The OpenAIRE Research Community Dashboard: On blending scientific workflows and scientific publishing

First Online 30 August 2019Despite the hype, the effective implementation of Open Science is hindered by several cultural and technical barriers. Researchers embraced digital science, use “digital laboratories” (e.g. research infrastructures, thematic services) to conduct their research and publish research data, but practices and tools are still far from achieving the expectations of transparency and reproducibility of Open Science. The places where science is performed and the places where science is published are still regarded as different realms. Publishing is still a post-experimental, tedious, manual process, too often limited to articles, in some contexts semantically linked to datasets, rarely to software, generally disregarding digital representations of experiments. In this work we present the OpenAIRE Research Community Dashboard (RCD), designed to overcome some of these barriers for a given research community, minimizing the technical efforts and without renouncing any of the community services or practices. The RCD flanks digital laboratories of research communities with scholarly communication tools for discovering and publishing interlinked scientific products such as literature, datasets, and software. The benefits of the RCD are show-cased by means of two real-case scenarios: the European Marine Science community and the European Plate Observing System (EPOS) research infrastructure.This work is partly funded by the OpenAIRE-Advance H2020 project (grant number: 777541; call: H2020-EINFRA-2017) and the OpenAIREConnect H2020 project (grant number: 731011; call: H2020-EINFRA-2016-1). Moreover, we would like to thank our colleagues Michele Manunta, Francesco Casu, and Claudio De Luca (Institute for the Electromagnetic Sensing of the Environment, CNR, Italy) for their work on the EPOS infrastructure RCD; and Stephane Pesant (University of Bremen, Germany) his work on the European Marine Science RCD

First Break Detection in Seismic Reflection Data with Fuzzy ARTMAP Neural Networks

. In this paper we investigate the use of a supervised, but self-organizing, Adaptive Resonance Theory type of neural network (Fuzzy-ARTMAP), for first break picking in seismic reflection data. First break picking is the accurate location of the leading energy pulse received by a geophone in response to a seismic shot. The performance of Fuzzy-ARTMAP is compared to our previous work with multi-layer perceptron and cascade-correlation neural nets[1]. Although the predictions of FuzzyARTMAP are less accurate by 2--8% for this problem, it has many features that make it a desirable candidate for a neural net implementation for first break detections: it learns quickly, efficiently and flexibly; it can be used in both on-line and off-line settings; it is easy to use, with few parameters; does not get trapped in local minima, and the fuzzy rules for mapping the input to the output can be extracted from the network. 1 Introduction 1.1 Objective Neural nets are being applied to a variety of ..

Publication Fp7 Funding Acknowledgment - Plos Openaire

Jahn N, Fenner M, Dimitropoulos H, Schirrwagen J. Publication Fp7 Funding Acknowledgment - Plos Openaire. Bielefeld University; 2013.The dataset contains a sample of metadata describing papers published in PLOS and their identified grant agreement number of FP7 projects. A second file shows the frequency of FP7 grants. The sample was created in July 2012

D8.3 Research Impact Services: OpenAIRE 2020

This deliverable relates to the work carried out under task T8.3, “Research Impact Services”. The task’s focus is on the development of pilots with selected National funding agencies and infrastructure initiatives in order to serve them with the OpenAIRE research impact suite of services. A major service that OpenAIRE provides is the linking of research results to funding. Aside from importing the links from the repositories and journals, OpenAIRE designs, develops and enhances mining algorithms that identify and extract funding information from the text of scientific publications. With the help of NOADs we have initiated bi-lateral, often informal, collaborations with national funding agencies to facilitate mining extraction on their data. This is an on-going activity throughout the duration of the project. Currently the national funding agencies that we are working with are: FCT (Portugal), ARC (Australia), NHMRC (Australia), NSF & NIH (USA), SFI (Ireland), “Ministry of Science Education and Sport” & "Croatian Science Foundation” (Croatia), NWO (Netherlands), and DFG (Germany). This deliverable describes the nature of the data of the identified National funding agencies, as well as their export technologies, and provides the specification of the general-purpose OpenAIRE services required to support research impact measurements

Publication FP7 Funding Acknowledgment - PLOS OpenAIRE

The dataset contains a sample of metadata describing papers published in PLOS and their identified grant agreement number of FP7 projects. A second file shows the frequency of FP7 grants. The sample was created in July 2012

