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Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"
<p>Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"</p>
Incidence of SARs-CoV-2 in Gütersloh county, Germany, after the outbreak in the slaughterhouse and meat packing plant Tönnies
<p>Figure </p> <p>Seven day incidence of SARS-CoV-2 per 100,000 people from March 15 to September 3, 2020 in Gütersloh, North Rhine-Westphalia, Germany</p> <p>Table</p> <p>Pandemic control measures in Gütersloh county</p>
Figure 3 in Seasonal incidence of Raoiella indica Hirst (Acari: Tenuipalpidae) on different varieties of date palm in Kachchh region of Western India
Figure 3. Pattern of distribution of red palm mite, Raoiella indica in different directions on three different varieties (pooled).
SNSPD traces given varying incident mean photon numbers
<p>The data set consists of 1.1 million electrical output signals (traces) from a superconducting nanowire single-photon detector (SNSPD) from Single Quantum. These traces were recorded with an oscilloscope (21 GHz bandwidth, 128GSa/s) for varying incident mean photon numbers between 0.5 and 5 in steps of 0.5 photons per pulse (generated with a laser, i.e., coherent states). More information can be found in the accompanying publication.</p>
Data from: Mean landscape-scale incidence of species in discrete habitats is patch size dependent
<p>Contains data and code for the manuscript 'Mean landscape-scale incidence of species in discrete habitats is patch size dependent'.</p> <p>Raw data consist of 202 published datasets collated from primary and secondary (e.g., government technical reports) sources. These sources summarise metacommunity structure for different taxonomic groups (birds, invertebrates, non-avian vertebrates or plants) in different types of discrete metacommunities including 'true' islands (i.e., inland, continental or oceanic archipelagos), habitat islands (e.g., ponds, wetlands, sky islands) and fragments (e.g., forest/woodland or grass/shrubland habitat remnants). </p> <p>The aim of the study was to test whether the size of a habitat patch influences the mean incidences of species within it, relative to the incidence of all species across the landscape. In other words, whether high-incidence (widespread) or low-incidence (narrow-range) species are found more often than expected in smaller or larger patches. To achieve this, a new standardized effect size metric was developed that quantifies the mean observed incidence of all species present in every patch (the geometric mean of the number of patches in which all species were observed) and compares this with an expectation based on re-sampling the incidences of all species in all patches. Meta-regression of the 202 datasets was used to test the relationship between this metric, the 'mean species landscape-scale incidences per patch' (MSLIP), and the size of habitat patches, and for differences in response among metacommunity types and taxonomic groups. </p>
Food Recall Incidents
<p>The Food Recall Incidents dataset consists of 7,546 short texts (from 5 to 360 characters each), which are the titles of food recall announcements (therefore referred to as <em>title</em>), and their full-length texts (from 56 to 48,318 characters). The data are crawled from 24 public food safety authority websites by <a href="https://agroknow.com/">Agroknow</a>. All texts are written in 6 languages, with English (6,644) and German (888) being the most common, followed by French (8), Greek (4), Italian (1) and Danish (1). Most of the texts have been authored after 2010 and they describe recalls of specific food products due to specific hazards. Experts manually classified each text to four groups of classes describing hazards and products on two levels of granularity:</p> <div> <div> <ul> <li><em>hazard</em>: fine-grained description of the hazards mentioned in the texts comprising 261 classes;</li> <li><em>hazard-category</em>: categorized version of the <em>hazard</em> classification task comprising 10 classes;</li> <li><em>product</em>: fine-grained description of the products mentioned in the texts comprising 1,256 classes;</li> <li><em>product-category</em>: categorized version of the <em>product</em> classification task comprising 22 classes.