Macroecology in the age of Big Data – Where to go from here?

Recent years have seen an exponential increase in the amount of data available in all sciences and application domains. Macroecology is part of this “Big Data” trend, with a strong rise in the volume of data that we are using for our research. Here, we summarize the most recent developments in macroecology in the age of Big Data that were presented at the 2018 annual meeting of the Specialist Group Macroecology of the Ecological Society of Germany, Austria and Switzerland (GfÖ). Supported by computational advances, macroecology has been a rapidly developing field over recent years. Our meeting highlighted important avenues for further progress in terms of standardized data collection, data integration, method development and process integration. In particular, we focus on (a) important data gaps and new initiatives to close them, for example through space- and airborne sensors, (b) how various data sources and types can be integrated, (c) how uncertainty can be assessed in data-driven analyses and (d) how Big Data and machine learning approaches have opened new ways of investigating processes rather than simply describing patterns. We discuss how Big Data opens up new opportunities, but also poses new challenges to macroecological research. In the future, it will be essential to carefully assess data quality, the reproducibility of data compilation and analytical methods, and the communication of uncertainties. Major progress in the field will depend on the definition of data standards and workflows for macroecology, such that scientific quality and integrity are guaranteed, and collaboration in research projects is made easier.     

Opportunities and challenges for the life sciences community

Twenty-first century life sciences have transformed into data-enabled (also called data-intensive, data-driven, or big data) sciences. They principally depend on data-, computation-, and instrumentation-intensive approaches to seek comprehensive understanding of complex biological processes and systems (e.g., ecosystems, complex diseases, environmental, and health challenges). Federal agencies including the National Science Foundation (NSF) have played and continue to play an exceptional leadership role by innovatively addressing the challenges of data-enabled life sciences. Yet even more is required not only to keep up with the current developments, but also to pro-actively enable future research needs. Straightforward access to data, computing, and analysis resources will enable true democratization of research competitions; thus investigators will compete based on the merits and broader impact of their ideas and approaches rather than on the scale of their institutional resources. This is the Final Report for Data-Intensive Science Workshops DISW1 and DISW2. The first NSF-funded Data Intensive Science Workshop (DISW1, Seattle, WA, September 19-20, 2010) overviewed the status of the data-enabled life sciences and identified their challenges and opportunities. This served as a baseline for the second NSF-funded DIS workshop (DISW2, Washington, DC, May 16-17, 2011). Based on the findings of DISW2 the following overarching recommendation to the NSF was proposed: establish a community alliance to be the voice and framework of the data-enabled life sciences. After this Final Report was finished, Data-Enabled Life Sciences Alliance (DELSA, www.delsall.org ) was formed to become a Digital Commons for the life sciences community.     

Evolutionism and the Teaching of Science: How Portugal Has Been “Playing with the Big Tree of Evolution”

The concept of evolution is fundamental to the teaching of biological sciences. Nevertheless, it seems frequently neglected and/or forgotten in our classrooms and absent from the school syllabus. These difficulties are present today in the Portuguese educational system, especially concerning the issue of human evolution. To overcome this difficulty, a multidisciplinary pilot project entitled Playing with the Big Tree of Evolution was developed by a nonprofit association called Group of Studies in Human Evolution in Portuguese schools and in other public and private organizations. Combining non-formal and informal apprenticeship, the project is composed of a set of pedagogical and experimental activities that aim to promote the broad concept of human evolution as well as to demystify the anthropocentric perspective that places humans at the top of the chain of life.     

Knowledge and Theme Discovery across Very Large Biological Data Sets Using Distributed Queries: A Prototype Combining Unstructured and Structured Data

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework.     

Invited review: Big Data in precision dairy farming

Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated expectations. A conceptual model was created, and a literature search in Scopus resulted in 1442 scientific peer reviewed papers. After thorough screening on relevance and classification by the authors, 142 papers remained for further analysis. The area of precision dairy farming (with classes in the primary chain (dairy farm, feed, breed, health, food, retail, consumer) and levels for object of interest (animal, farm, network)), the Big Data-V area (with categories on Volume, Velocity, Variety and other V’s) and the data analytics area (with categories in analysis methods (supervised learning, unsupervised learning, semi-supervised classification, reinforcement learning) and data characteristics (time-series, streaming, sequence, graph, spatial, multimedia)) were analysed. The animal sublevel, with 83% of the papers, exceeds the farm sublevel and network sublevel. Within the animal sublevel, topics within the dairy farm level prevailed with 58% over the health level (33%). Within the Big Data category, the Volume category was most favoured with 59% of the papers, followed by 37% of papers that included the Variety category. None of the papers included the Velocity category. Supervised learning, representing 87% of the papers, exceeds unsupervised learning (12%). Within supervised learning, 64% of the papers dealt with classification issues and exceeds the regression methods (36%). Time-series were used in 61% of the papers and were mostly dealing with animal-based farm data. Multimedia data appeared in a greater number of recent papers. Based on these results, it can be concluded that Big Data is a relevant topic of research within the precision dairy farming area, but that the full potential of Big Data in this precision dairy farming area is not utilised yet. However, the present authors expect the full potential of Big Data, within the precision dairy farming area, will be reached when multiple Big Data characteristics (Volume, Variety and other V’s) and sources (animal, groups, farms and chain parts) are used simultaneously, adding value to operational and strategic decision.     

