cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57745/n26qbs
title: European biodiversity data for fishes in the DRYvER project

description: Abundance, density, total length, and biomass of all fish taxa collected in the six European DRNs, together with associated environmental data collected during fish sampling. Environmental and background variables include sampled area, mean wetted width, and reach length.
DOI: 10.57745/rs8266
title: European biodiversity data for invertebrates in the DRYvER project

description: Abundance, density, mean length, mean dry mass, and mean secondary production of all macroinvertebrate taxa collected in the six European DRNs, together with associated environmental data collected during invertebrate sampling. Environmental and background variables include flow state, number of subsamples, sampled area, maximum, minimum, and mean wetted width, bankfull width at minimum and maximum wetted widths, pH, conductivity, temperature, dissolved oxygen (absolute concentration and % saturation), discharge, percentage of inorganic substrates, percentage cover of macrophytes and other organic substrates, mean water velocity, substrate embeddedness, maximum depth, and reach length.
DOI: 10.15468/dl.np6grf
title: Occurrence Download

description: A dataset containing 358115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "Continent is Asia",
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia",
 "Year 2019-2019"
 ]
}

 The dataset includes 358115 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063019-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23642/usn.7673105
title: Lesja, Soil Temperatures, 5 cm, 2011-2014

description: DATASET MIGRATED FROM FIGSHARE: Soil temperatures recorded between October 2011 and July 2014 at Lesja, Oppland County, Norway. Recorded at 5 cm depth. For more information on the locations of the study plots check the attached readme file.
DOI: 10.23642/usn.7673105/hdvetw
title: readme-soiltemps-5cm-lesja2011-2014.txt

description: readme-soiltemps-5cm-lesja2011-2014.txt
DOI: 10.23642/usn.7673105/sau4kj
title: soiltemps-5cm-lesja2011-2014.txt

description: soiltemps-5cm-lesja2011-2014.txt
DOI: 10.23642/usn.7673120
title: Lesja, Soil Temperatures, 30 cm, 2011-2014

description: DATASET MIGRATED FROM FIGSHARE: Soil temperatures at 30 cm depth recorded from October 2011 until July 2014 at Lesja, Oppland County, Norway. Detailed information about the plot locations is in the attached readme file.
DOI: 10.23642/usn.7673120/amt8eu
title: soiltemps-30cm-lesja2011-2014.txt

description: soiltemps-30cm-lesja2011-2014.txt
DOI: 10.23642/usn.7673120/vkndhb
title: readme-soiltemps-30cm-lesja2011-2014.txt

description: readme-soiltemps-30cm-lesja2011-2014.txt
DOI: 10.23642/usn.7454279
title: Soils at Haukeli, Imingfjell, and Lesja

description: DATASET MIGRATED FROM FIGSHARE: This dataset consist of two types of information. The first is a series of photographs of soil profiles at Haukeli, Imingfjell, and Lesja, all in southern Norway. The second is a set of spreadsheets with results of soil analyses of these profiles and of study plots at all three areas. &lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;The photographs can be re-used on the condition that I am listed as the photographer. &lt;/div&gt;
DOI: 10.23642/usn.7454279/wc7wpm
title: read-me-soils.txt

description: read-me-soils.txt
DOI: 10.23642/usn.7454279/gs2dpg
title: soil-chemical-data-plots.csv

description: soil-chemical-data-plots.csv
DOI: 10.23642/usn.7454279/kyc1ws
title: soil-texture-analysis-plots.csv

description: soil-texture-analysis-plots.csv
DOI: 10.23642/usn.7454279/q0bjfs
title: soil-chemical-data-profiles.csv

description: soil-chemical-data-profiles.csv
DOI: 10.23642/usn.7454279/4ebo2o
title: soilprofil-imingfjell-snowbed.JPG

description: soilprofil-imingfjell-snowbed.JPG
DOI: 10.23642/usn.7454279/2nmldz
title: soilprofile-haukeli-midslope.JPG

description: soilprofile-haukeli-midslope.JPG
DOI: 10.23642/usn.7454279/zsugg1
title: soilprofile-haukeli-ridgetop.JPG

description: soilprofile-haukeli-ridgetop.JPG
DOI: 10.23642/usn.7454279/hptyvx
title: soilprofile-haukeli-snowbed.JPG

description: soilprofile-haukeli-snowbed.JPG
DOI: 10.23642/usn.7454279/a62vke
title: soilprofile-imingfjell-midslope.JPG

description: soilprofile-imingfjell-midslope.JPG
DOI: 10.23642/usn.7454279/jlsfz6
title: soilprofile-imingfjell-ridgetop.JPG

