Datasets:

JerrySiRi

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SWN_Bert_based_2019_2023_100

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[ { "contexts": [ "In March 2019, PowerPoint educational materials were emailed to one academic institution's new surgical interns, who in June participated in an onsite 1-hour role-play \"hot seat\" group activity (GA) with an untrained simulated patient, and in October completed a single trained simulated patient (real-time raters) verification of proficiency (VOP) assessment. " ], "name": "PowerPoint" } ]

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[ { "contexts": [ "In the next step, the ArcGIS 9.3 was used to prepare geographic maps of the disease incidence and frequency. " ], "name": "ArcGIS 9.3" }, { "contexts": [ "using the Raster Calculator tool. " ], "name": "Raster Calculator tool" } ]

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[ { "contexts": [ "A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. ", "To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. ", "The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. ", "The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. ", "The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time." ], "name": "Faster R-CNN" } ]

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[ { "contexts": [ "Nutrient intake was determined using 'MicrodietTM', then compared to Dietary Reference Values (DRVs). " ], "name": "MicrodietTM" } ]

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[ { "contexts": [ "In the current study, we applied the Gene Set Cancer Analysis (GSCA), UCSC XENA, Gene Expression Omnibus (GEO), the Human Protein Atlas (HPA), LinkedOmics, DiseaseMeth version 2.0, TISIDB, and other databases for TGCT data mining. " ], "name": "Gene Set Cancer Analysis (GSCA)" }, { "contexts": [ "In the current study, we applied the Gene Set Cancer Analysis (GSCA), UCSC XENA, Gene Expression Omnibus (GEO), the Human Protein Atlas (HPA), LinkedOmics, DiseaseMeth version 2.0, TISIDB, and other databases for TGCT data mining. " ], "name": "UCSC XENA" }, { "contexts": [ "In the current study, we applied the Gene Set Cancer Analysis (GSCA), UCSC XENA, Gene Expression Omnibus (GEO), the Human Protein Atlas (HPA), LinkedOmics, DiseaseMeth version 2.0, TISIDB, and other databases for TGCT data mining. " ], "name": "Gene Expression Omnibus (GEO)" }, { "contexts": [ "In the current study, we applied the Gene Set Cancer Analysis (GSCA), UCSC XENA, Gene Expression Omnibus (GEO), the Human Protein Atlas (HPA), LinkedOmics, DiseaseMeth version 2.0, TISIDB, and other databases for TGCT data mining. " ], "name": "the Human Protein Atlas (HPA)" }, { "contexts": [ "In the current study, we applied the Gene Set Cancer Analysis (GSCA), UCSC XENA, Gene Expression Omnibus (GEO), the Human Protein Atlas (HPA), LinkedOmics, DiseaseMeth version 2.0, TISIDB, and other databases for TGCT data mining. " ], "name": "LinkedOmics" }, { "contexts": [ "In the current study, we applied the Gene Set Cancer Analysis (GSCA), UCSC XENA, Gene Expression Omnibus (GEO), the Human Protein Atlas (HPA), LinkedOmics, DiseaseMeth version 2.0, TISIDB, and other databases for TGCT data mining. " ], "name": "DiseaseMeth version 2.0" }, { "contexts": [ "In the current study, we applied the Gene Set Cancer Analysis (GSCA), UCSC XENA, Gene Expression Omnibus (GEO), the Human Protein Atlas (HPA), LinkedOmics, DiseaseMeth version 2.0, TISIDB, and other databases for TGCT data mining. " ], "name": "TISIDB" } ]

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[ { "contexts": [ "Ongoing and future trials registered on clinicaltrials.gov were also recorded, appraised, and considered in conjunction with the literature findings. " ], "name": "clinicaltrials.gov" } ]

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[ { "contexts": [ "Pre-interventional CT data of patients (n = 24) with LA-LVA (<0.5 mV) in voltage mapping after AF ablation were analyzed (Surgery Explorer, QuantMD LLC). " ], "name": "Surgery Explorer" }, { "contexts": [ "Pre-interventional CT data of patients (n = 24) with LA-LVA (<0.5 mV) in voltage mapping after AF ablation were analyzed (Surgery Explorer, QuantMD LLC). " ], "name": "QuantMD LLC" } ]

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[ { "contexts": [ "The aim of this retrospective infodemiological study is to investigate the public interest to the bakery products in the early period of coronavirus outbreak through Google Trends and open access data banks.\n\n\n", "Keywords were searched in Turkish, Italian and Swedish language by Google Trends. " ], "name": "Google Trends" } ]

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[ { "contexts": [ "The NSQIP-Pediatric database was queried for cases of posterior fusion for AIS. " ], "name": "NSQIP-Pediatric database" } ]

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