cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48597/kuww-b68c
title: Particle_number_concentration at Hohenpeissenberg

description: Measurement of Particle_number_concentration at Hohenpeissenberg
DOI: 10.48597/kz2d-vsm7
title: Particle_number_size_distribution at Mt Cimone

description: Measurement of Particle_number_size_distribution at Mt Cimone
DOI: 10.48597/m9qw-2583
title: Benz_a_anthracene at Els Torms

description: Measurement of Benz_a_anthracene at Els Torms
DOI: 10.48597/mcvw-azty
title: Precipitation_amount at Risco Llamo

description: Measurement of Precipitation_amount at Risco Llamo
DOI: 10.48597/mjt6-3e2h
title: Benz_a_anthracene at Zingst

description: Measurement of Benz_a_anthracene at Zingst
DOI: 10.48597/msga-4wj9
title: Particle_number_size_distribution at Mt Cimone

description: Measurement of Particle_number_size_distribution at Mt Cimone
DOI: 10.48597/mu4r-gspj
title: Inorganics in air and particle phase at Ähtäri II

description: Measurement of Inorganics in air and particle phase at Ähtäri II
DOI: 10.48597/nu9y-4usb
title: PAHs (polycyclic aromatic hydrocarbons) at Schmücke

description: Measurement of PAHs (polycyclic aromatic hydrocarbons) at Schmücke
DOI: 10.48597/pczk-j9cs
title: Op_DDD at Schauinsland

description: Measurement of Op_DDD at Schauinsland
DOI: 10.48597/pjn6-49nk
title: PAHs (polycyclic aromatic hydrocarbons) at Dunai

description: Measurement of PAHs (polycyclic aromatic hydrocarbons) at Dunai
DOI: 10.48597/pnx5-3aas
title: Heavy metals at Virolahti II

description: Measurement of Heavy metals at Virolahti II
DOI: 10.48597/pq6v-geec
title: POPs (persistent organic pollutants) at Kosetice (NAOK)

description: Measurement of POPs (persistent organic pollutants) at Kosetice (NAOK)
DOI: 10.48597/pwrk-2yr6
title: POPs (persistent organic pollutants) at Kosetice (NAOK)

description: Measurement of POPs (persistent organic pollutants) at Kosetice (NAOK)
DOI: 10.48597/pwxk-abvt
title: Heavy metals, inorganics in air and particle phase, organic_carbon and organic_mass at Indian Gardens

description: Measurement of Heavy metals, inorganics in air and particle phase, organic_carbon and organic_mass at Indian Gardens
DOI: 10.48597/q3fm-5xyq
title: Inorganics in air and particle phase at Harwell

description: Measurement of Inorganics in air and particle phase at Harwell
DOI: 10.48597/qem5-7hr3
title: Heavy metals, inorganics in air and particle phase, organic_carbon and organic_mass at Caney Creek

description: Measurement of Heavy metals, inorganics in air and particle phase, organic_carbon and organic_mass at Caney Creek
DOI: 10.48597/qxw2-3tvg
title: Particle_number_size_distribution at Mt Cimone

description: Measurement of Particle_number_size_distribution at Mt Cimone
DOI: 10.48597/skde-vjs3
title: Particle_number_size_distribution at BEO Moussala

description: Measurement of Particle_number_size_distribution at BEO Moussala
DOI: 10.48597/tcu6-umq7
title: Indeno_123cd_perylene at Tagish

description: Measurement of Indeno_123cd_perylene at Tagish
DOI: 10.48597/tfsd-rhbb
title: POPs (persistent organic pollutants) at Kosetice (NAOK)

description: Measurement of POPs (persistent organic pollutants) at Kosetice (NAOK)
DOI: 10.48597/thnn-apvr
title: Nitrogen_dioxide at Donon

description: Measurement of Nitrogen_dioxide at Donon
DOI: 10.48597/tmz5-rfwn
title: Mercury at Schauinsland

