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1,773 results for “Supplementary data,”
Supplementary material 4 from: Wayland MT, Vainio JK, Gibson DI, Herniou EA, Littlewood TDJ, Väinölä R (2015) The systematics of Echinorhynchus Zoega in Müller, 1776 (Acanthocephala, Echinorhynchidae) elucidated by nuclear and mitochondrial sequence data from eight European taxa. ZooKeys 484: 25-52. https://doi.org/10.3897/zookeys.484.9132
Patterns of COI sequence variation: Explanation note: Patterns of COI sequence variation. Graphs and discussion of patterns of nucleotide substitions in the COI data-set.
Supplementary material 1 from: Wayland MT, Vainio JK, Gibson DI, Herniou EA, Littlewood TDJ, Väinölä R (2015) The systematics of Echinorhynchus Zoega in Müller, 1776 (Acanthocephala, Echinorhynchidae) elucidated by nuclear and mitochondrial sequence data from eight European taxa. ZooKeys 484: 25-52. https://doi.org/10.3897/zookeys.484.9132
Aligned and concatenated partial sequences of COI and 28S rDNA: Explanation note: Aligned and concatenated partial sequences of COI and 28S rDNA in nexus format. Aligned partial sequences of COI and 28S rDNA from each acanthocephalan population have been concatenated. Gaps are indicated by '-'. The first 585 characters in each block correspond to COI and the remainder to 28S rDNA. The file contains data for all nine Echinorhynchus samples and the outgroup taxon, Acanthocephalus lucii. This nexus file was used in all phylogenetic analyses.
Supplementary material 3 from: Wayland MT, Vainio JK, Gibson DI, Herniou EA, Littlewood TDJ, Väinölä R (2015) The systematics of Echinorhynchus Zoega in Müller, 1776 (Acanthocephala, Echinorhynchidae) elucidated by nuclear and mitochondrial sequence data from eight European taxa. ZooKeys 484: 25-52. https://doi.org/10.3897/zookeys.484.9132
Nucleotide substitutions: Explanation note: Substitutions of nucleotides (transitions/transversions) for 28S rDNA (below the diagonal) and COI sequence data (above the diagonal).
Supplementary material 3 from: Balakirev A, Abramov A, Rozhnov V (2014) Phylogenetic relationships in the Niviventer-Chiromyscus complex (Rodentia, Muridae) inferred from molecular data, with description of a new species. ZooKeys 451: 109-136. https://doi.org/10.3897/zookeys.451.7210
Cranial measurements, range, and standard deviation: Explanation note: Cranial measurements, including range and standard deviation (SD), for Chiromyscus species from Vietnam (intact, adult skulls of both sexes).
Supplementary material 1 from: Zilletti B, Capdevila-Argüelles L (2014) Halting Biological Invasions in Europe: from Data to Decisions. A message from NEOBIOTA 2012. NeoBiota 21: 1-5. https://doi.org/10.3897/neobiota.21.7289
Time to act! Biological invasions need a strong European legal framework urgently!: Explanation note: Resolution adopted by the participants of the "7th European Conference on Biological Invasions" of the European Working Group on Biological Invasions - NEOBIOTA.
Supplementary Data for "Peptidomimetic blockade of MYB in acute myeloid leukemia"
<p>These files contain results of computational analysis of sequencing data, deposited in the Gene Expression Omnibus under the accession numbers GSE94242 and GSE107078:</p> <p> </p> <p>RNA-seq_data_MYBMIMvsControl.zip</p> <p>Supplementary Data S1: Analysis of gene expression of MOLM13 cells.</p> <p> </p> <p>ChIP-seq_data_gsea_reports.zip</p> <p>Supplementary Data S2: Analysis of MYB occupancy of MV411 cells</p> <p> </p> <p>H3K27Ac ChIP-seq normalized and peak counts.zip</p> <p>Supplementary Data S3: Analysis of H3K27Ac occupancy of MV411 cells</p> <p> </p> <p> </p>
Supplementary Data:Risk Analysis for Real-time Flood Control Operation of a Multi-reservoir System Using a Dynamic Bayesian Network
<p>The files in this record contain data for risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic bayesian network considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Reservoir data and river flood routing parameters</li> <li>Flood data</li> <li>Code and results of the Monte Carlo simulations</li> <li>Code and results of the Bayesian network</li> </ul>
Supplementary material 1 from: Di-Nizo CB, Banci KRS, Sato-Kuwabara Y, Silva MJJ (2017) Advances in cytogenetics of Brazilian rodents: cytotaxonomy, chromosome evolution and new karyotypic data. Comparative Cytogenetics 11(4): 833-892. https://doi.org/10.3897/CompCytogen.v11i4.19925
Table S1 : Explanation note: Sequences analysed for phylogenetic reconstruction (Maximum likelihood and Bayesian Inference) of Neacomys, with species, GenBank and lab/ field number, diploid and fundamental number (when available), locality and reference.
