Context

There are many developers in the world of video-games. Here they are!

Content

This is a short dataset that contains information about video-games publishers. The idea behind the data is to explain a little bit some information abut those video-games publishers.

Inspiration

The idea behind this dataset is to complement the video-games-sales-2019 dataset.

According to the survey, just under 18 percent of respondents identified PostgreSQQL as one of the most-wanted database skills. MongoDB ranked second with 17.89 percent stating they are not developing with it, but want to.

Data Statement for CommitBench

EXECUTIVE SUMMARY

CURATION RATIONALE

DOCUMENTATION FOR SOURCE DATASETS

LANGUAGE VARIETIES

SPEAKER DEMOGRAPHIC

ANNOTATOR DEMOGRAPHIC

SPEECH SITUATION AND CHARACTERISTICS

PREPROCESSING AND DATA FORMATTING

CAPTURE QUALITY

LIMITATIONS

METADATA

DISCLOSURES AND ETHICAL REVIEW

ABOUT THIS DOCUMENT

Content

The primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore the World Bank using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the World Bank's APIs and Kaggle's API.

Cover photo by Alex Block on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Monthly average radiance composite images using nighttime data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB). As these data are composited monthly, there are many areas of the globe where it is impossible to get good quality data coverage for that month. This can be due to …

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore the World Bank using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the World Bank's APIs and Kaggle's API.

Cover photo by Markus Spiske on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The Gridded Surface Meteorological dataset provides high spatial resolution (~4-km) daily surface fields of temperature, precipitation, winds, humidity and radiation across the contiguous United States from 1979. The dataset blends the high resolution spatial data from PRISM with the high temporal resolution data from the National Land Data Assimilation System (NLDAS) to produce spatially and temporally continuous fields that lend themselves to additional land surface modeling. This dataset contains provisional products that are replaced with updated versions when the complete source data become available. Products can be distinguished by the value of the 'status' property. At first, assets are ingested with status='early'. After several days, they are replaced by assets with status='provisional'. After about 2 months, they are replaced by the final assets with status='permanent'.

After 2022-01-25, Sentinel-2 scenes with PROCESSING_BASELINE '04.00' or above have their DN (value) range shifted by 1000. The HARMONIZED collection shifts data in newer scenes to be in the same range as in older scenes. Sentinel-2 is a wide-swath, high-resolution, multi-spectral imaging mission supporting Copernicus Land Monitoring studies, including the monitoring of vegetation, soil and water cover, as well as observation of inland waterways and coastal areas. The Sentinel-2 data contain 13 UINT16 spectral bands representing TOA reflectance scaled by 10000. See the Sentinel-2 User Handbook for details. QA60 is a bitmask band that contained rasterized cloud mask polygons until Feb 2022, when these polygons stopped being produced. Starting in February 2024, legacy-consistent QA60 bands are constructed from the MSK_CLASSI cloud classification bands. For more details, see the full explanation of how cloud masks are computed.. Each Sentinel-2 product (zip archive) may contain multiple granules. Each granule becomes a separate Earth Engine asset. EE asset ids for Sentinel-2 assets have the following format: COPERNICUS/S2/20151128T002653_20151128T102149_T56MNN. Here the first numeric part represents the sensing date and time, the second numeric part represents the product generation date and time, and the final 6-character string is a unique granule identifier indicating its UTM grid reference (see MGRS). The Level-2 data produced by ESA can be found in the collection COPERNICUS/S2_SR. For datasets to assist with cloud and/or cloud shadow detection, see COPERNICUS/S2_CLOUD_PROBABILITY and GOOGLE/CLOUD_SCORE_PLUS/V1/S2_HARMONIZED. For more details on Sentinel-2 radiometric resolution, see this page.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the World Bank. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore the World Bank using Kaggle and all of the data sources available through the World Bank organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using the World Bank's APIs and Kaggle's API.

