Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery
Summary: Daily weather reconstructions (called “reanalyses”) can help improve our understanding of meteorology and long-term climate changes. Adding undigitized historical weather observations to the datasets that underpin reanalyses is desirable; however, time requirements to capture those data fro...
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ftdoajarticles:oai:doaj.org/article:664a6e1423264c2e86b06a7cb7e5c78e 2023-05-15T13:53:19+02:00 Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery Andrew M. Lorrey Petra R. Pearce Rob Allan Clive Wilkinson John-Mark Woolley Emily Judd Stuart Mackay Sudhir Rawhat Laura Slivinski Sally Wilkinson Ed Hawkins Patrick Quesnel Gilbert P. Compo 2022-06-01T00:00:00Z https://doi.org/10.1016/j.patter.2022.100495 https://doaj.org/article/664a6e1423264c2e86b06a7cb7e5c78e EN eng Elsevier http://www.sciencedirect.com/science/article/pii/S2666389922000800 https://doaj.org/toc/2666-3899 2666-3899 doi:10.1016/j.patter.2022.100495 https://doaj.org/article/664a6e1423264c2e86b06a7cb7e5c78e Patterns, Vol 3, Iss 6, Pp 100495- (2022) DSML2: Proof-of-concept: Data science output has been formulated implemented and tested for one domain/problem Computer software QA76.75-76.765 article 2022 ftdoajarticles https://doi.org/10.1016/j.patter.2022.100495 2022-12-31T02:34:33Z Summary: Daily weather reconstructions (called “reanalyses”) can help improve our understanding of meteorology and long-term climate changes. Adding undigitized historical weather observations to the datasets that underpin reanalyses is desirable; however, time requirements to capture those data from a range of archives is usually limited. Southern Weather Discovery is a citizen science data rescue project that recovered tabulated handwritten meteorological observations from ship log books and land-based stations spanning New Zealand, the Southern Ocean, and Antarctica. We describe the Zooniverse-hosted Southern Weather Discovery campaign, highlight promotion tactics, and replicate keying levels needed to obtain 100% complete transcribed datasets with minimal type 1 and type 2 transcription errors. Rescued weather observations can augment optical character recognition (OCR) text recognition libraries. Closer links between citizen science data rescue and OCR-based scientific data capture will accelerate weather reconstruction improvements, which can be harnessed to mitigate impacts on communities and infrastructure from weather extremes. The bigger picture: Citizen science has the potential to capture historical handwritten scientific tabulated data that are not held in digital databases. However, undertaking a citizen science campaign for that purpose is not well described, which we address here. Our citizen science data rescue approach constrained data keying targets, developed participant instructions using clear examples, established replication levels to maximize completeness and confidence of data transcription, and demonstrated common data rescue pitfalls. We highlight how an effective communications strategy helps to maintain project momentum. Collaborating with industry to enhance optical character recognition (OCR) capability has the benefit of accelerating data rescue progress that can rapidly augment scientific data repositories. The resulting improvements to comprehensive historical weather datasets ... Article in Journal/Newspaper Antarc* Antarctica Southern Ocean Directory of Open Access Journals: DOAJ Articles Southern Ocean New Zealand Patterns 3 6 100495 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
DSML2: Proof-of-concept: Data science output has been formulated implemented and tested for one domain/problem Computer software QA76.75-76.765 |
spellingShingle |
DSML2: Proof-of-concept: Data science output has been formulated implemented and tested for one domain/problem Computer software QA76.75-76.765 Andrew M. Lorrey Petra R. Pearce Rob Allan Clive Wilkinson John-Mark Woolley Emily Judd Stuart Mackay Sudhir Rawhat Laura Slivinski Sally Wilkinson Ed Hawkins Patrick Quesnel Gilbert P. Compo Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery |
topic_facet |
DSML2: Proof-of-concept: Data science output has been formulated implemented and tested for one domain/problem Computer software QA76.75-76.765 |
description |
Summary: Daily weather reconstructions (called “reanalyses”) can help improve our understanding of meteorology and long-term climate changes. Adding undigitized historical weather observations to the datasets that underpin reanalyses is desirable; however, time requirements to capture those data from a range of archives is usually limited. Southern Weather Discovery is a citizen science data rescue project that recovered tabulated handwritten meteorological observations from ship log books and land-based stations spanning New Zealand, the Southern Ocean, and Antarctica. We describe the Zooniverse-hosted Southern Weather Discovery campaign, highlight promotion tactics, and replicate keying levels needed to obtain 100% complete transcribed datasets with minimal type 1 and type 2 transcription errors. Rescued weather observations can augment optical character recognition (OCR) text recognition libraries. Closer links between citizen science data rescue and OCR-based scientific data capture will accelerate weather reconstruction improvements, which can be harnessed to mitigate impacts on communities and infrastructure from weather extremes. The bigger picture: Citizen science has the potential to capture historical handwritten scientific tabulated data that are not held in digital databases. However, undertaking a citizen science campaign for that purpose is not well described, which we address here. Our citizen science data rescue approach constrained data keying targets, developed participant instructions using clear examples, established replication levels to maximize completeness and confidence of data transcription, and demonstrated common data rescue pitfalls. We highlight how an effective communications strategy helps to maintain project momentum. Collaborating with industry to enhance optical character recognition (OCR) capability has the benefit of accelerating data rescue progress that can rapidly augment scientific data repositories. The resulting improvements to comprehensive historical weather datasets ... |
format |
Article in Journal/Newspaper |
author |
Andrew M. Lorrey Petra R. Pearce Rob Allan Clive Wilkinson John-Mark Woolley Emily Judd Stuart Mackay Sudhir Rawhat Laura Slivinski Sally Wilkinson Ed Hawkins Patrick Quesnel Gilbert P. Compo |
author_facet |
Andrew M. Lorrey Petra R. Pearce Rob Allan Clive Wilkinson John-Mark Woolley Emily Judd Stuart Mackay Sudhir Rawhat Laura Slivinski Sally Wilkinson Ed Hawkins Patrick Quesnel Gilbert P. Compo |
author_sort |
Andrew M. Lorrey |
title |
Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery |
title_short |
Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery |
title_full |
Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery |
title_fullStr |
Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery |
title_full_unstemmed |
Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery |
title_sort |
meteorological data rescue: citizen science lessons learned from southern weather discovery |
publisher |
Elsevier |
publishDate |
2022 |
url |
https://doi.org/10.1016/j.patter.2022.100495 https://doaj.org/article/664a6e1423264c2e86b06a7cb7e5c78e |
geographic |
Southern Ocean New Zealand |
geographic_facet |
Southern Ocean New Zealand |
genre |
Antarc* Antarctica Southern Ocean |
genre_facet |
Antarc* Antarctica Southern Ocean |
op_source |
Patterns, Vol 3, Iss 6, Pp 100495- (2022) |
op_relation |
http://www.sciencedirect.com/science/article/pii/S2666389922000800 https://doaj.org/toc/2666-3899 2666-3899 doi:10.1016/j.patter.2022.100495 https://doaj.org/article/664a6e1423264c2e86b06a7cb7e5c78e |
op_doi |
https://doi.org/10.1016/j.patter.2022.100495 |
container_title |
Patterns |
container_volume |
3 |
container_issue |
6 |
container_start_page |
100495 |
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1766258364819439616 |