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1,569 results for “read until”
Themed Evidence: Reading Experiences - Gold Standard
<p>Exploring digital resources in search of pieces of evidence relevant to a certain research theme is a difficult and important task for humanities research. The concept of evidence is a particularly difficult one, as it relates to a fact being reported in a text, which is relevant to a certain subject of enquiry. </p> <p>The UK Reading Experience Database Project (UK RED) is an open-access dataset containing records that document the history of reading in Britain from 1450 to 1945. Evidence of reading presented in UK RED is drawn from published and unpublished sources as diverse as diaries, commonplace books, memoirs, sociological surveys, and criminal court and prison records. See <a href="http://www.open.ac.uk/Arts/reading/UK">http://www.open.ac.uk/Arts/reading/UK</a>. </p> <p>This dataset is a gold standard for supporting the evaluation of competing methods for detecting themed evidence of "reading experience".</p>
Dataset for "Progessive improvement of the Australian blacklip abalone (Haliotis rubra) genome assembly with Nanopore long reads, hybrid meta assembly and haplotig purging
<p>This Zenodo archive contains the blacklip abalone genome assemblies and and their BUSCO completeness calculations. Genome annotation (gff3 format), CDS, protein sequences and Orthofinder2 output were also included.</p>
NewsEye / READ OCR training dataset from Austrian Newspapers (19th C.)
<p>The dataset comprises Austrian newspaper pages from 19th and early 20th century with carefully corrected text. The page images were provided by the <a href="http://onb.ac.at/">Austrian National Library</a> and comprise 148 pages (training set) and 13 pages (validation set). The data are formed according to the PAGE format (cf. Cf. <a href="https://github.com/PRImA-Research-Lab/PAGE-XML/">https://github.com/PRImA-Research-Lab/PAGE-XML/</a>) and were produced with the <a href="http://read.transkribus.eu/">Transkribus </a>platform with support of the <a href="http://newseye.eu/">NewsEye</a> and the <a href="http://read.transkribus.eu/">READ </a>project.</p>
Reads and truth variant set for benchmarking variant calling/genotyping
<p><em>downsampled.fasta</em> is created by converting this file (ftp://<a href="http://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_HiSeq_HG002_Homogeneity-10953946/HG002Run01-11419412/HG002run1_S1.bam">ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_HiSeq_HG002_Homogeneity-10953946/HG002Run01-11419412/HG002run1_S1.bam</a>) to fasta and picking every second read (to get half the coverage and half the number of reads).</p> <p><em>The HG002_GRCh37_GIAB_highconf_CG</em>... file is created by picking variants on chromosome from this file (ftp://<a href="http://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG002_NA24385_son/NISTv3.3.2/GRCh37/HG002_GRCh37_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-22_v.3.3.2_highconf_triophased.vcf.gz">ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG002_NA24385_son/NISTv3.3.2/GRCh37/HG002_GRCh37_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-22_v.3.3.2_highconf_triophased.vcf.gz</a>).</p> <p> </p> <p><strong>These two files can be used in benchmarking variant calling/genotyping.</strong></p>
Polished Assemblies for "GoldPolish-Target: Targeted long-read genome assembly polishing"
<p>GoldPolish-Target is a targeted genome assembly polishing tool that uses long reads. We tested GoldPolish-Target on Oxford Nanopore Technologies datasets with a human cell line (NA24385) and Drosophila melanogaster (fruit fly). Here, we provide the data for the GoldRush baseline (unpolished) assembly and the GoldPolish-Target and medaka polished assemblies of these long-read datasets.</p>
Linked collectors and determiners for: Reading between the lines: revealing cryptic species diversity and colour patterns in Hypselodoris nudibranchs (Mollusca: Heterobranchia: Chromodorididae).
Natural history specimen data linked to collectors and determiners held within, "Reading between the lines: revealing cryptic species diversity and colour patterns in Hypselodoris nudibranchs (Mollusca: Heterobranchia: Chromodorididae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/49bea467-fac6-484a-9902-05483dec9a52">https://bionomia.net/dataset/49bea467-fac6-484a-9902-05483dec9a52</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/49bea467-fac6-484a-9902-05483dec9a52">https://gbif.org/dataset/49bea467-fac6-484a-9902-05483dec9a52</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Read between the lineata: A revision of the tattooed wasps, Zagrammosoma Ashmead (Hymenoptera: Eulophidae), with descriptions of eleven new species.
