Location of people

This page shows the location (by continent or country) of the Open Seeds people given their different roles

import geopandas
import matplotlib.pyplot as plt
import pandas as pd

path = geopandas.datasets.get_path('naturalearth_lowres')
world_df = geopandas.read_file(path)
baseurl = "https://raw.githubusercontent.com/open-life-science/open-life-science.github.io/main/_data/artifacts/openseeds/"
cohort_nb = 8

/tmp/ipykernel_2047/3598637579.py:1: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.
  path = geopandas.datasets.get_path('naturalearth_lowres')

people_df = pd.read_csv(f"{baseurl}people.csv", index_col=0)

roles_df = {}
roles = []
for r in ["role", "participant", "mentor", "expert", "speaker", "facilitator", "organizer"]:
    role = r.capitalize()
    roles.append(role)
    roles_df[role] = pd.read_csv(f"{baseurl}/roles/{r}.csv", index_col=0)

Continents

Proportion of people without continent information

people_df.continent.isna().sum() / len(people_df.continent)

0.07465277777777778

def plot_role_percentage(data, title, ylab):
    '''
    Generate role percentage plots
    
    :param data: data to plot
    :param title: title for plots
    :param ylab: ylab
    '''
    fig, ax = plt.subplots()
    fig.set_dpi(300)
    (data
         .drop(columns=['TOTAL'])
         .transpose()
         .plot(ax=ax, colormap='tab20c'))
    plt.title(title)
    plt.xlabel('Cohorts')
    plt.ylabel(ylab)
    ax.legend(
        loc='center left',
        bbox_to_anchor=(1, 0.5),
        frameon=False,
        fontsize='x-small')
    
    fig, ax = plt.subplots()
    fig.set_dpi(300)
    (data
         .drop(columns=['TOTAL'])
         .transpose()
         .plot.bar(stacked=True, ax=ax, colormap='tab20c'))
    plt.title(title)
    plt.xlabel('Cohorts')
    plt.ylabel(ylab)
    ax.legend(
        loc='center left',
        bbox_to_anchor=(1, 0.5),
        frameon=False,
        fontsize='x-small')

All roles

continents_df = {}
continents_perc_df = {}
all_roles_continent = {}
for r in roles:
    continents_df[r] = (roles_df[r].groupby(['continent']).count()
        .rename(columns = {'country_3': 'total'})
        .drop(columns = ['city','first-name','last-name','pronouns','country','longitude','latitude']))
    continents_df[r].columns = [x.upper() for x in continents_df[r].columns]
    all_roles_continent[r] = continents_df[r].sum(axis=1)
    continents_perc_df[r] = 100 * continents_df[r] / continents_df[r].sum(axis = 0)
continents_perc_df['Role']

	TOTAL	OLS-1	OLS-2	OLS-3	OLS-4	OLS-5	OLS-6	OLS-7	OLS-8
continent
Africa	16.536965	8.974359	13.970588	6.428571	3.921569	13.28125	19.587629	30.927835	25.757576
Asia	8.365759	7.692308	3.676471	13.571429	7.843137	10.15625	5.154639	2.061856	0.757576
Europe	48.832685	51.282051	55.882353	60.714286	58.823529	48.43750	55.670103	38.144330	47.727273
North America	12.840467	25.641026	18.382353	12.857143	20.588235	11.71875	9.278351	8.247423	4.545455
Oceania	1.750973	1.282051	3.676471	2.857143	1.960784	0.78125	1.030928	2.061856	0.000000
South America	11.673152	5.128205	4.411765	3.571429	6.862745	15.62500	9.278351	18.556701	21.212121

role = 'Role'
plot_role_percentage(continents_perc_df[role], f'All {role.lower()}s', f'{role} (%)')

all_roles_continent_df = (pd.DataFrame.from_dict(all_roles_continent)
    .fillna(0)
    .drop(columns=['Organizer', 'Role'])
    .reindex(['Participant', 'Mentor','Speaker','Expert','Facilitator'], axis=1)
    .assign(total=lambda df: df.sum(axis = 1)))
all_roles_continent_df = 100 * all_roles_continent_df / all_roles_continent_df.sum(axis = 0)
all_roles_continent_df

