This dataset contains metadata (title, abstract, date of publication, field, etc) for around 1 million academic articles. Each record contains additional information on the country of study and whether the article makes use of data. Machine learning tools were used to classify the country of study and data use.

Our data source of academic articles is the Semantic Scholar Open Research Corpus (S2ORC) (Lo et al. 2020). The corpus contains more than 130 million English language academic papers across multiple disciplines. The papers included in the Semantic Scholar corpus are gathered directly from publishers, from open archives such as arXiv or PubMed, and crawled from the internet.

We placed some restrictions on the articles to make them usable and relevant for our purposes. First, only articles with an abstract and parsed PDF or latex file are included in the analysis. The full text of the abstract is necessary to classify the country of study and whether the article uses data. The parsed PDF and latex file are important for extracting important information like the date of publication and field of study. This restriction eliminated a large number of articles in the original corpus. Around 30 million articles remain after keeping only articles with a parsable (i.e., suitable for digital processing) PDF, and around 26% of those 30 million are eliminated when removing articles without an abstract. Second, only articles from the year 2000 to 2020 were considered. This restriction eliminated an additional 9% of the remaining articles. Finally, articles from the following fields of study were excluded, as we aim to focus on fields that are likely to use data produced by countries’ national statistical system: Biology, Chemistry, Engineering, Physics, Materials Science, Environmental Science, Geology, History, Philosophy, Math, Computer Science, and Art. Fields that are included are: Economics, Political Science, Business, Sociology, Medicine, and Psychology. This third restriction eliminated around 34% of the remaining articles. From an initial corpus of 136 million articles, this resulted in a final corpus of around 10 million articles.

Due to the intensive computer resources required, a set of 1,037,748 articles were randomly selected from the 10 million articles in our restricted corpus as a convenience sample.

The empirical approach employed in this project utilizes text mining with Natural Language Processing (NLP). The goal of NLP is to extract structured information from raw, unstructured text. In this project, NLP is used to extract the country of study and whether the paper makes use of data. We will discuss each of these in turn.

To determine the country or countries of study in each academic article, two approaches are employed based on information found in the title, abstract, or topic fields. The first approach uses regular expression searches based on the presence of ISO3166 country names. A defined set of country names is compiled, and the presence of these names is checked in the relevant fields. This approach is transparent, widely used in social science research, and easily extended to other languages. However, there is a potential for exclusion errors if a country’s name is spelled non-standardly.

The second approach is based on Named Entity Recognition (NER), which uses machine learning to identify objects from text, utilizing the spaCy Python library. The Named Entity Recognition algorithm splits text into named entities, and NER is used in this project to identify countries of study in the academic articles. SpaCy supports multiple languages and has been trained on multiple spellings of countries, overcoming some of the limitations of the regular expression approach. If a country is identified by either the regular expression search or NER, it is linked to the article. Note that one article can be linked to more than one country.

The second task is to classify whether the paper uses data. A supervised machine learning approach is employed, where 3500 publications were first randomly selected and manually labeled by human raters using the Mechanical Turk service (Paszke et al. 2019).[1] To make sure the human raters had a similar and appropriate definition of data in mind, they were given the following instructions before seeing their first paper:

Each of these documents is an academic article. The goal of this study is to measure whether a specific academic article is using data and from which country the data came.

There are two classification tasks in this exercise:

1. identifying whether an academic article is using data from any country

2. Identifying from which country that data came.

For task 1, we are looking specifically at the use of data. Data is any information that has been collected, observed, generated or created to produce research findings. As an example, a study that reports findings or analysis using a survey data, uses data. Some clues to indicate that a study does use data includes whether a survey or census is described, a statistical model estimated, or a table or means or summary statistics is reported.

After an article is classified as using data, please note the type of data used. The options are population or business census, survey data, administrative data, geospatial data, private sector data, and other data. If no data is used, then mark "Not applicable". In cases where multiple data types are used, please click multiple options.[2]

For task 2, we are looking at the country or countries that are studied in the article. In some cases, no country may be applicable. For instance, if the research is theoretical and has no specific country application. In some cases, the research article may involve multiple countries. In these cases, select all countries that are discussed in the paper.

We expect between 10 and 35 percent of all articles to use data.

The median amount of time that a worker spent on an article, measured as the time between when the article was accepted to be classified by the worker and when the classification was submitted was 25.4 minutes. If human raters were exclusively used rather than machine learning tools, then the corpus of 1,037,748 articles examined in this study would take around 50 years of human work time to review at a cost of $3,113,244, which assumes a cost of $3 per article as was paid to MTurk workers.

A model is next trained on the 3,500 labelled articles. We use a distilled version of the BERT (bidirectional Encoder Representations for transformers) model to encode raw text into a numeric format suitable for predictions (Devlin et al. (2018)). BERT is pre-trained on a large corpus comprising the Toronto Book Corpus and Wikipedia. The distilled version (DistilBERT) is a compressed model that is 60% the size of BERT and retains 97% of the language understanding capabilities and is 60% faster (Sanh, Debut, Chaumond, Wolf 2019). We use PyTorch to produce a model to classify articles based on the labeled data. Of the 3,500 articles that were hand coded by the MTurk workers, 900 are fed to the machine learning model. 900 articles were selected because of computational limitations in training the NLP model. A classification of “uses data” was assigned if the model predicted an article used data with at least 90% confidence.

