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The analysis of utilising unmanned aerial vehicles (UAVs) to form flying networks in obstacle conditions and various algorithms for obstacle avoidance is conducted. A planning scheme for deploying a flying LiFi network based on UAVs in a production facility with obstacles is developed and described. Such networks are necessary to ensure reliable data transmission from sensors or other sources of information located in dangerous or hard-to-reach places to the crisis centre. Based on the planning scheme, the following stages are described: (1) laying the LiFi signal propagation route in conditions of interference, (2) placement of the UAV at the specified points of the laid route for the deployment of the LiFi network, and (3) ensuring the reliability of the deployed LiFi network. Strategies for deploying UAVs from a stationary depot to form a flying LiFi network in a room with obstacles are considered, namely the strategy of the first point for the route, the strategy of radial movement, and the strategy of the middle point for the route. Methods for ensuring the uninterrupted functioning of the flying LiFi network with the required level of reliability within a given time are developed and discussed. To implement the planning stages for deploying the UAV flying LiFi network in a production facility with obstacles, the "Simulation Way" and "Reliability Level" software tools are developed and described. Examples of utilising the proposed software tools are given. [ABSTRACT FROM AUTHOR]

The process of locating and identifying missing persons presents a complex challenge that hinges on the collection and comparison of diverse data sets. This commentary offers an overview of some of the difficulties and considerations associated with data management in the context of large‐scale missing person identifications. Such complexities include the uniqueness of each disaster event, the response time to the event, the variable quality and quantity of data, and the involvement of numerous stakeholders, all of which contribute to the intricacies of data management. In addition, the paramount considerations of privacy and ethical standards further compound these challenges, especially when dealing with sensitive information such as genetic data. This commentary describes the integrated Data Management System (iDMS) developed by the International Commission on Missing Persons (ICMP) as one example of a comprehensive, freely available solution for data collection, storage, protection, and analysis in missing person cases. The various advantages of the system are discussed, including the system's interoperability among the diverse array of stakeholders involved. While the iDMS streamlines data management processes and therefore represents a significant advancement in the field of missing person identification, it is concluded that the pending issue extends beyond the software tools to encompass the lack of political will among stakeholders to collaborate there remains a pressing need for all stakeholders involved in the identification process to commit to a mechanism that facilitates compatibility and interoperability if different tools are used in disaster victim identification (DVI) scenarios. [ABSTRACT FROM AUTHOR]

While robust tools exist for the analysis of single-cell datasets in both Python and R, interoperability is limited, and analysis tools generally only accept one object class. Considerable programming expertise is required to integrate tools across package ecosystems into a comprehensive analysis, due to their differing languages and internal data structures. This complicates validation of results and leads to inconsistent visualizations between analysis suites. Conversion between object formats is the most common solution, but this is difficult and error-prone due to the rapid pace of development of the analysis suites and their underlying data structures. To address this, we created SCUBA (Single-Cell Unified Backend API), an R package that implements a unified data access API for all common R and Python single-cell object formats. SCUBA extends the data access approach from the widely used Seurat package to SingleCellExperiment and anndata objects. SCUBA also implements new data-specific access functions for all supported object types. Performance scales well across all SCUBA-supported formats. In addition to performance, SCUBA offers several advantages over object conversion for the visualization and further analysis of pre-processed single-cell data. First, SCUBA extracts only data required for the operation at hand, leaving the original object unmodified. This process is simpler, less error prone, and less memory intensive than object conversion, which operates on the entire dataset. Second, code written with SCUBA can use any supported object class as input, with simple and consistent syntax across object formats. This allows a single analysis script or package (like our interactive single-cell browser, scExploreR) to work seamlessly with multiple object types, reducing the complexity of the code and improving both readability and reproducibility. Adoption of SCUBA will ultimately improve collaboration and reproducible research in single-cell analysis by lowering the barriers between package ecosystems.