Cover photo by İrfan Simsar on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

In the quest for advancing the field of continuous user authentication, we have meticulously crafted two comprehensive datasets: COUNT-OS-I and COUNT-OS-II, each harboring unique characteristics while sharing a common ground in their utility and design principles. These datasets encompass performance counters extracted from the Windows operating system, offering an intricate tapestry of data vital for evaluating and refining authentication models in real-world scenarios.Both datasets have been generated in real-world settings within public organizations in Brazil, ensuring their applicability and relevance to practical scenarios. Volunteers from diverse professional backgrounds participated in the data collection, contributing to the richness and variability of the data. Furthermore, both datasets were collected at a sample rate of every 5 seconds, providing a dense and detailed view of user interactions and system performance. The commitment to preserving user confidentiality is unwavering across both datasets, with pseudonymization applied meticulously to safeguard individual identities while maintaining data integrity and statistical robustness.The COUNT-OS-I dataset was specifically generated in a real-world scenario to evaluate our work on continuous user authentication. This dataset consist of performance counters extracted from the Windows operating system of 26 computers, representing 26 individual users. The data were collected on the computers of the Information Technology Department of a public organization in Brazil.The participants in this study were volunteers, with aged between 20 and 45 years old, consisting of both males and females. The majority of the participants were systems analysts and software developers who performed their routine work activities. There were no specific restrictions imposed on the tasks that the participants were required to perform during the data collection process.The participants used a variety of software applications as part of their regular work activities. This included web browsers such as Firefox, Chrome, and Edge, developer tools like Eclipse and SQL Developer, office programs such as Microsoft Office Word, Excel, and PowerPoint, as well as chat applications like WhatsApp. It's important to note that the list of applications mentioned is not exhaustive, and participants were not limited to using only these applications.For the COUNT-OS-I dataset, the data collected is based on computers with different characteristics and configurations in terms of hardware, operating system versions, and installed software. This diversity ensures a representative sample of real-world scenarios and allows for a comprehensive evaluation of the authentication model.During the data collection process, each sample was recorded at a frequency of every 5 seconds, capturing system data over a period of approximately 26 hours, on average, for each user. This duration provides sufficient data to analyze user behavior and system performance over an extended period. Each sample in the COUNT-OS-I dataset corresponds to a feature vector comprising 159 attributesThe COUNT-OS-II dataset was utilized to evaluate our work in a real-world setting. This dataset comprises performance counters extracted from the Windows operating system installed on 37 computers. These computers possess identical hardware configurations (CPU, memory, network, disk), operating systems, and software installations. The data collection was conducted within various departments of a public organization in Brazil.The participants in this study (37 users) were voluntary administration assistants who performed various administrative tasks as part of their routine work activities. No restrictions were imposed on the specific tasks they were assigned. The participants commonly utilized programs such as the Chrome browser and office applications like Office Word, Excel, and PowerPoint, in addition to the WhatsApp chat application.The data were collected over six days (approximately 48 hours), with sample collected at a 5-second interval. Each sample corresponds to a feature vector composed of 218 attributes. In this dataset, we also apply pseudonymization to hide users' sensitive information.

Region of Interest (ROI) is comprised of the Belgium, the Netherlands and Luxembourg

We use the communes administrative division which is standardized across Europe by EUROSTAT at: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units This is roughly equivalent to the notion municipalities in most countries.

From the link above, communes definition are taken from COMM_RG_01M_2016_4326.shp and country borders are taken from NUTS_RG_01M_2021_3035.shp.

images: Sentinel2 RGB from 2020-01-01 to 2020-31-12 filtered out pixels with clouds during the observation period according to QA60 band following the example given in GEE dataset info page, and took the median of the resulting pixels

  see https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2_SR_HARMONIZED


  see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/sentinel2rgbmedian2020.py

labels: ESA WorldCover 10m V100 labels mapped to the interval [1,11] according to the following map { 0:0, 10: 1, 20:2, 30:3, 40:4, 50:5, 60:6, 70:7, 80:8, 90:9, 95:10, 100:11 } pixel value zero is reserved for invalid data. see https://developers.google.com/earth-engine/datasets/catalog/ESA_WorldCover_v100

  see also https://github.com/rramosp/geetiles/blob/main/geetiles/defs/esaworldcover.py

_aschips.geojson the image chips geometries along with label proportions for easy visualization with QGIS, GeoPandas, etc.

