
Data Literate is an experiment in [data+content](#datacontent) display.

## Motivation

> I want to turn a (data-oriented) markdown file into an elegant data-driven web page

Data-oriented markdown file is the kind of thing in https://github.com/datasets/awesome-data or one of the issues https://github.com/datasets/awesome-data/issues

I want things like:

* Auto-conversion of links to csv or excel files into nice tables or previews
* Create charts using react components (in markdown) a la MDX
* Have datasets as markdown files using the frontmatter for metadata
* Datasets within markdown files using backtick notation e.g. \`\`\`dataset
* Embedded csv or json data turned into nie tables

> I want to publish a dataset to the web quickly, elegantly (and potentially interactively)

## Data Literate Documents

We term these kind of markdown documents with additional data-oriented functionality "data literate".

Our "data literate" document is a markdown (or MDX) file with the following additional features:

* [x] **Table of Contents**: quickly add a table of contents by adding a markdown heading 'Table of Contents', 'toc', or 'table-of-contents' (thanks to `remark-toc`)
* [x] Table rendering
* [ ] Auto-render linked tables: auto-convert links to CSV or Excel into inline tables. [in progress]

## Use cases (in full)

### Researching or writing up a (data-heavy) topic and sharing it

I want to jot down, notes and links, preview data, display or graph data etc. I want to do this in markdown as that's what i work in. I want to be able to preview and then share this with others.

### Curating data

I want to quickly turn some data I've found into a properly curated dataset. I might want to do this iteratively: starting with the minimum e.g. just a link and a few notes, then moving to caching the data, then previewing the data etc. The kind of thing you find in the issues of github.com/datasets/awesome-data/issues. I want to view this dataset as i've developing it and publish this dataset when its down (or as i go along)

## Data+Content

In reality, data and content usually go together. For example, consider two main and common use cases:

* Data-driven content: this is the case of research, data journalism, high end visualization etc.
* Data with content: most datasets end up with some content in the form of documentation. In fact, many datasets start out as content e.g. a few notes and a link to a possible data source and then evolve into a fully polished dataset.

## Desired features of the tooling:

* Extensible
* Open source
* Simple

## How is this different from ...

So one big thing is I've no interest to tackle interactivity like Jupyter or ObservableHQ. The aim is for the documents and the data themselves to be static. Any processing you do of them is done elsewhere and any loading and procesing of data is purely declarative (ie. here's the url of the data).


## Inspiration

* Bl.ocks https://bl.ocks.org/
* ObservableHQ https://observablehq.com/
* Jupyter Notebooks
* [DataHub.io v2](https://datahub.io/)
* RMarkdown


About


This evolving document represents my own investigation into the climate crisis and what we can (and should) be doing about it. The aim is to document and share my path through this topic rather than to offer anything original.

Motivating questions:

* What is actually happening? For example:
  * How much CO2 (and GHGs) are we emitting? How much is that relative to the past?
  * What impact has there been on the temperature and the climate? What is likely future impact?
  * What impact has there been on the planet and on humans -- already and in the future?
* What's causing this? For example, what is the role of different countries and different energy sources in generating CO2 and GHGs?
  * Also what actors -- countries, corporations etc -- are behind this, and who is blocking change?
* What can we do about all of this?
  * What is the case for action?
  * What policies can we use to reduce emissions?
  * What cleantech do we need, what investment do we need?
  * What kind of mitigation or adaptation is possible?
  * Is it "too late" to stop dangerous warming?
* What is being done? By whom?

## Table of Contents

## Conceptual Model

I am using the following very rough conceptual model for thinking about the climate crisis and the policies to address it.


```
1. Human Processes (e.g. energy production, travel etc)
      |
      |           <--- Policy (emissions controls, cleantech)
      |
      V
2. Green House Gases (GHG)
      |
      |           <--- Sequestration
      |
      V
3. Climate Change (temperature, sea level, weather patterns)
      |
      |
      |
      V
4. Direct Impact (storms, ecosystem changes etc)
      |
      |            <----- Policy (mitigation, adaptation, transfers)
      |
      V
5. Indirect Impact (homes lost, hurricance damage)
      |
      |            <----- Policy (rebuilding, compensation)
      |
      V
6. Impact on Lives  (measured in well-being $)
```

Reading IPCC reports, it appears that the greatest efforts -- and the greatest certainty -- is around steps two and three: documenting GHG emissions and their link to climate change.

My intention, for the present, is to focus on these: to gather the best summary data on these two areas and set it out in a simple and compelling way.

Later one could expand in two directions. First, backwards into the causes of GHG. In particular, what is the composition of energy production and use and how could that change. 

Second, forwards into the impact. The impacts is ultimately what matters but also the area where there is the greatest uncertainty. Right now we could focus on some of the things that have already been happening: the increasing number of forest fires, the growing number of extreme weather incidents etc. Just documenting them and assigning some probability score would be useful.

## Situation

### Temperatures are rising

Global temperatures have been rising, especially since 1980, relative to historical norm and this seems to be accelerating.

<VegaLite spec={ 
{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "data": {"url": "_files/HadCRUT.5.0.1.0.analysis.summary_series.global.annual.csv"},
  "width": 600,
  "height": 250,
  "mark": "line",
  "encoding": {
    "x": {"field": "Time", "type": "temporal"},
    "y": {"field": "Anomaly (deg C)", "type": "quantitative"}
  }
}
} />

### The Carbon Budget

Fn in IPCC 1.5 chapter 2 https://www.ipcc.ch/sr15/chapter/chapter-2/

> Chapter 1 has assessed historical warming between the 1850–1900 and 2006–2015 periods to be 0.87°C with a ±0.12°C likely (1-standard deviation) range, and global near-surface air temperature to be 0.97°C. The temperature changes from the 2006–2015 period are expressed in changes of global near-surface air temperature.
>
> Historical CO2 emissions since the middle of the 1850–1900 historical base period (mid-1875) are estimated at 1940 GtCO2 (1640–2240 GtCO2, one standard deviation range) until end 2010. Since 1 January 2011, an additional 290 GtCO2 (270–310 GtCO2, one sigma range) has been emitted until the end of 2017 (Le Quéré et al., 2018).

## What do we need to do?

### What emissions reductions do we need?

We have a remaining carbon budget of max 420 GtCO2 (from 2018). This implies reducing emissions to net zero by 2050 assuming rapid reductions now -- or much earlier if significant reductions dont' start now.

As of 2021, we are not following that pathway and hence net zero should already be earlier *and* reductions must start now and fast.

