The "Wikipedia Category Granularity (WikiGrain)" data consists of three files that contain information about articles of the English-language version of Wikipedia (https://en.wikipedia.org).

The data has been generated from the database dump dated 20 October 2016 provided by the Wikimedia foundation licensed under the GNU Free Documentation License (GFDL) and the Creative Commons Attribution-Share-Alike 3.0 License.

WikiGrain provides information on all 5,006,601 Wikipedia articles (that is, pages in Namespace 0 that are not redirects) that are assigned to at least one category.

The WikiGrain Data is analyzed in the paper

Jürgen Lerner and Alessandro Lomi: Knowledge categorization affects popularity and quality of Wikipedia articles. PLoS ONE, 13(1):e0190674, 2018.

===============================================================
Individual files (tables in comma-separated-values-format):

---------------------------------------------------------------
* article_info.csv contains the following variables:

- "id"
(integer) Unique identifier for articles; identical with the page_id in the Wikipedia database.

- "granularity"
(decimal) The granularity of an article A is defined to be the average (mean) granularity of the categories of A, where the granularity of a category C is the shortest path distance in the parent-child subcategory network from the root category (Category:Articles) to C. Higher granularity values indicate articles whose topics are less general, narrower, more specific.

- "is.FA"
(boolean) True ('1') if the article is a featured article; false ('0') else.

- "is.FA.or.GA"
(boolean) True ('1') if the article is a featured article or a good article; false ('0') else.

- "is.top.importance"
(boolean) True ('1') if the article is listed as a top importance article by at least one WikiProject; false ('0') else.

- "number.of.revisions"
(integer) Number of times a new version of the article has been uploaded.

---------------------------------------------------------------
* article_to_tlc.csv
is a list of links from articles to the closest top-level categories (TLC) they are contained in. We say that an article A is a member of a TLC C if A is in a category that is a descendant of C and the distance from C to A (measured by the number of parent-child category links) is minimal over all TLC. An article can thus be member of several TLC.
The file contains the following variables:

- "id"
(integer) Unique identifier for articles; identical with the page_id in the Wikipedia database.

- "id.of.tlc"
(integer) Unique identifier for TLC in which the article is contained; identical with the page_id in the Wikipedia database.

- "title.of.tlc"
(string) Title of the TLC in which the article is contained.

---------------------------------------------------------------
* article_info_normalized.csv
contains more variables associated with articles than article_info.csv. All variables, except "id" and "is.FA" are normalized to standard deviation equal to one. Variables whose name has prefix "log1p." have been transformed by the mapping x --> log(1+x) to make distributions that are skewed to the right 'more normal'.
The file contains the following variables:

- "id"
Article id.

- "is.FA"
Boolean indicator for whether the article is featured.

- "log1p.length"
Length measured by the number of bytes.

- "age"
Age measured by the time since the first edit.

- "log1p.number.of.edits"
Number of times a new version of the article has been uploaded.

- "log1p.number.of.reverts"
Number of times a revision has been reverted to a previous one.

- "log1p.number.of.contributors"
Number of unique contributors to the article.

- "number.of.characters.per.word"
Average number of characters per word (one component of 'reading complexity').

- "number.of.words.per.sentence"
Average number of words per sentence (second component of 'reading complexity').

- "number.of.level.1.sections"
Number of first level sections in the article.

- "number.of.level.2.sections"
Number of second level sections in the article.

- "number.of.categories"
Number of categories the article is in.

- "log1p.average.size.of.categories"
Average size of the categories the article is in.

- "log1p.number.of.intra.wiki.links"
Number of links to pages in the English-language version of Wikipedia.

- "log1p.number.of.external.references"
Number of external references given in the article.

- "log1p.number.of.images"
Number of images in the article.

- "log1p.number.of.templates"
Number of templates that the article uses.

- "log1p.number.of.inter.language.links"
Number of links to articles in different language edition of Wikipedia.

- "granularity"
As in article_info.csv (but normalized to standard deviation one).