Natural history specimen data linked to collectors and determiners held within, "Read between the lineata: A revision of the tattooed wasps, Zagrammosoma Ashmead (Hymenoptera: Eulophidae), with descriptions of eleven new species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1b50774e-3be8-4997-9f39-45e6f5d66cae">https://bionomia.net/dataset/1b50774e-3be8-4997-9f39-45e6f5d66cae</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1b50774e-3be8-4997-9f39-45e6f5d66cae">https://gbif.org/dataset/1b50774e-3be8-4997-9f39-45e6f5d66cae</a>. Formatted as a Frictionless Data package.
Example dataset of stLFR linked reads to run Aquila_stLFR
<p>We provide this small chromosome (chr21) example dataset to run Aquila_stLFR. </p> <p>Aquila_stLFR: <a href="https://github.com/maiziex/Aquila_stLFR">https://github.com/maiziex/Aquila_stLFR</a></p> <p> </p>
Figure 1 in Writing to be published or writing to be read?
Figure 1. Summary of the main ideas presented within a paper on tardigrade evolution (Kinchin 1995), given as an example of how dense text may be simplified as a concept map. Annotations to the map highlight key questions for the reader to consider that will clarify the ideas presented and help place the paper in the wider context of the literature.
TEST DATA for Enhanced protein isoform characterization through long-read proteogenomics
<p>Test data for The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.</p> <p>Companion Repositories:</p> <ol> <li><a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow GitHub Repository Release</a></li> <li><a href="https://doi.org/10.5281/zenodo.5920847">Long-Read-Proteogenomics Analysis GitHub Repository Release</a></li> </ol> <p>Companion Datasets</p> <ol> <li><a href="https://zenodo.org/deposit/5703754">Jurkat Samples and Reference Data</a></li> <li><a href="http://10.5281/zenodo.5920920">Long-Read-Proteogenomics Workflow Results using Jurkat Sample data</a></li> </ol> <p>This Repository contains the test data, specifically:</p> <p><a href="https://doi.org/10.5281/zenodo.5234651">TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions</a></p>
Code and Data for "Multiple re-reads of single proteins at single-amino-acid resolution using nanopores"
<p>The primary structures containing data and analysis products are peptidereads_fig2.mat (for figure 2) and peptiderereads_fig3.mat (for figure 3). The main analysis scripts for these data structures are callvariants_fig2.m and reread_analysis_fig3.m respectively. Data for figures S6 (S6_reread_data.dat) and S8 (S8_hetero_data.dat), and the analysis script used to produce figure S6 (S6_reread_analysis.m) are also included. Other files are dependencies of these main scripts.</p> <p> </p> <p>The fields in peptidereads_fig2 are as follows:</p> <p> </p> <p> </p> <p><strong>folder, eventnum, reducedStart, reducedEnd, suspicious, hasreread:</strong> notes for internal use</p> <p><strong>variant:</strong> the true identity of the single-amino-acid substitution variant</p> <p><strong>data: </strong>the ion current data for each read downsampled to 5 kHz.</p> <p><strong>omit: </strong>whether the read was omitted from analysis due to length</p> <p><strong>relativeDNAend</strong>: the index in the data where the DNA portion of the read ends.</p> <p><strong>relativeLinkerEnd:</strong> the index in the data where the linker portion of the read ends.</p> <p><strong>DNAlevels, Peplevels, Alllevels:</strong> extracted ion current levels for the DNA region, the peptide region, and everything.</p> <p><strong>cal: </strong>the multiplicative and additive constants applied to calibrate the read</p> <p><strong>caldata: </strong>the data with calibration constants applied</p> <p><strong>cons0D, cons0W, cons0G, cons0DNA: </strong>initial guesses for consensuses based on hand curation of data.</p> <p><strong>pepDcons0, pepWcons0, pepGcons0:</strong> the portion of the handmade consensus with the variant levels.</p> <p><strong>pepDcons, pepWcons, pepGcons:</strong> the portion of the iterated consensus with the variant levels.</p> <p><strong>inhandconsensus: </strong>whether the read was used in generation of the inital guess consensuses.</p> <p><strong>inconsensus</strong>: whether the read was used in generation of either the initial guess or iterated consensuses.</p> <p><strong>confidence:</strong> the relative likelihood of each variant assigned to the read</p> <p><strong>incalls:</strong> whether the data was used in variant calling (i.e., not used in consensus generation)</p> <p><strong>params</strong>: the analysis parameters used</p> <p> </p>