description: soilprofile-imingfjell-ridgetop.JPG
DOI: 10.23642/usn.7454279/xtkbom
title: soilprofile-lesja-midslope.JPG

description: soilprofile-lesja-midslope.JPG
DOI: 10.23642/usn.7454279/78gfv9
title: soilprofile-lesja-ridgetop.JPG

description: soilprofile-lesja-ridgetop.JPG
DOI: 10.23642/usn.7454279/duhxod
title: soilprofile-lesja-snowbed.JPG

description: soilprofile-lesja-snowbed.JPG
DOI: 10.5281/zenodo.17632107
title: Discharge simulations for the Seine river catchment, scenarios to analyse the future of low-flows and reservoir management

description: Discharge from GRSD model simulations over the Seine river catchment in France, under 4 contrasted climate scenarios and 9 management scenarios for the 4 reservoirs on the catchment.

More specifically, this dataset regroups all the simulation outputs used in the study of low-flows over the Seine river, integrating the influence of reservoirs within the semi-distributed hydrological model GRSD under different climate scenarios. This study is part of the STARS4Water project.

It covers 155 stations, with 17 stations downstream of the reservoirs. There is one simulation for each climate scenario for all stations covered, the different management scenarios outputs are only provided for the 17 influenced stations.
DOI: 10.6084/m9.figshare.30933363
title: Fréchet Sufficient Dimension Reduction for Metric Space-Valued Data via Distance Covariance

description: We propose a novel Fréchet sufficient dimension reduction (SDR) method based on kernel distance covariance, tailored for metric-space-valued responses such as count data, probability densities, and other complex structures. The method leverages a kernel-based transformation to map metric-space-valued responses into a feature space, enabling efficient dimension reduction. By incorporating kernel distance covariance, the proposed approach offers enhanced flexibility and adaptability for datasets with diverse and non-Euclidean characteristics. The effectiveness of the method is demonstrated through synthetic simulations and several real-world applications. In all cases, the proposed method runs faster and consistently outperforms the existing Fréchet SDR approaches, demonstrating its broad applicability and robustness in addressing complex data challenges.
DOI: 10.6084/m9.figshare.30933363.v1
title: Fréchet Sufficient Dimension Reduction for Metric Space-Valued Data via Distance Covariance

description: We propose a novel Fréchet sufficient dimension reduction (SDR) method based on kernel distance covariance, tailored for metric-space-valued responses such as count data, probability densities, and other complex structures. The method leverages a kernel-based transformation to map metric-space-valued responses into a feature space, enabling efficient dimension reduction. By incorporating kernel distance covariance, the proposed approach offers enhanced flexibility and adaptability for datasets with diverse and non-Euclidean characteristics. The effectiveness of the method is demonstrated through synthetic simulations and several real-world applications. In all cases, the proposed method runs faster and consistently outperforms the existing Fréchet SDR approaches, demonstrating its broad applicability and robustness in addressing complex data challenges.
DOI: 10.5281/zenodo.17632106
title: Discharge simulations for the Seine river catchment, scenarios to analyse the future of low-flows and reservoir management

description: Discharge from GRSD model simulations over the Seine river catchment in France, under 4 contrasted climate scenarios and 9 management scenarios for the 4 reservoirs on the catchment.

More specifically, this dataset regroups all the simulation outputs used in the study of low-flows over the Seine river, integrating the influence of reservoirs within the semi-distributed hydrological model GRSD under different climate scenarios. This study is part of the STARS4Water project.

It covers 155 stations, with 17 stations downstream of the reservoirs. There is one simulation for each climate scenario for all stations covered, the different management scenarios outputs are only provided for the 17 influenced stations.
DOI: 10.5281/zenodo.18018296
title: Standardized Diffusion Tensor Imaging Accurately Differentiates Early Relapsing–Remitting Multiple Sclerosis from Healthy Controls

description: This repository contains analysis-ready feature data and the computational notebook supporting the manuscript “Standardized Diffusion Tensor Imaging Accurately Differentiates Early Relapsing–Remitting Multiple Sclerosis from Healthy Controls.” The release provides (1) a CSV table of diffusion-derived regional metrics used for machine learning analyses and (2) a Jupyter notebook (.ipynb) with core analysis reported in the manuscript.

Files



metrics.csv - Tabular dataset of diffusion-derived features aggregated within anatomically defined regions of interest (ROIs).

analysis.ipynb - Jupyter notebook used to analyse the metrics 


CSV structure

Each row corresponds to a single analysed case. Columns include:



segmentation_subject - internal subject identifier

Group - diagnostic group label

Year - study year

ROI_<N>_<metric> - diffusion-derived metric aggregated in ROI N.



