Open Repositories Research Articles

Mapping Open Access Publishing Trends in Central Universities of North East India: A Scientometric Approach

ABSTRACT The study analyses open access publications across the 10 central universities of North East India, focusing on publication trends, disciplinary distribution, citation impact, collaboration patterns, and open access licensing patterns. It compiles scholarly outputs from the OpenAlex API, integrating various data sources such as Microsoft Academic Graph, ORCID, Crossref, and Unpaywall. A total of 28,832 publications from 2004–2023 were collected and analyzed using statistical methods. The findings reveal a positive trend in research output across the represented universities, with an increasing emphasis on OA publications. However, the proportion of OA publications remains relatively low compared to closed access. Citation impact varies among universities, reflecting differences in research quality and visibility. Open repositories such as SSRN, Research Square, and Zenodo are crucial in promoting OA publishing. The study also reveals common usage of licenses such as CC-BY and CC-BY-NC-ND, with variations across universities. The impact on libraries is also discussed, emphasizing the role of libraries in promoting OA, supporting researchers, and contributing to scholarly communication advancement. This study provides valuable insights for policymakers, academic administrators, and researchers seeking to promote a culture of openness, collaboration, and innovation in scholarly communication within the region, country, and at the global level.

Make data flow: Understanding the (re)usability of research data

Opening data and making datasets reusable is an extremely important and urgent need, especially as we move towards more data-driven studies worldwide. Various stakeholders are involved in the life cycle of data (re)use, including data providers/sharers, data (re)users, data managers/curators, and funding agencies. In response to the divergent demands of these stakeholders, data repositories have been established to bridge their needs and facilitate the cycle of data sharing and (re)use. However, recent studies have shown that data is being shared and (re)used through open data repositories at much lower levels than expected. This underscores a fundamental and often overlooked challenge in research data management, inviting a deeper and more comparative examination of the perspectives of various stakeholders on data (re)use. Using a mixed-method approach that includes metadata analysis, interview-based qualitative studies, and quantitative bibliometric analysis, this project first delves into the facilitating and inhibiting factors affecting a dataset's potential for (re)use, considering the perspectives of both data (re)users and data curators. It will then identify and elucidate the disparities between data (re)users and data curators, proposing suggestions for future data curation efforts. The findings will prompt data curators and data repositories to re-examine their current metadata frameworks and data curation practices, aligning them more closely with the actual needs of data (re)users. Moreover, this effort will make the flow of research data moves more smoothly within our academic communities, and contribute to the building of a more inclusive, equitable, open, and user-friendly research infrastructure.

Ostrich eggshell bead-making in East Africa: a study of crafting in the El Molo community, Kenya

ABSTRACT Ostrich eggshell beads have been produced by African societies for over 50,000 years and are still used in East Africa and elsewhere, yet the only well-documented ethnographic record of their manufacture comes from San and Bakgalagadi communities in the Kalahari region of southern Africa. Even so, some aspects of their social significance have not been extensively explored. In particular, research on ostrich eggshell beads in southern Africa has historically focused largely on the use of beads in exchange contexts with less emphasis on their use in bodily adornment or the social contexts of manufacture and consumption. This paper reports on the production process and cultural significance of ostrich eggshell beads among the El Molo community of Marsabit County, Kenya. Undertaken as part of the British Museum’s Endangered Materials Knowledge Program (EMKP), this project used unstructured interviews, participatory videos, photography and note-taking to document the entire process of ostrich eggshell bead manufacture, including the collection of hammer stones (soit), the making of strings (arkat) for stringing the beads, the breaking of shells, shaping and rounding, stringing and the making of diverse beadwork items, including rumirumi, kukuti, karkato, lilim and rarak. The social significance of these various items is also reported. This study helps to preserve this knowledge and the continuation of the craft in the community, while also providing data for the enrichment of interpretations of ostrich eggshell beads from archaeological collections in the region, which have received inadequate research attention. The project represents the first detailed ethnographic work ever undertaken on ostrich eggshell beads in East Africa. The materials produced will be available on the EMKP open access online repository.

