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WooCommerce stores list - 1.6M+ stores worldwide | scrapelabs.io
- datarade.ai
.csv, .xlsUpdated Sep 9, 2022
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EmailClick to copy linkLink copiedCiteScrapeLabs (2022). WooCommerce stores list - 1.6M+ stores worldwide | scrapelabs.io [Dataset]. https://datarade.ai/data-products/woocommerce-stores-list-1-6m-stores-worldwide-scrapelabs.csv, .xlsAvailable download formatsDataset updatedSep 9, 2022Dataset authored and provided byScrapeLabsArea coveredAnguilla, Estonia, Belize, Samoa, El Salvador, United Kingdom, Saint Lucia, Brunei Darussalam, Norfolk Island, OmanDescriptionOver 1.6M WooCommerce stores from around the world: United States (308k stores) Canada (68k stores) Europe (818k stores) Asia (279k stores) Oceania (94k stores) South America (100k stores)
Sample data: https://docs.google.com/spreadsheets/d/1CpJSjaVOUQGw2qwahGeQkgnNN2Lt6BGbY0wiqjHVlHU
We can create a custom list based on your criteria e.g. stores from specific countries and categories, using specific technologies and apps, with certain keywords on the homepage, with over 10k Instagram followers etc.
Pricing: $0.05 per lead
We also offer volume discounts on orders above 10,000 sellers.
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WP-Script | Web Hosting & Domain Names | Technology Data
- datastore.forage.ai
Updated Nov 20, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCite(2024). WP-Script | Web Hosting & Domain Names | Technology Data [Dataset]. https://datastore.forage.ai/searchresults/?resource_keyword=webDataset updatedNov 20, 2024DescriptionWP-Script is a company that provides WordPress themes and plugins for creating adult sites. They offer a range of products, including seven customizable adult WordPress themes and thirteen powerful adult WordPress plugins. Their products are designed to be easy to use and can help entrepreneurs create professional-looking adult sites with minimal technical expertise.
With WP-Script, you can start your adult site in six easy steps. They also offer a 14-day money-back guarantee, giving you the opportunity to test their products risk-free. Additionally, they provide premium support to help you resolve any issues you may encounter. Their customers love their products, citing excellent themes, easy installation, and good customer support.
- Z
Checkbot API raw results from Libraries, Archives and Museums websites for...
- data.niaid.nih.gov
- zenodo.org
Updated Jun 19, 2021ShareFacebookTwitterEmailClick to copy linkLink copiedCiteDaphne Kyriaki-Manessi (2021). Checkbot API raw results from Libraries, Archives and Museums websites for evaluating a data-driven Search Engine Optimization methodology [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_4992229Dataset updatedJun 19, 2021Dataset provided byGeorgios Giannakopoulos
Dimitrios Kouis
Daphne Kyriaki-Manessi
Ioannis DrivasLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionResults from Checkbot API to measure and collect 341 websites compatibility on multiple SEO variables (34 variables). Checkbot API indexes the website's code to find features capable of impacting SEO performance. Each website has been tested with the maximum number of links allowed to be crawled equally to 10.000 per test. In this way, we retrieved data about the overall websites performance including their sub-pages, and not only the main domain names. A scale from 0 (lowest rate) to 100 (highest rate) was adopted for each examined variable. This constitutes a useful managerial indicator of dealing with the quantification of websites performance while avoiding complex measurement systems that are difficult to be adopted by administrators. Websites tested were also categorized by the CMS type used. More information about the variables and the meaning of the results can be found at https://www.checkbot.io/
Data and results for the paper: "From Bugs to Benefits: Improving User...
- zenodo.org
bin, json, txtUpdated Jan 31, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteStefan Schwedt; Stefan Schwedt (2025). Data and results for the paper: "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC" [Dataset]. http://doi.org/10.5281/zenodo.12819736bin, txt, jsonAvailable download formatsUnique identifierhttps://doi.org/10.5281/zenodo.12819736Dataset updatedJan 31, 2025AuthorsStefan Schwedt; Stefan SchwedtLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionWe provide the following files used in the study "From Bugs to Benefits: Improving User Stories by Leveraging Crowd Knowledge with CrUISE-AC".The paper has been accepted for presentation in the research track of the IEEE/ACM International Conference on Software Engineering (ICSE) 2025 and will be included in the conference proceedings.The preprint is available on arXiv.
User stories e-commerce.xlsx
307 real-world user stories from 3 different eCommerce projects.
Project A defines a complete set of requirements for a B2C focused onlineshop of a publishing house who aims do sell his own publications directly.Project B contains a partial set of requirements for a B2C focused onlineshop of a bookseller.Project C includes a subset of B2C and B2B requirements for an online bookstore, supplemented by an eProcurement module designed to provide information and automation for industrial customers.Most of the user stories come with additional acceptance criteria, written in unstructured natural language.
