cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5564147
title: Processed data for the sRNA landscape chapter

description: <strong>Processed data to be used in analyses related to the sRNA landscape. </strong> <strong>1) small RNA processed data from stem trichomes</strong>: 2020-12-17_11-23_results_stem_trichomes.tar.gz Original small RNA-seq fastq files: available at https://doi.org/10.5281/zenodo.4105911 Software: small-rna-seq-pipeline v0.4.4 available at https://zenodo.org/record/4333786 <strong>2) small RNA processed data from bald stem, leaf primordium and leaf: </strong> xxxx === to be added === xxx <strong>3) mRNA-seq processed data (raw and scaled counts) from different tissues (stem trichomes, bald stem, leaf, leaf primordium): </strong> 20201117_snakemake_messenger_rnaseq_trichomes_and_other_tissues.tar.gz This file was obtained from the following original mRNA-seq fastq files: Stem trichomes of Moneymaker: dataset available here Stem trichomes of LA0716: dataset available here Stem trichomes of PI127826: dataset available here Bald stems, leaf primordia and leaves of Moneymaker, LA0716 and PI127826: datasets are available here. Samples S28 to S48 were used. The pipeline used was Snakemake RNA-seq release 0.3.4 The file contains: raw_counts.parsed.tsv: contains the raw counts that can be used for differential expression analysis (e.g. with DESeq2). scaled_counts.tsv: contains counts that are scaled between samples. This can be used for heatmap creation or PCA analysis for instance. NOT for differential analysis. samples.tsv.: a file listing the fastq files analysed. config.yaml: a file that contains the parameters used when running the pipeline.
DOI: 10.5281/zenodo.4277026
title: Processed data for the sRNA landscape chapter

description: <strong>Processed data to be used in analyses related to the sRNA landscape. </strong> <strong>1) small RNA processed data from stem trichomes</strong>: 2020-12-17_11-23_results_stem_trichomes.tar.gz Original small RNA-seq fastq files: available at https://doi.org/10.5281/zenodo.4105911 Software: small-rna-seq-pipeline v0.4.4 available at https://zenodo.org/record/4333786 <strong>2) small RNA processed data from bald stem, leaf primordium and leaf: </strong> xxxx === to be added === xxx <strong>3) mRNA-seq processed data (raw and scaled counts) from different tissues (stem trichomes, bald stem, leaf, leaf primordium): </strong> 20201117_snakemake_messenger_rnaseq_trichomes_and_other_tissues.tar.gz This file was obtained from the following original mRNA-seq fastq files: Stem trichomes of Moneymaker: dataset available here Stem trichomes of LA0716: dataset available here Stem trichomes of PI127826: dataset available here Bald stems, leaf primordia and leaves of Moneymaker, LA0716 and PI127826: datasets are available here. Samples S28 to S48 were used. The pipeline used was Snakemake RNA-seq release 0.3.4 The file contains: raw_counts.parsed.tsv: contains the raw counts that can be used for differential expression analysis (e.g. with DESeq2). scaled_counts.tsv: contains counts that are scaled between samples. This can be used for heatmap creation or PCA analysis for instance. NOT for differential analysis. samples.tsv.: a file listing the fastq files analysed. config.yaml: a file that contains the parameters used when running the pipeline.
DOI: 10.5281/zenodo.5564192
title: A SAT Approach to Twin-Width

description: Results for the paper "A SAT Approach to Twin-Width" to appear at ALENEX 2022.
DOI: 10.5281/zenodo.5564191
title: A SAT Approach to Twin-Width

description: Results for the paper "A SAT Approach to Twin-Width" to appear at ALENEX 2022.
DOI: 10.21954/ou.rd.14892132
title: Thermochemical Modelling Start Files for Early diagenesis at and below Vera Rubin ridge, Gale crater, Mars

description: Data to accompany manuscript published in Meteoritics and Planetary Science: "Early Diagenesis at and below Vera Rubin ridge, Gale crater, Mars" by S.M.R. Turner, S.P. Schwenzer, et al., (2021). DOI: 10.1111/maps.13748. CSV file contains data used for starting inputs in thermochemical models executed with CHIM-XPT, as described in the paper.
DOI: 10.21954/ou.rd.14892132.v1
title: Thermochemical Modelling Start Files for Early diagenesis at and below Vera Rubin ridge, Gale crater, Mars

description: Data to accompany manuscript published in Meteoritics and Planetary Science: "Early Diagenesis at and below Vera Rubin ridge, Gale crater, Mars" by S.M.R. Turner, S.P. Schwenzer, et al., (2021). DOI: 10.1111/maps.13748. CSV file contains data used for starting inputs in thermochemical models executed with CHIM-XPT, as described in the paper.
DOI: 10.5517/ccdc.csd.cc276gzx
title: CCDC 2061809: Experimental Crystal Structure Determination

