cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2c97db
title: CCDC 2183621: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2022|CSD Communication|||
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.cc2cb5kg
title: CCDC 2184525: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2022|CSD Communication|||
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.cc2cb5lh
title: CCDC 2184526: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, R.W. Harrington|2022|CSD Communication|||
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: 10.5519/50unozal
title: Das_Oligodon_melaneus

description: DNA sequence alignments for _Oligodon_ phylogenetics
DOI: 10.5281/zenodo.6948301
title: Navier-Stokes Dataset for Paper "Variable-Input DeepONets for Operator Learning"

description: This repository contains all datasets required to reproduce the Navier-Stokes tests presented in the paper "Variable-Input DeepONets for Operator Learning".
DOI: 10.5281/zenodo.6559024
title: Navier-Stokes Dataset for Paper "Variable-Input DeepONets for Operator Learning"

description: This repository contains all datasets required to reproduce the Navier-Stokes tests presented in the paper "Variable-Input DeepONets for Operator Learning".
DOI: 10.5281/zenodo.6948291
title: Darcy Flow Datasets for Paper "Variable-Input DeepONets for Operator Learning"

description: This repository contains all datasets required to reproduce the Darcy Flow tests presented in the paper "Variable-Input DeepONets for Operator Learning".
DOI: 10.5281/zenodo.6558988
title: Darcy Flow Datasets for Paper "Variable-Input DeepONets for Operator Learning"

description: This repository contains all datasets required to reproduce the Darcy Flow tests presented in the paper "Variable-Input DeepONets for Operator Learning".
DOI: 10.5281/zenodo.6948521
title: Database of thermal power plant of YASSA-DIBAMBA CAMEROUN (2017-2018-2019-2020-2021)

description: In this work, genetic algorithm coupled with neural networks was used to predict the concentration of pollutants in the Yassa area. The model takes into account the meteorological parameters of the study area and the source over 5 years, period from January 2017 to December 2021. To evaluate the model, two indices are used to indicate the prediction model’s performance; the Squared Correlation Coefficient R<sup>2</sup> whose value in the test case is 0.9996 and the Mean Square Error (MSE) whose best value is 0.0044756. We also evaluated the optimization methods and found that compared to the swarm by particle optimization, the genetic algorithm gives a better “fitness function curve” as a function of the number of iterations. The results showed that the GA-ANN coupling presented here is able to provide a reliable and accurate prediction of the distance of the area where the air quality standards and criteria are met. The safe distance where thermal power plant activity respects the air quality criteria is beyond 2200m. The results help not only in the choice of the location of the thermal power plant but also in the monitoring of the air quality in residential areas.
DOI: 10.5281/zenodo.6948520
title: Database of thermal power plant of YASSA-DIBAMBA CAMEROUN (2017-2018-2019-2020-2021)

description: In this work, genetic algorithm coupled with neural networks was used to predict the concentration of pollutants in the Yassa area. The model takes into account the meteorological parameters of the study area and the source over 5 years, period from January 2017 to December 2021. To evaluate the model, two indices are used to indicate the prediction model’s performance; the Squared Correlation Coefficient R<sup>2</sup> whose value in the test case is 0.9996 and the Mean Square Error (MSE) whose best value is 0.0044756. We also evaluated the optimization methods and found that compared to the swarm by particle optimization, the genetic algorithm gives a better “fitness function curve” as a function of the number of iterations. The results showed that the GA-ANN coupling presented here is able to provide a reliable and accurate prediction of the distance of the area where the air quality standards and criteria are met. The safe distance where thermal power plant activity respects the air quality criteria is beyond 2200m. The results help not only in the choice of the location of the thermal power plant but also in the monitoring of the air quality in residential areas.
DOI: 10.5281/zenodo.4778424
title: MELLODDY TUNER release v1 public data

description: A public dataset from ChEMBL (v25) for MELLODDY TUNER release v1. Data can be used for technical tests, template for your own dataset and machine learning with SparseChem (https://github.com/melloddy/SparseChem).
DOI: 10.5281/zenodo.4778423
title: MELLODDY TUNER release v1 public data

