The purpose of this paper is to assess the degree of openness of scientific articles on bioeconomy. Based on a WoS corpus of 2,489 articles published between 2015 and 2019, we calculated bibliometric indicators, explored the openness of each paper and assessed the share of journals, countries and research areas of these articles. The results show a sharp increase and diversification of articles in the field of bioeconomy, with a beginning long tail distribution. 45.6% of the articles are freely available, and the share of OA papers is steadily increasing, from 31% in 2015 to 52% in 2019. Gold is the most important variant of OA. Open access is low in the applied research areas of chemical, agricultural and environmental engineering but higher in the domains of energy and fuels, forestry, and green and sustainable science and technology. The UK and the Netherlands have the highest rates of OA papers, followed by Spain and Germany. The funding rate of OA papers is higher than of non-OA papers. This is the first bibliometric study on open access to articles on bioeconomy. The results can be useful for the further development of OA editorial and funding criteria in the field of bioeconomy.

Preprints is a multidisciplinary preprint platform that makes scientific manuscripts from all fields of research immediately available at www.preprints.org. Preprints is a free (not-for-profit) open access service supported by MDPI in Basel, Switzerland.

A common motivation for increasing open access to research findings and data is the potential to create economic benefits – but evidence is patchy and diverse. This study systematically reviewed the evidence on what kinds of economic impacts (positive and negative) open science can have, how these comes about, and how benefits could be maximized. Use of open science outputs often leaves no obvious trace, so most evidence of impacts is based on interviews, surveys, inference based on existing costs, and modelling approaches. There is indicative evidence that open access to findings/data can lead to savings in access costs, labour costs and transaction costs. There are examples of open science enabling new products, services, companies, research and collaborations. Modelling studies suggest higher returns to R&D if open access permits greater accessibility and efficiency of use of findings. Barriers include lack of skills capacity in search, interpretation and text mining, and lack of clarity around where benefits accrue. There are also contextual considerations around who benefits most from open science (e.g. sectors, small vs larger companies, types of dataset). Recommendations captured in the review include more research, monitoring and evaluation (including developing metrics), promoting benefits, capacity building and making outputs more audience-friendly.

Participating in collaborative scientific research through citizen science, a component of open science holds significance for both citizen scientists and professional researchers. Yet, the ad-vantages for those orchestrating citizen science initiatives are often overlooked. Organizers en-compass a diverse range, including governmental entities, non-governmental organizations, corporations, universities, and institutions like libraries. For libraries, citizen science holds im-portance by fostering heightened civic and research interests, promoting scientific publishing, and contributing to overall scientific progress. This paper aims to provide a comprehensive understanding of the specific ways in which citizen science can benefit libraries and how libraries can effectively utilize citizen science to achieve their goals. The paper is based on a systematic review of peer-reviewed articles that discuss the direct benefits of citizen science on libraries. A list of the main benefits of citizen science for libraries has been compiled from the literature. Additionally, the reasons why it is crucial for libraries to communicate the benefits of citizen science for their operations have been highlighted, particu-larly in terms of encouraging other libraries to actively engage in citizen science projects.

In the digital era in which over 4 billion people regularly access the internet, the conventional process of publishing scientific articles in academic journals following peer review is undergoing profound changes. Following physics and mathematics scholars who started to publish their work on the freely accessible arXiv server in the early 1990s, researchers of all disciplines increasingly publish scientific articles in the form of freely accessible and fully citeable preprints before or in parallel to conventional submission to academic journals for peer review. The full transition to open science, I argue in this study, requires to expand the education of students and young researchers to include scholarly communication in the digital era.

Chemistry is the last natural science discipline to embrace prepublishing, namely the publication of non-peer reviewed scientific articles on the internet. After a brief insight into the origins and the purpose of prepublishing in science, we conduct a concrete analysis of the concrete situation, aiming at providing an answer to several questions. Why the chemistry community has been late in embracing prepublishing? Is this in relation with the slow acceptance of open access publishing by the same community? Will prepublishing become a common habit also for chemistry scholars?

This paper explores whether preprints can better support open science by providing links to other early-stage research outputs. This potentially has benefits for transparency and discoverability of research projects. By looking at preprint submission systems, online preprints and surveying those who run preprint servers, I examined to what extent this is currently possible. No preprints server provided a complete service, however many allowed the linking of several open science elements from the abstract page. I looked at variation based on subject, age, and size of preprint server. In conclusion, authors posting preprints should consider the options provided by different preprint servers. It appears that open science is just one focus of preprint servers and further improvements will be dependent on preprint server policies and priorities rather than overcoming any technical difficulties.

We are witnessing a dramatic transformation in the way we do science. In recent years, significant flaws with existing scientific methods have come to light, including lack of transparency, insufficient involvement of stakeholders, disconnection from the public, and limited reproducibility of research findings. These concerns have sparked a global movement to revolutionize scientific practice and the emergence of Open Science. This new approach to science extends principles of openness to the entire research cycle, from hypothesis generation to data collection, analysis, replication, and translation from research to practice. Open Science seeks to remove all barriers to conducting high quality, rigorous, and impactful scientific research by ensuring that the data, methods, and opportunities for collaboration are open to all. Emerging digital technologies and "big data" (see "Ten simple rules for responsible big data research") have further accelerated the Open Science movement by affording new approaches to data sharing, connecting researcher networks, and facilitating the dissemination of research findings. Open scientific practices are also having a profound impact on the health sciences and medical research, and specifically how we conduct clinical research with human participants. Human health research necessitates careful considerations for practicing science in an ethical manner. There is also a particular urgency to human health research since the goal is to help people, so doing good science takes on a different meaning than simply doing science well. It also implores the scientist to reassess the conventional view of human health research as a pursuit conducted by scientists on human subjects, and lays a greater emphasis on inclusive and ethical practices to ensure that the research takes into account the interests of those who would be most impacted by the research. Openness in the context of human health research also raises greater concerns about privacy and security and presents more opportunities for people, including participants of research studies, to contribute in every capacity. At the core of open health research, scientific discoveries are not only the product of collaboration across disciplines, but must also be owned by the community that is inclusive of researchers, health workers, and patients and their families. To guide successful open health research practices, it is essential to carefully consider and delineate its guiding principles. This editorial is aimed at individuals participating in health science in any capacity, including but not limited to people living with medical conditions, health professionals, study participants, and researchers spanning all types of disciplines. We present ten simple rules that, while not comprehensive, offer guidance for conducting health research with human participants in an open, ethical, and rigorous manner. These rules can be difficult, resource-intensive, and can conflict with one another. They are aspirational and are intended to accelerate and improve the quality of human health research. Work that fails to follow these rules is not necessarily an indication of poor quality research, especially if the reasons for breaking the rules are considered and articulated (see rule 6: document everything). While most of the responsibility of following these rules falls on researchers, anyone involved in human health research in any capacity can apply them.

We present a discussion and analysis regarding the benefits and limitations of open and non-anonymized peer review based on literature results and responses to a survey on the reviewing process of alt.chi, a more or less open-review track within the CHI conference, the predominant conference in the field of human-computer interaction (HCI). This track currently is the only implementation of an open-peer-review process in the field of HCI while, with the recent increase in interest in open science practices, open review is now being considered and used in other fields. We collected 30 responses from alt.chi authors and reviewers and found that, while the benefits are quite clear and the system is generally well liked by alt.chi participants, they are reluctant to see it used in other venues. This concurs with a number of recent studies that suggest a divergence between support for a more open review process and its practical implementation. The data and scripts are available on https://osf.io/vuw7h/, and the figures and follow-up work on http://tiny.cc/OpenReviews.

During the last years, significant initiatives have been launched for the dissemination of Open Access as part of the Open Science movement. Nevertheless, the other major pillars of Open Science such as Open Research Data (ORD) and Open Peer Review (OPR) are still in an early stage of development among the communities of researchers and stakeholders. The present study sought to unveil the perceptions of a medical and health sciences community about these issues. Through the investigation of researchers’ attitude, valuable conclusions can be drawn, especially in the field of medicine and health sciences, where an explosive growth of scientific publishing exists. A quantitative survey was conducted based on a structured questionnaire, with 51.8% response rate (215 responses out of 415 electronic invitations). The participants in the survey agreed with the ORD principles However they ignored basic terms like FAIR (Findable, Accessible, Interoperable and Reusable) and appeared incentive to permit the exploitation of their data. Regarding OPR, participants expressed their agreement, implying their interest for a trustworthy evaluation system. Conclusively, researchers urge to receive proper training for both ORD principles and OPR processes which combined with a reformed evaluation system will enable them to take full advantage of the opportunities that arise from the new scholar publishing and communication landscape.

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

(1) Background: The 2002 Budapest Open Access Initiative recommended on self-archiving of scientific articles in open repositories as the “green road” to open access. Twenty years later, only one part of the researchers deposits their publications in open repositories; moreover, one part of the repositories’ content is not based on self-archived deposits but on mediated nonfaculty contributions. The purpose of the paper is to provide more empirical evidence on this situation and to assess the impact on the future of the green road. (2) Methods: We analyzed the contributions on the French national HAL repository from more than 1,000 laboratories affiliated to the ten most important French research universities, with a focus on 2020, representing 14,023 contributor accounts and 166,939 deposits. (3) Results: We identified seven different types of contributor accounts, including deposits from nonfaculty staff and import flows from other platforms. Mediated nonfaculty contribution accounts for at least 48% of the deposits. We also identified difference between institutions and disciplines. (4) Conclusions: Our empirical results reveal a transformation of open repositories from self-archiving and direct scientific communication towards research information management. Repositories like HAL are somewhere in the middle of the process. The paper describes data quality as the main issue and major challenge of this transformation.

Introduction: The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of our study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. Materials and methods: The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Results: Our preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, our results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. Conclusion: In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.

The 'big data revolution' has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the challenges, limitations and risks associated with it. Possible solutions to issues and research integrity considerations are also discussed. Due to the prominence, abundance and wide distribution of sequencing data, we focus on the reuse of publicly available sequence datasets. We define ‘successful reuse’ as the use of previously published data to enable novel scientific findings and use selected examples of such reuse from different disciplines to illustrate the enormous potential of the practice, while acknowledging their respective limitations and risks. A checklist to determine the reuse value and potential of a particular dataset is also provided. The open discussion of data reuse and the establishment of the practice as a norm has the potential to benefit all stakeholders in the life sciences.

