About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Clear Lake, IN, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Clear Lake, IN reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Clear Lake households based on income levels.

Key observations

• For Family Households: In Clear Lake, the majority of family households, representing NA%, earn NA, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising NA%, have incomes falling NA, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Clear Lake, the majority of non-family households, accounting for NA%, have income NA, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising NA%, earn NA, representing a smaller, yet notable, portion of non-family households in the community.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Clear Lake, IN (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Clear Lake, IN
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Clear Lake, IN
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Clear Lake, IN

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 Clear Lake median household income. You can refer the same here

We used this dataset to assess the strength of isolation due to geographic and macroclimatic distance across island and mainland systems, comparing published measurements of phenotypic traits and neutral genetic diversity for populations of plants and animals worldwide. The dataset includes 112 studies of 108 species (72 animals and 36 plants) in 868 island populations and 760 mainland populations, with population-level taxonomic and biogeographic information, totalling 7438 records. Methods Description of methods used for collection/generation of data: We searched the ISI Web of Science in March 2017 for comparative studies that included data on phenotypic traits and/or neutral genetic diversity of populations on true islands and on mainland sites in any taxonomic group. Search terms were 'island' and ('mainland' or 'continental') and 'population*' and ('demograph*' or 'fitness' or 'survival' or 'growth' or 'reproduc*' or 'density' or 'abundance' or 'size' or 'genetic diversity' or 'genetic structure' or 'population genetics') and ('plant*' or 'tree*' or 'shrub*or 'animal*' or 'bird*' or 'amphibian*' or 'mammal*' or 'reptile*' or 'lizard*' or 'snake*' or 'fish'), subsequently refined to the Web of Science categories 'Ecology' or 'Evolutionary Biology' or 'Zoology' or 'Genetics Heredity' or 'Biodiversity Conservation' or 'Marine Freshwater Biology' or 'Plant Sciences' or 'Geography Physical' or 'Ornithology' or 'Biochemistry Molecular Biology' or 'Multidisciplinary Sciences' or 'Environmental Sciences' or 'Fisheries' or 'Oceanography' or 'Biology' or 'Forestry' or 'Reproductive Biology' or 'Behavioral Sciences'. The search included the whole text including abstract and title, but only abstracts and titles were searchable for older papers depending on the journal. The search returned 1237 papers which were distributed among coauthors for further scrutiny. First paper filter To be useful, the papers must have met the following criteria: Overall study design criteria: Include at least two separate islands and two mainland populations; Eliminate studies comparing populations on several islands where there were no clear mainland vs. island comparisons; Present primary research data (e.g., meta-analyses were discarded); Include a field study (e.g., experimental studies and ex situ populations were discarded); Can include data from sub-populations pooled within an island or within a mainland population (but not between islands or between mainland sites); Island criteria: Island populations situated on separate islands (papers where all information on island populations originated from a single island were discarded); Can include multiple populations recorded on the same island, if there is more than one island in the study; While we accepted the authors' judgement about island vs. mainland status, in 19 papers we made our own judgement based on the relative size of the island or position relative to the mainland (e.g. Honshu Island of Japan, sized 227 960 km² was interpreted as mainland relative to islands less than 91 km²); Include islands surrounded by sea water but not islands in a lake or big river; Include islands regardless of origin (continental shelf, volcanic); Taxonomic criteria: Include any taxonomic group; The paper must compare populations within a single species; Do not include marine species (including coastline organisms); Databases used to check species delimitation: Handbook of Birds of the World (www.hbw.com/); International Plant Names Index (https://www.ipni.org/); Plants of the World Online(https://powo.science.kew.org/); Handbook of the Mammals of the World; Global Biodiversity Information Facility (https://www.gbif.org/); Biogeographic criteria: Include all continents, as well as studies on multiple continents; Do not include papers regarding migratory species; Only include old / historical invasions to islands (>50 yrs); do not include recent invasions; Response criteria: Do not include studies which report community-level responses such as species richness; Include genetic diversity measures and/or individual and population-level phenotypic trait responses; The first paper filter resulted in 235 papers which were randomly reassigned for a second round of filtering. Second paper filter In the second filter, we excluded papers that did not provide population geographic coordinates and population-level quantitative data, unless data were provided upon contacting the authors or could be obtained from figures using DataThief (Tummers 2006). We visually inspected maps plotted for each study separately and we made minor adjustments to the GPS coordinates when the coordinates placed the focal population off the island or mainland. For this study, we included only responses measured at the individual level, therefore we removed papers referring to demographic performance and traits such as immunity, behaviour and diet that are heavily reliant on ecosystem context. We extracted data on population-level mean for two broad categories of response: i) broad phenotypic measures, which included traits (size, weight and morphology of entire body or body parts), metabolism products, physiology, vital rates (growth, survival, reproduction) and mean age of sampled mature individuals; and ii) genetic diversity, which included heterozygosity,allelic richness, number of alleles per locus etc. The final dataset includes 112 studies and 108 species. Methods for processing the data: We made minor adjustments to the GPS location of some populations upon visual inspection on Google Maps of the correct overlay of the data point with the indicated island body or mainland. For each population we extracted four climate variables reflecting mean and variation in temperature and precipitation available in CliMond V1.2 (Kritikos et al. 2012) at 10 minutes resolution: mean annual temperature (Bio1), annual precipitation (Bio12), temperature seasonality (CV) (Bio4) and precipitation seasonality (CV) (Bio15) using the "prcomp function" in the stats package in R. For populations where climate variables were not available on the global climate maps mostly due to small island size not captured in CliMond, we extracted data from the geographically closest grid cell with available climate values, which was available within 3.5 km away from the focal grid cell for all localities. We normalised the four climate variables using the "normalizer" package in R (Vilela 2020), and we performed a Principal Component Analysis (PCA) using the psych package in R (Revelle 2018). We saved the loadings of the axes for further analyses. References:

