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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.

International audience; Academic dictionary writing is making greater and greater use of the TEI Guidelines’ dictionary module. And as increasing numbers of TEI dictionaries become available, there is an ever more palpable need to work towards greater interoperability among dictionary writing systems and other language resources that are needed by dictionaries and dictionary tools. In particular this holds true for the crucial role that statistical data obtained from language resources play in lexicographic workflow—a role that also has to be reflected in the model of the data produced in these workflows. Presenting a range of current projects, the authors address two main questions in this area: How can the relationship between a dictionary and other language resources be conceptualized, irrespective of whether they are used in the production of the dictionary or to enrich existing lexicographic data? And how can this be documented using the TEI Guidelines? Discussing a variety of options, this paper proposes a customization of the TEI dictionary module that tries to respond to the emerging requirements in an environment of increasingly intertwined language resources.

‘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 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

Abstract. The ancient mining landscape of Schwaz/Brixlegg in the Tyrol, Austria witnessed mining from prehistoric times to modern times creating a first order cultural landscape when it comes to one of the most important inventions in human history: the production of metal. In 1991 a part of this landscape was lost due to an enormous landslide that reshaped part of the mountain. With our work we want to propose a digital workflow to create a 3D semantic representation of this ancient mining landscape with its mining structures to preserve it for posterity. First, we define a conceptual model to integrate the data. It is based on the CIDOC CRM ontology and CRMgeo for geometric data. To transform our information sources to a formal representation of the classes and properties of the ontology we applied semantic web technologies and created a knowledge graph in RDF (Resource Description Framework). Through the CRMgeo extension coordinate information of mining features can be integrated into the RDF graph and thus related to the detailed digital elevation model that may be visualized together with the mining structures using Geoinformation systems or 3D visualization tools. The RDF network of the triple store can be queried using the SPARQL query language. We created a snapshot of mining, settlement and burial sites in the Bronze Age. The results of the query were loaded into a Geoinformation system and a visualization of known bronze age sites related to mining, settlement and burial activities was created.

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.

Abstract Background Negation occurs frequently in scientific literature, especially in biomedical literature. It has previously been reported that around 13% of sentences found in biomedical research articles contain negation. Historically, the main motivation for identifying negated events has been to ensure their exclusion from lists of extracted interactions. However, recently, there has been a growing interest in negative results, which has resulted in negation detection being identified as a key challenge in biomedical relation extraction. In this article, we focus on the problem of identifying negated bio-events, given gold standard event annotations. Results We have conducted a detailed analysis of three open access bio-event corpora containing negation information (i.e., GENIA Event, BioInfer and BioNLP’09 ST), and have identified the main types of negated bio-events. We have analysed the key aspects of a machine learning solution to the problem of detecting negated events, including selection of negation cues, feature engineering and the choice of learning algorithm. Combining the best solutions for each aspect of the problem, we propose a novel framework for the identification of negated bio-events. We have evaluated our system on each of the three open access corpora mentioned above. The performance of the system significantly surpasses the best results previously reported on the BioNLP’09 ST corpus, and achieves even better results on the GENIA Event and BioInfer corpora, both of which contain more varied and complex events. Conclusions Recently, in the field of biomedical text mining, the development and enhancement of event-based systems has received significant interest. The ability to identify negated events is a key performance element for these systems. We have conducted the first detailed study on the analysis and identification of negated bio-events. Our proposed framework can be integrated with state-of-the-art event extraction systems. The resulting systems will be able to extract bio-events with attached polarities from textual documents, which can serve as the foundation for more elaborate systems that are able to detect mutually contradicting bio-events.