Connecting the Dots is organized into three sections: “Our Data Selves,” “Networks Coming to Life,” and “Hitting the Big Time, Network Style.” After a short introduction, “Our Data Selves” eases the reader into the concept of individual datafication via online profiles. Janosov discusses both social media profiles as well as online shopping profiles, breaking down what kinds of data might be collected, how that data can be stored both statically and dynamically, and where technology may be collecting additional data about us and attaching it to our profiles, even if we don’t explicitly answer a question or survey. He concludes with the example of targeted coupon distribution and use, showing how data aggregated from many users over time can help companies predict consumer behavior.

“Networks Coming to Life” expands the reader’s purview to understand how interconnected profiles create a network. This section of the book is where Janosov’s unique approach to choosing examples shines. Unpacking everything from Game of Thrones character deaths to NFT art markets and even DJ popularity, Janosov explains the anatomy of networks, with their nodes and different types of links, and how networks are born, grow, and sometimes collapse. He engages the reader in discussions of rather heady scientific concepts, like weighted and directional relationships and preferential attachment, but keeps his writing accessible by using familiar topics as the backdrop.

Finally, “Hitting the Big Time, Network Style” brings the first two parts together and applies network theory to the real world. Instead of just showing where networks exist, Janosov demonstrates the utility of these mathematical concepts in the real world. He discusses how network theory can help predict the spread of disease, increase workplace productivity, engineer successful social media campaigns, and more. The final chapter also touches on a particularly timely subject: artificial intelligence. This chapter unpacks some of the inner workings of AI and is followed by a conclusion where Janosov ties all three parts together, leaving the reader with the feeling that they’ve tackled the challenge of the book, which will help them better understand more complex discussions of network theory.

When I first started reading Connecting the Dots, Janosov’s light and often joking voice immediately set this book apart from other network theory books and articles. His voice, paired with examples that I recognized, like Game of Thrones, electronic music, and oddly specific online ads, made me want to keep reading, even when I didn’t immediately recognize some of the more network theory-specific concepts. In addition to well-placed examples, Janosov’s organization is experimentation-forward. He often explains how he devised the idea for a project or analysis before unpacking what he actually did, which made me more invested in knowing what he found.

While Janosov provides ample links to view his network projects throughout the book, I was slightly disappointed to discover that none of the diagrams were printed on the page. That said, Janosov does not stop at just linking his own work, but often mentions further reading throughout the chapters and provides a comprehensive list of references by chapter at the end of the book, making Connecting the Dots a network of information in and of itself.

By the time I finished Connecting the Dots, I felt that my grasp of network theory was greatly improved. I would absolutely recommend this book for anyone curious about behavior prediction, datafication, or network theory. Connecting the Dots has a low barrier to entry and easily sheds light on what is often a very confusing topic.

Learn more about Connecting the Dots and preorder it on its website.

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1. Having four authors does not mean the book gets written four times as fast

Throughout our conversation, the topic of the sheer size of the author team came up a number of times. I asked explicitly about their experience working together because much of the book sounded like a single voice, not four. In response, Amanda said:

“You mentioned the kind of unified language, that it actually reads like a book that’s not written by four different people, despite having different authors on chapters. I think that took a lot of honing. I think we learned in the initial drafting process and my drafting content for Part I, that I write in very verbose prose that I really appreciate reading, but my co-authors are much more adept at writing for more business audiences. And so, that’s one of the reasons Andy and I collaborated on the first part, was him working with me on taking some of the big ideas and long sentences and helping to make sure that the content was accessible for folks who are reading the book and who might be skimming through it or looking for insights in bits.”

2. Disagreement is healthy, and sometimes it’s important to show it

With four authors, disagreement is inevitable. I asked the authors about how they handled those disagreements behind the scenes, and how they decided to put some of them on the page. Andy talked about how debates during the making of The Big Book of Dashboards (authored by Andy, Jeffrey, and Steve) resulted in them starting Chart Chat, to which they later invited Amanda. That experience helped prepare them for making Dashboards That Deliver, he said:

“There are challenges because you add one more person to the group, but that actually creates many more vertexes of disagreement and logistics. So it was a challenge, but you know, like in the first book, you end up with a better product, because even before anything gets to Wiley or a copy editor, it’s already gone through a painful process with the other three authors. So, even though it might have been difficult, we were always working to improve the end product. So, a challenging but fruitful process.”

Jeffrey also talked about how working together on previous projects helped him and his co-authors navigate disagreements:

“I think this would have been a lot harder had we come together, not working together, right? We worked together for years: Steve and Andy on the first book, and then Amanda on Chart Chat for, you know, years and years and years. So we work together on a regular basis. I know them, I trust them, you know? I respect them and their work. I think that really helps, especially when you get into a disagreement or something that’s really difficult.”

Steve mentioned how he appreciated the fresh perspectives his authors brought to the table, even when they were different from his:

“It’s good to have someone else reading the stuff that someone else has written, you know? Because, gee, this stuff’s so clear to me! I don’t need a figure here, I don’t need an illustration, I don’t need a call-out to accentuate the most important point, because I’m living and breathing it and thinking about it 24/7. Someone else reading it goes, ‘this isn’t clear, wait a minute, I don’t know what you’re talking about.’”

3. Authors really do think about how readers will use their book

I’ve always been told “think about your audience!” when taking writing courses, but was curious whether that was something real authors actually did when writing their books. Well, the authors of Dashboards That Deliver certainly did! Amanda talked about how the book is designed to be read in multiple ways:

“I think one of the best parts of the book is you can sit down and read it cover to cover if you want to, or you can pick and choose and read pieces of it. I hope it becomes a reference book that people have that, when they’re doing dashboard design work and they’re getting to their kind of prototyping and kind of layout design pieces, they can pop open the book and open that chapter and be reminded of some ideas. Or they’re looking for an example of a really great dashboard around financial data, and they can go pop open the big banking dashboard scenario. And so, I think that that was a big part of getting to a really accessible book—which we hope it is for a lot of people across different levels—to make sure that it reads as an accessible book and has lots of good examples, and can be read modularly or cover to cover.”

