ST_GRIDDED_TIME_SERIES_TEMPERATURE
Abstract
The product contains the gridded daily series of mean temperature at 250-m spatial resolution for the region Trentino – South Tyrol. The dataset currently spans the period 1980 – 2020, but it is expected to be regularly updated. It was obtained by applying an anomaly-based interpolation to the observations of more than 200 station sites of the regional meteorological network and some extra-regional sites close to the borders. All station series used for deriving the gridded fields were prior checked for quality and homogeneity and they are stored in the Climate Database: https://edp-portal.eurac.edu/cdb_doc/. Mean temperature was here defined as the daily average of maximum and minimum temperature. Further details can be found in the published paper (Crespi et al., 2021; https://doi.org/10.5194/essd-13-2801-2021). The dataset is also available in PANGAEA repository.
FAIR Overall Score
Keywords
Legal constraints
CC-BY-4.0
FAIR Overall Score: 77%
Evaluated by F-UJI - An Automated FAIR Data Assessment Tool: https://doi.org/10.5281/zenodo.6361400
Findable Moderate
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Metadata identifier follows a defined unique identifier syntax or scheme (IRI, URL, UUID, HASH or PID) (1/1)
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Metadata identifier follows a defined persistent identifier syntax (0/0.5)
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Persistent identifier for metadata is registered and maintained by a PID authority (0/0.5)
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Core data citation metadata is available (0/1)
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Core descriptive metadata is available (0/1)
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Metadata contains a PID or URL which indicates the location of the downloadable data content (1/1)
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Metadata is given in a way major search engines can ingest it for their catalogues (Dublin Core or schema.org or DCAT encoded in microdata, RDFa, embedded JSON-LD or meta tags see e.g. Google Dataset Search webmaster guidelines) (2/2)
Accessible Advanced
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Information about access restrictions or rights can be identified in metadata (1/1)
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Metadata are retrievable via their specified identifier (1/1)
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Data are retrievable via the identifiers given in metadata (1/1)
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Identifier leading to metadata matches a scheme indicating a standardized web communication protocol. (1/1)
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Identifier leading to data are matching a schema indicating a standardized web communication protocol. (1/1)
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The communication protocol found in identifiers (IRIs) leading to metadata supports authentication. (1/1)
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The communication protocol identified in data links (IRIs) supports authentication. (1/1)
Interoperable Moderate
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Parsable, structured metadata (JSON-LD, RDFa) is embedded in the landing page XHTML/HTML code (2/2)
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Parsable, structured metadata (RDF, JSON-LD) is accessible through content negotiation, typed links or sparql endpoint (2/2)
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Metadata uses terms from registered vocabularies that are identified by their namespaces (2/2)
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Related resources are referenced in plain text within appropriate metadata properties indicating the relation type (0/2)
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Related resources are referenced by machine readable links or identifiers within appropriate metadata properties indicating the relation type (0/2)
Reusable Moderate
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Minimum information (resource type) about the available data content is specified in the metadata (1/1)
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Information on the manner and form (file size and type or service (API) endpoint and protocol) in which data is delivered is provided (0/1)
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Licence information is given in an appropriate metadata element (1/1)
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Metadata contains elements which hold provenance information which can be mapped to PROV based on PROV-DC. (1/1)
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Metadata contains elements which hold provenance information using formal provenance ontologies (PROV, PAV). (0/1)
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Community specific metadata standard is detected using namespaces or schemas found in provided metadata (1/1)
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Multidisciplinary but community endorsed metadata (RDA Metadata Standards Catalog, fairsharing) standard is detected by namespace (1/1)
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Data is available in a file format recommended by the research community (long term file formats, open file formats or scientific file format) (1/1)
Snippet code
##### ----Explore and download STAC data with Python ----- #####
from pystac_client import Client
## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)
## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
print(f"Number of collections: {len(stac_collections)}")
## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
print(f"- {collection.id}")
print("-------------------------")
## Retrieve a specific collection
collection = catalog.get_collection("ID_OfTheCollection")
## Search for items in the collection
collection_items = list(catalog.search(collections=['ID_OfTheCollection'], max_items=10).items())
print(collection_items)
## Retrieve a list of the first 10 items belonging to a specific collection
item = collection.get_item("NameOfTheItem")
#print(list(item.assets.items())[0:10])
##Print the band name and the href of a specific item retrieved from the previous list
print(item.assets["ID_OfTheCollection"].title)
print(item.assets["ID_OfTheCollection"].href)
## Print Item’s assets through the assets attribute, which is a dictionary
for asset_key in item.assets:
asset = item.assets[asset_key]
print("{}: {} ({})".format(asset_key, asset.href, asset.media_type))
## ------------- DONWLOAD COG files from a specific collection ------- ####
from pystac_client import Client
import requests
## Read the example catalog
URL = 'https://stac.eurac.edu/'
catalog = Client.open(URL)
## List the Collections in the given Catalog
stac_collections = list(catalog.get_collections())
# print(f"Number of collections: {len(stac_collections)}")
## Print collection IDs
print("Collections IDs:")
for collection in stac_collections:
print(f"- {collection.id}")
print("-------------------------")
## Retrieve a specific collection
collection = catalog.get_collection("ID_OfTheCollection")
## Search for items in the collection
collection_items = list(catalog.search(collections=['ID_OfTheCollection'], max_items=10).items())
print(collection_items)
url_basepath = "https://eurac-eo.s3-eu-west-1.amazonaws.com/"
collection_url = url_basepath + "ID_OfTheCollection" + "/"
print(collection_url)
for item_id in collection_items:
# item = item.assets[asset_key]
print(item_id.id)
cog_file = str(item_id.id + '.tif')
url = collection_url + cog_file
response = requests.get(url)
with open(cog_file, "wb") as f:
f.write(response.content)
Related docs
| # | Name | Description | Link | Date published | Category |
|---|---|---|---|---|---|
| 1 | 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 |
| 2 | STAC guidelines | Documentation to browse and download items of the STAC catalog | Link | Feb. 22, 2024 | STAC |