cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.844630
title: Significantly enriched GO terms in heart jogging genes in zebrafish and their human and mouse orthologs

description: Zebrafish data: GO terms significantly enriched in zebrafish ‘heart jogging’ gene list. The GO biological process, molecular function and cellular component terms that are highly enriched in the zebrafish ‘jogging’ gene list. The GO terms are listed in order of decreasing p-values and the ‘universe set’ size included in the functional analysis using VLAD is 14,577. Human data: GO terms significantly enriched in human orthologs of zebrafish ‘heart jogging’ genes.<br>The GO biological process, molecular function and cellular component terms that are highly enriched in the human ‘jogging ortholog’ gene list. The GO terms are listed in order of decreasing p-values and the ‘universe set’ size included in the functional analysis using VLAD is 30,441. Mouse data: GO terms significantly enriched in mouse orthologs of zebrafish ‘heart jogging’ genes.<br>The GO biological process, molecular function and cellular component terms that are highly enriched in the mouse ‘jogging ortholog’ gene list. The GO terms are listed in order of decreasing p-values and the ‘universe set’ size included in the functional analysis using VLAD is 24,813. Biological process summary: Summary of all enriched biological process GO terms across all three species. The enriched biological process GO terms are grouped into general biological areas (‘development’, ‘patterning’, ‘cellular process’, ‘signaling’, ‘movement’ and ‘high level terms’, text in upper case). Each area is subdivided into more specific ontologies, based on the Gene Ontology structure. A subset of the biological process GO terms and data are reported in Table 2 in the main article. High-level terms were not included in any further discussion, as these terms are not specific enough to be informative. k: the number of genes in each gene list annotated to the GO term; M: the number of genes in the species proteome annotated to the GO term. Starred genes are annotated directly to the enriched term. Non-starred genes are annotated to a ‘child’ of the enriched term. DAR: zebrafish, HUM: human, MUS: mouse. Mouse gene list:‘Early heart development’ mouse gene list. The evidence that these 103 mouse genes have a role in the early stages of heart development comes from their orthology to the zebrafish ‘heart jogging’ gene list, from phenotypes associated with specific genotypes, or annotation to the GO term ‘determination of heart left/right asymmetry’ or it’s child terms (see method section in main article for full details).
DOI: 10.6084/m9.figshare.844761.v1
title: Supplementary Information Patra et al.

description: Supplementary material to the article "A genome wide dosage suppressor network reveals genetic robustness and a novel mechanism for Huntington’s disease", by Patra et al. o Supplementary Figures S1-S8 o Supplementary Table S1 o Supplementary Table S2 o Supplementary Table S3 o Supplementary Table S4 o Supplementary Table S5 o Supplementary Table S6 o Supplementary Table S7 o Supplementary Table S8 o Supplementary Table S9 o Supplementary Table S10 o Supplementary Table S11 o Supplementary Table S12
DOI: 10.6084/m9.figshare.844624
title: Buridan

description: this table is meant to present each parameter which is calculated from the Buridan's experiment in CeTrAn (www.github.com/jcolomb/CeTrAn)
DOI: 10.6084/m9.figshare.844630.v1
title: Significantly enriched GO terms in heart jogging genes in zebrafish and their human and mouse orthologs

