A large scale study of reader interactions with images on Wikipedia

AbstractWikipedia is the largest source of free encyclopedic knowledge and one of the most visited sites on the Web. To increase reader understanding of the article, Wikipedia editors add images within the text of the article’s body. However, despite their widespread usage on web platforms and the huge volume of visual content on Wikipedia, little is known about the importance of images in the context of free knowledge environments. To bridge this gap, we collect data about English Wikipedia reader interactions with images during one month and perform the first large-scale analysis of how interactions with images happen on Wikipedia. First, we quantify the overall engagement with images, finding that one in 29 pageviews results in a click on at least one image, one order of magnitude higher than interactions with other types of article content. Second, we study what factors associate with image engagement and observe that clicks on images occur more often in shorter articles and articles about visual arts or transports and biographies of less well-known people. Third, we look at interactions with Wikipedia article previews and find that images help support reader information need when navigating through the site, especially for more popular pages. The findings in this study deepen our understanding of the role of images for free knowledge and provide a guide for Wikipedia editors and web user communities to enrich the world’s largest source of encyclopedic knowledge.

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SKU performance and distribution: A large-scale analysis of the role of product characteristics with store scanner data

Journal of Retailing and Consumer Services ◽

10.1016/j.jretconser.2021.102533 ◽

2021 ◽

Vol 61 ◽

pp. 102533

Author(s):

Martin Hirche ◽

Luke Greenacre ◽

Magda Nenycz-Thiel ◽

Simone Loose ◽

Larry Lockshin

Keyword(s):

Large Scale ◽

Scale Analysis ◽

Scanner Data ◽

Product Characteristics ◽

Large Scale Analysis

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Evidence of Open Access of scientific publications in Google Scholar: a large-scale analysis

10.31235/osf.io/k54uv ◽

2018 ◽

Author(s):

Alberto Martín-Martín ◽

Rodrigo Costas ◽

Thed van Leeuwen ◽

Emilio Delgado López-Cózar

Keyword(s):

Open Access ◽

Large Scale ◽

Web Of Science ◽

Google Scholar ◽

Scale Analysis ◽

Scientific Publications ◽

Large Scale Analysis ◽

Citation Indexes ◽

Legal Forms ◽

The Web

This article uses Google Scholar (GS) as a source of data to analyse Open Access (OA) levels across all countries and fields of research. All articles and reviews with a DOI and published in 2009 or 2014 and covered by the three main citation indexes in the Web of Science (2,269,022 documents) were selected for study. The links to freely available versions of these documents displayed in GS were collected. To differentiate between more reliable (sustainable and legal) forms of access and less reliable ones, the data extracted from GS was combined with information available in DOAJ, CrossRef, OpenDOAR, and ROAR. This allowed us to distinguish the percentage of documents in our sample that are made OA by the publisher (23.1%, including Gold, Hybrid, Delayed, and Bronze OA) from those available as Green OA (17.6%), and those available from other sources (40.6%, mainly due to ResearchGate). The data shows an overall free availability of 54.6%, with important differences at the country and subject category levels. The data extracted from GS yielded very similar results to those found by other studies that analysed similar samples of documents, but employed different methods to find evidence of OA, thus suggesting a relative consistency among methods.

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Biochemical and Computational Approaches for the Large-Scale Analysis of Protein Arginine Methylation by Mass Spectrometry

Current Protein and Peptide Science ◽

10.2174/1389203721666200426232531 ◽

2020 ◽

Vol 21 (7) ◽

pp. 725-739

Author(s):

Daniele Musiani ◽

Enrico Massignani ◽

Alessandro Cuomo ◽

Avinash Yadav ◽

Tiziana Bonaldi

Keyword(s):

Mass Spectrometry ◽

Large Scale ◽

High Resolution Mass Spectrometry ◽

Arginine Methylation ◽

Research Field ◽

Scale Analysis ◽

Post Translational Modification ◽

Large Scale Analysis ◽

Protein Arginine Methylation

: The absence of efficient mass spectrometry-based approaches for the large-scale analysis of protein arginine methylation has hindered the understanding of its biological role, beyond the transcriptional regulation occurring through histone modification. In the last decade, however, several technological advances of both the biochemical methods for methylated polypeptide enrichment and the computational pipelines for MS data analysis have considerably boosted this research field, generating novel insights about the extent and role of this post-translational modification. : Here, we offer an overview of state-of-the-art approaches for the high-confidence identification and accurate quantification of protein arginine methylation by high-resolution mass spectrometry methods, which comprise the development of both biochemical and bioinformatics methods. The further optimization and systematic application of these analytical solutions will lead to ground-breaking discoveries on the role of protein methylation in biological processes.

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A large-scale analysis of alternative splicing reveals a key role of QKI in lung cancer

Molecular Oncology ◽

10.1016/j.molonc.2016.08.001 ◽

2016 ◽

Vol 10 (9) ◽

pp. 1437-1449 ◽

Cited By ~ 36

Author(s):

Fernando J. de Miguel ◽

María J. Pajares ◽

Elena Martínez-Terroba ◽

Daniel Ajona ◽

Xabier Morales ◽

...

Keyword(s):

Lung Cancer ◽

Alternative Splicing ◽

Large Scale ◽

Scale Analysis ◽

Large Scale Analysis

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Free and Open Source Software organizations: A large-scale analysis of code, comments, and commits frequency

PLoS ONE ◽

10.1371/journal.pone.0257192 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0257192

Author(s):

Tadeusz Chełkowski ◽

Dariusz Jemielniak ◽

Kacper Macikowski

Keyword(s):

Open Source ◽

Open Source Software ◽

Large Scale ◽

Source Code ◽

Business Strategies ◽

Scale Analysis ◽

Large Scale Analysis ◽

Global Corporations ◽

Frequency Source

As Free and Open Source Software (FOSS) increases in importance and use by global corporations, understanding the dynamics of its communities becomes critical. This paper measures up to 21 years of activities in 1314 individual projects and 1.4 billion lines of code managed. After analyzing the FOSS activities on the projects and organizations level, such as commits frequency, source code lines, and code comments, we find that there is less activity now than there was a decade ago. Moreover, our results suggest a greater decrease in the activities in large and well-established FOSS organizations. Our findings indicate that as technologies and business strategies related to FOSS mature, the role of large formal FOSS organizations serving as intermediary between developers diminishes.

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