Archives des mots-clés : research data

Documentation and Visualisation of Workflows for Effective Communication, Collaboration and Publication @ Source

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Authors : Cerys Willoughby, Jeremy G. Frey

Workflows processing data from research activities and driving in silico experiments are becoming an increasingly important method for conducting scientific research. Workflows have the advantage that not only can they be automated and used to process data repeatedly, but they can also be reused – in part or whole – enabling them to be evolved for use in new experiments.

A number of studies have investigated strategies for storing and sharing workflows for the benefit of reuse. These have revealed that simply storing workflows in repositories without additional context does not enable workflows to be successfully reused.

These studies have investigated what additional resources are needed to facilitate users of workflows and in particular to add provenance traces and to make workflows and their resources machine-readable.

These additions also include adding metadata for curation, annotations for comprehension, and including data sets to provide additional context to the workflow. Ultimately though, these mechanisms still rely on researchers having access to the software to view and run the workflows.

We argue that there are situations where researchers may want to understand a workflow that goes beyond what provenance traces provide and without having to run the workflow directly; there are many situations in which it can be difficult or impossible to run the original workflow.

To that end, we have investigated the creation of an interactive workflow visualization that captures the flow chart element of the workflow with additional context including annotations, descriptions, parameters, metadata and input, intermediate, and results data that can be added to the record of a workflow experiment to enhance both curation and add value to enable reuse.

We have created interactive workflow visualisations for the popular workflow creation tool KNIME, which does not provide users with an in-built function to extract provenance information that can otherwise only be viewed through the tool itself.

Making use of the strengths of KNIME for adding documentation and user-defined metadata we can extract and create a visualisation and curation package that encourages and enhances curation@source, facilitating effective communication, collaboration, and reuse of workflows.

URL : Documentation and Visualisation of Workflows for Effective Communication, Collaboration and Publication @ Source

DOI : https://doi.org/10.2218/ijdc.v12i1.532

Research Transparency: A Preliminary Study of Disciplinary Conceptualisation, Drivers, Tools and Support Services

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Authors : Liz Lyon, Wei Jeng, Eleanor Mattern

This paper describes a preliminary study of research transparency, which draws on the findings from four focus group sessions with faculty in chemistry, law, urban and social studies, and civil and environmental engineering.

The multi-faceted nature of transparency is highlighted by the broad ways in which the faculty conceptualised the concept (data sharing, ethics, replicability) and the vocabulary they used with common core terms identified (data, methods, full disclosure).

The associated concepts of reproducibility and trust are noted. The research lifecycle stages are used as a foundation to identify the action verbs and software tools associated with transparency.

A range of transparency drivers and motivations are listed. The role of libraries and data scientists is discussed in the context of the provision of transparency services for researchers.

URL : Research Transparency: A Preliminary Study of Disciplinary Conceptualisation, Drivers, Tools and Support Services

DOI : https://doi.org/10.2218/ijdc.v12i1.530

Amplifying Data Curation Efforts to Improve the Quality of Life Science Data

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Authors : Mariam Alqasab, Suzanne M. Embury, Sandra de F. Mendes Sampaio

In the era of data science, datasets are shared widely and used for many purposes unforeseen by the original creators of the data. In this context, defects in datasets can have far reaching consequences, spreading from dataset to dataset, and affecting the consumers of data in ways that are hard to predict or quantify.

Some form of waste is often the result. For example, scientists using defective data to propose hypotheses for experimentation may waste their limited wet lab resources chasing the wrong experimental targets. Scarce drug trial resources may be used to test drugs that actually have little chance of giving a cure.

Because of the potential real world costs, database owners care about providing high quality data. Automated curation tools can be used to an extent to discover and correct some forms of defect.

However, in some areas human curation, performed by highly-trained domain experts, is needed to ensure that the data represents our current interpretation of reality accurately.

Human curators are expensive, and there is far more curation work to be done than there are curators available to perform it. Tools and techniques are needed to enable the full value to be obtained from the curation effort currently available.

In this paper,we explore one possible approach to maximising the value obtained from human curators, by automatically extracting information about data defects and corrections from the work that the curators do.

This information is packaged in a source independent form, to allow it to be used by the owners of other databases (for which human curation effort is not available or is insufficient).

This amplifies the efforts of the human curators, allowing their work to be applied to other sources, without requiring any additional effort or change in their processes or tool sets. We show that this approach can discover significant numbers of defects, which can also be found in other sources.

