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1,569 results for “read until”
Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the first talk from our Landscape Report Funding Call Town Meeting that took place on the 03/02/2022.</p> <p>Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/03-02-2021-future-blood-testing-network-landscape-report-funding-town-meeting/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/I1xsig9C8w0</p>
Genome annotations of Drosophila melanogaster and Drosophila simulans wild-type strains from long read sequencing assemblies
<p>Genome assemblies were performed for eight wild-type strains of Drosophila melanogaster and Drosophila simulans from Oxford Nanopore long read sequencing (please refer to Mohamed et al. Cells 2020 (doi:10.3390/cells9081776)). Assemblies were deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB50024 (<a href="https://www.ebi.ac.uk/ena/browser/view/PRJEBxxxx">https://www.ebi.ac.uk/ena/browser/view/</a>PRJEB50024).</p> <p>Transposable Element annotations: we used RepeatMasker 4.1.0 (<a href="http://repeatmasker.org/">http://repeatmasker.org/</a>) -species Drosophila, followed by OneCodeToFindThemAll (Bailly-Bechet et al. 2014) with default parameters.</p> <p>Gene annotations: We retrieved gtf files from FlyBase : <a>ftp.flybase.net/genomes/D</a><a>rosophila_melanogaster/dmel_r6,46_FB2022_03/gft/dmel-all-r6.46.gtf.gz</a> and <a>ftp.flybase.net/genomes/Drosophila_simulans/dsim_r2,02_FB2017_04/gtf/dsim-all-</a><a>r2,02.gtf.gz</a>. The corresponding fasta files were also downloaded from FlyBase: <a>ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r6,46_FB2022_03/</a><a>fasta</a><a>/dmel-all-</a><a>chromosome-</a><a>r6.46.</a><a>fasta</a><a>.gz</a> and <a>ftp.flybase.net/genomes/Drosophila_simulans/dsim_r2,02_FB2017_04/</a><a>fasta</a><a>/dsim-all-</a><a>chromosome-</a><a>r2,02.</a><a>fasta</a><a>.gz</a>. We used Liftoff (Shumate and Salzberg, 2020) to lift over gene annotations from the references to our genome assemblies. We used -flank 0.2 and only kept the “gene” and “exon” terms.</p>
Data-Perpendicular reading of single confined magnetic skyrmions
<p>Thin-film sub-5 nm magnetic skyrmions constitute an ultimate scaling alternative for future<br> digital data storage. Skyrmions are robust noncollinear spin textures that can be moved and<br> manipulated by small electrical currents. Here we show here a technique to detect isolated<br> nanoskyrmions with a current perpendicular-to-plane geometry, which has immediate<br> implications for device concepts. We explore the physics behind such a mechanism by<br> studying the atomistic electronic structure of the magnetic quasiparticles. We investigate<br> from first principles how the isolated skyrmion local-density-of-states which tunnels into<br> the vacuum, when compared with the ferromagnetic background, is modified by the site<br> dependent spin mixing of electronic states with different relative canting angles. Local<br> transport properties are sensitive to this effect, as we report an atomistic conductance<br> anisotropy of up toB20% for magnetic skyrmions in Pd/Fe/Ir(111) thin films. In single<br> skyrmions, engineering this spin-mixing magnetoresistance could possibly be incorporated in<br> future magnetic storage technologies.</p>
Reading Information Technology and Libraries (volume 41, number 2, June 2022)
Today, for a good time, I applied my Reader Toolbox to the latest issue of ITAL for the two-fold purposes of: 1) just seing whether the Toolbox could function, and 2) determine the degree I could extract meaningful themes from the issue. Well, the Toolbox functioned, in that it did not crash nor output invalid data, and I do believe I could pull out themes, in that each issue's authors wrote about something distinctive, and I could identify those things. Below describes my process.
Reading Journal of eScience Librarianship: Responsible AI in Libraries and Archives
A special issue of Journal of eScience Librarianship was brought to my attention. The issue was on the topic of responsible AI in libraries and archives. I did a bit of distant reading against the issue, and outlined here are some of my take-aways. In short, AI is something to consider in Library Land, but not without some forethought.
University of Notre Dame News: A Reading
I have done a bit of analysis -- reading -- against the set of news distributed by the University of Notre Dame, and below is some of what I learned.
