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The progress of life science and social science research is contingent on effective modes of data storage, data sharing and data reproducibility. In the present digital era, data storage and data sharing play a vital role. For productive data-centric tasks, findable, accessible, interoperable and reusable (FAIR) principles have been developed as a standard convention. However, FAIR principles have specific challenges from computational implementation perspectives. The purpose of this paper is to identify the challenges related to computational implementations of FAIR principles. After identification of challenges, this paper aims to solve the identified challenges.

This paper deploys Petri net-based formal model and Petri net algebra to implement and analyze FAIR principles. The proposed Petri net-based model, theorems and corollaries may assist computer system architects in implementing and analyzing FAIR principles.

To demonstrate the use of derived petri net-based theorems and corollaries, existing data stewardship platforms – FAIRDOM and Dataverse – have been analyzed in this paper. Moreover, a data stewardship model – “Datalection” has been developed and conversed about in the present paper. Datalection has been designed based on the petri net-based theorems and corollaries.

This paper aims to bridge information science and life science using the formalism of data stewardship principles. This paper not only provides new dimensions to data stewardship but also systematically analyzes two existing data stewardship platforms FAIRDOM and Dataverse.

This paper presents results of a study on data managers' perspectives on the evolution of Designated Communities and the FAIR Principles using an example of a geological repository.

The study employed 10 semi-structured interviews with data managers at a state geological survey and qualitative analysis of the interview transcripts.

The Designated Community for a collection in this data repository has evolved from petroleum industry users to include academic researchers and the public. This change was accompanied by significant user interaction changes from in-person, reference interview-style conversations to anonymous digital, automated interactions. The main factors driving these changes were developments in technology which allowed the data managers to shift data discovery and access into the online environment. The online data portal has seen increasing non-expert use, driving the data team to develop additional services for these new communities. Repository data team participants varied in their familiarity with the FAIR Principles and their perceptions of the FAIRness of the data in the repository.

The study was limited to one organization in the United States. However, the results are applicable to other data environments working through the tensions between high-level global frameworks such as FAIR, and continuing to serve the day-to-day needs of their designated communities. Continued work on how to assess success in this complex space is needed.

This paper lies at the nexus of two digital preservation frameworks and contributes to a limited extant literature providing guidance on implementing the concept of a Designated Community in practice.

A striking feature of Jaques' work is his “no nonsense” attitude to the “manager‐subordinate” relationship. His blunt account of the origins of this relationship seems at first sight to place him in the legalistic “principles of management” camp rather than in the ranks of the subtler “people centred” schools. We shall see before long how misleading such first impressions can be, for Jaques is not making simplistic assumptions about the human psyche. But he certainly sees no point in agonising over the mechanism of association which brings organisations and work‐groups into being when the facts of life are perfectly straightforward and there is no need to be squeamish about them.

The data economy mainly relies on the surveillance capitalism business model, enabling companies to monetize their data. The surveillance allows for transforming private human experiences into behavioral data that can be harnessed in the marketing sphere. This study aims to focus on investigating the domain of data economy with the methodological lens of quantitative bibliometric analysis of published literature.

The bibliometric analysis seeks to unravel trends and timelines for the emergence of the data economy, its conceptualization, scientific progression and thematic synergy that could predict the future of the field. A total of 591 data between 2008 and June 2021 were used in the analysis with the Biblioshiny app on the web interfaced and VOSviewer version 1.6.16 to analyze data from Web of Science and Scopus.

This study combined findable, accessible, interoperable and reusable (FAIR) data and data economy and contributed to the literature on big data, information discovery and delivery by shedding light on the conceptual, intellectual and social structure of data economy and demonstrating data relevance as a key strategic asset for companies and academia now and in the future.

Findings from this study provide a steppingstone for researchers who may engage in further empirical and longitudinal studies by employing, for example, a quantitative and systematic review approach. In addition, future research could expand the scope of this study beyond FAIR data and data economy to examine aspects such as theories and show a plausible explanation of several phenomena in the emerging field.

The researchers can use the results of this study as a steppingstone for further empirical and longitudinal studies.

This study confirmed the relevance of data to society and revealed some gaps to be undertaken for the future.

This article describes the interviews the authors conducted in late 2021 with 19 researchers at the Department of Classical Philology and Italian Studies at the University of Bologna. The main purpose was to shed light on the definition of the word “data” in the humanities domain, as far as FAIR data management practices are concerned, and on what researchers think of the term.

The authors invited one researcher for each of the official disciplinary areas represented within the department and all 19 accepted to participate in the study. Participants were then divided into five main research areas: philology and literary criticism, language and linguistics, history of art, computer science and archival studies. The interviews were transcribed and analysed using a grounded theory approach.

A list of 13 research data types has been compiled thanks to the information collected from participants. The term “data” does not emerge as especially problematic, although a good deal of confusion remains. Looking at current research management practices, methodologies and teamwork appear more central than previously reported.

Our findings confirm that “data” within the FAIR framework should include all types of inputs and outputs humanities research work with, including publications. Also, the participants of this study appear ready for a discussion around making their research data FAIR: they do not find the terminology particularly problematic, while they rely on precise and recognised methodologies, as well as on sharing and collaboration with colleagues.

The purpose of this paper is to present empirical evidence on the opinion and behaviour of French scientists (senior management level) regarding research data management (RDM).

