scholarly journals Differences in Data Sharing Attitudes and Behaviours

2018 ◽  
Vol 42 (3) ◽  
pp. 1-39
Author(s):  
Flavio Bonifacio
Keyword(s):  
Data Sharing ◽  
Data Gathering ◽  
International Comparisons ◽  
Short Description ◽  
Data Archiving ◽  
Data Services ◽  
Univariate Distribution

This article reports the results of a survey conducted between 18th November and 18th December 2017 about different aspects of data sharing: tools used in building metadata, problems encountered in order to share the data, the propensity to share the data, the satisfaction obtained over different working tasks. After a short description of the data gathering task, the report describes the sample, the univariate distribution of the most important variables related to the work of data archiving and the attitudes concerning the data sharing activity: problems encountered, propensity to share the data, satisfaction obtained. Part of the report illustrates models suitable for interpreting the results and finally gives some advice for promoting data services. Some international comparisons of the results are proposed in the annex.

scholarly journals PsychData – Experiences from 12 Years of Research Data Archiving

2015 ◽  
Author(s):  
Peter Weiland ◽  
Ina Dehnhard
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Research Data ◽  
Data Reuse ◽  
German Research Foundation ◽  
Cross Sectional ◽  
Data Archiving ◽  
Wide Range ◽  
Domain Specific Knowledge ◽  
Meta Analyses

See video of the presentation.The benefits of making research data permanently accessible through data archives is widely recognized: costs can be reduced by reusing existing data, research results can be compared and validated with results from archived studies, fraud can be more easily detected, and meta-analyses can be conducted. Apart from that, authors may gain recognition and reputation for producing the datasets. Since 2003, the accredited research data center PsychData (part of the Leibniz Institute for Psychology Information in Trier, Germany) documents and archives research data from all areas of psychology and related fields. In the beginning, the main focus was on datasets that provide a high potential for reuse, e.g. longitudinal studies, large-scale cross sectional studies, or studies that were conducted during historically unique conditions. Presently, more and more journal publishers and project funding agencies require researchers to archive their data and make them accessible for the scientific community. Therefore, PsychData also has to serve this need.In this presentation we report on our experiences in operating a discipline-specific research data archive in a domain where data sharing is met with considerable resistance. We will focus on the challenges for data sharing and data reuse in psychology, e.g.large amount of domain-specific knowledge necessary for data curationhigh costs for documenting the data because of a wide range on non-standardized measuressmall teams and little established infrastructures compared with the "big data" disciplinesstudies in psychology not designed for reuse (in contrast to the social sciences)data protectionresistance to sharing dataAt the end of the presentation, we will provide a brief outlook on DataWiz, a new project funded by the German Research Foundation (DFG). In this project, tools will be developed to support researchers in documenting their data during the research phase.

scholarly journals Intelligent Data Management and Security in Cloud Computing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3458
Author(s):  
Lidia Ogiela ◽  
Marek R. Ogiela ◽  
Hoon Ko
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Service Management ◽  
Secret Data ◽  
Data Services ◽  
New Class ◽  
Management Processes ◽  
Confidential Data ◽  
Shared Secret ◽  
Linguistic Methods

This paper will present the authors’ own techniques of secret data management and protection, with particular attention paid to techniques securing data services. Among the solutions discussed, there will be information-sharing protocols dedicated to the tasks of secret (confidential) data sharing. Such solutions will be presented in an algorithmic form, aimed at solving the tasks of protecting and securing data against unauthorized acquisition. Data-sharing protocols will execute the tasks of securing a special type of information, i.e., data services. The area of data protection will be defined for various levels, within which will be executed the tasks of data management and protection. The authors’ solution concerning securing data with the use of cryptographic threshold techniques used to split the secret among a specified group of secret trustees, simultaneously enhanced by the application of linguistic methods of description of the shared secret, forms a new class of protocols, i.e., intelligent linguistic threshold schemes. The solutions presented in this paper referring to the service management and securing will be dedicated to various levels of data management. These levels could be differentiated both in the structure of a given entity and in its environment. There is a special example thereof, i.e., the cloud management processes. These will also be subject to the assessment of feasibility of application of the discussed protocols in these areas. Presented solutions will be based on the application of an innovative approach, in which we can use a special formal graph for the creation of a secret representation, which can then be divided and transmitted over a distributed network.

scholarly journals Enforcing public data archiving policies in academic publishing: A study of ecology journals

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171983625 ◽  
Author(s):  
Dan Sholler ◽  
Karthik Ram ◽  
Carl Boettiger ◽  
Daniel S Katz
Keyword(s):  
Data Sharing ◽  
Peer Review Process ◽  
Editorial Staff ◽  
Data Availability ◽  
Academic Publishing ◽  
Data Archiving ◽  
The Public ◽  
Public Data ◽  
Journal Editorial

