cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/v36-zn3-4cs
title: Harpoceras

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/887-gwa-14g
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wh6-pjj-gkk
title: †Protetragonites quadrisulcatus (d'Orbigny, 1841)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wre-gfd-hq0
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ywj-2gc-ljh
title: Canis lupus familiaris Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ycy-sty-aej
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/8fx-32q-k80
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0n9-y27-6we
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/a77-l6t-mt6
title: Baculites

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/1x5-33j-mb4
title: Plantae

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/ghj-mrq-rhc
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/92b-qxd-2lb
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/pas-8yp-6j7
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/73b-mc7-nvm
title: Cnidaria

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/a2v-l55-0x7
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/psz-gee-b2r
title: Balclutha Kirkaldy, 1900

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/kvx-pz3-h1z
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/yy3-5af-q13
title: Illaenus

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/cd0-j7v-dn1
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/c8w-5gw-ghe
title: †Goniophora J. Phillips, 1848

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/wt4-v24-6br
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.ndes8s
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Perna perna",
 "Country is Uruguay",
 "HasCoordinate is true"
 ]
}

 The dataset includes 44 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020019-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sjwgju
title: Occurrence Download

description: A dataset listing the 34 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Long-term monitoring of mammal communities in the Peneda-Gerês National Park using camera trap data"
 ]
}

 The dataset's 34 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020005-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y72fbz
title: Occurrence Download

description: A dataset containing 184697 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ambrosia artemisiifolia"
 ]
}

 The dataset includes 184697 records from 1562 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019982-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jra485
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ceropegia gigantea",
 "Country is Venezuela (Bolivarian Republic of)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020023-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18031868
title: Data for:Unlocking soil phages for global food security

description: This repository contains the processed data and code supporting the analysis of soil phage regulation of global crop yield. The dataset includes spatially harmonized raster layers of soil phage abundance and diversity, crop yield, soil properties, climate variables, and agricultural management indicators, as well as source data underlying the figures in the associated manuscript. Machine-learning code and trained model outputs are provided to enable reproducibility of the main analyses.
DOI: 10.5281/zenodo.18031867
title: Data for:Unlocking soil phages for global food security

description: This repository contains the processed data and code supporting the analysis of soil phage regulation of global crop yield. The dataset includes spatially harmonized raster layers of soil phage abundance and diversity, crop yield, soil properties, climate variables, and agricultural management indicators, as well as source data underlying the figures in the associated manuscript. Machine-learning code and trained model outputs are provided to enable reproducibility of the main analyses.
DOI: 10.34894/ngauwl
title: 2029-01-18 - Australian Ocean Data Network - CoreTrustSeal Requirements 2023-2025

description: CoreTrustSeal certification
DOI: 10.15468/dl.8s96r9
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((0.52569 41.07970,3.64384 41.07970,3.64384 42.32806,0.52569 42.32806,0.52569 41.07970))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Andrena pellucens"
 ]
}

 The dataset includes 19 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020025-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sgzrxf
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Kalanchoe daigremontiana",
 "Country is Venezuela (Bolivarian Republic of)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020027-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18298775
title: Rock magnetic induction intensity monitoring data
DOI: 10.5281/zenodo.18298774
title: Rock magnetic induction intensity monitoring data
DOI: 10.5281/zenodo.18234244
title: The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning (Dataset)

description: Wheelbot Dataset

A large, high-quality dynamics dataset of the [Mini Wheelbot](https://github.com/wheelbot/Mini-Wheelbot). The dataset contains 1 kHz data of all onboard sensor readings, the estimated state, ground-truth pose measurements from a motion capture system, and a third-person view video of the experiment. We perform a variety of experiments using pseudo-random binary excitation signals (PRBS) as setpoints of a linear controller, an MPC for driving, and an RL policy that races along a track. Experiments are performed across multiple hardware instances and on different surfaces. With this dataset, we hope to encourage researchers to use the Mini Wheelbot to benchmark their learning-based control methods, even without access to the real hardware. We include two example implementations of how to use the dataset, i.e., for dynamics learning and state estimation. A detailled description of this dataset is available in the brief on arxiv.



