This statistic shows data on media meshing and stacking in the United States in 2016. During the survey period, it was found that ** percent of U.S. internet users accessed program-related content on mobile devices while watching TV.

Dataset Card for The Stack

  Changelog

Release Description

v1.0 Initial release of the Stack. Included 30 programming languages and 18 permissive licenses. Note: Three included licenses (MPL/EPL/LGPL) are considered weak copyleft licenses. The resulting near-deduplicated dataset is 3TB in size.

v1.1 The three copyleft licenses ((MPL/EPL/LGPL) were excluded and the list of permissive licenses extended to 193 licenses in total. The list of programming languages… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack.

The Stack v2

The dataset consists of 4 versions:

bigcode/the-stack-v2: the full "The Stack v2" dataset <-- you are here bigcode/the-stack-v2-dedup: based on the bigcode/the-stack-v2 but further near-deduplicated bigcode/the-stack-v2-train-full-ids: based on the bigcode/the-stack-v2-dedup dataset but further filtered with heuristics and spanning 600+ programming languages. The data is grouped into repositories.bigcode/the-stack-v2-train-smol-ids: based on the… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-v2.

This dataset contains the predicted prices of the asset Stacking DAO Stacked Stacks BTC 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 data dump is sourced from the various sites in the Stack Exchange network of Q&A sites. This dump contains data up to and including 2024-06-30. This version was re-released in late August with bugfixes from the initial, flawed 2024-06-30 release The exact licenses for each bit of content is embedded in each entry. For license date ranges, see the root-level license.txt, or . For the schema, see the sede-and-data-dump-schema.md file within each .7z This torrent has been mirrored from

This statistic shows data on media meshing and stacking in Mexico in 2016. During the survey period, it was found that ************ of Mexican internet users accessed program-related content on mobile devices while watching TV.

This data dump is sourced from the various sites in the Stack Exchange network of Q&A sites. This dump contains data up to and including 2025-06-30. The exact licenses for each bit of content is embedded in each entry. For license date ranges, see the root-level license.txt, or . For the schema, see the sede-and-data-dump-schema.md file within each .7z This torrent has also been archived at

A variety of studies and disparate data sets track state energy storage policies, but these datasets do not cover all BTM-related storage policy. Moreover, these databases do not align policies with the policy stacking framework. Thus, it is unclear which BTM storage policies are adopted across the country, what should comprise a complete storage policy framework or stack, or how states policies compare with that stack. This first-of-its-kind BTM storage policy stack includes 11 parent policy categories and 31 policies across the market preparation, creation, and expansion policy components.

This dataset contains the predicted prices of Stacking DAO Stacked Stacks for the upcoming years based on user-defined projections.

This package contains SlowOps, and industrial dataset of stack traces introduced in our paper "Stack Trace Deduplication: Faster, More Accurately, and in More Realistic Scenarios".

SlowOps is an dataset of stack traces (reports) and their categories (issues), aimed at evaluating different models for stack trace deduplication. The dataset includes reports related to Slow Operation Assertion, collected at JetBrains from IntelliJ-based products in the time from 26.01.2021 to 29.02.2024. It contains 886,730 reports in 1,361 categories.

For more information about the dataset, please refer to the README.

This dataset contains the predicted prices of the asset 🥑Avocado Stacking 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.

Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patients. Given this, early intervention is particularly crucial, as it can not only significantly improve the prognosis of patients but also provide valuable reference information for clinical treatment. This study selected the BRFSS (Behavioral Risk Factor Surveillance System) dataset, which is publicly available on the Kaggle platform, as the research object, aiming to provide a scientific basis for the early diagnosis and treatment of diabetes through advanced machine learning techniques. Firstly, the dataset was balanced using various sampling methods; secondly, a Stacking model based on GA-XGBoost (XGBoost model optimized by genetic algorithm) was constructed for the risk prediction of diabetes; finally, the interpretability of the model was deeply analyzed using Shapley values. The results show: (1) Random oversampling, ADASYN, SMOTE, and SMOTEENN were used for data balance processing, among which SMOTEENN showed better efficiency and effect in dealing with data imbalance. (2) The GA-XGBoost model optimized the hyperparameters of the XGBoost model through a genetic algorithm to improve the model’s predictive accuracy. Combined with the better-performing LightGBM model and random forest model, a two-layer Stacking model was constructed. This model not only outperforms single machine learning models in predictive effect but also provides a new idea and method in the field of model integration. (3) Shapley value analysis identified features that have a significant impact on the prediction of diabetes, such as age and body mass index. This analysis not only enhances the transparency of the model but also provides more precise treatment decision support for doctors and patients. In summary, this study has not only improved the accuracy of predicting the risk of diabetes by adopting advanced machine learning techniques and model integration strategies but also provided a powerful tool for the early diagnosis and personalized treatment of diabetes.

Dataset Card for The Stack Metadata

  Changelog

Release Description

v1.1 This is the first release of the metadata. It is for The Stack v1.1

v1.2 Metadata dataset matching The Stack v1.2

  Dataset Summary

This is a set of additional information for repositories used for The Stack. It contains file paths, detected licenes as well as some other information for the repositories.

  Supported Tasks and Leaderboards

The main task is to recreate… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack-metadata.

Knuth introduced the problem of sorting with a sequence of stacks. Tarjan extended this idea to sorting with acyclic networks of stacks (and queues), where items to be sorted move from a source through the network to a sink while they may be stored temporarily at nodes (the stacks). Both characterized which permutations are sortable; but complexity of sorting was not an issue.

A new statistical model is presented for genomic prediction on maize and wheat data comprising multi-trait, multi-environment data.

Aim Species distribution models (SDMs) are powerful tools for assessing suitable habitats across large areas and at fine spatial resolution. Yet, the usefulness of SDMs for mapping species' realised distributions is often limited since data biases or missing information on dispersal barriers or biotic interactions hinder them from accurately delineating species' range limits. One way to overcome this limitation is to integrate SDMs with expert range maps, which provide coarse-scale information on the extent of species' ranges and thereby range limits that are complementary to information offered by SDMs.

Innovation Here, we propose a new approach for integrating expert range maps in SDMs based on an ensemble method called stacked generalisation. Specifically, our approach relies on training a meta-learner regression model using predictions from one or more SDM algorithms alongside the distance of training points to expert-defined ranges as predictor variables. We demonstrate our app...

In summer 2014, the U.S. Geological Survey conducted a 21-day geophysical program in deep water along the Atlantic continental margin by using R/V Marcus G. Langseth (Field Activity Number 2014-011-FA). The purpose of the seismic program was to collect multichannel seismic reflection and refraction data to determine sediment thickness. These data enable the United States to delineate its Extended Continental Shelf (ECS) along the Atlantic margin. The same data can also be used to understand large submarine landslides and therefore assess their potential tsunami hazard for infrastructure and communities living along the eastern seaboard. Supporting geophysical data were collected as marine magnetic data, gravity data, 3.5-kilohertz shallow seismic reflections, multibeam echo sounder bathymetry, and multibeam backscatter. The survey was conducted from water depths of approximately 1,500 meters to abyssal seafloor depths greater than 5,000 meters. Approximately 2,761 kilometers of multi-channel seismic data was collected along with 30 sonobuoy profiles. This field program had two primary objectives: (1) to collect some of the data necessary to establish the outer limits of the U.S. Continental Shelf, or Extended Continental Shelf, as defined by Article 76 of the United Nations Convention of the Law of the Sea and (2) to study the sudden mass transport of sediments down the continental margin as submarine landslides that pose potential tsunamigenic hazards to the Atlantic and Caribbean coastal communities.

This statistic shows data on media meshing and stacking in Turkey in 2016. During the survey period, it was found that ** percent of Turkish internet users accessed program-related content on mobile devices while watching TV.

This horizontal bar chart displays companies by stack using the aggregation count. The data is filtered where the industry is Household Durables. The data is about companies.