Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2021 submit to JAMES) Article Swipe
Peidong Wang
,
Janni Yuval
,
Paul A. O’Gorman
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.5794480
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.5794480
Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2021 submit to JAMES). Detailed description of the files in README.txt.
Related Topics
Concepts
Metadata
- Type
- dataset
- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.5794480
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393422741
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4393422741Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5281/zenodo.5794480Digital Object Identifier
- Title
-
Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2021 submit to JAMES)Work title
- Type
-
datasetOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-20Full publication date if available
- Authors
-
Peidong Wang, Janni Yuval, Paul A. O’GormanList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.5794480Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.5794480Direct OA link when available
- Concepts
-
Code (set theory), Artificial neural network, Meteorology, Environmental science, Geography, Computer science, Artificial intelligence, Programming language, Set (abstract data type)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4393422741 |
|---|---|
| doi | https://doi.org/10.5281/zenodo.5794480 |
| ids.doi | https://doi.org/10.5281/zenodo.5794480 |
| ids.openalex | https://openalex.org/W4393422741 |
| fwci | |
| type | dataset |
| title | Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2021 submit to JAMES) |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11588 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9203000068664551 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2306 |
| topics[0].subfield.display_name | Global and Planetary Change |
| topics[0].display_name | Atmospheric and Environmental Gas Dynamics |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2776760102 |
| concepts[0].level | 3 |
| concepts[0].score | 0.6495881676673889 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q5139990 |
| concepts[0].display_name | Code (set theory) |
| concepts[1].id | https://openalex.org/C50644808 |
| concepts[1].level | 2 |
| concepts[1].score | 0.508211612701416 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[1].display_name | Artificial neural network |
| concepts[2].id | https://openalex.org/C153294291 |
| concepts[2].level | 1 |
| concepts[2].score | 0.4139578342437744 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[2].display_name | Meteorology |
| concepts[3].id | https://openalex.org/C39432304 |
| concepts[3].level | 0 |
| concepts[3].score | 0.38698989152908325 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[3].display_name | Environmental science |
| concepts[4].id | https://openalex.org/C205649164 |
| concepts[4].level | 0 |
| concepts[4].score | 0.3623013496398926 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[4].display_name | Geography |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.3389943540096283 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.23463058471679688 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C199360897 |
| concepts[7].level | 1 |
| concepts[7].score | 0.1650661826133728 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[7].display_name | Programming language |
| concepts[8].id | https://openalex.org/C177264268 |
| concepts[8].level | 2 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[8].display_name | Set (abstract data type) |
| keywords[0].id | https://openalex.org/keywords/code |
| keywords[0].score | 0.6495881676673889 |
| keywords[0].display_name | Code (set theory) |
| keywords[1].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[1].score | 0.508211612701416 |
| keywords[1].display_name | Artificial neural network |
| keywords[2].id | https://openalex.org/keywords/meteorology |
| keywords[2].score | 0.4139578342437744 |
| keywords[2].display_name | Meteorology |
| keywords[3].id | https://openalex.org/keywords/environmental-science |
| keywords[3].score | 0.38698989152908325 |
| keywords[3].display_name | Environmental science |
| keywords[4].id | https://openalex.org/keywords/geography |
| keywords[4].score | 0.3623013496398926 |
| keywords[4].display_name | Geography |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.3389943540096283 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.23463058471679688 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/programming-language |
| keywords[7].score | 0.1650661826133728 |
| keywords[7].display_name | Programming language |
| language | en |
| locations[0].id | doi:10.5281/zenodo.5794480 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400562 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| locations[0].source.host_organization | https://openalex.org/I67311998 |
| locations[0].source.host_organization_name | European Organization for Nuclear Research |
| locations[0].source.host_organization_lineage | https://openalex.org/I67311998 |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | |
| locations[0].raw_type | dataset |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.5281/zenodo.5794480 |
| indexed_in | datacite |
| authorships[0].author.id | https://openalex.org/A5014281939 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6491-7171 |
| authorships[0].author.display_name | Peidong Wang |