A lumped kinetic model and experimental investigation of poly(ethylene terephthalate) condensed-phase pyrolysis

In a circular economy perspective, plastic waste (PW) is a valuable source of chemicals and energy vectors. Understanding the effect of poly(ethylene terephthalate) (PET) in thermochemical valorisation of complex and contaminated PW mixtures requires definition of suitable kinetic models. This work proposes a condensed-phase semi-detailed kinetic model for PET pyrolysis based on a consolidated functional group approach already validated for other polymers. The reaction network is built considering studies on thermal degradation of PET, model compounds, and small gas-phase esters. Reaction pathways proposed in the scientific literature are critically assessed through analogy with high accuracy gas-phase calculation and are complemented by new proposed pathways. The resulting model couples molecular and radical mechanism and consists of 85 gas and liquid species with 700 liquid-phase reactions, being suitable for CFD simulations of PW pyrolysis upon further reduction. This work also presents new experimental data including TG analysis coupled with GC × GC speciation measurements and elemental characterization of the solid residue. The model is validated by comparison with the new experimental data and a comprehensive set of literature data in terms of characteristic degradation times and detailed product yields. The present work expands the relevant data available for chemistry models development and extends the CRECK kinetic framework for thermochemical recycling of PW mixtures. The proposed kinetic model is attached as Supplementary material and freely available as an open GitHub repository.

Standardizing Vehicle Travel Speed Data for Road Safety

ABSTRACTIn 2022, almost 50,000 people died in road crashes in the United States, with speeding implicated in 29% of these fatalities. Despite known links between vehicle speed and crash occurrence and severity, there are no federal guidelines for collecting vehicle travel speed (VTS) data. Cities with open VTS data are using unstandardized datasets, which complicates large‐scale and cross‐jurisdictional analysis. We conducted a qualitative assessment of open data repositories for the 25 largest U.S. cities, using a framework of knowledge representation, evaluated twelve metadata components, and determined the potential usability of these datasets. Our knowledge representation framework includes five data elements: speed metric, timestamp, geospatial representation, posted speed and vehicle type. Findings show that one‐quarter of these cities have open VTS datasets. Of those cities, none has a VTS dataset containing all the elements defined in our framework. This suggests the need to design information policy standards for the collection and sharing of open VTS data.

RadLab: An open science resource for radiation studies relevant to human spaceflight

In response to the growing need of the space life sciences community for a publicly available single access point for radiation physics data relevant to human space exploration, an open data repository and analysis platform, RadLab, has been developed. RadLab consists of a database and a user-friendly data retrieval, visualization, and analysis toolkit, including a graphical user interface (GUI) and an application programming interface (API). RadLab complements the space biology data in the NASA Open Science Data Repository (OSDR) and aims to provide open, centralized access to radiation physics data relevant to spaceflight. Analysis tools contained in RadLab will facilitate intercomparison among different detectors and radiation data sets to better understand the radiation environment in spacecraft and planetary habitats, and facilitate the use of space radiation data by space biology investigators and radiation instrument developers.

Value of institutional repositories and their role in ethical scholarly communication

An institutional repository (IR) serves as an open access digital repository to showcase the academic output of that institute’s faculty members, staff, and students. The IR is important for scientifically peripheral countries whose research output is poorly represented on major bibliographic databases. IRs may function on a voluntary basis, in which academics are encouraged – rather than mandated – to self-archive copies of their work, either published papers, theses, conference proceedings, or other scholarly output. IRs can thus serve as a proxy metric to highlight institutes’ productivity. However, absent a mandatory archival requirement, content in IRs may be skewed, and absent motivational objectives, only conscientious academics will practice self-archival. Conversely, mandatory archival might be met with resistance, especially if free, viable and sustainable alternatives exist, such as academic social networking sites (e.g., ResearchGate) or preprint servers. Thus, investment in the creation of an IR will need to offer greater value and benefit than these alternative platforms. Not all output by an institute’s academics may be scholarly, such as publications in predatory venues, or publication of pseudoscience, fake science or fraudulent science. IR managers, including librarians, will thus also need to faithfully represent retracted and grey literature. These latter challenges might discourage institutions from establishing an IR, or might demotivate the maintenance of existing IRs because ethical scholarly communication ultimately requires the recognition and archival of both scholarly and unscholarly output.

