cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26142490
title: Belowground communities in lowlands are less stable to heat extremes across seasons - Dataset

description: Dataset and scripts of the publication Martínez-De León et al. 2025 "Belowground communities in lowlands are less stable to heat extremes across seasons". Ecology Letters.<br>Data free of use. When using this data, please cite the original publication.<br>Please make sure to download and extract all files in the same directory.List of data files (by order of use):- Chasseral_temperaturedata.csv - Temperature data from lowland meteo station- Neuchatel_temperaturedata.csv - Temperature data from highland meteo station- Data from temperature loggers.zip - Compressed folder with the temperature data from loggers deployed in the field and in the lab experiments- Initial physicochemical parameters.csv - Physicochemical parameters recorded in the field sites- General_database.csv - Database containing the treatments and Collembola species abundances- General_database_README.txt - Description of the variables included in the general database file.- Data metabarcoding fungi.zip - Compressed folder with the fungal metabarcoding reads- genera.xlsx - Table S1 from Põlme et al 2021 Fungal Diversity (FungalTraits data)- Output joint species distribution models.zip - Compressed folder containing R objects with fitted joint species distribution models- Plot Collembola-fungi association networks.pdf - High-resolution figure of the association networks for all spatiotemporal context (= Fig. S15)- session_info.txt - List of all packages loaded and their versions.<br><br>List of scripts:01_experimental_temperature_regimes.R02_temperature_loggers_lab_incubators.R03_temperature_loggers_field_soil.R04_plot_temperature_field_lab.R05_physicochemical_parameters.R06_database_preparation.R07_total_collembola_abundance.R08_diversity_indices_collembola.R09_joint_species_distribution_models_collembola.R10_preprocessing_fungal_taxa.R11_diversity_indices_fungi.R12_joint_species_distribution_models_fungi.R13_associations_fungi_collembola_models.R14_associations_fungi_collembola_posterior_samples.R15_associations_fungi_collembola_network_properties.R16_reads_trophicmodes_fungi.R17extra_average association strength
DOI: 10.6084/m9.figshare.26142490.v3
title: Belowground communities in lowlands are less stable to heat extremes across seasons - Dataset

description: Dataset and scripts of the publication Martínez-De León et al. 2025 "Belowground communities in lowlands are less stable to heat extremes across seasons". Ecology Letters.<br>Data free of use. When using this data, please cite the original publication.<br>Please make sure to download and extract all files in the same directory.List of data files (by order of use):- Chasseral_temperaturedata.csv - Temperature data from lowland meteo station- Neuchatel_temperaturedata.csv - Temperature data from highland meteo station- Data from temperature loggers.zip - Compressed folder with the temperature data from loggers deployed in the field and in the lab experiments- Initial physicochemical parameters.csv - Physicochemical parameters recorded in the field sites- General_database.csv - Database containing the treatments and Collembola species abundances- General_database_README.txt - Description of the variables included in the general database file.- Data metabarcoding fungi.zip - Compressed folder with the fungal metabarcoding reads- genera.xlsx - Table S1 from Põlme et al 2021 Fungal Diversity (FungalTraits data)- Output joint species distribution models.zip - Compressed folder containing R objects with fitted joint species distribution models- Plot Collembola-fungi association networks.pdf - High-resolution figure of the association networks for all spatiotemporal context (= Fig. S15)- session_info.txt - List of all packages loaded and their versions.<br><br>List of scripts:01_experimental_temperature_regimes.R02_temperature_loggers_lab_incubators.R03_temperature_loggers_field_soil.R04_plot_temperature_field_lab.R05_physicochemical_parameters.R06_database_preparation.R07_total_collembola_abundance.R08_diversity_indices_collembola.R09_joint_species_distribution_models_collembola.R10_preprocessing_fungal_taxa.R11_diversity_indices_fungi.R12_joint_species_distribution_models_fungi.R13_associations_fungi_collembola_models.R14_associations_fungi_collembola_posterior_samples.R15_associations_fungi_collembola_network_properties.R16_reads_trophicmodes_fungi.R17extra_average association strength
DOI: 10.22002/3g1s2-2j202
title: U2OS-Tubulin Tracker Green