description: A public dataset from ChEMBL (v25) for MELLODDY TUNER release v1. Data can be used for technical tests, template for your own dataset and machine learning with SparseChem (https://github.com/melloddy/SparseChem).
DOI: 10.5281/zenodo.5045055
title: Raw data extracted from ChEMBL

description: Raw data files extracted from ChEMBL for the MELLODDY project.
DOI: 10.5281/zenodo.5045054
title: Raw data extracted from ChEMBL

description: Raw data files extracted from ChEMBL for the MELLODDY project.
DOI: 10.5281/zenodo.4835670
title: MELLODDY TUNER release v2 public data

description: A public dataset from ChEMBL (v25) for MELLODDY TUNER release v2. Data extracted from ChEMBL and PubChem (input archives), and processed with https://github.com/melloddy/MELLODDY-TUNER release v2. Data can be used for technical tests, template for your own dataset and machine learning with SparseChem (https://github.com/melloddy/SparseChem).
DOI: 10.5281/zenodo.4835669
title: MELLODDY TUNER release v2 public data

description: A public dataset from ChEMBL (v25) for MELLODDY TUNER release v2. Data extracted from ChEMBL and PubChem (input archives), and processed with https://github.com/melloddy/MELLODDY-TUNER release v2. Data can be used for technical tests, template for your own dataset and machine learning with SparseChem (https://github.com/melloddy/SparseChem).
DOI: 10.48329/fsrx-cy41
title: CPRD GOLD August 2022

description: The CPRD GOLD database contains longitudinal routinely-collected electronic health records (EHR data) from UK primary care practices using Vision® general practice patient management software. The database captures information on demographic characteristics; diagnoses and symptoms; drug exposures; vaccination history; laboratory tests; and referrals to hospital and specialist care. Access to anonymised data from CPRD is subject to a full licence agreement containing detailed terms and conditions of use. Anonymised patient datasets can be extracted for researchers against specific study specifications, following protocol approval. Further information is available at https://www.cprd.com/data-access.
DOI: 10.5281/zenodo.6948603
title: Dialexical set of Proto-Tai simple onsets

description: A dialexical set for the study of variation and sound change in the simple onsets of Proto-Tai. Includes columns for the proto-onset, reconstructed form from Pittayaporn (2009), the posited Proto-Tai gloss, the standard Thai reflex in Thai script (if one exists), the cognate ID number from Pittayaporn's reconstructions, and the matching cognate ID number from Hudak (2008), which compiles Gedney's field data (if a match exists).
DOI: 10.5281/zenodo.6948602
title: Dialexical set of Proto-Tai simple onsets

description: A dialexical set for the study of variation and sound change in the simple onsets of Proto-Tai. Includes columns for the proto-onset, reconstructed form from Pittayaporn (2009), the posited Proto-Tai gloss, the standard Thai reflex in Thai script (if one exists), the cognate ID number from Pittayaporn's reconstructions, and the matching cognate ID number from Hudak (2008), which compiles Gedney's field data (if a match exists).
DOI: 10.5281/zenodo.6948536
title: MiaPaca2 shNISCH rilmenidine 48h

description: MiaPaca2 cell line, scramble and shNISCH treated with 100uM rilmenidine for 48h <em>in vitro</em>.
DOI: 10.5281/zenodo.6948535
title: MiaPaca2 shNISCH rilmenidine 48h

description: MiaPaca2 cell line, scramble and shNISCH treated with 100uM rilmenidine for 48h <em>in vitro</em>.
DOI: 10.5517/ccdc.csd.cc283c81
title: CCDC 2088602: Experimental Crystal Structure Determination

description: Related Article: sahid sk|2021|CSD Communication|||
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: 10.5281/zenodo.4537794
title: MD simulation of 50% POPC+50% cholesterol bilayer using Berger force field at T=303K

description: Unprotonated trajectory (1.2 microseconds) from a Berger simulation of 256 POPCs and 265 cholesterols hydrated with 20408 waters simulated with Gpu-version of Gromacs2020.1. The force field and inputs were downloaded from 10.5281/zenodo.13285. Prod.tpr was used to run the simulation. Simulation used Nose-Hoover thermostat and Parrinello-Rahman barostat.
DOI: 10.5281/zenodo.4537793
title: MD simulation of 50% POPC+50% cholesterol bilayer using Berger force field at T=303K

