Curation Process Research Articles

In Sharing We Trust. Taking Advantage of a Diverse Consortium to Build a Transparent Data Service in Catalonia

The Consorci de Serveis Universitaris de Catalunya (CSUC) is a consortium that serves 13 universities and 33 research centers in Catalonia and neighboring communities. In 2017 the Consortium created an Open Science department to collaborate with universities and research centers on facilitating the adoption of Open Science requirements. Even though CSUC also offers services to researchers directly (for example, its supercomputing resources), this report will focus on CSUC’s work with its member institutions to create and offer data management services. We will explain how CSUC has led the creation of a robust shared governance system, and how it takes advantage of the diversity of its members to create useful, high quality, and transparent services for all researchers in the Catalan research system. Through sharing each other’s experiences, values and priorities, the result is better than separate ad-hoc solutions. The process also creates a community of practitioners that develop expertise together with the help of professional development opportunities organized by CSUC, like recurrent self-learning labs focused on data curation tools, techniques and processes.

In Sharing We Trust. Taking Advantage of a Diverse Consortium to Build a Transparent Data Service in Catalonia

The Consorci de Serveis Universitaris de Catalunya (CSUC) is a consortium that serves 13 universities and 33 research centers in Catalonia and neighboring communities. In 2017 the Consortium created an Open Science department to collaborate with universities and research centers on facilitating the adoption of Open Science requirements. Even though CSUC also offers services to researchers directly (for example, its supercomputing resources), this report will focus on CSUC’s work with its member institutions to create and offer data management services. We will explain how CSUC has led the creation of a robust shared governance system, and how it takes advantage of the diversity of its members to create useful, high quality, and transparent services for all researchers in the Catalan research system. Through sharing each other’s experiences, values and priorities, the result is better than separate ad-hoc solutions. The process also creates a community of practitioners that develop expertise together with the help of professional development opportunities organized by CSUC, like recurrent self-learning labs focused on data curation tools, techniques and processes.

AECuration: Automated event curation for spike sorting.

Spike sorting is a commonly used analysis method for identifying single-units and multi-units from extracellular recordings. The extracellular recordings contain a mixture of signal components, such as neural and non-neural events, possibly due to motion and breathing artifacts or electrical interference. Identifying single and multi-unit spikes using a simple threshold-crossing method may lead to uncertainty in differentiating the actual neural spikes from non-neural spikes. The traditional method for classifying neural and non-neural units from spike sorting results is manual curation by a trained person. This subjective method suffers from human error and variability and is further complicated by the absence of ground truth in experimental extracellular recordings. Moreover, the manual curation process is time consuming and is becoming intractable due to the growing size and complexity of extracellular datasets. To address these challenges, we, for the first time, present a novel automatic curation method based on an autoencoder model, which is trained on features of simulated extracellular spike waveforms. The model is then applied to experimental electrophysiology datasets, where the reconstruction error is used as the metric for classifying neural and non-neural spikes. As an alternative to the traditional frequency domain and statistical techniques, our proposed method offers a time-domain evaluation model to automate the analysis of extracellular recordings based on learned time-domain features. The model exhibits excellent performance and throughput when applied to real-world extracellular datasets without any retraining, highlighting its generalizability. This method can be integrated into spike sorting pipelines as a pre-processing filtering step or a post-processing curation method.

Mars Sample Return: From collection to curation of samples from a habitable world

NASA’s Mars 2020 mission has initiated collection of samples from Mars’ Jezero Crater, which has a wide range of ancient rocks and rock types from lavas to lacustrine sedimentary rocks. The Mars Sample Return (MSR) Campaign, a joint effort between NASA and ESA, aims to bring the Perseverance collection back to Earth for intense scientific investigation. As the first return of samples from a habitable world, there are important challenges to overcome for the successful implementation of the MSR Campaign from the point of sample collection on Mars to the long-term curation of the samples on Earth. In particular, the successful execution of planetary protection protocols adds well-warranted complexity to every step of the process from the two MSR Program flight elements to the ground element at the sample receiving facility (SRF). In this contribution, we describe the architecture of the MSR Campaign, with a focus on infrastructure needs for the curation (i.e., the clean storage, processing, and allocation) of pristine Martian samples. Curation is a science-enabling and planetary protection-enabling activity, and the curation practices described in this contribution for the SRF and any long-term curation facility will enable the sample safety assessment, initial scientific investigations of the samples, and establish the MSR collection as a scientific resource that will enable generations of science and discovery through studies of the returned Mars samples. The planetary protection and curation processes established for MSR will provide critical insights into potential future sample return missions from other habitable worlds like Enceladus and Europa.

