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

description: A dataset containing 252 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Trachydoras steindachneri",
 "DecimalLatitude is not null",
 "DecimalLongitude is not null"
 ]
}

 The dataset includes 252 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009900-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18615737
title: Machine-Learning-Derived Dimethyl Sulphide (DMS) estimation for North Atlantic across 2002-2024 (2002-2009 Subset)

description: DMS Dataset for olfactory navigation and related research. The data was produced from machine learning ensemble methods, with a fine spatio-temporal resolution of 4km, daily. See the author's article for technical details and workflow.

The dataset covers 04/07/2002-22/06/2024, latitude [0, 60], and longitude [-80, 15]. 

Due to upload limitations, the daily DMS 2D fields were aggregated into monthly 3D fields with time dimensions, saved as netcdf files. The 22-year DMS dataset will be split into three uploads. For more convenient data query and annotation, the AniDMS Python package is recommended.

This upload contains data from 2002 to 2009.
DOI: 10.5281/zenodo.18615528
title: Scalable and multiplexed recorders of gene regulation dynamics across weeks

description: CytoTape-vivo recordings of transcriptional dynamics from cell populations in mouse brains in vivo.
DOI: 10.15468/dl.fxzwjm
title: Occurrence Download

description: A dataset containing 492 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Himantoglossum robertianum",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 492 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009904-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28881716
title: A hybrid method for forest aboveground biomass estimation: fusion of individual tree- and area-based approaches over northeast China

description: Currently, large-scale above-ground biomass (AGB) estimation mainly depends on the area-based approach (ABA). However, with advancements in individual tree detection technology and the availability of multi-platform remote sensing data, the individual tree-based approach (ITA) presents new opportunities for accurate, nondestructive AGB estimation. Nevertheless, research on integrating ITA with ABA for scaling AGB estimates from individual trees to the regional level remains limited. This study introduced an innovative hybrid framework incorporating ITA and ABA for regional-scale estimation of forest AGB through the combination of handheld, unmanned, and satellite LiDAR, together with Landsat 8 imagery. The results demonstrated that the segmentation of individual trees using a fusion of unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) was effective (<i>r</i> = 0.84, <i>p</i> = 0.76, F-score = 0.79) for the secondary forests in northeast China (NEC), particularly achieving high accuracy of diameter at breast height (DBH) with R<sup>2</sup> = 0.93 and RMSE of only 1.84 cm. Both individual tree and plot-level AGB estimates achieved satisfactory accuracy, with biases of  −3.9 kg and  −16,56 Mg/ha, respectively. The error-in-variable (EIV) model was established for stand-level AGB estimation using the plot AGB estimates from ITA (R<sup>2</sup> = 0.69; RMSE = 19.59 Mg/ha; rRMSE = 15.1%) and employed to estimate footprint-level AGB based on the canopy height data extracted from ICESat-2/ATL08, significantly expanding the limited sample size (by 150 times) for regional AGB estimation. Two periods (2019–2020, 2021–2022) of continuous AGB of NEC were mapped using the integrated method of random forests (RF) and empirical Bayesian Kriging (EBK). The accuracy of the RF-EBK model is markedly enhanced in comparison to that of the RF model for AGB estimation (R<sup>2</sup> increased by about 37% − 49%, RMSE and rRMSE declined by 25%). This study provides technical support for upscaling AGB estimation from individual tree to extensive forest and lays a solid foundation for precise forestry, sustainable forest management, and the attainment of carbon neutrality.
DOI: 10.6084/m9.figshare.28881716.v1
title: A hybrid method for forest aboveground biomass estimation: fusion of individual tree- and area-based approaches over northeast China

