cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28219109
title: Raw data for "Low-rank geostatistical inverse modeling of spatiotemporal heterogeneity in aquitard hydraulic parameters"

description: Many hydrogeological processes are intricately linked to both spatial heterogeneity and temporal dynamics of the involved parameters. The variability in hydrogeological parameters, across both space and time domains, is defined as the spatiotemporal heterogeneity. This study introduces a partitioning geostatistical inversion approach to characterize the spatiotemporal heterogeneity, and further applies this methodology to the long-term multi-extensometer data from the second aquitard beneath Qingliang Primary School, Changzhou City, China. The Principal Component Geostatistical Approach (PCGA) is used to capture the temporal variability in the hydraulic conductivity (K) and specific storage (Ss) across the three sub-layers of the aquitard of interest. Comparative analysis reveals that models accounting for spatiotemporal heterogeneity leads to significantly improved accuracy compared to those considering only spatial heterogeneity. PCGA effectively captures the temporal variability in aquitard K and Ss, with sub-layer 3 playing a dominant role in the inverted temporal dynamics. Additionally, neglecting the initially delayed drainage within the aquitard leads to a notable underestimation of K. Moreover, utilizing a stage-wise distribution of parameters as an initial guess significantly enhances inversion accuracy. The proposed methodology not only deepens the understanding of aquitard deformation but also holds broad potential for advancing the characterization of spatiotemporal heterogeneity in 4D hydrogeological modeling.
DOI: 10.6084/m9.figshare.28219109.v2
title: Raw data for "Low-rank geostatistical inverse modeling of spatiotemporal heterogeneity in aquitard hydraulic parameters"

description: Many hydrogeological processes are intricately linked to both spatial heterogeneity and temporal dynamics of the involved parameters. The variability in hydrogeological parameters, across both space and time domains, is defined as the spatiotemporal heterogeneity. This study introduces a partitioning geostatistical inversion approach to characterize the spatiotemporal heterogeneity, and further applies this methodology to the long-term multi-extensometer data from the second aquitard beneath Qingliang Primary School, Changzhou City, China. The Principal Component Geostatistical Approach (PCGA) is used to capture the temporal variability in the hydraulic conductivity (K) and specific storage (Ss) across the three sub-layers of the aquitard of interest. Comparative analysis reveals that models accounting for spatiotemporal heterogeneity leads to significantly improved accuracy compared to those considering only spatial heterogeneity. PCGA effectively captures the temporal variability in aquitard K and Ss, with sub-layer 3 playing a dominant role in the inverted temporal dynamics. Additionally, neglecting the initially delayed drainage within the aquitard leads to a notable underestimation of K. Moreover, utilizing a stage-wise distribution of parameters as an initial guess significantly enhances inversion accuracy. The proposed methodology not only deepens the understanding of aquitard deformation but also holds broad potential for advancing the characterization of spatiotemporal heterogeneity in 4D hydrogeological modeling.
DOI: 10.15468/dl.djkqm9
title: Occurrence Download

description: A dataset containing 7351164 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Amphibia",
 "Year 2000-2025"
 ]
}

 The dataset includes 7351164 records from 7293 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019719-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/3sdgb-sj961
title: Hippocampus Neurons

