Search

Open science (OS) awareness and skills are increasingly becoming an essential part of everyday scientific work as e.g., many journals require authors to share data. However, following an OS workflow can seem challenging at first. Thus, instructions by journals and other guidelines are important. But how comprehensive are they in the field of ecology and evolutionary biology (Ecol Evol)? To find this out, we reviewed 20 published OS guideline articles aimed for ecologists or evolutionary biologists, together with the data policies of 17 Ecol Evol journals to chart the current landscape of OS guidelines in the field, find potential gaps, identify field‐specific barriers for OS and discuss solutions to overcome these challenges. We found that many of the guideline articles covered similar topics, despite being written for a narrow field or specific target audience. Likewise, many of the guideline articles mentioned similar obstacles that could hinder or postpone a transition to open data sharing. Thus, there could be a need for a more widely known, general OS guideline for Ecol Evol. Following the same guideline could also enhance the uniformity of the OS practices carried on in the field. However, some topics, like long‐term experiments and physical samples, were mentioned surprisingly seldom, although they are typical issues in Ecol Evol. Of the journals, 15 out of 17 expected or at least encouraged data sharing either for all articles or under specific conditions, e.g. for registered reports and 10 of those required data sharing at the submission phase. The coverage of journal data policies varied greatly between journals, from practically non‐existing to very extensive. As journals can contribute greatly by leading the way and making open data useful, we recommend that the publishers and journals would invest in clear and comprehensive data policies and instructions for authors. [ABSTRACT FROM AUTHOR]

Abstract Open science (OS) awareness and skills are increasingly becoming an essential part of everyday scientific work as e.g., many journals require authors to share data. However, following an OS workflow can seem challenging at first. Thus, instructions by journals and other guidelines are important. But how comprehensive are they in the field of ecology and evolutionary biology (Ecol Evol)? To find this out, we reviewed 20 published OS guideline articles aimed for ecologists or evolutionary biologists, together with the data policies of 17 Ecol Evol journals to chart the current landscape of OS guidelines in the field, find potential gaps, identify field‐specific barriers for OS and discuss solutions to overcome these challenges. We found that many of the guideline articles covered similar topics, despite being written for a narrow field or specific target audience. Likewise, many of the guideline articles mentioned similar obstacles that could hinder or postpone a transition to open data sharing. Thus, there could be a need for a more widely known, general OS guideline for Ecol Evol. Following the same guideline could also enhance the uniformity of the OS practices carried on in the field. However, some topics, like long‐term experiments and physical samples, were mentioned surprisingly seldom, although they are typical issues in Ecol Evol. Of the journals, 15 out of 17 expected or at least encouraged data sharing either for all articles or under specific conditions, e.g. for registered reports and 10 of those required data sharing at the submission phase. The coverage of journal data policies varied greatly between journals, from practically non‐existing to very extensive. As journals can contribute greatly by leading the way and making open data useful, we recommend that the publishers and journals would invest in clear and comprehensive data policies and instructions for authors.

The relationship between people, place, and data presents challenges and opportunities for science and society. While there has been general enthusiasm for and work toward Findable, Accessible, Interoperable, and Reusable (FAIR) data for open science, only more recently have these data-centric principles been extended into dimensions important to people and place-notably, the CARE Principles for Indigenous Data Governance, which affect collective benefit, authority to control, responsibility, and ethics. The FAIR Island project seeks to translate these ideals into practice, leveraging the institutional infrastructure provided by scientific field stations. Starting with field stations in French Polynesia as key use cases that are exceptionally well connected to international research networks, FAIR Island builds interoperability between different components of critical research infrastructure, helping connect these to societal benefit areas. The goal is not only to increase reuse of scientific data and the awareness of work happening at the field stations but more generally to accelerate place-based research for sustainable development. FAIR Island works reflexively, aiming to scale horizontally through networks of field stations and to serve as a model for other sites of intensive long-term scientific study.

The article discusses the concept of data sovereignty and its implications for internet policy. Data sovereignty refers to the rights of nations or regions to govern the flow and management of data within their borders. It has far-reaching implications for sectors such as economics, law, security, privacy, and technology. Data sovereignty can enhance control, autonomy, and security over data, protect privacy rights, promote inclusivity and diversity, foster economic growth, and enhance trust and transparency in data governance. However, implementing data sovereignty poses challenges, such as balancing national sovereignty with the global nature of the internet and harmonizing regulations across jurisdictions. The article also highlights a case study in Aotearoa New Zealand, where data sovereignty is of concern to Indigenous users. The Te Mana Raraunga network advocates for Māori rights and interests in data and supports the development of Māori data infrastructure and digital businesses. The article concludes by discussing other articles in the issue that explore various aspects of infrastructure, policy, and participation in the digital realm. [Extracted from the article]

