This indicator shows how many days per year were assessed to have air quality that was worse than “moderate” in Champaign County, according to the U.S. Environmental Protection Agency’s (U.S. EPA) Air Quality Index Reports. The period of analysis is 1980-2024, and the U.S. EPA’s air quality ratings analyzed here are as follows, from best to worst: “good,” “moderate,” “unhealthy for sensitive groups,” “unhealthy,” “very unhealthy,” and "hazardous."[1]

In 2024, the number of days rated to have air quality worse than moderate was 0. This is a significant decrease from the 13 days in 2023 in the same category, the highest in the 21st century. That figure is likely due to the air pollution created by the unprecedented Canadian wildfire smoke in Summer 2023.

While there has been no consistent year-to-year trend in the number of days per year rated to have air quality worse than moderate, the number of days in peak years had decreased from 2000 through 2022. Where peak years before 2000 had between one and two dozen days with air quality worse than moderate (e.g., 1983, 18 days; 1988, 23 days; 1994, 17 days; 1999, 24 days), the year with the greatest number of days with air quality worse than moderate from 2000-2022 was 2002, with 10 days. There were several years between 2006 and 2022 that had no days with air quality worse than moderate.

This data is sourced from the U.S. EPA’s Air Quality Index Reports. The reports are released annually, and our period of analysis is 1980-2024. The Air Quality Index Report websites does caution that "[a]ir pollution levels measured at a particular monitoring site are not necessarily representative of the air quality for an entire county or urban area," and recommends that data users do not compare air quality between different locations[2].

[1] Environmental Protection Agency. (1980-2024). Air Quality Index Reports. (Accessed 13 June 2025).

[2] Ibid.

Source: Environmental Protection Agency. (1980-2024). Air Quality Index Reports. https://www.epa.gov/outdoor-air-quality-data/air-quality-index-report. (Accessed 13 June 2025).

Manually abtracted gestational age across different locations in the EHR. (XLSX 14 kb)

Turkey: Quality of railroad infrastructure, 1(low) - 7(high): The latest value from 2019 is 3.5 points, an increase from 3.3 points in 2018. In comparison, the world average is 3.61 points, based on data from 101 countries. Historically, the average for Turkey from 2009 to 2019 is 3.01 points. The minimum value, 2.48 points, was reached in 2009 while the maximum of 3.5 points was recorded in 2019.

Regulatory Quality: Percentile Rank in Belgium was reported at 85.85 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Belgium - Regulatory Quality: Percentile Rank - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Dr. Kevin Bronson provides a unique nitrogen and water management in cotton agricultural research dataset for compute, including notation of field events and operations, an intermediate analysis mega-table of correlated and calculated parameters, and laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs. This data was collected using a Hamby rig as a high-throughput proximal plant phenotyping platform. The Hamby 6000 rig Ellis W. Chenault, & Allen F. Wiese. (1989). Construction of a High-Clearance Plot Sprayer. Weed Technology, 3(4), 659–662. http://www.jstor.org/stable/3987560 Dr. Bronson modified an old high-clearance Hamby 6000 rig, adding a tank and pump with a rear boom, to perform precision liquid N applications. A Raven control unit with GPS supplied variable rate delivery options. The 12 volt Holland Scientific GeoScoutX data recorder and associated CropCircle ACS-470 sensors with GPS signal, was easy to mount and run on the vehicle as an attached rugged data acquisition module, and allowed the measuring of plants using custom proximal active optical reflectance sensing. The HS data logger was positioned near the operator, and sensors were positioned in front of the rig, on forward protruding armature attached to a hydraulic front boom assembly, facing downward in nadir view 1 m above the average canopy height. A 34-size class AGM battery sat under the operator and provided the data system electrical power supply. Data suffered reduced input from Conley. Although every effort was afforded to capture adequate quality across all metrics, experiment exterior considerations were such that canopy temperature data is absent, and canopy height is weak due to technical underperformance. Thankfully, reflectance data quality was maintained or improved through the implementation of new hardware by Bronson. See included README file for operational details and further description of the measured data signals. Summary: Active optical proximal cotton canopy sensing spatial data and including few additional related metrics and weak low-frequency ultrasonic derived height are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2014 season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig. Acquired data conforms to _location standard methodologies of the plant phenotyping. SAS and GIS compute processing output tables, including Excel formatted examples are presented, where data tabulation and analysis is available. Additional ultrasonic data signal explanation is offered as annotated time-series charts. The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake were also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).

