cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8214702
title: Processed Data and Inversion Results for Constraining the Io Plasma Torus

description: The processed and used data for constraining the plasma torus. FieldLines.txt are the mapped positions along the magnetic field lines calculated with the JRM33 model. The lines start at Io's orbit at 5.9 Jupiter Radii and are mapped towards the flattened Jupiter surface. The longitudinal resolution is 0.5°, resulting in 1440 total field lines, 720 for each the northern and the southern hemisphere. Therefore, the file contains of 1440 blocks, each introduced with the starting Easter System III longitude and the corresponding hemisphere. The block afterwards contains of 4 columns, three for the Cartesian position of the mapping and the last for the total field strength, which are formatted as X[RJ], Y[RJ], Z[RJ], B[nT] TravelTimesNorth.txt and TravelTimesSouth.txt contain the calculated travel times of the Alfven waves corresponding to the northern and southern Io Footprint (IFP), respectively. The travel times are calculated by using the observed positions of the IFP by Bonfond et al. (2017) and mapping along the magnetic field lines of the JRM33 model to Io's orbit. Then, the longitudinal distance to Io's position at the time of the observation is divided by Io's synodic orbital frequency. The file is formatted as Lon[°], Travel Time [s], Error [s] Lon is the system 3 eastern longitude of the footprint. Travel Time is the calculated travel time as described above and Error is the propagated uncertainty given by Bonfond et al. (2017) Dipole.txt and Multipole.txt are the CSV inversion results. For N = 10000 randomly chosen scale heights (between 0.4 and 1.6 Jupiter radii [RJ]) and peak densities (between 500 and 3500 particles per cm³), the resulting misfit for a torus centered at the dipole or multipole centrifugal equator has been calculated, respectively. The files have 10000 CSV lines which are formatted as Peak Density [1/cm³], Scale Height [RJ], Misfit Peak Density is the maximum particle density at the center of the torus and Scale Height is the scale height H of the torus with the density profile decreasing with distance z to the center of the torus corresponding to \(\rho = \rho_0 e^{-z^2 \over H^2}\)
DOI: 10.5281/zenodo.8214701
title: Processed Data and Inversion Results for Constraining the Io Plasma Torus

description: The processed and used data for constraining the plasma torus. FieldLines.txt are the mapped positions along the magnetic field lines calculated with the JRM33 model. The lines start at Io's orbit at 5.9 Jupiter Radii and are mapped towards the flattened Jupiter surface. The longitudinal resolution is 0.5°, resulting in 1440 total field lines, 720 for each the northern and the southern hemisphere. Therefore, the file contains of 1440 blocks, each introduced with the starting Easter System III longitude and the corresponding hemisphere. The block afterwards contains of 4 columns, three for the Cartesian position of the mapping and the last for the total field strength, which are formatted as X[RJ], Y[RJ], Z[RJ], B[nT] TravelTimesNorth.txt and TravelTimesSouth.txt contain the calculated travel times of the Alfven waves corresponding to the northern and southern Io Footprint (IFP), respectively. The travel times are calculated by using the observed positions of the IFP by Bonfond et al. (2017) and mapping along the magnetic field lines of the JRM33 model to Io's orbit. Then, the longitudinal distance to Io's position at the time of the observation is divided by Io's synodic orbital frequency. The file is formatted as Lon[°], Travel Time [s], Error [s] Lon is the system 3 eastern longitude of the footprint. Travel Time is the calculated travel time as described above and Error is the propagated uncertainty given by Bonfond et al. (2017) Dipole.txt and Multipole.txt are the CSV inversion results. For N = 10000 randomly chosen scale heights (between 0.4 and 1.6 Jupiter radii [RJ]) and peak densities (between 500 and 3500 particles per cm³), the resulting misfit for a torus centered at the dipole or multipole centrifugal equator has been calculated, respectively. The files have 10000 CSV lines which are formatted as Peak Density [1/cm³], Scale Height [RJ], Misfit Peak Density is the maximum particle density at the center of the torus and Scale Height is the scale height H of the torus with the density profile decreasing with distance z to the center of the torus corresponding to \(\rho = \rho_0 e^{-z^2 \over H^2}\)
DOI: 10.5281/zenodo.8215258
title: Hydrochemical indicators of Poyang Lake in 2018 and 2022

description: The data is the result of the water quality survey of Poyang Lake in China to explore the impact of extreme climate conditions on the lake. The water quality data in 2018 were derived from the Chinese ecosystem science data center (http://www.nesdc.org.cn/sdo/detail?id=5fa53680042ebb70d0c833af), reflecting the water quality characteristics under conventional climatic conditions. The water quality data in 2022 are derived from our field investigation and sampling. In October 2022, we entered the Poyang Lake area under the global extreme drought weather for investigation and sampling and conducted indoor analysis. The indicators contained in the two-period data include physical indicators and chemical indicators, including temperature, dissolved oxygen (DO), pH, NO2--N, NO3--N, NH4+-N, TN, K+, Na+, Ca+, Mg+, Cl-, SO42-, alkalinity, total dissolved solid (TDS) and COD, specifically.
description: Funding: Natural Science Foundation of Hebei Province Grant Number: D2018504002
DOI: 10.5281/zenodo.8215257
title: Hydrochemical indicators of Poyang Lake in 2018 and 2022

description: The data is the result of the water quality survey of Poyang Lake in China to explore the impact of extreme climate conditions on the lake. The water quality data in 2018 were derived from the Chinese ecosystem science data center (http://www.nesdc.org.cn/sdo/detail?id=5fa53680042ebb70d0c833af), reflecting the water quality characteristics under conventional climatic conditions. The water quality data in 2022 are derived from our field investigation and sampling. In October 2022, we entered the Poyang Lake area under the global extreme drought weather for investigation and sampling and conducted indoor analysis. The indicators contained in the two-period data include physical indicators and chemical indicators, including temperature, dissolved oxygen (DO), pH, NO2--N, NO3--N, NH4+-N, TN, K+, Na+, Ca+, Mg+, Cl-, SO42-, alkalinity, total dissolved solid (TDS) and COD, specifically.
description: Funding: Natural Science Foundation of Hebei Province Grant Number: D2018504002
DOI: 10.5066/p9wqprfb
title: Waterborne Resistivity Inverted Models, Mississippi Alluvial Plain, 2016-2018

description: This data release consists of two .csv files of inverted models of electrical resistivity created from the processed resistivity data in the accompanying two data releases: (Miller and Others, 2018) and (2018 Raw and Processed, in progress). During 2016, 17, and 18, the U.S. Geological Survey conducted continuous resistivity profiling along 15 rivers or lakes in the Mississippi Alluvial Plain of Mississippi, Arkansas, Louisiana, and Missouri to characterize streambed hydraulic conductivity values. These techniques characterize the near surface geomorphology of the streambed that controls the recharge to the alluvial aquifer. These data can be used to map changes in the lithology of the streambed and identify areas of groundwater-surface water interaction. A total of 2,017 kilometers (km) of continuous resistivity profiles were collected. Individual lengths of surveyed profiles per river include: 203 km on the Yazoo River, 197 km on the Floodway, 270 km on the Sunflower River, 317 km on the Black River, 83 km on the Bogue Phalia, 116 km on the Tallahatchie River, 95 km on the Quiver River, 37 km on the Yalobusha River, 448 km on the White River, 75 km on the Cache River, 42 km on the St. Francis River, 80 km on Eutah Bend, 25 km on Roebuck Lake, 9 km on Sky Lake, and 20 km on a U.S. Department of Agriculture On Farm Storage reservoir. Two data sets are presented in this data release: MAP_CRP_2016_2018_INV_MDLS_FULL.csv contains the entire 15-layer model output from the inversion software and MAP_CRP_2016_2018_INV_MDLS_DOI.csv is a dataset that has been created for studying groundwater-surface water interaction. The data have been post-processed after inversion to accurately reflect the depth of investigation of the instrument and to remove known modeling artifacts. Additionally, the water column, &amp;amp;iuml;&amp;amp;iquest;&amp;amp;frac12;RHO_DOI_1&amp;amp;iuml;&amp;amp;iquest;&amp;amp;frac12;, has been removed so only data below the bottom of the streambed is represented. References Miller, B.V., Adams, R.F., Stocks, S.J., Wilson, J.L., Smith, D.C., and Kress, W.H., 2018, Waterborne resistivity surveys for streams in the Mississippi Alluvial Plain, 2017: U.S. Geological Survey data release, https://doi.org/10.5066/F71J98ZQ.
DOI: 10.5281/zenodo.6963014
title: PROTECT-SLR BISICLES ice-sheet model simulations for Amundsen Sea Embayment to 2050

description: BISICLES ice-sheet model results. The NetCDF files in ASE_BISICLES.tar.gz contain simulated output variables for the Amundsen Sea Embayment sector of the West Antarctic Ice Sheet at a spatial resolution of 1 km. The model start date is 2007 and the outputs are yearly to 2052. Each of the 29 simulations is a result of a different combination of model parameters. The parameters are the u<sub>0</sub> value in a regularized Coulomb friction law, the rate of imposed thinning of floating ice, and scaling factors for sliding and viscosity coefficients between 0.9 and 1.1. The final part of each dataset name gives the sea-level equivalent (SLE) of loss of ice above floatation. Each output was randomly selected from a 2 cm 2050 SLE band of a histogram of a large ensemble of simulations. See the pdf report included for further details.
description: Datasets produced as a deliverable of the H2020 Protect-SLR
DOI: 10.5281/zenodo.7266826
title: PROTECT-SLR BISICLES ice-sheet model simulations for Amundsen Sea Embayment to 2050

description: BISICLES ice-sheet model results. The NetCDF files in ASE_BISICLES.tar.gz contain simulated output variables for the Amundsen Sea Embayment sector of the West Antarctic Ice Sheet at a spatial resolution of 1 km. The model start date is 2007 and the outputs are yearly to 2052. Each of the 30 simulations is a result of a different combination of model parameters. The parameters are the u<sub>0</sub> value in a regularized Coulomb friction law, the rate of imposed thinning of floating ice, and scaling factors for sliding and viscosity coefficients between 0.9 and 1.1. The final part of each dataset name gives the sea-level equivalent (SLE) of loss of ice above floatation within Pine Island and Thwaites Glacier catchment basins. Each output was randomly selected from a 2 cm 2050 SLE band of a histogram of a large ensemble of simulations. See the pdf report included for further details.
description: Datasets produced as a deliverable of the H2020 Protect-SLR
DOI: 10.5281/zenodo.8215225
title: PROTECT-SLR BISICLES ice-sheet model simulations for Amundsen Sea Embayment to 2050

description: BISICLES ice-sheet model results. The NetCDF files in ASE_BISICLES.tar.gz contain simulated output variables for the Amundsen Sea Embayment sector of the West Antarctic Ice Sheet at a spatial resolution of 1 km. The model start date is 2007 and the outputs are yearly to 2052. Each of the 30 simulations is a result of a different combination of model parameters. The parameters are the u<sub>0</sub> value in a regularized Coulomb friction law, the rate of imposed thinning of floating ice, and scaling factors for sliding and viscosity coefficients between 0.9 and 1.1. The final part of each dataset name gives the sea-level equivalent (SLE) of loss of ice above floatation within Pine Island and Thwaites Glacier catchment basins. Each output was randomly selected from a 2 cm 2050 SLE band of a histogram of a large ensemble of simulations. See the pdf report included for further details.
description: Datasets produced as a deliverable of the H2020 Protect-SLR
DOI: 10.5281/zenodo.6963013
title: PROTECT-SLR BISICLES ice-sheet model simulations for Amundsen Sea Embayment to 2050

description: BISICLES ice-sheet model results. The NetCDF files in ASE_BISICLES.tar.gz contain simulated output variables for the Amundsen Sea Embayment sector of the West Antarctic Ice Sheet at a spatial resolution of 1 km. The model start date is 2007 and the outputs are yearly to 2052. Each of the 30 simulations is a result of a different combination of model parameters. The parameters are the u<sub>0</sub> value in a regularized Coulomb friction law, the rate of imposed thinning of floating ice, and scaling factors for sliding and viscosity coefficients between 0.9 and 1.1. The final part of each dataset name gives the sea-level equivalent (SLE) of loss of ice above floatation within Pine Island and Thwaites Glacier catchment basins. Each output was randomly selected from a 2 cm 2050 SLE band of a histogram of a large ensemble of simulations. See the pdf report included for further details.
description: Datasets produced as a deliverable of the H2020 Protect-SLR
DOI: 10.5281/zenodo.7247911
title: Data from: Investigating the human and non-obese diabetic mouse MHC class II immunopeptidome using protein language modelling.

description: <strong>Background</strong>: Identifying peptides associated with the major histocompability complex class II (MHCII) is a central task in the evaluation of the immunoregulatory function of therapeutics and drug prototypes. MHCII-peptide presentation prediction has multiple biopharmaceutical applications, including the safety assessment of biologics and engineered derivatives in silico, or the fast progression of antigen-specific immunomodulatory drug discovery programs in immune disease and cancer. This has resulted in the collection of large–scale data sets on adaptive immune receptor antigenic responses and MHC-associated peptide proteomics. In parallel, recent deep learning algorithmic advances in natural language processing (NLP) and protein language modelling (PLM) have shown potential in leveraging large collections of sequence data and improve MHC presentation prediction. <strong>Methodology</strong>: We trained a compact transformer model (AEGIS) on human and mouse MHCII immunopeptidome data, including a preclinical murine model, and evaluated its performance on the peptide presentation prediction task. <strong>Data</strong>: The data and models used in AEGIS are contained in the uploaded tar files. <strong>Results</strong>: The transformer performs on par with existing deep learning algorithms and that combining datasets from multiple organisms increases model performance (see preprint). We trained variants of the model with and without MHCII information. In both alternatives, the inclusion of peptides presented by the I-Ag7 MHC class II molecule expressed by the non-obese diabetic (NOD) mice enabled the in silico prediction of presented peptides in a preclinical type 1 diabetes model organism, which has promising therapeutic applications.
description: Description
DOI: 10.5281/zenodo.7247910
title: Data from: Investigating the human and non-obese diabetic mouse MHC class II immunopeptidome using protein language modelling.

description: <strong>Background</strong>: Identifying peptides associated with the major histocompability complex class II (MHCII) is a central task in the evaluation of the immunoregulatory function of therapeutics and drug prototypes. MHCII-peptide presentation prediction has multiple biopharmaceutical applications, including the safety assessment of biologics and engineered derivatives in silico, or the fast progression of antigen-specific immunomodulatory drug discovery programs in immune disease and cancer. This has resulted in the collection of large–scale data sets on adaptive immune receptor antigenic responses and MHC-associated peptide proteomics. In parallel, recent deep learning algorithmic advances in natural language processing (NLP) and protein language modelling (PLM) have shown potential in leveraging large collections of sequence data and improve MHC presentation prediction. <strong>Methodology</strong>: We trained a compact transformer model (AEGIS) on human and mouse MHCII immunopeptidome data, including a preclinical murine model, and evaluated its performance on the peptide presentation prediction task. <strong>Data</strong>: The data and models used in AEGIS are contained in the uploaded tar files. <strong>Results</strong>: The transformer performs on par with existing deep learning algorithms and that combining datasets from multiple organisms increases model performance (see preprint). We trained variants of the model with and without MHCII information. In both alternatives, the inclusion of peptides presented by the I-Ag7 MHC class II molecule expressed by the non-obese diabetic (NOD) mice enabled the in silico prediction of presented peptides in a preclinical type 1 diabetes model organism, which has promising therapeutic applications.
description: Description
DOI: 10.5281/zenodo.8210867
title: Data from: Investigating the human and non-obese diabetic mouse MHC class II immunopeptidome using protein language modelling.

description: <strong>Background</strong>: Identifying peptides associated with the major histocompability complex class II (MHCII) is a central task in the evaluation of the immunoregulatory function of therapeutics and drug prototypes. MHCII-peptide presentation prediction has multiple biopharmaceutical applications, including the safety assessment of biologics and engineered derivatives in silico, or the fast progression of antigen-specific immunomodulatory drug discovery programs in immune disease and cancer. This has resulted in the collection of large–scale data sets on adaptive immune receptor antigenic responses and MHC-associated peptide proteomics. In parallel, recent deep learning algorithmic advances in natural language processing (NLP) and protein language modelling (PLM) have shown potential in leveraging large collections of sequence data and improve MHC presentation prediction. <strong>Methodology</strong>: We trained a compact transformer model (AEGIS) on human and mouse MHCII immunopeptidome data, including a preclinical murine model, and evaluated its performance on the peptide presentation prediction task. <strong>Data</strong>: The data and models used in AEGIS are contained in the uploaded tar files. <strong>Results</strong>: The transformer performs on par with existing deep learning algorithms and that combining datasets from multiple organisms increases model performance (see preprint). We trained variants of the model with and without MHCII information. In both alternatives, the inclusion of peptides presented by the I-Ag7 MHC class II molecule expressed by the non-obese diabetic (NOD) mice enabled the in silico prediction of presented peptides in a preclinical type 1 diabetes model organism, which has promising therapeutic applications.
description: Description
DOI: 10.26300/cbbs-3q39
title: Data documentation for Analgesic Prescribing in Post-Acute Skilled Nursing Facility Care Following Hip Fracture Hospitalization among Older Adults
DOI: 10.25612/837.kw67n5xmabpq
title: Public Crash Data Extract from TxDOT (2010-2020)

