#data analysis

[phanchester]

since there are so many data nerd phannies i decided to make a compilation of all the spreadsheets i could find - lmk if i missed any or if you want me to add any additional details <3

actively updating spreadsheets

dan and phil uploads from 2021-2024

dan and phil’s upload schedule from all their channels with days and dates

amount of days in between videos in each channel

pie charts of days of the week they upload

made by @ahappydnp 

everything dan and phil related

all of dan and phil’s video links from all their channels from all their accounts (including super amazing project, snapchat, vine, tiktok and more)

all of dan and phil’s radio shows, including reuploads and playlists, as well as the dan vs phil, fan war and internet news if available for each show with misc clips and written recaps

all of dan and phil’s liveshows, including some written recaps and the app where it was originally posted

all of dan and phil’s vyous including the question they were answering

all of dan and phil’s collaborations and video features (even if they were in the background), including the channel they were originally uploaded on 

all of dan and phil’s interviews

all of dan and phil’s merch, including originally shop links and links to the phandom wiki which has further information

all of dan and phil’s professional photos as well as some fan photos, including the event, photographer and platform

the dates and statuses of each of these videos (lost, archived, unlisted or public)

made by @stillarchivingdnp

dan and phil 2024 upload stats

each of their 2024 videos with channel, upload date, upload time in uk, length, sponsor and editor/s (if applicable) with an accompanying colour-coded calendar

(for amazingphil videos) whether dan featured and (for dapg videos) whether it was gaming/talking and who tweeted it

interactive part where you can see the time period between two videos

averages, maximums and minimums for times between uploads, upload times and runtimes with accompanying graphs

percentage of videos with other editors, with pie charts for all channels and each channel

made by @dnpbeats

all or nothing: dan vs phil season 2

all of the games for season 2, with the year they played them and the results with and without all or nothing coming into play

how often all or nothing came into play and who suggested it

the general impact of all or nothing

made by @organized-chaotic-disaster

dan and phil saying “i love you”

when dan and/or phil said ily

the video and timestamp from when they said ily and whether it was prompted

pie chart of dan or phil saying ily

made by @ahappydnp

games where one of them decides the winner

date and link for each video

overall winner with the winner for each round

breakdown of the amount of times each of them have won each round and the percentage phil has won

made by @dnpbeats

dan and phil 2024 upload schedule

upload date for each video, with the day of the week and approximate time it was uploaded in cst, including the most common and second most common upload day for dapg

days between each upload, including the longest gap, shortest gap, average gap and first and second most common gap for dapg

a colour-coded calendar displaying the upload schedule for dapg and amazingphil

made by @kat-aa

completed spreadsheets

all or nothing: dan vs phil season 1 with a great accompanying document with further details and analysis of the data

all of the games they played, with the year they played them and the results with and without all or nothing coming into play

how often all or nothing came into play and who suggested it

the general impact of all or nothing

made by @organized-chaotic-disaster 

youtuber tours

(not necessarily dnp but it includes them!)

120 different tours, including the creators, names, dates, countries, links (if available) and producers (if applicable)

each tours’ venue capacity range, average and total attendance

individual tour show breakdown with city, state, country and additional notes

data on each venue’s capacity, number of tours, and which youtuber went to each venue

data on each country’s amount of shows, broken down into states and cities

made by @stillarchivingdnp 

gamingmas 2023 schedule

all gamingmas video titles from 2023

the time each video was uploaded in gmt

made by @cactuslester

spreadsheet screenshots in posts

listening trends in all or nothing

scatter graph for the correlation between track number and number of listens

analysis of the data

made by @serendipnpipity

analysis of dnp’s letterboxd ratings and movies with part 1 and part 2

(pt 1) rating distributions for all the movies they’ve rated, including details about which movies one rated higher than the other, and which movies they rated the same

(pt 1) a list of their five-star movies

(pt 1) a list of movies one logged but not the other

(pt 1) cute little misc notes about the specific movies and dates

(pt 2) ratings broken down into genre, studio and franchise with accompanying bar charts

made by @philsrosesweatshirt

views on post-hiatus dapg videos after specific time frames

i believe this is a work of progress!

video titles with the dates and months, along with details of whether they were sponsored or had external editors

view count after 24 hours, 48 hours, 1 week, 2 weeks, 3 months and 6 months

made by @goldenpinof

#yes i was thinking abt making a spreadsheet for this... #but i thought that was too ridiculous #dan and phil #phan #phil lester #amazingphil #daniel howell #dan howell #danisnotonfire #spreadsheets #excel #data analysis

[thedatarogue]

All D&D 5E Spells, in one place

Hello, D&D players!