Andy pointed out how the framework for designing dashboards described throughout Part I of the book can be used for many applications outside of dashboards:

“I think the framework that Amanda’s come up with, and that we’ve obviously worked through the whole front section of the book, is focused on dashboards, but it is a data application design framework, right? You know, that framework took inspiration from Design Double Diamond, from Agile, from user-centered design, you know? Those are paradigms that are not dashboard-specific. So when you get into things like user stories and wireframing and prototyping, you’re just in application design. So we’ve framed it within a dashboard world, but, you know, that framework is applicable to anybody who’s trying to take data and to produce something that other people are going to use with data. This is a book for them.”

4. Even 500 pages can’t capture everything

I asked the authors about what was left on the cutting room floor when Dashboards That Deliver was finalized. Could there really be more than the nearly 500-page book? Turns out, there could be much more, but they didn’t feel having a thicker book was the most productive course of action. Steve reflected on their tool-agnostic approach to the book:

“The book is already fairly thick. And if we had something about, ‘and here’s how you make all these things in each of these tools,’ it would be 10,000 pages long. So, it’s wonderful for people who teach Tableau, people who teach PowerBI, because it will create this need for, ‘oh, you need to be adept in your tool? Here are these people who are great at it, and they’ll teach you, and they’ll help you with it.’ So, that’s where the frustration comes from. We’re not going to tell you, step by step, how to build this thing. We make it very clear, we’re not going to tell you how to do that. We can’t—there’s too many tools.”

In a similar vein, there were hundreds of real world projects and scenarios to choose from for the book. Ultimately, only so many could make the final draft and be useful to readers without too much overlap. Jeffrey addressed their process for deciding what stayed in:

“The list was probably twice as long, both on scenarios and real world examples. We had a long, long list of real world, and they just started getting cut. Some of them combined themselves. We said, ‘oh, that might fit with this chapter,’ and kind of moved in and combined. Some of them were written all the way to the 11th hour, and didn’t make it, and got cut, and didn’t make enough sense to have in the book. And some of them we felt like they just, you know, weren’t the right topic, or just didn’t make it.”

After chatting with Andy, Amanda, Jeffrey, and Steve, I have an even greater appreciation for the hard work and dedication it took to create Dashboards That Deliver. Even though I don’t make dashboards often, I definitely see myself referencing the book regularly as I complete other data visualization projects. If you want even more behind the scenes information about Dashboards That Deliver, you can check out the authors’ discussion of some of the book’s inner workings on Chart Chat 57: Under the Cover of Dashboards that Deliver.

Dashboards That Deliver is currently available for preorder and will publish on September 23, 2025.

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Part I: Process contains the first eleven chapters of the book. The first three chapters are primers for the following eight, describing why dashboards are important (Chapter 1), the authors’ Dashboards that Deliver framework (Chapter 2), and the roles often found on teams that create dashboards (Chapter 3). Each of the eight other chapters in Part I take a deep dive into the seven steps of the Dashboards That Deliver framework: spark (Chapter 4), discovery (Chapters 5 and 6), prototyping (Chapters 7 and 8), development and user testing (Chapter 9), release (Chapter 10), adoption (Chapter 10), and maintenance and enhancements (Chapter 11). The list of steps may seem overwhelming at first, but the chapters are conversational and broken down into short, labeled sections, making them easy to skim and find relevant sections at a glance.

After the reader has taken in the information in Part I, the authors move into Part II: Scenarios. In this section, fifteen dashboard projects are broken down, each in its own chapter. Breakdowns not only identify the dashboard designer, organization, audience, tools used, and creation timeline for each dashboard, but also go into the fine details of each project. Annotated still images of the final dashboards and photos and scans of the sketches and prototypes that preceded the final product make these scenarios come to life. Even more useful, the authors provide commentary on the dashboard at the end of each chapter, noting key takeaways and broadly applicable insights from each specific scenario. They even used an example from Outlier 2024—Michael Gethers’ Professional Racing Team Race Strategy Dashboard (Chapter 24)!

The book concludes with Part III: Succeeding in the Real World. This section has the fewest chapters—nine in total—that each address a debated issue or broadly applicable insight in dashboard design. The authors address the promises and pitfalls of software defaults, the actual definition of a dashboard, generative AI, and much more. This section feels the most broadly applicable, even to those who may not create dashboards specifically, but who still work on data analysis and design issues.

While reading Dashboards That Deliver, I found myself skipping around to different sections as I became interested or found them relevant to my work. I was pleased to find that when I later settled in to read the book cover to cover, it read just as well continuously as it did when used as a reference book. The accessible language, paired with many sidebars, well-labeled sections, and relevant graphics, makes the book feel more like an active discussion than a textbook and adds to its utility as a frequently referenced book on any data visualizer’s shelf.

Though the four authors write with a unified voice throughout the book, Dashboards That Deliver still showcases each of their individual personalities and areas of expertise. Some sidebars, end notes, and even entire chapters are written by individual authors or small groups and some parts of the book specifically showcase when two or more authors had contrasting ideas. Cotgreave, Makulec, Shaffer, and Wexler engage with each other on the pages of Dashboards That Deliver instead of flattening their opinions, proving there is always more than one solution to a problem. Their tactic encourages readers to formulate their own opinions and gives them the agency to apply their critical thinking to their own work, equipped with the Dashboards That Deliver framework.

Dashboards That Deliver is undoubtedly worth the 500-page read. The authors bring their expertise to the table and lay their process bare for all to benefit. The book is approachable, useful as a reference, and applicable beyond just dashboard design, even though it uses dashboards as a jumping off point for understanding the data analysis and visualization process. Cotgreave, Makulec, Shaffer, and Wexler have delivered a book that will be used by many data visualizers for years to come.

Dashboards That Deliver is currently available for preorder and will publish on September 23, 2025.