The performance of the models classifying articles to countries and as using data or not can be compared to the classification by the human raters. We consider the human raters as giving us the ground truth. This may underestimate the model performance if the workers at times got the allocation wrong in a way that would not apply to the model. For instance, a human rater could mistake the Republic of Korea for the Democratic People’s Republic of Korea. If both humans and the model perform the same kind of errors, then the performance reported here will be overestimated.

The model was able to predict whether an article made use of data with 87% accuracy evaluated on the set of articles held out of the model training. The correlation between the number of articles written about each country using data estimated under the two approaches is given in the figure below. The number of articles represents an aggregate total of

This dataset (known as "Stack Exchange Data Dump", Mathematics site) has been obtained from https://math.stackexchange.com/ (see also https://math.stackexchange.com/help/data-dumps). The dataset is very similar in structure to Stack Overflow Data Dump.

Licence information

This dataset is licensed under the Creative Commons CC BY-SA licence (2.5, 3.0, and/or 4.0). For the text of the licence(s) and other details, please see license.txt and https://stackoverflow.com/help/licensing.

Schema

See https://meta.stackexchange.com/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede.

Related notebooks and datasets

• https://www.kaggle.com/code/andreyvm/mathse-download-dump-from-se
• https://www.kaggle.com/datasets/andreyvm/math-stackexchange-dump-raw
• https://www.kaggle.com/code/andreyvm/mathse-dump-to-parquet
• https://www.kaggle.com/datasets/andreyvm/math-stackexchange-dump-parquet
• https://www.kaggle.com/code/andreyvm/mathse-recover-rephistory
• https://www.kaggle.com/datasets/andreyvm/math-stackexchange-rephistory
• https://www.kaggle.com/code/andreyvm/mathse-get-usersforbonus-from-sede
• https://www.kaggle.com/datasets/andreyvm/math-stackexchange-usersforbonus
• https://www.kaggle.com/code/andreyvm/mathse-get-postsdeleted-from-sede
• https://www.kaggle.com/datasets/andreyvm/math-stackexchange-postsdeleted
• https://www.kaggle.com/code/andreyvm/mathse-get-misc-data-from-sede
• https://www.kaggle.com/datasets/andreyvm/math-stackexchange-misc

Summary

This dataset contains biographical information derived from articles on English Wikipedia as it stood in early June 2024. It was created as part of the Structured Contents initiative at Wikimedia Enterprise and is intended for evaluation and research use.

The beta sample dataset is a subset of the Structured Contents Snapshot focusing on people with infoboxes in EN wikipedia; outputted as json files (compressed in tar.gz).

We warmly welcome any feedback you have. Please share your thoughts, suggestions, and any issues you encounter on the discussion page for this dataset here on Kaggle.

Data Structure

• File name: wme_people_infobox.tar.gz
• Size of compressed file: 4.12 GB
• Size of uncompressed file: 21.28 GB

Noteworthy Included Fields: - name - title of the article. - identifier - ID of the article. - image - main image representing the article's subject. - description - one-sentence description of the article for quick reference. - abstract - lead section, summarizing what the article is about. - infoboxes - parsed information from the side panel (infobox) on the Wikipedia article. - sections - parsed sections of the article, including links. Note: excludes other media/images, lists, tables and references or similar non-prose sections.

The Wikimedia Enterprise Data Dictionary explains all of the fields in this dataset.

Stats

Infoboxes - Compressed: 2GB - Uncompressed: 11GB

Infoboxes + sections + short description - Size of compressed file: 4.12 GB - Size of uncompressed file: 21.28 GB

Article analysis and filtering breakdown: - total # of articles analyzed: 6,940,949 - # people found with QID: 1,778,226 - # people found with Category: 158,996 - people found with Biography Project: 76,150 - Total # of people articles found: 2,013,372 - Total # people articles with infoboxes: 1,559,985 End stats - Total number of people articles in this dataset: 1,559,985 - that have a short description: 1,416,701 - that have an infobox: 1,559,985 - that have article sections: 1,559,921

This dataset includes 235,146 people articles that exist on Wikipedia but aren't yet tagged on Wikidata as instance of:human.

Maintenance and Support

This dataset was originally extracted from the Wikimedia Enterprise APIs on June 5, 2024. The information in this dataset may therefore be out of date. This dataset isn't being actively updated or maintained, and has been shared for community use and feedback. If you'd like to retrieve up-to-date Wikipedia articles or data from other Wikiprojects, get started with Wikimedia Enterprise's APIs

Initial Data Collection and Normalization

The dataset is built from the Wikimedia Enterprise HTML “snapshots”: https://enterprise.wikimedia.com/docs/snapshot/ and focuses on the Wikipedia article namespace (namespace 0 (main)).

Who are the source language producers?

