BackgroundWomen continue to have worse Coronary Artery Disease (CAD) outcomes than men. The causes of this discrepancy have yet to be fully elucidated. The main objective of this study is to detect gender discrepancies in the diagnosis and treatment of CAD.MethodsWe used data analytics to risk stratify ~32,000 patients with CAD of the total 960,129 patients treated at the UCSF Medical Center over an 8 year period. We implemented a multidimensional data analytics framework to trace patients from admission through treatment to create a path of events. Events are any medications or noninvasive and invasive procedures. The time between events for a similar set of paths was calculated. Then, the average waiting time for each step of the treatment was calculated. Finally, we applied statistical analysis to determine differences in time between diagnosis and treatment steps for men and women.ResultsThere is a significant time difference from the first time of admission to diagnostic Cardiac Catheterization between genders (p-value = 0.000119), while the time difference from diagnostic Cardiac Catheterization to CABG is not statistically significant.ConclusionWomen had a significantly longer interval between their first physician encounter indicative of CAD and their first diagnostic cardiac catheterization compared to men. Avoiding this delay in diagnosis may provide more timely treatment and a better outcome for patients at risk. Finally, we conclude by discussing the impact of the study on improving patient care with early detection and managing individual patients at risk of rapid progression of CAD.

We use content analysis to compare local media coverage with central media coverage in Chengdu, the main city of the protests, 6 online media platforms and 987 covid-related tweets are included to analyze the crisis coverage gap. We encode tweets and tables present the result of data analysis.

This data set contains frequency counts of target words in 16 million news and opinion articles from 10 popular news media outlets in the United Kingdom: The Guardian, The Times, The Independent, The Daily Mirror, BBC, Financial Times, Metro, Telegraph, The and The Daily Mail plus a few additional American-based outlets used for comparison reference. The target words are listed in the associated manuscript and are mostly words that denote some type of prejudice, social justice related terms or counterreaction to it. A few additional words are also available since they are used in the manuscript for illustration purposes.

The textual content of news and opinion articles from the outlets listed in Figure 3 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 derived relative 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.

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 million words of article content from an outlet.

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.

The list of compressed files in this data set is listed next:

-analysisScripts.rar contains the analysis scripts used in the main manuscript

-targetWordsInArticlesCounts.rar contains counts of target words in outlets articles as well as total counts of words in articles

-targetWordsInArticlesCountsGuardianExampleWords contains counts of target words in outlets articles as well as total counts of words in articles for illustrative Figure 1 in main manuscript

Usage Notes

In a small percentage of articles, outlet specific XPath expressions can fail 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.

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. To conclude, in a data analysis of 16 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 of main manuscript for supporting evidence).

This data set contains frequency counts of target words in 16 million news and opinion articles from 10 popular news media outlets in the United Kingdom. The target words are listed in the associated report and are mostly words that denote prejudice or are often associated with social justice discourse. A few additional words not denoting prejudice are also available since they are used in the report for illustration purposes of the method.

The textual content of news and opinion articles from the outlets 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.

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 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.

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.

In a small percentage of articles, outlet specific XPath expressions might fail 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.

Most of the incorrect frequency counts are often 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.To conclude, in a data analysis of over 16 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 2 of main manuscript for supporting evidence of the temporal precision of the method).

This data set belongs to an academic manuscript examining longitudinally (2000-2019) the prevalence of terms denoting far-right and far-left political extremism in a large corpus of more than 32 million written news and opinion articles from 54 news media outlets popular in the United States and the United Kingdom.

The textual content of news and opinion articles from the 54 outlets listed in 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.

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 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.

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.

The list of compressed files in this data set is listed next:

-analysisScripts.rar contains the analysis scripts used in the main manuscript

-articlesContainingTargetWords.rar contains counts of target words in outlets articles as well as total counts of words in articles

Usage Notes

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.

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 32 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 in the main manuscript for illustration of the accuracy of the frequency counts).

Stomach contents analysis is a standard dietary assessment method that potentially enables quantifying diet components with high taxonomic resolution. We compiled diet compositions from stomach content analysis from 75 unique species or genera: 32 fish, 19 marine mammals, 14 elasmobranchs, 9 seabirds and one marine turtle. Data were gathered from 89 published sources that included samples collected between 1885 and 2016 throughout the central and Northeast Atlantic, and the Mediterranean Sea. When available, we reported the percentage number of individuals of a prey type as a proportion of the total number of prey items (%N), the proportion of a prey item by weight (%W), and the proportion of stomachs containing a particular prey item (i.e. percent frequency of occurrence, %F). For each data record, we also provided the sampling location, geographic coordinates, month and year of sample collection, method of sample collection, taxonomic ranks (phylum, class, order, family), number and size (or size range) of sampled organisms, as well as the reference and DOI of the original data source, for further details on the samples analysed and/or the analytical techniques used.

Components obtained from each block of questions having applied a factorial analysis.