Get full metadata from CSV file - BEDMAP¶
This notebook shows how to get the rich and complete metadata from the Bedmap CSV file. It also shows how to convert the CSV file to NCCSV (NetCDF compatible CSV) as an example to the FAIR implementation of the CSV format used for Bedmap.
With only access to the CSV file, it is possible to programmatically obtain rich metadata from the file without having a complex metadata structure as header information in the file.
The data¶
The BEDMAP CSV files are available for downmoad from the UK Polar data Centre:
Upload the modules¶
For this conversion, we will need pandas, json, xarray, netCDF4 and urllib modules.
import pandas as pd
import json
import urllib.request
import xarray
import netCDF4 as nc
Opening and reading the CSV metadata¶
For this exercise, we only need to check the short metadata provided in the CSV file.
CSV_file = 'E:/BEDMAP/AWI_2015_GEA-DML_AIR_BM3.csv'
csv_metadata = pd.read_csv(CSV_file, nrows=18, sep = ': ', engine='python', header= None)
csv_metadata
| 0 | 1 | |
|---|---|---|
| 0 | #project | Dronning Maud Land (GEA). |
| 1 | #time_coverage_start | 2015 |
| 2 | #time_coverage_end | 2016 |
| 3 | #creator_name | Alfred Wegener Institute; Bundesanstalt für Ge... |
| 4 | #institution | Alfred Wegener Institute; Bundesanstalt für Ge... |
| 5 | #acknowledgement | Bundesanstalt für Geowissenschaften; Alfred We... |
| 6 | #source | https://doi.pangaea.de/10.1594/PANGAEA.915475 |
| 7 | #references | https://doi.org/10.1016/j.gr.2018.05.011 |
| 8 | #platform | airborne radar. |
| 9 | #instrument | AWI EMR. |
| 10 | #history | Incoherent processing |
| 11 | #electromagnetic_wave_speed_in_ice | 168 (meters/microseconds) |
| 12 | #firn_correction | 0 (m) |
| 13 | #centre_frequency | 150 (MHz) |
| 14 | #comment | Part of Bedmap3 |
| 15 | #metadata_link | https://doi.org/10.5285/91523ff9-d621-46b3-87f... |
| 16 | #license | https://creativecommons.org/licenses/by/4.0/ |
| 17 | #Conventions | ACDD-1.3, CF-1.8 |
First, we set the index as the first column (column 0 in our case).
csv_metadata[0] = csv_metadata[0].str.strip('#')
csv_metadata = csv_metadata.set_index(0)
The data from the CSV file are transformed to a dictionary for an easy handling of the metadata.
dict_metadata = csv_metadata.to_dict()[1]
dict_metadata
{'project': 'Dronning Maud Land (GEA).',
'time_coverage_start': '2015',
'time_coverage_end': '2016',
'creator_name': 'Alfred Wegener Institute; Bundesanstalt für Geowissenschaften.',
'institution': 'Alfred Wegener Institute; Bundesanstalt für Geowissenschaften.',
'acknowledgement': 'Bundesanstalt für Geowissenschaften; Alfred Wegener Institute.',
'source': 'https://doi.pangaea.de/10.1594/PANGAEA.915475',
'references': 'https://doi.org/10.1016/j.gr.2018.05.011',
'platform': 'airborne radar.',
'instrument': 'AWI EMR.',
'history': 'Incoherent processing',
'electromagnetic_wave_speed_in_ice': '168 (meters/microseconds)',
'firn_correction': '0 (m)',
'centre_frequency': '150 (MHz)',
'comment': 'Part of Bedmap3',
'metadata_link': 'https://doi.org/10.5285/91523ff9-d621-46b3-87f7-ffb6efcd1847',
'license': 'https://creativecommons.org/licenses/by/4.0/',
'Conventions': 'ACDD-1.3, CF-1.8'}
The additional metadata can be obtained from the doi itself referenced in the metadata_link.
