Category:Datasets: Difference between revisions

From Traxel Wiki
Jump to navigation Jump to search
 
(60 intermediate revisions by the same user not shown)
Line 1: Line 1:
To include your page here, tag it with <nowiki>[[Category:Datasets]]</nowiki>.
To include your page here, tag it with <nowiki>[[Category:Datasets]]</nowiki>.


= Datasets =
* CDC Mortality Stats Dataserver: https://wonder.cdc.gov/controller/datarequest/D158
** Raw Datasets: https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm#Mortality_Multiple
= Fusion Data =
* Open Source Fusion Conference: https://ossfe.github.io/
= Links =
= Links =
 
* Packaged Food Salt Mortality
** ~1.1 per 100k in Australia
** https://www.unsw.edu.au/newsroom/news/2024/10/tougher-limits-on-salt-in-packaged-foods-could-save-thousands-of-lives-study-shows
** https://old.reddit.com/r/science/comments/1gg6qfw/mandating_less_salt_in_packaged_foods_could/
* Race, Violence, and Equating Suicide and Homicide to Make A Study Sound Profound:
** https://old.reddit.com/r/science/comments/1avmovg/people_of_color_are_not_only_dying_more_often/
* World Mortality: https://github.com/akarlinsky/world_mortality
* World Mortality: https://github.com/akarlinsky/world_mortality
* US Mortality Delta: https://old.reddit.com/r/dataisbeautiful/comments/16torx0/oc_how_have_the_top_causes_of_death_in_the_us/
* US Suicide 2022: https://old.reddit.com/r/news/comments/15nsu5v/nearly_50000_americans_died_by_suicide_in_2022_a/
* US Suicide 2022: https://old.reddit.com/r/news/comments/15nsu5v/nearly_50000_americans_died_by_suicide_in_2022_a/
** 48,183 suicide in 2021 (all causes): https://www.cdc.gov/suicide/facts/index.html
** 2022 Again: https://old.reddit.com/r/news/comments/186sky1/number_of_suicides_in_the_us_in_2022_reaches/
** Men & Emotional Isolation: https://old.reddit.com/r/JustGuysBeingDudes/comments/18ic5cq/who_can_relate_to_this/
*** https://youtu.be/edv_bNEaYTQ?si=XCBiJ6Ve2zdGqs4N
* Overwork Mortality: https://old.reddit.com/r/worldnews/comments/186i7vo/working_more_than_55_hours_a_week_kills_750000/
* Gun Homicide
** Overall: 5.87 per 100k (329mm)
** Category of Males: 47 per 100k (22.7mm)
** All Others: 2.8 per 100k (306mm)
** All Males: 4.1 per 100k (139mm)
** Category of Males 20 - 34: 109 per 100k (5.53mm)
** Not previous: 4.1 per 100k (324mm)
** Homicide In General: https://old.reddit.com/r/MapPorn/comments/16lx9bh/cities_with_the_highest_homicide_rates_in_the/
** Violence Falling: https://old.reddit.com/r/UpliftingNews/comments/1ddgjr2/violent_crime_is_down_and_the_us_murder_rate_is/
*** https://old.reddit.com/r/news/comments/1dd2eel/violent_crime_is_down_and_the_us_murder_rate_is/
** US Surgeon General Gun Violence: https://old.reddit.com/r/news/comments/1do4cfy/us_surgeon_general_declares_gun_violence_a_public/
*** https://www.cbsnews.com/news/surgeon-general-declares-gun-violence-public-health-crisis/
* LEO Mortality: https://old.reddit.com/r/dataisbeautiful/comments/18gxkv4/most_dangerous_states_for_law_enforcement/
* Irregular Resistance Works
** https://old.reddit.com/r/CombatFootage/comments/164v42r/ukrainian_mass_drone_attack_on_russian_military/
*** https://old.reddit.com/r/pics/comments/163gqdx/ukraine_destroyed_several_russian_fighter_jets/
*** https://old.reddit.com/r/RCPlanes/comments/163y89j/ukraine_destroyed_several_russian_fighter_jets/
*** https://old.reddit.com/r/NonCredibleDefense/comments/11gukcc/australias_new_cardboard_drones_being_sent_to/
** Afghan Women, Suicide, Guns: https://old.reddit.com/r/worldnews/comments/1648z9b/report_disturbing_surge_in_afghan_female_suicides/jy74tr7/
* Cars