There have been few, if any, open data and information management policies openly published from natural science collections. This paper contextualises the rationale for publishing the Open Information and Exceptions Policy of the Natural History Museum, London and provides the policy itself. The policy outlines how the Natural History Museum puts the principle of 'open by default' into practice; and includes sections on purpose and scope, relationship to relevant legislation (which always takes precedence over the policy), the categories of possible exceptions to open information release, what happens when exceptions are declared, relations to UK government information security classifications and definition of terms. [ABSTRACT FROM AUTHOR]

This article advances understanding of the relationship between (a) people's awareness of and (b) their attitudes toward the ways in which data about them is collected, analyzed, shared, and used. It draws on an online survey of 2000 adults in the UK, which found that people with greater awareness of data uses hold more negative attitudes toward them. This finding is important because it challenges the deficit model which underlies initiatives that seek to improve the public's attitudes toward and trust in institutional data uses through improved transparency or better data literacy. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the first findings of a funded project (PreMiGro) that concerns the development of a start-up pilot prediction model of short and long-term migration flows in Greece using machine-learning tools. During the first phase of the project, which focused on the examination of data regarding historical number of immigrants to Greece associated with country of origin and its political status for the period 1990-2022 through a range of primary and secondary sources, a vital finding surfaced. The discontinuity of the Greek migration policy seems to be related, among others, to the absence of a consistent data policy on migration, ever since Greece became a host country of migrants in the 1990s up to 2022 when Greece had already witnessed major transformations in its migration policy domain. A first reading of PreMiGro's initial research outcomes sheds some light on the peculiarity of the Greek migration policy. Indeed, the securitization of migration in Greece, the (at times negative) perception of migrants and refugees from parts of the Greek Society and Public Sphere and the fragmented Greek migration policy per se, could be explained through the lens of the absence of a rationalized migration data policy in Greece, among other things. [ABSTRACT FROM AUTHOR]

The European public sector has for a long time tried to change its activities and its relation to the public through the production and provision of data and data-based technologies. Recent debates raised attention to data uses, through which societal value may be realized. However, often absent from these discussions is a conceptual and methodological debate on how to grasp and study such uses. This collection proposes a turn toward data practices—intended here as the analysis of data uses and policies, as they are articulated, understood, or turned into situated activities by different actors in specific contexts, involving organizational rules, socioeconomic factors, discourses, and artifacts. Through a mix of conceptual and methodological studies, the contributions explore how data-driven innovation within public institutions is understood, imagined, planned for, conducted, or assessed. The situations examined in this special issue show, for instance, that data initiatives carried out by different actors lack institutional rules to align data use to the actual needs of citizens; that data scientists are important moral actors whose ethical reasoning should be fostered; and that the materiality of data practices, such as databases, enables and constrains opportunities for public engagement. Collectively, the contributions offer new insights into what constitutes “data-driven innovation practices,” how different practices are assembled, and what their different political, moral, economic, and organizational implications are. The contributions focus on three particular topics of concern: the making of ethical and normative values in practice; organizational collaborations with and around data; and methodological innovations of studying data practices.

The three “data policies” were cross-interpreted and several thoughts on data industrial development from the perspective of data industry development trend, user data cognition, the scientific innovation and technology application on data industry were put forward. The authors believe that the development of both data industry and the industries who use data as key factor will accurately ignite the tipping point of economic development. Firstly, the acceleration impact of “data policies” on industrial development was discussed - data industry and data security industry will benefit from the “data policies” and became the backbone and main venue of high-quality development and empower the real economy. Secondly, the impact of “data policies” on user data cognition was expounded, which was helpful to understand the value symbiosis relationship between data factor and data security, establish the integrated governance cognition of data factor and data security, explore new ideas, new mechanisms, and new methods of data operation. Finally, the impact of “data policies” on technology and academic research around data circulation domain was described. It is necessary to adhere to the principle of “demand-driven, business-driven and practice-oriented” to achieve relevant technical breakthrough and strengthen technical weakness on supply side, furthering improve the supply capacity of scientific and technology innovation. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Digital technology has become an important driving force in the utilization of data production elements. To make full use of data resources, the underlying accompanying problem is rights management. In practice, the ownership and the right to use of data resource are separated in the circulation process. It has become an urgent problem to conduct data resource management activities based on reasonable and legal protection of users' rights. [Method/Process] Based on the general framework of "practical basis - abstract analysis - general concrete understanding", this paper firstly identified four practice statuses of distributed big data assets as followings: 1) the application of new technology leading to facilitating platforms; 2) attention needed to paid to intellectual properties of multiple users' groups; 3) users' needs to be satisfied on various contexts. For example, conducting resource procurement, collection, data processing and layout of resource allocation systems based on cloud service platforms; 4) maintainance and management of high quality data sets, including self-built and imported resources, the negotiation of ownership and use rights, and emphasis on the rights strategies. Next, this study conducted abstract analysis on the generalized idea of basic dimensions of data rights management from four aspects, namely, technical resource regulation, reasonable use boundary, complexity of rights, and analysis of use rights. [Results/Conclusions] Based on the abstract analysis, this paper put forward and explained four successive solutions at data resource management institute level, as described below. 1) the principle and terms of data policy, which include but not limited to general statement, policies of content use, policy statement on metadata, social media policies, and terms of service; 2) the typical contexts and content planning of rights potofolio. This paper took the resource procurement business of academic libraries as an example to illustrate and summarize the four content planning items on rights portfolio: strategic plan, operational policies, data policies, end-user policies; and 3) the allocation management of data assets, which lays the key part of data asset management. In this paper, we consider that data asset management refer to description and management of the constituent terms of data assets and the relationship between terms. The cost structure of data asset management contains tangible cost (such as consulting, software and hardware purchase fee, and digital resources purchase fee) and intangible cost (such as indirect human resource costs, risks, social credit, and loss compensation); and 4) finally, the establishment standard, working process, associated contrasts and regulations, and the evaluation measurements of data asset management businesses at the institute level. Such solutions are proposed to provide some inspirations to data resource management institutes（such as libraries） under the distributed big data circumstance.

