cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7504805
title: Dataset_Coradion.xlsx: Counts and measurements of examined specimens (page 1); list of distributional records of C. calendula and C. chrysozonus used for Figs. 10 and 11

description: DataSet_Coradion.xlsx: counts and measurements of examined specimens (page 1); list of distributional records of <em>C. calendula</em> and <em>C. chrysozonus</em> used for Figs. 10 and 11 (page 2) from: Matsunuma, M., Matsumoto, T., Motomura, H., Seah, Y.G. & Jaafar, T.N.A.M. (2023) <em>Coradion calendula</em>, a new butterflyfish from Australia (Teleostei: Chaetodontidae). <em>Journal of the Ocean Science Foundation</em>, 40, 1–28.
DOI: 10.5281/zenodo.7504804
title: Dataset_Coradion.xlsx: Counts and measurements of examined specimens (page 1); list of distributional records of C. calendula and C. chrysozonus used for Figs. 10 and 11

description: DataSet_Coradion.xlsx: counts and measurements of examined specimens (page 1); list of distributional records of <em>C. calendula</em> and <em>C. chrysozonus</em> used for Figs. 10 and 11 (page 2) from: Matsunuma, M., Matsumoto, T., Motomura, H., Seah, Y.G. & Jaafar, T.N.A.M. (2023) <em>Coradion calendula</em>, a new butterflyfish from Australia (Teleostei: Chaetodontidae). <em>Journal of the Ocean Science Foundation</em>, 40, 1–28.
DOI: 10.21233/pcn3-x109
title: Great Dismal Swamp pollen surface sample dataset

description: Raw data for the Great Dismal Swamp pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/x297-ms18
title: Hoedmeer (oever) pollen dataset

description: Raw data for the Hoedmeer (oever) pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/v2zz-w590
title: Marienvelde Halle - Vildersveen pollen dataset

description: Raw data for the Marienvelde Halle - Vildersveen pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/j5q0-ph68
title: Great Dismal Swamp pollen dataset

description: Raw data for the Great Dismal Swamp pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/j8dy-r487
title: Escharen raamvallei pollen dataset

description: Raw data for the Escharen raamvallei pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.25612/837.nbd8w879jg5y
title: Census 2020 PUMAs in Harris County

description: List of 2020 PUMAs (with names) in Harris County, Texas. Public Use Microdata Areas (PUMAs) are non-overlapping, statistical geographic areas that cover the entire USA.
DOI: 10.21233/g6nb-5510
title: Liesbos pollen dataset

description: Raw data for the Liesbos pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/pjvy-js36
title: Great Dismal Swamp pollen surface sample dataset

description: Raw data for the Great Dismal Swamp pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/3tam-w662
title: Ďurova mláka plant macrofossil dataset

description: Raw data for the Ďurova mláka plant macrofossil dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/vjva-8v89
title: Great Dismal Swamp pollen surface sample dataset

description: Raw data for the Great Dismal Swamp pollen surface sample dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.21233/fmz7-9474
title: DS-49 pollen dataset

description: Raw data for the DS-49 pollen dataset obtained from the Neotoma Paleoecology Database.
DOI: 10.5281/zenodo.7510797
title: Customized gtf file from Ensembl version 108 mm39

description: The gtf from Ensembl version 108 (mm39) was filtered to remove readthrough transcripts and all non-coding transcripts from a protein-coding gene. In addition, all genes with the same gene name which overlaps were merged under the same gene id to avoid ambiguous reads.<br> The procedure to generate the gtf is described in the bash file attached.<br>
DOI: 10.6084/m9.figshare.21834302
title: Raw_Data_Ziva_PLOS_AMK_GK_BLM

description: This is all of the raw data for the article published in PLOS One titled " Arrayed Imaging Reflectometry monitoring of anti-viral antibody production throughout <br> vaccination and breakthrough Covid-19"
DOI: 10.6084/m9.figshare.21834270
title: Antibiotic legacies shape the temperature response of soil microbial communities

