cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4620739
title: Antigen receptor repertoires profiling from RNA-Seq

description: <b>TCGA_SKCM_clinical_data_metadata_and_clonesets.xlsx</b> - Immune repertoires and sample metadata from the TCGA 48+48 nt paired-end RNA-Seq data for 460 patients with human skin cutaneous melanoma (SKCM).<br><br><b>CoverageStat.tsv</b> - data for calculation of TCR CDR3 extraction performance characteristics in respect to different TCR content, read-length and dataset type (single- or paired- end). Data for Supplementary Table 2.<b><br></b><b>Sorted_mice_TCR_clonesets.zip</b> - TCR clonesets for sorted mouse cell populations from section "Functional characterization of TRA and TRB repertoires based on RNA-Seq of sorted T-cells"<b><br></b><b>Data for comparison of MiXCR with TRUST and V'DJer on real data</b><br><b><br></b><b>SamplesForComparison.tsv</b> - list of all RNA-Seq samples used for MiXCR / V'DJer / TRUST comparison, including samples obtained by truncation of samples with longer read length. Summary analysis statistics for each sample. Tab-separated table. Data for <b>Figure 2e</b>.<br><b>analysis_results.zip</b> file contain all analysis results for all samples listed in SamplesForComparison.tsv. See <b>README.txt</b> file for exact commands used to generate this data. Archive has the following folder structure:<br><i>rnaseq/mixcr</i> - results of RNA-Seq samples using MiXCR. Individual files for each immunological chain: TRA, TRB, TRG, TRD, IGH, IGL, IGK.Combined data:TCR = TRA + TRB + TRG + TRDIG = IGH + IGL + IGK<br>ALL = all chainsTab-separated table. Unprocessed MiXCR output.<br><i>rnaseq/trust</i> - analysis results for TRUST. fasta files (some files are prepended with comment blocks, each line in comment block starts with #). Each file accumulate sequences for all chains (TRA + TRB + TRG + TRD). Unprocessed TRUST output.<br><i>rnaseq/vdjer</i> - analysis results for V'DJer. fasta files. Unprocessed V'DJer output.<br><br><i>repseq</i> - clonotypes from control TCR-Seq datasets assembled using MiXCR software with default settings.<br><br><i>notebooks</i> - contains IPython notebook for final data anaylsis and figure generation<br><br><i>scripts</i> - contains python and shell scripts to convert software-specific formatted data into msgpack format that is used by IPython notebook mentioned above<br><br><br>See also https://github.com/milaboratory/mixcr-rna-seq-paper repository for the code for <i>in-silico</i> experiments.
DOI: 10.6084/m9.figshare.4620745
title: Gamma and Base PLUS models -- skymaps

description: Base and Gamma model<br>full-sky skymap, resolution 8<br>Ferriere source term<br>variabile diffusion coefficient normalization and slope<br>gas: ringModel
DOI: 10.6084/m9.figshare.4620733
title: NCBI taxonomy database files

description: The NCBI taxonomy, from a database dump downloaded from the NCBI FTP server on 2017-02-3, imported in a SQLite database table as a phylogenetic tree.
DOI: 10.6084/m9.figshare.4620730
title: 16S database files

description: Green Genes tree of 16S rRNA haplotypes in Newick format, converted to CSV, and imported into SQLite
DOI: 10.6084/m9.figshare.4620724
title: Gamma and Base PLUS models

description: Base Model and Gamma Model PLUS<br>with Do(R) -- gradient problem fixed<br>Ferriere source term<br>resolution 8<br>full-sky<br>ringModel<br>XCO: p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px Monaco} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px Monaco; color: #931a68} span.s1 {color: #931a68} span.s2 {color: #000000} span.Apple-tab-span {white-space:pre} if (r &gt; 4.5) return 5.0; else return 1.9*1.8;
DOI: 10.6084/m9.figshare.4620736.v1
title: Response of digestive organs of Hypsiboas albopunctatus (Anura: Hylidae) to benzo[α]pyrene

description: Here, we tested the dose dependent and short- and long-term effects of benzo[alpha]pyrene (BAP) on the thickness of small intestine and liver melanomacrophages (MMC) of <i>Hypsiboas albopunctatus</i>. We conducted an experiment with a fully-crossed 3x2 factorial design to answer these two questions. Male specimens were separated into groups injected with a subcutaneous solution containing either 3 or 7 mg/Kg of BAP. Specimens were killed either 72h or 168h after the first injection with BAP. Then, we measured the the thickness of small intestine and the area occupied by MMCs in the liver. Experimental groups had 6 animals each. Our sampling units were pictures of histological sections (25 per animal). For the intestinal epithelium we only analyzed 5 out of the 6 animals.
DOI: 10.6084/m9.figshare.4620739.v1
title: Antigen receptor repertoires profiling from RNA-Seq

