guest ::
| Home > Publications database > AQ-Bench: a benchmark dataset for machine learning on global air quality metrics > print |
| Home > Publications database > AQ-Bench: a benchmark dataset for machine learning on global air quality metrics > print |
| 001 | 893368 | ||
| 005 | 20230127125339.0 | ||
| 024 | 7 | _ | |a 10.5194/essd-13-3013-2021 |2 doi |
| 024 | 7 | _ | |a 1866-3508 |2 ISSN |
| 024 | 7 | _ | |a 1866-3516 |2 ISSN |
| 024 | 7 | _ | |a 2128/28006 |2 Handle |
| 024 | 7 | _ | |a altmetric:108129381 |2 altmetric |
| 024 | 7 | _ | |a WOS:000668053300001 |2 WOS |
| 037 | _ | _ | |a FZJ-2021-02709 |
| 082 | _ | _ | |a 550 |
| 100 | 1 | _ | |a Betancourt, Clara |0 P:(DE-Juel1)171435 |b 0 |
| 245 | _ | _ | |a AQ-Bench: a benchmark dataset for machine learning on global air quality metrics |
| 260 | _ | _ | |a Katlenburg-Lindau |c 2021 |b Copernics Publications |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1632901328_6598 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a With the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air quality based on this metadata. AQ-Bench contains a unique collection of aggregated air quality data from the years 2010–2014 and metadata at more than 5500 air quality monitoring stations all over the world, provided by the first Tropospheric Ozone Assessment Report (TOAR). It focuses in particular on metrics of tropospheric ozone, which has a detrimental effect on climate, human morbidity and mortality, as well as crop yields. The purpose of this dataset is to produce estimates of various long-term ozone metrics based on time-independent local site conditions. We combine this task with a suitable evaluation metric. Baseline scores obtained from a linear regression method, a fully connected neural network and random forest are provided for reference and validation. AQ-Bench offers a low-threshold entrance for all machine learners with an interest in environmental science and for atmospheric scientists who are interested in applying machine learning techniques. It enables them to start with a real-world problem relevant to humans and nature. The dataset and introductory machine learning code are available at https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f (Betancourt et al., 2020) and https://gitlab.version.fz-juelich.de/esde/machine-learning/aq-bench (Betancourt et al., 2021). AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles. |
| 536 | _ | _ | |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) |0 G:(DE-HGF)POF4-5111 |c POF4-511 |f POF IV |x 0 |
| 536 | _ | _ | |a IntelliAQ - Artificial Intelligence for Air Quality (787576) |0 G:(EU-Grant)787576 |c 787576 |f ERC-2017-ADG |x 1 |
| 536 | _ | _ | |a Deep Learning for Air Quality and Climate Forecasts (deepacf_20191101) |0 G:(DE-Juel1)deepacf_20191101 |c deepacf_20191101 |f Deep Learning for Air Quality and Climate Forecasts |x 2 |
| 536 | _ | _ | |0 G:(DE-Juel-1)ESDE |a Earth System Data Exploration (ESDE) |c ESDE |x 3 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Stomberg, Timo |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Roscher, Ribana |0 P:(DE-HGF)0 |b 2 |
| 700 | 1 | _ | |a Schultz, Martin G. |0 P:(DE-Juel1)6952 |b 3 |e Corresponding author |
| 700 | 1 | _ | |a Stadtler, Scarlet |0 P:(DE-Juel1)180752 |b 4 |u fzj |
| 770 | _ | _ | |a Benchmark datasets and machine learning algorithms for Earth system science data (ESSD/GMD inter-journal SI) |
| 773 | _ | _ | |a 10.5194/essd-13-3013-2021 |g Vol. 13, no. 6, p. 3013 - 3033 |0 PERI:(DE-600)2475469-9 |n 6 |p 3013 - 3033 |t Earth system science data |v 13 |y 2021 |x 1866-3516 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/893368/files/AQ-Bench%20paper.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:893368 |p openaire |p open_access |p driver |p VDB |p ec_fundedresources |p dnbdelivery |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)171435 |
| 910 | 1 | _ | |a Universität Bonn |0 I:(DE-HGF)0 |b 1 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Universität Bonn |0 I:(DE-HGF)0 |b 2 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)6952 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)180752 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action |1 G:(DE-HGF)POF4-510 |0 G:(DE-HGF)POF4-511 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Enabling Computational- & Data-Intensive Science and Engineering |9 G:(DE-HGF)POF4-5111 |x 0 |
| 914 | 1 | _ | |y 2021 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2021-01-29 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2021-01-29 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2021-01-29 |
| 915 | _ | _ | |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0 |0 LIC:(DE-HGF)CCBYNCND4 |2 HGFVOC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1150 |2 StatID |b Current Contents - Physical, Chemical and Earth Sciences |d 2021-01-29 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b EARTH SYST SCI DATA : 2019 |d 2021-01-29 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2021-01-29 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2021-01-29 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2021-01-29 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2021-01-29 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2021-01-29 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b EARTH SYST SCI DATA : 2019 |d 2021-01-29 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2021-01-29 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2021-01-29 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)NIC-20090406 |k NIC |l John von Neumann - Institut für Computing |x 1 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)NIC-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|