cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.jwxb.00010
title: Regulation mechanism of Sparassis latifolia polysaccharide improving glucose metabolism disorder in skeletal muscle of mice based on transcriptomics analysis-Regarding the attached tables
title: 基于转录组学分析广叶绣球菌多糖改善小鼠骨骼肌糖代谢紊乱机制-有关附表

description: Primer sequence
description: Primer sequence
DOI: 10.71830/skbcsi
title: 3D Building Metrics Germany 2023

description: EN: The dataset &lt;strong&gt;3D Building Metrics Germany 2023&lt;/strong&gt; provides building footprints for Germany, enriched with geometric properties and structural metrics. 
&lt;br&gt;
It is derived from the official &lt;strong&gt;3D building models for Germany&lt;/strong&gt; in Level of Detail 2 (LoD2) created by the &lt;i&gt;Surveying Authorities of the Laender of the Federal Republic of Germany&lt;/i&gt; and represents data for the year 2023.
&lt;br&gt;
In addition to buildings, the models also include structures such as bridges, grain silos, wind turbines, or sound barriers, which can be identified through their attributed building functions.
&lt;br&gt;
Comprising &lt;strong&gt;56,917,103 modeled buildings and structures&lt;/strong&gt;, the dataset makes information from semantic 3D building models accessible to a broader range of users in GIS environments and facilitates integration with additional datasets.
&lt;br&gt;
It supports studies of urban morphology, building stock analysis, characterization of the built environment, and strategic planning across multiple disciplines. 
&lt;br&gt;
The dataset is provided as a nationwide &lt;strong&gt;GeoPackage&lt;/strong&gt; download, with separate tables for each of Germany’s 16 federal states.
description: DE: Der Datensatz &lt;strong&gt;3D Building Metrics Germany 2023&lt;/strong&gt; enthält Gebäudegrundrisse für Deutschland, angereichert mit geometrischen Eigenschaften und strukturellen Metriken.
&lt;br&gt;
Er ist abgeleitet aus den &lt;strong&gt;amtlichen 3D-Gebäudemodellen für Deutschland&lt;/strong&gt; im Level of Detail 2 (LoD2), die von den &lt;i&gt;Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland&lt;/i&gt; erstellt wurden, und entspricht dem Datenstand für das Jahr 2023.
&lt;br&gt;
Die Modelle enthalten neben Gebäuden auch Bauwerke wie Brücken, Getreidesilos, Windkraftanlagen oder Schallschutzwände, die über die ihnen zugeordneten Gebäudefunktionen identifiziert werden können.
&lt;br&gt;
Der Datensatz umfasst &lt;strong&gt;56 917 103 modellierte Gebäude und Strukturen&lt;/strong&gt; und macht Informationen aus semantischen 3D-Gebäudemodellen einem breiten Nutzerkreis in GIS-Umgebungen zugänglich und erleichtert die Integration mit weiteren Datensätzen.
&lt;br&gt;
Er unterstützt Studien zur städtischen Morphologie, zur Analyse des Gebäudebestands, zur Charakterisierung der bebauten Umwelt und zur strategischen Planung in verschiedenen Disziplinen.
&lt;br&gt;
Der Datensatz wird als bundesweites &lt;strong&gt;GeoPackage&lt;/strong&gt; zum Download angeboten, mit separaten Tabellen für jedes der 16 deutschen Bundesländer.
DOI: 10.71830/gvngua
title: 3D Building Metrics Germany 2022

