This general research goal refers to the activity of extracting any kind of information from open or closed, structured or unstructured collections of data, of discovering recurring phenomena, units, elements, patterns, groupings, and the like. This can refer to structural, formal or semantic aspects of data. Analysis also includes methods used to visualize results. Methods and techniques related to this goal may be considered to follow Capture and Enrichment; however, Enrichment depends upon assumptions, research questions and results related to Analysis.
Mediate is a collaborative time-based media annotation tool for the web that can be used both individually and collaboratively for synchronous and asynchronous digital annotation. One of its highlighting features is accessibility and customization, i.e. the ability to customize the schema that forms the basis of the analysis or the purpose of the project.
[Click ‘Read more’ for the full post!]
The Chinese Text Project is a well-established resource in Sinology, providing open access to a large number of ancient Chinese texts. As a digital medium, it utilizes crowdsourcing, linked data, knowledge graph and other computational technologies to provide an interactive interface for users who are interested in ancient Chinese texts. Beyond its main aim of providing open access to Chinese literature and philosophy texts, the project features an integrated Chinese character dictionary tool, images of scanned source texts, a search function for parallel passages, and much more. In terms of structured data, the project’s data wiki contains a wealth of records on entities such as persons, locations, and works.
[Click ‘Read more’ for the full post!]
The Closing the Gap in non-Latin script data aims at mapping the field of digital humanities projects outside and beyond the anglosphere with a particular focus on non-Latin scripts such as Arabic or Chinese in both machine-actionable and human readable form. The urgency and value of such a survey has been highlighted in recent discussions around global, decolonial, and multilingual digital humanities.
Everyone of us is accustomed to reading academic contributions using the Latin alphabet, for which we have already standard characters and formats. But what about texts written in languages featuring different, ideographic-based alphabets (for example, Chinese and Japanese)? What kind of recognition techniques and metadata are necessary to adopt in order to represent them in a digital context?
Following our last post focusing on Critical Discourse Analysis, today we highlight an automated document enrichment pipeline for automated interview coding, proposed by Ajda Pretnar Žagar, Nikola Ðukic´, Rajko Muršic in their paper presented at the Conference on Language Technologies & Digital Humanities, Ljubljana 2022. As described in the “Essential Guide to Coding Qualitative Data” (https://delvetool.com/guide), one of the main field of application of such a procedure is Ethnography, but not only.
Thanks to qualitative data coding it is possible to enrich texts through adding labels and descriptions to specific passages, that are generally pinpointed by means of computer-assisted qualitative data analysis softwares (CAQDAS). This can be valid for several fields of applications, from the humanities to biology, from sociology to medicine.
In their paper, Pretnar Žagar, Ðukic´ and Muršicˇ illustrate how relying on a couple of taxonomies (or onthologies) already known in anthropological studies may represent an asset to automatize and hasten the process of data labelling. These taxonomies are the Outline of Cultural Materials (OCM) and the ETSEO (acronym for Ethnological Topography of Slovenian Ethnic Territory) systematics. In both cases we deal with taxonomies elaborated and applied in ethnographic research in order to organize and better analyze concepts and categories related to human cultures and traditions.
[Click ‘Read more’ for the full post!]
In this video, Drs. Stephanie Vie and Jennifer deWinter explain some of the tools digital humanists can use for critical discourse analysis and visualization of data collected from social media platforms. Although not all the tools they mention are open source, the majority of them have free to use or freemium versions, including AntConc, a free-to-use concordancing tool, or several Twitter data visualisation tools such as Tweeps map or Tweetstats.
Even though the video does not provide just-as-good open source alternatives to Atlas.ti or MAXQDA (an obviously a recurrent question or shortcoming that is recurrently discussed on OpenMethods), it sets an excellent example for how to introduce tool criticism in the classroom alongside introduction to certain Digital Humanities Tools. After briefly touching upon both advantages and disadvantages of each tool, they encourage their audience (students in Digital Humanities study programs) to pilot each of them by using the same data-set and not only compare their results but also reflect on the epistemic processes in-between.
