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This article is about the two conflicting goals when teaching statistics or data science courses based on real-world data in a business school environment. We propose to look at structured socio-economic data about the Canadian economy. Canada was ranked 8th in 2017 by Open Data Watch (Government of Canada) for its data accessibility policy. Statistics Canada offers several ways to access data across its over 11,000 data tables. We built an R package to ease access to Statistics Canada's open economic data. With this package, we offer students another option to collect data about the Canadian economy.

Disasters affect multiple sectors; therefore, the need for interdisciplinary and collaborative efforts becomes increasingly apparent. The session "Transdisciplinary Approach: Toward Innovative Recovery and Disaster Risk Reduction" aimed to explore the importance and need for a transdisciplinary approach toward advancing disaster risk reduction and recovery. The approach can provide a systematic framework for organizing knowledge and perspectives across different disciplines. Panelists from different countries presented concepts and case studies to illustrate the effectiveness and challenges of this approach. Through presentations and discussions, it was found that this approach can foster innovation and inclusiveness, and that the data generated by science and technology are crucial for the formulation of disaster risk reduction policies. [ABSTRACT FROM AUTHOR]

DataPipe (https://pipe.jspsych.org) is a tool that allows researchers to save data from a behavioral experiment directly to the Open Science Framework. Researchers can configure data storage options for an experiment on the DataPipe website and then use the DataPipe API to send data to the Open Science Framework from any Internet-connected experiment. DataPipe is free to use and open-source. This paper describes the design of DataPipe and how it can help researchers adopt the practice of born-open data collection. [ABSTRACT FROM AUTHOR]

The Semantic Web in general and the Linked Open Data Initiative, in particular, are a growing movement for organisations to make their existing data available in a machine-readable format. Thus, institutions are highly encouraged to publish, share and interlink their data publicly. The more data are opened on the Web (Open Data), the more integrated sets of data will be connected in the Semantic Web (Linked Open Data). Within this context, libraries can complement their data by linking it to other, external data sources. The purpose of this article is to identify papers that refer to linked data in libraries, emphasising the ways that linked data empower libraries to put their knowledge in the context of the open-world, thus enhancing semantic technology innovations. The study considered papers published between 2008 and 2019 in English and presents the collected literature by grouping it according to the topic each paper refers to. The results show that libraries are facing a period of continuing change which present several challenges and indicate that they are moving towards developing new practices, policies and services. [ABSTRACT FROM AUTHOR]

The Open Science Movement aims to enhance the soundness, transparency, and accessibility of scientific research, and at the same time increase public trust in science. Currently, Open Science practices are mainly presented as solutions to the 'reproducibility crisis' in hypothetico‐deductive quantitative research. Increasing interest has been shown towards exploring how these practices can be adopted by qualitative researchers. In reviewing this emerging body of work, we conclude that the issue of diversity within qualitative research has not been adequately addressed. Furthermore, we find that many of these endeavours start with existing solutions for which they are trying to find matching problems to be solved. We contrast this approach with a natural incorporation of Open Science practices within interaction analysis and its constituent research traditions: conversation analysis, discursive psychology, ethnomethodology, and membership categorisation analysis. Zooming in on the development of conversation analysis starting in the 1960s, we highlight how practices for opening up and sharing data and analytic thinking have been embedded into its methodology. On the basis of this presentation, we propose a series of lessons learned for adopting Open Science practices in qualitative research. [ABSTRACT FROM AUTHOR]

This article examines the geographical distribution of the gender gap in unemployment in Andalusia and whether it exhibits territorial polarizations. It applies the methodological approach of exploratory spatial data analysis and tests local and global spatial dependencies using georeferenced open data produced by official organizations for the period 2011-2022. Based on this evaluation, it seeks to determine whether the gender gap in unemployment in Andalusia follows a homogeneous geographical distribution, or whether, on the contrary, it offers stable territorial polarizations over time. The results are presented in LISA (Local Indicators Spatial Association) maps formed by the local Moran's I differential statistic in each of the years of the series. Based on the results of the LISA clusters' colocation map, it may be concluded that the gender gap in unemployment in Andalusia has a strong structural, feminized, and geographically localized component. [ABSTRACT FROM AUTHOR]