To improve the quality and efficiency of research, groups within the scientific community seek to exploit the value of data sharing. Funders, institutions, and specialist organizations are developing and implementing strategies to encourage or mandate data sharing within and across disciplines, with varying degrees of success. Academic journals in ecology and evolution have adopted several types of public data archiving policies requiring authors to make data underlying scholarly manuscripts freely available. The effort to increase data sharing in the sciences is one part of a broader “data revolution” that has prompted discussion about a paradigm shift in scientific research. Yet anecdotes from the community and studies evaluating data availability suggest that these policies have not obtained the desired effects, both in terms of quantity and quality of available datasets. We conducted a qualitative, interview-based study with journal editorial staff and other stakeholders in the academic publishing process to examine how journals enforce data archiving policies. We specifically sought to establish who editors and other stakeholders perceive as responsible for ensuring data completeness and quality in the peer review process. Our analysis revealed little consensus with regard to how data archiving policies should be enforced and who should hold authors accountable for dataset submissions. Themes in interviewee responses included hopefulness that reviewers would take the initiative to review datasets and trust in authors to ensure the completeness and quality of their datasets. We highlight problematic aspects of these thematic responses and offer potential starting points for improvement of the public data archiving process.

User needs assessment for research data services in a research university

2019 ◽  
Vol 52 (3) ◽  
pp. 633-646 ◽  
Author(s):  
Soohyung Joo ◽  
Christie Peters
Keyword(s):  
Data Sharing ◽  
Research University ◽  
Research Data ◽  
User Needs ◽  
Data Services ◽  
Data Formats ◽  
Metadata Standards ◽  
Data Documentation ◽  
And Storage ◽  
Sharing Behavior

This study assesses the needs of researchers for data-related assistance and investigates their research data management behavior. A survey was conducted, and 186 valid responses were collected from faculty, researchers, and graduate students across different disciplines at a research university. The services for which researchers perceive the greatest need include assistance with quantitative analysis and data visualization. Overall, the need for data-related assistance is relatively higher among health scientists, while humanities researchers demonstrate the lowest need. This study also investigated the data formats used, data documentation and storage practices, and data-sharing behavior of researchers. We found that researchers rarely use metadata standards, but rely more on a standard file-naming scheme. As to data sharing, respondents are likely to share their data personally upon request or as supplementary materials to journal publications. The findings of this study will be useful for planning user-centered research data services in academic libraries.

scholarly journals Designing for Discovery and Re-Use: the ‘ANDS Data Sharing Verbs’ Approach to Service Decomposition

2009 ◽  
Vol 4 (3) ◽  
pp. 44-56 ◽  
Author(s):  
Adrian Burton ◽  
Andrew Treloar
Keyword(s):  
Data Sharing ◽  
National Data ◽  
Data Services

This paper describes how the Australian National Data Services (ANDS) is designing systems to support data sharing and Re-use. The paper commences with an overview of the setting for ANDS, before introducing ANDS itself. The paper then structures its discussion of ANDS services for Re-use in terms of the ANDS Data Sharing Verbs: Create, Store, Describe, Identify, Register, Discover, Access and Exploit. For each of the data verbs, a rationale for its importance is provided together with a description of how it is being implemented by ANDS. The paper concludes by arguing for the data verbs approach as a useful way to design and structure flexible services in a heterogenous environment.

scholarly journals Embracing the value of research data: introducing the JCHLA/JABSC Data Sharing Policy

2021 ◽  
Vol 42 (1) ◽  
Author(s):  
Kevin Read ◽  
Alanna Campbell ◽  
Vanessa Kitchin ◽  
Heather MacDonald ◽  
Sandra McKeown
Keyword(s):  
Data Sharing ◽  
Health Sciences ◽  
Research Data ◽  
Research Grants ◽  
Related Research ◽  
Data Services ◽  
Canadian Health ◽  
The Right ◽  
Positive Step ◽  
Worthwhile Investment

As health sciences researchers have been asked to share their data more frequently due to funder policies, journal requirements, or interest from their peers, health sciences librarians (HSLs) have simultaneously begun to provide support to researchers in this space through training, participating in RDM efforts on research grants, and developing comprehensive data services programs. If supporting researchers' data sharing efforts is a worthwhile investment for HSLs, it is crucial that we practice data sharing in our own research endeavours. sharing data is a positive step in the right direction, as it can increase the transparency, reliability, and reusability of HSL-related research outputs. Furthermore, having the ability to identify and connect with researchers in relation to the challenges associated with data sharing can help HSLs empathize with their communities and gain new perspectives on improving support in this area. To that end, the Journal of the Canadian Health Libraries Association / Journal de l’Association des bibliothèques de la santé du Canada (JCHLA / JABSC) has developed a Data Sharing Policy to improve the transparency and reusability of research data underlying the results of its publications. This paper will describe the approach taken to inform and develop this policy. 

