WHAT IS DATA MANAGEMENT IN THE DIGITAL HUMANITIES?
Digital Humanities (DH) data are not the objects in themselves, but a digital representation of those objects, or of structured information related to them, which is created and analyzed by computational methods. Most digital projects rely on the synthesis, analysis, and visualization of data. Broadly defined, data are any information collected or created in order to answer a research question, and may include the primary objects of study, such as texts, paintings, documentary sources, surveys, and secondary literature. Large projects can even be crowd-sources from the general public.
In DH research, data often also constitute valuable outputs in the form of digital resources, such as, for example, encoded texts, databases, images of artifacts, and digital collections.
In DH projects, "data management" refers to the systematic process of organizing, storing, archiving, preserving, and sharing research data collected through digital technologies. This includes planning for the handling of data throughout its lifecycle from collection to analysis and dissemination, ensuring its accessibility and long-term usability for future research. This process is often called the research data lifecycle.
KEY ASPECTS OF DATA MANAGEMENT IN DH PROJECTS
Key aspects of data management in DH projects:
- Data Collection:
Identifying relevant data sources, whether digitized texts, images, audio recordings, or other digital artifacts, and implementing appropriate methods for data capture.
- Data Cleaning and Standardization:
Cleaning up messy data by correcting errors, standardizing formats, and applying consistent metadata to ensure data quality and interoperability.
- Data Storage and Archiving:
Selecting appropriate platforms or repositories to securely store digital data, considering file formats, version control, and long-term preservation strategies.
- Metadata Creation:
Developing detailed descriptive information about the data (e.g., origin, date, content, context) to facilitate discoverability and understanding.
- Data Analysis Tools:
Utilizing software applications like OpenRefine, text analysis tools, and data visualization platforms to analyze and interpret the data.
- Data Sharing and Dissemination:
Making research data accessible to the wider community through open repositories, data portals, or published digital projects.
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Examples of Digital Humanities Projects that heavily rely on data management:
- Corpus-based analysis of literary texts:
Building and analyzing large digital collections of texts to study language patterns and themes.
- Historical mapping projects:
Geo-referencing and visualizing historical data on maps to explore spatial relationships.
- Digital archives of cultural heritage materials:
Digitizing and providing online access to collections like photographs, manuscripts, and audio recordings.
- Social media analysis for cultural studies:
Collecting and analyzing large volumes of social media data to understand public discourse and trends.
READINGS
Information and Knowledge Organisation in Digital Humanities by Call Number: Available OnlineISBN: 9781003131816Publication Date: 2021-12-24Information and Knowledge Organisation explores the role of knowledge organisation in the digital humanities. By focusing on how information is described, represented and organised in both research and practice, this work furthers the transdisciplinary nature of digital humanities. Including contributions from Asia, Australia, Europe, North America and the Middle East, the volume explores the potential uses of, and challenges involved in, applying the organisation of information and knowledge in the various areas of Digital Humanities. With a particular focus on the digital worlds of cultural heritage collections, the book also includes chapters that focus on machine learning, knowledge graphs, text analysis, text annotations and network analysis. Other topics covered include: semantic technologies, conceptual schemas and data augmentation, digital scholarly editing, metadata creation, browsing, visualisation and relevance ranking. Most importantly, perhaps, the book provides a starting point for discussions about the impact of information and knowledge organisation and related tools on the methodologies used in the Digital Humanities field. Information and Knowledge Organisation is intended for use by researchers, students and professionals interested in the role information and knowledge organisation plays in the Digital Humanities. (...) essential reading for those working in library and information science, computer science and across the humanities.
ISSUES TO TACKLE IN DH DATA MANAGEMENT
Challenges in Digital Humanities Data Management:
- Data Heterogeneity: Dealing with diverse data formats and structures from different sources.
- Data Volume: Managing large datasets that can be computationally demanding to process.
- Access and Copyright Issues: Obtaining permissions to use copyrighted materials and ensuring ethical data collection practices.
ESSENTIAL CONSIDERATIONS
Important Considerations:
- Data Management Plans (DMPs):
Creating a detailed plan outlining data collection, storage, access, and preservation strategies, often required by funding agencies.
- Collaboration with Librarians and Data Specialists:
Leveraging expertise from library staff and data scientists to navigate complex data management issues.
- Open Standards and Interoperability:
Utilizing open data formats and metadata standards to enable data sharing and reusability across different projects.
TOOLS
Tools that will allow you to work easily with collections of data:
Bulk Rename Utility: Simplifies the process for Windows Users by making it easy to establish and follow naming conventions.
Resource: ‘How to Use Bulk Rename Utility’ (video - 5.59 mins.)
DMPTool: Helps create the Data Management Plan, which will be required in a DH project that involves generating data.
From the Page: Transcription tool that allows users to crowdsource or collaborate with restricted individuals or volunteers to transcribe, index, and describe historic documents
Resource: From the Page "How-To"
OpenRefine: OpenRefine is a powerful free, open source tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.
Resource: OpenRefine User Manual
Tropy: Allows users to organize, annotate, tag, search visually, and export collections for research.
Resource: Introduction to Tropy (video) 4.12 mins.