Energy use is of crucial importance for the global challenge of climate change but also an essential part of daily life. Hence, research on energy needs to be robust and valid. Other scientific disciplines have experienced a reproducibility crisis, that is, existing findings could not be reproduced in new studies, and energy research might be impacted as well. In this paper, we suggest the ‘TReQ’ approach to improve the research practices in the energy field and arrive at greater Transparency, Reproducibility, and Quality. We acknowledge the specific challenges of energy research and suggest a highly adaptable suite of tools that can be applied to research approaches across this multi-disciplinary and fast-changing field. In particular, we introduce preregistration of studies, making data and code publicly available, using preprints, and employing reporting guidelines to heighten the standard of research practices within the energy field. We argue that through wider adoption of these tools, we will be able to have greater trust in the findings of research used to inform evidence-based policy and practice in the energy field.

Ongoing debates regarding the virtues and challenges of implementing open science for brain imaging research mirror those of the larger scientific community. The present commentary acknowledges the merits of arguments on both sides, as well as the underlying realities that have forced so many to feel the need to resist the implementation of an ideal. Potential sources of top-down reform are discussed, along with the factors that threaten to slow their progress. The potential roles of generational change and the individual are discussed, and a starter list of actionable steps that any researcher can take, big or small, is provided.

The research field of energy system analysis is dealing with increasingly complex energy systems and their respective challenges. Moreover, the requirement for open science has become a focal point of public interest. Both drivers have triggered the development of a broad range of (open) energy models and frameworks in recent years. However, there are hardly any approaches on how to evaluate these tools in terms of their capabilities to tackle energy system modelling challenges. This paper describes a first step towards a flexible evaluation of software to model energy systems. We propose a qualitative approach as an useful supplementary to existing model fact sheets and transparency checklists. We demonstrate the applicability by evaluating the newly developed “Open Energy Modelling Framework” with respect to existing challenges in energy system modelling. The case study results highlight that challenges related to complexity and scientific standards can be tackled to a large extent while the challenges of model utilization and interdisciplinary modelling are only tackled partially. However, the challenge of uncertainty remains for the most part unaddressed at present. Advantages of the evaluation approach lie in its simplicity, flexibility and transferability to other tools. Disadvantages mostly stem from its qualitative nature. Our analysis reveals that some challenges in the field of energy system modelling cannot be addressed by a software as they are on meta level like model result communication and interdisciplinary modelling.

This article challenges the assumption that journals and peer review are essential for developing, evaluating and disseminating scientific and other academic knowledge. It suggests a more flexible ecosystem, and examines some of the possibilities this might facilitate. The market for academic outputs should be opened up by encouraging the separation of the dissemination service from the evaluation service. Publishing research in subject-specific journals encourages compartmentalising research into rigid categories. The dissemination of knowledge would be better served by an open access, web-based repository system encompassing all disciplines. There would then be a role for organisations to assess the items in this repository to help users find relevant, high-quality work. There could be a variety of such organisations which could enable reviews from peers to be supplemented with evaluation by non-peers from a variety of different perspectives: user reviews, statistical reviews, reviews from the perspective of different disciplines, and so on. This should reduce the inevitably conservative influence of relying on two or three peers, and make the evaluation system more critical, multi-dimensional and responsive to the requirements of different audience groups, changing circumstances, and new ideas. Non-peer review might make it easier to challenge dominant paradigms, and expanding the potential audience beyond a narrow group of peers might encourage the criterion of simplicity to be taken more seriously - which is essential if human knowledge is to continue to progress.

Large datasets that enable researchers to perform investigations with unprecedented rigor are growing increasingly common in neuroimaging. Due to the simultaneous increasing popularity of open science, these state-of-the-art datasets are more accessible than ever to researchers around the world. While analysis of these samples has pushed the field forward, they pose a new set of challenges that might cause difficulties for novice users. Here, we offer practical tips for working with large datasets from the end-user’s perspective. We cover all aspects of the data life cycle: from what to consider when downloading and storing the data, to tips on how to become acquainted with a dataset one did not collect, to what to share when communicating results. This manuscript serves as a practical guide one can use when working with large neuroimaging datasets, thus dissolving barriers to scientific discovery.

Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.

Preprints are regularly cited in peer reviewed journal articles, books and conference paper. Are preprint citations somehow less important than citations to peer reviewed research papers? This study investigates citation patterns between 2017 and 2020 for preprints published in three preprint servers, one specializing in biology (bioRxiv), one in chemistry (ChemRxiv), and another hosting preprints in all disciplines (Research Square). As evaluation of scholarship continues to largely rely on citation-based metrics, this analysis and its outcomes will be useful to inform new research-based education in today’s scholarly communication.

The authors introduce the GRASS GIS add-on module g.citation as an initial implementation of a fine-grained software citation concept. The module extends the existing citation capabilities of GRASS GIS, which until now only provide for automated citation of the software project as a whole, authored by the GRASS Development Team, without reference to individual persons. The functionalities of the new module enable individual code citation for each of the over 500 implemented functionalities, including add-on modules. Three different classes of citation output are provided in a variety human- and machine-readable formats. The implications of this reference implementation of scientific software citation for both for the GRASS GIS project and the OSGeo foundation are outlined.

Energy system models have become indispensable to shape future energy systems by providing insights into different trajectories. However, sustainable systems with high shares of renewable energy are characterized by growing cross-sectoral interdependencies and decentralized structures. To capture important properties of increasingly complex energy systems, sophisticated and flexible modelling tools are needed. At the same time open science becomes increasingly important in energy system modelling. This paper presents the Open Energy Modelling Framework (oemof) as a novel approach in energy system modelling, representation and analysis. The framework forms a toolbox to construct comprehensive energy system models and has been published open source under a free license. With a collaborative development based on open processes the framework seeks for a maximum level of participation and transparency to facilitate open science principles in energy system modelling. Based on a generic graph based description of energy systems it is well suited to flexibly model complex cross-sectoral systems and incorporate various modelling approaches. This makes the framework a multi-purpose modelling environment for modelling and analyzing different systems ranging from an urban to a transnational scale.

Open science, accessibility and knowledge sharing, especially of articles and monographs stemming from a publicly funded research, seem to be quite a positive direction in the development of science and have received almost unanimous approval from a scientific community. However, when it comes to data sharing, the data obtained by qualitative methodology deserve special attention and treatment. Although FAIR principles provide ways to anonymise the data and interlocoutors (as explained in Celjak et al 2020) individuals coming from smaller communities or even communities of practice can sometimes be easily regcognized by members of the same communitiy, especially if the data refers to audio files! The researcher is obliged to thoroughly describe the ways in which the data will be managed and used, and if it will state that the data will be managed in line with FAIR principles, this will inevitably impact the narratives collected. The prerogative to make all the data open will inevitably lead to autocesorship i.e. in creating a kind of FAIRy Tales for the future, interlocutors will share with researchers. Having all that in mind I argue for a more vigilant approach when embracing the idea that all the data should be made FAIR. Not all the data should be made FAIR because this could, in the end, compromise the research process itself. Some data must remain in the space of trust created between main actors in the research process. These data should be treated with CARE principles.

Data4HumanRights training materials have been developed as open-source and tailored to limited resource settings where community data collectors often live and work. An overview of how the training material was developed together with community data collectors in Nigeria and Kenya is provided. The paper gives insights into the fundamental principles (i.e., inclusiveness, adaptive, limited resources, gender- and incentive-sensitive) and the structure of the open-access training materials. This resulted in 28 modules designed to be delivered in a face-to-face format in less than one day by a local trainer. To maximize adaptivity, the training modules can be mixed and matched (e.g., as individual modules or a learning path of several modules around a specific training need). The individual modules cover a range of methods and tools that are useful to human rights work, for example, documenting evictions, performing rapid needs assessments after acute crises, community profiling, and monitoring community development indicators. The training materials contain instructions for the facilitator(s) of the training and all necessary training materials to conduct the training. To maximize inclusivity, the training covers basic to advanced topics. Most training modules address basic training topics that can be optimally followed with a mobile phone (to avoid using a computer and printing handouts). The training was developed and implemented with local community groups in Nigeria and Kenya. The material is free to use, adapt, and translate into different contexts and languages.

Academic publishing is undergoing a highly transformative process, and many rules and value systems that were in place for years are being challenged in unprecedented forms leading to the evolution of novel ways of dealing with new pressures. One of the most important aspects of an integrated and valid academic literature is the ability to screen publications for errors during peer review to weed out mistakes, fraud and inconsistencies, such that the final published product represents a product that has value, intellectually, and otherwise. It is difficult to claim the existence of perfect manuscripts. The level of errors that exist in a manuscript will depend on the rigor of the research group and of the peer review process that was used to screen that paper. When errors slip into a final published paper, either through honest error or misconduct, and are not detected during peer review and editorial screening, but are spotted during post-publication peer review, an opportunity is created to set the record straight, and to correct it. To date, the most common forms of correcting the literature have been errata, corrigenda, expressions of concern, and retractions. Despite this range of corrective measures, which represent artificially created corrals around pockets of imperfect literature, certain cases do not quite fit this mold, and new suggested measures for correcting the literature have been proposed, including manuscript versioning, amendments, partial retractions and retract and replace. In this commentary, a discussion of the evolving correction of the literature is provided, as are perspectives of the risks and benefits of such new measures to improve it.

Modern research applies the Open Science approach that fosters the production and sharing of Open Data according to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. In the geospatial context this is generally achieved through the setup of OGC Web services that implements open standards that satisfies the FAIR requirements. Nevertheless, the requirement of Findability is not fully satisfied by those services since there’s no use of persistent identifiers and no guarantee that the same dataset used for a study can be immutably accessed in a later period: a fact that hinders the replicability of research. This is particularly true in recent years where data-driven research and technological advances have boosted frequent updates of datasets. Here, we review needs and practices, supported by some real case examples, on frequent data or metadata updates in geo-datasets of different data types. Additionally we assess the currently available tools that support data versioning for databases, files and log-structured tables. Finally we discuss challenges and opportunities to enable geospatial web services that are fully FAIR: a fact that would provide, due to the massive use and increasing availability of geospatial data, a great push toward open science compliance with ultimately impacts on the science transparency and credibility.

The energy transition requires integration of different energy carriers, including electricity, heat, and transport sectors. Energy modeling methods and tools are essential to provide a clear insight into the energy transition. However, the methodologies often overlook the details of small-scale energy systems. The study states an innovative approach to facilitate sub-national energy systems with 100% renewable penetration and sectoral integration. An optimization model, OSeEM-SN, is developed under the Oemof framework. The model is validated using the case study of Schleswig-Holstein. The study assumes three scenarios representing 25%, 50%, and 100% of the total available biomass potentials. OSeEM-SN reaches feasible solutions without additional offshore wind investment, indicating that they can be reserved for supplying other states’ energy demand. The annual investment cost varies between 1.02 bn – 1.44 bn €/yr for the three scenarios. The electricity generation decreases by 17%, indicating that with high biomass-based combined heat and power plants, the curtailment from other renewable plants can be decreased. Ground source heat pumps dominate the heat mix; however, their installation decreases by 28% as the biomass penetrates fully into the energy mix. The validation confirms OSeEM-SN as a beneficial tool to examine different scenarios for sub-national energy systems.