From the IPCC 1.5 report in 2018: https://www.ipcc.ch/sr15/chapter/chapter-2/

> Cumulative CO2 emissions are kept within a budget by reducing global annual CO2 emissions to net zero. This assessment suggests a remaining budget of about 420 GtCO2 for a two-thirds chance of limiting warming to 1.5°C, and of about 580 GtCO2 for an even chance (medium confidence). The remaining carbon budget is defined here as cumulative CO2 emissions from the start of 2018 until the time of net zero global emissions for global warming defined as a change in global near-surface air temperatures. Remaining budgets applicable to 2100 would be approximately 100 GtCO2 lower than this to account for permafrost thawing and potential methane release from wetlands in the future, and more thereafter. These estimates come with an additional geophysical uncertainty of at least ±400 GtCO2, related to non-CO2 response and TCRE distribution. Uncertainties in the level of historic warming contribute ±250 GtCO2. In addition, these estimates can vary by ±250 GtCO2 depending on non-CO2 mitigation strategies as found in available pathways. {2.2.2, 2.6.1}
> 
> Staying within a remaining carbon budget of 580 GtCO2 implies that CO2 emissions reach carbon neutrality in about 30 years, reduced to 20 years for a 420 GtCO2 remaining carbon budget (high confidence). The ±400 GtCO2 geophysical uncertainty range surrounding a carbon budget translates into a variation of this timing of carbon neutrality of roughly ±15–20 years. If emissions do not start declining in the next decade, the point of carbon neutrality would need to be reached at least two decades earlier to remain within the same carbon budget. {2.2.2, 2.3.5}

Here's a key diagram from IPCC SR15:

![](https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/Figure-2.5-1024x735.jpg)

Note that reading this carefully it is clear that electricity (white area under the graphs) is net-zero much earlier than 2050 by 2030 or 2040. That means a complete transition to net zero green energy in the next decade! This intuition is borne out by this next figure:

> Fig 2.14: Decomposition of transformation pathways into (a) energy demand, (b) carbon intensity of electricity, (c) the electricity share in final energy, and (d) the carbon intensity of the residual (non-electricity) fuel mix

If you focus on light blue and dark blue one sees that electricity has carbon intensity at or close to zero by 2030. Note that most models don't assume 100% renewable but rather increasing renewable plus carbon capture and storage for fossil fuel plants still runing. ("The share of energy from renewable sources (including biomass, hydro, solar, wind and geothermal) increases in all 1.5°C pathways with no or limited overshoot, with the renewable energy share of primary energy reaching 38–88% in 2050")

![](https://www.ipcc.ch/site/assets/uploads/sites/2/2019/01/Figure-2.14-1024x674.jpg)


Another one from OWID

![](https://ourworldindata.org/app/uploads/2020/11/15C-and-2C-mitigation-scenarios-1536x987.png)

Source: fn 10 in https://ourworldindata.org/worlds-energy-problem

### What options are there for doing that?

* Energy
* Food
* ...

TODO: sources of emissions

* transportation (29%), electricity (28%), industry (22%), commercial and residential (12%), and agriculture (9%) (https://www.climatecentral.org/gallery/graphics/emissions-sources-2020#:~:text=As%20defined%20by%20the%20Environmental,%2C%20and%20agriculture%20(9%25).)
* https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data - sourced from IPCC (2014). Climate Change 2014: Mitigation of Climate Change


### Which are feasible?

### Can we get enough from Solar (alone)?

See https://www.worldbank.org/en/topic/energy/publication/solar-photovoltaic-power-potential-by-country




## Reading List

See Zotero bibliography: https://www.zotero.org/groups/1488090/life-itself/collections/XA8U7WGT

* [Sustainable Energy without the Hot Air](./mckay-without-the-hot-air.md)
* [Project Drawdown](https://projectdrawdown.org/) - see also https://github.com/datasets/awesome-data/issues/329
* [IPCC - Special Report - Global Warming of 1.5 ºC](https://www.ipcc.ch/sr15/)
* https://www.unep.org/interactive/emissions-gap-report/2020/ - basically things are not looking good!

  > The world is still heading for a catastrophic temperature rise in excess of 3°C this century – far beyond the Paris Agreement goals of limiting global warming to well below 2°C and pursuing 1.5°C."
  >
  > ...
  >
  > So far, action on a green fiscal recovery has been limited. Around one-quarter of G20 members have dedicated shares of their spending, up to 3 per cent of GDP, to low-carbon measures. For most others, spending has mostly been high-carbon or neutral.



## Other work

* With Tommaso Venturini of KCL / Sciences Po I built the [COP21 Treaty Texts website][cop21] in November / December 2015
* With Tommaso Venturini of KCL / Sciences Po I have been working on [analyzing climate negotations][climate-talks]

[cop21]: http://cop21.okfnlabs.org/
[climate-talks]: https://github.com/rgrp/climate-negotiations


## TODO

* Base framework for understanding climate stuff.
  * Framework = sink/source
  * Framework for effects
  * Framework for overall question = an issue tree 
* How efficient could solar get? (Why? We can calculate amount of energy arriving from sun pretty well and combining that with solar efficiency tells us how energy we can obtain per m2 and hence how much area we need to obtain X amount of energy production)
  * What does efficiency mean? There is rate efficiency and actual 
  * McKay thought 10% was good. today in 2020 we are seeing things in 15-20% range
  * See https://en.wikipedia.org/wiki/Solar_cell_efficiency. Answer for single layer voltaics is theoretical limit is Shockley-Queiseer limit of ~33%. that is theoretical and given temperature, dust etc my guess is "real" efficiency is going to quite a bit lower so actual "real" efficiency of 20% would be good (?)
* how much money is spent on techno stuff (e.g. direct air capture - $100m alone with XPrize funded by Musk) vs social change e.g. better education for women?

### Data

* Solar capacity installed per year so that we know how fast we are making progress on supply in this area
  * Capacity (total and installed)
  * Supply (TWh or millions of KwH)
  * One answer is here: https://www.iea.org/reports/solar-pv.

    | Year | Generation (TWh) | % of total energy |
    |--|--|
    |2008|12|
    |2009|20|
    |2010|32|
    |2011|63|
    |2012|99|
    |2013|139|
    |2014|190|
    |2015|251|
    |2016|329|
    |2017|444|
    |2018|585|
    |2019|720| 2.7 |

* Energy demand (i.e. use) per person per country with breakdown if possible
* Wind capacity etc


Climate Change


The International Energy Agency (IEA; French: Agence internationale de l'énergie) is a Paris-based autonomous intergovernmental organisation established in the framework of the Organisation for Economic Co-operation and Development (OECD) in 1974 in the wake of the 1973 oil crisis. The IEA was initially dedicated to responding to physical disruptions in the supply of oil, as well as serving as an information source on statistics about the international oil market and other energy sectors. It is best known for the publication of its annual World Energy Outlook. [Wikipedia]

They claim on their site to provide:

> the world’s most authoritative and comprehensive source of global energy data. [We] collect, assess and disseminat energy statistics on supply and demand, compiled into energy balances in addition to a number of other key energy-related indicators, including energy prices, public RD&D and measures of energy efficiency, with other measures in development. https://www.iea.org/data-and-statistics

None of their is data is Open and most of it requires a paid subscription (see below for details). A few sample data series are provided for free. A large amount of valuable analysis and graphs are provided on their website (prominently marked "All rights reserved").

## Data

As listed on https://www.iea.org/data-and-statistics/data-products

Prices for a 12 month subscription in Euros. Price for 1, 5, 10 and unlimited users.

