Data visualization is the graphical representation of data, which researchers use to identify patterns, trends, outliers, etc., and for creating visual evidence to support a scholarly claim. The range of visualization chart types vary considerably, e.g., scatterplot, bar chart, and line graph. The kind you will see in the examples here are timeline, maptree, and network. GIS, a form of data visualization that uses geospatial data, will be discussed in the mapping section. (To learn more about data and data visualization, see Introduction to Data.)

Boston College Libraries' Digital Scholarship Handbook is intended to inform the Boston College community and beyond about digital scholarship (DS) concepts and methods and provide digital pedagogy guidance. The hope is that this resource will not only educate those new to these areas but also help them articulate their ideas and envision possibilities.

More Information

For questions about BC Libraries' digital scholarship initiatives, visit our website, or email the BCDS Group.

A complimentary site, BCDS Learn, provides tutorials and other DS skill-building resources. The Multimedia Guide provides learning resources and other information for media-oriented projects.

​CC BY-NC-SA

Timelines are a temporal form of data visualization and, depending on the tool, allow for varying degrees of detail and complexity.

Basic Timelines

This basic timeline of Mary Shelly's life was created with , a simple tool that allows for a single linear visualization.

Complex Timelines

DS methods enable scholars and students to conduct research and present scholarship in a variety of modes, including spatial, temporal, textual, hypertextual, immersive, graphical, and exhibitive. DS tools, e.g., ArcGIS, and skills, e.g., coding, are the means by which methods are enacted.

Here you will find an overview of different methods from a conceptual standpoint. To learn more about enacting methods and using tools, visit BCDS Learn.

Contents

• Data Visualization

  • Basic Charts

  • Timeline

  • Treemap

  • Network

• Mapping

  • GIS

  • Story Maps

  • Maps as Interface

• 3D & Immersive Technologies

  • Augmented Reality & Virtual Reality

  • 3D Modeling & Laser Scanning

  • Immersive Games

  • 360 Degree Capturing

• Digital Exhibits

  • Example Exhibits

• Hypertext

  • Publishing & Presenting

  • Multimedia

  • Narrative & Games

• Textual Encoding Initiative

  • What Does TEI Markup Look Like?

  • Facsimiles & Critical Editions

• Text Analysis

  • Out of the Box vs Coding and Scraping

  • Text Analysis Examples

What is Digital Scholarship?

Digital scholarship (DS) is the critical use of digital methods and tools in conducting research, presenting scholarship, and teaching. Digital humanities (DH) falls under the umbrella of DS and incorporates humanities specific practices and methodologies.

Why Digital Scholarship?

DS is a way in which faculty, librarians, and students engage with new areas of scholarship and engage with traditional scholarship in innovative ways.

There are many basic visualization types. Which one you use depends upon the kind of data you are displaying, how you want people to engage with it, and the kind of story you are trying to tell.

Some of the most common types of data visualization chart and graph formats include:

Bar Chart

Bar charts are one of the most common data visualizations. You can use them to quickly compare data across categories, highlight differences, show trends and outliers, and reveal historical highs and lows at a glance. Bar charts are especially effective when you have data that can be split into multiple categories.

Line Chart

The line chart, or line graph, connects several distinct data points, presenting them as one continuous evolution. Use line charts to view trends in data, usually over time (like stock price changes over five years or website page views for the month). The result is a simple, straightforward way to visualize changes in one value relative to another.

Pie Chart

Pie charts are powerful for adding detail to other visualizations. Alone, a pie chart doesn’t give the viewer a way to quickly and accurately compare information. Since the viewer has to create context on their own, key points from your data are missed. Instead of making a pie chart the focus of your dashboard, try using them to drill down on other visualizations.

Scatter Plot

Scatter plots are an effective way to investigate the relationship between different variables, showing if one variable is a good predictor of another, or if they tend to change independently. A scatter plot presents lots of distinct data points on a single chart. The chart can then be enhanced with analytics like cluster analysis or trend lines.

This treemap visualization, which shows exports around the world, is from the Harvard Growth Lab's The Atlas of Economic Complexity and was created using a platform designed by the Lab.

Visit to interact with this visualization.

Network visualization illustrates connections and relationships between different entities. As to be expected, the intricacy of the networks will impact the complexity of the visualization.

Simple Network

This relatively simple network visualization illustrates the relationships between characters in the film, Lord of the Rings: The Fellowship of the Ring.

Visit to interact with this visualization.

Complex Network

"Six Degrees of Francis Bacon," a more complex network visualization of early modern social networks, is a collaborative project to which multiple scholars and students from around the world have contributed.

Visit to interact with this visualization.

The use of 3D and immersive technologies, including 3D scanning, alternate reality (AR), virtual reality (VR), and immersive games, in academia has greatly increased over the past decade as scholars have explored how they can be best used in research and in the classroom. 3D technologies include the variety of ways that three-dimensional digital representations of real-world features can be created and shared. Immersive technologies take this experience a step further, using these digital representations to create a larger digital experience with its own narrative or argument that the user can experience firsthand.

Story mapping platforms are applications that use a variety of maps, text, and multimedia elements to present interactive narratives that engage users and provide instantly-accessible geographic context to any project.

Spatial Stories

This story map, created using Knightlab's StoryMaps, combines text, video, and images to highlight how Chicago's dialogue with classical antiquity has shaped the city's look, reputation, and identity.

Visit to interact with this story map.

Spatial Stories & Analysis

This story map, which explores the current healthcare system and soaring cancer drug prices, was created using ArcGIS Online Story Maps. A more flexible and complex platform than Knighlab's version, it combines text, various media elements, and a variety of maps and data visualizations.

Visit to interact with this story map.

Mapping is a broad term that describes adding marks, layers, images, etc. to maps. While GIS is part of mapping, not all mapping is GIS. Adding a pin to a Google Earth map, for example, is not GIS. Adding a data layer and using that data layer to perform an analysis is.

Augmented reality (AR) and virtual reality (VR) offers increased user engagement with the subject matter in question than a traditional image. AR involves applying a simulated layer that allows users to experience a layered, computer-generated enhancement to their real-world perception. VR involves creating and/or experiencing a computer-simulated world. AR and VR technologies are continuously evolving with AR, in particular, becoming easier and more affordable to create.

Augmented Reality

BC Libraries' Digital Studio has been working with the Center for Digital Innovation in Learning (CDIL) and Apple to develop ways to integrate AR into the classroom through Apple's new Reality Composer platform. The examples below will open on any iPhone or iPad and represent, in the first case, a "born-digital" object (the robot) while the lion stamp is a real-world object of which a 3D representation was made in the Digital Studio.

An animated robot AR model from the Apple Quick Look gallery projected on to a real-world table:

Lion-headed stamp AR model created using ARKit:

Virtual Reality

Created by Jessica Linker and her Bryn Mawr College students, this VR reconstruction of an early 1900s biology lab was based on Bryn Mawr archival records. It was created with Unity, a commonly used VR platform.

Visit to interact with this project.

Geographic Information Systems (GIS) is the combination of geospatial software (e.g., ArcGIS), tools (e.g., a GPS receiver), and geospatial data. While GIS is a form of data visualization, it also falls under the category of mapping. In the examples below, you can see how GIS can be used for visualizing all kinds of data including statistics and geographic areas. GIS is used for creating both static maps, such as the kind one sees in presentations and books, and interactive maps that can be shared online.

Traditional GIS Maps

This presentation poster created for the 2019 BC Libraries GIS contest has maps created in ArcGIS (see more information about the project in BC's eScholarship).

Interactive GIS Maps

This interactive map was created in Leaflet using curated spatial data of Gabii, an archaeological site outside of Rome.

Visit to interact with this map.

The map's interactive nature allows users to click on individual features to find out more about them as well as perform actions such as take measurements, search by feature number, and turn on and off different years of aerial imagery to see how the excavation evolved.

GIS Analysis

Mapping Islamophobia is an example of how GIS visualizes geospatial along with statistical data.

Visit to interact with this map.

The ability to engage with and manipulate 3D models of real-world artifacts and spaces can allow for a deeper understanding of the objects and space in question (particularly in comparison to a traditional 2D image). For example, a recent collection of public domain cultural heritage models from museums and libraries from across the world numbering more than 1700 models has been made freely available to view and download.

The creation of 3D objects can be divided into two general processes, creating models from photos called photogrammetry, which involves the construction of a 3D digital surface (known as a mesh) based on a series of overlapping photographs of an object, and laser scanning, which is the process of using lasers bouncing off from an object to identify the shape of its surface.

3D Models Made from Photographs

There are a variety of ways to make your 3D model publicly available, either as part of a research question or in the classroom. The models below are shared using Sketchfab, an online hosting platform.

Here is a 3D model of a pre-hominid skull made for a BC Biology class. (It is made up of approximately 150 images and the mesh is made of 1.5 million triangles.):

Here is a 3D model of the "Hail Flutie" statue set up outside of BC's Alumni Stadium. (It is made up of approximately 75 photos and the mesh is made of 762k triangles.):

3D Printing

3D models can also be 3D printed, such as this print of a Roman statue:

Like physical exhibits, digital exhibits require research, curation, the writing of captions and other contextual information, information organization, and design. Beyond displaying and contextualizing digitized or born-digital materials like historical documents, photographs, film footage, or audio clips, digital exhibits allow for such things as linking to outside resources and the incorporation of a variety of digital objects like maps and timelines. Digital exhibits often come from existing digital collections or begin with the creation of a digital collection, which requires digitization and metadata generation among other steps.

VR and AR based games, and interactive stories more broadly, are becoming more common and, with the increasing availability of VR and AR technology, are becoming easier to make. (Unity, one of the most popular gaming engines, offers free access to students and individuals for educational use.) The incorporation of games and the creation of them are also becoming more common in scholarly work and in the classroom where they promote multimodal learning patterns and offer a deeper understanding of subject matter.

Virtual Reality Based Experiences

VR based games involve fully computer-generated worlds. An example is Rome Reborn, which can be viewed on Oculus Rift and HTC Vive.

Closer to home, BC English professor Joseph Nugent and his team created “Joycestick,” an adaptation of James Joyce's Ulysses into an immersive, 3D virtual reality computer game developed in Unity. Users don a VR eyepiece and headphones and, with gaming devices, navigate and explore various scenes from the book.

Augmented Reality Based Games

AR based games involve applying a computer-generated layer onto the real world. Pokémon GO and Harry Potter: Wizards Unite mobile games are two.

