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Table 2 in Prevalence and new genotypes of Enterocytozoon bieneusi in sheltered dogs and cats in Sichuan province, southwestern China
<p><b>Table 2.</b> Prevalence and genotypes of <i>E. bieneusi</i> in sheltered cats from different cities and sources in Sichuan province, southwestern China.</p><table><tbody><tr><th>City</th><th>Source</th><th>No. examined</th><th>No. positive</th><th>Prevalence</th><th>OR (95% CI)</th><th><i>p-</i> value</th><th>Genotypes (<i>n</i>)</th></tr></tbody><tbody><tr><th></th><td></td><td></td><td></td><td>(%) (95% CI)</td><td></td><td></td><td></td></tr><tr><th>Chengdu</th><td>Shuangliu</td><td>85</td><td>13</td><td>15.3% (7.6–22.9)</td><td>Reference</td><td></td><td>CD9 (10); D (2); PtEb IX (1)</td></tr><tr><th>Ya’ an</th><td>Yucheng</td><td>23</td><td>3</td><td>13.0% (<i>—</i> 0.7–26.8)</td><td>0.831 (0.215–3.203)</td><td>0.788</td><td>CD9 (1); D (2)</td></tr><tr><th>Panzhihua</th><td>Dongqu</td><td>48</td><td>6</td><td>12.5% (3.1–21.9)</td><td>0.791 (0.280–2.237)</td><td>0.791</td><td>Type IV (6)</td></tr><tr><th>Total</th><td></td><td>156</td><td>22</td><td>14.1% (8.6–19.6)</td><td></td><td></td><td>CD9 (11); Type IV (6);</td></tr><tr><th></th><td></td><td></td><td></td><td></td><td></td><td></td><td>D (4); PtEb IX (1)</td></tr></tbody></table>
Figure 1 in Prevalence of Spiroplasma and interaction with wild Glossina tachinoides microbiota
Figure 1. Geographical locations of tsetse samples in Africa.
Prevalence of endoepicardial asynchrony and breakthrough patterns in a bilayer computational model of heterogeneous endoepicardial dissociation in the left atrium
Open the record for dataset details and reuse information.
Data from: Population-based screening for hepatitis C antibodies and active infection using a point-of-care test in a low prevalence area
<p><span><span><span><span><span><span><span><span><span><span><span><b>Background.</b> Data on the true prevalence of hepatitis C virus (HCV) infection in the </span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>general population is essential to health policies. We evaluated a program implementing free universal HCV screening using a non-invasive point-of-care test (POCT) (OraQuick-HCV rapid test) in oral fluid in an urban area in Valencia, South-Eastern Spain. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods.</b> A cross-sectional study was performed during 2015-2017. Free HCV screening was offered by regular mail to 11,500 individuals aged 18 and over, randomly selected from all census residents in the Health Department. All responding participants filled in a questionnaire about HCV infection risk factors and were tested in their tertiary Hospital. In those with a positive POCT, results were confirmed by enzyme-immunoassay and HCV-RNA.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results.</b> 1,206 persons agreed to participate (response rate: 11.16%). HCV antibodies were detected in 19 (1.60%) cases (age-sex standardized rate: 1.31%; 95%CI: 0.82-2.07), but only 8 showed positive HCV-RNA (age-sex standardized rate: 0.56%; 95%CI: 0.28-1.14). The majority (89%) of the cases were born before 1965 and 74% had at least one known risk factor for HCV infection. All anti-HCV positive individuals were already aware of their infection, and no undiagnosed cases were detected. The performance of the POCT was excellent for detecting active infection. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions.</b> These preliminary data suggest that HCV population screening with a POCT is feasible but, in our setting, mailing recruiting is not effective (11% response rate). The low prevalence of HCV antibodies and active infection in the participant population (with no new diagnoses made) suggests that, in our setting, underdiagnosis may be uncommon.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Files uploaded include the study database (Stata v.13) and the do.file of the study.</b></span></span></span></span></span></span></span></span></span></span></span></p>
Data for manuscript "Prevalence in News Media of two Competing Hypotheses about COVID-19 Origins"
