The Gaia-ESO Survey: processing FLAMES-UVES spectra

The Gaia-ESO Survey is a large public spectroscopic survey that aims to derive radial velocities and fundamental parameters of about 105 Milky Way stars in the field and in clusters. Observations are carried out with the multi-object optical spectrograph FLAMES, using simultaneously the medium-resolution (R ~ 20 000) GIRAFFE spectrograph and the high-resolution (R ~ 47 000) UVES spectrograph. In this paper we describe the methods and the software used for the data reduction, the derivation of the radial velocities, and the quality control of the FLAMES-UVES spectra. Data reduction has been performed using a workflow specifically developed for this project. This workflow runs the ESO public pipeline optimizing the data reduction for the Gaia-ESO Survey, automatically performs sky subtraction, barycentric correction and normalisation, and calculates radial velocities and a first guess of the rotational velocities. The quality control is performed using the output parameters from the ESO pipeline, by a visual inspection of the spectra and by the analysis of the signal-to-noise ratio of the spectra. Using the observations of the first 18 months, specifically targets observed multiple times at different epochs, stars observed with both GIRAFFE and UVES, and observations of radial velocity standards, we estimated the precision and the accuracy of the radial velocities. The statistical error on the radial velocities is σ ~ 0.4 km s-1 and is mainly due to uncertainties in the zero point of the wavelength calibration. However, we found a systematic bias with respect to the GIRAFFE spectra (~0.9 km s-1) and to the radial velocities of the standard stars (~0.5 km s-1) retrieved from the literature. This bias will be corrected in the future data releases, when a common zero point for all the set-ups and instruments used for the survey is be established.

Primary Raw Materials (1986-89) Volume 1 (Summary Reports of the R&D Programme). EUR 13647/I EN, 1992

"This document contains the Summary Reports of cost sharing Research and Development contracts funded under the "Primary Raw Materials" subprogramme of the Commission of the European Communities. This programme was part of the research and development programme on "Raw Materials and Advanced Materials" (1986 - 1989). The main objectives of the "Primary Raw Materials" subprogramme were to enhance the competitiveness of the European Community mining and metallurgical Industries and to reduce European Community vulnerability for minerals, particularly those of critical or strategic interest." Three research areas: Research and development exploration; Mining technology; Mineral processing.

International comparison of resource allocation in health sciences : an analysis of expenditures on biomedical research in 19 industrialized countries /

Item 507

Preparing a research grant application to the National Institutes of Health : selected articles /

Shipping list no.: 95-0133-P.

Federal budgeting for research and development. Hearings before the Subcommittee on Reorganization and International Organizations of the Committee on Government Operations, United States, Eighty-seventh Congress, first session. Agency coordination study (pursuant to S. Res. 26, 87th Cong.) July 26 and 27, 1961.

Includes bibliographies.

Bioenergy and climate change mitigation : an assessment

Date of Acceptance: 08/05/2014 Acknowledgements The authors are indebted to Julia Römer for assisting with editing several hundred references. Helmut Haberl gratefully acknowledges funding by the Austrian Academy of Sciences (Global Change Programme), the Austrian Ministry of Science and Research (BMWF, proVision programme) as well as by the EU-FP7 project VOLANTE. Carmenza Robledo-Abad received financial support from the Swiss State Secretariat for Economic Affairs.

Spatially explicit estimates of N2O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management

Acknowledgements We are grateful to Stefan Seibert for advice on reconciling the Monfreda datasets of yield and area and the Portmann dataset for irrigated area of rice. We thank Deepak Ray and Jonathan Foley for helpful comments. Research support to J.G. K.C., N.M, and P.W. was primarily provided by the Gordon and Betty Moore Foundation and the Institute on Environment, with additional support from NSF Hydrologic Sciences grant 1521210 for N.M., and additional support to J.G. and P.W. whose efforts contribute to Belmont Forum/FACCE-JPI funded DEVIL project (NE/M021327/1). M.H. was supported by CSIRO's OCE Science Leaders Programme and the Agriculture Flagship. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Bioenergy and climate change mitigation : an assessment

Date of Acceptance: 08/05/2014 Acknowledgements The authors are indebted to Julia Römer for assisting with editing several hundred references. Helmut Haberl gratefully acknowledges funding by the Austrian Academy of Sciences (Global Change Programme), the Austrian Ministry of Science and Research (BMWF, proVision programme) as well as by the EU-FP7 project VOLANTE. Carmenza Robledo-Abad received financial support from the Swiss State Secretariat for Economic Affairs.