TopoHub: Synthetic global-scale backbone networks topologies

This article introduces the latest features and enhancements in TopoHub, an open repository for network topologies used in networking research. The major update includes a new collection of global-scale backbone topologies, generated based on population density and incorporating submarine communication cables. The web interface has been upgraded to support interactive exploration of networks, including panning and zooming. Additionally, the new version addresses usability improvements and bug fixes informed by user feedback, enhancing the overall functionality and user experience of the platform.

Deep Reinforcement Learning using Deep-Q-Network for Global Maximum Power Point Tracking: Design and Experiments in Real Photovoltaic Systems

This paper presents a methodology for integrating Deep Reinforcement Learning (DRL) using a Deep-Q-Network (DQN) agent into real-time experiments to achieve the Global Maximum Power Point (GMPP) of Photovoltaic (PV) systems under various environmental conditions. Conventional methods, such as the Perturb and Observe (P&O) algorithm, often become stuck at the Local Maximum Power Point (LMPP) and fail to reach the GMPP under Partial Shading Conditions (PSC). The main contribution of this work is the experimental validation of the DQN agent’s implementation in a synchronous DC-DC Buck converter (step-down converter) un- der both uniform and PSC conditions. Additionally, we establish a testing pipeline for DRL models. The DQN agent’s performance is evaluated alongside the P&O algorithm. Results consistently indicate the DQN agent’s superiority over the P&O algorithm in all simulated scenarios. Although this trend does not entirely replicate in real-world test setups, significant results are observed. Specifically, in PSC sce- narios where the P&O algorithm becomes trapped at an LMPP, the DQN algorithm extracts up to 63.5% more power than the P&O algorithm. An open repository is available, containing PCB schematic designs and layouts, along with the code used for model training and deployment.

Impacts of Open Access and Open Repositories on Acquisitions and Collection Development

Impacts of Open Access and Open Repositories on Acquisitions and Collection Development

Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides.

Peptides are bioactive molecules whose functional versatility in living organisms has led to successful applications in diverse fields. In recent years, the amount of data describing peptide sequences and function collected in open repositories has substantially increased, allowing the application of more complex computational models to study the relations between the peptide composition and function. This work introduces AMP-Detector, a sequence-based classification model for the detection of peptides' functional biological activity, focusing on accelerating the discovery and de novo design of potential antimicrobial peptides (AMPs). AMP-Detector introduces a novel sequence-based pipeline to train binary classification models, integrating protein language models and machine learning algorithms. This pipeline produced 21 models targeting antimicrobial, antiviral, and antibacterial activity, achieving average precision exceeding 83%. Benchmark analyses revealed that our models outperformed existing methods for AMPs and delivered comparable results for other biological activity types. Utilizing the Peptide Atlas, we applied AMP-Detector to discover over 190,000 potential AMPs and demonstrated that it is an integrative approach with generative learning to aid in de novo design, resulting in over 500 novel AMPs. The combination of our methodology, robust models, and a generative design strategy offers a significant advancement in peptide-based drug discovery and represents a pivotal tool for therapeutic applications.

An open GNSS spoofing data repository: characterization and impact analysis with FGI-GSRx open-source software-defined receiver

Spoofing is becoming a prevalent threat to the users of Global Navigation Satellite Systems (GNSS). It is important to deepen our understanding of spoofing attacks and develop resilient techniques to effectively combat this threat. Detecting and mitigating these attacks requires thorough testing, typically conducted in a laboratory environment through the establishment of a spoofing test-bed. The complexity, cost and resource demands of creating such a test-bed underscore the necessity of utilizing openly available datasets. To address this need, this paper introduces a new GNSS spoofing data repository from Finnish Geospatial Research Institute (FGI) named hereafter as ‘FGI-SpoofRepo’. This data repository consists of raw In-phase and Quadrature (I/Q) data of live recordings of GPS L1 C/A, Galileo E1, GPS L5, and Galileo E5a signals. These datasets encompass three distinct types of spoofing characteristics (synchronous, asynchronous, and meaconing), making them very useful example candidates of open data for testing the performance of any anti-spoofing techniques (be it detection or mitigation). The inclusion of live signals in multiple GNSS frequencies and the presence of cryptographic signatures in Galileo E1 signal make these datasets potential benchmarks for assessing the resilience performance of multi-frequency multi-constellation receivers. The analysis of the datasets is carried out with an open-source MATLAB-based software-defined receiver, FGI-GSRx. An updated version of FGI-GSRx, equipped with the necessary modifications for processing and analyzing the new datasets, is released alongside the datasets. Therefore, the GNSS research community can utilize the open-source FGI-GSRx or any third-party SDR to process the publicly available raw I/Q data for implementation, testing and validation of any new anti-spoofing technique. The results show that time-synchronous spoofing seamlessly takes over positioning solution, while time-asynchronous spoofing acts as noise or in some cases, completely prevent the receiver from providing a positioning solution. Signal re-acquisition during an ongoing spoofing attack (cold start), the receiver tends to lock onto the spoofing signal with the highest peak, posing a potential threat to GNSS receivers without assisted information. Overall, this research aims to advance the understanding of complex spoofing attacks on GNSS signals, providing insight into enhancing resilience in navigation systems.