Background: The lack of standardized disability progression evaluation in multiple sclerosis (MS) hinders reproducibility of clinical study results, due to heterogeneous and poorly reported criteria. Objective: To demonstrate the impact of using different parameters when evaluating MS progression, and to introduce an automated tool for reproducible outcome computation. Methods: Re-analyzing BRAVO clinical trial data (NCT00605215), we examined the fluctuations in computed treatment effect on confirmed disability progression (CDP) and progression independent of relapse activity (PIRA) when varying different parameters. These analyses were conducted using the msprog package for R, which we developed as a tool for CDP assessment from longitudinal data, given a set of criteria that can be specified by the user. Results: The BRAVO study reported a hazard ratio (HR) of 0.69 (95% confidence interval (CI): 0.46–1.02) for CDP. Using the different parameter configurations, the resulting treatment effect on CDP varied considerably, with HRs ranging from 0.59 (95% CI: 0.41–0.86) to 0.72 (95% CI: 0.48–1.07). The treatment effect on PIRA varied from an HR = 0.62 (95% CI: 0.41–0.93) to an HR = 0.65 (95% CI: 0.40–1.04). Conclusions: The adoption of an open-access tool validated by the research community, with clear parameter specification and standardized output, could greatly reduce heterogeneity in CDP estimation and promote repeatability of study results. [ABSTRACT FROM AUTHOR]

Introduction: Drawing tasks are an elementary component of psychological assessment in the evaluation of mental health. With the rise of digitalization not only in psychology but healthcare in general, digital drawing tools (dDTs) have also been developed for this purpose. This scoping review aims at summarizing the state of the art of dDTs available to assess mental health conditions in people above preschool age. Methods: PubMed, PsycInfo, PsycArticles, CINAHL, and Psychology and Behavioral Sciences Collection were searched for dDTs from 2000 onwards. The focus was on dDTs, which not only evaluate the final drawing, but also process data. Results: After applying the search and selection strategy, a total of 37 articles, comprising unique dDTs, remained for data extraction. Around 75 % of these articles were published after 2014 and most of them target adults (86.5 %). In addition, dDTs were mainly used in two areas: tremor detection and assessment of cognitive states, utilizing, for example, the Spiral Drawing Test and the Clock Drawing Test. Conclusion: Early detection of mental diseases is an increasingly important field in healthcare. Through the integration of digital and artbased solutions, this area could expand into an interdisciplinary science. This review shows that the first steps in this direction have already been taken and that the possibilities for further research, e.g., on the optimized application of dDTs, are still open. [ABSTRACT FROM AUTHOR]

Teams working on creative projects, such as design thinking, mostly face complex problems as well as challenging situations characterized by uniqueness and value conflicts. To cope with these characteristics, teams usually start doing something by drawing on their current store of experiences and professional knowledge, and then (re-)assess the outcomes produced, and adjust future actions based on insights obtained during the process. In reflecting on actions, tacit knowledge is revealed that enables designers to handle challenging situations. Although there is great potential to support design thinking by adding a reflection lens, we lack guidance on how, when, and on what to perform reflection. Based on scientific and theoretical literature, semi-structured interviews, a case study and a software prototype, prescriptive design knowledge on how to integrate reflection into design thinking is deduced, which enriches the scarce body of knowledge at the intersection of reflection and (digital) design thinking. [ABSTRACT FROM AUTHOR]

xrdPlanner is a software package designed to aid in the planning and preparation of powder X-ray diffraction and total scattering beam times at synchrotron facilities. Many modern beamlines provide a flexible experimental setup and may have several different detectors available. In combination with a range of available X-ray energies, it often makes it difficult for the user to explore the available parameter space relevant for a given experiment prior to the scheduled beam time. xrdPlanner was developed to provide a fast and straightforward tool that allows users to visualize the accessible part of reciprocal space of their experiment at a given combination of photon energy and detector geometry. To plan and communicate the necessary geometry not only saves time but also helps the beamline staff to prepare and accommodate for an experiment. The program is tailored toward powder X-ray diffraction and total scattering experiments but may also be useful for other experiments that rely on an area detector and for which detector placement and achievable momentum-transfer range are important experimental parameters.