Are you tired of having to go through multiple books, pdf files or third-party websites just to find the one spell you're looking for? Don't want to pay hundreds of dollars to be able to access all the spells in DND Beyond?

Well, I've got just the thing for you.

In this Github repository, I've uploaded a dataset with information on all of the 5E spells (inside the datasets folder). The README file has all the information you need to use it, along with some other stuff.

If you have any problems using these assets, feel free to contact me either through here or Github. I'm thinking of adding pictures to the guide on Github, but it might be a while before I do that.

If the link isn't working, just remove the https://href.li/? part and you should be good.

Happy travels, adventurers.

#dnd #dnd5e #dungeons and dragons #dnd stuff #d&d #d&d 5e #ttrpg #data science #python #data analysis #data scientist #data visualization #programming #coding

[anotherobeymeblog]

Alright who wants some data??

I often see people talk about how underappreciated their favourite character is, regardless of which character it is, so it got me wondering which character actually has the most fics? Tumblr doesn't really allow for this type of search, but AO3 does, so here we have the number of fics tagged "(insert character)/Reader" on AO3!* (Disclaimer: other tags also exist, such as /Main Character, /Original Character, etc, but trying to incorporate multiple tags while avoiding fics that use both would be a nightmare, and /Reader seemed to be the most frequently used one across the board.)

*as of December 18th, 2023

No major surprises here, at least to me. The character with the most fics is Mammon (2020), followed pretty closely by Lucifer (1882), and the character with the least fics is Thirteen (46) followed by Raphael (59). I was mildly surprised that Asmo had more fics than either Beel or Belphie, but it was by a small enough margin that I wasn't completely shocked.

What I'd love to do is further break this down. See how many male, female, and gn mc fics there are, the ratio of sfw to nsfw fics for each character, maybe looking at which characters have the most sub! or dom!MC fics, but that would take a l o t more effort, so it'll be a bit before I can get that to you.

Anyway, hope this scratched your brain the way it did mine lol

#obey me #data analysis #obey me lucifer #obey me mammon #obey me leviathan #obey me satan #obey me asmodeus #obey me beelzebub #obey me belphegor

[mindblowingscience]

Cornell quantum researchers have detected an elusive phase of matter, called the Bragg glass phase, using large volumes of X-ray data and a new machine learning data analysis tool. The discovery settles a long-standing question of whether this almost–but not quite–ordered state of Bragg glass can exist in real materials. The paper, "Bragg glass signatures in PdxErTe3 with X-ray diffraction Temperature Clustering (X-TEC)," is published in Nature Physics. The lead author is Krishnanand Madhukar Mallayya, a postdoctoral researcher in the Department of Physics in the College of Arts and Sciences (A&S). Eun-Ah Kim, professor of physics (A&S), is the corresponding author. The research was conducted in collaboration with scientists at Argonne National Laboratory and at Stanford University.

Continue Reading.

#Science #Physics #Quantum Physics #Data Analysis #AI #Artifical Intelligence #Machine Learning

[jstor]

Frankenstein defeats Dracula

So, ITHAKA (JSTOR's parent nonprofit org) now has a new service called Constellate, and in it you can do data analysis, among other things. Not wanting to do what we're really supposed to be doing right now, we thought we'd check out how Dracula performed against Frankenstein, and to our surprise, the monster gets A LOT more mentions in scholarly literature than our Transylvanian Count. Y'all are a smart bunch, so feel free to do something less frivolous (or at least more interesting) at Constellate.org. All you need to get started is your free JSTOR login (or you can create one if you haven't yet).