A hypertext is a digital text that links to sections or pages within the text, to other texts, to media elements, and the like. Websites are the most common hypertext example. EBooks and eJournals are also often hypertextual, though not always with the same level of intricacy as a website. When designing a hypertext there are a number of technical and intellectual considerations including, but not limited to, information architecture, information organization, taxonomies, wayfinding, and navigation. All of these considerations impact usability and user experience. In digital scholarship, hypertexts are ways of publishing scholarship, e.g., making data visualizations and analysis available online. Through their structure, design, and the inclusion of media, they are also used to present scholarly arguments and tell narratives.

Multimedia projects are typically created in a hypertext format. This project, Sound and Documentary in Cardiff and Miller's Pandemonium, was created as a digital companion to a traditional master's thesis. Using Scalar, an open-source publishing platform, the project weaves images, video, and audio to bring greater light and meaning to the research topic.

Visit to interact with the project.

In digital scholarship, maps are often one component of a larger project and, in some cases, function as an interface to other aspects of a project.

Witches, a University of Edinburgh digital project that visualizes the locations of witch trials from 1550-1750, links a map marked with witch trial locations to The Survey of Scottish Witchcraft database.

Visit to interact with the map.

The hypertext medium affords scholars the opportunity to publish and present works in many different ways. As an example, the Digital Dante website contains scholarship ranging from commentary and analysis to a Divine Comedy digital edition to multimedia representations and responses to the author's works.

Visit to explore Digital Dante.

Hypertexts can be structured to create narrative paths and games. While a typical website platform like WordPress can be used to create such experiences, tools like Twine and Scalar were designed specifically for such purposes.

Paths

A Case of Hysteria, also an example of a digital exhibit, uses Scalar to create paths that take users through different topics.

Visit the site to explore the paths.

Games

The Anachronist, created in Twine, is an example of both a narrative and a game in which players make choices that determine the outcome.

Visit to interact with the game.

Textual Encoding Initiative (TEI) is a and an used for encoding literary texts, historical documents, and the like. During the process, scholars use TEI to "markup" texts to indicate different aspects such as titles, chapter headings, line breaks, and handwritten marginalia, as well as to note significance and inscribe interpretation. Most commonly TEI is used to create critical editions and facsimiles.

Related terms:

• Markup languages use tags to define elements within a digital document. XML (Extensible Markup Language) is used for encoding texts and employs a number of different standards and schemas.

• Markup tags are in and out marks that “wrap” a text element, e.g., a title, header, single word, paragraph, etc. Below is a simple example using the more commonly known markup language, HTML (Hypertext Markup Language):

In a web browse the above looks like:

Their Eyes are Watching God

Their Eyes are Watching God was written by Zora Neale Hurston in 1937.

The <h2> tag marks the title as a header and determines the specific size of the header. The <p> tag marks the sentence as a paragraph or a general body of text and the <em> tag marks the title as emphasized (italicized).

Narrative Exhibit

CSU Japanese American Digitization Project: An Exhibit uses Japanese-American internment documentation and other artifacts to tell a narrative. The linear format is facilitated by Scalar, the platform with which it is published and one that allows for the creation of narrative paths.

Visit to explore this exhibit.

Traditional Exhibit

The Burns Library's The Object in the Archive provides a more traditional exhibit experience, meaning one that groups objects under unifying themes and topics. Originally physically displayed in the Burns Library, it is also an example of how a physical exhibit can be translated into digital.

Visit to explore this exhibit.

Oral History Exhibit

Goin' North uses Omeka, an easy to use exhibit and digital collection platform. In addition to incorporating historical visual materials, the exhibit features a number of oral histories.

Visit to explore this exhibit.

Capturing the world in 360 degrees offers a different kind of immersive experience, one that began with the ability to create panorama shots with a phone and now is a common feature on everything from apartment tours to tourist attractions.

360 degree images, videos, and more complex experiences are becoming easier to create and share. In the academic world, these kinds of images can give the user a better understanding of a space or a more engaging digital experience on a particular subject. In many cases, these kinds of digital products can be combined with other immersive technologies, such as 3D modeling or VR.

360 tours are a common way to utilize these kinds of images, which can combine a "Google Street" view-like experience created from 360 images with further information. Recently, special exhibitions at BC's McMullen Museum of Art have been using 3DVista to create 360 versions of their exhibitions, including Indian Ocean Current: Six Artistic Narratives.

Visit to explore the exhibit.

360 tours also provide access to spaces people might not be able to travel to or visit. One example is the Anne Frank Family home, which is part of Google Arts and Culture.

Visit to explore the Frank Family home.

360 experiences can be combined with photogrammetry/laser scans, like this digital model of the interior of the Scrovegni Chapel (Padua, Italy).

Visit to explore the chapel.

Finally, 360 has entered the world of video, with 360 videos now available for locations such as Petra, Jordan.

The Chymistry of Newton includes TEI based facsimiles of Newton's alchemical manuscripts, critical materials, an analysis tool, and educational resources.

Visit to interact with this facsimile. (Note that you can click on "page image" to see an image of the original manuscript.)

The Cædmon’s Hymn: A Multimedia Edition and Archive is a critical edition, scholarly study, and textual archive of the Old English poem Cædmon’s Hymn. It is a good example of the use of TEI in medieval studies, which is heavily focused on manuscripts.

Visit to interact with this facsimile.

Text analysis involves using digital tools and one’s own analytical skills to explore texts, be they literary works, historical documents, scientific literature, or tweets. Approaches can be quantitative (e.g., word counting) and qualitative (e.g., topic modeling and sentiment analysis), and tools can range from coding and scripting languages to "out of the box" platforms like Voyant and Lexos.

In the humanities, text analysis is closely associated with the concept of distant reading, which essentially means using computational methods to explore and query large (sometimes massive) corpora. The corpa or datasets, as they are more commonly called in the sciences and social sciences, can be structured or unstructured, and the results can have a data visualization component.

Related Terms:

• Text mining (a term used more in the humanities), data mining (a term used more in the sciences and social sciences), and web scraping are techniques that use coding, scripting, and "out of the box" tools to gather text and create a corpus (or dataset).

Humanities Example

In this text analysis example, Ted Underwood and David Bamman used BookNLP, a Java-based natural language processing code, to explore gender in 93,708 English-language fiction volumes. They articulate one of their major discoveries as follows:

There is a clear decline from the nineteenth century (when women generally take up 40% or more of the “character space” in fiction) to the 1950s and 60s, when their prominence hovers around a low of 30%. A correction, beginning in the 1970s, almost restores fiction to its nineteenth-century state. (One way of thinking about this: second-wave feminism was a desperately-needed rescue operation.)

Visit their blog post to learn more about their methods and discoveries.

Science Example

Here CORD-19, a database containing thousands of scholarly articles about COVID-19 and other related coronaviruses, provides a topic model and visualization of 2437 journal articles. The approach they used, latent Dirichlet allocation (LDA), is a natural language processing based generative statistical model.

Visit to interact with the visualization.

Data is a collection of facts, statistics, measurements, and the like that are recorded (or should be recorded) using standardized methods. It is the smallest or rawest form of information and, as such, requires analysis and interpretation. A variety of means are used to collect data, some of which include questionnaire interviews, document analysis, machine measurements, and web scraping.

The terms "data" and "statistics" are often used interchangeably, however, in scholarly research, there is an important distinction between them. Data are individual pieces of factual information recorded and used for the purpose of analysis. It is the raw information from which statistics are created. Statistics are the results of data analysis, meaning its interpretation and presentation.

What Do Data Research Questions Look Like?

The following represent questions that would benefit from a data-oriented analysis and data DS methods, e.g., data visualization.

• Where in the texts and how often do children speak in Virginia Woolf's novels?

• What does Rodolfo Gonzales' correspondence reveal about his political networks?

• How closely does the rate of heart disease in adults correlate with economic class, race, gender, and area type (i.e., urban, suburban, or rural)?

• How do the rates of African American population increase in Philadelphia and Los Angeles between 1916-1940 correlate with changes in housing laws and redlining practices in both cities?

Introduction to Data explains fundamental concepts that inform data-related research, the use of data manipulation tools, and data project creation.

Contents

• What is Data?

  • Structured & Unstructured Data

  • Quantitative & Qualitative Data

  • Humanities & Data

• What is Data Visualization?

  • Visualization Tools

• DS Data Projects

  • Project Examples

• Research Data Lifecycle

  • Data Management Best Practices

• Glossary

Attributes are the describing characteristics or properties that define all items pertaining to a certain category applied to all cells of a column.

Data is a collection of facts, statistics, measurements, and the like that are recorded (or should be recorded) using standardized methods.

Data collection is a systematic process of gathering observations or measurements.

Data Visualization is a graphical representation of data.

Metadata is often simply defined as "data about data" or "information about information".

Data points are single units of data or single observations, e.g., a single measurement or a single geolocation point.

A Database is a systematic collection of data.

Dataset (or data set) is a collection of data. Typically, it is structured and housed in a tabular form (e.g., a spreadsheet).

The data life cycle represents all of the stages of data throughout its life from its creation for a study to its distribution and reuse. The data lifecycle begins with a researcher(s) developing a concept for a study; once a study concept is developed, data is then collected for that study.

Data Literacy is the ability to read, understand, create, and communicate data as information.

Geospatial data is defined in the ISO/TC 211 series of standards as data and information having an implicit or explicit association with a location relative to Earth.

Quantitative data relates to the quantity of something, and typical examples of quantitative data are numbers.

Qualitative data is used to characterize objects or observations, which can be collected in a non-numerical and non-binary way, such as languages.

Structure data refers to data that resides in a fixed field within a file or record, e.g., spreadsheet.

Unstructured data refers to a bucket of content or data points that are not organized and categorized, e.g., PDF files, image files.

There are two types of data, quantitative and qualitative. Generally speaking, when you measure something and give it a number value, you create quantitative data. When you classify or judge something, you create qualitative data. There are also different types of quantitative and qualitative data. (Also see, Qualitative vs Quantitative Data article.)

Qualitative Data

Qualitative data is used to characterize objects or observations, which can be collected in a non-numerical and non-binary way, such as languages. Qualitative data can include:

• Text

• Audio and video recordings

• Experiment notes, lab reports

• Interview transcripts

Two types of qualitative data include categorical, meaning data that can be organized in groups, and ordinal, meaning qualitative data that follows a natural order.

Quantitative Data

Quantitative data, as the name suggests, relates to the quantity of something, and typical examples of quantitative data are numbers. Quantitative data can include:

• Surveys data, including longitudinal and cross-sectional studies

• Count frequency

• Calculations such as calculating monthly gross margin

• Quantification: converting descriptive data to numbers such as satisfaction rating from 1-4

Two types of quantitative data include continuous, meaning numbers that can be made more precise and divided, e.g, a 4.3 earthquake, and discrete, meaning numbers that cannot be divided, e.g., the number of people in a household cannot include a fraction such as 3.5.

