Dataset

This dataset was created by Kelvin Ng

Contents

In the Official Property Register Information System (ALKIS®), all data of the real estate cadastre are merged. It contains the data of the formerly separate Automated Property Map (ALK), the Automated Property Book (ALB) and the Border Proof. ALKIS® includes a federal, object-based data model by systematically linking the spatial (map) and non-space-related (book) data and maintaining them without redundancy. Data storage is carried out with metadata and history management.

The user has a federally defined ALKIS basic database at the disposal of the user. These include: Parcels/location/points, Actual use, Buildings, Constructions/Facilities/Other facilities, Owners, Legislation/Treasury Units/Catalogues, Relief, User Profiles, Migration.

The data is provided via the uniform standard-based exchange interface (NAS) in a user-related inventory data update procedure (NBA procedure).

Dataset

This dataset was created by thuận huỳnh

Contents

Stock correlation networks use stock price data to explore the relationship between different stocks listed in the stock market. Currently this relationship is dominantly measured by the Pearson correlation coefficient. However, financial data suggest that nonlinear relationships may exist in the stock prices of different shares. To address this issue, this work uses mutual information to characterize the nonlinear relationship between stocks. Using 280 stocks traded at the Shanghai Stocks Exchange in China during the period of 2014-2016, we first compare the effectiveness of the correlation coefficient and mutual information for measuring stock relationships. Based on these two measures, we then develop two stock networks using the Minimum Spanning Tree method and study the topological properties of these networks, including degree, path length and the power-law distribution. The relationship network based on mutual information has a better distribution of the degree and larger value of the power-law distribution than those using the correlation coefficient. Numerical results show that mutual information is a more effective approach than the correlation coefficient to measure the stock relationship in a stock market that may undergo large fluctuations of stock prices.

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.

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.

This dataset aggregates real-time sentiment scores and metadata for financial news headlines, enabling rapid detection of market-moving events and trends. It includes headline text, publication details, sentiment analysis, relevance to financial markets, and links to affected stocks and sectors. Ideal for quantitative trading, risk monitoring, and financial news analytics.

Dataset

This dataset was created by Aashvi Shah

Contents

Dataset

This dataset was created by Le Minh Quy

Contents

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 : 254888  Domain Name : monster.usa.com  Date Range : 01st Jul 2021 - 30th Sep 2021   File Extension : ldjson

Available Fields : url, job_title, category, industry, company_name, logo_url, city, state, country, post_date, occupation_category, job_description, job_type, valid_through, html_job_description, extra_fields, uniq_id, crawl_timestamp, apply_url, job_board, geo, is_remote, test_contact_email, contact_email, test1_cities, test1_states, test1_countries, site_name, domain, postdate_yyyymmdd, predicted_language, inferred_iso3_lang_code, 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, dataset, postdate_in_indexname_format, segment_name, duplicate_status, job_desc_char_count, fitness_score  

Acknowledgements

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

Inspiration

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

This dataset provides detailed records of goods movement into, out of, and between warehouses, including product details, quantities, movement types, and timestamps. It enables comprehensive tracking for inventory optimization, order fulfillment, and operational analysis in logistics and supply chain management. The structure supports audit trails, loss prevention, and process improvement.

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

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.

This dataset provides comprehensive warehouse inventory records, including item details, stock levels, storage locations, supplier contacts, and detailed movement tracking. It is ideal for optimizing inventory management, automating reorder processes, and analyzing supply chain efficiency in logistics operations.

Network topology properties of six networks including networks in Figs 4 and 5 and four networks in S2–S5 Figs.

Effect of the Informed Push Model intervention on stockout duration for contraceptive and comparison products in Senegalese health facilities.

Dagelijkse eindedag-koersen met corporate actions voor aandelen, indices en ETF’s.

Dataset

This dataset was created by Tanmoy Das

Contents

Key Stock Trading StatisticsTop Stock Trading AppsFinance App Market LandscapeStock Trading App RevenueStock Trading Revenue by AppStock Trading App UsersStock Trading Users by AppStock Trading App...

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: 191371 Domain Name: amazon.fr Date Range: 01st Oct 2020 - 31st Dec 2020 File Extension: tsv

Available Fields: Uniq Id, Crawl Timestamp, Dataset Origin, Product Id, Product Barcode, Product Company Type Source, Product Brand Source, Product Brand Normalised Source, Product Name Source, Match Rank, Match Score, Match Type, Retailer, Product Category, Product Brand, Product Name, Product Price, Sku, Upc, Product Url, Market, Product Description, Product Currency, Product Available Inventory, Product Image Url, Product Model Number, Product Tags, Product Contents, Product Rating, Product Reviews Count, Bsr, Joining Key

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.