description: Related Article: Kaitlyn Varela, Hadi D. Arman, Francis K. Yoshimoto|2021|J.Nat.Prod.|84|1967|doi:10.1021/acs.jnatprod.1c00246
DOI: 10.5281/zenodo.5564198
title: raw data for submitted manuscript

description: the raw data of the submitted papaer"<strong>Biocontrol potential of five <em>Trichogramma</em> strains, in three species, against <em>Grapholita molesta</em></strong>"
DOI: 10.5281/zenodo.5564197
title: raw data for submitted manuscript

description: the raw data of the submitted papaer"<strong>Biocontrol potential of five <em>Trichogramma</em> strains, in three species, against <em>Grapholita molesta</em></strong>"
DOI: 10.6084/m9.figshare.16791118
title: GNSS coordinate time series

description: The GNSS coordinate time series of the GPS stations used in the paper 'Potential Contributors to Common Mode Error in Array GPS Displacement Fields in Taiwan Island' in Remote Sensing.
DOI: 10.21957/fzf9-te33
title: Global monthly reforecast sea surface temperatures from 1901 to 2010 with 24-month lead time
DOI: 10.5281/zenodo.5564236
title: SQA for AI. Interviews coding

description: This dataset includes interviews with practitioners on the topic "SQA for AI Systems" with a coding procedure.
DOI: 10.5281/zenodo.5564235
title: SQA for AI. Interviews coding

description: This dataset includes interviews with practitioners on the topic "SQA for AI Systems" with a coding procedure.
DOI: 10.5281/zenodo.5513421
title: Structural determinants of ligands recognition by the human mitochondrial basic amino acids transporter SLC25A29. Insights from molecular dynamics simulations of the c-state.

description: Initial coordinates, molecular dynamics trajectories and representative snapshots resulting from the study "Structural determinants of ligands recognition by the human mitochondrial basic amino acids transporter SLC25A29. Insights from molecular dynamics simulations of the c-state." by Pasquadibisceglie and Polticelli. The MD folders contain the parameter/topology (parm7) and initial coordinates (rst7) for the molecular dynamics simulations. Moreover, a NetCDF trajectory "prod.nc" of the production phase is also included.<br> In detail:<br> - MD0 -&gt; SLC25A29 in absence of ligands;<br> - MD1, MD3, MD4 -&gt; SLC25A29-ARG complex;<br> - MD1-LYS, MD3-LYS, MD4-LYS -&gt; SLC25A29-LYS complex. The folder PDB_figures contains the PDB files used to produce the figures presented in the manuscript.
DOI: 10.5281/zenodo.4836273
title: Structural determinants of ligands recognition by the human mitochondrial basic amino acids transporter SLC25A29. Insights from molecular dynamics simulations of the c-state.

description: Initial coordinates, molecular dynamics trajectories and representative snapshots resulting from the study "Structural determinants of ligands recognition by the human mitochondrial basic amino acids transporter SLC25A29. Insights from molecular dynamics simulations of the c-state." by Pasquadibisceglie and Polticelli. The MD folders contain the parameter/topology (parm7) and initial coordinates (rst7) for the molecular dynamics simulations. Moreover, a NetCDF trajectory "prod.nc" of the production phase is also included.<br> In detail:<br> - MD0 -&gt; SLC25A29 in absence of ligands;<br> - MD1, MD3, MD4 -&gt; SLC25A29-ARG complex;<br> - MD1-LYS, MD3-LYS, MD4-LYS -&gt; SLC25A29-LYS complex. The folder PDB_figures contains the PDB files used to produce the figures presented in the manuscript.
DOI: 10.5281/zenodo.5552394
title: Intrasexual cuticular hydrocarbon dimorphism in a wasp sheds light on hydrocarbon biosynthesis genes in Hymenoptera

