Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
29
datasets available to search
ShareScore release 0.9.0
Dataset results
29 results for “News Data”
Data for manuscript: "Prevalence of prejudice denoting words in news media discourse: a chronological analysis"
<p>This data set contains frequency counts of target words in 27 million news and opinion articles from 47 popular news media outlets in the United States. The target words are listed in the associated manuscript and are mostly words that denote some type of prejudice. A few additional words not denoting prejudice are also available since they are used in the manuscript for illustration purposes.</p> <p>The textual content of news and opinion articles from the outlets listed in Figure 4 of the main manuscript is available in the outlet's online domains and/or public cache repositories such as Google cache (https://webcache.googleusercontent.com), The Internet Wayback Machine (https://archive.org/web/web.php), and Common Crawl (https://commoncrawl.org). We used derived word frequency counts from these sources. Textual content included in our analysis is circumscribed to articles headlines and main body of text of the articles and does not include other article elements such as figure captions.</p> <p>Targeted textual content was located in HTML raw data using outlet specific xpath expressions. Tokens were lowercased prior to estimating frequency counts. To prevent outlets with sparse text content for a year from distorting aggregate frequency counts, we only include outlet frequency counts from years for which there is at least 1.25 million words of article content from an outlet. This threshold was chosen to maximize inclusion in our analysis of outlets with sparse amounts of articles text per year such as Reason, Alternet or The American Spectator. </p> <p>Yearly frequency usage of a target word in an outlet in any given year was estimated by dividing the total number of occurrences of the target word in all articles of a given year by the number of all words in all articles of that year. This method of estimating frequency accounts for variable volume of total article output over time.</p> <p>The list of compressed files in this data set is listed next:</p> <p>-analysisScripts.rar contains the analysis scripts used in the main manuscript </p> <p>-articlesContainingTargetWords.rar contains counts of target words in outlets articles as well as total counts of words in articles</p> <p>-cableNews.rar contains prevalence of target words in TV cable news. Data is from Stanford Cable TV News Analyzer (https://tvnews.stanford.edu/)</p> <p>-surveyData.rar contains longitudinal survey data used in the manuscript and links to original sources</p> <p>Usage Notes</p> <p>In a small percentage of articles, outlet specific XPath expressions failed to properly capture the content of the article due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. As a result, the total and target word counts metrics for a small subset of articles are not precise. In a random sample of articles and outlets, manual estimation of target words counts overlapped with the automatically derived counts for over 90% of the articles.</p> <p>Most of the incorrect frequency counts were minor deviations from the actual counts such as for instance counting the word "Facebook" in an article footnote encouraging article readers to follow the journalist’s Facebook profile and that the XPath expression mistakenly included as the content of the article main text. Some additional outlet-specific inaccuracies that we could identify occurred in "The Hill" and "Newsmax" news outlets where XPath expressions had some shortfalls at precisely capturing articles’ content. For "The Hill", in years 2007-2009, XPath expressions failed to capture the complete text of the article in about 40% of the articles. This does not necessarily result in incorrect frequency counts for that outlet but in a sample of articles’ words that is about 40% smaller than the total population of articles words for those three years. In the case of "NewsMax", the issue was that for some articles, XPath expressions captured the entire text of the article twice. Notice that this does not result in incorrect frequency counts. If a word appears x times in an article with a total of y words, the same frequency count will still be derived when our scripts count the word 2x times in the version of the article with a total of 2y words. To conclude, in a data analysis of 27 million articles, we cannot manually check the correctness of frequency counts for every single article and hundred percent accuracy at capturing articles’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our frequency metrics are representative of word prevalence in print news media content (see Figure 1 and Figure 2 of main manuscript for supporting evidence).</p>
Data from: Spotted in the news: using media reports to examine leopard distribution, depredation, and management practices outside protected areas in southern India
Open the record for dataset details and reuse information.
Data from: Combination of the National Early Warning Score (NEWS) and inflammatory biomarkers for early risk stratification in emergency department patients: results of a multi-national, observational study
Open the record for dataset details and reuse information.