Wikipedia is a human generated corpus of free knowledge, written, edited, and curated by a global community of editors since 2001. It is the largest and most accessed educational resource in history, accessed over 20 billion times by half a billion people each month. Wikipedia represents almost 25 years of work by its community; the creation, curation, and maintenance of millions of articles on distinct topics. This dataset includes the biographical contents of English Wikipedia language editions: English https://en.wikipedia.org/, written by the community.

Attribution

Terms and conditions

Wikimedia Enterprise provides this dataset under the assumption that downstream users will adhere to the relevant free culture licenses when the data is reused. In situations where attribution is required, reusers should identify the Wikimedia project from which the content was retrieved as the source of the content. Any attribution should adhere to Wikimedia’s trademark policy (available at https://foundation.wikimedia.org/wiki/Trademark_policy) and visual identity guidelines (ava...

This child item describes Python code used to query census data from the TigerWeb Representational State Transfer (REST) services and the U.S. Census Bureau Application Programming Interface (API). These data were needed as input feature variables for a machine learning model to predict public supply water use for the conterminous United States. Census data were retrieved for public-supply water service areas, but the census data collector could be used to retrieve data for other areas of interest. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Data retrieved by the census data collector code were used as input features in the public supply delivery and water use machine learning models. This page includes the following file: census_data_collector.zip - a zip file containing the census data collector Python code used to retrieve data from the U.S. Census Bureau and a README file.

The present is a manually labeled data set for the task of Event Detection (ED). The task of ED consists of identifying event triggers, the word that most clearly indicates the occurrence of an event.

The present data set consists of 2,200 news extracts from The New York Times (NYT) Annotated Corpus, separated into training (2,000) and testing (200) sets. Each news extract contains the plain text with the labels (event mentions), along with two metadata (publication date and an identifier).

Labels description: We consider as event any ongoing real-world event or situation reported in the news articles. It is important to distinguish those events and situations that are in progress (or are reported as fresh events) at the moment the news is delivered from past events that are simply brought back, future events, hypothetical events, or events that will not take place. In our data set we only labeled as event the first type of event. Based on this criterion, some words that are typically considered as events are labeled as non-event triggers if they do not refer to ongoing events at the time the analyzed news is released. Take for instance the following news extract: "devaluation is not a realistic option to the current account deficit since it would only contribute to weakening the credibility of economic policies as it did during the last crisis." The only word that is labeled as event trigger in this example is "deficit" because it is the only ongoing event refereed in the news. Note that the words "devaluation", "weakening" and "crisis" could be labeled as event triggers in other news extracts, where the context of use of these words is different, but not in the given example.

Further information: For a more detailed description of the data set and the data collection process please visit: https://cs.uns.edu.ar/~mmaisonnave/resources/ED_data.

Data format: The dataset is split in two folders: training and testing. The first folder contains 2,000 XML files. The second folder contains 200 XML files. Each XML file has the following format.

<?xml version="1.0" encoding="UTF-8"?>

The first three tags (pubdate, file-id and sent-idx) contain metadata information. The first one is the publication date of the news article that contained that text extract. The next two tags represent a unique identifier for the text extract. The file-id uniquely identifies a news article, that can hold several text extracts. The second one is the index that identifies that text extract inside the full article.

The last tag (sentence) defines the beginning and end of the text extract. Inside that text are the tags. Each of these tags surrounds one word that was manually labeled as an event trigger.

These four labeled data sets are targeted at ordinal quantification. The goal of quantification is not to predict the label of each individual instance, but the distribution of labels in unlabeled sets of data.

With the scripts provided, you can extract CSV files from the UCI machine learning repository and from OpenML. The ordinal class labels stem from a binning of a continuous regression label.

We complement this data set with the indices of data items that appear in each sample of our evaluation. Hence, you can precisely replicate our samples by drawing the specified data items. The indices stem from two evaluation protocols that are well suited for ordinal quantification. To this end, each row in the files app_val_indices.csv, app_tst_indices.csv, app-oq_val_indices.csv, and app-oq_tst_indices.csv represents one sample.

Our first protocol is the artificial prevalence protocol (APP), where all possible distributions of labels are drawn with an equal probability. The second protocol, APP-OQ, is a variant thereof, where only the smoothest 20% of all APP samples are considered. This variant is targeted at ordinal quantification tasks, where classes are ordered and a similarity of neighboring classes can be assumed.

Usage

You can extract four CSV files through the provided script extract-oq.jl, which is conveniently wrapped in a Makefile. The Project.toml and Manifest.toml specify the Julia package dependencies, similar to a requirements file in Python.

Preliminaries: You have to have a working Julia installation. We have used Julia v1.6.5 in our experiments.

Data Extraction: In your terminal, you can call either

make

(recommended), or

julia --project="." --eval "using Pkg; Pkg.instantiate()"
julia --project="." extract-oq.jl

Outcome: The first row in each CSV file is the header. The first column, named "class_label", is the ordinal class.