description: <b>TCGA_SKCM_clinical_data_metadata_and_clonesets.xlsx</b> - Immune repertoires and sample metadata from the TCGA 48+48 nt paired-end RNA-Seq data for 460 patients with human skin cutaneous melanoma (SKCM).<br><br><b>CoverageStat.tsv</b> - data for calculation of TCR CDR3 extraction performance characteristics in respect to different TCR content, read-length and dataset type (single- or paired- end). Data for Supplementary Table 2.<br><br>(temporary) <b>mixcr-rna-seq-paper-docker.zip</b> - source code and running instruction to reproduce performance evaluation on <i>in silico</i> data. <br><b><br></b><b>Sorted_mice_TCR_clonesets.zip</b> - TCR clonesets for sorted mouse cell populations from section "Functional characterization of TRA and TRB repertoires based on RNA-Seq of sorted T-cells"<b><br></b><b>Data for comparison of MiXCR with TRUST and V'DJer on real data</b><br><b><br></b><b>SamplesForComparison.tsv</b> - list of all RNA-Seq samples used for MiXCR / V'DJer / TRUST comparison, including samples obtained by truncation of samples with longer read length. Summary analysis statistics for each sample. Tab-separated table. Data for <b>Figure 2e</b>.<br><b>analysis_results.zip</b> file contain all analysis results for all samples listed in SamplesForComparison.tsv. See <b>README.txt</b> file for exact commands used to generate this data. Archive has the following folder structure:<br><i>rnaseq/mixcr</i> - results of RNA-Seq samples using MiXCR. Individual files for each immunological chain: TRA, TRB, TRG, TRD, IGH, IGL, IGK.Combined data:TCR = TRA + TRB + TRG + TRDIG = IGH + IGL + IGK<br>ALL = all chainsTab-separated table. Unprocessed MiXCR output.<br><i>rnaseq/trust</i> - analysis results for TRUST. fasta files (some files are prepended with comment blocks, each line in comment block starts with #). Each file accumulate sequences for all chains (TRA + TRB + TRG + TRD). Unprocessed TRUST output.<br><i>rnaseq/vdjer</i> - analysis results for V'DJer. fasta files. Unprocessed V'DJer output.<br><br><i>repseq</i> - clonotypes from control TCR-Seq datasets assembled using MiXCR software with default settings.<br>
DOI: 10.6084/m9.figshare.4620721
title: Main model - R script from Host species exploitation and discrimination by animal parasites

description: Host exploitation and discrimination model R script
DOI: 10.6084/m9.figshare.4620724.v1
title: Gamma and Base PLUS models

description: Base Model and Gamma Model PLUS<br>with Do(R) -- gradient problem fixed<br>Ferriere source term<br>resolution 8<br>full-sky<br>ringModel<br>XCO: p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px Monaco} p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px Monaco; color: #931a68} span.s1 {color: #931a68} span.s2 {color: #000000} span.Apple-tab-span {white-space:pre} if (r &gt; 4.5) return 5.0; else return 1.9*1.8;
DOI: 10.6084/m9.figshare.4620718
title: Raw Data from Uncertain links in host–parasite networks: lessons for parasite transmission in a multi-host system

description: For many parasites, the full set of hosts that are susceptible to infection is not known, and this could lead to a bias in estimates of transmission. We used counts of individual adult parasites from historical parasitology studies in southern Africa to map a bipartite network of the nematode parasites of herbivore hosts that occur in Botswana. Bipartite networks are used in community ecology to represent interactions across trophic levels. We used a Bayesian hierarchical model to predict the full set of host–parasite interactions from existing data on parasitic gastrointestinal nematodes of wild and domestic ungulates given assumptions about the distribution of parasite counts within hosts, while accounting for the relative uncertainty of less sampled species. We used network metrics to assess the difference between the observed and predicted networks, and to explore the connections between hosts via their shared parasites using a host–host unipartite network projected from the bipartite network. The model predicts a large number of missing links and identifies red hartebeest, giraffe and steenbok as the hosts that have the most uncertainty in parasite diversity. Further, the unipartite network reveals clusters of herbivores that have a high degree of parasite sharing, and these clusters correspond closely with phylogenetic distance rather than with the wild/domestic boundary. These results provide a basis for predicting the risk of cross-species transmission of nematode parasites in areas where livestock and wildlife share grazing land. This article is part of the themed issue ‘Opening the black box: re-examining the ecology and evolution of parasite transmission’.
DOI: 10.6084/m9.figshare.4620721.v1
title: Main model - R script from Host species exploitation and discrimination by animal parasites

