Data-Intensive Computing in the 21st Century

The deluge of data that future applications must process—in domains ranging from science to business informatics—creates a compelling argument for substantially increased R&D targeted at discovering scalable hardware and software solutions for data-intensive problems

Galaxy Zoo: Morphological Classification and Citizen Science

We provide a brief overview of the Galaxy Zoo and Zooniverse projects, including a short discussion of the history of, and motivation for, these projects as well as reviewing the science these innovative internet-based citizen science projects have produced so far. We briefly describe the method of applying en-masse human pattern recognition capabilities to complex data in data-intensive research. We also provide a discussion of the lessons learned from developing and running these community--based projects including thoughts on future applications of this methodology. This review is intended to give the reader a quick and simple introduction to the Zooniverse.Comment: 11 pages, 1 figure; to be published in Advances in Machine Learning and Data Mining for Astronom

Evidence-based implementation practices applied to the intensive treatment of eating disorders: Summary of research and illustration of principles using a case example

Implementation of evidence‐based practices (EBPs) in intensive treatment settings poses a major challenge in the field of psychology. This is particularly true for eating disorder (ED) treatment, where multidisciplinary care is provided to a severe and complex patient population; almost no data exist concerning best practices in these settings. We summarize the research on EBP implementation science organized by existing frameworks and illustrate how these practices may be applied using a case example. We describe the recent successful implementation of EBPs in a community‐based intensive ED treatment network, which recently adapted and implemented transdiagnostic, empirically supported treatment for emotional disorders across its system of residential and day‐hospital programs. The research summary, implementation frameworks, and case example may inform future efforts to implement evidence‐based practice in intensive treatment settings.Published versio

Linking Literature and Data: Status Report and Future Efforts

In the current era of data-intensive science, it is increasingly important for researchers to be able to have access to published results, the supporting data, and the processes used to produce them. Six years ago, recognizing this need, the American Astronomical Society and the Astrophysics Data Centers Executive Committee (ADEC) sponsored an effort to facilitate the annotation and linking of datasets during the publishing process, with limited success. I will review the status of this effort and describe a new, more general one now being considered in the context of the Virtual Astronomical Observatory.Comment: 9 pages, 2 figures, to appear in: Future Professional Communication in Astronomy II (FPCA-II

Are We at Risk of Losing the Current Generation of Climate Researchers to Data Science?

Climate model output has progressively increased in size over the past decades and is expected to continue to rise in the future. Consequently, the research time expended by Early Career Researchers (ECRs) on data-intensive activities is displacing the time spent in fostering novel scientific ideas and expanding the frontiers of climate sciences. Here, we highlight an urgent need for a better balance between data-intensive and foundational climate science activities, more open-ended research opportunities that reinforce the scientific freedom of the ECRs, and strong coordinated action to provide infrastructure and resources to the ECRs working in under-resourced environments

Are We at Risk of Losing the Current Generation of Climate Researchers to Data Science?

Climate model output has progressively increased in size over the past decades and is expected to continue to rise in the future. Consequently, the research time expended by Early Career Researchers (ECRs) on data-intensive activities is displacing the time spent in fostering novel scientific ideas and expanding the frontiers of climate sciences. Here, we highlight an urgent need for a better balance between data-intensive and foundational climate science activities, more open-ended research opportunities that reinforce the scientific freedom of the ECRs, and strong coordinated action to provide infrastructure and resources to the ECRs working in under-resourced environments

Population Data Science: The science of data about people

Introduction Societal and individual benefits of data-intensive science are substantial but raise challenges of balancing individual privacy and public good, while building appropriate governance and socio-technical systems to support data-intensive science. We set out to define a new field of inquiry to move collective interests forward. Objectives and Approach Our objectives were: 1. To create a concise definition of the emerging field of Population Data Science; 2. To highlight the characteristics and challenges of Population Data Science; 3. To differentiate Population Data Science from existing fields of data science and informatics; and 4. To discuss the implications and future opportunities for Population Data Science. Objectives 1 and 2 were met largely through International Population Data Linkage Network (IPDLN) member engagement, Objective 3 was evaluated via literature review, and Objective 4 was achieved through iterative and collective work on a peer-reviewed position paper. Results We define Population Data Science succinctly as the science of data about people. It is related to, but distinct from, the fields of data science and informatics. A broader definition includes four characteristics of: i) data use for positive impact on individuals and populations; ii) bringing together and analyzing data from multiple sources; iii) identifying population-level insights; and iv) developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few individuals or organisations possess all of the requisite knowledge and skills comprising Population Data Science, so this is by nature a multi-disciplinary “team science” field. There is a need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement. Conclusion/Implications These implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, will catalyze significant advances in our understanding of society, health, and human behavior and increase the impact of our research

Lexical Derivation of the PINT Taxonomy of Goals: Prominence, Inclusiveness, Negativity Prevention, and Tradition

What do people want? Few questions are more fundamental to psychological science than this. Yet, existing taxonomies disagree on both the number and content of goals. We thus adopted a lexical approach and investigated the structure of goal-relevant words from the natural English lexicon. Through an intensive rating process, 1,060 goal-relevant English words were first located. In Studies 1-2, two relatively large and diverse samples (total n = 1,026) rated their commitment to approaching or avoiding these goals. Principal component analyses yielded 4 replicable components: Prominence, Inclusiveness, Negativity prevention, and Tradition (the PINT Taxonomy). Study 3-7 (total n = 1,396) supported the 4-factor structure of an abbreviated scale and found systematic differences in their relationships with past goal-content measures, the Big 5 traits, affect, and need satisfaction. This investigation thus provides a data-driven taxonomy of higher-order goal-content and opens up a wide variety of fascinating lines for future research