description: Soil microbial communities are vulnerable to global change factors and land management decisions; these factors can therefore affect microbially-mediated ecosystem functions. Increasingly, multiple stressors are considered in investigations of ecological response to disturbances. Here we investigate how historical exposure to antibiotics affects the soil microbial response to a subsequent temperature change. <em>In-situ</em> antibiotic exposure initially increased soil respiration however this effect diminished over time. A subsequent incubation experiment showed that historical antibiotic exposure caused an acclimation-like response to increasing temperature. This response was likely driven by a differential response in the microbial community of antibiotic exposed soils. Microbial communities exposed to antibiotics tended to be dominated by slower-growing, oligotrophic taxa at higher temperatures. Therefore, historical exposure to one stressor is likely to influence the microbial community to subsequent stressors. To predict the response of soils to future stress, particularly increasing soil temperatures, historical context is necessary.
DOI: 10.6084/m9.figshare.21834270.v1
title: Antibiotic legacies shape the temperature response of soil microbial communities

description: Soil microbial communities are vulnerable to global change factors and land management decisions; these factors can therefore affect microbially-mediated ecosystem functions. Increasingly, multiple stressors are considered in investigations of ecological response to disturbances. Here we investigate how historical exposure to antibiotics affects the soil microbial response to a subsequent temperature change. <em>In-situ</em> antibiotic exposure initially increased soil respiration however this effect diminished over time. A subsequent incubation experiment showed that historical antibiotic exposure caused an acclimation-like response to increasing temperature. This response was likely driven by a differential response in the microbial community of antibiotic exposed soils. Microbial communities exposed to antibiotics tended to be dominated by slower-growing, oligotrophic taxa at higher temperatures. Therefore, historical exposure to one stressor is likely to influence the microbial community to subsequent stressors. To predict the response of soils to future stress, particularly increasing soil temperatures, historical context is necessary.
DOI: 10.5281/zenodo.7509964
title: dataset market share and profitability

description: Dataset of the research Market share and profitability: marketing investments as mechanisms to deal with competitiveness in the industry
DOI: 10.5281/zenodo.7509963
title: dataset market share and profitability

description: Dataset of the research Market share and profitability: marketing investments as mechanisms to deal with competitiveness in the industry
DOI: 10.22003/xfel.eu-data-900291-00
title: SCS commissioning
DOI: 10.22003/xfel.eu-data-900295-00
title: AGIPD at SPB  commissioning and calibration
DOI: 10.22003/xfel.eu-data-900296-00
title: AGIPD at HED commissioning and calibration
DOI: 10.22003/xfel.eu-data-900297-00
title: HED Commissioning
DOI: 10.22003/xfel.eu-data-900301-00
title: LPD SuperModule Commissioning
DOI: 10.22003/xfel.eu-data-900303-00
title: HXRSS studies comissioning
DOI: 10.22003/xfel.eu-data-900308-00
title: AGIPD at MID commissioning and calibration
DOI: 10.22003/xfel.eu-data-900315-00
title: SXP Commissioning
DOI: 10.22003/xfel.eu-data-900316-00
title: FXE HELIOS
DOI: 10.5281/zenodo.7436485
title: Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, high seq depth

description: <strong>File description:</strong> geneCounts: gene-level counts k_j: split counts spanning from one exon to another. k_theta: non-split counts covering a splice site n_psi3: total split counts from a given acceptor site n_psi5: total split counts from a given donor site n_theta: total split and non-split counts for a given splice site Sample annotation describing each sample from the dataset Description file with global information from the dataset The gene counts were originated using the GTF file from release 34 of GENCODE https://www.gencodegenes.org/human/release_34, and the split and non-split counts contain only the annotated junctions from the same release. <strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (https://github.com/gagneurlab/drop). <strong>Number of samples:</strong> 135<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34 <strong>Median mapped reads:</strong> 116 million<br> <strong>Disease</strong> (ICD-10: N): E88: 112, G31: 8, NONE: 5, K72: 2, G71: 2, E72: 1, G93: 1, I42: 1, F82: 1, E75: 1, F89: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True<br> <strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de <strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References
description: {"references": ["Y\u00e9pez, V. A., Gusic, M., Kopajtich, R., Mertes, C., et al. (2022). Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med 14, 38. doi: 10.1186/s13073-022-01019-9."]}
DOI: 10.5281/zenodo.7436486
title: Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, high seq depth