Open science has the capacity of boosting innovative solutions and knowledge development thanks to a transparent access to data shared within the research community and collaborative networks. Because of this, it has become a policy priority in various research and development strategy plans and roadmaps, but the awareness if its potential is still limited in industry. Additive manufacturing (AM) represents a field where open science initiatives may have a great impact, as large academic and industrial communities are working in the same area, enormous quantities of data are generated on a daily basis by companies and research centers, and many challenging problems still need to be solved. This article presents a case study based on an open science collaboration project between TRUMPF Laser- und Systemtechnik GmbH, one of the major AM systems developers and Politecnico di Milano. The case study relies on an open data set including in-line and in-situ signals gathered during the laser powder bed fusion of specimens of aluminum parts on an industrial machine. The signals were acquired by means of two photodiodes installed co-axially to the laser path. The specimens were designed to introduce, on purpose, anomalies in certain locations and in certain layers. The data set is specifically designed to support the development of novel in-situ monitoring methodologies for fast and robust anomaly detection while the part is being built. A layerwise statistical monitoring approach is proposed and preliminary results are presented, but the problem is open to additional research and to the exploration of novel solutions. [ABSTRACT FROM AUTHOR]

With the increased digitalisation of our society, new and emerging forms of data present new values and opportunities for improved data driven multimedia services, or even new solutions for managing future global pandemics (i.e., Disease X). This article conducts a literature review and bibliometric analysis of existing research records on new and emerging forms of multimedia data. The literature review engages with qualitative search of the most prominent journal and conference publications on this topic. The bibliometric analysis engages with statistical software (i.e. R) analysis of Web of Science data records. The results are somewhat unexpected. Despite the special relationship between the US and the UK, there is not much evidence of collaboration in research on this topic. Similarly, despite the negative media publicity on the current relationship between the US and China (and the US sanctions on China), the research on this topic seems to be growing strong. However, it would be interesting to repeat this exercise after a few years and compare the results. It is possible that the effect of the current US sanctions on China has not taken its full effect yet. [ABSTRACT FROM AUTHOR]

Historical data play an important role in our understanding of environmental change and ecosystem dynamics. By lengthening the temporal scale of scientific inquiry, historical data reveal insights into the dynamic nature of ecosystems. However, most historical data has yet to make a full contribution, remaining "dark" and out of reach to the broader scientific community. This article responds to several calls stressing the importance of empirical historical materials and urges their preservation and accessibility. Despite the importance of historical data collections, few standards have emerged to integrate historical dark data into the larger digital data landscape. To encourage greater use of historical data across scientific disciplines it is vital to make data findable, accessible, interoperable, and reusable (e.g., the FAIR principles). In this paper we discuss the potential of historical dark data to contribute to the modern digital ecological data landscape. We do this by focusing on three cases from the University of California field and research stations and the groups that have worked to make historical dark data discoverable. Despite the common goal of maximizing the potential use of these data collections, each case and the methods employed are unique, and showcase varying levels of success in achieving the FAIR principles and shepherding historical data into the twenty-first century.

Abstract Open science and open data within scholarly research programs are growing both in popularity and by requirement from grant funding agencies and journal publishers. A central component of open data management, especially on collaborative, multidisciplinary, and multi-institutional science projects, is documentation of complete and accurate metadata, workflow, and source code in addition to access to raw data and data products to uphold FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although best practice in data/metadata management is to use established internationally accepted metadata schemata, many of these standards are discipline-specific making it difficult to catalog multidisciplinary data and data products in a way that is easily findable and accessible. Consequently, scattered and incompatible metadata records create a barrier to scientific innovation, as researchers are burdened to find and link multidisciplinary datasets. One possible solution to increase data findability, accessibility, interoperability, reproducibility, and integrity within multi-institutional and interdisciplinary projects is a centralized and integrated data management platform. Overall, this type of interoperable framework supports reproducible open science and its dissemination to various stakeholders and the public in a FAIR manner by providing direct access to raw data and linking protocols, metadata and supporting workflow materials.

