Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
20
datasets available to search
ShareScore release 0.9.0
Dataset results
20 results for “STEC”
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
MERICS China Podcast: Europe's resilience vis-à-vis China, with Grzegorz Stec and Helena Legarda
<p>Building resilience, i.e., the ability to manage risks without compromising long-term development, is at the heart of Europe’s adjustment of its China policy. To contribute to this process, we recently launched the <a href="https://merics.org/en/report/europe-china-resilience-audit-insights-advancing-eu-resilience">MERICS Europe-China Resilience Audit</a>, which is based on a database of 98 indicators for the economy, security, politics and society. The analysis confirms some significant differences between the eleven countries studied in terms of vulnerability and progress in measures to strengthen resilience towards China. Among the countries covered, Sweden and the Netherlands stand out for their resilience building.</p> <p>In this episode of the MERICS China Podcast, project leads <strong>Grzegorz Stec</strong> and<strong> Helena Legarda </strong>explain the methodology of the index and discuss key findings from their analysis with MERICS Director Communications and Publications <strong>Claudia Wessling</strong>.</p>
MERICS China Podcast: China and the European Parliament election, with Ivana Karásková and Grzegorz Stec
<p>Ahead of the European Parliament election on June 6-9, 2024, this episode looks at the role of the European Parliament in EU-China relations and the possible impact of the election results on the European “de-risking” agenda among other topics. </p> <p><strong>Johannes Heller-John</strong> talks to <strong>Ivana Karásková</strong> and <strong>Grzegorz Stec</strong>. Ivana is a European China Policy Fellow at MERICS and the founder of MapInfluenCE and China Observers in Central and Eastern Europe (CHOICE) at the Association for International Affairs (AMO) in Prague. Grzegorz is the Head of the MERICS Brussels Office.</p> <p>Recently, Ivana co-authored two reports, one on <a href="https://www.amo.cz/en/foreign-electoral-interference-affecting-eu-democratic-processes/" target="_blank" rel="noopener noreferrer">foreign electoral interference in the EU</a> and one on the <a href="https://www.amo.cz/en/from-the-fringes-to-the-forefront-how-extreme-parties-in-the-european-parliament-can-shape-eu-china-relations/" target="_blank" rel="noopener noreferrer">rise of fringe parties in the EP and their impact on EU-China relations</a>. Grzegorz has published articles on <a href="https://www.merics.org/en/merics-briefs/how-ep-parties-see-china-ev-exports-trade-and-technology-council" target="_blank" rel="noopener">how EP parties see China</a> and on <a href="https://www.merics.org/en/comment/meps-key-lessons-eu-china-policy-during-last-mandate" target="_blank" rel="noopener">key lessons learned by Members of the EP during the last mandate</a>.</p>
User Guide – Dashboard on Shiga toxin-producing Escherichia coli (STEC)
<p>Frank Boelaert</p>
Assemblies of the STEC dataset
<p>The assembled contigs of the STEC dataset used in the manuscript "MetaBinner: a high-performance and stand-alone ensemble binning method to recover individual genomes from complex microbial communities"</p>
A Study to Assess Safety, Efficacy, and Pharmacokinetics of INM004 in Pediatric Patients With STEC-HUS
ClinicalTrials.gov study NCT05569746. IPD Sharing: NO. Countries: 1. Publications: 0.
Phase 2/3 Study to Evaluate PK, Safety & Efficacy of INM004 in STEC Positive Pediatric Patients for Prevention of HUS
ClinicalTrials.gov study NCT04132375. IPD Sharing: NO. Countries: 1. Publications: 2.
Shiga Toxin Producing Escherichia Coli (STEC) Volume Expansion
ClinicalTrials.gov study NCT03275792. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Data from: Investigating behavioral drivers of seasonal Shiga-Toxigenic Escherichia Coli (STEC) patterns in grazing cattle using an agent-based model
Open the record for dataset details and reuse information.
A HUS-associated STEC expressing a dicA transcriptional module is related to gene network dysregulation in Caco-2
GEO Series GSE45979. Homo sapiens; Escherichia coli CFT073; Escherichia coli O157:H7 str. Sakai; Escherichia coli; Escherichia coli O157:H7 str. EDL933; Escherichia coli str. K-12 substr. MG1655. 32 samples. Type: Expression profiling by array.