Primary Antibody Deficiencies: Curation Of Gene-Disease Relationships Using A ClinGen Validation Framework.

The Clinical Genome Resource (ClinGen) is an international collaborative effort between scientists and clinicians, diagnostic and research laboratories as well as the patient community. Using a standardized framework, ClinGen has established guidelines to classify gene-disease relationships as Definitive, Strong, Moderate, and Limited based on available scientific and clinical evidence. When the genetic and functional evidence for a gene-disease relationship has conflicting interpretations or contradictory evidence, they can be Disputed or Refuted. The ClinGen Antibody Deficiencies Gene Curation Expert Panel (AD-GCEP), using the ClinGen framework, has classified genes related to Primary Antibody Deficiencies (PAD) that primarily affect B cell development and/or function and accounts for the largest proportion of Inborn Errors of Immunity (IEI) or Primary Immunodeficiencies (PIDs). The AD-GCEP curated a total of 65 genes associated with humoral immune defects to validate 74 gene-disease relationships. Of these, 40 gene-disease relationships were classified as Definitive, 1 as Strong, 16 as Moderate, 15 as Limited, and two as Disputed. The curation process involved the review of 490 patient records and 3,546 associated Human Phenotype Ontology (HPO) entries. The most frequently observed HPO terms related to PAD was decreased circulating antibody (Ab) level, pneumonia and lymphadenopathy. These curations represent the first effort by the Immunology Clinical Domain Working Group (CDWG) of ClinGen to provide a comprehensive genetic and phenotypic revision of genetic disorders affecting humoral immunity, and these were reviewed and approved by experts in the field. The curations are publicly available at www.clinicalgenome.org.

Using text-mining to measure the scientific impact and legacy of ELIXIR, a distributed research infrastructure for life science data

Background ELIXIR is a pan-European public-funded research infrastructure dedicated to life science data. As such, it must demonstrate public value to its funders and stakeholders. We present methods to inventory research publications linked to ELIXIR that have received funding and support, as well as related citation metrics, used as performance metrics for these audiences. Methods To overcome challenges inherent in ELIXIR’s distributed structure, and the fact that those publishing ELIXIR-supported work are typically working part-time on ELIXIR matters, a semi-automated approach, consisting of text-mining followed by manual curation, is presented. A country-level case study (ELIXIR Italy) refines and expands the methods, notably by introducing more granularity in the curation process (e.g. considering all national-level grants, examining affiliations to report publication per institute) and by additionally looking at the scientific impact of the resources developed and operated by the Italian Node of ELIXIR. Results Overall, the methods described in this article have shown to: (1) be repeatable with acceptable levels of accuracy and consistency (notably across curators); (2) require reasonable effort in terms of curation of monthly ‘harvests’ of publications (obtained by text-mining); and (3) to be well-adapted to ELIXIR’s distributed nature. Conclusions Concrete examples are provided of downstream uses of the inventoried publications and their citations, both for ELIXIR as a whole and for the Italian case study. Limitations of the methods are discussed, particularly the challenges associated with using an ‘Open literature’ database (Europe PMC) for the text-mining, and the constraints related to curation capacity. The methods, along with the valuable lessons learned during their development, are sufficiently generic and pragmatic enough to be readily adapted by other similar research infrastructures.

Two decades of three-dimensional movement data from adult female northern elephant seals

Northern elephant seals (Mirounga angustirostris) have been integral to the development and progress of biologging technology and movement data analysis, which continue to improve our understanding of this and other species. Adult female elephant seals at Año Nuevo Reserve and other colonies along the west coast of North America were tracked annually from 2004 to 2020, resulting in a total of 653 instrument deployments. This paper outlines the compilation and curation process of these high-resolution diving and location data, now accessible in two Dryad repositories. The code used for data processing alongside the corresponding workflow is available through GitHub and Zenodo. This data set represents 3,844,927 dives and 596,815 locations collected from 475 individual seals with 178 repeat samplings over 17 years. We anticipate that these data will stimulate further analysis and investigation into elephant seal biology and aid in developing new analytical approaches for large marine predators.

