When will AI be able to tell you the risk / reward of cleaning up trees? Are conferences back? Bringing fidgets to the web, internet as an anxiety machine, and Chris is working on talk on modern CSS in real life.

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Maggie Appleton talks with us about her work at Elicit, working with large and small language models, how humans vet the responses from AI, the discussion around the Soggoth meme in AI, using Discord as UI, what to do if your boss wants AI in your app, and why does she call her blog a digital garden?

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SASTRA’s the School of Arts, Sciences, Humanities and Education, in collaboration with Sahitya Akademi, organises seminar on the celebrated playwright in Thanjavur

Rob Weychert reaches for the top notes to sing us a song of typographic scale. A little attention to scale and to the mathematics will help you to hit a high note with your designs this Christmas and beyond.

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I’ve been studying music theory this year. While some of its core concepts were already familiar to me, much of their specifics were not. Or so I thought. A funny thing happened when I was learning the major scales.

While playing through a song I had written some years before, I started picking it apart to see how it correlated with the theory I was learning. I had composed the melody without any thought to what the specific notes were, but as I started to transcribe them, a pattern quickly emerged: all the B’s and E’s were flat and the rest of the notes were natural. Lo and behold, long before my music theory studies began, I had written a song in B♭ major. My ears already knew how the major scales worked even if my brain didn’t. (If you know how “do re mi fa so la ti do” is supposed to sound tonally, then your ears know, too.)

When music is composed to a scale, it sounds “right” to us. And just as our ears appreciate harmony and melody with a rational basis, our eyes can appreciate the same concepts applied to spatial relationships.

Have you ever struggled with sizing type in a design project, especially when you need more than just one or two sizes? Have you ever despaired at the number of ad-hoc type sizes on your site spiraling out of control over time? It could be that you’ve been composing the typographic equivalent of a cacophonous symphony. And the first thing any composer will tell you to do is to get that thing on a scale.

Meet the typographic scale

You don’t need to know music theory to work with a typographic scale. You only need to know that a scale is a range of values with an established mathematic relationship. For a typographic scale, that relationship is frequently a steady interval between type sizes. Depending on what you need your type to do, the interval might be fixed (e.g. each size is two pixels bigger than the size before it) or it might be proportional (e.g. each size is twice as big as the size before it). I personally rarely find fixed intervals useful, so I’ll be focusing on proportional intervals.

The most important thing to understand about proportional intervals is thankfully not complicated: The bigger the intervals are, the more drastic the size differences will be in your scale. If your layout calls for contrast, a bigger interval might be the way to go. If you’re aiming for something more nuanced, go smaller. But keep these things in mind:

• There is such a thing as too much nuance: if a size on your scale is virtually indistinguishable from the sizes adjacent to it, it defeats the purpose of using a scale.
• On the flip side, too much contrast renders the sizes’ proportional relationship moot. At a certain point, massive display type is arguably more graphic than textual.
• More is less. The more sizes you use, the less they’ll mean.

Setting up the scale variables

The quickest way to get a scale up and running when working on the web is to drop its values into some CSS variables. The naming convention I typically use begins with --scale0, which is the body text size. The size below it is --scale-1 (as in “scale minus one”), the size above it is --scale1, and so on. Keeping the names relative to each other like this helps me move around the scale intuitively as I use it. If, say, --scale4 isn’t big enough for my h1, I can move up to --scale5 or --scale6, and I always know exactly how many steps away from the body text I am. Here’s a first pass at a simple set of scale variables using an interval of 1.5:

:root {
  --scale-2: 7.1px;  /* 10.7 ÷ 1.5 */
  --scale-1: 10.7px; /* 16 ÷ 1.5   */
  --scale0: 16px;    /* body text  */
  --scale1: 24px;    /* 16 × 1.5   */
  --scale2: 36px;    /* 24 × 1.5   */
}

I can use these variables with any CSS property that accepts a numeric value, like so:

p { font-size: var(--scale0); }

Rooting around in rems

I’m off to a good start. However, those px values are a little too absolute for my liking. If I convert them to rems, it’ll give my scale more flexibility. rem stands for “root em.” 1rem is equivalent to the html element’s text size, which in most browsers defaults to 16px. Crucially, though, users can adjust that size in their browser settings, and using rems in my CSS will respect those preferences.