description: Zebrafish data: GO terms significantly enriched in zebrafish ‘heart jogging’ gene list. The GO biological process, molecular function and cellular component terms that are highly enriched in the zebrafish ‘jogging’ gene list. The GO terms are listed in order of decreasing p-values and the ‘universe set’ size included in the functional analysis using VLAD is 14,577. Human data: GO terms significantly enriched in human orthologs of zebrafish ‘heart jogging’ genes.<br>The GO biological process, molecular function and cellular component terms that are highly enriched in the human ‘jogging ortholog’ gene list. The GO terms are listed in order of decreasing p-values and the ‘universe set’ size included in the functional analysis using VLAD is 30,441. Mouse data: GO terms significantly enriched in mouse orthologs of zebrafish ‘heart jogging’ genes.<br>The GO biological process, molecular function and cellular component terms that are highly enriched in the mouse ‘jogging ortholog’ gene list. The GO terms are listed in order of decreasing p-values and the ‘universe set’ size included in the functional analysis using VLAD is 24,813. Biological process summary: Summary of all enriched biological process GO terms across all three species. The enriched biological process GO terms are grouped into general biological areas (‘development’, ‘patterning’, ‘cellular process’, ‘signaling’, ‘movement’ and ‘high level terms’, text in upper case). Each area is subdivided into more specific ontologies, based on the Gene Ontology structure. A subset of the biological process GO terms and data are reported in Table 2 in the main article. High-level terms were not included in any further discussion, as these terms are not specific enough to be informative. k: the number of genes in each gene list annotated to the GO term; M: the number of genes in the species proteome annotated to the GO term. Starred genes are annotated directly to the enriched term. Non-starred genes are annotated to a ‘child’ of the enriched term. DAR: zebrafish, HUM: human, MUS: mouse. Mouse gene list:‘Early heart development’ mouse gene list. The evidence that these 103 mouse genes have a role in the early stages of heart development comes from their orthology to the zebrafish ‘heart jogging’ gene list, from phenotypes associated with specific genotypes, or annotation to the GO term ‘determination of heart left/right asymmetry’ or it’s child terms (see method section in main article for full details).
DOI: 10.6084/m9.figshare.844716.v1
title: Panama fungal phyllosphere sequences

description: Fungal LSU sequence data (primers LR0R/LR3) and metadata for fungal communities sampled from plant leaf surfaces on Barro Colorado Island, Panama. Data collection procedures are described in the paper "Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities" by Steven Kembel and Rebecca Mueller (manuscript in review at Botany). Multiple sets of files are included in this data archive: <strong>fungi_phyllosphere_metadata.txt</strong> - a tab-delimited text file with information on barcode sequences and host species identity for each sample. The columns of the text file include a sample identifier (SampleID), forward and reverse barcode sequences* (ForwardBarcodeSequence and ReverseBarcodeSequence), and the identity of the host plant species from which each sample was collected (HostSpecies). * note that in the raw sequence data, a combinatorial or dual-index barcoding approach was used to uniquely identify samples. The first 6bp of the barcode are on the forward read, and the final 6bp of the barcode are on the reverse read. The barcodes will need to be combined into a single 12bp construct prior to parsing into samples, or parsed with software capable of processing dual-indexed sequences. <strong>fungi1.fastq.tgz.part-xx</strong><br><strong>fungi2.fastq.tgz.part-xx</strong> - raw sequence data in FASTQ format, produced by two sequencing runs using Illumina MiSeq 250bp paired-end sequencing technology. Due to file size limitations, the original tar/gzip archives have been split into multiple parts. To recombine and uncompress the sequence files, the following commands work in a Linux/Unix environment: cat fungi1.fastq.tgz.part-* | tar xz<br>cat fungi2.fastq.tgz.part-* | tar xz
DOI: 10.6084/m9.figshare.844624.v2
title: Buridan

description: this table is meant to present each parameter which is calculated from the Buridan's experiment in CeTrAn (www.github.com/jcolomb/CeTrAn)
DOI: 10.6084/m9.figshare.844716.v2
title: Panama fungal phyllosphere sequences

description: Fungal LSU sequence data (primers LR0R/LR3) and metadata for fungal communities sampled from plant leaf surfaces on Barro Colorado Island, Panama. Data collection procedures are described in the paper "Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities" by Steven Kembel and Rebecca Mueller (manuscript in review at Botany). Multiple sets of files are included in this data archive: <strong>fungi_phyllosphere_metadata.txt</strong> - a tab-delimited text file with information on barcode sequences and host species identity for each sample. The columns of the text file include a sample identifier (SampleID), forward and reverse barcode sequences* (ForwardBarcodeSequence and ReverseBarcodeSequence), and the identity of the host plant species from which each sample was collected (HostSpecies). * note that in the raw sequence data, a combinatorial or dual-index barcoding approach was used to uniquely identify samples. The first 6bp of the barcode are on the forward read, and the final 6bp of the barcode are on the reverse read. The barcodes will need to be combined into a single 12bp construct prior to parsing into samples, or parsed with software capable of processing dual-indexed sequences. <strong>fungi1.fastq.tgz.part-xx</strong><br><strong>fungi2.fastq.tgz.part-xx</strong> - raw sequence data in FASTQ format, produced by two sequencing runs using Illumina MiSeq 250bp paired-end sequencing technology. Due to file size limitations, the original tar/gzip archives have been split into multiple parts. To recombine and uncompress the sequence files, the following commands work in a Linux/Unix environment: cat fungi1.fastq.tgz.part-* | tar xz<br>cat fungi2.fastq.tgz.part-* | tar xz
DOI: 10.6084/m9.figshare.844241
title: Images of laser ablation zones

description: This set consists of several images of the laser ablated zone. One image (HAZ.pdf) is a photo of the heat affected zone of a copper sample ablated at 20 different spots, each with an extra laser shot added as compared to the previous spot. Two other files (al.pdf and cu.pdf) show SEM images at two different magnifications of the ablated area of aluminum and copper.
DOI: 10.6084/m9.figshare.844241.v1
title: Images of laser ablation zones

description: This set consists of several images of the laser ablated zone. One image (HAZ.pdf) is a photo of the heat affected zone of a copper sample ablated at 20 different spots, each with an extra laser shot added as compared to the previous spot. Two other files (al.pdf and cu.pdf) show SEM images at two different magnifications of the ablated area of aluminum and copper.
DOI: 10.6084/m9.figshare.844716.v3
title: Panama fungal phyllosphere sequences

description: Fungal LSU sequence data (primers LR0R/LR3) and metadata for fungal communities sampled from plant leaf surfaces on Barro Colorado Island, Panama. Data collection procedures are described in the paper "Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities" by Steven Kembel and Rebecca Mueller (manuscript in review at Botany). Multiple sets of files are included in this data archive: <strong>fungi_phyllosphere_metadata.txt</strong> - a tab-delimited text file with information on barcode sequences and host species identity for each sample. The columns of the text file include a sample identifier (SampleID), forward and reverse barcode sequences* (ForwardBarcodeSequence and ReverseBarcodeSequence), and the identity of the host plant species from which each sample was collected (HostSpecies). * note that in the raw sequence data, a combinatorial or dual-index barcoding approach was used to uniquely identify samples. The first 6bp of the barcode are on the forward read, and the final 6bp of the barcode are on the reverse read. The barcodes will need to be combined into a single 12bp construct prior to parsing into samples, or parsed with software capable of processing dual-indexed sequences. <strong>fungi1.fastq.tgz.part-xx</strong><br><strong>fungi2.fastq.tgz.part-xx</strong> - raw sequence data in FASTQ format, produced by two sequencing runs using Illumina MiSeq 250bp paired-end sequencing technology. Due to file size limitations, the original tar/gzip archives have been split into multiple parts. To recombine and uncompress the sequence files, the following commands work in a Linux/Unix environment: cat fungi1.fastq.tgz.part-* | tar xz<br>cat fungi2.fastq.tgz.part-* | tar xz
DOI: 10.6084/m9.figshare.844624.v3
title: Buridan's exp: parameter table

description: this table is meant to present each parameter which is calculated from the Buridan's experiment in CeTrAn (www.github.com/jcolomb/CeTrAn)
DOI: 10.6084/m9.figshare.844716.v4
title: Panama fungal phyllosphere sequences

description: Fungal LSU sequence data (primers LR0R/LR3) and metadata for fungal communities sampled from plant leaf surfaces on Barro Colorado Island, Panama. Data collection procedures are described in the paper "Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities" by Steven Kembel and Rebecca Mueller (manuscript in review at Botany). Multiple sets of files are included in this data archive: <strong>fungi_phyllosphere_metadata.txt</strong> - a tab-delimited text file with information on barcode sequences and host species identity for each sample. The columns of the text file include a sample identifier (SampleID), forward and reverse barcode sequences* (ForwardBarcodeSequence and ReverseBarcodeSequence), and the identity of the host plant species from which each sample was collected (HostSpecies). * note that in the raw sequence data, a combinatorial or dual-index barcoding approach was used to uniquely identify samples. The first 6bp of the barcode are on the forward read, and the final 6bp of the barcode are on the reverse read. The barcodes will need to be combined into a single 12bp construct prior to parsing into samples, or parsed with software capable of processing dual-indexed sequences. <strong>fungi1.fastq.tgz.part-xx</strong><br><strong>fungi2.fastq.tgz.part-xx</strong> - raw sequence data in FASTQ format, produced by two sequencing runs using Illumina MiSeq 250bp paired-end sequencing technology. Due to file size limitations, the original tar/gzip archives have been split into multiple parts. To recombine and uncompress the sequence files, the following commands work in a Linux/Unix environment: cat fungi1.fastq.tgz.part-* | tar xz<br>cat fungi2.fastq.tgz.part-* | tar xz
DOI: 10.6084/m9.figshare.844624.v4
title: Buridan's experiment: calculated parameters

description: this table is meant to present each parameter which is calculated from the Buridan's experiment in CeTrAn (www.github.com/jcolomb/CeTrAn)
DOI: 10.6084/m9.figshare.843626
title: Reputation of medical treatments: Dataset and analysis files

description: Dataset and analysis code for forthcoming 2014 article in Journal of Medical Internet Research
DOI: 10.6084/m9.figshare.844624.v6
title: Buridan

description: this table is meant to present each parameter which is calculated from the Buridan's experiment in CeTrAn (www.github.com/jcolomb/CeTrAn)
DOI: 10.6084/m9.figshare.844624.v5
title: Buridan

description: this table is meant to present each parameter which is calculated from the Buridan's experiment in CeTrAn (www.github.com/jcolomb/CeTrAn)
DOI: 10.6084/m9.figshare.844716.v5
title: Panama fungal phyllosphere sequences

description: Fungal LSU sequence data (primers LR0R/LR3) and metadata for fungal communities sampled from plant leaf surfaces on Barro Colorado Island, Panama. Data collection procedures are described in the paper "Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities" by Steven Kembel and Rebecca Mueller (manuscript in review at Botany). Multiple sets of files are included in this data archive: <strong>fungi_phyllosphere_metadata.txt</strong> - a tab-delimited text file with information on barcode sequences and host species identity for each sample. The columns of the text file include a sample identifier (SampleID), forward and reverse barcode sequences* (ForwardBarcodeSequence and ReverseBarcodeSequence), and the identity of the host plant species from which each sample was collected (HostSpecies). * note that in the raw sequence data, a combinatorial or dual-index barcoding approach was used to uniquely identify samples. The first 6bp of the barcode are on the forward read, and the final 6bp of the barcode are on the reverse read. The barcodes will need to be combined into a single 12bp construct prior to parsing into samples, or parsed with software capable of processing dual-indexed sequences. <strong>fungi1.fastq.tgz.part-xx</strong><br><strong>fungi2.fastq.tgz.part-xx</strong> - raw sequence data in FASTQ format, produced by two sequencing runs using Illumina MiSeq 250bp paired-end sequencing technology. Due to file size limitations, the original tar/gzip archives have been split into multiple parts. To recombine and uncompress the sequence files, the following commands work in a Linux/Unix environment: cat fungi1.fastq.tgz.part-* | tar xz<br>cat fungi2.fastq.tgz.part-* | tar xz
DOI: 10.6084/m9.figshare.843615
title: Sugarbeet transcriptome unigenes

description: Unigenes assembled from RNA-seq of sugarbeet leaf and root tissues. Full description of research is available in manuscript, "Generation and characterization of a sugarbeet (<em>Beta vulgaris</em> L.) transcriptome and transcript-based SSR markers," by KK Fugate, D Fajardo, B Schlautman, JP Ferrareze, MD Bolton, LG Campbell, E Wiesman, and J. Zalapa, <em>The Plant Genome</em>. KEGG pathway maps and the unigenes assigned to these pathways can be found in figshare fileset "Sugarbeet unigenes assigned to KEGG pathways" (http://figshare.com/articles/KEGG_Pathway_Maps/930724)<strong></strong>
DOI: 10.6084/m9.figshare.843622
title: Data for: Cooperating to compete: altruism, sexual selection and causes of male reproductive cooperation

description: This figshare package includes data and code for the paper: Cooperating to compete: altruism, sexual selection and causes of male reproductive cooperation Samuel L. Díaz-Muñoz, Emily H. DuVal, Alan H. Krakauer, Eileen A. Lacey<br>Animal Behaviour (2014) 88, 67–78. doi:10.1016/j.anbehav.2013.11.008 The review article defines male reproductive cooperation among animals and proposes categories for different types of mal-male cooperation. The data are the result of a literature search detailed in the paper. If you use these data and code, please cite the article above and the figshare doi.
DOI: 10.6084/m9.figshare.843626.v1
title: How feedback biases give ineffective medical treatments a good reputation