<Table csv={`
Title,Description,Price_1,Price_5,Price_10,Price_Unlimited
CO2 Emissions from Fuel Combustion,https://www.iea.org/data-and-statistics/data-product/co2-emissions-from-fuel-combustion,"Annual time series of CO2 emissions from fossil fuel combustion, a major source of anthropogenic greenhouse gas emissions",550,1100,1650,2750
CO2 Emissions from Fuel Combustion Highlights,Free version of the IEA's annual time series of CO2 emissions from fossil fuel combustion,0,0,0,0
`} />

Here's the dump from a copy and paste (migration into the table in progress):

> CO2 Emissions from Fuel Combustion - https://www.iea.org/data-and-statistics/data-product/co2-emissions-from-fuel-combustion
> Annual time series of CO2 emissions from fossil fuel combustion, a major source of anthropogenic greenhouse gas emissions
> 
> Subscription
> 
> CO2 Emissions from Fuel Combustion Highlights
> Free version of the IEA's annual time series of CO2 emissions from fossil fuel combustion
> 
> Free

<Excel src='https://iea.blob.core.windows.net/assets/6296011e-4fed-40c3-95a0-1367a4c4484a/CO2Highlights2020.xls' />

> Coal Information
> A comprehensive review of historical and current market trends in the world coal sector
> 
> Subscription
> 
> Electricity Information
> Annual data on electricity and heat supply and consumption for OECD and non-OECD countries
> 
> Subscription
> 
> Emissions Factors 2020
> Annual time series of CO2 emissions from electricity generation
> 
> Buy once
> 
> Energy Efficiency Indicators
> Annual data from 2000 covering end-use energy consumption, now featuring end-use carbon emissions for the IEA member countries and beyond
> 
> Subscription
> 
> Energy Efficiency Indicators Highlights
> Free version of Energy Efficiency Indicators database with selected data highlights that includes total final energy consumption by end use and a selection of end uses and years
> 
> Free
> 
> Energy Prices
> Combination of regularly-updated world energy prices covering 135 countries, and energy prices and Taxes for OECD countries
> 
> Subscription
> 
> Energy Technology Perspectives 2017
> Free data and figures from Energy Technology Perspectives 2017
> 
> Free
> Energy Technology Perspectives 2020
> Includes projections at global level for the Sustainable Development Scenario (SDS) based on detailed modelling of the energy sector using the ETP Model
> 
> Buy once
> 
> Energy Technology RD&D Budget Database
> Tracking trends in spending by energy technology in IEA countries back to 1974
> 
> Free
> 
> Fossil Fuel Subsidies Database
> Fossil fuel consumption subsidies for select countries, 2010-2020
> 
> Free
> 
> Gas Trade Flows
> Monthly gas flow data by entry and exit point for 31 participating countries, principally covering the European natural gas network
> 
> Free
> 
> IEA Energy and Carbon Tracker 2020
> A new interactive product showcasing a wide set of indicators to analyse historical trends of CO2 emissions, energy, power and sectoral patterns at country level
> 
> Buy once
> 
> IEA Statistics Package
> A complete statistics package aimed at businesses and universities
> 
> Subscription
> 
> Monthly Electricity Statistics
> Monthly electricity production and trade data for 47 countries
> 
> Free
> 
> MODS & IEA Data Services Group Package
> The most complete and comprehensive package of IEA data: all of the IEA data services plus the Monthly Oil Data Service and Monthly Gas Data Service
> 
> Package
> 
> Monthly Gas Data Service
> Monthly data on natural gas balances, imports and exports for OECD countries
> 
> Subscription
> 
> Monthly Gas Statistics
> Monthly natural gas statistics for OECD countries
> 
> Free
> 
> Monthly Oil Data Service (MODS) Complete
> The complete data behind the IEA's monthly Oil Market Report
> 
> Subscription
> 
> Monthly Oil Data Service (MODS) Field by Field Supply
> Monthly, quarterly and annual production data with forecasts for major fields, and production streams in nearly one hundred OECD and non-OECD countries
> 
> Subscription
> 
> Monthly Oil Data Service (MODS) Supply, Demand, Balances and Stocks
> Historical and forecast monthly, quarterly and annual data for supply, demand, balances, stocks and refinery throughput.
> 
> Subscription
> 
> Monthly Oil Data Service (MODS) Trade
> Monthly information on OECD member countries' imports from over 90 origins and exports to over 80 destinations for 21 products
> 
> Subscription
> 
> Monthly Oil Price Statistics
> Monthly oil price data for selected countries
> 
> Free
> 
> Monthly Oil Statistics
> Monthly oil statistics for OECD countries
> 
> Free
> 
> Natural Gas Information
> Time series of annual gas supply balances, imports and exports for OECD and non-OECD countries.
> 
> Subscription
> 
> Net Zero by 2050 Scenario
> Figures and data along with projections at global level for the Net Zero Emissions by 2050 Scenario
> 
> Free
> 
> Net Zero by 2050 Scenario - Commercial usage
> Commercial license for the figures and data along with projections at global level for the Net Zero Emissions by 2050 Scenario
> 
> Buy once
> 
> OECD Energy Prices and Taxes quarterly
> A major international compilation of energy prices at all market levels for OECD countries, updated quarterly
> 
> Subscription
> 
> Oil 2021
> The data behind the Oil 2021 report with forecasts to 2026
> 
> Buy once
> 
> Oil Information
> Annual time series of oil data including oil supply and consumption, trade and oil demand for 148 countries and 24 regions
> 
> Subscription
> 
> Projections: Energy Policies of IEA Countries
> Projections for 31 IEA and Accession countries and the European Union collected directly from the member countries for the series on Energy Policies of IEA Countries
> 
> Subscription
> 
> Quarterly Coal Statistics
> Free coal production and trade data at quarterly level
> 
> Free
> 
> Renewables 2020
> Electricity and capacity forecast data for both the main case presented in the Renewables 2020 report and an accelerated case
> 
> Buy once
> 
> Renewables Information
> Annual time series on renewables and waste production, supply and consumption for the OECD countries and non-OECD countries
> 
> Subscription
> 
> Weather for Energy Tracker
> Weather-related data useful to understand, analyse and model the energy sector
> 
> Free
> 
> World Energy Balances
> Energy balances for over 180 countries and regions
> 
> Subscription
> 
> World Energy Balances Highlights
> Free highlights of energy balances for over 180 countries and regions
> 
> Free
> 
> World Energy Investment 2021 Datafile
> World and regional investment data for supply and end-use
> 
> Free
> 
> World Energy Statistics
> Basic energy statistics for over 180 countries and regions
> 
> Subscription
> 
> World Energy Statistics and Balances
> Combines World Energy Balances and World Energy Statistics in a single, discounted package
> 
> Subscription

## License

Not open. Most datasets have to be paid for and are quite expensive.

TODO: find the formal terms and conditions.


IEA Data and Statistics Products (International Energy Agency)


This demos and documents Data Literate features live.

You can see the raw source of this page here: https://raw.githubusercontent.com/datopian/data-literate/main/content/demo.mdx

## Table of Contents

## GFM

We can have github-flavored markdown including markdown tables, auto-linked links and checklists:

```
https://github.com/datopian/portal.js

| a | b |
|---|---|
| 1 | 2 |

* [x] one thing to do
* [ ] a second thing to do
```

https://github.com/datopian/portal.js

| a | b |
|---|---|
| 1 | 2 |

* [x] one thing to do
* [ ] a second thing to do

## Footnotes

```
here is a footnote reference[^1]

[^1]: a very interesting footnote.
```

here is a footnote reference[^1]

[^1]: a very interesting footnote.