There are a variety of data visualization tools available, many of them open source, to help you explore existing data visualization or to create your own. Below are a few examples.

Excel

Excel is a powerful tool for getting meaning out of vast amounts of data and offers a library of chart and graph types to help users visualize their spreadsheet data.

Tableau

Tableau is a data visualization and analytics platform that enables users to connect to a variety of data sources and explore the data in a simplified way. The drag and drop interface makes it very easy to visualize and create interactive dashboards without any programming skills. (Browse the Tableau public gallery to see examples of visuals and dashboards.)

Palladio

Palladio is a web-based data visualization tool for analyzing relationships across time and visualize historical or cultural networks.

Gephi

Gephi is free software for visualizing networks. The main website hosts official tutorials and also links to popular community-developed tutorials.

D3.JS

D3.js is a JavaScript library for producing dynamic, interactive data visualizations in web browsers. It is ideal for people who want to develop some JavaScript Programming skills and offers great power and flexibility.

Data can be in three different forms: unstructured, semi-structured, and structured.

Unstructured Data

Unstructured data is, essentially, a bucket of content or data points that are not organized and categorized. A folder full of images and digitized texts are a form of unstructured data. (In both cases, steps can be taken to structure them, however.)

Examples of unstructured data:

• Text files: such as word documents, PDFs, TXT files

• Multimedia content: image files, such as TIFF, JPEG, audio/video files. such MP3, MP4

• Qualitative data: such as survey responses, interview transcripts

Semi-structured Data

Semi-structured data lies midway between structured and unstructured data. It doesn't have a specific relational or tabular data model but includes tags and semantic markers that scale data into records and fields in a dataset. Common examples of semi-structured data are JSON and XML.

The following is an example of semi-structured data using JSON. The data describes an author's work.

{

    "name": 
    {
"surname": "Lee",

    "given-name": "Julia",
    
"viaf_id": 49595329
    
},

    "role": "author",

    "degrees": 
    "Ph.D.",

    "affiliation": 
        {
"class": "academic institution",

        "institution": "Acadimia as Colonialism"

        }

    }

Structured Data

Structured Data is data that is organized and categorized so that it can be more effectively analyzed, in particular by tools like databases and data visualization applications.

Understanding a little about structured data provides a lot of insight into how data works in various data tools. Data is structured in a tabular form (spreadsheets) or tables created using coding and markup languages. For the sake of simplicity, we will look at structured data through the lens of tabular data.

Structured Data Example

Tabular data, what we think of as spreadsheets, is structured data organized in rows. Rows represent a record (or unit of analysis) and each column represents a different attribute (also referred to as a variable or field).

An attribute describes everything that falls within it or, in this case, underneath it. Think of it like tagging. Everything in a column is tagged by the attribute. Each horizontal line is a row, and a single row makes up what is called a record, meaning a series of data points that go together.

To put tabular data or a spreadsheet into a more relatable context, here is an imaginary DMV database spreadsheet.

Notice that each data point falls under the appropriate attribute and each row represents a single driver's license (a record). Also notice that none of the driver's license numbers repeat. These are unique identifiers that help distinguish records from one another when information is the same or very similar. Moreover, the unique identifier is a datapoint by which the record can be searched.

As shown in the example, structured data is highly organized and easily understood by machine language. Those working within relational databases can input, search, and manipulate structured data relatively quickly using a relational database management system (RDBMS).

While data-oriented scholarship is perhaps more often associated with the sciences and social sciences, it has as much purpose and relevance in the humanities.

Data visualization can be used to illustrate social networks, how information spreads over time and place, historical, literary, and intellectual trends, and much more. The Belfast Group Poetry visualizes literary networks and Geography of the Post visualizes the spread of the US Postal Service in the nineteenth century.

Database creation also makes up a considerable amount of humanities data-related scholarship. Such databases often incorperate primary sources and facilitate the asking and answering of research questions. They Came on Waves of Ink is a database created from a nineteenth-century Puget Sound Customs District ledger and Enslaved.org, a highly collaborative and grant-funded project, is a database created from slavery-related records provided by different archives and datasets from existing projects like Voyages: The Trans-Atlantic Slave Trade Database.

Data visualization refers to representing data in a visual context, like a chart or a map, to help people understand the significance of that data. Visualization is a frequent final output of research. Putting some time and strategic thought into data visualization at the beginning of a research project can help you create more effective visualization. (For more on data visualization, see the "Data Visualization" section in DS Methodologies Overview.)

Three Types of Data Visualizations

Data visualization is usually one of three types:

• Scientific visualization, meaning the representation of scientific phenomena that tend to be tied to real-world objects with spatial properties e.g., modeling airflow over an airplane.

• Information visualization under which falls most statistical charts and graphs and also includes other visual and spatial representations.

• Infographic, meaning a specific sort of visualization that combines information visualization with narrative.

In the video below, David McCandless talks about how we can use visualizations to make data more meaningful. He explains who he turns complex data sets (like worldwide military spending, media buzz, Facebook status updates) into beautiful, simple diagrams that tease out unseen patterns and connections.

The following example is from The Walt Whitman Archive. It shows a portion of the markup written to create a facsimile of the original manuscript for Whitman's poem "The Argument."

As you can see in the TEI facsimile below, the Archive seeks to replicate the edits made in the manuscript as well as the layout of the text:

The TEI markup below looks very different from the facsimile but, if you look closely, you can probably understand some of what the markup is doing.

<text type="manuscript">
<body>
<pb facs="loc.00001.001.jpg" xml:id="leaf001r" type="recto"/>
<lg type="poem">
<head rend="underline" type="main-authorial">
After
<subst>
<del rend="overstrike" seq="1">an</del>
<add place="supralinear" rend="insertion" seq="2">
the
<del rend="overstrike">unsolv'd</del>
</add>
</subst>
argument
</head>
<l>
<del rend="overstrike">The</del>
<add place="supralinear" rend="insertion">
<del rend="overstrike">Coming in,</del>
<subst>
<del rend="overwrite" seq="1">a</del>
<add place="over" rend="overwrite" seq="2">A</add>
</subst>
group of
</add>
little children, and their
<lb/>
ways and chatter, flow
<add place="inline" rend="unmarked">in, </add>
<del rend="overstrike">
<add place="supralinear" rend="unmarked">upon me</add>
</del>
</l>
<l>
Like
<add place="supralinear" rend="insertion">welcome </add>
rippling water o'er my
<lb/>
heated
<add place="supralinear" rend="insertion">nerves and </add>
flesh.
</l>
<closer>
<signed>Walt Whitman</signed>
</closer>
</lg>
<pb facs="loc.00001.002.jpg" xml:id="leaf001v" type="verso"/>
</body>
</text>

Text analysis can be done using "out of the box" tools or coding and scripting with the latter approach enabling scholars to explore more nuanced research questions.

Out of the Box

Using "out of the box" tools, which don't require coding or scripting, is a good way to get started in text analysis as it will help users begin to understand possibilities and techniques. Voyant and Lexos are examples of such tools. (Mallet, used for topic modeling, is an example of a tool that requires coding but also provides users with a lot of guidance and preexisting code.)

Here is a Voyant instance that contains all of Shakespeare's plays. Stopwords like "thou" and "sir" have been applied to prevent them from dominating the results. (The selection of stopwords is part of the scholarly decision making that goes into text analysis.)

Visit to interact with this Voyant instance.

Coding and Scripting

Coding is an umbrella term that involves using coding (or programming) languages to do things like create applications and websites. Scripting falls under coding and involves using coding languages to do things like automate processes and make websites more dynamic. Coding and scripting are typically done using a computer's command line or platforms like Jupiter Notebooks.

To get a sense of what coding and scripting look like in text analysis, here is a basic example from the Natural Language Toolkit, which uses the Python language. Here you can see a script being run that tags the parts of speech in the sentence, "And now for something completely different." (CC = coordinating conjunction, RB = adverb, IN = preposition, NN = noun, JJ=adjective. )

import nltk
text = word_tokenize("And now for something completely different")
nltk.pos_tag(text)
[('And', 'CC'), ('now', 'RB'), ('for', 'IN'), ('something', 'NN'),
('completely', 'RB'), ('different', 'JJ')]

In this example from Programming Historian, you see a portion of a Python script used for counting word frequencies.

wordstring = 'it was the best of times it was the worst of times '
wordstring += 'it was the age of wisdom it was the age of foolishness'

wordlist = wordstring.split()

wordfreq = []
for w in wordlist:
    wordfreq.append(wordlist.count(w))

print("String\n" + wordstring +"\n")
print("List\n" + str(wordlist) + "\n")
print("Frequencies\n" + str(wordfreq) + "\n")
print("Pairs\n" + str(list(zip(wordlist, wordfreq))))

String
it was the best of times it was the worst of times it was the age of wisdom it was the age of foolishness

List
['it', 'was', 'the', 'best', 'of', 'times', 'it', 'was',
'the', 'worst', 'of', 'times', 'it', 'was', 'the', 'age',
'of', 'wisdom', 'it', 'was', 'the', 'age', 'of',
'foolishness']

Frequencies
[4, 4, 4, 1, 4, 2, 4, 4, 4, 1, 4, 2, 4, 4, 4, 2, 4, 1, 4,
4, 4, 2, 4, 1]

Pairs
[('it', 4), ('was', 4), ('the', 4), ('best', 1), ('of', 4),
('times', 2), ('it', 4), ('was', 4), ('the', 4),
('worst', 1), ('of', 4), ('times', 2), ('it', 4),
('was', 4), ('the', 4), ('age', 2), ('of', 4),
('wisdom', 1), ('it', 4), ('was', 4), ('the', 4),
('age', 2), ('of', 4), ('foolishness', 1)]

What is a DS Data Project?

Digital scholarship data projects usually involve data visualization and/or the creation of databases for the purposes of making data more manageable, navigable, and intelligible. Depending on the tools and methods used, different types of visualizations can be achieved and queries run for asking and answering scholarly questions. (See examples.)

What Do DS Data Projects Involve?

Among other takes, data projects require planning, the acquisition of existing data or collecting of new data, data cleaning, and structuring, and, of course, analysis. (Also see, Research Data Lifecycle.)

Where can I get DS Data Project Support at BC?

BC Libraries' Data Services facilitates, supports, and consults on data acquisition, management, curation, and visualization as well as design and provide data-related in-class instruction and workshops. BC's Research Services also provides support as well as licenses for platforms like ArcGIS.

