Archives des mots-clés : research data

Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

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Authors : Andra Waagmeester,  Martina Kutmon, Anders Riutta, Ryan Miller,  Egon L. Willighagen, Chris T.  Evelo , Alexander R. Pico

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data.

The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org.

Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries.

In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web.

WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development.

We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.

URL : Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

DOI : http://dx.doi.org/10.1371/journal.pcbi.1004989

Cloud-Based Big Data Management and Analytics for Scholarly Resources: Current Trends, Challenges and Scope for Future Research

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Authors : Samiya Khan, Kashish A. Shakil, Mansaf Alam

With the shifting focus of organizations and governments towards digitization of academic and technical documents, there has been an increasing need to use this reserve of scholarly documents for developing applications that can facilitate and aid in better management of research.

In addition to this, the evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing need for scholarly applications like collaborator discovery, expert finding and research recommendation systems.

This research paper reviews the current trends and identifies the challenges existing in the architecture, services and applications of big scholarly data platform with a specific focus on directions for future research.

URL : https://arxiv.org/abs/1606.01808

Towards a paradigm for open and free sharing of scientific data on global change science in China

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Authors : Changhui Peng, Xinzhang Song, Hong Jiang, Qiuan Zhu, Huai Chen, Jing M. Chen, Peng Gong, Chang Jie, Wenhua Xiang, Guirui Yu, Xiaolu Zhou

Despite great progress in data sharing that has been made in China in recent decades, cultural, policy, and technological challenges have prevented Chinese researchers from maximizing the availability of their data to the global change science community.

To achieve full and open exchange and sharing of scientific data, Chinese research funding agencies need to recognize that preservation of, and access to, digital data are central to their mission, and must support these tasks accordingly.

The Chinese government also needs to develop better mechanisms, incentives, and rewards, while scientists need to change their behavior and culture to recognize the need to maximize the usefulness of their data to society as well as to other researchers.

The Chinese research community and individual researchers should think globally and act personally to promote a paradigm of open, free, and timely data sharing, and to increase the effectiveness of knowledge development.

URL : Towards a paradigm for open and free sharing of scientific data on global change science in China

DOI : http://dx.doi.org/10.1002/ehs2.1225

Revisiting the Data Lifecycle with Big Data Curation

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Author : Line Pouchard

As science becomes more data-intensive and collaborative, researchers increasingly use larger and more complex data to answer research questions.

The capacity of storage infrastructure, the increased sophistication and deployment of sensors, the ubiquitous availability of computer clusters, the development of new analysis techniques, and larger collaborations allow researchers to address grand societal challenges in a way that is unprecedented.

In parallel, research data repositories have been built to host research data in response to the requirements of sponsors that research data be publicly available. Libraries are re-inventing themselves to respond to a growing demand to manage, store, curate and preserve the data produced in the course of publicly funded research.

As librarians and data managers are developing the tools and knowledge they need to meet these new expectations, they inevitably encounter conversations around Big Data. This paper explores definitions of Big Data that have coalesced in the last decade around four commonly mentioned characteristics: volume, variety, velocity, and veracity.

We highlight the issues associated with each characteristic, particularly their impact on data management and curation. We use the methodological framework of the data life cycle model, assessing two models developed in the context of Big Data projects and find them lacking.

We propose a Big Data life cycle model that includes activities focused on Big Data and more closely integrates curation with the research life cycle. These activities include planning, acquiring, preparing, analyzing, preserving, and discovering, with describing the data and assuring quality being an integral part of each activity.

We discuss the relationship between institutional data curation repositories and new long-term data resources associated with high performance computing centers, and reproducibility in computational science.

We apply this model by mapping the four characteristics of Big Data outlined above to each of the activities in the model. This mapping produces a set of questions that practitioners should be asking in a Big Data project

URL : Revisiting the Data Lifecycle with Big Data Curation

Alternative location : http://www.ijdc.net/index.php/ijdc/article/view/10.2.176

Research Data Sharing and Reuse Practices of Academic Faculty Researchers: A Study of the Virginia Tech Data Landscape

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Author : Yi Shen

This paper presents the results of a research data assessment and landscape study in the institutional context of Virginia Tech to determine the data sharing and reuse practices of academic faculty researchers.

Through mapping the level of user engagement in “openness of data,” “openness of methodologies and workflows,” and “reuse of existing data,” this study contributes to the current knowledge in data sharing and open access, and supports the strategic development of institutional data stewardship.

Asking faculty researchers to self-reflect sharing and reuse from both data producers’ and data users’ perspectives, the study reveals a significant gap between the rather limited sharing activities and the highly perceived reuse or repurpose values regarding data, indicating that potential values of data for future research are lost right after the original work is done.

The localized and sporadic data management and documentation practices of researchers also contribute to the obstacles they themselves often encounter when reusing existing data.

URL : Research Data Sharing and Reuse Practices of Academic Faculty Researchers: A Study of the Virginia Tech Data Landscape

Alternative location : http://www.ijdc.net/index.php/ijdc/article/view/10.2.157

State of Data Guidance in Journal Policies: A Case Study in Oncology

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Author: Deborah H. Charbonneau, Joan E. Beaudoin

This article reports the results of a study examining the state of data guidance provided to authors by 50 oncology journals. The purpose of the study was the identification of data practices addressed in the journals’ policies.

While a number of studies have examined data sharing practices among researchers, little is known about how journals address data sharing. Thus, what was discovered through this study has practical implications for journal publishers, editors, and researchers.

The findings indicate that journal publishers should provide more meaningful and comprehensive data guidance to prospective authors. More specifically, journal policies requiring data sharing, should direct researchers to relevant data repositories, and offer better metadata consultation to strengthen existing journal policies.

By providing adequate guidance for authors, and helping investigators to meet data sharing mandates, scholarly journal publishers can play a vital role in advancing access to research data.

URL : State of Data Guidance in Journal Policies: A Case Study in Oncology

Alternative location : http://www.ijdc.net/index.php/ijdc/article/view/10.2.136

Are Scientific Data Repositories Coping with Research Data Publishing?

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Research data publishing is intended as the release of research data to make it possible for practitioners to (re)use them according to “open science” dynamics. There are three main actors called to deal with research data publishing practices: researchers, publishers, and data repositories.

This study analyses the solutions offered by generalist scientific data repositories, i.e., repositories supporting the deposition of any type of research data. These repositories cannot make any assumption on the application domain.

They are actually called to face with the almost open ended typologies of data used in science. The current practices promoted by such repositories are analysed with respect to eight key aspects of data publishing, i.e., dataset formatting, documentation, licensing, publication costs, validation, availability, discovery and access, and citation.

From this analysis it emerges that these repositories implement well consolidated practices and pragmatic solutions for literature repositories.

These practices and solutions can not totally meet the needs of management and use of datasets resources, especially in a context where rapid technological changes continuously open new exploitation prospects.

URL : Are Scientific Data Repositories Coping with Research Data Publishing?

DOI : http://doi.org/10.5334/dsj-2016-006