Henslow's sparrow count dataCounts of Henslow's sparrows at Big Oaks National Wildlife Refuge with covariates for years since prescribed fire.data.csv

BackgroundThere is widespread evidence that statistical methods play an important role in original research articles, especially in medical research. The evaluation of statistical methods and reporting in journals suffers from a lack of standardized methods for assessing the use of statistics. The objective of this study was to develop and evaluate an instrument to assess the statistical intensity in research articles in a standardized way.MethodsA checklist-type measure scale was developed by selecting and refining items from previous reports about the statistical contents of medical journal articles and from published guidelines for statistical reporting. A total of 840 original medical research articles that were published between 2007–2015 in 16 journals were evaluated to test the scoring instrument. The total sum of all items was used to assess the intensity between sub-fields and journals. Inter-rater agreement was examined using a random sample of 40 articles. Four raters read and evaluated the selected articles using the developed instrument.ResultsThe scale consisted of 66 items. The total summary score adequately discriminated between research articles according to their study design characteristics. The new instrument could also discriminate between journals according to their statistical intensity. The inter-observer agreement measured by the ICC was 0.88 between all four raters. Individual item analysis showed very high agreement between the rater pairs, the percentage agreement ranged from 91.7% to 95.2%.ConclusionsA reliable and applicable instrument for evaluating the statistical intensity in research papers was developed. It is a helpful tool for comparing the statistical intensity between sub-fields and journals. The novel instrument may be applied in manuscript peer review to identify papers in need of additional statistical review.

This dataset mainly provides the statistical data on the results of our proactive investigation cases.

Requests taken and satisfied by Archives and Records Management. Gives details for each request including time to service the request and demonstrates efforts to provide public and Boston municipal government with access to public records.

Brochure Theme: A0 - Analysis and studies - General Under Theme: A000.01 - Statistical studies

Investigation of the types of fraud cases.........

Statistical Analysis Software Market size was valued at USD 7,963.44 Million in 2023 and is projected to reach USD 13,023.63 Million by 2030, growing at a CAGR of 7.28% during the forecast period 2024-2030.

Global Statistical Analysis Software Market Drivers

The market drivers for the Statistical Analysis Software Market can be influenced by various factors. These may include:

Growing Data Complexity and Volume: The demand for sophisticated statistical analysis tools has been fueled by the exponential rise in data volume and complexity across a range of industries. Robust software solutions are necessary for organizations to evaluate and extract significant insights from huge datasets. Growing Adoption of Data-Driven Decision-Making: Businesses are adopting a data-driven approach to decision-making at a faster rate. Utilizing statistical analysis tools, companies can extract meaningful insights from data to improve operational effectiveness and strategic planning. Developments in Analytics and Machine Learning: As these fields continue to progress, statistical analysis software is now capable of more. These tools' increasing popularity can be attributed to features like sophisticated modeling and predictive analytics. A greater emphasis is being placed on business intelligence: Analytics and business intelligence are now essential components of corporate strategy. In order to provide business intelligence tools for studying trends, patterns, and performance measures, statistical analysis software is essential. Increasing Need in Life Sciences and Healthcare: Large volumes of data are produced by the life sciences and healthcare sectors, necessitating complex statistical analysis. The need for data-driven insights in clinical trials, medical research, and healthcare administration is driving the market for statistical analysis software. Growth of Retail and E-Commerce: The retail and e-commerce industries use statistical analytic tools for inventory optimization, demand forecasting, and customer behavior analysis. The need for analytics tools is fueled in part by the expansion of online retail and data-driven marketing techniques. Government Regulations and Initiatives: Statistical analysis is frequently required for regulatory reporting and compliance with government initiatives, particularly in the healthcare and finance sectors. In these regulated industries, statistical analysis software uptake is driven by this. Big Data Analytics's Emergence: As big data analytics has grown in popularity, there has been a demand for advanced tools that can handle and analyze enormous datasets effectively. Software for statistical analysis is essential for deriving valuable conclusions from large amounts of data. Demand for Real-Time Analytics: In order to make deft judgments fast, there is a growing need for real-time analytics. Many different businesses have a significant demand for statistical analysis software that provides real-time data processing and analysis capabilities. Growing Awareness and Education: As more people become aware of the advantages of using statistical analysis in decision-making, its use has expanded across a range of academic and research institutions. The market for statistical analysis software is influenced by the academic sector. Trends in Remote Work: As more people around the world work from home, they are depending more on digital tools and analytics to collaborate and make decisions. Software for statistical analysis makes it possible for distant teams to efficiently examine data and exchange findings.

