cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.rxq82e
title: Occurrence Download

description: A dataset containing 15149 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudozizeeria maha"
 ]
}

 The dataset includes 15149 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045392-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yn73qu
title: Occurrence Download

description: A dataset containing 754 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Colias alfacariensis",
 "HasCoordinate is true",
 "Geometry POLYGON ((6.224439 46.56151, 6.224439 48.43216, 8.984551 48.43216, 8.984551 46.56151, 6.224439 46.56151))",
 "Year is greater than or equal to 2020",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 754 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045393-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hfma5h
title: Occurrence Download

description: A dataset containing 3787 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Austria",
 "TaxonKey is Boletus edulis"
 ]
}

 The dataset includes 3787 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045394-251025141854904/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.30656942
title: Terrace_Data

description: Terrace Farming
DOI: 10.6084/m9.figshare.30656942.v1
title: Terrace_Data

description: Terrace Farming
DOI: 10.5281/zenodo.17650000
title: Polarization Controlled Supercurrent in Ferroelectric Josephson Junction

description: Dataset for publication "Polarization Controlled Supercurrent in Ferroelectric Josephson Junction" by Yaozu Tang, Mazhar N. Ali, Gerrit E. W. Bauer, and Yaroslav M. Blanter.

Abstract: Josephson junctions are essential devices in superconducting electronics and quantum computing hardware. Here we predict electrical control of the supercurrent in composite superconductor-insulator-ferroelectric-insulator-superconductor (S-I-FE-I-S) Josephson junctions. Inversion symmetry broken by unequal dielectric barrier thicknesses and/or potentials converts ferroelectric polarization reversal into a substantial change of the critical current. With a WKB tunneling model we obtain non-volatile switching of the critical current with on-off efficiency up to 0.9 for physically realistic parameters. This can be achieved by optimizing the thicknesses and potential barriers of the insulating layers, as well as the thickness and dielectric constant of the ferroelectric layer. We also derive a compact linear expression for the critical current valid for small polarizations. Our results identify ferroelectric Josephson junctions as electrically programmable superconducting current switches for cryogenic memory and logic applications.

A detailed description of the dateset is included in the README.txt file.
DOI: 10.5281/zenodo.17649999
title: Polarization Controlled Supercurrent in Ferroelectric Josephson Junction

description: Dataset for publication "Polarization Controlled Supercurrent in Ferroelectric Josephson Junction" by Yaozu Tang, Mazhar N. Ali, Gerrit E. W. Bauer, and Yaroslav M. Blanter.

Abstract: Josephson junctions are essential devices in superconducting electronics and quantum computing hardware. Here we predict electrical control of the supercurrent in composite superconductor-insulator-ferroelectric-insulator-superconductor (S-I-FE-I-S) Josephson junctions. Inversion symmetry broken by unequal dielectric barrier thicknesses and/or potentials converts ferroelectric polarization reversal into a substantial change of the critical current. With a WKB tunneling model we obtain non-volatile switching of the critical current with on-off efficiency up to 0.9 for physically realistic parameters. This can be achieved by optimizing the thicknesses and potential barriers of the insulating layers, as well as the thickness and dielectric constant of the ferroelectric layer. We also derive a compact linear expression for the critical current valid for small polarizations. Our results identify ferroelectric Josephson junctions as electrically programmable superconducting current switches for cryogenic memory and logic applications.

A detailed description of the dateset is included in the README.txt file.
DOI: 10.5281/zenodo.17610440
title: Data and code for: Present-day and future global maps of non-native species richness across 17,343 islands worldwide

description: This repository contains the data and code necessary to reproduce the analyses described in the paper titled "Present-day and future global maps of non-native species richness across 17,343 islands worldwide." The primary objective of this paper was to train a model that enables the prediction of the current (2020) and future (2050) non-native species (NNS) richness for global islands. NNS richness prediction was based on a set of 11 biogeographic and anthropogenic predictors collected at the island level. We assessed the performance of four modeling strategies based on several algorithms and methods for accounting for data biases, and used the best model to predict NNS richness for the present day as well as for two future scenarios.

The data set used for building the four modeling strategies and for predicting the NNS richness of all islands for the present and the two future scenarios is available from the file: Observed_NNS_richness_Present_Future_predictors.xlsx. The first sheet, "METADATA," contains the column names and descriptions of each variable from the subsequent sheets. The sheet "Obs_NNS_PRESENT_PRED" contains the identifier (ID column), name, archipelago, the 11 anthropogenic and biogeographic predictors, the sampling effort, and the observed NNS richness of each island for the present day (2020). The sheets "FUTURE_PRED_SSP1" and "FUTURE_PRED_SSP3" contain the identifier (ID column) and the 11 anthropogenic and biogeographic predictors of each island for the SSP1 and SSP3 future scenarios, respectively. Raw data of the predictors were obtained from the sources listed in Table 1 of the paper. They were freely accessible, and we followed the conditions to access them.

The scripts to perform the NNS richness prediction are based on this dataset. However, the .rds files can be used directly in the scripts: Observed_NNS_richness_predictors.rds for the present predictors and observed NNS richness, 07_Data_isl_info_SSP1.RDS and 07_Data_isl_info_SSP3.RDS for the predictors for SSP1 and SSP3, respectively. One needs to run the scripts Model_building.R and Predicting_for_all_islands.R first, and then all the other scripts can be run in any order. 

The output dataset (Predicted_NNS_richness_17343_islands.xlsx) contains the identifier, name, mean predicted NNS richness across the 10 cross-validation folds for the present day and the two future scenarios, along with the associated standard deviation, for the hurdle random forest model, with sampling effort included as an observation weight during the calibration. Column names and descriptions are detailed in the METADATA sheet, and data are displayed in the "Output_Hurdle_RF_weighted_by_SE" sheet.
DOI: 10.5281/zenodo.17610441
title: Data and code for: Present-day and future global maps of non-native species richness across 17,343 islands worldwide

description: This repository contains the data and code necessary to reproduce the analyses described in the paper titled "Present-day and future global maps of non-native species richness across 17,343 islands worldwide." The primary objective of this paper was to train a model that enables the prediction of the current (2020) and future (2050) non-native species (NNS) richness for global islands. NNS richness prediction was based on a set of 11 biogeographic and anthropogenic predictors collected at the island level. We assessed the performance of four modeling strategies based on several algorithms and methods for accounting for data biases, and used the best model to predict NNS richness for the present day as well as for two future scenarios.

The data set used for building the four modeling strategies and for predicting the NNS richness of all islands for the present and the two future scenarios is available from the file: Observed_NNS_richness_Present_Future_predictors.xlsx. The first sheet, "METADATA," contains the column names and descriptions of each variable from the subsequent sheets. The sheet "Obs_NNS_PRESENT_PRED" contains the identifier (ID column), name, archipelago, the 11 anthropogenic and biogeographic predictors, the sampling effort, and the observed NNS richness of each island for the present day (2020). The sheets "FUTURE_PRED_SSP1" and "FUTURE_PRED_SSP3" contain the identifier (ID column) and the 11 anthropogenic and biogeographic predictors of each island for the SSP1 and SSP3 future scenarios, respectively. Raw data of the predictors were obtained from the sources listed in Table 1 of the paper. They were freely accessible, and we followed the conditions to access them.

The scripts to perform the NNS richness prediction are based on this dataset. However, the .rds files can be used directly in the scripts: Observed_NNS_richness_predictors.rds for the present predictors and observed NNS richness, 07_Data_isl_info_SSP1.RDS and 07_Data_isl_info_SSP3.RDS for the predictors for SSP1 and SSP3, respectively. One needs to run the scripts Model_building.R and Predicting_for_all_islands.R first, and then all the other scripts can be run in any order. 

The output dataset (Predicted_NNS_richness_17343_islands.xlsx) contains the identifier, name, mean predicted NNS richness across the 10 cross-validation folds for the present day and the two future scenarios, along with the associated standard deviation, for the hurdle random forest model, with sampling effort included as an observation weight during the calibration. Column names and descriptions are detailed in the METADATA sheet, and data are displayed in the "Output_Hurdle_RF_weighted_by_SE" sheet.
DOI: 10.15468/dl.8t49cf
title: Occurrence Download

description: A dataset containing 91777 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sayornis nigricans",
 "Country is Colombia"
 ]
}

