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| id ▼ | googleId | filename | defaultAlt | originalWidth | updatedAt | originalHeight |
|---|---|---|---|---|---|---|
| 1 | 1Hdvg7Oth-8t6Oslk9NttDzMtO2rEfYcn | Social media users over time.png | 3400 | 1678710426000 | ||
| 2 | 1zVzYcGot2Qc5iJbE30bF6XTxrgo_2dHd | Social media users by platform.png | 3400 | 1678710431000 | ||
| 3 | 1HfBcejZ9TCBT0d9OBKghBf85HWBpMvL9 | Adults using social media by gender.png | 3400 | 1678710436000 | ||
| 4 | 1FXep7e6_PR66GJ4n_dYSeGXef7zip1aU | Daily time online by young people.png | 3400 | 1678710468000 | ||
| 5 | 17OD3AgC6k6Wdn5Hm_dT6K35F5qg0OjR0 | daily-hours-spent-with-digital-media-per-adult-user.png | Stacked bar chart of the amount of time spent on digital media in the US over time, showing a doubling in the decade from 2008 to 2018. | 3400 | 1678717977846 | 2400 |
| 6 | 1yA9rvm1Egx-9f_byW5-MrM3sM2wn0Udf | Use-of-social-media-by-age.png | Grouped bar chart of social media users by platform which shows that young people are much more likely to use social media. | 800 | 1678717818908 | 455 |
| 7 | 1HfBcejZ9TCBT0d9OBKghBf85HWBpMvL9 | adults-using-social-media-by-gender.png | Scatterplot of the share of US adults using social media platforms, by gender showing that there are can be large differences depending on the platform. | 3400 | 1678718020660 | 2400 |
| 8 | 1FXep7e6_PR66GJ4n_dYSeGXef7zip1aU | daily-time-online-by-young-people.png | Bar chart of the time spent on the internet per day among young people, showing that most spend at least 4 hours. | 3400 | 1678717931017 | 2400 |
| 9 | 1zVzYcGot2Qc5iJbE30bF6XTxrgo_2dHd | social-media-users-by-platform.png | Bar chart of social media users by platform which shows that Facebook is the most popular, followed by YouTube and Whatsapp. | 3400 | 1678717854123 | 2400 |
| 10 | 1Hdvg7Oth-8t6Oslk9NttDzMtO2rEfYcn | social-media-users-over-time.png | Line chart of social media users by platform where most have grown rapidly over time. | 3400 | 1678717776253 | 2400 |
| 11 | 1e3ZhPkN8MbpKXlQ9TGRtqXMP3PyU4r0_ | share-of-young-people-networking-online.png | Bar chart of the percentage of young people that use social networking showing that most young people are online. | 3400 | 1678717895208 | 2400 |
| 13 | 1kzMwOniuvFHt5a0trWeLwIj5NbQr_WZC | eu-survey-responses-on-optimism.png | Line chart showing the share of people in the EU that thinks the economic situation in their country and personal situation is getting worse or better, showing that people are more optimistic about their personal sitatuion. | 1200 | 1678719956208 | 450 |
| 14 | 1C0AADHkRZi5syCoXLl8fm0-Z6j-jcMyP | local-optimism-global-pessimism.png | Bar chart of surveys responses in the UK about the extent of problems such as crime in their local area and the country as a whole, showing that people are more optimistic about their local area. | 2322 | 1705954556742 | 1584 |
| 15 | 1NC5LU1gUPioaHaw5BrNm7Q5VvfaPRT6b | share-think-world-getting-better.png | Bar chart of the share of people in each country that thinks the world is getting better showing that most people think the world is getting worse. | 3000 | 1705955074489 | 2100 |