‘Small Data’ for big insights in ecology

Big Data science has significantly furthered our understanding of complex systems by harnessing large volumes of data, generated at high velocity and in great variety. However, there is a risk that Big Data collection is prioritised to the detriment of ‘Small Data’ (data with few observations). This poses a particular risk to ecology where Small Data abounds. Machine learning experts are increasingly looking to Small Data to drive the next generation of innovation, leading to development in methods for Small Data such as transfer learning, knowledge graphs, and synthetic data. Meanwhile, meta-analysis and causal reasoning approaches are evolving to provide new insights from Small Data. These advances should add value to high-quality Small Data catalysing future insights for ecology.     

A Toolkit for Ecosystem Ecologists in the Time of Big Science

Ecosystem ecologists are being challenged to address the increasingly complex problems that comprise Big Science. These problems include multiple levels of biological organization that cross multiple interacting temporal and spatial scales, from individual plants, animals, and microbes to landscapes, continents, and the globe. As technology improves, the availability of data, derived data products, and information to address these complex problems are increasing at finer and coarser scales of resolution, and legacy, dark data are brought to light. Data analytics are improving as big data increase in importance in other fields that are improving access to these data. New data sources (crowdsourcing, social media) and ease of communication and collaboration among ecosystem ecologists and other disciplines are increasingly possible via the internet. It is increasingly important that ecosystem ecologists be able to communicate their findings, and to translate their concepts and findings into concrete bits of information that a general public can understand. Traditional approaches that portray ecosystem sciences as a dichotomy between empirical research and theoretical research will keep the field from fully contributing to the complexity of global change questions, and will keep ecosystem ecologists from taking full advantage of the data and technology available. Building on previous research, we describe a more forward-looking, integrated empirical–theoretical modeling approach that is iterative with learning to take advantage of the elements of Big Science. We suggest that training ecosystem ecologists in this integrated approach will be critical to addressing complex Earth system science questions, now and in the future.     

Criminal Prohibition of Wrongful Re‑identification: Legal Solution or Minefield for Big Data?

The collapse of confidence in anonymization (sometimes also known as de-identification) as a robust approach for preserving the privacy of personal data has incited an outpouring of new approaches that aim to fill the resulting trifecta of technical, organizational, and regulatory privacy gaps left in its wake. In the latter category, and in large part due to the growth of Big Data–driven biomedical research, falls a growing chorus of calls for criminal and penal offences to sanction wrongful re-identification of “anonymized” data. This chorus cuts across the fault lines of polarized privacy law scholarship that at times seems to advocate privacy protection at the expense of Big Data research or vice versa. Focusing on Big Data in the context of biomedicine, this article surveys the approaches that criminal or penal law might take toward wrongful re-identification of health data. It contextualizes the strategies within their respective legal regimes as well as in relation to emerging privacy debates focusing on personal data use and data linkage and assesses the relative merit of criminalization. We conclude that this approach suffers from several flaws and that alternative social and legal strategies to deter wrongful re-identification may be preferable.     

Standards for systems biology

High-throughput technologies are generating large amounts of complex data that have to be stored in databases, communicated to various data analysis tools and interpreted by scientists. Data representation and communication standards are needed to implement these steps efficiently. Here we give a classification of various standards related to systems biology and discuss various aspects of standardization in life sciences in general. Why are some standards more successful than others, what are the prerequisites for a standard to succeed and what are the possible pitfalls?     

Biology and data-intensive scientific discovery in the beginning of the 21st century

The life sciences are poised at the beginning of a paradigm-changing evolution in the way scientific questions are answered. Data-Intensive Science (DIS) promise to provide new ways of approaching scientific challenges and answering questions. This article is a summary of the life sciences issues and challenges as discussed in the DIS workshop in Seattle, September 19-20, 2010.     

Learning developmental biology has priority in the life sciences curriculum in Singapore

Singapore has embraced the life sciences as an important discipline to be emphasized in schools and universities. This is part of the nation's strategic move towards a knowledge-based economy, with the life sciences poised as a new engine for economic growth. In the life sciences, the area of developmental biology is of prime interest, since it is not just intriguing for students to know how a single cell can give rise to a complex, coordinated, functional life that is multicellular and multifaceted, but more importantly, there is much in developmental biology that can have biomedical implications. At different levels in the Singapore educational system, students are exposed to various aspects of developmental biology. The author has given many guest lectures to secondary (ages 12-16) and high school (ages 17-18) students to enthuse them about topics such as embryo cloning and stem cell biology. At the university level, some selected topics in developmental biology are part of a broader course which caters for students not majoring in the life sciences, so that they will learn to comprehend how development takes place and the significance of the knowledge and impacts of the technologies derived in the field. For students majoring in the life sciences, the subject is taught progressively in years two and three, so that students will gain specialist knowledge in developmental biology. As they learn, students are exposed to concepts, principles and mechanisms that underlie development. Different model organisms are studied to demonstrate the rapid advances in this field and to show the interconnectivity of developmental themes among living things. The course inevitably touches on life and death matters, and the social and ethical implications of recent technologies which enable scientists to manipulate life are discussed accordingly, either in class, in a discussion forum, or through essay writing.     