 The dataset includes 91777 records from 227 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045354-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17648984
title: PM_BC Method for BC coating des
DOI: 10.5281/zenodo.17648985
title: PM_BC Method for BC coating des
DOI: 10.6084/m9.figshare.30656924
title: Annual 30-meter Forest Cover Loss Maps for China (2000-2024) based on TST-EC Framework

description: <b>Overview</b>This dataset contains annual, 30-meter resolution forest cover loss (FCL) maps for China spanning the years 2000 to 2024. The data was generated using a novel detection framework, the Temporal-Spectral-Texture features fusion with Eco-regional Constraints (TST-EC), which incorporates independent XGBoost models trained for 35 distinct ecoregions within China. This approach is specifically designed to address the challenges of ecological heterogeneity across the country.<br><b>Methodology</b><br><b>Framework:</b> Temporal-Spectral-Texture features fusion with Eco-regional Constraints (TST-EC).<b>Base Data:</b> Landsat time-series imagery.<b>Model: </b>Separate XGBoost models were trained for each of the 35 ecoregions using a rigorously validated reference dataset of 9,202 samples.<b>Validation: </b>The overall accuracy of the FCL maps is 0.846 (±0.037), which significantly outperforms a global model without ecoregional constraints.<br><b>Data Details</b><br><b>Spatial Coverage: </b>China.<b>Temporal Coverage:</b> 2000-01-01 to 2024-12-31 (Annual maps).<b>Spatial Resolution:</b> 30 meters.<b>Projection: </b>WGS 84<b>File Format: </b>GeoTIFF<b>Map Values: </b>0 - No Loss, 2000-2024 - Loss Year]。<br><b>Usage Notes</b>This high-accuracy data product is valuable for studies on carbon cycling, forest dynamics, conservation policy assessment, and understanding the drivers of forest change in China and other highly heterogeneous regions. The dataset also supports analysis of spatiotemporal patterns and climatic thresholds related to forest loss.<br><b>Related Work</b>These maps form the basis for the analysis presented in the associated manuscript: "Spatiotemporal Patterns and Climate Tipping Points of Forest Loss in China Revealed by 20 Years of Remote Sensing and Ecoregion Modeling" (Under Review).
DOI: 10.6084/m9.figshare.30656924.v1
title: Annual 30-meter Forest Cover Loss Maps for China (2000-2024) based on TST-EC Framework

description: <b>Overview</b>This dataset contains annual, 30-meter resolution forest cover loss (FCL) maps for China spanning the years 2000 to 2024. The data was generated using a novel detection framework, the Temporal-Spectral-Texture features fusion with Eco-regional Constraints (TST-EC), which incorporates independent XGBoost models trained for 35 distinct ecoregions within China. This approach is specifically designed to address the challenges of ecological heterogeneity across the country.<br><b>Methodology</b><br><b>Framework:</b> Temporal-Spectral-Texture features fusion with Eco-regional Constraints (TST-EC).<b>Base Data:</b> Landsat time-series imagery.<b>Model: </b>Separate XGBoost models were trained for each of the 35 ecoregions using a rigorously validated reference dataset of 9,202 samples.<b>Validation: </b>The overall accuracy of the FCL maps is 0.846 (±0.037), which significantly outperforms a global model without ecoregional constraints.<br><b>Data Details</b><br><b>Spatial Coverage: </b>China.<b>Temporal Coverage:</b> 2000-01-01 to 2024-12-31 (Annual maps).<b>Spatial Resolution:</b> 30 meters.<b>Projection: </b>WGS 84<b>File Format: </b>GeoTIFF<b>Map Values: </b>0 - No Loss, 2000-2024 - Loss Year]。<br><b>Usage Notes</b>This high-accuracy data product is valuable for studies on carbon cycling, forest dynamics, conservation policy assessment, and understanding the drivers of forest change in China and other highly heterogeneous regions. The dataset also supports analysis of spatiotemporal patterns and climatic thresholds related to forest loss.<br><b>Related Work</b>These maps form the basis for the analysis presented in the associated manuscript: "Spatiotemporal Patterns and Climate Tipping Points of Forest Loss in China Revealed by 20 Years of Remote Sensing and Ecoregion Modeling" (Under Review).
DOI: 10.15468/dl.zzvhs3
title: Occurrence Download

description: A dataset containing 1840209 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Betula"
 ]
}

 The dataset includes 1840209 records from 3631 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045338-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.damh46
title: Occurrence Download

description: A dataset containing 2738 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((24.36440 46.35394,59.99020 46.35394,59.99020 70.07627,24.36440 70.07627,24.36440 46.35394))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Berberis vulgaris L."
 ]
}

 The dataset includes 2738 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009191-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ddwn6e
title: Occurrence Download

description: A dataset containing 1434 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((122.85269 24.92123,157.85961 24.92123,157.85961 52.38541,122.85269 52.38541,122.85269 24.92123))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Pristomyrmex punctatus (Smith, 1860)"
 ]
}

