cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.u2xjb3
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-111.61623 32.65312,-109.56717 32.65312,-109.56717 34.49282,-111.61623 34.49282,-111.61623 32.65312))",
 "TaxonKey is Sonorella magdalenensis"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001933-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0189893.10fu
title: SH0189893.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0189894.10fu
title: SH0189894.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0189895.10fu
title: SH0189895.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0189896.10fu
title: SH0189896.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0189897.10fu
title: SH0189897.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.25346/s6/cyfvlb/fjttrp
title: rbspa_ect-elec-L4_20180904_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/8syvkl
title: rbspa_ect-elec-L4_20171101_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/qhdafr
title: rbspa_ect-elec-L4_20170703_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/erbcca
title: rbspa_ect-elec-L4_20170624_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/pdakt1
title: rbspa_ect-elec-L4_20170824_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/dxbgcn
title: rbspa_ect-elec-L4_20170628_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/67tmb7
title: rbspa_ect-elec-L4_20170205_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/uwqgsq
title: rbspa_ect-elec-L4_20171110_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/yvtzf9
title: rbspa_ect-elec-L4_20171205_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/fahbq9
title: rbspa_ect-elec-L4_20170623_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/m7xo3f
title: rbspa_ect-elec-L4_20170909_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/kp8f3u
title: rbspa_ect-elec-L4_20171227_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/uvwx4c
title: rbspa_ect-elec-L4_20170427_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/xcea29
title: rbspa_ect-elec-L4_20171217_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ijknic
title: rbspa_ect-elec-L4_20171216_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/gvvind
title: rbspa_ect-elec-L4_20170304_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/fio4ua
title: rbspa_ect-elec-L4_20170329_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ltddz8
title: rbspa_ect-elec-L4_20171125_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ma2lg3
title: rbspa_ect-elec-L4_20170202_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/wdchdv
title: rbspa_ect-elec-L4_20170116_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/suzgm0
title: rbspa_ect-elec-L4_20171023_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/xj0vo3
title: rbspa_ect-elec-L4_20171231_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/n9q60t
title: rbspa_ect-elec-L4_20170212_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/zliz2t
title: rbspa_ect-elec-L4_20170428_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/t8gfmf
title: rbspa_ect-elec-L4_20170530_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/0b9tuz
title: rbspa_ect-elec-L4_20170505_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/4m9gic
title: rbspa_ect-elec-L4_20170430_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/qavyxr
title: rbspa_ect-elec-L4_20171124_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/x8x91j
title: rbspa_ect-elec-L4_20170801_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/9lzv2a
title: rbspa_ect-elec-L4_20171029_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/juydbm
title: rbspa_ect-elec-L4_20170307_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ljitoz
title: rbspa_ect-elec-L4_20170912_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/92pnp4
title: rbspa_ect-elec-L4_20170315_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/du1pgy
title: rbspa_ect-elec-L4_20171014_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/97hkhi
title: rbspa_ect-elec-L4_20170531_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/7nvgle
title: rbspa_ect-elec-L4_20171118_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/thd683
title: rbspa_ect-elec-L4_20170609_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/wdgrto
title: rbspa_ect-elec-L4_20171224_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/eytwj6
title: rbspa_ect-elec-L4_20170123_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/yzfnqn
title: rbspa_ect-elec-L4_20170619_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ub378g
title: rbspa_ect-elec-L4_20170110_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/tzxsdu
title: rbspa_ect-elec-L4_20170129_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ntlzq1
title: rbspa_ect-elec-L4_20170312_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/0qlmhk
title: rbspa_ect-elec-L4_20170821_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/s8prqq
title: rbspa_ect-elec-L4_20170416_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/s6zqut
title: rbspa_ect-elec-L4_20170405_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/kihcwj
title: rbspa_ect-elec-L4_20170818_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/iod4uk
title: rbspa_ect-elec-L4_20170228_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/edrwqv
title: rbspa_ect-elec-L4_20170701_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/jt3tkq
title: rbspa_ect-elec-L4_20170709_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/jggp3k
title: rbspa_ect-elec-L4_20170607_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/rutitc
title: rbspa_ect-elec-L4_20170327_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/q1rgjk
title: rbspa_ect-elec-L4_20171223_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/fp2q80
title: rbspa_ect-elec-L4_20171127_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ghtluj
title: rbspa_ect-elec-L4_20171031_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/muizzb
title: rbspa_ect-elec-L4_20171130_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/7ml06k
title: rbspa_ect-elec-L4_20171020_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/tafxbz
title: rbspa_ect-elec-L4_20170726_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/7giwog
title: rbspa_ect-elec-L4_20171018_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/te2llx
title: rbspa_ect-elec-L4_20170224_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ofcahy
title: rbspa_ect-elec-L4_20171010_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/bmtzaq
title: rbspa_ect-elec-L4_20170325_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/b7ili7
title: rbspa_ect-elec-L4_20170526_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/fylq2y
title: rbspa_ect-elec-L4_20171201_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/scmnw3
title: rbspa_ect-elec-L4_20171026_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/l8omf8
title: rbspa_ect-elec-L4_20170529_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ooarso
title: rbspa_ect-elec-L4_20171122_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ocvqeq
title: rbspa_ect-elec-L4_20170807_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/rmzm4r
title: rbspa_ect-elec-L4_20170114_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/jwwuhb
title: rbspa_ect-elec-L4_20171103_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/2funfw
title: rbspa_ect-elec-L4_20170201_v1.1.0.cdf
DOI: 10.15468/dl.wv2gdh
title: Occurrence Download