| 16 | 19ESuAr4g8X2T9m1VjiBXEQsPWlIq2Opr | optimistic-about-the-future.png | Stacked bar chart of the share of people that think the future will be better or worse by country showing that especially in rich countries, most think the world will be worse. | 4500 | 1705955010604 | 2415 |
| 17 | 1euKex8bRpCzVmVjY33WhVmwJDg30si6m | public-perception-of-change-in-poverty.png | Stacked bar chart of the share of people that think global extreme poverty has got better or worse, showing that most people incorrectly think it has stayed the same or gotten worse. | 4500 | 1680640571127 | 2388 |
| 18 | 1ChCDTN0CvVbzlIrXSrRFFMadrRVNxGAg | public-perception-of-change-child-mortality.png | Stacked bar chart of the share of people that think global child mortality has got better or worse, showing that most people incorrectly think it has stayed the same or gotten worse. | 4500 | 1705954879547 | 2415 |
| 19 | 16gFq-GNAb1pYWu0dVNlncBg7tXh8CkGd | environmental-optimism-pessimism.png | Bar chart showing the share of people that think the environment is bad in their local community, nationally and globally showing that people are more likely to be optimistic about their local community. | 617 | 1678720616450 | 617 |
| 20 | 18ptExwdrA1YRshMyZ6yppyHWD-FYb5Tk | share-future-optimism-vs-knowledge.png | Ladder chart of the share of people that think the future will be better, split by their knowledge of global development which shows that people tend to be more optimistic if they have greater knowledge of past changes. | 2100 | 1705955133656 | 2100 |
| 21 | 147puFI32iIz_C7G8Kf4b8vC67n70q1Kt | Optimistic-about-the-future.png | legacy-wordpress-upload | 3000 | 1696374335432 | 2100 |
| 23 | 1ajPUYy3r7AgMbfEjyq88dOW2VPDBNT1A | Healthy20height20growth20curves.png | legacy-wordpress-upload | 5596 | 1696374244919 | 4863 |
| 24 | 1gH3HHabrZsJAOwMQhnzb_kS4Trq2H96y | default-featured-image.png | The wordmark for Our World In Data | 1255 | 1707153080484 | 777 |
| 25 | 1kOwgy7JaH7l3En35jUoz3yzMZAFl8uao | checkers_2_1.webp | 2:1 test image | 1000 | 1679340138657 | |
| 26 | 1kiNpTkYWjlfO-cPVhwvNlM-bixP44JJD | un-population-projections-vs-estimates.png | Table that compares the latest UN population estimates with historical UN projections. Most historical projections were very close to the final estimates. | 720 | 1685442470230 | |
| 27 | 1F51SlyfZOCKONXVHnQeOChVAE-qJIO05 | population-projections-vs-estimates.svg | Table of UN population projections over time, compared to the real estimates of the world population. Most projections were a close match to reality. | 0 | 1685963861650 | |
| 28 | 174Vr1MWRlD_HxA6ZzBHAYLIEG6erbETq | Current-global-inequality-in-standard-of-living.png | legacy-wordpress-upload | 3000 | 1696374115654 | 1898 |
| 29 | 1wd6EPXBwBMmt9pnGNLvNuiqezDxYkNtv | Child-mortality-vs-GDP-per-capita-incl-Finland.png | legacy-wordpress-upload | 3043 | 1696374083496 | 2138 |
| 30 | 1Zz1W06Uy-eO_Uy_ilFaItuS0E1IjBrkR | Global-inequality-in-1800-1975-and-2015.png | legacy-wordpress-upload | 3000 | 1696374227391 | 5100 |
| 31 | 1dHVqkbMzee2ISwiC1_QGqtVRxi5jLkHU | the-history-of-global-inequality-featured-image.png | Image of the global distribution of income in 2015 | 12058 | 1688046822923 | 7417 |
| 32 | 1t_uqhiDM3mPY_mSs1ZnDBZjQrtWe3_30 | 4-World-Income-Distribution-2003-to-2035-growth-rates.png | legacy-wordpress-upload | 3000 | 1696373990810 | 2100 |