The Measurable AI Amazon Consumer Transaction Dataset is a leading source of email receipts and consumer transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan) - EMEA (Spain, United Arab Emirates) - Continental Europe - USA

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

Overview

Water companies in the UK are responsible for testing the quality of drinking water. This dataset contains the results of samples taken from the taps in domestic households to make sure they meet the standards set out by UK and European legislation. This data shows the location, date, and measured levels of determinands set out by the Drinking Water Inspectorate (DWI).

Key Definitions

Aggregation

Process involving summarizing or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes

Anonymisation

Anonymised data is a type of information sanitization in which data anonymisation tools encrypt or remove personally identifiable information from datasets for the purpose of preserving a data subject's privacy

Dataset

Structured and organized collection of related elements, often stored digitally, used for analysis and interpretation in various fields.

Determinand

A constituent or property of drinking water which can be determined or estimated.

DWI

Drinking Water Inspectorate, an organisation “providing independent reassurance that water supplies in England and Wales are safe and drinking water quality is acceptable to consumers.”

DWI Determinands

Constituents or properties that are tested for when evaluating a sample for its quality as per the guidance of the DWI. For this dataset, only determinands with “point of compliance” as “customer taps” are included.

Granularity

Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours

ID

Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.

LSOA

Lower-Level Super Output Area is made up of small geographic areas used for statistical and administrative purposes by the Office for National Statistics. It is designed to have homogeneous populations in terms of population size, making them suitable for statistical analysis and reporting. Each LSOA is built from groups of contiguous Output Areas with an average of about 1,500 residents or 650 households allowing for granular data collection useful for analysis, planning and policy- making while ensuring privacy.

ONS

Office for National Statistics

Open Data Triage

The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data. <

Sample

A sample is a representative segment or portion of water taken from a larger whole for the purpose of analysing or testing to ensure compliance with safety and quality standards.

Schema

Structure for organizing and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.

Units

Standard measurements used to quantify and compare different physical quantities.

Water Quality

The chemical, physical, biological, and radiological characteristics of water, typically in relation to its suitability for a specific purpose, such as drinking, swimming, or ecological health. It is determined by assessing a variety of parameters, including but not limited to pH, turbidity, microbial content, dissolved oxygen, presence of substances and temperature.

Data History

Data Origin

These samples were taken from customer taps. They were then analysed for water quality, and the results were uploaded to a database. This dataset is an extract from this database.

Data Triage Considerations

Granularity

Is it useful to share results as averages or individual?

We decided to share as individual results as the lowest level of granularity

Anonymisation

It is a requirement that this data cannot be used to identify a singular person or household. We discussed many options for aggregating the data to a specific geography to ensure this requirement is met. The following geographical aggregations were discussed:

<!--·
Water Supply Zone (WSZ) - Limits interoperability with other datasets

<!--·
Postcode – Some postcodes contain very few households and may not offer necessary anonymisation

<!--·
Postal Sector – Deemed not granular enough in highly populated areas

<!--·
Rounded Co-ordinates – Not a recognised standard and may cause overlapping areas

<!--·
MSOA – Deemed not granular enough

<!--·
LSOA – Agreed as a recognised standard appropriate for England and Wales

<!--·
Data Zones – Agreed as a recognised standard appropriate for Scotland

Data Specifications

Each dataset will cover a calendar year of samples

This dataset will be published annually

Historical datasets will be published as far back as 2016 from the introduction of of The Water Supply (Water Quality) Regulations 2016

The Determinands included in the dataset are as per the list that is required to be reported to the Drinking Water Inspectorate.

Context

Many UK water companies provide a search tool on their websites where you can search for water quality in your area by postcode. The results of the search may identify the water supply zone that supplies the postcode searched. Water supply zones are not linked to LSOAs which means the results may differ to this dataset

Some sample results are influenced by internal plumbing and may not be representative of drinking water quality in the wider area.

Some samples are tested on site and others are sent to scientific laboratories.

Data Publish Frequency

Annually

Data Triage Review Frequency

Annually unless otherwise requested

Supplementary information

Below is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.