description: A dataset containing 433 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-127.29681 26.10911,-93.2242 26.10911,-93.2242 54.79349,-127.29681 54.79349,-127.29681 26.10911))",
 "OccurrenceStatus is Present",
 "VerbatimScientificName is Rhaponticum repens"
 ]
}

 The dataset includes 433 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016929-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6/0ftezh/pxb31c
title: rbspa_ect-elec-L4_20170331_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/c9i6up
title: rbspa_ect-elec-L4_20171206_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/rvrirx
title: rbspa_ect-elec-L4_20170330_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh/ss8jqw
title: rbspa_ect-elec-L4_20170720_v1.1.0.cdf
DOI: 10.25346/s6/0ftezh
title: PSD_ECT_DATA_2017

description: This is the PSD ect dataset for the Van Allen Probes for 2017
DOI: 10.25346/s6/0ftezh/9ncoc2
title: rbspa_ect-elec-L4_20170219_v1.1.0.cdf
DOI: 10.5066/p1nxfglv
title: Modeled habitat suitability for five rare plants (Aliciella formosa, Sclerocactus cloverae, Townsendia gypsophila, Astragalus ripleyi, and Cymopterus spellenbergii) in New Mexico

description: This data bundle contains the final outputs from a VisTrails/SAHM workflow to model the potential distribution of 5 rare plants (Aliciella formosa, Sclerocactus cloverae, Townsendia gypsophila, Astragalus ripleyi, and Cymopterus spellenbergii) in northern New Mexico. These models utilized field data of spatially thinned occurrence locations and random background locations or random plus absence locations for the 5 species. Predictors included but were not limited to soil characteristics, topography, percent tree cover, bare ground, and continuous heat-insolation load index rasters. Details about both occurrence data and predictor inputs are included in the associated manuscript and Source Info section of this metadata. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2], and combined these to create an ensemble of models for each species. For more information on the model creation process and interpretation of the final maps, see "Process Step" section.

The bundle documentation files are: 1) 'NMrareplant_SDM_project_metadata.xml' (this file) which contains FGDC metadata describing the archive bundle. 2) Ensemble geotiff for each of the 5 modeled species: 'Code_vX_HML.tif' where code is the first two letters of the genus and species and X is the iteration of the final model product. 3) Tailored raster predictor layers not otherwise publicly available that were used during the modeling process and their corresponding metadata.
DOI: 10.15468/dl.dp7j6x
title: Occurrence Download

description: A dataset containing 31327 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Carpobrotus edulis (L.) N.E.Br."
 ]
}

 The dataset includes 31327 records from 349 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016928-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p145pcru
title: Rates of change in invasive annual grass cover to inform potential management opportunities across the sagebrush biome of the western United States

description: We used Rangeland Condition Monitoring Assessment and Projection (RCMAP) maps of annual herbaceous fractional components (mostly invasive annual grasses) to calculate mean rate of change in invasive annual grass cover over five-year time periods (ratesOfChange1987_2021.zip). We also created a map that identifies zones of the sagebrush biome that could be prioritized for different management goals (managementOpportunities.zip). The invasion of annual grasses has altered fire regimes and has contributed to the decline of sagebrush ecosystems. The vast expanse of annual grass invasions has required land managers to prioritize treatments in locations where they expect to be able to make a meaningful impact on invasion outcomes. Maps of invasive annual grass cover are useful in that they show the extent and severity of the invasion, but on their own, cover maps do not illustrate context such as how invasive cover is changing over time (i.e., increasing, stable, decreasing). The rate of change in invasive annual grass cover describes the trajectory of invasion. This information can be used by land managers to fine-tune priority locations and strategies for invasive species treatments in addition to other data sources (e.g., invasive annual grass cover maps).
DOI: 10.5281/zenodo.7743186
title: Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the South China Sea (2003-2019)

