Reliability Estimation of News Media Sources: "Birds of a Feather Flock Together"

Dataset introduced in the paper "Reliability Estimation of News Media Sources: Birds of a Feather Flock Together" published in the NAACL 2024 main conference. Similar to the news media bias and factual reporting dataset, this dataset consists of a collections of 5.33K new media domains names with reliability labels. Additionally, for some domains, there is also a human-provided reliability score… See the full description on the dataset page: https://huggingface.co/datasets/sergioburdisso/news_media_reliability.

Background and objectiveNutrition is a basic need for athletes; thus, adequate dietary intake is crucial for maintaining overall health, facilitating training adaptations and boosting athletic performance. Accurate dietary assessment tools are required to minimize the challenges faced by athletes. This study verifies the validity and reproducibility of a 157 item semi-quantitative food frequency questionnaire (FFQ) among Lebanese athletes. This is the only Arabic questionnaire in Lebanon that estimates food consumption for athletes which can also be used in Arabic speaking countries. There has been no previous validated food frequency questionnaire that estimated food consumption for athletes in Lebanon.MethodsA total of 194 athletes were included in the study to assess the validity of the food frequency questionnaire against four days dietary recalls by comparing the total nutrient intake values from the food frequency questionnaire with the mean values of four 24-hour dietary recalls using Spearman correlation coefficient and Bland Altman plots. In order to measure the reproducibility, the intra class correlation coefficients were calculated by repeating the same food frequency questionnaire after one month.ResultsThe intra-class correlation coefficient between the two-food frequency questionnaires ranged from average (0.739 for carbohydrates) to good (0.870 for energy (Kcal)), to excellent (0.919 for proteins) concerning macronutrients and ranged from average (0.688 for vitamin D), to excellent (0.952 for vitamin B12), indicating an acceptable reproducibility. Spearman’s correlation coefficients of dietary intake estimate from the food frequency questionnaire and the four dietary recalls varied between 0.304 for sodium, 0.469 for magnesium to 0.953 for caloric intake (kcal). Bland-Altman plots illustrated a percentage of agreement ranging between 94.3% for fats to 96.4% for proteins.ConclusionThis food frequency questionnaire has a reliable validity and reproducibility to evaluate dietary assessments and is an appropriate tool for future interventions to ensure the adoption of adequate eating strategies by athletes.

Latin American organizations have by far the lowest concerns regarding reliability risks with AI in 2024. This was in stark contrast to the rest of the world, where ** percent of respondents said reliability was relevant to their organization.

The objective of this project was to develop technical relationships between reliability improvement strategies and reliability performance metrics. This project defined reliability, explained the importance of travel time distributions for measuring reliability, and recommended specific reliability performance measures. The research reexamined the contribution of the various causes of nonrecurring congestion on urban freeway sections, however, some attention was also given to rural highways and urban arterials). Numerous actions that can potentially reduce nonrecurring congestion were identified with an indication of their relative importance. Models for predicting nonrecurring congestion were developed using three methods, all based on empirical procedures: The first involved before and after studies; the second was termed a 'data poor' approach and resulted in a parsimonious and easy-to-apply set of models; the third was entitled a 'data rich model' and used cross-section inputs including data on selected factors known to directly affect nonrecurring congestion. An important conclusion of the study is that actions to improve operations, reduce demand, and increase capacity all can improve travel time reliability. The 3 attached zip files contains comma separated value (.csv) files of data to support SHRP 2 report S2-L03-RR-1, Analytical procedures for determining the impacts of reliability mitigation strategies.Zip size is 1.83 MB. Files were accessed in Microsoft Excel 2016. Data will be preserved as is. To view publication see: https://rosap.ntl.bts.gov/view/dot/3605

Data set to accompany article.