description: Currently, large-scale above-ground biomass (AGB) estimation mainly depends on the area-based approach (ABA). However, with advancements in individual tree detection technology and the availability of multi-platform remote sensing data, the individual tree-based approach (ITA) presents new opportunities for accurate, nondestructive AGB estimation. Nevertheless, research on integrating ITA with ABA for scaling AGB estimates from individual trees to the regional level remains limited. This study introduced an innovative hybrid framework incorporating ITA and ABA for regional-scale estimation of forest AGB through the combination of handheld, unmanned, and satellite LiDAR, together with Landsat 8 imagery. The results demonstrated that the segmentation of individual trees using a fusion of unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) was effective (<i>r</i> = 0.84, <i>p</i> = 0.76, F-score = 0.79) for the secondary forests in northeast China (NEC), particularly achieving high accuracy of diameter at breast height (DBH) with R<sup>2</sup> = 0.93 and RMSE of only 1.84 cm. Both individual tree and plot-level AGB estimates achieved satisfactory accuracy, with biases of  −3.9 kg and  −16,56 Mg/ha, respectively. The error-in-variable (EIV) model was established for stand-level AGB estimation using the plot AGB estimates from ITA (R<sup>2</sup> = 0.69; RMSE = 19.59 Mg/ha; rRMSE = 15.1%) and employed to estimate footprint-level AGB based on the canopy height data extracted from ICESat-2/ATL08, significantly expanding the limited sample size (by 150 times) for regional AGB estimation. Two periods (2019–2020, 2021–2022) of continuous AGB of NEC were mapped using the integrated method of random forests (RF) and empirical Bayesian Kriging (EBK). The accuracy of the RF-EBK model is markedly enhanced in comparison to that of the RF model for AGB estimation (R<sup>2</sup> increased by about 37% − 49%, RMSE and rRMSE declined by 25%). This study provides technical support for upscaling AGB estimation from individual tree to extensive forest and lays a solid foundation for precise forestry, sustainable forest management, and the attainment of carbon neutrality.
DOI: 10.6084/m9.figshare.28034699
title: Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach

description: The seagrass ecosystems are among the most important organic carbon sinks on Earth, having a key role as climate change buffers. Among all seagrasses, <i>Posidonia oceanica</i>, an endemic seagrass species in the Mediterranean Sea, has been observed to feature the highest carbon stock and sequestration rate among all seagrasses. We developed a satellite-based workflow to complement <i>in situ</i> seagrass monitoring efforts in the Balearic Islands (Western Mediterranean), reducing field expenses while covering regional spatial scales. Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands’ coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. Machine learning algorithms have been applied to perform seagrass detection, obtaining a seagrass cartography up to 30 m of depth, estimating 505.6 km<sup>2</sup> of seagrass habitat extent. Using existing <i>in situ</i> soil carbon stock (C<sub>stock</sub>) data, we estimated a mean C<sub>stock</sub> value of 12.27 ± 2.1 million megagram (Mg) C<sub>org</sub>, while mapping a total annual C fixation (C<sub>fix</sub>) and C sequestration (C<sub>seq</sub>) rates of <i>P. oceanica</i> of 1,116.3 Mg C<sub>org</sub> and 227 Mg C<sub>org</sub>, according to depth. Our methodology highlights the key role of using a large image archive to generate the multi-temporal optical composite and an optimized bathymetry dataset to better map and account blue carbon in seagrass ecosystems across depth, showing the importance to integrate this Earth Observation approach to ensure a seagrass ecosystem monitoring at regional scales. This information aims to support the development of blue carbon strategies with synoptic time- and cost-efficient seagrass monitoring in the Mediterranean Sea.
DOI: 10.6084/m9.figshare.28034699.v1
title: Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach

description: The seagrass ecosystems are among the most important organic carbon sinks on Earth, having a key role as climate change buffers. Among all seagrasses, <i>Posidonia oceanica</i>, an endemic seagrass species in the Mediterranean Sea, has been observed to feature the highest carbon stock and sequestration rate among all seagrasses. We developed a satellite-based workflow to complement <i>in situ</i> seagrass monitoring efforts in the Balearic Islands (Western Mediterranean), reducing field expenses while covering regional spatial scales. Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands’ coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. Machine learning algorithms have been applied to perform seagrass detection, obtaining a seagrass cartography up to 30 m of depth, estimating 505.6 km<sup>2</sup> of seagrass habitat extent. Using existing <i>in situ</i> soil carbon stock (C<sub>stock</sub>) data, we estimated a mean C<sub>stock</sub> value of 12.27 ± 2.1 million megagram (Mg) C<sub>org</sub>, while mapping a total annual C fixation (C<sub>fix</sub>) and C sequestration (C<sub>seq</sub>) rates of <i>P. oceanica</i> of 1,116.3 Mg C<sub>org</sub> and 227 Mg C<sub>org</sub>, according to depth. Our methodology highlights the key role of using a large image archive to generate the multi-temporal optical composite and an optimized bathymetry dataset to better map and account blue carbon in seagrass ecosystems across depth, showing the importance to integrate this Earth Observation approach to ensure a seagrass ecosystem monitoring at regional scales. This information aims to support the development of blue carbon strategies with synoptic time- and cost-efficient seagrass monitoring in the Mediterranean Sea.
DOI: 10.6084/m9.figshare.30021706
title: Localized deformation detection and prediction in wide-range urban environments

description: Recent increases in acquisition and availability of SAR images have significantly advanced deformation monitoring. An existing challenge includes wide-area time-series InSAR (TSInSAR) analyses that demand high-performance computing resources and extensive storage capacity. Moreover, a clear definition of localized deformations and established methods for their detection are missing, and TSInSAR results are time-intensive to deliver. To address these challenges, this paper proposes to first generate wide-area TSInSAR results using PS-based concatenation. This is followed by a hierarchical classification to preliminarily identify regions of uplift or subsidence, combined with the Getis-Ord Gi∗ statistic. High-resolution network (HRNet) models are then applied for deformation prediction. We tested this process on area covering parts of the Netherlands, Germany, and Belgium. Three Sentinel-1 SAR frames were used, i.e. Frame 415 and 420 on Path 37 and Frame 421 on Path 110, each comprising 60 SAR acquisitions from June 2017 to June 2019. Compared to leveling data, the concatenated TSInSAR results demonstrated higher precision, with a mean value difference (MVD) of 0.2 mm, root mean square error (RMSE) of 0.1 mm, while the non-concatenated results were 1.8 mm and 1.7 mm, respectively. The hierarchical classification combined with Gi∗ successfully identified localized deformation regions. Additionally, the HRNet model, trained on benchmark TSInSAR datasets, accurately predicted deformations using newly wrapped interferograms (IFGs). In conclusion, this study presents an efficient solution for generating wide-ranging TSInSAR results and offers robust methods for detecting and analyzing localized deformation. Multi-scene integration is built upon StaMPS.A strategy is defined to detect the localized deformation.HRNet in wrapped IFG predicts deformation. Multi-scene integration is built upon StaMPS. A strategy is defined to detect the localized deformation. HRNet in wrapped IFG predicts deformation.
DOI: 10.6084/m9.figshare.30021706.v1
title: Localized deformation detection and prediction in wide-range urban environments

description: Recent increases in acquisition and availability of SAR images have significantly advanced deformation monitoring. An existing challenge includes wide-area time-series InSAR (TSInSAR) analyses that demand high-performance computing resources and extensive storage capacity. Moreover, a clear definition of localized deformations and established methods for their detection are missing, and TSInSAR results are time-intensive to deliver. To address these challenges, this paper proposes to first generate wide-area TSInSAR results using PS-based concatenation. This is followed by a hierarchical classification to preliminarily identify regions of uplift or subsidence, combined with the Getis-Ord Gi∗ statistic. High-resolution network (HRNet) models are then applied for deformation prediction. We tested this process on area covering parts of the Netherlands, Germany, and Belgium. Three Sentinel-1 SAR frames were used, i.e. Frame 415 and 420 on Path 37 and Frame 421 on Path 110, each comprising 60 SAR acquisitions from June 2017 to June 2019. Compared to leveling data, the concatenated TSInSAR results demonstrated higher precision, with a mean value difference (MVD) of 0.2 mm, root mean square error (RMSE) of 0.1 mm, while the non-concatenated results were 1.8 mm and 1.7 mm, respectively. The hierarchical classification combined with Gi∗ successfully identified localized deformation regions. Additionally, the HRNet model, trained on benchmark TSInSAR datasets, accurately predicted deformations using newly wrapped interferograms (IFGs). In conclusion, this study presents an efficient solution for generating wide-ranging TSInSAR results and offers robust methods for detecting and analyzing localized deformation. Multi-scene integration is built upon StaMPS.A strategy is defined to detect the localized deformation.HRNet in wrapped IFG predicts deformation. Multi-scene integration is built upon StaMPS. A strategy is defined to detect the localized deformation. HRNet in wrapped IFG predicts deformation.
DOI: 10.6084/m9.figshare.30517924
title: Hydrographic variations on decadal to longer timescales in the kuroshio extension and kuroshio-oyashio confluence

