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ShareScore release 0.7.1
Dataset results
8 results for “rapid risk assessment”
STudy to Assess Rapid Disease Progression by Clinical and Genetic Factors In Glaucoma patientS That Are High Risk
ClinicalTrials.gov study NCT01442896. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Supplementary material 3 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Plant pathogenic bacteria assessment for Zambia
Supplementary material 4 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Plant pathogenic protist assessment for Zambia
Supplementary material 2 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Guidelines for scoring species
Supplementary material 5 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
Assessment for vector species
Supplementary material 1 from: Mulema J, Phiri S, Bbebe N, Chandipo R, Chijikwa M, Chimutingiza H, Kachapulula P, Kankuma Mwanda F, Matimelo M, Mazimba-Sikazwe E, Mfune S, Mkulama M, Moonga M, Mphande W, Mufwaya M, Mulenga R, Mweemba B, Ndalamei Mabote D, Nkunika P, Nthenga I, Tembo M, Chowa J, Odunga S, Opisa S, Kasoma C, Charles L, Makale F, Rwomushana I, Phiri NA (2024) Rapid risk assessment of plant pathogenic bacteria and protists likely to threaten agriculture, biodiversity and forestry in Zambia. NeoBiota 91: 145-178. https://doi.org/10.3897/neobiota.91.113801
All data from horizon scanning for Zambia
Rapid Assessment of Esophageal Adenocarcinoma Risk Test
ClinicalTrials.gov study NCT03366012. IPD Sharing: NO. Countries: 1. Publications: 0.
Dataset related to article "Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"
<p>This record contains raw data related to article “Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"</p> <p>Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.</p>
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.