| 33 | 1wEVGuVSy-fY9pFVR-hB_dB4g4Xoq2RTj | Global-Inc-Distribution-2003-and-2013-log.png | legacy-wordpress-upload | 3000 | 1685654688091 | 2100 |
| 34 | 1ZN4pJFA7Cc0_zR44copDHznQnNKFp56B | Global-Inc-Distribution-2003-and-2013-linear-scale.png | legacy-wordpress-upload | 3000 | 1696374227298 | 2100 |
| 35 | 160ZEoQNae4pgBHQnbBS5BgmMxnFXq10i | Correlates-of-GDP-–-Income-matters-but-there-are-large-variations-at-each-income-level-1.png | legacy-wordpress-upload | 3000 | 1696374102487 | 1818 |
| 36 | 1NkPU7PfJ6_Sh_xQNs9LztDyNeJREsHTD | bananas.jpg | Bushels of ripe cavendishes | 2476 | 1713216332944 | 1300 |
| 37 | 1UiHCOL2lnE3VfCz7WpMcgTMoexjr9ARM | correlates-of-high-life-satisfaction-and-happiness-Kahneman-and-Krueger-2006.png | legacy-wordpress-upload | 688 | 1686926367995 | 484 |
| 38 | 1CsWvrspgFaZZQVffBFza-jnLnwgBO8MH | Five-income-distributions-national-poverty-and-IPL-2.png | legacy-wordpress-upload | 8633 | 1680639840336 | 7231 |
| 39 | 1tHZ4dbQ3fSj2U5wDbVG7I5av1ZKZfISo | female-to-male-wage-ratio-in-the-us.png | Bar chart of female to male wage ratios in the US for 1980, 1989, 1999, and 2010. The first bar for each year is the ratio unadjusted for co-variates; the second bar is the ratio adjusted for differences in education and experience; the third bar is the ratio also adjusted for other co-variates such as occupation, region and race. Both the unadjusted and adjusted ratios have increased | 12685 | 1687249220402 | 9440 |
| 40 | 1CdMLXz4Rry6Sy2HTBtiQRbnO_dV9QfB0 | gender-pay-gap-after-adjusting-for-education-and-occupation.png | World maps of the unadjusted gender wage gap, the gap adjusted for education, and the gap adjusted for occupation and industry of employment. Gender differences in occupation and industry, not education, account for a large fraction of the gender gap after accounting for individual characteristics. | 1506 | 1687249509148 | 1904 |
| 41 | 1rT5LPZbcgK-UcVebONlung8ZVD3_SjFZ | decomposition-of-the-gender-wage-gap-in-the-us.png | Bar chart of the fractions of the total gender wage gap accounted for by different labor-market variables in 1980 and 2010. Education and work experience have become much less important in explaining gender differences in wages over time, while occupation and industry have become more important. | 13359 | 1687249362666 | 9291 |
| 42 | 1PBOZA6LhgfhgAlAz2L0JfT8zzN80AKF2 | median-earnings-of-female pharmacists-relative-to-other-professions.png | Bar chart of median earnings of female pharmacists relative to other professions in the US in 1970, 1980, 1990, 2000, 2007, and 2010. Pharmacy has become a profession with a small gender earnings gap. | 866 | 1687249745572 | 536 |
| 43 | 1LPVwDsADwy5-lEz1YvLEH5-YNsUZiK-D | impacts-of-children-in-a-difference-in-differences-event-study-design.png | Event study plot of earnings before and after the birth of the first child for men and women, relative to men and women who sought to have children but did not. Women who have children have lower earnings up to ten years after having children, relative to women who did not have children. There is no difference for men. | 3000 | 1687249962676 | 1510 |
| 44 | 1e7HLd5EsUOFtIgvBYs9RBeN6jAnvohhv | fraction-of-couples-and-income-share-earned-by-the-wife.png | Line chart of the fraction of married couples depending on the income share earned by the wife. The fraction drops as the share crosses 0.5. | 976 | 1687251536459 | 689 |