:root {
  --scale-2: 0.4rem;  /* 0.7rem ÷ 1.5 */
  --scale-1: 0.7rem;  /* 1rem ÷ 1.5   */
  --scale0: 1rem;     /* body text    */
  --scale1: 1.5rem;   /* 1rem × 1.5   */
  --scale2: 2.25rem;  /* 1.5rem × 1.5 */
}

Another benefit of the relative nature of rems: I tend to use larger text sizes on large viewports and smaller text sizes on small viewports. Rather than adjusting dozens or hundreds of typographic CSS declarations per breakpoint, I can shift the whole scale up or down merely by adjusting the font-size on the html element:

html { font-size: 100%; }     /* 1rem = 16px */

@media screen and (min-width: 25em) {
  html { font-size: 112.5%; } /* 1rem = 18px */
}

Calculating with calc()

My scale is coming along. Its variables’ intuitive names make it easy for me to use, and its rem values respect the user’s browser preferences and allow me to easily shift the size of the entire scale at different viewport sizes. But my setup still isn’t optimized for one very important adjustment: the interval, which is currently 1.5. If 1.5 isn’t quite working for me and I want to see how an increase or decrease will affect the scale, I need to do the math all over again for every step in the scale every time I adjust the interval. The bigger the scale, the more time that will take. It’s time to put down the abacus and get calc() involved.

:root {
  --int: 1.5;
  --scale0: 1rem;
  --scale-1: calc(var(--scale0) / var(--int));
  --scale-2: calc(var(--scale-1) / var(--int));
  --scale1: calc(var(--scale0) * var(--int));
  --scale2: calc(var(--scale1) * var(--int));
}

My interval now has its very own variable, called --int. calc() determines each scale size by multiplying the preceding size by --int. Now that every size is ultimately dependent on --scale0’s value, --scale0 must appear first in the list. Since the sizes smaller than --scale0 are going down rather than up, their values require division rather than multiplication.

Scaling the scale

I can now quickly and easily tweak my scale’s interval by adjusting --int until the proportions are just right, but if I want to add more sizes to the scale, I need to add more variables and calc() values. This isn’t too big of a deal, but if I want to double or triple the number of sizes, it’s kind of a headache. Luckily, this is the sort of thing Sass is really good at. In the following code, adjusting the first four Sass variables at the top of :root will quickly spin up a set of CSS variables like the scale above, with any interval (proportional or fixed) and any number of scale sizes:

:root {
  $interval: 1.5;    // Unitless for proportional, unit for fixed
  $body-text: 1rem;  // Must have a unit
  $scale-min: -2;    // Unitless negative integer
  $scale-max: 2;     // Unitless positive integer

  --int: #{$interval};
  --scale0: #{$body-text};

  @if $scale-min < 0 {
  // Generate scale variables smaller than the base text size
    @for $i from -1 through $scale-min {
      @if type-of($interval) == number {
        @if unitless($interval) {
          --scale#{$i}: calc(var(--scale#{$i + 1}) / var(--int));
        } @else {
          --scale#{$i}: calc(var(--scale#{$i + 1}) - var(--int));
        }
      }
    }
  }
  @if $scale-max > 0 {
    // Generate scale variables larger than the base text size
    @for $i from 1 through $scale-max {
      @if type-of($interval) == number {
        @if unitless($interval) {
          --scale#{$i}: calc(var(--scale#{$i - 1}) * var(--int));
        } @else {
          --scale#{$i}: calc(var(--scale#{$i - 1}) + var(--int));
        }
      }
    }
  }
}

Go forth and scale

Typographic scales have been an indispensable part of my work for many years, and CSS variables and calc() make setup, adjustments, and experimentation easier than ever. I hope you find these techniques as useful as I do!

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About the author

Rob Weychert is a Brooklyn-based designer. He helps shape the reading experience at ProPublica and has previously helped make books at A Book Apart, games at Harmonix, and websites at Happy Cog. In his free time, he obsesses over music and film. Despite all this, he is probably best known as a competitive air guitarist.

