Emergence of Research Data Literacy with Special Reference to India

2019 ◽  
Vol 56 (2) ◽  
pp. 112-118 ◽  
Author(s):  
Prashant Shrivastava ◽  
Dinesh K. Gupta ◽  
◽  
Keyword(s):  
Special Reference ◽  
Research Data ◽  
Data Literacy

scholarly journals Информациона писменост наставника и сарадника на Универзитету у Београду

2018 ◽  
Vol 17 (32) ◽  
pp. 12
Author(s):  
Гордана Стокић Симончић ◽  
Драгана Сабовљев
Keyword(s):  
Data Management ◽  
Research Data ◽  
Management Research ◽  
Data Literacy ◽  
Research Data Management

У раду су представљени резултати истраживања које је спроведено током 2017. године на Универзитету у Београду, а имало је за циљ да утврди ниво информационе писмености наставника и сарадника, испитујући њихову оспособљеност за руковање истраживачким подацима. Истоветном онлајн анкетом (Вештина коришћења података и управљање истраживачким подацима – Data Literacy and Research Data Management Research) прикупљани су подаци о навикама истраживача (професора, сарадника, докторанада, библиотекара), у академским срединама већег броја европских држава упоредо, да би се омогућило компаративно сагледавање проблематике.Узорак од 85 испитаника потврдио је полазну претпоставку анкетара да ниво информационе писмености наставника и сарадника на Универзитету у Београду треба систематски унапређивати. Но, показао је и мањак институционалне инфраструктуре (политике, сервиси, средства, репозиторијуми, процедуре), па и недостатак, односно непознавање стручне терминологије у области управљања истраживачким подацима, што све заједно директно утиче на ефективност научног процеса.

scholarly journals Identifying new roles for academic libraries in supporting data-intensive research

Bibliosphere ◽  
2020 ◽  
pp. 97-102 ◽  
Author(s):  
Tibor Koltay
Keyword(s):  
Academic Libraries ◽  
Recent Literature ◽  
Service Providers ◽  
Literacy Education ◽  
Research Data ◽  
Service Development ◽  
Data Literacy ◽  
Intensive Research ◽  
Data Intensive ◽  
Research Data Management

Reacting to the appearance of data-intensive research prompts academic libraries to become service providers for scholars, who work with research data. Although this is an imperative for libraries worldwide, due to the differences between countries and institutions, the level of readiness to engage in related activities differs from country to country. While some of the related tasks are fairly novel, others heavily build on librarians’ traditional, well-known skills. To identify these tasks, as well as making an inventory of the required skills and abilities, this paper, based on a non-exhaustive review of the recent literature, presents both theoretical and practical issues. It is demonstrated that the most obvious directions of the service development in academic libraries to support data-intensive science are research data management, data curation, data literacy education for users, and data literacy education for librarians.

Research data management and research data literacy in Slovenian science

2019 ◽  
Vol 75 (1) ◽  
pp. 24-43 ◽  
Author(s):  
Polona Vilar ◽  
Vlasta Zabukovec
Keyword(s):  
Data Management ◽  
Online Survey ◽  
National Level ◽  
Public Domain ◽  
Research Data ◽  
Ethical Question ◽  
Valuable Insight ◽  
Content Type ◽  
Data Literacy ◽  
Research Data Management

PurposeThe purpose of this paper is to investigate the differences between scientific disciplines (SDs) in Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations regarding how to move things forward on the institutional and national level.Design/methodology/approachPurposive sample of active researchers was used from widest possible range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey (5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and multivariate methods (clustering) were used.FindingsThe authors identified three perception-related and four behavior-related connections; this gave three clusters per area. First, perceptions – skeptical group, mainly social (SocS) and natural sciences (NatS): no clear RDM and ethical issues standpoints, do not agree that every university needs a data management plan (DMP). Careful group, again including mainly SocS and NatS: RDM is problematic and linked to ethical dilemmas, positive toward institutional DMPs. Convinced group, mainly from humanities (HUM), NatS, engineering (ENG) and medicine and health sciences (MedHeS): no problems regarding RDM, agrees this is an ethical question, is positive toward institutional DMP’s. Second, behaviors – sparse group, mainly from MedHeS, NatS and HUM, some agricultural scientists (AgS), and some SocS and ENG: do not tag data sets with metadata, do not use file-naming conventions/standards. Frequent group – many ENG, SocS, moderate numbers of NatS, very few AgS and only a few MedHeS and HUM: often use file-naming conventions/standards, version-control systems, have experience with public-domain data, are reluctant to use metadata with their RD. Slender group, mainly from AgS and NatS, moderate numbers of ENG, SocS and HUM, but no MedHeS: often use public-domain data, other three activities are rare.Research limitations/implicationsResearch could be expanded to a wider population, include other stakeholders and use qualitative methods.Practical implicationsResults are useful for international comparisons but also give foundations and recommendations on institutional and national RDM and RDL policies, implementations, and how to bring academic libraries into the picture. Identified differences suggest that different educational, awareness-raising and participatory approaches are needed for each group.Originality/valueThe findings offer valuable insight into RDM and RDL of Slovenian scientists, which have not yet been investigated in Slovenia.