BackgroundUterine fibroids are the most common pelvic benign tumours found in reproductive-aged women and may affect up to 70% of all women by menopause. Uterine fibroids place a heavy burden on women and society resulting in poor quality of life, impaired self-image, and impaired social, sexual, emotional, and physical well-being of affected individuals.AimThis study aims to map the evidence on the burden of uterine fibroids in Sub-Saharan Africa; uterine fibroids’ burden by age, uterine fibroids’ geographic burden, uterine fibroids’ cost estimation and reported experiences among women diagnosed with uterine fibroids.SettingArticles will be selected from countries within Sub-Saharan AfricaMethods and analysisThis scoping review will be guided by the Arksey & O’Malley framework, enhanced by Levac et al (2010). The following electronic databases will be searched; PubMed, EBSCOhost (Cumulated Index to Nursing and Allied Health Literature and Health Source), Medical Literature Analysis and Retrieval System Online, Cochrane Library, Scopus, Web of Science, Africa Journal Online, and Google Scholar. The Population Concept and Context (PCC) framework will be used and the PRISMA flow diagram will also be used to show the literature search and selection of studies. Descriptive data analysis will be used; results will be presented in themes, narrative summaries, tables, and charts.DiscussionThe study anticipates finding relevant literature on the distribution of uterine fibroids, the burden of uterine fibroids in terms of geographic distribution, age distribution, and cost approximation related to the disease. This will assist in identifying research gaps to guide future research contribute to the body of scientific knowledge and develop preventative strategies for the disease.

Serbia: Quality of roads, 1(low) - 7(high): The latest value from 2019 is 3.5 points, an increase from 3.4 points in 2018. In comparison, the world average is 4.07 points, based on data from 141 countries. Historically, the average for Serbia from 2007 to 2019 is 2.81 points. The minimum value, 2.36 points, was reached in 2008 while the maximum of 3.5 points was recorded in 2019.

This is a systematic review. Ethical approval is not applicable. The study protocol was first developed and was registered in the PROSPERO international prospective register of systematic reviews (Chien, Khan, & Siassakos, 2012) (CRD42017077818). Employing standard systematic review strategies (Higgins & Green, 2011; Shamseer et al., 2015), we searched for all study types that focused on hospital/clinic based BC awareness and early detection services for healthy women in LMICs. The criteria for selecting the studies for review as well as the awareness and screening of interest (BCE, BSE and CBE) are shown in table 1.Table 1: A PICOS framework for determining the eligibility of the Studies for the Primary Research QuestionCriteria DeterminantsP- Population The population of this study will be healthy women with no evidence of malignancy accessing a BCE or screening intervention I- Intervention Patient-focused BCE and raising awareness over risk factors, early BC signs and symptoms, BC screening and/or detection among womenC- Comparison NoneO- Outcomes • Increased knowledge on BC• Practice BSE• Access CBE services• Early detection of BC• Reduced incidence of BC S- Study Setting LMICs; within a health care facility (hospital/clinic) The quality of included stuides were assessed using Effective Public Health Practice Project (EPHPP) Quality Assessment Tool recommended by Cochrane for quantitative studies (Effective Public Health Practice Project, 2010)PRISMA diagram was used to summerized the selection procedure of the studies

