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FIGURE 13 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 13. Ventral head view of the holotype of Bythaelurus immaculatus SCSFRI O 0094 (adult male ~708 mm TL).
FIGURE 18 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 18. Ventral view of snout of the holotype of Springeria stenosoma SCSFRI O 0065 (female 520 mm TL).
FIGURE 12 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 12. Lateral view of the holotype of Bythaelurus immaculatus SCSFRI O 0094 (adult male ~708 mm TL).
FIGURE 10 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 10. Lateral view of: A. holotype of Isistius labialis SCSFRI S 07257 (female 442 mm TL); B. Isistius brasiliensis (CSIRO H 5150–01, female 480 mm TL) from off Australia.
FIGURE 8 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 8. Lateral view of: A. holotype of Scymnodon niger SCSFRI S 07561 (female 482 mm TL); B. Zameus squamulosus (CSIRO H 2560–03, adult male ~500 mm TL) from Australia.
FIGURE 11 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 11. Holotype of Isistius labialis SCSFRI S 07257 (female 442 mm TL): A. anterior ventral view; B. dentition.
FIGURE 7 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 7. Lateral view of Centroscymnus coelolepis (CSIRO H 493, adult male 925 mm TL) from off southeastern Australia.
FIGURE 6 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 6. Lateral trunk denticles of the holotype of Centroscymnus macrops SCSFRI O 0150 (female 792 mm TL).
FIGURE 1 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 1. Holotype of Squalus acutirostris SCSFRI D 01562 (adult male 648 mm TL): A. lateral view; B. ventral head view; C. first dorsal fin; D. second dorsal fin.
FIGURE 3 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 3. Holotype of Centrophorus ferrugineus SCSFRI O 0094 (adult male 1044 mm TL): A. anterior lateral view; B. ventral head view; C. first dorsal fin; D. second dorsal fin.
FIGURE 5 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 5. Holotype of Centroscymnus macrops SCSFRI O 0150 (female 792 mm TL): A. lateral view; B. ventral view of head; C. dentition.
FIGURE 4 in Notes on shark and ray types at the South China Sea Fisheries Research Institute (SCSFRI) in Guangzhou, China
FIGURE 4. Lateral trunk denticles of the holotype of Centrophorus ferrugineus SCSFRI O 0094 (adult male 1044 mm TL).
PLATE 1 in Holotypes of ostracods of Order Podocopida from the collection of the Museum of A.V.Zhirmunsky Institute of Marine Biology FEB RAS (Vladivostok)
PLATE 1. SEM images of ostracod valves. 1, Trapezicandona taurica, RV f, holotype; 2, Heterocythereis reticulata, LV f, holotype; 3–6, Semicytherura calamitica: RV and LV f, holotype; 5, 6, LV m (No. 13416) and f (No. 13418), tuberculate variation, paratypes, the Black Sea near Crimea; 7–10, Semicytherura virgata: 7, 8, RV and LV f, holotype; 9, 10, RV and LV m, paratype (No. 13419), the Black Sea near Crimea, Planerskoye Village, 25 m depth, muddy sand. Scale 60 µm.
Map 1 in Braconidae (Hymenoptera) in the collection of the Institute of Zoology, NAS of Azerbaijan Republic Part IV. Subfamilies Orgilinae, Agathidinae, Ichneutinae, Cheloninae (Hymenoptera)
Map 1: Records of Bracconidae in Azerbaijan, places (open circles) and localities (filled triangles).