Data related to research article: Towards mouse genetic-specific RNA-sequencing read mapping
<p>This dataset contains data related to the research article: "Towards mouse genetic-specific RNA-sequencing read mapping".</p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (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. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</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/W6EH5l80NmU</p>
Data and Analysis of Reading and Assessment Activities in Moodle
<p><strong>Interactions of reading and assessment activities in Moodle</strong></p> <p>Reading and assessment are elementary activities for knowledge acquisition in online learning. Assessments represented as quizzes can help learners to identify gaps in their knowledge and understanding, which they can then overcome by reading the corresponding text-based course material. Reversely, quizzes can be used to evaluate reading comprehension. In this paper, we ex- amine the interactions between reading and quiz activities using scroll and log data from an online undergraduate course (N=142). By analyzing processes and sequential patterns in user sessions, we identified six session clusters for characteristic reading and quiz patterns potentially relevant for adaptive learning support. Using these session clusters, we further clustered students by their reading and quiz behavior over six time periods within the semester. The results hypothesize a personalization for seven groups of learners characterized by their temporal activity and predominant quiz and reading behavior.</p> <p> </p> <p><strong>Pre-requisites and install instructions</strong></p> <p>1. Make sure Python v3.9 is installed on your system.</p> <p>2. conda env create -f environment.yml</p> <p>3. conda activate analysis</p> <p>4. To replicate the analysis open the file Ananlysis.ipynb and execute the code blocks one by one or all together.</p> <p> </p> <p>`jupyter nbconvert --to python Analysis.ipynb`</p> <p>`jupyter notebook Analysis.py`</p> <p> </p> <p><strong>Files and folders</strong></p> <ul> <li>(File) Analysis.ipynb: Python Notebook containing all applied code blocks applied for data analysis.</li> <li>(File) requirements.txt: List of python modules to be installed to fulfill the requirements of the Analysis.ipynb script.</li> <li>(Folder) data: The folder contains anonymized CSV files for each Moodle database table that was necessary for the data analysis. All data files are text files encoded in UTF-8. The columns are separated with a semicolon (";"), and rows are indicated by line breaks ("\n"). <ul> <li>m_assign.csv: ...</li> <li>m_course_modules.csv: ...</li> <li>m_quiz.csv: ...</li> <li>no_students.csv: ...</li> <li>user_acceptances.csv: ...</li> <li>m_assign_grades.csv: ...</li> <li>m_course_sections.csv: ...</li> <li>m_quiz_attempts.csv: ...</li> <li>scroll.csv: ...</li> </ul> </li> </ul> <p> </p> <p><strong>Publications and citation</strong></p> <p><strong>Publications</strong></p> <ul> <li>Seidel, N., & Menze, D. (2022). Interactions of reading and assessment activities. In S. Sosnovsky, P. Brusilovsky, & A. Lan (Eds.), 4th Workshop on Intelligent Textbooks, 2022 (pp. 64–76). CEUR-WS. http://ceur-ws.org/Vol-3192/</li> <li>Menze, D., Seidel, Ni., & Kasakowskij, R. (2022). Interaction of reading and assessment behavior. In P. A. Henning, M. Striewe, & M. Wölfel (Eds.), DELFI 2022 – Die 21. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. (pp. 27–38). Gesellschaft für Informatik. https://doi.org/10.18420/delfi2022-011</li> </ul> <p><strong>Citation of the dataset</strong></p> <ul> <li>Seidel, Niels, & Menze, Dennis. (2022). Data and Analysis of Reading and Assessment Activities in Moodle (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.730007</li> </ul> <p>The source code and data are maintained at GitHub: <a href="https://github.com/nise/delfi22">https://github.com/nise/delfi22</a></p> <p> </p> <p><strong>Acknowledgments </strong>This research was supported by <em>CATALPA - Center of Advanced Technology for Assisted Learning and Predictive Analytics</em> of the FernUniversität in Hagen, Germany.</p>
Baseline assemblies for "ntLink: a toolkit for de novo genome assembly scaffolding and mapping using long reads" protocol
