Background Practice guidelines have been developed in response to the observation that variations exist in clinical medicine that are not related to variations in the clinical presentation and severity of the disease. Despite their widespread use, however, practice guideline evaluation lacks a rigorous scientific methodology to support its development and application. Discussion Firstly, we review the major epidemiological foundations of practice guideline development. Secondly, we propose a chronic disease epidemiological model in which practice patterns are viewed as the exposure and outcomes of interest such as quality or cost are viewed as the disease. Sources of selection, information, confounding and temporal trend bias are identified and discussed. Summary The proposed methodological framework for outcomes research to evaluate practice guidelines reflects the selection, information and confounding biases inherent in its observational nature which must be accounted for in both the design and the analysis phases of any outcomes research study.

This data set contains the replication data for the article "Knowing and doing: The development of information literacy measures to assess knowledge and practice." This article was published in the Journal of Information Literacy, in June 2021. The data was collected as part of the contact author's PhD research on information literacy (IL). One goal of this study is to assess students' levels of IL using three measures: 1) a 21-item IL test for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know. 2) a source-evaluation measure to assess students' abilities to critically evaluate information sources in practice. This is a "DO-measure," intended to measure what students do in practice, in actual assignments. 3) a source-use measure to assess students' abilities to use sources correctly when writing. This is a "DO-measure," intended to measure what students do in practice, in actual assignments. The data set contains survey results from 626 Norwegian and international students at three levels of higher education: bachelor, master's and PhD. The data was collected in Qualtrics from fall 2019 to spring 2020. In addition to the data set and this README file, two other files are available here: 1) test questions in the survey, including answer alternatives (IL_knowledge_tests.txt) 2) details of the assignment-based measures for assessing source evaluation and source use (Assignment_based_measures_assessing_IL_skills.txt) Publication abstract: This study touches upon three major themes in the field of information literacy (IL): the assessment of IL, the association between IL knowledge and skills, and the dimensionality of the IL construct. Three quantitative measures were developed and tested with several samples of university students to assess knowledge and skills for core facets of IL. These measures are freely available, applicable across disciplines, and easy to administer. Results indicate they are likely to be reliable and support valid interpretations. By measuring both knowledge and practice, the tools indicated low to moderate correlations between what students know about IL, and what they actually do when evaluating and using sources in authentic, graded assignments. The study is unique in using actual coursework to compare knowing and doing regarding students’ evaluation and use of sources. It provides one of the most thorough documentations of the development and testing of IL assessment measures to date. Results also urge us to ask whether the source-focused components of IL – information seeking, source evaluation and source use – can be considered unidimensional constructs or sets of disparate and more loosely related components, and findings support their heterogeneity.

Code and non-confidential data for Regulation Privacy Online: An Economic Evaluation of the GDPR.Modern websites rely on personal data to measure and improve their performance and to market to consumers. The European Union’s General Data Protection Regulation (GDPR) limited access to such personal data, with the goal of protecting consumer privacy. We examine the GDPR's impact on website pageviews and revenue for 1,084 diverse online firms using data from Adobe's website analytics platform. Among EU users, we find a reduction of approximately 12% in both website pageviews and e-commerce revenue, as recorded by the platform after the GDPR's enforcement deadline. We find evidence that the GDPR both reduced data recording and harmed real economic outcomes, and we derive bounds for the relative contribution of each explanation

ABSTRACT The aim of this article was to evaluate the effect of the National Program for Access and Quality Improvement in Primary Care (PMAQ-AB) on access and quality of Primary Care. Hospitalization for conditions susceptible to Primary Care were used as a dependent variable and indirect indicator of quality and access to that level of care. The quantitative method of study was applied, adopting all Brazilian municipalities as unit of analysis. The study was divided into two phases. The first one performed an exploratory descriptive time series analysis on the Brazilian municipalities for the period from 2010 to 2014. The second phase was characterized for evaluating the effect of the Program, during the same period, on access and quality of Primary Care by means of the statistical regression technique with counting data. The results, analyzed by region, showed that the Program exerts a significant effect on the quality and access of Primary Care, especially in the Northeast region of Brazil. This article contains an important report on health policies in Brazil, and is a mean of instructing managers and the various actors involved in the development and discussion of one of the main programs in Primary Care.