## Frontmatter

Posts can have frontmatter like:

```
---
title: Hello World
author: Rufus Pollock
---
```

The title and description are pulled from the MDX file and processed using `gray-matter`. Additionally, links are rendered using a custom component passed to `next-mdx-remote`.

## A Table of Contents

You can create a table of contents by having a markdown heading named `Table of Contents`. You can see an example at the start of this post.

## Test Component

This just shows a react component rendering in the page:

<TestComponent name="you" />

## A Table

You can create tables ...

```
<Table cols={[
    { key: 'id', name: 'ID' },
    { key: 'firstName', name: 'First name' },
    { key: 'lastName', name: 'Last name' },
    { key: 'age', name: 'Age' }
  ]} data={[
    { id: 1, lastName: 'Snow', firstName: 'Jon', age: 35 },
    { id: 2, lastName: 'Lannister', firstName: 'Cersei', age: 42 },
    { id: 3, lastName: 'Lannister', firstName: 'Jaime', age: 45 },
    { id: 4, lastName: 'Stark', firstName: 'Arya', age: 16 },
    { id: 7, lastName: 'Clifford', firstName: 'Ferrara', age: 44 },
    { id: 8, lastName: 'Frances', firstName: 'Rossini', age: 36 },
    { id: 9, lastName: 'Roxie', firstName: 'Harvey', age: 65 },
  ]}
  />
```

<Table cols={[
    { key: 'id', name: 'ID' },
    { key: 'firstName', name: 'First name' },
    { key: 'lastName', name: 'Last name' },
    { key: 'age', name: 'Age' }
  ]} data={[
    { id: 1, lastName: 'Snow', firstName: 'Jon', age: 35 },
    { id: 2, lastName: 'Lannister', firstName: 'Cersei', age: 42 },
    { id: 3, lastName: 'Lannister', firstName: 'Jaime', age: 45 },
    { id: 4, lastName: 'Stark', firstName: 'Arya', age: 16 },
    { id: 7, lastName: 'Clifford', firstName: 'Ferrara', age: 44 },
    { id: 8, lastName: 'Frances', firstName: 'Rossini', age: 36 },
    { id: 9, lastName: 'Roxie', firstName: 'Harvey', age: 65 },
  ]}
  />

### Table from Raw CSV

You can also pass raw CSV as the content ...

```
<Table csv={`
Year,Temp Anomaly
1850,-0.418
2020,0.923
`} />
```

<Table csv={`
Year,Temp Anomaly,
1850,-0.418
2020,0.923
`} />

### Table from a URL

<Table url='https://raw.githubusercontent.com/datopian/data-literate/main/public/_files/HadCRUT.5.0.1.0.analysis.summary_series.global.annual.csv' />

```
<Table url='https://raw.githubusercontent.com/datopian/data-literate/main/public/_files/HadCRUT.5.0.1.0.analysis.summary_series.global.annual.csv' />
```

## Charts

You can create charts using a simple syntax.

### Line Chart

<LineChart data={
    [
      ["1850",-0.41765878],
      ["1851",-0.2333498],
      ["1852",-0.22939907],
      ["1853",-0.27035445],
      ["1854",-0.29163003]
    ]
  }
  />

```
<LineChart data={
    [
      ["1850",-0.41765878],
      ["1851",-0.2333498],
      ["1852",-0.22939907],
      ["1853",-0.27035445],
      ["1854",-0.29163003]
    ]
  }
  />
```

NB: we have quoted years as otherwise not interpreted as dates but as integers ...


### Vega and Vega Lite

You can using vega or vega-lite. Here's an example using vega-lite:

<VegaLite data={ { "table": [
      {
        "y": -0.418,
        "x": 1850
      },
      {
        "y": 0.923,
        "x": 2020
      }
      ]
    }
  } spec={
    {
      "$schema": "https://vega.github.io/schema/vega-lite/v4.json",
      "mark": "bar",
      "data": {
          "name": "table"
      },
      "encoding": {
          "x": {
              "field": "x",
              "type": "ordinal"
          },
          "y": {
              "field": "y",
              "type": "quantitative"
          }
      }
    }
  } />


```jsx
<VegaLite data={ { "table": [
      {
        "y": -0.418,
        "x": 1850
      },
      {
        "y": 0.923,
        "x": 2020
      }
      ]
    }
  } spec={
    {
      "$schema": "https://vega.github.io/schema/vega-lite/v4.json",
      "mark": "bar",
      "data": {
          "name": "table"
      },
      "encoding": {
          "x": {
              "field": "x",
              "type": "ordinal"
          },
          "y": {
              "field": "y",
              "type": "quantitative"
          }
      }
    }
  } />

```

#### Line Chart from URL with Tooltip

https://vega.github.io/vega-lite/examples/interactive_multi_line_pivot_tooltip.html

<VegaLite spec={ 
{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "data": {"url": "_files/HadCRUT.5.0.1.0.analysis.summary_series.global.annual.csv"},
  "width": 600,
  "height": 250,
  "mark": "line",
  "encoding": {
    "x": {"field": "Time", "type": "temporal"},
    "y": {"field": "Anomaly (deg C)", "type": "quantitative"},
    "tooltip": {"field": "Anomaly (deg C)", "type": "quantitative"}
  }
}
} />

## Display Excel Files

Local file ...

```
<Excel src='/_files/eight-centuries-of-global-real-interest-rates-r-g-and-the-suprasecular-decline-1311-2018-data.xlsx' />
```

<Excel src='/_files/eight-centuries-of-global-real-interest-rates-r-g-and-the-suprasecular-decline-1311-2018-data.xlsx' />

Remote files work too (even without CORS) thanks to proxying:

```
<Excel src='https://github.com/datasets/awesome-data/files/6604635/eight-centuries-of-global-real-interest-rates-r-g-and-the-suprasecular-decline-1311-2018-data.xlsx' />
```

<Excel src='https://github.com/datasets/awesome-data/files/6604635/eight-centuries-of-global-real-interest-rates-r-g-and-the-suprasecular-decline-1311-2018-data.xlsx' />