Projects play a fundamental role in improving firms' performance. This is moderated by firms' ability to manage projects as well as on available methods and tools supporting project management. Many software tools exist in the market today, to meet different levels of requirements and offer various functionalities. Thus, there is a need to support the decision of project managers in terms of selecting the most suitable tool to use for their projects. This paper aims to provide a framework to support the comparison and the selection of project management software tools. A comprehensive list of functionalities to select software tools as well as a scoring system were specified. A prototypical implementation is presented to gain insights into framework comprehensiveness and practicality. The framework is expected to assist project managers in selecting the most suitable tools considering project context and stakeholders requirements. [ABSTRACT FROM AUTHOR]

xrdPlanner is a software package designed to aid in the planning and preparation of powder X-ray diffraction and total scattering beam times at synchrotron facilities. Many modern beamlines provide a flexible experimental setup and may have several different detectors available. In combination with a range of available X-ray energies, it often makes it difficult for the user to explore the available parameter space relevant for a given experiment prior to the scheduled beam time. xrdPlanner was developed to provide a fast and straightforward tool that allows users to visualize the accessible part of reciprocal space of their experiment at a given combination of photon energy and detector geometry. To plan and communicate the necessary geometry not only saves time but also helps the beamline staff to prepare and accommodate for an experiment. The program is tailored toward powder X-ray diffraction and total scattering experiments but may also be useful for other experiments that rely on an area detector and for which detector placement and achievable momentum-transfer range are important experimental parameters. [ABSTRACT FROM AUTHOR]

Abstract Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings.

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. [ABSTRACT FROM AUTHOR]

Precision medicine approaches often rely on complex and integrative analyses of multiple biomarkers from "omics" data to generate insights that can help with either diagnostic, prognostic, or therapeutical decisions. Such insights are often made using machine learning (ML) models that perform sample classification for a particular phenotype (yes/no). Building such models is a challenge and time-consuming, requiring advanced coding skills and mathematical modelling expertise. Artificial intelligence (AI) is a methodological solution that has the potential to facilitate, optimize, and scale model development. In this work, we developed an AI-based, user-friendly, and code-free platform that fully automated the development of predictive models from quantitative "omics" data. Here, we show the application of this tool with the development of cancer survival prognostics models using real-life data from breast, lung, and renal cancer transcriptomes. In comparison to other models, our generated models rendered performances with competitive sensitivities (72–85%), specificities (76–85%), accuracies (75–85%), and Receiver Operating Characteristic curves with superior Areas Under the Curve (ROC-AUC of 77–86%). Further, we reported the associated sets of genes (biomarkers) and their expression patterns that were predictive of cancer survival. Moreover, we made our models available as online tools to generate prognostic predictions based on the gene expressions of the biomarkers. In conclusion, we demonstrated that our tool is a robust, user-friendly solution for developing bespoke predictive tools from "omics" data, which facilitate precision medicine applications to the point-of-care. [ABSTRACT FROM AUTHOR]

Precision medicine approaches often rely on complex and integrative analyses of multiple biomarkers from “omics” data to generate insights that can help with either diagnostic, prognostic, or therapeutical decisions. Such insights are often made using machine learning (ML) models that perform sample classification for a particular phenotype (yes/no). Building such models is a challenge and time-consuming, requiring advanced coding skills and mathematical modelling expertise. Artificial intelligence (AI) is a methodological solution that has the potential to facilitate, optimize, and scale model development. In this work, we developed an AI-based, user-friendly, and code-free platform that fully automated the development of predictive models from quantitative “omics” data. Here, we show the application of this tool with the development of cancer survival prognostics models using real-life data from breast, lung, and renal cancer transcriptomes. In comparison to other models, our generated models rendered performances with competitive sensitivities (72–85%), specificities (76–85%), accuracies (75–85%), and Receiver Operating Characteristic curves with superior Areas Under the Curve (ROC-AUC of 77–86%). Further, we reported the associated sets of genes (biomarkers) and their expression patterns that were predictive of cancer survival. Moreover, we made our models available as online tools to generate prognostic predictions based on the gene expressions of the biomarkers. In conclusion, we demonstrated that our tool is a robust, user-friendly solution for developing bespoke predictive tools from “omics” data, which facilitate precision medicine applications to the point-of-care.