This dataset is the basis for the U.S. Geological Survey Scientific Investigations Report "An evaluation of methods for computing annual water quality loads", which utilized available data from Heidelberg University and the U.S. Geological Survey to evaluate the accuracy of various methods for computing annual water-quality loads. This dataset contains two files used in the report: "QW_FLOW.csv", which contains the water-quality sample and streamflow data used to develop water-quality load estimates, and "QW_LOAD.csv", which contains observed and estimated annual loads.

Synthetic Evaluation Data Generation Market Outlook

According to our latest research, the Synthetic Evaluation Data Generation market size was valued at $1.2 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a remarkable CAGR of 22.7% during the forecast period from 2025 to 2033. The primary factor driving the robust growth of the global synthetic evaluation data generation market is the increasing demand for high-quality, diverse, and privacy-compliant datasets to train, test, and validate artificial intelligence (AI) and machine learning (ML) models across industries. As organizations face growing regulatory scrutiny regarding data privacy and security, synthetic data generation offers a compelling solution by enabling the creation of realistic, anonymized datasets that accelerate AI innovation while minimizing compliance risks.

Regional Outlook

North America currently holds the largest share of the synthetic evaluation data generation market, accounting for approximately 38% of the global market value in 2024. This dominance is attributed to the region’s mature technology ecosystem, early adoption of artificial intelligence, and the presence of leading data-centric companies and research institutions. The United States, in particular, has been at the forefront of synthetic data innovation, fueled by significant investments in AI R&D, robust regulatory frameworks supporting data privacy, and a high concentration of enterprises seeking advanced data solutions. The region’s proactive approach to digital transformation, combined with stringent data governance policies such as CCPA and HIPAA, has further accelerated the adoption of synthetic evaluation data generation tools, especially in sectors like healthcare, finance, and autonomous vehicles.

The Asia Pacific region is emerging as the fastest-growing market for synthetic evaluation data generation, projected to achieve a CAGR of 27.3% between 2025 and 2033. Countries such as China, Japan, South Korea, and India are witnessing exponential growth in AI-driven applications and digital transformation initiatives. This surge is underpinned by rising investments in AI infrastructure, government-led digitalization programs, and the proliferation of startups specializing in synthetic data technologies. The region’s large, diverse populations and rapidly expanding digital economies create a unique demand for scalable, localized, and privacy-compliant data solutions, driving accelerated adoption of synthetic data generation platforms across industries such as e-commerce, fintech, and smart mobility.

Emerging economies in Latin America, the Middle East, and Africa are beginning to recognize the transformative potential of synthetic evaluation data generation, albeit at a relatively nascent stage. Adoption in these regions is often challenged by factors such as limited access to advanced AI infrastructure, lack of skilled talent, and evolving regulatory landscapes. However, increasing awareness of the benefits of synthetic data for overcoming data scarcity, enhancing model robustness, and ensuring compliance with emerging data protection laws is fostering gradual uptake. Governments and enterprises in these regions are exploring pilot projects and partnerships to address localized data challenges, with a focus on sectors like public health, smart cities, and financial inclusion. As policy frameworks mature and digital literacy improves, these markets are poised for significant growth over the next decade.

Report Scope

Rocket Engine Test Data Analytics Market Outlook

According to our latest research, the global rocket engine test data analytics market size in 2024 stands at USD 1.42 billion. The market is experiencing robust expansion, with a compounded annual growth rate (CAGR) of 12.8% from 2025 to 2033. By 2033, the market is forecasted to reach a value of USD 4.19 billion. This growth is primarily fueled by the increasing demand for advanced data analytics to enhance the reliability, safety, and performance of rocket engines, as well as the rising frequency of space missions and test launches across both governmental and commercial sectors.

