cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14846209
title: Dataset for: Arbuscular Mycorrhiza as a Bioagent for Striga hermonthica Management in Sorghum in Sub-Saharan Africa

description: This dataset contains experimental data collected from greenhouse conditions over two consecutive seasons, investigating the potential of Funneliformis mosseae, an arbuscular mycorrhizal fungus (AMF), in controlling Striga hermonthica (witchweed) parasitism in sorghum. The research examines the effects of AMF inoculation on S. hermonthica incidence and sorghum growth parameters, which are critical for sustainable agricultural practices in sub-Saharan Africa.

The dataset includes data on Striga emergence, sorghum growth, and biomass measurements under different treatment conditions: Striga-free, Striga-infested, Striga-infested with AMF inoculation, and Striga-free with AMF inoculation. Key metrics recorded include sorghum height, number of leaves, leaf area, and shoot biomass, as well as Striga plant counts and biomass at harvest.

This data supports the hypothesis that AMF inoculation significantly reduces Striga parasitism and improves sorghum growth, making it a promising biocontrol strategy for managing S. hermonthica infestations in sorghum production systems, particularly in greenhouse conditions. Further research, including field trials, is recommended to validate these findings for larger-scale agricultural use.
DOI: 10.5281/zenodo.14846210
title: Dataset for: Arbuscular Mycorrhiza as a Bioagent for Striga hermonthica Management in Sorghum in Sub-Saharan Africa

description: This dataset contains experimental data collected from greenhouse conditions over two consecutive seasons, investigating the potential of Funneliformis mosseae, an arbuscular mycorrhizal fungus (AMF), in controlling Striga hermonthica (witchweed) parasitism in sorghum. The research examines the effects of AMF inoculation on S. hermonthica incidence and sorghum growth parameters, which are critical for sustainable agricultural practices in sub-Saharan Africa.

The dataset includes data on Striga emergence, sorghum growth, and biomass measurements under different treatment conditions: Striga-free, Striga-infested, Striga-infested with AMF inoculation, and Striga-free with AMF inoculation. Key metrics recorded include sorghum height, number of leaves, leaf area, and shoot biomass, as well as Striga plant counts and biomass at harvest.

This data supports the hypothesis that AMF inoculation significantly reduces Striga parasitism and improves sorghum growth, making it a promising biocontrol strategy for managing S. hermonthica infestations in sorghum production systems, particularly in greenhouse conditions. Further research, including field trials, is recommended to validate these findings for larger-scale agricultural use.
DOI: 10.5281/zenodo.14839795
title: unimpeded: mlcdm des_y1.joint+sn.pantheon

description: cosmological model:mlcdm, dataset:des_y1.joint+sn.pantheon
DOI: 10.5281/zenodo.14839796
title: unimpeded: mlcdm des_y1.joint+sn.pantheon

description: cosmological model:mlcdm, dataset:des_y1.joint+sn.pantheon
DOI: 10.6084/m9.figshare.28266977
title: Joint Tomographic Inversion for P and S Velocity Models of the Middle East and Their Implications on the Regional Tectonic Framework

description: Input data and results of a joint tomographic inversion for P and S velocity models of the Middle East
DOI: 10.6084/m9.figshare.28266977.v1
title: Joint Tomographic Inversion for P and S Velocity Models of the Middle East and Their Implications on the Regional Tectonic Framework

description: Input data and results of a joint tomographic inversion for P and S velocity models of the Middle East
DOI: 10.13020/c43a-ew25
title: New Materials to re-create Synoptic Observations of Near-Inertial Motions in an Enclosed Basin

description: A recent manuscript examined near inertial currents and internal waves in Lake Superior using mooring observations. This submission includes the Matlab scripts and functions to analyze the observations and recreate the figures in the manuscript.
description: There is one folder (compressed in .zip format) with an associated plain-text documentation file that includes detailed information about its contents. The folders are: (1) analysis_scripts and (2) plotting_scripts. The first folder contains the Matlab scripts to process observations collected on the moorings. The second folder contains Matlab scripts and functions that re-create the figures included in Green et al. (2025). All of the data needed for these scripts are included in the folder.
DOI: 10.5281/zenodo.14827768
title: Guizhou Karst Rock Fracture, Soil Moisture, Sap Flow, and Meteorological Data

description: This dataset provides hydrological, ecological, and meteorological observations collected from the karst region of Guizhou, China. The data are useful for studying plant water use strategies, subsurface water movement, and ecohydrological processes in karst ecosystems. The data monitoring period spans from August 2021 to September 2024.

Dataset Contents:





Rock Fracture Water Data:



Water content and storage characteristics of karst rock fractures.

Monitoring data from different depths and apertures.





