delen/vistext huggingface dataset:
Chart description: Percentage of U.S. adults likely to change select behaviors if coronavirus (COVID-19) were to spread to their community is less likely for going to a concert and using public transportation. Percentage of U.S. adults likely to change select behaviors if coronavirus (COVID-19) were to spread to their community is more likely for voting and invest to stock market.

Chart data: Response Less likely Go to a concert 0.63 Use public transport 0.63 Go to a movie theater 0.62 Go to an amusement park 0.62 Go to a theater performance 0.62 Go to sporting events 0.61 Go to a museum 0.61 Go to a shopping mall 0.6 Go to a party or social event 0.59 Go to the gym 0.59 Go to a political rally 0.58 Use a ride-hailing service 0.57 Go out to eat in a restaurant or cafe 0.56 Take a vacation 0.51 Go to a work conference 0.51 Go to a religious gathering or meeting 0.5 Go to the grocery store 0.4 Invest in the stock market 0.37 Vote in a political election 0.33

Vega-Lite specification:
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Chart description: the chart isnt laid out very well I understand the figures to read between 0.% and 0.15%   This is a very low percentage. This shows that very few people are prepared to change to the imminance of covid covid.

Chart data: Don't know/no opinion Response 0.1 Go to a concert 0.11 Use public transport 0.09 Go to a movie theater 0.1 Go to an amusement park 0.1 Go to a theater performance 0.11 Go to sporting events 0.11 Go to a museum 0.08 Go to a shopping mall 0.09 Go to a party or social event 0.11 Go to the gym 0.12 Go to a political rally 0.12 Use a ride-hailing service 0.07 Go out to eat in a restaurant or cafe 0.09 Take a vacation 0.16 Go to a work conference 0.11 Go to a religious gathering or meeting 0.07 Go to the grocery store 0.18 Invest in the stock market 0.11 Vote in a political election

Vega-Lite specification:
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Chart description: There is a slight curve in numbers shown,  starting with an initial peak of numbers that decreases slightly before increasing again.  2018 surpasses the initial peak.

Chart data: Total Year 272967 2018 252585 2017 242659 2016 243427 2015 241009 2014 237247 2013 232087 2012* 237215 2011* 244991 2010 226254 2009 247650 2008 253295 2007

Vega-Lite specification:
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Chart description: All years had ecommerce valued between 60,000 and 70,000. 2020 was the year with the highest ecommerce value at about 69,000.

Chart data: Year E-commerce 2018 66396 2017 65643 2016 62607 2015 62243 2014 62754 2013 63243 2010 68344 2009 62704 2008 66265 2007 67489

Vega-Lite specification:
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Chart description: Consumption of coal increased between 1990 and 2005. Since 2005 the trend has broadly been for a reduction in the use of coal. The amount of coal used in 2020 is half of that used in 2005.

Chart data: Year Electric power sector Dec 31, 1989 16261 Dec 31, 1994 17466 Dec 31, 1999 20220 Dec 31, 2004 20737 Dec 31, 2005 20462 Dec 31, 2006 20808 Dec 31, 2007 20513 Dec 31, 2008 18225 Dec 31, 2009 19133 Dec 31, 2010 18035 Dec 31, 2011 15821 Dec 31, 2012 16451 Dec 31, 2013 16427 Dec 31, 2014 14138 Dec 31, 2015 12996 Dec 31, 2016 12622 Dec 31, 2017 12053 Dec 31, 2018 10203

Vega-Lite specification:
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Chart description: Even steady decline from 1990 until roughly 2008. Steep drop in 2009 before a steady increase in 2010. Overall consumption is significantly lower by 2019.

Chart data: Year Industrial sector Dec 31, 1989 2756 Dec 31, 1994 2488 Dec 31, 1999 2256 Dec 31, 2004 1954 Dec 31, 2005 1914 Dec 31, 2006 1865 Dec 31, 2007 1796 Dec 31, 2008 1392 Dec 31, 2009 1631 Dec 31, 2010 1561 Dec 31, 2011 1513 Dec 31, 2012 1546 Dec 31, 2013 1530 Dec 31, 2014 1380 Dec 31, 2015 1205 Dec 31, 2016 1195 Dec 31, 2017 1180 Dec 31, 2018 1186

Vega-Lite specification:
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Chart description: There's a decrease in consumption from the year 1990 to about 2000, then suddenly increases and drops in about 2005 and then there's an increase after that and continues with a decreasing pattern.

