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291 results for “chaptering”
Chapter 29. Supplementary material. Remote sensing of invasive Australian Acacia species: State of the art and future perspectives
<p>This is online supplementary material for the Chapter "Remote sensing of invasive Australian Acacia species: State of the art and future perspectives" authored by A Große-Stoltenberg, I Lizarazo, G Brundu, VP Gonçalves, LP Osco, C Masemola, J Müllerová, C Werner, I Kotze, and J Oldeland, corresponding to Chapter 29 In: "Wattles: Australian Acacia species around the world". Eds: D.M. Richardson, J.J. Le Roux, and E. Marchante (CABI, UK, 2023).”</p>
Supplemental datasets for Chapter 2 of Thesis
<p>Dataset S01. Mutants with significant fitness differences identified from randomization tests, grouped according to function, metabolism, and direction of fitness differences.</p> <p>Dataset S02. Normalized transposon insertions reads that are curated to the central 90% of coding regions.</p> <p>Dataset S03. Mean relative fitness (W) of mutants from four biological replicates. P-values were calculated for 10000 permutations of randomization tests, and were adjusted using a Benjamini-Hochberg correction for multiple comparisons.</p>
Supplementary material 1 for Thesis Chapter 2 - An obligate aerobe hybridises hydrogen fermentation and carbon storage to adapt to hypoxia
<p>Supplementary material for paired comparative metabolomics and proteomics on <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 during hypoxia, as part of chapter 2 for the thesis "Biochemistry and physiology of mycobacterial adaptations to energy starvation".</p> <p>Description below is identical to that provided in 'Summary.docx'. </p> <p>Proteomics_analysis.xlsx</p> <p>Includes raw and annotated data for comparative proteomics experiments for chapter 2.</p> <p>The tab ‘Annotated comparisons’ contains fold change and p values for the comparisons for each protein from <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 derived from LFQ-Analyst. Functional annotations are derived from KEGG pathways and modules, which utilise the spreadsheets in ‘MSMEG gene annotation’ ‘Protein ids to KEGG pathway’ and ‘KEGG Pathway and Modules’ to link KEGG annotations to MSMEG_XXXX gene identifiers and MSMEG_XXXX to Uniprot ID. Output from LFQ-Analyst is provided in the ‘Full_dataset’, ‘Imputed_matrix’ and ‘Original_matrix’ tabs.</p> <p>Data provided by the Monash Proteomics and Metabolomics Facility for upload into LFQ-analyst are provided as the ‘combined_protein.tsv’ and ‘LFQ-Analyst_experimental_design.txt’.</p> <p> </p> <p>Metabolism_analysis.xlsx</p> <p>Includes annotated data for comparative metabolomics experiments for chapter 2. Within the spreadsheet, TR refers to transition, ST refers to stationary phase and EXP refers to exponential phase. The tabs ‘TRvsEXP’, ‘STvsTR’ and ‘STvsEXP’ contain fold change and p values for each metabolite detected for each comparison. The remaining tabs categorise the metabolites based on KEGG database and IDEOM annotations. For broader categories (‘Lipid metabolism’,’ Carbohydrate metabolism’, ‘Cofactor metabolism’, ‘Nucleotide metabolism’, ‘Amino acid metabolism’ and ‘Peptides’ tabs), annotations were derived directly from filtering the ‘Map’ column of ‘Comparisons’ tab of the IDEOM worksheet (IDEOM_analysis.xlsb). Screenshots are pasted into each tab to show the filtering settings. The remaining tabs comprise narrower categories which were manually annotated with reference to KEGG pathways and maps, and also include rows corresponding to the proteomics data for these categories, so the proteomics and metabolomics data can be interpreted together. The ‘Proteomics’ tab contains the proteomics data referenced by these tabs, which is a copy of the ‘Annotated comparisons’ tab from the ‘Proteomics_analysis.xlsx’ file. A value of ‘N’ indicates the metabolite or protein (at least according to the name in the same row) was not found in these datasets.</p> <p>The IDEOM worksheet (IDEOM_analysis.xlsb) was provided by the Monash Proteomics and Metabolomics Facility and was used for further analysis and for annotations. ‘Data_for_MA_no_normalization.csv’ was also provided by the Monash Proteomics and Metabolomics Facility for upload into Metaboanalyst (https://www.metaboanalyst.ca/).</p>
Partners in Dementia Care: A Telephone Care Consultation Intervention Provided to Veterans in Partnership With Local Alzheimer's Association Chapters
ClinicalTrials.gov study NCT00291161. IPD Sharing: UNDECIDED. Countries: 1. Publications: 13.
Data from: Recent chapters of Neotropical history overlooked in phylogeography: shallow divergence explains phenotype and genotype uncoupling in Antilophia manakins
Open the record for dataset details and reuse information.