<!--1.
Drinking Water Inspectorate Standards and Regulations:

<!--2.
https://www.dwi.gov.uk/drinking-water-standards-and-regulations/

<!--3.
LSOA (England and Wales) and Data Zone (Scotland):

<!--4. https://www.nrscotland.gov.uk/files/geography/2011-census/geography-bckground-info-comparison-of-thresholds.pdf

<!--5.
Description for LSOA boundaries by the ONS: Census 2021 geographies - Office for National Statistics (ons.gov.uk)

<!--[6.
Postcode to LSOA lookup tables: Postcode to 2021 Census Output Area to Lower Layer Super Output Area to Middle Layer Super Output Area to Local Authority District (August 2023) Lookup in the UK (statistics.gov.uk)

<!--7.
Legislation history: Legislation - Drinking Water Inspectorate (dwi.gov.uk)

Seven excel files which includes the data supporting Figure 5, Figure 6, Figure 7, Figure 8, Figure 10, Figure 12 and Figure 14 is uploaded saparately.

Data Set S1. Data supporting the relationship between flow height and time in Figure 5.

Data Set S2. Data supporting normalized velocity profiles and normalized shear rate profiles in Figures 6(a)-6(d).

Data Set S3(a). Data supporting the relationships between mean grain size and global shear rate in Figure 7(a).

Data Set S3(b). Data supporting the relationships between mean grain size and Savage number in Figure 7(a).

Data Set S4(a). Data supporting the relationships between depth averaged velocity and time in Figure 8(a).

Data Set S4(b). Data supporting the relationships between depth averaged velocity and time in Figure 8(b).

Data Set S4(c). Data supporting the relationships between mean grain size and equivalent friction coefficient in Figure 8(c).

Data Set S5(a). Data supporting Figures 10(a) and 10(b).

Data Set S5(b). Data supporting the relationships between relative flow height and λ in Figure 10(c).

Data Set S5(c). Data supporting the relationships between relative flow height and λ in Figure 10(d).

Data Set S6. Data supporting the relationships between global shear rate and equivalent friction coefficient in Figure 12.

Data Set S6. Data supporting the relationships between normalized flow height and Savage number in Figure 14.

Datasets, video clips, and codes related to the paper 'Insight into granular flow dynamics relying on basal stress measurements: from experimental flume tests', submitted to the Journal of Geophysical Research: Solid Earth.

The datasets provides the raw and processed data for the laboratory flume tests of granular flow including parameters reflecting the granular flow behavior, basal normal stresses measured by a force plate, and deposit parameters of the granular flows.

S1_data_granular flow_velocity provides data of the velocity profiles with a 0.1 second time interval, the depth-averaged velocities, the depth-averaged shear rates, and the solid inertial stresses of the granular flows under different experimental conditions. The original data were calculated through particle image velocimetry (PIV) method. The images for PIV analysis were recorded by a high-speed camera.

S2_data_granular flow_stress provides the raw data of the measured basal normal stresses of the granular flows for all tests. The mean and fluctuating stress components extracted by applying a moving window average filter are also listed in the Table.

S3_data_granular flow_flow depth provides the data of the granular flow depth extracted every 0,02 s through a image processing method based on the high-speed photographs.

S4_data_granular flow_deposit provides the parameters of the granular flow deposits for all tests including the apparent friction coefficient and equivalent friction coefficient. The deposit parameters were calculated based on the digital surface model (DSM) of deposit, which were obtained through a oblique photogrammetry method.

S5_data_granular flow_density gives the data of the dynamic bulk flow densities of the granular flows under all experimental conditions. The dynamic bulk densities were calculated according to the measured and calculated normal stresses.

The videos of the granular flows under different experimental conditions during their propagation are provided in 'S6_video_granular flow.zip' to show the granular flow behavior and its evolution. S6_video_granular flow includes the side-view of the granular flows under all experimental conditions and front-view of the IMF-223 granular flow .

S7_codes_data analysis provides the computer codes for the extraction of mean and fluctuating components and the calculation of granular flow depth. The former includes one file for conducting moving average filter. The latter contains four files, which are used for median filter, image erosion, threshold segmentation and floodfill, extracting flow depth.

Experimental data for manuscript "Towards the end of drying of granular materials: enhanced evaporation and drying-induced collapse"Folder "collapses photos": photos taken with 10 minute interval during the drying experiment of 6 repeated experiments for glass beads with mean diameter 376 micrometers, initial volumetric water content ~2 percent, and initial packing fraction ~0.45. Note that both drying front propagation and collapse events can be visualised. Folder "weight vs time": contains data for sample weight as a function of time with time increment of 1 minute. The file name "rho0XXX_DYYY" represents the sample that has an initial packing fraction 0.XXX, and diameter YYY micrometers.

