Data Science as a Foundation towards Open Data and Open Science: The Case of Taiwan Indigenous Peoples Open Research Data (TIPD)

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.

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

Data Intelligence ◽

10.1162/dint_a_00088 ◽

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.

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Bridging the Data Talent Gap: Positioning the iSchool as an Agent for Change

International Journal of Digital Curation ◽

10.2218/ijdc.v10i1.349 ◽

2015 ◽

Vol 10 (1) ◽

pp. 111-122 ◽

Cited By ~ 14

Author(s):

Liz Lyon ◽

Aaron Brenner

Keyword(s):

Data Science ◽

Core Curriculum ◽

Open Data ◽

Open Science ◽

Research Data ◽

University Of Pittsburgh ◽

Data Infrastructures ◽

New Research ◽

Agent Of Change ◽

The University

This paper examines the role, functions and value of the “iSchool” as an agent of change in the data informatics and data curation arena. A brief background to the iSchool movement is given followed by a brief review of the data decade, which highlights key data trends from the iSchool perspective: open data and open science, big data and disciplinary data diversity. The growing emphasis on the shortage of data talent is noted and a family of data science roles identified. The paper moves on to describe three primary functions of iSchools: education, research intelligence and professional practice, which form the foundations of a new Capability Ramp Model. The model is illustrated by mini-case studies from the School of Information Sciences, University of Pittsburgh: the immersive (laboratory-based) component of two new Research Data Management and Research Data Infrastructures graduate courses, a new practice partnership with the University Library System centred on RDM, and the mapping of disciplinary data practice using the Community Capability Model Profile Tool. The paper closes with a look to the future and, based on the assertion that data is mission-critical for iSchools, some steps are proposed for the next data decade: moving data education programs into the mainstream core curriculum, adopting a translational data science perspective and strengthening engagement with the Research Data Alliance.

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Open Science in Österreich: Ansätze und Status

Information - Wissenschaft & Praxis ◽

10.1515/iwp-2015-0025 ◽

2015 ◽

Vol 66 (2-3) ◽

Author(s):

Katrin Buschmann ◽

Stefan Kasberger ◽

Peter Kraker ◽

Katja Mayer ◽

Falk Reckling ◽

...

Keyword(s):

Open Access ◽

Open Data ◽

Open Science ◽

Research Data ◽

Access Networks ◽

Open Research

Insbesondere in den letzten zwei Jahren hat Österreich im Bereich Open Science, vor allem was Open Access und Open Data betrifft, nennenswerte Fortschritte gemacht. Die Gründung des Open Access Networks Austria (OANA) und das Anfang 2014 gestartete Projekt e-Infrastructures Austria können als wichtige Grundsteine für den Ausbau einer österreichischen Open-Science-Landschaft gesehen werden. Auch das österreichische Kapitel der Open Knowledge Foundation leistet in den Bereichen Open Science Praxis- und Bewusstseinsbildung grundlegende Arbeit. Unter anderem bilden diese Initiativen die Grundlage für den Aufbau einer nationalen Open-Access-Strategie sowie einer ganz Österreich abdeckenden Infrastruktur für Open Access und Open (Research) Data. Dieser Beitrag gibt einen Überblick über diese und ähnliche nationale sowie lokale Open-Science-Projekte und -Initiativen und einen Ausblick in die mögliche Zukunft von Open Science in Österreich.

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Open Research Data and Open Peer Review: Perceptions of a Medical and Health Sciences Community in Greece

Publications ◽

10.3390/publications9020014 ◽

2021 ◽

Vol 9 (2) ◽

pp. 14

Author(s):

Eirini Delikoura ◽

Dimitrios Kouis

Keyword(s):