</li> </ul> <p>The columns <em>hazard-title</em> and <em>product-title</em> comprise character spans, generated based on feature importance of a Logistic Regression (LR) classifier. These signify parts of the <em>title</em> that are important for hazard and product classification. Due to their very low support for many classes, the fine-grained tasks of hazard and product classification may require further pre-processing (e.g. label clustering or filtering), dependent on the application. The dataset comprises also metadata, such as the release date of the text (<em>year</em>, <em>month</em>, <em>day</em>), the language of the text (<em>language</em>), and the country of issue (<em>country</em>).</p> <blockquote> <p><strong>DISCLAIMER: </strong><em>A random sample of 1,562 instances was used for validation and testing for the purposes of the <a href="https://food-hazard-detection-semeval-2025.github.io/">SemEval 2025 Task 9: The Food Hazard Detection Challenge</a>, labelled respectively in the <strong>semeval-split </strong>column.</em></p> </blockquote> </div> </div>
Incidents of death and missing people on migratory routes around the world
<pre>Dataset with data on incidents of people dead or missing on international migratory routes in different regions, from the website https://missingmigrants.iom.int/, from the Missing Migrants Project of the International Organisation for Migration (IOM). </pre>
Incidence and predictor of diabetic foot ulcer and its association with change in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia, 2021
<p>Abstract</p> <p> </p> <p><strong>Background</strong></p> <p>Diabetes mellitus is one of the global public health problems and fasting blood sugar is an important indicator of diabetes management. Uncontrolled diabetes can lead to diabetic foot ulcers, which is a common and disabling complication. The association between fasting blood glucose level and the incidence of diabetic foot ulcers is rarely considered, and knowing its predictors is good for clinical decision-making. Therefore, the aim of this study was to determine the incidence and predictors of diabetic foot ulcers and its association with changes in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia.</p> <p><strong>Methods</strong></p> <p>A multicenter retrospective follow-up study was conducted at a referral hospital in Northwest Ethiopia. A total of 539 newly diagnosed DM patients who had follow-up from 2010 to 2020 were selected using a computer-generated simple random sampling technique. Data was entered using Epi-Data 4.6 and analyzed in R software version 4.1. A Cox proportional hazard with a linear mixed effect model was jointly modeled and 95% Cl was used to select significant variables. AIC and BIC were used for model comparison.</p> <p><strong>Result</strong></p> <p>A total of 539 diabetes patients were followed for a total of 28727.53 person-month observations. Overall, 65 (12.1%) patients developed diabetic foot ulcers with incidence rate of 2.26/1000-person month observation with a 95% CI of [1.77, 2.88]. Being rural (AHR= 2.30, 95%CI: [1.23, 4.29]), being a DM patient with Diabetic Neuropathy (AHR= 2.61, 95%CI: [1.12, 6.06]), and having peripheral arterial disease(PAD) (AHR= 2.96, 95%CI: [1.37, 6.40]) were significant predictors of DFU. The time-dependent lagged value of fasting blood sugar change was significantly associated to the incident of DFU (α = 1.85, AHR=6.35, 95%CI [2.40, 16.79]).</p> <p><strong>Conclusion and recommendation</strong></p> <p>In this study, the incidence of DFU was higher than in previous studies and was influenced by multiple factors like rural residence, having neuropathy, and PAD were significant predictors of the incidence of DFU. In addition, longitudinal changes in fasting blood sugar were associated with an increased risk of DFU. Health professionals and DM patients should give greater attention to the identified risk factors for DFU were recommended.</p>
FDTD simulation of 290 nm PAAO with gold nanoparticles: varying incidence angle, s-polarization, n=1