Further Reading

Implementation of our experiments: https://github.com/mirkobunse/regularized-oq

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

The Ontario government, generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Open Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular organization, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. Read more about previewing data. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Have a look at our walk-through of how to make a chart in the catalogue. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or applications (including HTML, XML, Excel, etc.). The database file can be converted to a CSV/TXT to make the data machine-readable, but human-readable formatting will be lost. How to access the data: Open with Microsoft Office Access (a database management system used to develop application software). A file that keeps the original layout and

The self-documenting aspects and the ability to reproduce results have been touted as significant benefits of Jupyter Notebooks. At the same time, there has been growing criticism that the way notebooks are being used leads to unexpected behavior, encourage poor coding practices and that their results can be hard to reproduce. To understand good and bad practices used in the development of real notebooks, we analyzed 1.4 million notebooks from GitHub.

Paper: https://2019.msrconf.org/event/msr-2019-papers-a-large-scale-study-about-quality-and-reproducibility-of-jupyter-notebooks

This repository contains two files:

• dump.tar.bz2
• jupyter_reproducibility.tar.bz2

The dump.tar.bz2 file contains a PostgreSQL dump of the database, with all the data we extracted from the notebooks.

The jupyter_reproducibility.tar.bz2 file contains all the scripts we used to query and download Jupyter Notebooks, extract data from them, and analyze the data. It is organized as follows:

• analyses: this folder has all the notebooks we use to analyze the data in the PostgreSQL database.
• archaeology: this folder has all the scripts we use to query, download, and extract data from GitHub notebooks.
• paper: empty. The notebook analyses/N12.To.Paper.ipynb moves data to it

In the remaining of this text, we give instructions for reproducing the analyses, by using the data provided in the dump and reproducing the collection, by collecting data from GitHub again.

Reproducing the Analysis

This section shows how to load the data in the database and run the analyses notebooks. In the analysis, we used the following environment:

Ubuntu 18.04.1 LTS
PostgreSQL 10.6
Conda 4.5.11
Python 3.7.2
PdfCrop 2012/11/02 v1.38

First, download dump.tar.bz2 and extract it:

tar -xjf dump.tar.bz2

It extracts the file db2019-03-13.dump. Create a database in PostgreSQL (we call it "jupyter"), and use psql to restore the dump:

psql jupyter < db2019-03-13.dump

It populates the database with the dump. Now, configure the connection string for sqlalchemy by setting the environment variable JUP_DB_CONNECTTION:

export JUP_DB_CONNECTION="postgresql://user:password@hostname/jupyter";

Download and extract jupyter_reproducibility.tar.bz2:

tar -xjf jupyter_reproducibility.tar.bz2

Create a conda environment with Python 3.7:

conda create -n analyses python=3.7
conda activate analyses

Go to the analyses folder and install all the dependencies of the requirements.txt

cd jupyter_reproducibility/analyses
pip install -r requirements.txt

For reproducing the analyses, run jupyter on this folder:

jupyter notebook

Execute the notebooks on this order:

• Index.ipynb
• N0.Repository.ipynb
• N1.Skip.Notebook.ipynb
• N2.Notebook.ipynb
• N3.Cell.ipynb
• N4.Features.ipynb
• N5.Modules.ipynb
• N6.AST.ipynb
• N7.Name.ipynb
• N8.Execution.ipynb
• N9.Cell.Execution.Order.ipynb
• N10.Markdown.ipynb
• N11.Repository.With.Notebook.Restriction.ipynb
• N12.To.Paper.ipynb

Reproducing or Expanding the Collection

The collection demands more steps to reproduce and takes much longer to run (months). It also involves running arbitrary code on your machine. Proceed with caution.

Requirements

This time, we have extra requirements:

All the analysis requirements
lbzip2 2.5
gcc 7.3.0
Github account
Gmail account

Environment

First, set the following environment variables:

export JUP_MACHINE="db"; # machine identifier
export JUP_BASE_DIR="/mnt/jupyter/github"; # place to store the repositories
export JUP_LOGS_DIR="/home/jupyter/logs"; # log files
export JUP_COMPRESSION="lbzip2"; # compression program
export JUP_VERBOSE="5"; # verbose level
export JUP_DB_CONNECTION="postgresql://user:password@hostname/jupyter"; # sqlchemy connection
export JUP_GITHUB_USERNAME="github_username"; # your github username
export JUP_GITHUB_PASSWORD="github_password"; # your github password
export JUP_MAX_SIZE="8000.0"; # maximum size of the repositories directory (in GB)
export JUP_FIRST_DATE="2013-01-01"; # initial date to query github
export JUP_EMAIL_LOGIN="gmail@gmail.com"; # your gmail address
export JUP_EMAIL_TO="target@email.com"; # email that receives notifications
export JUP_OAUTH_FILE="~/oauth2_creds.json" # oauth2 auhentication file
export JUP_NOTEBOOK_INTERVAL=""; # notebook id interval for this machine. Leave it in blank
export JUP_REPOSITORY_INTERVAL=""; # repository id interval for this machine. Leave it in blank
export JUP_WITH_EXECUTION="1"; # run execute python notebooks
export JUP_WITH_DEPENDENCY="0"; # run notebooks with and without declared dependnecies
export JUP_EXECUTION_MODE="-1"; # run following the execution order
export JUP_EXECUTION_DIR="/home/jupyter/execution"; # temporary directory for running notebooks
export JUP_ANACONDA_PATH="~/anaconda3"; # conda installation path
export JUP_MOUNT_BASE="/home/jupyter/mount_ghstudy.sh"; # bash script to mount base dir
export JUP_UMOUNT_BASE="/home/jupyter/umount_ghstudy.sh"; # bash script to umount base dir
export JUP_NOTEBOOK_TIMEOUT="300"; # timeout the extraction