AbstractThe last decade has witnessed a debate about the disruptive role of emerging digital technologies. At the heart of this debate is the issue of big data, which underpins the operations of these digital technologies but creates a series of new risks and issues for society and governments. New policy problems concerning data require formulators of new governance strategies and innovative policy designs. In this introduction to the special issue Data Policy and Governance, we examine scholarship on data governance and data policy, with a particular focus on emerging contributions on these topics. We also present the six articles that make up this special issue and indicate trends and future research directions from the discussion. This review demonstrates how data governance and policy are central to the digital world and require new designs and dynamics to deal with instruments, mixes, practices, and regulatory mechanisms.

AbstractData governance is a decision-making process focused on authority building to specify decision rights and accountability that encourage desired behaviors regarding data use, security, integrity, and availability. The emergence of big data technologies to design public policy and deliver public services requires governments to design data policy and governance. This paper analyzes the dynamics of data governance design in the Brazilian Federal Government, based on institutional analysis and policy development. The paper reports a series of interviews with Brazilian Federal Government policymakers to frame the data policy design dynamics. The paper concludes that the policy design dynamics to data governance are path dependent and shape actions situations that reinforces previous institutions. In the case of the Brazilian Federal Government, the institutional framework is ambiguous, creating situations of conflict and ineffectiveness in the design of the data policy.

Agriculture is central to the Indian economy and suffers from widespread operational inefficiencies that could be corrected by the use of digital agriculture technologies (DA). We review and synthesise available literature concerning digital agriculture in India and anticipate its transformative potential in the coming decade. Although the initial growth of DA was more conspicuous in the downstream sectors and high-value crops, reaching smallholder farmers upstream is slowly emerging despite significant obstacles such as small fragmented holdings, inadequate data infrastructure and public policy, and unequal access to digital infrastructure. Agri-tech enables innovation at many locations within value chains, and a steady shift is occurring in change from individual farms to the whole value chain. Technology in the sector is progressing from information and communication technology-based solutions to Internet of Things and artificial intelligence–machine learning-enabled services. India's public policy shows signs of a longstanding investment and collaboration in the sector, with an explicit focus on data infrastructure development. We find smallholder predominance, diversity in production systems, the predominance of commodity crops, proximity to urban markets, and public policy as the major factors of DA's success in India. A stocktake of the available technologies and their applications by the public sector, tech giants, information technology leaders and agri-food tech startups in India strongly indicates a digital transformation of Indian agriculture. However, given the federal structure of governance and agriculture being a state (province) subject, we need to wait to see how DA policies are rolled out and taken up across the country. We try to determine whether digital agriculture technologies can correct widespread operational inefficiencies in Indian agriculture.. We observe digital agriculture's transition from individual farms to the whole value chain despite infrastructural and policy obstacles. India's public policy shows signs of a longstanding investment and collaboration in the sector. [ABSTRACT FROM AUTHOR]

Australia's distinctive colonial administrative history has resulted in the generation and capture of large quantities of personal data about Indigenous Peoples in Australia, which is currently controlled and processed by government agencies and departments without coherent regulation. From an Indigenous standpoint, these data constitute stranded assets. Established legal frameworks for pursuing recovery of other classes of asset alienated by governments from Indigenous Peoples in Australia, including land, natural resources, and unpaid wages, have not yet been extended to the recovery of Indigenous data assets. This legacy scenario has created a disproportionate administrative burden for Indigenous organisations by sustaining their dependency on government for necessary data, while simultaneously suppressing the value of their own contemporary community-owned data assets. In this article, we outline leading international legal, economic, and scientific frameworks by which an equitable arrangement for the governance of Indigenous data might be restored to Indigenous Peoples in Australia. [ABSTRACT FROM AUTHOR]

Abstract This article examines data-driven policy for smart cities and how the institutional analysis and development framework (IAD) is a tool to analyze data governance and data policy design. The research assumes that data-driven policy underpins smart city initiatives and incorporates collective action dilemmas, directly impacting policy design and data governance. The article’s motivation is to examine the elements of the IAD framework and decompose the constitutive components of data policy in smart cities, obtaining principles that guide data governance in complex situations of digital transformation. The article is a theoretical essay to discuss data policy in smart cities and how policy design understands dilemmas and conflicts of collective action motivated by data control.

