Global open data in agriculture and nutrition (Godan) initiative partner network analysis

Background: Ensuring healthy, safe and nutritious food for everyone is a global concern. Accessing the information to make the correct decisions regarding food security can be challenging. Open data has been shown to help solve practical problems related to agriculture and nutrition, enabling effective decision-making. In order to create a global data ecosystem that benefits everyone, a wide range of stakeholders must be included in the conversations. The GODAN initiative involves a network of over 500 partner organizations committed to open data in agriculture and nutrition. Methods: We analysed data from a survey of the partner organizations, with 225 respondents, to determine open data activities, including challenges, use of open data, stakeholder involvement and future directions. Respondents were asked a variety of free text and multiple choice questions. Results: 160 partners had at least one open data activity, 65 did not, or did not know. Of the 160, 36 had a second activity. Overall, GODAN partners are developing 200 open data activities. Agriculture is the most common focus for an open data activity. Nutrition-only activities are strongly underrepresented. The most frequently mentioned challenge was cost, which is linked to data governance, management, and human capacity; many do not have the funding to begin or maintain open data activities. Conclusions: The most common challenges were the ones related to the data itself, including how to access it, manage it, and how to keep the sensitive data secure. GODAN is already focusing on these issues through the Responsible Data and Data Ownership pieces. Capacity building, and empowering partners with the tools they need to act, is one of the most effective actions available for GODAN. Funding for open data, as well as research to create more sustainable business models, should be the focus of the open data agenda.</p

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images

We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules when using convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed AG models are evaluated on a variety of tasks, including medical image classification and segmentation. For classification, we demonstrate the use case of AGs in scan plane detection for fetal ultrasound screening. We show that the proposed attention mechanism can provide efficient object localisation while improving the overall prediction performance by reducing false positives. For segmentation, the proposed architecture is evaluated on two large 3D CT abdominal datasets with manual annotations for multiple organs. Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency. Moreover, AGs guide the model activations to be focused around salient regions, which provides better insights into how model predictions are made. The source code for the proposed AG models is publicly available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging with Deep Learning). arXiv admin note: substantial text overlap with arXiv:1804.03999, arXiv:1804.0533

USD Teacher Residency Program Impact: Classroom Management

USD’s yearlong Teacher Residency Program involves teacher candidates in a full year of teaching experience incorporated into their four-year program. This report examines the effect of two semesters of student teaching over a traditional one-semester model. Students’ confidence in classroom management greatly increased with the added semester.https://red.library.usd.edu/serc/1000/thumbnail.jp

USD Teacher Residency Program Impact: Culturally Responsive Pedagogy

USD’s yearlong Teacher Residency Program involves teacher candidates in a full year of teaching experience incorporated into their four-year program. This report examines the effect of two semesters of student teaching over a traditional one-semester model on teacher candidates’ confidence in culturally responsive pedagogy. Students reported much greater preparedness both to design and to implement instruction that incorporates students’ readiness, background, and culture, among other factors.https://red.library.usd.edu/serc/1001/thumbnail.jp

USD Teacher Residency Program Impact: Instructional Technology

USD’s yearlong Teacher Residency Program involves teacher candidates in a full year of teaching experience incorporated into their four-year program. This report examines the impact of the added semester of teaching experience on teacher candidates’ confidence to intentionally integrate technology with content and pedagogy. USD’s teach candidates indicated much greater confidence after a year-long residency than after a traditional single-semester student teaching experience.https://red.library.usd.edu/serc/1003/thumbnail.jp

Supportive Residency Instructors: University of South Dakota’s Teacher Residency Program

USD’s yearlong Teacher Residency Program involves teacher candidates in a full year of teaching experience incorporated into their four-year program. Residency instructors serve as coaches and mediators when issues arise, but they also provide timely instruction on such topics as classroom management, educational assessment, and others. Students in the residency program strongly agree that USD’s residency instructors support their instructional growth, assist them in overcoming challenging situations, and provide support and feedback to succeed.https://red.library.usd.edu/serc/1002/thumbnail.jp

Aerosol Optical Retrieval and Surface Reflectance from Airborne Remote Sensing Data over Land

Quantitative analysis of atmospheric optical properties and surface reflectance can be performed by applying radiative transfer theory in the Atmosphere-Earth coupled system, for the atmospheric correction of hyperspectral remote sensing data. This paper describes a new physically-based algorithm to retrieve the aerosol optical thickness at 550nm (τ550) and the surface reflectance (ρ) from airborne acquired data in the atmospheric window of the Visible and Near-Infrared (VNIR) range. The algorithm is realized in two modules. Module A retrieves τ550 with a minimization algorithm, then Module B retrieves the surface reflectance ρ for each pixel of the image. The method was tested on five remote sensing images acquired by an airborne sensor under different geometric conditions to evaluate the reliability of the method. The results, τ550 and ρ, retrieved from each image were validated with field data contemporaneously acquired by a sun-sky radiometer and a spectroradiometer, respectively. Good correlation index, r, and low root mean square deviations, RMSD, were obtained for the τ550 retrieved by Module A (r2 = 0.75, RMSD = 0.08) and the ρ retrieved by Module B (r2 ≤ 0.9, RMSD ≤ 0.003). Overall, the results are encouraging, indicating that the method is reliable for optical atmospheric studies and the atmospheric correction of airborne hyperspectral images. The method does not require additional at-ground measurements about at-ground reflectance of the reference pixel and aerosol optical thickness