Data content: Summarized the Mg and Zn isotope variation ranges of potassium magmas, porphyries, intraplate alkaline basalts, and peridotites during the carbonate cycle in subduction zones (see reference). Based on this, proposed a mixed model diagram of carbonate rocks in subduction zones (data and simulation diagrams), and provided a parameter table for the mixed model of carbonate rocks and mantle. Data source: Isotope Laboratory of China University of Geosciences (Beijing). Processing method: Collect data that meets the requirements from publicly published literature. Data quality: All data is checked to ensure reliability and can reflect scientific issues before being summarized into a graph; The table in the data is a parameter table for the mixed model of carbonate rocks and mantle. Data application: Display the process of carbonates in subduction zones (which can be divided into three cyclic processes according to different depths) and the approximate range of magnesium and zinc isotope compositions of mantle derived rocks produced by different subduction processes. The data can provide a basis for determining the source depth of rock samples and the process of carbonate subduction.

GPUZIP v2.0 Reproducibility Dataset This dataset provides all the necessary materials to reproduce the results presented in the GPUZIP v2.0 article. It is organized into folders, each containing a README.md.txt file that describes its contents and explains how to interpret the files. Note:This dataset is organized as a directory structure, so for better visualization change the "View type" to "Tree" before explore the dataset through this web application. Types of Files The repository contains the following file types: .md.txt: Markdown-formatted README files. For optimal readability, use a Markdown viewer such as VSCode or Learn More, however, as a straightforward approach any text reader (e.g., Notepad, cat, vi, nano) can also read them. .*.zipfile: Compressed file (usually called .zip). Files with the .extension.zipfile format (e.g., large-mod.su.zipfile) should be unzipped to access their original format (e.g., large-mod.su). Throughout the documentation, files are always referenced by their uncompressed extensions (e.g., .su). To ensure consistency and avoid confusion, it is recommended that all .zipfile files be unzipped before exploring the repository. Hint: Please see the scripts below for unzipping all files. .xlsx: Excel files. Compatible with LibreOffice, Google Sheets, and Numbers. .par: Configuration files for proprietary RTM runs. Readable with any text editor. .hdr: Header files for velocity models. Refer to Datasets/HowToReadDatasetFiles.md.txt for details. .bin: Raw binary data files containing velocity models in float format. See Datasets/HowToReadDatasetFiles.md.txt for parsing instructions. .data: Binary data files, similar to .bin. .su: Seismic Unix files containing seismic traces. Refer to Datasets/HowToReadDatasetFiles.md.txt for details. .png, .jpg, .jpeg, .gif: Rendered visuals of velocity models or diagrams. .qdrep: Nsight Systems profiling files. Compatible with Nsight Systems 2024.01.1. Root Directory Contents Datasets/ Contains input datasets, including velocity models, seismic traces, and configurations. Detailed information is provided in Datasets/HowToReadDatasetFiles.md.txt. DataWarmUp/ Holds results from compressor calibration experiments, including raw data, logs, and the compiled .xlsx summaries. Experiments were conducted with two shots. See DataWarmUp/README.md.txt for more information. GeometryScript/ Utility script for rendering shot distributions in the datasets. Helpful in visualizing experiment setups. NSight/ This folder contains a subset of Nsight profiling files for the Marmousi3D dataset, covering all compressors and a cache size of two across all checkpointing algorithms. If needed, contact the authors for additional profiling data. Quality/ Contains the results for all shots for quality assessment (Section 7.6). See Quality/README.md.txt. TimeBreakdown/ Complete results for Section 7.4 of the GPUZIP v2.0 article. This folder includes detailed breakdowns of two-shot experiments. See TimeBreakdown/README.md.txt for details. SpeedupAndMemory.xlsx Comprehensive data used to generate charts in Figure 6 and Table 4 (Sections 7.2 and 7.1) of the article. Extra: Util for Unzipping All Files We provide a simple script to unzip all files so that data exploration can be more fluid. Feel free to use it. Windows (.bat) @echo off setlocal enabledelayedexpansion for /r %%f in (.zipfile) do ( echo Decompressing: %%f powershell -Command "Expand-Archive -Path '%%f' -DestinationPath '%%~dpf' -Force" if not errorlevel 1 ( echo Decompressed successfully: %%f del "%%f" ) else ( echo Failed to decompress: %%f ) ) echo All zip files processed. pause Shell script (MacOS, Linux, Unix) #!/bin/bash find . -type f -name ".zipfile" | while read -r zipfile; do echo "Decompressing: $zipfile" unzip -o "$zipfile" -d "$(dirname "$zipfile")" if [ $? -eq 0 ]; then echo "Successfully decompressed: $zipfile" rm "$zipfile" else echo "Failed to decompress: $zipfile" fi done echo "All zip files processed." How Do I Read .bin, .data, and .su Files? See: Datasets/HowToReadDatasetFiles.md.txt How Do I Read .par and .hdr Files? See: Datasets/HowToReadDatasetFiles.md.txt How to Interpret Log Files? To analyze cache hits, misses, and memory consumption, refer to the logs in the TimeBreakdown folder (decom-*.txt files). Key metrics can be extracted as follows: Cache Hits: Search for RET_HIT. Cache Misses: Search for RET_MIS. Prefetched Items: Search for ===> Prefetching:. Prefetch Action Vector (PAV): Search for PAV:. Memory Consumption: Search for [MEM_TRACK]. Checkpoint Pool Size: Search for Checkpoint Pool Size. Each log file concludes with a summary from Nsight.