description: <strong>File description:</strong> geneCounts: gene-level counts k_j: split counts spanning from one exon to another. k_theta: non-split counts covering a splice site n_psi3: total split counts from a given acceptor site n_psi5: total split counts from a given donor site n_theta: total split and non-split counts for a given splice site Sample annotation describing each sample from the dataset Description file with global information from the dataset The gene counts were originated using the GTF file from release 34 of GENCODE https://www.gencodegenes.org/human/release_34, and the split and non-split counts contain only the annotated junctions from the same release. <strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (https://github.com/gagneurlab/drop). <strong>Number of samples:</strong> 135<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34 <strong>Median mapped reads:</strong> 116 million<br> <strong>Disease</strong> (ICD-10: N): E88: 112, G31: 8, NONE: 5, K72: 2, G71: 2, E72: 1, G93: 1, I42: 1, F82: 1, E75: 1, F89: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True<br> <strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de <strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References
description: {"references": ["Y\u00e9pez, V. A., Gusic, M., Kopajtich, R., Mertes, C., et al. (2022). Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med 14, 38. doi: 10.1186/s13073-022-01019-9."]}
DOI: 10.5281/zenodo.7510836
title: Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, high seq depth

description: <strong>File description:</strong> geneCounts: gene-level counts k_j: split counts spanning from one exon to another. k_theta: non-split counts covering a splice site n_psi3: total split counts from a given acceptor site n_psi5: total split counts from a given donor site n_theta: total split and non-split counts for a given splice site Sample annotation describing each sample from the dataset Description file with global information from the dataset The gene counts were originated using the GTF file from release 34 of GENCODE https://www.gencodegenes.org/human/release_34, and the split and non-split counts contain only the annotated junctions from the same release. <strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (https://github.com/gagneurlab/drop). <strong>Number of samples:</strong> 135<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34 <strong>Median mapped reads:</strong> 116 million<br> <strong>Disease</strong> (ICD-10: N): E88: 112, G31: 8, NONE: 5, K72: 2, G71: 2, E72: 1, G93: 1, I42: 1, F82: 1, E75: 1, F89: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True<br> <strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de <strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References
description: {"references": ["Y\u00e9pez, V. A., Gusic, M., Kopajtich, R., Mertes, C., et al. (2022). Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med 14, 38. doi: 10.1186/s13073-022-01019-9."]}
DOI: 10.6084/m9.figshare.21830283.v1
title: Arbóreas e regenerantes duas áreas.xlsx

description: Spreadsheets with raw data collected in the field
DOI: 10.6084/m9.figshare.3761562.v2448
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.7436578
title: Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, low seq depth

description: <strong>File description:</strong> geneCounts: gene-level counts k_j: split counts spanning from one exon to another. k_theta: non-split counts covering a splice site n_psi3: total split counts from a given acceptor site n_psi5: total split counts from a given donor site n_theta: total split and non-split counts for a given splice site Sample annotation describing each sample from the dataset Description file with global information from the dataset The gene counts were originated using the GTF file from release 34 of GENCODE https://www.gencodegenes.org/human/release_34, and the split and non-split counts contain only the annotated junctions from the same release. <strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (https://github.com/gagneurlab/drop). <strong>Number of samples:</strong> 127<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34 <strong>Median mapped reads:</strong> 71 million<br> <strong>Disease</strong> (ICD-10: N): E88: 84, NONE: 12, F89: 6, G31: 3, R27: 3, E72: 3, G40: 2, R16: 2, K72: 2, P94: 2, E77: 1, E75: 1, G71: 1, G93: 1, Q78: 1, G82: 1, R29: 1, Q02: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True <strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de <strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References
description: {"references": ["Y\u00e9pez, V. A., Gusic, M., Kopajtich, R., Mertes, C., et al. (2022). Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med 14, 38. doi: 10.1186/s13073-022-01019-9."]}
DOI: 10.5281/zenodo.7436579
title: Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, low seq depth