Recently emerging data-driven citizen sciences need to harness increasing amount of massive data with varying quality. This paper develops essential theoretical frameworks and example models and examine its computational complexity for interactive data-driven citizen science within the context of guided self-organization. We first define a conceptual model that incorporates quality of observation in terms of accuracy and reproducibility, ranging between subjectivity, inter-subjectivity, and objectivity. Next, we examine the database's algebraic and topological structure in relation to informational complexity measures, and evaluate its computational complexities with respect to exhaustive optimization. Conjectures of criticality are obtained on self-organizing processes of observation and dynamical model development. Example analysis is demonstrated with the use of biodiversity assessment database, the process that inevitably involves human subjectivity for the management in open complex systems.

The study analyzed Ecuador's presence in the Web of Science (WoS) post-pandemic from a multivariate dynamic, highlighting a significant transformation in scientific communication towards open access (OA). The methodology integrated multivariate statistical techniques, using R and specialized tools, to process 9,085 relevant articles, 52% of the Ecuadorian publications in WoS between 2019 and 2021 were open access. This movement reflects a commitment to quality research and accessibility, showing an increase in publications and citations under OA. The research underscores the importance of establishing robust national OA policies in Ecuador to promote scientific collaboration and an equitable distribution of knowledge. Despite funding challenges and the lack of specific regulations, educational institutions in Ecuador have maintained a constant scientific output, adapting to the global environment and the demand for transparent information. The study concludes that there is an urgent need for a holistic approach to OA in the country, recognizing the progress made despite the difficulties.

In most world’s countries, scholarship evaluation for tenure and promotion continues to rely on conventional criteria of publications in journals of high impact factor and grant funding. Continuing to hire and promote scholars for their achievements in research and in securing research funds exposes universities at risk because students, directly and indirectly through government funds, are the main source of revenues for academic institutions, whereas talented young researchers are those who actually carry out most of the published research. Purposeful scholarship evaluation needs to include all three areas of scholarly activity: research, teaching and mentoring, and service to society. Young scholars seeking tenure and promotion benefit from the practice of open science because it provides better and more impactful results with respect to each of the three areas of scholarship.

Easy to use satellite-based water quality visualizations are needed for monitoring and understanding coastal and inland waters, but to date, no publicly accessible real-time global visualization system was in place. Here we introduce the Ulyssys Water Quality Viewer (UWQV), a Sentinel Hub EO Browser Custom script designed for qualitative views of aquatic chlorophyll and suspended sediment concentrations. The viewer avoids unmixing of the chlorophyll and suspended sediment spectral signal by visualizing these parameters together, with high concentrations of suspended sediment obscuring chlorophyll if present. Cloud masking uses the Hollstein and Braaten algorithms (existing EO Browser custom script code), additionally water surfaces are masked using the Normalized Differential Water Index. Chlorophyll is estimated using reflectance line height-based indicators such as fluorescence line height and maximum chlorophyll index. Suspended sediment is visualized based on single-band reflectances at 620 or 700 nm. Data sources are Sentinel-2 and Sentinel-3 images, allowing either 20 m spatial resolution or up to daily imaging. This visualization system is easy to operate and interpret, and combined with the data service capacity of the Sentinel Hub, it is expected that UWQV will contribute to monitoring of remote water bodies and to our overall understanding of physical limnology and aquatic ecology.

Sector coupling is one of the emerging topics in recent energy and climate change policy discussions. It can play a significant role in creating the pathway of a renewable-based energy system in the European energy sector. The North Sea region is very likely to play a key role in the transition to a sustainable energy system. Though different energy modelling approaches allow a versatile use, they lead to the problem of an unclear understanding of specific aspects of sector coupling, and the relevance of existing tools and techniques to model and analyze such a system. This paper is aimed at providing a comprehensive understanding of sector coupling and its incorporation in energy system models. Followed by a thorough literature review on sector coupling and energy system modelling, the paper outlines an approach to select an appropriate tool based on the specific rationales of the research. The paper also presents ‘Oemof’ as an open model tool to address the complex challenges of energy systems. The conclusions from the literature review provide a detailed understanding of the concept of sector coupling and indicate that it can be advantageous from the viewpoints of decarbonization, flexibility, network optimization, and system efficiency. To solve the coupling barriers, diversified techno-socio-economic circumstances should be taken into account through the use of model collaboration. It is also demonstrated how a list of appropriate tools for model collaboration can be picked up methodologically from an available wide range of models. Finally, ‘Oemof’ is hypothesized as a progressive tool to design a sector-coupled and renewable-based energy system in the North Sea region.

Data creation is often the only way for researchers to produce basic geospatial information for the pursuit of more complex tasks and procedures such as those that lead to the production of new data for studies concerning river basins, slope morphodynamics, applied geomorphology and geology, urban and territorial planning, detailed studies, for example, in architecture and civil engineering, among others. This exercise results from a reflection where specific data processing tasks executed in Google Sketchup (Pro version, 2018) can be used in a context of interoperability with Geographical Information Systems (GIS) software. The focus is based on the production of contour lines and Digital Elevation Models (DEM) using an innovative sequence of tasks and procedures in both environments (GS and GIS). It starts in Google Sketchup (GS) graphic interface, with the selection of a satellite image referring to the study area—which can be anywhere on Earth's surface; subsequent processing steps lead to the production of elevation data at the selected scale and equidistance. This new data must be exported to GIS software in vector formats such as Autodesk Design Web format—DWG or Autodesk Drawing Exchange format—DXF. In this essay the option for the use of GIS Open Source Software (gvSIG and QGIS) was made. Correcting the original SHP by removing “data noise” that resulted from DXF file conversion permits the author to create new clean vector data in SHP format and, at a later stage, generate DEM data. This means that new elevation data becomes available, using simple but intuitive and interoperable procedures and techniques which confgures a costless work flow.

Environmental policy involving citizen science (CS) is of growing interest. In support of this open data stream, validation or quality assessment of the CS data and their appropriate usage for evidence-based policy making, needs a flexible and easily adaptable data curation process ensuring transparency. Addressing these needs, this paper describes an approach for automatic quality assurance as proposed by the Citizen OBservatory WEB (COBWEB) FP7 project. This approach is based upon a workflow composition that combines different quality controls, each belonging to seven categories or ‘pillars’. Each pillar focuses on a specific dimension in the types of reasoning algorithms for CS data qualification. These pillars attribute values to a range of quality elements belonging to three complementary quality models. Additional data from various sources, such as Earth Observation (EO) data, are often included as part of the inputs of quality controls within the pillars. However, qualified CS data can also contribute to the validation of EO data. Therefore, the question of validation can be considered as ‘two sides of the same coin’. Based on an invasive species CS study, concerning Fallopia japonica (Japanese knotweed), the paper discusses the flexibility and usefulness of qualifying CS data, either when using an EO data for the validation within the quality assurance process, or validating an EO data product that describes the risk of occurrence of the plant. Both validation paths are found to be improved by quality assurance of the CS data. Addressing the reliability of CS open data, issues and limitations of the role of quality assurance for validation, due to the quality of secondary data used within the automatic workflow, are described, e.g. error propagation, paving the route to improvements in the approach.

Sometimes there are clear and natural limits to the scope of action of a science, and in other cases they are simply convenient ones. Geographic Information Science (GISc) is a transversal science, with contacts with all geosciences but also with various formal sciences such as Mathematics, Logic and Computer Science. A first approach to specifying the limits of a science is through its definition. Definitions of GISc are often so expansive that they have been rightly criticized for practicing gerrymandering, in particular with the rest of the geosciences. To avoid this, an operational definition is proposed that places GISc among the sciences that handle Data and not Information. This solves the gerrymandering problem without really implying a significant cut of what is usually considered within GISc. As an unforeseen consequence, this delimitation will allow it to be characterized as Formal Science, leaving it as the only geoscience with this characteristic.

The digital format opens up new possibilities for interaction with monographic publications. In particular, annotation tools make it possible to broaden the discussion on the content of a book, to suggest new ideas, to report errors or inaccuracies, and to conduct open peer reviews. However, this requires the support of the users who might not yet be familiar with the annotation of digital documents. This paper will give concrete examples and recommendations for exploiting the potential of annotation in academic research and teaching. After presenting the annotation tool of Hypothesis, the article focuses on its use in the context of HIRMEOS (High Integration of Research Monographs in the European Open Science Infrastructure), a project aimed to improve the Open Access digital monograph. The general line and the aims of a post-peer review experiment with the annotation tool, as well as its usage in didactic activities concerning monographic publications are presented and proposed as potential best practices for similar annotation activities.

Adequate public spaces and urban green areas are key criteria for urban development and infrastructure implementation of healthy cities. Latterly, there has been an increasing number of research methods using artificial intelligence (AI) to monitor, quantify, and control the state of these spaces with an aim towards pioneering research in Urban Studies. However, in informal areas, open-data access tends to lack adequate and updated information, making it difficult to use AI methods. Hence, we propose a methodology for restricted open data collection and preparation for future use in machine learning or spatial data science models for similar areas. To that extent, we examine two peripheral and low-income neighborhoods in Quito, Ecuador, La Bota and Toctiuco, to analyze their public spaces, urban green areas, points of interest, and road networks, and how they address healthy cities criteria. We develop an original methodological approach that combines an index of proximity, accessibility, quantity, and quality of these spaces with geospatial and network analysis techniques. Results indicate that the connectivity and structure of these spaces are centralized and nodal, representing exclusion and segregation. This work provides insights on potential healthy spaces, and information to urban planners and policymakers in decision-making for healthy urban infrastructure.

Artificial intelligence (AI) has the potential to revolutionize research by automating data analysis, generating new insights, and supporting the discovery of new knowledge. The top 10 contribution areas of AI towards public health were gathered in this feasibility study. We utilized the “text-davinci-003” model of GPT-3, using OpenAI playground default parameters. The model was trained with the largest training dataset any AI had, limited to a cut-off date in 2021. This study aimed to test the ability of GPT-3 to advance public health and to explore the feasibility of using AI as scientific co-author. The AI was asked for input including scientific quotations and the human authors reviewed responses for plausibility. We found that GPT-3 was able to assemble, summarize, and generate plausible text blocks relevant for public health concerns, elucidating valuable areas of application for itself. However, most quotations were invented by GPT-3 and thus, invalid. Ac-cording to today’s rules, we conclude that AI can contribute to public health research as a team member. Nevertheless, good scientific practice needs to be also followed for AI contributions, and a broad scientific discourse on AI contributions is needed. Policies for good scientific practice should be updated timely following this discourse.