How to Read a Whole HathiTrust Collection
This Web pages outlines a process to use and understand ("read") the whole of a HathiTrust collection. Such a process is outlined here: 1) articulate a research question, 2)search the 'Trust and create a collection, 3) download the collection file and refine it, or at the least, remove duplicates, 4) use the result as input to htid2books; download the full text of each item, 5) use Reader Toolbox to build a "study carrel"; create a data set, 6) compute against the data set to address the research question, and 7) go to Step #1; repeat iteratively
Dataset: Reading International, Inc. (RDI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Reading International, Inc. (RDIB) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Reading the Poetry of António Ramos Rosa from a Distance: Bird's-Eye View, Kingdoms of Nature, The Four Elements (datasets-bundle-2)
<div> <p>Datasets created by Patrícia Esteves Reina with contributions from Bruno Ministro and Helena Costa Carvalho</p> <p> </p> <p>Corpus: António Ramos Rosa's complete poetry<br>Language: Portuguese (Portugal)</p> <p> </p> <p><strong>Contents</strong></p> <ul> <li>Visão Panorâmica (Bird’s-Eye View)</li> <li>Reinos Animal, Vegetal e Mineral (Kingdoms of Nature)</li> <li>Quatro Elementos (The Four Elements)</li> </ul> <p> </p> <p><strong>Data analysis and extraction using R language in RStudio<br></strong></p> <ul> <li>Timeframe: October 2023 - June 2024</li> <li>Work Package 2</li> <li>datasets-bundle-2</li> </ul> <p>Timestamps of the .csv files attached here: 29-06-2024</p> <p> </p> <p>Exploratory research project (2022.08122.PTDC)<br><strong>To See the Tree and the Forest. Reading the Poetry of António Ramos Rosa from a Distance<br></strong>https://doi.org/10.54499/2022.08122.PTDC<br>Funding: Fundação para a Ciência e a Tecnologia, Portugal<br>PI: Bruno Ministro<br>Host: Institute for Comparative Literature - Faculty of Arts and Humanities of the University of Porto<br><a href="https://arvore.ilcml.com/en/home-en/" rel="nofollow">https://arvore.ilcml.com/en/home-en/</a></p> </div>
Supplemental Results for Assembly, Annotation, and Analysis from HiFi reads of Gulf Toadfish Genome and Transcriptome fOpsBet2.1
<p>This repository contains gzipped tarballs of the results of the various assembly, annotation, and analysis steps performed during the assembly of the fOpsBet2.1 genome assembly for Opsanus beta at the University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science for the McDonald Toadfish Lab. These results are too numerous to include as supplemental data for a journal publication and so are available here for review. In this repository you will find results for:</p><p>Scripts:</p><p>-all bash and LSF scheduler job scripts used as part of the analysis, both exploratory and final. </p><p>QC:</p><p>-GenomeScope2 estimation of genome metrics from HiFi Reads</p><p>-QUAST genome statistics for each assembly step</p><p>-BUSCO completeness assessments for each assembly step </p><p>-inspector logs for polishing of initial assembly</p><p>-logs from Kraken2 contaminant screen</p><p>Assembly and Scaffolding:</p><p>-ntLINKS logs and intermediates for initial scaffolding</p><p>-ragtag logs and metrics for super-scaffolding to the ThaAma1.1 T. amazonica reference assembly</p><p>-mitoHIFI results for mitogenome assembly from HiFi reads, primary assembly, and purged alternate assembly</p><p>Annotation:</p><p>-PASA directory with full input and output for SQLite PASA assembly of transcriptome for gene predictors</p><p>-Results folder for Funannotate::annotate for gene models, annotations, and CDS/mRNA/protein fastas</p><p>-InterProSCan5 results for protein annotation used as input into Funannotate</p><p>-ghostKOALA KEGG assignment results for predicted proteins from funannotate results</p><p>Repetitive Elements:</p><p>-tidk telomere repeat analysis results</p><p>-TRAH satellite DNA analysis with subsequent analysis with HiCAT and StainedGlass</p><p>-RepeatModeler results for de novo TE prediction</p><p>-repclassifier results for TE curation</p><p>Comparative Analysis:</p><p>-OrthoFinder ortholog search for O. beta to several other vertebrates</p><p>-CAFE5 gene family expansion and contraction of Orthogroups from OrthoFinder results</p><p> </p>
Data for "A replicable and modular benchmark for long-read transcript quantification methods"
<p>This archive contains the input necessary to run the inital (TranSigner-protocol and IsoQuant-protocol) benchmarks associated with the <a href="https://github.com/COMBINE-lab/lr_quant_benchmarks" target="_blank" rel="noopener"><code>lr_quant_benchmarks repository</code></a>. The archive can be decompressed with <code>tar</code> and <code>zstd</code> using the command <code>tar --use-compress-program=zstd -xf input.tar.zstd</code>.</p>
Results of a research software programming and development survey at the University of Reading