description: Unprotonated trajectory (1.2 microseconds) from a Berger simulation of 256 POPCs and 265 cholesterols hydrated with 20408 waters simulated with Gpu-version of Gromacs2020.1. The force field and inputs were downloaded from 10.5281/zenodo.13285. Prod.tpr was used to run the simulation. Simulation used Nose-Hoover thermostat and Parrinello-Rahman barostat.
DOI: 10.5281/zenodo.4537207
title: Pure POPC bilayer MD simulation using Berger force field, T=303K

description: Protonated trajectory and .gro from a Berger simulation of 256 POPCs hydrated with 10342 waters simulated with Gpu-version of Gromacs2020.1. The force field and inputs were downloaded from https://zenodo.org/record/1402417/. Prod.tpr was used to run the simulation, protonated.tpr is a dummy .tpr used for the analysis of the protonated trajectory. Simulation used Nose-Hoover thermostat and Parrinello-Rahman barostat.
DOI: 10.5281/zenodo.6948680
title: ClinSpEn-OC (Ontology Concepts) Test + Background Set

description: This repository contains the test and background data for the ClinSpEn-Ontology Concepts sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content. The data is made up of a TSV file with two columns: concept number and English concept. The direction of this sub-track is EN&gt;ES. Ontologies and structured vocabularies represent a key resource for semantic interoperability, entity linking, biomedical knowledge bases and precision medicine, and thus there is a pressing need to generate multilingual biomedical ontologies for a range of clinical applications Related Links: <strong>- Data website with more information: </strong>https://temu.bsc.es/clinspen/ <strong>- WMT website (includes schedule, registration, ...): </strong>https://www.statmt.org/wmt22/ <strong>- CodaLab: </strong>https://codalab.lisn.upsaclay.fr/competitions/6696 ClinSpEn SAMPLE SETS: <strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> https://doi.org/10.5281/zenodo.6497350 <strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong>https://doi.org/10.5281/zenodo.6497372 <strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong>https://doi.org/10.5281/zenodo.6497388 ClinSpEn TEST SETS: <strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong>https://doi.org/10.5281/zenodo.6948634 <strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong>https://doi.org/10.5281/zenodo.6948669 <strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong>https://doi.org/10.5281/zenodo.6948679
DOI: 10.5281/zenodo.6948679
title: ClinSpEn-OC (Ontology Concepts) Test + Background Set

description: This repository contains the test and background data for the ClinSpEn-Ontology Concepts sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content. The data is made up of a TSV file with two columns: concept number and English concept. The direction of this sub-track is EN&gt;ES. Ontologies and structured vocabularies represent a key resource for semantic interoperability, entity linking, biomedical knowledge bases and precision medicine, and thus there is a pressing need to generate multilingual biomedical ontologies for a range of clinical applications Related Links: <strong>- Data website with more information: </strong>https://temu.bsc.es/clinspen/ <strong>- WMT website (includes schedule, registration, ...): </strong>https://www.statmt.org/wmt22/ <strong>- CodaLab: </strong>https://codalab.lisn.upsaclay.fr/competitions/6696 ClinSpEn SAMPLE SETS: <strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> https://doi.org/10.5281/zenodo.6497350 <strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong>https://doi.org/10.5281/zenodo.6497372 <strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong>https://doi.org/10.5281/zenodo.6497388 ClinSpEn TEST SETS: <strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong>https://doi.org/10.5281/zenodo.6948634 <strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong>https://doi.org/10.5281/zenodo.6948669 <strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong>https://doi.org/10.5281/zenodo.6948679
DOI: 10.5281/zenodo.4537206
title: Pure POPC bilayer MD simulation using Berger force field, T=303K

