As part of the planning for stimulation of the Newberry Volcano Enhanced Geothermal Systems (EGS) Demonstration project in Oregon, a high-resolution borehole televiewer (BHTV) log was acquired using the ALT ABI85 BHTV tool in the slightly deviated NWG 55-29 well. The image log reveals an extensive network of fractures in a conjugate set striking approximately N-S and dipping 50 deg that are well oriented for normal slip and are consistent with surface-breaking regional normal faults in the vicinity. Similarly, breakouts indicate a consistent minimum horizontal stress, Shmin, azimuth of 092.3 +/- 17.3 deg. In conjunction with a suite of geophysical logs, a model of the stress magnitudes constrained by the width of breakouts at depth and a model of rock strength independently indicates a predominantly normal faulting stress regime.

A supplementary report for the paper "A Preliminary Analysis on the Effect of Randomness in a CEGAR Framework" by Ákos Hajdu and Zoltán Micskei, presented at the 25th PhD Mini-Symposium (2018), organized by the Department of Measurement and Information Systems at the Budapest University of Technology and Economics.

An Open Context "projects" dataset item. Described with: "Content related to a chapter in "The Wide Lens in Archaeology: Honoring Brian Hesse's Contributions to Anthropological Archaeology"".

Preliminary Analysis of in Situ Strain Correlation New York and Vermont: Ii in Among Strain and Rock Properties (abs )

272level1、 272level1、 Cronbach's alpha、 Demographic characteristics、 descriptive statistics and correlation analysis

Dataset Overview:

This dataset contains simulated (hypothetical) but almost realistic (based on AI) data related to sleep, heart rate, and exercise habits of 500 individuals. It includes both pre-exercise and post-exercise resting heart rates, allowing for analyses such as a dependent t-test (Paired Sample t-test) to observe changes in heart rate after an exercise program. The dataset also includes additional health-related variables, such as age, hours of sleep per night, and exercise frequency.

The data is designed for tasks involving hypothesis testing, health analytics, or even machine learning applications that predict changes in heart rate based on personal attributes and exercise behavior. It can be used to understand the relationships between exercise frequency, sleep, and changes in heart rate.

File: Filename: heart_rate_data.csv File Format: CSV

- Features (Columns):

Age: Description: The age of the individual. Type: Integer Range: 18-60 years Relevance: Age is an important factor in determining heart rate and the effects of exercise.

Sleep Hours: Description: The average number of hours the individual sleeps per night. Type: Float Range: 3.0 - 10.0 hours Relevance: Sleep is a crucial health metric that can impact heart rate and exercise recovery.

Exercise Frequency (Days/Week): Description: The number of days per week the individual engages in physical exercise. Type: Integer Range: 1-7 days/week Relevance: More frequent exercise may lead to greater heart rate improvements and better cardiovascular health.

Resting Heart Rate Before: Description: The individual’s resting heart rate measured before beginning a 6-week exercise program. Type: Integer Range: 50 - 100 bpm (beats per minute) Relevance: This is a key health indicator, providing a baseline measurement for the individual’s heart rate.

Resting Heart Rate After: Description: The individual’s resting heart rate measured after completing the 6-week exercise program. Type: Integer Range: 45 - 95 bpm (lower than the "Resting Heart Rate Before" due to the effects of exercise). Relevance: This variable is essential for understanding how exercise affects heart rate over time, and it can be used to perform a dependent t-test analysis.

Max Heart Rate During Exercise: Description: The maximum heart rate the individual reached during exercise sessions. Type: Integer Range: 120 - 190 bpm Relevance: This metric helps in understanding cardiovascular strain during exercise and can be linked to exercise frequency or fitness levels.

Potential Uses: Dependent T-Test Analysis: The dataset is particularly suited for a dependent (paired) t-test where you compare the resting heart rate before and after the exercise program for each individual.

Exploratory Data Analysis (EDA):Investigate relationships between sleep, exercise frequency, and changes in heart rate. Potential analyses include correlations between sleep hours and resting heart rate improvement, or regression analyses to predict heart rate after exercise.

Machine Learning: Use the dataset for predictive modeling, and build a beginner regression model to predict post-exercise heart rate using age, sleep, and exercise frequency as features.

Health and Fitness Insights: This dataset can be useful for studying how different factors like sleep and age influence heart rate changes and overall cardiovascular health.

License: Choose an appropriate open license, such as:

CC BY 4.0 (Attribution 4.0 International).