* Cars
** 282,366,290 registered vehicles: https://www.statista.com/statistics/183505/number-of-vehicles-in-the-united-states-since-1990/
** 282,366,290 registered vehicles: https://www.statista.com/statistics/183505/number-of-vehicles-in-the-united-states-since-1990/
Line 11: Line 46:
** 124,010,992 households in the US: https://www.census.gov/quickfacts/fact/table/US/HSD410221
** 124,010,992 households in the US: https://www.census.gov/quickfacts/fact/table/US/HSD410221
*** 2.60 persons per household
*** 2.60 persons per household
** 2021: 16 year high at 42,915 traffic crash fatalities
** 2021: 16 year high at 42,915 traffic crash fatalities: https://www.nhtsa.gov/press-releases/early-estimate-2021-traffic-fatalities
* vegan / pro-life, 2a / anti-car
** 232.8mm drivers (18.43 deaths per 100k drivers)
** US Population 335mm (12.8 deaths per 100k people)
** Un-Walkable Cities: https://old.reddit.com/r/Damnthatsinteresting/comments/1dpg5r0/example_of_how_american_suburbs_are_designed_to/
** "Our society has developed to require cars and I've accepted that."
** "The 2022 data show that seat belt use is at 91.6%, and unrestrained occupant deaths currently account for 49.8% of deaths."
*** https://injuryfacts.nsc.org/motor-vehicle/occupant-protection/seat-belts/
*** https://old.reddit.com/r/todayilearned/comments/1dsiiue/til_in_2022_seat_belt_use_in_the_us_was_916_of/
* vegan / pro-life, anti-gun / anti-car, religious freedom / lgbtq freedom
* Pigs
** Pig Puzzle: https://old.reddit.com/r/aww/comments/8f1r62/pig_puzzle/
** Pig Friend: https://old.reddit.com/r/aww/comments/ciazd7/so_my_dad_surprised_my_sister_with_a_baby_pig_and/
** Pig Lassie: https://old.reddit.com/r/aww/comments/cot0oi/my_pet_pig_used_to_explore_the_backyard_as_i/
** Pig Family: https://old.reddit.com/r/aww/comments/q5gvg7/a_whole_family_of_pigs_going_on_an_adventure/
** Pig Cat Play: https://old.reddit.com/r/aww/comments/13dn2b2/pig_playing_with_the_cats/
** Pig Search: https://old.reddit.com/r/aww/search?q=pig&restrict_sr=on
** Pig Splash: https://old.reddit.com/r/aww/comments/pgvspr/ridiculously_happy_pigs_running_in_and_out_of_the/
** Pig Vacuum: https://old.reddit.com/r/aww/comments/nn7bu1/pig_annoyed_by_vacuum_keeps_unplugging_it/
** Big Pig: https://old.reddit.com/r/wholesomememes/comments/16g33w8/consolation_prize/
* Obesity (143 / 100k)
** Twinkie Commercial: https://www.youtube.com/watch?v=9crUKp682eE
** 500,000 excess deaths: https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(22)00159-6/fulltext
== IPH ==
* "Risk Factors for Male Perpetration and Female Victimization of Intimate Partner Homicide: A Meta-Analysis": https://legislature.vermont.gov/Documents/2022/WorkGroups/House%20Judiciary/Bills/H.133/Witness%20Documents/H.133~Sarah%20Robinson~Risk%20Factors%20for%20Male%20Perpretration%20and%20Femal%20Victimization%20of%20Intimate%20Partner%20Homicide-%20A%20Meta-Analysis~2-23-2021.pdf
** 11x as likely with access to a gun
* IPH Stats: https://www.ojp.gov/pdffiles1/jr000250.pdf
* 10mm victims experience domestic violence annually: https://zontausa.org/us-national-statistic-on-domestic-violence-2020/
* 2,237 cases of IPH annually (2017): https://www.nytimes.com/2019/04/12/us/domestic-violence-victims.html
 
