cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4641542
title: ChinaHighNO₂: Daily Seamless 10 km Ground-Level NO₂ Dataset for China (2008–2018)

description: ChinaHighNO2 is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level NO2 dataset for China from 2008 to 2018. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.84, a root-mean-square error (RMSE) of 7.99 µg m-3, and a mean absolute error (MAE) of 5.34 µg m-3 on a daily basis.

If you use the ChinaHighNO2 dataset in your scientific research, please cite the following references (Wei et al., ACP, 2023; Wei et al., EST, 2022):





Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023




Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834



Note that the ChinaHighNO2 dataset was improved to a 1 km resolution after 2019:

        all (including daily) data for the years after 2019 are accessible at: https://doi.org/10.5281/zenodo.4571660

More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
description: Note that the data are recorded in local time (i.e., Beijing time: GMT+8), and measured at the standard condition (i.e., 273 K and 1013 hPa). The concentrations can be converted to the room condition (i.e., 298 K and 1013 hPa) by dividing by a factor of 1.09375 (MEE, 2018).

This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
DOI: 10.5281/zenodo.4641543
title: ChinaHighNO2: Big Data Full-coverage Ground-level NO2 L3 Yearly 0.1 Deg Product

description: ChinaHighNO<sub>2</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the Big Data Level 3 (L3) yearly 0.1 degree (≈ 10 km) gridded <strong>full-coverage</strong> ground-level NO<sub>2</sub> products in Eastern China (ECHAP_NO2_Y10K) from 2013 to 2020, which are averaged from the Level 2 daily products. This dataset has high accuracy with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84 and a root-mean-square error (RMSE) of 7.99 µg m<sup>-3</sup> on a daily basis. If you use the ChinaHighNO<sub>2</sub> dataset for related scientific research, please cite the corresponding reference (Wei et al., RSE, 2021). <strong>Note that this dataset is continuously updated, and if you need a longer period or higher temporal-resolution (e.g., daily, monthly) data, please contact me.</strong>
description: Email: weijing_rs@163.com; weijing.rs@gmail.com.
description: {"references": ["Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136"]}
DOI: 10.5281/zenodo.5765561
title: ChinaHighNO2: Big Data Seamless 10 km Ground-level NO2 Dataset for China

description: ChinaHighNO<sub>2</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level NO<sub>2</sub> dataset in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84 and a root-mean-square error (RMSE) of 7.99 µg m<sup>-3</sup> on a daily basis. <strong>This old version was closed now since a new version has been published.</strong> <strong>More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html</strong>
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing.rs@gmail.com).
description: {"references": ["Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 269, 112775. https://doi.org/10.1016/j.rse.2021.112775", "Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136"]}
DOI: 10.5281/zenodo.7574177
title: ChinaHighNO2: Big Data Seamless 10 km Ground-level NO2 Dataset for China

description: ChinaHighNO2 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. 


This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level NO2 dataset in China from 2013 to 2018. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.84, a root-mean-square error (RMSE) of 7.99 µg m-3, and a mean absolute error (MAE) of 5.34 µg m-3 on a daily basis.


Note that the ChinaHighNO2 dataset is 10 km before 2019, and improved to 1 km after 2019, which is available at https://doi.org/10.5281/zenodo.4641538. If you use the ChinaHighNO2 dataset for related scientific research, please cite the corresponding references (Wei et al., ACP, 2023; Wei et al., ES&T, 2022):



	

	
Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023
	
	

	
Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834
	



More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html
description: Note that this dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing@umd.edu).
DOI: 10.15468/dl.5mrcu9
title: Occurrence Download

description: A dataset containing 75162 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Living Specimen, Occurrence evidence)",
 "GadmGid is one of (USA.10_1, USA.11_1, USA.41_1, USA.1_1, USA.34_1, USA.25_1, USA.43_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Polystichum acrostichoides (Michx.) Schott, Carpinus caroliniana Walter, Quercus nigra L., Fraxinus americana L., Celtis laevigata Willd., Phytolacca americana L., Ligustrum sinense Lour., Ilex opaca Aiton, Dioscorea villosa L., Asplenium platyneuron (L.) Britton, Sterns & Poggenb., Microstegium vimineum (Trin.) A.Camus)",
 "Year 1980-2025"
 ]
}

