Context

This dataset was created by our in house teams at PromptCloud(https://www.promptcloud.com/) and DataStock(https://datastock.shop/). This dataset contains 30K records. You can download the full dataset here (https://app.datastock.shop/?site_name=CareerBuilder Job Listing).

Content

This dataset contains the following: Total Records Count: 126490  Domain Name:: careerbuilder.uk.and.se  Date Range: 01st Aug 2019 - 31st Dec 2019   File Extension : ldjson

Available Fields : uniq_id, crawl_timestamp, url, job_title, category, company_name, city, state, country, post_date, job_description, job_requirements, job_type, job_board, geo, job_post_lang, valid_through, html_job_description, inferred_iso2_lang_code, inferred_iso3_lang_code, site_name, domain, postdate_yyyymmdd, has_expired, last_expiry_check_date, postdate_in_indexname_format, inferred_city, inferred_state, inferred_country, fitness_score 

Acknowledgements

We wouldn't be here without the help of our in house web scraping teams at PromptCloud(https://www.promptcloud.com/) and DataStock(https://datastock.shop/).

Inspiration

This dataset was created keeping in mind the data scientists and researchers across the world. Data is needed by all for various analytical purposes. We provide the best and quality data that is available out there.

Context

This dataset was created by PromptCloud and Datastock. This dataset has 30K record counts of various data fields. You can download the full dataset here.

Content

This file contains data fields of: - uniq_id, - crawl_timestamp, - URL, - job_title, - company_name, - city, state, - country, - inferred_city, - inferred_state, - inferred_country, - post_date, - job_description, - job_type, - job_board, - geo, - fitness_score 

Acknowledgements

We owe it to the in house web scraping and data mining team at PromptCloud and Datastock.

Analysis of ‘Amazon Product Reviews Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/amazon-product-reviews-datasete on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This dataset contains 30K records of product reviews from amazon.com.

This dataset was created by PromptCloud and DataStock

Content

This dataset contains the following:

• Total Records Count: 43729

• Domain Name: amazon.com

• Date Range: 01st Jan 2020 - 31st Mar 2020

• File Extension: CSV

• Available Fields:
  -- Uniq Id,
  -- Crawl Timestamp,
  -- Billing Uniq Id,
  -- Rating,
  -- Review Title,
  -- Review Rating,
  -- Review Date,
  -- User Id,
  -- Brand,
  -- Category,
  -- Sub Category,
  -- Product Description,
  -- Asin,
  -- Url,
  -- Review Content,
  -- Verified Purchase,
  -- Helpful Review Count,
  -- Manufacturer Response

Acknowledgements

We wouldn't be here without the help of our in house teams at PromptCloud and DataStock. Who has put their heart and soul into this project like all other projects? We want to provide the best quality data and we will continue to do so.

Inspiration

The inspiration for these datasets came from research. Reviews are something that is important wit everybody across the globe. So we decided to come up with this dataset that shows us exactly how the user reviews help companies to better their products.

This dataset was created by PromptCloud and contains around 0 samples along with Billing Uniq Id, Verified Purchase, technical information and other features such as: - Crawl Timestamp - Manufacturer Response - and more.

How to use this dataset

• Analyze Helpful Review Count in relation to Sub Category
• Study the influence of Review Date on Product Description
• More datasets

Acknowledgements

If you use this dataset in your research, please credit PromptCloud

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Context

This dataset was created by our in-house Web Scraping and Data Mining teams at PromptCloud and DataStock. You can download the full dataset here. This sample contains 30K records. You can download the full dataset here

Content

Total Records Count: 838451 Domain Name: monster.usa.com Date Range: 01st Jul 2020 - 30th Sep 2020 File Extension: ldjson

Available Fields: uniq_id, crawl_timestamp, url, job_title, category, company_name, country, post_date, job_description, job_board, geo, html_job_description, test1_countries, site_name, domain, postdate_yyyymmdd, predicted_language, test1_inferred_city, test1_inferred_state, test1_inferred_country, inferred_city, inferred_state, inferred_country, has_expired, last_expiry_check_date, latest_expiry_check_date, duplicate_status, dataset, is_remote, postdate_in_indexname_format, fitness_score

Acknowledgements

We wouldn't be here without the help of our in house web scraping and data mining teams at PromptCloud and DataStock.

Inspiration

This dataset was created keeping in mind our data scientists and researchers across the world.

the dataset can used for the test of models of deep learning which include structured data: stock price and unstructured data: stock bar posts. so, the dataset is Multi-source Heterogeneous Data.