The following are examples of data-related projects that highlight. Note more complex projects, especially ones with custom platforms, are grant or institutionally funded, which enables scholars to create more robust public-facing works.

Humanities

• Divisions of the Bible - a data visualization project that uses the tool Tableau

• Mapping Shakespeare’s Tragedies - a network visualization project that uses the tool Gephi.

• Navigating the Green Book - a mapping project that uses the tools Mapbox, Leaflet, and Open Street Map

• Also see, Humanities & Data for more projects

Social Sciences

• Simulated Dendrochronology of U.S. Immigration - a visualization project shows the historical immigration to the U.S. (1830 -2015)

• World Health Chart - A visualization project that displays fertility rate, life expectancy, and population of countries in six world regions using Gapminder

• The Opportunity Project - brings technologists, government, and communities to rapidly prototype digital products—powered by federal open data—that solve real-world problems for communities across the country.

• Facebook Data for Good - utilizing Facebook data, and featuring on providing insights on the topics including social connections, relative wealth, COVID-19 impact, climate change, etc.

Sciences

• Human Genome Project - an international, collaborative research program whose goal was the complete mapping and understanding of all the genes of human beings

• LIGO - a scientific collaboration of international physics institutes and research groups dedicated to the search for gravitational waves

• The New England Seismic Network - a BC science project that shows how much the ground moves in Weston, Massachusetts

• WorldWide Telescope - a science project for showcasing astronomical data and knowledge

Research data has a "life cycle" that describes and identifies the steps to be taken at the different stages of the research cycle to ensure successful data curation and preservation. The research data lifecycle can be divided into two main parts, Active Research Stage and Post-Active Research Stage.

During the active research stage, research activities mainly include data planning, acquiring, and analysis; while during the post-active research stage, the focus is on long-term data preservation, sharing, and re-use (Also see, Data Management Best Practices).

Active Research Stage

Planning - The stage it is determined how data will be managed. Typical considerations include:

• The type and format of data will be used.

• Whether any collected data will involve human subjects.

• Where the data will be stored and whether it will be re-used or shared at the end of the project.

Acquire (or "Find") - The stage of when data is found or collected. There are a few steps that can help you develop your approach:

• Define your topic as specifically as possible. For example:

  • What is the average SAT score by race for the last 10 years?

• Identify the unit of analysis, meaning what you will specifically be analyzing and by what measure. For example:

  • Geographic unit, e.g., local, national, international

  • Frequency, e.g., annual, quarterly, daily

  • Unit of analysis, e.g., individual, institution

  • Time series, e.g., cross-sectional, longitudinal (or panel)

• Identify data sources. For example:

  • Government agencies, e.g., census

  • Organization, e.g., International Monetary Fund (IMF)

  • Commercial Subscription Services, e.g., Inter-University Consortium for Political and Social Research (ICPSR), Statista

Collaborate and Analyze - The stage of your (and your collaborators') acitve use of the research data.

• What data processing tool(s) are you using? e.g., Excel, Stata, SPSS, Python, R

• What kind of data are you working on? e.g., numerical, categorical, text

• What kind of data tasks are you performing? e.g., data cleaning, descriptive statistics.

• Are you working in a team and is there a designated project manager?

• Are you looking for a web-based tool for working on your data?

Post-Active Research Stage

Store and Preserve - The planning stage for how the data will be archived for long-term preservation. Considerations include:

• What archive/repository/database have you identified as a place to deposit data? e.g., Dataverse

• How long will data be kept beyond the life of the project?

• What metadata schema will you use? Established domain-specific repositories will usually only accept data that meet their standards for file formats, documentation and metadata, e.g., DublinCore

Share (or Publish) - The stage in which data is shared (or re-used) after a project. Some considerations include:

• Through what resources/platforms the data be made available, e.g., a server or data repository

• When the data will be made available, e.g., immediately or after a 12 month embargo

• If the dataset was collected by the researchers, how it will be licensed to others e.g., a Creative Commons licenses

Discovery and Re-Use - this stage involves facilitating data sharing, which refers to publicly sharing data from completed (parts of) research, and having data reusable, i.e. outside your project or research team.

• Whether any permission restrictions need to be placed on the data, e.g., non-commercial use

• What are the intended or foreseeable uses of the data and who are the users

The following video explains the data management activities that can take place at different stages of the research process.

What Does is Mapping Project look like?

Mapping projects can be of all shapes and sizes, from the creation of a traditional figure with an explanatory legend and caption, such as might appear in an academic text, to an online interactive tool that allows for the searching or filtering of thousands of pieces of spatial data or hundreds of historical maps. They can combine different types of representing real world features (e.g., points, lines, or polygons which can contain both spatial data and other qualitative or quantitative information) with (e.g., satellite imagery, elevation data, vegetation data, or a historical map).

Visualization, analysis, and storytelling are three of the most common goals for any mapping project, and they may be intertwined. Visualization focuses on the presentation of a collection of data, either through straightforward stylistic choices or through the ability to allow the user to filter datasets in some way. is a process in which you model problems geographically, derive results by computer processing, and then explore and examine those results. Storytelling then combines the two, allowing the creator to present a cohesive narrative to their reader which is intrinsically tied to the data being presented at a particular moment.

Boston College Libraries Digital Scholarship Services

BC Libraries' facilitates, supports, and consults on mapping projects, as well as designs and provides mapping related in-class instruction and . BC's also provides support as well as for platforms like ArcGIS.

Data Visualization

• BLM Protests 2020: A traditional GIS analysis undertaken using ArcGIS Desktop (BC Library 2021 GIS Contest Winner); Main Goal: Data Analysis, Data Visualization; Platform: ArcGIS

• Interactive Gabii Site Map: An interactive version of a traditional GIS visualization map, focused on an archaeological site outside of Rome, Italy; Main Goal: Data Visualization; Platform: Leaflet, ArcGIS

• Authorial London Project: The Authorial London project is compiling and mapping references to London places found in the works and biographies of writers who have lived there; Main Goal: Data Visualization (raster through historical maps, vector through location references); Platform: Leaflet/Mapbox (within a larger developed application)

• Artists in Paris: Artists in Paris is the first project to map comprehensively where artistic communities developed in the eighteenth-century city and offer rich scope for subsequent investigations into how these communities worked and the impact they had on art practice in the period; Main Goal: Data Visualization and Filtering; Platform: Openlayers (within a larger developed application).

• Mapping the Gay Guides: Mapping the Gay Guides aims to understand often ignored queer geographies using the Damron Address Books, an early but longstanding travel guide aimed at gay men since the early 1960s. Similar in function to the green books used by African Americans during the Jim Crow era to help identify businesses that catered to black clients in the South, the Damron Guides aided a generation of queer people to identity sites of community, pleasure, and politics. Main Goal: Data Visualization and Filtering; Platform: Leaflet within larger developed application.

Data Analysis

• A Spatial Analysis Storymap: A clever use of an ArcGIS Online Story Map to discuss the use of spatial analysis in ArcGIS; Main Goal: Storytelling, Data Analysis; Platform: ArcGIS Online

• Interactive Atlas of Heart Disease and Stroke: This Interactive Atlas allows you to create and customize county-level maps of heart disease and stroke across the United States; Main Goal: Data Analysis, Data Visualization; Platform: ArcGIS Online

Storytelling

• Slave Revolt in Jamica, 1760-1761: This animated thematic map narrates the spatial history of the greatest slave insurrection in the eighteenth century British Empire; Main Goal: Storytelling; Platform: Leaflet

• Arya's Journey (Game of Thrones): An interactive, multimedia storymap detailing Arya's movements across the Game of Thrones books; Main Goal: Storytelling; Platform: Knightlab Storymaps

Digital exhibits are a form of online exhibit that, like physical exhibits, use objects to tell stories, make arguments, and demonstrate ideas. Attributes include:

• Objects can be digitized or “born digital” media of various types (e.g., digitized photographs, rare books, and films or born digital government documents)

• Special attention is given to the organization of both the objects and the site in which the exhibit lives.

• They might include a digital collection(s) component and interactive elements such as maps and timelines.

The following are some best practices that should be considered prior to starting a data project and provide guidance for managing data in the Research Data Lifecycle's post-active research stage.

Data Storage

To prevent data from being lost to incompatibility, store it as formats and on hardware that are open standard, not proprietary.

Data Documentation

In your documentation, use metadata to record details about the data collection process (e.g., a study) such as:

• its context

• the dates of data collections

• data collection methods, etc.

Sharing

Sharing data makes it possible for researchers to validate research results and to reuse data for teaching and further research. Sharing is also required by an increasing number of funders and publishers. Funders seek to maximize the impact of the research they fund by encouraging or requiring data sharing.

Depositing to an established repository will help to ensure that data are consistently available and accessible, and preserved for future use. Choosing a data repository can be determined by various factors, such as discipline, accepted data format, data sharing policies and etc. You can obtain assistance from Data Services to identify a repository to publish your research data.

Spatial datasets, in general, come in two distinct forms, vector data and raster (or pixel data). Raster and vector data can come together in the creation of a wide variety of mapping projects, from a traditional figure with an explanatory legend and caption, such as might appear in an academic text, to an online interactive platform that allows for the searching or filtering of thousands of pieces of spatial data or hundreds of historical maps.

Vector Data

Vector data includes points, lines, or polygons (shapes made up of straight lines) containing spatial information that represent some sort of feature or event in a physical or imagined landscape and may contain other types of qualitative or quantitative information, called attributes. A point may represent a tree, a city, or a moment in time. Lines might indicate the street grid of a town, the path someone traveled across the world, or a social link between two communities. Polygons can mark the boundaries of a country or voting district, the catchment area of a river, or a single city block.

Raster Data

Raster consists of "cells" of data covering a specific area (its extent), with attribute values in each cell representing a particular characteristic. It may still consist of points, lines, and polygons, but these shapes are themselves composed of pixels (the way a jpeg or other image file type is).

Data of this type may take many forms, such as satellite imagery containing vegetation or elevation data, precipitation maps, or even an historical map, which has been given a spatial reference. Unlike vector data, raster data has a particular resolution, meaning each pixel represents a particular geographic region of a specific size.

Introduction to Mapping discusses fundamental concepts that inform spatial data related research, the use of tools, and mapping project creation. While the focus in this section will be on spatial data based maps (i.e., GIS), it is important to note that simple maps can be created in tools like Knighlab StoryMaps, Google MyMaps, and even ArcGIS Online without sophisticated data. In this case, marks are made on maps using things like "pins" and "notes" that can either be added by searching coordinates or manually placing them on a designated image.