description: Hippocapmus mNeon-KDELnew DNA (1.6ug/ul)Quantifoil R2/2 on NH2 Au 200 mesh Extra thick carbon Cat# R2/2-LF-AU-NH2Low (grids) to high (dish) transferring
description: <strong>Tilt Series Date:</strong> 2020-02-16</p>
<strong>Data Taken By:</strong> Stephen Carter</p>
<strong>Species / Specimen:</strong> Rat Hippocampal Neuron (cultured)</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 120.0 eV/Å²,  defocus: -3.0 μm,  magnification: 33000x. </p>
<strong>Acquisition Software:</strong> SerialEM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> IMOD</p>
<strong>Collaborators and Roles:</strong> Zach Freyberg</p>
<strong>Purification / Growth Conditions / Treatment:</strong> ssociated rat cortical and hippocampal neurons were prepared from embryonic day 18 (E18) Long-Evans rats (Charles River Laboratories, Wilmington, MA). Neurons were plated onto 35-mm- diameter circular glass-bottom dishes (MatTek, Ashland, MA) coated with poly-D-lysine (20 mg/ml) and laminin (3.4 mg/ml) at a density of 3.25 × 105 cells/ml (total volume, 2 ml). The neurons were maintained in neurobasal medium supplemented with B27, penicillin/streptomycin (100 U/ml and 100 mg/ml, respectively), and 2 mM glutamine. One-fifth of the medium in each dish was replaced every 4 days.</p>
<strong>Sample Preparation:</strong> Manual Blotting</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/scd2020-02-16-4/rawdata/</p>neuron11.mrc,  Tilt Series (Pixel Size 2.65 nm), 2.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/scd2020-02-16-4/rawdata/neuron11.mrc"
        > <i class="download icon"></i> Download </a></p>

        neuron11_part58_6.rec,  Reconstruction (Pixel Size 10.6 nm), 1.5 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/scd2020-02-16-4/3dimage_118753/neuron11_part58_6.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.57760/sciencedb.29491
title: How do relational networks influence team innovation? The role of knowledge activities
title: How do relational networks influence team innovation? The role of knowledge activities

description: Although interest in team innovation continues to grow, existing research lacks a comprehensive understanding of how relational networks facilitate team innovation, particularly through the pathways and mechanisms of knowledge activities. This study examines the critical role of relational networks in promoting team innovation. Drawing on the theory of strong and weak ties and the SECI model of knowledge creation, it analyzes the relationships among strong/weak ties, knowledge sharing, knowledge acquisition, and team innovation. The research not only investigates how strong and weak ties promote knowledge sharing, knowledge acquisition, and team innovation but also explores how knowledge sharing and knowledge acquisition mediate and moderate the influence of strong/weak ties on team innovation. Based on an empirical analysis of 288 samples in China, the results indicate that strong and weak ties significantly enhance knowledge sharing and knowledge acquisition, thereby driving team innovation. Furthermore, knowledge sharing and knowledge acquisition play crucial mediating and moderating roles in the relationship between strong/weak ties and team innovation. This study contributes to the advancement of team innovation and offers practical insights for enterprises and policymakers.
description: Although interest in team innovation continues to grow, existing research lacks a comprehensive understanding of how relational networks facilitate team innovation, particularly through the pathways and mechanisms of knowledge activities. This study examines the critical role of relational networks in promoting team innovation. Drawing on the theory of strong and weak ties and the SECI model of knowledge creation, it analyzes the relationships among strong/weak ties, knowledge sharing, knowledge acquisition, and team innovation. The research not only investigates how strong and weak ties promote knowledge sharing, knowledge acquisition, and team innovation but also explores how knowledge sharing and knowledge acquisition mediate and moderate the influence of strong/weak ties on team innovation. Based on an empirical analysis of 288 samples in China, the results indicate that strong and weak ties significantly enhance knowledge sharing and knowledge acquisition, thereby driving team innovation. Furthermore, knowledge sharing and knowledge acquisition play crucial mediating and moderating roles in the relationship between strong/weak ties and team innovation. This study contributes to the advancement of team innovation and offers practical insights for enterprises and policymakers.
DOI: 10.57760/sciencedb.27140
title: 900nm nanosecond fiber laser
title: 900nm纳秒光纤激光器

