dc.contributor.author | Stadler, Konstantin | |
dc.date.accessioned | 2018-07-27T09:38:21Z | |
dc.date.available | 2018-07-27T09:38:21Z | |
dc.date.created | 2018-01-03T13:52:27Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | The Journal of Open Source Software. 2017, 2 (16), 332-332. | nb_NO |
dc.identifier.issn | 2475-9066 | |
dc.identifier.uri | http://hdl.handle.net/11250/2506671 | |
dc.description.abstract | Gathering the data basis for regional to global models and scenarios in many scientific fields regularly requires the parsing of multiple data sources. In particular for data related to economic (national accounts, trade statistics, etc.) and environmental (climate, ecosystem descriptions, etc.) variables, these data are mostly categorised per country. There is, however, no single standard of how to name or specify individual countries. As a consequence, parsing routines need to be adopted for each used data source. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Journal of Open Source Software | nb_NO |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | The country converter coco - a Python package for converting country names between different classification schemes | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | publishedVersion | nb_NO |
dc.source.pagenumber | 332-332 | nb_NO |
dc.source.volume | 2 | nb_NO |
dc.source.journal | The Journal of Open Source Software | nb_NO |
dc.source.issue | 16 | nb_NO |
dc.identifier.doi | 10.21105/joss.00332 | |
dc.identifier.cristin | 1534913 | |
dc.description.localcode | © 2017 Authors of JOSS papers retain copyright. This work is licensed under a Creative Commons Attribution 4.0 International License. | nb_NO |
cristin.unitcode | 194,64,25,0 | |
cristin.unitname | Institutt for energi- og prosessteknikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |