• Publicationsclose
• Research dataclose
• 2013-2022close
• Open Access close
• Article close
• GB close
• DARIAH EU close

The authors also acknowledge the support and collaboration of many other colleagues in their respective institutes, research communities and IT Infrastructures, together with the funding received by these from many different sources. These include but are not limited to the following: (i) The Worldwide LHC Computing Grid (WLCG) project is a global collaboration of more than 170 computing centres in 43 countries, linking up national and international grid infrastructures. Funding is acknowledged from many national funding bodies and we acknowledge the support of several operational infrastructures including EGI, OSG and NDGF/NeIC. (ii) EGI acknowledges the funding and support received from the European Commission and the many National Grid Initiatives and other members. EOSC-hub receives funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 777536. (iii) The work leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 730941 (AARC2). (iv) Work on the development of ESGF's identity management system has been supported by The UK Natural Environment Research Council and funding from the European Union's Seventh Framework Programme for research, technological development and demonstration through projects IS-ENES (grant agreement no 228203) and IS-ENES2 (grant agreement no 312979). (v) Ludek Matyska and Michal Prochazka acknowledge funding from the RI ELIXIR CZ project funded by MEYS Czech Republic No. LM2015047. (vi) Scott Koranda acknowledges support provided by the United States National Science Foundation under Grant No. PHY-1700765. (vii) GÉANT Association on behalf of the GN4 Phase 2 project (GN4-2).The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 731122(GN4-2). (viii) ELIXIR acknowledges support from Research Infrastructure programme of Horizon 2020 grant No 676559 EXCELERATE. (ix) CORBEL life science cluster acknowledges support from Horizon 2020 research and innovation programme under grant agreement No 654248. (x) Mirjam van Daalen acknowledges that the research leading to this result has been supported by the project CALIPSOplus under the Grant Agreement 730872 from the EU Framework Programme for Research and Innovation HORIZON 2020. (xi) EISCAT is an international association supported by research organisations in China (CRIRP), Finland (SA), Japan (NIPR), Norway (NFR), Sweden (VR), and the United Kingdom (NERC). This white-paper expresses common requirements of Research Communities seeking to leverage Identity Federation for Authentication and Authorisation. Recommendations are made to Stakeholders to guide the future evolution of Federated Identity Management in a direction that better satisfies research use cases. The authors represent research communities, Research Services, Infrastructures, Identity Federations and Interfederations, with a joint motivation to ease collaboration for distributed researchers. The content has been edited collaboratively by the Federated Identity Management for Research (FIM4R) Community, with input sought at conferences and meetings in Europe, Asia and North America.

‘Salvage’ evokes complex dynamics of loss, recovery and value, in such contexts\ud as waste management or shipwreck and maritime law. Similar dynamics, often\ud triggered by a collective or individual experience of a void or an absence, motivate\ud and inform much research into the history of women’s writing. The present article\ud explores, from the point of view of literary studies, the effects of understanding\ud research into the history of women’s writing as a salvage operation. This metaphor\ud bestows on the material studied the ambiguous status of remains. While\ud hindering the full integration of women’ s writing in more traditional accounts of\ud the literary past, the understanding of surviving material as remains can become\ud the starting point for constructing new, inclusive approaches to literary history.\ud This reframing of the problem is possible thanks to recent developments in the\ud Humanities, with an increasing interest in models and theories that allow us\ud to better understand complex and dynamic phenomena. In order to illustrate\ud the possibilities of this approach, the article draws on a brief analysis of nineteenth-century Spanish fashion magazines.