description: Host exploitation and discrimination model R script
DOI: 10.6084/m9.figshare.4620718.v2
title: Raw Data from Uncertain links in host–parasite networks: lessons for parasite transmission in a multi-host system

description: For many parasites, the full set of hosts that are susceptible to infection is not known, and this could lead to a bias in estimates of transmission. We used counts of individual adult parasites from historical parasitology studies in southern Africa to map a bipartite network of the nematode parasites of herbivore hosts that occur in Botswana. Bipartite networks are used in community ecology to represent interactions across trophic levels. We used a Bayesian hierarchical model to predict the full set of host–parasite interactions from existing data on parasitic gastrointestinal nematodes of wild and domestic ungulates given assumptions about the distribution of parasite counts within hosts, while accounting for the relative uncertainty of less sampled species. We used network metrics to assess the difference between the observed and predicted networks, and to explore the connections between hosts via their shared parasites using a host–host unipartite network projected from the bipartite network. The model predicts a large number of missing links and identifies red hartebeest, giraffe and steenbok as the hosts that have the most uncertainty in parasite diversity. Further, the unipartite network reveals clusters of herbivores that have a high degree of parasite sharing, and these clusters correspond closely with phylogenetic distance rather than with the wild/domestic boundary. These results provide a basis for predicting the risk of cross-species transmission of nematode parasites in areas where livestock and wildlife share grazing land. This article is part of the themed issue ‘Opening the black box: re-examining the ecology and evolution of parasite transmission’.
DOI: 10.6084/m9.figshare.4620718.v1
title: Raw Data from Uncertain links in host–parasite networks: lessons for parasite transmission in a multi-host system

description: For many parasites, the full set of hosts that are susceptible to infection is not known, and this could lead to a bias in estimates of transmission. We used counts of individual adult parasites from historical parasitology studies in southern Africa to map a bipartite network of the nematode parasites of herbivore hosts that occur in Botswana. Bipartite networks are used in community ecology to represent interactions across trophic levels. We used a Bayesian hierarchical model to predict the full set of host–parasite interactions from existing data on parasitic gastrointestinal nematodes of wild and domestic ungulates given assumptions about the distribution of parasite counts within hosts, while accounting for the relative uncertainty of less sampled species. We used network metrics to assess the difference between the observed and predicted networks, and to explore the connections between hosts via their shared parasites using a host–host unipartite network projected from the bipartite network. The model predicts a large number of missing links and identifies red hartebeest, giraffe and steenbok as the hosts that have the most uncertainty in parasite diversity. Further, the unipartite network reveals clusters of herbivores that have a high degree of parasite sharing, and these clusters correspond closely with phylogenetic distance rather than with the wild/domestic boundary. These results provide a basis for predicting the risk of cross-species transmission of nematode parasites in areas where livestock and wildlife share grazing land. This article is part of the themed issue ‘Opening the black box: re-examining the ecology and evolution of parasite transmission’.
DOI: 10.6084/m9.figshare.4620706
title: S1 Code from Mechanistic movement models to understand epidemic spread

description: S1 Code. Matlab code to simulate an epidemic when host perform unccorelated random walk
DOI: 10.6084/m9.figshare.4620703
title: S2 Code from Mechanistic movement models to understand epidemic spread

description: S2 Code. Matlab code to simulate epidemic when host perform a Biased random walk
DOI: 10.6084/m9.figshare.4620700
title: S3 Code from Mechanistic movement models to understand epidemic spread

description: S3 Code. Matlab code to simulate epidemic when hosts perform a Levy walk
DOI: 10.6084/m9.figshare.4620706.v1
title: S1 Code from Mechanistic movement models to understand epidemic spread

description: S1 Code. Matlab code to simulate an epidemic when host perform unccorelated random walk
DOI: 10.6084/m9.figshare.4620703.v1
title: S2 Code from Mechanistic movement models to understand epidemic spread

description: S2 Code. Matlab code to simulate epidemic when host perform a Biased random walk
DOI: 10.6084/m9.figshare.4620700.v1
title: S3 Code from Mechanistic movement models to understand epidemic spread

description: S3 Code. Matlab code to simulate epidemic when hosts perform a Levy walk
DOI: 10.17608/k6.auckland.4620664
title: Engaging with Open Science