	Participant	Mentor	Speaker	Expert	Facilitator	total
continent
Africa	20.348059	13.486005	9.049774	5.341880	46.666667	14.968487
Asia	10.843373	4.325700	4.072398	3.418803	5.333333	6.670168
Europe	43.373494	56.997455	59.276018	64.529915	32.000000	52.783613
North America	11.111111	11.450382	15.384615	17.094017	5.333333	12.920168
Oceania	0.803213	2.290076	4.072398	2.777778	0.000000	1.943277
South America	13.520750	11.450382	8.144796	6.837607	10.666667	10.714286

fig, ax = plt.subplots()
fig.set_dpi(300)
(all_roles_continent_df
     .drop(columns=['total'])
     .transpose()
     .plot.bar(stacked=True, ax=ax, colormap='tab20c'))
plt.xlabel('Roles')
ax.legend(
    loc='center left',
    bbox_to_anchor=(1, 0.5),
    frameon=False,
    fontsize='x-small')

<matplotlib.legend.Legend at 0x7f6f9974bf10>

Participants

continents_perc_df['Participant']

	TOTAL	OLS-1	OLS-2	OLS-3	OLS-4	OLS-5	OLS-6	OLS-7	OLS-8
continent
Africa	20.055710	17.241379	21.153846	4.6875	6.060606	16.923077	35.483871	41.666667	25.757576
Asia	10.584958	13.793103	7.692308	28.1250	15.151515	15.384615	6.451613	0.000000	0.000000
Europe	43.175487	27.586207	48.076923	53.1250	57.575758	36.923077	48.387097	29.166667	45.454545
North America	11.420613	34.482759	19.230769	10.9375	9.090909	9.230769	0.000000	4.166667	6.060606
Oceania	0.835655	0.000000	1.923077	1.5625	0.000000	0.000000	3.225806	0.000000	0.000000
South America	13.927577	6.896552	1.923077	1.5625	12.121212	21.538462	6.451613	25.000000	22.727273

role = 'Participant'
plot_role_percentage(continents_perc_df[role], f'{role}s', f'{role} (%)')

Mentors

continents_perc_df['Mentor']

	TOTAL	OLS-1	OLS-2	OLS-3	OLS-4	OLS-5	OLS-6	OLS-7	OLS-8
continent
Africa	13.970588	10.0	13.888889	8.823529	0.000	8.571429	19.354839	25.806452	18.421053
Asia	4.411765	5.0	2.777778	5.882353	3.125	8.571429	3.225806	3.225806	2.631579
Europe	52.941176	70.0	58.333333	61.764706	62.500	68.571429	67.741935	41.935484	47.368421
North America	12.500000	10.0	16.666667	11.764706	25.000	8.571429	3.225806	9.677419	2.631579
Oceania	2.941176	0.0	2.777778	5.882353	3.125	0.000000	0.000000	3.225806	0.000000
South America	13.235294	5.0	5.555556	5.882353	6.250	5.714286	6.451613	16.129032	28.947368

role = 'Mentor'
plot_role_percentage(continents_perc_df[role], f'{role}s', f'{role} (%)')

Speakers

continents_perc_df['Speaker']

	TOTAL	OLS-1	OLS-2	OLS-3	OLS-4	OLS-5	OLS-6	OLS-7	OLS-8
continent
Africa	10.465116	5.555556	12.500000	15.0	0.000000	6.666667	0.000000	14.285714	12.5
Asia	4.651163	0.000000	0.000000	5.0	5.882353	0.000000	10.526316	7.142857	0.0
Europe	54.651163	66.666667	66.666667	70.0	70.588235	46.666667	57.894737	42.857143	75.0
North America	16.279070	27.777778	8.333333	5.0	17.647059	26.666667	10.526316	14.285714	12.5
Oceania	4.651163	0.000000	8.333333	5.0	0.000000	6.666667	0.000000	7.142857	0.0
South America	9.302326	0.000000	4.166667	0.0	5.882353	13.333333	21.052632	14.285714	0.0

role = 'Speaker'
plot_role_percentage(continents_perc_df[role], f'{role}s', f'{role} (%)')