United States - 50-Year High Quality Market (HQM) Corporate Bond Spot Rate was 6.20% in June of 2025, according to the United States Federal Reserve. Historically, United States - 50-Year High Quality Market (HQM) Corporate Bond Spot Rate reached a record high of 12.54 in June of 1984 and a record low of 3.06 in December of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - 50-Year High Quality Market (HQM) Corporate Bond Spot Rate - last updated from the United States Federal Reserve on July of 2025.

Near real-time water observations available (water.data.sa.gov.au) from surface water and groundwater monitoring networks including water levels, flows, water quality and meteorology. Data is presented in plots (charts), table (grid) and map views. Users can select to download data for one or more sites (locations) in a variety of formats including chart images (PNG, PDF, JPG) or data exports (CSV, Excel, JSON).

Overview: The corpus of the plenary minutes of the German Bundestag (CPP-BT) is one of the largest freely available data sets of plenary minutes of the German Bundestag. It is a compilation of all plenary minutes up to the 18th electoral term, which were published in XML format on the Open Data Portal of the German Bundestag on the respective cut-off date.Please note the enclosed codebook! It contains important information on the correct use of the data set. It also helps in deciding which variant is best for you. I usually recommend the CSV files for quantitative research and the TXT collection for traditional research.The CPP-BT is the twin corpus of the corpus of printed matter of the German Bundestag (CDRS-BT). Both corpora are based on the same data structure, were constructed according to the same principles with largely identical code and are completely compatible with each other. By combining the two corpora, you can examine plenary minutes and printed matter - and thus all Bundestag proceedings - in uniform analyzes. Please note, however, that the CDRS-BT contains additional variables because the data basis is more extensive. UpdateThis dataset is updated several times per electoral term. I always publish notifications about new and updated data sets promptly on Twitter at @FobbeSean. HighlightsThe strengths of this data set are its enormous size, continuous updating, freedom from copyright and the formats (CSV, TXT, XML) that are suitable for both traditional research and quantitative studies.In the ANALYSIS ZIP archive I also provide 23 high-quality diagrams and tables for all purposes. Each diagram is in a format optimized for print (PDF) and web (PNG). Tables are provided in CSV format and are therefore easy to read for both people and machines. Key data: Deadline: February 17, 2021Contents: 4106 plenary minutes / ~ 310 million tokens (version 2021-02-17); Time frame: 1949 to 2017 (version 2021-02-17); Electoral terms: 1st to 18th electoral term (version 2021-02-17); Formats: CSV, TXT and XML Source code and compilation report. The entire creation process is fully automated and documented in detail. With each compilation of the complete data set, a comprehensive compilation report is created in an attractively designed PDF format (similar to the codebook).The compilation report contains the complete source code, documents relevant calculation results, provides time stamps accurate to the second and is provided with a clickable table of contents. It is stored together with the source code. If you are interested in details of the creation process, please read this first. The complete source code - both for the creation of the data set and for the codebook - is publicly available and permanently accessible in the scientific archive of CERN under this link: https://doi.org/10.5281/zenodo. 