description: Protonated trajectory and .gro from a Berger simulation of 256 POPCs hydrated with 10342 waters simulated with Gpu-version of Gromacs2020.1. The force field and inputs were downloaded from https://zenodo.org/record/1402417/. Prod.tpr was used to run the simulation, protonated.tpr is a dummy .tpr used for the analysis of the protonated trajectory. Simulation used Nose-Hoover thermostat and Parrinello-Rahman barostat.
DOI: 10.5281/zenodo.4382108
title: Pure POPC membrane simulations using Amber Lipid 14 Force Field

description: Pure POPC membrane simulations using the Amber Lipid 14 force field. <pre>@article{dickson2014lipid14, title={Lipid14: the amber lipid force field}, author={Dickson, Callum J and Madej, Benjamin D and Skjevik, {\AA}ge A and Betz, Robin M and Teigen, Knut and Gould, Ian R and Walker, Ross C}, journal={Journal of chemical theory and computation}, volume={10}, number={2}, pages={865--879}, year={2014}, publisher={ACS Publications} }</pre> The trajectories are centered such that the center of mass of the lipid tails are at the origin. <strong>Please check the imaging again to make sure that there are no problems. </strong> <strong>The trajectories do not contain water molecules.</strong> Simulation Details: Lipids : 72 POPC lipids, 36 per leaflet Water: 9560 TIP3P water molecules (<strong>water coordinates are not saved</strong>) Temperature: 303 K Pressure: 1 bar Thermostat: Langevin Barostat: Berendsen Pressure coupling: Semi-isotropic Trajectory Length: 100 ns (after 100 ns pre-equilibration) Saving frequency: 100 ps Further details are available at the 04_Run.in file All trajectories started from the same structure but equilibriated for 100 ns independently (using 03_Hold.in)
DOI: 10.5281/zenodo.4349753
title: Slipids simulations of a bilayer containing 50% of cholesterol, T=303K

description: Simulation of 50%POPC+50%cholesterol bilayer (122+122 lipids per leaflet) in full hydration (40 water/lipid) at T=303K in NPT ensemble. Simulations where done on GPU Gromacs 2020.1. The initial configurations were obtained from CHARMM36 simulations and then equilibrated with Slipids force field. The force field was downloaded from the Slipids website (http://www.fos.su.se/~sasha/SLipids/). Note that this is the pre-2020 version of the force field. Simulation uses Parrinello-Rahman barostat and Nose-Hoover thermostat.
DOI: 10.5281/zenodo.4382107
title: Pure POPC membrane simulations using Amber Lipid 14 Force Field

description: Pure POPC membrane simulations using the Amber Lipid 14 force field. <pre>@article{dickson2014lipid14, title={Lipid14: the amber lipid force field}, author={Dickson, Callum J and Madej, Benjamin D and Skjevik, {\AA}ge A and Betz, Robin M and Teigen, Knut and Gould, Ian R and Walker, Ross C}, journal={Journal of chemical theory and computation}, volume={10}, number={2}, pages={865--879}, year={2014}, publisher={ACS Publications} }</pre> The trajectories are centered such that the center of mass of the lipid tails are at the origin. <strong>Please check the imaging again to make sure that there are no problems. </strong> <strong>The trajectories do not contain water molecules.</strong> Simulation Details: Lipids : 72 POPC lipids, 36 per leaflet Water: 9560 TIP3P water molecules (<strong>water coordinates are not saved</strong>) Temperature: 303 K Pressure: 1 bar Thermostat: Langevin Barostat: Berendsen Pressure coupling: Semi-isotropic Trajectory Length: 100 ns (after 100 ns pre-equilibration) Saving frequency: 100 ps Further details are available at the 04_Run.in file All trajectories started from the same structure but equilibriated for 100 ns independently (using 03_Hold.in)
DOI: 10.5281/zenodo.4349752
title: Slipids simulations of a bilayer containing 50% of cholesterol, T=303K

description: Simulation of 50%POPC+50%cholesterol bilayer (122+122 lipids per leaflet) in full hydration (40 water/lipid) at T=303K in NPT ensemble. Simulations where done on GPU Gromacs 2020.1. The initial configurations were obtained from CHARMM36 simulations and then equilibrated with Slipids force field. The force field was downloaded from the Slipids website (http://www.fos.su.se/~sasha/SLipids/). Note that this is the pre-2020 version of the force field. Simulation uses Parrinello-Rahman barostat and Nose-Hoover thermostat.
DOI: 10.5281/zenodo.4071264
title: CHARMM36 simulations of pure POPC bilayer and a bilayer containing 50% of cholesterol, T=303K