More articles by Rob

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New York, NY : Routledge, 2016.

Berlin : Springer, [2005]

New York : Springer, [2005]

New York : Routledge, 2020

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Dr. S. Ramaratnam explains the relevance of the language

Full Schedule
Proceedings

Best quote: Librarians are like Mr. Paperclip from MS Office - we pop up when you least expect it and try to offer to you help...

This conference focused on the science library's role in supporting e-science and integrating into research collaborations and science departments. There was a mixture of speakers: government, library and institute directors, and a few librarians. The presentations were a mixture of big picture descriptions and some concrete examples. I felt like there wasn't as much hard solutions that we could take back to the library and implement, but perhaps just educating the library community on how radically different e-science is changing the research landscape is the necessary first step.

I've included the highlights from my session notes below (let me know if you'd like the see my full notes in gory detail). Check out the proceedings link above for powerpoint and document files for most of the speakers.

As a side note, our poster about GatorScholar was well-received with many people already aware of the project from either Val's USAIN presentations, the SLA poster, or from hearing about Cornell's project. Medha Devare was one of the panel reactors and she mentioned our collaboration in her presentation. Most of the poster visitors seemed very interested in starting their own version and perhaps at some point we'll have a network of databases.

Thursday

E-Science: Trends, Transformations & Responses

Convener and Moderator: Wendy Lougee, University of Minnesota
Speaker: Chris Greer, Director, National Coordination Office

2. A Case Study in E-Science: Building Ecological Informatics Solutions for Multi-Decadal Research

William Michener, Research Professor (Biology) and Associate Director, Long-Term Ecological Research Network Office, University of New Mexico


3. Rick Luce, Vice Provost and Director of University Libraries, Emory University Libraries

"Making a Quantum Leap to eResearch Support: a new world of opportunities and challenges for research libraries"


Wendy Lougee

Pitfalls: not to fall back on traditional roles, currently we don't respond to multi-institutional collaborations, our boundaries stop with the institution

We need to understand scientists' workflows, need to identify strategies for embedding librarians into project teams. We need to think about core expertise of librarians, reimaging roles of librarians

What do we do to build this collaborative action? We need to think outside the box.

Data Curation: Issues and Challenges

Convener and Moderator: James Mullins, Dean of Libraries, Purdue University

• Liz Lyon, Director, UKOLN

Transition or Transform? Repositiioning the Library for the Petabyte Era

How can libraries work with science (in a very general sense)?

1. Transition or Transform? Need to become embedded and integrated into team science. Many different models of engagement

Geosciences pilot where the library worked with the Geological department to curate their datasets (Edinborough):
Found: Time needed is longer than anticipated, inventory doesn't have to be comprehensive, little documentation exists
Outcomes: positive, requirement for researcher and auditor training, need to develop a data policy

2. Lots of opportunities of action: leadership by senior managers, faculty coordination, advocacy & tranining, data documentation best practices

People and Skills: there are not enough specialised data librarians. In UK 5 data librarians. Need to bring diverse communities together - facilitate cooperation between organizations and individuals.

Open science: new range of areas where results are being put onto the web (GalaxyZoo eg.) Librarians need to be aware of implications.

3. Need multidisciplinary teams and people in library, huge skill shortage, need to find core data skills and integrate it into the LIS curriculum. Recruit different people to the LIS team, rebrand the LIS career. Go from librarianship to Informatics.

• Fran Berman, Director of the San Diego Supercomputer Center, UC San Diego, and Co-chair Blue Ribbon Task Force on Sustainable Digital Preservation and Access

"Research and Data"

Researchers are detectives, shows different major questions (SAF, Brown Dwarfs, bridge stress, Income dynamics over 40 years, Disease spread-Protein Data Bank) - key collections all over.

CI Support: all these issues are crucial. researchers want a easy to use set of tools to make the most of their data.