Supplementary material 1 from: Shepherd KA, Lepschi BJ, Johnson EA, Gardner AG, Sessa EB, S. Jabaily R (2020) The concluding chapter: recircumscription of Goodenia (Goodeniaceae) to include four allied genera with an updated infrageneric classification. PhytoKeys 152: 27-104. https://doi.org/10.3897/phytokeys.152.49604
Summary of GenBank accession numbers, Taxon names, Project numbers, Herbarium Accession numbers, voucher collectors and collection numbers, phylogenetic position and taxonomy and classification according to Shepherd et al.
Data and code for Chapter 1: An expanded scope of biodiversity in urban agriculture, with implications for conservation.
<p>Data and code for Chapter 1: An expanded scope of biodiversity in urban agriculture, with implications for conservation, in <em>Urban Agroecology: Interdisciplinary Research and Future Directions</em> (Monika Egerer and Hamutahl Cohen eds). CRC Press, Taylor & Francis, Abingdon, UK</p> <p> </p> <p> </p>
Data generated for the EDO chapter case study
<p>This archive contains a ZIP archive, `data.zip`, that contains several directories and a `README`. Further details of this archive's contents are described in that file. The source code used to generate this data is available <a href="https://zenodo.org/record/4000316">here</a>.</p>
Data collection for Tsuji et al., 2020, Microbial ecology of phototrophs in Boreal Shield lakes, Chapter 3: Biogeography and activity of chlorophototrophs in the ferruginous water columns of Boreal Shield lakes (PhD thesis)
<p>This data collection includes supplementary or raw data files related to Chapter 3 of the PhD thesis of Jackson M. Tsuji, "Biogeography and activity of chlorophototrophs in the ferruginous water columns of Boreal Shield lakes" (in "Microbial ecology of phototrophs in Boreal Shield lakes"). Specifically, the following files are included:</p> <ul> <li>ASV_table_non_rarefied_counts.tsv.gz -- non-rarefied ASV table containing 16S rRNA gene amplicon data presented in this study as raw counts. Beyond the index column and sample columns, two additional columns, "Consensus.Lineage" and "Sequence" are included in the table. These columns include the taxonomic classification of the ASV (according to Silva) and the ASV sequence, respectively.</li> <li>ASV_table_non_rarefied_percent.tsv.gz -- same as above, but the data are normalized within each sample and expressed as percentages (i.e., sum to 100%).</li> <li>ASV_table_rarefied_counts.tsv.gz -- same as "ASV_table_non_rarefied_counts.tsv.gz", except that data is rarefied to 12,000 sequences per sample. Five samples were dropped due to having <12,000 sequences.</li> <li>ASV_table_rarefied_percent.tsv.gz -- same as above, but the data are normalized within each sample and expressed as percentages (i.e., sum to 100%).</li> <li>MAG_abundances_to_unassembled_reads.tsv.gz -- table like an ASV table showing the relative abundances (expressed as percentages) of metagenome-assembled genomes within metagenomes. Aside from the index column and sample columns, additional columns are included to provide the taxonomic classification of the MAGs (based on the Genome Taxonomy Database) and the CheckM statistics of the MAGs. Relative abundances of MAGs in a metagenome are calculated as the number of mapped reads to the MAGs from the given metagenome divided by the total number of unassembled metagenome reads for that metagenome (times 100%).</li> <li>MAG_abundances_to_assembled_reads.tsv.gz -- same as above, except that relative abundances are divided by the total number of unassembled metagenome reads for that metagenome that mapped to that metagenome's assembled contigs.</li> <li>core_sample_metadata.tsv -- table of core physico-chemical and geographic metadata for the samples in this study (used to build biplots presented in the chapter). Note that "nd" means "no data available", and any measurements below detection limits have been set to 0. A limited number of values were inferred from other sampling time points -- these are noted in the table for TDFe measurements, and in addition, the light attenuation coefficient for Lake 373 in Sept. 2017 was inferred from the Sept. 2016 coefficient due to no light data being available for Sept. 2017 samples.</li> <li>metadata_descriptions.tsv -- descriptions of all metadata columns in the above file.</li> </ul> <p> </p>
Appendices (A, B, C and D) from Chapter 1 of Piero Zannini's PhD Thesis - Biodiversity of Sacred Natural Sites in Italy
<p>This four files are appendices from the first chapter of Piero Zannini's PhD Thesis (see the text of the Thesis).</p>
Supplementary Information: CHAPTER 3 - Classification of genomic features of plant-associated bacteria using machine learning