description: The dataset contains Supplementary Files from "Intrasexual cuticular hydrocarbon dimorphism in a wasp sheds light on hydrocarbon biosynthesis genes in Hymenoptera": <em>Odynerus spinipes</em> draft genome assembly (ODSP_platanus.1.2.4.n0.s20.fa) Soft-masked version of the draft genome (ODSP_platanus.1.2.4.n0.s20.fasta.softmasked) Gene models of predicted protein-coding genes (ODSP_platanus.1.2.4.n0.s20_BRAKER1.9_annot.gff) mRNA file of predicted gene protein-coding genes (mRNA_ODSP_platanus.1.2.4.n0.s20.fa) Protein file of predicted gene protein-coding genes (ODSP_BRAKER_augustus_aa.fa) Deseq2 results table (age class comparison) (AGE_DESEQ2_sign_results_ordered.xlsx) EdgeR results table (age class comparison) (AGE_EdgeR_lrt_sign_results_ordered.xlsx) qRT-PCR raw values (Expression_knock_downs_all_Apis_ids.xlsx) Cuticular hydrocarbon compound abundances in <em>Apis mellifera</em> samples treated with dsRNA (CHCs_abundance_knock_downs_Apis_ids.xlsx) Nucleotide sequence encoding green fluorescent protein (GFP) (Primer_GFP_sequence_used_Negative_contro_RNAi.doc) Nucleotide sequence of candidate genes used for probe and dsRNA synthesis (Sequences_ISH_dsRNA_Apis_ids.fa)
DOI: 10.5281/zenodo.5552393
title: Intrasexual cuticular hydrocarbon dimorphism in a wasp sheds light on hydrocarbon biosynthesis genes in Hymenoptera

description: The dataset contains Supplementary Files from "Intrasexual cuticular hydrocarbon dimorphism in a wasp sheds light on hydrocarbon biosynthesis genes in Hymenoptera": <em>Odynerus spinipes</em> draft genome assembly (ODSP_platanus.1.2.4.n0.s20.fa) Soft-masked version of the draft genome (ODSP_platanus.1.2.4.n0.s20.fasta.softmasked) Gene models of predicted protein-coding genes (ODSP_platanus.1.2.4.n0.s20_BRAKER1.9_annot.gff) mRNA file of predicted gene protein-coding genes (mRNA_ODSP_platanus.1.2.4.n0.s20.fa) Protein file of predicted gene protein-coding genes (ODSP_BRAKER_augustus_aa.fa) Deseq2 results table (age class comparison) (AGE_DESEQ2_sign_results_ordered.xlsx) EdgeR results table (age class comparison) (AGE_EdgeR_lrt_sign_results_ordered.xlsx) qRT-PCR raw values (Expression_knock_downs_all_Apis_ids.xlsx) Cuticular hydrocarbon compound abundances in <em>Apis mellifera</em> samples treated with dsRNA (CHCs_abundance_knock_downs_Apis_ids.xlsx) Nucleotide sequence encoding green fluorescent protein (GFP) (Primer_GFP_sequence_used_Negative_contro_RNAi.doc) Nucleotide sequence of candidate genes used for probe and dsRNA synthesis (Sequences_ISH_dsRNA_Apis_ids.fa)
DOI: 10.5522/04/16732687
title: Perceived risk and condomless sex practice with commercial and non-commercial sexual partners of male migrant sex workers in London: Interview Schedule

description: Interview Schedule: face to face in-depth interview
DOI: 10.6084/m9.figshare.12656165.v27
title: COVID-19 Case Reports in China

description: Chinese prefectural level governments started to report daily confirmed COVID-19 cases online, starting from January 2020. The disclosures may contain the mobility, potential exposure scenario, epidemiological characteristics, and other useful information of individual cases. We organized a group of content coders since early March 2020, kept monitoring the information updates, manually extracted useful information from the public disclosures, and compiled these datasets.We welcome any form of collaborations with us and non-commercial reuse of our dataset. We highly encourage interested parties to examine the data, report errors in our coding, and help us to keep the data updated.The detailed data description can be found on SSRN preprint server https://dx.doi.org/10.2139/ssrn.3705815.
DOI: 10.6084/m9.figshare.12656165.v28
title: COVID-19 Case Reports in China

description: Chinese prefectural level governments started to report daily confirmed COVID-19 cases online, starting from January 2020. The disclosures may contain the mobility, potential exposure scenario, epidemiological characteristics, and other useful information of individual cases. We organized a group of content coders since early March 2020, kept monitoring the information updates, manually extracted useful information from the public disclosures, and compiled these datasets.We welcome any form of collaborations with us and non-commercial reuse of our dataset. We highly encourage interested parties to examine the data, report errors in our coding, and help us to keep the data updated.The detailed data description can be found on SSRN preprint server https://dx.doi.org/10.2139/ssrn.3705815.
DOI: 10.6084/m9.figshare.16792096
title: Supplementary Material for: Juzentaihoto Exerts Anti-Allergic Effects by Inhibiting Effector T-Cell Activation and Inducing and/or Activating Regulatory T Cells in a Murine Model of Contact Hypersensitivity