scholarly journals Data literacy for researchers and data librarians

2016 ◽  
Vol 49 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Tibor Koltay
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Literacy ◽  
Academic Library ◽  
Research Data ◽  
Review Of Literature ◽  
Data Literacy ◽  
Data Intensive ◽  
Research Data Management ◽  
Data Citation

This paper describes data literacy and emphasizes its importance. Data literacy is vital for researchers who need to become data literate science workers and also for (potential) data management professionals. Its important characteristic is a close connection and similarity to information literacy. To support this argument, a review of literature was undertaken on the importance of data, and the data-intensive paradigm of scientific research, researchers’ expected and real behaviour, the nature of research data management, the possible roles of the academic library, data quality and data citation, Besides describing the nature of data literacy and enumerating the related skills, the application of phenomenographic approaches to data literacy and its relationship to the digital humanities have been identified as subjects for further investigation.

scholarly journals Exploring the social activity of open research data on ResearchGate: implications for the data literacy of researchers

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Juliana Elisa Raffaghelli ◽  
Stefania Manca
Keyword(s):  
Peer Review ◽  
Social Activity ◽  
Open Science ◽  
Research Data ◽  
Invasive Approach ◽  
Content Type ◽  
Technological Infrastructure ◽  
Data Literacy ◽  
Open Research ◽  
The Social

Purpose Although current research has investigated how open research data (ORD) are published, researchers' behaviour of ORD sharing on academic social networks (ASNs) remains insufficiently explored. The purpose of this study is to investigate the connections between ORDs publication and social activity to uncover data literacy gaps.Design/methodology/approach This work investigates whether the ORDs publication leads to social activity around the ORDs and their linked published articles to uncover data literacy needs. The social activity was characterised as reads and citations, over the basis of a non-invasive approach supporting this preliminary study. The eventual associations between the social activity and the researchers' profile (scientific domain, gender, region, professional position, reputation) and the quality of the ORD published were investigated to complete this picture. A random sample of ORD items extracted from ResearchGate (752 ORDs) was analysed using quantitative techniques, including descriptive statistics, logistic regression and K-means cluster analysis.Findings The results highlight three main phenomena: (1) Globally, there is still an underdeveloped social activity around self-archived ORDs in ResearchGate, in terms of reads and citations, regardless of the published ORDs quality; (2) disentangling the moderating effects over social activity around ORD spots traditional dynamics within the “innovative” practice of engaging with data practices; (3) a somewhat similar situation of ResearchGate as ASN to other data platforms and repositories, in terms of social activity around ORD, was detected.Research limitations/implications Although the data were collected within a narrow period, the random data collection ensures a representative picture of researchers' practices.Practical implications As per the implications, the study sheds light on data literacy requirements to promote social activity around ORD in the context of open science as a desirable frontier of practice.Originality/value Researchers data literacy across digital systems is still little understood. Although there are many policies and technological infrastructure providing support, the researchers do not make an in-depth use of them.Peer reviewThe peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2021-0255.

scholarly journals Sharing qualitative research data, improving data literacy and establishing national data services

2020 ◽  
Vol 43 (4) ◽  
pp. 1-2
Author(s):  
Karsten Boye Rasmussen
Keyword(s):  
Social Science ◽  
Data Sharing ◽  
Quantitative Data ◽  
Qualitative Data ◽  
Research Data ◽  
National Data ◽  
Science Data ◽  
Data Infrastructure ◽  
Data Literacy ◽  
Social Science Data

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

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