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10,119 results for “IT Support”
The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering - Supporting Data
<p>Supporting code and data for the paper: </p> <p><em>The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering</em></p> <p>Raen (2020)</p> <p><strong>Supporting code</strong> includes `run_star_extras.f`, inlist templates, and our dark matter module (to be used in conjunction with MESA: <a href="http://mesa.sourceforge.net/index.html">Modules for Experiments in Stellar Astrophysics</a>). The full source code used in the production of this paper is available at <a href="https://github.com/troyraen/DM-in-Stars/">github.com/troyraen/DM-in-Stars</a> in the Raen2020 branch. (The master branch is intended for use by those wishing to use our module to explore DM effects beyond the scope of this paper.) We used MESA version 12115, and MESA SDK version 20190830.</p> <p><strong>Model data</strong> includes MESA history and profile data for the models highlighted in the paper (<span class="math-tex">\(1.0\ \mathrm{M}_\odot\)</span> and <span class="math-tex">\(3.5\ \mathrm{M}_\odot\)</span> models with <span class="math-tex">\(\Gamma_B = 0\)</span> (no dark matter), <span class="math-tex">\(\Gamma_B = 10^4\)</span>, and <span class="math-tex">\(\Gamma_B = 10^6\)</span>). The specific inlists used to generate the models are also included. Additional data will be shared on reasonable request to the paper's corresponding author.</p>
Support files for draft genome of the Asian buffalo leech, Hirudinaria manillensis
<p>The assembly fasta file, TE and gene gff files, and annotations for the draft genome of the Asian buffalo leech, Hirudinaria manillensis. </p>
Supporting data for "The methylome of Biomphalaria glabrata and other mollusks: enduring modification of epigenetic landscape and phenotypic traits by a new DNA methylation inhibitor"
<p>Methylome of the fresh water snail <em>Biomphalaria glabrata</em>. DNA was extracted from the feet of 10 individuals of <em>B. glabrata</em> originally isolated from Brazil. These snails have been cultivated in the laboratory since 1960. Tissue were grinded at 4°C and incubated in 1 ml volume of lysis buffer (20 mM TRIS pH 8; 1 mM EDTA; 100 mM NaCl; 0.5% SDS), with 0.3 mg of proteinase K at 55°C for 1 night. Afterwards, lysate was purified with phenol-chloroform and DNA was isopropanol precipitated. The extracted DNA (around 138ng/µL) was poled in equivalent amounts and Whole Genome Bisulfite Sequencing was done by GATC-biotech (www.gatc-biotech.com). The principle of this treatment is to convert non-methylated cytosines of gDNA into deoxy-uracil, whereas methylated cytosines remain intact. WGBS was done according to the Lister protocol (sequence 2 forward strands only). The reference genome (Biomphalaria-glabrata-BB02_SCAFFOLDS_BglaB1.fa) and annotation (Biomphalaria-glabrata-BB02_BASEFEATURES_BglaB1.3.gff3) used in this project are available on VectorBase (https://www.vectorbase.org/). To align our short reads, we chose to use two specific bisulfite mapping tools, BSMAP 1.0.0 (https://code.google.com/p/bsmap/) and Bismark 0.10.2 (www.bioinformatics.babraham.ac.uk /projects/bismark/), to compare their efficiency and convenience to finally work with the more suitable one on our datasets. IGV (Interactive Genomics Viewer, https://www.broadinstitute.org/igv/) was used to visualized final alignments.<br> BSMAP performed better than Bismark and was used for downstream analyses. Without default parameters alignement efficiency for BSMAP is 47.1%, allowing for 2 mismatches increases it to 55.6%. Methylation occurs predominantly in CpGs. (C methylated in CpG context: 12.4%, C methylated in CHG context: 0.5%, C methylated in CHH context: 0.5%) The major part of CpG sites, 95.7% were unmethylated, of the remaining 4.3% of CpG sites around 3.8% had low methylation, and 0.5% were completely methylated. Methylation is of the mosaic type. Methylation is relatively low with 1.2% of total cytosines. Our analyses suggested that conserved genes and genes with stable expression are localized in high methylated regions of the genome. Finally, we see that repetitive sequences were predominantly situated in low methylated regions of <em>B. glabrata</em>. </p> <p>Wiggle files were generated for CpG pairs only.</p> <p>Produced at IHPE (http://ihpe.univ-perp.fr/)</p>
A comprehensive evaluation of binning methods to recover human gut microbial species from a non-redundant reference gene catalog - Supporting Data
<p><strong>Description </strong></p> <p>The following files are available : </p> <ul> <li>Simulated non-redundant Gene Catalog (SGC) composed of 128267 genes;</li> <li>Gene abundance profiles across 40 samples: raw read counts, gene length normalized base counts, depth file computed by the jgi_summarize_bam_contig_depth script provided by MetaBAT;</li> <li>Gold Standard (GS) and Gold Standard Single Assignment (GS_SA) binning results;</li> <li>Binning results obtained on the SGC with nine binning methods: MSPminer, MGS-canopy, DAS Tool, MaxBin2, MetaBAT2, SolidBin, CONCOCT, COCACOLA and MyCC.</li> </ul> <p><strong>License</strong></p> <p>These files are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p>