description: Dataset and analysis code for forthcoming 2014 article in Journal of Medical Internet Research
DOI: 10.6084/m9.figshare.844716.v6
title: Panama fungal phyllosphere sequences

description: Fungal LSU sequence data (primers LR0R/LR3) and metadata for fungal communities sampled from plant leaf surfaces on Barro Colorado Island, Panama. Data collection procedures are described in the paper "Plant traits and taxonomy drive host associations in tropical phyllosphere fungal communities" by Steven Kembel and Rebecca Mueller (manuscript in review at Botany). Multiple sets of files are included in this data archive: <strong>fungi_phyllosphere_metadata.txt</strong> - a tab-delimited text file with information on barcode sequences and host species identity for each sample. The columns of the text file include a sample identifier (SampleID), forward and reverse barcode sequences* (ForwardBarcodeSequence and ReverseBarcodeSequence), and the identity of the host plant species from which each sample was collected (HostSpecies). * note that in the raw sequence data, a combinatorial or dual-index barcoding approach was used to uniquely identify samples. The first 6bp of the barcode are on the forward read, and the final 6bp of the barcode are on the reverse read. The barcodes will need to be combined into a single 12bp construct prior to parsing into samples, or parsed with software capable of processing dual-indexed sequences. <strong>fungi1.fastq.tgz.part-xx</strong><br><strong>fungi2.fastq.tgz.part-xx</strong> - raw sequence data in FASTQ format, produced by two sequencing runs using Illumina MiSeq 250bp paired-end sequencing technology. Due to file size limitations, the original tar/gzip archives have been split into multiple parts. To recombine and uncompress the sequence files, the following commands work in a Linux/Unix environment: cat fungi1.fastq.tgz.part-* | tar xz<br>cat fungi2.fastq.tgz.part-* | tar xz
DOI: 10.6084/m9.figshare.844627.v5
title: Visual Validation of the e-RUSLE Model Applied at the Pan-European Scale

description: Bosco, C., de Rigo, D., Dewitte, O., 2014. <strong>Visual Validation of the e-RUSLE Model Applied at the Pan-European Scale</strong>. <em>Scientific Topics Focus 1</em>, MRI-11a13. Notes Transdiscipl. Model. Env., Maieutike Research Initiative. https://doi.org/10.6084/m9.figshare.844627 <br>PDF: http://purl.org/mtv/STF/pdf/1843225<br>Version: DRAFT 0.4.2<br>This is a preliminary version. Any future version will be accessible from the same DOI code (https://doi.org/10.6084/m9.figshare.844627). The views expressed are those of the authors and may not be regarded as stating an official position of mentioned organisations. <strong>Visual Validation of the e-RUSLE Model Applied at the Pan-European Scale</strong> Claudio Bosco ¹ ² ⁴, Daniele de Rigo ² ³ ⁴ and Olivier Dewitte ⁵ 1 Loughborough University, Department of Civil and Building Engineering,<br>Loughborough, United Kingdom 2 Joint Research Centre of the European Commission,<br>Institute for Environment and Sustainability, Ispra, Italy 3 Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Milano, Italy 4 Maieutike Research Initiative, Milano, Italy 5 Royal Museum for Central Africa, Department of Earth Sciences, Tervuren, Belgium Validating soil erosion estimates at regional or continental scale is still extremely challenging. The common procedures are not technically and financially applicable for large spatial extents, despite this some options are still applicable. For validating the European map of soil erosion by water calculated using the approach proposed in Bosco et al. [1] we applied alternative qualitative methods based on visual evaluation. The 1 km 2 map was validated through a visual and categorical comparison between modelled and observed soil erosion. A procedure employing high-resolution Google Earth images and pictures as validation data is here shown. The resolution of the images, rapidly increased during the last years, allows for a visual qualitative estimation of local soil erosion rates. A cluster of 3x3 K m 2 around 85 selected points was analysed by the authors. The results corroborate the map obtained applying the e-RUSLE model. The 63% of a random sample of 732 grid cells are accurate, 83% at least moderately accurate with a bootstrap p ≤ 0.05). For each of the 85 clusters, the complete details of the validation also containing the comments of the evaluators and the geo-location of the analysed areas have been reported. <br><br>