cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8355348
title: Data for the publication titled " Improving Rechargeable Magnesium Batteries through Dual Cation Co-Intercalation Strategy"

description: Data sets were obtained from GCPL, CV, ICP-OES, XRD, Raman spectroscopy, TEM, STEM-EDX, EELS for the publication.


Abstract: The development of competitive rechargeable Mg batteries is hindered by the poor mobility of divalent Mg ions in cathode host materials. In this work, we have explored a novel dual cation co-intercalation strategy to mitigate the sluggishness of Mg2+ in model TiS2 material. The strategy involved pairing Mg2+ with Li+ or Na+ in dual-salt electrolytes in order to exploit their faster mobility with the aim to reach better electrochemical performance. A combination of experiments and theoretical calculations detailed the charge storage and redox mechanism of co-intercalating cationic charge carriers. Comparative evaluation revealed that the redox activity of Mg2+ can be improved significantly with the help of the dual cation co-intercalation strategy, although the ionic radius of the accompanying monovalent ion plays a critical role on the viability of the strategy. More specifically, a significantly higher Mg2+ quantity was intercalated with Li+ than with Na+ in TiS2. The reason being the absence of phase transition in the former case, which enabled improved Mg2+ storage. Our results highlight the dual cation co-intercalation strategy as an alternative approach to improve the electrochemical performance of rechargeable Mg batteries by opening the pathway to a rich playground of advanced cathode materials for multivalent battery applications.
DOI: 10.5519/qd.irovw0wq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1001 records)
DOI: 10.5519/qd.xyt2gvwp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (220274 records)
DOI: 10.6084/m9.figshare.24922905
title: 20240102_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24922905.v1
title: 20240102_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.5281/zenodo.10449163
title: Lista spraw CPAHwK

description: Lista + szczegółowy opis spraw dotyczących aktywności Komisji kodyfikacji prawa Hetmanatu w Centralnym Państwowym Archiwum Historycznym Ukrainy w Kijowie. Dokument zawiera numer sygnatury, numer starej sygnatury, opis spraw, ramy czasowe dokumentu, liczbę stron, odnośnik do dokumentu w elektronicznej bazie danych FamilySearch.
DOI: 10.5281/zenodo.10449164
title: Lista spraw CPAHwK

description: Lista + szczegółowy opis spraw dotyczących aktywności Komisji kodyfikacji prawa Hetmanatu w Centralnym Państwowym Archiwum Historycznym Ukrainy w Kijowie. Dokument zawiera numer sygnatury, numer starej sygnatury, opis spraw, ramy czasowe dokumentu, liczbę stron, odnośnik do dokumentu w elektronicznej bazie danych FamilySearch.
DOI: 10.34740/kaggle/dsv/7318380
title: MoviesDatasets
DOI: 10.34740/kaggle/dsv/7318383
title: Mahanagar Yatayat Transport Dataset
DOI: 10.25446/oxford.24922890
title: Prostitution Legislation_and_Sex Trafficking_joined dataset

description: This study was conducted to explore the effects prostitution legislation has on sex trafficking rates. This issue holds paramount importance in the fields of legal studies and human rights. By leveraging advanced machine learning techniques to analyze data from the Counter-Trafficking Data Collaborative (CTDC), encompassing 180 countries, this study aims to uncover the relationship between various prostitution legislation types and sex trafficking occurrences. The exploration begins with extensive cleaning, merging, and filtering of the CTDC dataset, integrating it with prostitution legislation data from the World Population Review. This process ensures a harmonized dataset that accurately reflects the global landscape of sex trafficking in relation to legislative frameworks. The machine learning model initially concentrated on prostitution legislation as a key variable but evolved to include a broader range of factors like registration year, population, growth rate, gender, and citizenship. This expansion was crucial in developing a more accurate and holistic model.This study offered a nuanced exploration of the impact of prostitution legislation on sex trafficking, employing sophisticated data analysis and machine learning models to parse through extensive data. The advanced RandomForestClassifier was key in the research, achieving an 87% accuracy rate for predicting instances of sex trafficking and demonstrating the need to incorporate diverse predictive features. Notably, the analysis emphasized the importance of the legislative feature in accurately predicting sex trafficking, despite the inclusion of other variables to improve overall model precision. These findings underscore the significance of a multifaceted approach, considering factors like demographics and socio-economic indicators, to gain a comprehensive understanding of sex trafficking trends.Complementing the machine learning insights, a logistic regression model scrutinized the specific effects of different legislative approaches on sex trafficking. The analysis revealed that legislative frameworks such as legalization, abolitionism, decriminalization, and neo-abolitionism have a considerable influence on reducing sex trafficking rates, suggesting their potential as effective legal strategies. Alternantively, prohibition legislation is found to corrrelate with significantly higher sex trafficking rates. These results serve as a critical resource for policymakers and advocates engaged in the development of informed, evidence-based approaches to address the global challenge of sex trafficking.
DOI: 10.25446/oxford.24922890.v1
title: Prostitution Legislation_and_Sex Trafficking_joined dataset

description: This research was created to address the question: How does prostitution legislation impact sex trafficking rates? This issue holds paramount importance in the fields of legal studies and human rights. By leveraging advanced machine learning techniques to analyze data from the Counter-Trafficking Data Collaborative (CTDC), encompassing 180 countries, this study aims to uncover the relationship between various prostitution legislation types and sex trafficking occurrences. The exploration begins with extensive cleaning, merging, and filtering of the CTDC dataset, integrating it with prostitution legislation data from the World Population Review. This process ensures a harmonized dataset that accurately reflects the global landscape of sex trafficking in relation to legislative frameworks. The machine learning model initially concentrated on prostitution legislation as a key variable but evolved to include a broader range of factors like registration year, population, growth rate, gender, and citizenship. This expansion was crucial in developing a more accurate and holistic model. This study offered a nuanced exploration of the impact of prostitution legislation on sex trafficking, employing sophisticated data analysis and machine learning models to parse through extensive data. The advanced RandomForestClassifier was key in the research, achieving an 87% accuracy rate and demonstrating the need to incorporate diverse predictive features. Notably, the analysis emphasized the importance of the legislative feature in accurately predicting sex trafficking, despite the inclusion of other variables to improve overall model precision. These findings underscore the significance of a multifaceted approach, considering factors like demographics and socio-economic indicators, to gain a comprehensive understanding of sex trafficking trends.Complementing the machine learning insights, a logistic regression model scrutinized the specific effects of different legislative approaches on sex trafficking. The analysis revealed that legislative frameworks such as Legalization, Abolitionism, Decriminalization, and Neo-Abolitionism have a considerable influence on reducing sex trafficking rates, suggesting their potential as effective legal strategies. These results serve as a critical resource for policymakers and advocates engaged in the development of informed, evidence-based approaches to address the global challenge of sex trafficking.
DOI: 10.5281/zenodo.10395772
title: Confocal data of Burmalestes jingruoyaae, holotype, NIGP203386

description: This dataset contains the raw confocal laser scanning microscopy (CLSM) slices for the holotype of Burmalestes jingruoyaae (NIGP203386) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "A new species of Burmalestes Tomaszewska & Ślipiński from mid-Cretaceous Kachin amber (Coleoptera: Endomychidae)" (DOI:10.11646/zootaxa.5396.1.15).

Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm (Argon) or 561 nm (DPSS 561-10) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.
DOI: 10.5281/zenodo.10395771
title: Confocal data of Burmalestes jingruoyaae, holotype, NIGP203386

description: This dataset contains the raw confocal laser scanning microscopy (CLSM) slices for the holotype of Burmalestes jingruoyaae (NIGP203386) from mid-Cretaceous Burmese amber. This dataset is associated with the publication "A new species of Burmalestes Tomaszewska & Ślipiński from mid-Cretaceous Kachin amber (Coleoptera: Endomychidae)" (DOI:10.11646/zootaxa.5396.1.15).

Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using the 488 nm (Argon) or 561 nm (DPSS 561-10) laser excitation line. The original CZI files are provided, which could be opened by the ZEISS ZEN software.
DOI: 10.6084/m9.figshare.24922911
title: Dataset

description: Effects of kinship and integration between adjacent/non-adjacent ramets on the growth and feedback with soil biota in a clonal invader
DOI: 10.6084/m9.figshare.24922911.v1
title: Dataset

description: Effects of kinship and integration between adjacent/non-adjacent ramets on the growth and feedback with soil biota in a clonal invader
DOI: 10.6084/m9.figshare.24922926
title: HFD_EJN data.xls

description: High fat diet and aerobic exercise during adolescence in sex-specific manner
DOI: 10.6084/m9.figshare.24922926.v1
title: HFD_EJN data.xls

description: High fat diet and aerobic exercise during adolescence in sex-specific manner
DOI: 10.6084/m9.figshare.22558378
title: AT & M raw data

description: The raw data and coding table of the manuscript: The relationship between insecure attachment and Machiavellianism
DOI: 10.6084/m9.figshare.22558378.v1
title: AT & M raw data

description: The raw data and coding table of the manuscript: The relationship between insecure attachment and Machiavellianism
DOI: 10.6084/m9.figshare.22558378.v2
title: AT & M raw data

description: The raw data and coding table of the manuscript: The relationship between insecure attachment and Machiavellianism
DOI: 10.34740/kaggle/dsv/7298157
title: College Placement Predictor Dataset
DOI: 10.34740/kaggle/dsv/7318848
title: Detailed Financials Data Of 4456 NSE & BSE Company
DOI: 10.34740/kaggle/ds/4233664
title: Detailed Financials Data Of 4492 NSE & BSE Company
DOI: 10.5281/zenodo.10407347
title: Data and Model Results Associated with Stochastic in Space and Time: Parts 1 and 2.

description: Processed datasets and model outputs associated with the two part  manuscript, two part manuscript in Journal of Geophysical Research - Earth Surface, "Stochastic in Space and Time: Part 1, Characterizing Orographic Gradients in Mean Runoff and Daily Runoff Variability" and "Stochastic in Space and Time: Part 2, Effects of Simulating Orographic Gradients in Daily Runoff Variability on Bedrock River Incision" by Adam M. Forte and Matthew W. Rossi. Contents include:



gagesII_wrr2_hcdn2009_routed.tgz - tarball containing individual textfiles of daily routed discharge from WaterGAP3 for selected HCDN2009 basins as described in the main text.

wrr2_derived_data_v4.csv - textfile containing processed results of individual pixels of WaterGAP3 data

wrr2_derived_Q.nc - NetCDF4 file containing processed results for WaterGAP3 related to the components of runoff

wrr2_derived_v2.nc - NetCDF4 file containing processed results for WaterGAP3 related to runoff and precipitation along with other variables

wrr2_events.tgz - tarball containing spatial statistics of identified runoff events by year above a given threshold in the WaterGAP3 data.

wrr2_raster_outputs.tgz - tarball containing individual rasters of processed WaterGAP3 data, largely reproducing what is contained in the NetCDF4 files, but also including versions upsampled to higher resolution and some derivative products

model_outputs_v2.tgz - tarball containing all 1D STIM profile model results presented in the paper. Individual timesteps are saved as pickle files and must be interpreted with the "stimpy" module included in the associated GitHub repository

model_figures_v2.tgz - tarball containing representative figures showing profile and other aspects of model evolution for all model runs. 
description: This is a shared repository for a two part manuscript in Journal of Geophysical Research - Earth Surface, "Stochastic in Space and Time: Part 1, Characterizing Orographic Gradients in Mean Runoff and Daily Runoff Variability" and "Stochastic in Space and Time: Part 2, Effects of Simulating Orographic Gradients in Daily Runoff Variability on Bedrock River Incision"
DOI: 10.5281/zenodo.7665886
title: Data and Model Results Associated with Stochastic in Space and Time: Parts 1 and 2.

description: Processed datasets and model outputs associated with the two part  manuscript, two part manuscript in Journal of Geophysical Research - Earth Surface, "Stochastic in Space and Time: Part 1, Characterizing Orographic Gradients in Mean Runoff and Daily Runoff Variability" and "Stochastic in Space and Time: Part 2, Effects of Simulating Orographic Gradients in Daily Runoff Variability on Bedrock River Incision" by Adam M. Forte and Matthew W. Rossi. Contents include:



gagesII_wrr2_hcdn2009_routed.tgz - tarball containing individual textfiles of daily routed discharge from WaterGAP3 for selected HCDN2009 basins as described in the main text.

wrr2_derived_data_v4.csv - textfile containing processed results of individual pixels of WaterGAP3 data

wrr2_derived_Q.nc - NetCDF4 file containing processed results for WaterGAP3 related to the components of runoff

wrr2_derived_v2.nc - NetCDF4 file containing processed results for WaterGAP3 related to runoff and precipitation along with other variables

wrr2_events.tgz - tarball containing spatial statistics of identified runoff events by year above a given threshold in the WaterGAP3 data.

wrr2_raster_outputs.tgz - tarball containing individual rasters of processed WaterGAP3 data, largely reproducing what is contained in the NetCDF4 files, but also including versions upsampled to higher resolution and some derivative products

model_outputs_v2.tgz - tarball containing all 1D STIM profile model results presented in the paper. Individual timesteps are saved as pickle files and must be interpreted with the "stimpy" module included in the associated GitHub repository

model_figures_v2.tgz - tarball containing representative figures showing profile and other aspects of model evolution for all model runs. 
description: This is a shared repository for a two part manuscript in Journal of Geophysical Research - Earth Surface, "Stochastic in Space and Time: Part 1, Characterizing Orographic Gradients in Mean Runoff and Daily Runoff Variability" and "Stochastic in Space and Time: Part 2, Effects of Simulating Orographic Gradients in Daily Runoff Variability on Bedrock River Incision"
DOI: 10.5281/zenodo.7665887
title: Data and Model Results Associated with Stochastic in Space and Time: Parts 1 and 2.

description: Processed datasets and model outputs associated with the manuscript, "Stochastic in space and time: Feedbacks between bedrock river incision and orographic patterns in runoff variability" by Adam M. Forte and Matthew W. Rossi, submitted to Journal of Geophysical Research - Earth Surface. Contents include: gagesII_wrr2_hcdn2009_routed.tgz - tarball containing individual textfiles of daily routed discharge from WaterGAP3 for selected HCDN2009 basins as described in the main text. wrr2_derived_data_v3.csv - textfile containing processed results of individual pixels of WaterGAP3 data wrr2_derived_Q.nc - NetCDF4 file containing processed results for WaterGAP3 related to the components of runoff wrr2_derived_v2.nc - NetCDF4 file containing processed results for WaterGAP3 related to runoff and precipitation along with other variables wrr2_raster_outputs.tgz - tarball containing individual rasters of processed WaterGAP3 data, largely reproducing what is contained in the NetCDF4 files, but also including versions upsampled to higher resolution and some derivative products model_outputs.tgz - tarball containing all 1D STIM profile model results presented in the paper. Individual timesteps are saved as pickle files and must be interpreted with the "stimpy" module included in the associated GitHub repository model_figures.tgz - tarball containing representative figures showing profile and other aspects of model evolution for all model runs.<br>
description: This is a shared repository for a two part manuscript in Journal of Geophysical Research - Earth Surface, "Stochastic in Space and Time: Part 1, Characterizing Orographic Gradients in Mean Runoff and Daily Runoff Variability" and "Stochastic in Space and Time: Part 2, Effects of Simulating Orographic Gradients in Daily Runoff Variability on Bedrock River Incision"
DOI: 10.5281/zenodo.10411367
title: Primitive groups of degree 4096 to 8191 for PrimGrp in GAP

description: This database contains the primitive groups of degree 4096 to 8191. It is intended to be used in GAP via the PrimGrp library (https://gap-packages.github.io/primgrp/).

The groups were originally computed by Benjamin Mark Stratford and reported in Chapter 3 of the following PhD thesis:

Benjamin Mark Stratford,Topics in computational group theory relating to classifications of permutation groups. PhD thesis, University of Warwick, 2022.(http://webcat.warwick.ac.uk/record=b3821786)

Additional information such as the order, simplicity, solubility, O'Nan-Scott type, collected suborbits, transitivity, and socle type have also been computed for the convenience of the user, and to facilitate identification of an arbitrary primitive group. The primitive identification number of each group has been assigned according to the ordering in which the data was originally provided. Along with the generators of the group, this information is stored as a GAP record object.