<p>ntLink is a flexible <em>de novo</em> genome scaffolding toolkit which can be run in various modes depending on the desired user output, with multiple new functionalities recently introduced. Here, we provide the baseline assembly datasets used in the ntLink protocol paper "ntLink: a toolkit for <em>de novo </em>genome assembly scaffolding and mapping using long reads". The provided assemblies are ABySS (short-read) and Flye (long-read) assemblies of <em>Caenorhabditis elegans </em>genome sequencing data. The ABySS (v2.1.4) assembly utilized paired-end short reads (accession DRR008444), and was run with the following parameters: k=64 l=40 s=1000 q=15 B=10G j=8 kc=3 H=4 S=1000-10000 N=9.The <em>C. elegans</em> Flye (v2.5) assembly was run using Oxford Nanopore long reads (accession SRR10028109) and the following parameters: --nano-raw SRR10028109.fastq -g100m -t48.</p>
De novo assembly of a long-read Amblyomma americanum genome
<p>Genome assemblies of Amblyomma americanum generated from PacBio HiFi sequencing of 50 individual female ticks. This repository contains the unphased diploid assembly generated by the Flye assembler (Arcadia_Amblyomma_americanum_asm001.fasta). In addition, there are two associated fasta files containing sequences generated by submitting the unphased diploid assembly to separation by the Purge_Dups pipeline (purged pseudo-haploid assembly and haplotig assembly).</p> <p>Flye assembler: https://github.com/fenderglass/Flye</p> <p>Purge_Dups pipeline: https://github.com/dfguan/purge_dups</p> <p>NCBI Bioproject: PRJNA932813</p>
De novo assembly of a long-read Amblyomma americanum genome (NCBI/Genbank deposited genome)
<p>Genome assembly of Amblyomma americanum generated from PacBio HiFi sequencing of 50 individual female ticks. This repository contains the phased pseudo-haploid tick genome generated after assembly using Flye, phasing using Purge_Dups, and clean-up using custom python scripts generated in-house. </p> <p>NCBI Bioproject: PRJNA932813</p>
DynamicRead: Eye Movement Data of Reading on Handheld Mobile Devices under Dynamic Conditions
<p>The DynamicRead dataset contains eye movement data from 20 participants engaged in reading tasks on a handheld mobile device, while sitting and walking. Participants read 10 texts using four gaze interaction methods based on scrolling techniques, as well as one touch-based interaction method. The dataset captures the impact of motion on eye movement and features an unstable eye-movement sampling frequency ranging from 8-12fps/hz, which poses a challenge for traditional eye-movement toolkits. This dataset can be valuable for academic research on the impacts of motion on eye movement and the development of robust gaze interaction methods for handheld mobile devices under dynamic conditions.<br> <br> ###<br> Folder Structure:<br> -'eye_data' -- contains eye movement of reading data<br> -'text_screenshot' -- contains different reading material screenshot<br> -'heatmap_img' -- contains heat map of each reading page <br> -'scanpath_img' -- contains scan path of each reading page<br> -'text' -- contains reading material<br> -'code' -- contains R code for reading eye movement visualisation</p> <p><br> Name Method<br> - GAP1_GazeA_log.txt -- Group A, Participant No1, Scrolling technique: GazeA<br> - Gaze A: Auto-scrolling<br> - Gaze B: Heatbox<br> - Gaze C: Eye-Swipe<br> - Gaze D: Moving-bar<br> </p>
Assemblies for "Linear time complexity de novo long read genome assembly with GoldRush"
<p>GoldRush is a <em>de novo</em> genome assembly algorithm with linear time complexity in the number of input long sequencing reads. We tested GoldRush on Oxford Nanopore Technologies datasets with different base error profiles describing the genomes of three human cell lines (NA24385, HG01243 and HG02055), Oryza sativa (rice), and Solanum lycopersicum (tomato). Here, we provide the assemblies for the GoldRush, Flye, Redbean and Shasta assemblies of these long read datasets.</p>
Long-read, chromosome-scale assembly of Vitis rotundifolia cv. Carlos and its unique resistance to Xylella fastidiosa subsp. fastidiosa.
<p>We assembled and annotated a new, long-read genome assembly for ‘Carlos’, a cultivar of muscadine that exhibits tolerance, to build upon the existing genetic resources available for muscadine. We are awaiting release of the genome through NCBI, so we have made the assembly and annotations available here.</p>
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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.