description: EN: The dataset &lt;strong&gt;3D Building Metrics Germany 2022&lt;/strong&gt; provides building footprints for Germany, enriched with geometric properties and structural metrics. 
&lt;br&gt;
It is derived from the official &lt;strong&gt;3D building models for Germany&lt;/strong&gt; in Level of Detail 2 (LoD2) created by the &lt;i&gt;Surveying Authorities of the Laender of the Federal Republic of Germany&lt;/i&gt; and represents data for the year 2022.
&lt;br&gt;
In addition to buildings, the models also include structures such as bridges, grain silos, wind turbines, or sound barriers, which can be identified through their attributed building functions.
&lt;br&gt;
Comprising &lt;strong&gt;56,377,167 modeled buildings and structures&lt;/strong&gt;, the dataset makes information from semantic 3D building models accessible to a broader range of users in GIS environments and facilitates integration with additional datasets.
&lt;br&gt;
It supports studies of urban morphology, building stock analysis, characterization of the built environment, and strategic planning across multiple disciplines. 
&lt;br&gt;
The dataset is provided as a nationwide &lt;strong&gt;GeoPackage&lt;/strong&gt; download, with separate tables for each of Germany’s 16 federal states.
description: DE: Der Datensatz &lt;strong&gt;3D Building Metrics Germany 2022&lt;/strong&gt; enthält Gebäudegrundrisse für Deutschland, angereichert mit geometrischen Eigenschaften und strukturellen Metriken.
&lt;br&gt;
Er ist abgeleitet aus den &lt;strong&gt;amtlichen 3D-Gebäudemodellen für Deutschland&lt;/strong&gt; im Level of Detail 2 (LoD2), die von den &lt;i&gt;Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland&lt;/i&gt; erstellt wurden, und entspricht dem Datenstand für das Jahr 2022.
&lt;br&gt;
Die Modelle enthalten neben Gebäuden auch Bauwerke wie Brücken, Getreidesilos, Windkraftanlagen oder Schallschutzwände, die über die ihnen zugeordneten Gebäudefunktionen identifiziert werden können.
&lt;br&gt;
Der Datensatz umfasst &lt;strong&gt;56 377 167 modellierte Gebäude und Strukturen&lt;/strong&gt; und macht Informationen aus semantischen 3D-Gebäudemodellen einem breiten Nutzerkreis in GIS-Umgebungen zugänglich und erleichtert die Integration mit weiteren Datensätzen.
&lt;br&gt;
Er unterstützt Studien zur städtischen Morphologie, zur Analyse des Gebäudebestands, zur Charakterisierung der bebauten Umwelt und zur strategischen Planung in verschiedenen Disziplinen.
&lt;br&gt;
Der Datensatz wird als bundesweites &lt;strong&gt;GeoPackage&lt;/strong&gt; zum Download angeboten, mit separaten Tabellen für jedes der 16 deutschen Bundesländer.
DOI: 10.6084/m9.figshare.30018646
title: <b>EBSD-Based Calibration of Differential Stress From Experimentally Deformed Black Hills Quartzite Using the Perimeter-Area Fractal Dimension</b>

description: The data files here have been used in Johnson et al. (2025, Journal of Geophysical Research). They are (1) two zipped files of raw EBSD data sets with 0.2 μm and 1.0 μm step sizes, (2) a MATLAB script to reconstruct grains from the EBSD data and calculate grain boundary dimension (<i>GBD</i>), (3) a MATLAB script to plot Figure 6a (comparison of piezometers) in Johnson et al. (2025), (4) an Excel file including Table 1 and Table 2 of Johnson et al. (2025), and (5) a summary Excel file including all the data used in Johnson et al. (2025).
DOI: 10.6084/m9.figshare.30018646.v6
title: <b>EBSD-Based Calibration of Differential Stress From Experimentally Deformed Black Hills Quartzite Using the Perimeter-Area Fractal Dimension</b>

description: The data files here have been used in Johnson et al. (2025, Journal of Geophysical Research). They are (1) two zipped files of raw EBSD data sets with 0.2 μm and 1.0 μm step sizes, (2) a MATLAB script to reconstruct grains from the EBSD data and calculate grain boundary dimension (<i>GBD</i>), (3) a MATLAB script to plot Figure 6a (comparison of piezometers) in Johnson et al. (2025), (4) an Excel file including Table 1 and Table 2 of Johnson et al. (2025), and (5) a summary Excel file including all the data used in Johnson et al. (2025).
DOI: 10.6084/m9.figshare.30018646.v7
title: <b>EBSD-Based Calibration of Differential Stress From Experimentally Deformed Black Hills Quartzite Using the Perimeter-Area Fractal Dimension</b>

description: The data files here have been used in Johnson et al. (2025, Journal of Geophysical Research). They are (1) two zipped files of raw EBSD data sets with 0.2 μm and 1.0 μm step sizes, (2) a MATLAB script to reconstruct grains from the EBSD data and calculate grain boundary dimension (<i>GBD</i>), (3) a MATLAB script to plot Figure 6a (comparison of piezometers) in Johnson et al. (2025), (4) an Excel file including Table 1 and Table 2 of Johnson et al. (2025), and (5) a summary Excel file including all the data used in Johnson et al. (2025).
DOI: 10.48548/pubdata-46
title: Eberswalde Carabid Monitoring 1999-2022 - Full Data

