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The publication is an important stage in a study. However, the publication stage in an accredited journal must go through a peer-reviewed process and sometimes requires a fee. The preprint server is a media archive for the distribution and distribution of articles that are available free of charge, enabling rapid distribution and use. Currently, there are many preprint servers available which are also recognized by peer-reviewed journals such as Arvix, Academia, Researchgate, OSF and in Indonesia itself there are INA-Rxiv servers. This article aims to explain the use of the preprint server as a publication media for scientific work among academics. The research method used is survey research on all academics in Indonesia. The results of the study showed that 42% of academics did not recognize the preprint server, 58% did not understand the function of the preprint server as a media publication that was fast, extensive, minimal in cost, accessible to the whole world and had DOI. They think the articles that have been published in the preprint can no longer be proposed to be published to an accredited journal. Ignorance causes its user to be very minimal, with only 24% of all academics filling out the questionnaire. The majority prefer to look directly for accredited journals without considering the time and cost required until the article is published.

Given the patient to doctor ratio of 50,000:1 in low income and middle-income countries, there is a need for automated heart sound classification system that can screen the Phonocardiogram (PCG) records in real-time. This paper proposes deep neural network architectures such as a one-dimensional convolutional neural network (1D-CNN) and Feed-forward Neural Network (F-NN) for the classification of unsegmented phonocardiogram (PCG) signal. The research paper aims to automate the feature engineering and feature selection process used in the analysis of the PCG signal. The original PCG signal is down-sampled at 500 Hz. Then they are divided into smaller time segments of 6 s epochs. Savitzky–Golay filter is used to suppress the high-frequency noises in the signal by data point smoothening. The processed data was then provided as an input to the proposed deep neural network (DNN) architectures. 1081 PCG records were used for training and validating the proposed DNN models. The Feed-forward Neural Network model with five hidden layers provided a better overall accuracy of 0.8565 with a sensitivity of 0.8673, and specificity of 0.8475. The balanced accuracy of the model was found to be 0.8574. The performance of the model was also studied using the Receiver Operating Characteristic (ROC) plot, which produced an Area Under the Curve (AUC) value of 0.857. The classification accuracy of the proposed models was compared to the related works on PCG signal analysis for cardiovascular disease detection. The DNN models studied in this study provided comparable performance in heart sound classification without the requirement of feature engineering and segmentation of heart sound signals.

To tackle the current global environmental crisis, operational science‐policy interfaces are needed. The Intergovernmental Science‐policy Platform on Biodiversity and Ecosystem Services (IPBES) provides governments with policy advice via its assessment reports. To expand the evidence‐base and to support the uptake of IPBES products, participation needs to be balanced across the globe. We found imbalance in authors’ distribution at both the UN regional and country level. It is more pronounced for IPBES‐related scientific papers than for the IPBES global assessment. The more detached from politics the decision of getting involved is, the more imbalanced the representation of the regions becomes. To improve the IPBES’ geographical balance, a strategy to increase the number of active member states is called for. We argued that without explicit efforts to reach the balance—for example, providing an attractive research environment for excellent researchers in their home country, improving cooperation among countries across the UN regions, and granting publication opportunity for all authors—the idea of geographic equality diminishes.

While controls over the Earth’s climate system have undergone rigorous hypothesis-testing since the 1800s, questions over the scientific consensus of the role of human activities in modern climate change continue to arise in public settings. We update previous efforts to quantify the scientific consensus on climate change by searching the recent literature for papers sceptical of anthropogenic-caused global warming. From a dataset of 88125 climate-related papers published since 2012, when this question was last addressed comprehensively, we examine a randomized subset of 3000 such publications. We also use a second sample-weighted approach that was specifically biased with keywords to help identify any sceptical peer-reviewed papers in the whole dataset. We identify four sceptical papers out of the sub-set of 3000, as evidenced by abstracts that were rated as implicitly or explicitly sceptical of human-caused global warming. In our sample utilizing pre-identified sceptical keywords we found 28 papers that were implicitly or explicitly sceptical. We conclude with high statistical confidence that the scientific consensus on human-caused contemporary climate change—expressed as a proportion of the total publications—exceeds 99% in the peer reviewed scientific literature.

