To enhance understanding of the process of climate change adaptation and to facilitate the planning and implementation of sustainable adaptation strategies deeper consideration of the factors that impede adaptation is required. Barriers to climate change adaptation are, consequently, being increasingly reported. But, despite this progress, knowledge of barriers that hamper adaptation in developing countries remains limited, especially in relation to underlying causes of vulnerability and low adaptive capacity. To further improve understanding of barriers to adaptation and identify gaps in the state-of-the-art knowledge, we undertook a synthesis of empirical literature from sub-Saharan Africa focusing on vulnerable, natural resource dependent communities and livelihoods. Our review illustrates that: 1) local level studies that reveal barriers to adaptation are diverse, although there is a propensity for studies on small-holder farmers; 2) many of the studies identify several barriers to adaptation, but appreciation of their interactions and compounded impacts remains scarce; and 3) most of the barriers uncovered relate broadly to biophysical, knowledge and financial constraints on agricultural production and rural development. More hidden and under-acknowledged political, social and psychological barriers are rarely mentioned, unless captured in studies that specifically set out to investigate these. We finish our review by highlighting gaps in understanding and by suggesting future research directions, focussing on issues of social justice. We argue that research on barriers needs to start asking why these barriers emerge, how they work together to shape adaptation processes, who they affect most, and what is needed to overcome them.

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Although growing in popularity in other countries, the business professional doctorate has yet to gain traction in the U.S. Such programs, intended to offer advanced disciplinary and research training to individuals who later plan to apply that training to employment in industry, are frequently seen to be inferior to their academically-focused Ph.D. program counterparts. Furthermore, if the sole purpose of a doctorate is to develop individuals focused on producing scholarly research articles, that assessment may well be correct. We argue, however, that such a narrowly focused view of the purpose of doctoral programs is self-defeating; by exclusively focusing on scholarly research and writings, we virtually guarantee that our research will never make it into practice. The paper begins by identifying a variety of types of doctoral programs that exist globally and placing these in a conceptual framework. We then present a detailed case study of the information systems (IS) doctoral programs offered in Osnabrueck, Germany-where as many as 90% of candidates choose careers in industry in preference to academia. Finally, we proposesupported using both conceptual arguments drawn from the study of complex informing and observed examples-that the greatest benefit of business professional doctorates may be the creation of enduring informing channels between practice and industry. Presented in this light, the business professional doctorate should be viewed as an essential part of the broader research ecology, rather than as a weak substitute for the disciplinary Ph.D.

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BioTuring’s BBrowser is a software solution that helps scientists effectively analyze single-cell omics data. It combines big data with big computation and modern data visualization to create a unique platform where scientists can interact and obtain important biological insights from the massive amounts of single-cell data. BBrowser has three main components: a curated single-cell database, a big-data analytics layer, and a data visualization module. BBrowser is available for download at: https://bioturing.com/bbrowser/download.

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Task complexity is a construct widely used in the behavioral sciences to explore and predict the relationship between task characteristics and information processing. Because the creation and use of IT in the performance of tasks is a central area of informing science (IS) research, it follows that better understanding of task complexity should be of great potential benefit to IS researchers and practitioners. Unfortunately, applying task complexity to IS is difficult because no complete, consistent definition exists. Furthermore, the most commonly adopted definition, objective task complexity, tends to be of limited use in situations where discretion or learning is present, or where information technology (IT) is available to assist the task performer. These limitations prove to be severe in many common IS situations.The paper presents a literature review identifying thirteen distinct definitions of task complexity, then synthesizes these into a new five-class framework, referred to as the Comprehensive Task Complexity Classes (CTCC). It then shows the potential relevance of the CTCC to IS, focusing on different ways it could be applied throughout a hypothetical information systems lifecycle. In the course of doing so, the paper also illustrates how the interaction between different classes of task complexity can serve as a rich source of questions for future investigations.

Task complexity is a construct widely used in the behavioral sciences to explore and predict the relationship between task characteristics and information processing. Because the creation and use of IT in the performance of tasks is a central area of informing science (IS) research, it follows that better understanding of task complexity should be of great potential benefit to IS researchers and practitioners. Unfortunately, applying task complexity to IS is difficult because no complete, consistent definition exists. Furthermore, the most commonly adopted definition, objective task complexity, tends to be of limited use in situations where discretion or learning is present, or where information technology (IT) is available to assist the task performer. These limitations prove to be severe in many common IS situations.The paper presents a literature review identifying thirteen distinct definitions of task complexity, then synthesizes these into a new five-class framework, referred to as the Comprehensive Task Complexity Classes (CTCC). It then shows the potential relevance of the CTCC to IS, focusing on different ways it could be applied throughout a hypothetical information systems lifecycle. In the course of doing so, the paper also illustrates how the interaction between different classes of task complexity can serve as a rich source of questions for future investigations.