MCHelper automatically curates transposable element libraries across eukaryotic species.

The number of species with high-quality genome sequences continues to increase, in part due to the scaling up of multiple large-scale biodiversity sequencing projects. While the need to annotate genic sequences in these genomes is widely acknowledged, the parallel need to annotate transposable element (TE) sequences that have been shown to alter genome architecture, rewire gene regulatory networks, and contribute to the evolution of host traits is becoming ever more evident. However, accurate genome-wide annotation of TE sequences is still technically challenging. Several de novo TE identification tools are now available, but manual curation of the libraries produced by these tools is needed to generate high-quality genome annotations. Manual curation is time-consuming, and thus impractical for large-scale genomic studies, and lacks reproducibility. In this work, we present the Manual Curator Helper tool MCHelper, which automates the TE library curation process. By leveraging MCHelper's fully automated mode with the outputs from three de novo TE identification tools, RepeatModeler2, EDTA, and REPET, in the fruit fly, rice, hooded crow, zebrafish, maize, and human, we show a substantial improvement in the quality of the TE libraries and genome annotations. MCHelper libraries are less redundant, with up to 65% reduction in the number of consensus sequences, have up to 11.4% fewer false positive sequences, and up to ∼48% fewer "unclassified/unknown" TE consensus sequences. Genome-wide TE annotations are also improved, including larger unfragmented insertions. Moreover, MCHelper is an easy-to-install and easy-to-use tool.

Rapidly developing NLP applications for content curation

AbstractTime and again we are faced, in a number of collaborative research projects, with the challenge of interconnecting various language processing tools to implement certain industry-driven use cases focusing, for the most part, upon digital content curation processes. In this paper we first describe several of the relevant projects and their technology platforms, followed by a description of the corresponding use cases and their requirements. The content curation platform we focus upon in this article and which has been implemented as a prototype makes use of a large number of NLP services, which we also build upon for other use cases and prototypes. In addition to the implemented NLP services, the article presents a workflow manager for the flexible creation and customisation of processing workflows that make use of the above mentioned NLP services. Based on the four key principles of generality, flexibility, scalability and efficiency, we present the first version of the workflow manager by providing details on its custom definition language, explaining the communication components and the general system architecture and setup. The paper also addresses challenges in interoperability across different NLP tasks and hardware-based resource use.

Clinical Implementation of Nephrologist-Led Genomic Testing for Glomerular Diseases in Singapore: Rationale and Protocol

Introduction: The early diagnosis and appropriate treatment of monogenic glomerular diseases can reduce kidney failure, avoid unnecessary investigations such as kidney biopsies and ineffective treatment with immunosuppressants, guide transplant decisions, and inform the genetic risks of their family members. Yet, genetic testing for kidney disease is underutilized in Singapore. We aimed to implement a nephrologist-led genetic service and evaluate the acceptance, adoption, utility, and cost-effectiveness of genetic testing for monogenic glomerular disease in Singapore. Methods: We will perform a prospective, multi-centre, type II hybrid effectiveness-implementation study with a post-design to evaluate both implementation and clinical outcomes of nephrologist-led genetic testing for suspected genetic glomerular kidney diseases. The multi-disciplinary implementation team will train “genetic nephrologists” to provide pre- and post-test counselling, order targeted exome panel sequencing for suspected glomerular kidney diseases (persistent microscopic haematuria and/or albuminuria or proteinuria in the absence of known causes, steroid-resistant primary nephrotic syndrome, apparent familial IgA nephropathy, or chronic kidney disease with no apparent cause), and interpret genetic test results; create workflows for patient referral, evaluation and management, and discuss genetic results at regular genomic board meetings. The outcomes are acceptance, appropriateness and adoption among patients and nephrologists, utility (proportion of patients who received genetic testing and have a confirmed diagnosis of genetic glomerular disease), and cost-effectiveness. Conclusion: This study will create and evaluate a nephrologist-led genetic service, develop an efficient variant curation process, and inform future recommendations on the optimal referral and genetic testing strategy for monogenic glomerular disease in Singapore. This will facilitate the future mainstreaming of genetic testing that will enable precision medicine in kidney care.