Supplementary material 1 from: Zúñiga JD, Gostel MR, Mulcahy DG, Barker K, Hill A, Sedaghatpour M, Vo SQ, Funk VA, Coddington JA (2017) Data Release: DNA barcodes of plant species collected for the Global Genome Initiative for Gardens Program, National Museum of Natural History, Smithsonian Institution. PhytoKeys 88: 119-122. https://doi.org/10.3897/phytokeys.88.14607
List of samples collected for the Global Genome Initiative for Gardens project selected for DNA barcoding, with GenBank accession numbers and genetic sample identification numbers. All the sequences are included in the GGI-Gardens BioProject. : Explanation note: List of samples collected for the Global Genome Initiative for Gardens project selected for DNA barcoding, with GenBank accession numbers and genetic sample identification numbers.
Supplementary material for "Can the anisotropic hydraulic conductivity of an aquifer be determined using surface displacement data? A case study"
<p>Supplementary material for ”Can the anisotropic hydraulic conductivity of an aquifer be determined using surface displacement data? A case study".</p> <p>The <a href="https://github.com/sonasalehian/AHC-Poroelastic-Model.git" target="_blank" rel="noopener">AHC-Poroelastic-Model</a> codes from repository is included in the AHC-Poroelastic-Model-main.zip file.</p>
Supplementary Data File
<p>Supplementary Data File for "DFF-ChIP: a method to detect and quantify complex interactions between RNA polymerase II, transcription factors, and chromatin"</p>
Supplementary figure 1 of Nabholz et al. A cautionary note on taxonomic changes based on mitochondrial data in Orthoptera: a comment of Hochkirch et al. 2023
<p>Boxplot of Fst computed using a variable number of randomly selected nuclear loci between Gomphocerippus biguttulus and G. brunneus. 1000 replications were made for each number of loci presented on the x-axis. The dotted line indicates Fst = 0.</p>
Supplementary data for: Convergent reductive evolution of cyanobacteria in symbiosis with Dinophysiales dinoflagellates
<p>Supplementary data for phylogenomic analysis in "<strong>Convergent reductive evolution of cyanobacteria in symbiosis with Dinophysiales dinoflagellates</strong>" by Nakayama, T., Nomura, M., Yabuki, A., Shiba, K., Inaba, K., & Inagaki, Y. (<a href="https://www.nature.com/articles/s41598-024-63502-0">https://www.nature.com/articles/s41598-024-63502-0</a>; <a href="https://doi.org/10.1101/2024.01.11.574452">https://doi.org/10.1101/2024.01.11.574452</a>).</p> <p>The text file <code>CregCyn_phylogenomic_tree.newick</code> contains a newick formatted phylogenomic tree shown in Figure 2 of the paper. Note that the tree is unrooted.<br>The compressed file <code>phylogenomic_analysis_dataset.tar.gz</code> contains the following directories and files.</p> <ul> <li><code>concatenated_dataset.fasta </code>: dataset used for the phylogenomic analysis, constructed by combining 143 protein alignments.</li> <li><code>single_protein_datasets</code>: directory containing each orthologous protein sequence from which the concatenated dataset was derived.<br>It also contains the following subdirectories. <ul> <li><code>original_sequences</code>: contains the multi-FASTA files of the original sequences for each orthologous protein.</li> <li><code>multiple_alignments</code>: contains multiple alignments for each orthologous protein.</li> <li><code>trimmed_alignments</code>: contains multiple alignments for each protein, with positions not suitable for analysis removed. These files are combined into <code>single_protein_dataset.fasta</code>.</li> </ul> </li> </ul>
Supplementary data to research paper "Linking local climate scenarios to global warming levels: Applicability, prospects and uncertainties"
<p>This file contains supplementary data for the research paper "<span>Linking local climate scenarios to global warming levels: Applicability, prospects and uncertainties"", submitted to IOP Publishing Environmental Research: Climate. The DOI and link to the paper will be added once it is published.</span></p> <p><span>Each file lists the annual mean temperature anomalies relative to the period 1991-2020 for a model of the Austrian climate scenarios OEKS15 (Leuprecht, 2018). The full dataset is available here: https://data.hub.geosphere.at/dataset/oks15_bias_corrected<br></span></p> <p> </p> <div> <div>Leuprecht, A. (2018). <em>ÖKS15 Bias Corrected EURO-CORDEX Model Precipitaion, Radiation, Temperature</em> [dataset]. <a href="https://doi.org/10.60669/B37Q-JD39">https://doi.org/10.60669/B37Q-JD39</a></div> </div>
Quantitative evidence for modelling electric vehicles - Supplementary Data