So, extremely roughly:
 
* 2,000 cases with access to a gun, 200 cases without.
* 20 per 100k with access to a gun.
* 2 per 100k without access to a gun.
* worst case 18 per 100k due to access to a gun. (could also be killers are more likely to have access to a gun - so maybe 10 - 15 per 100k is the realistic case)
* 10mm abusers annually
* 44% of households have guns
* 4mm abusers with guns kill 2,000 people (with roughly 1500 being avoidable)
* Each abuser has a 1:2000 chance of being a killer if they have a gun.
 
homicide:
* annual homicides: 26,031 or 7.8 per 100k
* annual gun homicides: 20,958 (81%)
* Population 350mm
* Each random person has at most a 1:13,461 chance of being a killer.
* domestic abuser without a gun is 1:20,000 to kill partner
* domestic abuser with a gun is 1:2,000 to kill partner
* domestic abuser with a gun is 1:1,346 to kill anyone (extremely rough)
* Which recapitulates the earlier stat: 10x as likely to kill. I think that's just how I mathed it, not an actual statistic.
 
= Life Expectancy =
* https://old.reddit.com/r/dataisbeautiful/comments/16lymi6/oc_life_expectancy_vs_health_expenditure/
* https://pbs.twimg.com/media/FsjYUjhWcAEatMo?format=jpg&name=medium
[[File:Us life expectancy gap.jpg|800px|frame]]
= Politics =
* Political Contemporary History Data
** Age Distribution Over Time
** Example Wikipedia Entry: https://en.wikipedia.org/wiki/Dan_Kildee
** Crime Over Time: https://en.wikipedia.org/wiki/List_of_American_federal_politicians_convicted_of_crimes
** Scandals Over Time
*** Political: https://en.wikipedia.org/wiki/List_of_federal_political_scandals_in_the_United_States
*** Sex: https://en.wikipedia.org/wiki/List_of_federal_political_sex_scandals_in_the_United_States
* Crowd Source Data
** Users tag data with classification tags, based on snippets from Wikipedia.
** Browser widget lets people jump to the next needed classification tag.
** "Needed" is defined by people putting money into refinement of the data.
*** "Needed" tips can include a desired outcome? "I'm only paying for data that confirms/contradicts my hypothesis?"
** "Provided" is defined by ... users putting cash in to vote on data quality and an ML algorithm allocating the correctness value of each classification based on other submissions and data quality votes.
*** I think it would be unavoidable that people would tip based on whether the answer agreed with their beliefs.
*** Payment for being right (high quality) could be made as an annuity with the future having the opportunity to correct the beliefs of the past. The residual revenue stream provides and extra incentive for not being disproven.
** Taggers get paid based on a function of "Needed" and "Provided"
* Collaborative Filtering of Content Displayed
** View content that matches what you've been tipping for.
** View content based on the tip history of other identities.
** Tip people for creating identities that have strong filter characteristics for you.

Latest revision as of 18:12, 31 October 2024

To include your page here, tag it with [[Category:Datasets]].

Datasets

Fusion Data

Links

IPH

So, extremely roughly:

  • 2,000 cases with access to a gun, 200 cases without.
  • 20 per 100k with access to a gun.
  • 2 per 100k without access to a gun.
  • worst case 18 per 100k due to access to a gun. (could also be killers are more likely to have access to a gun - so maybe 10 - 15 per 100k is the realistic case)
  • 10mm abusers annually
  • 44% of households have guns
  • 4mm abusers with guns kill 2,000 people (with roughly 1500 being avoidable)
  • Each abuser has a 1:2000 chance of being a killer if they have a gun.

homicide:

  • annual homicides: 26,031 or 7.8 per 100k
  • annual gun homicides: 20,958 (81%)
  • Population 350mm
  • Each random person has at most a 1:13,461 chance of being a killer.
  • domestic abuser without a gun is 1:20,000 to kill partner
  • domestic abuser with a gun is 1:2,000 to kill partner
  • domestic abuser with a gun is 1:1,346 to kill anyone (extremely rough)
  • Which recapitulates the earlier stat: 10x as likely to kill. I think that's just how I mathed it, not an actual statistic.

Life Expectancy

Us life expectancy gap.jpg

Politics

  • Political Contemporary History Data
  • Crowd Source Data
    • Users tag data with classification tags, based on snippets from Wikipedia.
    • Browser widget lets people jump to the next needed classification tag.
    • "Needed" is defined by people putting money into refinement of the data.
      • "Needed" tips can include a desired outcome? "I'm only paying for data that confirms/contradicts my hypothesis?"
    • "Provided" is defined by ... users putting cash in to vote on data quality and an ML algorithm allocating the correctness value of each classification based on other submissions and data quality votes.
      • I think it would be unavoidable that people would tip based on whether the answer agreed with their beliefs.
      • Payment for being right (high quality) could be made as an annuity with the future having the opportunity to correct the beliefs of the past. The residual revenue stream provides and extra incentive for not being disproven.
    • Taggers get paid based on a function of "Needed" and "Provided"
  • Collaborative Filtering of Content Displayed
    • View content that matches what you've been tipping for.
    • View content based on the tip history of other identities.
    • Tip people for creating identities that have strong filter characteristics for you.

Pages in category "Datasets"

This category contains only the following page.

B

Retrieved from "https://wiki.traxel.com/index.php?title=Category:Datasets&oldid=4714"