1littlecoder/sample-set dataset hosted on Hugging Face and contributed by the HF Datasets community

heissanjay/sample-set-gemma3n dataset hosted on Hugging Face and contributed by the HF Datasets community

Structure of the experimental stimuli with a sample set for each condition.

This database was prepared using a combination of materials that include aerial photographs, topographic maps (1:24,000 and 1:250,000), field notes, and a sample catalog. Our goal was to translate sample collection site locations at Yellowstone National Park and surrounding areas into a GIS database. This was achieved by transferring site locations from aerial photographs and topographic maps into layers in ArcMap. Each field site is located based on field notes describing where a sample was collected. Locations were marked on the photograph or topographic map by a pinhole or dot, respectively, with the corresponding station or site numbers. Station and site numbers were then referenced in the notes to determine the appropriate prefix for the station. Each point on the aerial photograph or topographic map was relocated on the screen in ArcMap, on a digital topographic map, or an aerial photograph. Several samples are present in the field notes and in the catalog but do not correspond to an aerial photograph or could not be found on the topographic maps. These samples are marked with “No” under the LocationFound field and do not have a corresponding point in the SampleSites feature class. Each point represents a field station or collection site with information that was entered into an attributes table (explained in detail in the entity and attribute metadata sections). Tabular information on hand samples, thin sections, and mineral separates were entered by hand. The Samples table includes everything transferred from the paper records and relates to the other tables using the SampleID and to the SampleSites feature class using the SampleSite field.

The samples in the training set and in the testing set.

The dataset contains the analytical results for environmental and quality-control replicate sample sets and the computed relative percent differences (RPD) greater than 25 percent for the data collected during the surface-water sampling for the Triangle Area Water Supply Monitoring Project. The data are from samples collected during October 2017 through September 2019. Several study sites contained in this dataset were sampled for other USGS projects during the same time frame. Unless the samples at these sites were collected in conjunction with the Triangle Area Water Supply Monitoring Project, the data for other projects are not included in the dataset.

After fast mean shift (FMS) clustering, the whole research area was divided to 10 subareas, so the new samples can characterize the geographical features of each subarea were collected through field investigations. Because of our limited human and material resources, it is difficult to conduct a mass of sampling in each subarea. In order to make the most of our limited resources, we need to conduct reasonable field sampling strategy. For the first two large subareas, we collected 70 field samples respectively, and labeled them as the first sample set and the second sample set that will be used to build their own GWR models for extend prediction of unobserved points in each area, i.e. local extension prediction; while the remaining 8 small subareas took moderate amounts of samples according to their size, if one subarea owns the size of raster points more than 5000, 16 samples will be collected from it, otherwise, take 12 samples. In this way, a total of 112 samples are put together as the third sample set, and the third GWR model is constructed to achieve the global extension prediction of 8 subareas. In addition, three sample sets were divided into training set and test set, respectively. For the first two sample sets, the ratio of sample size of training set and test set are all 5:2, i.e. training set contains 50 samples, test set has 20 samples. Because of the third sample set composed of samples from 8 subareas, we divided the samples of each subarea into training set and test set according to the ratio of 3:1. In the other word, the sample number of training set from third to tenth subarea is 12, 9, 9, 12, 9, 12, 12 and 9 respectively, and 84 training sample in total; and the sample number of test set from eight subarea is 4, 3, 3, 4, 3, 4, 4 and 3 respectively, a total of 28 samples.

*Risk Allele Lost refers to relative loss of the risk allele compared to the non-risk allele. Number in parentheses indicates percentage of total heterozygous samples showing relative loss of risk allele.†Non-risk Allele Lost refers to relative loss of the non-risk allele compared to the risk allele. Number in parentheses indicates percentage of total heterozygous samples showing relative loss of non-risk allele.§Total number of tumors with imbalance/total heterozygous samples (% of heterozygotes showing imbalance).‡Chi-squared statistical test, df = 1. Unadjusted for multiple comparisons.

Dataset

This dataset was created by VishalKJha

Contents

Dataset

This dataset was created by Lilian Mota Badu

Released under CC0: Public Domain

Contents

Benchmarking of various genome decontamination tools on an human microbiome (gut ùmicrobiota) dataset of Single-cell Amplified Genomes (SAGs) from Kawano-Sugara et al. (2024).

N = number of individuals.

Benchmarking of various genome decontamination tools on an rhizosphere environmental dataset of Single-cell Amplified Genomes (SAGs) from Aoki et al. (2022).