description: Eberswalde University for Sustainable Development has been sampling carabid beetles at different forest plots in the course of annually taught classes since 1995. From 1999 on, this has been done with highly standardized methods. Ground beetles were caught from 1999 to 2022, between the beginning of May and the end of July each year, on 13 forest plots at a forest area close to Eberswalde, Germany (52.820000, 13.790000). Not all plots were sampled each year and the researchers only included data from plots, which were sampled in three years or more. They used pitfall traps consisting of a 400ml glass jars positioned in a piece of PVC pipe, formaldehyde as trapping fluid and metal roofs. There were four pitfall traps at each plot, organised as either square or transect with approximately 20 meters between the traps. The layout of traps (square or transect) remained consistent for each plot throughout all sampled years. During the sampling period the traps were emptied three times, usually after four weeks (28 days), however, the exact sampling length occasionally varied. All data from traps that had been disturbed by factors such as rain, wild animals or vandalism were excluded. Carabid abundances are provided in full taxonomic resolution (species level).
description: Other
description: Main file with carabid beetle samples
description: Exact sampling dates
description: Trends for the 27 most abundant species and selected species traits
description: Calculated individual mean biomass for all species
DOI: 10.5281/zenodo.18491875
title: Simulated hourly NEE, CH4, DOC, and DIC fluxes at high-, middle-, and low-elevation sites

description: Simulated hourly NEE, CH4, DOC, and DIC fluxes at high-, middle-, and low-elevation sites
DOI: 10.5281/zenodo.18491874
title: Simulated hourly NEE, CH4, DOC, and DIC fluxes at high-, middle-, and low-elevation sites

description: Simulated hourly NEE, CH4, DOC, and DIC fluxes at high-, middle-, and low-elevation sites
DOI: 10.6084/m9.figshare.31073527
title: Groundwater Storage Changes within Europe: A Constrained Bayesian Integration of GRACE(-FO) Data into the W3RA Model

description: his dataset provides monthly estimates of <b>groundwater storage change (GWS)</b> across Europe at an effective spatial resolution of approximately <b>10 km</b>, generated using an extended <b>Constrained Bayesian (ConBay)</b> data integration framework.The ConBay framework integrates satellite gravimetric observations from <b>GRACE (2003–2017)</b> and <b>GRACE-FO (2018–present)</b> with outputs from the <b>W3RA hydrological model</b>, while additionally constraining surface soil moisture states using independent observations from the <b>ESA Climate Change Initiative (ESA CCI) soil moisture product (v09.1)</b>. This multi-source constraint improves the physical consistency between modeled and observed terrestrial water storage components and reduces equifinality in groundwater partitioning.To ensure spectral consistency, W3RA total water storage fields are transformed into the spectral domain and filtered using GRACE-consistent operators prior to assimilation. After Bayesian updating, residual components are reintroduced to enable physically coherent spatial downscaling of terrestrial water storage changes. ESA CCI soil moisture observations are incorporated as an independent constraint on surface soil moisture variability, improving consistency between surface and subsurface water storage dynamics.The resulting GWS estimates preserve the large-scale information content of GRACE while resolving sub-basin to regional-scale groundwater variability. Validation against in-situ groundwater level observations demonstrates improved representation of groundwater dynamics compared to existing gravity-based products, particularly in regions with complex hydrogeology and strong land–atmosphere coupling.<b>Key characteristics</b>Spatial coverage: EuropeSpatial resolution: ~10 kmTemporal resolution: MonthlyTime span: 2003–present (GRACE and GRACE-FO eras)Variable: Groundwater storage change (GWS)Units: [meter equivalent water height]Constraints: GRACE/GRACE-FO terrestrial water storage change; ESA CCI surface soil moisture<b>Intended use</b><br>This dataset is intended for research applications including continental-scale groundwater monitoring, hydro-climatic variability and drought assessment, model evaluation, and digital-twin development. Users should consider the intrinsic spatial smoothing of GRACE observations and the uncertainty characteristics provided with the dataset.<br><b>Citation</b><br>For the technical details, users of this dataset are requested to cite the associated publication "Exploring the Dynamics of Deep Stored Water Resources within Europe by Developing a 10-km resolution Groundwater Estimate for a Digital Twin of the Terrestrial Water Cycle".For the dataset itself, please cite the Figshare information.
DOI: 10.6084/m9.figshare.31073527.v2
title: Groundwater Storage Changes within Europe: A Constrained Bayesian Integration of GRACE(-FO) Data into the W3RA Model