Assessing the Transparency of Methods in Scientific Reporting

Enhancing transparency in scientific reports is crucial to foster trust, facilitate reproducibility, and ensure the integrity of research, ultimately advancing the progress of knowledge and innovation. To devise strategies for enhancing transparency in scientific reports, the initial step is to assess the current state: to objectively measure the current level of transparency, identifying its shortcomings and associated factors, and to gauge improvements, for instance, following interventions. Here we present a new tool and a proof of concept to this endeavor. Using a checklist, we evaluated the methods transparency of a corpus of 180 papers published in 2011 and 2021 in five top-tier psychology journals. We specifically focused on the materials, procedures, and characteristics of population and sample. We summarized the level of transparency in the methods of each paper with the Transparency Of Methods (TOM) score. This score consists of the proportion of relevant information regarding the method that is available to the reader of a scientific report, either in the main text of the paper, or the appendixes, supplementary materials, and online open repository. It ranges from 0 (i.e., no transparency in the relevant aspects of the methods and materials) to 1 (i.e., the scientific report is fully transparent in all the relevant materials and methods). The results affirm TOM’s potential for assessing the transparency of scientific reporting and offer two snapshots of transparency in the methods of published papers, a decade apart. While they indicate progress has been made, there remains room for further enhancements and highlight specific areas that require attention. In conclusion, this work underscores the ongoing need for improvement in methods’ transparency and introduces a valuable tool, demonstrating its applicability as a means to evaluate the transparency of scientific reports.

The IGCP 714 Project “3GEO – Geoclimbing & Geotrekking in Geoparks” – Selection of Geodiversity Sites Equipped for Climbing for Combining Outdoor and Multimedia Activities

The IGCP 714 project “3GEO – Geoclimbing & Geotrekking in Geoparks” is financed by the International Geoscience Programme (IGCP) and supported by the International Union of Geological Sciences (IUGS). In this paper, we report on the results of the first phase of the project focused on the criteria to be adopted to select geodiversity sites equipped for climbing or trekking. The selection of geoclimbing sites and geotrekking routes is aimed at combining multimedia tools and outdoor activities for Geosciences promotion and conservation in UNESCO Global Geoparks, aspiring geoparks or geoparks project, and also in protected areas featuring geoheritage sites. Indeed, both outdoor activities and multimedia tools favour the pursuing of many of the United Nations Sustainable Development Goals (e.g., 3, 4, 8, 11). An international consortium of geoscientists from 12 different countries selected, through the proposed procedure, 22 geoclimbing sites, and then they also detected 30 geotrekking routes mirroring Earth geodiversity. At some test sites (geoclimbing and geotrekking) multimedia tools and digital outcrop models have been developed through different methodological approaches (e.g., Structure from Motion and Multi-View Stereo photogrammetry), to open the way to the second part of the project still in progress. These sites and the relative virtual models are herein also shown. The final aim of the IGCP 714 project is indeed to create an open data repository (digital outcrop models, videos, virtual tours, photos, scientific information, and interpretations) to upload data of the selected sites to mirror Earth geodiversity for different users including tourists and school groups.

