Search Current Data Nuggets

As a supplement to the rubric provided for Data Nugget modules, I created a rubric containing specific point values for individual portions of the module as well as specific guidelines for answering written questions.

Big data, with N × P dimension where N is extremely large, has created new challenges for data analysis, particularly in the realm of creating meaningful clusters of data. Clustering techniques, such as K-means or hierarchical clustering are popular methods for performing exploratory analysis on large datasets. Unfortunately, these methods are not always possible to apply to big data due to memory or time constraints generated by calculations of order P*N(N−1)2. To circumvent this problem, typically the clustering technique is applied to a random sample drawn from the dataset; however, a weakness is that the structure of the dataset, particularly at the edges, is not necessarily maintained. We propose a new solution through the concept of “data nuggets”, which reduces a large dataset into a small collection of nuggets of data, each containing a center, weight, and scale parameter. The data nuggets are then input into algorithms that compute methods such as principal components analysis and clustering in a more computationally efficient manner. We show the consistency of the data nuggets based covariance estimator and apply the methodology of data nuggets to perform exploratory analysis of a flow cytometry dataset containing over one million observations using PCA and K-means clustering for weighted observations. Supplementary materials for this article are available online.

Materials and resources for the "Scientist Spotlights and Data Nuggets Workshop," presented at the 2021 Biology and Mathematics Educators (BIOME) Institute.

This two-minute overview video describes the work of the 2021 fall working group for Scientist Spotlight/Data Nugget course resources. Group members began developing new projects for a variety of courses and topics, and discussed key themes and important considerations for development of future projects.

This dataset provides information about the number of properties, residents, and average property values for Nugget Trail cross streets in Leo, IN.

AI-Powered Micro-Learning Nugget Market Outlook

According to our latest research, the global AI-powered micro-learning nugget market size reached USD 1.97 billion in 2024, demonstrating robust expansion fueled by the rapid adoption of digital learning solutions. The market is projected to grow at a remarkable CAGR of 18.2% from 2025 to 2033, anticipating a value of USD 9.13 billion by 2033. This surge is primarily driven by the increasing demand for personalized, just-in-time learning experiences across enterprises and educational institutions, as organizations seek to enhance employee skills, compliance, and productivity through AI-driven content delivery.

The exponential growth of the AI-powered micro-learning nugget market is underpinned by several transformative trends in the global learning and development landscape. One of the most significant growth factors is the shift towards continuous, on-demand learning, propelled by evolving workforce dynamics and the need for rapid skill acquisition. Organizations are increasingly recognizing the limitations of traditional, lengthy training programs and are turning to AI-powered micro-learning solutions that deliver concise, targeted content in digestible formats. This approach not only boosts learner engagement and retention but also enables real-time performance support, making it a preferred choice for both corporate and academic environments.

Another major driver for the AI-powered micro-learning nugget market is the integration of advanced artificial intelligence technologies such as natural language processing, machine learning, and predictive analytics within learning platforms. These technologies empower micro-learning solutions to deliver hyper-personalized learning paths, adapt content dynamically based on learner progress, and provide actionable insights to instructors and administrators. The ability of AI to analyze vast amounts of learner data and tailor content accordingly has proven invaluable in addressing diverse learning needs, reducing time-to-competency, and improving overall training ROI. Furthermore, the proliferation of mobile devices and the rise of remote work have accelerated the adoption of micro-learning, as learners can access AI-powered nuggets anytime, anywhere.

The increasing emphasis on compliance training, especially in highly regulated industries such as healthcare, finance, and manufacturing, is also fueling the demand for AI-powered micro-learning nuggets. Regulatory requirements often mandate frequent updates and assessments, which can be efficiently managed through bite-sized, AI-curated learning modules. These solutions enable organizations to ensure that employees remain up-to-date with the latest compliance standards, while also facilitating easy tracking and reporting of learning outcomes. Additionally, the global talent shortage and the need for rapid upskilling and reskilling have prompted enterprises and educational institutions alike to invest in agile learning technologies that can quickly bridge knowledge gaps and enhance workforce readiness.

Meta-Learning, a cutting-edge approach in the realm of artificial intelligence, is gaining traction in the AI-powered micro-learning nugget market. This technique involves the process of learning how to learn, allowing AI systems to adapt to new tasks with minimal data. By leveraging meta-learning, micro-learning platforms can enhance their adaptability, offering more personalized and efficient learning experiences. This is particularly beneficial in environments where learning needs are constantly evolving, such as corporate training and academic settings. The integration of meta-learning can significantly reduce the time required for AI models to optimize learning paths, thereby improving the overall effectiveness of micro-learning solutions. As organizations continue to seek innovative ways to enhance learning outcomes, the role of meta-learning in shaping the future of AI-powered education is expected to grow substantially.

From a regional perspective, North America continues to dominate the AI-powered micro-learning nugget market, accounting for the largest share in 2024. This leadership is attributed to the early adoption of advanced learning technologies, a strong presence of key market players, and substantial investments in corporate training and educational innovation. However, Asia Pac

Subscribers can access export and import data for 80 countries using HS codes or product names-ideal for informed market analysis.

