cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.12736842
title: Data for paper "Effects of Selective Simulated Seeding on the Lightning Potential Index in Northern Switzerland using the COSMO Model"

description: This data is used in the paper "Effects of Selective Simulated Seeding on the Lightning Potential Index in Northern Switzerland using the COSMO Model". This data is supplemented by software, i.e. the code which was used to conduct the data analysis as well as plot the data, which is separately available on Zenodo. "LINET.nc" is the data for the observed lightning activity on 2019-07-06, provided by Nowcast GmbH. Additionally, there are 10 files "ens**_ctrl_lpi_fullcosmodomain.nc" which contain the simulated lightning activtiy corresponding to that date. These aforementioned files are used in the jupyter notebook "LINET_LPI_comparison.ipynb". The remaining 40 files "ens**_[ctrl or seedlow or seedmed or seedhigh].nc" are used in "data_analysis.ipynb".
DOI: 10.6084/m9.figshare.3761562.v3016
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.13147272
title: Mean NDVI Values (1982-2018) and Future Predictions Using CHELSA Bioclim Variables for Türkiye

description: This dataset contains mean Normalized Difference Vegetation Index (NDVI) values from 1982 to 2018 and their future predictions based on CHELSA bioclimatic variables, specifically for the region of Türkiye. The data is provided in .asc format and includes both historical and projected NDVI values under different climate scenarios.

Contents:



Historical NDVI Data (1982-2018): Mean NDVI values derived from remote sensing data.

Future NDVI Predictions: NDVI projections for the periods 2011-2040, 2041-2070, and 2071-2100 under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5.


Methodology:



Model Training:



A Random Forest Regressor was used to model the relationship between NDVI and the selected bioclim variables.

The model achieved an R² of 0.9341, Mean Absolute Error of 0.0275, and Root Mean Squared Error of 0.0499.



Future Predictions:



Future NDVI values were predicted using the trained model and future CHELSA bioclim projections.

Predictions were made for three future periods (2011-2040, 2041-2070, 2071-2100) under three SSPs (SSP1-2.6, SSP3-7.0, SSP5-8.5).




Data Specifications:



Extent: Covers the geographical area of Türkiye and adjacents.


Sources:



NDVI Data:



Ma, Z., Dong, C., Lin, K., Yan, Y., Luo, J., Jiang, D., & Chen, X. (2022). A Global 250-m Downscaled NDVI Product from 1982 to 2018. Remote Sensing, 14(15), 3639.



CHELSA Bioclim Data:



Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017). Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4
DOI: 10.5281/zenodo.13147273
title: Mean NDVI Values (1982-2018) and Future Predictions Using CHELSA Bioclim Variables for Türkiye

description: This dataset contains mean Normalized Difference Vegetation Index (NDVI) values from 1982 to 2018 and their future predictions based on CHELSA bioclimatic variables, specifically for the region of Türkiye. The data is provided in .asc format and includes both historical and projected NDVI values under different climate scenarios.

Contents:



Historical NDVI Data (1982-2018): Mean NDVI values derived from remote sensing data.

Future NDVI Predictions: NDVI projections for the periods 2011-2040, 2041-2070, and 2071-2100 under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP3-7.0, and SSP5-8.5.


Methodology:



Model Training:



A Random Forest Regressor was used to model the relationship between NDVI and the selected bioclim variables.

The model achieved an R² of 0.9341, Mean Absolute Error of 0.0275, and Root Mean Squared Error of 0.0499.



Future Predictions:



Future NDVI values were predicted using the trained model and future CHELSA bioclim projections.

Predictions were made for three future periods (2011-2040, 2041-2070, 2071-2100) under three SSPs (SSP1-2.6, SSP3-7.0, SSP5-8.5).




Data Specifications:



Extent: Covers the geographical area of Türkiye and adjacents.


Sources:



NDVI Data:



Ma, Z., Dong, C., Lin, K., Yan, Y., Luo, J., Jiang, D., & Chen, X. (2022). A Global 250-m Downscaled NDVI Product from 1982 to 2018. Remote Sensing, 14(15), 3639.