A hermeneutic dialogical understanding of data reuse across different access regimes

ABSTRACT Policy and scholarly discourse emphasizing the panacea of Open (research) Data shapes expectations, and directs and legitimizes investments in data technologies and infrastructures. This is driven by the hope that Open Data will quicken the pace of research and innovation through data reuse, and that they do so more effectively than other access regimes, such as stewarded and proprietary data. Drawing on Leonelli’s relational framework and Gadamer’s hermeneutical conceptualization of a horizon of meanings, data reuse can be understood as a fitting process. In the latter, a researcher engages in a hermeneutical dialogical interaction with the data’s affordances with the goal of making a scientific contribution. Moreover, the fitting process takes place within a researcher’s bounded individual horizon (BIH), defined as an intentional orientation towards the future; it is made up of the relations and circumstances that modulate each researcher’s unique situation. Seen thus, data reuse is likely to result from the persistence of a researcher’s desire or need to make a scientific contribution, independently of the data access regime. What is more, the necessary interaction between potential reusers and data curators or owners can open up the interpretive affordances of data in the context of proprietary and stewarded data, making data more mutable compared to the relative immutability of data in open repositories. Accordingly, stewarded data, with the proper curation and digital preservation services, might provide a more sustainable form of sharing and reusing data where privacy is at stake.

Author self-archiving in open access institutional repositories for awareness creation in universities

Author self-archiving in open access institutional repositories for awareness creation in universities

UVLHub: A feature model data repository using UVL and open science principles

Feature models are the de facto standard for modelling variabilities and commonalities in features and relationships in software product lines. They are the base artefacts in many engineering activities, such as product configuration, derivation, or testing. Concrete models in different domains exist; however, many are in private or sparse repositories or belong to discontinued projects. The dispersion of knowledge of feature models hinders the study and reuse of these artefacts in different studies. The Universal Variability Language (UVL) is a community effort textual feature model language that promotes a common way of serializing feature models independently of concrete tools. Open science principles promote transparency, accessibility, and collaboration in scientific research. Although some attempts exist to promote feature model sharing, the existing solutions lack open science principles by design. In addition, existing and public feature models are described using formats not always supported by current tools. This paper presents ▪ , a repository of feature models in UVL format. ▪ provides a front end that facilitates the search, upload, storage, and management of feature model datasets, improving the capabilities of discontinued proposals. Furthermore, the tool communicates with Zenodo – one of the most well-known open science repositories – providing a permanent save of datasets and following open science principles. ▪ includes existing datasets and is readily available to include new data and functionalities in the future. It is maintained by three active universities in variability modelling.

Co-designing a wiki-based community knowledge management system for personal science.

Personal science is the practice of addressing personally relevant health questions through self-research. Implementing personal science can be challenging, owing to the need to develop and adopt research protocols, tools and methods. While online communities can provide valuable peer support, tools for systematically accessing community knowledge are lacking. The objective of this study is to apply a participatory design process involving a community of personal science practitioners to develop a peer-produced knowledge base that supports the needs of practitioners as consumers and contributors of knowledge. The process led to the development of the Personal Science Wiki, an open repository for documenting and accessing individual self-tracking projects while facilitating the establishment of consensus knowledge. After initial design iterations and a field testing phase, we performed a user study with 21 participants to test and improve the platform, and to explore suitable information architectures. The study deepened our understanding of barriers to scaling the personal science community, established an infrastructure for knowledge management actively used by the community and provided lessons on challenges, information needs, representations and architectures to support individuals with their personal health inquiries.

Teaching bioinformatics through the analysis of SARS-CoV-2: project-based training for computer science students.

We learn more effectively through experience and reflection than through passive reception of information. Bioinformatics offers an excellent opportunity for project-based learning. Molecular data are abundant and accessible in open repositories, and important concepts in biology can be rediscovered by reanalyzing the data. In the manuscript, we report on five hands-on assignments we designed for master's computer science students to train them in bioinformatics for genomics. These assignments are the cornerstones of our introductory bioinformatics course and are centered around the study of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). They assume no prior knowledge of molecular biology but do require programming skills. Through these assignments, students learn about genomes and genes, discover their composition and function, relate SARS-CoV-2 to other viruses, and learn about the body's response to infection. Student evaluation of the assignments confirms their usefulness and value, their appropriate mastery-level difficulty, and their interesting and motivating storyline. The course materials are freely available on GitHub at https://github.com/IB-ULFRI.

A Systematic Literature Review on Leaf Disease Recognition Using Computer Vision and Deep Learning Approach

Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability. Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers' model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed. Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance. Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models. Conclusion: While no singular ‘best' model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies. Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review