 The dataset includes 75162 records from 122 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015064-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.saw68c
title: Occurrence Download

description: A dataset containing 3183144 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Lepidothrix coronata (Spix, 1825), Tangara schrankii (Spix, 1825), Machaeropterus regulus (Hahn, 1819), Tachyphonus surinamus (Linnaeus, 1766), Sciurus igniventris Wagner, 1842, Pionites melanocephalus (Linnaeus, 1758), Ramphastos tucanus Linnaeus, 1758, Pteroglossus inscriptus Swainson, 1822, Pteroglossus azara (Vieillot, 1819), Ateles belzebuth É.Geoffroy Saint-Hilaire, 1806, Pionus menstruus (Linnaeus, 1766), Ara severus (Linnaeus, 1758), Aratinga leucophthalma (Statius Muller, 1776), Piculus flavigula (Boddaert, 1783), Amazona farinosa (Boddaert, 1783), Claravis pretiosa (Ferrari-Pérez, 1886), Patagioenas plumbea (Vieillot, 1818), Patagioenas subvinacea (Lawrence, 1868), Artibeus toltecus (Saussure, 1860), Vampyriscus nymphaea (Thomas, 1909), Querula purpurata (Statius Muller, 1776), Aotus zonalis Goldman, 1914, Taraba major (Vieillot, 1816), Trogon curucui Linnaeus, 1766, Ateles geoffroyi Kuhl, 1820, Pipile cumanensis (Jacquin, 1784), Saguinus geoffroyi (Pucheran, 1845), Setophaga striata (J.R.Forster, 1772), Trogon rufus J.F.Gmelin, 1788, Saimiri oerstedii (Reinhardt, 1872), Trogon viridis Linnaeus, 1766, Piranga flava (Vieillot, 1822), Cebus imitator Thomas, 1903, Carollia castanea H.Allen, 1890, Alouatta seniculus (Linnaeus, 1766), Psophia crepitans Linnaeus, 1758, Carollia brevicaudum (Schinz, 1821), Alouatta palliata (Gray, 1849), Pan troglodytes schweinfurthii (Giglioli, 1872), Bos javanicus d'Alton, 1823, Parum colligata Walker, 1856, Neptis pampanga Felder, 1863, Seriana indefinita Dworakowska, 1971, Urocissa caerulea Gould, 1863, Icerya aegyptiaca (Douglas, 1890), Acridotheres javanicus Cabanis, 1851, Asterocampa clyton (Boisduval & Le Conte, 1833), Entedon broussonetiae Yang, 1996, Limassolla emmrichi Dworakowska, 1972, Thapaia nema (Thapa, 1989))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3183144 records from 1082 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015065-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/b5s-fwc-9j6
title: Frangula alnus subsp. alnus

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.57907/mirri/0cxcwm
title: MIRRI0050334

description: Organism type: Filamentous Fungi ; Taxon name: Mucor lusitanicus ; Collection: CBS 968.68 ; Country: Mexico
DOI: 10.57907/mirri/2toysz
title: MIRRI0047922

description: Organism type: Filamentous Fungi ; Taxon name: Talaromyces wortmannii ; Collection: CBS 316.63 ; Country: Netherlands
DOI: 10.57907/mirri/5j1yv2/pvyhfq
title: MIRRI0050358.json

description: Strain json file
DOI: 10.57907/mirri/6aefz5
title: MIRRI0050365

description: Organism type: Filamentous Fungi ; Taxon name: Rasamsonia argillacea ; Collection: CBS 106.69 ; Country: Sweden
DOI: 10.57907/mirri/6vlrqq/gp4hj7
title: MIRRI0049219.json

description: Strain json file
DOI: 10.57907/mirri/9smzch
title: MIRRI0047939

description: Organism type: Filamentous Fungi ; Taxon name: Gelatoporia pannocincta ; Collection: CBS 342.63 ; Country: United States of America
DOI: 10.57907/mirri/cjjo3s
title: MIRRI0049239

description: Organism type: Filamentous Fungi ; Taxon name: Mucor silvaticus ; Collection: CBS 509.66 ; Country: Germany
DOI: 10.57907/mirri/ckxgoz/4miw2t
title: MIRRI0049200.json

description: Strain json file
DOI: 10.57907/mirri/dn7e5n/0d4avu
title: MIRRI0049283.json

description: Strain json file
DOI: 10.57907/mirri/exqsqm
title: MIRRI0047953

description: Organism type: Filamentous Fungi ; Taxon name: Remispora maritima ; Collection: CBS 360.63 ; Country: France
DOI: 10.57907/mirri/hagikp/ynwqkd
title: MIRRI0050347.json