Experts

continents_perc_df['Expert']

	TOTAL	OLS-1	OLS-2	OLS-3	OLS-4	OLS-5	OLS-6	OLS-7	OLS-8
continent
Africa	6.547619	5.405405	4.615385	3.174603	0.000000	4.0	2.631579	0.0	22.727273
Asia	4.166667	5.405405	1.538462	3.174603	4.347826	4.0	2.631579	0.0	0.000000
Europe	62.500000	67.567568	63.076923	68.253968	58.695652	76.0	68.421053	75.0	59.090909
North America	15.476190	16.216216	18.461538	17.460317	30.434783	12.0	15.789474	25.0	4.545455
Oceania	2.976190	2.702703	6.153846	3.174603	2.173913	0.0	0.000000	0.0	0.000000
South America	8.333333	2.702703	6.153846	4.761905	4.347826	4.0	10.526316	0.0	13.636364

role = 'Expert'
plot_role_percentage(continents_perc_df[role], f'{role}s', f'{role} (%)')

Facilitators

continents_perc_df['Facilitator']

	TOTAL	OLS-1	OLS-2	OLS-3	OLS-4	OLS-5	OLS-6	OLS-7	OLS-8
continent
Africa	46.666667	NaN	NaN	0.0	28.571429	40.0	42.857143	40.0	66.666667
Asia	3.333333	NaN	NaN	0.0	14.285714	20.0	14.285714	0.0	0.000000
Europe	30.000000	NaN	NaN	100.0	42.857143	40.0	28.571429	40.0	20.000000
North America	6.666667	NaN	NaN	0.0	14.285714	0.0	14.285714	0.0	0.000000
South America	13.333333	NaN	NaN	0.0	0.000000	0.0	0.000000	20.0	13.333333

role = 'Facilitator'
plot_role_percentage(continents_perc_df[role], f'{role}s', f'{role} (%)')

Countries

Proportion of people without country information

people_df.country.isna().sum() / len(people_df.country)

0.06076388888888889

Country repartition

countries_df = {}
countries_perc_df = {}
for r in roles: 
    countries_df[r] = (roles_df[r].groupby(['country']).count()
        .rename(columns = {'country_3': 'total'})
        .drop(columns = ['city','first-name','last-name','pronouns','continent','longitude','latitude']))
    countries_df[r].columns = [x.upper() for x in countries_df[r].columns]
    countries_perc_df[r] = 100 * countries_df[r] / countries_df[r].sum(axis = 0)

countries_perc_df['Role']