\<N\>: numeric ROI index

\<metric\>: metric name (e.g., lambda1, fractional anisotropy, tensor shape indices, mean b0 intensity, depending on the exported set)



Example column: ROI_2_lambda1 = principal diffusivity (λ1) in ROI 2.

Notes on privacy and reuse

The shared table contains derived diffusion metrics only and does not include raw imaging data.

Recommended citation

Please cite this Zenodo record and the associated manuscript:

Standardized Diffusion Tensor Imaging Accurately Differentiates Early Relapsing–Remitting Multiple Sclerosis from Healthy Controls.
DOI: 10.5281/zenodo.18018295
title: Standardized Diffusion Tensor Imaging Accurately Differentiates Early Relapsing–Remitting Multiple Sclerosis from Healthy Controls

description: This repository contains analysis-ready feature data and the computational notebook supporting the manuscript “Standardized Diffusion Tensor Imaging Accurately Differentiates Early Relapsing–Remitting Multiple Sclerosis from Healthy Controls.” The release provides (1) a CSV table of diffusion-derived regional metrics used for machine learning analyses and (2) a Jupyter notebook (.ipynb) with core analysis reported in the manuscript.

Files



metrics.csv - Tabular dataset of diffusion-derived features aggregated within anatomically defined regions of interest (ROIs).

analysis.ipynb - Jupyter notebook used to analyse the metrics 


CSV structure

Each row corresponds to a single analysed case. Columns include:



segmentation_subject - internal subject identifier

Group - diagnostic group label

Year - study year

ROI_<N>_<metric> - diffusion-derived metric aggregated in ROI N.



\<N\>: numeric ROI index

\<metric\>: metric name (e.g., lambda1, fractional anisotropy, tensor shape indices, mean b0 intensity, depending on the exported set)



Example column: ROI_2_lambda1 = principal diffusivity (λ1) in ROI 2.

Notes on privacy and reuse

The shared table contains derived diffusion metrics only and does not include raw imaging data.

Recommended citation

Please cite this Zenodo record and the associated manuscript:

Standardized Diffusion Tensor Imaging Accurately Differentiates Early Relapsing–Remitting Multiple Sclerosis from Healthy Controls.
DOI: 10.23642/usn.6022790
title: Supplementary Material for: Hagen et al. Prevalence and Possible Factors of Myopia in Norwegian Adolescents. Scientific Reports 2018.

description: DATASET MIGRATED FROM FIGSHARE: &lt;p&gt;&lt;b&gt;(i) Community profile and demographics.&lt;/b&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;(ii) Proportions of spherical equivalent refraction (SER) in smaller steps.&lt;/b&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;(iii) Mean SER and proportions of refractive error in participants of Norwegian, Asian, African, other European, South American, and mixed origin.&lt;/b&gt;&lt;br&gt;&lt;/p&gt;&lt;div&gt;&lt;b&gt;(iv) Mean self-reported hours per day spent on indoor and outdoor activities.&lt;/b&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;b&gt;&lt;br&gt;&lt;/b&gt;&lt;/div&gt;&lt;div&gt;&lt;b&gt;Reference:&lt;/b&gt;&lt;/div&gt;&lt;div&gt;Hagen LA, Gjelle JVB, Arnegard S, Pedersen HR, Gilson SJ, Baraas RC. (2018) Prevalence and Possible Factors of Myopia in Norwegian Adolescents. Nature Scientific Reports, in press.&lt;b&gt;&lt;br&gt;&lt;/b&gt;&lt;/div&gt;
DOI: 10.23642/usn.6022790/maftxs
title: Community Profile and Demographics.pdf

description: Community Profile and Demographics.pdf
DOI: 10.23642/usn.6022790/sirelb
title: Mean self-reported hours per day spent on activities.xlsx

description: Mean self-reported hours per day spent on activities.xlsx
DOI: 10.23642/usn.6022790/qe5n2q
title: Mean SER and proportion of refractive error in different ethnicities.xlsx

description: Mean SER and proportion of refractive error in different ethnicities.xlsx
DOI: 10.23642/usn.6022790/w1yivo
title: Proportions of SER in smaller steps.xlsx

description: Proportions of SER in smaller steps.xlsx
DOI: 10.23642/usn.6022790/s5kd4d
title: README.txt

description: README.txt
DOI: 10.5281/zenodo.18016597
title: Adult nutrition strongly affects insect cuticle thickness and injury resistance dataset

description: Data and Read.Me file and R script for manuscript 'Adult nutrition strongly affects insect cuticle thickness and injury resistance'