description: his dataset provides monthly estimates of <b>groundwater storage change (GWS)</b> across Europe at an effective spatial resolution of approximately <b>10 km</b>, generated using an extended <b>Constrained Bayesian (ConBay)</b> data integration framework.The ConBay framework integrates satellite gravimetric observations from <b>GRACE (2003–2017)</b> and <b>GRACE-FO (2018–present)</b> with outputs from the <b>W3RA hydrological model</b>, while additionally constraining surface soil moisture states using independent observations from the <b>ESA Climate Change Initiative (ESA CCI) soil moisture product (v09.1)</b>. This multi-source constraint improves the physical consistency between modeled and observed terrestrial water storage components and reduces equifinality in groundwater partitioning.To ensure spectral consistency, W3RA total water storage fields are transformed into the spectral domain and filtered using GRACE-consistent operators prior to assimilation. After Bayesian updating, residual components are reintroduced to enable physically coherent spatial downscaling of terrestrial water storage changes. ESA CCI soil moisture observations are incorporated as an independent constraint on surface soil moisture variability, improving consistency between surface and subsurface water storage dynamics.The resulting GWS estimates preserve the large-scale information content of GRACE while resolving sub-basin to regional-scale groundwater variability. Validation against in-situ groundwater level observations demonstrates improved representation of groundwater dynamics compared to existing gravity-based products, particularly in regions with complex hydrogeology and strong land–atmosphere coupling.<b>Key characteristics</b>Spatial coverage: EuropeSpatial resolution: ~10 kmTemporal resolution: MonthlyTime span: 2003–present (GRACE and GRACE-FO eras)Variable: Groundwater storage change (GWS)Units: [meter equivalent water height]Constraints: GRACE/GRACE-FO terrestrial water storage change; ESA CCI surface soil moisture<b>Intended use</b><br>This dataset is intended for research applications including continental-scale groundwater monitoring, hydro-climatic variability and drought assessment, model evaluation, and digital-twin development. Users should consider the intrinsic spatial smoothing of GRACE observations and the uncertainty characteristics provided with the dataset.<br><b>Citation</b><br>For the technical details, users of this dataset are requested to cite the associated publication "Exploring the Dynamics of Deep Stored Water Resources within Europe by Developing a 10-km resolution Groundwater Estimate for a Digital Twin of the Terrestrial Water Cycle".For the dataset itself, please cite the Figshare information.
DOI: 10.6084/m9.figshare.31073527.v1
title: Groundwater Storage Changes within Europe: A Constrained Bayesian Integration of GRACE(-FO) Data into the W3RA Model

description: his dataset provides monthly estimates of <b>groundwater storage change (GWS)</b> across Europe at an effective spatial resolution of approximately <b>10 km</b>, generated using an extended <b>Constrained Bayesian (ConBay)</b> data integration framework.The ConBay framework integrates satellite gravimetric observations from <b>GRACE (2003–2017)</b> and <b>GRACE-FO (2018–present)</b> with outputs from the <b>W3RA hydrological model</b>, while additionally constraining surface soil moisture states using independent observations from the <b>ESA Climate Change Initiative (ESA CCI) soil moisture product (v09.1)</b>. This multi-source constraint improves the physical consistency between modeled and observed terrestrial water storage components and reduces equifinality in groundwater partitioning.To ensure spectral consistency, W3RA total water storage fields are transformed into the spectral domain and filtered using GRACE-consistent operators prior to assimilation. After Bayesian updating, residual components are reintroduced to enable physically coherent spatial downscaling of terrestrial water storage changes. ESA CCI soil moisture observations are incorporated as an independent constraint on surface soil moisture variability, improving consistency between surface and subsurface water storage dynamics.The resulting GWS estimates preserve the large-scale information content of GRACE while resolving sub-basin to regional-scale groundwater variability. Validation against in-situ groundwater level observations demonstrates improved representation of groundwater dynamics compared to existing gravity-based products, particularly in regions with complex hydrogeology and strong land–atmosphere coupling.<b>Key characteristics</b>Spatial coverage: EuropeSpatial resolution: ~10 kmTemporal resolution: MonthlyTime span: 2003–present (GRACE and GRACE-FO eras)Variable: Groundwater storage change (GWS)Units: [meter equivalent water height]Constraints: GRACE/GRACE-FO terrestrial water storage change; ESA CCI surface soil moisture<b>Intended use</b><br>This dataset is intended for research applications including continental-scale groundwater monitoring, hydro-climatic variability and drought assessment, model evaluation, and digital-twin development. Users should consider the intrinsic spatial smoothing of GRACE observations and the uncertainty characteristics provided with the dataset.<br><b>Citation</b><br>For the technical details, users of this dataset are requested to cite the associated publication "Exploring the Dynamics of Deep Stored Water Resources within Europe by Developing a 10-km resolution Groundwater Estimate for a Digital Twin of the Terrestrial Water Cycle".For the dataset itself, please cite the Figshare information.
DOI: 10.5281/zenodo.18491337
title: RT-qPCR Gene Expression Dataset for Daphnia pulex (Predator-Induced, LG, 2019)

description: This dataset contains RT-qPCR measurements of relative gene expression in the freshwater crustacean *Daphnia pulex* collected during a summer fellowship research project conducted in Japan in 2019. The study investigated transcriptional responses across developmental stages under predator-induced conditions using linoleoyl glycine (LG) as a chemical cue, compared with untreated control samples.