description: U2OS cells stained with tubulin tracker to locatemidbodies using LM. Cryo-FIB milling is used to make the sample thinner.
description: <strong>Tilt Series Date:</strong> 2017-07-14</p>
<strong>Data Taken By:</strong> Wei Zhao</p>
<strong>Species / Specimen:</strong> U2OS</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-62.0°, 62.0°), step: 2°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -8.0 μm,  magnification: 22500x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> SerialEM</p>
<strong>Upload Method:</strong> reupload_downloaded</p>
<strong>Processing Software Used:</strong> IMOD-patch tracking</p>
<strong>Purification / Growth Conditions / Treatment:</strong> U2OS cells grown on Au finder grids for 28-48 hours.Grids coated with PL.</p>
<strong>Sample Preparation:</strong> Cells were stained with tubulin tracker and Hoechst before freezing. Plunge frozen using the vitrobot III blotted manually from back(drain time 1s).</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/wza2017-07-14-11/rawdata/</p>TS2.mrc,  Tilt Series (Pixel Size 0.4569 nm), 1.6 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/wza2017-07-14-11/rawdata/TS2.mrc"
        > <i class="download icon"></i> Download </a></p>

        TS2_bin2.rec,  Reconstruction (Pixel Size 0.0 nm), 231.6 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/wza2017-07-14-11/3dimage_86877/TS2_bin2.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.6084/m9.figshare.26142490.v2
title: Belowground communities in lowlands are less stable to heat extremes across seasons - Dataset

description: Dataset and scripts of the publication "Belowground communities in lowlands are less stable to heat extremes across seasons"<br>Data free of use. When using this data, please cite the original publication.<br>List of data files (by order of use):- Chasseral_temperaturedata.csv - Temperature data from lowland meteo station- Neuchatel_temperaturedata.csv - Temperature data from highland meteo station- Data from temperature loggers.zip - Temperature data from loggers deployed in the field and in the lab experiments- Initial physicochemical parameters.csv - Physicochemical parameters recorded in the field sites- General_database.csv - Database containing the treatments and Collembola species abundances- General_database_README.txt - Description of the variables included in the general database file.- Data metabarcoding fungi.zip - Database with the fungal metabarcoding reads- genera.xlsx - Table S1 from Põlme et al 2021 Fungal Diversity (FungalTraits data)- Output joint species distribution models.zip - R objects with fitted joint species distribution models- Plot Collembola-fungi association networks.pdf - High-resolution figure of the association networks for all spatiotemporal context (= Fig. S15)- session_info.txt - List of all packages loaded and their versions.<br><br>List of scripts:01_experimental_temperature_regimes.R02_temperature_loggers_lab_incubators.R03_temperature_loggers_field_soil.R04_plot_temperature_field_lab.R05_physicochemical_parameters.R06_database_preparation.R07_total_collembola_abundance.R08_diversity_indices_collembola.R09_joint_species_distribution_models_collembola.R10_preprocessing_fungal_taxa.R11_diversity_indices_fungi.R12_joint_species_distribution_models_fungi.R13_associations_fungi_collembola_models.R14_associations_fungi_collembola_posterior_samples.R15_associations_fungi_collembola_network_properties.R16_reads_trophicmodes_fungi.R17extra_average association strength
DOI: 10.22002/x0meb-fbw81
title: Vibrio cholerae delta TcpB

description: Raw data files of Vibrio cholerae delta TcpB
description: <strong>Tilt Series Date:</strong> 2015-10-16</p>
<strong>Data Taken By:</strong> Andreas Kjaer</p>
<strong>Species / Specimen:</strong> Vibrio cholerae</p>
<strong>Strain:</strong> O395-N1</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, 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> reupload_downloaded</p>
<strong>Processing Software Used:</strong> SIRT</p>
<strong>Collaborators and Roles:</strong> Ronald K. Taylor</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Grown ON in LB with ampicillin and IPTG, refreshed in LB shortly prior to freezing.</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aka2015-10-16-84/3dimage_73396/</p>10162015_TcpB_10084_SIRT_2K.rec,  Reconstruction (Pixel Size 0.0 nm), 7.1 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/aka2015-10-16-84/3dimage_73396/10162015_TcpB_10084_SIRT_2K.rec"
        > <i class="download icon"></i> Download </a></p>