<p>Appendix A- List of all bacterial genomes used in orthologous genes clustering in the feature extraction step and in the further steps to build and test classifiers’ models. The list includes the isolation source information and the related category for the genome classification and features selection purposes.</p> <p>Appendix B - Distribution of genomes by phylum, family, and genus among the categories defined according to bacteria lifestyle association.</p> <p>Appendix C - Enriched orthogroups by genus according to each enrichment test (Material and Methods). Values for each test are "Y" (enriched), "N" (not enriched), or "Untested" (clusters were untested when there was insufficient phylogenetic signal, they were too small or were found in all genomes).</p> <p>Appendix D - Classification performance of random forest and logistic regression techniques applied to genus-specific datasets of genomic features (orthogroups) using both matrices from gene count number and presence/absence values. Sensitivity is a measure of how well a test identifies true positives; Specificity: is a measure how well a test or model avoids false positives; Positive Predictive Value (Pos. Pred. Value): The probability that a positive prediction is correct; Negative Predictive Value (Neg. Pred. Value): The probability that a negative prediction is correct; Precision: The accuracy of positive predictions; Recall (Sensitivity): The ability to find all relevant cases; F1 Score: A combined measure of precision and recall; Prevalence: The proportion of positive cases in the total; Detection Rate: The proportion of true positive cases identified; Detection Prevalence: The proportion of positive predictions; Balanced Accuracy: An average of sensitivity and specificity; Area Under the Curve (AUC): The overall performance of the model in distinguishing between positive and negative cases.</p> <p>Appendix E - Orthogroups assigned with predicted COGs as an important feature for classifying plant-associated genomes. COG categories: A - RNA processing and modification; B - Chromatin structure and dynamics; C - Energy production and conversion; D - Cell cycle control, cell division, chromosome partitioning; E - Amino acid transport and metabolism; F - Nucleotide transport and metabolism; G - Carbohydrate transport and metabolism; H - Coenzyme transport and metabolism; I - Lipid transport and metabolism; J - Translation, ribosomal structure and biogenesis; K - Transcription; L - Replication, recombination and repair; M - Cell wall/membrane/envelope biogenesis; N - Cell motility; O - Posttranslational modification, protein turnover, chaperones; P - Inorganic ion transport and metabolism; Q - Secondary metabolites biosynthesis, transport and catabolism; R - General function prediction only; S - Function unknown; T - Signal transduction mechanisms; U - Intracellular trafficking, secretion, and vesicular transport; V - Defense mechanisms; W - Extracellular structures; X - Mobilome: prophages, transposons; Y - Nuclear structure; Z - Cytoskeleton.</p>
IPBES Invasive Alien Species Assessment: Chapter 4. Figures, tables and captions
<p>Figures, tables and captions from Chapter 4: Impacts of biological invasions on nature, nature’s contributions to people, and good quality of life. In: Thematic Assessment Report on Invasive Alien Species and their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
IPBES Invasive Alien Species Assessment: Chapter 5. Figures, tables and captions
<p>Figures, tables and caption from Chapter 5: Management; challenges, opportunities and lessons learned. In: Thematic Assessment Report on Invasive Alien Species and their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
IPBES Invasive Alien Species Assessment: Chapter 3. Figures, tables and captions
<p>Figures, tables and captions from Chapter 3: Drivers of biodiversity change affecting biological invasions. In: Thematic Assessment Report on Invasive Alien Species and their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
Supplementary Table Chapter 5 - Coco Duizer
<p><strong>Supplementary Table Chapter 5 - Coco Duizer</strong></p> <p><strong>Table S2: <strong><span>Results of the microarray analysis of HeLa 57A cells stimulated with <em>C</em>. <em>jejuni-</em>released ADP-heptose.</span></strong><span> <br></span></strong><span><span>Shown are all genes that are either up- or downregulated with a statistical significance of p <em>< </em>0.05.</span></span></p>
Supplementary tables for chapter 3: "Haplotype-resolved transcriptomics defines the inheritance and genetic architecture of response to Citrus Greening Disease"
<p>Dissertation chapter 3: "<span>Haplotype-resolved transcriptomics defines the inheritance and genetic architecture of response to Citrus Greening Disease"</span></p>
Dissertation Chapter 2 Tables
<p>Supplementary tables for chapter 2: "<span>Lineage-specific shifts in chromatin accessibility is associated with sequence divergence between <em>Citrus </em>species</span><span>"</span></p>
online Supplementary Data for INA Chapter 10
<p>online Supplementary Data for the Chapter 10 of the International Nitrogen Assessment.</p> <p>The .xlsx spreadsheet has been converted from a google sheet. The original formatting of the graphs and some formulas do not work in Excel. <br><br>The original spreadsheet can be found here: <br>https://docs.google.com/spreadsheets/d/1Yp6tqUz48Y7fA-PTsDQHPZo6qNLYZ_Wrh2Amf9_OH0U/edit?gid=422095820#gid=422095820</p>
Supplementary tables Chapter 4
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Steelquist Dissertation - Chapter 2 Supplement
<p>Clast size/precipitation datasets, imagery/elevation data products, and Jupyter Notebooks associated with Chapter 2 of Steelquist, A.T., (2021), Evolution of Fluvial Systems on the Colorado Plateau, Dissertation, Stanford University.</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.