Supporting data to M. Cavallaro, et al., 3'-5' crosstalk contributes to transcriptional bursting, 2019
<p>This repository contains supporting data to reference [1]. Please cite [1] if you find this repository useful. The data include:</p> <ul> <li>Flow cytometry data of HBB and HIV transgenes' expression in `.fcs` format.</li> <li>NanoString data for HIV expression.</li> <li>smFISH data for HBB and Akt1 gene expression.</li> </ul> <p>[1] M. Cavallaro, <em>et al.</em>, 3'-5' interactions contribute to transcriptional bursting, bioR$\chi$iv 514174. <a href="https://doi.org/10.1101/514174">https://doi.org/10.1101/514174</a></p>
Supporting Data Sets for "New Constraints on the Lunar Optical Space Weathering Rate"
<p>Data Sets supporting "New Constraints on the Lunar Optical Space Weathering Rate" submitted to Geophysical Research Letter on 12/18/2020. See Supporting Information (link TBD).</p>
Repository of data supporting the thesis "Poly-algorithmic Techniques in Real Quantifier Elimination"
<p>Dataset of various files (as a .zip) supporting the PhD thesis "Poly-algorithmic Techniques in Real Quantifier Elimination" by Zak Tonks, University of Bath. The PhD thesis is in the area of Quantifier Elimination over the Reals (QE) in Computer Algebra. The PhD thesis concerns implementation of algorithms in Quantifier Elimination, which largely culminates in the package QuantifierElimination for the Computer Algebra software Maple. Much of this repository is output of the benchmarking of this package against various other packes in Maple and otherwise. Otherwise there are some auxiliary tools and files to assist with working with QE in Maple, converting between various formats, and understanding case studies and the package QuantifierElimination via software demoes as Maple worksheets.</p> <p>An overview of the contents of this repository (as a .zip file, which contains subdirectories described in the README):</p> <ul> <li>The example databases contributed from the project, as files that can be read into Maple defining tables of examples, and associated functions to build or examine various examples,</li> <li>A pdf file providing the references for all examples from the example databases, and typesetting of the examples as associated QE problems,</li> <li>The benchmarking data produced from the benchmarking of the project as csv (comma separated value) files, and the Excel workbooks (xlsx files) processing said data into survival plots for the thesis,</li> <li>Copies of the survival plots themselves as .png files,</li> <li>The bash and Maple scripts used to generate the raw benchmarking data, that can be reused, including documentation how to do so in the associated README,</li> <li>Other auxiliary tools allowing for conversion of QE formulae between formats (such as that of SyNRAC, RegularChains, QuantifierElimination (amongst packages in Maple), and QEPCAD B.</li> <li>Maple worksheets and the associated exported pdf files used in software demos at conferences to demonstrate features of QuantifierElimination.</li> <li>Some pdf files demonstrating early case studies on Lazard curtains generated from an early development build of QuantifierElimination.</li> </ul> <p>Lastly, there is a README with more detail on the files of the repository further. To emulate the benchmarking of the thesis, an understanding of bash and potentially Maple is assumed, but the raw data from the project is provided here. Before QuantifierElimination's official release, the source code and/or Maple package as an .mla file is available for interested parties upon request to the author Zak Tonks (<a href="mailto:zak.p.tonks@bath.edu">zak.p.tonks@bath.edu</a>). Any other queries about this data or associated work should be directed to this email address. The author's PhD supervisor's email address is <a href="mailto:J.H.Davenport@bath.ac.uk">J.H.Davenport@bath.ac.uk</a>.</p>
Supporting Data for: Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation Technology
<p>This is the full data set of all reviewed research items obtained from Google Scholar, Web of Science and Scopus for the Scoping Literature Review <em><a href="https://doi.org/10.1371/journal.pone.0246398">Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation VR technology</a>.</em></p>
Supporting data for 'Hourly prediction of phytoplankton biomass and its environmental controls in lowland rivers'
<p>This dataset is used in the manuscript 'Hourly prediction of phytoplankton biomass and its environmental controls in lowland rivers' published in Water Resources Research. The dataset contains hourly observation of water quality in the lower Thames catchment, UK and were made available by the Environment Agency, UK. </p>
Raw and processed GO term data to support running GCEA analyses using ensemble-based nulls, as described in the manuscript, 'Overcoming bias in gene category enrichment analyses of brain-wide transcriptomic data'.