Overview Water companies in the UK are responsible for testing the quality of drinking water. This dataset contains the results of samples taken from the taps in domestic households to make sure they meet the standards set out by UK and European legislation. This data shows the location, date, and measured levels of determinands set out by the Drinking Water Inspectorate (DWI). Key Definitions  AggregationProcess involving summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes  Anonymisation Anonymised data is a type of information sanitisation in which data anonymisation tools encrypt or remove personally identifiable information from datasets for the purpose of preserving a data subject's privacy Dataset Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fields.  Determinand A constituent or property of drinking water which can be determined or estimated. DWI Drinking Water Inspectorate, an organisation “providing independent reassurance that water supplies in England and Wales are safe and drinking water quality is acceptable to consumers.”  DWI Determinands Constituents or properties that are tested for when evaluating a sample for its quality as per the guidance of the DWI. For this dataset, only determinands with “point of compliance” as “customer taps” are included.   Granularity Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours ID Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.  LSOA Lower-Level Super Output Area is made up of small geographic areas used for statistical and administrative purposes by the Office for National Statistics. It is designed to have homogeneous populations in terms of population size, making them suitable for statistical analysis and reporting. Each LSOA is built from groups of contiguous Output Areas with an average of about 1,500 residents or 650 households allowing for granular data collection useful for analysis, planning and policy- making while ensuring privacy.  ONS Office for National Statistics  Open Data Triage The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.  Sample A sample is a representative segment or portion of water taken from a larger whole for the purpose of analysing or testing to ensure compliance with safety and quality standards.  Schema Structure for organizing and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.  Units Standard measurements used to quantify and compare different physical quantities.  Water Quality The chemical, physical, biological, and radiological characteristics of water, typically in relation to its suitability for a specific purpose, such as drinking, swimming, or ecological health. It is determined by assessing a variety of parameters, including but not limited to pH, turbidity, microbial content, dissolved oxygen, presence of substances and temperature. Data History Data Origin  These samples were taken from customer taps. They were then analysed for water quality, and the results were uploaded to a database. This dataset is an extract from this database. Data Triage Considerations Granularity Is it useful to share results as averages or individual? We decided to share as individual results as the lowest level of granularity Anonymisation It is a requirement that this data cannot be used to identify a singular person or household. We discussed many options for aggregating the data to a specific geography to ensure this requirement is met. The following geographical aggregations were discussed: • Water Supply Zone (WSZ) - Limits interoperability with other datasets • Postcode – Some postcodes contain very few households and may not offer necessary anonymisation • Postal Sector – Deemed not granular enough in highly populated areas • Rounded Co-ordinates – Not a recognised standard and may cause overlapping areas • MSOA – Deemed not granular enough • LSOA – Agreed as a recognised standard appropriate for England and Wales • Data Zones – Agreed as a recognised standard appropriate for Scotland Data Triage Review Frequency  Annually unless otherwise requested Publish Frequency Annually Data Specifications • Each dataset will cover a year of samples in calendar year • This dataset will be published annually • Historical datasets will be published as far back as 2016 from the introduction of The Water Supply (Water Quality) Regulations 2016 • The determinands included in the dataset are as per the list that is required to be reported to the Drinking Water Inspectorate. • A small proportion of samples could not be allocated to an LSOA – these represented less than 0.1% of samples and were removed from the dataset in 2023. • See supplementary information for the lookup table applied to each calendar year of data. Context Many UK water companies provide a search tool on their websites where you can search for water quality in your area by postcode. The results of the search may identify the water supply zone that supplies the postcode searched. Water supply zones are not linked to LSOAs which means the results may differ to this dataset. Some sample results are influenced by internal plumbing and may not be representative of drinking water quality in the wider area. Some samples are tested on site and others are sent to scientific laboratories. Supplementary information Below is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.   1. Drinking Water Inspectorate Standards and Regulations: https://www.dwi.gov.uk/drinking-water-standards-and-regulations/   2. LSOA (England and Wales) and Data Zone (Scotland): https://www.nrscotland.gov.uk/files/geography/2011-census/geography-bckground-info-comparison-of-thresholds.pdf   3. Description for LSOA boundaries by the ONS: https://www.ons.gov.uk/methodology/geography/ukgeographies/censusgeographies/census2021geographies4. Postcode to LSOA lookup tables (2022 calendar year data): https://geoportal.statistics.gov.uk/datasets/3770c5e8b0c24f1dbe6d2fc6b46a0b18/about5. Postcode to LSOA lookup tables (2023 calendar year data): https://geoportal.statistics.gov.uk/datasets/b8451168e985446eb8269328615dec62/about6. Postcode to LSOA lookup tables (2024 calendar year data): https://geoportal.statistics.gov.uk/datasets/068ee476727d47a3a7a0d976d4343c59/about7. Legislation history: https://www.dwi.gov.uk/water-companies/legislation/

