For Students Page
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For Students Page
This fun interactive demo will show you!
Remember, submitting your project gives you:
Free support and suggestions from a DH expert.
A taste of peer review.
Potential publication - a boost for your CV/resume!
For Librarians and Academics page
This handy interactive demo will show you!
Remember, submitting their projects gives students:
Free support and suggestions from a DH expert.
A taste of peer review.
Potential publication - a boost for their CV/resume!
We are constantly working to develop and improve Gale Digital Scholar Lab, based on feedback from users, to best meet the needs of researchers, students, librarians and educators.
Gale strives to uphold values of openness and transparency that are core to the Digital Humanities, so we are happy to provide a list of the updates made to Gale Digital Scholar Lab below. Those at the top are most recent.
[October 2024]
Parts of Speech and Sentiment Analysis tools have a new view that allows users to see how the words in their documents have been identified by the tools. This is done via marking up the OCR*. In Parts of Speech, the mark-up indicates how each word has been categorised (for example as a noun or adjective), and in the Sentiment Analysis tool, it shows the sentiment of scored words.
Click to expand examples.
This new view improves the transparency of these tools, making it easier for users to understand how they’re working. It’s also easier to move between distant and close reading, and understand the context in which the identified words appear within each document.
This mark-up view already existed for Named Entity Recognition, but was the only view of the document the user could access. We have added a link to the original scan of the document within the tool, bringing it in line with all the other tools and ensuring that the user can easily access all of the information and context they might need about a document when conducting their research.
Click to expand example.
*OCR = This acronym is used to refer to a machine-readable text version of a document. It stands for Optical Character Recognition, which is the process by which machine-readable text is obtained from a photograph of a document.
[October 2024]
Sentiment by Timeframe is the third visualization in the Sentiment Analysis tool, and offers the following options: Century, Decade, Year, Month. With this visualization, users can navigate from the century level down to the month level, going in and out of different time periods within a Content Set to explore the nuances of specific timeframes. This update was made in direct response to user requests for increased granularity within the Sentiment Analysis tool and allows users to do more with their data.
Please note, available Timeframes are based on the metadata available for the documents being analyzed – if none of your documents include a publication month, then the month option will not appear for that year.
[July 2024]
Homepage redevelopments were made, giving users a cleaner and more intuitive experience, highlighting key features and research routes the user can take. Updates included:
[July 2024]
Sentiment Analysis tool migrated from using the 111 version of the AFINN lexicon, to the 165 version of the AFINN lexicon, which includes an additional 905 scored words/tokens.
Please note analysis runs made before the update are no longer directly replicable.
[July 2024]
The open-source algorithms that power the following tools were updated:
Thanks to the updates to these two Python-based software libraries, users will notice improved performance when using the tools.
[June 2024]
A number of accessibility and styling updates were made, including:
The ability to zoom for the Sentiment Over Time visualization was also introduced, so the user can make the visualization larger and explore it in more detail.
Lastly, the Word Cloud visualization for Ngrams was overhauled by moving to Highcharts. This brought it in line with our other visualizations, reduced white space and improved the readability of the words themselves. The results of an analysis were unchanged.
[April 2024]
In April 2024 we launched Gale Research Showcase, a free, open repository of student-authored digital scholarship. Designed to advance early career research, it showcases high-quality, peer-reviewed essays that demonstrate best practices in digital scholarship.
Students can use Gale Research Showcase to get inspiration and guidance on using Digital Humanities techniques in their own projects – and to get published!
Publishing in Gale Research Showcase is a great opportunity for early-career researchers to experience the Peer Review process, and get free feedback, support and suggestions from Gale’s DH experts on their DH project.
Gale Research Showcase is a great tool for faculty, librarians and educators to use when developing a DH curriculum, providing a hub of research examples to enhance instruction and engage and motivate students.
View Showcase Learn more – For Students Learn more – For Educators
[December 2022]
Released at the same time as Group Workspaces, the Group Notebook is a collaborative note-taking tool which allows students and researchers to communicate, ideate, and iterate on their projects together, helping to develop good research methodologies, as well as project management and report writing skills.
Significantly it allows students to create collaborative notes on content sets, clean configurations, and visualizations – from directly within those features, thus not disrupting their workflow. Users can also add images and links to their notes. The feature also records information such as the author of each note and date it was added, allowing group member contributions to be tracked.
The Group Notebook can also be used by educators to provide students with support materials, resources, and feedback to guide project-based learning.
Later updates included:
[December 2022]
Released at the same time as Group Notebooks, the Group Workspace feature was introduced to allow users to view other group members’ content, build content sets collaboratively, and share visualizations between group members.
This functionality helps students and researchers develop project management and collaboration skills. It also allows educators to oversee their class’s work by joining each group, meaning they can offer support when needed and keep projects on track.
Collaboration is a huge part of digital humanities work, and this update was in direct response to extensive user requests.
Later updates included:
Collaborators must all be at the same institution, due to rights limitations.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum
[October 2024]
Parts of Speech and Sentiment Analysis tools have a new view that allows users to see how the words in their documents have been identified by the tools. This is done via marking up the OCR*. In Parts of Speech, the mark-up indicates how each word has been categorised (for example as a noun or adjective), and in the Sentiment Analysis tool, it shows the sentiment of scored words.
Click to expand examples.
This new view improves the transparency of these tools, making it easier for users to understand how they’re working. It’s also easier to move between distant and close reading, and understand the context in which the identified words appear within each document.
This mark-up view already existed for Named Entity Recognition, but was the only view of the document the user could access. We have added a link to the original scan of the document within the tool, bringing it in line with all the other tools and ensuring that the user can easily access all of the information and context they might need about a document when conducting their research.
Click to expand example.
*OCR = This acronym is used to refer to a machine-readable text version of a document. It stands for Optical Character Recognition, which is the process by which machine-readable text is obtained from a photograph of a document.