Chest Radiograph at Diverse Institutes (CRADI) dataset
<p><strong>Introduction to the Chest Radiograph at Diverse Institutes (CRADI) dataset</strong></p> <p> </p> <p><strong>Background</strong></p> <p> </p> <p>Chest radiography is extensively used to screen and diagnose pulmonary and cardiac diseases. The advantages of its clinical practicality, efficiency, and cost-effectiveness make chest radiography the most accessible imaging test for pulmonary disorders, especially in primary hospitals. Currently, the interpretation of a chest radiograph mainly relies on radiologists.</p> <p> </p> <p>With the development of algorithms, convolutional neural networks (CNNs) have shown the ability to detect a single disorder in chest radiography, e.g., pneumothorax, lung cancer, pneumonia, and tuberculosis. Traditionally, expert annotation is applied to establish a CNN model for classifying medical images. This manual labeling procedure is time-consuming and highly demanding. Importantly, beyond the detection of a single disease, multi-label classification is necessary to interpret a chest radiograph in clinical practice.</p> <p> </p> <p>In order to promote the development of the artificial intelligence-assisted diagnosis of chest radiography, we launched the Chest Radiograph at Diverse Institutes (CRADI) dataset. This dataset is comprised of a large number of chest radiographs. Each radiograph has a 25-label disorder annotation, that was established by the terms adopted from the Fleischner’s glossary, and extracted from the original diagnostic report by natural language processing (NLP) and radiologist expertise.</p> <p> </p> <p>At present, the data of the CRADI dataset comes from two academic hospitals and multiple community clinics in Shanghai. The cases in the CRADI dataset are comprised of in-patients, out-patients, and screening participants.</p> <p> </p> <p>The CRADI dataset provides a better understanding of the multiple and different clinical data sources for chest radiography, which is potentially helpful for the training and test of CNN models.</p> <p> </p> <p>We welcome more data into the CRADI dataset. If you find it helpful to your research work or you want to contribute to this dataset, please feel free to contact us.</p> <p> </p> <p><strong>Data source</strong></p> <p><strong>Number of images</strong></p> <p><strong>Data_source</strong></p> <p>Academic hospital 1</p> <p>74,082</p> <p>0</p> <p>In- and out-patient from Academic hospital 2</p> <p>5,996</p> <p>1</p> <p>Screening participants from Academic hospital 2</p> <p>2,130</p> <p>2</p> <p>Community clinics</p> <p>1,804</p> <p>3</p> <p>Note. Each case includes one posterior-anterior (PA) view chest radiograph and the corresponding text label of disorder findings.</p> <p> </p> <p> </p> <p> </p> <p><strong>Preprocessing methods</strong></p> <p>Images: transformed and resized from DCM format to PNG, changed from 12-bit grayscale to 8-bit. All patient- or institute-related information is de-identified.</p> <p>Label: labels are extracted from the original diagnostic reports. The regular expression is applied by NLP and rules-based extraction methods. In total, 25 labels are extracted for each image.</p> <p><strong>Data</strong></p> <p><strong>Overview: </strong>All images are compressed into one file. All classification labels are listed in one CSV file. Data order is as following way:</p> <p><strong>Data organization</strong></p> <p><strong>Classification result</strong></p> <p>Each image links to the label by an item of ‘patientID’.</p> <p>data_resource<a href="#_msocom_1">[1]</a> stands for the resource of data. Data sources are listed in the previous table.</p> <p>Result table format: |pateintID|data_resource<a href="#_msocom_2">[2]</a> |label1|label2|.......|label25|</p> <p>The order of the 25 labels is as the following:</p> <p>1) pneumothorax, 2) emphysema, 3) pulmonary parenchymal calcification, 4) PICC implant, 5) aortic unfolding, 6) aortic arteriosclerosis, 7) aortic abnormalities, 8) small consolidation, 9) cardiomegaly, 10) patchy consolidation, 11) consolidation, 12) cavity, 13) mass, 14) prominent bronchovascular marking, 15) pulmonary edema, 16) pulmonary nodule, 17) hilar adenopathy, 18) pleural effusion, 19) pleural thickening, 20) pleural adhesion, 21) pleural calcification, 22) pleural abnormalities, 23) scoliosis, 24) pacemaker implant, 25) interstitial involvement.</p> <p>Data_resource or data_source?</p> <p>同上</p> <p><strong>Introduction to the Chest Radiograph at Diverse Institutes (CRADI) dataset</strong></p> <p> </p> <p><strong>Background</strong></p> <p> </p> <p>Chest radiography is extensively used to screen and diagnose pulmonary and cardiac diseases. The advantages of its clinical practicality, efficiency, and cost-effectiveness make chest radiography the most accessible imaging test for pulmonary disorders, especially in primary hospitals. Currently, the interpretation of a chest radiograph mainly relies on radiologists.</p> <p> </p> <p>With the development of algorithms, convolutional neural networks (CNNs) have shown the ability to detect a single disorder in chest radiography, e.g., pneumothorax, lung cancer, pneumonia, and tuberculosis. Traditionally, expert annotation is applied to establish a CNN model for classifying medical images. This manual labeling procedure is time-consuming and highly demanding. Importantly, beyond the detection of a single disease, multi-label classification is necessary to interpret a chest radiograph in clinical practice.</p> <p> </p> <p>In order to promote the development of the artificial intelligence-assisted diagnosis of chest radiography, we launched the Chest Radiograph at Diverse Institutes (CRADI) dataset. This dataset is comprised of a large number of chest radiographs. Each radiograph has a 25-label disorder annotation, that was established by the terms adopted from the Fleischner’s glossary, and extracted from the original diagnostic report by natural language processing (NLP) and radiologist expertise.