About the Dataset

This dataset presents a comprehensive overview of fashion product sales, customer demographics, and market trends across multiple European countries between the years 2024 and 2025. It includes detailed information on product characteristics, customer behavior, and sales channels, offering valuable insights into the fashion retail landscape.

Key features of the dataset include:

• Product Details: The dataset captures four main fashion products under the "Bold Boxy" series — Dress, Set, Shoes, and Tee — along with their respective catalog and cost prices. This allows for analysis of pricing strategies and profit margins.

• Color Distribution: Product color preferences are recorded, showing a nearly balanced demand across popular colors such as Black, Blue, Green, Red, and White. Black ranks highest with 104 items sold, indicating strong consumer preference.

• Size Variants: Product sizing includes at least size 35 and 36 (as shown in the filters), giving flexibility for market segmentation based on physical fit and regional size demand.

• Sales Channels: Two primary sales channels — E-commerce and Mobile App — are compared. E-commerce generated the highest total original price (57K), slightly above the Mobile App channel (54K), reflecting strong performance in digital shopping platforms.

• Customer Demographics: The dataset categorizes customers by age ranges (16–25, 26–35, 36–45, 46–55, 56–65) and country (France, Germany, Italy, Netherlands, Portugal, and Spain). This segmentation is useful for targeted marketing and personalized service strategies.

• Customer Service Trends: A year-over-year breakdown of customer service interactions per age group and country is visualized. For instance, in 2025, the 36–45 age group from France reported the highest service interaction (37), suggesting that middle-aged customers may require more support or are more engaged.

• Overall Product Count: A total of 500 fashion items are included, showcasing product volume and sales scope.

This dataset is suitable for business intelligence analysis, trend forecasting, and market strategy planning for fashion brands operating in Europe. It supports both descriptive analytics and predictive modeling aimed at understanding consumer preferences and optimizing product offerings.

The Haplotype Map (HapMap) project recently generated genotype data for more than 1 million single-nucleotide polymorphisms (SNPs) in four population samples. The main application of the data is in the selection of tag single-nucleotide polymorphisms (tSNPs) to use in association studies. The usefulness of this selection process needs to be verified in populations outside those used for the HapMap project. In addition, it is not known how well the data represent the general population, as only 90–120 chromosomes were used for each population and since the genotyped SNPs were selected so as to have high frequencies. In this study, we analyzed more than 1,000 individuals from Estonia. The population of this northern European country has been influenced by many different waves of migrations from Europe and Russia. We genotyped 1,536 randomly selected SNPs from two 500-kbp ENCODE regions on Chromosome 2. We observed that the tSNPs selected from the CEPH (Centre d'Etude du Polymorphisme Humain) from Utah (CEU) HapMap samples (derived from US residents with northern and western European ancestry) captured most of the variation in the Estonia sample. (Between 90% and 95% of the SNPs with a minor allele frequency of more than 5% have an r2 of at least 0.8 with one of the CEU tSNPs.) Using the reverse approach, tags selected from the Estonia sample could almost equally well describe the CEU sample. Finally, we observed that the sample size, the allelic frequency, and the SNP density in the dataset used to select the tags each have important effects on the tagging performance. Overall, our study supports the use of HapMap data in other Caucasian populations, but the SNP density and the bias towards high-frequency SNPs have to be taken into account when designing association studies.

ObjectiveSedentary behaviour is increasingly recognized as an important health risk, but comparable data across Europe are scarce. The objective of this study was to explore the prevalence and correlates of self-reported sitting time in adults across and within the 28 European Union Member States.MethodsThis study reports data from the Special Eurobarometer 412. In 2013, 27,919 randomly selected Europeans (approximately 1000 per Member State) were interviewed face-to-face. Sitting time on a usual day was self-reported and dichotomised into sitting less- and more than 7.5 hours per day. Uni- and multivariate odds ratios of sitting more than 7.5 hours per day were assessed by country and socio-demographic variables using binary logistic regression analyses. The analyses were stratified by country to study the socio-demographic correlates of sitting time within the different countries.ResultsA total of 26,617 respondents were included in the analyses. Median sitting time was five hours per day. Across Europe, 18.5 percent of the respondents reported to sit more than 7.5 hours per day, with substantial variation between countries (ranging from 8.9 to 32.1 percent). In general, northern European countries reported more sitting than countries in the south of Europe. ‘Current occupation’ and ‘age when stopped education’ were found to be the strongest correlates of sitting time, both across Europe and within most Member States. Compared to manual workers, the odds ratio of sitting more than 7.5 hours per day was 5.00 for people with white collar occupations, 3.84 for students, and 3.65 for managers.ConclusionsThere is substantial variation in self-reported sitting time among European adults across countries as well as socio-demographic groups. While regular surveillance of (objectively measured) sedentary behaviour is needed, the results of this study provide entry points for developing targeted interventions aimed at highly sedentary populations, such as people with sedentary occupations.

DRYAD stork microsat data