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "The Gabii Project" data publication.

Temporal and spatial distribution of niclosamide in the water column and sediment were evaluated after the application of granular Bayluscide in six lentic Sea Lamprey (Petromyzon marinus) larval assessment plots. Water and sediment were collected 0.25, 1, 3, 5, and 7 hours after application and were analyzed for niclosamide, the active ingredient in granular Bayluscide. Water samples were collected from five heights in the water column (1 cm, 13 cm, 26 cm, ½ water column, and water surface) at five locations inside and four locations 10 m outside of each assessment plot. Sediment was collected from 18 locations within each plot. Niclosamide water concentrations inside and outside of the plots did not vary by depth but did vary between plots and by time. Niclosamide water concentrations also varied by sampler location outside of the plots. Following granular Bayluscide applications the mean niclosamide concentration in water for all levels, within the plots, decreased from 0.12 mg∙L-1 (SD = 0.12 mg∙L-1) at 15 minutes to 0.061 mg∙L-1 (SD = 0.040 mg∙L-1) at hour 1. The mean niclosamide concentration in the top 4 cm of sediment was 2.9 mg∙kg-1 (SD = 2.4 mg∙kg-1) 15 minutes after application and was 1.3 mg∙kg-1 (SD = 1.8 mg∙kg-1) at hour 7. Concentrations in the sediment ranged from 0.000 to 30.730 mg∙kg-1 and varied between the six plots. Niclosamide concentrations measured in sediment samples were more than 1 order of magnitude greater than in the water and varied spatially by over 4 orders of magnitude. The datasets included are as follows: Niclosamide Sediment Concentrations Dataset (NicSed) Niclosamide Water Column Concentrations Dataset (NicWaterColumn) Niclosamide Water Concentrations Outside Plot Dataset (NicWaterOutside) Plot pH, depth and temperature dataset (PlotData) Plot Sediment pH before and after treatment (SedpHPlotTrt) Plot Sediment Temp before and after treatment (SedTempPlotTrt)

RESEARCH INFORMATION:

Starting in the 1930’s, many research projects have been performed with the intent to establish rules, which can be used to design granular, geometrically closed filter structures for hydraulic structures. However, for the vast majority of these research projects, the filter structure was loaded by uniform flow, either in the parallel or perpendicular direction. This means that, while for filters under flow loading multiple design options and diagrams are available, the knowledge on design rules for sloped, closed filters under wave loading remained limited. These knowledge gaps mean that, in design practice, often the design criteria for closed filters under flow loading, for instance the original criterion by Terzaghi, are used for the design of closed filters under wave loading, while these have not yet been experimentally verified for this use case.

This thesis combines model test results from two historically performed experiment programs on sloped, closed granular filters under wave loading with new experiments, which were conducted especially for this thesis. By analyzing the results from these three research projects, commonly used interface stability criteria for closed granular filters are tested for use in the case of a sloped, closed granular filter under wave loading. Afterwards, a new interface stability criterion for sloped, closed granular filters under wave loading is proposed.