| authorships[0].countries | RU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210109586 |
| authorships[0].affiliations[0].raw_affiliation_string | MIT |
| authorships[0].institutions[0].id | https://openalex.org/I4210109586 |
| authorships[0].institutions[0].ror | https://ror.org/021es5e59 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210109586 |
| authorships[0].institutions[0].country_code | RU |
| authorships[0].institutions[0].display_name | Moscow Institute of Thermal Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Peidong Wang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | MIT |
| authorships[1].author.id | https://openalex.org/A5049907180 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7519-0118 |
| authorships[1].author.display_name | Janni Yuval |
| authorships[1].countries | RU |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210109586 |
| authorships[1].affiliations[0].raw_affiliation_string | MIT |
| authorships[1].institutions[0].id | https://openalex.org/I4210109586 |
| authorships[1].institutions[0].ror | https://ror.org/021es5e59 |
| authorships[1].institutions[0].type | facility |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210109586 |
| authorships[1].institutions[0].country_code | RU |
| authorships[1].institutions[0].display_name | Moscow Institute of Thermal Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Janni Yuval |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | MIT |
| authorships[2].author.id | https://openalex.org/A5083734182 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1748-0816 |
| authorships[2].author.display_name | Paul A. O’Gorman |
| authorships[2].countries | RU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210109586 |
| authorships[2].affiliations[0].raw_affiliation_string | MIT |
| authorships[2].institutions[0].id | https://openalex.org/I4210109586 |
| authorships[2].institutions[0].ror | https://ror.org/021es5e59 |
| authorships[2].institutions[0].type | facility |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210109586 |
| authorships[2].institutions[0].country_code | RU |
| authorships[2].institutions[0].display_name | Moscow Institute of Thermal Technology |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Paul O'Gorman |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | MIT |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5281/zenodo.5794480 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2021 submit to JAMES) |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11588 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9203000068664551 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2306 |
| primary_topic.subfield.display_name | Global and Planetary Change |
| primary_topic.display_name | Atmospheric and Environmental Gas Dynamics |
| related_works | https://openalex.org/W2748952813, https://openalex.org/W2391251536, https://openalex.org/W2362198218, https://openalex.org/W1982750869, https://openalex.org/W2019521278, https://openalex.org/W1984922432, https://openalex.org/W2113077220, https://openalex.org/W2375008505, https://openalex.org/W2350679292, https://openalex.org/W2086348228 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.5281/zenodo.5794480 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400562 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| best_oa_location.source.host_organization | https://openalex.org/I67311998 |
| best_oa_location.source.host_organization_name | European Organization for Nuclear Research |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I67311998 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | |
| best_oa_location.raw_type | dataset |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.5281/zenodo.5794480 |
| primary_location.id | doi:10.5281/zenodo.5794480 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400562 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| primary_location.source.host_organization | https://openalex.org/I67311998 |
| primary_location.source.host_organization_name | European Organization for Nuclear Research |
| primary_location.source.host_organization_lineage | https://openalex.org/I67311998 |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | |
| primary_location.raw_type | dataset |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.5281/zenodo.5794480 |
| publication_date | 2021-12-20 |
| publication_year | 2021 |
| referenced_works_count | 0 |
| abstract_inverted_index.et | 14 |
| abstract_inverted_index.in | 25 |
| abstract_inverted_index.of | 6, 22 |
| abstract_inverted_index.to | 18 |
| abstract_inverted_index.al. | 15 |
| abstract_inverted_index.and | 1 |
| abstract_inverted_index.for | 3 |
| abstract_inverted_index.the | 23 |
| abstract_inverted_index.2021 | 16 |
| abstract_inverted_index.Data | 0 |
| abstract_inverted_index.code | 2 |
| abstract_inverted_index.with | 10 |
| abstract_inverted_index.(Wang | 13 |
| abstract_inverted_index.files | 24 |
| abstract_inverted_index.neural | 11 |
| abstract_inverted_index.submit | 17 |
| abstract_inverted_index.JAMES). | 19 |
| abstract_inverted_index.subgrid | 8 |
| abstract_inverted_index.Detailed | 20 |
| abstract_inverted_index.networks" | 12 |
| abstract_inverted_index.processes | 9 |
| abstract_inverted_index."Non-local | 4 |
| abstract_inverted_index.README.txt. | 26 |
| abstract_inverted_index.atmospheric | 7 |
| abstract_inverted_index.description | 21 |
| abstract_inverted_index.parameterization | 5 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |