"id": "word_vectors id",
"description": "word_vectors description",
"date": "2020-12-31",
"vector": {}
}]
}]
}
tags = [{"tag": "tag"}]
files = [{
"file_uri": "http://example.org/files/file.uri", # File name or URL
"format":
"application/excel", # The file format, physical medium, or dimensions of the resource.
"location": "100", # Page number or sheet name for the table
"note": "file note text" # file note
}]
response = create_and_import.create_document_dataset(
dataset_id=dataset_id,
repository_id=repository_id,
published=published,
overwrite=overwrite,
metadata_information=metadata_information,
document_description=document_description,
tags=tags,
files=files)
print(response)
# upload temporary thumbnail
thumbnail_path = utils.text_to_thumbnail("Document\nDataset")
create_and_import.upload_thumbnail(dataset_id, thumbnail_path)
create_and_import.set_api_url('http://training.ihsn.org/index.php/api/')
api_key = 'cf16a23a3cfc6a928f63dd3c8daf8796'
create_and_import.set_api_key(api_key)
#########################################
# create_survey_dataset_from_DDI example
#########################################
file = "SURVEY_DATASET_SAMPLE_02.xml"
overwrite = "yes"
repository_id = "central"
access_policy = "open"
published = 1
response = create_and_import.create_survey_dataset_from_DDI(
file=file,
overwrite=overwrite,
repository_id=repository_id,
access_policy=access_policy,
# data_remote_url=data_remote_url,
# rdf=rdf,
# published=published
)
print(response)
# upload temporary thumbnail
dataset_id = response['survey']['idno']
thumbnail_path = utils.text_to_thumbnail("Survey\nDataset")
create_and_import.upload_thumbnail(dataset_id, thumbnail_path)
"name": "album name",
"description": "album description",
"owner": "album owner",
"uri": "album uri"
}],
"tags": [{
"tag": "tag"
}],
"files": [{
"file_uri":
"http://example.org/image_description/files/file.uri", # File name or URL
"format":
"file format", # The file format, physical medium, or dimensions of the resource.
"note": "file note",
"show": True # Show the image file on the page
}]
}
response = create_and_import.create_image_dataset(
dataset_id=dataset_id,
repository_id=repository_id,
published=published,
overwrite=overwrite,
metadata_information=metadata_information,
image_description=image_description)
print(response)
# upload temporary thumbnail
thumbnail_path = utils.text_to_thumbnail("Image\nDataset")
create_and_import.upload_thumbnail(dataset_id, thumbnail_path)
"name": "Related document name",
"type":
"isPartOf" # "isPartOf""hasPart""isVersionOf""isFormatOf""hasFormat""references""isReferencedBy""isBasedOn""isBasisFor""requires""isRequiredBy"
}]
}
files = [{
"file_uri": "http://example.org/files/file.uri", # File name or URL
"format":
"file format", # The file format, physical medium, or dimensions of the resource.
"location": "file location",
"note": "file note"
}]
tags = [{"tag": "tag"}]
additional = {"additional": "additional info"}
response = create_and_import.create_table_dataset(
dataset_id=dataset_id,
repository_id=repository_id,
published=published,
overwrite=overwrite,
metadata_information=metadata_information,
table_description=table_description,
files=files,
tags=tags,
additional=additional)
print(response)
# upload temporary thumbnail
thumbnail_path = utils.text_to_thumbnail("Table\nDataset")
create_and_import.upload_thumbnail(dataset_id, thumbnail_path)
"data_quality_info lineage statement" # Data quality lineage statement
},
"spatial_representation_info":
{ # Resource spatial representation - Spatial representation information for the dataset (resource). Best practice is to include metadata for spatial representation if the described resource is a georeferenced dataset.
"topology_level":
"geometryOnly", # Topology Level Code: {geometryOnly, topology1D, planarGraph, fullPlanarGraph, surfaceGraph, fullSurfaceGraph, topology3D, fullTopology3D, abstract}
"Geometric_object_code":
"complex" # Geometric Object Type Code codes ={complex, composite, curve, point, solid, surface}
},
"reference_system_info":
{ # Resource’s spatial reference system - Description of the spatial and/or temporal reference systems used in the dataset.
"code": "EPSG:5701", # reference_system Identifier Code
"code_space": "urn:ogc:def:crs" # spatial reference system code_space
}
}
additional = {"additional": "additional info"}
response = create_and_import.create_geospatial_dataset(
dataset_id=dataset_id,
repository_id=repository_id,
published=published,
overwrite=overwrite,
metadata_maintenance=metadata_maintenance,
dataset_description=dataset_description,
additional=additional)
print(response)
# upload temporary thumbnail
thumbnail_path = utils.text_to_thumbnail("Geospatial\nDataset")
create_and_import.upload_thumbnail(dataset_id, thumbnail_path)
"word_vectors": [{
"id": "Vector Model ID",
"description": "Vector Model Description",
"date": "2020-12-31",
"vector": {}
}],
"series_groups": [ # Series included in groups
{
"name": "series_group name",
"version": "series_group version",
"uri": "http://example.org/series_groups/uri"
}
]
}
additional = {} # Any other custom metadata not covered by the schema
response = create_and_import.create_timeseries_dataset(
dataset_id=dataset_id,
repository_id=repository_id,
access_policy=access_policy,
published=published,
overwrite=overwrite,
metadata_creation=metadata_creation,
series_description=series_description,
additional=additional)
print(response)
# upload temporary thumbnail
thumbnail_path = utils.text_to_thumbnail("Timeseries\nDataset")
create_and_import.upload_thumbnail(dataset_id, thumbnail_path)
"isPartOf" # Valid values: "isPartOf" "hasPart" "isVersionOf" "isFormatOf" "hasFormat" "references" "isReferencedBy" "isBasedOn" "isBasisFor" "requires" "isRequiredBy"
}],
"tags": [{
"tag": "tag"
}]
}
files = [{
"file_uri":
"http://example.org/files/file.uri", # Provide file name, path or URL
"format":
"file format", # The file format, physical medium, or dimensions of the resource.
"location": "file location",
"note": "file note"
}]
additional = {"additional": "additional info"}
response = create_and_import.create_visualization_dataset(
dataset_id=dataset_id,
repository_id=repository_id,
published=published,
overwrite=overwrite,
metadata_information=metadata_information,
visualization_description=visualization_description,
files=files,
additional=additional)
print(response)
# upload temporary thumbnail
thumbnail_path = utils.text_to_thumbnail("Visualization\nDataset")
create_and_import.upload_thumbnail(dataset_id, thumbnail_path)