description: <b><i>Background:</i></b> Juzentaihoto (JTT) is a Kampo prescription that has been used clinically for treating skin diseases such as atopic dermatitis in Japan. We have previously studied the anti-allergic effects of JTT on 2,4,6-trinitrochlorobenzene (TNCB)-induced contact hypersensitivity (CHS) in mice and demonstrated that it significantly suppresses ear swelling in a dose-dependent manner. However, the mechanism underlying the anti-allergic actions of JTT is obscure. <b><i>Methods:</i></b> We investigated the mechanism underlying the anti-allergic effects of JTT using a TNCB-induced murine CHS model and adoptive cell transfer experiments. <b><i>Results:</i></b> We showed that the anti-allergic effects of JTT are due to inhibition of effector T-cell activation and induction and/or activation of regulatory T cells. Furthermore, ex vivo experiments confirmed the effect of JTT on the activation of effector T cells and regulatory T cells, as interferon-γ production decreased, whereas interleukin (IL)-10 production increased, in the cultured lymphocytes obtained from 5% TNCB-sensitized mice treated with anti-CD3ε and anti-CD28 monoclonal antibodies. Flow cytometry showed that the CD4<sup>+</sup>CD25<sup>+</sup>Foxp3<sup>+</sup>, CD4<sup>+</sup>CD25<sup>+</sup>Foxp3<sup>−</sup>, and CD8<sup>+</sup>CD122<sup>+</sup> cell population increased after oral administration of JTT. Finally, the anti-allergic effect of JTT by inducing and/or activating regulatory T cells (Tregs) was confirmed to be mediated by IL-10 through in vivo neutralization experiments with anti-IL-10 monoclonal antibodies. <b><i>Conclusion:</i></b> We suggested that JTT exerts anti-allergic effects by regulating the activation of effector T cells and Tregs involved in murine CHS model.
DOI: 10.6084/m9.figshare.16792096.v1
title: Supplementary Material for: Juzentaihoto Exerts Anti-Allergic Effects by Inhibiting Effector T-Cell Activation and Inducing and/or Activating Regulatory T Cells in a Murine Model of Contact Hypersensitivity

description: <b><i>Background:</i></b> Juzentaihoto (JTT) is a Kampo prescription that has been used clinically for treating skin diseases such as atopic dermatitis in Japan. We have previously studied the anti-allergic effects of JTT on 2,4,6-trinitrochlorobenzene (TNCB)-induced contact hypersensitivity (CHS) in mice and demonstrated that it significantly suppresses ear swelling in a dose-dependent manner. However, the mechanism underlying the anti-allergic actions of JTT is obscure. <b><i>Methods:</i></b> We investigated the mechanism underlying the anti-allergic effects of JTT using a TNCB-induced murine CHS model and adoptive cell transfer experiments. <b><i>Results:</i></b> We showed that the anti-allergic effects of JTT are due to inhibition of effector T-cell activation and induction and/or activation of regulatory T cells. Furthermore, ex vivo experiments confirmed the effect of JTT on the activation of effector T cells and regulatory T cells, as interferon-γ production decreased, whereas interleukin (IL)-10 production increased, in the cultured lymphocytes obtained from 5% TNCB-sensitized mice treated with anti-CD3ε and anti-CD28 monoclonal antibodies. Flow cytometry showed that the CD4<sup>+</sup>CD25<sup>+</sup>Foxp3<sup>+</sup>, CD4<sup>+</sup>CD25<sup>+</sup>Foxp3<sup>−</sup>, and CD8<sup>+</sup>CD122<sup>+</sup> cell population increased after oral administration of JTT. Finally, the anti-allergic effect of JTT by inducing and/or activating regulatory T cells (Tregs) was confirmed to be mediated by IL-10 through in vivo neutralization experiments with anti-IL-10 monoclonal antibodies. <b><i>Conclusion:</i></b> We suggested that JTT exerts anti-allergic effects by regulating the activation of effector T cells and Tregs involved in murine CHS model.
DOI: 10.5281/zenodo.5562841
title: Crossref Funder Registry - Mapping to top-level funding organizations