description: Measurement of Mercury at Schauinsland
DOI: 10.48597/udeq-bb7n
title: Inorganics in air and particle phase at Mace Head

description: Measurement of Inorganics in air and particle phase at Mace Head
DOI: 10.6084/m9.figshare.23550514
title: Additional file 3 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 3. KEGG pathway enrichment analysis of genes in expanded gene families of M. paniculata.
DOI: 10.6084/m9.figshare.23550514.v1
title: Additional file 3 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 3. KEGG pathway enrichment analysis of genes in expanded gene families of M. paniculata.
DOI: 10.6084/m9.figshare.23550517
title: Additional file 4 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 4. KEGG pathway enrichment analysis of genes with LTR insertions in the 3-kb upstream region in M. paniculata.
DOI: 10.6084/m9.figshare.23550517.v1
title: Additional file 4 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 4. KEGG pathway enrichment analysis of genes with LTR insertions in the 3-kb upstream region in M. paniculata.
DOI: 10.6084/m9.figshare.23550520
title: Additional file 5 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 5. Volatiles in three flowering stages of M. paniculata.
DOI: 10.6084/m9.figshare.23550520.v1
title: Additional file 5 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 5. Volatiles in three flowering stages of M. paniculata.
DOI: 10.6084/m9.figshare.23550523
title: Additional file 6 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 6. Volatiles in three flowering stages of C. maxima.
DOI: 10.6084/m9.figshare.23550523.v1
title: Additional file 6 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 6. Volatiles in three flowering stages of C. maxima.
DOI: 10.6084/m9.figshare.15161922
title: Additional file 2 of Impact on decision making framework for medicine purchasing in Chinese public hospital decision-making: determining the value of five dipeptidyl peptidase 4 (DPP-4) inhibitors

description: Additional file 2.
DOI: 10.6084/m9.figshare.15161922.v1
title: Additional file 2 of Impact on decision making framework for medicine purchasing in Chinese public hospital decision-making: determining the value of five dipeptidyl peptidase 4 (DPP-4) inhibitors

description: Additional file 2.
DOI: 10.6084/m9.figshare.23550529
title: Additional file 7 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 7. TPMs of genes in three flowering stages, leaves, and stemswith three replicates.
DOI: 10.6084/m9.figshare.23550529.v1
title: Additional file 7 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 7. TPMs of genes in three flowering stages, leaves, and stemswith three replicates.
DOI: 10.25452/figshare.plus.22344127
title: Metadata for scDrugPrio analysis of individual Crohn's disease (CD) patients' scRNA-sequencing data

description: <b>scDrugPrio: A framework for</b><b> </b><b>the</b><b> </b><b>analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases</b>Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. In our recent work, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive <i>in vitro, in vivo,</i><i> </i>and <i>in silico</i><i> </i>studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example,assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).<br>This data set includes metadata concerning the analysis of individual Crohn's disease patients' data. Specifically, this data set includes deep count auto encoder (DCA) denonised scRNA-seq matrix as well as clustering outcomes. The CD datasets analysed during the current study are available in the GEO, GSE134809.
DOI: 10.6084/m9.figshare.23550532
title: Additional file 8 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 8. Production of phenylacetaldehyde catalyzed by M. paniculata PAAS genes with three replicates.
DOI: 10.6084/m9.figshare.23550532.v1
title: Additional file 8 of Chromosome-level genome assembly of Murraya paniculata sheds light on biosynthesis of floral volatiles

description: Additional file 8. Production of phenylacetaldehyde catalyzed by M. paniculata PAAS genes with three replicates.
DOI: 10.25452/figshare.plus.22343980.v1
title: Metadata for scDrugPrio analysis of pooled Crohn's disease scRNA-sequencing data