Both the sustainability discourse and the debate on citizen science are strongly focused on the natural and technical sciences. Yet, numerous participatory research activities can be identified in the social sciences and humanities that address sustainability issues of various kinds. These have hardly been studied so far, and their contribution to addressing sustainability challenges is poorly known. The study investigates which sustainability topics are taken up by citizen science in the humanities and social sciences, which factors influence the choice of topics, and its implications. For this purpose, the concept of Citizen Social Science (CSS) is taken up and sustainability is operationalized via the Sustainable Development Goals (SDG) and its specific Targets. Based on a collection of CSS activities in Germany, the addressed sustainability topics are identified accordingly. It is then analyzed how these patterns depend on the main characteristics of CSS projects. The findings indicate a focus on three SDGs related to education, sustainable cities and partnerships for the Goals, while at the same time the project consortia are very heterogeneous. CSS shows particular strengths here through its multi-stakeholder approach. Going forward, the linkage of Citizen Science to the SDGs needs to be further formalized so that its transformative effects can be incorporated into SDG monitoring and the scientific institutions need additional incentives to participate in CSS.

Fundamental science and applied research and technology development (RTD) are facing significant challenges that particularly compound to the notorious credibility, reproducibility, funding and sustainability crises. The underlying, serious shortcomings are substantially amplified by a metrics-obsessed publication culture, and a growing cohort of academics fishing for fairly stagnant (public) funding budgets. This work presents, for the first time, a groundbreaking strategy to successfully address these severe issues; the novel strategy proposed here leverages the distributed ledger technology (DLT) “blockchain” to capitalize on cryptoeconomic mechanisms, such as tokenization, consensus, crowdsourcing, smart contracts, reputation systems as well as staking, reward and slashing mechanisms. This powerful toolbox, which is so far widely unfamiliar to traditional scientific and RTD communities (“TradSci”), is synergistically combined with the exponentially growing computing capabilities for virtualizing experiments through digital twin methods in a future scientific “metaverse”. Project contributions, such as hypotheses, methods, experimental data, modelling, simulation, assessment, predictions and directions are crowdsourced using blockchain, and captured by so-called non-fungible tokens (“NFTs”). The so enabled, highly integrative approach, termed decentralized science (“DeSci”), is destined to move research out of its present silos, and to markedly enhance quality, credibility, efficiency, transparency, inclusiveness, sustainability, impact, and sustainability of a wide spectrum of academic and commercial research initiatives.

Publicly-funded scientists have a responsibility to engage with the public on scientific information, but are lacking a standardized framework and assessment strategy to do it well. The PEPS (Public Engagement Practices for Scientists) Method is an outcomes-centered framework employing standardized pedagogical methods with quantifiable outcomes. This approach reveals that scientists often have unrealistic expectations for achieving affective learning outcomes (i.e. changing views from anti- to pro-vaccine) by solely cognitive learning strategies (i.e. supplying data). The PEPS Method can serve as a roadmap for standardized science communication assessments, complementing existing science communication training programs for the next generation of scientists.

Agricultural production needs technologies that assist the management of natural resources, for example, the collection of real-time data on soil, water, weather, crops, and biodiversity conditions. Sensor technology solutions and open-source software are appropriate for promoting more sustainable agricultural production. Among the advantages of using open-source technologies and software is its potential for extension, collaboration, customization, flexibility, maintenance cost, transparency, speed, and better security. Given the above, the objective of this research was to find, in different electronic databases, exclusively open-source software for precision agriculture, offering a systematic review, and addressing considerations and challenges. This survey considers up-to-date open-source software available in repositories such as GitHub and GitLab, to understand its characteristics and application formats.

Participatory landscape conservation is an innovative approach that weaves theory and practice to bridge the gap between theoretical models and practical applications. Intertropical regions as the case of Mexico face challenges to conciliate regional governability, social justice, and nature conservation. The State of Michoacan is one of these regions where the challenges exacerbate since nature conservation is last due to its ongoing territorial disputes. We implemented the participatory landscape conservation approach by creating a complementary form of protected areas with ongoing conflicts, drought conditions, and extreme poverty. We conducted participatory mapping and land cover/use analyses as main methodological tools to reach consensus among stakeholders. We integrated, macro, micro and social scales to provide sound arguments to integrate local, scholar and policy makers perceptions. The outcomes of the participatory mapping analyses were assessed. The present papers provide evidence of the positive outcome of using a Participatory Landscape Conservation to establish a Biosphere Reserve, safeguarding one of the most biologically diverse and delicate ecosystems consisting of seasonally dry tropical forests within a rather disputed region. We discussed the relevance of our findings and compared them to ongoing regional and global trends in the light of other forms of establishing protected areas.

The Science-Sustainability poses an interdisciplinary paradox. On the one hand, the science for sustainability has increased in OECD economies in and in China as well as in the US in particular; on the other hand; the sustainability situation has worsened (Co2 emission has risen). On the face value, the adverse correlation shows a paradox. However, without explicating the science-sustainability relationship, it leads to a premature conclusion. In this study, we have drawn on three concrete questions for concrete answers. First, whether and how interdisciplinary sciences—energy science and environmental science—contribute to the sustainability. Second, whether and how the Sino-US inter-institutional analysis varies in the science-sustainability paradox. The empirical analysis from a panel data in the interdisciplinary and inter-institutional context show mixed patterns in three ways. First, the increase in the environmental science shows an improvement in the sustainability; the energy science shows a decline in the sustainability. Second, the Chinese environmental science has a comparative advantage to American environment science for the sustainability development, and the Chinese energy science has a comparative disadvantage to the US in the sustainability development. Third, the environmental science mediates the energy science in the science-sustainability relationships. Standing alone, the increase in the energy science harms sustainability; mediated by environmental science, it benefits sustainability. The study explains the adverse role of energy science in Jevons Paradox. The study also offers some policy paths for further research how capitalisms differently innovate, form strategies, and implement the practice.

The primary barrier to understanding visual and abstract information in STEM fields is representational competence the ability to generate, transform, analyze and explain representations. The relationship is known between the foundational visual literacy and the domain specific science literacy, however how science literacy is a function of science learning is still not well understood despite investigation across many fields. To support the improvement of students’ representational competence and promote learning in science, identification of visualization skills is necessary. This project details the development of an artificial neural network (ANN) capable of measuring and modeling visual science literacy (VSL) via neurological measurements using functional near infrared spectrometry (fNIRS). The developed model has the capacity to classify levels of scientific visual literacy allowing educators and curriculum designers the ability to create more targeted and immersive classroom resources such as virtual reality, to enhance the fundamental visual tools in science.

Open-source devices are nowadays used in a vast number of research fields like medicine, education, agriculture, and sports among others. In this work, an open-source, portable, low cost pH logger, appropriate for in situ meas-urements was designed and developed to assist in experiments on agricultural produce manufacturing. Τhe device was calibrated manually using pH buffers for values of 4.01 and 7.01. Then it was tested by manually measuring pH from the juice of citrus fruits. A waterproof temperature sensor was added to the device for temperature compen-sation when measuring pH. A formal method comparison process between the open-source device and a Hanna HI9024 Waterproof pH Meter was designed to assess their agreement. We derived indices of agreement and graph-ical assessment tools using mixed-effects models. Advantages and disadvantages for interpreting agreement through the proposed procedure are discussed. In our illustration, indices reported mediocre agreement and the subsequent similarity analysis revealed a fixed bias of 0.22 pH units. After recalibration, agreement between the devices im-proved to excellent levels. The process can be followed in general to avoid misleading or over-simplistic results of studies reporting solely correlation coefficients for formal comparison purposes.

The COVID-19 pandemic caused by the novel coronavirus emerged in Wuhan City, Hubei province of China at the end of 2019, has radically transformed the lives of people around the world. Due to its fast spreading, it is currently considered as a worldwide health, social and economic concern. The lack of knowledge on this area has encouraged academic sphere for extensive research, which is reflected in exponentially growing scientific literature in this area. However, current state of COVID-19 research reveals only early development of knowledge, while a comprehensive and in-depth overview remains neglected. Accordingly, the main aim of this paper is to fill the aforementioned gap in the literature and provide an extensive bibliometric analysis of COVID-19 research across science and social science research landscape, using innovative and sophisticated bibliometric approaches (e.g. Venn diagram, Biblioshiny descriptive statistics, VOSviewer co-occurrence network analysis, Jaccard distance cluster analysis, text mining based on logistic regression). The bibliometric analysis is based on the Scopus database including all relevant and latest information on COVID-19 related publications (n=16,866) in the first half of 2020. The empirical results indicate that there is still a lack of publications of COVID-19 and its implications in less-explored (non-health) sciences, especially in social sciences. Accordingly, the findings emphasize an importance of a comprehensive and in-depth approach considering different scientific disciplines in COVID-19 research. The understanding of the evolution of emerging scientific knowledge on COVID-19 is beneficial not only for scientific community but also for evidence-based policymaking in order to prevent and address the COVID-19 pandemic.

Industrial pilot projects often rely on proprietary and expensive electronic hardware to control and monitor experiments. This raises costs and retards innovation. Open-source hardware tools exist for implementing these processes individually, however, they are not easily integrated with other designs. The Broadly Reconfigurable and Expandable Automation Device (BREAD) framework provides many open-source devices which can be connected to create more complex data acquisition and control systems. This article explores the feasibility of using BREAD plug-and-play open hardware to quickly design and test monitoring and control electronics for an industrial materials processing prototype pyrolysis reactor. Generally, pilot-scale pyrolysis plants are expensive custom designed systems. The plug-and-play prototype approach is first tested by connecting it to the pyrolysis reactor and ensuring it can measure temperature and actuate heaters and a stirring motor. Next, a single circuit board system was created and tested using the designs from the BREAD prototype to reduce the number of microcontrollers required. Both open-source control systems were capable of reliably running the pyrolysis reactor continuously and the overall cost of control was reduced by more than 10X. Open-source, plug-and-play hardware provides a reliable avenue for researchers to quickly develop data acquisition and control electronics for industrial-scale experiments.

Variability is the predisposition of phenomena to assume different values over time. Variability is higher when the differences of cases from each other are larger or diverge from a specific value, such as arithmetic mean. This study focuses on the variability of research topics within and between research fields to explain, whenever possible, how the variability shapes the structure and dynamics of technological trajectories. Analysis of variability here applies the entropy and variance decomposition on main research fields in quantum technologies. Empirical evidence shows that variability in research topics is inversely proportional to the age of research fields. Inductive implications are that higher variability in research topics of recent scientific fields is due to uncertain dynamics that generates a heterogeneity of different technological trajectories some of them can growth and develop towards fruitful directions others can be dismissed. Hence the variability seems to be one of the drivers that shapes technological evolution and can provide policy and managerial implications to improve the scientific and technological forecasting that may support appropriate decisions of R&D investments towards promising research fields and technologies.

