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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
6 | 275kB | csv zip | 5 years ago | 5 years ago | ODC-PDDL-1.0 | EPA (United States Environmental Protection Agency) CSIRO (Commonwealth Scientific and Industrial Research Organisation) |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
csiro_alt_gmsl_mo_2015 | 4kB | csv (4kB) , json (10kB) | ||
csiro_alt_gmsl_yr_2015 | 382B | csv (382B) , json (812B) | ||
csiro_recons_gmsl_mo_2015 | 36kB | csv (36kB) , json (100kB) | ||
csiro_recons_gmsl_yr_2015 | 3kB | csv (3kB) , json (8kB) | ||
epa-sea-level | 7kB | csv (7kB) , json (22kB) | ||
sea-level-rise_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 46kB | zip (46kB) |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
Time | 1 | date (%Y-%m-%d) | Year and month of GMSL calculation (YYYY-MMM) |
GMSL | 2 | number | Global Mean Sea Level in mm |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
Time | 1 | date (%Y-%m-%d) | Year of GMSL calculation (YYYY) |
GMSL | 2 | number | Global Mean Sea Level in mm |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
Time | 1 | date (%Y-%m-%d) | Year and month of GMSL calculation (YYYY-MMM) |
GMSL | 2 | number | Reconstructed Global Mean Sea Level in mm |
GMSL uncertainty | 3 | number | uncertainty due to reconstruction |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
Time | 1 | date (%Y-%m-%d) | Year of GMSL calculation (YYYY) |
GMSL | 2 | number | Reconstructed Global Mean Sea Level in mm |
GMSL uncertainty | 3 | number | Uncertainty due to reconstruction |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
Year | 1 | date (%Y-%m-%d) | Year of measurement (YYYY) |
CSIRO Adjusted Sea Level | 2 | number | Comulative changes (in inches) in sea level for the world’s oceans based on the combination of long-term tide gauge measurements and recent satellite measurements. |
Lower Error Bound | 3 | number | inches |
Upper Error Bound | 4 | number | inches |
NOAA Adjusted Sea Level | 5 | number | inches |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/sea-level-rise
data info core/sea-level-rise
tree core/sea-level-rise
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/sea-level-rise/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/sea-level-rise/r/0.csv
curl -L https://datahub.io/core/sea-level-rise/r/1.csv
curl -L https://datahub.io/core/sea-level-rise/r/2.csv
curl -L https://datahub.io/core/sea-level-rise/r/3.csv
curl -L https://datahub.io/core/sea-level-rise/r/4.csv
curl -L https://datahub.io/core/sea-level-rise/r/5.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/core/sea-level-rise/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
# get list of all resources:
print(json_data$resources$name)
# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
if(json_data$resources$datahub$type[i]=='derived/csv'){
path_to_file = json_data$resources$path[i]
data <- read.csv(url(path_to_file))
print(data)
}
}
Note: You might need to run the script with root permissions if you are running on Linux machine
Install the Frictionless Data data package library and the pandas itself:
pip install datapackage
pip install pandas
Now you can use the datapackage in the Pandas:
import datapackage
import pandas as pd
data_url = 'https://datahub.io/core/sea-level-rise/datapackage.json'
# to load Data Package into storage
package = datapackage.Package(data_url)
# to load only tabular data
resources = package.resources
for resource in resources:
if resource.tabular:
data = pd.read_csv(resource.descriptor['path'])
print (data)
For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):
pip install datapackage
To get Data Package into your Python environment, run following code:
from datapackage import Package
package = Package('https://datahub.io/core/sea-level-rise/datapackage.json')
# print list of all resources:
print(package.resource_names)
# print processed tabular data (if exists any)
for resource in package.resources:
if resource.descriptor['datahub']['type'] == 'derived/csv':
print(resource.read())
If you are using JavaScript, please, follow instructions below:
Install data.js
module using npm
:
$ npm install data.js
Once the package is installed, use the following code snippet:
const {Dataset} = require('data.js')
const path = 'https://datahub.io/core/sea-level-rise/datapackage.json'
// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
const dataset = await Dataset.load(path)
// get list of all resources:
for (const id in dataset.resources) {
console.log(dataset.resources[id]._descriptor.name)
}
// get all tabular data(if exists any)
for (const id in dataset.resources) {
if (dataset.resources[id]._descriptor.format === "csv") {
const file = dataset.resources[id]
// Get a raw stream
const stream = await file.stream()
// entire file as a buffer (be careful with large files!)
const buffer = await file.buffer
// print data
stream.pipe(process.stdout)
}
}
})()
Global Average Absolute Sea Level Change, 1880-2014 from the US Environmental Protection Agency using data from CSIRO, 2015; NOAA, 2015.
This data contains “cumulative changes in sea level for the world’s oceans since 1880, based on a combination of long-term tide gauge measurements and recent satellite measurements. It shows average absolute sea level change, which refers to the height of the ocean surface, regardless of whether nearby land is rising or falling. Satellite data are based solely on measured sea level, while the long-term tide gauge data include a small correction factor because the size and shape of the oceans are changing slowly over time. (On average, the ocean floor has been gradually sinking since the last Ice Age peak, 20,000 years ago.)”
We estimate the rise in global average sea level from satellite altimeter data for 1993–2009 and from coastal and island sea-level measurements from 1880 to 2009. For 1993–2009 and after correcting for glacial isostatic adjustment, the estimated rate of rise is 3.2 ± 0.4 mm year−1 from the satellite data and 2.8 ± 0.8 mm year−1 from the in situ data. The global average sea-level rise from 1880 to 2009 is about 210 mm. The linear trend from 1900 to 2009 is 1.7 ± 0.2 mm year−1 and since 1961 is 1.9 ± 0.4 mm year−1. There is considerable variability in the rate of rise during the twentieth century but there has been a statistically significant acceleration since 1880 and 1900 of 0.009 ± 0.003 mm year−2 and 0.009 ± 0.004 mm year−2, respectively. Since the start of the altimeter record in 1993, global average sea level rose at a rate near the upper end of the sea level projections of the Intergovernmental Panel on Climate Change’s Third and Fourth Assessment Reports. However, the reconstruction indicates there was little net change in sea level from 1990 to 1993, most likely as a result of the volcanic eruption of Mount Pinatubo in 1991.
For further information you may visit the following websites:
EPA is Federal Government, so public domain we would assume.
Figures marked “CSIRO”, are copyright CSIRO, but please feel free to use them, conditional on the figures not being altered, and their source being acknowledged, and with a link to this site where possible. All other figures are copyright. Please do not copy without the owner’s permission.
All the additional work made to build this Data Package is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/
The paper describing this data reconstruction is: Church, J. A. and N.J. White (2011), Sea-level rise from the late 19th to the early 21st Century. Surveys in Geophysics, doi:10.1007/s10712-011-9119-1. This paper is published “Open Access” and is available as a pdf from here.
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