scholarly journals Examining barriers for establishing a national data service

2020 ◽  
Vol 43 (4) ◽  
pp. 1-14
Author(s):  
Janez Štebe

A system for monitoring the current situation of Data Archive Services (DAS) maturity in European countries was developed during the CESSDA Strengthening and Widening in (SaW 2016 and 2017) and further adapted in CESSDA Widening Activities 2018 (WA 2018) projects for continuous monitoring. An assessment of the existing national data sharing culture, the development of the social science sector and its production of high-quality research data, the funders’ research data policy requirements, and the capacity and skills of national grassroots initiatives, provide a framework for understanding the current situation in different countries. Methods used in the projects, included desk research of  existing documents and a survey, combined with extensive interviews focused on the area of expertise of the informants (individuals from data services, research and decision makers’ representatives from each country). The focus of the paper is the situation in 20 non-member CESSDA European countries with emerging and immature DAS initiatives. Results show that countries are slowly but persistently removing the key obstacles in establishing a DAS initiative in their respective countries. The remaining obstacles reside mainly outside the control of the data professional community – namely research funders slowly adopt data sharing policies and incentives for data sharing, including the provision of a sustainable DAS infrastructure, capable of supporting researchers with publishing and accessing research data. The results show that the lack of expertise and skills of DAS initiatives, their understanding of tools and services or organizational settings are not such an issue, as more mature DAS are organising training and mentorship activities. Detailed guidance in the DAS advocacy and planning was prepared in the framework of the above-mentioned pan-European and some past regional projects. The tools and framework of those activities will be referred to in the discussions as a resource that can be used in other countries and continents.

2019 ◽  
Vol 52 (3) ◽  
pp. 633-646 ◽  
Author(s):  
Soohyung Joo ◽  
Christie Peters

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.


2009 ◽  
Vol 4 (3) ◽  
pp. 44-56 ◽  
Author(s):  
Adrian Burton ◽  
Andrew Treloar

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.


2020 ◽  
Vol 43 (4) ◽  
pp. 1-2
Author(s):  
Karsten Boye Rasmussen

Welcome to the fourth issue of volume 43 of the IASSIST Quarterly (IQ 43:4, 2019). The first article is authored by Jessica Mozersky, Heidi Walsh, Meredith Parsons, Tristan McIntosh, Kari Baldwin, and James M. DuBois – all located at the Bioethics Research Center, Washington University School of Medicine, St. Louis, Missouri in USA. They ask the question “Are we ready to share qualitative research data?”, with the subtitle “Knowledge and preparedness among qualitative researchers, IRB Members, and data repository curators.” The subtitle indicates that their research includes a survey of key personnel related to scientific data sharing. The report is obtained through semi-structured in-depth interviews with 30 data repository curators, 30 qualitative researchers, and 30 IRB staff members in the USA. IRB stands for Institutional Review Board, which in other countries might be called research ethics committee or similar. There is generally an increasing trend towards data sharing and open science, but qualitative data are rarely shared. The dilemma behind this reluctance to share is exemplified by health data where qualitative methods explore sensitive topics. The sensitivity leads to protection of confidentiality, which hinders keeping sufficient contextual detail for secondary analyses. You could add that protection of confidentiality is a much bigger task in qualitative data, where sensitive information can be hidden in every corner of the data, that consequently must be fine-combed, while with quantitative data most decisions concerning confidentiality can be made at the level of variables. The reporting in the article gives insights into the differences between the three stakeholder groups. An often-found answer among researchers is that data sharing is associated with quantitative data, while IRB members have little practice with qualitative. Among curators, about half had curated qualitative data, but many only worked with quantitative data. In general, qualitative data sharing lacks guidance and standards.   The second article also raises a question: “How many ways can we teach data literacy?” We are now in Asia with a connection to the USA. The author Yun Dai is working at the Library of New York University Shanghai, where they have explored many ways to teach data literacy to undergraduate students. These initiatives, described in the article, included workshops and in-class instruction - which tempted students by offering up-to-date technology, through online casebooks of topics in the data lifecycle, to event series with appealing names like “Lying with Data.” The event series had a marketing mascot - a “Lying with Data” Pinocchio - and sessions on being fooled by advertisements and getting the truth out of opinion surveys. Data literacy has a resemblance to information literacy and in that perspective, data literacy is defined as “critical thinking applied to evaluating data sources and formats, and interpreting and communicating findings,” while statistical literacy is “the ability to evaluate statistical information as evidence.” The article presents the approaches and does not conclude on the question, “How many?” No readers will be surprised by the missing answer, and I am certain readers will enjoy the ideas of the article and the marketing focus.   With the last article “Examining barriers for establishing a national data service,” the author Janez Štebe takes us to Europe. Janez Štebe is head of the social science data archives (Arhiv Družboslovnih Podatkov) at the University of Ljubljana, Slovenia. The Consortium of European Social Science Data Archives (CESSDA) is a distributed European social science data infrastructure for access to research data. CESSDA has many - but not all - European countries as members. The focus is on the situation in 20 non-CESSDA member European countries, with emerging and immature data archive services being developed through such projects as the CESSDA Strengthening and Widening (SaW 2016 and 2017) and CESSDA Widening Activities (WA 2018). By identifying and comparing gaps and differences, a group of countries at a similar level may consider following similar best practice examples to achieve a more mature and supportive open scientific data ecosystem. Like the earlier articles, this article provides good references to earlier literature and description of previous studies in the area. In this project 22 countries were selected, all CESSDA non-members, and interviewees among social science researchers and data librarians were contacted with an e-mail template between October 2018 and January 2019. The article brings results and discussion of the national data sharing culture and data infrastructure. Yes, there is a lack of money! However, it is the process of gradually establishing a robust data infrastructure that is believed to impact the growth of a data sharing culture and improve the excellence and the efficiency of research in general.   Submissions of papers for the IASSIST Quarterly are always very welcome. We welcome input from IASSIST conferences or other conferences and workshops, from local presentations or papers especially written for the IQ. When you are preparing such a presentation, give a thought to turning your one-time presentation into a lasting contribution. Doing that after the event also gives you the opportunity of improving your work after feedback. We encourage you to login or create an author login to https://www.iassistquarterly.com (our Open Journal System application). We permit authors to “deep link” into the IQ as well as to deposit the paper in your local repository. Chairing a conference session with the purpose of aggregating and integrating papers for a special issue IQ is also much appreciated as the information reaches many more people than the limited number of session participants and will be readily available on the IASSIST Quarterly website at https://www.iassistquarterly.com.  Authors are very welcome to take a look at the instructions and layout: https://www.iassistquarterly.com/index.php/iassist/about/submissions Authors can also contact me directly via e-mail: [email protected]. Should you be interested in compiling a special issue for the IQ as guest editor(s) I will also be delighted to hear from you. Karsten Boye Rasmussen - December 2019