# Opening JSON file
doi = dict_metadata['metadata_link'].strip('https://doi.org/')
with urllib.request.urlopen("https://api.datacite.org/dois/application/vnd.datacite.datacite+json/" + doi) as url:
DOI_metadata = json.load(url)
DOI_metadata
{'id': 'https://doi.org/10.5285/91523ff9-d621-46b3-87f7-ffb6efcd1847',
'doi': '10.5285/91523FF9-D621-46B3-87F7-FFB6EFCD1847',
'url': 'https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01614',
'types': {'ris': 'DATA',
'bibtex': 'misc',
'citeproc': 'dataset',
'schemaOrg': 'Dataset',
'resourceType': 'Dataset',
'resourceTypeGeneral': 'Dataset'},
'creators': [{'name': 'Fremand, Alice',
'givenName': 'Alice',
'familyName': 'Fremand',
'affiliation': [{'name': 'British Antarctic Survey',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01rhff309',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0001-8272-0981',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Fretwell, Peter',
'givenName': 'Peter',
'familyName': 'Fretwell',
'affiliation': [{'name': 'British Antarctic Survey',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01rhff309',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-1988-5844',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Bodart, Julien',
'givenName': 'Julien',
'familyName': 'Bodart',
'affiliation': [{'name': 'British Antarctic Survey',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01rhff309',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-8237-0675',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Pritchard, Hamish',
'givenName': 'Hamish',
'familyName': 'Pritchard',
'affiliation': [{'name': 'British Antarctic Survey',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01rhff309',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0003-2936-1734',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Aitken, A.',
'givenName': 'A.',
'familyName': 'Aitken',
'affiliation': [{'name': 'Centre for Exploration Targeting, University of Western Australia',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/047272k79',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-6375-2504',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Bamber, Jonathan',
'givenName': 'Jonathan',
'familyName': 'Bamber',
'affiliation': [{'name': 'University of Bristol',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/0524sp257',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-2280-2819',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Bell, R.E.',
'givenName': 'R.E.',
'familyName': 'Bell',
'affiliation': [{'name': 'Lamont Doherty Earth Observatory, Columbia University, Palisades, NY, USA',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/02e2tgs60',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0003-2570-3856',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Bianchi, C',
'givenName': 'C',
'familyName': 'Bianchi',
'affiliation': [{'name': 'Istituto Nazionale di Geofisica e Vulcanologia',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00qps9a02',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Bingham, Robert',
'givenName': 'Robert',
'familyName': 'Bingham',
'affiliation': [{'name': 'University of Edinburgh',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01nrxwf90',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-0630-2021',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Blankenship, Donald',
'givenName': 'Donald',
'familyName': 'Blankenship',
'affiliation': [{'name': 'Institute for Geophysics, Jackson School of Geosciences, University of Texas',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00hj54h04',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0003-0205-4830',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Casassa, G.',
'givenName': 'G.',
'familyName': 'Casassa',
'affiliation': [{'name': 'Centro de Estudios Cientificos',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00t8xfq63',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Catania, Ginny',
'givenName': 'Ginny',
'familyName': 'Catania',
'affiliation': [{'name': 'University of Texas'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-7561-5902',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Christianson, K',
'givenName': 'K',
'familyName': 'Christianson',
'affiliation': [{'name': 'Earth and Space Sciences, University of Washington',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00cvxb145',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-5116-3032',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Conway, H',
'givenName': 'H',
'familyName': 'Conway',
'affiliation': [{'name': 'Earth and Space Sciences, University of Washington',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00cvxb145',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0003-4634-3474',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Corr, Hugh',
'givenName': 'Hugh',
'familyName': 'Corr',
'affiliation': [{'name': 'British Antarctic Survey',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01rhff309',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Cui, X.',
'givenName': 'X.',
'familyName': 'Cui',
'affiliation': [{'name': 'Polar Research Institute of China',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/027fn9x30',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-4269-8086',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Damaske, D',