	TOTAL	OLS-1	OLS-2	OLS-3	OLS-4	OLS-5	OLS-6	OLS-7	OLS-8
country
Argentina	5.836576	0.000000	1.470588	1.428571	3.921569	6.766917	3.061224	8.163265	15.909091
Australia	1.750973	1.282051	3.676471	2.857143	1.960784	0.751880	1.020408	2.040816	0.000000
Austria	0.194553	0.000000	0.735294	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Bangladesh	0.194553	0.000000	0.735294	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Belgium	0.389105	0.000000	0.000000	1.428571	0.000000	0.000000	0.000000	0.000000	0.000000
Benin	0.194553	0.000000	0.000000	0.000000	0.000000	0.751880	0.000000	0.000000	0.000000
Bolivia	0.000000	0.000000	0.000000	0.000000	0.000000	3.007519	0.000000	0.000000	0.000000
Brazil	3.112840	5.128205	2.941176	1.428571	1.960784	2.255639	4.081633	4.081633	1.515152
Cameroon	2.140078	0.000000	0.000000	0.714286	0.000000	1.503759	2.040816	9.183673	3.030303
Canada	2.918288	5.128205	7.352941	2.142857	0.980392	3.007519	2.040816	2.040816	0.757576
Chile	0.389105	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2.040816	0.757576
China	0.194553	1.282051	0.735294	0.000000	0.000000	0.751880	1.020408	0.000000	0.000000
Colombia	0.778210	0.000000	0.000000	0.714286	0.980392	0.751880	2.040816	4.081633	2.272727
Czechia	0.778210	0.000000	0.735294	2.142857	0.000000	0.000000	0.000000	0.000000	0.000000
Denmark	0.389105	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.020408	0.757576
Egypt	0.194553	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.020408	0.000000
Estonia	0.389105	0.000000	0.000000	0.714286	0.000000	0.000000	0.000000	1.020408	0.757576
Eswatini	0.194553	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.757576
Ethiopia	0.194553	0.000000	0.735294	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
France	2.334630	3.846154	4.411765	3.571429	5.882353	1.503759	1.020408	3.061224	3.030303
Germany	6.809339	10.256410	8.088235	10.000000	7.843137	6.766917	5.102041	7.142857	3.787879
Ghana	0.778210	0.000000	0.735294	0.714286	0.980392	1.503759	0.000000	1.020408	0.000000
Greece	0.583658	2.564103	1.470588	1.428571	0.980392	0.751880	1.020408	0.000000	0.757576
Hungary	0.194553	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.020408	0.000000
India	3.696498	2.564103	0.735294	5.714286	3.921569	3.007519	3.061224	1.020408	0.757576
Indonesia	0.972763	0.000000	0.735294	2.857143	0.000000	0.000000	0.000000	0.000000	0.000000
Ireland	0.972763	0.000000	0.000000	0.714286	0.000000	0.000000	2.040816	4.081633	0.757576
Italy	1.945525	1.282051	0.735294	2.857143	1.960784	0.751880	4.081633	2.040816	0.757576
Japan	0.194553	1.282051	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Kazakhstan	0.194553	0.000000	0.000000	0.000000	0.000000	0.751880	0.000000	0.000000	0.000000
Kenya	4.280156	7.692308	4.411765	1.428571	1.960784	3.007519	9.183673	8.163265	8.333333
Luxembourg	0.194553	1.282051	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Mali	0.389105	0.000000	1.470588	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Mexico	0.389105	0.000000	0.735294	1.428571	0.980392	0.751880	1.020408	1.020408	0.757576
Nepal	0.778210	1.282051	0.000000	2.142857	0.000000	0.000000	0.000000	0.000000	0.000000
Netherlands	8.560311	3.846154	4.411765	3.571429	6.862745	6.766917	7.142857	5.102041	22.727273
Nigeria	3.891051	0.000000	1.470588	1.428571	0.980392	1.503759	3.061224	9.183673	9.090909
Norway	0.583658	2.564103	0.000000	0.000000	0.980392	0.751880	0.000000	0.000000	0.000000
Peru	1.361868	0.000000	0.000000	0.000000	0.000000	5.263158	0.000000	0.000000	0.000000
Poland	0.194553	0.000000	0.000000	0.000000	0.980392	0.000000	0.000000	0.000000	0.000000
Portugal	0.389105	0.000000	0.000000	1.428571	0.000000	0.000000	0.000000	0.000000	0.000000
Romania	0.194553	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.020408	0.757576
Russian Federation	0.194553	1.282051	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
Saudi Arabia	0.778210	0.000000	0.000000	1.428571	2.941176	0.751880	1.020408	1.020408	0.000000
South Africa	3.891051	1.282051	4.411765	2.142857	0.000000	4.511278	5.102041	1.020408	3.787879
South Korea	0.000000	0.000000	0.000000	0.000000	0.000000	0.751880	1.020408	0.000000	0.000000
Spain	3.112840	1.282051	2.205882	6.428571	1.960784	3.759398	1.020408	0.000000	1.515152
Sweden	0.389105	0.000000	0.735294	0.000000	0.980392	0.751880	1.020408	0.000000	0.757576
Switzerland	1.750973	1.282051	4.411765	1.428571	1.960784	0.751880	3.061224	1.020408	0.757576
Thailand	0.194553	1.282051	0.000000	0.000000	0.980392	0.000000	0.000000	0.000000	0.000000
Turkey	0.972763	0.000000	0.735294	1.428571	0.000000	3.759398	0.000000	0.000000	0.000000
Uganda	0.389105	0.000000	0.735294	0.000000	0.000000	0.000000	0.000000	1.020408	0.757576
United Kingdom	18.287938	21.794872	27.941176	25.000000	28.431373	24.060150	29.591837	11.224490	10.606061
United Kingdom / Ukraine	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.020408	0.000000
United States	9.533074	20.512821	10.294118	9.285714	18.627451	7.518797	6.122449	5.102041	3.030303
Uruguay	0.194553	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.757576
Uzbekistan	0.194553	0.000000	0.000000	0.000000	0.000000	0.751880	0.000000	0.000000	0.000000

