cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16887934
title: Improving cassava bacterial blight resistance by editing the epigenome

description: The zipped folder contains all of the data used in figures for Veley et al. organized by figure number.
DOI: 10.6084/m9.figshare.16887934.v1
title: Improving cassava bacterial blight resistance by editing the epigenome

description: The zipped folder contains all of the data used in figures for Veley et al. organized by figure number.
DOI: 10.5281/zenodo.7430956
title: Transcriptomics for clinical and experimental biology research: hang on a seq

description: Additional R script, source data files and figures associated with the paper. The following source files can generate the corresponding figures using the R script (Omics_review_HC_final.R). Figure 1: Fig1_upset_new_V2_general.type.csv Figure 2 & 3: SMP_ENSG_biotypes_average_data_All_genes_V2.xlsx; FUSION_study_CPM-data_fusion_annotated.xlsx Extra Figures (RNA_review_figures_brain_HC.pdf) uploaded here : GSE47774_SEQC_ILM_BGI_Human_Brain_Only.xlsx; GSE47774_SEQC_ILM_BGI_Human_Brain_Only.xlsx Additional raw counts source files: RNA-seq.A: summary data is available at https://www.ebi.ac.uk/birney-srv/FUSION/. Full dataset is available at dbGaP accession phs001048.v2.p1. RNA-seq.B: GSE164471_GESTALT_Muscle_ENSG_counts_annotated.csv RNA-seq.C: GSE97084_Robinson_GeneCount_raw.tsv; GSE97084_Robinson_GeneCount_raw_2.tsv RNA-seq.D: GSE157585_Kulkarni_Peck_et_al_MASTERS_raw_counts.txt RNA-seq.E: GSE151066_Rubenstein_rsem_genes_count.csv HTA2.0 array: SMP191_iron_output_M_GC-HTA_ENST_Grch38_25.500.25.10_log2_2_minimum.csv; ENSG_biotypes_RNA_review_SMP_data Figure 5 & 6 are generated from various source files above following the R script.
DOI: 10.5281/zenodo.7430955
title: Transcriptomics for clinical and experimental biology research: hang on a seq

description: Additional R script, source data files and figures associated with the paper. The following source files can generate the corresponding figures using the R script (Omics_review_HC_final.R). Figure 1: Fig1_upset_new_V2_general.type.csv Figure 2 & 3: SMP_ENSG_biotypes_average_data_All_genes_V2.xlsx; FUSION_study_CPM-data_fusion_annotated.xlsx Extra Figures (RNA_review_figures_brain_HC.pdf) uploaded here : GSE47774_SEQC_ILM_BGI_Human_Brain_Only.xlsx; GSE47774_SEQC_ILM_BGI_Human_Brain_Only.xlsx Additional raw counts source files: RNA-seq.A: summary data is available at https://www.ebi.ac.uk/birney-srv/FUSION/. Full dataset is available at dbGaP accession phs001048.v2.p1. RNA-seq.B: GSE164471_GESTALT_Muscle_ENSG_counts_annotated.csv RNA-seq.C: GSE97084_Robinson_GeneCount_raw.tsv; GSE97084_Robinson_GeneCount_raw_2.tsv RNA-seq.D: GSE157585_Kulkarni_Peck_et_al_MASTERS_raw_counts.txt RNA-seq.E: GSE151066_Rubenstein_rsem_genes_count.csv HTA2.0 array: SMP191_iron_output_M_GC-HTA_ENST_Grch38_25.500.25.10_log2_2_minimum.csv; ENSG_biotypes_RNA_review_SMP_data Figure 5 & 6 are generated from various source files above following the R script.
DOI: 10.6084/m9.figshare.16644619
title: Arctic COSMO-CLM hindcast 1995

description: COSMO-CLM Russian Arctic climate hindcast
DOI: 10.6084/m9.figshare.16644619.v3
title: Arctic COSMO-CLM hindcast 1995

description: COSMO-CLM Russian Arctic climate hindcast
DOI: 10.6084/m9.figshare.16644619.v2
title: Arctic COSMO-CLM hindcast 1995

description: COSMO-CLM Russian Arctic climate hindcast
DOI: 10.25318/4210004001-eng
title: Type of non-parental child care used in the evenings or on weekends, children in school aged 4 to 12 years