Bruno Vilela (2020). normalizer: Making data normal again.. R package version 0.1.0. Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., et al.(2012). CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol., 3, 53--64. Revelle, W. (2018) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 1.8.12. Tummers, B. (2006). DataThief III. https://datathief.org/

Survey data used in a perception study of stalked barnacle harvesters on the effectiveness of fisheries management practices in Spain, Portugal and France. Harvesters from the following six regions along the Atlantic Arc participated: Morbihan in Brittany (France), Asturias-East, Asturias-West and Galicia (Spain), the Reserva Natural das Berlengas (RNB; Portugal) and the Parque Natural do Sudoeste Alentejano e Costa Vicentina (PNSACV; Portugal). We administered 184 surveys from October 2019 to September 2020 and each region was treated as an independent population. The data includes: general demographic data (Region, Age, Gender, Level of Education, Main income source, Years of Experience); perception data of the effectiveness of the currently implemented management strategies in each region (coded: e_name_of_strategy – using Likert Scale with scores ranging from 1 = completely ineffective to 5 = very effective); data of the willingness for change of the currently implemented management (Yes, No, NA); and data of harvesters’ perceptions regarding the most important strategy to achieve sustainability in the fishery. Because the surveys were conducted both before and during the Covid-19 pandemic (the column Covid indicates whether the data was collected before or during the pandemic), we had to make adjustments in our data collection methods. We provided the following options for survey completion (see the Recollection_of_data column): by hand in a written format, online, or via an oral interview conducted with the assistance of a scientist per telephone. Our results indicate that the majority of harvesters in the regions in Portugal and France were willing to make changes to current management strategies, reflecting their awareness of the need for improvement. Based on the AIC model selection analysis results, the model with the single variable region explained 83% of the cumulative model weight. The variable region was the best predictor of the trends in management strategy preferences, and presented a highly significant goodness-of-fit result (p<0.001), suggesting that regional differences play a significant role in shaping these preferences. No clear trend emerged regarding a single "optimal" management strategy preferred by harvesters across regions. Harvesters in less developed co-management systems favored general input and output restrictions and expressed a desire for greater involvement in co-management processes. Conversely, harvesters in highly developed co-management systems with Territorial User Rights for Fishers (TURFs) preferred the most restrictive and spatially explicit management strategies, such as implementing harvest bans and establishing marine reserves. Our findings emphasise that management strategies do not only need to be tailored to each region's particular practices, needs, and characteristics, but that resource users’ readiness for specific strategies also needs to be considered.