The user stories have been anonymized and the merchant's real names were replaced with neutral terms.
Columns- ID: a unique ID we assigned across all projects- Project: user story belongs to project A, B or C- Connextra: user story in connextra pattern- Acceptance Criteria: acceptance criteria that came with the user story
User stories CMS.xlsx
34 CMS related user stories from a dataset that was originally created byLucassen, G., Dalpiaz, F., van der Werf, J.M.E., Brinkkemper, S.: Visualizing userstory requirements at multiple granularity levels via semantic relatedness. In: Con-ceptual Modeling: 35th International Conference, ER 2016, Gifu, Japan, November14-17, 2016, Proceedings 35. pp. 463–478. Springer (2016)
Columns- ID: a unique ID we assigned- Connextra: user story in connextra pattern
Issues e-commerce.xlsx
54,396 issues, we harvested from seven different issue trackers between June 2011 and July 2024- magento2 (https://github.com/magento/magento2/issues)- nopCommerce (https://github.com/nopSolutions/nopCommerce/issues)- OpenCart (https://github.com/opencart/opencart/issues)- PrestaShop (https://github.com/PrestaShop/PrestaShop/issues)- Shopware5 (https://issues.shopware.com/?products=SW-5)- Shopware6 (https://issues.shopware.com/?products=SW-6)- WooCommerce (https://github.com/woocommerce/woocommerce/issues)
Columns- id: unique ID we have assigned- Issue Tracker: issue tracker this issue originates from- Title: title of the original issue- Body: body / description of the original issue- Preprocessed: result of preprocessing the issue as described in the paper- Sample: issue was part of our 3,500 sample issues we used to evaluate CrUISE-AC
Issues CMS.xlsx
64,500 issues, we harvested from two different issue trackers between April 2002 and August 2024- Moodle (https://github.com/magento/magento2/issues)
Columns are the the same as for "Issues e-commerce.xlsx"
trivia-trainingdata.csv
Manually labelled dataset to train the trivia classifier.The dataset contains 1916 phrases with an even distribution of 958 trivia and 958 non-trivia phrases.
- Label = 1: this sentence is trivia- Label = 0: this sentence is not considered trivia
Any source code was replaced by [CODE] to simplify the classification process. Source code in markdown could be identified easily as it is enclosed by a special character https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks
Prompts
prompt_match.txt: prompt we used across all LLMs to assess, if an issue potentially might affect a given user story
prompt_generate.txt: GPT4-turbo prompt to convert an issue text into gherkin-style acceptance criteria for a given user storyprompt_evaluate.txt: GPT4-turbo prompt to assess the usefulness of a newly generated acceptance criteria for a given user story
Evaluation e-commerce.xlsx
issue / user story pairs, generated acceptance criteria and result of manual evaluation.
Columns- StoryID: unique ID of the user story (refer to User stories e-commerce.xlsx)- IssueID: unique ID of the issue (refer to Issues e-commerce.xlsx)- Issue: preprocessed issue text used as basis to generate the acceptance criterion- Connextra: user story in connextra pattern- Existing AC: acceptance criteria that originally came with the user story- AC: by CrUISE-AC generated acceptance criterion- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)- E2: evaluation result by expert 2 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)- E3: evaluation result by expert 3 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)- 3/4 majority: did at least 3 experts assess this AC as relevant (1 = yes; 0 = no)
Evaluation CMS.xlsx
- StoryID: unique ID of the user story (refer to User stories CMS.xlsx)- IssueID: unique ID of the issue (refer to Issues CMS.xlsx)- Issue: preprocessed issue text used as basis to generate the acceptance criterion- Connextra: user story in connextra pattern- AC: by CrUISE-AC generated acceptance criterion- AC_Explanation: explanation generated by CrUISE-AC why this AC adds new knowledge to the current user story- E1: evaluation result by expert 1 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)- E4: evaluation result by expert 4 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)- E5: evaluation result by expert 5 (1 = AC adds relevant knowledge; 0 = AC is irrelevant)- 2/3 majority: did at least 2 experts assess this AC as relevant (1 = yes; 0 = no)
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FacebookTwitterEmailOver 1.6M WooCommerce stores from around the world: United States (308k stores) Canada (68k stores) Europe (818k stores) Asia (279k stores) Oceania (94k stores) South America (100k stores)
Sample data: https://docs.google.com/spreadsheets/d/1CpJSjaVOUQGw2qwahGeQkgnNN2Lt6BGbY0wiqjHVlHU
We can create a custom list based on your criteria e.g. stores from specific countries and categories, using specific technologies and apps, with certain keywords on the homepage, with over 10k Instagram followers etc.
Pricing: $0.05 per lead
We also offer volume discounts on orders above 10,000 sellers.