In order to use this data, download the file "ExtendedPrimitiveGroupsData.tar" into the "data" directory of the PrimGrp library. Untar the file in this directory. This should result in a subdirectory called "ExtendedPrimitiveGroupsData" which contains files of the format "PrimitiveGroups_N_ID.g.gz" where N is the degree of the permutation group and ID is its primitive identification.  Do not decompress the files of the format "PrimitiveGroups_N_ID.g.gz". Do not move them into the "data" directory, but rather leave them in the "ExtendedPrimitiveGroupsData" subdirectory.

Note that the extended primitive groups data is reasonably large (~3GB) and ensure you have sufficient space available on your computer.

PrimGrp version 4.0.0 or later is required. For more information about how to use the primitive groups library in GAP see the PrimGrp documentation (https://gap-packages.github.io/primgrp/)
DOI: 10.5281/zenodo.10411366
title: Primitive groups of degree 4096 to 8191 for PrimGrp in GAP

description: This database contains the primitive groups of degree 4096 to 8191. It is intended to be used in GAP via the PrimGrp library (https://gap-packages.github.io/primgrp/).

The groups were originally computed by Benjamin Mark Stratford and reported in Chapter 3 of the following PhD thesis:

Benjamin Mark Stratford,Topics in computational group theory relating to classifications of permutation groups. PhD thesis, University of Warwick, 2022.(http://webcat.warwick.ac.uk/record=b3821786)

Additional information such as the order, simplicity, solubility, O'Nan-Scott type, collected suborbits, transitivity, and socle type have also been computed for the convenience of the user, and to facilitate identification of an arbitrary primitive group. The primitive identification number of each group has been assigned according to the ordering in which the data was originally provided. Along with the generators of the group, this information is stored as a GAP record object.

In order to use this data, download the file "ExtendedPrimitiveGroupsData.tar" into the "data" directory of the PrimGrp library. Untar the file in this directory. This should result in a subdirectory called "ExtendedPrimitiveGroupsData" which contains files of the format "PrimitiveGroups_N_ID.g.gz" where N is the degree of the permutation group and ID is its primitive identification.  Do not decompress the files of the format "PrimitiveGroups_N_ID.g.gz". Do not move them into the "data" directory, but rather leave them in the "ExtendedPrimitiveGroupsData" subdirectory.

Note that the extended primitive groups data is reasonably large (~1GB) and ensure you have sufficient space available on your computer.

PrimGrp version 4.0.0 or later is required. For more information about how to use the primitive groups library in GAP see the PrimGrp documentation (https://gap-packages.github.io/primgrp/)
DOI: 10.5281/zenodo.10426628
title: Primitive groups of degree 4096 to 8191 for PrimGrp in GAP

description: This database contains the primitive groups of degree 4096 to 8191. It is intended to be used in GAP via the PrimGrp library (https://gap-packages.github.io/primgrp/).

The groups were originally computed by Benjamin Mark Stratford and reported in Chapter 3 of the following PhD thesis:

Benjamin Mark Stratford,Topics in computational group theory relating to classifications of permutation groups. PhD thesis, University of Warwick, 2022.(http://webcat.warwick.ac.uk/record=b3821786)

Additional information such as the order, simplicity, solubility, O'Nan-Scott type, collected suborbits, transitivity, and socle type have also been computed for the convenience of the user, and to facilitate identification of an arbitrary primitive group. The primitive identification number of each group has been assigned according to the ordering in which the data was originally provided. Along with the generators of the group, this information is stored as a GAP record object.

In order to use this data, download the file "ExtendedPrimitiveGroupsData.tar" into the "data" directory of the PrimGrp library. Untar the file in this directory. This should result in a subdirectory called "ExtendedPrimitiveGroupsData" which contains files of the format "PrimitiveGroups_N_ID.g.gz" where N is the degree of the permutation group and ID is its primitive identification.  Do not decompress the files of the format "PrimitiveGroups_N_ID.g.gz". Do not move them into the "data" directory, but rather leave them in the "ExtendedPrimitiveGroupsData" subdirectory.

Note that the extended primitive groups data is reasonably large (~1GB) and ensure you have sufficient space available on your computer.

PrimGrp version 4.0.0 or later is required. For more information about how to use the primitive groups library in GAP see the PrimGrp documentation (https://gap-packages.github.io/primgrp/)