description: The variability of the Kuroshio Extension (KE) system, modulated by large-scale atmospheric forcing, critically influences North Pacific climate responses. However, despite the ongoing unprecedented northward KE shift, the low-frequency modulations on hydrography within the KE system and their impacts on the Kuroshio-Oyashio Confluence (KOC) region remain unclear. We analyzed hydrographic properties and mesoscale activities in the KE-KOC system and evaluated the effects of atmospheric forcing using satellite and reanalysis data from 1993 to 2023. Our findings indicate a poleward KE shift (0.45 ± 0.02°/decade) and increases in Sea Level Anomaly (SLA, 8.07 ± 0.30 cm/decade in the KE, 2.45 ± 0.27 cm/decade in the KOC) driven by negative wind stress curl (WSC) trends across the mid-latitude North Pacific. Following 2010, the KE system transitioned to a predominantly stable state, except for 2016–2017. The KOC region experienced enhanced eddy kinetic energy during convoluted KE meander phases (2021–2023), indicating baroclinic instability-driven energy transfer. The enhanced negative WSC anomalies north of 40°N and positive anomalies southward created a meridional dipole pattern in the eastern North Pacific after 2010. Accompanied by this shift, westward Rossby wave propagation was disrupted by some mesoscale signals, altering negative phase relationships between KE dynamics and climate indices to near-zero lags, resulting in the synchronized record-high SLA both in the KE and eastern North Pacific, coinciding with extreme negative Pacific Decadal Oscillation conditions during 2019–2023. These results reveal a new state of the North Pacific climate system and highlight the KE-KOC system’s accelerated adjustment to atmospheric forcing.
DOI: 10.6084/m9.figshare.30517924.v3
title: Hydrographic variations on decadal to longer timescales in the kuroshio extension and kuroshio-oyashio confluence

description: The variability of the Kuroshio Extension (KE) system, modulated by large-scale atmospheric forcing, critically influences North Pacific climate responses. However, despite the ongoing unprecedented northward KE shift, the low-frequency modulations on hydrography within the KE system and their impacts on the Kuroshio-Oyashio Confluence (KOC) region remain unclear. We analyzed hydrographic properties and mesoscale activities in the KE-KOC system and evaluated the effects of atmospheric forcing using satellite and reanalysis data from 1993 to 2023. Our findings indicate a poleward KE shift (0.45 ± 0.02°/decade) and increases in Sea Level Anomaly (SLA, 8.07 ± 0.30 cm/decade in the KE, 2.45 ± 0.27 cm/decade in the KOC) driven by negative wind stress curl (WSC) trends across the mid-latitude North Pacific. Following 2010, the KE system transitioned to a predominantly stable state, except for 2016–2017. The KOC region experienced enhanced eddy kinetic energy during convoluted KE meander phases (2021–2023), indicating baroclinic instability-driven energy transfer. The enhanced negative WSC anomalies north of 40°N and positive anomalies southward created a meridional dipole pattern in the eastern North Pacific after 2010. Accompanied by this shift, westward Rossby wave propagation was disrupted by some mesoscale signals, altering negative phase relationships between KE dynamics and climate indices to near-zero lags, resulting in the synchronized record-high SLA both in the KE and eastern North Pacific, coinciding with extreme negative Pacific Decadal Oscillation conditions during 2019–2023. These results reveal a new state of the North Pacific climate system and highlight the KE-KOC system’s accelerated adjustment to atmospheric forcing.
DOI: 10.6084/m9.figshare.30517924.v2
title: Hydrographic variations on decadal to longer timescales in the kuroshio extension and kuroshio-oyashio confluence