'givenName': 'D',
'familyName': 'Damaske',
'affiliation': [{'name': 'Center for Marine Environmental Sciences, University Bremen',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/04ers2y35',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Damn, V.',
'givenName': 'V.',
'familyName': 'Damn',
'affiliation': [{'name': 'Federal Institute for Geosciences and Natural Resources',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/04d77de73',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Drews, R.',
'givenName': 'R.',
'familyName': 'Drews',
'affiliation': [{'name': 'Tubingen University, Department of Geosciences',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/03a1kwz48',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-2328-294X',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Eagles, G.',
'givenName': 'G.',
'familyName': 'Eagles',
'affiliation': [{'name': 'Alfred-Wegener-Institut Helmholtz-Zentrum fur Polar und Meeresforschung',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/032e6b942',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0001-5325-0810',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Eisen, O.',
'givenName': 'O.',
'familyName': 'Eisen',
'affiliation': [{'name': 'Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/032e6b942',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-6380-962X',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Eisermann, H.',
'givenName': 'H.',
'familyName': 'Eisermann',
'affiliation': [{'name': 'Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/032e6b942',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Ferraccioli, Fausto',
'givenName': 'Fausto',
'familyName': 'Ferraccioli',
'affiliation': [{'name': 'British Antarctic Survey',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01rhff309',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-9347-4736',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Forsberg, Rene',
'givenName': 'Rene',
'familyName': 'Forsberg',
'affiliation': [{'name': 'DTU Space',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/04qtj9h94',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-7288-9545',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Franke, S',
'givenName': 'S',
'familyName': 'Franke',
'affiliation': [{'name': 'Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/032e6b942',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0001-8462-4379',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Fujita, S.',
'givenName': 'S.',
'familyName': 'Fujita',
'affiliation': [{'name': 'National Institute of Polar Research',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/05k6m5t95',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0003-0127-0777',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Gim, Y.',
'givenName': 'Y.',
'familyName': 'Gim',
'affiliation': [{'name': 'Jet Propulsion Laboratory. California Institute of Technology',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/05dxps055',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Goel, V.',
'givenName': 'V.',
'familyName': 'Goel',
'affiliation': [{'name': 'National Centre for Polar Ocean Research (NCPOR)',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/05af1fm66',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Gogineni, P.',
'givenName': 'P.',
'familyName': 'Gogineni',
'affiliation': [{'name': 'University of Alabama',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/03xrrjk67',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Greenbaum, J.S.',
'givenName': 'J.S.',
'familyName': 'Greenbaum',
'affiliation': [{'name': 'Institute for Geophysics, Jackson School of Geosciences, University of Texas',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00hj54h04',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Hills, B.',
'givenName': 'B.',
'familyName': 'Hills',
'affiliation': [{'name': 'Department of Earth and Space Sciences, University of Washington',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00cvxb145',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0003-4490-7416',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Hindmarsh, Richard',
'givenName': 'Richard',
'familyName': 'Hindmarsh',
'affiliation': [{'name': 'British Antarctic Survey',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01rhff309',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Holmlund, P.',
'givenName': 'P.',
'familyName': 'Holmlund',
'affiliation': [{'name': 'Stockholm University',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/05f0yaq80',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Holschuh, N.',
'givenName': 'N.',
'familyName': 'Holschuh',
'affiliation': [{'name': 'Amherst College',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/028vqfs63',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0003-1703-5085',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Holt, J.W.',
'givenName': 'J.W.',
'familyName': 'Holt',
'affiliation': [{'name': 'University of Arizona',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/03m2x1q45',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Humbert, A.',
'givenName': 'A.',
'familyName': 'Humbert',
'affiliation': [{'name': 'Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/032e6b942',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-0244-8760',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Jacobel, R.',