Content curation in a university library : Case study of scientific dissemination in Geology and Marine Sciences with the TELLUS and NEREUS newsletters

The article aims to analyze the content curation process and the impact of TELLUS and NEREUS newsletters on subscribers . These newsletters , focusing on geology and ocean sciences , disseminate curated news to keep readers informed about scientific developments . The study seeks to establish metrics for accurately measuring subscriber interest and satisfaction ​A key challenge is the lack of specificity and standardized metrics for such specialized newsletters . To achieve the objectives, this study employees the almost study methodology . The evolution of the newsletters are analyzed based on data extracted from messaging platforms​ Additionally , participant observation is used to examine key aspects such as​ platforms used , the curation system , information sources , topics covered , and the developed metrics system​ Findings reveal stable open and click -to-open rates over time , indicating that subscribers maintain consistent expectations of interest regardless of the specific content Additionally , subscribers are classified into 16 categories based on interaction habits , providing valuable insights into reader behavior . These classifications and metrics are essential for understanding the audience and measuring the newsletters ' impact and success​ The study highlights the importance of continuous adaptation to new technologies, the value of specialized information for a diverse audience, the relevance of email as a communication channel, and the development of own metrics focused on user satisfaction. These contributions can be applied to other libraries and scientific dissemination newsletters.

Developing a Multiscale Neural Connectivity Knowledgebase of the Autonomic Nervous System.

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a U.S. National Institutes of Health (NIH) funded effort to enhance our understanding of the neural circuitry responsible for visceral control. SPARC's mission is to identify, extract, and compile our overall existing knowledge and understanding of the autonomic nervous system (ANS) connectivity between the central nervous system and end organs. A major goal of SPARC is to use this knowledge to promote the development of the next generation of neuromodulation devices and bioelectronic medicine for nervous system diseases. As part of the SPARC program, we have been developing SCKAN, a dynamic knowledge base of ANS connectivity that contains information about the origins, terminations, and routing of ANS projections. The distillation of SPARC's connectivity knowledge into this knowledge base involves a rigorous curation process to capture connectivity information provided by experts, published literature, textbooks, and SPARC scientific data. SCKAN is used to automatically generate anatomical and functional connectivity maps on the SPARC portal. In this article, we present the design and functionality of SCKAN, including the detailed knowledge engineering process developed to populate the resource with high quality and accurate data. We discuss the process from both the perspective of SCKAN's ontological representation as well as its practical applications in developing information systems. We share our techniques, strategies, tools and insights for developing a practical knowledgebase of ANS connectivity that supports continual enhancement.

Biocollections Managers: Perspectives and Processes for Curating Physical Collections and their Digital Objects

Natural history collections retain a plethora of samples and objects for research purposes across domains. The data derived from these physical collections informs scientific discovery, but often aggregating data within even a single domain relies on navigating institutional and discipline-specific catalogs and repositories. Differing curation practices, shifts in methods for measurement, and changing theoretical and funding priorities, make the United States biocollections infrastructure a patchwork quilt of objects and their associated metadata. While the efforts of many have greatly improved the system, it still needs additional investment in these invaluable collections. In today’s context of possible data reuse in AI-ready test beds and public access, building a cyberinfrastructure atop this foundation should include revisiting the existing practices in preserving, conserving, and managing the physical collections and the subsequent research data curation processes of the digital objects (Bishop and Hank 2016). Scientific domains that rely on physical collections to create knowledge and work within today’s machine-actionable context need more FAIR (Findable, Accessible, Interoperable, and Resuable)-aligned data that also accounts for ethical reuse (i.e., CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) Principles for Indigenous Data Governance (Carroll et al. 2020)), as well as funding agency expectations for data that are as open as possible. The purpose of the Institute of Museum and Library Services study is to understand the curation perceptions and behaviors of managers of physical collections across biocollections to inform cross-disciplinary research, data management services, and resources. Six focus groups were conducted with eighteen participants across six physical biocollections: entomology, two herbaria, herpetology, ichthyology, and palaeontology. Participants responded to open-ended questions about their collection overview, storage, data collection, metadata, organization, findability, and reuse. Results indicate that these natural history collection managers use global metadata and storage standards to increase discoverability but reuse of these physical collections and associated digital objects require more investment in personnel and cyberinfrastructure to enhance reusability of these invaluable natural history collections.