<p>Data to support a toolbox for performing gene category enrichment analyses, including against ensembles of null phenotypes.</p> <p>Descriptions of how these data files can be used for this purpose are in the documentation for the toolbox, at https://github.com/benfulcher/GCEA_FalsePositives</p>
Supporting Data: Complementary Organic Logic Gates on Plastic Formed by Self-Aligned Transistors with Gravure and Inkjet Printed Dielectric and Semiconductors
<p>The file contains the supporting data for the publication:</p> <p>S.G. Higgins, B.V.O. Muir, G. Dell'Erba, A. Perinot, M. Caironi, A.J. Campbell. Complementary Organic Logic Gates on Plastic Formed by Self-Aligned Transistors with Gravure and Inkjet Printed Dielectric and Semiconductors. doi: 10.1002/aelm.201500272. <em>Advanced Electronic Materials </em>(2015)</p> <p>See 'README.txt' for a description of the contents of the compressed file.</p>
Supporting Data: Indacenodithiophene-benzothiadiazole Organic Field-Effect Transistors with Gravure Printed Semiconductor and Dielectric on Plastic
<p>The file contains the supporting data for the publication:</p> <p>S.G. Higgins, B.V.O. Muir, M. Heeney, A.J. Campbell. Indacenodithiophene-benzothiadiazole Organic Field-Effect Transistors with Gravure Printed Semiconductor and Dielectric on Plastic. doi: 10.1557/mrc.2015.66. <em>MRS Communications</em> (2015)</p> <p>See 'README.txt' for a description of the contents of the compressed file.</p>
Supporting Data: Self-Aligned Organic Field-Effect Transistors on Plastic with Picofarad Overlap Capacitances and Megahertz Operating Frequencies
<p>The file contains the supporting data for the publication:</p> <p>S.G. Higgins, B.V.O. Muir, G. Dell'Erba, A. Perinot, M. Caironi, A.J. Campbell. Self-Aligned Organic Field-Effect Transistors on Plastic with Picofarad Overlap Capacitances and Megahertz Operating Frequencies. doi: 10.1063/1.4939045. <em>Applied Physics Letters</em> (2016)</p> <p>See 'README.txt' for a description of the contents of the compressed file.</p>
An intense, cold, velocity-controlled molecular beam by frequency-chirped laser slowing - supporting data
<p>These are the data presented in figures 3, 4, 5, 6 and 7 of our paper "An intense, cold, velocity-controlled molecular beam by frequency-chirped laser slowing". The first line of each data file explains the content. The second line labels the columns. The remaining rows give the data.</p>
Dataset supporting "Using Machine Learning to decide when to Precondition Cylindrical Algebraic Decomposition with Groebner Bases"
<p>Dataset supporting the paper:</p> <p>Z. Huang, M. England, J.H. Davenport and L.C. Paulson<br> Using Machine Learning to decide when to Precondition Cylindrical Algebraic Decomposition with Groebner Bases.<br> Proceedings of the 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC '16), pp. 45--52. IEEE, 2016. Digital Object Identifier: 10.1109/SYNASC.2016.020 </p>
Dataset supporting the paper: "A Case Study on the Parametric Occurrence of Multiple Steady States"
<p>Dataset supporting the paper:</p> <p>Russell Bradford, James H. Davenport, Matthew England, Hassan Errami, Vladimir Gerdt, Dima Grigoriev, Charles Hoyt, Marek Košta, Ovidiu Radulescu, Thomas Sturm, and Andreas Weber. A Case Study on the Parametric Occurrence of Multiple Steady States. In Proceedings of ISSAC ’17, Kaiserslautern, Germany, July 25-28, 2017, 8 pages. ACM, 2017.<br> https://doi.org/http://dx.doi.org/10.1145/3087604.3087622<br> Preprint: https://arxiv.org/abs/1704.08997</p> <p>We provide all the accompanying material for the main symbolic computations in Section 2.1</p> <p>###############<br> Section 2.1.1<br> ###############</p> <p>The computations were carried out within the following setting:</p> <p>1. Compiled and functional Reduce system r3606 is available and the<br> environment variable $trunk points to the trunk of Reduce source tree<br> (e.g. ~/reduce-algebra/trunk). To compile and install Reduce see:<br> http://redlog.eu/reduce-wiki/index.php/Installation</p> <p>2. Compiled and functional QEPCAD B v1.69 is installed and it is<br> possible to call QEPCAD from Reduce (via rlqepcad). To compile and<br> install QEPCAD see: https://www.usna.edu/CS/qepcadweb/B/QEPCAD.html</p> <p>To reproduce the experiments reported in the paper go:</p> <p>./generate-problems-onevar.py</p> <p>./compute-all.sh --csl ~/reduce-algebra/trunk ./problems-onevar 4</p> <p>./einsetzen.sh</p> <p>A few notes BEFORE YOU RUN:</p> <p>1. To try a different value of k19, edit the file solbiomod26.red.