'givenName': 'R.',
'familyName': 'Jacobel',
'affiliation': [{'name': 'St. Olaf College',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01q7w1f47',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Jansen, D.',
'givenName': 'D.',
'familyName': 'Jansen',
'affiliation': [{'name': 'Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/032e6b942',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-4412-5820',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Jenkins, Adrian',
'givenName': 'Adrian',
'familyName': 'Jenkins',
'affiliation': [{'name': 'Northumbria University',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/049e6bc10',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-9117-0616',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Jokat, Wilfried',
'givenName': 'Wilfried',
'familyName': 'Jokat',
'affiliation': [{'name': 'Alfred Wegener Institute Helmholtz Center for Polar and Marine Research',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/032e6b942',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-7793-5854',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Jordan, Tom',
'givenName': 'Tom',
'familyName': 'Jordan',
'affiliation': [{'name': 'British Antarctic Survey',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01rhff309',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0003-2780-1986',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'King, Edward',
'givenName': 'Edward',
'familyName': 'King',
'affiliation': [{'name': 'British Antarctic Survey',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01rhff309',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0003-3793-3915',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Kohler, Jack',
'givenName': 'Jack',
'familyName': 'Kohler',
'affiliation': [{'name': 'Norwegian Polar Institute',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/03avf6522',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Krabill, W.',
'givenName': 'W.',
'familyName': 'Krabill',
'affiliation': [{'name': 'NASA Wallops Flight Facility',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00r57r863',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Langley, K.',
'givenName': 'K.',
'familyName': 'Langley',
'affiliation': [{'name': 'Department of Geosciences, University of Oslo',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01xtthb56',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Lee, J.',
'givenName': 'J.',
'familyName': 'Lee',
'affiliation': [{'name': 'Korean Polar Research Institute',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00n14a494',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Leitchenkov, G.',
'givenName': 'G.',
'familyName': 'Leitchenkov',
'affiliation': [{'name': 'Institute for Geology and Mineral Resources of the World Ocean',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01fe6wg83',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0001-6316-8511',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Leuschen, C.',
'givenName': 'C.',
'familyName': 'Leuschen',
'affiliation': [{'name': 'Centre for Remote Sensing of Ice Sheets, University of Kansas',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/04t2m2598',
'affiliationIdentifierScheme': 'ROR'}]},
{'name': 'Luyendyk, B.',
'givenName': 'B.',
'familyName': 'Luyendyk',
'affiliation': [{'name': 'Earth Research Institute, University of California',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/02t274463',
'affiliationIdentifierScheme': 'ROR'}],
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-8722-2807',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'MacGregor, J.',
'givenName': 'J.',
'familyName': 'MacGregor',
'affiliation': [{'name': 'Cryospheric Sciences Lab, NASA Goddard Space Flight Center',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/0171mag52',
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'nameIdentifier': 'https://orcid.org/0000-0002-9065-0570',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Welch, B.',
'givenName': 'B.',
'familyName': 'Welch',
'affiliation': [{'name': 'St. Olaf College',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/01q7w1f47',
'affiliationIdentifierScheme': 'ROR'}],
'contributorType': 'Researcher'},
{'name': 'Wilson, D.S.',
'givenName': 'D.S.',
'familyName': 'Wilson',
'affiliation': [{'name': 'Marine Science Institute, University of California',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/02t274463',
'affiliationIdentifierScheme': 'ROR'}],
'contributorType': 'Researcher',
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0001-8800-2462',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Young, Duncan',
'givenName': 'Duncan',
'familyName': 'Young',
'affiliation': [{'name': 'Institute for Geophysics, Jackson School of Geosciences, University of Texas',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00hj54h04',
'affiliationIdentifierScheme': 'ROR'}],
'contributorType': 'Researcher',
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0002-6866-8176',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'Zirizzotti, A.',
'givenName': 'A.',
'familyName': 'Zirizzotti',
'affiliation': [{'name': 'Istituto Nazionale di Geofisica e Vulcanologia',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/00qps9a02',
