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Digital Scholarship

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.

DS Methods Overview

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.

¶ Data Visualization

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.)

Basic Charts

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.

Timeline

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 TimelineJS, a simple tool that allows for a single linear visualization.

Visit to interact with this timeline.

Complex Timelines

"Conflicts of the World," a more complex timeline than the one above, was created with Tableau Public, a popular tool used for creating multiple types of data visualization.

Visit to interact with this timeline.

Treemap

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

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.

¶ Mapping

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.

GIS

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.

Story Maps

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.

Maps as Interface

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.

¶ 3D & Immersive Technologies

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.

Augmented Reality & Virtual Reality

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.

3D Modeling & Laser Scanning

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:

Immersive Games

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.

360 Degree Capturing

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.

¶ Digital Exhibits

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.

Example Exhibits

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.

¶ Hypertext

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.

Publishing & Presenting

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.

Multimedia

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.

Narratives & Games

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

Textual Encoding Initiative (TEI) is a consortium and an XML standardized markup language 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):

<h2>Their Eyes are Watching God</h2>
<p><em>Their Eyes are Watching God</em> was written by Zora Neale Hurston in 1937.
</p>

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).

What Does TEI Markup Look Like?

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>

Facsimiles & Critical Editions

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

(Note that you can click on "page image" to see an image of the original manuscript.)

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.

¶ Text Analysis

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).

Out of the Box vs Coding and Scripting

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)]

Text Analysis Examples

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.

Introduction to Data

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

What is Data?
What is Data Visualization?
- Visualization Tools
DS Data Projects
- Project Examples
Research Data Lifecycle
- Data Management Best Practices
Glossary

¶ What is Data?

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?

Structured & Unstructured Data

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.

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).

Humanities & Data

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.

¶ What is Data Visualization?

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.

¶ DS Data Projects

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.

Getting Started Questions

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?

Project Examples

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

- a data visualization project that uses the tool
- a network visualization project that uses the tool .
- a mapping project that uses the tools , , and
Also see, for more projects

- a visualization project shows the historical immigration to the U.S. (1830 -2015)
- A visualization project that displays fertility rate, life expectancy, and population of countries in six world regions using
- 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.
- utilizing Facebook data, and featuring on providing insights on the topics including social connections, relative wealth, COVID-19 impact, climate change, etc.

Sciences

- an international, collaborative research program whose goal was the complete mapping and understanding of all the genes of human beings
- a scientific collaboration of international physics institutes and research groups dedicated to the search for gravitational waves
- a BC science project that shows how much the ground moves in Weston, Massachusetts
- a science project for showcasing astronomical data and knowledge

Visualization Tools

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.

¶ Research Data Lifecycle

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, ).

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?

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.

Data Management Best Practices

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 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.

¶ 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 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.

¶ What is Spatial Data?

Spatial Data

The concept of data is discussed more broadly in Introduction to Data. 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, Mapping Islamophobia, 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?

¶ Vector and Raster 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.

Vector and Raster Data Examples

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 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.

¶ Starting a Mapping Project

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.

Getting Started Questions

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)?

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?

Project Examples

Data Visualization

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

: This animated thematic map narrates the spatial history of the greatest slave insurrection in the eighteenth century British Empire; Main Goal: Storytelling; Platform: Leaflet
: An interactive, multimedia storymap detailing Arya's movements across the Game of Thrones books; Main Goal: Storytelling; Platform: Knightlab Storymaps

Mapping Tools and Platforms

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

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

Google Earth (Cloud and Desktop)

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

Tableau Mapping

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

ArcGIS (Pro and Desktop) and QGIS

Leaflet

Introduction to Digital Exhibits

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.

What is a Digital Exhibit?
- Related Concepts
Starting a Digital Exhibit Project
Exhibit Examples
Exhibit Platforms

¶ What is a Digital Exhibit?

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.

Related Concepts

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.

¶ Exhibit Examples

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)

File Format Examples

Raster Data File Formats

While raster data 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 georeferencing, 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 Boston Open Data), 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

A CSV (comma-separated value) file is one of the most well-known formats for both spatial and non-spatial data and is created by using commas to separate each value of an attribute, with each line of the file equal to a single data record. If we return to thinking about structured data sets, comma-separated value file simply puts a comma in between attribute value, returning an output that looks something like this:

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

CSV files are especially useful if you simply have a collection of point data. A file of this kind, sometimes called "XY data" or "long/lat data" can be easily imported into software like ArcGIS or QGIS in order to visualize it. (Note that it will not have any attributes).

GeoJSON

GeoJSON is an open standard format for spatial data based on the JSON file format, which includes spatial information (points, lines, polygons) along with other qualitative and quantitative attributes. If you are using Python, a coding language, or platforms like Leaflet to manipulating spatial data, a GeoJSON export is probably your best choice.

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.

{
"type": "FeatureCollection",
"name": "food_truck_schedule",
"features": [
{ "type": "Feature", "properties": { 
    "Day": "Monday", 
    "Time": "11-3pm", 
    "Truck": "Papi's Stuffed Sopapillas", 
    "Location": "Back Bay", 
    "Pinpoint": "Clarendon Street", 
    "Hours": "11-3pm", 
    "Management": "City of Boston", 
    "ObjectId": 1 }, 
    
  "geometry": {
       "type": "Point", 
       "coordinates": [ -71.0751258, 42.3505372 ] } 
 }

It's worth noting that there are tools for converting CSV to GeoJSON and vice versa.

KML

A KML file (Keyhole-Markup-Language) is an open standard spatial data format most commonly associated with displaying geographic data in an Earth browser such as Google Earth. Although not necessarily as easily compatible with other platforms such as ArcGIS or Leaflet, there are a variety of conversion tools and plugins built-in within these platforms that may help ease the process.

Shapefile

A Shapefile is a simple format for storing the geometric location and attribute information of geographic features represented by points, lines, or polygons (areas), and is most commonly associated with ESRI's ArcGIS program suite. From within ArcGIS/QGIS, you may then export it as other file types, though this sometimes requires purchasing the Interoperability extension.