Factor distance to water
FAIR Overall Score
Abstract
Distance [m] is calculated at each location to the nearest lakes, water reservoirs, and rivers. Rivers were filtered to Strahler order greater than 3.
Keywords
Supplemental information
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Hydrosheds terms of use.
Contact for metadata
Eurac Research - Institute for Earth Observation
bartolomeo.ventura@eurac.edu
Viale Druso, 1 / Drususallee 1, Eurac Research, Bolzano, Autonomous Province of Bolzano, 39100, Italy
FAIR Overall Score: 58%
| Principle | Score | Earned | Level |
|---|---|---|---|
| Findable | 43% | 3 of 7 | initial |
| Accessible | 57% | 4 of 7 | advanced |
| Interoperable | 67% | 4 of 6 | moderate |
| Reusable | 67% | 4 of 6 | moderate |
Evaluated by F-UJI web service: Anusuriya Devaraju, & Robert Huber. (2020). F-UJI - An Automated FAIR Data Assessment Tool. Zenodo. https://doi.org/10.5281/zenodo.6361400
Snippet code
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install.packages("openeo")
library(openeo)
# login ----
host = "https://openeo.eurac.edu"
con = connect(host = host)
login()
# check login ---
con$isConnected()
con$isLoggedIn()
describe_account()
# load collection - save result ----
p = processes()
data = p$load_collection(id = "ADO_factor_distance_to_water",
spatial_extent = list(west = 4.00916,
east = 17.508308,
south = 42.884932,
north = 50.318551),
temporal_extent = list("STARTTIME", "ENDTIME"))
result = p$save_result(data = data, format="netCDF")
# download results ----
# either directly (suitable for smaller requests)
compute_result(result,
format = "netCDF",
output_file = "ADO_factor_distance_to_water.nc",
con = con)
# or start a batch job (suitable for larger requests)
job_id = create_job(graph = result,
title = "ADO_factor_distance_to_water",
description = "ADO_factor_distance_to_water",
format = "netCDF")
start_job(job = job_id)
result_list = list_results(job = job_id)
download_results(job = job_id, folder = ".")
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#pip install openeo
import openeo
# login ----
euracHost = "https://openeo.eurac.edu"
conn = openeo.connect(euracHost).authenticate_oidc(client_id="openEO_PKCE")
# load collection - save result ----
data = conn.load_collection("ADO_factor_distance_to_water",spatial_extent={'west':4.00916,'east':17.508308,'south':42.884932,'north':50.318551},temporal_extent=["STARTTIME", "ENDTIME"])
result = data.save_result(format="NetCDF")
# download results ----
# either directly (suitable for smaller requests, closes the connection after 2 minutes)
result.download("ADO_factor_distance_to_water.nc",format="netCDF")
# or start a batch job (suitable for larger requests, e.g. when .download() timeouts)
job = result.create_job(title = "ADO_factor_distance_to_water",description = "ADO_factor_distance_to_water",out_format = "netCDF")
jobId = job.job_id
job.start_job()
jobResults = job.get_results()
jobResults.download_files('.')Related docs
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| 1 | openEO for ADO project | Tutorial and snippets on how to use openEO in the ADO project | Link | Sept. 15, 2021 | OpenEO |
| 2 | EDP video tutorial | Presentation of edp-platform and tutorial for data analysis and processing | Link | Sept. 15, 2021 | OpenEO |
| 3 | Official OpenEO documentation and project site | Official Documentation provided in the project web site for a deeper overview and introduction. | Link | June 10, 2021 | OpenEO |
| 4 | OpenEO doc | Documentation for OpenEO API | Link | June 9, 2021 | OpenEO |
| 5 | Eurac - OpenEO | openEO endpoint | Link | April 28, 2021 | OpenEO |
| 6 | MOOC Cubes and Clouds | Free Online Course teaching the concepts of data cubes, cloud platforms and open science in geospatial and EO. | Link | March 8, 2024 | OpenEO, STAC |