One of the key factors propelling the growth of the rocket engine test data analytics market is the rapid technological advancement in data acquisition and processing systems. Modern rocket engine tests generate colossal volumes of data, encompassing parameters such as thrust, temperature, vibration, and fuel flow. The integration of sophisticated analytics platforms enables stakeholders to derive actionable insights from this data, facilitating real-time monitoring, anomaly detection, and root-cause analysis. This technological leap not only shortens development cycles but also significantly reduces the risk of catastrophic failures, making it indispensable for organizations aiming to maintain a competitive edge in the aerospace and defense sector.

Another significant growth driver is the escalating investment in space exploration and commercial spaceflight activities. Both government agencies like NASA and ESA, as well as private players such as SpaceX and Blue Origin, are conducting more frequent and complex test campaigns. These organizations increasingly rely on data analytics to validate engine designs, optimize test procedures, and ensure compliance with stringent safety standards. The advent of reusable rocket technology further amplifies the need for predictive maintenance and performance analytics, as understanding wear and tear across multiple launches becomes critical to mission success and cost efficiency.

The convergence of artificial intelligence (AI) and machine learning (ML) with rocket engine test data analytics is also catalyzing market expansion. Advanced algorithms are now capable of identifying subtle patterns and correlations within vast datasets, enabling predictive maintenance and early fault detection with unprecedented accuracy. This capability is particularly valuable for commercial space companies and research institutes seeking to maximize engine uptime and minimize unplanned downtimes. Moreover, the growing adoption of cloud-based analytics platforms is democratizing access to high-performance computing resources, allowing smaller organizations and emerging space nations to participate in the market and drive further innovation.

From a regional perspective, North America continues to dominate the rocket engine test data analytics market, accounting for over 43% of the global revenue in 2024. This leadership is attributed to the presence of major aerospace companies, robust government funding, and a vibrant ecosystem of technology providers. However, Asia Pacific is emerging as the fastest-growing region, with countries like China and India ramping up their space programs and investing heavily in indigenous rocket engine development and testing infrastructure. Europe also remains a significant market, driven by collaborative initiatives and strong research capabilities. The Middle East & Africa and Latin America, while still nascent, are expected to witness steady growth as regional space ambitions intensify.

Component Analysis

The component segment of the rocket engine test data analytics market is categorized into software, hardware, and services. The software component is witnessing the highest growth, driven by the increasing demand for advanced analytics platforms capable of handling large-scale, high-velocity data streams generated during engine tests. These so

We evaluated several animations for transitions between scatter plots in a crowd-sourcing study. We published the results in a paper and provide additional information within this supplemental material. Contents: Tables that did not fit into the original paper, due to page limits. An anonymized print-out of the preregistration. The original preregistration is available at OSF (DOI) and on the internet archive. Videos demonstrating the tasks used in the study: used to record samples for the study, used for participant training, and used to detect distracted participants and bots. An interactive demonstration of all study tasks (including training and attention checks). The source code is contained within the directory ./interactive-demo/ of this supplemental material and also available at GitHub. The animation library that we used for the study. We also include a test page for readers to use with their own data sets. The source code is contained within the directory ./animation-library/ of this supplemental material and also avialable at GitHub. The list of nonsensical statements that we used for attention checks on Prolific. The statistical tests with the recorded study data, some of which we reported in the main paper. We also provide reports from the preliminary power analysis that we performed to determine the number of participants for the study. The recorded pseudo-anonymized study data for further analysis.

Since the beginning of the 1960s, Statistics Sweden, in collaboration with various research institutions, has carried out follow-up surveys in the school system. These surveys have taken place within the framework of the IS project (Individual Statistics Project) at the University of Gothenburg and the UGU project (Evaluation through follow-up of students) at the University of Teacher Education in Stockholm, which since 1990 have been merged into a research project called 'Evaluation through Follow-up'. The follow-up surveys are part of the central evaluation of the school and are based on large nationally representative samples from different cohorts of students.

Evaluation through follow-up (UGU) is one of the country's largest research databases in the field of education. UGU is part of the central evaluation of the school and is based on large nationally representative samples from different cohorts of students. The longitudinal database contains information on nationally representative samples of school pupils from ten cohorts, born between 1948 and 2004. The sampling process was based on the student's birthday for the first two and on the school class for the other cohorts.