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The book begins with informative writing, orienting the reader to the state of environmental data and climate issues, which are some of the more straightforward topics addressed in the book. From the start, the focus is on community-building and collective environmental data governance and stewardship. With each subsequent piece, more layers of complexity are added, including philosophical discussions of how data contributes to worldbuilding and calls to action alongside wildfire safety zines and traditional American Indigenous artwork-turned-data exploration.

As the volume progresses, readers are brought on a journey through data art and speculative fiction, exploring historical pasts through queer and decolonial lenses and imagined futures of data and technology integration without sacrificing humanity, individuality, and environmental balance. It is a challenge to incorporate explanatory writings with creative fiction in the same book without startling or confusing the reader, but the Data Science x Design Collective team executes this beautifully. Once I picked up the book, it was difficult to put down because each standalone piece compelled me to read the next. At times, individual pieces within this section of the anthology could feel slightly heady, but this feeling rarely persisted for long.

Toward the closing of Our Environment, the tone turns again to more straightforward and practical topics, this time with a more actionable bent, including how-tos and tips for advancing the practice of data science and communication. I found that this reemergence into actionable work not only grounded the more cerebral pieces contained in the middle of the book, but also made my reading experience seem purposeful. By the time I read the final piece, I felt equipped with more knowledge than I had before to meet the world of data with confidence and understanding.

The first thing that struck me when I initially flipped through Our Environment was the abundance of colorful visualizations which cover full pages of the book. Each written piece is accompanied by at least one but often many images, from digital data visualizations and data quilts to maps and related photographs. For pieces not directly associated with a specific visualization project, the Data Science x Design team worked with illustrator Noemi De Feo to produce appropriate and eye-catching illustrations. With every turn of the page, more images come into view, providing the reader with plenty of visual aids and inspiration related to each article. I also quickly noticed how useful having footnotes written in a column along the side of the page was as opposed to printing them at the bottom. This design choice allows the note to align horizontally with where it was mentioned in the text, making it more intuitive to read. For both of these aspects, I applaud Barbara Borko, the designer behind the book’s layout.

Beyond the beautiful graphics and ergonomic design, I appreciated how carefully the editors arranged the contributing pieces into a cohesive anthology of work. As discussed above, each piece of writing led the reader to the next, producing a smooth and comfortable reading experience. In addition to ordering the pieces well, the editors also did an excellent job of varying the length and visual weight of each piece. This allowed the reader to feel like the content had a consistent topical flow without becoming bogged down by many long articles or rushed by many short ones.

Overall, Our Environment by the Data Science x Design Collective is an excellent collection of data-oriented work contemplating our world, how we occupy it, and how data is woven through it. With a focus on community engagement, decolonization, queer resistance, and advocacy, this book provides engaging and diverse perspectives about how to honor history and build futures with data.

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Chart Spark’s preface begins with a no-frills question: “is this book for you?” Torban lays out the goals and purpose of the book plainly, inviting readers to consider what they will gain from continuing their journey through the book. She also provides an overview of her qualifications for writing the book. The last section of the preface is the one I found most useful: a reading plan for the entire book. Torban recommends reading Chart Spark in chunks, tackling a maximum of thirty minutes of reading per day for seven days. In total, readers can expect to spend about two and a half hours cover to cover, which I found was an accurate estimate.

After the preface, Torban jumps right into the introduction, defining and breaking down creativity. I appreciated her adaptation of the different types of creativity from James C. Kaufman and Ronald A. Beghetto’s research paper, “Beyond Big and Little: The Four C Model of Creativity.” The Four C Model encompasses mini-c, little-c, Pro-c, and Big-C creativity, which range from being new and creative just for you personally to so creative that it changes the world. Torban urges readers not to focus on Big-C creativity, but instead to build creative habits and practices that contribute to consistently producing mini-c, little-c and Pro-c work. This section also contains a personal narrative where Torban describes her journey from unfulfilled government data worker to creative freelance information designer. She provides a note that reading this narrative is optional, but I felt that reading it contributed to the heart and relatability of the book as a whole.

Section I: Care, explores the necessary work that allows space for our minds to foster creative ideas. I appreciated Torban’s explanation of not only short-term creative cycles, where we alternate between ideation, execution, and rest, but also larger creative seasons that alternate between productive summers and slower winters. Understanding and accepting that no one can be at peak creative productivity all the time and that there are ways to prepare for slow seasons is key to any creative care routine. 

Moving to Section II: Coax, Torban provides actionable tasks to help readers bring out their creative energy, even when they feel creatively blocked. I enjoyed her focus on small tasks to jumpstart creative processes. I also found that Torban’s use of personal examples was particularly effective in this section, as she walked through multiple projects of her own to showcase how she moved past moments of feeling stuck.

The final section of Chart Spark – Section III: Communicate, tackles both communication with the intended audience for data visualizations and clients throughout the project development process. Significant time is spent deconstructing the idea of visual metaphors; when to use them, and how to execute them properly. Beyond this, I loved Torban’s “4Q” prompt, which helps data visualization designers determine when to be more experimental or creative in their work and when to stick to tried and true methods. By taking into account the reader’s sense of urgency, attention span, and familiarity with the material, as well the designer’s own time and energy, it becomes much easier to discern when creativity is a must and when it might need to take a backseat.

Torban uses the conclusion of her book to remind readers that their creative output does not equate to their self worth. Among all of the advice in the book, I feel that this is the most important for people (including myself) to hear and internalize. In this industry, zeroing in on the quality of deliverables is essential, but that can draw focus away from physical and mental wellbeing. Torban describes this not as a balance to be struck between the personal and professional, but as a constant tug-of-war. My experience has been similar and I recognize the vulnerability it requires to publish this struggle, showcasing once again the empathy and courage with which Torban works and writes.

Taken as a cohesive work, Chart Spark is a practical guide to increasing and maintaining creative output with accessible and actionable prompts at the conclusion of every section. The book is concise and would serve as a good reference book for any data visualization designer or other creative professional to keep on their shelf.