Authors: EV(Ginny) Greenway1,2, Tamsin Woodman1,3, Zachary Emberts4,5, Simon Chen6, Walter Federle6and Christine W. Miller1,6*

1 Entomology and Nematology Department, University of Florida, Gainesville, Florida 326112 School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK3 The Institute of Ecology and Evolution, University of Edinburgh, Edinburgh EH9 3FL, UK4 Department of Biology, University of Florida, Gainesville, Florida 326115 Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma 740786 Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK 

*Corresponding author
DOI: 10.5281/zenodo.18016598
title: Adult nutrition strongly affects insect cuticle thickness and injury resistance dataset

description: Data and Read.Me file and R script for manuscript 'Adult nutrition strongly affects insect cuticle thickness and injury resistance'

Authors: EV(Ginny) Greenway1,2, Tamsin Woodman1,3, Zachary Emberts4,5, Simon Chen6, Walter Federle6and Christine W. Miller1,6*

1 Entomology and Nematology Department, University of Florida, Gainesville, Florida 326112 School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK3 The Institute of Ecology and Evolution, University of Edinburgh, Edinburgh EH9 3FL, UK4 Department of Biology, University of Florida, Gainesville, Florida 326115 Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma 740786 Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK 

*Corresponding author
DOI: 10.15468/dl.77ab3g
title: Occurrence Download

description: A dataset listing the 1825 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Viet Nam",
 "Geometry POLYGON((105.36006 9.15359,107.037 9.15359,107.037 11.23252,105.36006 11.23252,105.36006 9.15359))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Chordata"
 ]
}

 The dataset's 1825 records were derived from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063023-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nu8tbq
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sabicea klugii"
 ]
}

 The dataset includes 17 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0063025-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.23642/usn.6148562
title: Questionnaire and results: MCQ SIM project

description: DATASET MIGRATED FROM FIGSHARE: &lt;div&gt;The files contains description and data from a quasi-experimental study examining students’ acquisition of theoretical knowledge during simulation based learning.&lt;br&gt;&lt;/div&gt;&lt;div&gt;&lt;br&gt;&lt;/div&gt;&lt;div&gt;The study is part of a research project that examines different aspects of students' learning after implementing an educational
intervention during simulation-based learning. &lt;br&gt;&lt;/div&gt;
DOI: 10.23642/usn.6148562/domjkd
title: MCQ-spÃ¸rreskjema.docx

description: MCQ-spørreskjema.docx
DOI: 10.23642/usn.6148562/dua5o4
title: MCQ-spÃ¸rreskjema.pdf

description: MCQ-spørreskjema.pdf
DOI: 10.23642/usn.6148562/0wbgsn
title: Readme1.txt

description: Readme1.txt
DOI: 10.23642/usn.6148562/mfahhd
title: Samlet svar pÃ¥ MCQ.sav

description: Samlet svar på MCQ.sav
DOI: 10.34740/kaggle/dsv/14257777
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.18019057
title: LINF_300024400

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18019058
title: LINF_300024400

description: Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.18018782
title: Data accompanying the paper "Trading oxygen for iron II. Oxygen- versus iron-dependent cosmic star formation history" (Chruslinska et al. 2025)

description: This repository contains the numerical outputs of the calculations (accompanying code MetalCosfr GitHub public release is in prep.) that underpin the results described in the paper.

[0] "Trading oxygen for iron II. Oxygen- versus iron-dependent cosmic star formation history"by Chruślińska, M. ; Curti, M. ; Pakmor, R. ; De Cia, A. ; Matthee, J. ; Bhagwat, A. ; Monty, S. (2025)arXiv:2511.15782

These calculations rely on the observation-based framework developed in [1,2], extended and validated against more recent and additional empirical constraints in [0]. This work [0] further applies the [O/Fe]–sSFR relation of galaxies [3] to derive the iron-dependent cosmic star formation history, fSFR(Z_Fe/H, t), alongside its oxygen-based counterpart, fSFR(Z_O/H, t). The theoretical underpinnings of the relation and empirical constraints on the relation are described in [3]. 