_communes.geojson the communes geometries with their label prortions for easy visualization with QGIS, GeoPandas, etc.

splits.csv contains two splits of image chips in train, test, val - with geographical bands at 45° angles in nw-se direction - the same as above reorganized to that all chips within the same commune fall within the same split.

data/ a pickle file for each image chip containing a dict with - the 100x100 RGB sentinel 2 chip image - the 100x100 chip level lavels - the label proportions of the chip - the aggregated label proportions of the commune the chip belongs to

Data for replicating The Global Spatial Distribution of Economic Activity: Nature, History, and the Role of Trade (forthcoming 2018; with Vernon Henderson, Tim Squires and David N. Weil) Quarterly Journal of Economics We explore the role of natural characteristics in determining the worldwide spatial distribution of economic activity, as proxied by lights at night, observed across 240,000 grid cells. A parsimonious set of 24 physical geography attributes explains 47% of worldwide variation and 35% of within-country variation in lights. We divide geographic characteristics into two groups, those primarily important for agriculture and those primarily important for trade, and confront a puzzle. In examining within-country variation in lights, among countries that developed early, agricultural variables incrementally explain over 6 times as much variation in lights as do trade variables, while among late developing countries the ratio is only about 1.5, even though the latter group is far more dependent on agriculture. Correspondingly, the marginal effects of agricultural variables as a group on lights are larger in absolute value, and those for trade smaller, for early developers than for late developers. We show that this apparent puzzle is explained by persistence and the differential timing of technological shocks in the two sets of countries. For early developers, structural transformation due to rising agricultural productivity began when transport costs were still high, so cities were localized in agricultural regions. When transport costs fell, these agglomerations persisted. In late-developing countries, transport costs fell before structural transformation. To exploit urban scale economies, manufacturing agglomerated in relatively few, often coastal, locations. Consistent with this explanation, countries that developed earlier are more spatially equal in their distribution of education and economic activity than late developers. This dataset is part of the Global Research Program on Spatial Development of Cities funded by the Multi-Donor Trust Fund on Sustainable Urbanization of the World Bank and supported by the U.K. Department for International Development.

About Dataset (strawberries, peaches, pomegranates) Photo requirements: 1-White background 2-.jpg 3- Image size 300*300 The number of photos required is 250 photos of each fruit when it is fresh and 250 photos of each Fruit Dataset for Classification when it is rotten. Total 1500 images

Diverse Collection With a diverse collection of Product images, the files provides an excellent foundation for developing and testing machine learning models designed for image recognition and allocation. Each image is captured under different lighting conditions and backgrounds, offering a realistic challenge for algorithms to overcome.

Real-World Applications The variability in the dataset ensures that models trained on it can generalize well to real-world scenarios, making them robust and reliable. The dataset includes common fruits such as apples, bananas, oranges, and strawberries, among others, allowing for comprehensive training and evaluation.

Industry Use Cases One of the significant advantages of using the Fruits Dataset for Classification is its applicability in various fields such as agriculture, retail, and the food industry. In agriculture, it can help automate the process of fruit sorting and grading, enhancing efficiency and reducing labor costs. In retail, it can be used to develop automated checkout systems that accurately identify fruits, streamlining the purchasing process.

Educational Value The dataset is also valuable for educational purposes, providing students and educators with a practical tool to learn and teach machine learning concepts. By working with this dataset, learners can gain hands-on experience in data preprocessing, model training, and evaluation.