description: Percentage of children in school aged 4 to 12 years participating in non-parental child care in the evenings or on weekends, by type of child care arrangement.
DOI: 10.25318/4210004001-fra
title: Type de services de garde fournis par une personne sans lien de parenté utilisé le soir ou la fin de semaine, enfants âgés de 4 à 12 ans allant à l’école

description: Pourcentage d'enfants âgés de 4 à 12 ans allant à l’école recevant des services de garde fournis par une personne sans lien de parenté le soir ou la fin de semaine, selon le type de garde.
DOI: 10.25318/3910005501-eng
title: Number of marriages and nuptiality indicators

description: Number of marriages and various nuptiality indicators (crude marriage rate, number of marriages of different-gender or same-gender couples [when available], total first-marriage rate and probability of ever marrying according to the first-marriage table), by place of occurrence, 1991 to most recent year.
DOI: 10.25318/3910005501-fra
title: Nombre de mariages et indicateurs de nuptialité

description: Nombre de mariages et divers indicateurs de nuptialité (taux brut de nuptialité, nombre de mariages de couples de genre différent ou de même genre [si disponible], indice synthétique de primonuptialité et probabilité de se marier au moins une fois d'après la table de primonuptialité), selon le lieu de l'événement, 1991 à l'année la plus récente.
DOI: 10.6084/m9.figshare.16680925
title: Arctic COSMO-CLM hindcast 1996

description: Russian Arctic COSMO-CLM hindcast 1996
DOI: 10.6084/m9.figshare.16680925.v3
title: Arctic COSMO-CLM hindcast 1996

description: Russian Arctic COSMO-CLM hindcast 1996
DOI: 10.6084/m9.figshare.16680925.v2
title: Arctic COSMO-CLM hindcast 1996

description: Russian Arctic COSMO-CLM hindcast 1996
DOI: 10.5281/zenodo.7429795
title: Data sets for "Automated cell segmentation for reproducibility in bioimage analysis"

description: This is the raw data sets used in "Automated cell segmentation for reproducibility in bioimage analysis", published in Synthetic Biology (Oxford Academic)
description: All inquires, email Michael.Robitaille@nrl.navy.mil For each experiment, scaling and time information are stored in the metadata .XML file of the parent folder, separated out by +/- rapamycin.
DOI: 10.5281/zenodo.7429794
title: Data sets for "Automated cell segmentation for reproducibility in bioimage analysis"

description: This is the raw data sets used in "Automated cell segmentation for reproducibility in bioimage analysis", published in Synthetic Biology (Oxford Academic)
description: All inquires, email Michael.Robitaille@nrl.navy.mil For each experiment, scaling and time information are stored in the metadata .XML file of the parent folder, separated out by +/- rapamycin.
DOI: 10.6084/m9.figshare.16697920
title: Arctic COSMO-CLM hindcast 1997

description: Russian Arctic COSMO-CLM climate hindcast 1997
DOI: 10.6084/m9.figshare.16697920.v3
title: Arctic COSMO-CLM hindcast 1997

description: Russian Arctic COSMO-CLM climate hindcast 1997
DOI: 10.6084/m9.figshare.16697920.v2
title: Arctic COSMO-CLM hindcast 1997

description: Russian Arctic COSMO-CLM climate hindcast 1997
DOI: 10.5281/zenodo.7414744
title: Pearl River Delta FVCOM model mangrove forests scenarios

description: Model data presented in: De Dominicis, M., Wolf, J., van Hespen, R., Zheng, P., Hu. Z. "Mangrove forests can be an effective coastal defence in the Pearl River Delta, China", <em>Communications Earth & Environment</em> (2023). To explore the effects of vegetation on storm surge dynamics and currents, we used a Finite Volume Community Ocean Model implementation for the South China Sea and the Pearl River Delta and simulated Typhoon Hato (2017) one of the strongest typhoons to affect the coastal areas of the Pearl River Delta in recent decades. We numerically modelled the protection capability of the mangrove wetlands in Shenzhen Bay and in the upper estuary river branches close to Guangzhou to determine their ability to mitigate coastal flooding. Additionally, we analyzed how the effectiveness of mangroves changes under different sea level rise scenarios. The dataset consists of water elevation and horizontal currents for 40 model experiments (see Table 1 in De Dominicis et al, 2023).
DOI: 10.5281/zenodo.7414743
title: Pearl River Delta FVCOM model mangrove forests scenarios