Why Mapping?

Integrating mapping methods into a DS project can offer a variety of helpful advantages in terms of visualization, storytelling, and analysis. Mapping can make complex information and arguments more digestible to the average reader, combining a multitude of information into a single comprehensible figure. It can also help an author tell a story with their data, guiding the viewer around a landscape as the story unfolds with tools such as Knightlab Storymaps. Finally, it opens the door to more specialized spatial analysis through the use of programs like ArcGIS or QGIS, taking advantage of modern computing to reinforce an academic argument to an audience.

Contents

Spatial Data

The concept of data is discussed more broadly in . Here we take a quick look at spatial data in particular. In short, spatial data adds a geographic dimension to a qualitative and/or quantitative data set, situating it in a particular location within a coordinate system relative to other data points. (The coordinate system can be a real-world system or a locally created one used to meet the needs of a particular project.)

For example, the project, , demonstrates how geospatial data can be combined with other data points (i.e., date, gender, and type of incident). Collectively, the data brings to light, from a geospatial perspective, trends in hostility and hate toward Muslim Americans.

What Do Spatial Research Questions Look Like?

The following represent questions that would benefit from a spatial data-oriented analysis and DS mapping methods.

• How has the spatial patterning or characteristics of Hmong immigration to Minneapolis–Saint Paul changed over time?

• Where in the city has the increased number of health clinics had the greatest impact on reducing diabetes-based illnesses? How does this data correlate with race and economic status?

• How do characters in War and Peace move around a physical environment over the course of the book, and how does this connect with larger themes?

• How are historical monuments clustered in downtown Boston in comparison to other major cities?

• How can we visualize the movement of Jesuit missionaries travel over the course of the 18th century?

Introduction to Digital Exhibits explains fundamental concepts that inform digital exhibit creation. Much of the information here originated with an introduction to digital exhibits workshop (see slides), which also resulted in the Getting Started with Digital Exhibits tutorial.

Contents

• What is a Digital Exhibit?

  • Related Concepts

• Starting a Digital Exhibit Project

• Exhibit Examples

• Exhibit Platforms

Digital Collections

Digital collections are an organized and described (using metadata) group of media objects. Objects can be digitized or “born digital,” media of various types, and are usually searchable and/or browsable. Digital collections can be included in digital exhibit sites--platforms like Omeka lend themselves to this as they allow for the creation of both.

Metadata

Metadata is information about different types of data, which includes media objects. Some types of metadata are descriptive, which explains characteristics of data/media objects, administrative, which explains things like resource type and copyright, and structural, which explains things like the version of the data/media object and the relationships between different objects within the collection.

Digitization

Digitization is the act of making a digital version of analog media. It involves the scanning of a physical photograph and transferring of an analog audio tape to a digital audio format, for example.

Born Digital

Born digital refers to media that is digital in its original form, e.g., a Word doc, a photograph taken with a digital camera, and a Photoshop file.

The following questions are helpful to consider when beginning a mapping project. They are broken down into different sections based on different potential parts of a mapping project.

Note that some questions are good to consider in multiple contexts and that you do not need to be able to answer them all to get started. If you need assistance, contact BC's Digital Scholarship Group.

General Project Questions

What are the goals for my project?

• What is your research topic or subject focus?

• Generally, where does my mapping project fit into the visualization - analysis - storytelling trifecta?

• What is the timeline for my project?

• What do I imagine the final product of my project might look like?

Spatial Data Acquisition

How can I obtain the data I need for my project?

• Are you collecting your own data for your research or looking for data from a data provider (e.g. census or other governmental data)?

• Are you dealing with vector data, raster data, both, or unsure?

• Are you gathering spatial data from "pre-digital" sources, such as historical maps, that need to be digitized (the maps themselves and/or the spatial data inherent within the maps)?

• Are you looking for spatial data for a certain time period or geographical focus?

• Have you started searching for data sources? What are the most relevant ones?

Spatial Data Manipulation

How can I prepare my data to be used in a mapping tool?

• What format(s) is your spatial data in (e.g. csv/spreadsheet, shapefile, kml, geojson, etc)?

• Do you need to add qualitative or quantitative data to your vector data?

• Do you need to clean your data (i.e. make the spatial data internally consistent, fix errors within the attributes of the data, etc)?

• Do you need to merge your data (i.e. bring together or combine multiple data sources into one source that contains spatial data, possibly with different attributes or information attached)?

Spatial Data Visualization

How do I want people to be able to see or engage with my data?

• How much data do I have, and is it divided into multiple layers or types?

• How do I imagine users engaging with my data (as a pre-created fixed figure, as an interactive map, etc. Also see Sharing below)?

• How can my visualization emphasize the argument I am trying to make with the spatial data? What is the main idea I want to convey to my audience?

Spatial Data Analysis

Is spatial analysis part of my mapping project goals?

• What question am I trying to answer with spatial analysis (i.e. what kind of spatial analysis am I trying to do)?

• Do I have the necessary data to answer the question I am exploring?

• What GIS tool is most appropriate for me to use to answer that question, and do I have access to it (QGIS/GrassGIS/ArcGIS)?

Spatial Data Storytelling

Is storytelling (e.g. a StoryMap, a GeoTour, etc) a part of my project goals, where the spatial data integrated into a larger interactive narrative with different kinds of media?

• Do you have any preferred StoryMapping tool to use (Knightlab, ArcGIS online, etc.) or do you need help with choosing the visualization tools?

• Do you have a general outline of how viewers will move through your story?

• What kinds of media do you want to integrate with your story (text, images, audio, video, etc)?

Spatial Data Sharing

• What kind of mapping output is ideal for my project (static figure online or printed, interactive online figure, etc.)?

• How will my map be tied to my larger narrative (if desired)?

• Where should my spatial data and map project appear/be hosted/preserved? Will it be accessible to the general public?

• Will others be able to download my spatial data to work with on their own projects?

• Racing to Change - An exhibit that focuses on the Civil Rights Movement in 1960s and 70s Oregon. Platform: A developer customized site

• Japanese Digitization Project - An exhibit about Japanese nationals and Japanese American WWII internment. Platform: Scalar

• A Gospel of Health: Hilla Sheriff's Crusade Against Malnutrition in South Carolina - An exhibit about Dr. Hilla Sheriff, a pioneering crusader for the public health system in Progressive Era South Carolina. Platform: Wordpress (.com and .org)

• Drawn to the Nines - An exhibit about the use of costuming in mid-century illustration. Platform: Omeka (.net and .org)

• Quack Cures and Self-Remedies - An exhibit about dubious "cures" and patient care in the mid-19th to early 20th century. Platform: A developer customized site

• #LovecraftCountry - An exhibit that examines historical, literary, and cultural events presented in the show Love Craft Country with published and primary source materials. Platform: Wordpress (.com and .org)

In addition to introducing digital pedagogy as a concept, this section provides guidance on how to implement digital pedagogy practices. The Digital Scholarship Group facilitates digital pedagogy in the classroom. For assistance or questions, contact Melanie Hubbard.

• Also, see Introduction to Digital Pedagogy, for videos and interactive content.

Contents

• What is Digital Pedagogy?

• Considerations

• Recommendations

• Assignment Design

  • Learning Outcomes

  • Mode/Method/Tool Process

  • Assignment Examples

• Evaluation

  • Assignment Criteria

• Maintenance & Archiving

  • Recommended File Formats

The following questions are helpful to consider when beginning a data project.

Data Acquisition

• Are you looking for data & statistics with a time period or geography focus?

• Are you looking for a specific data type? e.g., qualitative, qualitative, GIS, multimedia

• Are you collecting your own data for your research?

• Have you started searching for data sources?

• Do you need support on data management (DMP), preservation, or sharing?

Data Manipulation

• What data format are you using? e.g., Excel, Stata, SPSS

• What data tasks do you need to conduct?

  • Data cleaning: the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted.

  • Data merging: the process of combining two or more data sets into a single data set.

  • Aggregation (Summarization): the process of gathering data and presenting it in a summarized format.

Data Visualization

• What format does your data come in? e.g., Excel, text file, JSON, PDF, spatial

• Do you have any preferred visualization tool you want to use?

• Do you need help with choosing the visualization tools?

The following examples of vector and raster data demonstrate some of the many forms the data can take.

Vector data as Polygons

Vector data in mapping generally appears either as points, lines, or polygons. In this example of polygons as raster data, spatial information tied to the data defines the shape's boundaries while further information (such as a title and image) is included as additional attributes. Attributes can include any type of additional information that is useful for a viewer to know about a particular location appearing in your dataset.

Raster as a Basemap

A basemap is a georeferenced raster image. They help give a larger context to vector data sets, which would otherwise simply appear as points, lines, or polygons on a blank space. Satellite imagery and other top-down images (orthophotos) are the most common raster data of this kind, seen, for example, when using satellite imagery from GoogleMaps as seen below.

Raster as a Surface Map

Raster surface maps contain attributes that mark change over a particular landscape instead of representing the visual world as basemaps do. Common attributes indicated on surface maps include elevation (which is specifically termed a digital elevation map, or DEM), rainfall, or temperature. Once these types of surface maps are imported into a GIS platform like ArcGIS or QGIS, they can be analyzed in a myriad of ways.

Raster as Thematic Map

Raster thematic maps are similar to surface maps as they use attributes of a particular landscape, be they physical or cultural. Combining several types of data to create what are called thematic data sets, thematic maps group together certain specified attributes from vector or raster data into specific categories, and then mapping out the categories.

The example below shows vegetation types from a dataset that breaks land-cover types into categories. The data is multispectral, meaning it is the acquisition of reflected wavelength data in the visible, near infrared, and short-wave infrared spectrums.

Depending on your goals, there are a wide variety of pre-built platforms that are useful for visualizing, analyzing, and telling stories about spatial data. Below are some common platforms, starting with ones with the generally lowest level learning curve and moving to the highest.

Knight Lab StoryMapsJS

StoryMapJS, a free online tool developed by Northwestern University's Knight Lab, allows creators to combine spatial information with multimedia and textual data to tell location based narratives. Its simple user interface and ability to use both standard and customized underlays make it a good platform for simple story-mapping projects. Its limitations include the fact that it cannot be hosted locally and little customization in terms of styling or functionality is possible. Spatial data points must also be inserted individually either on a map or through long/lat coordinates, making work with large datasets difficult.

Main use: Storytelling, Access: Free, requires a Google account

The example project seen below combines text, video, and images to highlight how Chicago's dialogue with classical antiquity has shaped the city's look, reputation, and identity.