Soil Moisture Data:



Soil water content measurements from multiple soil depths.

Spatiotemporal variations in soil moisture.





Sap Flow Data:



Tree sap flow measurements to assess plant transpiration rates.

Diurnal and seasonal variations in tree water use.

Data from multiple tree species in different locations.





Meteorological Data:



Precipitation, temperature, relative humidity, and vapor pressure deficit (VPD).

Continuous weather observations from local meteorological stations.




Usage and Applications:



Ecohydrological studies of karst ecosystems.

Analysis of plant water use and subsurface water dynamics.

Model calibration and validation for karst hydrological processes.

Understanding tree water uptake strategies under varying environmental conditions.


When citing this dataset, please reference the following paper:

Liu X., Liu W., Chen X., et al. Estimating fracture characteristics and hydraulic conductivity from slug tests in epikarst of southwest China. Journal of Hydrology: Regional Studies, 2024, 53. https://doi.org/10.1016/j.ejrh.2024.101777.
DOI: 10.5281/zenodo.14827769
title: Guizhou Karst Rock Fracture, Soil Moisture, Sap Flow, and Meteorological Data

description: This dataset provides hydrological, ecological, and meteorological observations collected from the karst region of Guizhou, China. The data are useful for studying plant water use strategies, subsurface water movement, and ecohydrological processes in karst ecosystems. The data monitoring period spans from August 2021 to September 2024.

Dataset Contents:





Rock Fracture Water Data:



Water content and storage characteristics of karst rock fractures.

Monitoring data from different depths and apertures.





Soil Moisture Data:



Soil water content measurements from multiple soil depths.

Spatiotemporal variations in soil moisture.





Sap Flow Data:



Tree sap flow measurements to assess plant transpiration rates.

Diurnal and seasonal variations in tree water use.

Data from multiple tree species in different locations.





Meteorological Data:



Precipitation, temperature, relative humidity, and vapor pressure deficit (VPD).

Continuous weather observations from local meteorological stations.




Usage and Applications:



Ecohydrological studies of karst ecosystems.

Analysis of plant water use and subsurface water dynamics.

Model calibration and validation for karst hydrological processes.

Understanding tree water uptake strategies under varying environmental conditions.


When citing this dataset, please reference the following paper:

Liu X., Liu W., Chen X., et al. Estimating fracture characteristics and hydraulic conductivity from slug tests in epikarst of southwest China. Journal of Hydrology: Regional Studies, 2024, 53. https://doi.org/10.1016/j.ejrh.2024.101777.
DOI: 10.34740/kaggle/dsv/10716675
title: PROTEINS Structure Function
DOI: 10.25919/bv4y-3z85
title: Parkes observations for project P1348 semester 2024OCTS_03

description: We have discovered 6 candidate eclipsing binary millisecond pulsar (BMSPs) systems via image-plane searches for steep-spectrum radio sources with periodic variability. The radio emission, short eclipse periods (&lt;1day), and Fermi association suggest they could be spider PSRs. Millisecond pulsations have recently been detected from a similarly identified candidate system. The discovery of millisecond pulsars (MSPs) in these systems would provide insights into the formation and evolution of spider PSRs. It would also improve predictions of the BMSP and MSP populations detectable by upcoming, highly sensitive image-plane surveys at low frequencies using precursors to, and eventually, the Square Kilometre Array (SKA) itself. Finally, 5 of our 6 candidates are also located in the Galactic bulge. If all such systems include MSPs, it would provide further compelling evidence that the Fermi GeV gamma-ray excess is coming from thousands of MSPs.
DOI: 10.6084/m9.figshare.28385834
title: Figure 5

description: Data to produce Figure 5
DOI: 10.6084/m9.figshare.28385834.v1
title: Figure 5

description: Data to produce Figure 5
DOI: 10.25345/c5513v75f
title: MassIVE MSV000097094 - An intrinsically disordered region of histone demethylase KDM5A regulates catalysis through interactions with the nucleosomal acidic patch and DNA
title: KDM5A lysine demethylase conformation was probed by BS3 crosslinking in the presence and absence of DNA. Crosslinked proteins were reduced, alkylated with iodoacetamide, trypsin digest, and labeled with TMT6plex reagent prior to mixing and acquisition on an Orbitrap Exploris equiped with a FAIMS source. Three biological replicates are included split over multipe technical replicates and/or SEC fractions. The SampleKey.txt file explains the labeling scheme.
DOI: 10.25959/3pfk-4d36
title: Rubicon estuary native saltmarsh spatial change data

description: At the Rubicon estuary in North Tasmania, native marsh changes were evaluated at three sites 1946-2023 using two spatial methods, shoreline movement and marsh polygonal area.
DOI: 10.5281/zenodo.14847199
title: NACHOS dataset: OCT and Xray

description: The following datasets are part of a future paper.