For each cohort, data of mainly two types are collected. School administrative data is collected annually by Statistics Sweden during the time that pupils are in the general school system (primary and secondary school), for most cohorts starting in compulsory school year 3. This information is provided by the school offices and, among other things, includes characteristics of school, class, special support, study choices and grades. Information obtained has varied somewhat, e.g. due to changes in curricula. A more detailed description of this data collection can be found in reports published by Statistics Sweden and linked to datasets for each cohort.

Survey data from the pupils is collected for the first time in compulsory school year 6 (for most cohorts). Questionnaire in survey in year 6 includes questions related to self-perception and interest in learning, attitudes to school, hobbies, school motivation and future plans. For some cohorts, questionnaire data are also collected in year 3 and year 9 in compulsory school and in upper secondary school.

Furthermore, results from various intelligence tests and standartized knowledge tests are included in the data collection year 6. The intelligence tests have been identical for all cohorts (except cohort born in 1987 from which questionnaire data were first collected in year 9). The intelligence test consists of a verbal, a spatial and an inductive test, each containing 40 tasks and specially designed for the UGU project. The verbal test is a vocabulary test of the opposite type. The spatial test is a so-called ‘sheet metal folding test’ and the inductive test are made up of series of numbers. The reliability of the test, intercorrelations and connection with school grades are reported by Svensson (1971).

For the first three cohorts (1948, 1953 and 1967), the standartized knowledge tests in year 6 consist of the standard tests in Swedish, mathematics and English that up to and including the beginning of the 1980s were offered to all pupils in compulsory school year 6. For the cohort 1972, specially prepared tests in reading and mathematics were used. The test in reading consists of 27 tasks and aimed to identify students with reading difficulties. The mathematics test, which was also offered for the fifth cohort, (1977) includes 19 assignments. After a changed version of the test, caused by the previously used test being judged to be somewhat too simple, has been used for the cohort born in 1982. Results on the mathematics test are not available for the 1987 cohort. The mathematics test was not offered to the students in the cohort in 1992, as the test did not seem to fully correspond with current curriculum intentions in mathematics. For further information, see the description of the dataset for each cohort.

For several of the samples, questionnaires were also collected from the students 'parents and teachers in year 6. The teacher questionnaire contains questions about the teacher, class size and composition, the teacher's assessments of the class' knowledge level, etc., school resources, working methods and parental involvement and questions about the existence of evaluations. The questionnaire for the guardians includes questions about the child's upbringing conditions, ambitions and wishes regarding the child's education, views on the school's objectives and the parents' own educational and professional situation.

The students are followed up even after they have left primary school. Among other things, data collection is done during the time they are in high school. Then school administrative data such as e.g. choice of upper secondary school line / program and grades after completing studies. For some of the cohorts, in addition to school administrative data, questionnaire data were also collected from the students.

he sample consisted of students born on the 5th, 15th and 25th of any month in 1953, a total of 10,723 students.

The data obtained in 1966 were: 1. School administrative data (school form, class type, year and grades). 2. Information about the parents' profession and education, number of siblings, the distance between home and school, etc.

This information was collected for 93% of all born on the current days. The reason for this is reduced resources for Statistics Sweden for follow-up work - reminders etc. Annual data for cohorts in 1953 were collected by Statistics Sweden up to and including academic year 1972/73.

1. Answers to certain questions that shed light on students' school motivation, leisure activities and study and career plans. Some of the questions changed significantly compared to the cohort in 1948 due to the fact that they did not function satisfactorily from a metrological point of view.
2. Results on three aptitude tests, one verbal, one spatial and one inductive.
3. Standard test results in reading, writing, mathematics and English, which were offered to the students who belonged to year 6.

Response rate for test and questionnaire data is 88% Standard test results were received for just over 85% of those who took the tests.

The sample included a total of 9955 students, for whom some form of information was obtained.

Part of the "Individual Statistics Project" together with cohort 1953.