<p>In 2017 an online survey of University of Reading staff active in or supporting research and registered PhD students was undertaken to assess the nature and extent of research programming and software development activities in the University, and to understand how the University might provide guidance, training and support. The survey was a administered by the Research Data Manager on behalf of the University's Research Data Management Steering Group. The survey ran from 1st November to 15th December 2017 and collected a total of 170 responses.</p> <p>The survey sought responses from anyone in the University who was involved in any of the following activities:</p> <ul> <li>writing code and using software for numerical and statistical analysis;</li> <li>creating and contributing to computational models or simulations;</li> <li>conducting Text and Data Mining (TDM) and content analysis;</li> <li>creating and contributing to software distributed as a product or implemented as a service;</li> <li>creating data visualisations;</li> <li>using markup languages to structure and render content.</li> </ul> <p>The survey was distributed using the Bristol Online Survey. A dataset of anonymised survey responses and a PDF of the survey questions are here included.</p>
Supplemental material to the article "Improving silent reading performance through feedback on eye movements: A feasibility study"
<p>This repository accompanies the article:</p> <p>Korinth, S., & Fiebach, C.J. (2018). Improving silent reading performance through feedback on eye movements: A feasibility study. Scientific Studies of Reading.</p> <p>For a more convenient download files were zipped. Organized into four subfolders it provides the data and the R-scripts allowing the replication of all analyses and the manuscript's figures. For further information please refer to the README.txt files in the individual subfolders.</p> <p>Please note, a former version of this repository contained the same data and the same analyses and was used for manuscript review purposes. Changes to the current version concern only the manuscript structure (i.e., analyses presented in previous manuscript version as appendices were integrated into the main text). </p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 3. Estimate coefficients distribution of model on read rate as response of Terasort application
<p>Figure 3 shows estimate coefficients distribution of model on read rate as the response of Terasort application. The filled triangle point-up indicates the position of the minimum stable sampling time for the corresponding estimate coefficients as well as the number above it shows the exact position value. The dashed line represents the average estimated coefficient of the regression model.</p>
Extension of Partial Gene Transcripts by Iterative Mapping of RNA-Seq Raw Reads
<p>Trinity assembled transcriptome of <em>Drosophila melanogaster</em> and <em>Osmia </em><em>bicornis</em></p>
Supplementary files for the manuscript "Assembly of Long Error-Prone Reads Using Repeat Graphs"
<p>Supplementary files for the manuscript "Assembly of Long Error-Prone Reads Using Repeat Graphs"</p> <p> </p> <p>Contents<br> ---------</p> <p>* `human_assemblues` - Flye assemblies of the human ONT sequencing data + QUAST benchmarking<br> of Flye, Canu and MaSuRCA assemblies. Scripts for assembly graph analysis are also included.</p> <p>* `nctc_assemblis` - Flye assemblies of the NCTC 21 bacterial dataset.</p> <p>* `yeast_assemblies` - working directories Flye, Canu, Falcon, Hinge and Miniasm assemblies of <br> yeast PB and ONT datasets + final assemblies + quast report. Some large files <br> (such as read alignments) were deleted.</p> <p>* `worm_assemblies` - working directories Flye, Canu, Falcon, Hinge and Miniasm assemblies of <br> the c. elegans dataset + final assemblies + quast report. Some large files <br> (such as read alignments) were deleted. `tandem_misassemblies` directory contain<br> the detailed analysis of nine tandem misassemblies. We recommend "gepard" dot-plotter for visualization.</p> <p>* `metagenome_assemblies` - Flye and Canu assemblies of a PacBio mock metagenome dataset.<br> In addition to metagenome assemblies, each bacteria was reassembled separately to<br> estimate the rate of divergence between the target genomes and the available references.</p> <p>* `simulated_data` - two assemblies of the simulated data illustrating Figure 1 (from Appendix I),<br> as well as simulated unbridged repeats benchmark.</p> <p><br> Software versions and parameters<br> --------------------------------</p> <p>* Flye - 2.3.5 (commit 20afeda)<br> * Canu - 1.7.1 (commit dfa60b8)<br> * Falcon - 0.3.0 (FALCON-Integrate commit 7498ef9)<br> * HINGE - 0.5.0 (commit 79fdf66)<br> * Miniasm - 0.2-r168-dirty (commit 40ec280) / Minimap2 2.8-r711-dirty (commit 8fc5f8d)<br> * Quast - 5.0.0 (commit de6973bb)</p> <p>Flye and Canu were run with the default parameters. The config files / scripts for<br> Falcon, HINGE and Miniasm could be found in the 'asm_config' archive folder.