description: <b>scDrugPrio: A framework for</b><b> </b><b>the</b><b> </b><b>analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases</b>Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. In our recent work, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive <i>in vitro, in vivo,</i><i> </i>and <i>in silico</i><i> </i>studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example,assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).<br>This data set includes metadata concerning the analysis of pooled Crohn's disease (CD) data. Specifically, this data set includes deep count auto encoder (DCA) denonised scRNA-seq matrix as well as clustering outcomes. The CD datasets analysed during the current study are available in the GEO, GSE134809.
DOI: 10.25452/figshare.plus.22343980
title: Metadata for scDrugPrio analysis of pooled Crohn's disease scRNA-sequencing data

description: <b>scDrugPrio: A framework for</b><b> </b><b>the</b><b> </b><b>analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases</b>Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. In our recent work, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive <i>in vitro, in vivo,</i><i> </i>and <i>in silico</i><i> </i>studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example,assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).<br>This data set includes metadata concerning the analysis of pooled Crohn's disease (CD) data. Specifically, this data set includes deep count auto encoder (DCA) denonised scRNA-seq matrix as well as clustering outcomes. The CD datasets analysed during the current study are available in the GEO, GSE134809.
DOI: 10.25452/figshare.plus.22343932
title: Multiple sclerosis metadata for scDrugPrio

description: <b>scDrugPrio: A framework for</b><b> </b><b>the</b><b> </b><b>analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases</b>Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. In our recent work, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive <i>in vitro, in vivo,</i><i> </i>and <i>in silico</i><i> </i>studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example,assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).<br>This data set includes metadata concerning the analysis of multiple sclerosis (MS) data. Specifically, this data set includes deep count auto encoder (DCA) denonised scRNA-seq matrix as well as clustering outcomes. The MS datasets analysed during the current study are available in the GEO, GSE138266.
DOI: 10.6084/m9.figshare.20929115
title: Spatio-and temporal patterns of Jurassic–Early Cretaceous volcanism in the Great Xing’an range, NE China: constraints on the geodynamic evolution

description: Tracing coupled ocean closure and intra-continental orogenic processes using tectonomagmatic thermal events is important for performing plate reconstructions. In this study, we collected geochronological data obtained from Jurassic–Early Cretaceous volcanic sequences in the Great Xing apos; an Range (GXR) and compared and merged the data to obtain a better understanding of the timing of volcanism in the GXR. Jurassic–Early Cretaceous volcanism, which is mainly represented by the Tamulangou, Manketouebo, and Manitu formations, are ubiquitous in the GXR and do not contain a magmatic hiatus. Thus, ‘diachronous’ volcanism did not occur in the GXR. The volcanic sequences in these formations are also slightly ambiguous and therefore cannot be used to determine the timing of volcanism. However, absolute ages and rock assemblages can provide better constraints on this timing. Combined with data from Jurassic–Early Cretaceous tectonomagmatic thermal events in Mongolia, Siberia, and NE China, we re-evaluated the influences of the Jurassic–Early Cretaceous closure of the Mongol–Okhotsk Ocean (MOO), as well as the subduction and rollback of the Izanagi Plate, at different times in the GXR. The GXR likely underwent flat-slab subduction during the Late Jurassic–early Cretaceous as the MOO closed in an irregular ‘scissor-like’ fashion (i.e. not in a progressive west to east manner). Large-scale lithospheric delamination likely led to a large pulse of magmatism and mineralisation in the GXR during the late Early Cretaceous.
DOI: 10.6084/m9.figshare.20929115.v1
title: Spatio-and temporal patterns of Jurassic–Early Cretaceous volcanism in the Great Xing’an range, NE China: constraints on the geodynamic evolution