There are six diferent kinds of widgets we have;

Ticker - This Widget is used for your websites top or bottom for navigation bar. It is horizontal bar with symbols last prices, daily changes and daily percentage changes.

Tape Ticker - This is a stock market classic widget that simply displays symbols (prices, daily changes and daily changes of percentages ) with a sliding cursor that stops when your cursor stops in a position it will stop too. Simple, fancy and useful.

Single Ticker - It's a simple one-symbol sized ticker.

Converter - This widget works best on the right or left sidebar of your website with a fast, useful currency converter with the latest updates and unit prices.

Mini Converter - It’s also simple and beautiful converter best for mobile websites.

Historical Chart - You can view the historical data details for a single symbol with the Historical Chart Widget.

Context

This dataset was created by PromptCloud and Datastock. This dataset holds 30K record counts of job feed data from dice.com USA.

You can download the full dataset here.

Content

This file contains the following data fields:

• Total Records Count : 9856000 
• Domain Name : dice.com 
• Date Range : 25th Mar 2018 - 31st Dec 2018  
• File Extension : xml
• Available Fields : -- uniq_id, -- crawl_timestamp, -- url, -- job_title, -- company_name, -- city, -- state, -- post_date, -- job_description, -- job_requirements, -- job_type, -- job_board, -- geo, -- location 

Acknowledgements

We owe it to the Team at PromptCloud and DataStock.

File 1 includes seven subsets respectively named according to the year as “2007” to “2013”. Specifically, in file “2007”, there are subsets of 25 texts and 50 files. According to our samples, we constructed 25 portfolios using all of the individual stocks, which are named “s1b1”, “s1b2”, “s1b3”, “s1b4”, “s1b5”, …, “s5b1”, “s5b2”, “s5b3”, “s5b4”, “s5b5”. And the 25 subset texts are the constituent stocks for each portfolio, named “s1b1_07” to “s5b5_07”. As for the 50 files, 25 of them are the original price data for 25 portfolios named “s1b1_07” to “s5b5_07”; the other 25 are the processed data for 25 portfolios named “s1b1_07result” to “s5b5_o7result”,in which realized jump measures data are calculated by the non-parametric method, for all constituent stocks of portfolio “s1b1” to portfolio “s5b5”, respectively.

The goal of the Kattegat cod survey is to provide fisheries independent data for estimating the abundance, biomass, recruitment index and distribution of Kattegat cod. The results of the survey is used in the Kattegat cod assessment. The the survey is conducted by DTU Aqua in Denmark and SLU Aqua in Sweden and this data set is only covering the Danish part. The survey is conducted in late November -early December and has 40 stations allocated. All species caught are registered in the data set but cod is the target species.

Context

This dataset was created by our in house teams at PromptCloud(https://www.promptcloud.com/) and DataStock(https://datastock.shop/). We have about 5K samples in this dataset. You can download the full dataset here(https://app.datastock.shop/?site_name=Articles_From_BuzzFeed_2020). We have a 30% discount on all datasets in our data repository. Feel free to head over to DataStock(https://datastock.shop/) and avail the discount.

Content

This dataset contains the following: Total Records Count :: 14831  Domain Name: buzzfeed.com  Date Range: 01st Jan 2020 - 30th Apr 2020   File Extension :: csv

Available Fields: Uniq Id, Crawl Timestamp, Title Headline, Short Description Sub Headline, Content Body, Author, Date And Time Of Posting, Image Urls 

Acknowledgements

We wouldn't be here without the help of our web scraping and data mining experts at PromptCloud and DataStock.

Inspiration

The inspiration for this dataset came from Buzzfeed itself. We thought long and hard about the informative articles that we have on Buzzfeed. So we came up with a dataset for it.

1-minute price data from 2015-01-05 to 2020-10-30 of the 56 constituent stocks of the Shanghai Composite Index.

Descripción del servicio de descarga INSPIRE (Atom predefinido): En el Sistema Oficial de Información del Registro de la Propiedad (ALKIS®), todos los datos del catastro inmobiliario se fusionan y mantienen de forma integrada. Esto incluye datos del antiguo mapa de propiedades y el antiguo libro de propiedades en ALKIS. La base de ALKIS® es un concepto técnico desarrollado por la Asociación de administraciones de topografía de los Länder de la República Federal de Alemania (AdV) para la gestión de todos los datos básicos del sistema oficial de topografía. Todos los estados federales se comprometen a mantener una base de datos de base de datos ALKIS de acuerdo con este concepto. Además, hay datos adicionales específicos de cada país según el modelo de datos.