[1] 'Metallicity of stars formed throughout the cosmic history based on the observational properties of star forming galaxies' by Chruślińska, M. and Nelemans, G., MNRAS, 2019 (DOI: 10.1093/mnras/stz2057)https://ui.adsabs.harvard.edu/abs/2019MNRAS.tmp.1974C/abstract

[2] "The impact of the FMR and starburst galaxies on the (low-metallicity) cosmic star formation history"by Chruślińska, M., Nelemans, G., Boco, L., Lapi. A, 2021, MNRAS (2021)https://arxiv.org/abs/2109.06187

[3] ' Trading oxygen for iron. I. The [O/Fe]-specific star formation rate relation of galaxies 'by  Chruślińska, M., Pakmor, R., Matthee, J. and Matsuno, T., A&A 686, A186 (2024);doi:10.1051/0004-6361/202347602 

If you use this data, please cite [0], this Zenodo repository (10.5281/zenodo.18018783) and the relevant papers listed above.

_________________________________________________________________________________________Contents:

Data filesNumerical outputs allowing to reproduce all the results presented in [0].Each file contains the distribution of the cosmic star formation rate density over "metallicity" (for different metallicity probes, see below) and redshift. Each number in the output file corresponds to stellar mass density [Msun / cMp^3] formed in a given "metallicity" (columns) and redshift (rows) bins.Folders contain outputs obtained under different assumptions (model variations), as discussed in the paper."Metallicity":-for oxygen abundance 12+log(O/H): outputs in FOH_z_dM.dat-for iron abundance 12+log(Fe/H): outputs in FFeH_z_dM.dat -for oxygen to iron abundance ratio [O/Fe]: outputs in OFe_z_dM.dat

Python script: get_model_ref.pyContains a helper function that allows you to find the data corresponding to the model variations of interest, as defined in Table C.1 in [0]. It also explains the naming convention.
DOI: 10.5281/zenodo.18018783
title: Data accompanying the paper "Trading oxygen for iron II. Oxygen- versus iron-dependent cosmic star formation history" (Chruslinska et al. 2025)

description: This repository contains the numerical outputs of the calculations (accompanying code MetalCosfr GitHub public release is in prep.) that underpin the results described in the paper.

[0] "Trading oxygen for iron II. Oxygen- versus iron-dependent cosmic star formation history"by Chruślińska, M. ; Curti, M. ; Pakmor, R. ; De Cia, A. ; Matthee, J. ; Bhagwat, A. ; Monty, S. (2025)arXiv:2511.15782

These calculations rely on the observation-based framework developed in [1,2], extended and validated against more recent and additional empirical constraints in [0]. This work [0] further applies the [O/Fe]–sSFR relation of galaxies [3] to derive the iron-dependent cosmic star formation history, fSFR(Z_Fe/H, t), alongside its oxygen-based counterpart, fSFR(Z_O/H, t). The theoretical underpinnings of the relation and empirical constraints on the relation are described in [3]. 

[1] 'Metallicity of stars formed throughout the cosmic history based on the observational properties of star forming galaxies' by Chruślińska, M. and Nelemans, G., MNRAS, 2019 (DOI: 10.1093/mnras/stz2057)https://ui.adsabs.harvard.edu/abs/2019MNRAS.tmp.1974C/abstract

[2] "The impact of the FMR and starburst galaxies on the (low-metallicity) cosmic star formation history"by Chruślińska, M., Nelemans, G., Boco, L., Lapi. A, 2021, MNRAS (2021)https://arxiv.org/abs/2109.06187

[3] ' Trading oxygen for iron. I. The [O/Fe]-specific star formation rate relation of galaxies 'by  Chruślińska, M., Pakmor, R., Matthee, J. and Matsuno, T., A&A 686, A186 (2024);doi:10.1051/0004-6361/202347602 

If you use this data, please cite [0], this Zenodo repository (10.5281/zenodo.18018783) and the relevant papers listed above.

_________________________________________________________________________________________Contents:

Data filesNumerical outputs allowing to reproduce all the results presented in [0].Each file contains the distribution of the cosmic star formation rate density over "metallicity" (for different metallicity probes, see below) and redshift. Each number in the output file corresponds to stellar mass density [Msun / cMp^3] formed in a given "metallicity" (columns) and redshift (rows) bins.Folders contain outputs obtained under different assumptions (model variations), as discussed in the paper."Metallicity":-for oxygen abundance 12+log(O/H): outputs in FOH_z_dM.dat-for iron abundance 12+log(Fe/H): outputs in FFeH_z_dM.dat -for oxygen to iron abundance ratio [O/Fe]: outputs in OFe_z_dM.dat

Python script: get_model_ref.pyContains a helper function that allows you to find the data corresponding to the model variations of interest, as defined in Table C.1 in [0]. It also explains the naming convention.