Environmental Impacts of the Deep-Water Oil and Gas Industry : A Review to Guide Management Strategies

The authors would like to thank the leadership of the Deep Ocean Stewardship Initiative (DOSI), including Lisa Levin, Maria Baker, and Kristina Gjerde, for their support in developing this review. This work evolved from a meeting of the DOSI Oil and Gas working group supported by the J.M. Kaplan Fund, and associated with the Deep-Sea Biology Symposium in Aveiro, Portugal in September 2015. The members of the Oil and Gas working group that contributed to our discussions at that meeting or through the listserve are acknowledged for their contributions to this work. We would also like to thank the three reviewers and the editor who provided valuable comments and insight into the work presented here. DJ and AD were supported by funding from the European Union's Horizon 2020 research and innovation programme under the MERCES (Marine Ecosystem Restoration in Changing European Seas) project, grant agreement No 689518. AB was supported by CNPq grants 301412/2013-8 and 200504/2015-0. LH acknowledges funding provided by a Natural Environment Research Council grant (NE/L008181/1). This output reflects only the authors' views and the funders cannot be held responsible for any use that may be made of the information contained therein.

See-saw relationship of the Holocene East Asian-Australian summer monsoon

D.E. and N.M. acknowledge support by the Leibniz Association (WGL) under Grant No. SAW-2013-IZW-2. F.H.M.’s research is funded through an Australian Postgraduate Award. I.O. is financially supported from TUBITAK under 2214/A program and by Ege University under the Research Project number 2015FEN028. This study received funding from the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska-Curie grant agreement No 691037. The publication of this article was funded by the Open Access Fund of the Leibniz Association. K.H.W. thank Rhawn F. Denniston for his wider involvement in the northwest Australian monsoon project and the Kimberley Foundation Australia for financial support for this project and Paul Wyrwoll for helpful comments. We are also grateful to Yanjun Cai for providing the Lake Qinghai record.

Document Cohesion Flow: Striving towards Coherence

Text cohesion is an important element of discourse processing. This paper presents a new approach to modeling, quantifying, and visualizing text cohesion using automated cohesion flow indices that capture semantic links among paragraphs. Cohesion flow is calculated by applying Cohesion Network Analysis, a combination of semantic distances, Latent Semantic Analysis, and Latent Dirichlet Allocation, as well as Social Network Analysis. Experiments performed on 315 timed essays indicated that cohesion flow indices are significantly correlated with human ratings of text coherence and essay quality. Visualizations of the global cohesion indices are also included to support a more facile understanding of how cohesion flow impacts coherence in terms of semantic dependencies between paragraphs.

D1.4 – First version applied gaming asset methodology

This deliverable (D1.4) is an intermediate document, expressly included to inform the first project review about RAGE’s methodology of software asset creation and management. The final version of the methodology description (D1.1) will be delivered in Month 29. The document explains how the RAGE project defines, develops, distributes and maintains a series of applied gaming software assets that it aims to make available. It describes a high-level methodology and infrastructure that are needed to support the work in the project as well as after the project has ended.

Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain?

The speed at which new scientific papers are published has increased dramatically, while the process of tracking the most recent publications having a high impact has become more and more cumbersome. In order to support learners and researchers in retrieving relevant articles and identifying the most central researchers within a domain, we propose a novel 2-mode multilayered graph derived from Cohesion Network Analysis (CNA). The resulting extended CNA graph integrates both authors and papers, as well as three principal link types: coauthorship, co-citation, and semantic similarity among the contents of the papers. Our rankings do not rely on the number of published documents, but on their global impact based on links between authors, citations, and semantic relatedness to similar articles. As a preliminary validation, we have built a network based on the 2013 LAK dataset in order to reveal the most central authors within the emerging Learning Analytics domain.

Integrating Scientific Publication into an Applied Gaming Ecosystem

Salman, M. et al. (2016). Integrating Scientific Publication into an Applied Gaming Ecosystem. GSTF Journal on Computing (JoC), Volume 5 (Issue 1), pp. 45-51.

Game Learning Analytics: Learning Analytics for Serious Games

Video games have become one of the largest entertainment industries, and their power to capture the attention of players worldwide soon prompted the idea of using games to improve education. However, these educational games, commonly referred to as serious games, face different challenges when brought into the classroom, ranging from pragmatic issues (e.g. a high development cost) to deeper educational issues, including a lack of understanding of how the students interact with the games and how the learning process actually occurs. This chapter explores the potential of data-driven approaches to improve the practical applicability of serious games. Existing work done by the entertainment and learning industries helps to build a conceptual model of the tasks required to analyze player interactions in serious games (gaming learning analytics or GLA). The chapter also describes the main ongoing initiatives to create reference GLA infrastructures and their connection to new emerging specifications from the educational technology field. Finally, it explores how this data-driven GLA will help in the development of a new generation of more effective educational games and new business models that will support their expansion. This results in additional ethical implications, which are discussed at the end of the chapter.