SW Expression — Los enlaces para descargar los registros se generan dinámicamente desde las solicitudes de GetFeature a un WFS 1.1.0

Analysis of ‘World Happiness Report 2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/PromptCloudHQ/world-happiness-report-2019 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

The data has been released by SDSN and extracted by PromptCloud's custom web crawling solution.

Context

The World Happiness Report is a landmark survey of the state of global happiness that ranks 156 countries by how happy their citizens perceive themselves to be. This year’s World Happiness Report focuses on happiness and the community: how happiness has evolved over the past dozen years, with a focus on the technologies, social norms, conflicts and government policies that have driven those changes.

Content

What is Dystopia?

Dystopia is an imaginary country that has the world’s least-happy people. The purpose in establishing Dystopia is to have a benchmark against which all countries can be favorably compared (no country performs more poorly than Dystopia) in terms of each of the six key variables, thus allowing each sub-bar to be of positive (or zero, in six instances) width. The lowest scores observed for the six key variables, therefore, characterize Dystopia. Since life would be very unpleasant in a country with the world’s lowest incomes, lowest life expectancy, lowest generosity, most corruption, least freedom, and least social support, it is referred to as “Dystopia,” in contrast to Utopia.

What are the residuals?

The residuals, or unexplained components, differ for each country, reflecting the extent to which the six variables either over- or under-explain average 2016-2018 life evaluations. These residuals have an average value of approximately zero over the whole set of countries. Figure 2.7 shows the average residual for each country if the equation in Table 2.1 is applied to average 2016- 2018 data for the six variables in that country. We combine these residuals with the estimate for life evaluations in Dystopia so that the combined bar will always have positive values. As can be seen in Figure 2.7, although some life evaluation residuals are quite large, occasionally exceeding one point on the scale from 0 to 10, they are always much smaller than the calculated value in Dystopia, where the average life is rated at 1.88 on the 0 to 10 scale. Table 7 of the online Statistical Appendix 1 for Chapter 2 puts the Dystopia plus residual block at the left side, and also draws the Dystopia line, making it easy to compare the signs and sizes of the residuals in different countries.

Why do we use these six factors to explain life evaluations?

The variables used reflect what has been broadly found in the research literature to be important in explaining national-level differences in life evaluations. Some important variables, such as unemployment or inequality, do not appear because comparable international data are not yet available for the full sample of countries. The variables are intended to illustrate important lines of correlation rather than to reflect clean causal estimates, since some of the data are drawn from the same survey sources, some are correlated with each other (or with other important factors for which we do not have measures), and in several instances there are likely to be two-way relations between life evaluations and the chosen variables (for example, healthy people are overall happier, but as Chapter 4 in the World Happiness Report 2013 demonstrated, happier people are overall healthier). In Statistical Appendix 1 of World Happiness Report 2018, we assessed the possible importance of using explanatory data from the same people whose life evaluations are being explained. We did this by randomly dividing the samples into two groups, and using the average values for .e.g. freedom gleaned from one group to explain the life evaluations of the other group. This lowered the effects, but only very slightly (e.g. 2% to 3%), assuring us that using data from the same individuals is not seriously affecting the results.

Data source: http://worldhappiness.report/ed/2019/

More such datasets can be downloaded from DataStock.

--- Original source retains full ownership of the source dataset ---

Context

This dataset was created by PromptCloud and DataStock. This dataset holds around 30K records for the date range of 1st May 2019 to 31st July 2019.

You can download the full dataset here.

Content

This dataset contains the following:

• Total Records Count : 1203379 
• Domain Name: indeed.com 
• Date Range: 01st May 2019 - 31st Jul 2019  
• File Extension: LDJSON
• Available Fields : -- uniq_id, -- crawl_timestamp, -- url, -- job_title, -- category, -- company_name, -- city, -- state, -- country, -- post_date, -- job_description, -- job_board, -- geo, -- inferred_city, -- inferred_state, -- inferred_country, -- fitness_score 

Acknowledgements

We wouldn't be here without the help of our in house web scraping team at PromptCloud and DataStock. Please feel free to reach out to us at marketing@promptcloud.com

The effect of the Informed Push Model intervention on odds of any monthly stockout for contraceptive and comparison products, in Senegalese health facilities.