The Washington site used the reliability guide from Project L02, analysis tools for forecasting reliability and estimating impacts from Project L07, Project L08, and Project C11 as well as the guide on reliability performance measures from the Project L05 product. The Washington site focused on the I-5 and I-405 corridors from Lynnwood to Tukwila (approximately 30 miles long for each corridor running through the Puget Sound metropolitan region), and the SR-522 urban arterial near Seattle. The pilot testing demonstrated that the SHRP 2 Reliability data and analytical products clearly addressed the practical challenges that transportation agencies face when monitoring and analyzing travel time reliability. However, most tools require significant improvements at the application level. Project L38D was intended to evaluate a suite of projects to determine their readiness for implementation. Those projects had a logical structure consisting of data collection, analysis, and project prioritization. The datasets in this zip file, which is 90.5 MB in size, are in support of SHRP 2 reliability project L38D, "Pilot testing of SHRP 2 reliability data and analytical products: Washington." The project report can be accessed via the following URL: https://rosap.ntl.bts.gov/view/dot/3610 This zip file contains 20 Comma Separated Values (CSV) files, which can be opened using most text editing programs.

"SHRP 2 initiated the L38 project to pilot test products from five of the program’s completed projects. The products support reliability estimation and use based on data analyses, analytical techniques, and decision-making framework. The L38 project has two main objectives: (1) to assist agencies in using travel time reliability as a measure in their business practices and (2) to receive feedback from the project research teams on the applicability and usefulness of the products tested, along with their suggested possible refinements. SHRP 2 selected four teams from California, Minnesota, Florida, and Washington. Project L38C tested elements from Projects L02, L05, L07, and L08. Project L02 identified methods to collect, archive, and integrate required data for reliability estimation and methods for analyzing and visualizing the causes of unreliability based on the collected data. Projects L07 and L08 produced analytical techniques and tools for estimating reliability based on developed models and allowing the estimation of reliability and the impacts on reliability of alternative mitigating strategies. Project L05 provided guidance regarding how to use reliability assessments to support the business processes of transportation agencies. The datasets in this zip file, which is 7.83 MB in size, support of SHRP 2 reliability project L38C, "Pilot testing of SHRP 2 reliability data and analytical products: Florida." The accompanying report can be accessed at the following URL: https://rosap.ntl.bts.gov/view/dot/3609 There are 12 datasets in this zip file, including 2 Microsoft Excel worksheets (XLSX) and 10 Comma Separated Values (CSV) files. The Microsoft Excel worksheets can be opened using the 2010 and 2016 versions of Microsoft Word, the CSV files can be opened using most text editors.

As of 2024, the Latin America was the region with the largest share of organizations with fully operationalized reliability measures worldwide. About ** percent of the respondents in this industry reported to have fully operationalized at least ** percent of the listed measures to mitigate reliability risks across the development, deployment, and use of artificial intelligence (AI) — from these, about *** percent claim to have fully operationalized all the listed measures. Nevertheless, Europe was the one with the highest overall adoption of AI-related reliability measures by the surveyed organizations, having an average of **** adopted measures.

Abstract Purpose: The aim of this study was to develop, validate, and test the clarity and reliability of the Brief Instrument to Assess Workers’ Productivity during a Working Day. Design/methodology/approach: The content of the instrument was chosen using research containing other valid instruments and after this the construct was developed. Relevance and clarity validations were conducted with experts using Likert scales (from 0 to 10), convergent validity was performed using the Health and Productivity Questionnaire (HPQ) and Health & Labor Questionnaire (HLQ) instruments, and reliability measures were carried out using the Split Half Test and Cronbach’s Alpha coefficient. Findings: The instrument proved to be clear and relevant with an average of 9.11±0.93 in the relevance test and 9.23±0.75 in the clarity test. Regarding convergent validity, the instrument showed a high correlation with the HPQ (r2= 0.86) and the HLQ (r2 = 0.82). The reliability results were r2 = 0.78 in the Split Half Test and a Cronbach’s Alpha coefficient of α = 0.91 for the Management variables and α = 0.80 for the Physical and Mental Variables. Originality/value: The proposed instrument was shown to have an adequate content and construct, in addition to converging results with other recognized instruments, and it had very high levels of reliability. All these factors define it as a good tool for research regarding productivity in companies.

Market Research Intellect's Asset Reliability Management Market Report highlights a valuation of USD 3.5 billion in 2024 and anticipates growth to USD 7.2 billion by 2033, with a CAGR of 8.5% from 2026-2033.Explore insights on demand dynamics, innovation pipelines, and competitive landscapes.

The Reliability Score layer shows the results of combining TTI, OTP, and scheduled speed to calculate the overall measure of reliability. A high reliability score (reliscore) is indicative of segments that may benefit from targeted improvements to improve transit operations. The Reliability Score was weighted by ridership (riderrelis) to highlight segments that impact high ridership surface transit service and allow for prioritization of improvements.