<p>The Covid-19 pandemic has been one of the most disruptive and painful phenomena of the last few decades. As of July 2021, the origins of the SARS-CoV-2 virus that caused the outbreak remain a mystery. This work analyzes the prevalence in news media articles of two popular hypotheses about SARS-CoV-2 virus origins: the natural emergence and the lab-leak hypotheses. </p> <p>This data set contains frequency counts of target words in news and opinion articles from 12 popular news media outlets. The target words are listed in the associated manuscript and are mostly words associated with the Covid-19 pandemic. </p> <p>The list of compressed files in this data set is listed next:</p> <p>targetWordsInArticlesCounts.rar contains counts of target words in outlets articles as well as total counts of words in articles</p> <p>targetWordsFrequencies.rar daily, weekly, monthly word frequencies</p> <p>wordEmbeddingModels.rar monthly embedding models of news outlets content</p> <p>analysisScripts.rar analysis notebooks</p> <p>The textual content of news and opinion articles from the outlets is available in the outlet's online domains and/or public cache repositories such as Google cache, The Internet Wayback Machine, and Common Crawl. We used derived word frequency counts from these sources. Textual content included in our analysis is circumscribed to articles headlines and main body of text of the articles and does not include other article elements such as figure captions.</p> <p>Targeted textual content was located in HTML raw data using outlet specific XPath expressions. Tokens were lowercased prior to estimating frequency counts. </p> <p>Yearly frequency usage of a target word in an outlet in any given temporal interval ( daily, weekly, monthly) was estimated by dividing the total number of occurrences of the target word in all articles of a given temporal interval by the number of all words in all articles of that temporal interval. This method of estimating frequency accounts for variable volume of total article output over time.</p> <p>In a small percentage of articles, outlet specific XPath expressions might fail to properly capture the content of the article due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. As a result, the total and target word counts metrics for a small subset of articles are not precise. In a random sample of articles and outlets, manual estimation of target words counts overlapped with the automatically derived counts for over 90% of the articles. Most of the incorrect frequency counts are minor deviations from the actual counts such as for instance counting a word in an article footnote encouraging article readers to find related articles and that the XPath expression might mistakenly include as the content of the article main text. Some additional outlet-specific inaccuracies that we could identify occurred in the WSJ where in less than 5% of the articles XPath expressions failed to capture the article's main text content. Other outlets articles samples sizes might not be comprehensive but, to the best of our knowledge, they are representative and include tens of thousands of articles per outlet/year. To conclude, in a data analysis of over 1.5 million articles, we cannot manually check the correctness of frequency counts for every single article and hundred percent accuracy at capturing articles’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our frequency metrics are representative of word prevalence in print news media content (see Figure 1 of main manuscript for supporting evidence).</p> <p> </p> <p> </p>
Figure 4 in A redescription of Neophilopterus tricolor (Burmeister, 1838) (Insecta: Phthiraptera: Ischnocera: Philopteridae) from the black stork Ciconia nigra (L.) (Aves) with notes on its prevalence
Figure 4. Terminal segments of the male Neophilopterus tricolor.
Figure 2 in A redescription of Neophilopterus tricolor (Burmeister, 1838) (Insecta: Phthiraptera: Ischnocera: Philopteridae) from the black stork Ciconia nigra (L.) (Aves) with notes on its prevalence
Figure 2. Female ventral view of Neophilopterus tricolor.
Figure 3 in A redescription of Neophilopterus tricolor (Burmeister, 1838) (Insecta: Phthiraptera: Ischnocera: Philopteridae) from the black stork Ciconia nigra (L.) (Aves) with notes on its prevalence
Figure 3. Copulatory apparatus of male Neophilopterus tricolor.
Figure 1 in A redescription of Neophilopterus tricolor (Burmeister, 1838) (Insecta: Phthiraptera: Ischnocera: Philopteridae) from the black stork Ciconia nigra (L.) (Aves) with notes on its prevalence
Figure 1. Female dorsal view of Neophilopterus tricolor.