description: Model data presented in: De Dominicis, M., Wolf, J., van Hespen, R., Zheng, P., Hu. Z. "Mangrove forests can be an effective coastal defence in the Pearl River Delta, China", <em>Communications Earth & Environment</em> (2023). To explore the effects of vegetation on storm surge dynamics and currents, we used a Finite Volume Community Ocean Model implementation for the South China Sea and the Pearl River Delta and simulated Typhoon Hato (2017) one of the strongest typhoons to affect the coastal areas of the Pearl River Delta in recent decades. We numerically modelled the protection capability of the mangrove wetlands in Shenzhen Bay and in the upper estuary river branches close to Guangzhou to determine their ability to mitigate coastal flooding. Additionally, we analyzed how the effectiveness of mangroves changes under different sea level rise scenarios. The dataset consists of water elevation and horizontal currents for 40 model experiments (see Table 1 in De Dominicis et al, 2023).
DOI: 10.5517/ccdc.csd.cc24d04q
title: CCDC 1977742: Experimental Crystal Structure Determination

description: Related Article: Zhu Baili, He Pengzhen, Wang Qinghua, Cui Shuxin|2021|Rengong Jingti Xuebao|50|1471|
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.cc27h01t
title: CCDC 2069995: Experimental Crystal Structure Determination

description: Related Article: Hui Yang, Min Li, Yahui Cai, Mi Zhou, Guoliang Liu|2021|Cryst.Growth Des.|21|6642|doi:10.1021/acs.cgd.1c00895
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.cc27mwvm
title: CCDC 2074701: Experimental Crystal Structure Determination

description: Related Article: Arpan Bera, Srishti Gautam, Somarupa Sahoo, Apurba Kumar Pal, Paturu Kondaiah, Akhil R. Chakravarty|2022|RSC Med. Chem.|13|1526|doi:10.1039/D2MD00225F
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: 10.6084/m9.figshare.21727928
title: Questionnaire Criss-crossing the Sahara Questionnaire 2022.docx

description: This questionnaire is administered in order to obtain information on the above research topic. The objectives of this research are to: i. examine the new dangers that West African migrants who travel through the Sahara Desert to Europe contend with; and ii. investigate the implications that the new dangers to such migrants’ aspirations pose to the states within the domain.
DOI: 10.6084/m9.figshare.21727928.v1
title: Questionnaire Criss-crossing the Sahara Questionnaire 2022.docx

description: This questionnaire is administered in order to obtain information on the above research topic. The objectives of this research are to: i. examine the new dangers that West African migrants who travel through the Sahara Desert to Europe contend with; and ii. investigate the implications that the new dangers to such migrants’ aspirations pose to the states within the domain.
DOI: 10.6084/m9.figshare.17067277
title: Reliability assessment of compressive and splitting tensile strength prediction of roller compacted concrete pavement: introducing MARS-GOA-MCS

description: It is required to determine the mechanical properties of Roller Compacted Concrete Pavement (RCCP) such as Compressive Strength (CS) and Splitting Tensile Strength (STS) for constructing and maintaining the road pavement because of the inevitable uncertainties in the experimental setup. In this study, a reliable hybrid intelligent model based on the integration of Grasshopper Optimisation Algorithm (GOA) and Multivariate Adaptive Regression Splines (MARS), called MARS-GOA, was developed to present new closed-form equations for CS and STS prediction of RCCP. Initially, GOA was developed to serve as a search engine of the proposed algorithm to optimise MARS control parameters by minimising the error of prediction. Statistical metrics indicate that proposed hybrid MARS-GOA outperformed ELM, M5p, and standard MARS for prediction of both the CS (CoD = 0.811, PMARE = 22.146% and U95 = 34.670) and STS (CoD = 0.816, PMARE = 16.192% and U95 = 2.725) of RCCP. The results of reliability analysis, namely Monte-Carlo Simulation (MCS), revealed that TCS, TTS, and uncertainty level of the used dataset play a major role in the determination of the reliability of the RCCP mix design.
DOI: 10.6084/m9.figshare.17067277.v1
title: Reliability assessment of compressive and splitting tensile strength prediction of roller compacted concrete pavement: introducing MARS-GOA-MCS

