cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10034265
title: DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification

description: The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potentialfor advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology.
DOI: 10.5281/zenodo.10034266
title: DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification

description: The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potentialfor advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology.
DOI: 10.5281/zenodo.10048012
title: DistSNE: Distributed computing and online visualization of DNA methylation-based central nervous system tumor classification

description: The current state-of-the-art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed-computing-based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t-distributed neighborhood embedding (t-SNE) model for dimensionality reduction and visualization of tumor classification results in two-dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user-friendly local data management and federated methylome-based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large-scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potentialfor advancing human brain tumor classification and fostering collaborative precision medicine in neuro-oncology.
DOI: 10.35003/7d3vb4/otgmc6
title: 1-s2.0-S2352340918313179-mmc1.pdf

description: :unav
DOI: 10.35003/7d3vb4/wdbp83
title: 1-s2.0-S2352340918313179-mmc3.xls

description: :unav
DOI: 10.35003/7d3vb4/ihbvun
title: 1-s2.0-S2352340918313179-mmc2.xls

description: :unav
DOI: 10.5519/qd.i9d6fii4
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (45 records)
DOI: 10.5281/zenodo.6328446
title: Duke Liver Dataset (MRI) v2

description: The Duke Liver DataSet (DLDS) provides over 2000 anonymized MRI image series acquired in routine liver MRI protocols across 105 subjects that can be used to train algorithms for two applications:


(1) Series identification
(2) Liver segmentation


The series identification grouping (Series_Classification.zip) includes 2146 unique image series across all 105 subjects. The image series encompass seventeen different contrast types including different pulse sequences, contrast bolus phases, and imaging orientations. A randomized, alphabetical series label corresponding to one of these seventeen contrast types is assigned to each contrast type. The file SeriesClassificationKey.csv includes a list of all image series included in the dataset and their corresponding label. The file SequenceTypes.csv shows the mapping of these labels to contrast type. It is intended that these labels can be used to train an automated series classification model for abdominal MRI. 


The liver segmentation sub-grouping (Segmentation.zip) includes 310 unique image series across a subset of 95 subjects. The image series encompass four main contrast types: axial in-phase, axial opposed, axial precontrast fat-suppressed T1w, and contrast-enhanced portal venous t1w. It is intended that this data can be used to train an automated liver segmentation model. The file SegmentationKey.csv includes a list of the cases with included masks files. Since there is overlap in these series with the series identification dataset, corresponding series labels are also included.


In both datasets, cases are organized in directories by DLDS ID -> Series number. The segmentation cases are further divided into image and mask sub-directories.


Please note that a previous version of this dataset (version 1.0.0) withheld roughly 25% of series labels and segmentations. These have been restored to the dataset for completeness. As reference for users wishing to know which data was added (or looking for a list of subjects to remove for a convenient test set), labels and masks were previously withheld for the following DLDS id's: [0002, 0003, 0004, 0017, 0020, 0023, 0031, 0032, 0033, 0035, 0038, 0042, 0046, 0050, 0063, 0074, 0075, 0078, 0083, 0084, 0096, 0097, 0098, 0100, 0103, 0104].
DOI: 10.5281/zenodo.6328447
title: Duke Liver Dataset (MRI)

description: <strong>*******************************************************************************************************************</strong> <strong>There is a new version of this dataset available now. Please see the link at the top of the screen and request access to that version instead.</strong> <strong>********************************************************************************************************************</strong> This dataset contains images and supporting files for two corresponding liver MRI applications: (1) Liver segmentation application: 310 MRI image series (4 different contrast types) across 95 subjects are provided. Roughly 75% of series are accompanied by mask files corresponding to manual segmentations of the liver. Participants can use these images and masks to train an automated liver segmentation model. The remaining 25% of series can be used as the test set for these models. (2) Series classification application: 2146 MRI image series across 105 subjects are provided. An alphabetical series label is provided for roughly 75% of the series and withheld for the other 25%. Participants can use these images and labels to train an automated series classification model for liver MRI. The models can be tested on the unlabeled 25% of series.
DOI: 10.5281/zenodo.7774566
title: Duke Liver Dataset (MRI) v2

description: The Duke Liver DataSet (DLDS) provides over 2000 anonymized MRI image series acquired in routine liver MRI protocols across 105 subjects that can be used to train algorithms for two applications:


(1) Series identification
(2) Liver segmentation


The series identification grouping (Series_Classification.zip) includes 2146 unique image series across all 105 subjects. The image series encompass seventeen different contrast types including different pulse sequences, contrast bolus phases, and imaging orientations. A randomized, alphabetical series label corresponding to one of these seventeen contrast types is assigned to each contrast type. The file SeriesClassificationKey.csv includes a list of all image series included in the dataset and their corresponding label. The file SequenceTypes.csv shows the mapping of these labels to contrast type. It is intended that these labels can be used to train an automated series classification model for abdominal MRI. 