description: Presentation given at the Brain Research New Zealand (CoRE) Early Career Researcher Workshop 3 February 2017.Otago Museum, Dunedin, New Zealand<br>Licenced under CC-BY-SA Kubke
DOI: 10.6084/m9.figshare.4620634
title: Markers for Glioblastoma subtypes and stem-ness from Cancer cells exhibit clonal diversity in phenotypic plasticity

description: Genes marking different glioblastoma subtypes. Drawn from DOI:10.1016/j.ccr.2009.12.020 and DOI:10.1126/science.1254257
DOI: 10.6084/m9.figshare.4620628
title: Normalized reads for each clone from Cancer cells exhibit clonal diversity in phenotypic plasticity

description: Supplementary file (.csv) of the normalized reads for each barcoded clone. Each sorted population is normalized so that it sums to the fractional-size of that population in the unsorted cells
DOI: 10.6084/m9.figshare.4620631
title: Supplementary Table 1: Phenotypic ratios and estimated population sizes of barcoded clones from Cancer cells exhibit clonal diversity in phenotypic plasticity

description: Supplementary table (.xlsx) with the phenotypic ratios and estimated population sizes of barcoded clones
DOI: 10.6084/m9.figshare.4620631.v1
title: Supplementary Table 1: Phenotypic ratios and estimated population sizes of barcoded clones from Cancer cells exhibit clonal diversity in phenotypic plasticity

description: Supplementary table (.xlsx) with the phenotypic ratios and estimated population sizes of barcoded clones
DOI: 10.6084/m9.figshare.4620628.v1
title: Normalized reads for each clone from Cancer cells exhibit clonal diversity in phenotypic plasticity

description: Supplementary file (.csv) of the normalized reads for each barcoded clone. Each sorted population is normalized so that it sums to the fractional-size of that population in the unsorted cells
DOI: 10.6084/m9.figshare.4620634.v1
title: Markers for Glioblastoma subtypes and stem-ness from Cancer cells exhibit clonal diversity in phenotypic plasticity

description: Genes marking different glioblastoma subtypes. Drawn from DOI:10.1016/j.ccr.2009.12.020 and DOI:10.1126/science.1254257
DOI: 10.6084/m9.figshare.4620625
title: Reads for each clone from Cancer cells exhibit clonal diversity in phenotypic plasticity

description: supplementary file (.txt, tab-delimited) of the count of reads for each barcoded clone
DOI: 10.6084/m9.figshare.4620616
title: Data from Small birds, big effects: the little auk (<i>Alle alle</i>) transforms high Arctic ecosystems

description: In some arctic areas, marine-derived nutrients (MDN) resulting from fish migrations fuel freshwater and terrestrial ecosystems, increasing primary production and biodiversity. Less is known, however, about the role of seabird-MDN in shaping ecosystems. Here, we examine how the most abundant seabird in the North Atlantic, the little auk (<i>Alle alle</i>), alters freshwater and terrestrial ecosystems around the North Water Polynya (NOW) in Greenland. We compare stable isotope ratios (<i>δ</i><sup>15</sup>N and <i>δ</i><sup>13</sup>C) of freshwater and terrestrial biota, terrestrial vegetation indices and physical–chemical properties, productivity and community structure of fresh waters in catchments with and without little auk colonies. The presence of colonies profoundly alters freshwater and terrestrial ecosystems by providing nutrients and massively enhancing primary production. Based on elevated <i>δ</i><sup>15</sup>N in MDN, we estimate that MDN fuels more than 85% of terrestrial and aquatic biomass in bird influenced systems. Furthermore, by using different proxies of bird impact (colony distance, algal <i>δ</i><sup>15</sup>N) it is possible to identify a gradient in ecosystem response to increasing bird impact. Little auk impact acidifies the freshwater systems, reducing taxonomic richness of macroinvertebrates and truncating food webs. These results demonstrate that the little auk acts as an ecosystem engineer, transforming ecosystems across a vast region of Northwest Greenland.
DOI: 10.6084/m9.figshare.4620622
title: Empirical data for simulations from Cancer cells exhibit clonal diversity in phenotypic plasticity

description: Data used for simulations of clones under different treatments. Contains growth rate, estimated cell count, and log2(epithelial/mesenchymal) ratio for each clone.
DOI: 10.6084/m9.figshare.4620625.v1
title: Reads for each clone from Cancer cells exhibit clonal diversity in phenotypic plasticity

description: supplementary file (.txt, tab-delimited) of the count of reads for each barcoded clone
DOI: 10.6084/m9.figshare.4620616.v1
title: Data from Small birds, big effects: the little auk (<i>Alle alle</i>) transforms high Arctic ecosystems