Chart data: Year Commercial sector Dec 31, 1989 124 Dec 31, 1994 117 Dec 31, 1999 92 Dec 31, 2004 97 Dec 31, 2005 65 Dec 31, 2006 70 Dec 31, 2007 81 Dec 31, 2008 73 Dec 31, 2009 70 Dec 31, 2010 62 Dec 31, 2011 44 Dec 31, 2012 41 Dec 31, 2013 40 Dec 31, 2014 31 Dec 31, 2015 24 Dec 31, 2016 21 Dec 31, 2017 19 Dec 31, 2018 17

Vega-Lite specification:
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Chart description: The highest net investment of insurance companies was in 2007, with £40 billion short term assets. The lowest net investment of insurance companies was in 2018  with £-20 billion short term assets.

Chart data: Year Short-term assets Dec 31, 2006 41.1 Dec 31, 2007 −4.8 Dec 31, 2008 −4.2 Dec 31, 2009 −7.6 Dec 31, 2010 10.9 Dec 31, 2011 15 Dec 31, 2012 24.9 Dec 31, 2013 5.9 Dec 31, 2014 4.5 Dec 31, 2015 −8.5 Dec 31, 2016 23.7 Dec 31, 2017 −22.1

Vega-Lite specification:
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Chart description: Since 2007 the highest investment by insurance companies in pension funds and trusts was in 2007 at over £40bn. In 2018 the investment by insurance companies in Pension Funds and Trusts was less than £-20bn.

Chart data: Year Short-term assets Dec 31, 2006 41.1 Dec 31, 2007 −4.8 Dec 31, 2008 −4.2 Dec 31, 2009 −7.6 Dec 31, 2010 10.9 Dec 31, 2011 15 Dec 31, 2012 24.9 Dec 31, 2013 5.9 Dec 31, 2014 4.5 Dec 31, 2015 −8.5 Dec 31, 2016 23.7 Dec 31, 2017 −22.1

Vega-Lite specification:
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Chart description: Net investments peak at 2016 and lowest point is at 2008. Each 2 years the data increases and decreases consistently.

Chart data: Year Gilts* Dec 31, 2006 −0.4 Dec 31, 2007 −19.6 Dec 31, 2008 13.9 Dec 31, 2009 29.2 Dec 31, 2010 −0.8 Dec 31, 2011 −10.2 Dec 31, 2012 12.6 Dec 31, 2013 10.2 Dec 31, 2014 0.8 Dec 31, 2015 37.8 Dec 31, 2016 18.5 Dec 31, 2017 15

Vega-Lite specification:
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Chart description: The years differentiates for how many net investments of insurance companies, pension funds and trusts in the United Kingdom.

Chart data: Year Overseas securities Dec 31, 2006 44.2 Dec 31, 2007 15.3 Dec 31, 2008 43.3 Dec 31, 2009 24.8 Dec 31, 2010 13.3 Dec 31, 2011 46.5 Dec 31, 2012 18.1 Dec 31, 2013 −0.7 Dec 31, 2014 18.8 Dec 31, 2015 −31.9 Dec 31, 2016 1.3 Dec 31, 2017 −49.7

Vega-Lite specification:
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Chart description: As time increases, employment steadily decreases in the agriculture sector.

Chart data: Year Agriculture Dec 31, 2009 0.0255 Dec 31, 2010 0.0237 Dec 31, 2011 0.0223 Dec 31, 2012 0.0221 Dec 31, 2013 0.0225 Dec 31, 2014 0.0201 Dec 31, 2015 0.0209 Dec 31, 2016 0.0206 Dec 31, 2017 0.0211 Dec 31, 2018 0.0206 Dec 31, 2019 0.0201

Vega-Lite specification:
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Chart description: The distribution of employment in Norway has changed little in recent years.