Inspiration for Kaggle Users: How does exercise frequency influence the reduction in resting heart rate? Is there a relationship between sleep and heart rate improvements post-exercise? Can we predict the post-exercise heart rate using other health variables? How do age and exercise frequency interact to affect heart rate?

Acknowledgments: This is a simulated dataset for educational purposes, generated to demonstrate statistical and machine learning applications in the field of health analytics.

Open-file report; contains unpublished data that has not yet been peer-reviewed.

Mutualistic networks in space, morphological traits and colors, or how to put all together to understand rare and common species in rapidly changing landscapes

Preliminary analysis report generated automatically by the iMAP to provide a summary of conserved taxonomy assigned to OTUs and the initial analysis of OTUs and taxa data. The preliminary analysis report was automatically saved in the “reports” folder as “report4_preliminary_analysis.html”. (HTML 20379 kb)

Abstract The aims of this study were: 1) to evaluate the sports potential of young basketball players; 2) to identify variables that discriminate sports potential assessed by coaches; 3) to verifythe relationship between classification of the multidimensional profile of athletes and classification of the sports potential by coaches. Sixty-two young basketball players aged 15.6±1.1 years from U-15 (n = 24) and U-17 (n = 38) categories participated in the study. A test battery was applied to evaluate sports potential indicators: 1) anthropometric; 2) physicomotor; 3) psychological;4) skills;5) socio-environmental;6) maturational and 7) sports potential.Clusteranalysis was performed in three groups: high, medium and low potential. Student’s t-test was used for the comparison between athletes evaluated by the coach as excellent and the others and the Chi-Square test to verify the relationship between sports potential classifications. It was observed that in the high-potential group, athletes were chronologically older, with higher % predicted adult height (PMS), competitive and determined sports orientation, higher body size, lower skinfold summation, and greater physicomotor performance. In comparison with other athletes, high-potential basketball players presented higher stature, wider wingspan,longer limb length, greater predicted adult stature and higher Z score of the % PMS. It could be concluded that the multidimensional approach was useful for the evaluation of the sports potential of young basketball players, requiring the use of multidimensional variables, in addition to coaches’ opinion regarding the potential of their athletes.

A comparative analysis of the amount and type of attention received by OA v non-OA articles published in Nature Communciations in 2013-14. Original study conducted by Euan Adie and published on the Altmetric blog: http://www.altmetric.com/blog/attentionoa/ Poster created and presented by Catherine Williams at the 2015 COASP conference, from 15th - 17th September 2015 in Amsterdam.

BACKGROUND: The Health Insurance Institute of Slovenia (ZZZS) began publishing service-related data in May 2023, following a directive from the Ministry of Health (MoH). The ZZZS website provides easily accessible information about the services provided by individual doctors, including their names. The user is provided relevant information about the doctor's employer, including whether it is a public or private institution. The data provided is useful for studying the public system's operations and identifying any errors or anomalies.

METHODS: The data for services provided in May 2023 was downloaded and analysed. The published data were cross-referenced using the provider's RIZDDZ number with the daily updated data on ambulatory workload from June 9, 2023, published by ZZZS. The data mentioned earlier were found to be inaccurate and were improved using alerts from the zdravniki.sledilnik.org portal. Therefore, they currently provide an accurate representation of the current situation. The total number of services provided by each provider in a given month was determined by adding up the individual services and then assigning them to the corresponding provider.

RESULTS: A pivot table was created to identify 307 unique operators, with 15 operators not appearing in both lists. There are 66 public providers, which make up about 72% of the contractual programme in the public system. There are 241 private providers, accounting for about 28% of the contractual programme. In May 2023, public providers accounted for 69% (n=646,236) of services in the family medicine system, while private providers contributed 31% (n=291,660). The total number of services provided by public and private providers was 937,896. Three linear correlations were analysed. The initial analysis of the entire sample yielded a high R-squared value of .998 (adjusted R-squared value of .996) and a significant level below 0.001. The second analysis of the data from private providers showed a high R Squared value of .904 (Adjusted R Squared = .886), indicating a strong correlation between the variables. Furthermore, the significance level was < 0.001, providing additional support for the statistical significance of the results. The third analysis used data from public providers and showed a strong level of explanatory power, with a R Squared value of 1.000 (Adjusted R Squared = 1.000). Furthermore, the statistical significance of the findings was established with a p-value < 0.001.