| 45 | 1jCJG6sZ8WzvbyTNKrodkupcxIxY1275y | cable-tv-access-and-preference-for-a-son.png | Bar chart of the share of Indian households who report wanting their next child to be a boy in 2001, 2002, and 2003, depending on whether they had cable TV in 2001, got cable TV in 2002 or 2003, or never had cable TV. The preference for a son declined for households in the year they got cable TV. | 12502 | 1687251728713 | 8212 |
| 46 | 1JCVDuR8aIgqgOnPtuzQ37WwbOuM133Jb | share-of-women-in-top-income-groups.png | Plot of the shares of women in the top 10%, 1%, and 0.1% income groups in selected high-income countries between 2000 and 2013. The shares have increased in most countries, but women remain very underrepresented. | 2215 | 1687257783804 | 2127 |
| 47 | 18-sgJf-UyAhw5m_ys8uGXX1siQ7STUWx | population-projections-thumbnail.png | Line chart showing various population projections over time. They are all close together. | 5000 | 1687261469839 | 2617 |
| 48 | 17315d4uVSYl53OmSoLSGX1OVBq7-LZcq | labor-force-participation-of-women-in-the-usa.png | Line chart of of labor force participation of women in the US between 1955 and 2005, distinguishing between the shares of all women, of women that are single and never married, of married women with a present spouse, and of divorced, widowed, and separated women. The overall increase in the labor force participation of women is driven by married women with a present spouse. | 3000 | 1687262341606 | 1658 |
| 49 | 1UOCTswq25r72-qFm-myqbW1xhsZxd0o0 | share-of-us-households-with-basic-electrical-appliances.png | Line charts of the share of US households with basic electrical appliances, such as refrigerators and microwaves, and of housework working hours per week to prepare meals, do the laundry and the cleaning. Basic electrical appliances have soared over the last century, and housework working hours have steadily declined. | 3000 | 1687268616965 | 2100 |
| 50 | 1s-6ZAxGkfvaBEt4Yo7BONLM8bga_hFX5 | how-many-women-could-we-save -from-dying-in-pregnancy-or-childbirth.png | Bar chart of maternal deaths per year if we still had the poor health of 1800, at today's global mortality rate, and if all regions achieved the current maternal mortality rate of the European Union. Many more women are saved from dying in pregnancy or childbirth than in the past, but many more could be saved still. | 6605 | 1687282978576 | 2594 |
| 51 | 1tICZbC0ZDO0r6H2hR3h4T-8bde7LRnW5 | Gender Pay Gap Key Facts.png | 850 | 1685024033000 | ||
| 52 | 1yEBi6x8tQDqcehhGcGj5o892ih5Pofsl | Gender Pay Gap Why.png | 850 | 1685087832000 | ||
| 53 | 1Vorzs9dnHqlx82R2uYbIriNvpC1t5tCG | Gender Pay Gap Biological.png | 850 | 1685024034000 | ||
| 54 | 17x0jCcNgKc9pnf8ylqfTQytKaJseH6v4 | Women Top Incomes.png | 850 | 1685023290000 | ||
| 55 | 17x0jCcNgKc9pnf8ylqfTQytKaJseH6v4 | women-top-incomes.png | Featured image for the article on top incomes and women. Stylized lines slowly going up. | 850 | 1687283471609 | 600 |
| 56 | 1tICZbC0ZDO0r6H2hR3h4T-8bde7LRnW5 | gender-pay-gap-key-facts.png | Featured image for the article on the key facts about the gender pay gap. Stylized scatter plot with dots in different sizes and colors. | 850 | 1687283613377 | 600 |