Gene expression was examined for a set of developmental and signaling pathway genes, including members of the **Wnt signaling pathway**, **Sox transcription factors**, and **Armadillo (β-catenin)**, which are associated with embryonic development and morphological regulation. Three life stages were analyzed: embryo, first instar, and second instar. For each life stage and treatment combination, three biological replicates were included.

Total RNA was extracted and reverse-transcribed to cDNA, and gene expression was quantified using RT-qPCR using reference gene normalization. Crossing point (Ct/Cp) values were determined using the second derivative maximum method implemented in the instrument software.

The dataset includes raw Ct values, sample metadata, and associated experimental information. These data provide insight into developmental stage–specific transcriptional responses of *Daphnia pulex* to predator-associated chemical cues and may be useful for comparative studies of inducible defense mechanisms, developmental gene regulation, and stress-related gene expression.
DOI: 10.5281/zenodo.18491338
title: RT-qPCR Gene Expression Dataset for Daphnia pulex (Predator-Induced, LG, 2019)

description: This dataset contains RT-qPCR measurements of relative gene expression in the freshwater crustacean *Daphnia pulex* collected during a summer fellowship research project conducted in Japan in 2019. The study investigated transcriptional responses across developmental stages under predator-induced conditions using linoleoyl glycine (LG) as a chemical cue, compared with untreated control samples.

Gene expression was examined for a set of developmental and signaling pathway genes, including members of the **Wnt signaling pathway**, **Sox transcription factors**, and **Armadillo (β-catenin)**, which are associated with embryonic development and morphological regulation. Three life stages were analyzed: embryo, first instar, and second instar. For each life stage and treatment combination, three biological replicates were included.

Total RNA was extracted and reverse-transcribed to cDNA, and gene expression was quantified using RT-qPCR using reference gene normalization. Crossing point (Ct/Cp) values were determined using the second derivative maximum method implemented in the instrument software.

The dataset includes raw Ct values, sample metadata, and associated experimental information. These data provide insight into developmental stage–specific transcriptional responses of *Daphnia pulex* to predator-associated chemical cues and may be useful for comparative studies of inducible defense mechanisms, developmental gene regulation, and stress-related gene expression.
DOI: 10.3929/ethz-c-000795469
title: Memristor with optically tunable dynamics
DOI: 10.6084/m9.figshare.31073497
title: Processed Global Groundwater Level (GWL) Observations in Europe

description: This dataset provides <b>processed, quality-controlled, and gap-filled monthly groundwater level (GWL) observations</b> across Europe, derived from in-situ measurements available through the <b>Global Groundwater Monitoring Network (GGMN)</b>. The dataset was developed to support large-scale groundwater analysis, model evaluation, and satellite–model integration studies.The original GWL observations were obtained from the GGMN portal and comprise heterogeneous station records with varying temporal coverage and data completeness. To ensure data reliability and representativeness at the continental scale, only monitoring stations with <b>at least 40% temporal coverage</b> over the study period were retained. Stations with shorter, highly fragmented, or insufficiently documented records were excluded.To improve temporal continuity while preserving observed variability, missing values in the retained time series were reconstructed using a <b>hybrid statistical gap-filling approach</b> that combines <b>Least Squares Principal Component Analysis (LS-PCA)</b> and <b>Iterative PCA (IPCA)</b>. This method exploits both spatial and temporal correlations among groundwater monitoring stations, enabling robust estimation of missing observations while minimizing reconstruction error and avoiding over-smoothing of groundwater dynamics.The resulting dataset provides spatially distributed, temporally consistent monthly groundwater level time series suitable for validating large-scale groundwater storage products, assessing hydro-climatic variability, and supporting hydrological and data assimilation studies. Groundwater levels are reported relative to local station reference datums; therefore, the dataset is most appropriate for analyzing <b>temporal variability, anomalies, and trends</b>, rather than absolute hydraulic head comparisons across stations.