The liver segmentation sub-grouping (Segmentation.zip) includes 310 unique image series across a subset of 95 subjects. The image series encompass four main contrast types: axial in-phase, axial opposed, axial precontrast fat-suppressed T1w, and contrast-enhanced portal venous t1w. It is intended that this data can be used to train an automated liver segmentation model. The file SegmentationKey.csv includes a list of the cases with included masks files. Since there is overlap in these series with the series identification dataset, corresponding series labels are also included.


In both datasets, cases are organized in directories by DLDS ID -> Series number. The segmentation cases are further divided into image and mask sub-directories.


Please note that a previous version of this dataset (version 1.0.0) withheld roughly 25% of series labels and segmentations. These have been restored to the dataset for completeness. As reference for users wishing to know which data was added (or looking for a list of subjects to remove for a convenient test set), labels and masks were previously withheld for the following DLDS id's: [0002, 0003, 0004, 0017, 0020, 0023, 0031, 0032, 0033, 0035, 0038, 0042, 0046, 0050, 0063, 0074, 0075, 0078, 0083, 0084, 0096, 0097, 0098, 0100, 0103, 0104].
DOI: 10.5281/zenodo.8393134
title: C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.

description: There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.
DOI: 10.5281/zenodo.8393135
title: C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.

description: There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.
DOI: 10.5281/zenodo.8173412
title: Analytical method for reconstructing the stress on a spherical particle from its surface deformation

description: Supplemental data for the following manuscript: Lea Johanna Krüger, Michael te Vrugt, Stephan Bröker, Bernhard Wallmeyer, Timo Betz, Raphael Wittkowski, "Analytical method for reconstructing the stress on a spherical particle from its surface deformation".
Bead1_ExperimentalData.tif and Bead2_ExperimentalData.tif contain the measured bead shapes as TIF files. Bead1_ExperimentalData.txt and Bead2_ExperimentalData.txt contain the measured bead shapes as point clouds. Bead1_SphericalHarmonicsExpansionCoefficients.txt and Bead2_SphericalHarmonicsExpansionCoefficients.txt contain expansions of the bead shapes into spherical harmonics.
description: M.t.V., T.B., and R.W. are funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Project-IDs 525063330, BE 6270/2-1, and 433682494 - SFB 1459.
DOI: 10.5281/zenodo.10048023
title: CICE gx3 grid and initial condition files. 2023.10.27

description: This is an updated version of the gx3 grid and initial condition files. This now includes monthly initial files for 2005 and also netCDF versions of the grid files.
DOI: 10.5281/zenodo.4670593
title: CICE gx3 grid and initial condition files. 2023.10.27

description: This is an updated version of the gx3 grid and initial condition files. This now includes monthly initial files for 2005 and also netCDF versions of the grid files.
DOI: 10.5281/zenodo.4670594
title: CICE gx3 grid and initial condition files. 2021.03.19

description: This is an updated version of the gx3 grid and initial condition files. This now includes monthly initial files for 2005.
DOI: 10.5281/zenodo.10048048
title: Metocean Conditions at Utsira Nord with NORA3 (1982-2022)

description: Utsira Nord Dataset
Description
This dataset focuses on metocean conditions at Utsira Nord (UN), an offshore Norwegian area located in deep-sea waters. The dataset contains hourly time series data from 1982 to 2022 for each grid point in the area, making it highly valuable for long-term climate studies and the development of floating offshore wind turbines.
 