Peer Review ◽

Evaluation System ◽

Early Stage ◽

Open Science ◽

Health Sciences ◽

Research Data ◽

Stage Of Development ◽

Open Research ◽

Proper Training ◽

To Receive

Recently significant initiatives have been launched for the dissemination of Open Access as part of the Open Science movement. Nevertheless, two other major pillars of Open Science such as Open Research Data (ORD) and Open Peer Review (OPR) are still in an early stage of development among the communities of researchers and stakeholders. The present study sought to unveil the perceptions of a medical and health sciences community about these issues. Through the investigation of researchers` attitudes, valuable conclusions can be drawn, especially in the field of medicine and health sciences, where an explosive growth of scientific publishing exists. A quantitative survey was conducted based on a structured questionnaire, with 179 valid responses. The participants in the survey agreed with the Open Peer Review principles. However, they ignored basic terms like FAIR (Findable, Accessible, Interoperable, and Reusable) and appeared incentivized to permit the exploitation of their data. Regarding Open Peer Review (OPR), participants expressed their agreement, implying their support for a trustworthy evaluation system. Conclusively, researchers need to receive proper training for both Open Research Data principles and Open Peer Review processes which combined with a reformed evaluation system will enable them to take full advantage of the opportunities that arise from the new scholarly publishing and communication landscape.

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Tutkimusaineiston avoin jakaminen – tutkimusorganisaatioiden jäsenten käsityksiä, kokemuksia ja mielipiteitä

Informaatiotutkimus ◽

10.23978/inf.77411 ◽

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.

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Towards Open Science for the Qualitative Researcher: From a Positivist to an Open Interpretation

International Journal of Qualitative Methods ◽

10.1177/16094069211034641 ◽

2021 ◽

Vol 20 ◽

pp. 160940692110346

Author(s):

Barbara Class ◽

Miguel de Bruyne ◽

Claire Wuillemin ◽

Dimitri Donzé ◽

Jean-Blaise Claivaz

Keyword(s):

Open Science ◽

Research Data ◽

Decision Makers ◽

Second Phase ◽

Data Handling ◽

Optimal Solutions ◽

Qualitative Researcher ◽

Open Research ◽

Concrete Experience ◽

Handling Practices

This reflection by a qualitative researcher stems from a concrete experience with data handling in a funded research project. The researcher followed Open Research Data guidelines and found optimal solutions to pseudonymise data, but this later evolved into a deep epistemological questioning on praxis. During the first phase of the project, a tailor-made software was developed with help from librarians and an IT professional to automate the pseudonymisation of the 150 data chunks generated by 16 students, 3 tutors and 3 decision makers. In the second phase of the project, this experience sparked questions about the meaning of such data handling and interpretations of Open Science, which led the researcher to suggest a framework for the professional development of qualitative researchers in their understanding of Open Science. The article raises awareness of normative frameworks in institutional data handling practices and calls for active contributions to defining qualitative research in an Open Science perspective, particularly taking as a reference the recent draft recommendation by UNESCO (2020)

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NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

Research Ideas and Outcomes ◽

10.3897/rio.6.e55852 ◽

2020 ◽

Vol 6 ◽

Cited By ~ 3

Author(s):

Christoph Steinbeck ◽

Oliver Koepler ◽

Felix Bach ◽

Sonja Herres-Pawlis ◽

Nicole Jung ◽

...

Keyword(s):

Data Management ◽

Data Science ◽

Open Data ◽

Research Data ◽

Data Standards ◽

Data Repositories ◽

Data Infrastructure ◽

Research Data Management ◽

Wide Range ◽

Chemistry Community

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

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The reproducibility challenge – what researchers need

Septentrio Conference Series ◽

10.7557/5.4257 ◽

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.

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FAIR Digital Objects for Science: From Data Pieces to Actionable Knowledge Units

Publications ◽

10.3390/publications8020021 ◽

2020 ◽

Vol 8 (2) ◽

pp. 21 ◽

Cited By ~ 2

Author(s):

Koenraad De Smedt ◽

Dimitris Koureas ◽

Peter Wittenburg

Keyword(s):

Data Science ◽

Open Science ◽

Research Data ◽

Use Cases ◽

Digital Object ◽

Data Interoperability ◽

Actionable Knowledge ◽

The Past ◽

Research Communities ◽

Digital Objects

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

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