The physics of granular materials, including rheology and jamming, is strongly influenced by cohesive forces between the constituent grains. Despite significant progress in understanding the mechanical properties of granular materials, it remains unresolved how the range and strength of cohesive interactions influence mechanical failure or avalanches. In this study, we use molecular dynamics simulations to investigate simple shear flows of soft cohesive particles. The particles are coated with thin sticky layers, and both the range and strength of cohesive interactions are determined by the layer thickness. We examine shear strength, force chains, particle displacements, and avalanches, and find that these quantities change drastically even when the thickness of the sticky layers is only 1% of the particle diameter. We also analyze avalanche statistics and find that the avalanche size, maximum stress drop rate, and dimensionless avalanche duration are related by scaling laws. Remarkably, the scaling exponents of the scaling laws are independent of the layer thickness but differ from the predictions of mean-field theory. Furthermore, the power-law exponents for the avalanche size distribution and the distribution of the dimensionless avalanche duration are universal but do not agree with mean-field predictions. We confirm that the exponents estimated from numerical data are mutually consistent. In addition, we show that particle displacements at mechanical failure tend to be localized when the cohesive forces are sufficiently strong.

This dataset contains high-speed video recordings and particle image velocimetry (PIV) analysis results from granular flow experiments performed on an inclined flume with a fixed rough substrate, at the University of Edinburgh. Included are the high-speed videos (.mp4), a Word document outlining the experimental details and analysis methods, and figures displaying key analytical results of vertical velocity and granular temperature profiles. The flows consist of sand particles with a volumetric mean diameter of 875 µm, propagating over a substrate of coarser sand with a mean diameter of 1063 µm. Experimental conditions include varying slope angles (34°–42°) to investigate the influence of inclination on flow dynamics. PIV was used to analyse the videos, generating vertical velocity profiles and granular temperature profiles. Lens distortion was corrected using MATLAB to ensure accurate measurements. This dataset is relevant to those interested in granular flow dynamics, natural hazard modelling (e.g., landslides, pyroclastic density currents), and granular flow industrial applications.

This information will not be updated while the Cincinnati Police Department undergoes transfer to a new data management system.

Data Description: This data represents use of force incidents by the Cincinnati Police Department. Use of force can generally be defined as the means of compelling compliance or overcoming resistance to an officer’s command(s) in order to protect life or property or to take a person into custody.

Data Creation: This data is created through reporting by the Cincinnati Police Department.

Data Created By: The source of this data is the Cincinnati Police Department.

Refresh Frequency: This information will not be updated while the Cincinnati Police Department undergoes transfer to a new data management system.

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/quk6-rcaw

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Disclaimer: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

Granular Certificate Trading for 24/7 CFE Market Outlook

According to our latest research, the global market size for Granular Certificate Trading for 24/7 Clean Energy (CFE) reached USD 1.47 billion in 2024. The market is experiencing robust growth, with a compound annual growth rate (CAGR) of 24.3% expected from 2025 to 2033. By 2033, the market is forecasted to attain a value of approximately USD 11.28 billion. This rapid expansion is primarily driven by the increasing demand for real-time renewable energy verification, stringent sustainability mandates, and the global transition toward net-zero carbon emissions.

One of the primary growth factors propelling the Granular Certificate Trading for 24/7 CFE market is the rising adoption of renewable energy sources by corporations and utilities aiming to meet ambitious sustainability targets. As organizations intensify their focus on decarbonization and environmental responsibility, there is a growing necessity for mechanisms that can verify and certify the use of clean energy on an hourly or sub-hourly basis. Granular certificates, which provide detailed temporal and locational data, allow companies to align their energy consumption with actual renewable generation, thus enabling authentic 24/7 clean energy claims. This level of transparency is increasingly sought after by stakeholders, investors, and regulatory authorities, making granular certificate trading an essential tool in the evolving energy landscape.

Another significant driver behind the market’s growth is the technological advancements in trading platforms, particularly the integration of blockchain technology. Blockchain-based platforms offer enhanced security, transparency, and traceability, which are critical for the integrity of granular certificate transactions. These platforms facilitate automated, real-time trading and settlement of clean energy certificates, reducing administrative overhead and operational costs. Furthermore, the interoperability of blockchain solutions with existing energy and certificate management systems is accelerating adoption across utilities, corporates, and data centers. As digitalization sweeps through the energy sector, the synergy between advanced trading platforms and granular certification is catalyzing market expansion.