ArcGIS Online/ArcGIS Online Story Map

ArcGIS Online is ESRI's free online data visualization tool and can be integrated directly with ArcGIS Desktop or Pro. It is possible to create a free personal account or to join the Boston College account system by contacting Research Services.

While ArcGIS Online can be useful for making simple interactive maps, where it really shines is in its storymapping functionality. Like Knight Lab StoryMapJS, ArcGIS StoryMaps allow you to create inspiring, immersive stories and tours by combining text, interactive maps, and other multimedia content. ArcGIS Online, however, is a much more flexible platform, allowing creators to present their data in a wider variety of formats and utilize a wider variety of map types. Its learning curve is steeper than Knight Lab's StoryMaps and it requires the user to have a greater understanding of how they want to organize and share their spatial data.

Main Use: Storytelling, Data Visualization, Access: Free version available, license version also available to BC faculty, staff, and students

This example ArcGIS StoryMap from BC student Wenwei Su won the 2020 Boston College Libraries GIS Contest (digital division) for looking at health care expenditures and mortality rates in the US through the lens of the movie "Dying to Survive." (For more examples, check out the ArcGIS online StoryMap gallery.)

Google Earth (Cloud and Desktop)

The free tool Google Earth Pro (available for use online or for download to mobile and desktop) is a common tool for creating simple maps with points, lines, and polygons to share with others. The program maps the Earth by superimposing satellite images, aerial photography, and GIS data onto a 3D globe, allowing users to see cities and landscapes from various angles.

The new GoogleEarth Creation tools allow you to create a "story map" experience by adding items such as images and videos into location descriptions. It is possible to share your creation in the cloud (through standard google sharing methods) or download your spatial data as a .kml file to open on a variety of platforms.

Main Use: Storytelling, Simple Visualization, Access: Free, requires a Google account

The example project seen below uses historical photographs, artwork, Google street views, and satellite imagery to tell the story of Henry Box Brown, an enslaved person who shipped himself from Virginia to Pennsylvania to obtain his freedom.

Tableau Mapping

Tableau is a data visualization and analytics platform that enables users to connect to a variety of data sources and explore the data in a simplified way. The drag and drop interface makes it very easy to visualize and create interactive dashboards without any programming skills. The mapping features of Tableau Desktop give users the ability to get the answers to spatial questions. Tableau's spatial file connector allows you to easily connect to and join Esri Shapefiles, KML, MapInfo tables, GeoJSON files, and other forms of geospatial data. You can also import geographic data from R or GIS (or whatever other spatial files or custom geocode data you have) and make it more easily accessible, interactive, and shareable. Census-based population, income, and other standard demographic datasets are built-in.

With Tableau, you can create the following common map types:

• Proportional symbol maps

• Choropleth maps (filled maps)

• Point distribution maps

• Heatmaps (density maps)

• Flow maps (path maps)

• Spider maps (origin-destination maps)

Main Use: Data Visualization, Access: Tableau Public is a free version available to the general public, a free version of Tableau Desktop can be acquired by academic faculty, staff, and students (see instructions)

The example map below from the project, Immigrant Settlement in Greater Boston, shows the foreign-born population around the Boston area community from 1870 to 2010.

ArcGIS (Pro and Desktop) and QGIS

ArcGIS and QGIS are the two most common platforms for organizing your data into a true database and for analyzing data using common spatial methodologies. As such, they are often the go-to for someone wanting to move beyond an Excel or Google Sheet file for recording their datasets. The two platforms are similar, though ArcGIS only runs on Windows computers while QGIS is a free and open source GIS platform that runs on Windows and Macs. While both platforms can be used for sharing visualizations as exports in traditional file formats such as .jpg and .tiff (commonly used in publications), sharing interactive online visualizations requires integration with a secondary platform like ArcGIS Online or Leaflet.

Boston College has an ArcGIS campus license for students, faculty, and staff, so please see the BC Services ArcGIS page for more information on how to get a license for your computer. Both platforms are available for use in the Digital Studio. If you just want to start from the basics in ArcGIS or QGIS, we recommend running through a few of the beginner tutorials from the LearnArcGIS Hub or the QGIS tutorials page.

Main use: Spatial Organization, Spatial Analysis, Spatial Visualization, Access: ArcGIS licenses are available to BC faculty, staff, and students; QGIS is free and open source, download from QGIS

Leaflet

Leaflet is an open-source JavaScript library for interactive web maps. It is lightweight and flexible and is probably the most popular open-source mapping library at the moment. Of the web mapping platforms discussed here, it is certainly the most powerful, but at the same time is the least user friendly, as a knowledge of coding in javascript is required. Many types of functionalities may be performed more easily using the other platforms described above, yet Leaflet (especially with its many plugins) is by far the most customizable.

Main use: Data Visualization, Access: Free and open source, download from Leaflet

The example map below is an interactive Leaflet based map from the Institute for Advanced Jesuit Studies (IAJS) Jesuit Online Necrology Project, which the lives of more than 33000 members of the Jesuit Priesthood. The map shows the myriad of locations mentioned in the necrology, allowing the user to search by location and identify the Jesuits associated with a location.

Raster Data File Formats

While can be stored in many different formats, GeoTIFF is one of the most common. GeoTIFF is a standard .tif image that also contains spatial data (e.g., coordinates and projections) and attributes. In general, however, any standard image (e.g., jpeg), if given proper spatial data, a practice called , can act like a raster file.

Vector Data File Formats

Vector data includes data in the form of points, lines, and polygons along with any connected attributes. This type of data can come in several different formats, such as GeoJSON, CSV, and KML. Below you can see an array of data formats (found in ), GeoJason, CSV, KML, Shapefile, ESRI Rest API, and ArcGIS Hub Dataset. All contain the same food truck information:

While most platforms allow for some interoperability (the ability to work with several file formats), it is helpful to understand the basic distinction between them.

CSV

CSV data is useful simply because it is easy to create and manipulate in software like Excel or GoogleSheets, and these programs often allow you to export your data as a CSV to make it more interoperable. It is also easy to transform a CSV file into other formats, like GeoJSON, making it an easy "go-to" for sharing spatial data

GeoJSON

The example below is a selection of GeoJSON data. It is organized similarly to the well-known CSV, with each feature containing a series of attributes, including (in this case) the geometry for point data, latitude, and longitude values for where the food truck will be located.

KML

Shapefile

Platform selection will influence decisions about site organization and design, among other things. You may or may not start out knowing what platform you want to use, or you may change your mind about the chosen platform based on the way you want to organize your exhibit, the way you want visitors to interact with it, and whether you want to have the object available in digital collection form as well.

Some considerations that should be made when considering platforms include:

• Ease of use and customizability

• Whether there needs to be a searchable collections component

• Whether exhibits should be navigable linearly, non-linearly, or both

• The types and level of interactivity desired

• Accessibility for the disabled

Some common platform options:

• Omeka (.net and .org) [accessibility statement]

• Exhibit.so - [accessibility statement]

• Scalar

• Wordpress (.com and .org) [accessibility statement]

• ArcGIS Story Maps [accessibility guidance]

• For more information, see Comparing Systems for Creating Digital Exhibits by Dr. Pamella Lach

The following recommendations are intended to help reduce the challenges that can arise when leading a class through digital pedagogy assignments.

• Learn the technology, at least a little. If a librarian or instructional technologist is supporting the technology components of an assignment or activity, it is still a good idea for instructors to learn the technology, even if it is to gain only a basic understanding. Knowing something about the technology will enable instructors to have more in-depth conversations with students about the assignment or activity, maximize learning outcomes, and better understand students’ experiences.

• Scaffold larger projects. Scaffolding larger projects into smaller assignments can help students stay more organized, better manage their time, and develop their skills systematically.

• Strive to make digital projects ADA compliant. If a digital project (e.g., a website) is going to be public, try to make it as ADA compliant as possible. Compliance means doing things like making sure that web pages have headers and images have labels. Being conscientious about ADA standards will make the project accessible to a broader audience and will make students more aware of and sensitive to such needs. (See Accessibility Basics and W3C Accessibility Guidelines.)

‌• Have conversations about copyright and fair use before students begin incorporating media into their work. Even though the fair use doctrine enables students to incorporate media into projects with less concern about copyright, they should understand why they can more “freely” use media for a class assignment and not for other venues. (See more on intellectual property rights.)

• Include a disclaimer statement on public student projects that explains they are student work. A disclaimer will help prevent less information literate people from seeing such projects as an authoritative source. Additionally, it will signal to educators that the projects are potential teaching models. To that end, it is also helpful to include the assignment(s) that led to the projects' creation.

• Have at least a basic plan (or better yet a detailed one) for how you will evaluate student work. You and the students should have a clear idea of what the expectations are for the assignment(s). Waiting until the work is completed before determining how to evaluate it can make grading difficult and can leave students feeling unsure about the quality of their work and the fairness of the grading (see "Evaluation").

• Decide whether you want to maintain or archive student work. Digital technology is inherently impermanent (e.g., links break, and file types become obsolete). For this reason, it is important to decide whether you want to maintain or archive student work. (See the “Maintenance and Archiving” for more information.)

Like with all assignment design, designing a digital pedagogy-based assignment should be driven by the learning outcomes. If you have already decided on the method (e.g., mapping) and tool (e.g., ArcGIS), designing or refining your assignment might be mainly a matter of selecting the desired learning outcomes and developing them further.

Related digital pedagogy learning outcomes include, but are not limited to, the ability to:

• Use digital tools to investigate scholarly questions

• Digitally present critical and creative scholarship

• Effectively organize information

• Navigate multiple literacies, e.g., information literacy, digital literacy, data literacy, and visual literacy

• Work collaboratively

• Manage projects

• Write for the web and/or public audiences

• Visually communicate

• Navigate intellectual property rights

• Comply with web accessibility standards

Guiding questions like these will help you identify learning outcomes:

• What literacies might students improve on or gain from this assignment?

• What communication, information organization, analytical, technical, and problem-solving skills does this assignment require?

• What will the assignment teach students about incorporating media (e.g., video and images) into their work?

• What will the assignment teach students about intellectual property rights?

• How will this assignment teach students to work in groups and manage projects?

After having identified learning outcomes, you can enhance them by creating more specific requirements. You can, for example, enhance an assignment's data literacy component by not just having students use datasets they can find but also having them create their own. This act of creation will give them a much deeper understanding of how datasets work. Another example would be having students create metadata for objects they incorporate into a digital exhibit. Creating metadata on top of writing captions will get students more engaged with the objects as it will require them to analyze them on multiple levels. An alternative to increasing specific and practical requirements is having students critically reflect on their work in the form of an essay, presentation, or by including a praxis component to their digital project that explains how they approached and accomplished their work (see praxis example).