description: The Crossref Funder Registry is an open registry of names and identifiers of research funding organizations. The registry has a hierarchical structure. Each organization may have relations with parent and child organizations.<br> <br> We provide a mapping of organizations in the Crossref Funder Registry to the corresponding top-level organizations. The mapping is based on version 1.34 of the Crossref Funder Registry, made available in an RDF file in the Crossref Funder Registry GitLab repository.<br> <br> To create the mapping to top-level organizations, we first extracted the hierarchical structure of the Crossref Funder Registry based on all ‘narrower’ and ‘broader’ assignments. Using this hierarchical structure, we then identified for each of the 27,681 active organizations in the registry the corresponding top-level organizations. For most organizations, we identified a single top-level organization. However, organizations may have multiple parent organizations, and for some organizations we therefore identified multiple top-level organizations.<br> <br> Example: ‘H2020 Societal Challenges’ has ‘Horizon 2020 Framework Programme’ as its only parent organization, ‘Horizon 2020 Framework Programme’ has ‘European Commission’ as its only parent organization, and ‘European Commission’ does not have a parent organization. The top-level organization corresponding to ‘H2020 Societal Challenges’ therefore is ‘European Commission’.<br> <br> We ignored the following parent-child relations in order to avoid having cycles in the network of parent-child relations: Child organization: Ministry of Science and Technology of the People's Republic of China (CHN) [DOI: 10.13039/501100002855]<br> Parent organization: National Science and Technology Major Project (CHN) [DOI: 10.13039/501100018537] Child organization: University of Georgia (USA) [DOI: 10.13039/100007699]<br> Parent organization: College of William and Mary (USA) [DOI: 10.13039/100008277] We ignored the following parent-child relations because they seem to be mistakes: Child organization: College of William and Mary (USA) [DOI: 10.13039/100008277]<br> Parent organization: University of Georgia (USA) [DOI: 10.13039/100007699] Child organization: Fakulti Pertanian, Universiti Putra Malaysia (MYS) [DOI: 10.13039/501100006018]<br> Parent organization: Department of Child Health, University of Manchester (GBR) [DOI: 10.13039/100007546] Child organization: Pusat Penyelidikan dan Inovasi, Universiti Malaysia Sabah (MYS) [DOI: 10.13039/501100006243]<br> Parent organization: Workplace Health, Safety and Compensation Commission (CAN) [DOI: 10.13039/501100000183] Child organization: Workplace Health, Safety and Compensation Commission (CAN) [DOI: 10.13039/501100000183]<br> Parent organization: Shahid Beheshti University of Medical Sciences (IRN) [DOI: 10.13039/501100005851]
DOI: 10.5281/zenodo.5564335
title: Scripts to perform dynamic optimization of a model of invasive aspergillosis

description: Matlab scripts to perform optimization and analyses of a dynamic optimization model of invasive aspergillosis:<br> "Dynamic optimization reveals alveolar epithelial cells as key mediators of host defense in invasive aspergillosis"<br> Jan Ewald, Flora Rivieccio, Lukáš Radosa, Stefan Schuster, Axel A. Brakhage, Christoph Kaleta<br> https://doi.org/10.1101/2021.05.12.443764 File structure: --- Core optimization files ---<br> -- simulation.cpp --<br> Description of ODE model in 'C', used by solver to calculate the time course given the parameters, initial states and control variables -- simulation.mexa64 --<br> Compiled mex-File from simulation.cpp used as interface by MATLAB to 'C' files. -- optFun.m --<br> Definition of the objective function. Uses a control vector 'u' to calculate the, objective function value F, the time course of state variables 'x' and gradients used by the solver to determine the optimal control of 'u'. -- single_opt.m --<br> Function which performs an optimization run for a given parameter set and a randomized initial solution u0. Optimization can be conducted by SNOPT or in conjunction with IPOPT. Both solver are not provided here (proprietary). <br> --- Additional files ---<br> -- solve_with_ipopt.m --<br> Configuration file and interface to call the solver IPOPT. -- lognorm_mu.m --<br> Function to provide log-normal distributed samples given the standard deviation, reference value (mode of distribution) and number of samples which should be calculated. -- mice_param_alv.m --<br> Reference parameter set of the model for mice as host organism and cell numbers normalized per alveolus. -- parameter_model.m --<br> Definition of parameter container to handle interface to optFun-call by solvers which do not allow parameter in function call. --- Optimization Runs ---<br> -- rand_params.m --<br> Performs optimization runs for multiple randomized parameter sets and several scenarios to determine parameter sensitivity. -- doseresponse.m --<br> Performs optimization runs for a sequnce of initial conidial dosages to determine the dose response of the system. --- Scripts for post-analysis ---<br> -- rand_analysis.m --<br> Loads the data created by the optimization runs (rand_params.m) and performs parameter sensitivity analysis as well as produces plots for analysis related to parameter influence. -- doseanalysis.m --<br> Loads the data created by the optimization runs (doseresponse.m ) and produces dose response curves as well as additional plots and analyses. --- Optimization output files ---<br> -- rand_param_out/ --<br> Folder contains optimization results as MATLAB session-file for randomized parameters used for parameter sensitivity analysis. -- dose_out/ --<br> Folder contains optimization results as MATLAB session-file for dose response curve calculation.