'affiliationIdentifierScheme': 'ROR'}],
'contributorType': 'Researcher',
'nameIdentifiers': [{'schemeUri': 'https://orcid.org',
'nameIdentifier': 'https://orcid.org/0000-0001-7586-9219',
'nameIdentifierScheme': 'ORCID'}]},
{'name': 'UK Polar Data Centre',
'affiliation': [{'name': 'Natural Environment Research Council',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/02b5d8509',
'affiliationIdentifierScheme': 'ROR'}],
'contributorType': 'DataManager',
'nameIdentifiers': []},
{'name': 'UK Polar Data Centre',
'affiliation': [{'name': 'Natural Environment Research Council',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/02b5d8509',
'affiliationIdentifierScheme': 'ROR'}],
'contributorType': 'Distributor',
'nameIdentifiers': []},
{'name': 'UK Polar Data Centre',
'affiliation': [{'name': 'Natural Environment Research Council',
'schemeUri': 'https://ror.org/',
'affiliationIdentifier': 'https://ror.org/02b5d8509',
'affiliationIdentifierScheme': 'ROR'}],
'contributorType': 'HostingInstitution',
'nameIdentifiers': []}],
'dates': [{'date': '1971-01-01/2020-12-31', 'dateType': 'Collected'},
{'date': '2022-02-28', 'dateType': 'Accepted'},
{'date': '2022-02-28', 'dateType': 'Created'},
{'date': '2022-02-28', 'dateType': 'Submitted'},
{'date': '2022-10-18', 'dateType': 'Issued'}],
'publicationYear': 2022,
'language': 'en',
'identifiers': [],
'sizes': ['84 files', '6.9 GB'],
'formats': ['text/csv'],
'version': '1.0',
'rightsList': [{'lang': 'en-GB',
'rights': 'Creative Commons Attribution 4.0 International',
'rightsUri': 'https://creativecommons.org/licenses/by/4.0/legalcode',
'schemeUri': 'https://spdx.org/licenses/',
'rightsIdentifier': 'cc-by-4.0',
'rightsIdentifierScheme': 'SPDX'}],
'descriptions': [{'lang': 'en-GB',
'description': "We present here the Bedmap3 ice thickness, bed and surface elevation standardised CSV data points that are used to create the Bedmap3 gridding products in addition to the previous data releases. The data consists of 50 million points acquired by 17 different data providers in Antarctica. The associated datasets consist of: - Bedmap1 standardised CSV data points: https://doi.org/10.5285/f64815ec-4077-4432-9f55-0ce230f46029 - Bedmap2 standardised CSV data points: https://doi.org/10.5285/2fd95199-365e-4da1-ae26-3b6d48b3e6ac - Bedmap3 statistically-summarised data points (shapefiles): https://doi.org/10.5285/a72a50c6-a829-4e12-9f9a-5a683a1acc4a This work is supported by the SCAR Bedmap project and the British Antarctic Survey's core programme: National Capability - Polar Expertise Supporting UK Research",
'descriptionType': 'Abstract'},
{'lang': 'en-GB',
'description': 'The primary source data consist of survey point measurements of ice thickness, bed elevation and surface elevation, which principally comes from airborne radar surveys and seismic soundings, and to a smaller extent from ground-based radar surveys. All the data have been standardised to a specific CSV format. The format consists of (i) an extended header section where (a) each line is introduced by a comment (#) character, (b) each line contains a single header item, (c) the colon character (:) is used as the key/value separator, (d) units are in parentheses, (e) attributes preferably use a common vocabulary such as the CF convention and includes attributes from the ACDD; (ii) a header row composed of the column name following the CF convention and units in parentheses; and finally (iii) the data using comma as the separator. The extended header consists of general information regarding each campaign such as the year, the name of the main investigator, funding and processing details. The variable names are as follows: trajectory_id Line or Flight ID trace_number Trace number from the specific line given in Line_ID longitude Longitude (WGS84 EPSG: 4326) in decimal degrees latitude Latitude (WGS84 EPSG: 4326) in decimal degrees date Date following ISO 8601 format: YYYY: year, MM: month, DD: day time_UTC UTC time following ISO 8601 format: HH: hours, MM: minutes, SS: seconds surface_altitude Surface elevation or altitude (referenced to WGS84) in meters land_ice_thickness Ice thickness in meters bedrock_altitude Bed elevation or altitude (referenced to WGS84) in meters two_way_travel_time Two-way travel time in seconds aircraft_altitude Aircraft elevation or altitude when applicable (referenced to WGS84) in meters along_track_distance Distance in the along-track direction in meters',
'descriptionType': 'Methods'},
{'lang': 'en-GB',
'description': "All non-value data were converted to -9999, including any negative ice thickness values and any points with clear outliers. We checked the minimum and maximum values of each field to ensure the data are in a reasonable range and calculated mean and standard deviation on each dataset to identify potential issues. For example, if longitude/latitude values did not fall within the expected -180 to 180 or -50 to -90 degrees range respectively, the entire row was removed. When no ice thickness values were provided but surface and bed elevation values existed, we simply calculated ice thickness by subtracting the surface value from the bed value. At times, bed elevation was higher than surface elevation, likely due to issues with the semi-automatic picker used to extract the surface