Dyslipidemia (Dys) is a disease characterised by abnormally high levels of lipids in the 1 blood. It causes the deposits of lipids on the arterial wall, leading to the insurgence of atherosclerosis, 2 i.e. the increased thickness of arterial walls, which finally increases the risk of cardiovascular diseases. 3 Dyslipidemia is as comorbidity of Type 2 Diabetes Mellitus (T2DM). A set of genes has been associated 4 with dyslipidemia, but examining the regulation of such genes in age and sex is still an open research 5 field. In this study, starting from publicly available databases, we select genes associated with 6 dyslipidemia and we analyse their basal level changes by means of age and sex. Also studying the 7 networks associated to the genes, we identify the rewiring changes showing that there exists a set of 8 genes whose expression changes with age in: artery tibial, artery aorta and blood. Finally, we report 9 about changes associated to gender atributes in the extracted data

Dyslipidemia (Dys) is a disease characterised by abnormally high levels of lipids in the blood. It causes the deposits of lipids on the arterial wall leading to the insurgence of atherosclerosis, i.e. the increased thickness of arterial walls, which finally increases the risk of cardiovascular diseases. Dyslipidemia is a comorbidity of Type 2 Diabetes Mellitus (T2DM). A set of genes has been associated with dyslipidemia, but examining the regulation of such genes in age and sex is still an open research field. In this study, we select from publicly available databases the genes associated with dyslipidemia and analyse changes in the basal level of such genes by age and sex. Moreover, we also distinct differential rewiring of the related gene association network in males and females. Our results show that there exist a set of genes whose expression changes with age in artery tibial, artery aorta and blood. Moreover, there exist significant differences between sex.

Distributed digital manufacturing offers a solution to medical supply and technology shortages during pandemics. To prepare for the next pandemic, this study reviews the state-of-the-art for open hardware designs needed in a COVID-19-like pandemic. It evaluates the readiness of the top twenty technologies requested by the Government of India. The results show that the majority of the actual medical products have had some open source development, however, only 15% of the supporting technologies that make the open source device possible are freely available. The results show there is still considerable work needed to provide open source paths for the development of all the medical hardware needed during pandemics. Five core areas of future work are discussed that include: i) technical development of a wide-range of open source solutions for all medical supplies and devices, ii) policies that protect the productivity of laboratories, makerspaces and fabrication facilities during a pandemic, as well as iii) streamlining the regulatory process, iv) developing Good-Samaritan laws to protect makers and designers of open medical hardware, as well as to compel those with knowledge that will save lives to share it, and v) requiring all citizen-funded research to be released with free and open source licenses.

The American Academy of Arts and Sciences suggested the definition of science literacy emphasize how crucial understanding the scientific process and the ability to evaluate conflicting scientific evidence is. The purpose of this article is to present an evidence-supported curriculum covering the fundamentals of logic, reasoning, and argumentation skills to address the emphasized basic knowledge, skills, and abilities required to be scientifically literate, which will prepare the public to understand and engage with science meaningfully. An analytic-synthetic approach toward understanding the notion of public is taken using a theoretical biomimetics framework that identifies naturally occurring objects or phenomena that descriptively captures the essence of a construct to facilitate creative problem-solving. In the present case, the problem being solved is how to reconcile what is meant by public, how it ought to be interpreted, the different levels of confidence in science that exist, and various understandings of science all with one another. The results demonstrate there is an inherent denotative-connotative inconsistency in the traditional notion of public that can be explicated through the concept of a fractal allowing for comprehension of the relationship between public confidence in, and understanding of, science.

Open-source 3-D printing has played a pivotal role in revolutionizing the additive manufacturing (AM) landscape, by making distributed manufacturing economic, democratizing access, and fostering far more rapid innovation than antiquated proprietary systems. Unfortunately, some 3-D printing manufacturing companies began deviating from open-source principles and violating licenses for the detriment of the community. To determine if a pattern has emerged of companies patenting clearly open-source innovations, this study presents three case studies from the three primary regions of open-source 3-D printing development (EU, U.S. and China) as well as three aspects of 3-D printing technology (AM materials, an open-source 3-D printer, and core open-source 3-D printing concepts used in most 3-D printers). The results of this review have shown that non-inventing entities called patent parasites are patenting open-source inventions already well-established in the open source community and in the most egregious cases commercialized by one (or several) firms at the time of the patent filing. Patent parasites are able to patent open-source innovations by using a different language, vague patent titles and broad claims that encompass enormous swaths of widely diffused open-source innovation space. This practice poses a severe threat to innovation and several approaches to irradicate the threat are discussed.

Business processes are continuously evolving in order to adapt to changes due to various factors. One important process drift perspective yet to be investigated is the detection of branching condition changes in the process model. None of the existing process drift detection methods focus on detecting changes of branching conditions in process models. Existing branching condition detection methods do not take changes within the process into account, hence results are inadequate to represent the changes of decision criteria of the process. In this paper, we present a method which can detect branching condition changes in process models. The method takes both process models and event logs as input, and translates event logs into decision sequences for change points detection. The proposed method is evaluated by simulated event logs.

This study provides designs for a low-cost, easily replicable open source lab-grade digital scale that can be used as a precision balance. The design is such that it can be manufactured for use in most labs throughout the world with open source RepRap-class material extrusion-based 3-D printers for the mechanical components and readily available open source electronics including the Arduino Nano. Several versions of the design were fabricated and tested for precision and accuracy for a range of load cells. The results showed the open source scale was found to be repeatable within 0.1g with multiple load cells, with even better precision (0.01g) depending on load cell range and style. The scale tracks linearly with proprietary lab-grade scales, meeting the performance specified in the load cell data sheets, indicating that it is accurate across the range of the load cell installed. The smallest loadcell tested(100g) offers precision on the order of a commercial digital mass balance. The scale can be produced at significant cost savings compared to scales of comparable range and precision when serial capability is present. The cost savings increase significantly as the range of the scale increases and are particularly well-suited for resource-constrained medical and scientific facilities.

Leisure-time physical activity (LTPA) is associated with access and use of public open spaces. The “President João Goulart Elevated Avenue” and current denominated “Minhocão” is a facility for leisure activities that are open for people during the night/weekends. The aim of this study was to examine if the prevalence of LTPA among individuals living in the surroundings of Minhocão is different according to proximity to, and use of, the facility. We conducted a cross-sectional study with cluster sampling with people aged ≥18 years who lived in households until 500m and between 501m to 1500m of Minhocão. The survey was conducted between December/2017 until March/2019 with an electronic questionnaire self-responded. We conducted bivariate analysis and Poisson regression to examine possible differences in LTPA according to the proximity of residences and use of Minhocão. The analysis used post-stratification weights. A total of 12,030 telephone numbers of people were drawn (≤500m = 6,942; and >500m to ≤1500m = 5,088). The final sample analyzed were of 235 residents who returned the questionnaires. There was a higher prevalence of individuals engaging in at least 150 minutes per week of LTPA among users than non-users (Prevalence Ratio=2.23, IC95%1.72-2.90). People who used the park had higher prevalence of all types of LTPA than non-users. The results can serve to inform government decision-making on the future of Minhocão.

In order to deal with imprecision, ambiguity, and uncertainty in data analysis, Pawlak introduced rough set theory in 1982. This paper aims to expand the scope of basic set theory developed by presenting the notions of δP-upper and δP-lower approximations, that are based on the notion of δP-open sets, we additionally examine a few of their fundamental characteristics.

The terms 'open' and 'openness' are widely used across the current higher education environment particularly in the areas of repository services and scholarly communications. Open-access licensing and open-source licensing are two prevalent manifestations of open culture within higher education research environments. As theoretical ideals, open-licensing models aim at openness and academic freedom. But operating as they do within the context of global neoliberalism, to what extent are these models constructed by, sustained by, and co-opted by neoliberalism? In this paper, we interrogate the use of open-licensing within scholarly communications and within the larger societal context of neoliberalism. Through synthesis of various sources, we will examine how open access licensing models have been constrained by neoliberal or otherwise corporate agendas, how open access and open scholarship have been reframed within discourses of compliance, how open-source software models and software are co-opted by politico-economic forces, and how the language of 'openness' is widely misused in higher education and repository services circles to drive agendas that run counter to actually increasing openness. We will finish by suggesting ways to resist this trend and use open-licensing models to resist neoliberal agendas in open scholarship.

The recent remarkable progress in Artificial Intelligence (AI) has renewed the discussion on the possibility of human-level AI. Despite the difficulty of the problem and the presence of many large gaps and challenges, most people working in the field think that human-level AI is achievable, but they disagree on the date. In this paper, I briefly discuss the possibility of human-level AI from the perspective of two traditions in the Islamic world, namely Kalam and Sufism, and despite their different mode of investigation, they converge to the same conclusion on this topic.

The acoustic conditions of open-plan office spaces influence wellbeing and productivity perceived by users, but with an inadequate evaluation for workspace, acoustic design in open-plan offices can be a factor that alters user performance. Such is the case in Mexico, where there are no adequate standards to evaluate specific acoustic conditions such as intelligibility. For this reason, this case study aims to evaluate different types of measurement methods for intelligibility. This study was carried out in a university in northern Mexico. The sound measurements were based on the Mexican standard for noise analysis and the ISO 3382- part 3 standards for acoustic measurements for open-plan offices. The psychoacoustic parameters evaluated were Reverberation and Intelligibility, using objective methods determined on S/N and subjective methods based on loss of consonant; where it was analyzed distance between sound source and zones classified by building design characteristics. The results indicated in which points the intelligibility effects increased, observing that Reverberation remained stable in this office, and that the subjective methods presented greater size of the measured sound effect than the objective methods. This finding establishes that subjective methods conformed to Lognormal behavior, which is applicable to other linguistic elements describing speech behavior.

Forest recreation can be successfully used for the psychological relaxation of respondents and can be used as a remedy for common problems with stress. The special form of forest recreation intended for restoration is forest bathing. These activities might be distracted by some factors, such as viewing buildings in the forest or using a computer in nature, which interrupt psychological relaxation. One factor that might interrupt psychological relaxation is the occurrence of an open dump in the forest during an outdoor experience. To test the hypothesis that an open dump might decrease psychological relaxation, a case study was planned that used a randomized, controlled crossover design. For this purpose, two groups of healthy young adults viewed a control forest or a forest with an open dump in reverse order and filled in psychological questionnaires after each stimulus. A pretest was used. Participants wore oblique eye patches to stop their visual stimulation before the experimental stimulation, and the physical environment was monitored. The results were analyzed using the two-way repeated measures ANOVA. The measured negative psychological indicators significantly increased after viewing the forest with waste, and the five indicators of the Profile of Mood States increased: Tension-Anxiety, Depression-Dejection, Anger-Hostility, Fatigue, and Confusion. In addition, the negative aspect of the Positive and Negative Affect Schedule increased in comparison to the control and pretest. The measured positive indicators significantly decreased after viewing the forest with waste, the positive aspect of the Positive and Negative Affect Schedule decreased, and the Restorative Outcome Scale and Subjective Vitality scores decreased (in comparison to the control and pretest). The occurrence of an open dump in the forest might interrupt a normal restorative experience in the forest by reducing psychological relaxation. Nevertheless, the mechanism of these relevancies is not known, and thus, it will be further investigated. In addition, in a future study, the size of the impact of these open dumps on normal everyday experiences should be investigated. It is proposed that different mechanisms might be responsible for these reactions; however, the aim of this manuscript is to only measure this reaction. The identified psychological reasons for these mechanisms can be assessed in further studies.