# Frequenci of log report
export JUP_ASTROID_FREQUENCY="5";
export JUP_IPYTHON_FREQUENCY="5";
export JUP_NOTEBOOKS_FREQUENCY="5";
export JUP_REQUIREMENT_FREQUENCY="5";
export JUP_CRAWLER_FREQUENCY="1";
export JUP_CLONE_FREQUENCY="1";
export JUP_COMPRESS_FREQUENCY="5";

export JUP_DB_IP="localhost"; # postgres database IP

Then, configure the file ~/oauth2_creds.json, according to yagmail documentation: https://media.readthedocs.org/pdf/yagmail/latest/yagmail.pdf

Configure the mount_ghstudy.sh and umount_ghstudy.sh scripts. The first one should mount the folder that stores the directories. The second one should umount it. You can leave the scripts in blank, but it is not advisable, as the reproducibility study runs arbitrary code on your machine and you may lose your data.

Scripts

Download and extract jupyter_reproducibility.tar.bz2:

tar -xjf jupyter_reproducibility.tar.bz2

Install 5 conda environments and 5 anaconda environments, for each python version. In each of them, upgrade pip, install pipenv, and install the archaeology package (Note that it is a local package that has not been published to pypi. Make sure to use the -e option):

Conda 2.7

conda create -n raw27 python=2.7 -y
conda activate raw27
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Anaconda 2.7

conda create -n py27 python=2.7 anaconda -y
conda activate py27
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Conda 3.4

It requires a manual jupyter and pathlib2 installation due to some incompatibilities found on the default installation.

conda create -n raw34 python=3.4 -y
conda activate raw34
conda install jupyter -c conda-forge -y
conda uninstall jupyter -y
pip install --upgrade pip
pip install jupyter
pip install pipenv
pip install -e jupyter_reproducibility/archaeology
pip install pathlib2

Anaconda 3.4

conda create -n py34 python=3.4 anaconda -y
conda activate py34
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Conda 3.5

conda create -n raw35 python=3.5 -y
conda activate raw35
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Anaconda 3.5

It requires the manual installation of other anaconda packages.

conda create -n py35 python=3.5 anaconda -y
conda install -y appdirs atomicwrites keyring secretstorage libuuid navigator-updater prometheus_client pyasn1 pyasn1-modules spyder-kernels tqdm jeepney automat constantly anaconda-navigator
conda activate py35
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Conda 3.6

conda create -n raw36 python=3.6 -y
conda activate raw36
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Anaconda 3.6

conda create -n py36 python=3.6 anaconda -y
conda activate py36
conda install -y anaconda-navigator jupyterlab_server navigator-updater
pip install --upgrade pip
pip install pipenv
pip install -e jupyter_reproducibility/archaeology

Conda 3.7

<code

ZARA is one of the world's largest apparel and fashion retailers. The CrawlFeeds team has successfully extracted over 10,000 product records from ZARA USA, including titles, prices, images, availability, and more.

You can customize the dataset to match your specific needs, such as format adjustments, re-extraction, or additional data points.

If you're looking for retail data solutions, you can customize the current dataset or extract ZARA product data from other countries like Spain, the UK, and India.

Find here latest zara us products listings (https://crawlfeeds.com/datasets/download-the-complete-zara-product-dataset)

Obtain Dataset Metadata API.......................

A collection of 22 data set of 50+ requirements each, expressed as user stories.

The dataset has been created by gathering data from web sources and we are not aware of license agreements or intellectual property rights on the requirements / user stories. The curator took utmost diligence in minimizing the risks of copyright infringement by using non-recent data that is less likely to be critical, by sampling a subset of the original requirements collection, and by qualitatively analyzing the requirements. In case of copyright infringement, please contact the dataset curator (Fabiano Dalpiaz, f.dalpiaz@uu.nl) to discuss the possibility of removal of that dataset [see Zenodo's policies]

The data sets have been originally used to conduct experiments about ambiguity detection with the REVV-Light tool: https://github.com/RELabUU/revv-light

This collection has been originally published in Mendeley data: https://data.mendeley.com/datasets/7zbk8zsd8y/1

Overview of the datasets [data and links added in December 2024]

The following text provides a description of the datasets, including links to the systems and websites, when available. The datasets are organized by macro-category and then by identifier.