| 57 | 1yEBi6x8tQDqcehhGcGj5o892ih5Pofsl | gender-pay-gap-why.png | Featured image for the article on the causes of the gender pay gap. Stylized world map with a few countries in orange. | 850 | 1687283573700 | 600 |
| 58 | 1Vorzs9dnHqlx82R2uYbIriNvpC1t5tCG | gender-pay-gap-biological.png | Featured image for the article on the extent to which the gender pay gap is biological. Stylized scatter plot with dots in different sizes and colors. | 850 | 1687283646459 | 600 |
| 59 | 1He-77io_JsUhMUq9c2ExtV_1tG9HGqUB | working-women-key-facts.png | Featured image for the article on the key facts and trends in female labor force participation. Several stylized lines going up. | 850 | 1687283389095 | 600 |
| 60 | 1g_g3ZeMgBRDKcsF6HYB_PEBaD105KwbD | working-women-determinants.png | Featured image for the article on what determines the female labor force participation. Stylized blue bars of different lengths. | 850 | 1687283482333 | 600 |
| 61 | 1OhlD0cZvPEnETVDcFMWD5ujtJFFk7P2L | maternal-mortality-saving.png | Featured image for the article on how many maternal deaths could be avoided. Stylized bar chart with lines indicating the differences in size between them. | 3170 | 1687283720543 | 1244 |
| 62 | 1UPPafrLuZTo_5WJc0DfTf0kzEe3j7bDF | maternal-mortality-where.png | Featured image for the article on where women are most at risk of dying in childbirth. Stylized lines going down at different speeds. | 850 | 1687283519823 | |
| 63 | 1JuVYTo-gcpwXzv3lPC_t6OstRZwDqGN9 | Causes-of-deaths-2019.png | legacy-wordpress-upload | 3086 | 1680639773543 | |
| 64 | 1qifRk8_N8qS8H79zBPABobh5_yDqAK6P | causes-of-deaths-globally.png | Featured image for the article on causes of death globally. Stylized tree map with tiles of blue, red, and green. | 2716 | 1687335000759 | 1634 |
| 65 | 1ha-sPvacqMtYpBhjZsWa6i4HrvxAgdfh | what-do-people-die-from-causes-of-deaths-globally.png | Tree map of causes of death globally in 2019, with non-communicable diseases in blue, communicable or infectious diseases in red, and injuries in green. The most common causes of deaths are non-communicable diseases such as heart diseases and cancers, while injuries and especially deaths from violence are rare. | 3086 | 1687333724183 | |
| 66 | 1-HLRK463in-MgWuOhUV7Bnwe1FfWQI4Q | does-the-news-reflect-what-we-die-from-featured-image.png | Featured image for the article on whether the news reflect what we die from. Bar chart of what Americans die from, what they search on Google, and what the media reports on. | 4080 | 1687335043491 | |
| 67 | 153sOJd2ViucmJ75Sn4L-zP6yxygI_t38 | causes-of-deaths-google-searches-media-reporting.png | Bar chart of what Americans die from, what they search on Google, and what the media reports on. Homicides and terrorism are very rare causes of deaths, while deaths from heart disease and cancer are common. Americans rarely search for heart disease on Google, and search for homicide, terrorism, and even cancer more frequently than they are causes of death. And deaths from terrorism and homicides account for more than half of media coverage. | 4080 | 1687333953513 | |
| 68 | 1KZnNlnBybn7Wv--_Iqc3UV6xvbXggj2Z | does-the-news-reflect-what-we-die-from.png | Bar chart of deaths from 13 different causes in the US relative to the share of media coverage these topics get in the New York Times and The Guardian newspapers. Strokes, pneumonia & influenza, suicide, and especially homicide and terrorism are massively overrepresented in the media. Other causes of deaths, such as kidney and heart diseases as well drug overdoses are underrepresented. | 3461 | 1687334122695 | |