description: Strain json file
DOI: 10.57907/mirri/hhvie6
title: MIRRI0050343

description: Organism type: Filamentous Fungi ; Taxon name: Mucor hiemalis f. hiemalis ; Collection: CBS 976.68 ; Country: Germany
DOI: 10.57907/mirri/hhvie6/6pdkqt
title: MIRRI0050343.json

description: Strain json file
DOI: 10.57907/mirri/iijljj/jz1yjl
title: MIRRI0047949.json

description: Strain json file
DOI: 10.57907/mirri/jfqile/ltguvn
title: MIRRI0047947.json

description: Strain json file
DOI: 10.57907/mirri/kf3tx9
title: MIRRI0049207

description: Organism type: Filamentous Fungi ; Taxon name: Spirosphaera beverwijkiana ; Collection: CBS 472.66 ; Country: Netherlands
DOI: 10.57907/mirri/kllky2/nb47kd
title: MIRRI0050362.json

description: Strain json file
DOI: 10.57907/mirri/lg4xev/wxgvn5
title: MIRRI0047975.json

description: Strain json file
DOI: 10.57907/mirri/lommna/hxgc06
title: MIRRI0050348.json

description: Strain json file
DOI: 10.57907/mirri/mhpnjw/a71u0f
title: MIRRI0050355.json

description: Strain json file
DOI: 10.57907/mirri/ndcglw/tk2fwc
title: MIRRI0047921.json

description: Strain json file
DOI: 10.57907/mirri/nyi1fl/a8kvt4
title: MIRRI0050597.json

description: Strain json file
DOI: 10.57907/mirri/oc86bh/w7yqck
title: MIRRI0047985.json

description: Strain json file
DOI: 10.57907/mirri/ospomm
title: MIRRI0048051

description: Organism type: Filamentous Fungi ; Taxon name: Chrysosporium indicum ; Collection: CBS 504.63 ; Country: India
DOI: 10.57907/mirri/owdzpp/ayexvm
title: MIRRI0049202.json

description: Strain json file
DOI: 10.57907/mirri/qhv7xa/baftbx
title: MIRRI0047942.json

description: Strain json file
DOI: 10.57907/mirri/ti8fot
title: MIRRI0050357

description: Organism type: Filamentous Fungi ; Taxon name: Mucor flavus ; Collection: CBS 991.68 ; Country: Belgium
DOI: 10.57907/mirri/tu8jnp
title: MIRRI0050371

description: Organism type: Filamentous Fungi ; Taxon name: Acremonium longisporum ; Collection: CBS 113.69 ; Country: Italy
DOI: 10.57907/mirri/vgsx7e
title: MIRRI0050367

description: Organism type: Filamentous Fungi ; Taxon name: Amesia atrobrunnea ; Collection: CBS 108.69 ; Country: Japan
DOI: 10.57907/mirri/xqbtk5/gu2rbg
title: MIRRI0050429.json

description: Strain json file
DOI: 10.57907/mirri/znwzf3
title: MIRRI0047980

description: Organism type: Filamentous Fungi ; Taxon name: Elsinoë pongamiae ; Collection: CBS 402.63 ; Country: India
DOI: 10.3535/3cn-erz-r75
title: Eupatorium cannabinum L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/g5a-lzn-h41
title: Astrantia major L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/60p-tlj-9t7
title: Campanula persicifolia L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/lm8-fb2-enf
title: Hieracium L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.57907/mirri/19mva3/gz4rhd
title: MIRRI0049790.json

description: Strain json file
DOI: 10.57907/mirri/24lgt2/aytnka
title: MIRRI0050653.json

description: Strain json file
DOI: 10.57907/mirri/2bbyqi/jbnckr
title: MIRRI0049825.json

description: Strain json file
DOI: 10.57907/mirri/2kcpwg
title: MIRRI0049224

description: Organism type: Filamentous Fungi ; Taxon name: Eupenicillium cinnamopurpureum ; Collection: CBS 490.66 ; Country: South Africa
DOI: 10.57907/mirri/3x05qe/zzx9tb
title: MIRRI0049811.json

description: Strain json file
DOI: 10.57907/mirri/5vxq1j/cb3mdu
title: MIRRI0050645.json

description: Strain json file
DOI: 10.57907/mirri/6bitay
title: MIRRI0047998

description: Organism type: Filamentous Fungi ; Taxon name: Trichophyton rubrum ; Collection: CBS 432.63 ; Country: Netherlands
DOI: 10.57907/mirri/6hkfkp/3kg0ss
title: MIRRI0047994.json