and bed reflector or a lack of distinctive reflectors in areas of shallow ice. To prevent this affecting the gridded product, we converted these values to -9999 for both the surface and the bed. Finally, we also conducted routine checks on the ice thickness data by comparing the given ice thickness value with the inferred ice thickness calculated from subtracting surface with bed. If these did not match, we placed -9999 on the ice thickness values. File naming conventions were also used throughout to easily identify a specific dataset as follows: DataProvider_Year_CampaignName_TypeofData_BM3. The type of data used was separated into three categories: Airborne Radar (AIR), Ground-based Radar (GRN), and Seismic (SEI) data. The 'BM3' abbreviation at the end identifies the datasets was part of the Bedmap3 compilation to differentiate from the Bedmap 1 and 2 (BM1 and BM2) compilations. For instance, the file named 'NASA_2019_ICEBRIDGE_AIR_BM3.csv' refers to the ICEBRIDGE airborne campaign led by NASA in 2019.",
'descriptionType': 'Other'}],
'geoLocations': [{'geoLocationBox': {'eastBoundLongitude': '180',
'northBoundLatitude': '-60',
'southBoundLatitude': '-90',
'westBoundLongitude': '-180'},
'geoLocationPlace': 'Antarctica'}],
'fundingReferences': [{'awardUri': 'https://gtr.ukri.org/projects?ref=NE/R016038/1',
'awardTitle': 'National Capability - Polar Expertise Supporting UK Research',
'funderName': 'Natural Environment Research Council',
'awardNumber': 'NE/R016038/1',
'funderIdentifier': 'https://ror.org/02b5d8509',
'funderIdentifierType': 'ROR'}],
'relatedIdentifiers': [{'relationType': 'References',
'relatedIdentifier': '10.5285/2fd95199-365e-4da1-ae26-3b6d48b3e6ac',
'resourceTypeGeneral': 'Dataset',
'relatedIdentifierType': 'DOI'},
{'relationType': 'IsReferencedBy',
'relatedIdentifier': '10.5285/2fd95199-365e-4da1-ae26-3b6d48b3e6ac',
'resourceTypeGeneral': 'Dataset',
'relatedIdentifierType': 'DOI'},
{'relationType': 'References',
'relatedIdentifier': '10.5285/a72a50c6-a829-4e12-9f9a-5a683a1acc4a',
'resourceTypeGeneral': 'Dataset',
'relatedIdentifierType': 'DOI'},
{'relationType': 'IsReferencedBy',
'relatedIdentifier': '10.5285/a72a50c6-a829-4e12-9f9a-5a683a1acc4a',
'resourceTypeGeneral': 'Dataset',
'relatedIdentifierType': 'DOI'},
{'relationType': 'References',
'relatedIdentifier': '10.5285/f64815ec-4077-4432-9f55-0ce230f46029',
'resourceTypeGeneral': 'Dataset',
'relatedIdentifierType': 'DOI'},
{'relationType': 'IsReferencedBy',
'relatedIdentifier': '10.5285/f64815ec-4077-4432-9f55-0ce230f46029',
'resourceTypeGeneral': 'Dataset',
'relatedIdentifierType': 'DOI'},
{'relationType': 'IsPartOf',
'relatedIdentifier': 'https://www.bas.ac.uk/project/bedmap/',
'resourceTypeGeneral': 'InteractiveResource',
'relatedIdentifierType': 'URL'},
{'relationType': 'References',
'relatedIdentifier': '10.1029/2000jb900449',
'resourceTypeGeneral': 'Text',
'relatedIdentifierType': 'DOI'},
{'relationType': 'References',
'relatedIdentifier': '10.5194/tc-7-375-2013',
'resourceTypeGeneral': 'Text',
'relatedIdentifierType': 'DOI'}],
'relatedItems': [],
'schemaVersion': 'http://datacite.org/schema/kernel-4',
'providerId': 'rckq',
'clientId': 'bl.nerc',
'agency': 'datacite',
'state': 'findable'}
It is now possible to add the relevant and rich DOI metadata to the simple metadata specific to the survey. Depending on the standard, the name of the field may differ. The example below shows how the metadata are transformed to NetCDF compliant metadata.
Getting the full metadata from the CSV file¶
dict_metadata['title'] = DOI_metadata['titles'][0]['title']
dict_metadata['summary'] = DOI_metadata['descriptions'][0]['description']
dict_metadata['publisher_name'] = DOI_metadata['publisher']
dict_metadata['geospatial_lat_min'] = DOI_metadata['geoLocations'][0]['geoLocationBox']['southBoundLatitude']
dict_metadata['geospatial_lat_max'] = DOI_metadata['geoLocations'][0]['geoLocationBox']['northBoundLatitude']
dict_metadata['geospatial_lon_min'] = DOI_metadata['geoLocations'][0]['geoLocationBox']['westBoundLongitude']
dict_metadata['geospatial_lon_max'] = DOI_metadata['geoLocations'][0]['geoLocationBox']['eastBoundLongitude']
dict_metadata['comment'] = DOI_metadata['descriptions'][1]['description'] + DOI_metadata['descriptions'][2]['description']
dict_metadata['acknowledgement'] = DOI_metadata['fundingReferences'][0]['awardTitle']
for i in range(0, len(DOI_metadata['dates'])):
if DOI_metadata['dates'][i]['dateType']== 'Created':
dict_metadata['date_created'] = DOI_metadata['dates'][i]['date']
dict_metadata['keywords'] = ''
for i in range(0, len(DOI_metadata['subjects'])):
if '"' not in DOI_metadata['subjects'][i]['subject']:
dict_metadata['keywords'] = dict_metadata['keywords'] + DOI_metadata['subjects'][i]['subject'] + ', '
dict_metadata['keywords'] = dict_metadata['keywords'][:-2]
dict_metadata
{'project': 'Dronning Maud Land (GEA).',
'time_coverage_start': '2015',
'time_coverage_end': '2016',
'creator_name': 'Alfred Wegener Institute; Bundesanstalt für Geowissenschaften.',
'institution': 'Alfred Wegener Institute; Bundesanstalt für Geowissenschaften.',
'acknowledgement': 'National Capability - Polar Expertise Supporting UK Research',
'source': 'https://doi.pangaea.de/10.1594/PANGAEA.915475',
'references': 'https://doi.org/10.1016/j.gr.2018.05.011',
'platform': 'airborne radar.',