<p><strong>Please cite as:</strong></p> <p>Malte Jansen, Rob Gross, and Iain Staffell. ‘Quantitative Evidence for Modelling Electric Vehicles’. <em>Renewable and Sustainable Energy Reviews</em> 199 (1 July 2024): 114524. <a href="https://doi.org/10.1016/j.rser.2024.114524">https://doi.org/10.1016/j.rser.2024.114524</a>.</p> <p><strong>Abstract:</strong></p> <p>Electric vehicles are now a major contributor to decarbonising the transport sector. Their rollout has accelerated rapidly since 2020, reaching a global fleet of 40 million in 2023. This presents both problems and opportunities for electricity systems, with charging increasing peak loads, but also providing a large new source of flexibility to help manage increased shares of wind and solar generation, shift peak demand and improve network management.</p> <p>While EV flexibility is widely discussed, there is uncertainty surrounding the magnitude to which EVs could help electricity systems, and a distinct lack of quantitative evidence around adoption, charging behaviour and technical capabilities for load shifting. This study employs the rapid evidence assessment method to synthesise recent information. We find that studies expect that EVs could provide 1–11 GW of flexible capacity per million vehicles (median: 3.7 GW), with the ability to shift demand by 1.5–5 hours (median: 4 hours) and a price elasticity of –0.77 to –0.10 (median: –0.15). Diurnal profiles of charging demand and availability for providing flexibility are aggregated across multiple studies. The results are relevant for energy modellers and show that the interaction between EVs and electricity systems can be generalised on a widely-applicable basis.</p>
Supplementary collection of preprocessed and analysed data objects for the paper: "Gene-level alignment of single cell trajectories"
<p>Supplementary collection of h5ad data files containing preprocessed and analysed single-cell reference and query datasets discussed in the case studies of the paper:<em><strong>"Gene-level alignment of single cell trajectories"</strong></em> (<a href="https://doi.org/10.1101/2023.03.08.531713" rel="nofollow">https://doi.org/10.1101/2023.03.08.531713</a>).</p> <p>Our artificial thymic organoid raw sequencing data are available from ArrayExpress (E-MTAB-12720).</p> <p>Below lists the original sources of the literature datasets we downloaded prior to our own preprocessing and trajectory analysis.</p> <ul> <li> <p>Original pan fetal reference data from: Suo, C. et al. (2022) "Mapping the developing human immune system across organs" (<a href="https://doi.org/10.1126/science.abo0510">https://doi.org/10.1126/science.abo0510</a><a href="http://paperpile.com/b/XTpgLG/TQtQ3">), </a>downloaded from <a href="https://developmental.cellatlas.io/fetal-immune">https://developmental.cellatlas.io/fetal-immune</a>.</p> </li> </ul> <ul> <li> <p>Original healthy/IPF data from: Adams, T. S. et al. (2020), "Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis" (<a href="https://doi.org/10.1126/sciadv.aba1983">https://doi.org/10.1126/sciadv.aba1983</a>), downloaded from GEO (GSE136831).</p> </li> </ul> <p>We have run our scripts in <a href="https://github.com/Teichlab/G2G_notebooks">https://github.com/Teichlab/G2G_notebooks</a> to generate new latent embeddings and infer pseudotime trajectories for the above datasets, and used them to model trajectory alignments with our newly developed framework: ‘Genes2Genes’ <a href="https://github.com/Teichlab/Genes2Genes">https://github.com/Teichlab/Genes2Genes</a>. </p>
Supplementary data (excel tables) for manuscritpt under revision (more details when the manuscript will be accepted for publication)
Open the record for dataset details and reuse information.
Appendix 5: Supplementary data for Chapter 4 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development'
<p>Appendix 5: Supplementary data for Chapter 4 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development' by Adam R. Mason, Department of Civil and Environmental Engineering, Imperial College London</p>
Appendix 4: Supplementary data for Chapter 3 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for the management of biodiversity alongside human development'
<p>Appendix 4: Supplementary data for Chapter 3 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development' by Adam R. Mason, Department of Civil and Environmental Engineering, Imperial College London</p>
Appendix 6: Supplementary data for Chapter 5 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development'
<p>Appendix 6: Supplementary data for Chapter 5 in the thesis: 'Quantifying the natural resource requirements of terrestrial ecosystems for managing biodiversity alongside human development' by Adam R. Mason, Department of Civil and Environmental Engineering, Imperial College London</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.