Abstract

Air pollution (both outdoor and indoor) is an important public health challenge especially in the developing world where legislation on emissions control is either weak or non-existent. With these countries preparing for industrial take off, outdoor air pollution will continue to remain important as it concerns the health consequences, owing to possibly higher levels of emissions. In these countries, majority of households rely on biomass derived fuels for cooking and heating that have been classified as highly polluting and have been shown to have deleterious effects on human health. Studies have documented the negative effects of both outdoor and indoor air pollution on health; however, there have been very few studies in Africa. The objectives of this study are to assess the perceptions and attitudes of people living in two informal settlements in Nairobi regarding their exposure to air pollution; estimate the effect of indoor air pollution on pregnancy outcomes and model the effect of air pollution on mortality in the two settlements. The study shall use mixed methods approach where a qualitative study will be done to look at the perceptions and attitudes of residents regarding air pollution. In addition to this, a panel study measuring levels of outdoor air pollutants shall be done. This will be done in such a way that seasonal variations are accounted for. To assess the effect of air pollution on pregnancy outcomes, a follow up study of pregnant women will be done and measurements of indoor air pollution levels will be done in their homes. The study is expected to take 12 months.

Geographic coverage

Nairobi's slums (Korogocho and Viwandani)

Analysis unit

The households.

Universe

Indoor air data covered households with pregnant women while outdoor air data was collected from various villages in each of the slum

Sampling procedure

The qualitative study involved a total of eight focus group discussions with men and women living in Korogocho and Viwandani. In addition to this, outdoor air pollution was measured as panel data to ensure seasonal variations are accounted for. This part of the study involved assessment by the measurement team who were carrying the measuring equipment. The study on indoor air pollution and pregnancy outcomes was a prospective study of a cohort of pregnant women recruited during their first or early second trimester. They were followed up until they delivered and the birth weight of the newborn was taken. Measurement of indoor air pollution levels was done and other information on fuel and stove types used in the household collected. The study was nested on an ongoing intervention study following up 600 pregnant women and providing nutritional counseling pre- and post-pregnancy for optimal child health.

Sampling deviation

na

Mode of data collection

Face-to-face [f2f]

Research instrument

Qualitative guide into the enquiry of people's perceptions of exposure to air pollution 1. Understand the perceptions, attitudes and beliefs of individuals regarding air pollution. 2. Assess the communities' understanding of the health risks associated with air pollution.

Questionnaire on IAP and pregnancy outcomes

Cleaning operations

Data collection for the quantitative study was done electronically using netbooks. This removed the need for data entry and therefore once data had been collected, the investigators obtained it for cleaning and analysis using Stata software. Descriptive analysis and multivariable regression analysis was applied as appropriate. Qualitative data was transcribed by the investigators and coded and thematic analysis conducted using Nvivo software.

Response rate

na

Sampling error estimates

na

This dataset presents the raw data obtained from 1415 online and representative Australian that were asked to aesthetically rate 180 photos of typical coral reef landscapes. Mean aesthetic ratings of 180 photos were collected from the survey, as well as from an expert research team, contributing mean ratings of coral reef health, coral cover, coral pattern, coral topography, fish abundance, and visibility.

Please note that CSIRO have published a version of this dataset on 29 May 2019, which should be considered the primary source of data information (i.e. citation for data files found on the CSIRO Portal). The published eAtlas version includes files supplied by the project to the eAtlas for publication. The eAtlas version differs in format (RatingsAesthetics.csv - includes the photo mean score) and includes a second spreadsheet containing information not available in the CSIRO version (Ratings-All.csv) which captures each photo's ratings against five factors (coral health, coral cover, coral topography, fish abundance and visibility), as outlined in point two below. The CSIRO version contains the SPSS data extract and codebook (xlsx file), as well as the photo ratings summary (PhotoRatingsInd.xlsx) without the calculated mean.

Methods:
1. A survey was constructed to collect simple demographic information about each participant, the self-rated level of interest in coral reefs, and aesthetic ratings for each photo on a scale of 1-10 (where 1=extremely unattractive, and 10=extremely attractive). Once an individual agreed to partake in the survey, they were sent a survey with 50 photographs randomly chosen from the pool of 181 photographs. It was noted that the quality of responses could be affected if more than 50 photos were viewed (where 50 photos represented a ten-minute survey). The style of the survey was not dissimilar from very popular online games in which individuals are asked to rank aesthetic preferences of fashion or interior design items. A full list of the images used in the survey is available in Appendix 1 (1-90)

A total of 1,417 individuals participated in the study, where each photo was rated at least 380 times on the ten-point scale. Twenty-nine percent of the sample came from Queensland, and 71% were distributed across Australia. Some 62.3% of people came from Metropolitan Australia, whilst 37.7 came from rural/regional Australia. Some 51.4% were female. Participants represented a range of experiences with the Great Barrier Reef, where 7.2% had never visited, and 7.9% did not find coral reefs that interesting. Most participants (99.6%) were not part of a GBR based club or community groups, such as a spear-fishing club. The average age for the sample population was 46.96 (standard error=0.471), and ranged from 16 to 89.