Open data has revolutionized knowledge-sharing, providing economic and cultural benefits worldwide. However, releasing government, personal, or research data often raises concerns about data security and ethical implications, leading to infringements on privacy and related disputes. The Privacy Protection Framework for Open Data (PPFOD) is proposed to address these challenges. This framework aims to establish clear privacy protection measures and safeguard individuals' privacy rights. Existing privacy protection practices were examined using content analysis, and 36 indicators across five dimensions were developed and validated through an empirical study with 437 participants. The PPFOD offers comprehensive guidelines for data openness, empowering individuals to identify privacy risks, guiding businesses to ensure legal compliance and prevent data leaks, and assisting libraries and data institutions in implementing effective privacy education and training programs, fostering a more privacy-conscious and secure data era. • A Privacy Protection Framework for Open Data (PPFOD) is presented with five dimensions and 36 measures. • The PPFOD was developed using content analysis and questionnaire study methods. • The PPFOD empowers individuals and guides businesses in ensuring privacy. • Data processing requires the utmost attention to privacy protection. [ABSTRACT FROM AUTHOR]

My objective in this text is to present a counterpoint to the positivist bias that has dominated the debate on open science and eventually highlight some problems and provide a more plural and inclusive perspective on the subject. I reflect on three key points that have pervaded the debate on open science, namely: (a) open access to the knowledge produced, (b) transparency in research processes, and (c) replication and reproducibility of previous research. My focus is on highlighting the need for a plural and inclusive view of science, one which is grounded on otherness assumptions. [ABSTRACT FROM AUTHOR]

Research data are growingly becoming part of an information ecosystem that emphasises broad and often overlapping themes, also touching on civic data and on research information. Accordingly, issues such as open science and open data, data reproducibility, misinterpretation and misuse are addressed. Distinguished attention is given to the fact that the World Health Organization (WHO) does not only require data management plans for the projects it supports, but it will also expect them to include information on data sharing. The study underlines the importance of the FAIR principles as well. We also touch on the Data Champions initiative and the related (so far pilot) activities in Hungary, as well as the Love Data Week. Although it is not definetely part of librarianship yet, some of the characteristics of data donation are also described. [ABSTRACT FROM AUTHOR]

The array of problems presented to Extension professionals is broad and growing in number and complexity. As Extension has demonstrated its adaptability in addressing these issues, new ways of working have emerged. Access to an expanding pool of scientific reports and data can potentially provide Extension professionals with the greater tools and knowledge they need to collaboratively engage with their communities. However, a key challenge and possible impediment will be access to rapidly emerging research and data. Open science and open data can broaden the evidence base available to Extension educators, and the emerging field of data science offers new tools to help Extension stakeholders make data-informed decisions. A new data-sharing partnership among Canada, Mexico, and the United States may serve as a model for other countries’ rural advisory services and national Extension systems. Fully implementing this expanded role for Extension will require resources to establish a National Community Learning Network and a national data commons as well as advocacy for open access policies at all levels of government. As abstract as open science and open data may seem to local and regional Extension practitioners, equal access to scientific knowledge and underlying research data is not only imperative for local community engagement but also integral for locally appropriate decision-making. Widening access to research and data directly supports the democratization of science and Extension. Opening scientific research and providing effective access to publicly financed data will become essential platforms for university engagement and Extension. It is critical for Extension professionals to understand the analytic powers and emerging policies that easily-accessed research and data can bring to collaborative community engagement.

This paper carries out a lightweight review to explore the potentials of data science in the last two decades and especially focuses on the four essential components: data resources, technologies, data infrastructures, and data education. Considering the barriers of data science, the analysis has been mapped into four essential components, highlighting priorities and challenges in social and cultural, epistemological, scientific and technical, economic, legal, and ethical aspects. As a result, the future development of data science tends to shift toward datafication, data technicity, infrastructuralism, and data literacy empowerment. The data ecosystem, at the macro level, has also been analyzed under the open science umbrella, providing a snapshot for the future development of data science.