Predicting Fear of Breast Cancer Recurrence in women five years after diagnosis using Machine Learning and healthcare reimbursement data from the French nationwide VICAN survey

ObjectiveA major concern for cancer survivors after treatment is the Fear of Cancer Recurrence (FCR), which is the fear that cancer will reappear or progress. This fear can be exacerbated by medical uncertainty about the future, leading to harmful obsession and having a negative impact on quality of life. This study aims to develop a predictive Machine Learning (ML) model using healthcare reimbursement data to better predict FCR and understand the factors influencing FCR in women with breast cancer five years after their diagnosis. Materials and MethodsWe used data from the VICAN (VIe après le CANcer) survey to propose an interpretable model to identify patients at risk of developing clinical FCR. The reimbursement data for each patient were analyzed beyond the first two years of treatment, excluding the initial phase influenced by the cancer curative therapeutic process. Data experiments were conducted, including the use of algorithms such as Random Forest, Support Vector Machines, Gradient Boosting, eXtreme Gradient Boosting, and Multilayer Perceptron. The AUC was used to choose the optimal model. ResultsThe dataset is composed of 918 patients classified regarding FCR. The experimental design incorporated classification levels of medications, biological and medical procedures. Subsequently, data was generated for two experiments, facilitating examination at the ultimate healthcare classification level in Experiment 1, while Experiment 2 targeted the penultimate classification level. Overall, the best-performing model achieved an AUC of 66%. This demonstrates some effectiveness of the algorithms in detecting patients at risk of developing clinical FCR and encourages further investigations to enhance the model's performance and assess its generalizability. ConclusionML applied to reimbursement data has shown promise in predicting FCR, aiding in the identification of patients at risk of developing it. The results pave the way for personalized prevention and intervention strategies, representing a significant advancement in postcancer care focusing on the needs of survivors.

Curating genomic disease-gene relationships with Gene2Phenotype (G2P)

Genetically determined disorders are highly heterogenous in clinical presentation and underlying molecular mechanism. The evidence underpinning these conditions in the peer-reviewed literature requires robust critical evaluation for diagnostic use. Here, we present a structured curation process for Gene2Phenotype (G2P). This draws on multiple lines of clinical, bioinformatic and functional evidence. The process utilises and extends existing terminologies, allows for precise definition of the molecular basis of disease, and confidence levels to be attributed to a given gene-disease assertion. In-depth disease curation using this process will prove useful in applications including in diagnostics, research and development of targeted therapeutics. G2P: www.ebi.ac.uk/gene2phenotype.

Evaluating the Accuracy of Artificial Intelligence(AI)-Generated Synopses for Plasma Cell Disorder Treatment Regimens