2. We identified 180 underwater coral reef photographs from those that were publicly available (www.gbrmpa.gov.au) or existed in the combined image libraries of the study authors. They represented typical underwater images from the GBR, with a common oblique perspective taken from approximately 5-10 m above a coral substrate. This perspective characterised the image that a person would see as soon as they placed their head beneath the water, and it was similar to the visual perspective used in monitoring surveys conducted by manta-towing at the Australian Institute of Marine Science. Some photos were duplicated and placed randomly, and some were modified using photo editing software to manipulate one feature independent of others, for the purposes of ‘checking’ the consistency and subtleties associated with making aesthetic judgements.

Each photo was rated for each of the five factors (on a scale of low, medium, high) by members of the research team with experience in coral reefs; coral health, coral cover, coral topography, fish abundance, and visibility. Given that there were insufficient photos representing abundant fish and poor visibility, a total of 20 photos were manipulated to enhance or de-emphasise certain factors. These photos ensured that we could attribute differences in aesthetic appeal of each photo to at least one of the five factors. The final set of photos represented realistic coral reef images across all five factors, with a greater representation of images containing moderately high coral cover to capture the nuances across the scale of potential ratings and also to aide engagement during online rating sessions



Format:
This dataset consists of two CSV files and two PDF files. The two CSV files contain the data on aesthetic ratings from an online survey, and ratings on reef health and abundance. eAtlas Note: The original files were provided as Excel spreadsheet tables and were converted to CSV files. Photographs and analysis were originally supplied as word document files and have been converted to PDF files.



References:

Marshall, N.A., Marshall, P.A., and Smith, A.K. (2017) Managing for Aesthetic Values in the Great Barrier Reef: Identifying indicators and linking Reef Aesthetics with Reef Health. Report to the National Environmental Science Programme. Reef and Rainforest Research Centre Limited, Cairns (102 pp.).

Data Location:

This dataset is filed in the eAtlas enduring data repository at: eAtlas/nesp3/3.2.4_Defining-assessing-GBR-aesthetics

During the fourth quarter 2024, approximately 20.6 million TikTok accounts were removed from the platform due to suspicion of being operated by users under the age of 13. During the last measured period, around 185 million fake accounts were removed from fake accounts removed from TikTok.

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Abstract

This dataset contains the electricity metering data from the NRS Load Research Programme collected at 5 minute intervals. From 1994 to 2008 electricity meters were installed at households to measure the voltage and current. From 2009 to 2014 loggers were upgraded and the current, voltage, real and reactive power and power frequency of households were metered.

The NRS Load Research Programme was started in 1994 to provide inputs towards policy development and technical design guidelines for the domestic electricity distribution business in South Africa. The programme was overseen by the National Rationalised Specification (NRS) 034 Working Group at Eskom. Under this programme the Domestic Electrical Load (DEL) Study (also referred to as the Domestic Load Research Project) was designed and managed to collect electricity meter readings and conduct an annual socio-demographic survey of metered households. The resulting DEL data collection and research outputs present a collaborative, multi-party public-academic-private collaboration.

Initiated by Dr Ron Herman (Stellenbosch University) and Prof. Trevor Gaunt (University of Cape Town), the study was promoted by the NRS 034 Working Group established within Eskom for this purpose. Early funders and collaborators included the Department of Minerals and Energy Affairs (now Department of Energy), the Council for Scientific and Industrial Research, as well as Stellenbosch, eThekwini and Nelson Mandela Bay Municipalities. From 1994 to 2009 eight municipalities contributed to data collection. Eskom Research, Testing and Development became actively involved in the study in 1997. From 2001 onwards Eskom was the major data contributor and funder of the study. Prior to 1994, the National Energy Council and Development Bank of Southern Africa funded the development of the data loggers used in the study, as well as early research efforts by Dr Ron Herman and J.J. Kritzinger that influenced the study.