description: The 900nm nanosecond fiber laser has important application value in differential absorption lidar, atmospheric environment monitoring, and other fields. Recently, the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, has made a breakthrough in the research of all-optical fiber 900 nm band nanosecond fiber lasers. Based on the full link self-developed domestic neodymium doped silica fiber, it has achieved 100 μ focal laser output for the first time. The laser uses a continuous narrow spectrum 922nm fiber oscillator as the seed source. After acousto-optic modulation, it is amplified in three stages to achieve an average power of 12W, a repetition rate of 100kHz, a pulse width of 37ns, and a single pulse energy of 120 μ J. The research results are expected to further promote the development and application of 900nm nanosecond fiber lasers 
description: The 900nm nanosecond fiber laser has important application value in differential absorption lidar, atmospheric environment monitoring, and other fields. Recently, the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, has made a breakthrough in the research of all-optical fiber 900 nm band nanosecond fiber lasers. Based on the full link self-developed domestic neodymium doped silica fiber, it has achieved 100 μ focal laser output for the first time. The laser uses a continuous narrow spectrum 922nm fiber oscillator as the seed source. After acousto-optic modulation, it is amplified in three stages to achieve an average power of 12W, a repetition rate of 100kHz, a pulse width of 37ns, and a single pulse energy of 120 μ J. The research results are expected to further promote the development and application of 900nm nanosecond fiber lasers
DOI: 10.22002/eb7ca-9ps72
title: Hpylori_dFliF_FISE

description: These are H_pylori cells that are lacking FliP protein. In these cells, FliF protein was knocked out  .  to examine its relation to the hat-like complex.
description: <strong>Tilt Series Date:</strong> 2020-10-08</p>
<strong>Data Taken By:</strong> Mohammed Kaplan</p>
<strong>Species / Specimen:</strong> Helicobacter pylori</p>
<strong>Strain:</strong> 26695</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 3°, constant angular increment, dosage: 120.0 eV/Å²,  defocus: -8.0 μm,  magnification: 19500x. </p>
<strong>Microscope:</strong> Caltech Titan Krios</p>
<strong>Acquisition Software:</strong> Serial EM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> The cells were prepared and sent by Carrie Schaffer from the university of Kentucky. The cells were grown at 37C with 5% CO2 on sheep blood agar plates for 2 days from a -80C. No growth was seen . on the plates after 24 hr therefore the plates were kept in the . incubator for another 24Hr.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> The samples were prepared with Vitrobot IV. Blotting conditions were either: blot force 6, blot . time 3, drain time 1 OR blot force -3, blot time 4, drain time 1, 100%  humidity. Cu grids. 10 nm . gold</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2020-10-08-322/rawdata/</p>Hpylori_DFliF_FISE_327.mrc,  Tilt Series (Pixel Size 0.452 nm), 1.9 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2020-10-08-322/rawdata/Hpylori_DFliF_FISE_327.mrc"
        > <i class="download icon"></i> Download </a></p>

        Hpylori_DFliF_FISE_327_full.rec,  Reconstruction (Pixel Size 1.808 nm), 737.3 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2020-10-08-322/3dimage_114120/Hpylori_DFliF_FISE_327_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.57760/sciencedb.27129
title: XRD and Raman Characterization of Microstructural Changes in Coke during Mixed Pyrolysis of Coking Coal and Additives Dataset
title: 炼焦煤与添加剂混合热解过程中煤焦微观结构演变机制数据集