In December 2012, DOAJ’s parent company, IS4OA, announced they would introduce new criteria for inclusion in DOAJ [1] and that DOAJ would collect vastly more information from journals as part of the accreditation process – and that journals already included, would need to reapply in order to be kept in the registry. My hypothesis was that the journals removed from DOAJ on May 9th 2016 would chiefly be journals from small publishers (mostly single journal publishers) and that DOAJ journal metadata information would reveal that they were journals with a lower level of publishing competence than those that would remain in the DOAJ. Among indicators of publishing competence could be the use of APCs, permanent article identifiers, journal licenses, article level metadata deposited with DOAJ, archiving policy/solutions and/or having a policy in SHERPA/RoMEO. The analysis shows my concerns to be correct.

Due to resource constraints, most Diamond Open Access journals publish less than 25 articles per year, and 75% of journals are not able to provide their content in XML and HTML, primarily providing only PDFs (Bosman et al., 2021, p. 7-8). In order to keep up with larger commercial publishers, a high degree of automation and streamlining of processes is necessary. The Open Source Academic Publishing Suite (OS-APS) project funded by the German Federal Ministry of Education and Research aims to achieve this. OS-APS automatically extracts the underlying XML from Word manuscripts and offers optimization and export options in various formats (PDF, HTML, EPUB). The professional corporate design, e.g., of the PDFs, is managed automatically by using templates or creating one's own using a Template Development Kit. OS-APS will also connect to scholarly-led and community-driven publishing platforms such as Open Journal Systems (OJS), Open Monograph Press (OMP), and DSpace: the software will be able to be integrated into a wide range of publication processes, whether at small, low-resource commercial Open Access Publishers, or institutional and Diamond Open Access Publishers. References: Bosman, J., Frantsvåg, J. E., Kramer, B., Langlais, P.‑C., & Proudman, V. (2021). Oa Diamond Journals Study. Part 1: Findings. https://doi.org/10.5281/zenodo.4558703

We demonstrate a proof-of-principle of simple all-solid-state post-compression setup for low energy, high repetition rate laser pulses, where spectral broadening was performed using a combination of highly nonlinear bulk materials in a simple single pass geometry. 75 fs, 210 nJ pulses from an amplified 76 MHz, 15.7 W Yb:KGW oscillator after sequential spectral broadening in ZnS and YAG samples of 2 mm and 15 mm thickness, respectively, were compressed to 37 fs by means of Gires–Tournois interferometric mirrors. The post-compressed pulses with an average power of 11.47 W demonstrated reasonable spatial-spectral homogeneity of the beam with the spectral overlap parameter V>83% and good beam quality with Mx2=1.28 and My2=1.14.

This article explores the lack of unanimity regarding the nomenclature used to refer to the field of research that explores the intersection between art and science. A series of examples of nomenclatures and the context in which they are applied are listed and referenced. The diverse terminology reflects the heterogeneity, transdisciplinarity, and complexity of a research field in full expansion.

A naked human eye can perceive objects down to a millimeter length. While lenses and microscopes have overcome this limit, the human mind still lacks perspective when navigating conventional scales (1), especially in the range that are less palpable to naked human eye (2,3). This problem is particularly acute in the context of science communication, where the conventional scale bar units facilitate little comprehension regarding the perception for factorial size differences (3). Here we aim to bridge the gap of scale factors and perspectives using a universal toolkit of objects, which can help comprehend the relative change in length dimensions up to 13 orders of magnitude difference. We further have demonstrated the use of such a universal object toolkit as a length perceptive scale by illustrating and narrating biological phenomena. The meter to picometer ‘length perceptive scale’ proposed here has the potential to cover majority of length scales present in the biological realm, and is analogous to the time compression methods widely used in explaining cosmos timeline (4). Our toolkit can also be calibrated according to the users need in their scientific communication and illustrations, which will aid the readers’ benefit in understanding the length scale perception of illustrated phenomenon.

The understanding of the issues that affect the training of information professionals requires reflection on the impact of Digital Information and Communication Technologies in the current working world, characterized by the rupture of the notion of space / time and the crossing of jurisdictions and professional boundaries, where dilemmas pedagogies to qualify these professionals considering future perspectives. It was based on the assumption that "studies of graduates" are subsidies for the evaluation of the educational system in face of the demands of in the labor market, helping in the progression of the researches in information, education and work. The objective was to map the results obtained in the studies of graduates promoted in the postgraduate and undergraduate courses in Information Science and Librarianship to verify if the demands pointed out in the surveys with graduates are in line with the studies of Education and Training, the orientations of the National Curricular Guidelines (DCN) and the regulatory actions of the Federal Library Board (CFB). The sample consisted of 45 studies published in CI journals on their associated areas, in the Brazilian context, from 2000 to 2018, of which only 10 articles related to the Library Sciences were considered for the qualitative analysis of this initial phase. to enable a comparative analysis between the proposals of the DCN, the critical analysis of the academic area of ​​CI in the studies on education and professional formation (pedagogical projects and curricula) and the orientations of the class councils. The results were categorized according to the reflection of the graduates about their education, the experience of education, vision about professional practice and the labor market; and followed the structure of the DCN axes: profile; skills and abilities; curricular contents, internships and complementary activities; course structure and institutional evaluation. Prevalence of studies on the identification of graduates (gender, age group, employment and work place) was predominant; few focused on salary aspects; and there were no questions about cultural habits or perceptions about changes in the world of work. Results on issues related to job placement and employability have indicated the need to expand the range of possibilities that go beyond traditional information units and the importance of internships for understanding new virtual work spaces. The dimension of the academic formation was evaluated questioning the compatibility of the disciplines with the demands of the labor market and the satisfaction of the graduates with the course and profession, being indicated as more important contents for the professional life the more technical disciplines and that characterize the course of Librarianship, complemented by those related to management; and with less relevance to the language subjects; marketing; statistic; information and information technology, as opposed to the flexibility proposed by the DCN, adopted after 2002. It is concluded that the process has been guided by isolated initiatives, and that the collective debate involving university and the professional world lacks actions for the construction of dialogue, which may result in the definition of pedagogical and curricular projects suited to the demands of graduates in their regions of origin and demands of the world of work.

Public trust in science was tested and relied on during the SARS-CoV-2 pandemic, which has shaped global events since the WHO declaration in March 11, 2020. Public trust has been impacted through the government recommendations and mandates informed by public health guidance, including non-pharmaceutical and pharmaceutical interventions. The free-flow of ideas and in-formation so essential to the functioning of science has faced unprecedented challenge from widespread censorship in both the media and in scientific journals. This has created a poisoned environment for the building of trust between science and society. Scientific norms and ac-countability must be restored in order to rebuild the vital relationship between scientists and the public they serve.

The study investigated the usage of open data in public organizations, focusing on the motives, preferences, and governance ideals that determine their quality. The study shed light on how open data platforms are created and improved, as well as their influence on government operations. The primary goal was to understand the motivations behind the usage of open data, evaluate gov-ernance ideals, and examine institutions' efforts to standardize data formats. Hierarchical regres-sion analysis divides variables into core reasons and different preferences, demonstrating a need for "diversification" in order to use open data effectively. Efficiency, accountability, community impact, knowledge, and high-quality service delivery are all emphasized in governance value analysis. The institution's dedication to standardizing open data formats is evidenced by legislative measures and the integration of many technologies. The findings illustrate the institution's dual strategy of em-ploying traditional techniques and new technologies for effective data management. The report suggests increasing data openness and standardization, investing in ongoing training, upgrading alerting and awareness systems, and adopting specific regulations for open data use. Monitoring and feedback techniques are critical for adjusting and refining open data services to satisfy de-velopment needs. This study adds to the scientific knowledge of open data use by highlighting important motives, preferences, and governance ideals. The offered guidelines provide practical insights for public organizations looking to improve their open data platforms by promoting openness, efficiency, and community participation.

To stabilize the Earth’s climate a large-scale transition is needed to non-carbon emitting renewable energy technologies like wind and solar energy. Although these renewable energy sources are now lower-cost than fossil fuels, their inherent intermittency make them unable to supply a constant load without storage. To address these challenges rechargeable electric batteries are currently the most promising option; however, their high capital costs limit current deployment velocities. To both reduce the cost as well as improve performance 3D printing technology has emerged as a promising solution. This literature review provides the state-of-the-art enhancements of battery properties with 3D printing including efficiency, mechanical stability, energy and power density, customizability and sizing, production process efficiency, material conservation and environmental sustainability as well as the progress in solid-state batteries. The principles, advantages, limitations, and recent advancements associated with the most common types of 3D printing are reviewed focusing on their contributions to the battery field. 3D printing batteries offer design flexibility, material flexibility, reduces pack weight, minimizes material waste, increases the range of applications and has the potential to reduce costs. As 3D printing technologies become more accessible, the prospect of cost-effective production for customized batteries is extremely promising.

Thermal sterilization is generally avoided for 3-D printed components because of the relatively low deformation temperatures for common thermoplastics used for material extrusion-based additive manufacturing. 3-D printing materials required for high-temperature heat sterilizable components for COVID-19 and other applications demands 3-D printers with heated beds, hot ends that can reach higher temperatures than polytetrafluoroethylene (PTFE) hot ends and heated chambers to avoid part warping and delamination. There are several high temperature printers on the market, but their high costs make them inaccessible for full home-based distributed manufacturing required during pandemic lockdowns. To allow for all these requirements to be met for under $1,000, the Cerberus – an open source three-headed self-replicating rapid prototyper (RepRap) was designed and tested with the following capabilities: i) 200^oC-capable heated bed, ii) 500^oC-capabel hot end, iii) isolated heated chamber with 1kW space heater core and iv) mains voltage chamber and bed heating for rapid start. The Cereberus successfully prints polyetherketoneketone (PEKK) and polyetherimide (PEI, ULTEM) with tensile strengths of 77.5 and 80.5 MPa, respectively. As a case study, open source face masks were 3-D printed in PEKK and shown not to warp upon widely home-accessible oven-based sterilization.