 The dataset includes 1434 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009192-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ncqcx5
title: Occurrence Download

description: A dataset containing 77032 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ricinus communis L.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 77032 records from 729 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009181-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/kyzf4-zwb38
title: Xanthomonas vs Caulobacter

description: Xac WT pBRaφ pBRR MCS5 GFP (code 9) vs Caulobacter competition
description: <strong>Tilt Series Date:</strong> 2017-03-30</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Xanthomonas citri</p>
<strong>Strain:</strong> pv citri 306</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-50.0°, 50.0°), step: 1.5°, constant angular increment, dosage: 160.0 eV/Å²,  defocus: -8.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Chuck Farah lab made the strains, data collected by Debnath Ghosal.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> 4 µL mixture of Xac and Caulobacter cells were pipetted on top of Au-Finder grids. These grids were glow discharged in advance and kept on top of Agar pads. Carbon side up. These were then kept at the BSL-II hood and left for about 10 min before freezing.</p>
<strong>Sample Preparation:</strong> Freezing conditions: Blot force 6, wait time=0, drain time =1 sec, blot time= 10 sec</p>
description: Files available via S3 at ps://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-03-30-31/rawdata/</p>Xac_Cc_201703294tha0001.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.3 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-03-30-31/rawdata/Xac_Cc_201703294tha0001.mrc"
        > <i class="download icon"></i> Download </a></p>

        Xac_Cc_201703294tha0001.rec,  Reconstruction (Pixel Size 1.56 nm), 890.9 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-03-30-31/3dimage_83382/Xac_Cc_201703294tha0001.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/tch4v-5cc34
title: perfusiondcrp_fixd_good track

description: MOh and Sahand
description: <strong>Tilt Series Date:</strong> 2015-08-24</p>
<strong>Data Taken By:</strong> Poorna Subramanian</p>
<strong>Species / Specimen:</strong> Shewanella oneidensis</p>
<strong>Strain:</strong> MR-1</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.0°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -8.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Batchruntomo</p>
<strong>Collaborators and Roles:</strong> The sample was grown in perfusion chamber and fixed for 30 minutes with 4% formaldehyde. Then washed 3 times in distilled water for 1 minute each. The sample was blotted in a manual plunger with 1.5ul gold beads.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Lm is taken before and after fixing. In the LM after fixing the nanowires were shrunk. Although the cells looked good with formaldehyde we had lost long nanowires. 2 squares were imaged with correlation anyway.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2015-08-24-13/3dimage_60716/</p>perfusiondcrp_sq130005.rec,  Reconstruction (Pixel Size 1.56 nm), 889.0 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2015-08-24-13/3dimage_60716/perfusiondcrp_sq130005.rec"
        > <i class="download icon"></i> Download </a></p>

        perfusiondcrp_sq130005.mrc,  Tilt Series (Pixel Size 0.39 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2015-08-24-13/rawdata/perfusiondcrp_sq130005.mrc"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/rvf63-a4p74
title: Coxiella_burnetii_ wtACCMD_pH4d5

description: Coxiella burnetii Nine Mile phase II clone 4 (NMIIC4) strain. Wild-type cells day-5 culture. Cells grown in acidic media ACCMD and pH 4.75
description: <strong>Tilt Series Date:</strong> 2017-10-17</p>
<strong>Data Taken By:</strong> Debnath Ghosal</p>
<strong>Species / Specimen:</strong> Coxiella burnetii</p>
<strong>Strain:</strong> Nine Mile phase II clone 4</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 2°, constant angular increment, dosage: 130.0 eV/Å²,  defocus: -7.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Raptor</p>
<strong>Collaborators and Roles:</strong> Strain sent to us by Robert Heinzen. Data collected and processed by Debnath Ghosal and Mohammed Kaplan using Caltech tomography pipeline.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells were grown in Robert Heinzen's lab @NIAID and ~50 ul cells were sent to us.</p>
<strong>Sample Preparation:</strong> Refer to Heinzen lab. Freezing: 100% humidity, 4µl sample, Blot force 6, blot time 4 sec. Wait time 0 sec. Drain time 1 sec. Ethane/propane mix. Grid: Quantifoil Cu 2/2 200. Add gold to cells prior to freezing: 1ul of gold to 4 ul of cells, freeze.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-10-17-137/rawdata/</p>Cb_d5pH4_20171017g0019.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.7 GB  
        
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        > <i class="download icon"></i> Download </a></p>

        Cb_d5pH4_20171017g0019_full.rec,  Reconstruction (Pixel Size 1.56 nm), 445.4 MB  
        
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        > <i class="download icon"></i> Download </a></p>