Public administration and transparency

g02-federalspending.txt (2018) originates from early data in the Federal Spending Transparency project, which pertain to the website that is used to share publicly the spending data for the U.S. government. The website was created because of the Digital Accountability and Transparency Act of 2014 (DATA Act). The specific dataset pertains a system called DAIMS or Data Broker, which stands for DATA Act Information Model Schema. The sample that was gathered refers to a sub-project related to allowing the government to act as a data broker, thereby providing data to third parties. The data for the Data Broker project is currently not available online, although the backend seems to be hosted in GitHub under a CC0 1.0 Universal license. Current and recent snapshots of federal spending related websites, including many more projects than the one described in the shared collection, can be found here.

g03-loudoun.txt (2018) is a set of extracted requirements from a document, by the Loudoun County Virginia, that describes the to-be user stories and use cases about a system for land management readiness assessment called Loudoun County LandMARC. The source document can be found here and it is part of the Electronic Land Management System and EPlan Review Project - RFP RFQ issued in March 2018. More information about the overall LandMARC system and services can be found here.

g04-recycling.txt(2017) concerns a web application where recycling and waste disposal facilities can be searched and located. The application operates through the visualization of a map that the user can interact with. The dataset has obtained from a GitHub website and it is at the basis of a students' project on web site design; the code is available (no license).

g05-openspending.txt (2018) is about the OpenSpending project (www), a project of the Open Knowledge foundation which aims at transparency about how local governments spend money. At the time of the collection, the data was retrieved from a Trello board that is currently unavailable. The sample focuses on publishing, importing and editing datasets, and how the data should be presented. Currently, OpenSpending is managed via a GitHub repository which contains multiple sub-projects with unknown license.

g11-nsf.txt (2018) refers to a collection of user stories referring to the NSF Site Redesign & Content Discovery project, which originates from a publicly accessible GitHub repository (GPL 2.0 license). In particular, the user stories refer to an early version of the NSF's website. The user stories can be found as closed Issues.

(Research) data and meta-data management

g08-frictionless.txt (2016) regards the Frictionless Data project, which offers an open source dataset for building data infrastructures, to be used by researchers, data scientists, and data engineers. Links to the many projects within the Frictionless Data project are on GitHub (with a mix of Unlicense and MIT license) and web. The specific set of user stories has been collected in 2016 by GitHub user @danfowler and are stored in a Trello board.

g14-datahub.txt (2013) concerns the open source project DataHub, which is currently developed via a GitHub repository (the code has Apache License 2.0). DataHub is a data discovery platform which has been developed over multiple years. The specific data set is an initial set of user stories, which we can date back to 2013 thanks to a comment therein.

g16-mis.txt (2015) is a collection of user stories that pertains a repository for researchers and archivists. The source of the dataset is a public Trello repository. Although the user stories do not have explicit links to projects, it can be inferred that the stories originate from some project related to the library of Duke University.

g17-cask.txt (2016) refers to the Cask Data Application Platform (CDAP). CDAP is an open source application platform (GitHub, under Apache License 2.0) that can be used to develop applications within the Apache Hadoop ecosystem, an open-source framework which can be used for distributed processing of large datasets. The user stories are extracted from a document that includes requirements regarding dataset management for Cask 4.0, which includes the scenarios, user stories and a design for the implementation of these user stories. The raw data is available in the following environment.

g18-neurohub.txt (2012) is concerned with the NeuroHub platform, a neuroscience data management, analysis and collaboration platform for researchers in neuroscience to collect, store, and share data with colleagues or with the research community. The user stories were collected at a time NeuroHub was still a research project sponsored by the UK Joint Information Systems Committee (JISC). For information about the research project from which the requirements were collected, see the following record.

g22-rdadmp.txt (2018) is a collection of user stories from the Research Data Alliance's working group on DMP Common Standards. Their GitHub repository contains a collection of user stories that were created by asking the community to suggest functionality that should part of a website that manages data management plans. Each user story is stored as an issue on the GitHub's page.

g23-archivesspace.txt (2012-2013) refers to ArchivesSpace: an open source, web application for managing archives information. The application is designed to support core functions in archives administration such as accessioning; description and arrangement of processed materials including analog, hybrid, and
born digital content; management of authorities and rights; and reference service. The application supports collection management through collection management records, tracking of events, and a growing number of administrative reports. ArchivesSpace is open source and its

The data sets provide an overview of selected data on waterworks registered with the Norwegian Food Safety Authority. The information has been reported by the waterworks through application processing or other reporting to the Norwegian Food Safety Authority. Drinking water regulations require, among other things, annual reporting. The Norwegian Food Safety Authority has created a separate form service for such reporting. The data sets include public or private waterworks that supply 50 people or more. In addition, all municipal owned businesses with their own water supply are included regardless of size. The data sets also contain decommissioned facilities. This is done for those who wish to view historical data, i.e. data for previous years or earlier. There are data sets for the following supervisory objects: 1. Water supply system. It also includes analysis of drinking water. 2. Transport system 3. Treatment facility 4. Entry point. It also includes analysis of the water source. Below you will find datasets for: 1. Water supply system_reporting In addition, there is a file (information.txt) that provides an overview of when the extracts were produced and how many lines there are in the individual files. The withdrawals are done weekly. Furthermore, for the data sets water supply system, transport system and intake point it is possible to see historical data on what is included in the annual reporting. To make use of that information, the file must be linked to the “moder” file. to get names and other static information. These files have the _reporting ending in the file name. Description of the data fields (i.e. metadata) in the individual data sets appears in separate files. These are available in pdf format. If you double-click the csv file and it opens directly in excel, then you will not get the æøå. To see the character set correctly in Excel, you must: & start Excel and a new spreadsheet & select data and then from text, press Import & select separator data and file origin 65001: Unicode (UTF-8) and tick of My Data have headings and press Next & remove tab as separator and select semicolon as separator, press next & otherwise, complete the data sets can be imported into a separate database and compiled as desired. There are link keys in the files that make it possible to link the files together. The waterworks are responsible for the quality of the datasets.