scholarly journals Why You Need Soft and Non-Technical Skills for Successful Data Librarianship

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Margaret Henderson
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Practical Knowledge ◽  
Soft Skills ◽  
Technical Skills ◽  
Life Cycles ◽  
Research Data ◽  
Relationship Building ◽  
Data Services ◽  
Research Data Management

There are many courses available to teach research data management to librarians and researchers. While these courses can help with technical skills, like programming or statistics, and practical knowledge of data life cycles or data sharing policies, there are “soft skills” and non-technical skills that are needed to successfully start and run data services. While there are many important characteristics of a good data librarian, reference skills, relationship building, collaboration, listening, and facilitation are some of the most important. Giving consideration to these skills will help any data librarian with their multifaceted job.

scholarly journals Reading Faculty’s Research Publications Helps to Determine Which Professors to Target for Data Services

2014 ◽  
Vol 9 (1) ◽  
pp. 54
Author(s):  
Giovanna Badia
Keyword(s):  
Data Sharing ◽  
Journal Article ◽  
Faculty Members ◽  
Data Sources ◽  
Data Sets ◽  
Data Repositories ◽  
Data Services ◽  
Data Practices ◽  
Rich Data ◽  
Bibliographic Study

Objective – The research project examined university faculty’s publications in order to find professors with previous data experiences. The professors could then be approached with an offer of the library’s data services. Design – Bibliographic study. Setting – Department of Crop Sciences in the College of Agricultural, Consumer, and Environmental Sciences at the University of Illinois at Urbana-Champaign. Subjects – A total of 62 assistant, associate, and full professors. Methods – The author searched Web of Science and faculty web pages to find each of the subjects’ two most recent research or review articles. Altogether, 124 articles were read to check whether data sources were used and shared. Data sources were defined as sources other than traditional citations to literature for information or ideas, such as data repositories, supplementary files, and weather stations. Data sharing was defined as publicly sharing data beyond that published in the journal article, such as providing supplementary files with the article or submitting data sets to a disciplinary repository (p. 205). Main Results – Thirty of the 124 articles, which were written by 20 different professors, referred to additional data that was made openly accessible. The analysis of the articles uncovered a variety of data experiences, such as faculty who utilized repository data, published supplementary files, submitted their own data to repositories, or posted data on their university’s website. These 20 faculty members were contacted and asked for a meeting “to discuss their data sharing thoughts and experiences and to ask whether they [saw] a role for the library in facilitating data sharing” (p. 206). The author received a positive response from seven of the faculty members and had a successful meeting with each of them. Conclusion – A bibliographic study can be employed to select which professors to target for data services. While this method is time-consuming, it allows librarians to gather rich data about faculty research that will help them to create customized, relevant messages to professors about the library’s data services. It also allows them to become more knowledgeable about data practices and resources in a particular discipline.

scholarly journals Guidelines for the content and format of PET brain data in publications and archives: A consensus paper

2020 ◽  
Vol 40 (8) ◽  
pp. 1576-1585 ◽  
Author(s):  
Gitte M Knudsen ◽  
Melanie Ganz ◽  
Stefan Appelhoff ◽  
Ronald Boellaard ◽  
Guy Bormans ◽  
...  
Keyword(s):  
Best Practices ◽  
Data Sharing ◽  
Statistical Power ◽  
Sample Sizes ◽  
Data Archiving ◽  
Positron Emission ◽  
Neuroimaging Data ◽  
Brain Data ◽  
Larger Sample ◽  
Publication Standards

It is a growing concern that outcomes of neuroimaging studies often cannot be replicated. To counteract this, the magnetic resonance (MR) neuroimaging community has promoted acquisition standards and created data sharing platforms, based on a consensus on how to organize and share MR neuroimaging data. Here, we take a similar approach to positron emission tomography (PET) data. To facilitate comparison of findings across studies, we first recommend publication standards for tracer characteristics, image acquisition, image preprocessing, and outcome estimation for PET neuroimaging data. The co-authors of this paper, representing more than 25 PET centers worldwide, voted to classify information as mandatory, recommended, or optional. Second, we describe a framework to facilitate data archiving and data sharing within and across centers. Because of the high cost of PET neuroimaging studies, sample sizes tend to be small and relatively few sites worldwide have the required multidisciplinary expertise to properly conduct and analyze PET studies. Data sharing will make it easier to combine datasets from different centers to achieve larger sample sizes and stronger statistical power to test hypotheses. The combining of datasets from different centers may be enhanced by adoption of a common set of best practices in data acquisition and analysis.

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