Sharing the video on Humanities Commons with stable archiving, DOI and rich metadata is among the best things that could happen to teaching resources of all kinds.
Introduction: Folgert Karsdorp, Mike Kestemont and Allen Riddell ‘s interactive book, Humanities Data Analysis: Case Studies with Python had been written with the aim in mind to equip humanities students and scholars working with textual and tabular resources with practical, hands-on knowledge to better understand the potentials of data-rich, computer-assisted approaches that the Python framework offers to them and eventually to apply and integrate them to their own research projects.
The first part introduces a “Data carpentry”, a collection of essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. This sets the stage for the second part that consists of 5 case studies (Statistics Essentials: WhoReads Novels? ; Introduction to Probability ; Narrating with Maps ; Stylometry and the Voice of Hildegard ; A Topic Model of United States Supreme Court Opinions, 1900–2000 ) showcasing how to draw meaningful insights from data using quantitative methods. Each chapter contains executable Python codes and ends with exercises ranging from easier drills to more creative and complex possibilities to adapt the apply and adopt the newly acquired knowledge to their own research problems.
The book exhibits best practices in how to make digital scholarship available in an open, sustainable ad digital-native manner, coming in different layers that are firmly interlinked with each other. Published with Princeton University Press in 2021, hardcopies are also available, but more importantly, the digital version is an Open Access Jupyter notebook that can be read in multiple environments and formats (.md and .pdf). The documentation, coda and data materials are available on Zenodo (https://zenodo.org/record/3560761#.Y3tCcn3MJD9). The authors also made sure to select and use packages which are mature and actively maintained.
Introduction: This short video teaser summarizes the main characteristics of PixPlot, a Python-based tool for clustering images and analyzing them from a numerical perspective as well as its pedagogical relevance as far as
machine learning is concerned.
The paper “Visual Patterns Discovery in Large Databases of Paintings”, presented at the Digital Humanities 2016 Conference held in Poland,
can be considered the foundational text for the development of the PixPlot Project at Yale University.
[Click ‘Read more’ for the full post!]
In this post, we reach back in time to showcase an older project and highlight its impact on data visualization in Digital Humanities as well as its good practices to make different layers of scholarship available for increased transparency and reusability.
Developed at Stanford with other research partners (‘Cultures of Knowledge’ at Oxford, the Groupe d’Alembert at CNRS, the KKCC-Circulation of Knowledge and Learned Practices in the 17th-century Dutch Republic, the DensityDesign ResearchLab), the ‘Mapping of the Republic of Letters Project’ aimed at digitizing and visualizing the intellectual community throughout the XVI and XVIII centuries known as ‘Republic of Letters’ (an overview of the concept can be found in Bots and Waquet, 1997), to get a better sense of the shape, size and associated intellectual network, its inherent complexities and boundaries.
Below we highlight the different, interrelated
layers of making project outputs available and reusable on the long term (way before FAIR data became a widespread policy imperative!): methodological reflections, interactive visualizations, the associated data and its data model schema. All of these layers are published in a trusted repository and are interlinked with each other via their Persistent Identifiers.
[Click ‘Read more’ for the full post!]
The conversation below is a special, summer episode of our Spotlight series. It is a collaboration between OpenMethods and the Humanista podcast and this it comes as a podcast, in which Alíz Horváth, owner of the Humanista podcast series and proud Editorial Team member of OpenMethods, is asking Shih-Pei Chen, scholar and Digital Content Curator at the Max Plank Institute for the History of Science about the text analysis tools LoGaRT, RISE and SHINE; non-Latin scripted Digital Humanities, why local gazetteers are goldmines to Asian Studies, how digitization changes, broadens the kinds research questions one can study, where are the challenges in the access to cultural heritage and liaising with proprietary infrastructure providers… and many more! Enjoy!