Disclaimer: Some of the links in this post are Amazon Affiliate links. This means that if you click on the link and make a purchase, we may receive a small commission at no extra cost to you. Thank you for your support!

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The book is organized like a manual, with sections for tables, graphs, matrix graphs, and publishing and presenting. Boers himself even suggests in the introduction that some may find it most useful to skip around the book instead of reading it straight through in order to absorb the information that is most relevant to them. I chose to read it from cover to cover and was impressed by how well each of the six chapters stood alone while still maintaining a steady flow from one to the next.

Data Visualization for Biomedical Scientists is packed with practical examples about how to make research visualizations more reader-friendly. There are numerous figures of graphs from his and other research papers before Boers edited them and after he used his method to optimize them for readability and comprehension, or “clear vision and clear understanding.” In addition, the book is accompanied by a website, which contains the code (written in prism) used to create the majority of the graphs and tables contained in the print volume in addition to a range of video tutorials. I found that both of these elements greatly enhanced the utility and readability of the book, especially as someone who is not a practicing scientist.

Beginning in the first line of the preface and continuing throughout the book, Boers consistently references the work of the data visualization “giants” from whom he draws inspiration. These people include Edward Tufte, William Cleveland, and Stephen Few. But it’s Boers’s analysis and synthesis of their data visualization philosophies combined with his own opinions and best practices that make this book a must-read alongside publications by Tufte, Cleveland, and Few.

Boers starts with an introduction, which lays out his general data visualization philosophy and some of the key questions he hopes to answer with the book. Following the introduction are three chapters that discuss the proper use and design of tables, graphs, and matrix graphs, respectively. The fifth chapter is more tailored to Boers’s scientific audience and discusses best practices for publishing and presenting biomedical visualizations. Lastly, Boers ends the book with a section titled “The Bottom Line,” which contains a single succinct paragraph describing the main takeaways of the book as a whole.

I particularly appreciated how Boers balanced the need to remove as much “ink” as possible, as Tufte instructs, with the desire to have indicators (headlines, arrows, captions, highlighted elements, etc.) that guide the viewer through a data visualization or table. In pursuit of this end, as well as the need to lower the bar for prerequisite knowledge about scientific research, Boers developed a new way to visualize error, which he calls the “null zone.” This mark is a gray bar that runs across a graph showing the measurements for two different groups. Where the lines fall outside the null zone, the difference between the groups is statistically significant. When the lines fall within the null zone, there is not a statistically significant difference. I find this method of visualization much more readable than trying to estimate whether error bars for adjacent marks overlap and hope that Boers’s method of visualizing error will be more widely employed.

Though Boers is an accomplished scientist and academic, he writes as if he is sitting across from you at a coffee shop and explaining concepts in real time. He employs carefully presented logic paired with plain language, exclamations, and jokes that make Data Visualization for Biomedical Scientists an incredibly approachable book even for someone who was not trained as a scientist.

Boers covers a range of concepts that I consider in my everyday work as a data journalist, including accessibility, formatting for print and digital, reader (un)familiarity with complex concepts, and constructing a cohesive narrative. I wholeheartedly recommend this book, especially for scientists interested in communicating their research beyond their collaborators and journalists and other communicators who want to hone their science visualization skills.

Disclaimer: Some of the links in this post are Amazon Affiliate links. This means that if you click on the link and make a purchase, we may receive a small commission at no extra cost to you. Thank you for your support!

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After graduating from college at the beginning of May, there was just one thing on my mind: what I was going to do now that I was no longer a student. Because of this mindset, it seemed fitting that I collect my next set of data about plans as I tried to make sense of all the potential options ahead of me. I started with a simple prompt, writing down every time I made a plan for that day or the future that was occupying so much of my mental space as a recent graduate.

With this week 21 visualization (above), I wanted to show how plans blossom during the day as well as the flow of plans from one day to the next. As a result, I decided to create a flower-inspired design, with each bloom sprouting petals and leaves depending on the plans I made. The first trend that struck me when I finished this visualization was how many plans I actually make, especially ones that are made and executed on the same day. I anticipated having flowers with five or even eight petals, but the largest one has fifteen!

I was also surprised by how much variance there was in the number of plans I made each day. I consider my routine to be relatively consistent, so realizing that there are some days when I make fifteen plans and others when I only make one gives me a new appreciation for how much my life changes from one day to the next. However, that variance made me wonder: how many of these plans are broad in scope and how many are just day-to-day decisions?

Tracking conversations is always a challenge, but I was prepared to determine exactly what kinds of plans I was formulating throughout my week. About a month after graduation, it was no surprise that I had many discussions about important topics like finding a job, moving to a new city, and continuing to pursue personal projects. But despite the importance of those conversations, there were still many more chats about small plans, such as what to eat for dinner, which game to play with my siblings, and what movie my family should watch.

Together, these conversations made a constellation of plans, distilled into a scattered, yet beautiful dataviz. But after two weeks of seeing my planning laid out before me, I was curious about what I was doing with my less structured time.

Not surprisingly, my unplanned time was sparse; however, I was shocked at the variety of activities I did when I didn’t have a specific plan. In most spans of unplanned time I was doing four or more separate activities! These ranged from completing tasks and chores to watching a movie and hanging out with family and friends.

Although I was first struck by the number of different things I did in each chunk of unplanned time, after reflecting more I recognized that they all had something in common—nearly every one of these activities helped me relax or attend to my own needs. Because of my busy schedule, I don’t often build self care into my calendar, so it was comforting to see that I still prioritized  those pursuits subconsciously.

For the final week of plans data, I decided to take a more abstract approach. Instead of documenting activities or concrete actions, I tracked how I was feeling about plans for different aspects of my life. I was encouraged to see that I ended the week equally or more optimistic about most of my plans than I was when I started the week. But I also noticed that my emotions tended to move together: if I became discouraged with one plan, I was often discouraged about all of them.