The mode/method/tool process will help you identify the method(s) you want to use, get you thinking about possible additional learning outcomes, and point you in the right direction regarding tools. (Also, see "Tool" for a list of possibilities.)

The Process

The mode/method/tool process walks you through a series of questions and considerations to help you narrow down the options and identify additional learning outcomes.

Mode in this context means the way in which students engage with course materials, experience them, or express ideas about them. Examples of modes include spatial, temporal, textual, hypertextual, immersive, graphical, and exhibitive. ‌The choice of mode should be rooted in the type of thinking and reasoning you want students to do. For example, a map is a spatial mode. Creating one compels students to think and process information spatially.

Method is the way in which students are doing the work, meaning conducting research or presenting scholarship. Examples of methods include text analysis, GIS, and augmented reality. Modes that correlate with these methods include textual, spatial, and immersive, respectively.

Tools are the technologies that enable the execution of a method and the creation or presentation of content. Tools mean anything from a digital platform like Tableau and ArcGIS to a markup language like HTML and coding language like Python. The tool selection process should take into account key factors like complexity, accessibility, and support availability.

Guiding Questions

Answer these questions to begin the decision-making process.

1.) Mode

To select the mode(s), begin by asking the questions:

• How do you want students to experience the content associated with the assignment?

  (e.g., Do you want them experiencing it spatially or temporally or both?)

• Relatedly, what kind of thinking and reasoning do you want them to do?

To get you thinking more about potential learning outcomes ask:

• What impact will the mode(s) have on their learning?

‌2.) Method

To select the method(s), begin by asking the questions:

• In what way should students engage with the assignment subject matter that will align with the determined mode? (The Mode/Method/Tool Combinations list below will help with this question.)

To get you thinking more about potential learning outcomes ask:

• What impact will the method(s) have on their learning?

‌3.) Tool

To select the tools(s), begin by asking the questions:

• What tool(s) facilitate the methodological approach?

• What level of learning curve should the tool(s) have?

• Who will be teaching and supporting the tool(s)?

• Are the tool(s) accessible for students with disabilities? If not, what alternative tool(s) can be used, and what additional help might the student need to use them?

To get you thinking more about potential learning outcomes ask:

• How might the tool(s) create opportunities for additional learning outcomes?

• What technical and scholarly skills will students gain from using the tool?

Mode/Method/Tool Combinations

The following are only some of the possible combinations:

Scenarios

The following scenarios are intended to demonstrate ways in which the process might work.

‌Scenario One

Mode: An instructor of a lower division history class wants students to understand and “experience” how the Himalayan mountain range impacted the relationship between ancient India and China. The instructor's main objective is to help students understand the affect of typology and distance, so it is decided that spatial is the best mode.

Method: For the methods, they choose GIS and mapping, because both methods will allow students to deeply engage with the geography of the region and will, through the use of such features as topographical layers, help them better understand the complexity of landforms.

‌Tool: For tool, the instructor chooses ArcGIS Online because it allows students to add marks (e.g., pins) and data layers as well as provides topographic base layers. The online version (as opposed to ArcGIS Pro) was chosen because it is supported on campus, it has less of a learning curve, and it is cloud-based and, therefore, accessible from any computer.

Learning outcomes: In addition to original learning outcomes the instructor identified such as students will understand how the typology of the Himalayan mountains affected encounters between ancient India and China, they identify other learning outcomes such as students will learn to find, evaluate and incorporate spatial data into their scholarship.

Scenario Two

‌Mode: An instructor of both a lower-division and upper-division political science class wants their students to see the evolution of political rhetoric in presidential inauguration speeches over time and, therefore, they decide that a temporal and textual mode are most appropriate.

Method: The methods they choose are timeline visualization and text analysis, which will enable students to see patterns and trends in the speeches as well as present those changes visually.

‌Tool: For the lower-division class, the instructor wants to keep the technology as simple as possible so that students can focus more on gaining foundational rhetorical analysis skills. In response, the instructor chooses, TimelineJS, which requires only basic data entry skills, and Voyant, a free cloud-based text analysis tool with an easy-to-use graphic interface.

For the upper-division class, the instructor wants students to learn more complex text analysis skills. They stick with using TimelineJS because they know the text analysis component will be demanding. For the text analysis tool, they choose Python for “scraping” the speeches from the web and for running the anaysis. The instructor feels comfortable giving this assignment because all of the tools are free, work on both Mac and PC, is supported by the Library, and there are multiple online resources.

‌Learning outcomes: In addition to original learning outcomes the instructor identified like, students will be able to identify and analyze trends in inauguration speeches over time, the instructor identifies other learning outcomes such as students will learn the basics of cleaning and structuring data and, in the case of the upper-division assignment, gain a basic understanding of coding.

What is Digital Pedagogy?

Digital pedagogy is the deeply considered and conscious incorporation of digital tools, concepts, and methods into teaching and learning. The journal, Hybrid Pedagogy, describes it as being “precisely not about using digital technologies for teaching and, rather, about approaching those tools from a critical pedagogical perspective.”

Digital pedagogy ranges in scope and complexity and can be about the incorporation of digitally-driven class assignments and projects (e.g., students creating a website or interactive online map.) Or, it can be about the creation of teaching materials (e.g., digital learning objects) and the use of digital tools in teaching. The critical components include the selection of the methods and tools used, how they are used, and issues around them (e.g., bias in algorithms).

Why Digital Pedagogy?

As a practice, digital pedagogy fosters multimodal teaching and learning and can provide new and creative ways into class subject matters. Some compelling reasons for the incorporation of digital pedagogy into courses include:

• Digital pedagogy assignments and activities have multiple learning outcomes and prepare students to be critically engaged with technology postgraduation.

• Digital pedagogical approaches give less conventional learners alternative ways to demonstrate their knowledge and abilities otherwise difficult to show in more traditional coursework.

• Digital pedagogy approaches can be used to make online course content more engaging and interactive.

The following information is intended to assist instructors who want to maintain or archive student work.

Due to constant changes in technology, digital projects, whether they are online or stored in a digital file format, are subject to loss and obsolescence. There are steps you can take to help maintain them, meaning keeping a project live and fully functional, and there are steps that can be taken to archive them, meaning capturing a version of a project that preserves the information but not necessarily its interactive component. Taking the following steps will help with project maintenance:

• To reduce obsolescence, save projects and media incorporated into projects using file formats that are open, non-proprietary, and widely available. (See "Recommended File Formats")

• When it is not possible to export a file, take a screenshot, e.g., if a cloud-based data visualization tool will not allow you to download the visualization as an image file, take a screenshot of it instead.

• If the project is online, periodically view it to make sure it is still viewable and that nothing needs to be updated, e.g., if it is Wordpress, the platform version or a plugin might need to be updated by simply clicking “update.”

• To prevent media loss, upload media so that it “lives” in a project or in a permanent repository linked to the project.

• When linking to media, choose media from more reliable repositories like the Internet Archive and Wikicommons as opposed to YouTube or an image found on a random webpage.

• To reduce “link rot” or “dead links, link to source less likely to be taken down or moved, e.g., sources in the Internet Archive, Wikipedia, and museum digital collections. Also, periodically check for dead links using a free link checker tool (Search “link checker” in a web browser, and multiple tool options will come up.)

For more information, talk to the BCDS Group, which can help you create a maintenance or preservation plan.

Grading digital assignments can be challenging and can feel subjective if you have not articulated your expectations from the start. The following points will help you begin creating an evaluation criterion:

• Decide what is more important, the process students go through when conducting the assignment, the final product they create, or both. Based on what you decide, determine the weight you will give to each area. For example, the process could be 60%, and the product could be 40%.

• When evaluating the process component, determine how you will observe and measure it. Having students journal or write a longer essay about the process is one way. Having them present on it is another. When you scaffold an assignment, it is easier to keep an eye on how well students are following instructions and the care they are giving to each step.

• When evaluating the final product(s), determine what specific elements you will be grading. For example, in addition to grading the content, you can grade the writing quality, how well media is incorporated the interpretation and incorporation of data information organization, and the visual design principles applied. (See the "Student Project Checklist" for possible grading elements.)

• If students are working in groups, determine whether everyone will get the same grade or whether they will be graded individually. When you choose to grade students as a group, decide ahead of time how you will manage situations where students do not contribute enough to the project.

When incorporating digital pedagogy into the classroom, it is important to take several considerations into account.

‌• Ask the question, why digital? Before designing a digital pedagogy course component, consider why you are choosing digital and what it will achieve that other approaches will not. It’s possible that digital is not the best medium and that using it will make coursework more cumbersome than necessary.

• There is no such thing as a “digital native.” Some students are tech-savvy, especially if their major is technology-oriented. There are many other students, however, who are not inherently good at or comfortable with using technology in course work, despite being proficient with certain technologies like social media. Therefore, do not make assumptions about their technical abilities.

• Make sure there is enough room in the syllabus for students to execute assignments fully. Students need time to learn the technology, which means they need time to experiment, fail, and then succeed. Also, keep in mind that the primary learning curve might not be learning the technology itself as much as it is learning how to apply the technology to the specific assignment.

‌• Not all students have equal access to technology. Even if every student has a laptop, smartphone, etc., this does not mean their computer is fast enough or current enough, or they have the necessary bandwidth or data plan to meet the requirements of an assignment. Similarly, even if the necessary technology is readily available on campus, this does not mean every student can be on campus for the time it takes to complete an assignment. (Think about students who live off-campus or who work full-time.) For this reason, instructors should be prepared to meet the needs of students with less access, e.g., know where students can borrow equipment and give extra time to complete an assignment. One way to level the technology “playing field” is to allow class time for such work.

Conducting a class survey asking about student access to technology is one way to identify access issues. Ensure students’ responses are kept private.

• Be prepared to adjust an assignment for students with disabilities. If a student has a disability, it might be necessary to provide them with an alternative version of an assignment. For example, give a visually impaired student a podcast assignment in place of a video one. Additional support might be needed to help disabled students navigate the technology. Talk to a Digital Scholarship librarian or the Center for Digital Innovation in Learning for guidance on this matter and inquire with Disability Support Services about any assistance they can provide.

• Take privacy and online safety into account‌ when having students work publicly. When students are working online, be it a project they are creating, e.g., a website, or engaging with an online community, e.g., TikTok, discuss issues of privacy and safety. Allow students not to put their name on their work or put only their first name and last initial. When doing social media-based assignments, it might be best if they don’t use their personal accounts.