[Purpose/Significance] Digital technology has become an important driving force in the utilization of data production elements. To make full use of data resources, the underlying accompanying problem is rights management. In practice, the ownership and the right to use of data resource are separated in the circulation process. It has become an urgent problem to conduct data resource management activities based on reasonable and legal protection of users' rights. [Method/Process] Based on the general framework of "practical basis - abstract analysis - general concrete understanding", this paper firstly identified four practice statuses of distributed big data assets as followings: 1) the application of new technology leading to facilitating platforms; 2) attention needed to paid to intellectual properties of multiple users' groups; 3) users' needs to be satisfied on various contexts. For example, conducting resource procurement, collection, data processing and layout of resource allocation systems based on cloud service platforms; 4) maintainance and management of high quality data sets, including self-built and imported resources, the negotiation of ownership and use rights, and emphasis on the rights strategies. Next, this study conducted abstract analysis on the generalized idea of basic dimensions of data rights management from four aspects, namely, technical resource regulation, reasonable use boundary, complexity of rights, and analysis of use rights. [Results/Conclusions] Based on the abstract analysis, this paper put forward and explained four successive solutions at data resource management institute level, as described below. 1) the principle and terms of data policy, which include but not limited to general statement, policies of content use, policy statement on metadata, social media policies, and terms of service; 2) the typical contexts and content planning of rights potofolio. This paper took the resource procurement business of academic libraries as an example to illustrate and summarize the four content planning items on rights portfolio: strategic plan, operational policies, data policies, end-user policies; and 3) the allocation management of data assets, which lays the key part of data asset management. In this paper, we consider that data asset management refer to description and management of the constituent terms of data assets and the relationship between terms. The cost structure of data asset management contains tangible cost (such as consulting, software and hardware purchase fee, and digital resources purchase fee) and intangible cost (such as indirect human resource costs, risks, social credit, and loss compensation); and 4) finally, the establishment standard, working process, associated contrasts and regulations, and the evaluation measurements of data asset management businesses at the institute level. Such solutions are proposed to provide some inspirations to data resource management institutes (such as libraries) under the distributed big data circumstance. [ABSTRACT FROM AUTHOR]

Coproduction of Knowledge is critical in complex settings and environments with multiple transdisciplinary actors from the practitioners and scholars grounded in practice. In complex projects, joint work inquiry is often challenging due to differences in actors' backgrounds and interests, which slows the pace of scientifically proffered research solutions to life challenges. This study offers new insights into the reasoning and motivations behind actors with different disciplinary backgrounds working together to achieve the organisational goal of providing productive research results and how communication and collaboration play an essential role in the process. This paper examines the nexus of coproduction and the management process in knowledge generation in transdisciplinary organisations through active participation, communication, and collaboration. The data was gathered using ethnographic organisational entry strategies, including participant observations, official document reviews and interviews with scientists in ARO. The dataset was grouped and analyzed into the specific theme (knowledge management, data policy and copyright issues, communication processes, the integration of technical and non-technical knowledge from all actors in ARO etc.) using NVIVO software with narrations and phenomenal experiences from actors. The findings revealed that, for the production and management of the knowledge process to be effective and sustainable in organisations and projects, team members must acknowledge each actor's contribution to the inquiry process. Again, framing a data management policy that regulates and addresses each actor's interest is essential and tends to build confidence from other theme members. It further argues that integrating implicit and explicit knowledge of practitioners and scholars, the experienced and the inexperienced actors in teams, consolidates the quest to generate grounded knowledge to address life challenges. Each member's understanding of the others' areas of work and research has a corresponding and positive effect on the general underutilised knowledge, which in effect, means that a deeper level of interactions is required than it is happening. Complex project implementation requires a broader level of support, collaboration, and stakeholder buy-in, and a sense of respect for different opinions and team members' specialisations or responsibilities remains. The study recommends institutional leaders and policymakers promote internal social enhancement programs for effective knowledge co-production, management, and fluid communication. [ABSTRACT FROM AUTHOR]

Recent events of global impact have led the scientific community to re-evaluate and re-affirm the role of science in crisis situations. In particular, the COVID-19 health emergency required the abrupt (re-)allocation of scientific resources to address a pandemic which had demonstrated the vulnerability of science as well as the essential role data science has in responding to a pandemic. In a digital world, data collection, data processing, and data reuse are critical to science and its use for critical decision-making in society’s response. This paper reports a workshop undertaken by the CODATA International Data Policy Committee (IDPC), together with leading international partners, on the role of data policy in times of crisis. A critical investigation of the role of data policy in crisis situations was engaged by a leading group of scientists and data experts representing a wide audience of various scientific disciplines and expertise from inter-governmental organisations. This paper presents preliminary results as to how data policy, specifically designed to address the need for science in crisis situations, can contribute to building a more robust scientific enterprise that is appropriately prepared for and capable of acting with confidence in the urgencies of crises. The workshop identified a need for establishing principles for data policy in times of crisis as developing concrete recommendations to support engagement with the international scientific community and inter-governmental organisations.

