cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12511457.v1
title: Additional file 4 of Dried fruit pomace inclusion in poultry diet: growth performance, intestinal morphology and physiology

description: Additional file 4. Raw data of phyla detected in broiler chickens fed experimental diets.
DOI: 10.6084/m9.figshare.12511439
title: Additional file 5 of Urbanisation, risk stratification and house infestation with a major vector of Chagas disease in an endemic municipality of the Argentine Chaco

description: Additional file 5: Table S3. Individual data including house infestation, environmental and demographic variables, Avia Terai, Chaco, 2015–2016.
DOI: 10.6084/m9.figshare.12511439.v1
title: Additional file 5 of Urbanisation, risk stratification and house infestation with a major vector of Chagas disease in an endemic municipality of the Argentine Chaco

description: Additional file 5: Table S3. Individual data including house infestation, environmental and demographic variables, Avia Terai, Chaco, 2015–2016.
DOI: 10.6084/m9.figshare.12511403
title: Additional file 1 of Intravenous and oral copper kinetics, biodistribution and dosimetry in healthy humans studied by [64Cu]copper PET/CT

description: Additional file 1.
DOI: 10.6084/m9.figshare.12511403.v1
title: Additional file 1 of Intravenous and oral copper kinetics, biodistribution and dosimetry in healthy humans studied by [64Cu]copper PET/CT

description: Additional file 1.
DOI: 10.6084/m9.figshare.12511385
title: Additional file 1 of COVID-19: viral–host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection

description: Additional file 1: Table S1. List of accession numbers of H-CoV. Table S2. List of genes selected by RWR algorithm for HCoV-229E, along with proximity score. Table S3. List of genes selected by RWR algorithm for SARS-CoV, along with proximity score. Table S4. List of genes selected by RWR algorithm for MERS-CoV, along with proximity score.
DOI: 10.6084/m9.figshare.12511385.v1
title: Additional file 1 of COVID-19: viral–host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection

description: Additional file 1: Table S1. List of accession numbers of H-CoV. Table S2. List of genes selected by RWR algorithm for HCoV-229E, along with proximity score. Table S3. List of genes selected by RWR algorithm for SARS-CoV, along with proximity score. Table S4. List of genes selected by RWR algorithm for MERS-CoV, along with proximity score.
DOI: 10.6084/m9.figshare.12511361
title: Additional file 8 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 8:Suppl. Table 8. List of oligonucleotide primers used for the qPCR analysis.
DOI: 10.6084/m9.figshare.12511355
title: Additional file 7 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 7:Suppl. Table 7. Gene ontologies (GO-term) obtained by functional enrichment analysis of genes which were inversely regulated by cold and light in a priming-dependent manner (Suppl. Tab. 6). The GOs grouping the seven genes mentioned in the text are labelled in orange.
DOI: 10.6084/m9.figshare.12511361.v1
title: Additional file 8 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 8:Suppl. Table 8. List of oligonucleotide primers used for the qPCR analysis.
DOI: 10.6084/m9.figshare.12511349
title: Additional file 5 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 5:Suppl. Table 5. Crude data on the gene ontologies (GO-terms) in the functional enrichment analysis of genes regulated at a threshold of log2 (primed/unprimed) ≥ I 0.5 I and FPKM ≥5 after priming. The table lists (from left to right) the number of genes associated with the GO-term in the query, the number of genes in the query, the number of genes associated with the GO-term in the reference background (TAIR10), the total number of genes used as background, the ratio of genes associated with the respective GO-term relative to the total number of genes analysed in the query (transcriptome) and in the background (reference), the enrichment in the query relative to the reference, the p-value for the significance of enrichment and the false discovery rate (FDR).
DOI: 10.6084/m9.figshare.12511352
title: Additional file 6 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 6:Suppl. Table 6. Identities of the genes showing inverse priming-dependent regulation after cold and light triggering. The seven genes associated with defence responses are labelled in dark orange. log2 (primed/unprimed) values ≥0.5 is highlighted in orange, log2 (primed/unprimed) values ≤ − 0.5 in blue.
DOI: 10.6084/m9.figshare.12511346
title: Additional file 4 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 4:Suppl. Table 4. Identities of the genes showing priming-dependent regulation at a threshold of log2 (primed/unprimed) ≥ I 0.5 I and FPKM ≥5 in at least one treatment. log2 (primed/unprimed) values ≥0.5 are highlighted in orange, log2 (primed/unprimed) values ≤ − 0.5 in blue. Genes tested by qPCR for their regulation are highlighted in bright yellow.
DOI: 10.6084/m9.figshare.12511346.v1
title: Additional file 4 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 4:Suppl. Table 4. Identities of the genes showing priming-dependent regulation at a threshold of log2 (primed/unprimed) ≥ I 0.5 I and FPKM ≥5 in at least one treatment. log2 (primed/unprimed) values ≥0.5 are highlighted in orange, log2 (primed/unprimed) values ≤ − 0.5 in blue. Genes tested by qPCR for their regulation are highlighted in bright yellow.
DOI: 10.6084/m9.figshare.12511349.v1
title: Additional file 5 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 5:Suppl. Table 5. Crude data on the gene ontologies (GO-terms) in the functional enrichment analysis of genes regulated at a threshold of log2 (primed/unprimed) ≥ I 0.5 I and FPKM ≥5 after priming. The table lists (from left to right) the number of genes associated with the GO-term in the query, the number of genes in the query, the number of genes associated with the GO-term in the reference background (TAIR10), the total number of genes used as background, the ratio of genes associated with the respective GO-term relative to the total number of genes analysed in the query (transcriptome) and in the background (reference), the enrichment in the query relative to the reference, the p-value for the significance of enrichment and the false discovery rate (FDR).
DOI: 10.6084/m9.figshare.12511352.v1
title: Additional file 6 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 6:Suppl. Table 6. Identities of the genes showing inverse priming-dependent regulation after cold and light triggering. The seven genes associated with defence responses are labelled in dark orange. log2 (primed/unprimed) values ≥0.5 is highlighted in orange, log2 (primed/unprimed) values ≤ − 0.5 in blue.
DOI: 10.6084/m9.figshare.12511355.v1
title: Additional file 7 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 7:Suppl. Table 7. Gene ontologies (GO-term) obtained by functional enrichment analysis of genes which were inversely regulated by cold and light in a priming-dependent manner (Suppl. Tab. 6). The GOs grouping the seven genes mentioned in the text are labelled in orange.
DOI: 10.6084/m9.figshare.12511340
title: Additional file 3 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 3:Suppl. Table 3. Identities of the genes showing priming-dependent regulation at a threshold of log2 (primed/unprimed) ≥ I 1 I and FPKM ≥10 in at least one treatment. log2 (primed/unprimed) values ≥1 are highlighted in orange, log2 (primed/unprimed) values ≤ − 1 in blue. Genes tested by qPCR for their regulation are highlighted in bright yellow.
DOI: 10.6084/m9.figshare.12511343
title: Additional file 8 of Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer

description: Additional file 8: Supplementary Table S6.
DOI: 10.6084/m9.figshare.12511343.v1
title: Additional file 8 of Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer

description: Additional file 8: Supplementary Table S6.
DOI: 10.6084/m9.figshare.12511337
title: Additional file 2 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 2:Suppl. Table 2. FPKM values in control plants (C5) and primed plants (P5) at the end of the 5 day long lag-phase, in only cold (T-C) or only light-triggered (T-L) plants and in cold-primed and cold (PT-C) or light-triggered (PT-L) plants arranged according to the FPKM-value in control plants. For each gene, the transcript variants used for aligning the RNASeq reads are listed.
DOI: 10.6084/m9.figshare.12511340.v1
title: Additional file 3 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 3:Suppl. Table 3. Identities of the genes showing priming-dependent regulation at a threshold of log2 (primed/unprimed) ≥ I 1 I and FPKM ≥10 in at least one treatment. log2 (primed/unprimed) values ≥1 are highlighted in orange, log2 (primed/unprimed) values ≤ − 1 in blue. Genes tested by qPCR for their regulation are highlighted in bright yellow.
DOI: 10.6084/m9.figshare.12511328
title: Additional file 1 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 1:Suppl. Table 1. General information on the quality of RNA sequencing and RNA sequence alignments.
DOI: 10.6084/m9.figshare.12511337.v1
title: Additional file 2 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 2:Suppl. Table 2. FPKM values in control plants (C5) and primed plants (P5) at the end of the 5 day long lag-phase, in only cold (T-C) or only light-triggered (T-L) plants and in cold-primed and cold (PT-C) or light-triggered (PT-L) plants arranged according to the FPKM-value in control plants. For each gene, the transcript variants used for aligning the RNASeq reads are listed.
DOI: 10.6084/m9.figshare.12511325
title: Additional file 6 of Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer

description: Additional file 6: Supplementary Table S4.
DOI: 10.6084/m9.figshare.12511322
title: Additional file 5 of Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer

description: Additional file 5: Supplementary Table S3.
DOI: 10.6084/m9.figshare.12511325.v1
title: Additional file 6 of Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer

description: Additional file 6: Supplementary Table S4.
DOI: 10.6084/m9.figshare.12511316
title: Additional file 4 of Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer

description: Additional file 4: Supplementary Table S2.
DOI: 10.6084/m9.figshare.12511322.v1
title: Additional file 5 of Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer

description: Additional file 5: Supplementary Table S3.
DOI: 10.6084/m9.figshare.12511328.v1
title: Additional file 1 of Cold priming uncouples light- and cold-regulation of gene expression in Arabidopsis thaliana

description: Additional file 1:Suppl. Table 1. General information on the quality of RNA sequencing and RNA sequence alignments.
DOI: 10.6084/m9.figshare.12511316.v1
title: Additional file 4 of Therapy-induced lipid uptake and remodeling underpin ferroptosis hypersensitivity in prostate cancer

description: Additional file 4: Supplementary Table S2.
DOI: 10.6084/m9.figshare.12511292
title: Additional file 2 of To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions

description: Additional file 2.
DOI: 10.6084/m9.figshare.12511292.v1
title: Additional file 2 of To screen or not to screen: an interactive framework for comparing costs of mass malaria treatment interventions

description: Additional file 2.
DOI: 10.6084/m9.figshare.12511241
title: Additional file 3 of Proteomic analysis in lupus mice identifies Coronin-1A as a potential biomarker for lupus nephritis

description: Additional file 3. List of significantly enriched KEGG pathways identified by pathway analysis of discovery proteomic data at each time. Table S3.1: List of significantly enriched KEGG pathways at 12 weeks. Table S3.2: List of significantly enriched KEGG pathways at 24 weeks. Table S3.3: List of significantly enriched KEGG pathways at 36 weeks.
DOI: 10.6084/m9.figshare.12511235
title: Additional file 2 of Proteomic analysis in lupus mice identifies Coronin-1A as a potential biomarker for lupus nephritis

description: Additional file 2. Discovery proteomics data (accession number, protein names, p value, q-value, fold change status of identified proteins). List of common proteins between mouse kidney discovery proteomics and human kidney transcriptomics. Table S2.1: List of all proteins quantified by discovery proteomics in the kidneys of Sle123 compared to control mice at 12 weeks. Table S2.2: List of all proteins quantified by discovery proteomics in the kidneys of Sle123 compared to control mice at 24 weeks. Table S2.3: List of all proteins quantified by discovery proteomics in the kidneys of Sle123 compared to control mice at 36 weeks. Table S2.4: List of 15 common proteins between mouse kidney discovery proteomics and human kidney transcriptomics.
DOI: 10.6084/m9.figshare.12511241.v1
title: Additional file 3 of Proteomic analysis in lupus mice identifies Coronin-1A as a potential biomarker for lupus nephritis

description: Additional file 3. List of significantly enriched KEGG pathways identified by pathway analysis of discovery proteomic data at each time. Table S3.1: List of significantly enriched KEGG pathways at 12 weeks. Table S3.2: List of significantly enriched KEGG pathways at 24 weeks. Table S3.3: List of significantly enriched KEGG pathways at 36 weeks.
DOI: 10.6084/m9.figshare.12511235.v1
title: Additional file 2 of Proteomic analysis in lupus mice identifies Coronin-1A as a potential biomarker for lupus nephritis