BACKGROUND Concise, accurate, and real time synopses of the evidence base are critical to support treatment decision-making in hematology, especially in a very rapidly evolving space such as the treatment of plasma cell disorders (PCDs). Synopses are used in clinical practice guidelines, educational settings, and general clinical practice. This process of knowledge curation is time consuming and cumbersome, even when performed by a clinical expert. Artificial intelligence (AI) - specifically, Large Language Models (LLMs) have promise in this context; however, they are prone to hallucination and may provide inaccurate and out of date information. Morever, the degree to which individual LLMs perform in summarizing the clinical evidence base may vary widely. We objectively assessed the abilities of four LLMs to generate accurate, coherent, and relevant synopses for six widely used PCD regimens. METHODS We compared the performance of four popular LLMs: 1) Claude 3.5 (“Claude”); 2) ChatGPT 4.0 (“ChatGPT”); 3) Gemini; and 4) Llama-3.1 (“Llama”). Each LLM was prompted exactly as follows: “write a synopsis for the development and evolution of therapy with [regimen name] for PCD, using citations from the literature”, where [regimen name] was replaced with: 1) “Dara-RVd”; 2) “KRd”; 3) “VDT-PACE”; 4) “Dara-CyBorD”; 5) “Elranatamab”; and 6) “Talquetamab.” The generated synopses were assessed by two PCD physician specialists using Likert scales on six criteria: accuracy, completeness, relevance, clarity, hallucinations, and coherence; lower scores correspond to poor performance and higher scores to excellent performance. The reviewers were blinded to LLMs to minimize bias and conducted their evaluations independently. The evaluation process was recorded using REDCap. Mean scores with 95% confidence intervals (CI) were calculated across all domains. RESULTS There were marked differences in LLM performance across the six criteria. Claude demonstrated the highest performance in all domains, notably outperforming the other LLMs in accuracy: mean Likert score 3.92 (95% CI 3.54-4.29); ChatGPT 3.25 (2.76-3.74); Gemini 3.17 (2.54-3.80); Llama 1.92 (1.41-2.43); completeness: mean Likert score 4.00 (3.66-4.34); GPT 2.58 (2.02-3.15); Gemini 2.58 (2.02-3.15); Llama 1.67 (1.39-1.95); and lack of hallucinations: mean Likert score 4.00 (4.00-4.00); ChatGPT 2.75 (2.06-3.44); Gemini 3.25 (2.65-3.85); Llama 1.92 (1.26-2.57). Llama performed considerably poorer across all the studied domains, frequently providing inaccurate information and misinterpreted results. ChatGPT and Gemini had intermediate performance. Notably, none of the LLMs including Claude registered perfect accuracy, completeness, or relevance. CONCLUSION To our knowledge, this is the first rigorous evaluation of widely available LLMs for the task of evidence summarization in the PCD domain. In our evaluation of four LLMs for six PCD regimens, meaningful differences were evident across the LLMs. While Claude performed at a notably higher level, evidence synopses need to be as close to perfectly accurate and comprehensive to be usable for real-world clinical decision support. By this standard, even the best performing LLMs continue to require careful editing from a domain expert to become usable. Inaccurate and incoherent synopses could lead to suboptimal patient care if taken literally. We plan to repeat this experiment with newer generations of LLMs to evaluate their potential improvement over time.

MOTHER-DB: A Database for Sharing Nonhuman Ovarian Histology Images.

The goal of the Multispecies Ovary Tissue Histology Electronic Repository (MOTHER) project is to establish a collection of nonhuman ovary histology images for multiple species as a resource for researchers and educators. An important component of sharing scientific data is the inclusion of the contextual metadata that describes the data. MOTHER extends the Ecological Metadata Language (EML) for documenting research data, leveraging its data provenance and usage license with the inclusion of metadata for ovary histology images. The design of the MOTHER metadata includes information on the donor animal, including reproductive cycle status, the slide and its preparation. MOTHER also extends the ezEML tool, called ezEML+MOTHER, for the specification of the metadata. The design of the MOTHER database (MOTHERDB) captures the metadata about the histology images, providing a searchable resource for discovering relevant images. MOTHER also defines a curation process for the ingestion of a collection of images and its metadata, verifying the validity of the metadata before its inclusion in the MOTHER collection. A Web search provides the ability to identify relevant images based on various characteristics in the metadata itself, such as genus and species, using filters.

Curation and classification procedures for the UK Antarctic meteorite collection

AbstractThe field of advanced curation is important for existing astromaterials collections, which includes samples returned by space missions, and meteorites and cosmic dust samples that have been recovered from here on Earth. In order to maximize the scientific return of the samples, contamination needs to be minimized at all stages of sample collection, preliminary examination, classification, and curation. Utilizing best practice methods, a detailed acquisition and curation plan was implemented during the UK's first two expeditions to collect Antarctic meteorites from two new blue icefields, Hutchison Icefields and Outer Recovery Icefields. This article documents the design and execution of the procedures used during the project's curation and classification processes. It describes two case studies showing the processes applied to the recovered meteorites, and reviews our experiences and lessons learned for the future.