• The open science movement calls for making research data openly available. • Sharing data is not trivial, especially if their interpretation is context dependent as in psychoneuroendocrinology (PNE). • A standardized data format for PNE may support researchers in sharing their data and using openly available data of others. • We call for an open discussion within the community to start working on a standard data structure and nomenclature for PNE. [ABSTRACT FROM AUTHOR]

The Polar Environment Data Science Center (PEDSC) is one of the centers of the Joint Support-Center for Data Science Research (DS) of the Research Organization of Information and Systems (ROIS), which was established in 2017. The purpose of the PEDSC is to promote the opening and sharing of the scientific data obtained by research activities in the polar region led by the National Institute of Polar Research (NIPR). Activities of the PEDSC have been carried out along a five year plan with the following seven specific tasks since 2017: (1) construction of an integrated database; (2) upgrade and interoperable use of the three existing database systems (NIPR Science Database, Arctic Data archive System (ADS), and Inter-university Upper atmosphere Global Observation NETwork system (IUGONET)); (3) processing of the time-series digital data; (4) processing of the sample data; (5) data publication in the Polar Data Journal; (6) collaboration with external communities; and (7) promoting data science using the database and database system.

Open science and open data within scholarly research programs are growing both in popularity and by requirement from grant funding agencies and journal publishers. A central component of open data management, especially on collaborative, multidisciplinary, and multi-institutional science projects, is documentation of complete and accurate metadata, workflow, and source code in addition to access to raw data and data products to uphold FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although best practice in data/metadata management is to use established internationally accepted metadata schemata, many of these standards are discipline-specific making it difficult to catalog multidisciplinary data and data products in a way that is easily findable and accessible. Consequently, scattered and incompatible metadata records create a barrier to scientific innovation, as researchers are burdened to find and link multidisciplinary datasets. One possible solution to increase data findability, accessibility, interoperability, reproducibility, and integrity within multi-institutional and interdisciplinary projects is a centralized and integrated data management platform. Overall, this type of interoperable framework supports reproducible open science and its dissemination to various stakeholders and the public in a FAIR manner by providing direct access to raw data and linking protocols, metadata and supporting workflow materials. [ABSTRACT FROM AUTHOR]

The search for new strategies for better understanding cardiovascular (CV) disease is a constant one, spanning multitudinous types of observations and studies. A comprehensive characterization of each disease state and its biomolecular underpinnings relies upon insights gleaned from extensive information collection of various types of data. Researchers and clinicians in CV biomedicine repeatedly face questions regarding which types of data may best answer their questions, how to integrate information from multiple datasets of various types, and how to adapt emerging advances in machine learning and/or artificial intelligence to their needs in data processing. Frequently lauded as a field with great practical and translational potential, the interface between biomedical informatics and CV medicine is challenged with staggeringly massive datasets. Successful application of computational approaches to decode these complex and gigantic amounts of information becomes an essential step toward realizing the desired benefits. In this review, we examine recent efforts to adapt informatics strategies to CV biomedical research: automated information extraction and unification of multifaceted -omics data. We discuss how and why this interdisciplinary space of CV Informatics is particularly relevant to and supportive of current experimental and clinical research. We describe in detail how open data sources and methods can drive discovery while demanding few initial resources, an advantage afforded by widespread availability of cloud computing-driven platforms. Subsequently, we provide examples of how interoperable computational systems facilitate exploration of data from multiple sources, including both consistently formatted structured data and unstructured data. Taken together, these approaches for achieving data harmony enable molecular phenotyping of CV diseases and unification of CV knowledge. [ABSTRACT FROM AUTHOR]

Cloud-based services introduce a paradigm shift in how users access, process and analyse Big Earth data. A key challenge is to align the current state of how users access, process and analyse the data with trends and roadmaps large data organisations layout. In addition, due to the increased availability of open data, a more diverse user base wants to take advantage of Earth science data leading to new user requirements. We run a web-based survey among Big Earth data users to better understand the motivation to migrate to cloud-based services as well as the challenges and opportunities that might arise. Results show an overall interest in moving to cloud-based services but air an insufficient literacy in cloud systems and a lack of trust due to security concerns and opacity of emerging costs. These gaps demand efforts on three levels. First, cloud services shall be targeted at intermediate users instead of policy- and decision-makers and over-engineered systems with a high level of abstraction should be avoided. Second, more substantial capacity-building efforts are required to decrease the existing gap in cloud skills and uptake. Third, a cloud certification mechanism could help in building up overall trust in cloud-based services. [ABSTRACT FROM AUTHOR]