Kidney OCT dataset

Three type of tissues were sampled: cortex, medulla, and pelvis. Image size: 185*210 pixels. The OCT dataset comes from 10 porcine kidneys. For each kidney and tissue, there are 30 volumes. Each volume contain 20 images. Total number of images: 10*3*30*20 = 18,000. The same dataset was partitioned in three different levels: image (folder: split_random), volumen (folder: split_volume), and subject (folder: split_subject).

Chest X-ray repository

A chest X-ray repository was built using the ChestX-ray8 dataset, the CheXpert dataset, the MIMIC-CXR dataset, and the PadChest dataset from the TorchXRayVision library. A chest X-ray repository was built using the ChestX-ray8 dataset, the CheXpert dataset, the MIMIC-CXR dataset, and the PadChest dataset from the TorchXRayVision library. The chest X-ray repository was partitioned into four folds using three different partitioning levels. In image-level partitioning(folder: split1_random), images were randomly distributed across four folds. In patient-level partitioning (folder: split2_patient), all images from the same patient were assigned to the same fold. Finally, in dataset-level partitioning (folder: split3_dataset), each dataset was exclusively allocated to a separate fold.
DOI: 10.5281/zenodo.14847200
title: NACHOS dataset: OCT and Xray

description: The following datasets are part of a future paper.

Kidney OCT dataset

Three type of tissues were sampled: cortex, medulla, and pelvis. Image size: 185*210 pixels. The OCT dataset comes from 10 porcine kidneys. For each kidney and tissue, there are 30 volumes. Each volume contain 20 images. Total number of images: 10*3*30*20 = 18,000. The same dataset was partitioned in three different levels: image (folder: split_random), volumen (folder: split_volume), and subject (folder: split_subject).

Chest X-ray repository

A chest X-ray repository was built using the ChestX-ray8 dataset, the CheXpert dataset, the MIMIC-CXR dataset, and the PadChest dataset from the TorchXRayVision library. A chest X-ray repository was built using the ChestX-ray8 dataset, the CheXpert dataset, the MIMIC-CXR dataset, and the PadChest dataset from the TorchXRayVision library. The chest X-ray repository was partitioned into four folds using three different partitioning levels. In image-level partitioning(folder: split1_random), images were randomly distributed across four folds. In patient-level partitioning (folder: split2_patient), all images from the same patient were assigned to the same fold. Finally, in dataset-level partitioning (folder: split3_dataset), each dataset was exclusively allocated to a separate fold.
DOI: 10.5281/zenodo.14847071
title: Sensitivity of atmospheric carbon dioxide to dust iron solubility during the last glacial-interglacial cycle: simulation output data

description: This dataset, archived in Output_simulations.tar.gz, contains the output of model simulations from the study "Sensitivity of atmospheric carbon dioxide to dust iron solubility during the last glacial-interglacial cycle." It is structured to facilitate access to regional (Regional) and global (Global) simulation outputs and associated data derived from various sources of aeolian dust deposition fields.

Organizational Structure:

Global Simulations: Files in this directory are derived from global models and are organized by the source of the dust deposition fields used to force the cGENIE model. The sources include:



Albani (Albani et al. (2014))

Lambert ( Lambert et al. (2015))

Takemura (Takemura et al. (2009))

Ohgaito (Ohgaito et al. (2018))

MIROC-ESM (Sueyoshi et al. (2013))

MRI-CGCM3 (Yukimoto et al. (2012))


Each sub-directory contains simulation outputs for different scaling factors (0.33, 0.67, 1.00, 2.00, 3.00) reflecting variations in dust iron solubility.

Regional Simulations: These directories are structured similarly to the global simulations but are further organized by High Nutrient, Low Chlorophyll (HNLC) regions, which include:



SA (South Atlantic)

SP (South Pacific)

NP (North Pacific)

CP (Central East Pacific)

NA (North Atlantic)


Files in these sub-directories detail the outcomes from simulations that apply the same scaling factors as the global simulations but focused on specific oceanic regions noted for their unique biogeochemical characteristics.