4542666 Cryptographic signatures The integrity and authenticity of the individual archives of the data record are ensured by a two-phase signature. In phase I, hash values ​​are calculated for each ZIP archive during compilation using two different methods (SHA2-256 and SHA3-512) and documented in a CSV file.In phase II this CSV file is signed with my personal secret GPG key. This procedure ensures that the compilation can be carried out by anyone, especially in the context of replications, but that the personal guarantee of results still remains.The CSV file with the hash checksums created during the compilation of the data set is provided with my personal GPG signature. The public key corresponding to this version is stored with both the data set and the source code. It has the following characteristics: Name: Sean Fobbe (fobbe-data@posteo.de)Fingerprint: FE6F B888 F0E5 656C 1D25 3B9A 50C4 1384 F44A 4E42 No copyright: public domain. In accordance with Section 5 (1) UrhG, there is no copyright in the plenary minutes, as they are official works. § 5 UrhG is to be applied analogously to official databases (BGH, decision of September 28, 2006 - I ZR 261/03, "Saxon tendering service"). All my own contributions (e.g. by compiling and adapting the metadata) and thus the entire data set are completely copyright-free in accordance with a CC0 1.0 Universal Public Domain License. Disclaimer: This data set is a private scientific initiative and has no connection to the German Bundestag or other official bodies of the Federal Republic of Germany.

This is a replication package and online appendix for the TOSEM Journal paper "What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects".

Contents

This repository contains the following:

• INSTALL: Detailed installation instructions for each of the used tools as well as the required Python dependencies.

• FIGURES: Graphical contents shared in the manuscript.

  • Study design:

    • Study design: Vertical diagram with the steps undergone during the study design.
    • Data collection process diagram: Workflow diagram with the data collection process undergone.
    • Sampling process diagram: Workflow diagram with the sampling process undergone during the data collection stage.
    • Data analysis process diagram: Workflow diagram with the data analysis process undergone.
    • CoT learning example: Example prompt from the performed prompt engineering stage based on the CoT learning technique.

  • Results:

    • RMC frequencies histogram: Histogram with the RMCs resulting from the performed Open-Coding.
    • RF feature importance for process metrics: Results for process metrics based on the RF algorithm with MDA and MDG feature importance metrics.
    • RF feature importance for product metrics: Results for product metrics based on the RF algorithm with MDA and MDG feature importance metrics.
    • RF feature importance for all SW metrics: Results for all SW metrics based on the RF algorithm with MDA and MDG feature importance metrics.
    • XGB feature importance for process metrics: Results for process metrics based on the XGB algorithm with the IG feature importance metric.
    • XGB feature importance for product metrics: Results for product metrics based on the XGB algorithm with the IG feature importance metric.
    • XGB feature importance for all SW metrics: Results for all SW metrics based on the XGB algorithm with the IG feature importance metric.
    • Spearman's correlation matrix: Heatmap with the Spearman's correlation matrix with the performed uncorrected correlations, and the Bonferroni and BH-corrected correlations.
    • Kendall's correlation matrix: Heatmap with the Kendall's correlation matrix with the performed uncorrected correlations, and the Bonferroni and BH-corrected correlations.

• TABLES: Tables appearing in the article.