'instrument': 'AWI EMR.',
'history': 'Incoherent processing',
'electromagnetic_wave_speed_in_ice': '168 (meters/microseconds)',
'firn_correction': '0 (m)',
'centre_frequency': '150 (MHz)',
'comment': "The primary source data consist of survey point measurements of ice thickness, bed elevation and surface elevation, which principally comes from airborne radar surveys and seismic soundings, and to a smaller extent from ground-based radar surveys. All the data have been standardised to a specific CSV format. The format consists of (i) an extended header section where (a) each line is introduced by a comment (#) character, (b) each line contains a single header item, (c) the colon character (:) is used as the key/value separator, (d) units are in parentheses, (e) attributes preferably use a common vocabulary such as the CF convention and includes attributes from the ACDD; (ii) a header row composed of the column name following the CF convention and units in parentheses; and finally (iii) the data using comma as the separator. The extended header consists of general information regarding each campaign such as the year, the name of the main investigator, funding and processing details. The variable names are as follows: trajectory_id Line or Flight ID trace_number Trace number from the specific line given in Line_ID longitude Longitude (WGS84 EPSG: 4326) in decimal degrees latitude Latitude (WGS84 EPSG: 4326) in decimal degrees date Date following ISO 8601 format: YYYY: year, MM: month, DD: day time_UTC UTC time following ISO 8601 format: HH: hours, MM: minutes, SS: seconds surface_altitude Surface elevation or altitude (referenced to WGS84) in meters land_ice_thickness Ice thickness in meters bedrock_altitude Bed elevation or altitude (referenced to WGS84) in meters two_way_travel_time Two-way travel time in seconds aircraft_altitude Aircraft elevation or altitude when applicable (referenced to WGS84) in meters along_track_distance Distance in the along-track direction in metersAll non-value data were converted to -9999, including any negative ice thickness values and any points with clear outliers. We checked the minimum and maximum values of each field to ensure the data are in a reasonable range and calculated mean and standard deviation on each dataset to identify potential issues. For example, if longitude/latitude values did not fall within the expected -180 to 180 or -50 to -90 degrees range respectively, the entire row was removed. When no ice thickness values were provided but surface and bed elevation values existed, we simply calculated ice thickness by subtracting the surface value from the bed value. At times, bed elevation was higher than surface elevation, likely due to issues with the semi-automatic picker used to extract the surface and bed reflector or a lack of distinctive reflectors in areas of shallow ice. To prevent this affecting the gridded product, we converted these values to -9999 for both the surface and the bed. Finally, we also conducted routine checks on the ice thickness data by comparing the given ice thickness value with the inferred ice thickness calculated from subtracting surface with bed. If these did not match, we placed -9999 on the ice thickness values. File naming conventions were also used throughout to easily identify a specific dataset as follows: DataProvider_Year_CampaignName_TypeofData_BM3. The type of data used was separated into three categories: Airborne Radar (AIR), Ground-based Radar (GRN), and Seismic (SEI) data. The 'BM3' abbreviation at the end identifies the datasets was part of the Bedmap3 compilation to differentiate from the Bedmap 1 and 2 (BM1 and BM2) compilations. For instance, the file named 'NASA_2019_ICEBRIDGE_AIR_BM3.csv' refers to the ICEBRIDGE airborne campaign led by NASA in 2019.",
'metadata_link': 'https://doi.org/10.5285/91523ff9-d621-46b3-87f7-ffb6efcd1847',
'license': 'https://creativecommons.org/licenses/by/4.0/',
'Conventions': 'ACDD-1.3, CF-1.8',
'title': 'BEDMAP3 - Ice thickness, bed and surface elevation for Antarctica - standardised data points',
'summary': "We present here the Bedmap3 ice thickness, bed and surface elevation standardised CSV data points that are used to create the Bedmap3 gridding products in addition to the previous data releases. The data consists of 50 million points acquired by 17 different data providers in Antarctica. The associated datasets consist of: - Bedmap1 standardised CSV data points: https://doi.org/10.5285/f64815ec-4077-4432-9f55-0ce230f46029 - Bedmap2 standardised CSV data points: https://doi.org/10.5285/2fd95199-365e-4da1-ae26-3b6d48b3e6ac - Bedmap3 statistically-summarised data points (shapefiles): https://doi.org/10.5285/a72a50c6-a829-4e12-9f9a-5a683a1acc4a This work is supported by the SCAR Bedmap project and the British Antarctic Survey's core programme: National Capability - Polar Expertise Supporting UK Research",
'publisher_name': 'NERC EDS UK Polar Data Centre',
'geospatial_lat_min': '-90',
'geospatial_lat_max': '-60',
'geospatial_lon_min': '-180',
'geospatial_lon_max': '180',
'date_created': '2022-02-28',
'keywords': 'Antarctica, BEDMAP, Elevation, Ice thickness'}
Converting the CSV file to NetCDF with rich metadata¶
First, we will load the data, convert them to an array and convert them to NetCDF.