Before starting the exhibit creation process, you need to closely consider your topic, desired effects, and objectives with the understanding that these decisions might change as you progress.

1.) Determine the Topic

What is the main focus or theme of the exhibit?

• Some examples: a historical period or movement, an event, a person's biography, a process or technique (e.g., silk screen printing), an idea or concept (e.g., the law of gravity), an industry (e.g., whaling), a single object (e.g., a specific book, painting, or musical instrument)

2.) Determine the Desired Effect

Effects to consider (as cited in Barth, et al. 2018):

• Aesthetic: organized around the beauty of objects

• Emotive: designed to elicit an emotion in the viewer

• Evocative: designed to create a specific atmosphere

• Didactic: designed to teach the viewer about a specific topic

• Entertaining: designed for the amusement or enjoyment of the viewer

3.) Determine the Objectives

What do you want people "walking away" with? This means considering things like:

• What is the motivation for creating the exhibit? (Why this exhibit?)

• What are the intended learning outcomes?

• How do you want visitors to be able to apply what they learn beyond the exhibit?

4.) Creating the Exhibit

Once the above considerations have been made, it is time to begin the exhibit creation process. See our "Getting Started with Digital Exhibits" tutorial for more information.

References

Barth, G. L., Drake Davis, L., Mita, A. (2018). "Digital Exhibitions: Concepts and Practices". Mid-Atlantic Regional Archives Conference Technical Leaflet Series no. 12.

Below is a list of possible assignment criteria to incorperate into requirements and/or rubrics.

‌External Links

• External sites linked to are selected based on how much they add to the content of the project.

• The external sites are carefully selected based on the quality of information they provide

‌Media

• All media, e.g., images and video, are relevant to the topic.

• All media is chosen based on the intellectual value or meaning they bring to the project.

• There are captions or text that explain the relevance of the media.

• All media is properly cited, and/or credit is given.

Organization

• The information architecture is logical and easy to navigate.

• The menu/navigation bar labels are logical, concise, and relevant to the information they link to.

• Page headers and subheaders are logical, concise, and relevant.

• The layout of the content on individual pages is well organized and logical.

Technical Basics

• All hyperlinks work and link to the appropriate site/page.

• All images appear.

• All URLs (web addresses) have been turned into hyperlinked text. The specific words chosen to be hyperlinked should be logical, i.e., they lead to where users would expect.

Usability

• The project addresses basic ADA compliance requirements, including:

  • Headers are consistently used.

  • Images have alt-text.

  • Fonts and color choices are ADA approved.

Visual Design

• There is an appropriate (aesthetically pleasing and intellectually stimulating) balance between text and images.

• Text and images complement each other.

• Images are tastefully formatted, i.e., they are an appropriate dimension, cropped, so that rough edges are removed, they align well with the text, and they are of an appropriate resolution so that they do not look pixelated (more info on image size).

• All text is easy to read, be it due to layout (e.g., spacing) or the choice of size, font, and color.

Written Content

• The written content is concise while being highly informative.

• The written content is a synthesis and analysis of authoritative information/data and one’s own thoughts.

• Written content is logically organized and distributed throughout the site.

• Titles, headers, and captions are relevant, clear, descriptive, and thoughtful.

• As with an essay or long paper, there is a clear flow of ideas.

• Quotes and paraphrased ideas are properly cited.

The following file formats are recommended for maintaining and preserving digital content. They were taken from the Smithsonian’s “Recommended Preservation Formats for Electronic Records.”

BC Guides

• Data Visualization

• Digital History

• Multimedia Production

• Text and Data Mining

Critical Readings

• Algorithms of Oppression

• Bodies of Information: Intersectional Feminism and Digital Humanities

• Debates in the Digital Humanities

• Digital Redlining, Access, and Privacy

• Disability, Universal Design, and the Digital Humanities

• Tactical Tech

Digital Pedagogy

• Guides & Teaching Materials

  • Community of Online Research Assignments

  • Digital Pedagogy in the Humanities

  • DLFteach Toolkit: Lesson Plans for Digital Library Instruction

  • HASTAC Pedagogy Project

  • Humanities Commons Syllabi Repository

• Journals

  • Exploring Literacies Through Digital Humanities: A dh+lib Special Issue

  • Hybrid Pedagogy

  • Journal of Interactive Technology and Pedagogy

Introductions

• Digital Humanities Literacy Guidebook

• How Did They Make That?

• Introduction to Digital Humanities

• Northeastern Digital Scholarship Glossary

Journals

• dh+lib

• Digital Humanities Quarterly

• Digital Studies

• Reviews in DH

The following information is intended to support creating digital scholarship projects that are more accessible for the disabled and for those affected by the digital divide and digital redlining. If you have any recommendations on how to improve the content, please let us know.

Common Practices

It's important to design projects from the beginning with accessibility in mind. This involves, among other steps, making sure that:

• Screen readers can effectively navigate and communicate page content and interfaces

• Auditory based information, e.g., podcasts, is also available in text form

• Videos are captioned

• There is an alternative way for users to access video-based information and any medium that requires more bandwidth, e.g., a text and still image version of the content

• Digital project are mobile-friendly (responsive design)

General Resources

The following resources are useful for a variety of digital project types:

• Accessibility Cheatsheet

• Digital A11y Cheatsheets

• Essential Components of Web Accessibility (full guidelines)

• Web browser extensions run tests for the visually impaired, e.g., check contrast

DS Tools Accessibility

The following is a list of commonly used tools and platforms and their related accessibility information:

• Esri - Accessibility Resources provides information ranging from creating accessible ArcGIS Story Maps to creating accessible pdfs in ArcGIS Pro to creating accessible basemaps

• Exhibit.so - Accessibility statement

• Tableau - Accessibility FAQ includes information on such things as creating data views and dashboards for the visually impaired

• Omeka - Accessibility statement

• Wordpress - Accessibility statement

Related Tools

• Adobe Acrobat DC - Guides for created accessible PDFs in Acrobat

• Powerpoint - Guide on how to create an accessible PDF

Digital Scholarship can sometimes require more complex skills such as coding and using more intricate tools such as ArcGIS. There are a number of educational opportunities for gaining such skills, including BC Libraries workshops and the BCDS Learn site, which has tutorials based on workshops among other resources. Some learning opportunities outside of BC include:

• Code Academy

• Digital Humanities Summer Institute

• Digital Pedagogy Lab online courses

• Geospatial Historian

• Humanities Intensive Learning and Teaching (HILT)

• OU Digital Scholarship Lessons and Tutorials

• Programing Historian

• Tooling Up for Digital Humanities

There are numerous digital pedagogy assignment examples out there. You can find many at the University of Michigan’s Digital Pedagogy Repository and the #DLFteach Toolkit. The BC Digital History libguide can also help faculty from multiple disciplines imagine possibilities. A few examples by the creator of this digital pedagogy guide include:

Mapping Parable of the Sower (Basic mapping)

• See assignment

• See associated visual image exercise

The Watts Collection (Digital collection creation)

• See syllabus and assignments

Evolution of International Policy and Policy Areas (Timeline)

• See assignment

Introduction to Voyant (Text analysis)

• See assignment

Archives, Race, and Justice (Digital storytelling)

• See project instructions

Digital scholarship projects often incorperate different types of media, some of which are from existing sources. Using existing or outside media requires navigating intellectual property rights.

The media resources below have a number of reusable and public domain materials that lessen concerns about violating copyright. Please keep in mind that even when a site says media may be used, it is imperative that you read the sites’ terms of use policies to verify that you are in full compliance. It is also important that you credit the materials.

Images & Video

• Artstor (Public collections)

• British Library About | Search Images

• British Library Catalog of Manuscripts About | Search Images

• Creative Commons Search (Search for images, videos, and music)

• Digital Public Library of America (You have to navigate to where the image lives. Look for links that say things like, "View in [source library name])

• Flickr Commons

• Getty Open Content Program About | Search Images

• Internet Archive (Select "Text" under "Media Type")

• Library of Congress & Library of Congress on Flickr

• National Gallery for the Arts About | Search Images

• New York Public Library

• Old Book Illustrations

• PICRYL

• Unsplash

• Wellcome Images

• Wiki Commons

Music & Sound Effects

• Jamendo

• Free Music Archive

• Creative Commons Audio

• Free Play Music (choose the education license)

• Opsound

• Partners in Rhyme

• Podsafe Music

• Wikimedia Commons

Below is a list of tools that is far from exhaustive. The majority of them are free and cloud-based or downloadable. The * indicates tools that are in the O'Neill Library's Digital Studio. (See the Digital Studio software and equipment lists.)

Audio

• Audacity (sound editing)*

• SoundCite (embedding sound)

• SoundCloud (hosting and embedding)

Code Editors

• Atom

• Oxygen XML Editor*

• Visual Studio Code

Data Cleaning & Manipulation

• Anaconda (multiple Python and R platforms)

• Excel*

• Google Sheets

• Jupyter Notebook (Python)

• Lexos

• OpenRefine

• R Studio (R)

Data Visualization

• Palladio

• Excel*

• Storyline

• Tableau Desktop* (accessibility FAQ)

• Tableau Public (accessibility FAQ)

• Gephi

• Data Wrapper

Digital Collections & Exhibits

• CollectionBuilder

• Exhibit.so (accessibility statement)

• Omeka [see introductory instructions] (accessibility statement)

Image Editing & Graphic Design

• Adobe Creative Suite*

• GIMP*

Mapping & GIS

• ArcGIS Online (accessibility resources)

• ArcGIS Pro* (accessibility resources)

• ArcGIS Storymaps (accessibility guidance)

• Google Earth

• Knight Lab StoryMap

• QGIS*

Media Hosting

• Internet Archive

• Soundcloud

• Youtube

Network Visualization

• Cytoscape

• Gephi*

• Palladio

Publishing

• Scalar

• WordPress (accessibility statement)

Text Analysis

• Lexos

• MeaningCloud

• Voyant

Timeline

• TimelineJS

• TimeMapper

• Neatline (Omeka plugin)

• Tableau Desktop* (accessibility FAQ)

• Tableau Public (accessibility FAQ)

Video

• iMovie (Mac video editing)*

• Movie Maker (PC video editing)*

Digital pedagogy assignments can be stand-alone components of a course or be complementary or supplementary to more traditional coursework.

Assignments may be digital in origin or begin as a traditional assignment such as a paper and then be moved into a digital format. In the case of the latter, students are given the opportunity to learn about translation, be it the act of translating a paper into a digital project and/or translating academic writing and content for the general public.

The two sections and will assist with assignment design. The second section is especially useful if you do not know the method(s) and tool(s) you want to use.