Although the impact of hydrology on the ecological status of surface water bodies has been highly recognised, the hydrological regime alteration assessment has proven to be a challenging task. In this context, an extensive structured review analysis was used as a research method to investigate the strength and limitations of the hydrological regime alteration assessment methods as adopted by each member of the European Environment Agency and the cooperating countries, according to the Water Framework Directive 2000/60, as well as to propose future directions. The review was also widened to include the methods currently used worldwide in the hydrological alteration studies and the supporting software tools developed. The implementation of a common methodology on a European scale is not applicable, since a single approach would not be able to cope with the regional needs and conditions. The main limitation in almost all the methods developed by European countries and worldwide is the need for a flow time series of high temporal resolution, so as to also capture the systems' extreme high and low flows. Automatic monitoring systems for rivers can provide a solution. Additionally, hydrological modelling may provide the necessary data for the definition of the reference conditions. Nevertheless, the main limitations of the methodologies reviewed and the challenge for future development are the incorporation of the groundwater contribution to the hydrological regime and the development of quantitative relationships between flow alteration and ecological response. [ABSTRACT FROM AUTHOR]

The EU‐FORA programme 'Quantitative tools in microbial and chemical risk assessment' was dedicated to training on predictive microbiology fundamentals, implementation of different modelling strategies, design of experiments and software tools such as MATLAB, GInaFiT and DMFit. The fellow performed MATLAB training on maximum specific growth rate (μmax) determination according to the Ratkowsky model. GInaFiT training on different models for bacterial inactivation and DMFit training on growth parameters of Vibrio parahaemolyticus were also carried out. Optical density measurements of V. parahaemolyticus bacterial cultures were performed. The obtained kinetics of optical density measurements were used to estimate μmax. Hereafter, Minimum inhibitory concentrations and non‐inhibitory concentrations of aminoglycoside antibiotics were estimated based on the quantification of the fractional areas of the optical density vs time. It can be concluded that the results of the quantitative characterisation of V. parahaemolyticus are reliable and can be used for exposure assessments. Also, the turbidimetric assay can be applied for successful estimation of minimum inhibitory concentrations and non‐inhibitory concentrations. [ABSTRACT FROM AUTHOR]

The article examines the issues of the application of modern software tools and technologies for digitization of the main processes and services in the higher education institutions of the Republic of Bulgaria. The topic is complex and debatable, both from the point of view of the applied methods and technologies, and in terms of reporting the effectiveness of these services. The acceleration of the digitization processes necessitates and requires the taking of specific measures to ensure the level of cyber security of the organization. One of the measures in this direction is for the developed web applications to be based on the latest technologies, as well as to carry out specific vulnerability tests before launching the services. As a result of the analyzed theoretical statements, as well as on the basis of the specific software implementation, some of the positive moments in expanding students’ access to modern digital services have been outlined. [ABSTRACT FROM AUTHOR]

The advent of Software Defined Networking (SDN) has ushered in an era where the functions of interconnected devices are no longer constrained by their original design. Instead, these devices, now transformed into "general-purpose" nodes within the network, have roles that are defined by their configuration settings. Given that these configurations can be compiled into a computer file, software tools have been developed to consolidate and automate the administration of configuration parameters across all devices in an SDN network. These tools, akin to source code control tools used in programming languages, are capable of managing configurations for individual or groups of devices simultaneously. This study presents an evaluation of three such tools--Ansible, Puppet, and Chef--assessing their merits and demerits across various performance and usability dimensions, including configuration, installation, ease of use, and management capabilities. The comparative analysis reveals Ansible as a remarkably versatile tool, offering a wealth of advantages that make it a compelling choice for a majority of automation and configuration management tasks. [ABSTRACT FROM AUTHOR]