csv_data = pd.read_csv(CSV_file, skiprows=18, low_memory=False)
After opening the data as a dataframe, we convert the data to an array using xarray library:
xr = xarray.Dataset.from_dataframe(csv_data)
We then add the attributes from our metadata dictionary that we just created.
xr.attrs = dict_metadata
xr
<xarray.Dataset>
Dimensions: (index: 23382)
Coordinates:
* index (index) int64 0 1 2 3 ... 23378 23379 23380 23381
Data variables:
trajectory_id (index) object '20162002_01' ... '20162020_04'
trace_number (index) int64 -9999 -9999 -9999 ... -9999 -9999
longitude (degree_east) (index) float64 -5.795 -5.794 ... -7.318 -7.323
latitude (degree_north) (index) float64 -71.03 -71.03 ... -70.66 -70.66
date (index) object '2015-12-15' ... '2016-01-03'
time_UTC (index) object '12:43:36' ... '14:51:38'
surface_altitude (m) (index) int64 -9999 -9999 -9999 ... -9999 -9999
land_ice_thickness (m) (index) float64 463.7 463.7 456.3 ... 252.5 253.0
bedrock_altitude (m) (index) int64 -9999 -9999 -9999 ... -9999 -9999
two_way_travel_time (m) (index) float64 5.52e-06 5.52e-06 ... 3.012e-06
aircraft_altitude (m) (index) int64 -9999 -9999 -9999 ... -9999 -9999
along_track_distance (m) (index) int64 0 56 325 ... 161406 161636 161832
Attributes: (12/27)
project: Dronning Maud Land (GEA).
time_coverage_start: 2015
time_coverage_end: 2016
creator_name: Alfred Wegener Institute; Bundesansta...
institution: Alfred Wegener Institute; Bundesansta...
acknowledgement: National Capability - Polar Expertise...
... ...
geospatial_lat_min: -90
geospatial_lat_max: -60
geospatial_lon_min: -180
geospatial_lon_max: 180
date_created: 2022-02-28
keywords: Antarctica, BEDMAP, Elevation, Ice th...We can add the standard names and units for all the variables:
name_dict = {}
for name, variables in xr.variables.items():
xr[name].attrs['standard_name'] = name.split(' (')[0]
xr[name].attrs['long_name'] = name.split(' (')[0]
if '(' in name:
xr[name].attrs['units'] = name.split(' (')[1][:-1]
else:
xr[name].attrs['units'] = ''
name_dict[name] = name.split(' (')[0]
xr = xr.rename(name_dict=name_dict)
We save the data to NetCDF
filename = CSV_file.strip('.csv')
xr.to_netcdf('%s.nc' %filename)
Checking completness of the metadata¶
It is possible to check the metadata completness thanks to the compliance-checker library
from compliance_checker.runner import ComplianceChecker, CheckSuite
# Load all available checker classes
check_suite = CheckSuite()
check_suite.load_all_available_checkers()
# Run cf and adcc checks
path = filename + '.nc'
checker_names = ['cf', 'acdd']
verbose = 0
criteria = 'normal'
output_filename = filename + '_report.json'
output_format = 'json'
"""
Inputs to ComplianceChecker.run_checker
path Dataset location (url or file)
checker_names List of string names to run, should match keys of checkers dict (empty list means run all)
verbose Verbosity of the output (0, 1, 2)
criteria Determines failure (lenient, normal, strict)
output_filename Path to the file for output
output_format Format of the output
@returns If the tests failed (based on the criteria)
"""
return_value, errors = ComplianceChecker.run_checker(path,
checker_names,
verbose,
criteria,
output_filename=output_filename,
output_format=output_format)
# Open the JSON output and get the compliance scores
with open(output_filename, 'r') as fp:
cc_data = json.load(fp)
for i in range(0, len(checker_names)):
scored = cc_data[checker_names[i]]['scored_points']
possible = cc_data[checker_names[i]]['possible_points']
print('Convention: {} - CC Scored {} out of {} possible points'.format(checker_names[i], scored, possible))
Convention: cf - CC Scored 235 out of 246 possible points
Convention: acdd - CC Scored 49 out of 77 possible points
Although the CSV metadata are quite small, all the enriched metadata can be easily retrieved from the DOI metadata. The missing points are linked to parameters that are not referenced in the conventions cited.