description: Crash records for many counties in Texas, including crash type, date/time, roadway system, speed limit, roadway type, geographic information (including census block and ZCTA geoids), crash severity, and day of the week.
DOI: 10.48366/r604317
title: Potential of Sentinel (1 and 2) satellite data in Crop classification

description: This comparison shows the significant potential of Sentinel-1 and Sentinel-2 satellite datasets for crop classification due to their complementary capabilities and rich information content. Thi comparison will be helpful for young researchers in the field of remote sensing.
DOI: 10.34740/kaggle/dsv/6248777
title: Mahanagar Yatayat Transport Dataset
DOI: 10.25612/837.mg67xbr0499e
title: Public Crash Data Extract from TxDOT (2021)

description: Crash records for many counties in Texas, including crash type, date/time, roadway system, speed limit, roadway type, geographic information, crash severity, and day of the week.
DOI: 10.25612/837.erql6w45mm2g
title: Public Crash Data Extract from TxDOT (2022)

description: Crash records for H-GAC counties, including crash type, date/time, roadway system, speed limit, roadway type, geographic information, crash severity, and day of the week for 2022.
DOI: 10.5281/zenodo.7066767
title: Croatian teachers' MTEBI AND TPACK

description: Data of a surveying of 606 Croatian clasroom teachers.
description: {"references": ["Isteni\u010d, A., Sim\u010di\u010d, B., Crnkovi\u0107, V. M., & Volk, M. (2023). Croatian Elementary School Teachers' Maths Teaching Efficacy Beliefs: Knowledge Domains and Cross-Curricular Maths in the Post-Digital Era. SAGE Open, 13(3). https://doi.org/10.1177/21582440231187990"]}
DOI: 10.5281/zenodo.7066766
title: Croatian teachers' MTEBI AND TPACK

description: Data of a surveying of 606 Croatian clasroom teachers.
description: {"references": ["Isteni\u010d, A., Sim\u010di\u010d, B., Crnkovi\u0107, V. M., & Volk, M. (2023). Croatian Elementary School Teachers' Maths Teaching Efficacy Beliefs: Knowledge Domains and Cross-Curricular Maths in the Post-Digital Era. SAGE Open, 13(3). https://doi.org/10.1177/21582440231187990"]}
DOI: 10.25402/fsg.23867625.v1
title: Table S1 - Diagnostic performances and therapeutic impact of the Unyvero Implant and Tissue Infection multiplex PCR in periprosthetic joint infections

description: <strong>Aim: </strong>We evaluated the diagnostic performances of Unyvero Implant and Tissue Infection multiplex PCR (mPCR) (Curetis) and the clinical impact of this PCR on therapeutic decisions. <strong>Materials & methods:</strong>AmPCR was performed on 33 joint fluids in addition to standard culture. A group of experts analyzed <em>a posteriori </em>the impact of the mPCR in the patient management. <strong>Results: </strong>The rate of concordance with culture was 74% (20<strong>/</strong>27). The sensitivity of the PCR was 59% and the specificity 90%. Clinicians would have started an appropriate treatment sooner for six patients (from 2 to 22 days earlier). <strong>Conclusion: </strong>The PCR would improve the management of 22% of the patients. For other patients, mPCR results have to be completed with the culture.
DOI: 10.25402/fsg.23867628
title: Table S2 list of pathogens and resistance genes - Diagnostic performances and therapeutic impact of the Unyvero Implant and Tissue Infection multiplex PCR in periprosthetic joint infections

description: <strong>Aim: </strong>We evaluated the diagnostic performances of Unyvero Implant and Tissue Infection multiplex PCR (mPCR) (Curetis) and the clinical impact of this PCR on therapeutic decisions. <strong>Materials & methods:</strong>AmPCR was performed on 33 joint fluids in addition to standard culture. A group of experts analyzed <em>a posteriori </em>the impact of the mPCR in the patient management. <strong>Results: </strong>The rate of concordance with culture was 74% (20<strong>/</strong>27). The sensitivity of the PCR was 59% and the specificity 90%. Clinicians would have started an appropriate treatment sooner for six patients (from 2 to 22 days earlier). <strong>Conclusion: </strong>The PCR would improve the management of 22% of the patients. For other patients, mPCR results have to be completed with the culture.