In 2014, Nigeria halted transmission of wild poliovirus for the first time in its history. A critical enabling component in this historic achievement was the use of satellite data to produce more accurate maps and population estimates used in planning and implementing vaccination campaigns. This article employs a value-of-information approach to estimate the net socioeconomic benefits associated with this use of satellite data. We calculate the increase in the likelihood of halting transmission of polio associated with the use of satellite-based information compared to traditional data sources, and we consider the benefits associated with savings to the healthcare system as well as health benefits. Using a conservative approach focused on just 1 year of benefits, we estimate net socioeconomic benefits of between $46.0 million and $153.9 million. In addition to these quantified benefits, we also recognize qualitative benefits associated with improving human health, reaching marginalized communities, and building capacity among local populations. We also explore the substantial benefits associated with follow-on projects that have made use of the satellite-based data products and methodologies originally developed for the Nigeria polio eradication effort.

For smart cities, data-driven innovation promises societal benefits and increased well-being for residents and visitors. At the same time, the deployment of data-driven innovation poses significant ethical challenges. Although cities and other public-sector actors have increasingly adopted ethical principles, employing them in practice remains challenging. In this commentary, we use a virtue-based approach that bridges the gap between abstract principles and the daily work of practitioners who engage in and with data-driven innovation processes. Inspired by Aristotle, we describe practices of data-driven innovation in a smart city applying the concepts of virtue and phronêsis, meaning good judgment of and sensitivity to ethical issues. We use a dialogic case-study approach to study two cases of data-driven innovation in the city of Helsinki. We then describe as an illustration of how our approach can help bridge the gap between concrete practices of data-driven innovation and high-level principles. Overall, we advance a theoretically grounded, virtue-based approach, which is practice oriented and linked to the daily work of data scientists and other practitioners of data-driven innovation. Further, this approach helps understand the need for and importance of individual application of phronêsis, which is particularly important in public-sector organizations that can experience gaps between principle and practice. This importance is further intensified in cases of data-driven innovation in which, by definition, novel and unknown contexts are explored.

This issue introduces Marketing Letters' data policy, formulated by our data policy board, comprised of Dina Mayzlin, Darren Dahl, Anirban Mukhopadhyay, and Joachim Vosgerau (Chair). In line with the vision of the founding editors of Marketing Letters to publish interesting findings that are not wrong, this policy is designed to guard against "being wrong," without precluding publication of interesting research. It is designed to be (a) simple, i.e., easy to understand and execute; (b) effective, i.e., serving to improve integrity of science and the journal; and (c) protective, i.e., defining the rights, expectations, and responsibilities of all authors and readers of Marketing Letters. [ABSTRACT FROM AUTHOR]

This article examines data-driven policy for smart cities and how the institutional analysis and development framework (IAD) is a tool to analyze data governance and data policy design. The research assumes that data-driven policy underpins smart city initiatives and incorporates collective action dilemmas, directly impacting policy design and data governance. The article's motivation is to examine the elements of the IAD framework and decompose the constitutive components of data policy in smart cities, obtaining principles that guide data governance in complex situations of digital transformation. The article is a theoretical essay to discuss data policy in smart cities and how policy design understands dilemmas and conflicts of collective action motivated by data control. [ABSTRACT FROM AUTHOR]

This paper reports on the Hackathon Sessions organised at the Polar Data Forum IV (PDF IV) (20–24 September 2021), during which 351 participants from 50 different countries discussed collaboratively about the latest developments in polar data management. The 4th edition of the PDF hosted lively discussions on (i) best practices for polar data management, (ii) data policy, (ii) documenting data flows into aggregators, (iv) data interoperability, (v) polar federated search, (vi) semantics and vocabularies, (vii) Virtual Research Environments (VREs), and (viii) new polar technologies. This paper provides an overview of the organisational aspects of PDF IV and summarises the polar data objectives and outcomes by describing the conclusions drawn from the Hackathon Sessions.