| 70 | 14F61OMVPxjEz01iN8bUVjVPfETbGPlqi | LE-vs-Health-Exp-2020-version.png | legacy-wordpress-upload | 1874 | 1680639641276 | |
| 71 | 1O-I1cQO38_9oi5fE3TloP6dcybSXPxUO | why-is-life-expectancy-in-the-us-lower-than-in-other-rich-countries.png | Featured image for the article on why life expectancy in the US is lower than in other rich countries. Scatter plot of life expectancy and health expenditure per capita, with each country between 1970 and 2018 represented as a line, the USA in red and other OECD countries in grey. | 1874 | 1687335090350 | 2042 |
| 72 | 1nZ84o6IqNhOCP_KjbTcnaX9HhqfeeLfo | life-expectancy-vs-health-expenditure-1970-to-2018.png | Scatter plot of life expectancy and health expenditure per capita, with each country between 1970 and 2018 represented as a line, the USA in red and other OECD countries in grey. Starting in the early 1980s, other OECD countries have increased their life expectancies by a lot with limited increases in health expenditures per capita. The USA has started to spend much more on life expectancy, but its life expectancy has increased far less. | 1874 | 1687334508200 | 2042 |
| 73 | 1Ecm7e1ZJWC5Oh8UkvcHSZXxEWIShV7Da | population-projections-vs-estimates.png | Table of UN population projections over time, compared to the real estimates of the world population. Most projections were a close match to reality. | 5088 | 1687344998993 | 3009 |
| 74 | 1RyWm_QAABM_9xJ8f4ZhDCmrjVLranuk_ | violent_deaths_nonstate_societies_featured_image.png | Featured image for the article on the ethnographic and archaeological evidence on violent deaths. Stylized bar chart with bars of different sizes in blue. | 850 | 1687347600739 | |
| 75 | 1RyWm_QAABM_9xJ8f4ZhDCmrjVLranuk_ | violent-deaths-nonstate-societies-featured-image.png | Featured image for the article on the ethnographic and archaeological evidence on violent deaths. Stylized bar chart with bars of different sizes in blue. | 850 | 1687364485467 | 600 |
| 76 | 1CpcLi8mtnD0S1MiQNZ0pYCxubx73TwqG | homicide-rates-across-sources.png | Featured image for the article on the different homicide data sources. Stylized lines that all go down. | 850 | 1687524499064 | 638 |
| 77 | 191XFDa1nVUUjvMphm1pWH03Qjf3qya3z | Diet-costs-thumbnail.png | legacy-wordpress-upload | 1200 | 1710503215937 | 630 |
| 78 | 1Ia7V0f5yhxv5IXkQMPk_HqIVup1GYWIh | Poverty-line-thumbnail.png | legacy-wordpress-upload | 1200 | 1696374426541 | 628 |
| 79 | 1Q9LGXjNJjPF6YVpvyMC6EhmYwcM5UMY4 | poverty-featured-image.png | A crop of a red, orange, yellow, and blue stacked bar chart. | 768 | 1686688111761 | 437 |
| 80 | 1dLFCz7AJmqm9dU5XriPR0aQLU-hB6JKe | higher-poverty-global-line-featured-image.png | legacy-wordpress-upload | 1312 | 1686167410434 | 622 |
| 81 | 133DP-0KaEOR2bRSX4L3nVTFLZq_UjklB | history-of-poverty-has-just-begun-featured-image.png | legacy-wordpress-upload | 2718 | 1686155609412 | 982 |
| 82 | 1o6e9onB-cXS48O9B2T4tB3MIw7VJgOo- | history-extreme-poverty-featured-image.png | legacy-wordpress-upload | 2334 | 1686166572769 | 1356 |