We use data from eight satellites to statistically examine the role of chorus as a potential source of plasmaspheric hiss. We find that the strong equatorial (|λm| < 6°) chorus wave power in the frequency range 50 < f < 200 Hz does not extend to high latitudes in any MLT sector and is unlikely to be the source of the low frequency plasmaspheric hiss in this frequency range. In contrast, strong equatorial chorus wave power in the medium frequency range 200 < f < 2000 Hz is observed to extend to high latitudes and low altitudes in the pre-noon sector, consistent with ray tracing modelling from a chorus source and supporting the chorus to hiss generation mechanism. At higher frequencies, chorus may contribute to the weak plasmaspheric hiss seen on the dayside in the frequency range 2000 < f < 3000 Hz band, but is not responsible for the weak plasmaspheric hiss on the night-side in the frequency range 3000 < f < 4000 Hz.

The research leading to these results has received funding from the Natural Environment Research Council (NERC) Highlight Topic grant NE/P01738X/1 (Rad-Sat) and the NERC grants NE/V00249X/1 (Sat-Risk) and NE/R016038/1. Jacob Bortnik received funding from NASA grant NNX14AI18G, and RBSP-ECT and EMFISIS funding provided by JHU/APL contracts 967399 and 921647 under NASA''s prime contract NAS5-01072. Wen Li and Xiao-Chen Shen received funding from NASA grants 80NSSC20K0698 and 80NSSC19K0845, NSF grant AGS-1847818, and the Alfred P. Sloan Research Fellowship FG-2018-10936.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

The datasets containing simulation performance results during the current study, in addition to the code to replicate the simulation study in its entirety, are available here. See the README file for a description the Stata do-files, R-script files, tips to run the code, and the performance result dataset dictionaries.

The objective of this study was to identify the patterns of juvenile salmonid distribution and relative abundance in relation to habitat correlates. It is the first dataset of its kind because the entire river was snorkeled by one person in multiple years. During two consecutive summers, we completed a census of juvenile salmonids and stream habitat across a stream network. We used the data to test the ability of habitat models to explain the distribution of juvenile coho salmon (Oncorhynchus kisutch), young-of-the-year (age 0) steelhead (Oncorhynchus mykiss), and steelhead parr (= age 1) for a network consisting of several different sized streams. Our network-scale models, which included five stream habitat variables, explained 27%, 11%, and 19% of the variation in the density of juvenile coho salmon, age 0 steelhead, and steelhead parr, respectively. We found weak to strong levels of spatial auto-correlation in the model residuals (Moran's I values ranging from 0.25 - 0.71). Explanatory power of base habitat models increased substantially and the level of spatial auto-correlation decreased with sequential inclusion of variables accounting for stream size, year, stream, and reach location. The models for specific streams underscored the variability that was implied in the network-scale models. Associations between juvenile salmonids and individual habitat variables were rarely linear and ranged from negative to positive, and the variable accounting for location of the habitat within a stream was often more important than any individual habitat variable. The limited success in predicting the summer distribution and density of juvenile coho salmon and steelhead with our network-scale models was apparently related to variation in the strength and shape of fish-habitat associations across and within streams and years. Summary of statistical analysis of the Calawah Riverscape data. NOAA was not involved and did not pay for the collection of this data. This data represents the statistical analysis carried out by Martin Liermann as a NOAA employee.

Quantities of the Anderson-Darling statistics and p-values, for S1 (Chol dataset) and S2 (Hep datasets).

ITS data collected as part of Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study. Code used to analyse the ITS studies.

Brochure Theme: A0 – Analysis and studies – General Under Theme: A000.01 – Statistical studies

The Ministry of Justice's Investigation Bureau's anti-corruption work has primarily applied legal statistics for cases transferred over the past five years.

Global Statistical Analysis Software market size is expected to reach $15.49 billion by 2029 at 10.6%, segmented as by software, on-premise software, cloud-based software, desktop-based software, mobile-based software

Brochure Theme: A0 – Analysis and studies – General Under Theme: A000.01 – Statistical studies

This dataset is about books. It has 1 row and is filtered where the book is College crime : a statistical study of offenses on American campuses. It features 7 columns including author, publication date, language, and book publisher.

These are the tab and csv files from the Methods in Biostatistics with R Book

Analysis of structure, intensity, course and results of persecution of non-political fringe groups in the Third Reich as well as the connection of this persecution with the rule structures of German fascism.

Topics: Date of birth, concentration camp, incarceration date, command, end of KZ-detention, manner of end, death, special position, occupation, religion, marital status, children, criminal law paragraph, length of previous convictions, length of police detention, reason for incarceration, place of incarceration.