The results are part of a nationwide survey on scientific information and documentation with 432 directors of French public research laboratories conducted by the French Research Center CNRS in 2014.

The paper presents empirical results about data production (types), management (human resources, IT, funding, and standards), data sharing and related needs, and highlights significant disciplinary differences. Also, it appears that RDM and data sharing is not directly correlated with the commitment to open access. Regarding the FAIR data principles, the paper reveals that 68 per cent of all laboratory directors affirm that their data production and management is compliant with at least one of the FAIR principles. But only 26 per cent are compliant with at least three principles, and less than 7 per cent are compliant with all four FAIR criteria, with laboratories in nuclear physics, SSH and earth sciences and astronomy being in advance of other disciplines, especially concerning the findability and the availability of their data output. The paper concludes with comments about research data service development and recommendations for an institutional RDM policy.

For the first time, a nationwide survey was conducted with the senior research management level from all scientific disciplines. Surveys on RDM usually assess individual data behaviours, skills and needs. This survey is different insofar as it addresses institutional and collective data practice. The respondents did not report on their own data behaviours and attitudes but were asked to provide information about their laboratory. The response rate was high (>30 per cent), and the results provide good insight into the real support and uptake of RDM by senior research managers who provide both models (examples for good practice) and opinion leadership.

With reference to IAS/IFRS, the purpose of this paper is to examine the value relevance of the two amortised cost/fair value measurement methods applied to loans, and test whether loan fair values are an incremental explanatory factor for a bank's stock price, beyond that provided by loan book values.

The value relevance of 83 European banks from 2005‐2008 is analyzed. The authors employ a regression model in which the stock price (dependent variable) is related to accounting variables typically affecting the firms' market value (book value and earnings).

Book values and earnings affect banks' market values. Investors appreciate the difference between loan book and fair values, and attribute to this difference an expected negative value. Furthermore, the control variable for banks headquartered in countries most affected by the financial crisis proves to be strongly significant as the crisis dummy variable itself.

The results have important implications for bank managers, who should consider the importance that financial markets attribute to loan fair values. There are also implications for regulators and standard setters, though these are less obvious.

This is the first study on the explanatory power of loan fair values in Europe. It addresses loan fair values, and the European market, while previous literature has mainly concerned the US market. In addition, the authors use an original dataset containing information on the loan fair values of European banks during a timeframe which covers both pre‐crisis and crisis periods.

Media users daily exchange personal data for “free” personalised media. Is this a fair trade, or user “exploitation”? Do personalisation benefits outweigh privacy risks?

This study surveyed experts in three consecutive online rounds (e-Delphi). The authors explored personal data processing value for media, personalisation relevance, benefits and risks for users. The authors scrutinised the value-exchange between media and users and determined whether media communicate transparently, or use “dark patterns” to obtain more personal data.

Communication to users must be clear, correct and concise (prevent user deception). Experts disagree on “payment” with personal data for “free” personalised media. This study discerned obstacles and solutions to substantially balance the interests of media and users (fair value exchange). Personal data processing must be transparent, profitable to media and users. Media can agree “sector-wide” on personalisation transparency. Fair, secure and transparent information disclosure to media is possible through shared responsibility and effort.

This study’s innovative contribution is threefold: Firstly, focus on professional stakeholders’ opinion in the value network. Secondly, recommendations to clearly communicate personalised media value, benefits and risks to users. This allows media to create codes of conduct that increase user trust. Thirdly, expanding literature explaining how media realise personal data value, deal with stakeholder interests and position themselves in the data processing debate. This research improves understanding of personal data value, processing benefits and potential risks in a regional context and European regulatory framework.

The purpose of this paper is to look at the journey and experience of the University of Hong Kong (HKU) Research Data Management (RDM) practice to respond to the needs of researchers in an academic library.

The research data services (RDS) practice is based on the FAIR data principle. And the authors designed the RDM Stewardship framework to implement the RDS step by step.

The HKU Libraries developed and implemented a set of RDS under a research data stewardship framework in response to the recent evolving research needs for RDM amongst the academic communities. The services cover policy and procedure settings for research data planning, research data infrastructure establishment, data curation services and provision of online resources and instructional guidelines.

This study provides an example of an approach to respond to the needs of the academic libraries about how to start the RDS including the data policy, data repository, data librarianship and data curation.

Upcycling is conceptualised as a digital historical research practice aimed at increasing the scientific value of historical data collections produced in print or in electronic form between the eighteenth and the late twentieth centuries. The concept of upcycling facilitates data rescue and reuse as well as the study of information creation processes deployed by previous generations of researchers.

Based on a selection of two historical reference works and two legacy collections, an upcycling workflow consisting of three parts (input, processing and documentation and output) is developed. The workflow facilitates the study of historical information creation processes based on paradata analysis and targets the cognitive processes that precede and accompany the creation of historical data collections.

The proposed upcycling workflow furthers the understanding of computational methods and their role in historical research. Through its focus on the information creation processes that precede and accompany historical research, the upcycling workflow contributes to historical data criticism and digital hermeneutics.

Many historical data collections produced between the eighteenth and the late twentieth century do not comply with the principles of FAIR data. The paper argues that ignoring the work of previous generations of researchers is not an option, because it would make current research practices more vulnerable and would result in losing access to the experiences and knowledge accumulated by previous generations of scientists. The proposed upcycling workflow takes historical data collections seriously and makes them available for future generations of researchers.