She finds different preservation profiles: timescale, datascale, well-tended to poor, level of policy restrictions, planned vs. ad hoc approach

Researchers focused on new projects, customization of solutions to problems, collaboration

Researchers need help: developing management, preservation and use environments, proper curation and annotation, navigating policy, regulation, IP, sustainability

Questions about preservation: what should we save and who should pay for it? Just saving everything isn't an option. 2007 was the crossover year - digital data exceeded the amount of available storage. What do we want to save? Who is we?
Society: official and historically valuable data, Fed agency or inst normally takes part.
Research community: PDB, NVO.
Me: medical record, financial data, digital photos - real commercial market for preservation solutions.

What do we have to save?
private sector: HIPAA, Sarbanes-Oxley,
OMB regulations for fed funded research data (3 years, not always easy to do).

Economics: many costs associated with preservation. Maintenance upkeep, software, utilities, space, networking, security, etc.

UCSD forged partnership with library. Trying to create a preservation grid with formal policies, nationwide grid with other institutions.

Panel Responders:

• Sayeed Choudhury, Associate Dean of University Libraries and Hodson Director of the Digital Research and Curation Center, Johns Hopkins University

Data Curation Issues and Challenges:

It makes sense to help scientists deal with public and higher levels of data, not the raw data.

Considerations: need to work within their systems, consider gateways for systems as part of infrastructure development (think about railroad gauge), focus on both human and tech components of infrastructure, human interoperability is more difficult than tech interoperability, trust is key!

Questions: What about the cloud or the crowd? Can Flickr help us with data curation? What are the fundamental differences between data and collections? Human readable vs. machine readable? How do we transfer principles into new practices? What are we trying to sustain? Data? Scholarship? Our organizations?


Supporting Virtual Orgs

• Thomas A. Finholt, Director, Collaboratory for Research on Electronic Work (CREW) and Research Professor & Associate Dean for Research and Innovation, School of Information, University of Michigan

Changing nature of geographically-distributed collaboration:

history: transition in terms of distributed work. Much of what came before (collaboratory, video conf) had a precedent but new emerging has no precedent (crowdsourcing, VO's), no traditional context leaves us a bit adrift.

Lesson 1: anticipate cultural differences.
Domain scientists: characteristics: power distance (bias toward seniority, hierarchical), individualist(solo PI, individual genius), masculine(adversial and competitive), uncertainty avoidance
CI developers: power distance (bias toward talent, egalitarian), collectivist(project model), masculine, embrace risk

Lesson 2: plan for first contact.

It can be tough to recognize successful innovations: first efforts are often awkward hybrids



Crowdsourcing: idea that we send out challenges and solutions come to us (ex. Innocentive website, Games with a Purpose). We don't know who is going to do the work, effort is contributed voluntarily -> incentives are important to motivate work

Delegation of organizational work: people can count on organizations to do some of the basic policy work. Much attention has focused on technology and processes to support social ties, alternative course is the use of technology to supplant social ties - > think of this as organizing without the work of organizing, questions of who to trust, who pays, permitted to use the resources are managed by middleware.

Group work is an inevitable fact of org life.

• Medha Devare, Life Sciences and Bioinformatics Librarian, Mann Library, Cornell University

Idea of Virtual Organization: boundary crossing, pooling of competencies, participants or activities geographically separated, fluid, flat structure, participant equality

Library contributions: technology choices, tools; tech support/guidance; subject expertise; understanding of research landscape; vision - user needs of the future?

Examples of library support: VIVO, DataStar (supports data-sharing among researchers)

DataStar: Data Staging Repository: supports data sharing, esp during research process, promotes publishing or archiving to discipline specific data centers and/or to Cornell's DR. Nascent stage

Reinventing the library? Librarians as middle-ware to facilitate process of connecting and creating coherence across disciplines - both VIVO and DataStar aid this.

Hope that both tools seamlessly interact with each other.


D. Scott Brandt, Associate Dean for Research, Purdue University Library

Tries to embed librarians in research teams. We have to redefine what we do, collect.

Let’s spend some time looking at disclosures, the Dialog API, the Popover API, and more. We’ll look at the right time to use each one depending on your needs. Modal or non-modal? JavaScript or pure HTML/CSS? Not sure? Don’t worry, we’ll go into all that.

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The Different (and Modern) Ways to Toggle Content originally published on CSS-Tricks, which is part of the DigitalOcean family. You should get the newsletter.

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