AbstractTropical peatlands are currently being rapidly cleared and drained for the establishment of oil palm plantations, which threatens their globally significant carbon sequestration capacity. Large-scale land conversion of tropical peatlands is important in the context of greenhouse gas emission factors and sustainable land management. At present, quantification of carbon dioxide losses from tropical peatlands is limited by our understanding of the relative contribution of heterotrophic and autotrophic respiration to net peat surface CO2 emissions. In this study we separated heterotrophic and autotrophic components of peat CO2 losses from two oil palm plantations (one established in ‘2000’ and the other in 1978, then replanted in ‘2006’) using chamber-based emissions sampling along a transect from the rooting to non-rooting zones on a peatland in Selangor, Peninsular Malaysia over the course of 3 months (June–August, 2014). Collar CO2 measurements were compared with soil temperature and moisture at site and also accompanied by depth profiles assessing peat C and bulk density. The soil respiration decreased exponentially with distance from the palm trunks with the sharpest decline found for the plantation with the younger palms with overall fluxes of 1341 and 988 mg CO2 m−2 h−1, respectively, at the 2000 and 2006 plantations, respectively. The mean heterotrophic flux was 909 ± SE 136 and 716 ± SE 201 mg m−2 h−1 at the 2000 and 2006 plantations, respectively. Autotrophic emissions adjacent to the palm trunks were 845 ± SE 135 and 1558 ± SE 341 mg m−2 h−1 at the 2000 and 2006 plantations, respectively. Heterotrophic CO2 flux was positively related to peat soil moisture, but not temperature. Total peat C stocks were 60 kg m−2 (down to 1 m depth) and did not vary among plantations of different ages but SOC concentrations declined significantly with depth at both plantations but the decline was sharper in the second generation 2006 plantation. The CO2 flux values reported in this study suggest a potential for very high carbon (C) loss from drained tropical peats during the dry season. This is particularly concerning given that more intense dry periods related to climate change are predicted for SE Asia. Taken together, this study highlights the need for careful management of tropical peatlands, and the vulnerability of their carbon storage capability under conditions of drainage.

This paper describes the achievements of the H2020 project INDIGO-DataCloud. The project has provided e-infrastructures with tools, applications and cloud framework enhancements to manage the demanding requirements of scientific communities, either locally or through enhanced interfaces. The middleware developed allows to federate hybrid resources, to easily write, port and run scientific applications to the cloud. In particular, we have extended existing PaaS (Platform as a Service) solutions, allowing public and private e-infrastructures, including those provided by EGI, EUDAT, and Helix Nebula, to integrate their existing services and make them available through AAI services compliant with GEANT interfederation policies, thus guaranteeing transparency and trust in the provisioning of such services. Our middleware facilitates the execution of applications using containers on Cloud and Grid based infrastructures, as well as on HPC clusters. Our developments are freely downloadable as open source components, and are already being integrated into many scientific applications. Comment: 39 pages, 15 figures.Version accepted in Journal of Grid Computing

This paper explores key issues in the development of open access to research data. The use of digital means for developing, storing and manipulating data is creating a focus on ‘data-driven science’. One aspect of this focus is the development of ‘open access’ to research data. Open access to research data refers to the way in which various types of data are openly available to public and private stakeholders, user communities and citizens. Open access to research data, however, involves more than simply providing easier and wider access to data for potential user groups. The development of open access requires attention to the ways data are considered in different areas of research. We identify how open access is being unevenly developed across the research environment and the consequences this has in terms of generating data gaps. Data gaps refer to the way data becomes detached from published conclusions. To address these issues, we examine four main areas in developing open access to research data: stakeholder roles and values; technological requirements for managing and sharing data; legal and ethical regulations and procedures; institutional roles and policy frameworks. We conclude that problems of variability and consistency across the open access ecosystem need to be addressed within and between these areas to ensure that risks surrounding a data gap are managed in open access. 11 authors. Missing: Sally Wyatt

Although there has been a drive in the cultural heritage sector to provide large-scale, open data sets for researchers, we have not seen a commensurate rise in humanities researchers undertaking complex analysis of these data sets for their own research purposes. This article reports on a pilot project at University College London, working in collaboration with the British Library, to scope out how best high-performance computing facilities can be used to facilitate the needs of researchers in the humanities. Using institutional data-processing frameworks routinely used to support scientific research, we assisted four humanities researchers in analysing 60,000 digitized books, and we present two resulting case studies here. This research allowed us to identify infrastructural and procedural barriers and make recommendations on resource allocation to best support non-computational researchers in undertaking ‘big data’ research. We recommend that research software engineer capacity can be most efficiently deployed in maintaining and supporting data sets, while librarians can provide an essential service in running initial, routine queries for humanities scholars. At present there are too many technical hurdles for most individuals in the humanities to consider analysing at scale these increasingly available open data sets, and by building on existing frameworks of support from research computing and library services, we can best support humanities scholars in developing methods and approaches to take advantage of these research opportunities.