This paper develops the notion of "open data product". We define an open data product as the open result of the processes through which a variety of data (open and not) are turned into accessible information through a service, infrastructure, analytics or a combination of all of them, where each step of development is designed to promote open principles. Open data products are born out of a (data) need and add value beyond simply publishing existing datasets. We argue that the process of adding value should adhere to the principles of open (geographic) data science, ensuring openness, transparency and reproducibility. We also contend that outreach, in the form of active communication and dissemination through dashboards, software and publication are key to engage end-users and ensure societal impact. Open data products have major benefits. First, they enable insights from highly sensitive, controlled and/or secure data which may not be accessible otherwise. Second, they can expand the use of commercial and administrative data for the public good leveraging on their high temporal frequency and geographic granularity. We also contend that there is a compelling need for open data products as we experience the current data revolution. New, emerging data sources are unprecedented in temporal frequency and geographical resolution, but they are large, unstructured, fragmented and often hard to access due to privacy and confidentiality concerns. By transforming raw (open or "closed") data into ready to use open data products, new dimensions of human geographical processes can be captured and analysed, as we illustrate with existing examples. We conclude by arguing that several parallels exist between the role that open source software played in enabling research on spatial analysis in the 90 s and early 2000s, and the opportunities that open data products offer to unlock the potential of new forms of (geo-)data. [ABSTRACT FROM AUTHOR]

Open Science is a founding principle of ELIXIR, a pan‐European research infrastructure for life science data, with 21 Member countries plus the European Molecular Biology Laboratory. The mission of ELIXIR is to coordinate bioinformatics resources so that they form a single, integrated and pan‐European infrastructure, which can be used freely by academic and private‐sector researchers across the globe. As a recipient of public and charitable funding, ELIXIR must demonstrate its value, and the need to produce evidence in support of this is intensifying. Our practice‐led journey towards demonstrating public value is articulated around five main challenges and, for each, we present our pragmatic approach for tackling it. We begin by showing how we are working towards demystifying what research infrastructures do. We then shed light on the sort of evidence our funders and other stakeholders are asking us for, how this evidence varies in nature and scope, and our tactics to satisfy them. We follow‐on by providing our thoughts on possible barriers and solutions to embedding impact evaluation in our activities. Finally, we provide lessons learned, which we believe are sufficiently transferable and will be inspirational to other research infrastructures as they embark on their own journeys to demonstrate public value. [ABSTRACT FROM AUTHOR]

This paper explores the values of open data in the context of differential power relations in academia and, in particular, the significance of open data policies for early career researchers and career development. The values of open data in terms of accessibility, research scrutiny, and data transparency are important ethical values that all scientists should aspire to achieve. As part of the drive for open science in general, the development of open data and, associated with it, open access, are key characteristics. Although these may be the key drivers for open data in science, the potential to inform policies and to support non‐academic sectors are also high on the political agenda for geographical science, placing the issue of "open data" firmly within the context of the neoliberalisation of universities and academics. Open data becomes a common resource from which other researchers, business, and government can extract value. This paper addresses the contexts in which open data is being encouraged and identifies concerns that these policies may need to be sensitive to, such as existing and emerging power relations within which data is produced and consumed. Viewing open data in this context illuminates some potential ethical issues that the benign ideal of open data could produce. We explore the importance of viewing research and research outputs within the dimensions of a dynamic set of power relationships, which often go unnoticed by practitioners. This suggests the debate over open data needs to be sensitive to the contexts within which researchers work and implicitly emphasise the key role that data, and its production and sharing, play in these relationships. By thinking about the different values of "data" defined by these relations, it is possible to untangle how the seemingly unquestionable drive to open data may pose problems for some researchers. It is suggested, however, that some of these potential issues could be creatively used to address issues of unequal power relations and so enable a greater and mutually rewarding flow of open data between researchers. [ABSTRACT FROM AUTHOR]