Chart data: Year Industry Dec 31, 2009 0.197 Dec 31, 2010 0.2025 Dec 31, 2011 0.2023 Dec 31, 2012 0.2032 Dec 31, 2013 0.2044 Dec 31, 2014 0.2012 Dec 31, 2015 0.1947 Dec 31, 2016 0.1941 Dec 31, 2017 0.1947 Dec 31, 2018 0.1928 Dec 31, 2019 0.191

Vega-Lite specification:
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Chart description: The number of services has been maintained over the last ten years.

Chart data: Year Services Dec 31, 2009 0.7776 Dec 31, 2010 0.7738 Dec 31, 2011 0.7754 Dec 31, 2012 0.7748 Dec 31, 2013 0.7731 Dec 31, 2014 0.7787 Dec 31, 2015 0.7844 Dec 31, 2016 0.7853 Dec 31, 2017 0.7843 Dec 31, 2018 0.7867 Dec 31, 2019 0.7889

Vega-Lite specification:
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Chart description: Uttar Pradesh has the most government and private testing facilities for covid in India.

Chart data: Government Laboratories state of India 1196 Total 124 Uttar Pradesh 94 Maharashtra 78 Andhra Pradesh 77 Madhya Pradesh 74 West Bengal 67 Tamil Nadu 60 Karnataka 53 Uttarakhand 48 Bihar 43 Odisha 42 Kerala 42 Gujarat 39 Rajasthan 37 Punjab 36 Chhattisgarh 33 Jharkhand 30 Himachal Pradesh 28 Delhi 25 Jammu & Kashmir 23 Haryana 21 Telangana 21 Arunachal Pradesh 16 Assam 15 Meghalaya 14 Nagaland 11 Mizoram 7 Manipur 7 Chandigarh 7 Goa 6 Puducherry 5 Andaman & Nicobar islands 4 Tripura 4 Leh-Ladakh 2 Sikkim 2 Lakshadweep 1 Dadra & Nagar Haveli 0 Mobile testing labs

Vega-Lite specification:
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Chart description: London has the highest ratio of household rent to income with the North east being the lowest.

Chart data: region of United Kingdom Dec '19 London 0.359 South West 0.325 South East 0.318 UK Average 0.311 East England 0.307 UK Average (Excluding London) 0.297 Wales 0.287 East Midlands 0.288 North West 0.287 West Midlands 0.296 Scotland 0.27 Northern Ireland 0.277 Yorkshire & Humber 0.267 North East 0.244

Vega-Lite specification:
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Chart description: Winning NFL teams progressively earn significantly more money in USD every year.

Chart data: Year Winning team Dec 31, 2019 130 Dec 31, 2020 150 Dec 31, 2021 157 Dec 31, 2022 164 Dec 31, 2023 171 Dec 31, 2024 178 Dec 31, 2025 188 Dec 31, 2026 198 Dec 31, 2027 208 Dec 31, 2028 218 Dec 31, 2029 228

Vega-Lite specification:
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Chart description: Basketball fund increases when the academic enhancement fund decreases.

Chart data: Basketball fund Academic Enhancement fund 167.6 49.04 168.5 49.22 164.94 48.02 159.71 46.73 205 26.54 199.2 26.92 193.58 25.1 188.31 24.41 202 24.6 193.9 22.4

Vega-Lite specification:
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Chart description: The bar graph is confusing and does not present the data well. The highest amount from the basketball fund is 168.5. The highest amount from the Special Assistance Fund is 17.

Chart data: Basketball fund Special Assistance Fund 167.6 18.75 168.5 18.63 164.94 18.18 159.71 17.69

Vega-Lite specification:
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Chart description: As the years increase, so does the conference grant. however there was a dip in the conference grant in the year 2019/2020.

Chart data: Conference Grant* Year 9.93 2019/20 9.97 2018/19 9.72 2017/18 9.46 2016/17 9.2 2015/16 8.96 2014/15 8.7 2013/14 8.47 2012/13 8.3 2011/12

Vega-Lite specification:
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Chart description: The visulazation shows that the department that has the most Proposed federal spending by the U.S. governmenton cyber security for selected governmentagencies from FY 2020 to FY 2021 was the department of defense. Numerous other departments received no proposed funding for cyber security.