If you find this dataset helpful, please cite the Mini Wheelbot paper and/or dataset directly:

```bibtex

@inproceedings{hose2025mini,

title={The {Mini Wheelbot}: A Testbed for Learning-based Balancing, Flips, and Articulated Driving},

booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)},

publisher={IEEE},

author={Hose, Henrik and Weisgerber, Jan and Trimpe, Sebastian},

year={2025},

}


@article{hose2026dataset,

title={The {Mini Wheelbot} Dataset: High-Fidelity Data for Robot Learning},

author={Henrik Hose and Paul Brunzema and Devdutt Subhasish and Sebastian Trimpe},

year={2026},

url={https://arxiv.org/abs/2601.11394},

}

```
DOI: 10.5281/zenodo.18260659
title: The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning (Dataset)

description: Wheelbot Dataset

A large, high-quality dynamics dataset of the [Mini Wheelbot](https://github.com/wheelbot/Mini-Wheelbot). The dataset contains 1 kHz data of all onboard sensor readings, the estimated state, ground-truth pose measurements from a motion capture system, and a third-person view video of the experiment. We perform a variety of experiments using pseudo-random binary excitation signals (PRBS) as setpoints of a linear controller, an MPC for driving, and an RL policy that races along a track. Experiments are performed across multiple hardware instances and on different surfaces. With this dataset, we hope to encourage researchers to use the Mini Wheelbot to benchmark their learning-based control methods, even without access to the real hardware. We include two example implementations of how to use the dataset, i.e., for dynamics learning and state estimation. A detailled description of this dataset is available in the brief on arxiv.



If you find this dataset helpful, please cite the Mini Wheelbot paper and/or dataset directly:

```bibtex

@inproceedings{hose2025mini,

title={The {Mini Wheelbot}: A Testbed for Learning-based Balancing, Flips, and Articulated Driving},

booktitle={2025 IEEE International Conference on Robotics and Automation (ICRA)},

publisher={IEEE},

author={Hose, Henrik and Weisgerber, Jan and Trimpe, Sebastian},

year={2025},

}


@article{hose2026dataset,

title={The {Mini Wheelbot} Dataset: High-Fidelity Data for Robot Learning},

author={Henrik Hose and Paul Brunzema and Devdutt Subhasish and Sebastian Trimpe},

year={2026},

url={https://arxiv.org/abs/2601.11394},

}

```
DOI: 10.15468/dl.bcepjb
title: Occurrence Download

description: A dataset containing 413 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((106.17666 -5.59485,104.51152 -7.17264,114.46362 -9.31864,114.89159 -6.79253,106.17666 -5.59485))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Begonia"
 ]
}

 The dataset includes 413 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020028-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18201633
title: Double-Pumped Kerr Parametric Amplifier Beyond the Gain-Bandwidth Limit

description: Raw and processed data as well as a jupyter notebooks for the evaluation associated with the paper "Double-Pumped Kerr Parametric Amplifier Beyond the Gain-Bandwidth Limit" (publicly available on arXiv: arXiv:2601.00078) by N. Zapata et al. acquired and prepared at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

The notebooks show how to analyze the main results of the manuscript.

For additional information please contact: nicolas.gonzalez2@kit.edu or ioan.pop@kit.edu.
DOI: 10.5281/zenodo.18201634
title: Double-Pumped Kerr Parametric Amplifier Beyond the Gain-Bandwidth Limit

description: Raw and processed data as well as a jupyter notebooks for the evaluation associated with the paper "Double-Pumped Kerr Parametric Amplifier Beyond the Gain-Bandwidth Limit" (publicly available on arXiv: arXiv:2601.00078) by N. Zapata et al. acquired and prepared at the Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

The notebooks show how to analyze the main results of the manuscript.

For additional information please contact: nicolas.gonzalez2@kit.edu or ioan.pop@kit.edu.
DOI: 10.21227/kxed-hj49
title: "Integrated Hourly Electricity Load and Meteorological Dataset for Smart Grid Demand Analysis and Forecasting"

