Read, Hot & Digitized: Visualizing Wikipedia’s Gender Gap | TexLibris

“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….”

Knowledge curation work in Wikidata WikiProject discussions | Emerald Insight

Abstract:  Purpose

The purpose of this paper is to investigate how editors participate in Wikidata and how they organize their work.


This qualitative study used content analysis of discussions involving data curation and negotiation in Wikidata. Activity theory was used as a conceptual framework for data collection and analysis.


The analysis identified six activities: conceptualizing the curation process, appraising objects, ingesting objects from external sources, creating collaborative infrastructure, re-organizing collaborative infrastructure and welcoming newcomers. Many of the norms and rules that were identified help regulate the activities in Wikidata.

Research limitations/implications

This study mapped Wikidata activities to curation and ontology frameworks. Results from this study provided implications for academic studies on online peer-curation work.

Practical implications

An understanding of the activities in Wikidata will help inform communities wishing to contribute data to or reuse data from Wikidata, as well as inform the design of other similar online peer-curation communities, scientific research institutional repositories, digital archives and libraries.


Wikidata is one of the largest knowledge curation projects on the web. The data from this project are used by other Wikimedia projects such as Wikipedia, as well as major search engines. This study explores an aspect of Wikidata WikiProject editors to the author’s knowledge has yet to be researched.

Visualizing the research ecosystem via Wikidata and Scholia | Zenodo

“Research takes place in a sociotechnical ecosystem that connects researchers with the objects of study and the natural and cultural worlds around them.

Wikidata is a community-curated open knowledge base in which concepts covered in any Wikipedia — and beyond — can be described and annotated collaboratively.

This session is devoted to demoing Scholia, an open-source tool to visualize the global research ecosystem based on information in Wikidata about research fields, researchers, institutions, funders, databases, locations, publications, methodologies and related concepts….”

Call 2020 Librarian Community Call – OpenCon

“This talk will focus on discussing the Scholarly Profiles as Service (SPaS) model developed and implemented at Indiana University-Purdue University Indianapolis. The SPaS model aims to provide representation for IUPUI-affiliated faculty and their scholarly research in Wikidata. By sharing these data in the knowledge base, IUPUI University Library is actively contributing to the growth of the bibliographic citation ecosystem in a repository that is free and open.


This call brings together all librarians working with, or learning about, all things Open–and gives folks an opportunity to connect with each other to better their work and librarianship. …”

Award ceremony for the best PhD theses during the IwZ’2020 ? Lewoniewski

“In the 23rd Edition of the Scientific Competition of the Economic Informatics Society in the group of doctoral dissertations, the third place was awarded to the work “The method of comparing and enriching information in multilingual wikis based on the analysis of their quality“. Author of the thesis: Dr. W?odzimierz Lewoniewski; The thesis supervisor: Prof. Witold Abramowicz; the auxiliary supervisor: Prof. Krzysztof W?cel.

The doctoral dissertation presents methods and tools that allow to determine the values of quality measures on the basis of data in various formats and with the use of various sources. As part of scientific research, data with a total volume of over 10 terabytes were analyzed and over a billion values of information quality measures were obtained from the multilingual Wikipedia. The automatic quality assessment models presented in the doctoral dissertation can be used not only to automatically enrich various language versions of Wikipedia, but also to enrich other knowledge bases (such as DBpedia, Wikidata) with information of better quality….”

Nitpicking online knowledge representations of governmental leadership. The case of Belgian prime ministers in Wikipedia and Wikidata.

Abstract:  A key pitfall for knowledge-seekers, particularly in the political arena, is informed complacency, or an over-reliance on search engines at the cost of epistemic curiosity. Recent scholarship has documented significant problems with those sources of knowledge that the public relies on the most, including instances of ideological and algorithmic bias in Wikipedia and Google. Such observations raise the question of how deep one would actually need to dig into these platforms’ representations of factual (historical and biographical) knowledge before encountering similar epistemological issues. The present article addresses this question by ‘nitpicking’ knowledge representations of governments and governmental leadership in Wikipedia and Wikidata. Situated within the emerging framework of ‘data studies’, our micro-level analysis of the representations of Belgian prime ministers and their governments thereby reveals problems of classification, naming and linking of biographical items that go well beyond the affordances of the platforms under discussion. This article thus makes an evidence-based contribution to the study of the fundamental challenges that mark the formalisation of knowledge in the humanities.


Pop! Familiar Wikidata

“Wikipedia is far from perfect. The same can be said of its sister project, Wikidata. And yet, excluding the World Wide Web itself, Wikipedia and Wikidata together represent the world’s largest structured humanities data source. This methods paper offers an introduction to the value of Wikidata for humanities research and makes the case for humanities researchers’ intervention in its development. It concludes with a short case study to illustrate how Wikidata can support humanities research projects. The case study project, Linked Familiarity, uses Wikidata data about the people quoted in the first ten editions of Bartlett’s Familiar Quotations to look for patterns in the people Bartlett’s Familiar editorial team thought readers find quotable from 1855 and 1910. These patterns will, we hope, clarify a corner of the zeitgeist: Bartlett’s Familiar Quotations readers voted with their purchases—the book’s popularity suggests the quotes the volume’s editorial team compiled really did meet a public desire, or even need. The Linked Familiarity’s team is using Wikidata data to find out about the people worth quoting in this 55-year stretch, to examine the characteristics that unite them, and to uncover the outliers….”