• ./data/train.tsv and ./data/dev.tsv are TSV files consisting of topics (queries), facets, clarifying questions, user's answers, and labels for how much clarification is needed (clarification needs).
• ./data/test.tsv is a TSV file consisting of test topic ID's, as well as queries (text).
• ./data/test_with_labels.tsv is a TSV file consiting of test topic ID's with the labels. It can be used with the evaluation script.
• ./data/multi_turn_human_generated_data.tsv is a TSV file containing the human-generated multi turn conversations which is the result of of the human-in-the-loop process.
• ./data/question_bank.tsv is a TSV file containing all the questions in the collection, as well as their ID's. Participants' models should select questions from this file.
• ./data/top10k_docs_dict.pkl.tar.gz is a dict containing the top 10,000 document ID's retrieved from ClueWeb09 and ClueWeb12 collections for each topic. This may be used by the participants who wish to leverage documents content in their models.
• ./data/single_turn_train_eval.pkl is a dict containing the performance of each topic after asking a question and getting the answer. The evaluation tool that we provide uses this file to evaluate the selected questions.
• ./data/multi_turn_train_eval.pkl.tar.gz.** and ./data/multi_turn_dev_eval.pkl.tar.gz are dicts that contain the performance of each conversation after asking a question from the question_bank and getting the answer from the user. The evaluation tool that we provide uses this file to evaluate the selected questions. Notice that these dicts are built based on the synthetic multi-turn conversations.
• ./data/dev_synthetic.pkl.tar.gz and ./data/train_synthetic.pkl.tar.gz are two compressed pickle files that contain dicts of synthetic multi-turn conversations. We have generated these conversations following the method explained in [1].
• ./src/clariq_eval_tool.py is a python script to evaluate the runs. The participants may use this tool to evaluate their models on the dev set. We would use the same tool to evaluate the submitted runs on the test set.
• ./sample_runs/ contains some sample runs and baselines. Among them, we have included the two oracle models BestQuestion and WorstQuestion, as well as NoQuestion, the model choosing no question. Participants may check these files as sample run files. Also, they could test the evaluation tool using these files.

This archive contains:

- model_output.tar.gz: Model output
- extracted_fluxes.tar.gz: Sensible heat, latent heat and CO2 fluxes extracted from the FLUXNET2015 dataset, used to evaluate the model output
- extracted_climate.tar.gz: Climate data extracted from the FLUXNET2015 dataset, used to force the simulations
- scripts.tar.gz: Python scripts used to analyze the data and produce the results reported in the model description paper

The climate forcing data has been extracted from the FLUXNET2015 dataset [1]. Input and output data is stored in netCDF files. Evaluation data is stored in python serialized files (pickle).

[1] Pastorello, G., Trotta, C., Canfora, E. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7, 225 (2020). https://doi.org/10.1038/s41597-020-0534-3

Data obtained from n=67 untrained attendants at an outreach Pint of Science festival, online because of the COVID-19 pandemic but usually held at bars. The participants used images of brand logos to evaluate eight beers among the most commonly consumed in Spain. Three sensory analysis techniques were used: Projective Mapping, Acceptability and Check-All-That-Apply (CATA).

This dataset is based on the unit and regression testing available on https://github.com/USEPA/Stormwater-Management-Model/actions

The Supporting Healthy Marriage (SHM) evaluation was launched in 2003 to develop, to implement, and to test the effectiveness of a program aimed at strengthening low-income couples' marriages as one approach for supporting stable and nurturing family environments and parents' and children's well-being. The evaluation was led by MDRC and was sponsored by the Office of Planning, Research and Evaluation in the Administration for Children and Families, United States Department of Health and Human Services.The SHM program was a voluntary yearlong marriage education program for low-income married couples who had children or were expecting a child. The program provided a series of group workshops based on structured curricula designed to enhance couples' relationships; supplemental activities to build on workshop themes; and family support services to address participation barriers, connect families with other services, and reinforce curricular themes. The study sample consists of 6,298 couples (12,596 adult sample members) who were expecting a child or had a child under 18 years old at the time of study entry. The sample consists primarily of low-to-modest income, married couples with diverse racial and ethnic backgrounds. In each family, one child was randomly selected to be the focus of any child-related measures gathered in the data collection activities. These children ranged from pre-birth to 14 years old at the time of enrollment in the study. Follow-up interviews were conducted at 12 and 30 months after baseline data collection. More detail is provided in the study documentation.