#data analysis #text analysis #text mining #dracula #frankenstein #jstor

[chocolatepot]

A while back, when there was concurrent ship discourse on Twitter and Tumblr, I started looking at the AO3 statistics for OFMD to get a sense of what the actual proportions are of different ships. The basic stats are pretty clear from the sorting sidebar:

Blackbeard | Edward Teach/Stede Bonnet (12321)

Blackbeard | Edward Teach/Israel Hands (2269)

Black Pete/Lucius Spriggs (1561)

Blackbeard | Edward Teach/Stede Bonnet/Israel Hands (1411)

Oluwande Boodhari/Jim Jimenez (1394)

Stede Bonnet/Israel Hands (976)

Israel Hands/Lucius Spriggs (839)

Blackbeard | Edward Teach & Israel Hands (677)

Israel Hands/"Calico" Jack Rackham (474)

Minor or Background Relationships (283) (label not appearing in pie chart)

However! There's a significant amount of overlap with some of these. Pete/Lucius and Jim/Olu are common side ships to Ed/Stede, for instance - 327 fics are tagged with only Ed/Stede and Pete/Lucius (23% of the Pete/Lucius tag), 195 fics are tagged with only Ed/Stede and Olu/Jim (14% of the Olu/Jim tag), and 749 fics are tagged with Ed/Stede, Pete/Lucius, and Olu/Jim (48% of the Pete/Lucius tag and 54% of the Olu/Jim tag).

It's also common for fics with Ed/Stede/Izzy to tag one or more of the two-person ships that make up the triad - 378 (27% of the Steddyhands total) include all four ships, and each of the side ships have 600-700 fics that overlap with Steddyhands in some way. As best as I can tell from careful inclusions and exclusions in the filters, there are 11,106 fics that are just Ed/Stede and not the other three, 1,113 that are just Ed/Izzy, 456 that are just Ed/Stede/Izzy, and 248 that are just Stede/Izzy.

So then I went in and tried to find the data for fics with each of the ships as the only pairing out of the group (italics indicate that the label wouldn't generate because the slice was so small, but this is the order they appear in in the chart):

Blackbeard | Edward Teach/Stede Bonnet (9294)

Blackbeard | Edward Teach/Israel Hands (898)

Black Pete/Lucius Spriggs (214)

Blackbeard | Edward Teach/Stede Bonnet/Israel Hands (426)

Oluwande Boodhari/Jim Jimenez (320)

Stede Bonnet/Israel Hands (236)

Israel Hands/Lucius Spriggs (411)

Blackbeard | Edward Teach & Israel Hands (160)

Israel Hands/"Calico" Jack Rackham (279)

Minor or Background Relationships (31)

This isn't perfect - there's no way to filter main vs. background ships, so there's no way for me to differentiate between an Ed/Stede fic with Lucius/Pete and Olu/Jim tagged for background mentions and an Olu/Jim fic with Lucius Pete and Ed/Stede tagged for background mentions; there are also even smaller ships that come into play once I cropped out the heavy hitters. However, I think this data gives a clearer picture of authorial priorities on AO3.

#ofmd #our flag means death #ao3 #data analysis #I don't really have a point to make #I just think it's neat #okay maybe there's a little bit of a point #I used to get concerned about Izzy ships being in the upper tiers of popularity #but then I realized what a huge leap there is between Ed/Stede and everything else #when you refine the data for the last graph the Izzy ships do gain ground compared to Pucius and Tealoranges #but Ed/Stede gets even more overwhelming #the gentlebeard juggernaut cannot be stopped

[artwithlama]

11.02.24

Morning <3 I I hope you guys have a lovely day.

Going over my notes and reviewing my proposal this morning while enjoying the rainy weather.

Who’s your favorite artist atm?

#study motivation #study aesthetic #studyinspo #study notes #studyblr #study space #studyspo #study blog #studyspiration #student #notes #goodnotes #ipad #ipad notes #computer science #IT #data analysis #hozier #hozier to be alone #mine

[mothdapple]

I always knew that first arc-centric fics dominated most of Warriors fanfiction, but I was curious to see how much that was really the case. Being the data-driven nerd that I am, I took the top 100 Kudos Warriors fics on AO3, sorted them into different types, and these are the results.