<br> Currently we set k19 = 500.</p> <p>2. It is advised to run these test on a multicore machine, e.g., we<br> used 32 cores (the last argument of ./compute-all.sh script) for<br> trying the 3^11 candidates (einsetzen.sh script).</p> <p>3. The script generate-problems-assignmnets.py (called from<br> einsetzen.sh) creates 3^11 test files!</p> <p>4. The potential values to be tried using interval refinement are<br> described as real algebraic numbers, i.e., pairs of polynomial and<br> isolating interval. These numbers are stored in assignment files<br> (assignment-<var>-<possibility#>) produced by ./gml-to-red.py (called<br> from einsetzen.sh)</p> <p>###############<br> Section 2.1.2<br> ###############</p> <p>These computation were conducted in commercial Computer Algebra System, Maple 2016. We have included the Maple Worksheet file which is annotated to describe the calulations in detail. We also include a pdf printout of the worksheet which those without access to Maple can read.</p>
Supporting data for 'Simultaneous maximum a posteriori longitudinal PET image reconstruction'
<p>This dataset contains the data used to produce the paper: <em>'Simultaneous maximum </em>a posteriori l<em>ongitudinal PET image reconstruction' </em>by Ellis and Reader, Physics in Medicine and Biology (2017). DOI: http://dx.doi.org/10.1088/1361-6560/aa7b49. Please see the article for a full description of methodology used to obtain this data. </p> <p>The dataset comprises a number of MATLAB data files (.mat), MATLAB scripts (.m), and plain text files (.txt), corresponding to each figure in the article. Running the .m script in MATLAB for each figure will reproduce that figure approximately as it appears in the article. Furthermore, the .txt files describe the contents of the .mat data files in order to allow independent exploration of the data. Note that the function plotSparseMarker is required to be able to run fig5.m.</p> <p>This work was funded by the King’s College London & Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging (grant number EP/L015226/1) and supported by the EPSRC grant number EP/M020142/1. This data has been made available in accordance with the EPSRC's policy framework on research data.</p>
Biolinks, datasets and algorithms supporting semantic-based distribution and similarity for scientific publications
<p><strong>Background: </strong>Finding articles related to a publication of interest remains a challenge in the Life Sciences domain as the number of scientific publications grows day by day. Publication repositories such as PubMed and Elsevier provides a list of similar articles. There, similarity is commonly calculated based on title, abstract and some keywords assigned to articles. Here we present the datasets and algorithms used in Biolinks. Biolinks uses ontological concepts extracted from publication and makes it possible to calculate a distribution score according to semantic groups as well as a semantic similarity based on either all identified annotations or narrowed to one or more particular semantic groups. Biolinks supports both title and abstract only as well as full-text.