This dataset is about: Sedimentologic and magnetic data of sediment core GeoB6428-1, complete sample set. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.779170 for more information.

This dataset is about: Bulk sediment x-ray diffraction analyses (weight percentage) of surface sediment samples from the southern Florida Straits and the Bahama Platform, sample set 2. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.758234 for more information.

Sample Packs Market Outlook

According to our latest research, the global Sample Packs market size reached USD 7.2 billion in 2024, driven by surging demand across multiple industries and innovative distribution strategies. The market is projected to expand at a robust CAGR of 8.1% from 2025 to 2033, with the total market value forecasted to hit USD 13.9 billion by 2033. This impressive growth trajectory is underpinned by evolving consumer preferences, the proliferation of e-commerce channels, and increased investment in product sampling as a strategic marketing tool by key players across sectors.

The growth of the Sample Packs market is largely propelled by the rising importance of experiential marketing and product trial before purchase, especially in highly competitive segments such as cosmetics, food & beverage, and music production. Brands are increasingly leveraging sample packs as a cost-effective way to introduce new products, gather consumer feedback, and boost brand loyalty. In the digital age, consumers are more inclined to try before they buy, and sample packs offer a low-risk entry point, making them a preferred marketing strategy for companies aiming to reduce product return rates and enhance customer satisfaction.

Another significant factor fueling the expansion of the Sample Packs market is the rapid growth of the online retail ecosystem. E-commerce platforms have revolutionized the distribution of sample packs, making it easier for brands to reach a global audience and for consumers to access a wider variety of samples. This shift has been particularly beneficial for niche and emerging brands, allowing them to compete with established players by providing innovative sample packs that cater to specific consumer needs. The rise of subscription box services and influencer-driven campaigns has further amplified the reach and appeal of sample packs, fostering sustained market momentum.

The increasing focus on sustainability and eco-friendly packaging is also shaping the future of the Sample Packs market. As environmental concerns become more prominent, both consumers and companies are seeking sustainable solutions for sample pack production and distribution. This has led to a surge in the adoption of biodegradable materials, recyclable packaging, and refillable sample containers. Brands that prioritize sustainability in their sample pack offerings are not only meeting regulatory requirements but are also capturing the growing segment of environmentally conscious consumers, thereby driving further market growth.

Regionally, North America currently dominates the Sample Packs market due to its mature retail infrastructure, high consumer awareness, and the presence of major players. However, the Asia Pacific region is poised for the fastest growth, fueled by rising disposable incomes, urbanization, and the rapid expansion of e-commerce platforms. Europe remains a strong market, particularly in the cosmetics and food & beverage sectors, while Latin America and the Middle East & Africa are emerging as promising markets due to increasing brand penetration and changing consumer lifestyles.

Product Type Analysis

The Product Type segment of the Sample Packs market is highly diverse, encompassing music sample packs, food & beverage sample packs, cosmetic sample packs, health & wellness sample packs, and several other emerging categories. Music sample packs are particularly popular among musicians and producers, offering curated sets of sounds, loops, and effects that streamline the music production process. The increasing adoption of digital audio workstations (DAWs) and the democratization of music production tools have significantly boosted demand for music sample packs, making them a staple in both professional and amateur music creation.

Food & beverage sample packs are another rapidly growing category, driven by consumer interest in exploring new flavors, dietary options, and health-focused products without committing to full-sized purchases. Brands in this sector use sample packs to introduce limited-edition items, seasonal flavors, or new product lines, often leveraging them as part of larger marketing campaigns. The convenience and affordability of food & beverage sample packs appeal to a broad demographic, from health-conscious individuals to adventurous eaters, thereby expanding the addressable market.

Cosmeti

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.

This dataset is about: Bulk sediment x-ray diffraction analyses (peak area) of surface sediment samples from the southern Florida Straits, sample set 1. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.758234 for more information.

This dataset is about: Fine fraction (<63 µm) x-ray diffraction analyses (weight percentage) of surface sediment samples from the southern Florida Straits and the Bahama Platform, sample set 2. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.738225 for more information.

The median MFI readings from three technical replicates per sample were analysed in R, and a positive or negative status was assigned to each serum sample. Positivity or negativity was based on a cutoff MFI value that was determined for each viral NP based on the 99th percentile for MFI values in the German blood bank sample set. Statistically significant differences in seroprevalence rates between the two sample sets and odds ratios were calculated using Fisher’s exact test.