Walking of humanoid robots is dependent on precise tracking of the center of gravity and foot trajectories. Trajectory tracking is achieved by mobilizing the robot joints to produce the correct trajectory. Errors are being occurred because of assumptions on tracking the center of gravity and the foot trajectories. In this study, a numerical algorithm has been developed that produces an exact and single kinematic solution in which the center of gravity and foot trajectories can be tracked with the desired precision. The effectiveness of the algorithm was examined with a dynamic simulation and compared with the method given in the literature The main highlight of this study, using the presented algorithm, the robot can walk even if the position of its center of gravity is lower than its hips, resulting tracking error was smaller than reported in the literature.

This study aimed to identify the most beneficial attitudes adopted by enterprise participants during their engagement in open-source software (OSS) activities. For this purpose, we selected repositories from GitHub, featuring the top 10 with the highest stars and forks, classified the participants according to their sentiment states, and compared their productivity. The results indicated that participants with positive sentiments were more productive than those with negative sentiments. Consequently, it can be inferred that encouraging organizations to express positive sentiments towards their developers publicly may stimulate OSS community activities and enhance productivity, thereby potentially leading to increased open innovation and sustainability.

This proposal describes the design and development of an interoperable application that supports green open access with long-term sustainability and improved user experience of article deposit. Introduction: The lack of library resources and unfriendly repository user interface are two significant barriers that hinder green open access. Tasked to implement the open access mandate, librarians at an American research university developed a comprehensive system called Easy Deposit 2 to automate the support workflow of green open access. Implementation: Easy Deposit 2 is a web application that is able to harvest newly publications, outreach for manuscript on behalf of the library, and facilitate self-archiving to IR. It is developed and maintained by the library and integrated with the IR. Results and Discussion: The article deposit rate is about 25% with Easy Deposit 2, which increases significantly comparing to the previous period. It also serves as a local database for faculty publications with open access status. The lesson learned is that library cannot rely on a single commercial provider for publication data due to mismatched priorities. Conclusion: Recent IT developments provides new opportunities of innovation like Easy Deposit 2 in supporting open access. Academic librarians are vital in promoting "openness" in scholarly communication such as transparency and diversity in the sharing of publication data.

The number of documents published on the Web other languages than English grows every year. As a consequence, it increases the necessity of extracting useful information from different languages, pointing out the importance of researching Open Information Extraction (OIE) techniques. Different OIE methods have been dealing with features from a unique language. On the other hand, few approaches tackle multilingual aspects. In such approaches, multilingual is only treated as an extraction method, which results in low precision due to the use of general rules. Multilingual methods have been applied to a vast amount of problems in Natural Language Processing achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We state that a multilingual approach can enhance OIE methods, being ideal to evaluate and compare OIE systems, and as a consequence, to applying it to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase the acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning the state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.

Universities, like cities, have embraced novel technologies and data-based solutions to improve their campuses with ‘smart’ becoming a welcomed concept. Campuses in many ways are small-scale cities. They increasingly seek to address similar challenges and to deliver improved experiences to their users. How can data be used in making this vision a reality? What can we learn from smart campuses that can be scale up to smart cities? A short research study was conducted over a three-month period at a public university in the United Kingdom employing stakeholder interviews and user surveys, aiming at gaining insight into these questions. Based on the study, the authors suggest that making data publicly available could bring many benefits to different groups of stakeholders and campus users. These benefits come with risks and challenges such as data privacy and protection and infrastructure hurdles. However, if these challenges can be overcome, open data could contribute significantly to improving campuses and user experiences, and potentially set an example for smart cities.

The implementation of the European Cohesion Policy aiming at fostering regions competitiveness, economic growth and creation of new jobs is documented over the period 2014–2020 in the publicly available Open Data Portal for the European Structural and Investment funds. On the base of this source, this paper aims at describing the process of data mining and visualization for information production on regional programmes performace in achieving effective expenditure of resouces.

Network effects, economies of scale, and lock-in-effects increasingly lead to a concentration of digital resources and capabilities, hindering the free and equitable development of digital entrepreneurship (SDG9), new skills, and jobs (SDG8), especially in small communities (SDG11) and their small and medium-sized enterprises (“SMEs”). To ensure the affordability and accessibility of technologies, promote digital entrepreneurship and community well-being (SDG3), and protect digital rights, we propose data cooperatives [1,2] as a vehicle for secure, trusted, and sovereign data exchange [3,4]. In post-pandemic times, community/SME-led cooperatives can play a vital role by ensuring that supply chains to support digital commons are uninterrupted, resilient, and decentralized [5]. Digital commons and data sovereignty provide communities with affordable and easy access to information and the ability to collectively negotiate data-related decisions. Moreover, cooperative commons (a) provide access to the infrastructure that underpins the modern economy, (b) preserve property rights, and (c) ensure that privatization and monopolization do not further erode self-determination, especially in a world increasingly mediated by AI. Thus, governance plays a significant role in accelerating communities’/SMEs’ digital transformation and addressing their challenges. Cooperatives thrive on digital governance and standards such as open trusted Application Programming Interfaces (APIs) that increase the efficiency, technological capabilities, and capacities of participants and, most importantly, integrate, enable, and accelerate the digital transformation of SMEs in the overall process. This policy paper presents and discusses several transformative use cases for cooperative data governance. The use cases demonstrate how platform/data-cooperatives, and their novel value creation can be leveraged to take digital commons and value chains to a new level of collaboration while addressing the most pressing community issues. The proposed framework for a digital federated and sovereign reference architecture will create a blueprint for sustainable development both in the Global South and North.

The term artificial intelligence refers to the field of computer science that deals with the design and implementation of computer systems that mimic elements of human behavior that imply basic intelligence: learning, adaptability, inference, contextual understanding, and problem solving among others. Artificial intelligence is a point of intersection between multiple sciences such as computer science, psychology, philosophy, neurology, linguistics and engineering science, with the aim of synthesizing intelligent behavior, with elements of reasoning, learning and adaptation to the environment, while it is usually applied to specially made machines or computers.

Within rehabilitation, there is great need for a simple method to monitor wheelchair use, especially whether it is active or passive. For this purpose, an existing measurement technique was extended with a method for detecting self or attendant pushed wheelchair propulsion. The validity was determined by comparison with manual annotation of wheelchair use. Twenty-four amputation and stroke patients completed a semi-structured course of active and passive wheelchair use. Based on a machine learning approach, a method was developed that detected the type of movement. The machine learning method was trained based on the data of a single wheel sensor as well as a setup using an additional sensor on the frame. The method showed high accuracy (F1 = 0.886, frame and wheel sensor) even if only a single wheel sensor was used (F1 = 0.827). The developed and validated measurement method is ideally suited to easily determine wheelchair use and the corresponding activity level of patients in rehabilitation.

A modern binary executable is a composition of various networks. Control flow graphs are commonly used to represent an executable program in labeled datasets used for classification tasks. Control flow and term representations are widely adopted, but provide only a partial view of program semantics. This study is an empirical analysis of the networks composing malicious binaries in order to provide a complete representation of the structural properties of a program. This is accomplished by the measurement of structural properties of program networks in a malicious binary executable dataset. We demonstrate the presence of Scale-Free properties of network structure for program data dependency and control flow graphs, and show that data dependency graphs also have Small-World structural properties. We show that program data dependency graphs have a degree correlation that is structurally disassortative, and that control flow graphs have a neutral degree assortativity, indicating the use of random graphs to model the structural properties of program control flow graphs would show increased accuracy. By providing an increase in feature resolution within labeled datasets of executable programs we provide a quantitative basis to interpret the results of classifiers trained on CFG graph features. An increase in feature resolution allows for the structural properties of program classes to be analyzed for patterns as well as their component parts. By capturing a complete picture of program graphs we can enable theoretical solutions for the mapping a program's operational semantics to its structure.

Horticulture crops (fruit trees) had been grown and cultivated from ancient times in Central Asia. Few researchers have addressed the problem of this profitable sector in the former Great Silk Road, which was at the crossroads of trading avenues. Horticulture has received much attention in the last twenty years. To investigate the current state of research activity of horticulture in Central Asia, we downloaded 4205 English papers from the Scopus database between 2000-2020. We identified a total of 50 papers, and the last four years have witnessed significant growth in publication number, an average of 5 articles per year. Acta Horticulturea was one of the most productive journal. Institute of Plant Biology and Biotechnology (Almaty) and the Academy of Sciences of the Republic of Uzbekistan have worked productively to study high issues. United States Department of Agriculture and Swiss National Science Foundation with Karl Popper Foundation have supported scientific activity in the region. Top 15 highly cited articles were published within the framework of funded projects with international researchers. Researchers of Central Asia focused on walnut, grape and apple, studied on molecular level and cryopreservation of wild relatives for future use. Cherry, apricot, almond and pomegranate crops were less studied by researchers.

A considerable body of research exists on women in leadership and likewise on women in STEM (science, technology, engineering, mathematics) fields. However, the intersection of the two is terra incognita: women in leadership in STEM. At the most fundamental level, we don’t even have a solid idea of how many women hold leadership positions in STEM. This study determined the proportion of women in leadership positions in several academic STEM areas via a sampling of institutions across the United States and other countries. In every area studied, women held fewer leadership positions than the proportion of female PhDs in those fields. The proportion of women in non-STEM specific top academic leadership roles was also examined to see what proportion of those individuals leading academic institutions might have background in a STEM discipline and how that compares to men in the same positions. This study opens the door to exploring the experiences of women who lead in STEM, which is likely to promote women’s participation in these fields.

A significant number and range of challenges besetting sustainability can be traced to the actions and interactions of multiple autonomous agents (people mostly) and the entities they create (e.g., institutions, policies, social network) in the corresponding social-environmental systems (SES). To address these challenges, we need to understand decisions made and actions taken by agents, the outcomes of their actions, including the feedbacks on the corresponding agents and environment. The science of Agent-based Complex Systems—ACS science—has a significant potential to handle such challenges. The advantages of ACS science for sustainability are addressed by way of identifying the key elements and challenges in sustainability science, the generic features of ACS, and the key advances and challenges in modeling ACS. Artificial intelligence and data science promise to improve understanding of agents’ behaviors, detect SES structures, and formulate SES mechanisms.

Abstract This essay presents a real-world demonstration of the evidence-based science communication process, showing how it can be used to create scientific data visualizations for public audiences. Visualizing research data can be an important science communication tool. Maximizing its effectiveness has the potential to benefit millions of viewers. As with many forms of science communication, creators of such data visualizations typically rely on their own judgments and the views of the scientists providing the data to inform their science communication decision-making. But that leaves out a critical stakeholder in the communications pipeline: the intended audience. Here, we show the practical steps that our team - the Advanced Visualization Lab at the University of Illinois at Urbana-Champaign - has taken to shift towards more evidence-based practice to enhance our science communication impact. We do this using concrete examples from our work on two scientific documentary films, one on the theme of ‘solar superstorms’ and the other focusing on the black hole at the center of the Milky Way galaxy. We used audience research with each of these films to inform our strategies and designs. We describe how such research evidence informed our understanding of ‘what works and why’ with cinematic-style data visualizations for the public. We close the essay with our key ‘take home’ messages from this evidence-based science communication process.