Beyond the data, I experimented with a new treatment variable in this visualization. I had never considered using opacity to show the overlap of different categories before, but demonstrating the inherent layering of my emotions made a uniquely fitting choice.

As this installment of my personal data project concludes, I’m leaving with a fresh perspective about how I organize and execute my life. It’s easy to get caught up in day-to-day doings, but seeing the broader picture and all the possibilities ahead is more important than ever as I transition into life after college. I can’t wait to see what’s in store!

Now that my plans data is officially in the books, my next set of data and visualizations about color is on the way. If you’re ready for more personal dataviz content right now, I’m updating my data collection journey weekly on my Twitter, Instagram, and TikTok, so check it out for a sneak peek before the articles! I invite you to follow along with me or even try some data collection of your own and share it with someone. Until next time, stay tuned!

The post Plan Planet: Part 6 of a Year-Long Personal Data Project appeared first on Nightingale.

Following my entertainment data collection, I was inspired by week fifteen’s data, which tracked each time I shared entertainment media with someone else. The idea of connecting my data to my interactions with others intrigued me, so I decided to make my next four weeks communication-themed. To begin, I wanted to know how I messaged people by tracking each instance of using a messaging app.

Just looking at the visualization above, it’s immediately obvious that I do most of my messaging through texts and Facebook Messenger. This didn’t surprise me, as most of my friends communicate with me through those platforms and all of the group chats I’m in use one or the other. However, I was surprised by two other trends.

First, I respond to group chats consistently, but infrequently. Most days, I send at least one group message, but the messages I read from those groups far outnumber those that I send. Second, I seldom hold conversations via written message. This is in part because I prefer to schedule a phone or video call with people to have more in-depth discussions and shy away from doing much more than plan a time to talk via text or chat.

After collecting data connected to my phone for a week, I was eager to explore other methods of communication. For my second week of messages data, I decided to pivot to tracking my emails.

I often find myself overwhelmed by the number of emails I receive, so I expected this visualization to contain a large quantity of data. In order to show the continuous nature of emails, I decided to let each day run into the next in a snakelike form. This revealed that emails truly do not sleep; however, they do tend to be concentrated in the middle of the day, with many messages being sent and received around the lunch hour.

An unexpected revelation I had was the overall lack of email chains that I participated in throughout the week. These are shown in the visualization by the solid connecting arcs between dots. During the entire week, I only sent or received emails in nine different email chains across all three of my email accounts.

Now that I had a good idea of how and when I sent and received different types of messages from my previous two weeks of data, I was curious about the content of my messages. It seemed that there was a treasure trove of discoveries waiting to happen, but I knew I couldn’t track the content of every message. Instead, I decided to focus on one theme: gratitude.

I had no idea what to expect from this data, as I had never thought about how often I thank people before. Something that immediately jumped out at me was that I most often thank individuals instead of groups, demonstrated by the abundance of teal triangles as opposed to orange triangles. I also didn’t predict that I would need to include handwritten cards as one of the types of thank you messages I sent. Though I don’t write thank you cards often, I did send a few during this particular week because some friends and family members had sent me congratulations for my college graduation!

Despite tracking messages of gratitude for a week, I still found myself looking at the emails data I had collected. I noticed that the amount of time between responses to email chains varied widely and I was curious whether that was true for all types of messages. As a result, I decided the final week of messages data would be dedicated to tracking my response time.

When I finished this visualization, the divide in the data jumped out at me immediately. The vast majority of messages I sent were texts and I responded to nearly all of them within ten minutes. On the other hand, all but three of the emails I received took me more than an hour. This is in part because many of the emails I write have to do with professional matters, so I take my time reading and responding to them. In addition, emails sometimes include instructions to complete tasks or send documents, so that accounts for the additional response time.

All in all, during these four weeks of data collection I learned that my messaging habits are consistent, but sometimes surprising. Though I try not to enter my data collection with too many preconceived notions about the outcome, I found that my methods of communications were far different from what I had expected, with less variety and more skew toward dominant platforms and practices. I became more aware of how and how often I communicate with others and got the chance to think more deeply about what I send. I was also able to experiment with new visualization techniques, like playing with how time is represented and simplifying complicated data.

After wrapping up my exploration of messages, I’ve started collecting data for the next theme: plans. If you’re anxious for more personal data viz content, I’m updating my data collection journey weekly on my Twitter, Instagram, and TikTok, so find me there if you don’t want to wait for the articles! While I keep track of my own data for the next installment, I invite you to follow along or even try some data collection of your own and share it with someone. Until next time, stay tuned!

The post You’ve Got Mail: Part 5 of a Year-Long Personal Data Project appeared first on Nightingale.

For this set of four weeks, I wanted to pick a type of data that would balance out my previous visualizations about work and allow me to focus on something other than productivity. I decided on entertainment. In order to begin understanding my own entertainment landscape, I collected information about sources of entertainment for the first week.

Collecting this data consisted of making a tally mark in a chart every time I used each entertainment source because I wanted to get a sense of the frequency with which I used each platform. I don’t often think much about how many places my entertainment comes from, so it was surprising to me what a wide variety of games and other media I engage with each day. However, there was an obvious depletion of variety as my data collection continued. Each week, I begin collecting data on Friday and continued through the following Thursday, so the first three days of data (03/36-03/28) were weekends, when I had more time to relax and, therefore, entertain myself. For the other four days of the week, there was less variety in my entertainment and I used each source less frequently overall.

Beyond variety and frequency, this visualization shows how I access most of my entertainment: through my phone. Though I expected this to be the case, I was also pleasantly surprised to note that I used analog entertainment at least once a day, often by reading, but sometimes by playing a board game or painting.

After seeing the sources of my entertainment laid out, I wondered how I chose between such a wide variety. This prompted me to write down why I decided to use each form of entertainment I engaged with for the following week.

