis aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.Commercial Risks and AnonymisationIndividual Identification RisksThere is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.Meter and Property AssociationChallenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.Interpretation of Null ConsumptionInstances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.Meter Re-readsThe dataset must account for instances where meters are read multiple times for accuracy.Joint Supplies & Multiple Meters per HouseholdSpecial consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.Schema Consistency with the Energy IndustryIn formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.SchemaThe dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.Aggregation to Mitigate RisksThe dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.Data FreshnessUsers should be aware that this dataset reflects historical consumption patterns and does not represent real-time data.Publish FrequencyAnnually (1st of April to the 31st of March)Data Triage Review FrequencyAn annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.Data SpecificationsFor the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include:Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption.Where it is necessary to estimate consumption, this is calculated based on actual meter readings.Meters of all types (smart, dumb, AMR) are included in this dataset.The dataset is updated and published annually (1st of April to the 31st of March).Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release.The dataset includes LSOAs with 10 or more meters. Any LSOAs with less than 10 meters have been excluded.ContextUsers are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.The geographical data provided does not pinpoint locations of water meters within an LSOA.The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.Supplementary InformationBelow is a curated selection of links for additional reading, which provide a deeper understanding of this dataset:Stream - The Value of a Domestic Consumption Data Use Case: https://www.streamwaterdata.co.uk/apps/0742154c5c1547cb8df235ccf2f66d57/exploreOfwat - Guidance on water meters: https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf

The Energy Release Component (ERC) is a calculated output of the National Fire Danger Rating System (NFDRS). The ERC is a number related to the available energy (BTU) per unit area (square foot) within the flaming front at the head of a fire. The ERC is considered a composite fuel moisture index as it reflects the contribution of all live and dead fuels to potential fire intensity. As live fuels cure and dead fuels dry, the ERC will increase and can be described as a build-up index. The ERC has memory. Each daily calculation considers the past 7 days in calculating the new number. Daily variations of the ERC are relatively small as wind is not part of the calculation.

The data in this dataset were collected in the result of the survey of Latvian society (2021) aimed at identifying high-value data set for Latvia, i.e. data sets that, in the view of Latvian society, could create the value for the Latvian economy and society.
The survey is created for both individuals and businesses.
It being made public both to act as supplementary data for "Towards enrichment of the open government data: a stakeholder-centered determination of High-Value Data sets for Latvia" paper (author: Anastasija Nikiforova, University of Latvia) and in order for other researchers to use these data in their own work.

The survey was distributed among Latvian citizens and organisations. The structure of the survey is available in the supplementary file available (see Survey_HighValueDataSets.odt)

***Description of the data in this data set: structure of the survey and pre-defined answers (if any)***
1. Have you ever used open (government) data? - {(1) yes, once; (2) yes, there has been a little experience; (3) yes, continuously, (4) no, it wasn’t needed for me; (5) no, have tried but has failed}
2. How would you assess the value of open govenment data that are currently available for your personal use or your business? - 5-point Likert scale, where 1 – any to 5 – very high
3. If you ever used the open (government) data, what was the purpose of using them? - {(1) Have not had to use; (2) to identify the situation for an object or ab event (e.g. Covid-19 current state); (3) data-driven decision-making; (4) for the enrichment of my data, i.e. by supplementing them; (5) for better understanding of decisions of the government; (6) awareness of governments’ actions (increasing transparency); (7) forecasting (e.g. trendings etc.); (8) for developing data-driven solutions that use only the open data; (9) for developing data-driven solutions, using open data as a supplement to existing data; (10) for training and education purposes; (11) for entertainment; (12) other (open-ended question)
4. What category(ies) of “high value datasets” is, in you opinion, able to create added value for society or the economy? {(1)Geospatial data; (2) Earth observation and environment; (3) Meteorological; (4) Statistics; (5) Companies and company ownership; (6) Mobility}
5. To what extent do you think the current data catalogue of Latvia’s Open data portal corresponds to the needs of data users/ consumers? - 10-point Likert scale, where 1 – no data are useful, but 10 – fully correspond, i.e. all potentially valuable datasets are available
6. Which of the current data categories in Latvia’s open data portals, in you opinion, most corresponds to the “high value dataset”? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
7. Which of them form your TOP-3? - {(1)Foreign affairs; (2) business econonmy; (3) energy; (4) citizens and society; (5) education and sport; (6) culture; (7) regions and municipalities; (8) justice, internal affairs and security; (9) transports; (10) public administration; (11) health; (12) environment; (13) agriculture, food and forestry; (14) science and technologies}
8. How would you assess the value of the following data categories?
8.1. sensor data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.2. real-time data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
8.3. geospatial data - 5-point Likert scale, where 1 – not needed to 5 – highly valuable
9. What would be these datasets? I.e. what (sub)topic could these data be associated with? - open-ended question
10. Which of the data sets currently available could be valauble and useful for society and businesses? - open-ended question
11. Which of the data sets currently NOT available in Latvia’s open data portal could, in your opinion, be valauble and useful for society and businesses? - open-ended question
12. How did you define them? - {(1)Subjective opinion; (2) experience with data; (3) filtering out the most popular datasets, i.e. basing the on public opinion; (4) other (open-ended question)}
13. How high could be the value of these data sets value for you or your business? - 5-point Likert scale, where 1 – not valuable, 5 – highly valuable
14. Do you represent any company/ organization (are you working anywhere)? (if “yes”, please, fill out the survey twice, i.e. as an individual user AND a company representative) - {yes; no; I am an individual data user; other (open-ended)}
15. What industry/ sector does your company/ organization belong to? (if you do not work at the moment, please, choose the last option) - {Information and communication services; Financial and ansurance activities; Accommodation and catering services; Education; Real estate operations; Wholesale and retail trade; repair of motor vehicles and motorcycles; transport and storage; construction; water supply; waste water; waste management and recovery; electricity, gas supple, heating and air conditioning; manufacturing industry; mining and quarrying; agriculture, forestry and fisheries professional, scientific and technical services; operation of administrative and service services; public administration and defence; compulsory social insurance; health and social care; art, entertainment and recreation; activities of households as employers;; CSO/NGO; Iam not a representative of any company
16. To which category does your company/ organization belong to in terms of its size? - {small; medium; large; self-employeed; I am not a representative of any company}
17. What is the age group that you belong to? (if you are an individual user, not a company representative) - {11..15, 16..20, 21..25, 26..30, 31..35, 36..40, 41..45, 46+, “do not want to reveal”}
18. Please, indicate your education or a scientific degree that corresponds most to you? (if you are an individual user, not a company representative) - {master degree; bachelor’s degree; Dr. and/ or PhD; student (bachelor level); student (master level); doctoral candidate; pupil; do not want to reveal these data}

***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt

***Licenses or restrictions***
CC-BY