A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market wherein firms and consumers tailor their choices to the demand data. The social dimension of the individual data—whereby a consumer's data are predictive of others' behavior—generates a data externality that can reduce the intermediary's cost of acquiring the information. The intermediary optimally preserves the privacy of consumers' identities if and only if doing so increases social surplus. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large. [ABSTRACT FROM AUTHOR]

The innovation fields of municipalities are based on the creation of added value for the local population by creating a portfolio of digital implementation projects as a "proof of concept". The Objective of the research project is focussed on answers to the questions of Strategies on multi-time acceptable and responsible governance standards in a portfolio of data-based, bureaucracy-reducing and trend-based topics. The Prior work serves the elements of Open Data, Public Value Modeling, Business Process Optimization, Public Corporate Responsibility and Digital Leadership in a holistic orientation question of municipal departments. The Approach are adjusting screws in the interaction of control options from the spectrum of local governance. Between different surveys there are relationships and effects. The Results show that basic prerequisites at the level of values, competencies and management are necessary in order to effectively implement targeted instruments of digital transformation in everyday life. Transparency tools and creativity technology support the introduction of targeted governance. The Implications of this interdisziplinary project should give an impact to academics in the public management and data sciences as well as specialists of leadership and strategic management. For practioneers and leaders of municipalities the possibilities of wise governance should get visual. The Value of the paper lies in the direction of the tension between stable efficiency measures and visionary holisticness of adoption capability of ambiguous requirements in the public sector.

In an era of digitalization, governments often turn to digital solutions for pressing policy issues, and the use of digital contact tracing and quarantine enforcement for COVID-19 is no exception. The longterm impacts of the digital solutions, however, cannot be taken for granted. The development and use of data tools for pandemic control, for example, may have potentially detrimental and irreversible impacts on data governance and, more broadly, society, in the long run. In this paper, we aim to explore the extent to which COVID-19 and digital contact tracing have led to policy change in data governance, if at all, and what the implications of such change would be for a post-COVID world. We compare the use of contact tracing and monitoring applications across mainland China, Hong Kong, and Singapore to illustrate both the enormous benefits and potential risks arising from the design of contact tracing applications and the involvement of stakeholders in the various stages of the policy cycle to combat the COVID-19 pandemic. We argue that, while COVID-19 has not changed the nature of issues, such as public trust in data governance, the increasing involvement of big tech in data policies, and data privacy risks, it has exacerbated those issues through the accelerated adoption of data technologies. [ABSTRACT FROM AUTHOR]

This study explores data reuse and data sharing based on a review of the two most recent publications of each faculty member in crop sciences at the University of Illinois at Urbana-Champaign. Following the methodology of a study conducted in 2011, this 2021 study reveals current practices and compares present-day findings with those of the original study and other literature on data practices. In particular, this work addresses the variety of data sources used by scientists, data citation practices, common data sharing methods, and the challenges of determining the effects of funder policies on data sharing. [ABSTRACT FROM AUTHOR]

This paper contributes to a project that maps the concept of 'data provenance' into qualitative data research. Data provenance is a concept from computer science that allows us to trace the history of sets of data to determine the accuracy and validity of a database. We transform this into a critical concept by arguing that data provenance can be used to trace the acts of governance and rhetoric. We then analyse the forms of discursive power that shape transactions in data. Our approach can create a history of the governance and justifications that are used to build and assemble datasets from multiple sources. Presently, data often lacks this information about its own discursive origins, unlike other forms of data provenance. Critical data provenance gives us a model for thinking about how governance could be mapped onto data. And thus, critical data provenance is also a framework for critiquing the justifications used when dataset owners acquire data – for instance, whether data stewards or users are encouraged to be 'open', to 'share', or be 'transparent'. To this end, we demonstrate this model of thinking and provide the analytical tools necessary to redeploy these ideas into new contexts. [ABSTRACT FROM AUTHOR]

The article addresses the roles of Facebook as a social network, a social media and a social actor and how the platform positions itself among such concepts, especially after the Cambridge Analytica consultancy scandal in 2018. In this context, the objective is to identify the relationship between Facebook Data Policy, the Cambridge Analytica case and the conceptual ambiguity that the company presents. Specifically, it seeks to delimit the multiplicity of Facebook as a social network, social media and social actor. Based on bibliographic research, theoretical discussion on the distinction and integration of the concepts that define Facebook and govern the use of platform data is allowed. Therefore, it is questioned how the scandal acts on the use of individuals' records. As a methodology, the speech analysis of the Data Policy pre and post-Cambridge Analytica is applied to verify the effectiveness of the updates made by the company and whether they reconfigure the company's relationship with its data. The results bring observations regarding data collection and the structure of Facebook. [ABSTRACT FROM AUTHOR]

Data innovation is at the core of the Fourth Industrial Revolution. While the catastrophic COVID-19 pandemic has accelerated the societal shift toward a data-driven society, the direction of overall data regulation remains unclear and data policy experts have yet to reach a consensus. This study identifies and examines the ideal regulator models of data-policy experts and suggests an appropriate method for developing policy in the data economy. To identify different typologies of data regulation, this study used Q methodology with 42 data policy experts, including public officers, researchers, entrepreneurs, and professors, and additional focus group interviews (FGIs) with six data policy experts. Using a Q survey, this study discerns four types of data policy regulators: proactive activists, neutral conservatives, pro-protection idealists, and pro-protection pragmatists. Based on the results of the analysis and FGIs, this study suggests three practical policy implications for framing a nation's data policy. It also discusses possibilities for exploring diverse methods of data industry regulation, underscoring the value of identifying regulatory issues in the data industry from a social science perspective. [ABSTRACT FROM AUTHOR]

This article details a correction to the article: Task Group on Data Citation Standards and Practices, C.-I., 2013. Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data. Data Science Journal, 12, pp. CIDCR1–CIDCR7.