OpenAIRE Graph dataset: new collected projects

<p>The dataset includes metadata about projects grants collected by OpenAIRE until September 2023. This dump involves </p><ul><li>280 new HE (Horizon Europe) projects</li><li>951 FCT (Fundação para a Ciência e a Tecnologia) new projects</li><li>358 ANR (French National Research Agency) new projects</li><li>144 SNSF (Swiss National Science Foundation) new projects</li><li>11 WT (Wellcome Trust) new projects</li></ul><p>This upload includes only the new projects until September. For the complete set of projects you can download the project.tar file in the latest version of the dataset of the OpenAIRE Graph available at https://zenodo.org/record/8217359</p&gt

Visual-Based Classification of Figures from Scientific Literature

Authors of scientific publications and books use images to present a wide spectrum of information. Despite the richness of the visual content of scientific publications the figures are usually not taken into consideration in the context of text mining methodologies towards the automatic indexing and retrieval of scientific corpora. In this work, we present a system for automatic categorization of figures from scientific literature to a set of predefined classes. We have employed a wide range of visual features that achieve high discrimination ability between the adopted classes. A real-world dataset has been compiled and annotated in order to train and evaluate the proposed method using three different classification schemata

ARIADNE: Final Report on Data Mining

Recent years have witnessed a growing interest from archaeological communities in Linked Data. ARIADNE, the Advanced Research Infrastructure for Archaeological Data set Networking in Europe, facilitates a central web portal that provides access to archaeological data from various sources. Parts of these data have been being published as Linked Data, and are currently available in the Linked Open Data cloud. With it, the nature of these data has shifted from unstructured to structured. This presents new opportunities for data mining. While general-purpose software exists, recent studies have revealed the importance of two domain-specific requirements: 1) produce interpretable results, and 2) allow trust in the underlying model. In this work, we investigate to what extend interpretable data mining can contribute to the understanding of linked archaeological data. A case study was held, which involved the mining of semantic association rules over data sets of increasing levels of knowledge granularity, followed by the qualitative evaluation of these rules by domain experts. Experiments have shown that the approach yielded mostly plausible patterns, some of which were seen as highly relevant

Therapist adherence to family‐based treatment for adolescents with anorexia nervosa: A multi‐site exploratory study

ObjectiveThis exploratory study is the first to examine family-based treatment (FBT) adherence and association to treatment outcome in the context of a large-scale, multi-centre study for the treatment of adolescents with anorexia nervosa.MethodOne hundred and ninety recorded FBT sessions from 68 adolescents with anorexia nervosa and their families were recruited across multiple sites (N = 6). Each site provided 1-4 tapes per family over four treatment time points, and each was independently rated for therapist adherence.ResultsThere were differences in adherence scores within and between sites. ANOVA produced a main effect for site, F(5, 46) = 8.6, p < .001, and phase, F(3, 42) = 12.7, p < .001, with adherence decreasing in later phases. Adherence was not associated to end of treatment percent ideal body weight after controlling for baseline percent ideal body weight (r = .088, p = .48).ConclusionsResults suggest that FBT can be delivered with adherence in phase one of treatment. Adherence was not associated with treatment outcome as determined using percent ideal body weight

OpenAIRE: Advancing open science

OpenAIRE, the point of reference for Open Access in Europe, is now addressing the problem of enabling the Open Science paradigm. To this aim it will provide services to: (i) overcome the limits of today’s scientific communication landscape, by allowing research communities and the relative e-infrastructures to fully publish, interlink, package and reuse their research artefacts (e.g. literature, data, and software) and their funding grants within the European and global ecosystem as supported/promoted by OpenAIRE, (ii) enable end-users (e.g. researchers, funder officers) to search and consult a rich and up-to-date knowledge graph of research results and (iii) enable scientific and educational information repositories and publishers to subscribe and be notified of changes in the OpenAIRE knowledge graph. These combined actions will bring long-term and immediate benefits to research communities, research organisations, repository managers, and funders by affecting the way research results are disseminated and reused. On the one hand, publishing the interlinked and packaged research literature, data and software via OpenAIRE drives research communities to an Open Science transition in a consistent and interoperable fashion. On the other hand, the resulting infrastructure concretely enables the construction of Open Science oriented services, supporting practices such as machine-assisted research reproducibility and evaluation.This research was supported by EU funded project OpenAIRE-Connect (grant agreement: 731011; Call: H2020-EINFRA-2016-1). We thank our colleague Stefania Biagioni for her help during the writing of this paper