This article identifies promising research areas on the PETROLEUM SCIENCE topic via bibliometric analysis of the 2018-2021 publications in the highly cited Journal of Petroleum Science and Engineering, which is included in the Journal Citation Reports Section: ENERGY & FUELS — Q2 Quartile, ENGINEERING, PETROLEUM — Q1 Quartile. Bibliometric metadata from Web of Science were used for 866 articles in 2018, 1,142 — in 2019, 1,138 — in 2020, and 1,832 in 2021. The clustering of articles was performed using the texts of the Title, Abstract, Keywords, and Keywords Plus fields. The demo version of the Lingo3G algorithm was used. For the two major clusters, the most promising research topics were determined by comparing the titles of the 350 most cited and 350 least cited articles for each year. The hypothesis that low-cited papers often have the same subject matter as high-cited papers of previous years is discussed.

Precision medicine emphasizes fine-grained diagnostics, taking individual variability into account to enhance treatment effectiveness. Parkinson's Disease (PD) heterogeneity among individuals is a proof that disease subtypes exist, and assigning individuals to subgroups is necessary for a better understanding of disease mechanisms and designing precise treatment approaches. The purpose of this study was to identify PD subtypes using RNA-Seq data in a combined pipeline including unsupervised machine learning, bioinformatics, and network analysis. 210 post mortem brain RNA-Seq samples from PD (n = 115) and Normal Controls (NC, n = 95) were obtained with a systematic data retrieval following PRISMA statements and a fully data-driven clustering pipeline was performed to identify PD subtypes. Bioinformatics and Network analyses were performed to characterize the disease mechanisms of the identified PD subtypes and to identify target genes for drug repurposing. Two PD clusters were identified and 42 DEGs were found (p.adjusted ≤ 0.01). PD clusters had significantly different gene network structures (p < 0.0001) and phenotype-specific disease mechanisms, highlighting the differential involvement of the Wnt/β-catenin pathway regulating adult neurogenesis. NEUROD1 was identified as a key regulator of gene networks and ISX9 and PD98059 were identified as NEUROD1-interacting compounds with disease-modifying potential, reducing the effects of dopaminergic neurodegeneration. This hybrid data analysis approach could enable precision medicine applications by providing insights for the identification and characterization of pathological subtypes. This workflow has proven useful on PD brain RNA-Seq, but its application to other neurodegenerative diseases is encouraged.

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

In order to establish a new discipline specializing in accident science from the perspective of safety science. Under the guidance of the current research theories and methods of safety science, combined with the research paradigm of humanities and social medicine, this paper puts forward new viewpoints, new theories and new models about accident research. First of all, through literature retrieval, this paper analyzes the relevant research results of accidents at home and abroad, and expounds the existing problems and the basic trend of accident science research. Secondly, it puts forward eight kinds of attribute relations of the accident, and makes clear the characteristics and connotation of the accident. In the study of accident types, a hierarchical classification model based on accident cognition is created for the first time. It also points out the logical relevance of five levels of accident science research and the realistic relevance of three levels. At the same time, according to the thought of science of science, this paper puts forward a new definition of safety under the thinking of accident science and other basic concepts related to safety science, and explains the connotation. In addition, it creates and constructs the basic concept of accident science, establishes the conceptual model of accident science, and points out the “3-4-5” model of accident science research and its connotation. Thirdly, draw lessons from the interdisciplinary paradigm to study the relevant theoretical basis and discipline classification relationship of accident science, and construct the tree of accident science. Finally, the research contents of three main aspects of accident science are summarized. The results show that the research results in this paper not only play a fundamental role in the basic construction of accident science, but also further enrich and perfect the discipline system of safety science, which has a certain theoretical significance.

Predatory research field in science is when an emerging scientific topic destroys current topics and characterizes a main scientific change. Predatory research field can be a basic driver of scientific and technological change that generates a 'creative destruction' in science and society in contexts of knowledge-based competition and rapid changes. The prediction of proposed theory of predatory research fields is that it destroys with a fast growth other research fields. The theoretical approach is tested here in research fields of large language models (LLM) by analyzing the transformers (a deep learning architecture based on the multi-head attention mechanism) proposed in 2017 and from November 2022 started main applications in generative artificial intelligence with innovations of BERT, ChatGPT, Microsoft Copilot (launched on February 7, 2023) and other natural language processing tools driven by AI technology for engaging conversations, gain insights, automate tasks, etc., etc. Statistical evidence suggests that growth rate of transformer technologies is 80.58%, a high level compared to all other research fields in machine learning (having a growth rate of 13.83%). Moreover, predatory research field of transformers has a destructive power such that all other domains in LLM from 2021 to 2023 have a general reduction of scientific growth. The impact of transformers is much more drastic of previous radical technologies such as CNN having a temporal growth rate of 0.16%, lower than 0.38% by transformers, ceteris paribus. These analysis reveals that transformers have characteristics to generate a radical scientific and technological change in a not-too-distant future. Overall, then, the study suggests that predatory research field on emerging topics and technologies can generate path-breaking innovations and the examination here can clarify the essential elements of the science dynamics for a better theory of scientific and technological change, providing also main implications for knowledge policy to support promising research fields and technologies to guide economic and social change.

Centrifuges are commonly required devices in medical diagnostics facilities as well as scientific laboratories. Although there are commercial and open source centrifuges, costs of the former and required electricity to operate the latter, limit accessibility in resource-constrained settings. There is a need for low-cost, human-powered, verified and reliable lab-scale centrifuge. This study provides the designs for a low-cost 100% 3-D printed centrifuge, which can be fabricated on any low-cost RepRap-class fused filament fabrication (FFF) or fused particle fabrication (FPF)-based 3-D printer. In addition, validation procedures are provided using a web camera and free and open source software. This paper provides the complete open source plans including instructions for fabrication and operation for a hand-powered centrifuge. This study successfully tested and validated the instrument, which can be operated anywhere in the world with no electricity inputs obtaining a radial velocity of over 1750rpm and over 50N of relative centrifugal force. Using commercial filament the instrument costs about US$25, which is less than half of all commercially available systems; however, the costs can be dropped further using recycled plastics on open source systems for over 99% savings. The results are discussed in the contexts of resource-constrained medical and scientific facilities.

Visits to coastal and inland-waterways have strong associations with many health and economic benefits but are also opportunities for exposure to environmental pollution. In England, few balanced survey samples exist which can facilitate accurate identification of correlates with recreation in blue spaces. This information could support risk assessment for likelihood of exposure and message targeting in case of environmental threats. We process data from a recent (2022-2023) and large (n = 177,551) survey of English residents to describe demographic, seasonal and regional correlates with any participation in water-based recreational activities in previous year (logistic regression model), and in the previous 28 days (zero-inflated negative binomial regression). Activities studied were: surfing and paddleboarding; open water swimming; scuba-diving and snorkelling; canoeing and kayaking; angling. Summer was associated with most frequent participation, winter with fewest sessions except for rowing. Anglers were group most likely to be socially vulnerable: less likely to be university educated, more likely to be disabled and especially likely to be male. People resident in rural locations were more likely to participate at least once in prior year, but did not have higher participation rates in the previous 28 days. Regional variations in specific activities may suggest where demand for water recreation is unmet. The results provide a reference description of outdoor water-recreationalists doing specific types of activity in England and may be useful when designing emergency messages and otherwise setting policy with regard to management of microbiological, radiological or chemical hazards in coastal and inland waters.

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chro-matography (LC), but choosing the ‘correct’ pipeline isn’t straight-forward. This tutorial, in con-junction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide explor-atory analysis in order to inform decision-making regarding costly and time-consuming down-stream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/ changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

There is great current interest in systems represented by non-Hermitian Hamiltonians, including a wide variety of real systems that may be dissipative and whose behaviour can be represented by a "phase" parameter that characterises the way "exceptional points" (singularities of various sorts) determine the system. These systems are briefly reviewed with an emphasis on their geometrical thermodynamics properties.

There is great current interest in systems represented by non-Hermitian Hamiltonians, including a wide variety of real systems that may be dissipative and whose behaviour can be represented by a "phase" parameter that characterises the way "exceptional points" (singularities of various sorts) determine the system. These systems are briefly reviewed with an emphasis on their geometrical thermodynamics properties.

Microalgae biomass is a budding raw material for the origination of food, fuel, and other value-added products. However, bulk production of microalgal biomass at commercial level is a herculean task for the current microalgal mass production technologies due to the undesirable contaminations by biological pollutants. These contaminants hamstring the production of microalgae biomass by debilitating the growth of cultures, crumble the quality of biomass and sometimes may crash the whole culture. The best utilization of the microalgae biomass at industrial level could be attained by avoiding various possible biological contaminations in mass cultivation system, understanding the contamination mechanisms, and the complex interactions of algae with other microorganisms. This review explores the various types of biological pollutants, their possible mode of infection along with mechanisms, different controlling methods to maintain desired microalgae culture.

The Open Access (OA) publishing model that is based on article processing charges (APC) is often associated with the potential for more transparency regarding the expenditures for publications. However, the extent to which transparency can be achieved depends not least on the completeness of data in APC monitoring systems. This article investigates two blind spots of the largest collection of APC payment information, OpenAPC. It aims to identify likely APC-liable publications for German universities that contribute to this system and for those that do not provide data to it. The calculation combines data from Web of Science, the ISSN-Gold-OA-list and OpenAPC. The results show that for the group of universities contributing to the monitoring system, more than half of the APC payments are not covered by it and the average payments for non-covered APCs is higher than for APCs covered by the system. In addition, the group of universities that does not contribute to OpenAPC accounts for two thirds of the number of APC-liable publications recorded for contributing universities. Regarding the size of these blind spots, the value of the monitoring system is limited at present.

This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS Graphical Modeler to calculate the shoreline change by End Point Rate method. The EPR4Q tries to fill the gap of user-friendly and free open-source tool for shoreline analysis in Geographic Information System environment, since the most used software - Digital Shoreline Analysis System (DSAS) - although is a free extension, is suited for commercial software. Besides, the best free open-source option to calculate EPR called Analyzing Moving Boundaries Using R (AMBUR), since it is a robust and powerful tool, the complexity and heavy processes can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR on different examples of shorelines found in nature, extracted from the U.S. Geological Survey Open-File. The obtained results of each tool were compared with Pearson correlation coefficient. The validation results indicate that the EPR4Q tool created acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, guarantying that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.