#this is in progress

[megalommi]

I'm building a playlist

[wormsslime]

fuck it im doing analysis on the blizzard diversity data

[id: a screenshot of an application, showing a breakdown of information for Overwatch. The left panel shows unselected categories “Difficulty * (8)”, “Difficulty ** (12)”, and “Difficulty *** (9)”, and selected categories “Gender: Men (14)”, “Gender: Women (14)”, “Morale: Evil (8)”, “Morale: Good (14)”, “Morale: Neutral (7)”, “Role: Damage (16)”, “Role: Support (6)”, and “Role: Tanks (7)”.

The second panel shows the members of the Support role, with Ana, Brigitte, Lucio, Mercy, Moira, and Zenyatta, all individually unselected.

The final and largest panel shows a table and a radial line graph for the selected categories across the metrics “Culture”, “Race”, “Age”, “Cognitive Ability”, “Physical Ability”, “Body Type”, “Facial Features/Beauty”, “Gender Identity”, “Sexual Orientation”, and “Socioeconomic Background”. Interestingly, the graph and the table seem to be using different data. data under the cut]

the table shows numeric values, while the graph has unscaled axes but has dots that i will be treating as a linear scale from 0 to 10. also, some of the lines overlap in unclear ways, and i dont have the interactive version that would allow me to highlight categories, so i will be making my best guess. i’m not going to use the graph in the analysis, anyway.

from the graph, with the metrics, as above: “Culture”, “Race”, “Age”, “Cognitive Ability”, “Physical Ability”, “Body Type”, “Facial Features/Beauty”, “Gender Identity”, “Sexual Orientation”, and “Socioeconomic Background”

Gender: Men: 7, 6, 8, 5, 7, 6, 7, 0, 5, 3 Gender: Women: 7, 7, 7, 5, 4, 8, 4, 5, 0, 2 Morale: Evil: 7, 6, 5, 3, 7, 3, 4, 5, 0, 3 Morale: Good: 7, 7, 8, 5, 4, 0, 2, 5, 5, 3 Morale: Neutral: 8, 8, 7, 5, 6, 8, 7, 5, 5, 3 Role: Damage: 8, 8, 8, 5, 7, 6, 7, 5, 5, 3 Role: Support: 7, 7, 7, 5, 4, 2, 4, 5, 0, 2 Role: Tank: 6, 4, 8, 5, 4, 8, 3, 5, 0, 0

the table has the following data instead, using the same columns:

Gender: Men: 2.929, 1.643, 2.857, 0.571, 1.857, 0.714, 1.286, 0.000, 0.357, 0.929 Gender: Women: 3.286, 2.357, 1.357, 0.714, 0.500, 1.143, 0.571, 5.000, 0.357, 0.357 Morale: Evil: 2.250, 1.500, 2.125, 0.375, 1.375, 0.375, 1.000, 2.500, 0.000, 1.250 Morale: Good: 3.214, 1.500, 1.714, 0.714, 1.000, 1.643, 1.071, 2.500, 0.357, 0.357 Morale: Neutral: 4.286, 3.286, 2.571, 1.429, 1.286, 0.000, 0.429, 2.143, 0.714, 0.429 Role: Damage: 3.250, 2.438, 1.563, 0.813, 1.438, 0.750, 1.063, 2.188, 0.625, 1.000 Role: Support: 2.833, 2.167, 2.333, 0.833, 1.000, 0.333, 0.833, 3.333, 0.000, 0.333 Role: Tank: 3.429, 0.571, 2.857, 0.714, 0.714, 1.714, 0.571, 2.143, 0.000, 0.000

[laws-hat-headcanons]

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She/Her – 32 – In the UK

I basically just write One Piece nonsense and reblog stuff!

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[thepastel-prince]

My yuri jacket just came in.

[swiftology101]

The Courses

Swiftology 101

The basics! Introductory Course for Swiftology (the study of Taylor Swift and her discography). Songs included in the syllabus are her most popular, best known songs. These are the number one hits, the songs that non-swifties know. If you were to meet someone who doesn't know anything about Taylor Swift, these are the songs you show them first.

Swiftology 102

Okay, you got the basics, now it's time to go a little deeper. These are songs that have entered the public consciousness, but aren't ubiquitous. People might know the song but not know it's by Taylor Swift; it might come on the radio randomly or be added to a random Spotify playlist. Songs on this syllabus are NOT deep cuts, but not necessarily singles either.

201: Understanding the Artist

Songs included in this syllabus are songs that are necessary to understanding the songwriter. These songs make up key aspects of Taylor Alison Swift. They may chronical major events in her life, "core memories," as they say, or highlight key parts of her personality. These are keystones within Swiftology. Some songs may be well known, others maybe not, but all are integral to the artist.

202: Understanding the Music

These are the songs that define Taylor Swift's sound - she bends and jumps genres, yes, but her songs still sound like Taylor. These are songs that represent her musicality, her constants. When you hear one of these songs, no matter the era it originates from, you know it's a Taylor Swift song because of how it sounds.

301: tbd

[chaotic-peeps-yo-blog]

"Fear us, for we are the wild."

- from an unfinished chapter

~mod cat ✨

[tendersounds]

I think sometimes, about what it means to love Never felt it. Brushes with destiny That I missed through doubts, Dreams of something greater I was too stuck too chase It’s a goose chase between “what they find in me won’t be good enough” and “I can’t do this alone…can’t let them see my bare rugged heart, scrape it and break it as we wrestle this thing out, maybe for eternity, I don’t know what to do, I don’t know how to do this” I think my mind is my biggest enemy and my heart is my no-good lover, It makes promises it can’t keep and For risk of breaking, shattering into pieces in the hands of another. How do I shed off the fear? When do I become fearless? Truly? When can I have faith that things will work out for ME, and me and me? For me and my soul? For the lighter voices that whisper into my ears that maybe I should Take the jump. Over the balcony with a wine bottle in hand Nothing to lose but a life well lived, a heart well travelled