Kriging is commonly used for developing emulators as surrogates for computationally intensive simulations. One difficulty with kriging is the potential numerical instability in the computation of the inverse of the covariance matrix, which can lead to large variability and poor performance of the kriging predictor. First, we study some causes of ill-conditioning in kriging. We then study the use of nugget in kriging to overcome the numerical instability. Some asymptotic results on its interpolation bias and mean squared prediction errors are presented. Finally, we study the choice of the nugget parameter based on some algebraic lower bounds and use of a regularizing trace. A simulation study is performed to show the differences between kriging with and without nugget and to demonstrate the advantages of the former. This article has supplementary materials online.

ABSTRACT: Crab meat is rich in nutrients such as polyunsaturated fatty acids, minerals, and vitamins. Its catch is one of the most important fishing modalities in the North Brazilian state of Pará; however,crab is under utilized in the food industry and mainly sold as fresh meat. The study aimed to develop three formulations of gluten-free crab nuggets with added fiber. To reduce the cost of the final product, three formulations(F1, F2, and F3) were developed by the addition of rice flour at 0% (F1), 15% (F2), and 30% (F3) as a partial replacement of crab meat. Physicochemical and microbiological analyses were performed according to the legislation. In sensory intent analysis, F1 and F2 stood out in relation to F3, butall formulations were well accepted. The cost per unit of the formulations was 0.37 Brazilian reals (R$) for F1, R$ 0.27 for F2, and R$ 0.24 for F3; the formulations obtained a 90.47% yield. For fibers, the same result (0.96 g-dry basis) was obtained for all three formulations.

Get the latest USA Chicken Nuggets import data with importer names, shipment details, buyers list, product description, price, quantity, and major US ports.

This dataset provides information about the number of properties, residents, and average property values for Nugget Drive cross streets in Foresthill, CA.

Non-traditional data signals from social media and employment platforms for GNOG stock analysis

Global Plant-Based Nuggets Market size valued at USD 518.1 Mn in 2023 & predicted to grow at USD 824.85 Mn by 2032 at 6.10% CAGR from 2024 - 2032

This dataset contains the predicted prices of the asset Nugget Rush over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This resource, a MS Excel refresher, extends the level for this Data Nugget. Students are given an Excel workbook with the data and asked to graph and calculate diversity using Excel functions (rather than drawing graphs by hand as in the original data nugget). The data set used is the same. I use this activity in an upper division Environmental Science course for majors that focuses on Restoration Ecology. The simplicity of the data set and the comparisons of reptile diversity among urban, non-urban and urban rehabilitated lend for a great example for doing calculations in spreadsheets.

This data sheet provides the mass of chicken nuggets from Wendy's and McDonald's to see: how much is my money work? Wendy's chicken nuggets taste better in my opinion, so I wanted to know about how much real meat is being used or if the mass is just fake chicken.

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

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Overview

This dataset contains scored and assigned nuggets for 5k single-turn battles based on the lmarena-ai/search-arena-v1-7k dataset. For each battle, gpt-4.1 is used to generate nuggets of information from the two model responses. Each nugget is scored based on its importance, and then the same model is used to assign the nuggets to the two model responses. In addition to the original fields from lmarena-ai/search-arena-v1-7k, each data point includes:

nugget_documents: A list… See the full description on the dataset page: https://huggingface.co/datasets/castorini/search-arena-v1-nuggets-5k.

Timeseries data from 'Nugget Bench (Imiq: 10988)' (imiq_10988_50n01) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=satellite@gina.alaska.edu,,,,feedback@axiomdatascience.com contributor_name=Geographic Information Network of Alaska (GINA),North Slope Science Initiative (NSSI),US Fish and Wildlife Service (US FWS),Arctic Landscape Conservation Cooperative (Arctic LCC, defunded 2019),Axiom Data Science contributor_role=contributor,sponsor,sponsor,sponsor,processor contributor_role_vocabulary=NERC contributor_url=https://gina.alaska.edu/,https://northslopescience.org/,https://www.fws.gov/,https://lccnetwork.org/lcc/arctic,https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=snow_water_equivalent_qc_agg,z,time,surface_snow_thickness_qc_agg,snow_water_equivalent,surface_snow_thickness&time>=max(time)-3days Easternmost_Easting=-150.94 featureType=TimeSeries geospatial_lat_max=62.52 geospatial_lat_min=62.52 geospatial_lat_units=degrees_north geospatial_lon_max=-150.94 geospatial_lon_min=-150.94 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from Imiq - Hydroclimate Database and Data Portal at id=112944 infoUrl=https://sensors.ioos.us/#metadata/112944/station institution=US Department of Agriculture (USDA) naming_authority=com.axiomdatascience Northernmost_Northing=62.52 platform=fixed platform_name=Nugget Bench (Imiq: 10988) platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://www.usda.gov/,, sourceUrl=https://www.usda.gov/ Southernmost_Northing=62.52 standard_name_vocabulary=CF Standard Name Table v72 station_id=112944 time_coverage_end=2014-03-31T00:00:00Z time_coverage_start=1968-02-06T00:00:00Z Westernmost_Easting=-150.94

Comprehensive dataset containing 27 verified Banana Nugget locations in Indonesia with complete contact information, ratings, reviews, and location data.