This paper initially conducts a literature review and content analysis of the open research data policies in China. Next, a series of exemplars describe data practices to promote and enable the use of open research data, including open data practices in research programs, data repositories, data journals, and citizen science. Moreover, the top four driving forces are identified and analyzed along with their responsible guiding work. In addition, the “landscape of open research data ecology in China” is derived from the literature review and from observations of actual cases, where the interaction and mutual development of data policies, data programs, and data practices are recognized. Finally, future trends of research data practices within China and internationally are discussed. We hope the analysis provides perspective on current open data practices in China along with insight into the need for additional research on scientific data sharing and management.

University governance on the use of student feedback was studied by looking at the availability and clarity of public policies, FAQ 's, websites, and other documents made available by a sample of Canadian higher education institutions. Educational data, including student feedback data, offers institutions new opportunities to analyze and evaluate their programs in order to improve teaching and learning. However, it is imperative that datasets be used and protected in a manner that respects student privacy and promotes trust and transparency. The research revealed a number of inconsistencies in privacy governance practices, information availability, and proactivity of Canadian higher education institutions. [ABSTRACT FROM AUTHOR]

The paper aims to present the implementation of the RDA research data policy framework in Slovenian scientific journals within the project RDA Node Slovenia. The activity aimed to implement the practice of data sharing and data citation in Slovenian scientific journals and was based on internationally renowned practices and policies, particularly the Research Data Policy Framework of the RDA Data Policy Standardization and Implementation Interest Group. Following this, the RDA Node Slovenia coordination prepared a guidance document that allowed the four pilot participating journals (from fields of archaeology, history, linguistics and social sciences) to adjust their journal policies regarding data sharing, data citation, adapted the definitions of research data and suggested appropriate data repositories that suit their disciplinary specifics. The comparison of results underlines how discipline-specific the aspects of data-sharing are. The pilot proved that a grass-root approach in advancing open science can be successful and well-received in the research community, however, it also pointed out several issues in scientific publishing that would benefit from a planned action on a national level. The context of an underdeveloped data sharing culture, slow implementation of open data strategy by the national research funder and sparse national data service infrastructure creates a unique environment for this study, the result of which can be used in similar contexts worldwide.

Background Many scholarly journals have established their own data-related policies, which specify their enforcement of data sharing, the types of data to be submitted, and their procedures for making data available. However, except for the journal impact factor and the subject area, the factors associated with the overall strength of the data sharing policies of scholarly journals remain unknown. This study examines how factors, including impact factor, subject area, type of journal publisher, and geographical location of the publisher are related to the strength of the data sharing policy. Methods From each of the 178 categories of the Web of Science’s 2017 edition of Journal Citation Reports, the top journals in each quartile (Q1, Q2, Q3, and Q4) were selected in December 2018. Of the resulting 709 journals (5%), 700 in the fields of life, health, and physical sciences were selected for analysis. Four of the authors independently reviewed the results of the journal website searches, categorized the journals’ data sharing policies, and extracted the characteristics of individual journals. Univariable multinomial logistic regression analyses were initially conducted to determine whether there was a relationship between each factor and the strength of the data sharing policy. Based on the univariable analyses, a multivariable model was performed to further investigate the factors related to the presence and/or strength of the policy. Results Of the 700 journals, 308 (44.0%) had no data sharing policy, 125 (17.9%) had a weak policy, and 267 (38.1%) had a strong policy (expecting or mandating data sharing). The impact factor quartile was positively associated with the strength of the data sharing policies. Physical science journals were less likely to have a strong policy relative to a weak policy than Life science journals (relative risk ratio [RRR], 0.36; 95% CI [0.17–0.78]). Life science journals had a greater probability of having a weak policy relative to no policy than health science journals (RRR, 2.73; 95% CI [1.05–7.14]). Commercial publishers were more likely to have a weak policy relative to no policy than non-commercial publishers (RRR, 7.87; 95% CI, [3.98–15.57]). Journals by publishers in Europe, including the majority of those located in the United Kingdom and the Netherlands, were more likely to have a strong data sharing policy than a weak policy (RRR, 2.99; 95% CI [1.85–4.81]). Conclusions These findings may account for the increase in commercial publishers’ engagement in data sharing and indicate that European national initiatives that encourage and mandate data sharing may influence the presence of a strong policy in the associated journals. Future research needs to explore the factors associated with varied degrees in the strength of a data sharing policy as well as more diverse characteristics of journals related to the policy strength.