Based on my observations, I created a decision web showcasing how I choose what media or entertainment to use. I found that much of my decision-making process was based on the content medium, so that is the center of my web. From there, I asked myself questions about more specific criteria, like who I’m with, who the content comes from, and how much time I have. Each combination of choices leads to an entertainment source. When I came up with the idea for this visualization, I was concerned that I would have to squeeze a ton of questions into a (relatively) small space, but was shocked when even the most involved decisions only took three questions to surface a conclusion.

Although I enjoy finding insights into my own habits, I thought it would be interesting to widen my gaze for the third week and explore how I shared entertainment with other people in my life.

With this visualization, I wanted to emphasize the different sets of people I shared entertainment with, all of whom live at home with me. Based on my viz, I most often share with three of my sisters: Liza, who is two years older than me, and Talia and Nola, who are eight and eleven years younger than me, respectively. Interestingly Zane, my younger brother, is most often the person with whom I share social media posts. A factor that impacted this data was the fact that my dad and sister Anya were out of town for a portion of the week in order to visit colleges. Since they weren’t in the house, I couldn’t easily sit down to watch a movie or share other media with them, thus they were absent from the dataset. Overall, I wanted this visualization to convey togetherness, represented by the blossoming flower format.

As I approached my last week of entertainment, I realized that though I had painted an excellent picture of the culture and habits around my entertainment, I hadn’t actually tracked the content of the media I was consuming. Music is the most pervasive type of entertainment in my life, so I set out to track the genres of music I listened to for the week.

Much like the previous visualization, the symbols of this viz were inspired by objects in the world: this time, records. Each record is a song I listened to and is grouped with others in its genre, determined by the artist of the track.

I wanted to show who chose the music because there were two events throughout the week when someone other than myself was in charge of the music, a household dance party to celebrate my sister’s birthday and a bonfire in our backyard celebrating the good weather. For both of those, my brother dj’ed. He has an affinity for rock and rap music, so those genres contain many blue records. On the other end of the spectrum, most people in my family don’t listen to indie or folk music, so those genres were almost exclusively played by me.

In summary, variety is the name of the game when it comes to entertainment in my life. Not only did I discover how much entertainment I have access to, but I also learned how often I share it with others. That sharing fosters connection in my life and I’m glad I took the opportunity to fully quantify and appreciate that.

Now that this set of entertainment vizzes is finished, I’ve started collecting data for the next theme: messages. If you’re chomping at the bit for more, I’m updating my personal data collection journey weekly on my Twitter, Instagram, and TikTok, so find me there if you don’t want to wait for the articles! While I continue collecting data for my next installment, I invite you to follow along or even try some data collection of your own and share it with someone. Until next time, stay tuned!

The post Are You Not Entertained?: Part 4 of a Year-Long Personal Data Project appeared first on Nightingale.

For this timeframe, I collected data about work. My gut told me that I should start by measuring how much I get done every day, since the goal of work is usually to be productive. As a metric, I counted how many tasks I completed for the first week, including what the task was and what time I finished it.

To celebrate the accomplishment of completing tasks, I represented my data as blooming flowers. Each long, thin petal is a task I completed and the color shows the type of task. Upon review, I was proud to see that I cleared my inbox at least once a day for the entire week. I pride myself on responding to people in a timely manner, and this was a gratifying way to illustrate that I’m meeting my goal!

The second facet of this visualization is the height of the stems. Because I tracked when I completed each task, I thought it would be interesting to represent the span of time between when I completed the first task of the day and the last task of the day. Each leaf is one hour of time, so for the first day, there were 14 hours between the completion of my first and last tasks. Visualizing this aspect of the data shows that the span of time I spend completing tasks does not necessarily correlate with how many tasks I complete. For instance, the amount of time between the completion of my first and last tasks is the same for days four and five, but I clearly completed more tasks on the fifth day.

I was intrigued by the lack of pattern in the time span of my tasks, so for the second week of work data I tracked when I was working and what type of work I was doing. I was hoping to gain a little more clarity about how much time I actually spent working because I know that I often work in spurts with significant breaks in the middle, which wasn’t reflected in my previous data. I also wanted to see the separation between time spent on daily and weekly tasks versus time spent working on longer-term goals, since my previous visualization only counted a completed task when it was entirely finished.

This visualization is a pretty straightforward schedule, with each row being a different day and the time progressing from let to right. In order to make the data points as clear as possible, I put a break in the timeline from 2am-8am because I never did work during that time. One thing I noticed when creating this visualization was that I occasionally work on other tasks while in class or large group meetings. To represent this, I tried a few different overlapping techniques, but ultimately decided to split the rectangles in half so that they could occupy the same space on the timeline.

An unusual trend that surfaced when I completed this visualization was how much time I spent on career planning and preparation during this week. I realized that this happened because I attended the National Institute for Computer-Assisted Reporting (NICAR) conference from March 3–5, sending me on a data journalism conference-induced whirlwind of trying to figure out what I’m doing post-graduation. Another anomaly in this work schedule is the time I spent working on my tiny exhibit, which was a virtual exhibition called Isolation Celebrations that I was selected to curate. The exhibition was scheduled to go live the week after this data was collected, so I was making my final preparations.

The end of this visualization showed a large chunk of time during which I worked on my senior thesis. Since the following week was the last week before I had to submit my complete rough draft, I kept note of how my thesis work progressed.

The form of this visualization really encompasses how I was feeling as I wrote the final chapters of my thesis — increasingly stressed, but pushing myself to be productive. The outer ring shows my mood and ability to focus, which varied widely throughout the week. It also provides a countdown until my draft was due, from seven days to one day. The inner ring shows my productivity levels which, despite my boundless anxiety about finishing, did increase substantially as the deadline loomed closer. Retrospectively, I shouldn’t have been as worried as I was, but hindsight is 20/20!

After collecting productivity-related data for three weeks, I wanted to look at work from a different angle. To expand beyond myself as an isolated individual doing work, I collected data about my work environment, including where I was and who was with me.

