scholarly journals Key Aspects of Open Data in Finnish RDI Cooperation between Higher Education and Businesses

2021 ◽  
pp. 1-13
Author(s):  
Seliina Päällysaho ◽  
Jaana Latvanen ◽  
Anttoni Lehto ◽  
Jaakko Riihimaa ◽  
Pekka Lahti ◽  
...  
Keyword(s):  
Higher Education ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
General Level ◽  
Joint Research ◽  
Research Organizations ◽  
Universities Of Applied Sciences ◽  
Key Aspects ◽  
Five Phases

The article highlights aspects that should be considered during an open research, development, and innovation (RDI) process cycle to improve the utilization of research data and foster open cooperation between higher education and businesses. The viewpoint here is in publicly funded joint research projects of the universities of applied sciences (UAS), the concept is, however, applicable in other higher education and research organizations as well. There are various challenges related to research data management in general as well as to the openness and reuse of data and results. The findings of this article are based on the results of a two-day expert workshop, and these results are interlinked with five phases of an open RDI process cycle: planning, implementation, documentation, sharing, and commercialization. Various drivers and barriers can be identified in different stages of the process. On a general level, special attention must be paid to critical factors such as ownership and sharing of data and results, confidential information and business secrets as well as following the requirements of the open science policies of the participating organizations and funders. This article also highlights several best practices that should be considered in each phase of an open RDI process cycle with businesses.

scholarly journals Tutkimusaineiston avoin jakaminen – tutkimusorganisaatioiden jäsenten käsityksiä, kokemuksia ja mielipiteitä

2018 ◽  
Vol 37 (4) ◽  
Author(s):  
Heidi Enwald
Keyword(s):  
Data Sharing ◽  
Data Storage ◽  
Online Survey ◽  
Open Science ◽  
Research Data ◽  
Research Field ◽  
Open Research ◽  
Research Organizations ◽  
Data Collection Instrument ◽  
In The Beginning

Open research data is data that is free to access, reuse, and redistribute. This study focuses on the perceptions, opinions and experiences of staff and researchers of research institutes on topics related to open research data. Furthermore, the differences across gender, role in the research organization and research field were investigated. An international questionnaire survey, translated into Finnish and Swedish, was used as the data collection instrument. An online survey was distributed through an open science related network to Finnish research organizations. In the end, 469 responded to all 24 questions of the survey. Findings indicate that many are still unaware or uncertain about issues related to data sharing and long-term data storage. Women as well as staff and researchers of medical and health sciences were most concerned about the possible problems associated with data sharing. Those in the beginning of their scientific careers, hesitated about sharing their data.

scholarly journals The reproducibility challenge – what researchers need

2017 ◽  
Author(s):  
Federica Rosetta
Keyword(s):  
Scientific Discovery ◽  
Open Data ◽  
Science Research ◽  
Open Science ◽  
Research Data ◽  
Data Sets ◽  
Scientific Practices ◽  
Data Citation ◽  
Materials Used ◽  
High Level

Watch the VIDEO here.Within the Open Science discussions, the current call for “reproducibility” comes from the raising awareness that results as presented in research papers are not as easily reproducible as expected, or even contradicted those original results in some reproduction efforts. In this context, transparency and openness are seen as key components to facilitate good scientific practices, as well as scientific discovery. As a result, many funding agencies now require the deposit of research data sets, institutions improve the training on the application of statistical methods, and journals begin to mandate a high level of detail on the methods and materials used. How can researchers be supported and encouraged to provide that level of transparency? An important component is the underlying research data, which is currently often only partly available within the article. At Elsevier we have therefore been working on journal data guidelines which clearly explain to researchers when and how they are expected to make their research data available. Simultaneously, we have also developed the corresponding infrastructure to make it as easy as possible for researchers to share their data in a way that is appropriate in their field. To ensure researchers get credit for the work they do on managing and sharing data, all our journals support data citation in line with the FORCE11 data citation principles – a key step in the direction of ensuring that we address the lack of credits and incentives which emerged from the Open Data analysis (Open Data - the Researcher Perspective https://www.elsevier.com/about/open-science/research-data/open-data-report ) recently carried out by Elsevier together with CWTS. Finally, the presentation will also touch upon a number of initiatives to ensure the reproducibility of software, protocols and methods. With STAR methods, for instance, methods are submitted in a Structured, Transparent, Accessible Reporting format; this approach promotes rigor and robustness, and makes reporting easier for the author and replication easier for the reader.