description: The South China Sea (SCS) is one of the largest marginal seas worldwide. It includes a river-dominated, highly productive marginal sea on the north shelf and a wide, oligotrophic ocean-dominated basin with various dynamic sub-regions. Based on an in situ seawater partial pressure of CO2 (pCO2) datasets of 44 cruises/legs collected for the last two decades in the SCS, we proposed a seawater pCO2 retrieval algorithm by combining the semi-mechanistic and machine learning (ML) methods (MeSAA-ML). The parameter selection strategy was based on the mechanistic analysis of pCO2 variation, separating impacts of thermodynamics, biological activities, water mixing, and the atmospheric CO2 forcing. We set a few semi-analytical parameters: pCO2_therm, which was a proxy for the combined effect of thermodynamics and the atmospheric CO2 forcing on seawater pCO2; an upwelling index (UISST) and mixing layer depth (MLD) to characterize the multiple mixing processes; chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm (Rrs(443) and Rrs(555)), which were the inputs to proxy the biological effect and other characteristics for distinguishing shelf, basin, and sub-regions. As the seawater pCO2 and atmospheric pCO2 ( pCO2air) have similar data values and characteristics in the vast SCS oligotrophic basin, it will cause instability of the model if one is input and the other is output; thus the difference between them (ΔpCO2sea-air) was set as the output, and the seawater pCO2 was obtained finally by summing pCO2air and ΔpCO2sea-air. We compared several ML models, and the XGBoost model was confirmed as the best model. Completely independent cruise-based and observed datasets from Southeastern Asia Time-series Study (SEATS) were used to validate the satellite products, with low root mean square error (RMSE = 11.69 μatm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of satellite-derived pCO2 (2.44 ± 0.24 μatm/yr) at the location of SEATS was found to be consistent with observed data. We presented that the SCS as a whole is a source of atmospheric CO2, releasing an average of 11.00 ± 2.45 Tg C/yr from a total area of 3.32 × 106 km2, and the northern shelf is a sink (1.69 ± 0.53 Tg C/yr). The area-integrated CO2 efflux over the entire SCS may decrease with a rate of 0.34 Tg C/yr during 2003–2019. This high-accuracy dataset with 1 km resolution provides a refined understanding of the air-sea CO2 exchange dynamics in the SCS during 2003–2019.
description: Reference

Song, Z., Yu, S., Bai, Y.*, Guo, X., He, X., Zhai, W., & Dai, M. (2023). Construction of a high spatiotemporal resolution dataset of satellite-derived pCO2 and air-sea CO2 flux in the South China Sea (2003-2019). IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2023.3306389.

For any questions and/or file corruption/missing, feel free to contact the data producer at yushujie@sio.org.cn.
DOI: 10.5281/zenodo.7743187
title: Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the South China Sea (2003-2019)

description: The South China Sea (SCS) is one of the largest marginal seas worldwide. It includes a river-dominated, highly productive marginal sea on the north shelf and a wide, oligotrophic ocean-dominated basin with various dynamic sub-regions. Based on an in situ seawater partial pressure of CO2 (pCO2) datasets of 44 cruises/legs collected for the last two decades in the SCS, we proposed a seawater pCO2 retrieval algorithm by combining the semi-mechanistic and machine learning (ML) methods (MeSAA-ML). The parameter selection strategy was based on the mechanistic analysis of pCO2 variation, separating impacts of thermodynamics, biological activities, water mixing, and the atmospheric CO2 forcing. We set a few semi-analytical parameters: pCO2_therm, which was a proxy for the combined effect of thermodynamics and the atmospheric CO2 forcing on seawater pCO2; an upwelling index (UISST) and mixing layer depth (MLD) to characterize the multiple mixing processes; chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm (Rrs(443) and Rrs(555)), which were the inputs to proxy the biological effect and other characteristics for distinguishing shelf, basin, and sub-regions. As the seawater pCO2 and atmospheric pCO2 ( pCO2air) have similar data values and characteristics in the vast SCS oligotrophic basin, it will cause instability of the model if one is input and the other is output; thus the difference between them (ΔpCO2sea-air) was set as the output, and the seawater pCO2 was obtained finally by summing pCO2air and ΔpCO2sea-air. We compared several ML models, and the XGBoost model was confirmed as the best model. Completely independent cruise-based and observed datasets from Southeastern Asia Time-series Study (SEATS) were used to validate the satellite products, with low root mean square error (RMSE = 11.69 μatm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of satellite-derived pCO2 (2.44 ± 0.24 μatm/yr) at the location of SEATS was found to be consistent with observed data. We presented that the SCS as a whole is a source of atmospheric CO2, releasing an average of 11.00 ± 2.45 Tg C/yr from a total area of 3.32 × 106 km2, and the northern shelf is a sink (1.69 ± 0.53 Tg C/yr). The area-integrated CO2 efflux over the entire SCS may decrease with a rate of 0.34 Tg C/yr during 2003–2019. This high-accuracy dataset with 1 km resolution provides a refined understanding of the air-sea CO2 exchange dynamics in the SCS during 2003–2019.
description: Reference

Song, Z., Yu, S., Bai, Y.*, Guo, X., He, X., Zhai, W., & Dai, M. (2023). Construction of a high spatiotemporal resolution dataset of satellite-derived pCO2 and air-sea CO2 flux in the South China Sea (2003-2019). IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2023.3306389.

For any questions and/or file corruption/missing, feel free to contact the data producer at yushujie@sio.org.cn.