This study made a major contribution to the electrification of South African households and enabled the development of planning tools and applications that Eskom and municipalities to accurately forecast and right-size new power transmission and distribution infrastructure. The research outputs that emerged from the data collected in this study, such as the Hermann-Beta distribution and the Geo-based Load Forecasting Standard, informed the design of South Africa's power system and have been used in the design of power grids in other developing countries.

Geographic coverage

The study had national coverage.

Analysis unit

Households

Universe

The metering study covers electrified households that received electricity either directly from Eskom or from their local municipality. Particular attention was devoted to rural and low income households, as well as surveying households electrified over a range of years, thus having had access to electricity from recent times to several decades.

Kind of data

Process-produced data

Sampling procedure

The sampling procedure and sample design are described in detail in the annual NRS Load Research Reports and in particular in the Load Data Collection Guides. The sample design was reviewed annually and updated from time to time as the need arose.

SAMPLE POPULATION CHARACTERISTICS Sampling communities were selected based on the following requirements outlined in programme reports: The target community should have a high degree of electrification, should be stable and willing to co-operate with the project. There should not be many gapsi n connectivity. As first-time consumers require a period of adjustment to the use of electrical power, it was assumed that individual load patterns would be erratic for the first two years. Thus "newly electrified" communities should have had access to electricity for at least 24 months before being selected to participate in the study.

SAMPLE SIZE 70 - 100 consumers (households) were deemed a sufficient sample population for statistically significant load metering.

SAMPLE SELECTION A random systematic method was suggested and where possible used to select households to be monitored. In general sample selection was optimised to fully utilise data loggers, meaning that loggers were installed on electrical poles that had the most connections so that all logger channels could be utilised. The approach taken at the beginning of the study was as follows: 1. List all the dwelling stand numbers from the township plans. 2. Divide the number of stands by the number of available loggers (call the resulting number sl) 3. Select a random starting point, say at stand sp. 4. Add multiples of sl to sp to give the stand numbers at which to site the loggers. 5. Check (4) to ensure that all or most of the data channels can be used at the point under consideration. If necessary move one pole forward or backward to optimise logger utilisation. 6. Repeat the process until all the loggers have been sited. Meticulous attention must now be given to identifying each monitored dwelling with its logger and channel.

Mode of data collection

Other

Research instrument

NA

Cleaning operations

This dataset has been produced by extracting all electrcity metering data from the original NRS Load Research SQL database using the saveRawProfiles function from the delretrieve python package (https://github.com/wiebket/delretrieve: release v1.0). Full instructions on how to use delretrieve to extract data are in the README file contained in the package.

DATA EXTRACTION AND FILE STRUCTURE To manage data volumes, meter readings were extracted in batches and are stored in a file hierarchy arranged by metering unit (A, Hz, kVA, kW, V) and collection year (1994 - 2015).

MISSING VALUES No post-processing was done after data extraction and all database records, including missing values, are stored exactly as retrieved.

Data appraisal

CALIBRATION of voltages and instruments Prior to 2009 data loggers were built inhouse and only elementary calibration was done (insufficient for commercial standards). After 2009 all loggers were changed to commercial loggers with standard industry calibration of electricity meters.

TIME SYNCHRONISATION Meter readings have date and time stamps. Every time data was downloaded from the logger, the meter clock was adjusted to the laptop clock, which was set before going into the field.

LOGGING ERRORS Early logging devices had a 6 week storage capacity. When this capacity was exceeded a "data buffer full" error would occur. Other common modes of technical failure included 'floating' data channels, readings failing to '0' load and readings failing to full scale Amps.

DATA VALIDATION MODELS A data marking table was generated to validate profile IDs on each day against a set of data quality rules (incuded as external resoure). Based on these rules readings were marked as 'Y' (valid) or 'N' (invalid).

SAMPLING SUFFICIENCY Sampling sufficiency was determined by calculating the standard deviation on customer behaviour at the time of annual peak demand (ie 60 or more customers were require to contribute to the annual peak demand, within an acceptable standard deviation)

During a January 2024 global survey among marketers, nearly 60 percent reported plans to increase their organic use of YouTube for marketing purposes in the following 12 months. LinkedIn and Instagram followed, respectively mentioned by 57 and 56 percent of the respondents intending to use them more. According to the same survey, Facebook was the most important social media platform for marketers worldwide.

The global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.