Purpose Systems to support literature review (LR) and manuscript preparation tend to focus on only one or two of the tasks involved. The purpose of this paper is to describe an intervention framework that redesigns a particular set of tasks, allowing for interconnectivity between the tasks and providing appropriate user interface display features for each task in a prototype system. Design/methodology/approach A user evaluation study was conducted on the prototype system. The system supports the three tasks: building a reading list (RL) of research papers, finding similar papers based on a set of papers and shortlisting papers from the final RL for inclusion in manuscript based on article type. A total of 119 researchers who had experience in authoring research papers, participated in the evaluation study. They had to select one of the provided 43 topics and execute the tasks offered by the system. Three questionnaires were provided for evaluating the tasks and system. Both quantitative and qualitative analyses were performed on the collected evaluation data. Findings Task redesign aspects had a positive impact in user evaluation for the second task of finding similar papers while improvement was found to be required for the first and third tasks. The tasks interconnectivity features seed basket and RL were helpful for the participants in conveniently searching for papers within the system. Two of the four proposed informational display features, namely, information cue labels and shared co-relations were the most preferred features of the system. Student user group found the task recommendations and the overall system to be more useful and effective than the staff group. Originality/value This study validates the importance of interconnected task design and novel informational display features in accentuating task-based recommendations for LR and manuscript preparatory tasks. The potential for improvement in recommendations was shown through the task redesign exercise where new requirements for the tasks were identified. The resultant prototype system helps in bridging the gap between novices and experts in terms of LR skills.

We study the citation network of scientific articles in the hypothesis that the likelihood of connecting to a node depends on the node's degree, a mechanism called the preferential attachment. We show that the rate at which articles acquire citations linearly depends on the number of citations already received and we find the functional form of the dependence basing on the analysis of the dynamic source data. We also investigate the effect of the article age on receiving citations and show that there is the exponential decay of interest to old articles. Nevertheless the distribution of citations has the power-law form.

This paper aims to provide an understanding of designing a problem statement that must be present in every research proposal or other scientific work. A study, from proposals to scientific papers, requires a problem statement. The design of the program statement is basically always present from research interests. Objectives as a translation of the problem statement will be described through a methodology that matches the answers to be given to the interests. In the industry 4.0 era, problem statement in research prioritize innovation or change to improve welfare.

Peer review is a well-established cornerstone of the scientific process, yet it is not immune to biases like status bias, which we explore in this paper. Merton described this bias as prominent researchers getting disproportionately great credit for their contribution, while relatively unknown researchers get disproportionately little credit [R. K. Merton, Science 159, 56–63 (1968)]. We measured the extent of this bias in the peer-review process through a preregistered field experiment.We invited more than 3,300 researchers to review a finance research paper jointly written by a prominent author (a Nobel laureate) and by a relatively unknown author (an early career research associate), varying whether reviewers saw the prominent author’s name, an anonymized version of the paper, or the less-well-known author’s name. We found strong evidence for the status bias: More of the invited researchers accepted to review the paper when the prominent name was shown, and while only 23% recommended “reject” when the prominent researcher was the only author shown, 48% did so when the paper was anonymized, and 65% did when the little-known author was the only author shown. Our findings complement and extend earlier results on double-anonymized vs. single-anonymized review [R. Blank, Am. Econ. Rev. 81, 1041–1067 (1991); M. A. Ucci, F. D’Antonio, V. Berghella, Am. J. Obstet. Gynecol. MFM 4, 100645 (2022)].

Humans learn about the world by collectively acquiring information, filtering it, and sharing what we know. Misinformation undermines this process. The repercussions are extensive. Without reliable and accurate sources of information, we cannot hope to halt climate change, make reasoned democratic decisions, or control a global pandemic. Most analyses of misinformation focus on popular and social media, but the scientific enterprise faces a parallel set of problems—from hype and hyperbole to publication bias and citation misdirection, predatory publishing, and filter bubbles. In this perspective, we highlight these parallels and discuss future research directions and interventions.