  • Study design:
    • Study Context: Summary descriptive statistics of the projects considered in the study context.
    • RMT detectable refactoring types: Refactorings Types detectable by RefactoringMiner 3.0 and newer versions.
    • RMT mined output: Detected density of refactoring for each of the refactoring types detected by RefactoringMiner 3.0 and newer version.
    • Computed Software metrics: Product and process metrics adopted in this study.
    • Motivations: Motivations derived from developers’ answers based on their refactoring activity based on related work.
    • Correlation analysis interpretability: Spearman’s and Kendall’s correlation coefficients' interpretation.
    • refactoring-motivations-related: Definitions of the considered refactoring types with ground truth motivations.
    • refactorings: Refactorings Types detectable by RefactoringMiner 3.0 and newer versions.
  • Results:
    • RQ1:
      • LLMvsHumCT: LLM Motivation Alignment Agreement Contingency Table.
      • Refactoring Type representativeness: Representativeness level for the refactoring types sampled in the manual validation for RQ$_1$.
      • AgreementHvsLLM: Testing Human Motivation against LLM based motivation.
      • Disagreement categories: Summary of manually detected disagreement categories between LLM extracted motivations and the literature.
    • RQ2:
      • ExtendedMotivations: Summary of LLM Motivations that Extend Human-Reported Motivations.
      • HumanValidatedAlignment: Human Validated LLM Alignment with Literature.
    • RQ3:
      • Resulting RMCs from the Open-Coding stage: Description of the refactoring motivation categories (RMC) resulting from the Open Coding process that group the extracted refactoring motivations.
      • CCR RMC: Refactoring Types Frequencies (#) for CCR RMC.
      • CSRR RMC: Refactoring Types Frequencies (#) for CSRR RMC.
  • Related Works:
    • Related Work: Related work on the existing previous research on Refactoring Recommendation
  • Appendix:
    • Abbreviations: Table including all the refactoring types considered in this study with their abbreviations.
    • Unique LLM-based refactoring motivations: List of unique motivation categories and their respective frequencies grouped into the defined RMCs.

• PROJECTS: List of the initial 124 Software projects considered in the study context. (Links to GitHub repositories provided).

• DATA: A folder containing all raw data extracted

  • Compressed Software Repositories: Since the sampled refactoring observations were based on different versions of the adopted repositories, we cloned them and analyzed them at that specific stager of their

The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. These indicators track facility-based releases to water of 3 substances that are defined as toxic under the Canadian Environmental Protection Act, 1999: mercury, lead and cadmium and their compounds. For each substance, data are provided at the national, regional (provincial and territorial) and facility level, as well as by source. The indicators inform Canadians about releases to water of these 3 substances from facilities in Canada. The Releases of harmful substances to water indicators also help the government to identify priorities and develop or revise strategies to inform further risk management and to track progress on policies put in place to reduce or control these 3 substances and water pollution in general. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated. Canadian Environmental Sustainability Indicators: https://www.canada.ca/environmental-indicators

You can also access a zipped csv file version of this

dataset.TMS

(traffic monitoring system) daily-updated traffic counts CSVData reuse caveats: as per license.

Data quality

statement: please read the accompanying user manual, explaining:

how

 this data is collected identification 

 of count stations traffic 

 monitoring technology monitoring 

 hierarchy and conventions typical 

 survey specification data 

 calculation TMS 

 operation. 

Traffic

monitoring for state highways: user manual

[PDF 465 KB]

The data is at daily granularity. However, the actual update

frequency of the data depends on the contract the site falls within. For telemetry

sites it's once a week on a Wednesday. Some regional sites are fortnightly, and

some monthly or quarterly. Some are only 4 weeks a year, with timing depending

on contractors’ programme of work.

Data quality caveats: you must use this data in

conjunction with the user manual and the following caveats.