When creating this visualization, I wanted to focus on grounding myself in my work environment. To emphasize this, I purposefully didn’t include any indication of how long I was working for or what kinds of tasks I was completing. Without this information, the data forces the reader to focus on the way my environment and I move. For instance, on the first day, I switch environments a few times, from the rec room to the kitchen, my parents’ room, and back to the rec room again. On the other hand, when I work in rooms with heavy foot traffic, like the kitchen and living room, my environment is constantly changing around me as people walk in and out. The last string of purple ovals even show me listening in on a webinar while my family and I make lunch in the kitchen!

In the end, quantifying how and how much I work has allowed me to see how much I commit myself to what I do. I love what I study and try my best to make the most of every opportunity, even if it means 14-hour days or finishing in the early hours of the morning. These visualizations have also shown me how much pressure I put on myself to succeed, which both motivates and tires me. I hope that with these new insights, I’ll be able to better balance my drive to be productive and my need to take care of myself.

Now that my four weeks of work vizzes are complete, I’ve begun collecting data for the next theme: entertainment. I’m updating my personal data collection journey weekly on my Twitter, Instagram, and TikTok, so if you’re too anxious to wait for the articles, check it out! While I continue through the four weeks until my next installment, I invite you to follow along or even try some data collection of your own and share it with someone. Until next time, stay tuned!

Emilia Ruzicka studies data journalism at Brown University and will graduate in May 2021. Her current work includes a year-long personal data collection project, a podcast about the USPS, and her newly-completed senior thesis. Find out more at emiliaruzicka.com.

The post All in a Day’s Work: Part 3 of a Yearlong Personal Data Project appeared first on Nightingale.

Over the course of the last four weeks, I’ve been collecting data about different types of movement in my life. Just like the first visualization I did with my last theme, I started with a literal interpretation of “movement” before moving on to more conceptual definitions. When I think of moving, I think about walking places, especially because COVID has understandably limited travel, so I decided to record every time I walked to a different room of my house.

I had a ton of data at the end of week five because I recorded not only rooms I intended to go to, like my bedroom or the kitchen, but also rooms that I just happened to pass through, like the foyer or sitting room. Due to the sheer amount of noise in this dataset, I decided to only visualize the times when that room was my destination. This decision led to cleaner data and told a more specific story that gave me some insight into where I spend most of my time.

It’s obvious from the floor plan that I spend a large share of my days on the third floor in my bedroom, my bathroom, and the rec room. I have a table set up in the rec room where I attend virtual meetings, zoom into class, and do all of my work, so it didn’t surprise me that the rec room was a frequent destination. However, something that popped out at me after making this visualization was how often I visit the dining room. The dining room is where we keep the fancy table and chairs, but we almost always eat at the kitchen table instead (hence why I was in the kitchen so often). But I realized after making this that since the pandemic started, my family has set up the dining room table as a puzzle station: my frequent visits were to work on the puzzle with my siblings.

My inspiration for week six’s data was to attempt to monitor more localized body movement. Each time I stretched, I wrote down what part of my body I was stretching, whether or not I cracked a joint in that area, and a quick note about why I stretched. This ranged from just waking up to being stiff and even just stretching because I was restless.

The layout for this viz is simple, but it revealed localized data without extraneous labels or explanations. Upon review, it did not surprise me that I stretched my back the most often, as that tends to be a body part that I pay attention to more than others because of some physical therapy I did for back pain in middle school. One point that did surprise me was that I only fidget on the lower half of my body. This may be because I like to move a little during Zoom classes, but when my camera is on, anyone in the meeting can see the upper half of my body. Fidgeting with my legs and ankles keeps my restlessness covert.

During week seven, I started to explore somewhat more creative interpretations of the “movement” theme. I wanted to record the pace of my days — whether they went by quickly or slowly, and how that impacted my mood. This exploration forced me to be more aware of what I was doing in order to record as much data as possible.

At first, I was unsure how to display this data because it was more abstract than my last two weeks’ had been. However, since the data was abstract, it was an interesting opportunity to experiment with more abstract visualization styles as well. In this viz, each triangle is a time when I noticed that the pace of my day was either unusually fast or unusually slow. I picked triangles because they imitate the “play” symbol.

What struck me about this visualization was how often I felt a wide range of emotion despite the pace of my day. “Bored” was the only feeling that exclusively appeared during slow-paced moments. All other emotions appeared during both slow- and fast-paced times, which shows how versatile emotions can be.

For the final week of the movement theme, I was excited to explore more conceptual data, so I settled on recording times when I felt emotionally moved. I knew that I needed to track more than just how I was feeling, so I also wrote down any physical reactions I had when I felt moved, like smiling, having a tight throat, or welling up with tears.

Once again, with abstract data, using more symbolic visualization techniques makes a fun experiment. The droplet shape of each data point looks like a teardrop when the point is up (negative emotion) and the upper part of an exclamation point when the point is down (positive emotion). The size of the droplet also corresponds with how long the feeling lasted — the bigger the droplet, the more time I spent with that feeling.

The most glaring trend in this visualization is how many times I was moved in a positive way compared to the very few times I was moved in a negative way. Though there are many crises to confront right now, seeing how often I felt grateful, loved, and comforted reassures me that there is still hope for things to get better. Another important trend I noticed was that the vast majority of the instances when I felt moved happened while having a conversation with another person. This shows how much I rely on human connection, even though I’m still confined to my house to keep the people I love safe.

Overall, this second set of data collection has taught me that my life is more dynamic than I expected. I often feel stuck because I haven’t been able to physically go to as many places as I used to before the COVID-19 pandemic, but I’m still constantly walking around, moving my body, changing pace, and reacting to those around me.

After wrapping up my exploration of movement, I’ve already started collecting data for the next theme: work. I’m updating my personal data collection journey weekly on my Twitter, Instagram, and TikTok, so if you’re raring to see my visualizations as I make them, check it out! While I continue through the four weeks until my next article, I invite you to follow along or even try some data collection of your own and share it with someone. Until next time, stay tuned!