</p> <p>The HUMAN (but not the HUMAN+) assembly was generated with the earlier <br> Flye version 2.3.2 (released on Feb 20 2018) to provide a fair comparison <br> with the Canu and MaSuRCA assemblies (which were not updated since the release of Flye 2.3.2).<br> We note that the HUMAN assembly using the latest Flye version 2.3.5 has <br> NGA50 = 7.3 Mb and improves over the Flye 2.3.2 assembly (NGA50 = 6.3Mb). <br> HUMAN+ was assembled using the latest Flye and Canu versions (as of September 2018).</p> <p>The code for unbridged repeat resolution is currently available <br> in a separate 'flye-trestle' branch (commit 6100d32)</p>
READ ABP WI Dataset - Writer Identification over decades
<p>A hand is usually considered as a unique characteristic of a person. However, it may slightly change over their whole lifespan. This change might be due to some physical or mental issues. To the best of our knowledge, there is no dataset available, which covers this aspect of evolvement of handwriting of a single person.</p> <p>When dealing with archival documents, it is important to show that methods are invariant against these changes or investigate how much of these changes are covered. Thus, a new dataset was created with data of the Passau Diocesan Archives (ABP, <a href="https://www.bistum-passau.de/bistum/archiv">https://www.bistum-passau.de/bistum/archiv</a> ).</p> <p>The documents originate from death records of different villages or towns in the Diocese of Passau. Usually the writer of these records (mostly the priest) remains the same over several years. In total, the dataset consists of 1766 pages, which originate from 28 different writers. The number of pages per writer varies from 7 up to 311. For some writers, we only have data from 3 different years, whereas the largest time span between two documents of the same writer is 31 years.</p> <p>The dataset is organized as follows:</p> <p>[ID]_[Name]\[YEAR]\[ID]_filename.png</p> <p>The corresponding PAGE XML file is provided along with the dataset and contains the regions of the image where text is included. This file can be used to calculate features of the writer solely on the handwriting and not on the table lines.</p> <p>Currently no research tasks are defined on the dataset; we leave this up to the community. Drop us a note how you are using this dataset.</p>
MinION 1D² Reads From Mus musculus GL261 Cell Lines
<p>Called FASTQ and raw FAST5 MinION cDNA reads (1D²) from a murine GL261 neuroblastoma cell line, cultured at the Malaghan Institute of Medical Research, sequenced on a R9.5 flow cell in August 2017 using the LSK309 1D² kit for ligating ONT adapters to cDNA generated using strand-switching primers.</p> <p>The called reads for the entire sequencing run are available:</p> <ul> <li>called_reads_1Dsq_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- called reads from both/all runs (1D²-corrected fastq files only).</li> <li>called_reads_uncorrected_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- uncorrected reads from both/all runs.</li> <li>metadata_called_reads_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- metadata associated with all called sequences (e.g. sequencing_summary.txt)</li> </ul> <p>This dataset only includes a subset of the total reads as raw signal / FAST5 files:</p> <ul> <li>Actb_GL261_cDNA_2017-Aug-04_1D2.tar -- reads from one run that mapped (in whole or in part) to a mouse beta-actin transcript [<a href="http://asia.ensembl.org/Mus_musculus/Transcript/Summary?db=core;g=ENSMUSG00000029580;r=5:142903234-142903654;t=ENSMUST00000100497">ENSMUST00000100497.10</a>].</li> <li>Ubb_GL261_cDNA_2017-Aug-04_1D2.tar -- reads from one run that mapped (in whole or in part) to a mouse ubiquitin transcript [<a href="http://asia.ensembl.org/Mus_musculus/Transcript/Summary?db=core;g=ENSMUSG00000019505;r=11:62551171-62553213;t=ENSMUST00000019649">ENSMUST00000019649.3</a>].</li> </ul>
Assembly, annotation, and read counts for 2016 Sargasso Sea incubations
<p>Data sets for:</p> <p>Lampe RH, Wang S, Cassar N, and Marchetti A. (in prep) Strategies among phytoplankton in response to alleviation of nutrient stress in a subtropical gyre.</p> <p>File descriptions:</p> <ul> <li>site1_clust99.fa.gz - Assembled contigs for site 1 after clustering based on 99% similarity.</li> <li>site2_clust99.fa.gz - Assembled contigs for site 2 after clustering based on 99% similarity.</li> <li>site1_results.tsv.gz - annotations and counts for each contig from site 1 (tab-delimited)</li> <li>site2_results.csv.gz - annotations and counts for each contig from site 2 (tab-delimited)</li> </ul>
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.