Springer Nature seeks early adopter institutions and funders to shape new research data services

Springer Nature is rolling out new support services to help the research community manage and share data. Following a successful pilot offering data deposition support to Springer Nature authors, announced in April 2017 and extended to all researchers in January 2018, a wider range of research data services are now being introduced for institutions and funders.

The recent report from Digital Science, State of Open Data 2017, shows that while the proportion of researchers sharing their data has grown, efforts to archive, publish and share data continue to be hampered by time constraints and a lack of knowledge around data standards, repository options, and funder and institutional requirements.

Developed in response to these challenges, Springer Nature's Research Data Support services for institutions and funders aim to overcome many of these problems.

To support data deposit into repositories, Research Data Editors will curate and enhance metadata to improve its discoverability and encourage reuse in accordance with the Findable, Accessible, Interoperable and Reusable (FAIR) data principles. Editors will also help authors draft summaries and data availability statements to improve human readability and data linking and citation. Initially made available on Figshare, the service can be used for any scholarly research data (including code, videos, images, text, raw and processed data) whether or not they are associated with a publication.

Research Data Editors and in-house data policy experts will also offer webinars, workshops and 'train-the-trainer' resources on policy and best practice in data management, along with hands-on data curation workshops.

Working with institutions and funders, Springer Nature also aims to develop data availability statement reporting to support policy compliance and track research outputs and impact. 

Framework for Research Data Management Makes Life Simpler for Researchers

AlphaGalileo
18 January 2018 Science Europe

The new framework, developed by the Science Europe Working Group on Research Data, provides a structured format that sets a number of minimum requirements for data management plans. Scientific communities are encouraged to build on this with the requirements that are specific to their field of research. This will result in domain data protocols that can be used as blank templates by individual researchers. Such protocols will both reduce the time needed for researchers to create data management plans, and simplify the process of evaluating them for research organisations.

The framework neatly fits into existing requirements for data management plans, and is easy to integrate in data policies that have already been or are currently being created. It was developed with a bottom-up approach in mind, allowing researchers to create data management plans that match their needs and requirements. At the same time, it enables research organisations to use them to ensure the efficacy and accountability of publicly funded research.

Over the coming months, the Science Europe Working Group on Research Data will engage with various scientific communities to promote the implementation of the framework and the creation of the first domain protocols based on it.

The guidance document with the framework is available at http://scieur.org/guidance-rdmps

https://www.alphagalileo.org/ViewItem.aspx?ItemId=182835&CultureCode=en

Special Issue on Large-Scale Data Curation and Metadata Management

Some light reading for you all from Distributed and Parallel Databases 

The aim of this special issue is to give an overview of current research in distributed data curation and metadata management. The scope of the special issue covered a wide range of sub-topics including:

• Metadata summarization, mining, and deep analytics
• Distributed data curation and metadata management
• Big data curation and annotation in emerging infrastructures, e.g., MapReduce, Spark, and NoSQL engines
• Metadata validation and verification
• New annotation management and metadata models
• Proactive and automated data curation and annotation
• Metadata cleaning and quality control strategies
• Metadata-driven query processing and optimizations
• Storage and indexing techniques for big metadata
• Crowd-Sourcing models for data curation and annotation
• Scalable metadata and provenance visualization
• Interoperability of metadata formats and engines

https://link.springer.com/article/10.1007/s10619-017-7217-x

The Realities of Research Data Management

OCLC Research (2017-2018)

The Realities of Research Data Management is a four-part series that explores how research universities are addressing the challenge of managing research data throughout the research lifecycle. In this series, we examine the context, influences, and choices higher education institutions face in building or acquiring RDM capacity—in other words, the infrastructure, services, and other resources needed to support emerging data management practices. Our findings are based on case studies of four institutions: University of Edinburgh (UK), the University of Illinois at Urbana-Champaign (US), Monash University (Australia) and Wageningen University & Research (the Netherlands), in four very different national contexts.

https://www.oclc.org/research/publications/2017/oclcresearch-research-data-management.html