Dependency Management Bots in Open-Source Systems - Prevalence and Adoption
<p>This is a re-analysis package for the paper: Dependency Management Bots in Open-Source Systems - Prevalence and Adoption. The package includes scripts and processed data collected from GitHub about five investigated dependency management bots: Dependabot, Depfu, Greenkeeper, Pyup and Renovate.</p>
Evaluating noninvasive methods for estimating cestode prevalence in a wild carnivore population
<p>This repository holds the datasets and R code files needed to run the models in: Brandell et al., 2022. Evaluating noninvasive methods for estimating cestode prevalence in a wild carnivore population. <em>PLOS ONE</em>.</p> <p>Excel files have associated KEYs for each data column; CSVs are analyzed with their associated R code.</p>
Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs [DATA]
<p>Catalogs for Nature Communications article: Seismic Magnitude Clustering is Prevalent in Field and Laboratory Catalogs.</p> <p> </p> <p>Update: In DataVariableExplanation, two catalogs from University of Minnesota</p> <p>Mixed mode and mode I bending data description needs to show that the third column is in seconds.</p>
Fig 1 in Prevalence Of Fascioliasis In Ruminants Of The World - Meta-Analysis
Fig 1. Flow diagram of the study design process.
Prevalence data complementing the European Union One Health 2021 Zoonoses Report
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation is: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - Croatia
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - the United Kingdom (Northern Ireland)
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - Finland
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - Luxembourg
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2021 Zoonoses Report - Ireland
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Drought, infection complexity (COI), and prevalence of Plasmodium mexicanum
<p>This data and R code were used for the research reported in the manuscript listed below, which was being reviewed by a journal at the time this data set was published.</p> <ul> <li>Title: Drought correlates with reduced infection complexity and possibly prevalence in a decades-long study of the lizard malaria parasite Plasmodium mexicanum</li> <li>Authors: Allison Neal, Joshua Sassi, Anne Vardo-Zalik</li> </ul> <p>There are 6 files:</p> <ol> <li><strong>HOPDroughtAnalysis20201221.R</strong>- an R script; the main data analysis file</li> <li><strong>DroughtAnalysisDataFull.csv</strong>- a data file containing data on all of the lizards collected at the University of California's Hopland Research and Extension Center from 1978 to 2016. This file does NOT contain any information on drought, but HOPDroughtAnalysis20201221.R will import data from both this file and the droughtMonitorDataUkiah.csv and align them. Alternatively, use DroughtData_AsAnalyzed.csv (see below). Metadata are provided in a separate file (see below).</li> <li><strong>DroughtData_AsAnalyzed.csv</strong>- this data set combines data from DroughtAnalysisDataFull.csv and droughtMonitorDataUkiah.csv. It contains only the lizards that were analyzed for this paper. This file may be useful for repeating the data analysis outside of R, but does not allow for easy adjustment of what data were excluded or how drought measures were aligned with prevalence and COI measures.</li> <li><strong>DroughtAnalysisMetadata.csv</strong>- a description of the data in DroughtAnalysisDataFull.csv and DroughtData_AsAnalyzed.csv.</li> <li><strong>droughtMonitorDataUkiah.csv</strong>- a data file containing a summary of data from the US Drought Monitor. The original data were downloaded here: <a href="https://droughtmonitor.unl.edu/DmData/DataDownload.aspx">https://droughtmonitor.unl.edu/DmData/DataDownload.aspx</a>. Week is reported as YYYYMMDD. The original data, as downloaded, were recorded as the percentage of Ukiah (Mendocino County, California) that was in each of six categories: no drought or drought of severity D0 (abnormally dry) to D4 (exceptional drought). We converted this data to a single number by converting the categories into a numerical scale (0 = no drought, 1 = D0, etc.) and calculating a weighted average of the drought severity for the county for each week. These averages are recorded in this file.</li> <li><strong>HRECRainfall.csv</strong>- a data file containing rainfall data from the Hopland Research and Extension Center (<a href="https://hrec.ucanr.edu/">https://hrec.ucanr.edu/</a>), posted with permission from John Bailey (HREC director, 2022). It reports the monthly rainfall at HREC from 1952 to 2017.</li> </ol>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.