No DotC, TBC, or ASC fics were in the 100 most kudos-ed fics.

I think the most surprising thing to me is how few TNP fics there are in the top fics. I know that TNP isn't the most popular arc, but TONS of iconic characters originate in it, and as the 2nd arc it also has time on its side to gain power with nostalgia for fics (or just rack up the kudos since the longer a fic is on AO3, the more readers/kudos it gets, hence I think most of these fics are biased to be older.)

There are also very few OC fics here (exactly 2) which isn't terribly surprising since almost every fandom favors canon character fics over OC fics, but I maybe expected a bit higher since Warriors in particular is a fandom rich with OCs. I am immensely proud, though, to say that one of those two fics in the top 100 kudos is mine, Cold Bloodlines :)

#warrior cats fanfic #warrior cats #data analysis

[herpersonafire]

Hey everyone! enjoying my (two) week break of uni, so I've been lazy and playing games. Today, working on Python, I'm just doing repetition of learning the basics; Variables, Data types, Logic statements, etc. Hope everyone has a good week!

#codeblr #coding #python #university #uni life #studying #datascience #data analytics #data analysis #studyblr #student life #study motivation #study blog #student

[amtrak-official]

Analysis of the test posts is complete.

The 3 most popular metro and intercity rail systems in the US are Amtrak, California High-speed Rail, and the BART.

The Least popular are the LA Metro, The Seattle Link and MARC

Based on this we can conclude that Intercity Rail is far more popular than local rail. Additionally the West Coast is much less popular than the East Coast.

Next data test should include METRA, Long Island Railroad, Brightline, and Metrolink to establish more clear modes. Additionally future data collection endeavors will exclude the Portland Street car in favor of MAX

#data analysis

[wouldtheybecomeafearavatar]

Stats Time!

All right, are you ready for some data analysis? I'll give an overview in this post, and then I'll probably analyse the data per Fear over the next while if there's demand for it (This post is long enough already).

Global stats

So far, as of the polls currently finished (up to the Bigby Wolf poll when I started making this) there have been 331 characters polled from 247 different sources. The average percentage for each option (leaving out the see results option and the polls from before the other fears option) were:

Yes, willingly: 44.48%

Yes, by coercion: 9.67%

Yes, by unawareness or conditioning: 24.31%

No, they would decline/resist from the start: 8.03%

No, they would go along but stop before transformation: 4.41%

No, they would already be an avatar for another Fear: 9.10%

I doubt people are submitting characters because they think they won't be an avatar, which I think accounts for how much more "Yes" votes there are in general, especially for "Yes, willingly".

For more heroic characters, if they are going to be an avatar it would make sense it would be by unawareness or conditioning. Some characters would probably have to get to the point that the alternative is death before they'd choose to become an avatar (though it's important to remember that anyone who becomes an avatar does have to make a choice).

Coercion and stopping before transformation are proportionally less used than other options. I suppose both are more unusual situations and less generally applicable for many characters, but it's possible that these options are just being underutilised. You guys will have to be the judges of that!

The Fears

Fear Submitted Avatars

The Buried: 7 3

The Corruption: 23 21

The Dark: 4 4

The Desolation: 32 31

The End: 27 26

The Extinction: 17 14

The Eye: 25 25

The Flesh: 13 13

The Hunt: 29 27

The Lonely: 31 27

The Slaughter: 30 28

The Spiral: 31 30

The Stranger: 21 19

The Vast: 10 9

The Web: 45 43

(Sorry about the funky table, Tumblr isn't really built for this)

The Web had by far the most submissions and avatars. I'd guess that this is probably due to the fact that villains are in general going to be more likely to want to become an avatar (few moral scruples, desire for power) and more likely to be Web aligned specifically (all that scheming). That, or Web aligned characters are just more popular!

The Dark was the least popular Fear with a grand total of only 4 submissions! I suppose it's a bit basic, maybe? Compared to the other Fears it's kind of hard to know what defines a Dark avatar in terms of personality. Still, I'm sure there's more out there! There are a couple of recent submissions in the inbox actually now that I think about it, but it'll take a while to get to those.