To achieve national and global air quality and climate change objectives, the agricultural sector increasingly requires dependable decision support tools for gaseous emissions at the farm level. We evaluated thirteen greenhouse gas (GHG)-based decision support systems (DSS), considering criteria such as not only the accessibility, user-friendliness, stakeholder involvement, sustainability methodology, and modeling aspects, but also the input parameters and outputs provided, all crucial for decision making. While most DSSs provide information for facilitating their use, only four are suitable for inexperienced users, and stakeholder participation in DSS development is infrequent. The dominant methodology for farm-level GHG estimation is IPCC 2006, with quantitative models primarily used for indicators' assessment. Scenario and contribution analyses are the prevailing decision support approaches. Soil, crop, and fertilizer types are the most implemented non-livestock-related inputs, while climate- and feed-related costs are the least required. All DSSs assess farm-level mitigation measures, but less than half offer sustainability consultation. These tools promote environmental sustainability by evaluating mitigation strategies, disseminating farm sustainability information, and guiding sustainable farm management. Yet, challenges such as disparate estimation methods, result variations, comparison difficulties, usability concerns, steep learning curves, the lack of automation, the necessity for multiple tools, the limited integration of the results, and changing regulations hinder their wider adoption. [ABSTRACT FROM AUTHOR]

This paper analyses four SDN controllers that support this architecture not only from a technical point of view but also from an academic point of view by including it in the university curriculum. The integration of network controller analysis into an academic curriculum can provide a comprehensive training in theoretical and practical aspects related to network management and SDN technologies. The controllers analyzed were FloodLight, HP SDN VAN Controller, ONOS (Open Network Operating System) and AGILE SDN. Their comparison was based on criteria such as ease of installation, interaction with other communication protocols, ability to monitor network topologies and experience in using their graphical user interfaces. ONOS was found to be the most secure, reliable, robust and scalable controller. Notwithstanding the above, it is important to note that the network technology landscape is constantly evolving, so it is essential to keep updating drivers and comparing features, performance, etc. on these platforms before making a decision. The following are the factors that make ONOS the best choice: 1. Flexibility and customization: ONOS is known for being highly flexible and customizable. This means that you can adapt and customize its functionality to meet the specific needs of your network. Extensions and custom applications can be implemented more easily in ONOS than in some other controllers. 2. Scalability: ONOS is designed to be scalable and can handle large networks with a large number of devices and flows. This makes it suitable for applications in service provider and enterprise network environments. 3. multitechnology support: ONOS is known for its ability to manage a variety of network technologies, including OpenFlow and others. This makes it versatile in terms of support for different network equipment and technologies.4. Active community and continuous development: ONOS has an active community of developers and continuous development. This means that updates and new features are more likely to be found on a regular basis. Among the criteria used, ease of installation was chosen, allowing the controller to be deployed quickly and efficiently, which is beneficial in terms of time and cost. On the other hand, the ability to monitor network topologies provides visibility and control, which is essential for network performance, efficiency and security. [ABSTRACT FROM AUTHOR]

Abstract Objective To investigate the usefulness and performance metrics of three freely-available softwares (Rayyan®, Abstrackr® and Colandr®) for title screening in systematic reviews. Study design and setting In this methodological study, the usefulness of softwares to screen titles in systematic reviews was investigated by the comparison between the number of titles identified by software-assisted screening and those by manual screening using a previously published systematic review. To test the performance metrics, sensitivity, specificity, false negative rate, proportion missed, workload and timing savings were calculated. A purposely built survey was used to evaluate the rater's experiences regarding the softwares’ performances. Results Rayyan® was the most sensitive software and raters correctly identified 78% of the true positives. All three softwares were specific and raters correctly identified 99% of the true negatives. They also had similar values for precision, proportion missed, workload and timing savings. Rayyan®, Abstrackr® and Colandr® had 21%, 39% and 34% of false negatives rates, respectively. Rayyan presented the best performance (35/40) according to the raters. Conclusion Rayyan®, Abstrackr® and Colandr® are useful tools and provided good metric performance results for systematic title screening. Rayyan® appears to be the best ranked on the quantitative and on the raters’ perspective evaluation. The most important finding of this study is that the use of software to screen titles does not remove any title that would meet the inclusion criteria for the final review, being valuable resources to facilitate the screening process.

This paper presents a comprehensive review of existing literature on the life cycle analysis (LCA) of ceramic tiles. By critically evaluating these studies, the paper aims to provide insights into controlling the factors that contribute to a more sustainable production of ceramic tiles. The study identifies the relationships between variables that impact the shaping process, and highlights the priority areas that require intervention for reducing the environmental impact of ceramic tile production. The results of the LCA analysis emphasize the significance of the manufacturing process and the dependence on non-renewable sources of fuels in the ceramic tile industry. This paper provides a hierarchical framework of the variables that have the most significant impact and offers a guide for reducing the environmental footprint of ceramic tile production.