Wastewater data stock of the Zweckverband Kremmen (WFS)

NTT DATA stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

MealMe offers in-depth restaurant menu data, including prices, from the top 100,000 restaurants across the USA and Canada. Our proprietary technology collects accurate, real-time menu and pricing information, enabling businesses to make data-driven decisions in competitive intelligence, pricing optimization, and market research. With comprehensive coverage that spans major restaurant platforms and chains, MealMe ensures your business has access to the most reliable data to excel in a rapidly evolving industry.

Platforms and Restaurants Covered: MealMe's database includes data from leading restaurant platforms such as UberEats, Postmates, ToastTakeout, SkipTheDishes, Square, Appfront, Olo, TouchBistro, and Clover, as well as direct menu data from major restaurant chains including Raising Cane’s, Panda Express, Popeyes, Burger King, and Subway. This extensive coverage ensures a detailed view of the market, helping businesses monitor trends, pricing, and availability across a broad spectrum of restaurant types and sizes.

Key Features: Comprehensive Menu Data: Access detailed menu information, including item descriptions, categories, sizes, and customizations. Real-Time Pricing: Monitor up-to-date menu prices for accurate competitive analysis. Restaurant-Specific Insights: Analyze individual restaurant chains such as Raising Cane’s and Panda Express, or platforms like UberEats, for market trends and pricing strategies. Cross-Platform Analysis: Compare menu items and pricing across platforms like ToastTakeout, Olo, and SkipTheDishes for a holistic industry view. Regional Data: Understand geographic variations in menu offerings and pricing across the USA and Canada.

Use Cases: Competitive Intelligence: Track menu offerings, pricing strategies, and seasonal trends across platforms like UberEats and Postmates or chains like Popeyes and Subway. Market Research: Identify gaps in the market by analyzing menus and pricing from top restaurants. Pricing Optimization: Use real-time pricing data to inform dynamic pricing strategies and promotions. Trend Monitoring: Stay ahead by tracking popular menu items, regional preferences, and emerging food trends. Platform Analysis: Assess how restaurants perform across delivery platforms such as SkipTheDishes, Olo, and Square. Industries Benefiting from Our Data Restaurant Chains: Optimize menu offerings and pricing strategies with detailed competitor data. Food Delivery Platforms: Benchmark menu pricing and availability across competitive platforms. Market Research Firms: Conduct detailed analyses to identify opportunities and market trends. AI & Analytics Companies: Power recommendation engines and predictive models with robust menu data. Consumer Apps: Enhance app experiences with accurate menu and pricing data. Data Delivery and Integration

MealMe offers flexible integration options to ensure seamless access to our comprehensive menu data. Whether you need bulk exports for in-depth research or real-time updates via API, our solutions are designed to scale with your business needs.

Why Choose MealMe? Extensive Coverage: Menu data from 100,000+ restaurants, including major chains like Burger King and Raising Cane’s. Real-Time Accuracy: Up-to-date pricing and menu details for actionable insights. Customizable Solutions: Tailored datasets to meet your specific business objectives. Proven Expertise: Trusted by top companies for delivering reliable, actionable data. MealMe empowers businesses with the data needed to thrive in a competitive restaurant and food delivery market. For more information or to request a demo, contact us today!

Context

This dataset was created by our in-house web scraping and data mining teams at PromptCloud and DataStock. This dataset is a sample of the full dataset that can be seen on our data repository. The property listing is one of the key factors most people are on the lookout for these days. Real-Estate data is required by many to make sure they can quote the correct price and keep the competitive pricing is present.

You can download the full dataset from our data repository at DataStock. I am attaching the link of the dataset below. Link: https://app.datastock.shop/?site_name=Property_Listing_from_Homes.com

Content

Total Records Count : 798088  Domain Name : homes.com  Date Range : 01st Mar 2020 - 31st May 2020   File Extension : xml

Available Fields : uniq_id, crawl_timestamp, ad_title, location, price, bedrooms, bathrooms, sqft, overview, home_details, mls_number, listing_source, listing_agent, offered_by, image_urls

Acknowledgments

We wouldn't be here without the help of our in house web scraping and data mining teams at PromptCloud and DataStock.

Inspiration

This dataset was created keeping in mind our data scientists and researchers across the world.

Servicio en línea de información sobre el nivel del agua para EE. UU., Reino Unido, IE, DE, AT, CH y Südtirol. Se puede activar el aviso por SMS y correo electrónico. Te avisa en caso de inundaciones.