Demo



Column 1 is the date.
Column 2 is the median of the 100 ensemble member time series.
Columns 3 and 4 are the lower and upper bounds of the 95% confidence interval of bias uncertainty computed from the 100 member ensemble.
Columns 5 and 6 are the lower and upper bounds of the 95% confidence interval of measurement and sampling uncertainties around the ensemble median. These are the combination of fully uncorrelated measurement and sampling uncertainties and partially correlated uncertainties described by the HadCRUT4 error covariance matrices.
Columns 7 and 8 are the lower and upper bounds of the 95% confidence interval of coverage uncertainties around the ensemble median.
Columns 9 and 10 are the lower and upper bounds of the 95% confidence interval of the combination of measurement and sampling and bias uncertainties.
Columns 11 and 12 are the lower and upper bounds of the 95% confidence interval of the combined effects of all the uncertainties described in the HadCRUT4 error model (measurement and sampling, bias and coverage uncertainties).

```csv
year,temperature_anomaly,lower1,upper1,lower2,upper2,lower3,upper3,lower4,upper4,lower5,upper5
1850,-0.373,-0.425,-0.339,-0.502,-0.243,-0.536,-0.210,-0.515,-0.238,-0.591,-0.162
1851,-0.218,-0.274,-0.184,-0.350,-0.086,-0.368,-0.068,-0.365,-0.081,-0.429,-0.016
1852,-0.228,-0.280,-0.196,-0.353,-0.103,-0.378,-0.078,-0.367,-0.098,-0.434,-0.030
1853,-0.269,-0.321,-0.239,-0.378,-0.160,-0.428,-0.110,-0.393,-0.155,-0.472,-0.075
1854,-0.248,-0.301,-0.218,-0.349,-0.147,-0.391,-0.105,-0.365,-0.143,-0.435,-0.072
1855,-0.272,-0.324,-0.241,-0.369,-0.176,-0.415,-0.130,-0.385,-0.171,-0.455,-0.099
1856,-0.358,-0.413,-0.327,-0.456,-0.260,-0.501,-0.215,-0.471,-0.253,-0.541,-0.182
1857,-0.461,-0.512,-0.431,-0.558,-0.363,-0.594,-0.328,-0.573,-0.358,-0.636,-0.294
1858,-0.467,-0.521,-0.435,-0.570,-0.364,-0.600,-0.334,-0.583,-0.357,-0.644,-0.295
1859,-0.284,-0.340,-0.249,-0.396,-0.171,-0.418,-0.150,-0.412,-0.166,-0.470,-0.106
1860,-0.343,-0.405,-0.310,-0.455,-0.231,-0.478,-0.208,-0.472,-0.227,-0.531,-0.166
1861,-0.407,-0.484,-0.356,-0.525,-0.290,-0.562,-0.252,-0.542,-0.276,-0.613,-0.204
1862,-0.524,-0.597,-0.471,-0.643,-0.404,-0.689,-0.358,-0.659,-0.391,-0.737,-0.312
1863,-0.278,-0.359,-0.226,-0.403,-0.154,-0.444,-0.112,-0.423,-0.144,-0.499,-0.065
1864,-0.494,-0.564,-0.448,-0.617,-0.372,-0.649,-0.340,-0.636,-0.364,-0.705,-0.293
1865,-0.279,-0.358,-0.232,-0.404,-0.155,-0.454,-0.105,-0.428,-0.147,-0.511,-0.062
1866,-0.251,-0.338,-0.197,-0.370,-0.133,-0.413,-0.089,-0.396,-0.120,-0.470,-0.043
1867,-0.321,-0.406,-0.265,-0.434,-0.208,-0.485,-0.157,-0.459,-0.193,-0.535,-0.113
1868,-0.238,-0.326,-0.185,-0.344,-0.132,-0.399,-0.078,-0.372,-0.118,-0.448,-0.038
1869,-0.262,-0.333,-0.221,-0.368,-0.156,-0.403,-0.122,-0.388,-0.148,-0.451,-0.082
1870,-0.276,-0.340,-0.239,-0.377,-0.174,-0.400,-0.151,-0.393,-0.165,-0.447,-0.110
1871,-0.335,-0.404,-0.293,-0.437,-0.232,-0.470,-0.199,-0.453,-0.221,-0.514,-0.157
1872,-0.227,-0.290,-0.187,-0.326,-0.128,-0.351,-0.103,-0.342,-0.118,-0.397,-0.062
1873,-0.304,-0.364,-0.258,-0.400,-0.207,-0.422,-0.186,-0.417,-0.196,-0.468,-0.144
1874,-0.368,-0.435,-0.325,-0.462,-0.273,-0.487,-0.249,-0.480,-0.261,-0.532,-0.209
1875,-0.395,-0.464,-0.352,-0.488,-0.302,-0.512,-0.277,-0.509,-0.290,-0.558,-0.238
1876,-0.384,-0.453,-0.335,-0.475,-0.294,-0.500,-0.269,-0.494,-0.279,-0.543,-0.228
1877,-0.075,-0.146,-0.026,-0.163,0.013,-0.193,0.043,-0.183,0.028,-0.235,0.081
1878,0.035,-0.026,0.084,-0.053,0.124,-0.081,0.152,-0.070,0.139,-0.121,0.192
1879,-0.230,-0.291,-0.189,-0.310,-0.151,-0.344,-0.117,-0.328,-0.139,-0.380,-0.084
1880,-0.227,-0.288,-0.182,-0.304,-0.150,-0.347,-0.107,-0.322,-0.136,-0.380,-0.076
1881,-0.200,-0.259,-0.157,-0.276,-0.125,-0.324,-0.076,-0.293,-0.112,-0.355,-0.048
1882,-0.213,-0.274,-0.171,-0.287,-0.138,-0.330,-0.096,-0.307,-0.127,-0.363,-0.068
1883,-0.296,-0.350,-0.251,-0.364,-0.229,-0.405,-0.187,-0.380,-0.215,-0.433,-0.160
1884,-0.409,-0.457,-0.373,-0.471,-0.346,-0.519,-0.298,-0.488,-0.335,-0.545,-0.276
1885,-0.389,-0.444,-0.354,-0.450,-0.328,-0.504,-0.274,-0.467,-0.317,-0.528,-0.254
1886,-0.367,-0.419,-0.334,-0.428,-0.307,-0.480,-0.255,-0.445,-0.298,-0.505,-0.236
1887,-0.418,-0.476,-0.382,-0.481,-0.356,-0.526,-0.310,-0.501,-0.346,-0.555,-0.289
1888,-0.307,-0.359,-0.268,-0.370,-0.244,-0.413,-0.201,-0.390,-0.232,-0.442,-0.178
1889,-0.171,-0.235,-0.129,-0.237,-0.105,-0.279,-0.063,-0.262,-0.092,-0.313,-0.038
1890,-0.416,-0.484,-0.369,-0.482,-0.351,-0.521,-0.312,-0.510,-0.335,-0.557,-0.285
1891,-0.330,-0.408,-0.276,-0.398,-0.262,-0.436,-0.224,-0.429,-0.245,-0.476,-0.195
1892,-0.455,-0.526,-0.400,-0.527,-0.383,-0.560,-0.350,-0.558,-0.366,-0.603,-0.318
1893,-0.473,-0.547,-0.416,-0.545,-0.402,-0.576,-0.370,-0.573,-0.384,-0.617,-0.337
1894,-0.410,-0.487,-0.352,-0.479,-0.341,-0.514,-0.307,-0.507,-0.319,-0.551,-0.272