CONCLUSION: Our analysis shows a strong linear correlation between contract size of the program signed and number services rendered by family medicine providers. A stronger linear correlation is observed among providers in the public system compared to those in the private system. Our study found that private providers generally offer more services than public providers. However, it is important to acknowledge that the evaluation framework for assessing services may have inherent flaws when examining the data. Prescribing a prescription and resuscitating a patient are both assigned a rating of one service. It is crucial to closely monitor trends and identify comparable databases for pairing at the secondary and tertiary levels.

A preliminary analysis of associations between Ureaplasma, Mycoplasma and the vaginal microbiome

Preliminary results from an ongoing analysis of citation practices in quantitative studies that analyze data from Altmetric.com (supporting data for our submission to http://altmetrics.org/altmetrics20/).Data sources: https://web.archive.org/web/20200929163109/https://www.altmetric.com/blog/altmetrics-research-2019/ and https://web.archive.org/web/20200929163146/https://www.altmetric.com/blog/altmetric-supported-research-2018-in-review/The dataset shows that only 32% of quantitative studies that build upon Altmetric’s attention score mention the day on which the data was collected, and that 50% mention no version information at all.

{"Preliminary check on the provisions on self-consumption and energy communities in the draft National Energy and Climate Plans (NECPs) of nine EU Member States (BE, DE, ES, FR, HR, IT, NL, PT, UK). The main findings are that only France and Spain put a reasonable emphasis on the importance of self-consumption and energy communities as suggested by RED II. Germany and Italy show some efforts while the NECPs of the other five member states contain only weak or no provisions on prosumerism. Most countries don’t define neither targets nor measures.","H2020 project PROSEU - Prosumers for the Energy Union: Mainstreaming active participation of citizens in the energy transition. Work package 3 (WP3) - Policy, regulation and governance"}

No Publication Abstract is Available

An Open Context "projects" dataset item. Described with: "Content related to a chapter in 'The Archaeology of Mesoamerican Animals'".

Offshore wind represents a potentially significant source of low-carbon energy for Canada, and ensuring that relevant, high-quality data and scientifically sound analyses are brought forward into decision-making processes will increase the chances of success for any future deployment of offshore wind in Canada. To support this objective, CanmetENERGY-Ottawa (CE-O), a federal laboratory within Natural Resources Canada (NRCan), completed a preliminary analysis of relevant considerations for offshore wind, with an initial focus on Atlantic Canada. To conduct the analysis, CE-O used geographic information system (GIS) software and methods and engaged with multiple federal government departments to acquire relevant data and obtain insights from subject matter experts on the appropriate use of these data in the context of the analysis. The purpose of this work is to support the identification of candidate regions within Atlantic Canada that could become designated offshore wind energy areas in the future. The study area for the analysis included the Gulf of St. Lawrence, the western and southern coasts of the island of Newfoundland, and the coastal waters south of Nova Scotia. Twelve input data layers representing various geophysical, ecological, and ocean use considerations were incorporated as part of a multi-criteria analysis (MCA) approach to evaluate the effects of multiple inputs within a consistent framework. Six scenarios were developed which allow for visualization of a range of outcomes according to the influence weighting applied to the different input layers and the suitability scoring applied within each layer. This preliminary assessment resulted in the identification of several areas which could be candidates for future designated offshore wind areas, including the areas of the Gulf of St. Lawrence north of Prince Edward Island and west of the island of Newfoundland, and areas surrounding Sable Island. This study is subject to several limitations, namely missing and incomplete data, lack of emphasis on temporal and cumulative effects, and the inherent subjectivity of the scoring scheme applied. Further work is necessary to address data gaps and take ecosystem wide impacts into account before deployment of offshore wind projects in Canada’s coastal waters. Despite these limitations, this study and the data compiled in its preparation can aid in identifying promising locations for further review. A description of the methodology used to undertake this study is contained in the accompanying report, available at the following link: https://doi.org/10.4095/331855. This report provides in depth detail into how these data layers were compiled and details any analysis that was done on the data to produce the final data layers in this package.

Legacy product - no abstract available

The study is mixed methods research.Quantitative Data: Datasets are of sociodemographic data of women accessing cervical cancer screening at a woman's clinic. The datasets and do files can be opened in analytic software, STATA . Qualitative data: Qualitative data consists of preliminary analysis tables and reflective notes from in-depth interviews with female patients and healthcare providers. .