The open science movement continues to gain momentum, attention, and discussion. However, there are a number of different interpretations, viewpoints, and perspectives as to what the term "open science" means. In this study, we define open science as a collaborative culture enabled by technology that empowers the open sharing of data, information, and knowledge within the scientific community and the wider public to accelerate scientific research and understanding. As science has become increasingly data driven, data programs now play a critical role in enabling and accelerating open science. In this study, we describe specific actions that data programs can take to make the open science paradigm shift a reality. These actions range from implementing open data and software policies to reimaging data systems that move data out of organizational silos and into cyberinfrastructures that enable efficient research processes and accelerate knowledge dissemination. There are still a number of obstacles to be overcome by data programs which range from mitigating the risk of open data misuse to overcoming the inertia of legacy systems. Data programs need to support open science through the thoughtful development of open policies, systematic investment in innovative and collaborative infrastructures, and the promotion of cultural change. On the other hand, individual researchers play an equally important role by serving as advocates for open science principles and by adopting a number of best practices outlined in this study. By working together, a new and more open age of scientific research can be achieved to benefit science and society. Key Points: Open science is a collaborative culture enabled by technology that empowers the open sharing of data, information, and knowledge within the scientific community and the wider public to accelerate scientific research and understandingThis study provides a synopsis of various open science activities occurring throughout the community and synthesizes those activities around three broad open science focus areasScience has become increasingly data driven, and data programs now play a critical role in enabling and accelerating open scienceData programs can set strategic policies and directions that are critical to enabling and promoting open science [ABSTRACT FROM AUTHOR]

The 2016 Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report emphasized six recommendations to teach introductory courses in statistics. Among them: use of real data with context and purpose. Many educators have created databases consisting of multiple datasets for use in class; sometimes making hundreds of datasets available. Yet “the context and purpose” component of the data may remain elusive if just a generic database is made available. We describe the use of open data in introductory courses. Countries and cities continue to share data through open data portals. Hence, educators can find regional data that engage their students more effectively. We present excerpts from case studies that show the application of statistical methods to data on: crime, housing, rainfall, tourist travel, and others. Data wrangling and discussion of results are recognized as important case study components. Thus, the open data based case studies attend most GAISE College Report recommendations. Reproducible R code is made available for each case study. Example uses of open data in more advanced courses in statistics are also described. Supplementary materials for this article are available online.

This paper analyses the environment of open data and its perception by Mexicans based on the National Survey on Access to Public Information and Protection of Personal Data (ENAID 2016), and the Survey on Public Perception of Science and Technology (ENPECYT 2015). The literature review was focused on how the open data age represents a new social paradigm that has revolutionized the way that people and organizations obtain, analyse and use large amounts of information to make decisions. Mexico has mechanisms and platforms for access to open data, however, its use and contribution to real innovation is unknown. This work focuses on this problem, the data reflect important weaknesses in the perception of open data implementation as well as little interest in innovation and ignorance about it.

Sharing research data from public funding is an important topic, especially now, during times of global emergencies like the COVID-19 pandemic, when we need policies that enable rapid sharing of research data. Our aim is to discuss and review the revised Draft of the OECD Recommendation Concerning Access to Research Data from Public Funding. The Recommendation is based on ethical scientific practice, but in order to be able to apply it in real settings, we suggest several enhancements to make it more actionable. In particular, constant maintenance of provided software stipulated by the Recommendation is virtually impossible even for commercial software. Other major concerns are insufficient clarity regarding how to finance data repositories in joint private-public investments, inconsistencies between data security and user-friendliness of access, little focus on the reproducibility of submitted data, risks related to the mining of large data sets, and sensitive (particularly personal) data protection. In addition, we identify several risks and threats that need to be considered when designing and developing data platforms to implement the Recommendation (e.g., not only the descriptions of the data formats but also the data collection methods should be available). Furthermore, the non-even level of readiness of some countries for the practical implementation of the proposed Recommendation poses a risk of its delayed or incomplete implementation.

