2019-05-14

Nobel Laureate Publications

"The Nobel Prize is a set of annual international awards bestowed in several categories by Swedish and Norwegian institutions in recognition of academic, cultural, or scientific advances." - Wikipedia.

This week's h/t goes to Georgios Karamanis for sharing the Nobel data links on GitHub! The data covers metadata for all of the Nobel prize winners. An additional dataset looks at all the publications from each winner. From the data aggregators for the publications dataset, "We constructed the publication records for almost all Nobel laureates in physics, chemistry, and physiology or medicine from 1900 to 2016 (545 out of 590, 92.4%). We first collected information manually from Nobel Prize official websites, their university websites, and Wikipedia. We then match it algorithmically with big data, tracing publication records from the MAG database."

The raw data for all the papers come from the Harvard dataverse as seen here and the information about the winners themselves comes from Kaggle as seen here.

If you want to dive deeper on some network analysis - Wagner et al, 2015 examined Do Nobel Laureates Create Prize-Winning Networks? and shared some data.

There is also a nice run through with tidyverse codepic on Kaggle here.

Get the data!

nobel_winners <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-14/nobel_winners.csv")
nobel_winner_all_pubs <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-14/nobel_winner_all_pubs.csv")

Data Dictionary

nobel_winners.csv

variable	class	description
prize_year	double	Year that Nobel Prize was awarded
category	character	Field of study/category
prize	character	Prize Name
motivation	character	Motivation of the award
prize_share	character	Share eg 1 of 1, 1 of 2, 1 of 4, etc
laureate_id	double	ID assigned to each winner
laureate_type	character	Individual or organization
full_name	character	name of the winner
birth_date	double	birth date of winner
birth_city	character	birth city/state of winner
birth_country	character	birth country of winner
gender	character	binary gender of the winner
organization_name	character	organization name
organization_city	character	organization city
organization_country	character	organization country
death_date	double	death date of the winner (if dead)
death_city	character	death city (if dead)
death_country	character	death country (if dead)

nobel_winner_all_pubs.csv

variable	class	description
laureate_id	double	Assigned Laureate ID (doesn't match other dataset)
laureate_name	character	Abbreviated name
prize_year	double	Prize year
title	character	Title of paper
pub_year	double	Publication year
paper_id	double	Paper ID
doi	character	DOI of paper
journal	character	Journal paper published in
affiliation	character	Affiliation of the author
is_prize_winning_paper	character	Is associated as the Nobel winning paper (yes or no)
category	character	Category/field of study

Cleaning script

library(tidyverse)
library(here)
library(janitor)

# read in the specific category/field datasets and the overall winners

nobel_winners <- read_csv(here("2019", "2019-05-14", "archive.csv")) %>% 
  janitor::clean_names() %>% 
  rename("prize_year" = year,
         "gender" = sex)

chem_pubs <- read_csv(here("2019", "2019-05-14", "Chemistry publication record.csv")) %>% 
  janitor::clean_names() %>% 
  mutate(category = "chemistry")

med_pubs <- read_csv(here("2019", "2019-05-14", "Medicine publication record.csv")) %>% 
  janitor::clean_names() %>% 
  mutate(category = "medicine")

physics_pubs <- read_csv(here("2019", "2019-05-14", "Physics publication record.csv")) %>% 
  janitor::clean_names() %>% 
  mutate(category = "physics")

all_pubs <- bind_rows(chem_pubs, med_pubs, physics_pubs)

all_pubs %>% 
  write_csv(here("2019", "2019-05-14", "nobel_winner_all_pubs.csv"))

nobel_winners %>% 
  write_csv(here("2019", "2019-05-14", "nobel_winners.csv"))