</p> <p><strong>Materials: </strong>In a previous work [1], 4,240 articles from the TREC-05 collection [2] were selected. The title-and-abstract for those 4,240 articles were annotated with Unified Medical Language System (UMLS) concepts, such annotations are refer to as our TA-dataset and correspond to the JSON files under the pubmed folder in the JSON-LD.zip file. From those 4,240 articles, full-text was available for only 62. The title-and-abstract annotations for those 62 articles, TAFT-dataset, are located under the pubmed-pmc folder in the JSON-LD.zip file, which also contains the full-text annotations under the folder pmc, FT-dataset. The list corresponding to articles with title-and-abstract is found in the genomics.qrels.large.pubmed.onlyRelevants.titleAndAbstract.tsv file, while those with full-text are recorded in the genomics.qrels.large.pmc.onlyRelevants.fullContent.tsv file.</p> <p>Here we include the annotations on title and abstract as well as those for full-text for all our datasets (profiles.zip). We also provide the global similarity matrices (similarity.zip).</p> <p><strong>Methods:</strong> The TA-dataset was used to calculate the Information Gain (IG) according to the UMLS semantic groups, see IG_umls_groups.PMID.xlsx. A new grouping is proposed for Biolinks, see biolinks_groups.tsv. The IG was calculated for Biolinks groups as well, IG_biolinks_groups.PMID.xlsx, showing a improvement around 5%.</p> <p>In order to assess the similarity metric regarding the cohesion of TREC-05 groups, we used Silhouette Coefficient analyses. An additional dataset Stem-TAFT-dataset was used and compared to TAFT and FT datasets.</p> <p>Biolinks groups were used to calculate a semantic group distribution score for each article in all our datasets. A semantic similarity metric based on PubMed related articles [3] is also provided; the Biolinks groups can be used to narrow the similarity to one or more selected groups. All the corresponding algorithms are open-access and available on GitHub under the license Apache-2.0, a frozen version, biotea-io-parser-master.zip, is provided here. In order to facilitate the analysis of our datasets based on the annotations as well as the distribution and similarity scores, some web-based visualization components were created. All of them open-access and available in GitHub under the license Apache-2.0; frozen versions are provided here, see files biotea-vis-annotation-master.zip, biotea-vis-similarity-master.zip, biotea-vis-tooltip-master.zip and biotea-vis-topicDistribution-master.zip. These components are brought together by biotea-vis-biolinks-master.zip. A demo is provided at http://ljgarcia.github.io/biotea-biolinks/; this demo was built on top of GitHub pages, a frozen version of the gh-pages branch is provided here, see biotea-biolinks-gh-pages.zip.</p> <p><strong>Conclusions: </strong>Biolinks assigns a weight to each semantic group based on the annotations extracted from either title-and-abstract or full-text articles. It also measures similarity for a pair of documents using the semantic information. The distribution and similarity metrics can be narrowed to a subset of the semantic groups, enabling researchers to focus on what is more relevant to them.</p> <p> </p> <p>[1] Garcia Castro, L.J., R. Berlanga, and A. Garcia, <em>In the pursuit of a semantic similarity metric based on UMLS annotations for articles in PubMed Central Open Access.</em> Journal of Biomedical Informatics, 2015. <strong>57</strong>: p. 204-218</p> <p>[2] Text Retrieval Conference 2005 - Genomics Track. <em>TREC-05 Genomics Track ad hoc relevance judgement</em>. 2005 [cited 2016 23rd August]; Available from: http://trec.nist.gov/data/genomics/05/genomics.qrels.large.txt</p> <p>[3] Lin, J. and W.J. Wilbur, <em>PubMed related articles: a probabilistic topic-based model for content similarity.</em> BMC Bioinformatics, 2007. <strong>8</strong>(1): p. 423</p>
Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis (supporting data)