<p>Version 2 has the same files as version 1 and some additional files.</p> <p>FDTD software: Lumerical (Ansys, version 2021 R2.3).</p> <p>Structure: aluminum (Palik) substrate; 290 nm thickness (<em>h</em>) aluminum oxide (Palik) layer with 35 nm diameter (<em>RPo</em>) cylindrical pores with 100 nm distance (<em>D</em>) between the pore centers (representing porous anodized aluminum oxide - PAAO); 60 nm diameter (<em>RNP</em>) gold (Johnson and Christy) nanoparticles placed directly above each pore.</p> <p>Refractive index of the surrounding medium (<em>n</em>): 1.0.</p> <p>Simulation region: from 300 nm below the substrate/PAAO interface to 1.3 µm above PAAO surface; x and y spans are equal to one period of the structure.</p> <p>Mesh override region: from 50 nm below the PAAO to 50 nm above the nanoparticles; 2 nm step size in each direction.</p> <p>Light source: BFAST plane wave light source; 500 nm above PAAO; varying (20° - 70° in steps of 5°) angle of incidence (<em>ang</em>); 300 nm – 1000 nm wavelength range; s-polarization (<em>pol</em>).</p> <p>Monitor (frequency domain field and power): 2D Z-normal; 1 µm above PAAO; results are in "<em>_reflection.txt</em>" files.</p> <p>Information in the file name: <em>h</em> - thickness of PAAO; <em>pol</em> - polarization; <em>RNP</em> - diameter of gold nanoparticles; <em>RPo</em> - diameter of pores; <em>D</em> - distance between pore centers; <em>ang</em> - angle of incidence; <em>n</em> - refractive index of surrounding medium.</p> <p>Files: (1) "<em>_reflection.txt</em>" - lambda(nm) (first column) - wavelength in nanometers; Y (second column) - T data from the monitor above the structure. (2) "<em>_p0.log</em>" - log file produced by the software while running the simulation. (3) "<em>.fsp</em>" - Lumerical software file containing the simulation project (license required to open these files). Consecutive numbering corresponds to the angles of incidence: 1 - 20°, 2 - 25°, 3 - 30°, 4 - 35°, 5 - 40°, 6 - 45°, 7 - 50°, 8 - 55°, 9 - 60°, 10 - 65°, 11 - 70°. (4) "<em>Lumerical_Screenshots.pdf</em>" - shows software screenshots for every object and its every property; red text is added to show which values are different for different simulations. (5) "<em>Structure_Illustration.png</em>" - a schematic of modeled structure. (6) "290nm-Spol_varying-angle<em>.jpg</em>" - a preview of data from "<em>_reflection.txt</em>" files.</p>
Dengue incidence and climatic variables in Cali from 2015 to 2021
<p>In this work we studied the relationship between dengue incidence in Cali and the climatic variables that are known to have an impact on the mosquito and were available (precipitation, relative humidity, minimum, mean, and maximum temperature). Since the natural processes of the mosquito imply that any changes on climatic variables need some time to be visible on the dengue incidence, a lagged correlation analysis was done in order to choose the predictor variables of count regression models. A Principal Component Analysis was done to reduce dimensionality and study the correlation among the climatic variables. Finally, aiming to predict the monthly dengue incidence, three different regression models were constructed and compared using de Akaike information criterion. The best model was the negative binomial regression model, and the predictor variables were mean temperature with a 3-month lag and mean temperature with a 5-month lag as well as their interaction. The other variables were not significant on the models. And interesting conclusion was that according to the coefficients of the regression model, a 1°C increase in the monthly mean temperature will reflect as a 45% increase in dengue incidence after 3 months. The rises to a 64% increase after 5 months.</p>
Figure 5 in Incidence of Oscheius onirici (Nematoda: Rhabditidae), a potentially entomopathogenic nematode from the marshlands of Wisconsin, USA
Figure 5: Bayesian consensus tree inferred from 28S D2/D3 under GTR+I+G model (−ln L = 4799.2056; AIC = 9618.4111; freqA = 0.2557; freqC = 0.182; freqG = 0.2892; freqT = 0.2732; R(a) = 0.6259; R(b) = 2.263; R(c) = 1.6102; R(d) = 0.5689; R(e) = 5.2205; R(f) = 1; Pinva = 0.2558; Shape = 0.6827). Posterior probability values exceeding 50% are given on appropriate clades.
Figure 2 in Incidence of Oscheius onirici (Nematoda: Rhabditidae), a potentially entomopathogenic nematode from the marshlands of Wisconsin, USA
Figure 2: Photographs of Oscheius onirici female. (A) Entire body (arrow showing vulva). (B) Pharyngeal region (arrow showing excretory pore). (C) Head region. (D, E) Lateral view of vulva region (arrow showing vulva). (F) Lateral view of tail region (arrow showing anus). (G) Basal bulb (arrow showing excretory pore).