The attached file contains the data provided by the SimaPro software during a series of tests to evaluate the environmental benefit of adding an agro-industrial residue, alpeorujo or olive pomace, to a clay matrix for the production of lightweight aggregates. Life Cycle Analysis and CML 2000 methodology were used.

In this article, we propose a multiscale adaptive test to detect differences between two samples of intrinsically smoothed image data in high-dimensional context. The test aggregates data from nearby locations using adaptive weights, significantly enhancing statistical power. We demonstrate that the test statistic converges to a Gumbel extreme value distribution under the null hypothesis. Moreover, we investigate its multiscale nature, showing that the chosen scales can grow at a specific polynomial rate of the sample size. We also evaluate its power against sparse alternatives and establish that with probability approaching one, the proposed method can identify the locations where the two means differ from each other. Additionally, we extend the proposed method to multi-sample ANOVA tests. Simulation results suggest that the proposed test outperforms the non-multiscale method that ignores spatial features of imaging data. The procedures are illustrated using a real dataset from the Alzheimer’s Disease Neuroimaging Initiative study. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

ABSTRACT: Purpose: to analyze the phonoaudiological practice in the applicability of the "Tongue-tie Test" in Distrito Federal. Methods: it was prepared a self-explanatory questionnaire, composed by 10 questions. The questionnaire was disposed in a specific website created for this purpose. Participated in this research 44 phonoaudiologists from Distrito Federal. The statistical analysis of data was initially performed in simple frequency tables and then in cross-frequency tables. Results: participated in this research phonoaudiologists experienced in several areas, public and private sector. Only 27.27% of the participants evaluate the lingual frenulum in infants; most evaluates less than one year. The phonoaudiologists is the best known professional in Distrito Federal which performs evaluation of the lingual frenulum in infants. Conclusion: most of the phonoaudiologists who evaluate the lingual frenulum in newborns do not use specific protocol for the analysis. The criteria used during the examination vary.

Abstract

This dataset was supplied to the Biorgeional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on known details at the time of acquisition.

This dataset contains the pdf report: Kruseman G and de Ridder N (1994) Analysis and Evaluation of Pumping Test Data (2nd Edition). International Institute for Land Reclamation and Improvement, Wageningen, The Netherlands.

Dataset History

This dataset contains the pdf report: Kruseman G and de Ridder N (1994) Analysis and Evaluation of Pumping Test Data (2nd Edition). International Institute for Land Reclamation and Improvement, Wageningen, The Netherlands.

Viewed from: http://www.hydrology.nl/images/docs/dutch/key/Kruseman_and_De_Ridder_2000.pdf on 18 December 2015

Dataset Citation

International Institute for Land Reclamation and Improvement (1994) Analysis and Evaluation of Pumping Test Data.. Bioregional Assessment Source Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/c66b744e-e9bb-4d88-a82c-a6593efe91d2.

Boost your software testing efficiency with our comprehensive analysis of the Test Data Generation Tools market. Discover key trends, growth drivers, and leading companies shaping this booming $1500 million market (2025). Learn about regional market share, segmentation, and future forecasts.

Precision and Recall, as well as their combination in terms of FMeasure, are widely used measures in computer science and generally used to evaluate the overall performance of ontology matchers in fully automatic, unsupervised scenarios. In this paper, we investigate the case of supervised matching,where automatically created ontology alignments are verified by an expert. We motivate and describe this use case and its characteristics and discuss why traditional, F-measure based evaluation measures are not suitable to choose the best matching system for this task. Therefore, we investigate several alternative evaluation measures and propose the use of Precision@N curves as a means to assess different matching systems for supervised matching. We compare the ranking of ontology matchers from the last OAEI campaign using Precision@N curves to the traditional F-measure based ranking, and discuss means to combine matchers in a way that optimizes the user support in supervised ontology matching.