ADO - Discharge hydrological datasets
FAIR Overall Score
Abstract
Daily time-series of discharge stations present within the Alpine space, obtained from different providers. Is made up of four columns: id station, date, discharge and the data quality information given by its different providers. The database contains observational daily discharge data deriving from the first measurement (differs for each region) to the present, with more than 1400 stations. These data were collected from multiple data providers within the ADO study region, covering the countries Austria, France, Germany, Italy, Slovenia and Switzerland. The spanned period is 1869-2021. The missing dates were added in order to have continuous time-series.
Keywords
Digital Object Identifier (DOI)
https://doi.org/10.48784/9e195271-02ae-40be-b3a7-525f57f53c80
Legal constraints
Restricted data access to project's partners
Contact for metadata
Eurac Research - Center for Sensing Solutions
supportcss@eurac.edu
Viale Druso, 1 / Drususallee 1, eurac research, Bolzano, Autonomous Province of Bolzano, 39100, Italy
FAIR Overall Score: 92%
| Principle | Score | Earned | Level |
|---|---|---|---|
| Findable | 100% | 7 of 7 | advanced |
| Accessible | 100% | 7 of 7 | advanced |
| Interoperable | 100% | 6 of 6 | advanced |
| 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
#To set up the connection, the following two packages are required.
library(DBI)
library(RPostgres)
#Using the tidyverse in R, we can circumvent the direct usage of SQL language, and can use the more user-friendly dplyr syntax. For this we need these packages.
library(dplyr)
library(dbplyr)
#For data wrangling and visualization we will use these packages.
library(sf)
library(lubridate)
library(ggplot2)
library(mapview)
#Set up the connection
#The following sets up the connection to the SQL database. This is currently only possible within the ScientificNet. The parameters are copied from the documentation.
conn <- dbConnect(Postgres(),
dbname = "climate_data",
host = "10.7.18.68",
port = 5432,
user = "climate_user",
password = "meteo_data")
##################################
#List the available tables.
dbListTables(conn)
#The daily values are stored in the table stations_data.
tbl(conn, "stations_data")
#The climatologies (averages for the period 1981-2010) values are stored in the table climatologies.
tbl(conn, "climatologies")
#And finally the metadata with the station locations and more info on the processing in metadata.
tbl(conn, "metadata")
##########################
#Additional examples for create map and plots are available at: http://edp-doc.eurac.edu/cdb_doc/notebooks/Access-CDB-R-ubuntu.html
##########################
Related docs
| # | Name | Description | Link | Date published | Category |
|---|---|---|---|---|---|
| 1 | Connection to PostgreSQL database | Necessary steps to connect to the PostgreSQL DB. Example with the Cimate Database. Available only inside ScientificNet | Link | June 10, 2021 | PostgreSQL |
| 2 | Climatic and Hydrological timeseries | This page contains description and documentastion of Climate DB, ADO and 4DMED projects | Link | April 14, 2023 | PostgreSQL |