—

Purpose: Make data for drinking water supply available to the public.

Sample data for exercises in Further Adventures in Data Cleaning.

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy. Methods Prior to collection of data, the researcher was be guided by all ethical training certification on data collection, right to confidentiality and privacy reserved called Institutional Review Board (IRB). Data was be collected from the manual archive of the Hospitals purposively selected using stratified sampling technique, transform the data to electronic form and store in MYSQL database called malaria. Each patient file was extracted and review for signs and symptoms of malaria then check for laboratory confirmation result from diagnosis. The data was be divided into two tables: the first table was called data1 which contain data for use in phase 1 of the classification, while the second table data2 which contains data for use in phase 2 of the classification. Data Source Collection Malaria incidence data set is obtained from Public hospitals from 2017 to 2021. These are the data used for modeling and analysis. Also, putting in mind the geographical location and socio-economic factors inclusive which are available for patients inhabiting those areas. Naive Bayes (Multinomial) is the model used to analyze the collected data for malaria disease prediction and grading accordingly. Data Preprocessing: Data preprocessing shall be done to remove noise and outlier. Transformation: The data shall be transformed from analog to electronic record. Data Partitioning The data which shall be collected will be divided into two portions; one portion of the data shall be extracted as a training set, while the other portion will be used for testing. The training portion shall be taken from a table stored in a database and will be called data which is training set1, while the training portion taking from another table store in a database is shall be called data which is training set2. The dataset was split into two parts: a sample containing 70% of the training data and 30% for the purpose of this research. Then, using MNB classification algorithms implemented in Python, the models were trained on the training sample. On the 30% remaining data, the resulting models were tested, and the results were compared with the other Machine Learning models using the standard metrics. Classification and prediction: Base on the nature of variable in the dataset, this study will use Naïve Bayes (Multinomial) classification techniques; Classification phase 1 and Classification phase 2. The operation of the framework is illustrated as follows: i. Data collection and preprocessing shall be done. ii. Preprocess data shall be stored in a training set 1 and training set 2. These datasets shall be used during classification. iii. Test data set is shall be stored in database test data set. iv. Part of the test data set must be compared for classification using classifier 1 and the remaining part must be classified with classifier 2 as follows: Classifier phase 1: It classify into positive or negative classes. If the patient is having malaria, then the patient is classified as positive (P), while a patient is classified as negative (N) if the patient does not have malaria.
Classifier phase 2: It classify only data set that has been classified as positive by classifier 1, and then further classify them into complicated and uncomplicated class label. The classifier will also capture data on environmental factors, genetics, gender and age, cultural and socio-economic variables. The system will be designed such that the core parameters as a determining factor should supply their value.

Looking for a free Walmart product dataset? The Walmart Products Free Dataset delivers a ready-to-use ecommerce product data CSV containing ~2,100 verified product records from Walmart.com. It includes vital details like product titles, prices, categories, brand info, availability, and descriptions — perfect for data analysis, price comparison, market research, or building machine-learning models.

Key Features

Complete Product Metadata: Each entry includes URL, title, brand, SKU, price, currency, description, availability, delivery method, average rating, total ratings, image links, unique ID, and timestamp.

CSV Format, Ready to Use: Download instantly - no need for scraping, cleaning or formatting.

Good for E-commerce Research & ML: Ideal for product cataloging, price tracking, demand forecasting, recommendation systems, or data-driven projects.

Free & Easy Access: Priced at USD $0.0, making it a great starting point for developers, data analysts or students.

Who Benefits?

• Data analysts & researchers exploring e-commerce trends or product catalog data.
• Developers & data scientists building price-comparison tools, recommendation engines or ML models.
• E-commerce strategists/marketers need product metadata for competitive analysis or market research.
• Students/hobbyists needing a free dataset for learning or demo projects.

Why Use This Dataset Instead of Manual Scraping?

• Time-saving: No need to write scrapers or deal with rate limits.
• Clean, structured data: All records are verified and already formatted in CSV, saving hours of cleaning.
• Risk-free: Avoid Terms-of-Service issues or IP blocks that come with manual scraping.
  Instant access: Free and immediately downloadable.

Stay ahead with our comprehensive News Dataset, designed for businesses, analysts, and researchers to track global events, monitor media trends, and extract valuable insights from news sources worldwide.

Dataset Features

News Articles: Access structured news data, including headlines, summaries, full articles, publication dates, and source details. Ideal for media monitoring and sentiment analysis. Publisher & Source Information: Extract details about news publishers, including domain, region, and credibility indicators. Sentiment & Topic Classification: Analyze news sentiment, categorize articles by topic, and track emerging trends in real time. Historical & Real-Time Data: Retrieve historical archives or access continuously updated news feeds for up-to-date insights.