Conclusion The Fruits Dataset for Classification is a versatile and indispensable resource for advancing the field of image classification. Its diverse and high-quality images, coupled with practical applications, make it a go-to dataset for researchers, developers, and educators aiming to improve and innovate in machine learning and computer vision.

This dataset is sourced from Kaggle.

This dataset contains labeled data for gun detection collected from various videos on YouTube. The dataset has been specifically curated and labeled by me to aid in training machine learning models, particularly for real-time gun detection tasks. It is formatted for easy use with YOLO (You Only Look Once), one of the most popular object detection models.

Key Features: Source: The videos were sourced from YouTube and feature diverse environments, including indoor and outdoor settings, with varying lighting conditions and backgrounds. Annotations: The dataset is fully labeled with bounding boxes around guns, following the YOLO format (.txt files for annotations). Each annotation provides the class (gun) and the coordinates of the bounding box. YOLO-Compatible: The dataset is ready to be used with any YOLO model (YOLOv3, YOLOv4, YOLOv5, etc.), ensuring seamless integration for object detection training. Realistic Scenarios: The dataset includes footage of guns from various perspectives and angles, making it useful for training models that can generalize to real-world detection tasks. This dataset is ideal for researchers and developers working on gun detection systems, security applications, or surveillance systems that require fast and accurate detection of firearms.

GitHub is how people build software and is home to the largest community of open source developers in the world, with over 12 million people contributing to 31 million projects on GitHub since 2008.

This 3TB+ dataset comprises the largest released source of GitHub activity to date. It contains a full snapshot of the content of more than 2.8 million open source GitHub repositories including more than 145 million unique commits, over 2 billion different file paths, and the contents of the latest revision for 163 million files, all of which are searchable with regular expressions.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.github_repos.[TABLENAME]. Fork this kernel to get started to learn how to safely manage analyzing large BigQuery datasets.

Acknowledgements

This dataset was made available per GitHub's terms of service. This dataset is available via Google Cloud Platform's Marketplace, GitHub Activity Data, as part of GCP Public Datasets.

Inspiration

• This is the perfect dataset for fighting language wars.
• Can you identify any signals that predict which packages or languages will become popular, in advance of their mass adoption?

Description:

👉 Download the dataset here

This dataset consists of a diverse collection of images featuring Paimon, a popular character from the game Genshin Impact. The images have been sourced from in-game gameplay footage and capture Paimon from various angles and in different sizes (scales), making the dataset suitable for training YOLO object detection models.

The dataset provides a comprehensive view of Paimon in different lighting conditions, game environments, and positions, ensuring the model can generalize well to similar characters or object detection tasks. While most annotations are accurately labeled, a small number of annotations may include minor inaccuracies due to manual labeling errors. This is ideal for researchers and developers working on character recognition, object detection in gaming environments, or other AI vision tasks.

Download Dataset

Dataset Features:

Image Format: .jpg files in 640×320 resolution.

Annotation Format: .txt files in YOLO format, containing bounding box data with:

class_id

x_center

y_center

width

height

Use Cases:

Character Detection in Games: Train YOLO models to detect and identify in-game characters or NPCs.

Gaming Analytics: Improve recognition of specific game elements for AI-powered game analytics tools.

Research: Contribute to academic research focused on object detection or computer vision in animated and gaming environments.

Data Structure:

Images: High-quality .jpg images captured from multiple perspectives, ensuring robust model training across various orientations and lighting scenarios.

Annotations: Each image has an associated .txt file that follows the YOLO format. The annotations are structured to include class identification, object location (center coordinates), and

bounding box dimensions.

Key Advantages:

Varied Angles and Scales: The dataset includes Paimon from multiple perspectives, aiding in creating more versatile and adaptable object detection models.

Real-World Scenario: Extracted from actual gameplay footage, the dataset simulates real-world detection challenges such as varying backgrounds, motion blur, and changing character scales.

Training Ready: Suitable for training YOLO models and other deep learning frameworks that require object detection capabilities.

This dataset is sourced from Kaggle.