The

 road sensors used in data collection are subject to both technical errors and 

 environmental interference.Data 

 is compiled from a variety of sources. Accuracy may vary and the data 

 should only be used as a guide.As 

 not all road sections are monitored, a direct calculation of Vehicle 

 Kilometres Travelled (VKT) for a region is not possible.Data 

 is sourced from Waka Kotahi New Zealand Transport Agency TMS data.For 

 sites that use dual loops classification is by length. Vehicles with a length of less than 5.5m are 

 classed as light vehicles. Vehicles over 11m long are classed as heavy 

 vehicles. Vehicles between 5.5 and 11m are split 50:50 into light and 

 heavy.In September 2022, the National Telemetry contract was handed to a new

contractor. During the handover process, due to some missing documents and aged technology, 40 of the 96 national telemetry traffic count sites went offline. Current contractor has continued to upload data from all active sites and have gradually worked to bring most offline sites back online. Please note and account for possible gaps in data from National Telemetry Sites.

The NZTA Vehicle

Classification Relationships diagram below shows the length classification (typically dual loops) and axle classification (typically pneumatic tube counts),

and how these map to the Monetised benefits and costs manual, table A37,

page 254.

Monetised benefits and costs manual [PDF 9 MB]

For the full TMS

classification schema see Appendix A of the traffic counting manual vehicle

classification scheme (NZTA 2011), below.

Traffic monitoring for state highways: user manual [PDF 465 KB]

State highway traffic monitoring (map)

State highway traffic monitoring sites

TMS

(traffic monitoring system) traffic – historic quarter hourly

Summary of basic hydrologic data and trends. Illustrations include hydrographs and maps of water levels and changes, precipitation table, water quality, charts of storage and diversions. A complete list of unpublished and published reports on the ground-water resources of Arizona by the U. S. Geological Survey.

Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. From there, electricity is distributed along the 33 kV circuits to bring it closer to the home. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month in our South Eastern Power Networks (SPN) licence area. The data are aligned with the same naming convention as the LTDS for improved interoperability.

Care is taken to protect the private affairs of companies connected to the 33 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 33kV Circuits Monthly Data, which describes by month what circuits were triaged, if they could be made public, and what the monthly statistics are of that site.

If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint

This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach The dataset is not derived, it is the measurements from our network stored in our historian. The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps. We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer. The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control Statement The data is provided "as is".
In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that. Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible. Additional Information Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information:Open Data Portal Reuses — UK Power Networks

IntroductionAlthough various methods exist for osteoporosis prevention, most patients fail to receive optimal treatment due to information asymmetry between physicians and patients, as well as limited consultation time. Current literatures suggest that decision aids can support clinical decision-making by improving patients’ risk perception and treatment acceptance.ObjectiveThis scoping review described the use and effectiveness of decision aids in clinical decision-making among individuals with osteoporosis.Inclusion criteriaThe review will include studies conducted in various countries that focus on decision-aiding interventions for people with osteoporosis in different settings and are published in English or Chinese.MethodsPubMed, CINAHL, Web of Science, Embase, Cochrane Library, China Knowledge Network, Wanfang Database, and China Biomedical Literature Database were searched. The search timeframe was from the establishment of the database to June 30, 2024. Studies that meet the inclusion criteria will be eligible for selection. The process of selecting eligible studies will then be summarized on a PRISMA-ScR chart. Collated in data-extraction tables will be authorship information, publication date, country, study site, sample information, study type, intervention form, content elements, application scope, and outcome indicators. The content elements, application scope, and outcome indicators will be analyzed using a thematic analysis and summarized using a narrative summary.ConclusionWith strong efficacy and viability, DA greatly enhances patients’ decision-making experience and decision quality. In order to provide patients with osteoporosis with high-quality decision-making support, it will be necessary to conduct large-scale, randomized controlled studies in the future with the goal of guaranteeing homogeneous interventions, expand the scope and meaning of the application of DA in osteoporosis, improve professional support during the decision-making process, create scientific and useful decision-making aids, and take specific actions.