description: Additional file 2. Discovery proteomics data (accession number, protein names, p value, q-value, fold change status of identified proteins). List of common proteins between mouse kidney discovery proteomics and human kidney transcriptomics. Table S2.1: List of all proteins quantified by discovery proteomics in the kidneys of Sle123 compared to control mice at 12 weeks. Table S2.2: List of all proteins quantified by discovery proteomics in the kidneys of Sle123 compared to control mice at 24 weeks. Table S2.3: List of all proteins quantified by discovery proteomics in the kidneys of Sle123 compared to control mice at 36 weeks. Table S2.4: List of 15 common proteins between mouse kidney discovery proteomics and human kidney transcriptomics.
DOI: 10.6084/m9.figshare.12511169
title: Additional file 5 of Transcriptome analysis of the brain provides insights into the regulatory mechanism for Coilia nasus migration

description: Additional file 5 Appendix file 5: Table S4: FPKM of DEGs in the control and stressed group.
DOI: 10.6084/m9.figshare.12511163
title: Additional file 3 of Transcriptome analysis of the brain provides insights into the regulatory mechanism for Coilia nasus migration

description: Additional file 3 Appendix file 3: Table S2: pathway hierarchy.
DOI: 10.6084/m9.figshare.12511169.v1
title: Additional file 5 of Transcriptome analysis of the brain provides insights into the regulatory mechanism for Coilia nasus migration

description: Additional file 5 Appendix file 5: Table S4: FPKM of DEGs in the control and stressed group.
DOI: 10.6084/m9.figshare.12511154
title: Additional file 2 of Transcriptome analysis of the brain provides insights into the regulatory mechanism for Coilia nasus migration

description: Additional file 2 Appendix file 2: Table S1: top 10 GO terms, top 10 KEGG pathways and all DEGs in this study.
DOI: 10.6084/m9.figshare.12511154.v1
title: Additional file 2 of Transcriptome analysis of the brain provides insights into the regulatory mechanism for Coilia nasus migration

description: Additional file 2 Appendix file 2: Table S1: top 10 GO terms, top 10 KEGG pathways and all DEGs in this study.
DOI: 10.6084/m9.figshare.12511157
title: Additional file 9 of pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

description: Additional file 9 Supplementary Table 7. pyMeSHSim perfectly recognized MeSH term, but DNorm and TaggerOne failed. The semantic similarity between them calculated by pyMeSHSim. pyMeSHSim_Score is semantic similarity between Nelson_MeSH _ID and pyMeSHSim_MeSH_ID, taggerOne_score is semantic similarity between Nelson_MeSH _ID and TaggerOne_MeSH_ID, DNorm_score is semantic similarity between Nelson_MeSH _ID and Dnorm_MeSH_ID.
DOI: 10.6084/m9.figshare.12511148
title: Additional file 8 of pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

description: Additional file 8 Supplementary Table 6. MeSH term number in each category correctly identified by pyMeSHSim, Dnorm, TaggerOne and Nelson’s manual work.
DOI: 10.6084/m9.figshare.12511157.v1
title: Additional file 9 of pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

description: Additional file 9 Supplementary Table 7. pyMeSHSim perfectly recognized MeSH term, but DNorm and TaggerOne failed. The semantic similarity between them calculated by pyMeSHSim. pyMeSHSim_Score is semantic similarity between Nelson_MeSH _ID and pyMeSHSim_MeSH_ID, taggerOne_score is semantic similarity between Nelson_MeSH _ID and TaggerOne_MeSH_ID, DNorm_score is semantic similarity between Nelson_MeSH _ID and Dnorm_MeSH_ID.
DOI: 10.6084/m9.figshare.12511163.v1
title: Additional file 3 of Transcriptome analysis of the brain provides insights into the regulatory mechanism for Coilia nasus migration

description: Additional file 3 Appendix file 3: Table S2: pathway hierarchy.
DOI: 10.6084/m9.figshare.12511142
title: Additional file 7 of pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