description: It is required to determine the mechanical properties of Roller Compacted Concrete Pavement (RCCP) such as Compressive Strength (CS) and Splitting Tensile Strength (STS) for constructing and maintaining the road pavement because of the inevitable uncertainties in the experimental setup. In this study, a reliable hybrid intelligent model based on the integration of Grasshopper Optimisation Algorithm (GOA) and Multivariate Adaptive Regression Splines (MARS), called MARS-GOA, was developed to present new closed-form equations for CS and STS prediction of RCCP. Initially, GOA was developed to serve as a search engine of the proposed algorithm to optimise MARS control parameters by minimising the error of prediction. Statistical metrics indicate that proposed hybrid MARS-GOA outperformed ELM, M5p, and standard MARS for prediction of both the CS (CoD = 0.811, PMARE = 22.146% and U95 = 34.670) and STS (CoD = 0.816, PMARE = 16.192% and U95 = 2.725) of RCCP. The results of reliability analysis, namely Monte-Carlo Simulation (MCS), revealed that TCS, TTS, and uncertainty level of the used dataset play a major role in the determination of the reliability of the RCCP mix design.
DOI: 10.15784/601625
title: KIS-1 ROV Icefin Missions

description: This work was primarily funded by the NASA-funded RISE-UP: Ross Ice Shelf & Europa Underwater Probe (PI Britney E. Schmidt) project. Field work at the KIS-1 site during the 2019-2020 season was completed through international collaboration between NASA, NSF/USAP, and Antarctica New Zealand, funded by the Aotearoa New Zealand Antarctic Science Platform (ANTA1801) Antarctic Ice Dynamics Project (ASP02101), and the NZARI-funded Aotearoa New Zealand Ross Ice Shelf Programme, “Vulnerability of the Ross Ice Shelf in a Warming World” (grant no. 2014-11). The data contained in this directory are merged and calibrated data files from three missions conducted with ROV Icefin through a borehole drilled through Ross Ice Shelf at KIS1 (-82.78410, -155.26267), near the Kamb Ice Stream grounding zone. Missions were conducted in 2019 on December 16th, 18th, and 21st (UTC). For questions or assistance working with the data please contact Britney Schmidt (britneys@cornell.edu) and Justin Lawrence (jdl287@cornell.edu).
DOI: 10.6084/m9.figshare.21727967
title: data

description: arxiv
DOI: 10.6084/m9.figshare.21727967.v2
title: data

description: arxiv
DOI: 10.6084/m9.figshare.21727967.v1
title: 20221215

description: arxiv
DOI: 10.6084/m9.figshare.16777681
title: Arctic COSMO-CLM hindcast 1998

description: Russian Arctic COSMO-CLM hindcast 1998
DOI: 10.6084/m9.figshare.16777681.v3
title: Arctic COSMO-CLM hindcast 1998

description: Russian Arctic COSMO-CLM hindcast 1998
DOI: 10.6084/m9.figshare.16777681.v2
title: Arctic COSMO-CLM hindcast 1998

description: Russian Arctic COSMO-CLM hindcast 1998
DOI: 10.6084/m9.figshare.21727970
title: Table 2

description: Table 2. Global model fit measures
DOI: 10.6084/m9.figshare.21727970.v1
title: Table 2

description: Table 2. Global model fit measures
DOI: 10.5281/zenodo.7075664
title: Data for "Understanding density driven errors via reaction barrier heights"