Data from: Discovering periodic patterns in historical news
Open the record for dataset details and reuse information.
Data of A Study on the Presentation of China's COVID-19 News in American Mainstream Media: Party Logic and Mirror Image Effect Hypothesis
Open the record for dataset details and reuse information.
FactDrill: A Data Repository of Fact-checked Social Media Content to Study Fake News Incidents in India
<p>A dataset containing 22,435 fact-checked social media content to study fake news incidents in India. The dataset comprises news stories from 2013 to the year 2020, covering 13 different languages spoken in the country. There are 14 different attributes present in the dataset.</p>
Unlocking the data in BBC news
<p>Unlocking the data in BBC news</p>
Data and code for project "What makes users click: the effect of news values negativity and surprise in news headlines on the CTR"
<p>This repository includes the following files and corresponding Python codes saved in Jupiter Notebooks:</p> <p><strong>upworthy-archive-confirmatory-packages-03.12.2020.csv:</strong> Original dataset downloaded from The Upworthy Research Archive (https://osf.io/jd64p/)</p> <p><strong>cleaned_data.csv</strong>: <strong>upworthy-archive-confirmatory-packages-03.12.2020.csv</strong> cleaned with <strong>notebook_clean_data.ipynb</strong></p> <ul> <li>Data sorted by <em>clickability_test_id</em></li> <li>Headlines with 0 clicks deleted</li> <li>Variables <em>ctr</em>, <em>mean_ctr</em>, and <em>lift</em> created</li> <li>Variable <em>winner</em> converted to dummy variable</li> <li>Only kept first occurrence of headlines that occur multiple times in the dataset per test</li> <li>Deleted tests with only one headline</li> </ul> <p><strong>sample_manual_annotations.csv</strong>: sample of 300 headlines of <strong>cleaned_data.csv </strong>retrieved with <strong>notebook_sample_manual_annotations.ipynb</strong></p> <p><strong>inter-annotator_sentiment.csv</strong>: input for <strong>notebook_inter-annotator_scores_confusion_matrix.ipynb</strong> to calculate inter-annotator agreement scores with regard to annotating sentiment</p> <p><strong>inter-annotator_emotion.csv</strong>: input for <strong>notebook_inter-annotator_scores_confusion_matrix.ipynb</strong> to calculate inter-annotator agreement scores with regard to annotating emotion</p> <p><strong>input_sentiment.tsv</strong>: input for <strong>notebook_sentiment_emotion_test_set.ipynb </strong>for sentiment analysis on sample of 300 headlines in <strong>sample_manual_annotations.csv</strong></p> <p><strong>input_emotion.tsv</strong>: input for <strong>notebook_sentiment_emotion_test_set.ipynb </strong>for emotion analysis on sample of 300 headlines in <strong>sample_manual_annotations.csv</strong></p> <p><strong>input_all_headlines.tsv</strong>: input for <strong>notebook_sentiment_emotion_final_annotation.ipynb</strong> to annotate all headlines in <strong>cleaned_data.csv</strong></p> <p><strong>final_dataset.csv</strong>: output from <strong>notebook_merge_dataframes.ipynb</strong> that merges all csv output files from <strong>notebook_sentiment_emotion_final_annotation.ipynb </strong>with <strong>cleaned_data.csv</strong></p> <p><strong>notebook_descriptive_statistics.ipynb</strong>: Python code to retrieve descriptive statistics of various variables in <strong>cleaned_data.csv</strong></p> <p><strong>NRC-Emotion-Lexicon-Wordlevel-v0.92.txt:</strong> NRC Emotion Lexicon used in <strong>notebook_sentiment_emotion_test_set.ipynb </strong>and<strong> notebook_sentiment_emotion_final_annotation.ipynb</strong></p>
Restricted access data for the paper Fake News on Twitter During the 2016 U.S. Presidential Election
<p>Restricted access data for replicating results in the paper Fake News on Twitter During the 2016 U.S. Presidential Election.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.