description: <strong>File description:</strong> geneCounts: gene-level counts k_j: split counts spanning from one exon to another. k_theta: non-split counts covering a splice site n_psi3: total split counts from a given acceptor site n_psi5: total split counts from a given donor site n_theta: total split and non-split counts for a given splice site Sample annotation describing each sample from the dataset Description file with global information from the dataset The gene counts were originated using the GTF file from release 34 of GENCODE https://www.gencodegenes.org/human/release_34, and the split and non-split counts contain only the annotated junctions from the same release. <strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (https://github.com/gagneurlab/drop). <strong>Number of samples:</strong> 127<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34 <strong>Median mapped reads:</strong> 71 million<br> <strong>Disease</strong> (ICD-10: N): E88: 84, NONE: 12, F89: 6, G31: 3, R27: 3, E72: 3, G40: 2, R16: 2, K72: 2, P94: 2, E77: 1, E75: 1, G71: 1, G93: 1, Q78: 1, G82: 1, R29: 1, Q02: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True <strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de <strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References
description: {"references": ["Y\u00e9pez, V. A., Gusic, M., Kopajtich, R., Mertes, C., et al. (2022). Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med 14, 38. doi: 10.1186/s13073-022-01019-9."]}
DOI: 10.5281/zenodo.7510845
title: Gene expression and splicing counts from the Yepez, Gusic et al study - fibroblast, hg19, strand-specific, low seq depth

description: <strong>File description:</strong> geneCounts: gene-level counts k_j: split counts spanning from one exon to another. k_theta: non-split counts covering a splice site n_psi3: total split counts from a given acceptor site n_psi5: total split counts from a given donor site n_theta: total split and non-split counts for a given splice site Sample annotation describing each sample from the dataset Description file with global information from the dataset The gene counts were originated using the GTF file from release 34 of GENCODE https://www.gencodegenes.org/human/release_34, and the split and non-split counts contain only the annotated junctions from the same release. <strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired-end specifications match. Afterwards, DROP can be used to compute expression and splicing outliers (https://github.com/gagneurlab/drop). <strong>Number of samples:</strong> 127<br> <strong>Tissue:</strong> Fibroblast<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode34 <strong>Median mapped reads:</strong> 71 million<br> <strong>Disease</strong> (ICD-10: N): E88: 84, NONE: 12, F89: 6, G31: 3, R27: 3, E72: 3, G40: 2, R16: 2, K72: 2, P94: 2, E77: 1, E75: 1, G71: 1, G93: 1, Q78: 1, G82: 1, R29: 1, Q02: 1<br> <strong>Strand specific:</strong> True<br> <strong>Paired end:</strong> True <strong>Dataset contact:</strong> Vicente Yepez, yepez at in.tum.de; Christian Mertes, mertes at in.tum.de; Julien Gagneur, gagneur at in.tum.de; Holger Prokisch, prokisch at helmholtz-muenchen.de <strong>Citation:</strong> Cite both the resource using Zenodo's citation and the publication under References
description: {"references": ["Y\u00e9pez, V. A., Gusic, M., Kopajtich, R., Mertes, C., et al. (2022). Clinical implementation of RNA sequencing for Mendelian disease diagnostics. Genome Med 14, 38. doi: 10.1186/s13073-022-01019-9."]}
DOI: 10.6084/m9.figshare.21830283.v2
title: Arbóreas e regenerantes duas áreas.xlsx

description: Dado brutos coletados em campo, tabelas com lista de espécies para cada área e mapa de vegetação da área de estudo.
DOI: 10.26093/cds/vizier.35300147
title: Megamaser detection in Seyfert galaxies

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'Megamaser detection and nuclear obscuration in Seyfert galaxies.' (bibcode: 2011A&A...530A.147R)
DOI: 10.26093/cds/vizier.35350038
title: Observations of NOAO Bootes field at 153MHz

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'Deep low-frequency radio observations of the NOAO Bootes field. I. Data reduction and catalog construction.' (bibcode: 2011A&A...535A..38I)
DOI: 10.26093/cds/vizier.35490055
title: GMRT 153MHz (2m) Radio Mini Survey I.

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'T-RaMiSu: The Two-meter Radio Mini Survey I. The Bootes Field.' (bibcode: 2013A&A...549A..55W)
DOI: 10.26093/cds/vizier.35610142
title: Galactic extinction from SDSS BHB stars

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'Exploring the total Galactic extinction with SDSS BHB stars.' (bibcode: 2014A&A...561A.142T)
DOI: 10.26093/cds/vizier.35670109
title: Carina nebula optically identified YSOs

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'Investigation of the stellar content in the western part of the Carina nebula.' (bibcode: 2014A&A...567A.109K)
DOI: 10.26093/cds/vizier.35750087
title: Deuteration in massive star formation process

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'Deuteration and evolution in the massive star formation process. The role of surface chemistry.' (bibcode: 2015A&A...575A..87F)