| 83 | 1ILWBPpvYzoRmNvzoONN4q1yGooWZXmvy | reducing-global-poverty-featured-image.png | legacy-wordpress-upload | 1936 | 1686155579212 | 966 |
| 84 | 1Pqr1cDzFYGeU0ylwKZn95wzS1wdk6VxN | Featured-Image-for-the-post.png | legacy-wordpress-upload | 2886 | 1696374184700 | 1586 |
| 85 | 1pAFwYU9JJIbM74K5Hmq5uV8AkIMgi9g7 | social-spending-oecd-longrun-3.png | legacy-wordpress-upload | 2040 | 1680639308762 | 1440 |
| 86 | 1OYLmhHw88W789l5Hz7Ci7h_4zSw7ivBh | malthusian-trap-featured-image.png | legacy-wordpress-upload | 2056 | 1686166619670 | 1346 |
| 87 | 1f_S1q0TXHKp9GlKULVF4hSXYM7_em6cJ | dissatisfied-vs-income-1.png | legacy-wordpress-upload | 3000 | 1696374144552 | 2100 |
| 88 | 1bre5WGzZaWwsomJG1blvbJZajtdjJROu | Two-centuries-World-as-100-people.png | legacy-wordpress-upload | 3690 | 1680639846427 | 2442 |
| 89 | 1-oC6hga7yJ40yNAKfer3Fq-fBhddUOLj | poverty-growth-needed-featured-image.png | legacy-wordpress-upload | 2004 | 1686167280352 | 1070 |
| 90 | 1hkk5T9_WPOXTN7EUiv4el9Iy5f_MEFtZ | projections-to-2030-by-world-region.png | legacy-wordpress-upload | 9334 | 1696374435153 | 7285 |
| 91 | 1FcEj9U76kF6RpH1oEUMKCdIoQ89cTcOC | number-of-poor-plus-projections-1.png | legacy-wordpress-upload | 9938 | 1696374329159 | 8017 |
| 92 | 16rwZNeEND5ahCjSlqEqc2T4cBV0XHlS8 | national-poverty-lines-vs-gdp-per-capita-1.png | legacy-wordpress-upload | 3400 | 1696374317333 | 2400 |
| 93 | 1QjW0TpM_upH6VHqV6Yd-fcGK6JVCbLHo | Share-of-the-world-population-in-extreme-poverty-Moatsos-2021-–-Reworked-with-2017-PPP-data-from-PIP.png | legacy-wordpress-upload | 9981 | 1696374524475 | 6244 |
| 94 | 1ovEE--4QDSIeVqOoThQjnqZd3J4lYgpW | share-of-population-who-have-ever-been-homeless-2003.png | legacy-wordpress-upload | 3400 | 1696374523959 | 2400 |
| 95 | 16foWKL7pKv1NeZUsdrNy21ZEQFmvD6Hk | Thumbnail-SDG-8.png | Sustainable development goal 8: Decent Work and Economic Growth | 1200 | 1687937099878 | 630 |
| 96 | 1BCxdzgHTNMuf1_FTyWd5Ac4lYxeIN46r | Thumbnail-SDG-1.png | Sustainable development goal 1: No Poverty | 1200 | 1687937029440 | 630 |
| 97 | 1PwFqYbNih2ZH-HZdpaTuX0vkeDA-GiIk | Thumbnail-SDG-9.png | Sustainable development goal 9: Industry, Innovation and Infrastructure | 1200 | 1687937110833 | 630 |
| 98 | 1pOlVADU9UQFPWjbbDtIlbZybUPvuzYvi | Thumbnail-SDG-10.png | Sustainable development goal 10: Reduced Inequalities | 1200 | 1687937119317 | 630 |
| 99 | 1lKTl0ITUGnnTsmQ38-TYq6_O5d3zoL8w | github-mark.png | The octacat logo of GitHub | 240 | 1699987118489 | 240 |
| 100 | 1Q8OraVHyujkqht2k2xe9Qw1VQnnz3dxC | Thumbnail-SDG-16.png | Sustainable development goal 16: Peace, Justice and Strong Institutions | 1200 | 1687937177284 | 630 |
| 101 | 1sLISU8q754ay1OsRy79tLyyshUW7NbwE | Energy.png | legacy-wordpress-upload | 1200 | 1680639682211 | |
| 102 | 1HnWAMKt1NManmCo9_tEiKn1hQYK6HjiM | Demographic-Transition.png | Timeline chart that shows 5 stages of the demographic transition. Birth and death rates are plotted on the y-axis. Death rates first fall, and are later followed by a decline in birth rates. | 2364 | 1687963775111 | 1875 |
| 103 | 11kApds_vsoQu18St95zYVarNPy1Xq560 | demographic-transition-5-countries.png | The chart shows the demographic transition (birth and death rates) in Germany, Sweden, Chile, Mauritius, and China. | 3000 | 1687884304782 | 2223 |
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