Nav: Home

73 percent of academics say access to research data helps them in their work; 34 percent do not publish their data

April 05, 2017

Combining results from bibliometric analyses, a global sample of researcher opinions and case-study interviews, a new report reveals that although the benefits of open research data are well known, in practice, confusion remains within the researcher community around when and how to share research data.

The report, Open Data: The Researcher Perspective, is the result of a year-long, co-conducted study between Elsevier, the information analytics company specializing in science and health, and the Centre for Science and Technology Studies (CWTS), part of Leiden University, the Netherlands.

The study is based on a complementary methods approach consisting of a quantitative analysis of bibliometric and publication data, a global survey of 1,200 researchers and three case studies including in-depth interviews with key individuals involved in data collection, analysis and deposition in the fields of soil science, human genetics and digital humanities.

Report findings include:
  • Researchers acknowledge the benefits of open data, but data sharing practices are still limited. Reasons mentioned include: not enough training in data sharing, sharing data is not associated with credit or reward, research data management and privacy issues, proprietary aspects and ethics.

  • Data sharing mandates by funders (or publishers) are not considered a driver by researchers to increase their data sharing practices; 64% of researchers believe they own the data they generated for their research.

  • Public data sharing primarily occurs through the current publishing system; less than 15% of researchers share data in a data repository. When researchers do share their data directly, most (>80%) share with direct collaborators.

  • 34% of researchers surveyed do not publish data at all. Those who do share data still use more traditional processes, such as through publication of data aggregated into tables and annexes.

  • Analysis of publication in data journals reveals scattered practices: dedicated data journals are a new and small-scale phenomenon; the popularity is increasing quickly.

  • There is an almost even split between researchers who believe there are no clear standards for citing published data (45%) and those who believe there are clear standards (41%).

  • Data-sharing practices depend on the field: there is no general approach. In intensive data-sharing fields, data sharing practice is embedded into the research design and execution.


Wouter Haak, Vice-President of Research Data Management Solutions at Elsevier, said: "The findings presented in this report help us -- as well as research leaders, university and government policy makers -- better understand where pain points lie when it comes to the sentiment around and the reality of data sharing practices among researchers. These are invaluable insights for us to ensure researchers are given the tools and knowledge they need to successfully share their data."

Paul Wouters, Director of CWTS and Professor of Scientometrics, said: "The science system is undergoing a major transition: from a professional system where the researcher is in the lead, to an open innovation system with multiple stakeholders. One of the ambitions in the Dutch National Plan Open Science is to make research data available in a standardized way for reuse. The study presented here shows that this reality is still far away. It calls for stronger incentives and rewards to implement open data practices. It is time we address the fundamental questions around accessibility in order to be responsible to society. CWTS will continue to raise these issues."

The report and key findings will be presented at the Research Data Alliance conference (RDA) in Barcelona on April 7, 14.00-16.00.

The report and all underling report raw data are freely available.
-end-
Note for editors

Journalists who would like to schedule an interview to discuss the report and its findings can contact Sacha Boucherie at s.boucherie@elsevier.com or +31 20 485 3564. The report and all underling report raw data are freely available.

About CWTS

The ambition of the Centre for Science and Technology Studies (CWTS) is to be a globally leading centre in science and technology studies, with an emphasis on research evaluation, research management, and science policy. CWTS aims to be unique not only in its diversity of theoretical approaches (e.g. citation and communication theories, neo-institutional theory, actor network theory) and methodological approaches (e.g., scientometrics, computer simulation, surveys, interviews, ethnography), but especially in the way these different approaches are combined and integrated. VOSviewer and CitNetExplorer are two popular freely available scientometric software tools for visualizing bibliometric networks developed at CWTS. http://www.cwts.nl

About Elsevier

Elsevier is a global information analytics company that helps institutions and professionals progress science, advance healthcare and improve performance for the benefit of humanity. Elsevier provides digital solutions and tools in the areas of strategic research management, R&D performance, clinical decision support, and professional education; including ScienceDirect, Scopus, ClinicalKey and Sherpath. Elsevier publishes over 2,500 digitized journals, including The Lancet and Cell, more than 35,000 e-book titles, and many iconic reference works, including Gray's Anatomy. Elsevier is part of RELX Group, a world-leading provider of information and analytics to professionals and business customers, in a wide range of industries. http://www.elsevier.com

Media contacts

Sacha Boucherie
Senior Press Officer, Elsevier
+31 20 485 3564
s.boucherie@elsevier.com

Ingeborg Meijer
Senior researcher, CWTS
+31 71 527 6074
i.meijer@cwts.leidenuniv.nl

Elsevier

Related Data Articles:

Discrimination, lack of diversity, & societal risks of data mining highlighted in big data
A special issue of Big Data presents a series of insightful articles that focus on Big Data and Social and Technical Trade-Offs.
Journal AAS publishes first data description paper: Data collection and sharing
AAS published its first data description paper on June 8, 2017.
73 percent of academics say access to research data helps them in their work; 34 percent do not publish their data
Combining results from bibliometric analyses, a global sample of researcher opinions and case-study interviews, a new report reveals that although the benefits of open research data are well known, in practice, confusion remains within the researcher community around when and how to share research data.
Designing new materials from 'small' data
A Northwestern and Los Alamos team developed a novel workflow combining machine learning and density functional theory calculations to create design guidelines for new materials that exhibit useful electronic properties, such as ferroelectricity and piezoelectricity.
Big data for the universe
Astronomers at Lomonosov Moscow State University in cooperation with their French colleagues and with the help of citizen scientists have released 'The Reference Catalog of galaxy SEDs,' which contains value-added information about 800,000 galaxies.
What to do with the data?
Rapid advances in computing constantly translate into new technologies in our everyday lives.
Why keep the raw data?
The increasingly popular subject of raw diffraction data deposition is examined in a Topical Review in IUCrJ.
Infrastructure data for everyone
How much electricity flows through the grid? When and where?
Finding patterns in corrupted data
A new 'robust' statistical method from MIT enables efficient model fitting with corrupted, high-dimensional data.
Big data for little creatures
A multi-disciplinary team of researchers at UC Riverside has received $3 million from the National Science Foundation Research Traineeship program to prepare the next generation of scientists and engineers who will learn how to exploit the power of big data to understand insects.

Related Data Reading:

Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
by Seth Stephens-Davidowitz (Author)

An Economist Best Book of the Year

A PBS NewsHour Book of the Year

An Entrepeneur Top Business Book

An Amazon Best Book of the Year in Business and Leadership

New York Times Bestseller

Foreword by Steven Pinker, author of The Better Angels of our Nature

Blending the informed analysis of The Signal and the Noise with the instructive iconoclasm of Think Like a Freak, a fascinating,... View Details


Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann (Author)

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?

In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various... View Details


Storytelling with Data: A Data Visualization Guide for Business Professionals
by Cole Nussbaumer Knaflic (Author)

Don't simply show your datatell a story with it!  Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examplesready for immediate application to your next graph or presentation. 
Storytelling is not an inherent skill,... View Details


Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
by Foster Provost (Author), Tom Fawcett (Author)

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll... View Details


Practical Statistics for Data Scientists: 50 Essential Concepts
by Peter Bruce (Author), Andrew Bruce (Author)

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to... View Details


Confident Data Skills: Master the Fundamentals of Working with Data and Supercharge Your Career (Confident Series)
by Kirill Eremenko (Author)

Data science is the most exciting skill you can master. Data has dramatically changed how our world works. From entertainment to politics, from technology to advertising and from science to the business world, data is integral and its only limit is our imagination. If you want to have a vibrant and valuable professional life, being skilled with data is the key to a cutting-edge career. Learning how to work with data may seem intimidating or difficult but with Confident Data Skills you will be able to master the fundamentals and supercharge your professional abilities. This... View Details


Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
by Wes McKinney (Author)

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.

Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new... View Details


Data Smart: Using Data Science to Transform Information into Insight
by John W. Foreman (Author)

Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.

But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.

Data science is little more than using straight-forward steps to process raw data into... View Details


Dear Data
by Giorgia Lupi (Author), Stefanie Posavec (Author), Maria Popova (Foreword)

Equal parts mail art, data visualization, and affectionate correspondence, Dear Data celebrates "the infinitesimal, incomplete, imperfect, yet exquisitely human details of life," in the words of Maria Popova (Brain Pickings), who introduces this charming and graphically powerful book. For one year, Giorgia Lupi, an Italian living in New York, and Stefanie Posavec, an American in London, mapped the particulars of their daily lives as a series of hand-drawn postcards they exchanged via mail weekly—small portraits as full of emotion as they are data, both mundane and magical. Dear... View Details

Best Science Podcasts 2018

We have hand picked the best science podcasts for 2018. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

The Person You Become
Over the course of our lives, we shed parts of our old selves, embrace new ones, and redefine who we are. This hour, TED speakers explore ideas about the experiences that shape the person we become. Guests include aerobatics pilot and public speaker Janine Shepherd, writers Roxane Gay and Taiye Selasi, activist Jackson Bird, and fashion executive Kaustav Dey.
Now Playing: Science for the People

#478 She Has Her Mother's Laugh
What does heredity really mean? Carl Zimmer would argue it's more than your genes along. In "She Has Her Mother’s Laugh: The Power, Perversions, and Potential of Heredity", Zimmer covers the history of genetics and what kinship and heredity really mean when we're discovering how to alter our own DNA, and, potentially, the DNA of our children.