1895,-0.390,-0.460,-0.335,-0.457,-0.322,-0.492,-0.287,-0.487,-0.304,-0.531,-0.256
1896,-0.186,-0.255,-0.136,-0.255,-0.117,-0.290,-0.082,-0.282,-0.099,-0.327,-0.050
1897,-0.206,-0.273,-0.153,-0.276,-0.137,-0.309,-0.104,-0.304,-0.118,-0.348,-0.071
1898,-0.412,-0.479,-0.362,-0.482,-0.343,-0.514,-0.310,-0.508,-0.323,-0.552,-0.277
1899,-0.289,-0.355,-0.231,-0.358,-0.220,-0.389,-0.189,-0.385,-0.198,-0.428,-0.154
1900,-0.203,-0.272,-0.147,-0.274,-0.132,-0.303,-0.102,-0.303,-0.111,-0.345,-0.066
1901,-0.259,-0.326,-0.208,-0.328,-0.189,-0.366,-0.151,-0.357,-0.168,-0.404,-0.118
1902,-0.402,-0.461,-0.355,-0.464,-0.339,-0.505,-0.298,-0.490,-0.320,-0.538,-0.270
1903,-0.479,-0.543,-0.431,-0.541,-0.417,-0.585,-0.374,-0.567,-0.400,-0.617,-0.348
1904,-0.520,-0.580,-0.471,-0.582,-0.459,-0.627,-0.413,-0.606,-0.441,-0.657,-0.387
1905,-0.377,-0.438,-0.326,-0.436,-0.318,-0.481,-0.273,-0.461,-0.299,-0.511,-0.247
1906,-0.283,-0.338,-0.232,-0.341,-0.224,-0.388,-0.177,-0.363,-0.207,-0.415,-0.152
1907,-0.465,-0.523,-0.423,-0.524,-0.406,-0.568,-0.363,-0.546,-0.392,-0.596,-0.339
1908,-0.511,-0.563,-0.468,-0.568,-0.455,-0.613,-0.409,-0.590,-0.441,-0.640,-0.388
1909,-0.522,-0.576,-0.476,-0.574,-0.471,-0.626,-0.419,-0.596,-0.456,-0.649,-0.400
1910,-0.490,-0.547,-0.448,-0.539,-0.442,-0.594,-0.387,-0.564,-0.424,-0.617,-0.368
1911,-0.544,-0.595,-0.498,-0.591,-0.498,-0.646,-0.442,-0.614,-0.480,-0.668,-0.424
1912,-0.437,-0.486,-0.392,-0.485,-0.390,-0.538,-0.337,-0.508,-0.375,-0.561,-0.320
1913,-0.424,-0.471,-0.381,-0.473,-0.375,-0.527,-0.321,-0.493,-0.362,-0.548,-0.305
1914,-0.244,-0.291,-0.197,-0.302,-0.185,-0.346,-0.141,-0.321,-0.173,-0.373,-0.120
1915,-0.141,-0.202,-0.085,-0.209,-0.073,-0.250,-0.032,-0.232,-0.061,-0.284,-0.007
1916,-0.383,-0.437,-0.328,-0.442,-0.323,-0.489,-0.276,-0.466,-0.308,-0.518,-0.254
1917,-0.468,-0.525,-0.413,-0.532,-0.404,-0.578,-0.358,-0.556,-0.387,-0.610,-0.332
1918,-0.333,-0.396,-0.270,-0.401,-0.264,-0.449,-0.217,-0.427,-0.244,-0.483,-0.187
1919,-0.275,-0.327,-0.217,-0.339,-0.210,-0.388,-0.161,-0.361,-0.192,-0.417,-0.135
1920,-0.247,-0.296,-0.198,-0.309,-0.186,-0.358,-0.137,-0.328,-0.172,-0.385,-0.114
1921,-0.187,-0.237,-0.149,-0.243,-0.132,-0.288,-0.086,-0.262,-0.124,-0.314,-0.069
1922,-0.302,-0.346,-0.262,-0.355,-0.249,-0.404,-0.200,-0.370,-0.239,-0.425,-0.183
1923,-0.276,-0.323,-0.237,-0.326,-0.227,-0.374,-0.179,-0.343,-0.219,-0.395,-0.165
1924,-0.294,-0.340,-0.259,-0.345,-0.244,-0.396,-0.192,-0.361,-0.236,-0.417,-0.178
1925,-0.215,-0.265,-0.178,-0.265,-0.166,-0.316,-0.115,-0.284,-0.157,-0.338,-0.101
1926,-0.108,-0.155,-0.070,-0.157,-0.059,-0.209,-0.007,-0.175,-0.051,-0.230,0.007
1927,-0.210,-0.253,-0.177,-0.256,-0.164,-0.310,-0.110,-0.271,-0.156,-0.327,-0.097
1928,-0.206,-0.253,-0.174,-0.254,-0.158,-0.303,-0.108,-0.269,-0.152,-0.323,-0.096
1929,-0.350,-0.394,-0.320,-0.399,-0.301,-0.448,-0.252,-0.414,-0.295,-0.468,-0.239
1930,-0.137,-0.181,-0.104,-0.186,-0.088,-0.235,-0.039,-0.200,-0.081,-0.254,-0.025
1931,-0.087,-0.133,-0.055,-0.135,-0.038,-0.185,0.011,-0.150,-0.031,-0.204,0.025
1932,-0.137,-0.183,-0.107,-0.185,-0.088,-0.231,-0.043,-0.199,-0.082,-0.250,-0.029
1933,-0.273,-0.320,-0.240,-0.320,-0.225,-0.366,-0.179,-0.335,-0.219,-0.386,-0.166
1934,-0.131,-0.177,-0.100,-0.178,-0.084,-0.224,-0.038,-0.191,-0.078,-0.243,-0.024
1935,-0.178,-0.221,-0.150,-0.224,-0.132,-0.270,-0.086,-0.235,-0.126,-0.287,-0.073
1936,-0.147,-0.186,-0.120,-0.189,-0.105,-0.235,-0.059,-0.199,-0.098,-0.250,-0.046
1937,-0.026,-0.064,0.001,-0.068,0.016,-0.112,0.061,-0.079,0.022,-0.128,0.072
1938,-0.006,-0.044,0.017,-0.049,0.038,-0.096,0.085,-0.061,0.043,-0.112,0.096
1939,-0.052,-0.088,-0.029,-0.103,-0.001,-0.142,0.038,-0.113,0.005,-0.161,0.054
1940,0.014,-0.018,0.045,-0.044,0.072,-0.089,0.117,-0.054,0.079,-0.110,0.135
1941,0.020,-0.021,0.053,-0.032,0.072,-0.077,0.117,-0.047,0.081,-0.098,0.134
1942,-0.027,-0.081,0.015,-0.084,0.029,-0.125,0.071,-0.105,0.042,-0.152,0.093
1943,-0.004,-0.056,0.036,-0.060,0.051,-0.107,0.099,-0.081,0.063,-0.133,0.118
1944,0.144,0.094,0.180,0.091,0.198,0.044,0.245,0.070,0.210,0.019,0.264
1945,0.025,-0.017,0.059,-0.030,0.081,-0.080,0.131,-0.048,0.090,-0.104,0.149
1946,-0.071,-0.113,-0.042,-0.124,-0.017,-0.160,0.019,-0.140,-0.009,-0.184,0.038
1947,-0.038,-0.098,0.001,-0.089,0.012,-0.126,0.050,-0.115,0.028,-0.155,0.072
1948,-0.039,-0.101,0.000,-0.086,0.008,-0.124,0.046,-0.113,0.027,-0.152,0.069
1949,-0.074,-0.119,-0.036,-0.118,-0.031,-0.158,0.009,-0.140,-0.018,-0.182,0.025
1950,-0.173,-0.216,-0.135,-0.217,-0.130,-0.255,-0.091,-0.237,-0.118,-0.279,-0.075
1951,-0.052,-0.091,-0.018,-0.093,-0.010,-0.137,0.034,-0.108,0.001,-0.155,0.049
1952,0.028,-0.010,0.063,-0.012,0.068,-0.056,0.112,-0.024,0.084,-0.070,0.130
1953,0.097,0.060,0.132,0.057,0.137,0.012,0.182,0.043,0.151,-0.004,0.198
1954,-0.129,-0.166,-0.093,-0.169,-0.089,-0.213,-0.045,-0.182,-0.077,-0.228,-0.031
1955,-0.190,-0.222,-0.160,-0.227,-0.152,-0.272,-0.107,-0.239,-0.140,-0.285,-0.093
