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For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.

In the past decade, there have been remarkable advances in proteomic technologies. Mass spectrometry has emerged as the preferred method for in-depth characterization of the protein components of biological systems. Using mass spectrometry, key insights into the composition, regulation and function of molecular complexes and pathways have been gained. From these studies, it is clear that mass-spectrometry-based proteomics is now a powerful 'hypothesis-generating engine' that, when combined with complementary molecular, cellular and pharmacological techniques, provides a framework for translating large data sets into an understanding of complex biological processes.

Complex coupled multi-physics simulations are playing increasingly important roles in scientific and engineering applications such as fusion plasma and climate modeling. At the same time, extreme scales, high levels of concurrency and the advent of multicore and many core technologies are making the high-end parallel computing systems on which these simulations run, hard to program. While the Partitioned Global Address Space (PGAS) languages is attempting to address the problem, the PGAS model does not easily support the coupling of multiple application codes, which is necessary for the coupled multi-physics simulations. Furthermore, existing frameworks that support coupled simulations have been developed for fragmented programming models such as message passing, and are conceptually mismatched with the shared memory address space abstraction in the PGAS programming model. This paper explores how multi-physics coupled simulations can be supported within the PGAS programming framework. Specifically, in this paper, we present the design and implementation of the XpressSpace programming system, which enables efficient and productive development of coupled simulations across multiple independent PGAS Unified Parallel C (UPC) executables. XpressSpace provides the global-view style programming interface that is consistent with the memory model in UPC, and provides an efficient runtime system that can dynamically capture the data decomposition of global-view arrays and enable fast exchange of parallel data structures between coupled codes. In addition, XpressSpace provides the flexibility to define the coupling process in specification file that is independent of the program source codes. We evaluate the performance and scalability of Xpress Space prototype implementation using different coupling patterns extracted from real world multi-physics simulation scenarios, on the Jaguar Cray XT5 system of Oak Ridge National Laboratory.

Dexy is a tool for writing about code and data, so it's an ideal tool for writing about data science which involves lots of both.

Widespread use of a comprehensive CI framework has the potential to revolutionize every science and engineering discipline as well as education. Computing power, data volumes, software, and network capacities are all on exponential growth paths. Highly diverse, multidisciplinary collaborations and partnerships are growing dramatically, greatly enabled by new and emerging technologies, spanning multiple agencies and international domains to address complex grand challenge problems. Scientific discovery is being advanced by linking computational facilities and instruments to build highly-capable simulation models, sophisticated algorithms, software, and other tools and services. CIF21 will enable new approaches to research and education - supporting new modalities such as distributed collaborative networks, allowing researchers to more easily adapt to changes in the research and education process, and providing an integrated framework for people, instruments, and tools to address complex problems and conduct multidisciplinary research. CIF21 will consist of secure, geographically distributed, and connected CI: advanced computing facilities, scientific instruments, software environments, advanced networks, data storage capabilities, and the critically important human capital and expertise.

Driven by input from its scientific community, the Cancer Imaging Program (CIP) finds itself at the junction of two powerful scientific requisites; the need for cross-disciplinary research and inter-institutional data-sharing to speed scientific discovery and reduce redundancy, and the need to provide imaging phenotype data to augment large scale genomic analysis.

Policies that request and require investigators to share research data are becoming more common. This research study, The impact of public data archiving policies on attitudes, experiences, and practices of authors, explores the impact of journal policies on the experiences, attitudes, and practices of active scientists.

In occasion of the Haiti earthquake an European Commission's Joint Research Center team used the damage reports mapped on the Ushahidi-Haiti platform to show that this crowdsourced data can help predict the spatial distribution of structural damage in Port-au-Prince