International audience; In this paper we describe the development and evaluation of a visual analytics tool to support historical research. Historians continuously gather data related to their scholarly research from archival visits and background search. Organising and making sense of all this data can be challenging as many historians continue to rely on analog or basic digital tools. We built an integrated note-taking environment for historians which unifies a set of func-tionalities we identified as important for historical research including editing, tagging, searching, sharing and visualization. Our approach was to involve users from the initial stage of brainstorming and requirement analysis through to design, implementation and evaluation. We report on the process and results of our work, and conclude by reflecting on our own experience in conducting user-centered visual analytics design for digital humanities.
In her capacity as guest editor, the author introduces a set of essays examining the trends, risks, needs, pressures, and prospects of the humanities after recent reforms to tertiary education throughout Europe. By focusing on the educational, cultural, and social value of research in the humanities, which also provide economic and democratic benefits, this special issue focuses on three key topics: “funding policies”, “evaluation”, and “cultural resources”. This article provides the background to the subject matter (Section 1) the context and a synopsis of the contributions, showing how and why these position papers by members of the humanities cluster of the Academia Europaea can provide this debate with new tools of analysis and diagnosis (Section 5). Finally, the concluding remarks highlight the Academia Europaea’s actions for the humanities (Section 6). a reflection on the controversial issues of quality control, measures of research productivity, and funding decisions as key drivers changing the humanities (Section 2) an overview of the current difficulties and prospects for “modernizing” the humanities (Section 3) the rationale for this special issue (Section 4)
Countries: Spain, Spain, Netherlands, Netherlands, France
Project: FCT | EXPL/BBB-BEP/1356/2013 (EXPL/BBB-BEP/1356/2013), AKA | ELIXIR - Data for Life Eu... (273655), WT , EC | WENMR (261572), EC | EGI-INSPIRE (261323), EC | BIOMEDBRIDGES (284209), FCT | SFRH/BPD/78075/2011 (SFRH/BPD/78075/2011), FCT | EXPL/BBB-BEP/1356/2013 (EXPL/BBB-BEP/1356/2013), AKA | ELIXIR - Data for Life Eu... (273655), WT ,...
With the increasingly rapid growth of data in life sciences we are witnessing a major transition in the way research is conducted, from hypothesis-driven studies to data-driven simulations of whole systems. Such approaches necessitate the use of large-scale computational resources and e-infrastructures, such as the European Grid Infrastructure (EGI). EGI, one of key the enablers of the digital European Research Area, is a federation of resource providers set up to deliver sustainable, integrated and secure computing services to European researchers and their international partners. Here we aim to provide the state of the art of Grid/Cloud computing in EU research as viewed from within the field of life sciences, focusing on key infrastructures and projects within the life sciences community. Rather than focusing purely on the technical aspects underlying the currently provided solutions, we outline the design aspects and key characteristics that can be identified across major research approaches. Overall, we aim to provide significant insights into the road ahead by establishing ever-strengthening connections between EGI as a whole and the life sciences community. AD was supported by Fundação para a Ciência e a Tecnologia, Portugal (SFRH/BPD/78075/2011 and EXPL/BBBBEP/1356/2013). FP has been supported by the National Grid Infrastructure NGI_GRNET, HellasGRID, as part of the EGI. IFB acknowledges funding from the “National Infrastructures in Biology and Health” call of the French “Investments for the Future” initiative. The WeNMR project has been funded by a European FP7 e-Infrastructure grant, contract no. 261572. AF was supported by a grant from Labex CEBA (Centre d’études de la Biodiversité Amazonienne) from ANR. MC is supported by UK’s BBSRC core funding. CSC was supported by Academy of Finland grant No. 273655 for ELIXIR Finland. The EGI-InSPIRE project (Integrated Sustainable Pan-European Infrastructure for Researchers in Europe) is co-funded by the European Commission (contract number: RI-261323). The BioMedBridges project is funded by the European Commission within Research Infrastructures of the FP7 Capacities Specific Programme, grant agreement number 284209. This is an open-access article.-- et al. Peer Reviewed
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
National audience; Il 1° ottobre 2015 il MIUR firma l'adesione dell'Italia a CLARIN-ERIC, l'infrastruttura di ricerca che offre risorse e tecnologie linguistiche dedicate al settore delle scienze del linguaggio e delle scienze umane e sociali. Questo articolo intende fornire alla comunità italiana una ampia panoramica di CLARIN, la sua missione, i suoi pilastri, i servizi, la sua organizzazione tecnica ed amministrativa e la struttura di governance, sia a livello europeo che locale. Viene introdotto il network italiano, con il primo centro nazionale ILC4CLARIN, ospitato ed in via di sviluppo presso l'ILC-CNR, le funzionalità, le risorse ed i servizi offerti; viene presentato infine il primo nucleo del consorzio nazionale CLARIN-IT, illustrando i criteri di costituzione, le attività previste e le prospettive future.
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.
Biased language commonly occurs around topics which are of controversial nature, thus, stirring disagreement between the different involved parties of a discussion. This is due to the fact that for language and its use, specifically, the understanding and use of phrases, the stances are cohesive within the particular groups. However, such cohesiveness does not hold across groups. In collaborative environments or environments where impartial language is desired (e.g. Wikipedia, news media), statements and the language therein should represent equally the involved parties and be neutrally phrased. Biased language is introduced through the presence of inflammatory words or phrases, or statements that may be incorrect or one-sided, thus violating such consensus. In this work, we focus on the specific case of phrasing bias, which may be introduced through specific inflammatory words or phrases in a statement. For this purpose, we propose an approach that relies on a recurrent neural networks in order to capture the inter-dependencies between words in a phrase that introduced bias. We perform a thorough experimental evaluation, where we show the advantages of a neural based approach over competitors that rely on word lexicons and other hand-crafted features in detecting biased language. We are able to distinguish biased statements with a precision of P=0.92, thus significantly outperforming baseline models with an improvement of over 30%. Finally, we release the largest corpus of statements annotated for biased language. The Twelfth ACM International Conference on Web Search and Data Mining, February 11--15, 2019, Melbourne, VIC, Australia