Opportunities for Adopting Open Research Data in Learning Analytics

2021 ◽  
pp. 29-60
Author(s):  
Katarzyna Biernacka ◽  
Niels Pinkwart
Keyword(s):  
Learning Analytics ◽  
Open Data ◽  
Publishing Research ◽  
Open Science ◽  
Research Data ◽  
Research Institutions ◽  
Open Research ◽  
Main Emphasis ◽  
Published Research ◽  
Made In

The relevance of open research data is already acknowledged in many disciplines. Demanded by publishers, funders, and research institutions, the number of published research data increases every day. In learning analytics though, it seems that data are not sufficiently published and re-used. This chapter discusses some of the progress that the learning analytics community has made in shifting towards open practices, and it addresses the barriers that researchers in this discipline have to face. As an introduction, the movement and the term open science is explained. The importance of its principles is demonstrated before the main focus is put on open data. The main emphasis though lies in the question, Why are the advantages of publishing research data not capitalized on in the field of learning analytics? What are the barriers? The authors evaluate them, investigate their causes, and consider some potential ways for development in the future in the form of a toolkit and guidelines.

scholarly journals eLabFTW as an Open Science tool to improve the quality and translation of preclinical research

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 292
Author(s):  
Michael Hewera ◽  
Daniel Hänggi ◽  
Björn Gerlach ◽  
Ulf Dietrich Kahlert
Keyword(s):  
Data Management ◽  
Open Source ◽  
Academic Research ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
Preclinical Research ◽  
Research Data Management ◽  
New Methods ◽  
Set Up

Reports of non-replicable research demand new methods of research data management. Electronic laboratory notebooks (ELNs) are suggested as tools to improve the documentation of research data and make them universally accessible. In a self-guided approach, we introduced the open-source ELN eLabFTW into our lab group and, after using it for a while, think it is a useful tool to overcome hurdles in ELN introduction by providing a combination of properties making it suitable for small preclinical labs, like ours. We set up our instance of eLabFTW, without any further programming needed. Our efforts to embrace open data approach by introducing an ELN fits well with other institutional organized ELN initiatives in academic research.

scholarly journals A brief introduction to Open Data, Open Source Software and Collective Intelligence for environmental data creators and users

2018 ◽  
Author(s):  
Tomislav Hengl ◽  
Ichsani Wheeler ◽  
Robert A MacMillan
Keyword(s):  
Open Access ◽  
Open Source ◽  
Open Source Software ◽  
Collective Intelligence ◽  
Open Data ◽  
Open Science ◽  
Environmental Data ◽  
Key Aspects

Using the term "Open data" has become a bit of a fashion, but using it without clear specifications is misleading i.e. it can be considered just an empty phrase. Probably even worse is the term "Open Science" — can science be NOT open at all? Are we reinventing something that should be obvious from start? This guide tries to clarify some key aspects of Open Data, Open Source Software and Crowdsourcing using examples of projects and business. It aims at helping you understand and appreciate complexity of Open Data, Open Source software and Open Access publications. It was specifically written for producers and users of environmental data, however, the guide will likely be useful to any data producers and user.

scholarly journals Research Data Management Implementation at Peking University Library: Foster and Promote Open Science and Open Data

2021 ◽  
Vol 3 (1) ◽  
pp. 189-204
Author(s):  
Hua Nie ◽  
Pengcheng Luo ◽  
Ping Fu
Keyword(s):  
Data Management ◽  
Open Data ◽  
Implementation Process ◽  
Open Science ◽  
Research Data ◽  
Data Repository ◽  
Research Data Management ◽  
University Library ◽  
Open Research ◽  
Peking University

Research Data Management (RDM) has become increasingly important for more and more academic institutions. Using the Peking University Open Research Data Repository (PKU-ORDR) project as an example, this paper will review a library-based university-wide open research data repository project and related RDM services implementation process including project kickoff, needs assessment, partnerships establishment, software investigation and selection, software customization, as well as data curation services and training. Through the review, some issues revealed during the stages of the implementation process are also discussed and addressed in the paper such as awareness of research data, demands from data providers and users, data policies and requirements from home institution, requirements from funding agencies and publishers, the collaboration between administrative units and libraries, and concerns from data providers and users. The significance of the study is that the paper shows an example of creating an Open Data repository and RDM services for other Chinese academic libraries planning to implement their RDM services for their home institutions. The authors of the paper have also observed since the PKU-ORDR and RDM services implemented in 2015, the Peking University Library (PKUL) has helped numerous researchers to support the entire research life cycle and enhanced Open Science (OS) practices on campus, as well as impacted the national OS movement in China through various national events and activities hosted by the PKUL.

scholarly journals A brief introduction to Open Data, Open Source Software and Collective Intelligence for environmental data creators and users

2018 ◽  
Author(s):  
Tomislav Hengl ◽  
Ichsani Wheeler ◽  
Robert A MacMillan
Keyword(s):  
Open Access ◽  
Open Source ◽  
Open Source Software ◽  
Collective Intelligence ◽  
Open Data ◽  
Open Science ◽  
Environmental Data ◽  
Key Aspects

Using the term "Open data" has become a bit of a fashion, but using it without clear specifications is misleading i.e. it can be considered just an empty phrase. Probably even worse is the term "Open Science" — can science be NOT open at all? Are we reinventing something that should be obvious from start? This guide tries to clarify some key aspects of Open Data, Open Source Software and Crowdsourcing using examples of projects and business. It aims at helping you understand and appreciate complexity of Open Data, Open Source software and Open Access publications. It was specifically written for producers and users of environmental data, however, the guide will likely be useful to any data producers and user.