Data for validation STEC workflow
<p>Additional data<br> ========</p> <p>This archive contains additional data for the manuscript "Validation of a bioinformatics workflow for characterization of Shiga Toxin-Producing *Escherichia coli*, applied to a high-quality reference dataset, demonstrates high performance for using WGS for routine pathogen typing"</p> <p># Notes</p> <p>Samples were analyzed with anonymized file names. They can be linked back to the original sample name as indicated in the Excel sheet.</p> <p># Content</p> <p>## Validation results ('all_results.xlsx')</p> <p>This spreadsheet contains detailed results for the validation. It contains all workflow output, corresponding metadata and classification (TP, FN, TN or FP).</p> <p>## KMA output ('kma.tar')</p> <p>This folder contains the output from KMA for the various assays. They were executed in isolation for each of the assays (instead of with the workflow). </p> <p><br> ## Example reports ('report_EH1236_*.zip')</p> <p>Example output reports of the workflow for each of the three detection methods on the same sample (EH1236).</p> <p><br> ## Virulence gene custom database ('virulence_genes_db.fasta')</p> <p>This folder contains a FASTA file with the sequences that were used to evaluate the performance of the virulence gene detection.</p> <p>## Virulence gene detection ('virulence_gene_detection.tar')</p> <p>This folder contains the output of the virulence gene detection for the three detection methods.<br> This was executed separately because the custom virulence gene database is not included in the bioinformatics workflow.</p> <p>## Workflow reports ('workflow_reports_updated.tar')</p> <p>This folder contains the output of the workflow for all of the validation samples with the three detection methods. <br> All runs were executed in August 2020, with database updated to the latest available version. <br> A single archive is created for each sample, containing the output for the three bioinformatics approaches (BLAST+, KMA, SRST2).<br> BAM files were omitted from the archives due to their large sizes.</p> <p>## Workflow reports - validation ('workflow_reports_validation.tar')</p> <p>This folder contains the output reports used for the validation (with older database version, etc).<br> This does not include KMA results because they were validated per-assay (see KMA archive).</p> <p># Contact</p> <p>For further questions you can contact Bert Bogaerts (bert.bogaerts@sciensano.be)<br> </p>
Safety and Efficacy Study of Eculizumab in Shiga-Toxin Producing Escherichia Coli Hemolytic-Uremic Syndrome (STEC-HUS)
ClinicalTrials.gov study NCT01410916. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Efficacy of INM004 in Children With STEC-HUS
ClinicalTrials.gov study NCT06389474. IPD Sharing: Not stated. Countries: 9. Publications: 0.
MAPS integrates regulation of actin-targeting effector SteC into the virulence control network of Salmonella small RNA PinT
GEO Series GSE157499. Mus musculus; Salmonella enterica subsp. enterica serovar Typhimurium str. SL1344. 15 samples. Type: Expression profiling by high throughput sequencing.
FIGURE 4 in Distribution of Macrobiotus shonaicus Stec, Arakawa & Michalczyk, 2018 (Tardigrada: Eutardigrada: Macrobiotidae) in Japan
FIGURE 4. Egg morphology in the type strain (SHONAI) and nine new populations of Macrobiotus shonaicus as seen under light microscopy. Scale bar = 20 µm.
FIGURE 2 in Distribution of Macrobiotus shonaicus Stec, Arakawa & Michalczyk, 2018 (Tardigrada: Eutardigrada: Macrobiotidae) in Japan
FIGURE 2. The BI phylogenetic trees based on concatenated COI + ITS-2 sequences (A) and haplotype networks for each of the two markers (B—COI; C—ITS-2). Values at the nodes indicate Bayesian posterior probabilities (BP). Scale bars indicate substitutions per position. The size of circles in haplotype networks correlates with the number of haplotypes; values above the lines indicate the number of mutations.
FIGURE 1 in Distribution of Macrobiotus shonaicus Stec, Arakawa & Michalczyk, 2018 (Tardigrada: Eutardigrada: Macrobiotidae) in Japan
FIGURE 1. Map of Japan with the locations of the type population (the open circle) nine new populations (filled circles) of Macrobiotus shonaicus. Map edited with GMT v6.0.0 (Wessel et al. 2019).
FIGURE 5 in Distribution of Macrobiotus shonaicus Stec, Arakawa & Michalczyk, 2018 (Tardigrada: Eutardigrada: Macrobiotidae) in Japan
FIGURE 5. Karyotypes in the nine newly found populations of Macrobiotus shonaicus. The labels indicate localities. Scale bar = 10 µm.
FIGURE 3 in Distribution of Macrobiotus shonaicus Stec, Arakawa & Michalczyk, 2018 (Tardigrada: Eutardigrada: Macrobiotidae) in Japan
FIGURE 3. Principal component analyses (PCA) for animal pt values (A) and egg morphometric traits (B) of M. shonaicus from the type strain (SHONAI) and nine new populations of M. shonaicus.
RNA SEQ STEC FHI96 Type III mod
GEO Series GSE129787. Escherichia coli. 6 samples. Type: Expression profiling by high throughput sequencing.
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.