description: Contains gzipped tarballs for BH76 data, inputs, and outputs computed with PySCF and the UTEP-NRLMOL code. Contents: r2SCAN_BH76_FODs.tar.gz : all relaxed Fermi orbital descriptors (FODs) computed self-consistently for r<sup>2</sup>SCAN-FLOSIC All entries are space-separated The first line contains the number of up-spin FODs followed by the number of down-spin FODs The first three values in each ensuing line are the x, y, and z coordinates of the FOD results_aug-cc-pvqz.tar.gz : all PySCF inputs/outputs for BH76 computed using the aug-cc-pVQZ basis set (spherical representation) DFAs = HF/EXOA, LSDA, PBE, BLYP, SCAN, r<sup>2</sup>SCAN, M06-L, MN15-L, B3LYP, LC-wPBE And DFA @ DFA2, where DFA2 = HF, LSDA, LC-wPBE results_def2-QZVP.tar.gz : all PySCF inputs/outputs for BH76 computed using the def2-QZVP basis set (spherical representation) DFAs = HF/EXOA, LSDA, PBE, SCAN, r<sup>2</sup>SCAN, LC-wPBE And DFA @ DFA2, where DFA2 = HF, LC-wPBE results_NRLMOL_cart.tar.gz : all PySCF inputs/outputs for BH76 computed using the default NRLMOL (density-functional optimized or DFO) basis set (Cartesian representation) DFAs = HF/EXOA, LSDA, PBE, SCAN, r<sup>2</sup>SCAN, LC-wPBE And DFA @ DFA2, where DFA2 = HF, LC-wPBE FLOSIC.tar.gz : data computed using the UTEP-NRLMOL code (NRLMOL basis set, Cartesian representation) DFAs = LSDA, PBE, SCAN, r<sup>2</sup>SCAN And DFA@SCAN-FLOSIC DC_DFT_processed_data.tar.gz : processed density-driven and functional-driven errors, and density sensitivity data r2SCAN_hybids.tar.gz : self-consistent r2 SCAN hybrid calculations (R2X-<em>x</em>) and DFA@R2X-<em>x</em> calculations All PySCF input/output, aug-cc-pVQZ basis (spherical representation) <em>x </em>= 0, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9, 1.0 DFA = LSDA, PBE, BLYP, SCAN, r2SCAN, B3LYP, LC-wPBE SCAN_hybids.tar.gz : self-consistent SCAN hybrid calculations (SX-<em>x</em>) and DFA@SX-<em>x</em> calculations All PySCF input/output, aug-cc-pVQZ basis (spherical representation) <em>x </em>= 0, 0.1, 0.25, 0.5, 0.75, 1.0 DFA = LSDA, PBE, SCAN, r2SCAN, LC-wPBE
DOI: 10.5281/zenodo.7075663
title: Data for "Understanding density driven errors via reaction barrier heights"

description: Contains gzipped tarballs for BH76 data, inputs, and outputs computed with PySCF and the UTEP-NRLMOL code. Contents: r2SCAN_BH76_FODs.tar.gz : all relaxed Fermi orbital descriptors (FODs) computed self-consistently for r<sup>2</sup>SCAN-FLOSIC All entries are space-separated The first line contains the number of up-spin FODs followed by the number of down-spin FODs The first three values in each ensuing line are the x, y, and z coordinates of the FOD results_aug-cc-pvqz.tar.gz : all PySCF inputs/outputs for BH76 computed using the aug-cc-pVQZ basis set (spherical representation) DFAs = HF/EXOA, LSDA, PBE, BLYP, SCAN, r<sup>2</sup>SCAN, M06-L, MN15-L, B3LYP, LC-wPBE And DFA @ DFA2, where DFA2 = HF, LSDA, PBE, LC-wPBE results_def2-QZVP.tar.gz : all PySCF inputs/outputs for BH76 computed using the def2-QZVP basis set (spherical representation) DFAs = HF/EXOA, LSDA, PBE, SCAN, r<sup>2</sup>SCAN, LC-wPBE And DFA @ DFA2, where DFA2 = HF, LC-wPBE results_NRLMOL_cart.tar.gz : all PySCF inputs/outputs for BH76 computed using the default NRLMOL (density-functional optimized or DFO) basis set (Cartesian representation) DFAs = HF/EXOA, LSDA, PBE, SCAN, r<sup>2</sup>SCAN, LC-wPBE And DFA @ DFA2, where DFA2 = HF, LC-wPBE FLOSIC.tar.gz : data computed using the UTEP-NRLMOL code (NRLMOL basis set, Cartesian representation) DFAs = LSDA, PBE, SCAN, r<sup>2</sup>SCAN And DFA@SCAN-FLOSIC DC_DFT_processed_data.tar.gz : processed density-driven and functional-driven errors, and density sensitivity data r2SCAN_hybids.tar.gz : self-consistent r2 SCAN hybrid calculations (R2X-<em>x</em>) and DFA@R2X-<em>x</em> calculations All PySCF input/output, aug-cc-pVQZ basis (spherical representation) <em>x </em>= 0, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9, 1.0 DFA = LSDA, PBE, BLYP, SCAN, r2SCAN, B3LYP, LC-wPBE SCAN_hybids.tar.gz : self-consistent SCAN hybrid calculations (SX-<em>x</em>) and DFA@SX-<em>x</em> calculations All PySCF input/output, aug-cc-pVQZ basis (spherical representation) <em>x </em>= 0, 0.1, 0.25, 0.5, 0.75, 1.0 DFA = LSDA, PBE, SCAN, r2SCAN, LC-wPBE
description: Version 2 adds @PBE results using the aug-cc-pVQZ basis set. These are used in the SM
DOI: 10.6084/m9.figshare.21727982
title: Table 3