URL : Amplifying Data Curation Efforts to Improve the Quality of Life Science Data

DOI : https://doi.org/10.2218/ijdc.v12i1.495

Connecting Data Publication to the Research Workflow: A Preliminary Analysis

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Authors : Sünje Dallmeier-Tiessen, Varsha Khodiyar, Fiona Murphy, Amy Nurnberger, Lisa Raymond, Angus Whyte

The data curation community has long encouraged researchers to document collected research data during active stages of the research workflow, to provide robust metadata earlier, and support research data publication and preservation.

Data documentation with robust metadata is one of a number of steps in effective data publication. Data publication is the process of making digital research objects ‘FAIR’, i.e. findable, accessible, interoperable, and reusable; attributes increasingly expected by research communities, funders and society.

Research data publishing workflows are the means to that end. Currently, however, much published research data remains inconsistently and inadequately documented by researchers.

Documentation of data closer in time to data collection would help mitigate the high cost that repositories associate with the ingest process. More effective data publication and sharing should in principle result from early interactions between researchers and their selected data repository.

This paper describes a short study undertaken by members of the Research Data Alliance (RDA) and World Data System (WDS) working group on Publishing Data Workflows. We present a collection of recent examples of data publication workflows that connect data repositories and publishing platforms with research activity ‘upstream’ of the ingest process.

We re-articulate previous recommendations of the working group, to account for the varied upstream service components and platforms that support the flow of contextual and provenance information downstream.

These workflows should be open and loosely coupled to support interoperability, including with preservation and publication environments. Our recommendations aim to stimulate further work on researchers’ views of data publishing and the extent to which available services and infrastructure facilitate the publication of FAIR data.

We also aim to stimulate further dialogue about, and definition of, the roles and responsibilities of research data services and platform providers for the ‘FAIRness’ of research data publication workflows themselves.

URL : Connecting Data Publication to the Research Workflow: A Preliminary Analysis

DOI : https://doi.org/10.2218/ijdc.v12i1.533

Data Sharing and Cardiology : Platforms and Possibilities

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Authors : Pranammya Dey, Joseph S. Ross, Jessica D. Ritchie, Nihar R. Desai, Sanjeev P. Bhavnani, Harlan M. Krumholz

Sharing deidentified patient-level research data presents immense opportunities to all stakeholders involved in cardiology research and practice. Sharing data encourages the use of existing data for knowledge generation to improve practice, while also allowing for validation of disseminated research.

In this review, we discuss key initiatives and platforms that have helped to accelerate progress toward greater sharing of data. These efforts are being prompted by government, universities, philanthropic sponsors of research, major industry players, and collaborations among some of these entities.

As data sharing becomes a more common expectation, policy changes will be required to encourage and assist data generators with the process of sharing the data they create.

Patients also will need access to their own data and to be empowered to share those data with researchers. Although medicine still lags behind other fields in achieving data sharing’s full potential, cardiology research has the potential to lead the way.

URL : http://www.onlinejacc.org/content/70/24/3018

 

Research Data Management Instruction for Digital Humanities

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Author : Willow Dressel

eScience related library services at Princeton University started in response to the National Science Foundation’s (NSF) data management plan requirements, and grew to encompass a range of services including data management plan consultation, assistance with depositing into a disciplinary or institutional repository, and research data management instruction.

These services were initially directed at science and engineering disciplines on campus, but the eScience Librarian soon realized the relevance of research data management instruction for humanities disciplines with digital approaches.

Applicability to the digital humanities was initially recognized by discovery of related efforts from the history department’s Information Technology (IT) manager in the form of a graduate-student workshop on file and digital-asset management concepts.

Seeing the common ground these activities shared with research data management, a collaboration was formed between the history department’s IT Manager and the eScience Librarian to provide a research data management overview to the entire campus community.

The eScience Librarian was then invited to participate in the history department’s graduate student file and digital asset management workshop to provide an overview of other research data management concepts. Based on the success of the collaboration with the history department IT, the eScience Librarian offered to develop a workshop for the newly formed Center for Digital Humanities at Princeton.

To develop the workshop, background research on digital humanities curation was performed revealing similarities and differences between digital humanities curation and research data management in the sciences. These similarities and differences, workshop results, and areas of further study are discussed.

URL : Research Data Management Instruction for Digital Humanities

DOI : https://doi.org/10.7191/jeslib.2017.1115

Business models for sustainable research data repositories

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Author : OECD

There is a large variety of repositories that are responsible for providing long term access to data that is used for research. As data volumes and the demands for more open access to this data increase, these repositories are coming under increasing financial pressures that can undermine their long-term sustainability.

This report explores the income streams, costs, value propositions, and business models for 48 research data repositories. It includes a set of recommendations designed to provide a framework for developing sustainable business models and to assist policy makers and funders in supporting repositories with a balance of policy regulation and incentives.

DOI : http://dx.doi.org/10.1787/302b12bb-en