Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap

Insights from expert software design practice

Software is a designed artifact. In other design disciplines, such as architecture, there is a well-established tradition of design studies which inform not only the discipline itself but also tool design, processes, and collaborative work. The 'challenge' of this paper is to consider software from such a 'design studies' perspective. This paper will present a series of observations from empirical studies of expert software designers, and will draw on examples from actual professional practice. It will consider what experts' mental imagery, software visualisations, and sketches suggest about software design thinking. It will also discuss some of the deliberate practices experts use to promote innovation. Finally, it will open discussion on the tensions between observed software design practices and received methodology in software engineering

Passion-based co-creation

As our world is getting evermore interconnected and entwined across professional, organizational and national boundaries, challenges rarely fall neatly into the realm of single functions, departments or disciplines any more. While it is uncertain what the world will look like in a few decades, and many of the needed skills and approaches are unknown, we do know we need a way of creating the future together. Counting on a few heroic innovation champions will not suffice in transforming our organizations. Passion-based co-creation describes the approach to tackling these issues that has led to the creation of Aalto Design Factory and the Global Design Factory Network of 20 co-creation platforms around the globe. Our approach, in a nutshell, is a way of creating something new together, sprinkled with a hefty dose of intrinsic motivation. Sound too hype-y? Worry not, we aren’t preaching the adoption of yet another ‘’perfect’ tool, licensed process, or turnkey solution. Rather, we want to share some principles we have found effective, offer a look into the scientific backbone of our approach, and provide tangible examples on how to bring the mindset and ways of working into your organization. Mix, match, and adapt these elements to create your own personalized stack of building blocks for passion-based co-creation in your unique context

Managing social capital as knowledge management – some specification and representation issues.

‘Classic’ accounts of social capital have emergedin accounts of stable networks or institutionalenvironments. These conditions do not apply inthe case of many firms – a case in point beingsmall firm networks that rely on rapid turnover ofprojects. Our research team is attempting toidentify how social capital is manifest in thesecontexts, and thus to make suggestions forbuilding, maintaining and refreshing such capital.We present work to date that converts this type oftacit knowledge into sets of explicit andmanageable local data, and provide examples ofinformation visualizations for profiling andretrieval that support the management of socialcapital

Engineering for a Science-Centric Experimentation Platform

Netflix is an internet entertainment service that routinely employs experimentation to guide strategy around product innovations. As Netflix grew, it had the opportunity to explore increasingly specialized improvements to its service, which generated demand for deeper analyses supported by richer metrics and powered by more diverse statistical methodologies. To facilitate this, and more fully harness the skill sets of both engineering and data science, Netflix engineers created a science-centric experimentation platform that leverages the expertise of data scientists from a wide range of backgrounds by allowing them to make direct code contributions in the languages used by scientists (Python and R). Moreover, the same code that runs in production is able to be run locally, making it straightforward to explore and graduate both metrics and causal inference methodologies directly into production services. In this paper, we utilize a case-study research method to provide two main contributions. Firstly, we report on the architecture of this platform, with a special emphasis on its novel aspects: how it supports science-centric end-to-end workflows without compromising engineering requirements. Secondly, we describe its approach to causal inference, which leverages the potential outcomes conceptual framework to provide a unified abstraction layer for arbitrary statistical models and methodologies.Comment: 10 page