description: Tracing coupled ocean closure and intra-continental orogenic processes using tectonomagmatic thermal events is important for performing plate reconstructions. In this study, we collected geochronological data obtained from Jurassic–Early Cretaceous volcanic sequences in the Great Xing apos; an Range (GXR) and compared and merged the data to obtain a better understanding of the timing of volcanism in the GXR. Jurassic–Early Cretaceous volcanism, which is mainly represented by the Tamulangou, Manketouebo, and Manitu formations, are ubiquitous in the GXR and do not contain a magmatic hiatus. Thus, ‘diachronous’ volcanism did not occur in the GXR. The volcanic sequences in these formations are also slightly ambiguous and therefore cannot be used to determine the timing of volcanism. However, absolute ages and rock assemblages can provide better constraints on this timing. Combined with data from Jurassic–Early Cretaceous tectonomagmatic thermal events in Mongolia, Siberia, and NE China, we re-evaluated the influences of the Jurassic–Early Cretaceous closure of the Mongol–Okhotsk Ocean (MOO), as well as the subduction and rollback of the Izanagi Plate, at different times in the GXR. The GXR likely underwent flat-slab subduction during the Late Jurassic–early Cretaceous as the MOO closed in an irregular ‘scissor-like’ fashion (i.e. not in a progressive west to east manner). Large-scale lithospheric delamination likely led to a large pulse of magmatism and mineralisation in the GXR during the late Early Cretaceous.
DOI: 10.48597/ugen-efz2
title: Pressure at Donon

description: Measurement of Pressure at Donon
DOI: 10.48597/v2za-dpd9
title: Aerosol_light_scattering_coefficient at Monte Cimone

description: Measurement of Aerosol_light_scattering_coefficient at Monte Cimone
DOI: 10.48597/w9cb-jer6
title: Nitrogen_dioxide at Oulanka

description: Measurement of Nitrogen_dioxide at Oulanka
DOI: 10.48597/w9sk-3z3x
title: Heavy metals at Montelibretti

description: Measurement of Heavy metals at Montelibretti
DOI: 10.48597/wb5b-3exu
title: Particle_number_size_distribution at Mt Cimone

description: Measurement of Particle_number_size_distribution at Mt Cimone
DOI: 10.48597/wcsc-523q
title: Inorganics in air and particle phase at Payerne

description: Measurement of Inorganics in air and particle phase at Payerne
DOI: 10.48597/wjcp-gvrd
title: POPs (persistent organic pollutants) at Zingst

description: Measurement of POPs (persistent organic pollutants) at Zingst
DOI: 10.48597/wkft-46d4
title: Inorganics in precipitation at Montelibretti

description: Measurement of Inorganics in precipitation at Montelibretti
DOI: 10.48597/wrtu-bxtz
title: Heavy metals, inorganics in air and particle phase, organic_carbon and organic_mass at Hance Camp at Grand Canyon NP

description: Measurement of Heavy metals, inorganics in air and particle phase, organic_carbon and organic_mass at Hance Camp at Grand Canyon NP
DOI: 10.48597/wtm7-xncc
title: PAHs (polycyclic aromatic hydrocarbons, 16) at Penausende

description: Measurement of PAHs (polycyclic aromatic hydrocarbons, 16) at Penausende
DOI: 10.48597/wv2r-r7ka
title: Sulphate_total at Payerne

description: Measurement of Sulphate_total at Payerne
DOI: 10.48597/wx46-t5hm
title: Precipitation_amount_off at Zavizan

description: Measurement of Precipitation_amount_off at Zavizan
DOI: 10.48597/xbmj-b2z7
title: Heavy metals at Ähtäri II

description: Measurement of Heavy metals at Ähtäri II
DOI: 10.48597/xc9u-hh9q
title: Particle_number_size_distribution at Mt Cimone

description: Measurement of Particle_number_size_distribution at Mt Cimone
DOI: 10.5281/zenodo.10728949
title: Multicentre RCTs involving Ireland as a collaborating partner

description: RCTs conducted on humans in a healthcare setting with centres based in Ireland, and <80% of participants recruited in Ireland. Variables include vtrial characteristics and reporting rates for: trial registration, use of standardised reporting guidelines, number of Irish centres and number of participants recruited in Ireland.