This study reviews research data policies and author instructions of 31 journals from the Earth sciences and from Biodiversity that are published by German learned societies or research institutions. 12 journals don’t address data sharing at all. The statements on data sharing of the journal’s data policies/author guidelines were matched to 14 defined features of journal research data policies. A brief discussion on quality of data policies is presented to raise awareness of German learned societies/research institutions and to guide them towards improved data policies of their journals.

Open data platforms provide free access to datasets in key areas of urban development. Often managed by the local state, open data platforms constitute a part of smart city strategies worldwide, serving different, potentially contradictory aims, i.e., fostering economic development, pursuing civic objectives or improving administrative efficiency. Reflecting these diverse orientations, our interest lies in understanding how open data platforms are coproduced by different actors based on their conceptions of open data. Taking a critical stance toward the often functionalistic interpretation of open data initiatives as ecosystems, we conceptualize the development of open data initiatives through a dynamic approach to agency, focusing on the strategies of different actor groups shaping open data initiatives and platforms in specific local governance contexts. Empirically, the paper analyses the development of open data initiatives and platforms in two European cities, Lyon and Berlin. Starting with the initial steps for setting open data on the agenda, we apply a process perspective unpacking actors' conceptions of open data and their strategies to shape open data initiatives and platform design. The analysis is based on tracing development processes and on interviews with representatives of city administrations, open data initiatives and civil society. Our findings indicate the objectives of open data initiatives were narrowed down to economic development and administration efficiency, despite broader visions on open data among other actors. This can be traced back to the high importance of personnel, financial and technical resources for defining the implementation of open data initiatives.

Abstract With today's increasing attention to open science and open data, knowledge production and data production have significant impacts on both research and policy arenas. We scrutinize the concept of data production in relation to knowledge production in the earth and environmental sciences. We present three empirical cases that illustrate issues arising when knowledge production and data production as interdependent but distinct processes are not clear in the mind of researchers and policymakers. A two‐stream model is developed that highlights their interplay yet avoids the conflation of knowledge and data production. This approach highlights knowledge and data in terms of processes rather than stable objects or assets. Further, we suggest that considering knowledge and data production in relation to their development by communities rather than as commodities helps in understanding the debates and issues that arise in contemporary research practice.

This article details a correction to the article: Hrynaszkiewicz, I., Simons, N., Hussain, A., Grant, R. and Goudie, S., 2020. Developing a Research Data Policy Framework for All Journals and Publishers. 'Data Science Journal', 19(1), p. 5. DOI: http://doi.org/10.5334/dsj-2020-005

An output of the Data policy standardisation and implementation Interest Group (IG) of the Research Data Alliance (RDA) More journals and publishers – and funding agencies and institutions – are introducing research data policies. But as the prevalence of policies increases, there is potential to confuse researchers and support staff with numerous or conflicting policy requirements. We define and describe 14 features of journal research data policies and arrange these into a set of six standard policy types or tiers, which can be adopted by journals and publishers to promote data sharing in a way that encourages good practice and is appropriate for their audience’s perceived needs. Policy features include coverage of topics such as data citation, data repositories, data availability statements, data standards and formats, and peer review of research data. These policy features and types have been created by reviewing the policies of multiple scholarly publishers, which collectively publish more than 10,000 journals, and through discussions and consensus building with multiple stakeholders in research data policy via the Data Policy Standardisation and Implementation Interest Group of the Research Data Alliance. Implementation guidelines for the standard research data policies for journals and publishers are also provided, along with template policy texts which can be implemented by journals in their Information for Authors and publishing workflows. We conclude with a call for collaboration across the scholarly publishing and wider research community to drive further implementation and adoption of consistent research data policies.

With today's increasing attention to open science and open data, knowledge production and data production have significant impacts on both research and policy arenas. We scrutinize the concept of data production in relation to knowledge production in the earth and environmental sciences. We present three empirical cases that illustrate issues arising when knowledge production and data production as interdependent but distinct processes are not clear in the mind of researchers and policymakers. A two‐stream model is developed that highlights their interplay yet avoids the conflation of knowledge and data production. This approach highlights knowledge and data in terms of processes rather than stable objects or assets. Further, we suggest that considering knowledge and data production in relation to their development by communities rather than as commodities helps in understanding the debates and issues that arise in contemporary research practice. [ABSTRACT FROM AUTHOR]

Data journalism is emerging as a new subspecialty in Indian journalism. Indian data journalism practice is in nascent stage and newsrooms have lately started recognizing it as a new practice. There were specific issues and challenges faced by journalists in western news rooms as they adopted the practice. To generate a comparative perspective, the study carried out semi-structured interviews with Indian data journalists about their perceptions on the challenges of practicing data journalism. The results show that there are external and internal bottlenecks in practicing data journalism in India. While irregular data releases, use of unfriendly formats by governmental authorities is a major concern; reluctance of media organizations to invest in data teams is identified as an internal challenge. Indian data journalists are skeptical about the audience's participatory potential, which has restricted them from relying on practices like crowd-sourcing. [ABSTRACT FROM AUTHOR]