description: "This dataset delivers a high-resolution, city-scale representation of Delhi\u2019s electricity demand behavior through the rigorous integration of hourly electrical load measurements with temporally aligned meteorological observations for the National Capital Territory of Delhi. Capturing the intricate and non-linear coupling between atmospheric forcing and urban energy consumption, the dataset reflects Delhi\u2019s pronounced climatic heterogeneity, including extreme summer heatwaves, monsoon-driven humidity regimes, transitional shoulder seasons, and winter cold spells associated with atmospheric stagnation. These conditions critically shape electricity demand through air-conditioning and heating loads, behavioral adaptations, and evolving urban infrastructure. The dataset synchronizes hourly load profiles with key weather variables\u2014such as ambient temperature, relative humidity, wind speed, and associated atmospheric indicators\u2014thereby enabling detailed analysis of weather-sensitive demand fluctuations and peak-load stress phenomena. Structured for advanced energy analytics, it is well suited for state-of-the-art time-series modeling, including deep learning, transformer-based architectures, hybrid physics-informed approaches, and climate-aware forecasting frameworks. The dataset supports investigations into heatwave-induced demand surges, demand response potential, dynamic tariff design, renewable energy integration, and power system resilience under climate variability and extreme events. By providing a unified, machine-readable depiction of the electricity\u2013weather nexus in one of the world\u2019s largest and most climate-vulnerable megacities, this dataset facilitates reproducible research, robust model benchmarking, and evidence-based policy analysis for smart grid planning, sustainable urban energy management, and climate-adaptive infrastructure development in Delhi."
DOI: 10.5281/zenodo.17698818
title: Dataset of KDE+ hotspots of vegetation fires along railway lines in Czechia

description: Jedná se o data o vlhkosti půdy v okolí tratí, data o lokalizaci požárů a stavu krajiny. Výzkumná data a metadata jsou rámci repozitářů trvale k dispozici. Data jsou sdílena pod licencí CC-BY, pokud nebudou zjištěny další relevantní důvody, proč není možné data sdílet pouze s podmínkou uvedení autora (About CC Licenses - Creative Commons).
DOI: 10.5281/zenodo.17698819
title: Dataset of KDE+ hotspots of vegetation fires along railway lines in Czechia

description: Jedná se o data o vlhkosti půdy v okolí tratí, data o lokalizaci požárů a stavu krajiny. Výzkumná data a metadata jsou rámci repozitářů trvale k dispozici. Data jsou sdílena pod licencí CC-BY, pokud nebudou zjištěny další relevantní důvody, proč není možné data sdílet pouze s podmínkou uvedení autora (About CC Licenses - Creative Commons).
DOI: 10.17031/10.17031/1810.1
title: 2015 Alderney Wildlife Trust (AWT) Alderney intertidal strandline survey

description: The intertidal strandline survey aims to record marine life within the intertidal envrionments. This survey is conducted within the intertidal environments of Alderney, Channel Islands. The rationale of the survey is to a) record marine life on Alderney and b) engage local residents on the marine environment.
DOI: 10.15468/dl.rzbv85
title: Occurrence Download

description: A dataset containing 2646 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Lithuania",
 "TaxonKey is Galliformes"
 ]
}

 The dataset includes 2646 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020029-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.94cn2e
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Decalobanthus boisianus",
 "Country is Viet Nam",
 "HasCoordinate is true"
 ]
}

 The dataset includes 19 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020030-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.95b4b7
title: Occurrence Download

description: A dataset containing 46 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acanthocereus rosei"
 ]
}

 The dataset includes 46 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048641-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/kmzwj-5r359
title: Coxiella_ Verocells_28dpi

description: Coxiella burnetii Nine Mile phase II clone 4 (NMIIC4) strain. Coxiella cells purified from infected Vero cells.
description: <strong>Tilt Series Date:</strong> 2017-09-25</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 using Caltech tomography pipeline.</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Refer to Heinzen lab.</p>
<strong>Sample Preparation:</strong> 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.T</p>
  dga2017-09-25-110_slicer19059.jpg: multilayer structure beneath the inner membrane
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-09-25-110/rawdata/</p>Cb_vero_201709250110.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-09-25-110/rawdata/Cb_vero_201709250110.mrc"
        > <i class="download icon"></i> Download </a></p>

        Cb_vero_201709250110_part61_7.rec,  Reconstruction (Pixel Size 1.56 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-09-25-110/3dimage_88601/Cb_vero_201709250110_part61_7.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/me7tr-kmj40
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/dga2017-10-17-21/rawdata/</p>Cb_d5pH4_201710170015.mrc,  Tilt Series (Pixel Size 0.39 nm), 1.7 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-10-17-21/rawdata/Cb_d5pH4_201710170015.mrc"
        > <i class="download icon"></i> Download </a></p>

        Cb_d5pH4_201710170015_full.rec,  Reconstruction (Pixel Size 1.56 nm), 445.4 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/dga2017-10-17-21/3dimage_89209/Cb_d5pH4_201710170015_full.rec"
        > <i class="download icon"></i> Download </a></p>