description: Additional file 7 Supplementary Table 5. GWAS phenotypes parsed by Nelson’s group and pyMeSHSim, TaggerOne and DNorm. the semantic similarity between them calculated by pyMeSHSim. pyMeSHSim_Score is semantic similarity between Nelson_MeSH _ID and pyMeSHSim_MeSH_ID, taggerOne_score is semantic similarity between Nelson_MeSH _ID and TaggerOne_MeSH_ID, DNorm_score is semantic similarity between Nelson_MeSH _ID and Dnorm_MeSH_ID.
DOI: 10.6084/m9.figshare.12511142.v1
title: Additional file 7 of pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

description: Additional file 7 Supplementary Table 5. GWAS phenotypes parsed by Nelson’s group and pyMeSHSim, TaggerOne and DNorm. the semantic similarity between them calculated by pyMeSHSim. pyMeSHSim_Score is semantic similarity between Nelson_MeSH _ID and pyMeSHSim_MeSH_ID, taggerOne_score is semantic similarity between Nelson_MeSH _ID and TaggerOne_MeSH_ID, DNorm_score is semantic similarity between Nelson_MeSH _ID and Dnorm_MeSH_ID.
DOI: 10.6084/m9.figshare.12511148.v1
title: Additional file 8 of pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

description: Additional file 8 Supplementary Table 6. MeSH term number in each category correctly identified by pyMeSHSim, Dnorm, TaggerOne and Nelson’s manual work.
DOI: 10.6084/m9.figshare.12511130
title: Additional file 4 of pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

description: Additional file 4 Supplementary Table 2. GWAS phenotypes parsed by Nelson’s group and pyMeSHSim, and the semantic similarity between them calculated by pyMeSHSim and meshes.
DOI: 10.6084/m9.figshare.12511130.v1
title: Additional file 4 of pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

description: Additional file 4 Supplementary Table 2. GWAS phenotypes parsed by Nelson’s group and pyMeSHSim, and the semantic similarity between them calculated by pyMeSHSim and meshes.
DOI: 10.6084/m9.figshare.12511109
title: Additional file 10 of pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

description: Additional file 10 : Supplementary Table 8. DNorm or TaggerOne perfectly recognized MeSH terms, but pyMeSHSim failed. The semantic similarity between them calculated by pyMeSHSim. pyMeSHSim_Score is semantic similarity between Nelson_MeSH _ID and pyMeSHSim_MeSH_ID, taggerOne_score is semantic similarity between Nelson_MeSH _ID and TaggerOne_MeSH_ID, DNorm_score is semantic similarity between Nelson_MeSH _ID and Dnorm_MeSH_ID.
DOI: 10.6084/m9.figshare.12511109.v1
title: Additional file 10 of pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms

description: Additional file 10 : Supplementary Table 8. DNorm or TaggerOne perfectly recognized MeSH terms, but pyMeSHSim failed. The semantic similarity between them calculated by pyMeSHSim. pyMeSHSim_Score is semantic similarity between Nelson_MeSH _ID and pyMeSHSim_MeSH_ID, taggerOne_score is semantic similarity between Nelson_MeSH _ID and TaggerOne_MeSH_ID, DNorm_score is semantic similarity between Nelson_MeSH _ID and Dnorm_MeSH_ID.
DOI: 10.6084/m9.figshare.12510872
title: Additional file 2 of Immune characterization of metastatic colorectal cancer patients post reovirus administration

description: Additional file 2: Supplementary Figure 1. a – Transcriptome analysis post-Reovirus administration (Genes up-regulated 2-fold, p
DOI: 10.6084/m9.figshare.12510872.v1
title: Additional file 2 of Immune characterization of metastatic colorectal cancer patients post reovirus administration

description: Additional file 2: Supplementary Figure 1. a – Transcriptome analysis post-Reovirus administration (Genes up-regulated 2-fold, p
DOI: 10.6084/m9.figshare.12510752
title: Database.sav

description: This was a cross-sectional and correlational study, using convenience sampling to recruit participants across four early intervention centers in Singapore. Primary caregivers had to complete five questionnaires including demographic, Strengths and Difficulties Questionnaire-Parent Form (SDQ), Parental Stress Scale (PSS), Family Support Scale (FSS) and Reaction to Diagnosis Questionnaire (RDQ). The study strictly adhered to ethical requirements outlined by the Human Ethics Research Committee in James Cook University. Correlational analysis conducted to explore relationships between factors, followed by moderation analysis.