description: Strain json file
DOI: 10.57907/mirri/98uigv
title: MIRRI0050588

description: Organism type: Filamentous Fungi ; Taxon name: Pochonia chlamydosporia var. catenulata ; Collection: CBS 397.69 ; Country: Canada
DOI: 10.57907/mirri/9oxsd6/g89npa
title: MIRRI0047951.json

description: Strain json file
DOI: 10.57907/mirri/ah6ru9
title: MIRRI0050620

description: Organism type: Filamentous Fungi ; Taxon name: Penicillium striatisporum ; Collection: CBS 434.69 ; Country: Turkey
DOI: 10.57907/mirri/avokcv
title: MIRRI0050590

description: Organism type: Filamentous Fungi ; Taxon name: Sagenomella diversispora ; Collection: CBS 399.69 ; Country: Canada
DOI: 10.57907/mirri/avokcv/dgcad4
title: MIRRI0050590.json

description: Strain json file
DOI: 10.57907/mirri/ayyotq
title: MIRRI0049234

description: Organism type: Filamentous Fungi ; Taxon name: Pochonia chlamydosporia var. catenulata ; Collection: CBS 504.66 ; Country: Canada
DOI: 10.57907/mirri/cwwlnn
title: MIRRI0049813

description: Organism type: Filamentous Fungi ; Taxon name: Malbranchea cinnamomea ; Collection: CBS 115.68 ; Country: China
DOI: 10.57907/mirri/dpd7kn/xofj6b
title: MIRRI0050618.json

description: Strain json file
DOI: 10.57907/mirri/e2fsvf
title: MIRRI0047990

description: Organism type: Filamentous Fungi ; Taxon name: Trichophyton mentagrophytes ; Collection: CBS 424.63 ; Country: Netherlands
DOI: 10.57907/mirri/e6bwca
title: MIRRI0050601

description: Organism type: Filamentous Fungi ; Taxon name: Penicillium gorlenkoanum ; Collection: CBS 411.69 ; Country: Syria
DOI: 10.57907/mirri/eaugrk
title: MIRRI0049294

description: Organism type: Filamentous Fungi ; Taxon name: Lecanicillium aphanocladii ; Collection: CBS 581.66 ; Country: South Africa
DOI: 10.57907/mirri/f3alwe/ltukje
title: MIRRI0049796.json

description: Strain json file
DOI: 10.57907/mirri/f4izt3
title: MIRRI0049222

description: Organism type: Filamentous Fungi ; Taxon name: Eupenicillium shearii ; Collection: CBS 488.66
DOI: 10.57907/mirri/fvs9e5
title: MIRRI0047976

description: Organism type: Filamentous Fungi ; Taxon name: Sordaria fimicola ; Collection: CBS 398.63 ; Country: Argentina
DOI: 10.57907/mirri/hhhdvl
title: MIRRI0047964

description: Organism type: Filamentous Fungi ; Taxon name: Botryotinia squamosa ; Collection: CBS 382.63 ; Country: Canada
DOI: 10.57907/mirri/ifpmj6/xl0jgm
title: MIRRI0050580.json

description: Strain json file
DOI: 10.57907/mirri/ikccza
title: MIRRI0048027

description: Organism type: Filamentous Fungi ; Taxon name: Chaetomium apiculatum ; Collection: CBS 472.63
DOI: 10.57907/mirri/ikxmni/uwhl5j
title: MIRRI0050619.json

description: Strain json file
DOI: 10.57907/mirri/kgqf9f/lq1vfb
title: MIRRI0047992.json

description: Strain json file
DOI: 10.57907/mirri/kkw9pb
title: MIRRI0049245

description: Organism type: Filamentous Fungi ; Taxon name: Epicoccum nigrum ; Collection: CBS 517.66 ; Country: Austria
DOI: 10.57907/mirri/koyhkq/swtvn1
title: MIRRI0048015.json

description: Strain json file
DOI: 10.57907/mirri/ljbv8k
title: MIRRI0049268

description: Organism type: Filamentous Fungi ; Taxon name: Mucor mucedo ; Collection: CBS 542.66 ; Country: Ukraine
DOI: 10.57907/mirri/llfhfr
title: MIRRI0049243

description: Organism type: Filamentous Fungi ; Taxon name: Arcopilus aureus ; Collection: CBS 515.66 ; Country: United States of America
DOI: 10.57907/mirri/lodsgg/mgdzox
title: MIRRI0049230.json