The limits of reductionism in the life sciences

In the contemporary life sciences more and more researchers emphasize the "limits of reductionism" (e.g. Ahn et al. 2006a, 709; Mazzocchi 2008, 10) or they call for a move "beyond reductionism" (Gallagher/Appenzeller 1999, 79). However, it is far from clear what exactly they argue for and what the envisioned limits of reductionism are. In this paper I claim that the current discussions about reductionism in the life sciences, which focus on methodological and explanatory issues, leave the concepts of a reductive method and a reductive explanation too unspecified. In order to fill this gap and to clarify what the limits of reductionism are I identify three reductive methods that are crucial in the current practice of the life sciences: decomposition, focusing on internal factors, and studying parts in isolation. Furthermore, I argue that reductive explanations in the life sciences exhibit three characteristics: first, they refer only to factors at a lower level than the phenomenon at issue, second, they focus on internal factors and thus ignore or simplify the environment of a system, and, third, they cite only the parts of a system in isolation.     

从科研论文量看世界生命科学的发展

应用美国ESI基本科学指标数据库,对1993年至2003年世界科研产出成果进行统计分析,反映国内外生命科学重点研究领域和学科分布特点,揭示我国与国外相比存在的差距。从文献计量学角度,对我国生命科学的发展提出了建设性意见。     

Biotech round the world: Focus on Mexico

Trends in trade Trends in life sciences education and workforce Increasing numbers of life sciences publications and researchers Manufacturing of medical devices Pharmaceutical & clinical research Mexican Life Sciences Alliance The four main research regions in Mexico     

An integrated,modular approach to data science education in microbiology

We live in an increasingly data-driven world, where high-throughput sequencing and mass spectrometry platforms are transforming biology into an information science. This has shifted major challenges in biological research from data generation and processing to interpretation and knowledge translation. However, postsecondary training in bioinformatics, or more generally data science for life scientists, lags behind current demand. In particular, development of accessible, undergraduate data science curricula has the potential to improve research and learning outcomes as well as better prepare students in the life sciences to thrive in public and private sector careers. Here, we describe the Experiential Data science for Undergraduate Cross-Disciplinary Education (EDUCE) initiative, which aims to progressively build data science competency across several years of integrated practice. Through EDUCE, students complete data science modules integrated into required and elective courses augmented with coordinated cocurricular activities. The EDUCE initiative draws on a community of practice consisting of teaching assistants (TAs), postdocs, instructors, and research faculty from multiple disciplines to overcome several reported barriers to data science for life scientists, including instructor capacity, student prior knowledge, and relevance to discipline-specific problems. Preliminary survey results indicate that even a single module improves student self-reported interest and/or experience in bioinformatics and computer science. Thus, EDUCE provides a flexible and extensible active learning framework for integration of data science curriculum into undergraduate courses and programs across the life sciences.     

Aerodynamically assisted jetting and threading for processing concentrated suspensions containing advanced structural,functional and biological materials

In recent years material sciences have been interpreted right across the physical and the life sciences. Essentially this discipline broadly addresses the materials, processing, and/or fabrication right up to the structure. The materials and structures areas can range from the micro- to the nanometre scale and, in a materials sense, span from the structural, functional to the most complex, namely biological (living cells). It is generally recognised that the processing or fabrication is fundamental in bridging the materials with their structures. In a global perspective, processing has not only contributed to the materials sciences but its very nature has bridged the physical with the life sciences. In this review we discuss one such swiftly emerging fabrication approach having a plethora of applications spanning the physical and life sciences.     

网格技术在生命科学中的应用研究

生命科学尤其是生物信息学中庞大的数据处理和高性能计算大大超出了某一单个机构的计算能力,而网格技术的出现使这些困难应刃而解,并逐渐成为生命科学标准的网络基础.从计算网格、数据网格和知识网格3个方面综述了最新的网格技术.最后展望了网格技术在生命科学领域中广阔的应用前景.     

Physiology in Russia: state, problems

The issue of the content of physiology as science, its methods, directions of modern physiology, and its place in the system of life sciences is discussed. Structure of the system of regulations, the role of endocrine system and autacoids in particular, and physiological significance of homeostasis are analyzed in detail. Problems are considered concerning the applied significance of physiology, interaction of universities and research institutions, organization ofphysiological studies in Russia. Data on members in the regional divisions of Pavlov Physiological Society are presented.