There were 25 characters who were deemed to not serve the Fear they were submitted for. Most Fears had 1 or 2 rejections, while Lonely and Buried had 4 each. No submitted characters rejected The Dark, Flesh or Eye though!

The Buried had by far the largest proportion (4/7) of characters rejecting it. I suppose that's pretty appropriate for a Fear that has "poor working conditions" as part of its domain. Like Dark, it also had quite few submissions. There's only a couple in the inbox at the moment too.

Top Characters by Poll Option

To calculate this, I divided the number of votes for each option by the total, and found the character with the max percentage. I decided to separate the characters into low vote characters and high vote characters based on if they had over 50 voters who knew them, as there is more uncertainty with lower votes.

Most willing:

High Voted - Jareth the Goblin King from Labyrinth for The Spiral, 112/121 votes (93%)

Low Voted - Nikolai Gogol from Bungou Stray Dogs for The Spiral, 20/21 votes (95%)

Funny that they're both for The Spiral. The Twisting Deceit has some enthusiastic followers on it's hands!

Most likely to be coerced:

High Voted - Anthy Himemiya from Revolutionary Girl Utena for The Buried, 21/56 votes (38%)

Low Voted - Nathan Wallace from Repo! The Genetic Opera for The Hunt, 16/34 votes (47%)

Wow, The Buried had to coerce one of it's 3 avatars? People really don't want to serve that Fear in particular. Interesting to note that even with the most coerced characters that option was still under 50% of the votes! It's definitely not being used as much.

Most unaware:

High Voted - Siffrin from In Stars and Time for The Lonely, 74/113 votes (66%)

Low Voted - Nelson Tethers from Nelson Tethers: Puzzle Agent for The Eye, 9/13 (69%)

Uh, oops. I still haven't reblogged Siffrin's poll, have I? Sorry In Stars and Time fans (Mostly because I want to get around to playing that game, but I've already spoiled several major plot points for myself, so that's hardly an excuse). Anyway, these are most likely to stumble into a Fear, and not realise they are transforming until they get to the point of it being an avatarhood or death choice.

Most resistant:

High Voted - Murderbot from The Murderbot Diaries for The Stranger, 52/83 votes (63%)

Low Voted - Captain Hawkeye Pierce from M*A*S*H for The Slaughter, 25/49 votes (51%)

Funny, these two are both associated with being asked to be violent and resisting. Congrats to them for being able to fight off a Fear outright.

Most likely to stop before transformation:

High Voted - Zuko from Avatar: The Last Airbender for The Hunt, 77/188 votes (41%)

Low Voted - The Traveler from Chants of Sennaar from The Lonely, 7/34 votes (21%)

This one seems to be even less utilised than the Coercion option proportionally. Zuko is a pretty good example of a character that people thought would fit this scenario, while The Traveller had more votes for decline and unawareness than stopping before transformation.

Most likely to serve another Fear:

High Voted - Jevil from Deltarune for The Buried, 43/74 votes (58%)

Low Voted - Sarah from The Shaperaverse for The Web, 11/30 votes (37%)

Interesting. For Jevil there was quite a clear consensus that the alternative Fear he would serve is The Spiral, but Sarah was voted a Web avatar and there was no clear consensus on what other Fear she would serve instead. She's nearly a rival for Monika in the number suggested. Most of the characters with a high number of votes for this option must be in the High Voted category.

Most unknown:

High Voted - Five Pebbles from Rain World for The Corruption, 41/52 (ratio of 0.79 for voters who did not vs did know this character)

Low Voted - Hijiri Kanna from Puella Magi Kazumi Magica: The Innocent Malice for The Extinction, 28/5 (ratio of 5.6 for voters who did not vs did know this character)

Five Pebbles is a character who has been polled twice, and somehow less people knew him in the second poll! That poll also got less votes, putting it in the Low Voted category, which is why this poll is being featured here. This goes to show that the characters more people knew tend to have more votes on the poll, which is not surprising. The most unknown character is Hijiri Kanna! Not super surprising for a character it was very difficult to find a decent picture of. I'm a fan of Puella Magi Madoka Magica and I had no idea this spinoff existed.