Included Files:

fields_biogem_2d.nc: Contains two-dimensional fields of ocean and atmospheric properties, formatted as NetCDF.fields_biogem_3d.nc: Includes three-dimensional fields of ocean properties at multiple depth levels, provided as NetCDF.biogem_series_atm_pCO2.res: Outputs the atmospheric concentration of pCO2 from each simulation, crucial for understanding the impacts of varied iron solubility on atmospheric carbon dioxide levels.
DOI: 10.5281/zenodo.14847072
title: Sensitivity of atmospheric carbon dioxide to dust iron solubility during the last glacial-interglacial cycle: simulation output data

description: This dataset, archived in Output_simulations.tar.gz, contains the output of model simulations from the study "Sensitivity of atmospheric carbon dioxide to dust iron solubility during the last glacial-interglacial cycle." It is structured to facilitate access to regional (Regional) and global (Global) simulation outputs and associated data derived from various sources of aeolian dust deposition fields.

Organizational Structure:

Global Simulations: Files in this directory are derived from global models and are organized by the source of the dust deposition fields used to force the cGENIE model. The sources include:



Albani (Albani et al. (2014))

Lambert ( Lambert et al. (2015))

Takemura (Takemura et al. (2009))

Ohgaito (Ohgaito et al. (2018))

MIROC-ESM (Sueyoshi et al. (2013))

MRI-CGCM3 (Yukimoto et al. (2012))


Each sub-directory contains simulation outputs for different scaling factors (0.33, 0.67, 1.00, 2.00, 3.00) reflecting variations in dust iron solubility.

Regional Simulations: These directories are structured similarly to the global simulations but are further organized by High Nutrient, Low Chlorophyll (HNLC) regions, which include:



SA (South Atlantic)

SP (South Pacific)

NP (North Pacific)

CP (Central East Pacific)

NA (North Atlantic)


Files in these sub-directories detail the outcomes from simulations that apply the same scaling factors as the global simulations but focused on specific oceanic regions noted for their unique biogeochemical characteristics.

Included Files:

fields_biogem_2d.nc: Contains two-dimensional fields of ocean and atmospheric properties, formatted as NetCDF.fields_biogem_3d.nc: Includes three-dimensional fields of ocean properties at multiple depth levels, provided as NetCDF.biogem_series_atm_pCO2.res: Outputs the atmospheric concentration of pCO2 from each simulation, crucial for understanding the impacts of varied iron solubility on atmospheric carbon dioxide levels.
DOI: 10.6084/m9.figshare.28385900
title: Figure 1

description: Data to generate Figure 1
DOI: 10.5281/zenodo.8312438
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Władysław Szafer, http://www.wikidata.org/entity/Q322052. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q322052
DOI: 10.5281/zenodo.14847320
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Władysław Szafer, http://www.wikidata.org/entity/Q322052. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q322052
DOI: 10.5281/zenodo.8312437
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Władysław Szafer, http://www.wikidata.org/entity/Q322052. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q322052
DOI: 10.5281/zenodo.13855437
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Margery Claire Carlson, http://www.wikidata.org/entity/Q21392328. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21392328
DOI: 10.5281/zenodo.13864265
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Margery Claire Carlson, http://www.wikidata.org/entity/Q21392328. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21392328
DOI: 10.5281/zenodo.13855436
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Margery Claire Carlson, http://www.wikidata.org/entity/Q21392328. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21392328
DOI: 10.5281/zenodo.14847321
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Margery Claire Carlson, http://www.wikidata.org/entity/Q21392328. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21392328
DOI: 10.5281/zenodo.10836072
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ursula Göllner-Scheiding, http://www.wikidata.org/entity/Q55038831. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55038831
DOI: 10.5281/zenodo.11043799
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ursula Göllner-Scheiding, http://www.wikidata.org/entity/Q55038831. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55038831
DOI: 10.5281/zenodo.14847326
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ursula Göllner-Scheiding, http://www.wikidata.org/entity/Q55038831. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55038831
DOI: 10.5281/zenodo.8149168
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ursula Göllner-Scheiding, http://www.wikidata.org/entity/Q55038831. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55038831
DOI: 10.5281/zenodo.8149169
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ursula Göllner-Scheiding, http://www.wikidata.org/entity/Q55038831. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55038831
DOI: 10.5281/zenodo.14847323
title: berkhs2025
DOI: 10.5281/zenodo.14847324
title: berkhs2025
DOI: 10.5281/zenodo.14807588
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ferdinand Albert Heidenreich, http://www.wikidata.org/entity/Q55675410. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55675410
DOI: 10.5281/zenodo.14847328
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by Ferdinand Albert Heidenreich, http://www.wikidata.org/entity/Q55675410. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q55675410
DOI: 10.5281/zenodo.14819388
title: Natural history specimens collected and/or identified and deposited.

description: Natural history specimen data collected and/or identified by William Andrew Shoolbred, http://www.wikidata.org/entity/Q21608819. Claims or attributions were made on Bionomia, https://bionomia.net using specimen data from the Global Biodiversity Information Facility, https://gbif.org.
description: http://www.wikidata.org/entity/Q21608819