description: Table 3. Reliability and convergent validity
DOI: 10.6084/m9.figshare.16730323
title: Arctic COSMO-CLM hindcast 1999

description: Russian Arctic COSMO-CLM hindcast 1999
DOI: 10.6084/m9.figshare.16730323.v3
title: Arctic COSMO-CLM hindcast 1999

description: Russian Arctic COSMO-CLM hindcast 1999
DOI: 10.6084/m9.figshare.16730323.v2
title: Arctic COSMO-CLM hindcast 1999

description: Russian Arctic COSMO-CLM hindcast 1999
DOI: 10.34740/kaggle/dsv/4723847
title: Egypt Gold Prices | Daily Updated
DOI: 10.5281/zenodo.7374580
title: Source data for the publication "SiGe quantum wells with oscillating Ge concentrations for quantum dot qubits"

description: This repository contains data reported in the figures of the publication "SiGe quantum wells with oscillating Ge concentrations for quantum dot qubits."
DOI: 10.5281/zenodo.7276407
title: 100ns REST2 MD Simulation Trajectory of Wild-Type Alpha-Synuclein using the DES-Amber Forcefield

description: Molecular dynamics simulation trajectory and for wild-type acylated alpha-Synuclein (Ac-AS) using the REST2 algorithm. The system was parameterised with the DES-Amber force field published by Maxwell et al. The simulation was run for 100 nanoseconds using GROMACS on the Forschungszentrum Jülich JUWELS supercomputer. The reposited trajectory is the lowest temperature replica at 300 K from 32 replicas between 300-500 K. This work was partially performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) and received funding from the Helmholtz Association of German Research Centers. The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at Jülich Supercomputing Centre (JSC).
description: This trajectory is provided to support Jupyter Notebooks and CoLabs demonstrating the free and open-source software, SPEADI, developed by the authors.
description: {"references": ["Tucker, M. R., Piana, S., Tan, D., LeVine, M. V., & Shaw, D. E. (2022). Development of force field parameters for the simulation of single- and double-stranded dna molecules and dna-protein complexes. The Journal of Physical Chemistry B, 126(24), 4442\u20134457. http://dx.doi.org/10.1021/acs.jpcb.1c10971", "Abraham, M. J., Murtola, T., Schulz, R., Szil\u00e1rd P\u00e1ll, Smith, J. C., Hess, B., & Lindahl, E. (2015). Gromacs: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1-2(nil), 19\u201325. http://dx.doi.org/10.1016/j.softx.2015.06.001", "Wang, L., Friesner, R. A., & Berne, B. J. (2011). Replica exchange with solute scaling: a more efficient version of replica exchange with solute tempering (rest2). The Journal of Physical Chemistry B, 115(30), 9431\u20139438. http://dx.doi.org/10.1021/jp204407d", "Alvarez, D. (2021). Juwels cluster and booster: Exascale pathfinder with modular supercomputing architecture at J\u00fclich Supercomputing Centre. Journal of large-scale research facilities JLSRF, 7, 183. http://dx.doi.org/10.17815/jlsrf-7-183"]}