Hypothesis: The reliability can be adopted to quantitatively measure the sustainability of mega-projects.

Presentation: This dataset shows two scenario based examples to establish an initial reliability assessment of megaproject sustainability. Data were gathered from the author’s assumption with regard to assumed differences between scenarios A and B. There are two sheets in this Microsoft Excel file, including a comparison between two scenarios by using a Fault Tree Analysis model, and a correlation analysis between reliability and unavailability.

Notable findings: It has been found from this exploratory experiment that the reliability can be used to quantitatively measure megaproject sustainability, and there is a negative correlation between reliability and unavailability among 11 related events in association with sustainability goals in the life-cycle of megaproject.

Interpretation: Results from data analysis by using the two sheets can be useful to inform decision making on megaproject sustainability. For example, the reliability to achieve sustainability goals can be enhanced by decrease the unavailability or the failure at individual work stages in megaproject delivery.

Implication: This dataset file can be used to perform reliability analysis in other experiment to access megaproject sustainability.

These data are part of the NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed excepted as noted below. All direct identifiers have been removed and replaced with text enclosed in square brackets (e.g.[MASKED]). Due to the masking of select information, variables/content described in the data documentation may not actually be available as part of the collection. Users should consult the investigator(s) if further information is needed. This collection is one part of the Department of Justice's response to 42 USC 14142, a law which requires the U.S. Attorney General to 1) "acquire data about the use of excessive force by law enforcement officers" and 2) "publish an annual summary of the data." Researchers compared agency-level data reported in the 2003 (ICPSR 4411) and 2007 (ICPSR 31161) waves of the Law Enforcement Management and Administrative Statistics (LEMAS) surveys with available external sources including publicly available reports and direct contact with agency personnel. The purpose of this study was to assess validity and reliability of the available agency-level reported data on citizen complaints about police use of force.

California Local Reliability Areas.

The present study sought to assess and establish the reliability of a bespoke coding window to assess volleyball performance. This coding window was the first to combine a six-phase model of complexes along with efficacy scales that were individualised to each of the six key skills (Serve, Serve Receive, Set, Spike, Block, and Defence). The results of the study demonstrated Kappa and Weighted Kappa values above the acceptable thresholds for both inter- and intra-reliability, and that it was possible to utilise such a detailed system without the impact of Observer Drift.

Files whose names contain 'r1' concern reliability estimation for the network from Fig. 2 with component reliabilities given by the vector r1=[0.944, 0.964, 0.958, 0.946, 0.946, 0.948, 0.955, 0.954, 0.93, 0.955, 0.962, 0.952, 0.966]. These files whose names contain 'r2' concern reliability estimation for the network from Fig. 2 with component reliabilities given by the vector r2=[0.707, 0.699, 0.706, 0.693, 0.692, 0.704, 0.707, 0.693, 0.702, 0.708, 0.705, 0.679, 0.684]. Files whose names contain 'MC' concern reliability estimation based on d-MCs, otherwise the network reliability is based on d-MPs. If a file's name contains 'RX', then the file contains data concerning the performance of Chang's procedure for reliability estimation without steps 1 and 2 and Kozyra's procedure IFB (algorithm 1) for a given dataset of random vectors. Otherwise, the file contains data concerning the performance of Chang's procedure for reliability estimation using his first procedure generating random vectors and Kozyra's procedure IRE (algorithm 2). The meaning of columns in all datasets whose name does not contain 'RX' is as follows: 'd' - demand level, 'number' - the number of d-MPs or d-MCs; 'Chang R' and 'Chang T' - reliability estimation and running time, respectively, for Chang's reliability estimation procedure, 'Kozyra R' and 'Kozyra T' - reliability estimation and running time, respectively, for Kozyra's reliability estimation procedure (algorithm 2); 'Chang/Kozyra' - the ratio of the running time of Chang's procedure and the running time of Kozyra's algorithm. The meaning of columns in all datasets whose name contains 'RX' is as follows: 'd' - demand level, 'number' - the number of d-MPs or d-MCs; 'Simple R'/'Simple T' - reliability estimation/running time for Chang's estimation reliability procedure without steps 1-2 for X from a given dataset of random vectors, 'Intersection R'/'Intersection T' - reliability estimation/running time for Kozyra's estimation reliability procedure IFB (algorithm 1); 'Simple/Intersection' - the ration of the computational time of Chang's estimation reliability procedure without steps 1-2 and the running time of Kozyra's estimation reliability procedure IFB (algorithm 1).