Figure 4 in Incidence of Oscheius onirici (Nematoda: Rhabditidae), a potentially entomopathogenic nematode from the marshlands of Wisconsin, USA
Figure 4: Bayesian consensus tree inferred from 18S under GTR+I+G model (−ln L = 5789.8442; AIC = 11599.6885; freqA = 0.2657; freqC = 0.202; freqG = 0.258; freqT = 0.2743; R(a) = 0.9275; R(b) = 3.6026; R(c) = 2.6802; R(d) = 0.6926; R(e) = 6.3237; R(f) = 1; Pinva = 0.4134; Shape = 0.6825). Posterior probability values exceeding 50% are given on appropriate clades.
Figure 3 in Incidence of Oscheius onirici (Nematoda: Rhabditidae), a potentially entomopathogenic nematode from the marshlands of Wisconsin, USA
Figure 3: Scanning electron microscope photographs of Oscheius onirici female. (A) Lip region en-face view showing one amphideal aperture (am), six labial sensilla (ls) and two cephalic sensilla (cs). (B) Excrotory pore. (C) Ventral view of vulva. (D) Lateral lines. (E) Entire body lateral view (arrow showing vulva). (F) Esophageal region (arrow showing excretory pore). (G) Lateral field showing lateral lines. (H) Tail region ventral view (Top arrow showing four bacteria, middle arrow showing anus, two bottom arrows showing phasmids).
Figure 6 in Incidence of Oscheius onirici (Nematoda: Rhabditidae), a potentially entomopathogenic nematode from the marshlands of Wisconsin, USA
Figure 6: Bayesian consensus tree inferred from ITS under TVM+I model (−ln L = 9235.373; AIC = 18488.7461; freqA = 0.2255; freqC = 0.2115; freqG = 0.2393; freqT = 0.3237; R(a) = 1.3757; R(b) = 3.2802; R(c) = 1.847; R(d) = 1.2014; R(e) = 3.2802; R(f) = 1; Pinva = 0.1375; Shape = 1.5896). Posterior probability values exceeding 50% are given on appropriate clades.
Figure 1 in Incidence of Oscheius onirici (Nematoda: Rhabditidae), a potentially entomopathogenic nematode from the marshlands of Wisconsin, USA
Figure 1: Oscheius onirici female. (A) Entire body. (B) Pharyngeal region. (C) Lateral view of vulva region and lateral field. (D) Schematic representation of En-face view of lip region. (E) Lateral view of tail region.
Plate 1 in Incidence of parasitic infection in adult and juvenile Clarias gariepinus in a private fish farm, Yola, Adamawa state
Plate 1: Adult Clarias gariepinus placed on adissecting board after measurement and weighing for dissection
Figure 7 in Incidence of Oscheius onirici (Nematoda: Rhabditidae), a potentially entomopathogenic nematode from the marshlands of Wisconsin, USA
Figure 7: The mortality rates for different insect types treated with either an Oscheius onirici population, or a control treatment of two milliliters of distilled water. (B) The average number of days that it took for a larva to die after exposure to the treatment. Insects were treated with either an Oscheius onirici population, or a control treatment of two milliliters of distilled water.
Fig. 1 in First report of Colotis amata (Lepidoptera: Pieridae) on Salvadora persica (Capparales: Salvadoraceae) in Rajasthan, India: incidence and morphometric analysis
Fig. 1. Aggregation of Colotis amata on a pilu plant (Salvadora persica) during the winter season at the ICAR Central Institute for Arid Horticulture, Bikaner, India.
Fig. 2 in First report of Colotis amata (Lepidoptera: Pieridae) on Salvadora persica (Capparales: Salvadoraceae) in Rajasthan, India: incidence and morphometric analysis
Fig. 2. Different developmental stages of Colotis amata. A, eggs; B, 1st instars; C, 2nd instar; D, 3rd instar; E, 4th instar; F, 5th instar; G, pupae; H, adult male; I, adult female.
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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.