Empirical estimation of metal powder bed fusion technological improvement rate

AbstractThis study empirically estimates the technological improvement rate (TIR) of metal powder bed fusion (PBF) technology, widely used in aerospace, automotive, and medical industries. PBF's continuous long-term adoption growth is driven by its ability to enhance manufacturing efficiency in terms of time and raw material use, as well as its capability to produce high-quality, high-strength, complex-shaped parts. Measuring the technological development of PBF is crucial as itis enlarging its application domain and is increasingly considered a viable alternative to traditional manufacturing technologies across a broader range of applications. We resorted to the literature to collect information and assess which technical parameters are most relevant to measure the capabilities of PBF. With those, we established an ideal functional performance metric (FPM) capable of comprehensively assessing PBF's technological performance improvement. Considering all available data sources and PBF machines ever made commercially available, a data set of technical parameters was constructed. This was followed by a data curation process focusing on data availability and reliability. The resultant practical FPM was used to estimate the TIR of PBF technology. By employing regression analysis, we estimate a yearly improvement of 26.8%. This empirical rate comes as a more accurate and reliable substitute to the previously indirectly estimated patent-derived rate of 33.3%. Our findings underscore PBF's capability of keeping pace with its growing significance and wider industrial applications. The results of this study provide a key metric for those in the industry and research, confirming the rapid performance growth and establishing a standard for future industrial uses.

Development and Validation of the Emergency Medical Technicians Occupational Stress Scale

Introduction. The population of interested workers for this study included Emergency Medical Technicians (EMTs) in the United States. This group of workers was chosen because of the nature of their work, which requires their presence at the scenes of life and death situations. Certainly, their work conditions can have an effect on their psychological well-being. Yet, the work of these workers, with all of its stressors, has not been adequately studied through the use of appropriate measuring instruments. Emergency Medical Technicians are not unknown in occupational stress research. This report discusses previous studies on EMT workers and occupational stressors and explains the curative process through the development and validation of an EMT Occupational Stress Scale. The report also highlights the psychometric investigation of this scale. Paramedics/EMTs face increased risk of physical and psychological stress due to the nature of their work. It is crucial to understand the stressors affecting their well-being in order to develop appropriate management strategies. Research suggests that EMTs may lack preparation for their roles, requiring additional training in empathy and emotional intelligence. The relationship between stressors experienced and clinical service provided needs further investigation. Standardized measures of stress for paramedics/EMTs are lacking and more comprehensive instruments are needed. This report aims to develop a comprehensive stress scale specifically for EMTs/paramedics, addressing validation concerns and incorporating environmental stress within a larger theoretical framework. Methods. This study was This study was conducted using a mixed-methods approach to develop and validate the Emergency Medical Technicians Occupational Stress Scale. The scale was pilot tested with a sample of EMTs to ensure reliability and validity. by the ethics committee and is based on a literature review. The Canadian EMT Job Satisfaction Questionnaire was used to measure organizational stress levels of EMTs. The questionnaire was reviewed by professionals and academics, resulting in the inclusion of five new items. The final item list consisted of 58 questions. Permission was obtained. Results. The outcomes and findings obtained from the comprehensive study conducted on the Development and Validation of the Emergency Medical Technicians Occupational Stress Scale have shed light on significant discoveries pertaining to the intricate realm of stress levels experienced by our dedicated EMT professionals. Conclusion. The rapidly evolving scope of Emergency Medical Services (EMS) practice has placed increasing demands on the relatively small professional community that consists of EMTs and paramedics. An Occupational Stress model was developed to examine interpersonal work stressors unique to the EMT role, and a measure was developed to assess the construct. Initial results suggest that the OSM is an accurate, reliable, and potentially valuable assessment tool. High levels of interpersonal stress may influence the EMTs to cope with their daily activities by seeking outlets for tension and depression through increased use of alcohol, legal drugs, and caffeine. Alcohol is used more frequently than other legal drugs are to modify interpersonal stress symptoms. The EMTs are in a prime position to help each other identify these symptoms when arising and offer assistance. Staff stress management programs are essential in solving and preventing occupational stress. A move toward prevention rather than reaction to such stress, which for many results in alcoholism, drug addiction, or job related emotional illness, will serve to preserve the health of the EMTs. The medical community has a vested interest in the health of these dedicated professionals. Not only does such prevention attempt to manage the human tragedy of alcoholism, legal drug addiction, and job related emotional illness, but it also prevents the more tangible, but no less important cost factors to the EMT.