Chart data: 2020* United States federal executive department 231.2 Department of Agriculture 514.3 Department of Commerce 10075 Department of Defense 166.2 Department of Education 550.4 Department of Energy 475.7 Department of Health & Human Services 2574.1 Department of Homeland Security 68.2 Department of Housing & Urban Development 900.5 Department of Justice 92.2 Department of Labor 405.8 Department of State 121.4 Department of the Interior 588.4 Department of the Treasury 262.1 Department of Transportation 524.6 Department of Veterans Affairs 32.5 Environmental Protection Agency 82.4 General Services Administration 166.6 National Aeronautics & Space Administration 226.3 National Science Foundation 27.5 Nuclear Regulatory Commission 47.1 Office of Personnel Management 15.7 Small Business Administration 207.6 Social Security Administration 42.5 U.S. Agency for International Developmen 393.6 Non-CFO Act Agencies

Vega-Lite specification:
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Chart description: India 65+ year old population increased .013 somethings in 10 years.

Chart data: Year 65 years + Dec 31, 2008 0.0502 Dec 31, 2009 0.0508 Dec 31, 2010 0.0517 Dec 31, 2011 0.0527 Dec 31, 2012 0.0536 Dec 31, 2013 0.0548 Dec 31, 2014 0.0561 Dec 31, 2015 0.0579 Dec 31, 2016 0.0598 Dec 31, 2017 0.0618 Dec 31, 2018 0.0638

Vega-Lite specification:
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Chart description: The number of the population accessing the internet increased every year. The largest increase of population accessing the internet was in 2017.

Chart data: World Year 0.536 2019* 0.514 2018 0.49 2017 0.448 2016 0.415 2015 0.391 2014 0.37 2013 0.348 2012 0.318 2011 0.293 2010 0.258 2009 0.231 2008 0.206 2007 0.184 2006 0.168 2005

Vega-Lite specification:
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Chart description: The global population access to internet increases steadily over the researches period proportionally to the increase of LDC.

Chart data: Year LDCs 2019* 0.191 2018 0.176 2017 0.161 2016 0.143 2015 0.124 2014 0.108 2013 0.093 2012 0.081 2011 0.066 2010 0.055 2009 0.043 2008 0.031 2007 0.02 2006 0.016 2005 0.014

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Chart description: Between 2010 and 2012, the distribution of workers in the agricultural sector remained the same at 0.028. This amount then rose steadily to around 0.032 in 2013 which was the highest year recorded. The agricultural workforce then dropped steadily until 2015, rose a little in 2016 but then continued to drop until 2018 where only 0.023 was recorded.

Chart data: Year Agriculture Dec 31, 2009 0.0291 Dec 31, 2010 0.0291 Dec 31, 2011 0.0292 Dec 31, 2012 0.0308 Dec 31, 2013 0.0285 Dec 31, 2014 0.0275 Dec 31, 2015 0.0287 Dec 31, 2016 0.0263 Dec 31, 2017 0.0251 Dec 31, 2018 0.0244 Dec 31, 2019 0.0238

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Chart description: 2010 saw the highest distribution of economic sectors with a value of 0.225. Since then, distribution has steadily dropped to just under 0.20 in 2020.

Chart data: Year Industry Dec 31, 2009 0.2225 Dec 31, 2010 0.2217 Dec 31, 2011 0.2176 Dec 31, 2012 0.2131 Dec 31, 2013 0.2053 Dec 31, 2014 0.2038 Dec 31, 2015 0.2029 Dec 31, 2016 0.2049 Dec 31, 2017 0.2029 Dec 31, 2018 0.2009 Dec 31, 2019 0.1989

Vega-Lite specification:
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Chart description: The distribution of the workforce in the industry sector has steadily declined since 2010, with the exception of a very small increase in 2016.

Chart data: Year Industry Dec 31, 2009 0.2225 Dec 31, 2010 0.2217 Dec 31, 2011 0.2176 Dec 31, 2012 0.2131 Dec 31, 2013 0.2053 Dec 31, 2014 0.2038 Dec 31, 2015 0.2029 Dec 31, 2016 0.2049 Dec 31, 2017 0.2029 Dec 31, 2018 0.2009 Dec 31, 2019 0.1989

Vega-Lite specification:
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