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

77%

Keywords

Temperature, Daily, High-resolution, cct

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 57%
Accessible 100%
Interoperable 67%
Reusable 83%
Findable Moderate
Metadata and data are assigned a globally unique identifier.
FsF-F1-01MD · 1/1
  • Metadata identifier follows a defined unique identifier syntax or scheme (IRI, URL, UUID, HASH or PID) (1/1)
Metadata and data are assigned a persistent identifier.
FsF-F1-02MD · 0/1
  • Metadata identifier follows a defined persistent identifier syntax (0/0.5)
    • Could not find any persistent identifier for metadata which is registered
  • Persistent identifier for metadata is registered and maintained by a PID authority (0/0.5)
Metadata includes descriptive core elements (creator, title, data identifier, publisher, publication date, summary and keywords) to support data findability.
FsF-F2-01M · 0/2
  • Core data citation metadata is available (0/1)
    • Failed to parse RDF, trying to fix RDF string and retry parsing everything before line -: 1
    • Not all required core descriptive metadata elements exist, missing -: ['publisher', 'creator', 'object_identifier']
  • Core descriptive metadata is available (0/1)
    • Failed to extract Datacite JSON -: 'bytes' object has no attribute 'get'
Metadata includes the identifier of the data it describes.
FsF-F3-01M · 1/1
  • Metadata contains a PID or URL which indicates the location of the downloadable data content (1/1)
Metadata is offered in such a way that it can be registered or indexed by search engines.
FsF-F4-01M · 2/2
  • 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
Metadata contains access level and access conditions of the data.
FsF-A1-01M · 1/1
  • Information about access restrictions or rights can be identified in metadata (1/1)
Metadata and data are retrievable by their identifier
FsF-A1-02MD · 2/2
  • Metadata are retrievable via their specified identifier (1/1)
  • Data are retrievable via the identifiers given in metadata (1/1)
A standardized communication protocol is used to access metadata and data.
FsF-A1.1-01MD · 2/2
  • Identifier leading to metadata matches a scheme indicating a standardized web communication protocol. (1/1)
  • Identifier leading to data are matching a schema indicating a standardized web communication protocol. (1/1)
Metadata and data are accessible through a standardized communication protocol which supports authentication.
FsF-A1.2-01MD · 2/2
  • The communication protocol found in identifiers (IRIs) leading to metadata supports authentication. (1/1)
  • The communication protocol identified in data links (IRIs) supports authentication. (1/1)
Interoperable Moderate
Metadata is represented using a formal knowledge representation language.
FsF-I1-01M · 2/2
  • Parsable, structured metadata (JSON-LD, RDFa) is embedded in the landing page XHTML/HTML code (2/2)
  • Parsable, structured metadata (RDF, JSON-LD) is accessible through content negotiation, typed links or sparql endpoint (2/2)
Metadata uses registered semantic resources
FsF-I2-01M · 2/2
  • Metadata uses terms from registered vocabularies that are identified by their namespaces (2/2)
Metadata includes qualified references between the data and its related entities.
FsF-I3-01M · 0/2
  • Related resources are referenced in plain text within appropriate metadata properties indicating the relation type (0/2)
    • Could not identify qualified related resources in metadata
  • Related resources are referenced by machine readable links or identifiers within appropriate metadata properties indicating the relation type (0/2)
Reusable Moderate
Metadata specifies the content of the data.
FsF-R1-01M · 1/2
  • Minimum information (resource type) about the available data content is specified in the metadata (1/1)
  • 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)
Metadata includes license information under which data can be reused.
FsF-R1.1-01M · 1/1
  • Licence information is given in an appropriate metadata element (1/1)
Metadata includes provenance information about data creation or generation.
FsF-R1.2-01M · 1/1
  • Metadata contains elements which hold provenance information which can be mapped to PROV based on PROV-DC. (1/1)
  • Metadata contains elements which hold provenance information using formal provenance ontologies (PROV, PAV). (0/1)
Metadata follows a standard recommended by the target research community of the data.
FsF-R1.3-01M · 1/1
  • Community specific metadata standard is detected using namespaces or schemas found in provided metadata (1/1)
  • Multidisciplinary but community endorsed metadata (RDA Metadata Standards Catalog, fairsharing) standard is detected by namespace (1/1)
Data is available in a file format recommended by the target research community.
FsF-R1.3-02D · 1/1
  • 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)

Assessed 2026-08-26 (metrics 0.8)

Snippet code

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##### ----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)

# 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