<div> <div>This archive contains the supporting data and code for <a href="https://doi.org/10.1093/ismeco/ycaf089" target="_blank" rel="noopener">Meilander et al., 2024: <em>Upcycling Human Excrement: The Gut Microbiome to Soil Microbiome Axis</em></a>.</div> <div> </div> <div><strong>Clicking the links below will open the corresponding files using QIIME 2 View (<a href="https://view.qiime2.org" target="_blank" rel="noopener">https://view.qiime2.org</a>). </strong></div> <div> </div> <div> <div> <div>Summaries of master data files:</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/asv-table.qzv/content" target="_blank" rel="noopener">Summary of master feature table (<code>asv-table.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/sample-metadata.qzv/content">Tabulated view of sample metadata (<code>sample-metadata.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/asv-seqs-ms10.qzv?download=1" target="_blank" rel="noopener">Summary of ASV sequences observed in at least 10 samples: (<code>asv-seqs-ms10.qzv</code>)</a></div> <div> </div> <div>PCoA plots:</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/braycurtis.qzv/content" target="_blank" rel="noopener">Bray-Curtis Emperor plot (<code>braycurtis.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/jaccard.qzv/content" target="_blank" rel="noopener">Jaccard Emperor plot (<code>jaccard.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/unweighted_unifrac.qzv/content" target="_blank" rel="noopener">Unweighted UniFrac Emperor plot (<code>unweighted_unifrac.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/weighted_unifrac.qzv/content" target="_blank" rel="noopener">Weighted UniFrac Emperor plot (<code>weighted_unifrac.qzv</code>)</a></div> <div> </div> <div>Taxonomy barplots:</div> <div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/taxa-bar-plots-bucket2-gtdb-r214.1-weighted-stool-taxonomy.qzv?download=1">Taxonomy bar plot for Bucket 2 only (<code>taxa-bar-plots-bucket2-gtdb-r214.1-weighted-stool-taxonomy.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/records/15390940/files/taxa-bar-plots-bucket3-gtdb-r214.1-weighted-stool-taxonomy.qzv?download=1" target="_blank" rel="noopener">Taxonomy bar plot for Bucket 3 only (<code>taxa-bar-plots-bucket3-gtdb-r214.1-weighted-stool-taxonomy.qzv</code>)</a></div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/taxa-bar-plots-gtdb-r214.1-weighted-stool-taxonomy.qzv/content" target="_blank" rel="noopener">Taxonomy bar plot for all samples (<code>taxa-bar-plots-gtdb-r214.1-weighted-stool-taxonomy.qzv</code></a>)</div> </div> <div> </div> <div>q2-fmt "raincloud plots":</div> <div><a href="https://view.qiime2.org/visualization/?src=https://zenodo.org/api/records/13887457/files/hec-raincloud.qzv/content">Raincloud plot (<code>hec-raincloud.qzv</code>)</a></div> <div> </div> </div> </div> <div> </div> <div>The linked <code>.qzv</code> files are also contained in the <code>gut-to-soil-qiime2.zip</code> zip file, along with all relevant data artifacts (<code>.qza</code> files).</div> <div>The <code>.qzv</code> files are also maintained outside of the <code>.zip</code> file to facilitate their viewing with QIIME 2 View.</div> </div> <div> </div> <div> <div>Code for generating figures 1 and 2 (and corresponding supplemental figures):</div> <div><code>gut-to-soil-manuscript-figures-main.zip</code> (also see: <a href="https://github.com/caporaso-lab/gut-to-soil-manuscript-figures" target="_blank" rel="noopener">https://github.com/caporaso-lab/gut-to-soil-manuscript-figures</a>)</div> <div> </div> <div>Code for generating ridgeline plots (Figure S6):</div> <div><code>gut-to-soil-ridgeline-plots-main.zip</code> (also see: <a href="https://github.com/caporaso-lab/gut-to-soil-ridgeline-plots" target="_blank" rel="noopener">https://github.com/caporaso-lab/gut-to-soil-ridgeline-plots</a>)</div> </div> <div> <div> </div> </div>
Research data supporting: "Self-assembly of cyclic peptide monolayers by hydrophobic supramolecular hinges"
<p>This repository contains the set of modelling data shown in the paper:<strong> "Self-assembly of cyclic peptide monolayers by hydrophobic supramolecular hinges"</strong>, published on Chemical Science (DOI: 10.1039/d3sc03930g)</p>
ScienceDex guides
Understand access before you commit
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.