Open science and data are yet to make a real breakthrough and research policies will have a critical role in it. The history and general context around open data is hence firstly addressed, including how researchers perceive the existing incentives, leading to recommendations on how to foster data sharing. Subsequently, the focus is on catalysis, with a particular emphasis on benchmarking the data sharing practices against other fields and surveying the type of data currently being shared. The current infrastructure, including data repositories, and standards formats is maped. The striking differences among different disciplines are discussed, serving as a basis to propose specific actions to promote data sharing in catalysis. Short‐term initiatives are needed to boost the amount of openly available data, particularly in heterogeneous catalysis, but a high degree of standardization in data formats will be needed to ensure optimal and automated data mining in the long run. Because of its unique, central role in understanding the catalytic action, kinetic catalytic data is of particular interest. As formats and mining tools are dependant on the type of data, kinetic catalytic data is firstly characterized. Guidelines for a standardized sharing format are proposed, taking into account the small, well‐structured nature of this type of data. To maximize the extraction of information, the low volume of kinetic catalytic data will be compensated by incorporating fundamental knowledge into statistics‐based tools. Whencoupled with knowledge generation tools, i. e. kinetic models, new insights at the active site and mechanism levels will be reached in an ever more automated and powerful way. [ABSTRACT FROM AUTHOR]

Sharing research data from public funding is an important topic, especially now, during times of global emergencies like the COVID-19 pandemic, when we need policies that enable rapid sharing of research data. Our aim is to discuss and review the revised Draft of the OECD Recommendation Concerning Access to Research Data from Public Funding. The Recommendation is based on ethical scientific practice, but in order to be able to apply it in real settings, we suggest several enhancements to make it more actionable. In particular, constant maintenance of provided software stipulated by the Recommendation is virtually impossible even for commercial software. Other major concerns are insufficient clarity regarding how to finance data repositories in joint private-public investments, inconsistencies between data security and user-friendliness of access, little focus on the reproducibility of submitted data, risks related to the mining of large data sets, and sensitive (particularly personal) data protection. In addition, we identify several risks and threats that need to be considered when designing and developing data platforms to implement the Recommendation (e.g., not only the descriptions of the data formats but also the data collection methods should be available). Furthermore, the non-even level of readiness of some countries for the practical implementation of the proposed Recommendation poses a risk of its delayed or incomplete implementation. [ABSTRACT FROM AUTHOR]

Contemporary cities are witnessing momentous shifts in how institutions and individuals produce and circulate data. Despite recent trends claiming that anyone can create and use data, cities remain marked by persistently uneven access and usage of digital technologies. This is the case as well within the emergent phenomenon of the 'smart city,' where open data are a key strategy for achieving 'smartness,' and increasingly constitute a fundamental dimension of urban life, governance, economic activity, and epistemology. The digital ethnography has extended traditional ethnographic research practices into such digital realms, yet its applicability within open data and smart cities is unclear. The method has tended to overlook the important roles of particular digital artifacts such as the database in structuring and producing knowledge. In this paper, we develop the database ethnography as a rich methodological resource for open data research. This approach centers the database as a key site for the production and materialization of social meaning. The database ethnography draws attention to the ways digital choices and practices—around database design, schema, data models, and so on—leave traces through time. From these traces, we may infer lessons about how phenomena come to be encoded as data and acted upon in urban contexts. Open databases are, in other words, key ways in which knowledges about the smart city are framed, delimited, and represented. More specifically, we argue that open databases limit data types, categorize and classify data to align with technical specifications, reflect the database designer's episteme, and (re)produce conceptions of the world. We substantiate these claims through a database ethnography of the open data portal for the city of Calgary, in Western Canada. [ABSTRACT FROM AUTHOR]

Data librarianship and the role of the data librarian are an established reality in many countries, even though at different levels. Particularly, academic librarians have been involved in research data management for a long time and this role is acquiring precise features. In Italy, the data librarian is a figure still to be built and defined. The aim of the article is to offer a first systematic exploration in the fields of data librarianship and the role of the data librarian, both in their practical (what activities) and methodological (how activities are performed) features. The hope is to encourage the beginning of a necessary reflection on these topics. [ABSTRACT FROM AUTHOR]

