Identifying Design Requirements of a User-Centered Research Data Management System

Bugaje, Maryam; Chowdhury, Gobinda

doi:10.1007/978-3-030-04257-8_35

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…Open Science advocates list a standard set of benefits of Open Science practices for researchers, such as increased citation rates (Piwowar, Day and Fridsma, 2007;Piwowar and Vision, 2013b), increased media coverage (McKiernan et al, 2016), more transparent research evaluation (Pöschl 2012;Beck et al 2018), increased reproducibility (Toelch and Ostwald, 2018), increased control over research outputs through retaining copyright and publishing under Creative Commons (CC) licenses (McKiernan et al, 2016), and establishment of priority via preprints (Vale and Hyman, 2016). Yet, as McKiernan et al (2016) have pointed out, arguments promoting Open Science policies rarely address the practical barriers involved in changing researchers' behaviours, such as existing incentive structures for research careers (Nosek et al, 2015) or a lack of infrastructures (Amorim et al, 2017;Cox et al, 2017;Bugaje and Chowdhury, 2018). Indeed, Open Science depends heavily on digital infrastructures allowing the free exchange of research outputs (Bardi, Casarosa and Manghi, 2018), which explains Mirowski's (2018) critical review of the role of platforms in Open Science.…”

Section: A Brief Exposition Of Open Sciencementioning

confidence: 99%

Open Science at the Science-policy Interface: Bringing in the Evidence?

Reichmann¹,

Wieser²

2022

Preprint

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Part of the current enthusiasm about Open Science stems from its promises to reform scientific practicein service of the common good, to ensure that scientific outputs will be found and reused more easily,and to enhance scientific impact on policy and society. With this article, we question this optimism byanalysing the potential for Open Science practices to enhance research uptake at the science-policyinterface. Science advice is critical to help policy makers make informed decisions. Likewise, someinterpretations of Open Science hold that making research processes and outputs more transparent andaccessible will also enhance uptake of results by policy and society at large. However, we argue thatthis hope is based on an unjustifiably simplistic understanding of the science-policy interface that leaveskey terms (“impact”, “uptake”) undefined. We show that this understanding – based upon linear modelsof research uptake – likewise grounds the influential "evidence-policy gap" diagnosis which holds thatto improve research uptake, communication and interaction between researchers and policymakers needto be improved. The overall normative stance of both discussions has side-lined empirical descriptionof the science-policy interface, ignoring questions about the underlying differences between the policydomain and academia. Importantly, both Open Science and literature on closing the evidence-policy gaprecommend improving communication (either in terms of the content or the means) as a viable strategy.To correct some of these views, we combine insights from policy theory with a systematic review of theliterature on the evidence-policy gap in the health domain and find that removing barriers to access byitself will not be enough to foster research uptake.

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Section: A Brief Exposition Of Open Sciencementioning

confidence: 99%

Open Science at the Science-policy Interface: Bringing in the Evidence?

Reichmann¹,

Wieser²

2022

Preprint

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“…A vast literature studies data management practices (Tenopir et al, 2011(Tenopir et al, , 2015Unal et al, 2019), including governance (Grant, 2015), and infrastructures (Knight, 2015;Bugaje and Chowdhury, 2018). Studies show little awareness of RDM (Mancilla et al, 2019), a lack of technical skills (Aydinoglu, Dogan and Taskin, 2017) and missing institutional guidelines (Chigwada, Chiparausha and Kasiroori, 2017).…”

Section: Research Data Management and Data Mobilitymentioning

confidence: 99%

Data Sharing in the High-throughput University - Mobility and Precarity

Reichmann¹

2021

Preprint

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The nascent field of data science and the expansion of the higher education sector share surprising affinities. The emergence of the “entrepreneurial university” has brought increasing differentiation of the work roles of academics in addition to increased mobility and, for some, precarity. At the same time, researchers are dealing with unprecedented amounts of data. The present article describes how policies and infrastructures implemented to support researchers with data curation tasks might be repurposed by research administrators to tackle problems of academic mobility rooted in increasing precarity of non-tenured research staff. Findings suggest that the organizational benefit of research data management (RDM) is not increased efficiency or reusability of research, but rather increased control over data left behind by non-tenured staff. Recent interest in data mobility needs to be understood by reference to increased researcher mobility. While the view of data as context-independent evidence has been challenged by reference to the investments necessary to mobilize data as evidence in the first place, the material presented here suggests that RDM is repurposed by universities as a strategy to manage, not data, but rather increasing rates of staff turnover. The mobility of data producers and the immobility of data are frequently in tension. Handing over data is problematic irrespective of domain, data type, and funding source. The term “high-throughput university” is introduced in opposition to “high-throughput” data production techniques to suggest that findability and reusability of data need to be recontextualized with reference to increased academic mobility.

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“…Los repositorios de datos de investigación tienen características particulares. Realizan muchas funciones útiles para la ciencia abierta, siendo claves el almacenamiento de los datasets y el hacer que se puedan descubrir para su posible reutilización (Bugaje y Chowdhury, 2018). Además, entre otras opciones, deben brindar soporte a la gestión de datos de investigación (RDM).…”

Section: Tipos De Repositoriosunclassified

“…Optimización para cautelar la correcta indexación en motores de búsqueda (Bugaje y Chowdhury, 2018;Codina, 2019;Rovira et al, 2007):…”

Section: Posicionamientounclassified

Atributos de calidad web para repositorios de datos de investigación en universidades

Morales-Vargas¹,

Codina²

2019

Hipertext.net

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El nuevo modelo de ciencia abierta requiere de una infraestructura adecuada para preservar y compartir los datos de investigación. Ello ha implicado un desafío tanto para los investigadores como para las universidades y centros. Como respuesta se han creado más de dos mil repositorios de datos en el mundo, un cuarto por parte de estas instituciones, con diferentes softwares especializados. El presente trabajo tiene como objetivo proponer atributos de calidad web para evaluar la interfaz de este tipo de sitios. Para ello se realizó una revisión sistematizada de literatura con directrices internacionales, requisitos técnicos y recomendaciones de expertos, detectando coincidencias. Como resultado se ofrece un protocolo de análisis especialmente formulado para repositorios de datos de investigación, con parámetros e indicadores sobre la interfaz de usuario, localización, acceso y reutilización, conservación, difusión, evaluación y compromiso institucional. A modo de conclusión se observa la necesidad de atender los aspectos específicos de este tipo de datos, relevando el papel de las bibliotecas universitarias para gestionarlos.

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Identifying Design Requirements of a User-Centered Research Data Management System

Cited by 5 publications

References 29 publications

Open Science at the Science-policy Interface: Bringing in the Evidence?

Open Science at the Science-policy Interface: Bringing in the Evidence?

Data Sharing in the High-throughput University - Mobility and Precarity

Atributos de calidad web para repositorios de datos de investigación en universidades

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