        10162015_TcpB_10084.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/aka2015-10-16-84/rawdata/10162015_TcpB_10084.mrc"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.34740/kaggle/dsv/12871184
title: Multi-Class Semiconductor Wafer Image Dataset
DOI: 10.22002/9vhzc-nct72
title: T5_wt_control_Aumalemaide_5nm

description: This is a wt sample in which Mini cells have been isolated by high speed centrifugation and cells are Au malemaide labelled. This is a control sample. No fiducials added.
description: <strong>Tilt Series Date:</strong> 2017-02-14</p>
<strong>Data Taken By:</strong> Poorna Subramanian</p>
<strong>Species / Specimen:</strong> Escherichia coli</p>
<strong>Strain:</strong> EC93 stick, UPEC536</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-65.0°, 65.0°), step: 1.0°, constant angular increment, dosage: 120.0 eV/Å²,  defocus: -10.0 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> autoPatch</p>
<strong>Collaborators and Roles:</strong> Zach Ruhe and Chris hayes</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Freezing in vitrobot. Humidifier was not working. I wouldn't really trust the conditions.</p>
<strong>Sample Preparation:</strong> Sample prepared by Taylor Steven</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2017-02-14-14/rawdata/</p>sq90014.mrc,  Tilt Series (Pixel Size 0.39 nm), 3.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2017-02-14-14/rawdata/sq90014.mrc"
        > <i class="download icon"></i> Download </a></p>

        sq90014.rec,  Reconstruction (Pixel Size 1.56 nm), 1.1 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2017-02-14-14/3dimage_82417/sq90014.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/91bnb-xnp06
title: LBculture_wt

description: This is a LB culture of wt from a flask grown into stationary phase. The purpose was to to check that there are no nanowires  and nothing was seen in sem, and when we do cryo-em to ensure we see flagellum. This would ensure that flagellum can't be seen in sem. But we did see long wires in sem. Sahand opined that is because we grew them into stationary phase. I imaged these to capture nanowires since they were present in sem
description: <strong>Tilt Series Date:</strong> 2015-07-25</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: 2.0°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -10.0 μm,  magnification: 22500x. </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> Moh and Sahand</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Manual blotting Cu side glow discharged. Sample was put on opposite side and blotted. Similar to vitrobot</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2015-07-25-13/rawdata/</p>lbculture_nanowirepresent_atlas10012.mrc,  Tilt Series (Pixel Size 0.48 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2015-07-25-13/rawdata/lbculture_nanowirepresent_atlas10012.mrc"
        > <i class="download icon"></i> Download </a></p>

        lbculture_nanowirepresent_atlas10012_full.rec,  Reconstruction (Pixel Size 1.92 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/psa2015-07-25-13/3dimage_59514/lbculture_nanowirepresent_atlas10012_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.15468/dl.krryx6
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Canada, United States of America)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Carex demissa",
 "VerbatimScientificName is carex demissa"
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045331-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/6x2qf-sym58
title: Caulobacter crescentus

description: Raw data files of Caulobacter crescentus
description: <strong>Tilt Series Date:</strong> 2018-10-08</p>
<strong>Data Taken By:</strong> Lujia Zhang</p>
<strong>Species / Specimen:</strong> Caulobacter crescentus</p>
<strong>Strain:</strong> NA1000</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.0°, constant angular increment, dosage: 252.8 eV/Å²,  defocus: -10.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</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> For the research of pilus, Del E from Partick</p>
<strong>Purification / Growth Conditions / Treatment:</strong> swarmer cell, PVP</p>
<strong>Sample Preparation:</strong> force 4, time 6, humidity 100%, temp. 22</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/lza2018-10-08-8/rawdata/</p>targ08.mrc,  Tilt Series (Pixel Size 0.3712 nm), 3.4 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/lza2018-10-08-8/rawdata/targ08.mrc"
        > <i class="download icon"></i> Download </a></p>

        targ08_full.rec,  Reconstruction (Pixel Size 1.4848 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/lza2018-10-08-8/3dimage_99503/targ08_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.6084/m9.figshare.29986123
title: Development and validation of machine learning models for predicting acute kidney injury in acute-on-chronic liver failure: a multimodel comparative study