</p> <p> </p> <p>At present, the data of the CRADI dataset comes from two academic hospitals and multiple community clinics in Shanghai. The cases in the CRADI dataset are comprised of in-patients, out-patients, and screening participants.</p> <p> </p> <p>The CRADI dataset provides a better understanding of the multiple and different clinical data sources for chest radiography, which is potentially helpful for the training and test of CNN models.</p> <p> </p> <p>We welcome more data into the CRADI dataset. If you find it helpful to your research work or you want to contribute to this dataset, please feel free to contact us.</p> <p><strong>Data source </strong></p> <p>training data data source: 0</p> <p>In- and out-patient from external hospital data source: 1</p> <p>Screening participants from external hospital data source : 2</p> <p>Community clinics datasource: 3</p> <p>Note. Each case includes one posterior-anterior (PA) view chest radiograph and the corresponding text label of disorder findings.</p> <p><strong>Preprocessing methods</strong></p> <p>Images: transformed and resized from DCM format to PNG, changed from 12-bit grayscale to 8-bit. All patient- or institute-related information is de-identified.</p> <p>Label: labels are extracted from the original diagnostic reports. The regular expression is applied by NLP and rules-based extraction methods. In total, 25 labels are extracted for each image.</p> <p><strong>Data</strong></p> <p><strong>Overview: </strong>All images are compressed into one file. All classification labels are listed in one CSV file. Data order is as following way:</p> <p><strong>Classification result</strong></p> <p>Each image links to the label by an item of ‘patientID’.</p> <p>data_resource<a href="#_msocom_1">[1]</a> stands for the resource of data. Data sources are listed in the previous table.</p> <p>Result table format: |pateintID|data_resource<a href="#_msocom_2">[2]</a> |label1|label2|.......|label25|</p> <p>The order of the 25 labels is as the following:</p> <p>1) pneumothorax, 2) emphysema, 3) pulmonary parenchymal calcification, 4) PICC implant, 5) aortic unfolding, 6) aortic arteriosclerosis, 7) aortic abnormalities, 8) small consolidation, 9) cardiomegaly, 10) patchy consolidation, 11) consolidation, 12) cavity, 13) mass, 14) prominent bronchovascular marking, 15) pulmonary edema, 16) pulmonary nodule, 17) hilar adenopathy, 18) pleural effusion, 19) pleural thickening, 20) pleural adhesion, 21) pleural calcification, 22) pleural abnormalities, 23) scoliosis, 24) pacemaker implant, 25) interstitial involvement.</p> <p>Data will be released after anonymilization process.</p>
A SARS-Cov2 infection high-uptake surveillance program on healthcare workers and cancer patients in an Italian Cancer Institute
<p>Background: From the beginning of the 2020, SARS-CoV-2 quickly spread worldwide becoming the main problem for the healthcare systems. Healthcare workers (HCWs) are at higher risk of infection and can be a dangerous vehicle for the spread of the virus. Furthermore, cancer patients (CPs) are a vulnerable population with an increased risk of developing severe and lethal forms of COVID-19. Therefore, at the National Cancer Institute of Naples, a surveillance program aimed to prevent the hospital access of SARS-CoV-2 positive subjects (HCWs and CPs) was implemented. The study aims to describe the results of the monitoring activity for the SARS-CoV-2 spread among HCWs and CPs, from March 2020 to March 2021. Methods: This surveillance program included a periodic sampling through nasopharyngeal molecular swab for SARS-CoV-2 (real time-PCR). CPs were submitted to the molecular test at least 48 hours before hospital admission. Multiple logistic regression models were performed among HCWs and CPs to assess the main SARS-CoV-2 risk factors. Results: Overall 1510 HCWs and 12401 CPs were tested with PCR for the detection of SARS-CoV-2 in different time points. SARS-CoV-2 was detected in 20 (1.7%) HCWs of the 1204 subjects during 1st wave, and 127 (9.2%) of 1385 in the 2nd wave (p<0.001); among CPs, the prevalence varied from 3.8% during 1st and 3.4% during the 2nd wave (p=0.5). Multivariate logistic analysis provided a significant OR for nurse (OR = 2.24, 95% CI 1.23-4.08 p<0.001) compared to Research, Administrative staff and other job titles. Conclusions: Our findings underline that the adoption of stringent measures has been essential to contain the shock wave of SARS-CoV-2 infection in the hospital setting. In a far-sighted way, these measures involved both staff and patients. Among HCWs, nurses are more exposed to contagion.</p>
Health-promoting lifestyle behavior and its associated factors among pregnant women attending antenatal care service in public health institutions of Debre Markos town, Ethiopia
<p>Here is the dataset for the manuscripit entitled "Health-promoting lifestyle and related factors among pregnant women at public health institutions in Debre Markos, Ethiopia"</p>
Performance Evaluation of SARS-CoV-2 Viral Transport Medium Produced by Bangladesh Reference Institute for Chemical Measurements
<p>Supplementary Table S1: Stability study design of BRiCM VTM<br> Supplementary Table S2: Stability study result of BRiCM VTM</p>
Players of the Innovation Ecosystem: A Quantitative Analysis of the Scientific Collaboration of a State-Run Research Institute in Brazil
<p>Análise de colaborações em artigos do Instituto Nacional de Tecnologia. </p>
Supplemental Material for Manuscript: The Heterogeneity of Social Network and Institutional Covariance in the American Southeast
<p>Supplemental Material for Manuscript: The Heterogeneity of Social Network and Institutional Covariance in the American Southeast</p>
ScienceDex guides
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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.