1956,-0.267,-0.297,-0.236,-0.304,-0.229,-0.345,-0.188,-0.314,-0.219,-0.357,-0.174
1957,-0.007,-0.035,0.024,-0.043,0.030,-0.084,0.071,-0.054,0.040,-0.097,0.083
1958,0.046,0.020,0.075,0.010,0.082,-0.031,0.122,0.002,0.092,-0.042,0.136
1959,0.017,-0.008,0.043,-0.018,0.051,-0.058,0.091,-0.026,0.060,-0.069,0.103
1960,-0.049,-0.073,-0.023,-0.083,-0.015,-0.123,0.025,-0.091,-0.007,-0.134,0.036
1961,0.038,0.009,0.060,0.006,0.070,-0.038,0.113,-0.002,0.078,-0.047,0.124
1962,0.014,-0.012,0.034,-0.017,0.046,-0.062,0.090,-0.026,0.053,-0.071,0.100
1963,0.048,0.022,0.074,0.016,0.080,-0.029,0.125,0.005,0.089,-0.040,0.136
1964,-0.223,-0.246,-0.197,-0.254,-0.192,-0.299,-0.148,-0.262,-0.181,-0.307,-0.136
1965,-0.140,-0.163,-0.113,-0.171,-0.109,-0.216,-0.064,-0.181,-0.099,-0.227,-0.054
1966,-0.068,-0.090,-0.042,-0.098,-0.038,-0.145,0.009,-0.107,-0.029,-0.154,0.018
1967,-0.074,-0.093,-0.052,-0.103,-0.045,-0.150,0.003,-0.109,-0.038,-0.158,0.011
1968,-0.113,-0.133,-0.097,-0.141,-0.085,-0.188,-0.037,-0.147,-0.080,-0.196,-0.031
1969,0.032,0.014,0.050,0.004,0.059,-0.045,0.108,-0.002,0.066,-0.051,0.115
1970,-0.027,-0.048,-0.009,-0.055,-0.000,-0.103,0.048,-0.060,0.008,-0.109,0.057
1971,-0.186,-0.204,-0.163,-0.213,-0.160,-0.261,-0.112,-0.218,-0.150,-0.267,-0.103
1972,-0.065,-0.080,-0.040,-0.093,-0.038,-0.141,0.010,-0.098,-0.031,-0.147,0.018
1973,0.062,0.051,0.082,0.035,0.089,-0.012,0.136,0.032,0.094,-0.017,0.143
1974,-0.214,-0.227,-0.197,-0.241,-0.187,-0.289,-0.139,-0.244,-0.182,-0.294,-0.132
1975,-0.149,-0.163,-0.134,-0.175,-0.122,-0.224,-0.073,-0.179,-0.118,-0.230,-0.067
1976,-0.241,-0.255,-0.227,-0.269,-0.214,-0.317,-0.166,-0.272,-0.211,-0.323,-0.160
1977,0.047,0.034,0.064,0.020,0.074,-0.027,0.121,0.017,0.078,-0.033,0.128
1978,-0.062,-0.077,-0.044,-0.089,-0.035,-0.138,0.014,-0.092,-0.030,-0.143,0.021
1979,0.057,0.041,0.076,0.035,0.080,-0.016,0.131,0.029,0.086,-0.022,0.136
1980,0.092,0.077,0.107,0.067,0.118,0.017,0.168,0.062,0.122,0.010,0.173
1981,0.140,0.125,0.153,0.113,0.166,0.063,0.216,0.108,0.170,0.056,0.222
1982,0.011,-0.011,0.030,-0.017,0.040,-0.064,0.087,-0.025,0.045,-0.073,0.094
1983,0.194,0.174,0.211,0.166,0.222,0.119,0.269,0.159,0.227,0.110,0.276
1984,-0.014,-0.033,0.006,-0.041,0.014,-0.089,0.061,-0.048,0.019,-0.096,0.068
1985,-0.030,-0.048,-0.013,-0.056,-0.003,-0.103,0.043,-0.063,0.002,-0.111,0.050
1986,0.045,0.024,0.067,0.020,0.071,-0.030,0.120,0.010,0.077,-0.038,0.126
1987,0.192,0.171,0.220,0.167,0.217,0.116,0.268,0.157,0.226,0.109,0.275
1988,0.198,0.175,0.221,0.173,0.224,0.123,0.274,0.163,0.233,0.115,0.282
1989,0.118,0.091,0.147,0.091,0.145,0.042,0.193,0.081,0.155,0.034,0.202
1990,0.296,0.267,0.330,0.269,0.323,0.220,0.373,0.258,0.337,0.211,0.383
1991,0.254,0.220,0.289,0.228,0.280,0.178,0.330,0.214,0.296,0.169,0.341
1992,0.105,0.071,0.139,0.079,0.130,0.027,0.182,0.064,0.146,0.017,0.192
1993,0.148,0.112,0.186,0.123,0.174,0.071,0.226,0.106,0.191,0.061,0.237
1994,0.208,0.174,0.247,0.183,0.233,0.129,0.286,0.165,0.252,0.119,0.298
1995,0.325,0.289,0.361,0.302,0.348,0.249,0.402,0.281,0.369,0.236,0.412
1996,0.183,0.146,0.217,0.162,0.203,0.103,0.262,0.140,0.224,0.091,0.270
1997,0.390,0.356,0.425,0.367,0.412,0.313,0.466,0.349,0.433,0.303,0.477
1998,0.539,0.500,0.578,0.516,0.563,0.464,0.615,0.493,0.584,0.450,0.627
1999,0.306,0.265,0.343,0.284,0.329,0.230,0.382,0.260,0.351,0.217,0.395
2000,0.294,0.259,0.338,0.273,0.316,0.217,0.371,0.252,0.342,0.206,0.386
2001,0.441,0.406,0.483,0.419,0.462,0.364,0.517,0.398,0.490,0.353,0.532
2002,0.496,0.463,0.538,0.476,0.516,0.417,0.575,0.454,0.546,0.406,0.590
2003,0.505,0.473,0.546,0.487,0.524,0.427,0.584,0.465,0.554,0.417,0.600
2004,0.447,0.413,0.483,0.430,0.464,0.368,0.526,0.406,0.491,0.358,0.539
2005,0.545,0.507,0.583,0.528,0.561,0.464,0.625,0.503,0.590,0.454,0.639
2006,0.506,0.468,0.544,0.491,0.521,0.427,0.584,0.464,0.550,0.416,0.597
2007,0.491,0.457,0.531,0.477,0.506,0.414,0.569,0.451,0.534,0.404,0.581
2008,0.395,0.355,0.432,0.380,0.409,0.318,0.471,0.353,0.438,0.308,0.483
2009,0.506,0.465,0.546,0.492,0.520,0.427,0.584,0.463,0.549,0.415,0.595
2010,0.560,0.523,0.603,0.546,0.573,0.480,0.639,0.519,0.605,0.470,0.651
2011,0.425,0.393,0.466,0.410,0.440,0.347,0.503,0.384,0.470,0.336,0.515
2012,0.470,0.437,0.509,0.452,0.488,0.392,0.548,0.429,0.515,0.381,0.561
2013,0.514,0.478,0.552,0.495,0.533,0.435,0.593,0.470,0.559,0.423,0.606
2014,0.579,0.545,0.616,0.560,0.598,0.503,0.655,0.537,0.624,0.491,0.669
2015,0.763,0.730,0.802,0.747,0.779,0.686,0.840,0.722,0.808,0.675,0.853
2016,0.797,0.762,0.835,0.781,0.812,0.720,0.873,0.755,0.842,0.709,0.886
2017,0.677,0.636,0.716,0.655,0.698,0.599,0.754,0.632,0.725,0.587,0.769
2018,0.597,0.560,0.636,0.582,0.612,0.522,0.672,0.556,0.642,0.511,0.686
2019,0.736,0.700,0.776,0.720,0.753,0.656,0.817,0.693,0.781,0.644,0.829
2020,0.768,0.733,0.811,0.752,0.784,0.687,0.849,0.726,0.816,0.678,0.863
2021,0.551,0.513,0.597,0.526,0.577,0.363,0.740,0.504,0.605,0.358,0.749
```


