“However, Wikipedia has a long-standing problem of gender imbalance both in terms of article content and editor demographics. Only 18% of content across Wikimedia platforms are about women. The gaps on content covering non-binary and transgender individuals are even starker: less than 1% of editors identify as trans, and less than 1% of biographies cover trans or nonbinary individuals. When gender is combined with other factors, such as race, nationality, or ethnicity, the numbers get even lower. This gender inequity has long been covered in the scholarly literature via editor surveys and analysis of article content (Hill and Shaw, 2013; Graells-Garrido, Lalmas, and Menczer, 2015; Bear and Collier, 2016; Wagner, Graells-Garrido, Garcia, and Menczer, 2016; Ford and Wajcman, 2017). To visualize these inequalities in nearly real time, the Humaniki tool was developed….”
“Adding multimedia files to Wikipedia articles has never become a common practice as adding images, although there are topics that would clearly benefit more from having video and audio files. Many articles on scientific phenomena involving physical and chemical changes can not be properly explained through the staticity of images and require dynamicity that can be provided with videos. Furthermore, digital learning in education is increasing and it has especially gained momentum during the COVID-19 pandemic when many educational systems switched to online learning. The latter underlines the importance of educational resources in digital form.
In order to address the foregoing issues, Shared Knowledge in collaboration with Ss. Cyril and Methodius University of Skopje started a project known as Wikiexperiments with the goal of recording and uploading free high-definition videos of scientific experiments for the purposes of illustrating important scientific concepts and phenomena across the Wikimedia projects. After a year-long break, the project that began in September 2015 continued with new recordings produced throughout 2020 that brought the total number of physics and chemistry experiments recorded so far up to 90….”
“We’re absolutely thrilled to be joining forces with Wiki Education to offer GO-GN’ers training in Wikipedia editing and the chance to collaboratively improve the depth and coverage of open education topics on Wikipedia….”
Abstract: This project seeks to conduct language translation on metadata labels for research publications, attribution data, and clinical trials information to make data about medical research queriable in underserved languages through Wikidata and the Linked Open Web. This project has the benefit of distributing content through Wikipedia and Wikidata, which already have an annual userbase of a billion users and which already have established actionable standards to practice diversity, inclusion, openness, FAIRness, and transparency about program development. The impact will be localized access to basic research information in various Global South languages to integrate with existing community efforts for establishing the same. Although Wikidata development in this direction seems inevitable, the cultural and social exchange required to establish global multilingual research partnerships could begin now with support rather than later as a second phase effort for including the developing world. Wikipedia and Wikidata are established forums with an existing active userbase for multilingual research collaboration, but the research practices there still are immature. By applying metadata expertise through this project, we will elevate the current amateur development with more stable Linked Open Data compatibility to English language databases. Using the wiki distribution and discussion platform to develop the global conversation about data sharing will set good precedents for the trend of global research collaboration.
Abstract: With the COVID-19 pandemic’s outbreak at the beginning of 2020, millions across the world flocked to Wikipedia to read about the virus. Our study offers an in-depth analysis of the scientific backbone supporting Wikipedia’s COVID-19 articles. Using references as a readout, we asked which sources informed Wikipedia’s growing pool of COVID-19-related articles during the pandemic’s first wave (January-May 2020). We found that coronavirus-related articles referenced trusted media sources and cited high-quality academic research. Moreover, despite a surge in preprints, Wikipedia’s COVID-19 articles had a clear preference for open-access studies published in respected journals and made little use of non-peer-reviewed research uploaded independently to academic servers. Building a timeline of COVID-19 articles on Wikipedia from 2001-2020 revealed a nuanced trade-off between quality and timeliness, with a growth in COVID-19 article creation and citations, from both academic research and popular media. It further revealed how preexisting articles on key topics related to the virus created a framework on Wikipedia for integrating new knowledge. This “scientific infrastructure” helped provide context, and regulated the influx of new information into Wikipedia. Lastly, we constructed a network of DOI-Wikipedia articles, which showed the landscape of pandemic-related knowledge on Wikipedia and revealed how citations create a web of scientific knowledge to support coverage of scientific topics like COVID-19 vaccine development. Understanding how scientific research interacts with the digital knowledge-sphere during the pandemic provides insight into how Wikipedia can facilitate access to science. It also sheds light on how Wikipedia successfully fended of disinformation on the COVID-19 and may provide insight into how its unique model may be deployed in other contexts.
“Katherine Maher, the Chief Executive Officer of the Wikimedia Foundation, the global nonprofit that operates Wikipedia, will be leaving the organization in April.
Under her leadership, Wikipedia attained its highest public trust in the institution’s history. Since becoming CEO in 2016, Katherine defined an expansive strategic direction for Wikimedia’s next decade, significantly expanded Wikipedia’s presence in emerging markets, increased the diversity and number of editors, significantly grew readership and contributors across Wikipedia and its sister free knowledge projects, and solidified the financial position and future of the Wikimedia movement….”
“In celebration of Wikipedia’s 20th anniversary on January 15th, Boston Public Library has uploaded more than 8,000 historical photographs from its archival collections to Wikimedia Commons. These images include some of the library’s most important photographic collections, and contribute to the single largest batch of uploads ever contributed to Wikimedia Commons. By uploading these public domain images, BPL is making them available so that they can be freely used to enhance Wikipedia articles, re-printed in publications, or incorporated in student projects and papers. …”
“Wikipedia, the world’s largest free online encyclopedia, turns 20 years old on 15 January. This birthday commemorates two decades of global efforts to support free knowledge, open collaboration, and trust on the internet. In a time when disinformation and polarization challenge our trust in information and institutions, Wikipedia is more relevant than ever. Wikipedia celebrates its past and looks ahead to how it will meet the challenges of tomorrow to grow into a more resilient, equitable knowledge resource….”
“More intriguing than Wikipedia itself was, and remains, the idea at its core: that the Internet can be a place not just for communication and entertainment, but collaboration and truth-seeking. It has rightfully been hailed many times as the pinnacle achievement of the philosophy of the “open web,” which has many definitions, but to me simply means: you can do almost anything here, together, without corporate influence. Today, it’s the open web’s last stand—Apple, Google and Amazon’s decision to ban right-wing darling social media app Parler from their respective platforms and services was absolutely the right choice, but also made abundantly clear that the days of hoping for a truly open web have long since passed….”
Abstract: Identifying important scholarly literature at an early stage is vital to the academic research community and other stakeholders such as technology companies and government bodies. Due to the sheer amount of research published and the growth of ever-changing interdisciplinary areas, researchers need an efficient way to identify important scholarly work. The number of citations a given research publication has accrued has been used for this purpose, but these take time to occur and longer to accumulate. In this article, we use altmetrics to predict the short-term and long-term citations that a scholarly publication could receive. We build various classification and regression models and evaluate their performance, finding neural networks and ensemble models to perform best for these tasks. We also find that Mendeley readership is the most important factor in predicting the early citations, followed by other factors such as the academic status of the readers (e.g., student, postdoc, professor), followers on Twitter, online post length, author count, and the number of mentions on Twitter, Wikipedia, and across different countries.