description: Acute kidney injury (AKI) is one of the serious complications in acute-on-chronic liver failure (ACLF), and the mortality rate is very high. Early identification of high-risk patients is critical. Therefore, this study aimed to develop prediction models for AKI in ACLF patients based on machine learning (ML) algorithms. This retrospective study enrolled 1,076 adult patients diagnosed with ACLF, with AKI defined according to the International Club of Ascites criteria. Participants were randomly allocated into training (<i>n</i> = 753, 70%) and test (<i>n</i> = 323, 30%) sets. Six ML models were developed: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and extreme gradient boosting (XGBoost). The performance of each model was assessed using the area under the receiver operating characteristic curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), the calibration curve, and decision curve analysis. Among participants, 250 (23.2%) developed AKI during hospitalization. Multivariate LR analyses identified ten significant variables in the training set: age, hypertension, total bilirubin, blood urea nitrogen, serum creatinine, blood uric acid, international normalized ratio, hepatic encephalopathy, abdominal infection, and sepsis. The RF model performed best in the test set (AUC-ROC = 0.899; AUC-PR = 0.806). The ML models can be reliable tools for predicting AKI in patients with ACLF. The RF model performed the best and can help medical clinicians to better identify patients with high risk of AKI in ACLF.
DOI: 10.6084/m9.figshare.29986123.v1
title: Development and validation of machine learning models for predicting acute kidney injury in acute-on-chronic liver failure: a multimodel comparative study

description: Acute kidney injury (AKI) is one of the serious complications in acute-on-chronic liver failure (ACLF), and the mortality rate is very high. Early identification of high-risk patients is critical. Therefore, this study aimed to develop prediction models for AKI in ACLF patients based on machine learning (ML) algorithms. This retrospective study enrolled 1,076 adult patients diagnosed with ACLF, with AKI defined according to the International Club of Ascites criteria. Participants were randomly allocated into training (<i>n</i> = 753, 70%) and test (<i>n</i> = 323, 30%) sets. Six ML models were developed: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and extreme gradient boosting (XGBoost). The performance of each model was assessed using the area under the receiver operating characteristic curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), the calibration curve, and decision curve analysis. Among participants, 250 (23.2%) developed AKI during hospitalization. Multivariate LR analyses identified ten significant variables in the training set: age, hypertension, total bilirubin, blood urea nitrogen, serum creatinine, blood uric acid, international normalized ratio, hepatic encephalopathy, abdominal infection, and sepsis. The RF model performed best in the test set (AUC-ROC = 0.899; AUC-PR = 0.806). The ML models can be reliable tools for predicting AKI in patients with ACLF. The RF model performed the best and can help medical clinicians to better identify patients with high risk of AKI in ACLF.
DOI: 10.22002/nztw4-bbx97
title: Shewanella oneidensis dFlgH

description: Raw data files of Shewanella oneidensis dFlgH
description: <strong>Tilt Series Date:</strong> 2018-05-27</p>
<strong>Data Taken By:</strong> Mohammed Kaplan</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°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -8.0 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> Jeff Granlick (Uni. of Minnesota) supplied the mutant cells.</p>
<strong>Sample Preparation:</strong> Samples were prepared from overnight LB culture</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2018-05-27-12/rawdata/</p>tilt20.mrc,  Tilt Series (Pixel Size 0.57 nm), 1.1 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2018-05-27-12/rawdata/tilt20.mrc"
        > <i class="download icon"></i> Download </a></p>

        tilt20_full.rec,  Reconstruction (Pixel Size 2.28 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mka2018-05-27-12/3dimage_93298/tilt20_full.rec"
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DOI: 10.22002/sz001-52v26
title: Caulobacter crescentus

description: Raw data files of Caulobacter crescentus
description: <strong>Tilt Series Date:</strong> 2018-09-11</p>
<strong>Data Taken By:</strong> Lujia Zhang</p>
<strong>Species / Specimen:</strong> Unspecified</p>
<strong>Strain:</strong> None</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 180.0 eV/Å²,  defocus: -6.0 μm,  magnification: 27500x. </p>
<strong>Microscope:</strong> Caltech Polara</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> For the research of pilus, WT</p>
<strong>Purification / Growth Conditions / Treatment:</strong> swarmer cell, PVP</p>
<strong>Sample Preparation:</strong> force 4, time 6, humidity 100%, temp. 22</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/lza2018-09-11-26/rawdata/</p>targ43.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/lza2018-09-11-26/rawdata/targ43.mrc"
        > <i class="download icon"></i> Download </a></p>

        targ43_part120_13.rec,  Reconstruction (Pixel Size 1.56 nm), 445.4 MB  
        
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DOI: 10.5281/zenodo.16634646
title: nf-core/proteinfamilies reproducibility benchmark results

description: To evaluate the applicability of the nf-core/proteinfamilies pipeline, we explored how well the automatically generated protein families produced by the pipeline matched expert curated protein families from established databases (NCBIFAM, PANTHER, Pfam, and HAMAP), which have undergone extensive curation, including multiple rounds of search and refinement.

This upload contains the original family FASTA files,  the additional UniProt-SwissProt sequences, and the generated family models and alignments.