Author(s):  
Kevin Read ◽  
Alanna Campbell ◽  
Vanessa Kitchin ◽  
Heather MacDonald ◽  
Sandra McKeown

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. 


2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Angeletta Leggio

Since 2009 the Australian National Data Services (ANDS) has evolved and matured as a national infrastructure project. This has involved a change in its engagement model; primarily moving from a compliance and milestone driven model, towards a partnering organisation. In 2013 ANDS streamlined its contract management and reporting process and initiated the Institutional Engagement program to assist partnering organisations achieve their research data ambitions. These, amongst other initiatives, helped ANDS move towards operating as a collaborator and partner, rather than solely as a funder. Between 2013 and 2017 ANDS changed its engagement model during four of its funding programs by offering funding and expertise into projects. However, the uptake of expertise was not as successful in the earlier programs as anticipated. As a result, changes in how ANDS engaged, including working more closely with project partners at the project initiation stage, were introduced. These changes improved ANDS’ ability to become embedded as a trusted and invested partner in the project team. Feedback provided by project partners during surveys and interviews suggests the shift from funder to partner is slowly evolving and moving in the right direction. To continue this process, ANDS, RDS and Nectar have adopted a Partnership Strategy as part of delivering its aligned business plan in 2018.


2016 ◽  
Vol 11 (1) ◽  
pp. 183 ◽  
Author(s):  
Andrew E. Treloar ◽  
Mingfang Wu

This article introduces the provenance activities that are being carried out at the Australia National Data Services (ANDS). Since its beginning, ANDS has been promoting four data transformations so that Australia’s research data become more valuable and reusable by researchers. Among many other activities that enable the four transformations, ANDS has been encouraging ANDS partners to capture and describe rich context at the time when a data collection is created. In 2015, ANDS funded a number of external projects that had provenance components. In addition, ANDS is working on the interoperability between the schema that is used by the ANDS research data registration and discovery service – Research Data Australia (RDA) – and the W3C recommended provenance standard, Provenance Ontology (PROV-O), and investigating how to enrich the schema to access provenance information. The article concludes by discussing the lessons we learnt and our future planned activity.


2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Margaret Henderson

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.


CJC Open ◽  
2020 ◽  
Author(s):  
Frederic Dallaire ◽  
Marie-Claude Battista ◽  
Steven C. Greenway ◽  
Kevin Harris ◽  
Emilie Jean St-Michel ◽  
...  

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