Emilia Ruzicka studies data journalism at Brown University and will graduate in May 2021. Her current work includes a year-long personal data collection project, a podcast about the USPS, and her senior thesis. Find out more at emiliaruzicka.com.

The post Moving Right Along: Part 2 of a Yearlong Personal Data Project appeared first on Nightingale.

Hello! I’m a student, data journalist, and designer with a love for all things dataviz. In accordance with annual traditions, I spent a lot of time thinking about my New Year’s resolution this year. Because the COVID-19 pandemic has kept me confined to my family’s home since March, I wanted to take more control of my everyday life and learn more about myself in the process.

I was inspired by Dear Data: a personal data collection project that Giorgia Lupi and Stefanie Posavec conducted for an entire year. In March, I did a three-week version to track the immediate changes to my communication style after evacuating my college’s campus. I decided to follow in Lupi’s and Posavec’s footsteps and collect a different piece of personal data every week for a year, but since I don’t have a partner, I added a twist.

Each week for fifty-two weeks, I’ll collect a different data point about my habits, activities, or attitudes, but those data points won’t be chosen at random. Fifty-two can be evenly split into thirteen groups of four, so each chunk of four weeks will have a theme. These themes can be anything: from entertainment to energy or color to connection. The important part is that those four weeks of data collection will be connected and (hopefully) reveal a greater truth about my relationship to that topic, which I’ll be able to share!

For the last four weeks, I’ve been collecting data about food. I started by tracking when I eat. I simply wrote down the time that I started and finished eating every meal. I also added little notes next to the time if there was anything that directly impacted the timing of my meal.

Because I recorded specific times instead of just how long I spent eating, I was able to organize this data visualization just like a typical hourly schedule. This layout revealed a few things immediately. First, dinner is by far my most consistent meal of the day. I’ve never been one to skip meals, but I know that I often prioritize doing work or attending meetings over having a meal at a “normal” time. However, since I eat dinner with my entire family, it’s at approximately the same time every night. My lunches show the opposite trend — they vary widely both in timing and length, likely because I am most busy during the middle of the day. I tend to just run down to the kitchen for a quick bite between meetings or working on assignments instead of taking the time to have a heavy meal.

Another thing I noticed is that I don’t snack as often as I’d imagined. I thought that I ate at least one snack a day, but during the week I recorded my mealtimes, I only snacked on three days, including one day when much of my eating was fueled by stress.

Moving into week two of data collection, I wanted to see in more detail what a typical week of meals looked like. I anticipated a relatively wide variety of dishes, as I do my best to eat a healthy mix of foods. I also challenged myself to be a bit more illustrative with my visualization, so I decided to draw simplified versions of each of my meals. These little drawings were made possible because I wrote down what I ate and the main colors of the foods, as well as how much I enjoyed the meal and how satisfied I felt when I was done on a 1–5 scale.

Overall, my meals were more monochromatic than I would have liked, though this may be in part because I standardized the size of the sides for aesthetic purposes instead of making them proportional to the meal that I had. Despite this flaw, the format of this visualization did allow me to size the “plates” of food by how large the meal was. Large plates primarily went along with dinners, while medium and small plates encompassed breakfasts, lunches, and snacks.

The data for week three were by far the most challenging to track. I tried to write down every conversation I had with another person that involved food, including the topic and participants. This was difficult because I live in a house with my two parents and five siblings, which adds up to a ton of conversations! I found myself tracking conversations in my head as they flowed organically and writing them down when there was a break or lull instead of trying to take notes in real time.

As is apparent from all the solid circles on this visualization, the vast majority of my conversations about food were with my family. Occasionally food would come up while chatting with a friend or neighbor, but because I cohabitate with my parents and siblings, I’m far more likely to talk to them about meal plans, how something tastes, or simply what’s in the fridge.

I also noticed that the blue rings, which symbolize conversations where someone gave their opinion about a food, tended to cluster together. Two big clumps of blue rings can be seen on January 16 and January 19, which coincided with a baking itch that I decided to scratch. On each of those two days, I baked a different kind of pie and most of the blue rings were compliments about my handiwork!

After reflecting on my first three weeks of data collection, I chose to make my last week of food data collection slightly more reflective. During week four, I wrote down all of the thoughts I had about food and when I ate within the web of those thoughts. This manifested into a noticeably different type of visualization than the first three weeks.

When I examined my week four data to make this visualization, I realized that all of my food-related thoughts could be placed into four categories: feeling hungry, craving a particular food, thinking about my next meal, or telling myself that I should take a break to eat. I represented each of these thoughts with a leaf on a stem. That stem can either be cut off by eating (a flower) or by ending my day (an unadorned stem). It’s also possible for me to eat a meal without actively thinking about it in advance, which is depicted as a flower in the dirt.

This visualization suggests that after I think about food two or three times, I usually decide that a meal is in order; however, there were occasions throughout the week where I thought about food five or even six times before giving in to my mind’s insistent messages. Once again, I noticed that all of my snacks came at the end of my days, as opposed to in between lunch and dinner, which is when my younger siblings typically insist on a treat.

These last four weeks of personal data collection have allowed me to examine my relationship with food in a unique way that revealed some surprising trends. I’m not as much of a snacker as I thought I was and I eat fewer greens than I had imagined. I’m also pretty responsive to my body’s signals for food, which was a welcome revelation!

With the first four weeks of data collection finished, I’ve already begun the next theme: movement. I’m updating my personal data collection journey weekly on my Twitter, Instagram, and TikTok, so if you’re interested in following along with my visualizations in real time, look no further! In the space between this article and the next, I invite you to follow along or even try some data collection of your own and share it with someone. Until then, stay tuned!

Emilia Ruzicka studies data journalism at Brown University and will graduate in May 2021. Her current work includes a year-long personal data collection project, a podcast about the USPS, and her senior thesis. Find out more at emiliaruzicka.com.

The post Food for Thought: Part 1 of a Yearlong Personal Data Project appeared first on Nightingale.