Open science refers to all things open in research and scholarly communication: from publications and research data to code, models and methods as well as quality evaluation based on open peer review. However, getting started with implementing open science might not be as straightforward for all stakeholders. For example, what do research funders expect in terms of open access to publications and/or research data? Where and how to publish research data? How to ensure that research results are reproducible? These are all legitimate questions and, in particular, early career researchers may benefit from additional guidance and training. In this paper we review the activities of the European-funded FOSTER project which organized and supported a wide range of targeted trainings for open science, based on face-to-face events and on a growing suite of e-learning courses. This article reviews the approach and experiences gained from the first two years of the project. The FOSTER project has received funding from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 612425. The authors gratefully acknowledge the contributions of all project partners to the design and implementation of the FOSTER project.

International audience; The CENDARI infrastructure is a research-supporting platform designed to provide tools for transnational historical research, focusing on two topics: medieval culture and World War I. It exposes to the end users modern Web-based tools relying on a sophisticated infrastructure to collect, enrich, annotate, and search through large document corpora. Supporting researchers in their daily work is a novel concern for infrastructures. We describe how we gathered requirements through multiple methods to understand historians' needs and derive an abstract workflow to support them. We then outline the tools that we have built, tying their technical descriptions to the user requirements. The main tools are the note-taking environment and its faceted search capabilities; the data integration platform including the Data API, supporting semantic enrichment through entity recognition; and the environment supporting the software development processes throughout the project to keep both technical partners and researchers in the loop. The outcomes are technical together with new resources developed and gathered, and the research workflow that has been described and documented.

AbstractInformation about verb subcategorization frames (SCFs) is important to many tasks in natural language processing (NLP) and, in turn, text mining. Biomedicine has a need for high-quality SCF lexicons to support the extraction of information from the biomedical literature, which helps biologists to take advantage of the latest biomedical knowledge despite the overwhelming growth of that literature. Unfortunately, techniques for creating such resources for biomedical text are relatively undeveloped compared to general language. This paper serves as an introduction to subcategorization and existing approaches to acquisition, and provides motivation for developing techniques that address issues particularly important to biomedical NLP. First, we give the traditional linguistic definition of subcategorization, along with several related concepts. Second, we describe approaches to learning SCF lexicons from large data sets for general and biomedical domains. Third, we consider the crucial issue of linguistic variation between biomedical fields (subdomain variation). We demonstrate significant variation among subdomains, and find the variation does not simply follow patterns of general lexical variation. Finally, we note several requirements for future research in biomedical SCF lexicon acquisition: a high-quality gold standard, investigation of different definitions of subcategorization, and minimally-supervised methods that can learn subdomain-specific lexical usage without the need for extensive manual work.

Background: Biomedical natural language processing (NLP) applications that have access to detailed resources about the linguistic characteristics of biomedical language demonstrate improved performance on tasks such as relation extraction and syntactic or semantic parsing. Such applications are important for transforming the growing unstructured information buried in the biomedical literature into structured, actionable information. In this paper, we address the creation of linguistic resources that capture how individual biomedical verbs behave. We specifically consider verb subcategorization, or the tendency of verbs to ''select'' co-occurrence with particular phrase types, which influences the interpretation of verbs and identification of verbal arguments in context. There are currently a limited number of biomedical resources containing information about subcategorization frames (SCFs), and these are the result of either labor-intensive manual collation, or automatic methods that use tools adapted to a single biomedical subdomain. Either method may result in resources that lack coverage. Moreover, the quality of existing verb SCF resources for biomedicine is unknown, due to a lack of available gold standards for evaluation. Results: This paper presents three new resources related to verb subcategorization frames in biomedicine, and four experiments making use of the new resources. We present the first biomedical SCF gold standards, capturing two different but widely-used definitions of subcategorization, and a new SCF lexicon, BioCat, covering a large number of biomedical sub-domains. We evaluate the SCF acquisition methodologies for BioCat with respect to the gold standards, and compare the results with the accuracy of the only previously existing automatically-acquired SCF lexicon for biomedicine, the BioLexicon. Our results show that the BioLexicon has greater precision while BioCat has better coverage of SCFs. Finally, we explore the definition of subcategorization using these resources and its implications for biomedical NLP. All resources are made publicly available. Conclusion: The SCF resources we have evaluated still show considerably lower accuracy than that reported with general English lexicons, demonstrating the need for domain- and subdomain-specific SCF acquisition tools for biomedicine. Our new gold standards reveal major differences when annotators use the different definitions. Moreover, evaluation of BioCat yields major differences in accuracy depending on the gold standard, demonstrating that the definition of subcategorization adopted will have a direct impact on perceived system accuracy for specific tasks.