Met Office Hadley Centre temperature observations data


Status: we have a good source (Schmelzing) which we could use ...

## Research

### Eight centuries of global real interest rates, R-G, and the ‘suprasecular’ decline, 1311–2018

This is a 2020 BoE staff working paper and looks pretty spot on. I've attached the xlsx data file.

> Staff Working Paper No. 845 By Paul Schmelzing
>
> With recourse to archival, printed primary, and secondary sources, this paper reconstructs global real interest rates on an annual basis going back to the 14th century, covering 78% of advanced economy GDP over time. I show that across successive monetary and fiscal regimes, and a variety of asset classes, real interest rates have not been ‘stable’, and that since the major monetary upheavals of the late middle ages, a trend decline between 0.6–1.6 basis points per annum has prevailed. A gradual increase in real negative‑yielding rates in advanced economies over the same horizon is identified, despite important temporary reversals such as the 17th Century Crisis. Against their long‑term context, currently depressed sovereign real rates are in fact converging ‘back to historical trend’ — a trend that makes narratives about a ‘secular stagnation’ environment entirely misleading, and suggests that — irrespective of particular monetary and fiscal responses — real rates could soon enter permanently negative territory. I also posit that the return data here reflects a substantial share of ‘non‑human wealth’ over time: the resulting R-G series derived from this data show a downward trend over the same timeframe: suggestions about the ‘virtual stability’ of capital returns, and the policy implications advanced by Piketty (2014) are in consequence equally unsubstantiated by the historical record.

* [eight-centuries-of-global-real-interest-rates-r-g-and-the-suprasecular-decline-1311-2018.pdf](https://github.com/datasets/awesome-data/files/6604637/eight-centuries-of-global-real-interest-rates-r-g-and-the-suprasecular-decline-1311-2018.pdf)
  * [eight-centuries-of-global-real-interest-rates-r-g-and-the-suprasecular-decline-1311-2018-appendix.pdf](https://github.com/datasets/awesome-data/files/6604641/eight-centuries-of-global-real-interest-rates-r-g-and-the-suprasecular-decline-1311-2018-appendix.pdf)
* **Data**: [eight-centuries-of-global-real-interest-rates-r-g-and-the-suprasecular-decline-1311-2018-data.xlsx](https://github.com/datasets/awesome-data/files/6604635/eight-centuries-of-global-real-interest-rates-r-g-and-the-suprasecular-decline-1311-2018-data.xlsx)

#### p.13 Figure III: 454 personal/non-marketable loans to sovereigns, 1310-1946, and U.S. savings bonds – plus resulting real rate trend.1

![image](https://user-images.githubusercontent.com/180658/120935592-d0fcf480-c703-11eb-9dee-9b264833c747.png)

#### p.14 Figure IV: Headline global real rate, GDP-weighted, and trend declines, 1317-2018.

![image](https://user-images.githubusercontent.com/180658/120935615-f12cb380-c703-11eb-96bf-305857ce013a.png)

#### Figure V: Nominal bond yields, GDP- and arithmetically-weighted, 1314-2018

![image](https://user-images.githubusercontent.com/180658/120935660-1d483480-c704-11eb-93a5-74747df6b06a.png)

#### Figure VIII: Private R, and R-G trends, “Carolingian Europe”, 1250-2019.39

[Ed: Evidence that "private R[eal] rate" is similar to "public R[eal] rate"]

![image](https://user-images.githubusercontent.com/180658/120935753-8760d980-c704-11eb-985d-9916123345ce.png)

## Aside

When I read Piketty (Capital In 21st Century), I immediately felt one of the weakest parts of his argument was the claim about the stability of real interest rates. Usually, one would imagine that as capital accumulates (as it did in his model) the "price" of it i.e. the interest rate should fall. Much of Piketty's simple model and argument about the domination of capital(ists) rested on the fact that this was not happening. This seemed dubious IMO. My cursory historical knowledge threw up memories that real interest rates a century or a couple of centuries ago definitely seemed higher than today (e.g. British Government consols in the 18th century yielding a real 5 or 6%. Keynes talking about 6.5% yields back in Economic Prospects for our Grandchildren etc).

Interesting that the above BoE paper a) lends substantial data for this claim of declining real interest rates b) specifically mentions that this undermines Piketty's claims:

> the resulting R-G series derived from this data show a downward trend over the same timeframe: suggestions about the ‘virtual stability’ of capital returns, and the policy implications advanced by Piketty (2014) are in consequence equally unsubstantiated by the historical record.

Some damning critical asides from Schmelzing in the footnotes e.g. this one in fn 46 on p. 32 implying in academic language that Piketty has not been entirely straight with some sources:

>  In a footnote, Piketty (2014, 354) refers to Homer and Sylla (1996), contending that the reference would show that
“for interest on loans, we often find rates above 5% in earlier periods, typically on the order of 6-8 percent, even for
loans with real estate collateral”. I have been unable to find such a statement in the History of Interest Rates. Even
ignoring this, it still remains unexplained why Piketty then opts for 4.5%, rather than at least choosing a figure
between 6-8%.


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