description: In some arctic areas, marine-derived nutrients (MDN) resulting from fish migrations fuel freshwater and terrestrial ecosystems, increasing primary production and biodiversity. Less is known, however, about the role of seabird-MDN in shaping ecosystems. Here, we examine how the most abundant seabird in the North Atlantic, the little auk (<i>Alle alle</i>), alters freshwater and terrestrial ecosystems around the North Water Polynya (NOW) in Greenland. We compare stable isotope ratios (<i>δ</i><sup>15</sup>N and <i>δ</i><sup>13</sup>C) of freshwater and terrestrial biota, terrestrial vegetation indices and physical–chemical properties, productivity and community structure of fresh waters in catchments with and without little auk colonies. The presence of colonies profoundly alters freshwater and terrestrial ecosystems by providing nutrients and massively enhancing primary production. Based on elevated <i>δ</i><sup>15</sup>N in MDN, we estimate that MDN fuels more than 85% of terrestrial and aquatic biomass in bird influenced systems. Furthermore, by using different proxies of bird impact (colony distance, algal <i>δ</i><sup>15</sup>N) it is possible to identify a gradient in ecosystem response to increasing bird impact. Little auk impact acidifies the freshwater systems, reducing taxonomic richness of macroinvertebrates and truncating food webs. These results demonstrate that the little auk acts as an ecosystem engineer, transforming ecosystems across a vast region of Northwest Greenland.
DOI: 10.6084/m9.figshare.4620607
title: Dataisotopes from Small birds, big effects: the little auk (<i>Alle alle</i>) transforms high Arctic ecosystems

description: In some arctic areas, marine-derived nutrients (MDN) resulting from fish migrations fuel freshwater and terrestrial ecosystems, increasing primary production and biodiversity. Less is known, however, about the role of seabird-MDN in shaping ecosystems. Here, we examine how the most abundant seabird in the North Atlantic, the little auk (<i>Alle alle</i>), alters freshwater and terrestrial ecosystems around the North Water Polynya (NOW) in Greenland. We compare stable isotope ratios (<i>δ</i><sup>15</sup>N and <i>δ</i><sup>13</sup>C) of freshwater and terrestrial biota, terrestrial vegetation indices and physical–chemical properties, productivity and community structure of fresh waters in catchments with and without little auk colonies. The presence of colonies profoundly alters freshwater and terrestrial ecosystems by providing nutrients and massively enhancing primary production. Based on elevated <i>δ</i><sup>15</sup>N in MDN, we estimate that MDN fuels more than 85% of terrestrial and aquatic biomass in bird influenced systems. Furthermore, by using different proxies of bird impact (colony distance, algal <i>δ</i><sup>15</sup>N) it is possible to identify a gradient in ecosystem response to increasing bird impact. Little auk impact acidifies the freshwater systems, reducing taxonomic richness of macroinvertebrates and truncating food webs. These results demonstrate that the little auk acts as an ecosystem engineer, transforming ecosystems across a vast region of Northwest Greenland.
DOI: 10.6084/m9.figshare.4620607.v1
title: Dataisotopes from Small birds, big effects: the little auk (<i>Alle alle</i>) transforms high Arctic ecosystems

description: In some arctic areas, marine-derived nutrients (MDN) resulting from fish migrations fuel freshwater and terrestrial ecosystems, increasing primary production and biodiversity. Less is known, however, about the role of seabird-MDN in shaping ecosystems. Here, we examine how the most abundant seabird in the North Atlantic, the little auk (<i>Alle alle</i>), alters freshwater and terrestrial ecosystems around the North Water Polynya (NOW) in Greenland. We compare stable isotope ratios (<i>δ</i><sup>15</sup>N and <i>δ</i><sup>13</sup>C) of freshwater and terrestrial biota, terrestrial vegetation indices and physical–chemical properties, productivity and community structure of fresh waters in catchments with and without little auk colonies. The presence of colonies profoundly alters freshwater and terrestrial ecosystems by providing nutrients and massively enhancing primary production. Based on elevated <i>δ</i><sup>15</sup>N in MDN, we estimate that MDN fuels more than 85% of terrestrial and aquatic biomass in bird influenced systems. Furthermore, by using different proxies of bird impact (colony distance, algal <i>δ</i><sup>15</sup>N) it is possible to identify a gradient in ecosystem response to increasing bird impact. Little auk impact acidifies the freshwater systems, reducing taxonomic richness of macroinvertebrates and truncating food webs. These results demonstrate that the little auk acts as an ecosystem engineer, transforming ecosystems across a vast region of Northwest Greenland.