description: The variability of the Kuroshio Extension (KE) system, modulated by large-scale atmospheric forcing, critically influences North Pacific climate responses. However, despite the ongoing unprecedented northward KE shift, the low-frequency modulations on hydrography within the KE system and their impacts on the Kuroshio-Oyashio Confluence (KOC) region remain unclear. We analyzed hydrographic properties and mesoscale activities in the KE-KOC system and evaluated the effects of atmospheric forcing using satellite and reanalysis data from 1993 to 2023. Our findings indicate a poleward KE shift (0.45 ± 0.02°/decade) and increases in Sea Level Anomaly (SLA, 8.07 ± 0.30 cm/decade in the KE, 2.45 ± 0.27 cm/decade in the KOC) driven by negative wind stress curl (WSC) trends across the mid-latitude North Pacific. Following 2010, the KE system transitioned to a predominantly stable state, except for 2016–2017. The KOC region experienced enhanced eddy kinetic energy during convoluted KE meander phases (2021–2023), indicating baroclinic instability-driven energy transfer. The enhanced negative WSC anomalies north of 40°N and positive anomalies southward created a meridional dipole pattern in the eastern North Pacific after 2010. Accompanied by this shift, westward Rossby wave propagation was disrupted by some mesoscale signals, altering negative phase relationships between KE dynamics and climate indices to near-zero lags, resulting in the synchronized record-high SLA both in the KE and eastern North Pacific, coinciding with extreme negative Pacific Decadal Oscillation conditions during 2019–2023. These results reveal a new state of the North Pacific climate system and highlight the KE-KOC system’s accelerated adjustment to atmospheric forcing.
DOI: 10.6084/m9.figshare.28777154
title: An interpretable physics-informed deep learning model for estimating multiple air pollutants

description: Accurate and effective monitoring of atmospheric components is urgently needed due to the compound air pollution in China. Despite numerous efforts to estimate various air pollutant concentrations, the focus on individual pollutants has overlooked their interactions, thereby limiting estimation accuracy. In addition, the use of shared data and model structures in these studies has led to inefficiencies due to duplicated efforts. Moreover, widely applied machine learning models have demonstrated a lack of interpretability and a weak physical basis. To address these issues, this study developed an interpretable physics-informed deep neural network to estimate multiple air pollutants simultaneously (IPMDNN). The model inputs were initially categorized based on prior atmospheric knowledge. To account for the varying impact of each input on different pollutants, a tanh-based self-attention mechanism was followed to dynamically adjust their contributions. A deep interaction module was then introduced to capture the complex physicochemical interactions among pollutants. To ensure consistency with physical laws, a physics-constrained loss function was incorporated. Finally, layer-wise relevance propagation was employed to interpret the model results. Experiments conducted in China in 2019 and 2020 demonstrated that the proposed model effectively estimated ozone (O<sub>3</sub>) and particulate matter (PM<sub>2.5</sub> and PM<sub>10</sub>) concentrations simultaneously. Compared with separate estimation models, the proposed model achieved higher sample-based cross-validation R<sup>2</sup> values of 0.92, 0.90, and 0.87 for O<sub>3</sub>, PM<sub>2.5</sub>, and PM<sub>10</sub>, respectively, and demonstrated more than twice the efficiency. Furthermore, the model interpretation identified formaldehyde, carbon monoxide, hydroxyl radical, and temperature as key contributors in the joint estimation. Mapping results derived from the model highlighted severe O<sub>3</sub> and PM pollution in East, Central, and North China. In terms of seasonal trends, severe O<sub>3</sub> pollution was observed in the summer, whereas high levels of PM pollution occurred in the winter. These findings suggested that the proposed model, with its high accuracy and efficiency, could provide valuable insights for the coordinated control of air pollution in China.