scholarly journals Data Access for the Social Sciences in Austria. Open Data, Closed Data, Research Infrastructures and Re-Use

2021 ◽  
Vol 50 (1) ◽  
pp. 15
Author(s):  
Matthias Reiter-Pázmándy
Keyword(s):  
Social Sciences ◽  
Research Policy ◽  
Open Data ◽  
Data Access ◽  
Open Science ◽  
Research Data ◽  
The Social ◽  
Research Infrastructures ◽  
Barriers And Challenges ◽  
Access To Data

Open science and open access to research data are important aspects of research policy in Austria. In the last years, the social sciences have seen the building of research infrastructures that generate data and archives that store data. Data standards have been established, several working groups exist and a number of activities aim to further develop various aspects of open science, open data and access to data. However, some barriers and challenges still exist in the practice of sharing research data. One aspect that should be emphasised and incentivised is the re-use of research data.

scholarly journals FAIR Forever? Accountabilities and Responsibilities in the Preservation of Research Data

2021 ◽  
Vol 16 (1) ◽  
pp. 16
Author(s):  
Amy Currie ◽  
William Kilbride
Keyword(s):  
Community Of Practice ◽  
Working Group ◽  
Digital Preservation ◽  
Open Science ◽  
Research Data ◽  
Final Report ◽  
Research Organizations ◽  
Fair Principles ◽  
Science And Research

Digital preservation is a fast-moving and growing community of practice of ubiquitous relevance, but in which capability is unevenly distributed. Within the open science and research data communities, digital preservation has a close alignment to the FAIR principles and is delivered through a complex specialist infrastructure comprising technology, staff and policy. However, capacity erodes quickly, establishing a need for ongoing examination and review to ensure that skills, technology, and policy remain fit for changing purpose. To address this challenge, the Digital Preservation Coalition (DPC) conducted the FAIR Forever study, commissioned by the European Open Science Cloud (EOSC) Sustainability Working Group and funded by the EOSC Secretariat Project in 2020, to assess the current strengths, weaknesses, opportunities and threats to the preservation of research data across EOSC, and the feasibility of establishing shared approaches, workflows and services that would benefit EOSC stakeholders. This paper draws from the FAIR Forever study to document and explore its key findings on the identified strengths, weaknesses, opportunities, and threats to the preservation of FAIR data in EOSC, and to the preservation of research data more broadly. It begins with background of the study and an overview of the methodology employed, which involved a desk-based assessment of the emerging EOSC vision, interviews with representatives of EOSC stakeholders, and focus groups with digital preservation specialists and data managers in research organizations. It summarizes key findings on the need for clarity on digital preservation in the EOSC vision and for elucidation of roles, responsibilities, and accountabilities to mitigate risks of data loss, reputation, and sustainability. It then outlines the recommendations provided in the final report presented to the EOSC Sustainability Working Group. To better ensure that research data can be FAIRer for longer, the recommendations of the study are presented with discussion on how they can be extended and applied to various research data stakeholders in and outside of EOSC, and suggest ways to bring together research data curation, management, and preservation communities to better ensure FAIRness now and in the long term.

scholarly journals An Analysis of Peer-Reviewed Publications on Open Educational Practices (OEP) from 2007 to 2020: A Bibliometric Mapping Analysis

2021 ◽  
Vol 13 (19) ◽  
pp. 10798
Author(s):  
Ahmed Tlili ◽  
Daniel Burgos ◽  
Ronghuai Huang ◽  
Sanjaya Mishra ◽  
Ramesh Chander Sharma ◽  
...  
Keyword(s):  
Higher Education ◽  
Students With Disabilities ◽  
Open Data ◽  
Open Science ◽  
Future Research ◽  
Educational Practices ◽  
Bibliometric Mapping ◽  
Mapping Analysis ◽  
Open Educational Practices ◽  
New Research

This study undertook a bibliometric mapping analysis of research papers on Open Educational Practices (OEP) in the Web of Science and Scopus databases. This study functions as a guide for new research, serving to identify trends in the OEP field and compare the existing research so as to identify gaps and promising future paths. A total of 635 studies were obtained; however, only 156 were finally selected for the analysis. VOSviewer software was used to identify the most frequently used keywords and terms in the abstracts and titles. This software was also used to analyse the studies for co-authorship and citations. The findings showed that research on OEP started in 2007 and focused on higher education, including open and distance higher education. Most of the OEP studies were published in English as journal articles, in particular, many were published in Distance Education, International Review of Research in Open and Distributed Learning, and Open Praxis. The United Kingdom, Spain, and Australia were the top contributors to the OEP literature. The analysis of keywords and terms in the titles and abstracts revealed that current OEP trends covered only open pedagogy and open collaboration, suggesting a need for more research on other trends, such as open assessment, open data, and open science. The results also suggested that future research needs to focus more on inclusive open educational practices that accommodate students with disabilities.

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