In this paper we study the changes in academic library services inspired by the Open Science movement and especially the changes prompted from Open Data as a founding part of Open Science. We argue that academic libraries face the even bigger challenges for accommodating and providing support for Open Big Data composed from existing raw data sets and new massive sets generated from data driven research. Ensuring the veracity of Open Big Data is a complex problem dominated by data science. For academic libraries, that challenge triggers not only the expansion of traditional library services, but also leads to adoption of a set of new roles and responsibilities. That includes, but is not limited to development of the supporting models for Research Data Management, providing Data Management Plan assistance, expanding the qualifications of library personnel toward data science literacy, integration of the library services into research and educational process by taking part in research grants and many others. We outline several approaches taken by some academic libraries and by libraries at the City University of New York (CUNY) to meet necessities imposed by doing research and education with Open Big Data – from changes in libraries' administrative structure, changes in personnel qualifications and duties, leading the interdisciplinary advisory groups, to active collaboration in principal projects. [ABSTRACT FROM AUTHOR]

In the current era of big data, huge quantities of valuable data, which may be of different levels of veracity, are being generated at a rapid rate. Embedded into these big data are implicit, previously unknown and potentially useful information and valuable knowledge that can be discovered by data science solutions, which apply techniques like data mining. There has been a trend that more and more collections of these big data have been made openly available in science, government and non-profit organizations so that people could collaboratively study and analysis these open big data. In this article, we focus on open big data for public transit because public transit (e.g., bus) as a means of transportation is a vital part of many people's lives. As time is a precious resource, bus delays could negatively affect commuters' plans. Unfortunately, they are inevitable. Hence, many existing works focused on predicting bus delays. However, predicting on-time or early buses is also important. For instance, commuters who come to a bus stop on time may still miss their buses if the buses leave early. So, in this article, we examine open big data about bus performance (e.g., early, on-time, and late stops). We analyze the data with frequent pattern mining and make predictions with decision-tree based classification. For illustration, we perform predictive analytics on real-life open big data available on Winnipeg Open Data Portal, about bus performance from Winnipeg Transit. It shows the benefits of predictive analytics on open big data for supporting smart transportation services. [ABSTRACT FROM AUTHOR]

Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This paper provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and integrate all investigated quality characteristics in a composite indicator. We stress the need to maintain existing gauging networks, and highlight opportunities in extending existing global databases, understanding drivers for trends and inhomogeneity, and in innovative acquisition methods in data-scarce regions. [ABSTRACT FROM AUTHOR]

The evolution of Smart Cities conveys continuous changes involving a great number of variables, which might hamper the development of evaluation tools and methodologies. Most of the metric models for Smart City are based on the selection of key performance indicators (KPI) according to the specific model objectives. As different organisations propose their own indicators generating different models, it is difficult to get a straightforward comparison among models. With the aim of dealing with this and other disadvantages, in this study, a framework based on the application of Data Science to the KPIs is proposed. This framework represents an infrastructure that goes through the treatment of Open Data, facilitating the evaluation of different models comparison intended for decision‐making, and to the final stage of dispatching service reports. There are four components that integrate this framework (i) a tree structure to manage the KPIs; (ii) a designed JavaScript Object Notation document for service dispatch; (iii) Web applications for evaluations based on Smart People with four scenarios and; (iv) the infrastructure for reception and attention of reports. [ABSTRACT FROM AUTHOR]

The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code. [ABSTRACT FROM AUTHOR]

Open data and openness is a phenomenal paradigm shift in the knowledge society. The purpose of the present paper is to identify the open data repositories especially re3data.org. The authors used re3data.org registry as the source for the present study. Re3data. org is a registry which indexes the data repositories, i.e. mainly open data repositories. There were a total of 2327 repositories found during the study. The results of the study indicate that the data repositories consist the research data largely on Science and Technology discipline; the European and North American countries are the major contributors to the data repositories; the repositories indexed in re3data use variety of software; the data sets are mainly available in English language and the Dublin core metadata standard is widely used by the research data repositories. [ABSTRACT FROM AUTHOR]