description: Strain json file
DOI: 10.57907/mirri/makurm/f12acg
title: MIRRI0049794.json

description: Strain json file
DOI: 10.57907/mirri/mdt6ma/l6rxec
title: MIRRI0047983.json

description: Strain json file
DOI: 10.57907/mirri/minjhn
title: MIRRI0048001

description: Organism type: Filamentous Fungi ; Taxon name: Trichophyton schoenleinii ; Collection: CBS 435.63 ; Country: Netherlands
DOI: 10.57907/mirri/mnkp6v/iil1vk
title: MIRRI0050612.json

description: Strain json file
DOI: 10.57907/mirri/n4dyma
title: MIRRI0049831

description: Organism type: Filamentous Fungi ; Taxon name: Septoria arundinacea ; Collection: CBS 133.68 ; Country: Netherlands
DOI: 10.57907/mirri/nbzpwn
title: MIRRI0050412

description: Organism type: Filamentous Fungi ; Taxon name: Rhizomucor pusillus ; Collection: CBS 179.69 ; Country: Ukraine
DOI: 10.57907/mirri/neikh1
title: MIRRI0049853

description: Organism type: Filamentous Fungi ; Taxon name: Pythium salpingophorum ; Collection: CBS 165.68 ; Country: United States of America
DOI: 10.57907/mirri/ogmmkq/8wedfy
title: MIRRI0047989.json

description: Strain json file
DOI: 10.57907/mirri/qkh8b0
title: MIRRI0050430

description: Organism type: Filamentous Fungi ; Taxon name: Mucor hiemalis f. hiemalis ; Collection: CBS 206.69 ; Country: Ukraine
DOI: 10.57907/mirri/qso3tm
title: MIRRI0050646

description: Organism type: Filamentous Fungi ; Taxon name: Cytospora acaciae ; Collection: CBS 468.69 ; Country: Spain
DOI: 10.57907/mirri/rykfw0
title: MIRRI0050638

description: Organism type: Filamentous Fungi ; Taxon name: Penicillium canescens ; Collection: CBS 458.69 ; Country: Turkey
DOI: 10.57907/mirri/tafjsz
title: MIRRI0049807

description: Organism type: Filamentous Fungi ; Taxon name: Graphium fructicola ; Collection: CBS 107.68 ; Country: Ireland
DOI: 10.57907/mirri/tftw5a
title: MIRRI0049774

description: Organism type: Filamentous Fungi ; Taxon name: Penicillium kurssanovii ; Collection: CBS 625.67 ; Country: Ukraine
DOI: 10.57907/mirri/tgz2mo/yptxky
title: MIRRI0049211.json

description: Strain json file
DOI: 10.57907/mirri/tjeiyk/iftpxi
title: MIRRI0050632.json

description: Strain json file
DOI: 10.57907/mirri/ulrge3
title: MIRRI0047967

description: Organism type: Filamentous Fungi ; Taxon name: Sclerotium hydrophilum ; Collection: CBS 385.63 ; Country: Italy
DOI: 10.57907/mirri/uxkzcu
title: MIRRI0047957

description: Organism type: Filamentous Fungi ; Taxon name: Seaverinia geranii ; Collection: CBS 374.63 ; Country: United States of America
DOI: 10.57907/mirri/ve2a9o/stmb1y
title: MIRRI0049233.json

description: Strain json file
DOI: 10.57907/mirri/wkwxup/jfmt2v
title: MIRRI0047979.json

description: Strain json file
DOI: 10.57907/mirri/xduhun
title: MIRRI0047982

description: Organism type: Filamentous Fungi ; Taxon name: Elsinoe anacardii ; Collection: CBS 404.63 ; Country: India
DOI: 10.57907/mirri/xfcdgc
title: MIRRI0050610

description: Organism type: Filamentous Fungi ; Taxon name: Ophiostoma microsporum ; Collection: CBS 420.69 ; Country: United States of America
DOI: 10.57907/mirri/xfcdgc/btmo0p
title: MIRRI0050610.json

description: Strain json file
DOI: 10.57907/mirri/yov2fh
title: MIRRI0048006

description: Organism type: Filamentous Fungi ; Taxon name: Trichophyton violaceum ; Collection: CBS 442.63 ; Country: Germany
DOI: 10.57907/mirri/znwzf3/fxaicn
title: MIRRI0047980.json

description: Strain json file