description: This dataset originates from the study "Mechanism of Microstructural Evolution of Coal Char during Co-pyrolysis of Coking Coal and Additives". It includes proximate and ultimate analysis data for Kunpeng fat coal (FM), Yonghesheng lean coal (SM), coal tar pitch (CTP), and high-density polyethylene (HDPE), as well as thermogravimetric (TG/DTG) analysis data for the blended coal sample BC (FM:SM = 9:1) with 0%, 2%, 4%, 6%, and 8% additions of either CTP or HDPE, obtained during stepwise pyrolysis from 350 to 600°C. The TG/DTG data comprise the initial temperature (Tₛ), the temperature at maximum mass loss rate (Tₚ), the final temperature (Tf), the maximum mass loss rate (DTGmax), and the total mass loss. The dataset also provides X-ray diffraction (XRD) and Raman spectroscopy analysis results for pyrolysis products at different temperature points (350°C, 400°C, 450°C, 500°C, 550°C, 600°C). The XRD data include interlayer spacing (d₀₀₂), stacking height (Lc), lateral size (La), and aromaticity (fa), while the Raman data consist of the ID/IG ratio and the full width at half maximum of the G-band (FWHM-G). Additionally, correlation analysis data between d₀₀₂ and FWHM-G are included (R² &gt; 0.85). All data were obtained using a STA449 F3 thermogravimetric analyzer, a KYOWAGLAS-XA XRD instrument, and a HORIBA Jobin Yvon confocal Raman microscope.
description: This dataset originates from the study "Mechanism of Microstructural Evolution of Coal Char during Co-pyrolysis of Coking Coal and Additives". It includes proximate and ultimate analysis data for Kunpeng fat coal (FM), Yonghesheng lean coal (SM), coal tar pitch (CTP), and high-density polyethylene (HDPE), as well as thermogravimetric (TG/DTG) analysis data for the blended coal sample BC (FM:SM = 9:1) with 0%, 2%, 4%, 6%, and 8% additions of either CTP or HDPE, obtained during stepwise pyrolysis from 350 to 600°C. The TG/DTG data comprise the initial temperature (Tₛ), the temperature at maximum mass loss rate (Tₚ), the final temperature (Tf), the maximum mass loss rate (DTGmax), and the total mass loss. The dataset also provides X-ray diffraction (XRD) and Raman spectroscopy analysis results for pyrolysis products at different temperature points (350°C, 400°C, 450°C, 500°C, 550°C, 600°C). The XRD data include interlayer spacing (d₀₀₂), stacking height (Lc), lateral size (La), and aromaticity (fa), while the Raman data consist of the ID/IG ratio and the full width at half maximum of the G-band (FWHM-G). Additionally, correlation analysis data between d₀₀₂ and FWHM-G are included (R² &gt; 0.85). All data were obtained using a STA449 F3 thermogravimetric analyzer, a KYOWAGLAS-XA XRD instrument, and a HORIBA Jobin Yvon confocal Raman microscope.
DOI: 10.22002/0n5x8-r8w09
title: JV9082_K3_janelia

description: JV9082:DotF-sfGFP fusion
description: <strong>Tilt Series Date:</strong> 2019-12-04</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Legionella pneumophila</p>
<strong>Strain:</strong> Philadelphia-1_Lp02</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-54.0°, 54.0°), step: 3.0°, constant angular increment, dosage: 90.0 eV/Å²,  defocus: -3.0 μm,  magnification: 26000x. </p>
<strong>Microscope:</strong> Janelia Titan</p>
<strong>Acquisition Software:</strong> SerialEM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent to us by Joseph P. Vogel, Washington University School of Medicine. Data collected and processed by Debnath Ghosal.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells were grown in AYE liquid culture with following supplements: ferric nitrate, cysteine hydrochloride and thymidine.</p>
<strong>Sample Preparation:</strong> Vitrobot (MK iv) settings: 100% humidity, 4µl sample, Blot force 6, blot time 4.5 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Data collected at Janelia Krios (K3 camera). Mag: 26000, pixel size: 2.61A. Defocus range -2 to -4 um.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-12-04-36/rawdata/</p>9082_Box1_No4_S73.mrc,  Tilt Series (Pixel Size 0.261 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-12-04-36/rawdata/9082_Box1_No4_S73.mrc"
        > <i class="download icon"></i> Download </a></p>

        9082_Box1_No4_S73_full.rec,  Reconstruction (Pixel Size 1.044 nm), 737.3 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2019-12-04-36/3dimage_113007/9082_Box1_No4_S73_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/mqc8g-z1z76
title: P. gingivalis wild type