Libraries and institutions around the world are working with publishers to transform scholarly publishing into an open access ecosystem. To accomplish this goal, publisher contracts are being renegotiated and modified to include transformative elements which will move in this direction. With costs shifting from traditional subscriptions to fees which incorporate Read and Publish cost elements, tools are needed to provide underlying analytics both at the negotiating level but also for the ongoing implementation of new agreements. The ongoing workflow will be more complicated since institutions will need to more closely track authors and their associated article processing charge payments. A variety of open source and commercial tools and services are reviewed which have been developed to move to a new open access publishing environment. [ABSTRACT FROM AUTHOR]

Abstract The EU‐FORA programme ‘Quantitative tools in microbial and chemical risk assessment’ was dedicated to training on predictive microbiology fundamentals, implementation of different modelling strategies, design of experiments and software tools such as MATLAB, GInaFiT and DMFit. The fellow performed MATLAB training on maximum specific growth rate (μmax) determination according to the Ratkowsky model. GInaFiT training on different models for bacterial inactivation and DMFit training on growth parameters of Vibrio parahaemolyticus were also carried out. Optical density measurements of V. parahaemolyticus bacterial cultures were performed. The obtained kinetics of optical density measurements were used to estimate μmax. Hereafter, Minimum inhibitory concentrations and non‐inhibitory concentrations of aminoglycoside antibiotics were estimated based on the quantification of the fractional areas of the optical density vs time. It can be concluded that the results of the quantitative characterisation of V. parahaemolyticus are reliable and can be used for exposure assessments. Also, the turbidimetric assay can be applied for successful estimation of minimum inhibitory concentrations and non‐inhibitory concentrations.

Developing error-free applications is a major challenge for computer scientists. Tools to remedy this problem have been developed, notably Rule Checkers and proof assistants. As a particular case of error, a bug is by nature intangible, invisible and difficult to trace. We propose to investigate the correlations between the alerts generated by rule checkers and the internal quality of the software system. In this first version of the work, we present M2SmallLint, a tool for visualizing and navigating through source code properties in order to locate potential errors. This tool enables the visualization of software health.

Introduction The increasing need for secondary use of clinical study data requires FAIR infrastructures, i.e. provide findable, accessible, interoperable and reusable data. It is crucial for data scientists to assess the number and distribution of cohorts that meet complex combinations of criteria defined by the research question. This so-called feasibility test is increasingly offered as a self-service, where scientists can filter the available data according to specific parameters. Early feasibility tools have been developed for biosamples or image collections. They are of high interest for clinical study platforms that federate multiple studies and data types, but they pose specific requirements on the integration of data sources and data protection. Methods Mandatory and desired requirements for such tools were acquired from two user groups -- primary users and staff managing a platform's transfer office. Open Source feasibility tools were sought by different literature search strategies and evaluated on their adaptability to the requirements. Results We identified seven feasibility tools that we evaluated based on six mandatory properties. Discussion We determined five feasibility tools to be most promising candidates for adaption to a clinical study research data platform, the Clinical Communication Platform, the German Portal for Medical Research Data, the Feasibility Explorer, Medical Controlling, and the Sample Locator. [ABSTRACT FROM AUTHOR]

The Research Resource Identifier (RRID) was introduced in 2014 to better identify biomedical research resources and track their use across the literature, including key digital resources such as databases and software. Authors include an RRID after the first mention of any resource used. Here, we provide an overview of RRIDs and analyze their use for digital resource identification. We quantitatively compare the output of our RRID curation workflow with the outputs of automated text mining systems used to identify resource mentions in text. The results show that authors follow RRID reporting guidelines well, and that our natural language processing based text mining was able to identify nearly all of the resources identified by RRIDs as well as thousands more. Finally, we demonstrate how RRIDs and text mining can complement each other to provide a scalable solution to digital resource citation.