Open Science principles for accelerating trait-based science across the Tree of Life.

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges

Open Science principles for accelerating trait-based science across the Tree of Life

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges

Does bereavement support save lives?

This report aims to provide Cittimani Hospice Service with an overview of the findings and language texts derived from the study that provides feedback from the participants on the services provided by the Hospice and commentary on a range of service delivery issues.Associated Grant:Cittimani Hospice Service

Psycho-Social Update: A quarterly newsletter of IPP-SHR, Central Queensland University, Australia

‘Psycho-Social Update’ is a newsletter from the InternationalProgram of Psycho-Social Health Research IPP-SHR, Central Queensland University, circulated four times a year to an international audience of service providers, policy makers and academics with an interest in the human experience of serious illness.The aim of the newsletter is to translate the wealth of research findings available in the academic psycho-socialhealth literature into lay summaries that can be useful for service delivery and health policy. The newsletter also showcases the diversity of excellent psycho-social programinitiatives designed by practitioners and organisations to assist people to deal with the many challenges associated with serious physical and/ or mental illness. The newsletter celebrates the fact that psycho-social research is now a well established discipline that is ‘making a difference’ in the real world of health care. </div

Eight seasons : our family's journey with childhood leukaemia

"Tahlia is four when she is diagnosed with acute lymphoblastic leukaemia, a rapidly progressing form of childhood cancer. Her life is irrevocabley changed, and her family are confronted with the many difficult challenges associated with childhood leukaemia. This is a powerful and honest story, told through the eyes of a mother, about a little girl's strength and courage. This journey not only leaves the family with a new perspective of what life is about, but is an inspiration to all who come across it."--cove

Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

In this paper, we propose a supervised selection based method to decrease both the computation and the feature dimension of the original bilinear pooling. Different from currently existing compressed second-order pooling methods, the proposed selection method is matrix normalization applicable. Moreover, by extracting the selected highly semantic feature channels, we proposed the Fisher- Recurrent-Attention structure and achieved state-of-the-art fine-grained classification results among the VGG-16 based models

Fine-Grained Categorization by Deep Part-Collaboration Convolution Net

© 2018 IEEE. In part-based categorization context, the ability to learn representative feature from quantitative tiny object parts is of similar importance as to exactly localize the parts. We propose a new deep net structure for fine-grained categorization that follows the taxonomy workflow, which makes it interpretable and understandable for humans. By training customized sub-nets on each manually annotated parts, we increased the state-of-the-art part-based classification accuracy for general fine-grained CUB-200-2011 dataset by 2.1%. Our study shows the proposed method can produce more activation to discriminate detail part difference while maintaining high computing performance by applying a set of strategies to optimize the deep net structure

Squeezed Bilinear Pooling for Fine-Grained Visual Categorization

In this paper, we propose a supervised selection based method to decrease both the computation and the feature dimension of the original bilinear pooling. Different from currently existing compressed second-order pooling methods, the proposed selection method is matrix normalization applicable. Moreover, by extracting the selected highly semantic feature channels, we proposed the Fisher- Recurrent-Attention structure and achieved state-of-the-art fine-grained classification results among the VGG-16 based models