Customizable Subsets for Specific Needs Our News Dataset is fully customizable, allowing you to filter data based on publication date, region, topic, sentiment, or specific news sources. Whether you need broad coverage for trend analysis or focused data for competitive intelligence, we tailor the dataset to your needs.

Popular Use Cases

Media Monitoring & Reputation Management: Track brand mentions, analyze media coverage, and assess public sentiment. Market & Competitive Intelligence: Monitor industry trends, competitor activity, and emerging market opportunities. AI & Machine Learning Training: Use structured news data to train AI models for sentiment analysis, topic classification, and predictive analytics. Financial & Investment Research: Analyze news impact on stock markets, commodities, and economic indicators. Policy & Risk Analysis: Track regulatory changes, geopolitical events, and crisis developments in real time.

Whether you're analyzing market trends, monitoring brand reputation, or training AI models, our News Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

This dataset contains all data and analysis scripts pertaining to the research conducted for the PLOSOne paper: “Natural language processing for cognitive therapy: extracting schemas from thought records.” The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. To date, the schemas underlying such thought records have been largely manually identified. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. We used the Amazon Mechanical Turk crowd sourcing platform to collect a set of 1600 thought records. In total, these thought records contain 5747 thoughts of various depth levels, with the automatic thought constituting the most shallow level and the core belief the deepest level. We here deliver:
1. a natural language dataset: the thoughts delineated by participants in the scenario-based and open thought records2. reliability analyses: all thoughts were labeled with respect to the degree to which they reflect a set of 9 possible schemas by the first author. An independent second coder also labeled a sample of the thoughts.3. analyses to determine whether automatic identification of thoughts is possible.4. additional materials (scenarios, instruction videos, qualtrics survey, osf preregistration form) that could assist in the replication of the study.

analyze the health and retirement study (hrs) with r the hrs is the one and only longitudinal survey of american seniors. with a panel starting its third decade, the current pool of respondents includes older folks who have been interviewed every two years as far back as 1992. unlike cross-sectional or shorter panel surveys, respondents keep responding until, well, death d o us part. paid for by the national institute on aging and administered by the university of michigan's institute for social research, if you apply for an interviewer job with them, i hope you like werther's original. figuring out how to analyze this data set might trigger your fight-or-flight synapses if you just start clicking arou nd on michigan's website. instead, read pages numbered 10-17 (pdf pages 12-19) of this introduction pdf and don't touch the data until you understand figure a-3 on that last page. if you start enjoying yourself, here's the whole book. after that, it's time to register for access to the (free) data. keep your username and password handy, you'll need it for the top of the download automation r script. next, look at this data flowchart to get an idea of why the data download page is such a righteous jungle. but wait, good news: umich recently farmed out its data management to the rand corporation, who promptly constructed a giant consolidated file with one record per respondent across the whole panel. oh so beautiful. the rand hrs files make much of the older data and syntax examples obsolete, so when you come across stuff like instructions on how to merge years, you can happily ignore them - rand has done it for you. the health and retirement study only includes noninstitutionalized adults when new respondents get added to the panel (as they were in 1992, 1993, 1998, 2004, and 2010) but once they're in, they're in - respondents have a weight of zero for interview waves when they were nursing home residents; but they're still responding and will continue to contribute to your statistics so long as you're generalizing about a population from a previous wave (for example: it's possible to compute "among all americans who were 50+ years old in 1998, x% lived in nursing homes by 2010"). my source for that 411? page 13 of the design doc. wicked. this new github repository contains five scripts: 1992 - 2010 download HRS microdata.R loop through every year and every file, download, then unzip everything in one big party impor t longitudinal RAND contributed files.R create a SQLite database (.db) on the local disk load the rand, rand-cams, and both rand-family files into the database (.db) in chunks (to prevent overloading ram) longitudinal RAND - analysis examples.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create tw o database-backed complex sample survey object, using a taylor-series linearization design perform a mountain of analysis examples with wave weights from two different points in the panel import example HRS file.R load a fixed-width file using only the sas importation script directly into ram with < a href="http://blog.revolutionanalytics.com/2012/07/importing-public-data-with-sas-instructions-into-r.html">SAScii parse through the IF block at the bottom of the sas importation script, blank out a number of variables save the file as an R data file (.rda) for fast loading later replicate 2002 regression.R connect to the sql database created by the 'import longitudinal RAND contributed files' program create a database-backed complex sample survey object, using a taylor-series linearization design exactly match the final regression shown in this document provided by analysts at RAND as an update of the regression on pdf page B76 of this document . click here to view these five scripts for more detail about the health and retirement study (hrs), visit: michigan's hrs homepage rand's hrs homepage the hrs wikipedia page a running list of publications using hrs notes: exemplary work making it this far. as a reward, here's the detailed codebook for the main rand hrs file. note that rand also creates 'flat files' for every survey wave, but really, most every analysis you c an think of is possible using just the four files imported with the rand importation script above. if you must work with the non-rand files, there's an example of how to import a single hrs (umich-created) file, but if you wish to import more than one, you'll have to write some for loops yourself. confidential to sas, spss, stata, and sudaan users: a tidal wave is coming. you can get water up your nose and be dragged out to sea, or you can grab a surf board. time to transition to r. :D