description: Type IX secretion system, t9ss
description: <strong>Tilt Series Date:</strong> 2020-10-16</p>
<strong>Data Taken By:</strong> Georges Chreifi</p>
<strong>Species / Specimen:</strong> Porphyromonas gingivalis</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 3°, constant angular increment, dosage: 160.0 eV/Å²,  defocus: -6.0 μm,  magnification: 26000x. </p>
<strong>Microscope:</strong> Caltech Titan Krios</p>
<strong>Acquisition Software:</strong> Serial EM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Eric Reynolds</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells grown anaerobically on a TSA+BHI plate at 37C for 3-4 days.</p>
<strong>Sample Preparation:</strong> Harvested directly from plates, resuspended in Tris buffer, fiducials added, then plunge-frozen.. Vitrobot (MK iv) settings: 100% humidity, 3ul sample, Blot force 3, blot time 3.5 sec. Wait time 0 . sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Acquired using the FISE . method.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/gca2020-10-16-70/rawdata/</p>PginWT_8_090.mrc,  Tilt Series (Pixel Size 0.333 nm), 1.9 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/gca2020-10-16-70/rawdata/PginWT_8_090.mrc"
        > <i class="download icon"></i> Download </a></p>

        PginWT_8_090_part41_2.rec,  Reconstruction (Pixel Size 1.332 nm), 884.7 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/gca2020-10-16-70/3dimage_114503/PginWT_8_090_part41_2.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.57760/sciencedb.29543
title: A collection of Clair3 models in a single place
title: A collection of Clair3 models in a single place

description: Clair3 is widely-used variant calling tools for high-throuput sequencing data. However, since nanopore sequencing, a emerging third-generation high-throughput sequecing technology, keep evolve, different flowcell type and basecaller configuration have very different error pattern. Therefore, one needs different Clair3 models for different nanopore sequencing data. Here we collect all nanopore sequencing data model for Clair3, where were stored in different places for fully automatic pipelines that involves Clair3.
description: Clair3 is widely-used variant calling tools for high-throuput sequencing data. However, since nanopore sequencing, a emerging third-generation high-throughput sequecing technology, keep evolve, different flowcell type and basecaller configuration have very different error pattern. Therefore, one needs different Clair3 models for different nanopore sequencing data. Here we collect all nanopore sequencing data model for Clair3, where were stored in different places for fully automatic pipelines that involves Clair3.
DOI: 10.6084/m9.figshare.30006322
title: raw data.xlsx

description: Extracellular matrix (ECM)-mimetic biomaterials have recently gained attention due to their ability to recapitulate the native microenvironment and regulate stem cell behavior
DOI: 10.6084/m9.figshare.30006322.v1
title: raw data.xlsx

description: Extracellular matrix (ECM)-mimetic biomaterials have recently gained attention due to their ability to recapitulate the native microenvironment and regulate stem cell behavior
DOI: 10.22002/gz01e-bk315
title: P. gingivalis wild type

description: Type IX secretion system, t9ss
description: <strong>Tilt Series Date:</strong> 2020-10-16</p>
<strong>Data Taken By:</strong> Georges Chreifi</p>
<strong>Species / Specimen:</strong> Porphyromonas gingivalis</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 3°, constant angular increment, dosage: 160.0 eV/Å²,  defocus: -6.0 μm,  magnification: 26000x. </p>
<strong>Microscope:</strong> Caltech Titan Krios</p>
<strong>Acquisition Software:</strong> Serial EM</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Eric Reynolds</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells grown anaerobically on a TSA+BHI plate at 37C for 3-4 days.</p>
<strong>Sample Preparation:</strong> Harvested directly from plates, resuspended in Tris buffer, fiducials added, then plunge-frozen.. Vitrobot (MK iv) settings: 100% humidity, 3ul sample, Blot force 3, blot time 3.5 sec. Wait time 0 . sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Acquired using the FISE . method.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/gca2020-10-16-102/rawdata/</p>PginWT_8_052.mrc,  Tilt Series (Pixel Size 0.333 nm), 1.9 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/gca2020-10-16-102/rawdata/PginWT_8_052.mrc"
        > <i class="download icon"></i> Download </a></p>

        PginWT_8_052_part41_4.rec,  Reconstruction (Pixel Size 1.332 nm), 884.7 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/gca2020-10-16-102/3dimage_114565/PginWT_8_052_part41_4.rec"
        > <i class="download icon"></i> Download </a></p>