Conclusion

Well, that's all for now! Hope you found that interesting, I know I certainly did. I think it would be interesting to dig into the data Fear by Fear, so let me know if you'd be interested in that! If you have any questions fell free to ask. The spreadsheet is in the pinned post, so you can dig into the data yourself if you want.

#archivist activity #the magnus archives #tma #mod Moth 🦋 #data analysis #poll statistics #data my beloved #stats

[alwaysbewoke]

here is the sheet. it's an excel spreadsheet so it will download to your computer. this is really important for all the people who constantly say "democrats and republicans are the same." if they're not willing to do the work to either prove or disprove that notion, they're NOT someone ANYONE should be taking political advice from.

#politics #data analysis #voting records #bipartisan analysis #representation effectiveness #Democrats #Republicans #legislation tracking #political myths #factual analysis

[cbirt]

PCA (Principle Component Analysis) is a commonly used technique in biomedical research to identify similarities and differences between groups of samples. Though conventional PCA is a great tool, it still has some limitations in visualizing and quantifying batch effects; therefore, to overcome these limitations, researchers from the Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, USA, developed PCA-Plus. 

It is an enhanced version of conventional PCA with advanced and additional features for improved diagnosis, detection, and quantification of differences in a group of samples. It appears to be a useful and valuable tool for researchers working on large datasets, particularly in the field of bioinformatics and related areas, where batch effects and group differences are important considerations.

To untangle the complexities of datasets with many variables, PCA (Principal Component Analysis) is an important tool to help researchers in data analysis. PCA is a powerful statistical technique that sorts data into a simpler and much more understandable form. It identifies the main components that explain the most variance in the given dataset. PCA reduces the complexity of your data while preserving the essential information by focusing on the main components. This makes it easier to visualize trends and patterns, dimensionality reduction, feature extraction, and anomaly detection. It has been used in a wide variety of contexts, for example, image processing and compression, characterization of molecular dynamics, linguistic information retrieval, and assessment of batch effects.

Continue Reading

#data analysis #big data #pca #pca plus #bioinformatics #genomics

[d0nutzgg]

Tonight I am hunting down venomous and nonvenomous snake pictures that are under the creative commons of specific breeds in order to create one of the most advanced, in depth datasets of different venomous and nonvenomous snakes as well as a test set that will include snakes from both sides of all species. I love snakes a lot and really, all reptiles. It is definitely tedious work, as I have to make sure each picture is cleared before I can use it (ethically), but I am making a lot of progress! I have species such as the King Cobra, Inland Taipan, and Eyelash Pit Viper among just a few! Wikimedia Commons has been a huge help!

I'm super excited.

Hope your nights are going good. I am still not feeling good but jamming + virtual snake hunting is keeping me busy!

#programming #data science #data scientist #data analysis #neural networks #image processing #artificial intelligence #machine learning #snakes #snake #reptiles #reptile #herpetology #animals #biology #science #programming project #dataset #kaggle #coding

[jstor]

Yes, there are truly important things people do with text analysis, but we like to use it as kind of a competition. For example, we compared the number of mentions of "cat" vs "dog" in newspapers from 1900 to the present, and we were shocked to find out that cats were briefly in the lead up until about 1920, when it all started going downhill. Look at what happens in 2020!

You can run your own little competitions (or, you know, do some genuine research if you want) for free at https://constellate.org/builder.

Us, we're going to keep trying dumb competitions until the workday is over.

Image: A dog chasing a cat through a window. Etching by W. R. Smith after J. Pitman. From the Open: Wellcome Collection on JSTOR. Creative Commons: Attribution

#data analysis #text analysis #unigrams #cat vs dog #cats and dogs #we need some coffee #research

[chocolatepot]

I was semi-arguing with someone on HobbyDrama about the poll wank, and couldn't stop myself from looking up some stats ...

Number of fics total: Stucky - 21,616; Gentlebeard - 15,268

Earliest fic posted: Stucky - 2011 (ignoring the two backdated to 2002 and 1950); Gentlebeard - 9 March 2022

Average fics per year: Stucky - 1,801; Gentlebeard - 10,178

They do beat us in longfics, with nearly 500 more-than-100k-word fics to our 100.

#ofmd #pirate poll #data analysis