CHELSA Bioclim Data:



Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017). Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M. Data from: Climatologies at high resolution for the earth’s land surface areas. Dryad Digital Repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4
DOI: 10.5517/ccdc.csd.cc2gmzmq
title: CCDC 2283324: Experimental Crystal Structure Determination

description: Related Article: James H. W. LaFortune, Ala'aeddeen Swidan, Jeffrey S. Ovens, Zheng-Wang Qu, Stefan Grimme, Michael A. Land, Charles L. B. Macdonald|2023|Chem.-Eur.J.|29|e202302558|doi:10.1002/chem.202302558
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2gmznr
title: CCDC 2283325: Experimental Crystal Structure Determination

description: Related Article: James H. W. LaFortune, Ala'aeddeen Swidan, Jeffrey S. Ovens, Zheng-Wang Qu, Stefan Grimme, Michael A. Land, Charles L. B. Macdonald|2023|Chem.-Eur.J.|29|e202302558|doi:10.1002/chem.202302558
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2gmzps
title: CCDC 2283326: Experimental Crystal Structure Determination

description: Related Article: James H. W. LaFortune, Ala'aeddeen Swidan, Jeffrey S. Ovens, Zheng-Wang Qu, Stefan Grimme, Michael A. Land, Charles L. B. Macdonald|2023|Chem.-Eur.J.|29|e202302558|doi:10.1002/chem.202302558
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.4nsbcs
title: Occurrence Download

description: A dataset containing 626044 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Xestia c-nigrum (Linnaeus, 1758)"
 ]
}

 The dataset includes 626044 records from 645 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047649-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48366/r711816
title: What are the machine learning application in veterans studies research (based on open access papers of the official journal of the American Medical Informatics Association)

description: What are the machine learning application in veterans studies research (based on open access papers of the official journal of the American Medical Informatics Association, https://academic.oup.com/jamia)
DOI: 10.15468/dl.qmnw3e
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Peces de la subcuenca hidrográfica del río Bata Embalse, cuenca hidrográfica del río Garagoa, macrocuenca del río Orinoco, Boyacá, Colombia"
 ]
}

 The dataset includes 108 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047652-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f5qcnv
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eugenia altissima Sobral & Faria"
 ]
}

 The dataset's 1 records were derived from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047650-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2kyubs
title: Occurrence Download

description: A dataset containing 3485 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "TaxonKey is Sarcoramphus papa (Linnaeus, 1758)"
 ]
}

 The dataset includes 3485 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047654-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10975867
title: PointPrompt: A Visual Prompting Dataset based on the Segment Anything Model

description: Each folder in 'Prompting data.zip' corresponds to a single category (Bird, Cat, Bus etc), and each of these contain folders corresponding to a single participant (st1, st2 etc). Each participant folder should contain 5 subfolders:



'masks' contains the binary masks produced for each image, in the format a_b_mask.png, where 'a' corresponds to the image number (0 to 399) and 'b' indexes through timestamps in the prompting process

'points' contains the inclusion and exclusion points formatted as a_green.npy and a_red.npy respectively, where 'a' corresponds to the image number. Each of these files is a list of lists corresponding to the prompted points at each timestep. The outer list is of size (t,), where 't' is the number of timesteps for that image, an each inner list is fo size (n,2), where 'n' is the number of points at a given timestep

'scores' contains the scores at each timestep for every image (mIoU)

'sorts' contains sorted timestamp indexes, going from max to min based on the score

'eachround' indicates which timesteps belong to each of the two rounds (if they exist). Each file contains a list of lenght t (number of timestamps) where values of 0 corresponds to timestamps that belong to the first round and values of 1 correspond to timestamps that belong to the second round


Quick usage:

-To get the best (highes score) mask for a given image : masks[sorts[0]] -To get the best set of prompts for that image : green[sorts[0]] and red[sorts[0]]-To get which round produced the highest score in that image : eachround[sorts[0]]

The codebase associated with this work can be found at this Github.

Please refer to our lab-wide github for more information regarding the code associated with our other papers.
DOI: 10.5281/zenodo.10975868
title: PointPrompt: A Multi-modal Prompting Dataset for Segment Anything Model

description: Each folder in 'Prompting data.zip' corresponds to a single category (Bird, Cat, Bus etc), and each of these contain folders corresponding to a single participant (st1, st2 etc). Each participant folder should contain 5 subfolders:



'masks' contains the binary masks produced for each image, in the format a_b_mask.png, where 'a' corresponds to the image number (0 to 399) and 'b' indexes through timestamps in the prompting process

'points' contains the inclusion and exclusion points formatted as a_green.npy and a_red.npy respectively, where 'a' corresponds to the image number. Each of these files is a list of lists corresponding to the prompted points at each timestep. The outer list is of size (t,), where 't' is the number of timesteps for that image, an each inner list is fo size (n,2), where 'n' is the number of points at a given timestep

'scores' contains the scores at each timestep for every image (mIoU)

'sorts' contains sorted timestamp indexes, going from max to min based on the score

'eachround' indicates which timesteps belong to each of the two rounds (if they exist). Each file contains a list of lenght t (number of timestamps) where values of 0 corresponds to timestamps that belong to the first round and values of 1 correspond to timestamps that belong to the second round


Quick usage:

-To get the best (highes score) mask for a given image : masks[sorts[0]] -To get the best set of prompts for that image : green[sorts[0]] and red[sorts[0]]-To get which round produced the highest score in that image : eachround[sorts[0]]
DOI: 10.5281/zenodo.11187949
title: PointPrompt: A Multi-modal Prompting Dataset for Segment Anything Model

description: Each folder in 'Prompting data.zip' corresponds to a single category (Bird, Cat, Bus etc), and each of these contain folders corresponding to a single participant (st1, st2 etc). Each participant folder should contain 5 subfolders:



'masks' contains the binary masks produced for each image, in the format a_b_mask.png, where 'a' corresponds to the image number (0 to 399) and 'b' indexes through timestamps in the prompting process

'points' contains the inclusion and exclusion points formatted as a_green.npy and a_red.npy respectively, where 'a' corresponds to the image number. Each of these files is a list of lists corresponding to the prompted points at each timestep. The outer list is of size (t,), where 't' is the number of timesteps for that image, an each inner list is fo size (n,2), where 'n' is the number of points at a given timestep

'scores' contains the scores at each timestep for every image (mIoU)

'sorts' contains sorted timestamp indexes, going from max to min based on the score

'eachround' indicates which timesteps belong to each of the two rounds (if they exist). Each file contains a list of lenght t (number of timestamps) where values of 0 corresponds to timestamps that belong to the first round and values of 1 correspond to timestamps that belong to the second round


Quick usage:

-To get the best (highes score) mask for a given image : masks[sorts[0]] -To get the best set of prompts for that image : green[sorts[0]] and red[sorts[0]]-To get which round produced the highest score in that image : eachround[sorts[0]]
DOI: 10.5281/zenodo.11580815
title: PointPrompt: A Visual Prompting Dataset based on the Segment Anything Model

description: Each folder in 'Prompting data.zip' corresponds to a single category (Bird, Cat, Bus etc), and each of these contain folders corresponding to a single participant (st1, st2 etc). Each participant folder should contain 5 subfolders:



'masks' contains the binary masks produced for each image, in the format a_b_mask.png, where 'a' corresponds to the image number (0 to 399) and 'b' indexes through timestamps in the prompting process

'points' contains the inclusion and exclusion points formatted as a_green.npy and a_red.npy respectively, where 'a' corresponds to the image number. Each of these files is a list of lists corresponding to the prompted points at each timestep. The outer list is of size (t,), where 't' is the number of timesteps for that image, an each inner list is fo size (n,2), where 'n' is the number of points at a given timestep

'scores' contains the scores at each timestep for every image (mIoU)

'sorts' contains sorted timestamp indexes, going from max to min based on the score

'eachround' indicates which timesteps belong to each of the two rounds (if they exist). Each file contains a list of lenght t (number of timestamps) where values of 0 corresponds to timestamps that belong to the first round and values of 1 correspond to timestamps that belong to the second round


Quick usage:

-To get the best (highes score) mask for a given image : masks[sorts[0]] -To get the best set of prompts for that image : green[sorts[0]] and red[sorts[0]]-To get which round produced the highest score in that image : eachround[sorts[0]]

The codebase associated with this work can be found at this Github.

Please refer to our lab-wide github for more information regarding the code associated with our other papers.
DOI: 10.15468/dl.tn4p2t
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eugenia aerosa Mc Vaugh"
 ]
}