Reliability Test Equipment Market Outlook

According to our latest research, the global Reliability Test Equipment market size reached USD 5.42 billion in 2024, exhibiting robust expansion across key end-use sectors. The market is projected to grow at a CAGR of 7.1% from 2025 to 2033, reaching an estimated USD 10.06 billion by 2033. This impressive growth is primarily driven by the increasing demand for advanced testing solutions in industries such as automotive, electronics, aerospace, and healthcare, alongside the continuous evolution of product safety and quality standards worldwide.

One of the primary growth drivers for the Reliability Test Equipment market is the rapid technological advancements in manufacturing and product design. As products become more sophisticated and integrated with complex electronic systems, the need for precise and reliable testing equipment has intensified. Industries are compelled to ensure that their products meet rigorous safety, durability, and compliance standards before reaching the market. This is particularly evident in the automotive and electronics sectors, where even minor defects can lead to significant safety risks and costly recalls. The integration of IoT, AI, and automation in manufacturing processes further escalates the demand for reliability test equipment that can handle intricate and high-volume testing requirements efficiently.

Another significant factor fueling market growth is the tightening of regulatory frameworks and quality assurance protocols globally. Regulatory bodies in regions such as North America, Europe, and Asia Pacific have implemented stringent guidelines mandating comprehensive reliability testing for products, especially in sectors like aerospace, healthcare, and defense. These regulations necessitate the adoption of advanced reliability test equipment to guarantee product safety, longevity, and performance under various environmental and operational conditions. As a result, manufacturers are increasingly investing in state-of-the-art test systems, including environmental chambers, vibration and shock test systems, and burn-in test equipment, to comply with regulatory requirements and safeguard their brand reputation.

Moreover, the rising trend of digital transformation and Industry 4.0 initiatives has significantly impacted the Reliability Test Equipment market. Automation and data analytics are being leveraged to enhance the accuracy, repeatability, and efficiency of testing processes. Companies are adopting smart test equipment capable of real-time monitoring, predictive maintenance, and remote diagnostics, which not only improve testing outcomes but also reduce operational costs and downtime. The integration of cloud-based platforms and IoT-enabled devices has further expanded the scope of reliability testing, enabling seamless data collection, analysis, and reporting across geographically dispersed facilities. This digital shift is expected to continue driving market growth, as organizations seek to optimize their testing operations and stay competitive in an increasingly dynamic industrial landscape.

From a regional perspective, Asia Pacific remains at the forefront of the Reliability Test Equipment market, accounting for the largest share in 2024, followed closely by North America and Europe. The dominance of Asia Pacific is attributed to the presence of major manufacturing hubs, rapid industrialization, and significant investments in R&D activities. Countries like China, Japan, South Korea, and India are witnessing heightened demand for reliability test equipment across automotive, electronics, and semiconductor sectors. Meanwhile, North America and Europe are characterized by advanced technological infrastructure and stringent regulatory standards, driving steady market growth. The Middle East & Africa and Latin America are also emerging as promising markets, supported by expanding industrial bases and growing focus on quality assurance.

Thermal Cycling Test Equipment plays a crucial role in the Reliability Test Equipment market, particularly in industries where products are subjected to extreme temperature variations. This equipment is designed to simulate rapid temperature changes, assessing the thermal endurance and stability of materials and components. As products become more sophisticated, the need for preci

Data validation and reliability using Cronbach’s formula.

Reliability and validity of measurement model.

I worked on a Credit Reliability project for credit card issuance, covering the complete machine learning pipeline: from collecting and preprocessing applicant data (income, credit history, employment status) to feature engineering and model selection. I applied algorithms such as logistic regression and random forests, evaluating performance using metrics including accuracy, precision, recall, F1, and AUC-ROC. After fine-tuning, the models were prepared for real-world deployment, with all methods and insights documented in a comprehensive report. This project provided valuable experience in leveraging data to support informed credit decisions.