description: Simulation of POPC and 50%POPC+50%cholesterol bilayer in full hydration (40 water/lipid) at T=303K in NPT ensemble. Simulations where done on GPU Gromacs 2020.1. The initial configurations and force field (CHARMM36) were dowloaded from CHARMM-GUI. Simulation uses Parrinello-Rahman barostat and Nose-Hoover thermostat.
DOI: 10.5281/zenodo.4071263
title: CHARMM36 simulations of pure POPC bilayer and a bilayer containing 50% of cholesterol, T=303K

description: Simulation of POPC and 50%POPC+50%cholesterol bilayer in full hydration (40 water/lipid) at T=303K in NPT ensemble. Simulations where done on GPU Gromacs 2020.1. The initial configurations and force field (CHARMM36) were dowloaded from CHARMM-GUI. Simulation uses Parrinello-Rahman barostat and Nose-Hoover thermostat.
DOI: 10.5281/zenodo.3997214
title: POPG MD simulation with Lipid17 in water with Na+ counter ions

description: Lipid17 POPG bilayer simulation water. The initial structure was taken from https://zenodo.org/record/3832274#.X0OlNxmEZGE . Temperature is 310 K. The system contains 500 POPG, 500 sodium ions and 25000 TIP3 water molecules. Total length of simulation is 400 ns. First 100 ns were discarded. Saving frequency of 100-300ns.xtc is 20 ps.
DOI: 10.5281/zenodo.3997213
title: POPG MD simulation with Lipid17 in water with Na+ counter ions

description: Lipid17 POPG bilayer simulation water. The initial structure was taken from https://zenodo.org/record/3832274#.X0OlNxmEZGE . Temperature is 310 K. The system contains 500 POPG, 500 sodium ions and 25000 TIP3 water molecules. Total length of simulation is 400 ns. First 100 ns were discarded. Saving frequency of 100-300ns.xtc is 20 ps.
DOI: 10.5281/zenodo.3864993
title: LIPID17 POPC-POPG 50:50 MD simulation, Na+ counterions and 1000mM CaCl2, 298K

description: The last 200ns of 720ns of MD simulation trajectory with Amber lipid 17 force field. POPC-POPG 50:50 (150 POPC, 150 POPG) with Na+ counterions and 1000mM CaCl2. The starting structure and lipid 17 parameters from here: https://zenodo.org/record/2585523#.Xbf0FC17FBx The starting structure was generated by removing appropriate number of POPC lipids to get 50:50 ratio. Dihedral types are corrected to type 9 as discussed here: https://github.com/NMRLipids/NMRlipidsIVPEandPG/issues/12.
DOI: 10.5281/zenodo.6948635
title: ClinSpEn-CC (Clinical Cases) Test + Background Set

description: This repository contains the test and background data for the ClinSpEn-Clinical Cases sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content. The data is made up of a TSV file with three columns: document number, line number and English line. The direction of this sub-track is EN&gt;ES. The clinical cases themselves include COVID-19 case reports as well as diverse content extracted from PubMed. Related Links: <strong>- Data website with more information: </strong>https://temu.bsc.es/clinspen/ <strong>- WMT website (includes schedule, registration, ...): </strong>https://www.statmt.org/wmt22/ <strong>- CodaLab: </strong>https://codalab.lisn.upsaclay.fr/competitions/6696 ClinSpEn SAMPLE SETS: <strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> https://doi.org/10.5281/zenodo.6497350 <strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong>https://doi.org/10.5281/zenodo.6497372 <strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong>https://doi.org/10.5281/zenodo.6497388 ClinSpEn TEST SETS: <strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong>https://doi.org/10.5281/zenodo.6948634 <strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong>https://doi.org/10.5281/zenodo.6948669 <strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong>https://doi.org/10.5281/zenodo.6948679