Technical Specifications

Format: netcdf4_classic
Variables in Utisra Nord (UN) netCDF File
time

Size: 8760x1
Dimensions: time
Datatype: double
Description: Represents the time for each data entry, starting from the year 1982 and extending through 2022.
Attributes: Units are in 'hours since 1970-01-01 00:00:00'.
z

Size: 8x1
Dimensions: z
Datatype: double
Description: Specifies the heights above the surface at which measurements were taken.
lat

Size: 317x1
Dimensions: coordinate
Datatype: double
Description: Latitude coordinates for each grid point in the area.
lon

Size: 317x1
Dimensions: coordinate
Datatype: double
Description: Longitude coordinates for each grid point in the area.
Dir

Size: 8760x317x8
Dimensions: time, coordinate, z
Datatype: double
Description: Mean wind direction at each time, location, and height.
U

Size: 8760x317x8
Dimensions: time, coordinate, z
Datatype: double
Description: Mean wind speed at each time, location, and height.
hs

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Significant wave height at each time and location.
tp

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Peak wave period at each time and location.
u_star

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Friction velocity at each time and location.
wd

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Wave heading at each time and location.
DOI: 10.5281/zenodo.10048047
title: Metocean Conditions at Utsira Nord with NORA3 (1982-2022)

description: Utsira Nord Dataset
Description
This dataset focuses on metocean conditions at Utsira Nord (UN), an offshore Norwegian area located in deep-sea waters. The dataset contains hourly time series data from 1982 to 2022 for each grid point in the area, making it highly valuable for long-term climate studies and the development of floating offshore wind turbines.
 
Technical Specifications

Format: netcdf4_classic
Variables in Utisra Nord (UN) netCDF File
time

Size: 8760x1
Dimensions: time
Datatype: double
Description: Represents the time for each data entry, starting from the year 1982 and extending through 2022.
Attributes: Units are in 'hours since 1970-01-01 00:00:00'.
z

Size: 8x1
Dimensions: z
Datatype: double
Description: Specifies the heights above the surface at which measurements were taken.
lat

Size: 317x1
Dimensions: coordinate
Datatype: double
Description: Latitude coordinates for each grid point in the area.
lon

Size: 317x1
Dimensions: coordinate
Datatype: double
Description: Longitude coordinates for each grid point in the area.
Dir

Size: 8760x317x8
Dimensions: time, coordinate, z
Datatype: double
Description: Mean wind direction at each time, location, and height.
U

Size: 8760x317x8
Dimensions: time, coordinate, z
Datatype: double
Description: Mean wind speed at each time, location, and height.
hs

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Significant wave height at each time and location.
tp

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Peak wave period at each time and location.
u_star

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Friction velocity at each time and location.
wd

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Wave heading at each time and location.
DOI: 10.5281/zenodo.7057407
title: Metocean Conditions at Sørlige Nordsjø II with NORA3 (1982-2022)

description: Sørlige Nordsjø II Dataset
Description
This dataset concentrates on metocean conditions at Sørlige Nordsjø II (SN2), an offshore Norwegian area in intermediate waters. The dataset features hourly time series data for each grid point, covering the years from 1982 to 2022. This extensive temporal range makes the dataset indispensable for research on long-term climate patterns and offshore wind energy potential in the North Sea.
 
Technical Specifications

Format: netcdf4_classic
Variables in Sørlige Nordsjø II (SN2) netCDF File
time

Size: 8760x1
Dimensions: time
Datatype: double
Description: Represents the time for each data entry, starting from the year 1982 and extending through 2022.
Attributes: Units are in 'hours since 1970-01-01 00:00:00'.
z

Size: 8x1
Dimensions: z
Datatype: double
Description: Specifies the heights above the surface at which measurements were taken.
lat

Size: 317x1
Dimensions: coordinate
Datatype: double
Description: Latitude coordinates for each grid point in the area.
lon

Size: 317x1
Dimensions: coordinate
Datatype: double
Description: Longitude coordinates for each grid point in the area.
Dir

Size: 8760x317x8
Dimensions: time, coordinate, z
Datatype: double
Description: Mean wind direction at each time, location, and height.
U

Size: 8760x317x8
Dimensions: time, coordinate, z
Datatype: double
Description: Mean wind speed at each time, location, and height.
hs

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Significant wave height at each time and location.
tp

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Peak wave period at each time and location.
u_star

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Friction velocity at each time and location.
wd

Size: 8760x317
Dimensions: time, coordinate
Datatype: double
Description: Wave heading at each time and location.