 The dataset's 1 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047651-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26491948
title: Genomic signatures of diversification in large plant genera Begonia and Cyrtandra

description: Diverse plant groups often emerge from bursts of speciation and may experience reductions in effective population size through recurrent lineage splitting. This process is hypothesised to influence genome-wide patterns of variation, including increased genetic drift, random fixation of nearly neutral mutations, and relaxed selection. Such diversification signatures might contribute to stocks of genetic variation, enhancing populations' adaptive potential to future challenges. To investigate these signals in large, diverse taxa, we compared two phylogenetically disparate plant genera, <i>Begonia</i> (Begoniaceae) and <i>Cyrtandra</i> (Gesneriaceae), generating new transcriptomic resources for the speciose genus <i>Cyrtandra </i>and taking advantage of comparable transcriptomic resources in <i>Begonia</i>. Leveraging closely related species-poor outgroups for each diverse genus, we examined shifts in selection strength and gene duplication patterns. Contrary to expectations, we found no evidence of genome-wide relaxed selection in the diverse genera compared to their species-poor counterparts, with only approximately one fifth of tested loci showing signs of relaxed selection. Additionally, there were no substantial changes in gene duplication patterns between the diverse and species-poor genera. Our findings suggest that the effects of diversification are not strong enough to be detected on a genome-wide scale, and that any effects of reduced effective population size on the stochastic fixation of mutations or duplicate genes may be short lived. This study showcases the utility of transcriptomic data for studying patterns of evolution and diversification in non-model organisms, and underscores the importance of investment in genomic resources for understudied taxa.
DOI: 10.6084/m9.figshare.26491948.v1
title: Genomic signatures of diversification in large plant genera Begonia and Cyrtandra

description: Diverse plant groups often emerge from bursts of speciation and may experience reductions in effective population size through recurrent lineage splitting. This process is hypothesised to influence genome-wide patterns of variation, including increased genetic drift, random fixation of nearly neutral mutations, and relaxed selection. Such diversification signatures might contribute to stocks of genetic variation, enhancing populations' adaptive potential to future challenges. To investigate these signals in large, diverse taxa, we compared two phylogenetically disparate plant genera, <i>Begonia</i> (Begoniaceae) and <i>Cyrtandra</i> (Gesneriaceae), generating new transcriptomic resources for the speciose genus <i>Cyrtandra </i>and taking advantage of comparable transcriptomic resources in <i>Begonia</i>. Leveraging closely related species-poor outgroups for each diverse genus, we examined shifts in selection strength and gene duplication patterns. Contrary to expectations, we found no evidence of genome-wide relaxed selection in the diverse genera compared to their species-poor counterparts, with only approximately one fifth of tested loci showing signs of relaxed selection. Additionally, there were no substantial changes in gene duplication patterns between the diverse and species-poor genera. Our findings suggest that the effects of diversification are not strong enough to be detected on a genome-wide scale, and that any effects of reduced effective population size on the stochastic fixation of mutations or duplicate genes may be short lived. This study showcases the utility of transcriptomic data for studying patterns of evolution and diversification in non-model organisms, and underscores the importance of investment in genomic resources for understudied taxa.
DOI: 10.5281/zenodo.13214905
title: Different lung microbiota community states are associated with pulmonary nontuberculous mycobacterial disease prognosis
DOI: 10.5281/zenodo.13214906
title: Different lung microbiota community states are associated with pulmonary nontuberculous mycobacterial disease prognosis
DOI: 10.15468/dl.wgmnkp
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Monocerotesa strigata (Warren, 1893)"
 ]
}

 The dataset includes 14 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047655-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13213973
title: Life in the fastlane? A comparative analysis of gene expression profiles across annual, semi-annual, and non-annual killifishes (Cyprinodontiformes: Nothobranchiidae)

description: Read Counts per gene

All read counts by inidividual and species

Species abbreviations key

R script for data analyses
DOI: 10.5281/zenodo.13213974
title: Life in the fastlane? A comparative analysis of gene expression profiles across annual, semi-annual, and non-annual killifishes (Cyprinodontiformes: Nothobranchiidae)

description: Read Counts per gene

All read counts by inidividual and species

Species abbreviations key

R script for data analyses
DOI: 10.6084/m9.figshare.22715158.v14
title: Matbench Discovery v1.0.0

description: These are the v1 data files for Matbench Discovery, a benchmark for machine learning energy models on inorganic crystal stability prediction from unrelaxed structures. It contains relaxed structures of the MP training set, initial+relaxed structures of the WBM test set, plus several checkpoints for models trained on this data specifically for this benchmark. For a description of each file, see https://matbench-discovery.materialsproject.org/contribute#--direct-download.The full force field training set containing 1.3M structures along with their energies, forces, stresses and magmons is available at https://figshare.com/articles/dataset/23713842.
DOI: 10.15468/dl.csj9yu
title: Occurrence Download

description: A dataset listing the 2364 species recorded in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Phaeophyceae"
 ]
}

 The dataset's 2364 records were derived from 1048 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047656-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.