len(countries_perc_df['Role'])

57

Maps

country_code_df = {}
country_world_df = {}
for r in roles: 
    country_code_df[r] = (roles_df[r].groupby(['country_3']).count()
        .rename(columns = {'country': 'total'})
        .drop(columns = ['city','first-name','last-name','pronouns','continent','longitude','latitude'])
        )
    country_code_df[r] = 100 * country_code_df[r] / country_code_df[r].sum(axis = 0)
    country_code_df[r] = (country_code_df[r]
        .rename_axis("iso_a3")
        .reset_index())
    
    country_world_df[r] = pd.merge(world_df, country_code_df[r], on='iso_a3', how='outer')
    country_world_df[r]['total'].fillna(0, inplace=True)
    for i in range(1, cohort_nb):
        country_world_df[r][f'ols-{i}'].fillna(0, inplace=True)
    country_world_df[r].head()

def plot_role_map(data, role):
    '''
    Generate role percentage plots
    
    :param data: data to plot
    :param title: title for plots
    :param ylab: ylab
    '''
    fig, ax = plt.subplots(figsize=(15, 10))
    fig.set_dpi(300)
    
    data.plot(
        column='total',
        ax=ax,
        legend=True,
        legend_kwds={'label': "Percentage by Country", 'orientation': "horizontal"},
        cmap='YlGn')
    plt.title(f'All cohorts / All {role}s')
    ax.set_axis_off()
        
    #plt.savefig(f'../../results/openseeds/location/{role}-all-cohorts.png', bbox_inches='tight')
    
    for i in range(1,cohort_nb):
        fig, ax = plt.subplots(figsize=(15, 10))
        fig.set_dpi(300)

        data.plot(
            column=f'ols-{i}',
            ax=ax,
            legend=True,
            missing_kwds={"color": "lightgrey"},
            legend_kwds={'label': "Percentage by Country", 'orientation': "horizontal"},
            cmap='YlGn')
        plt.title(f'OLS-{i} / All {role}s')
        ax.set_axis_off()
        
        #plt.savefig(f'../../results/openseeds/location/{role}-ols-{i}.png', bbox_inches='tight')

All roles

role = 'Role'
plot_role_map(country_world_df[role], role.lower())

Participants

role = 'Participant'

Number of countries

len(country_code_df[role])

49

plot_role_map(country_world_df[role], role.lower())

Mentors

role = 'Mentor'
plot_role_map(country_world_df[role], role.lower())

Speakers

role = 'Speaker'
plot_role_map(country_world_df[role], role.lower())

Experts

role = 'Expert'
plot_role_map(country_world_df[role], role.lower())

Facilitators

role = 'Facilitator'
plot_role_map(country_world_df[role], role.lower())