Regulatory frameworks and government policies are also pivotal in shaping the growth trajectory of the Granular Certificate Trading for 24/7 CFE market. Many regions, particularly in Europe and North America, are implementing stringent renewable portfolio standards and carbon neutrality regulations that require precise tracking and reporting of clean energy usage. These policies are encouraging market participants to adopt granular certification as a means to comply with legal requirements and demonstrate progress toward sustainability goals. Additionally, international initiatives such as the EnergyTag standard and the United Nations’ push for 24/7 carbon-free energy are fostering global harmonization and standardization, further boosting market growth.

From a regional perspective, Europe and North America are leading the adoption of granular certificate trading, driven by progressive regulatory environments and high corporate sustainability commitments. The Asia Pacific region is emerging as a fast-growing market, fueled by rapid industrialization, increasing renewable energy investments, and supportive government policies. Latin America and the Middle East & Africa are also witnessing gradual uptake, primarily through pilot projects and collaborations with international organizations. As more regions recognize the value of granular certificates for 24/7 CFE, the global market is expected to witness widespread adoption and integration into mainstream energy procurement strategies.

Certificate Type Analysis

The certificate type segment in the Granular Certificate Trading for 24/7 CFE market is characterized by the emergence of innovative products designed to meet the evolving needs of energy buyers and sellers. Time-matched certificates are at the forefront, providing verification that energy consumed during a specific hour or minute is matched by renewable generation during the same period. This level of granularity is essential for organizations striving for true 24/7 clean energy usage, as it eliminates the discrepancies associated with annual or monthly averaging. The adoption of time-matched certificates is gaining momentum amo

The file contains data corresponding to all the figures included in the article. Each sheet presents the data for a specific figure. The profiles of scaled mean velocity, solid volume fraction, and scaled fluctuating velocity are shown across the domain (scaled height) for various wall velocities, overburden pressures (fig2), stiffness constants (fig3), and different configurations (fig6). The scaling of midplane velocity with wall velocity for various overburden pressures is provided in Sheet fig4. Sheet fig5 shows the variation of scaled midplane velocity with scaled overburden pressure for different wall velocities, stiffness constants, and coefficients of friction. Transient profiles of mean velocity, solid fraction, and fluctuating velocity showing the system's transition from a stable plug in the lower region to the top (fig7), and plug fluctuations under intermediate pressures (fig8), are shown across the domain for the Parallel case. The midplane velocity over time for the Parallel case is shown in Sheet fig9a, with its corresponding power spectral density (PSD) data in Sheet fig9b, alongside the PSD of midplane velocity in the fixed wall case and wall fluctuations in the normal direction. Sheet fig10 displays the PSD for various overburden pressures in the Parallel case. Midplane velocity distributions for various wall velocities and overburden pressures are shown in Sheet fig11 for the Parallel case, followed by their standard deviations in Sheet fig12 and the effects of the coefficient of friction on standard deviation are shown in Sheet fig13.

Data and codes used to support the research article entitled "A multicriteria optimization framework for the definition of the spatial granularity of urban social media analytics", which was published in the International Journal of Geographical Information Science.

We are currently experiencing technical difficulties. The data will be available again as soon as possible.

Data Description: This data represents use of force incidents reported by the Cincinnati Police Department. Use of force can generally be defined as the means of compelling compliance or overcoming resistance to an officer’s command(s) in order to protect life or property or to take a person into custody.

The demographic information for the subjects and officers are available at the following links. The datasets can be linked using the UNIQUE_REPORT_ID. Please keep in mind an incident may have more than one subject and more than one officer involved.

Subjects: https://data.cincinnati-oh.gov/safety/Use-of-Force-Subjects/4gu6-tz3f/about_data Officers: https://data.cincinnati-oh.gov/safety/Use-of-Force-Officers/28j3-kqky/about_data

Data Creation: This data is created through reporting by the Cincinnati Police Department.

Data Created By: The source of this data is the Cincinnati Police Department.

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/quk6-rcaw

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Disclaimer: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

Data Origin:Samples were taken from customer taps. They were then analysed, and the results were uploaded to a database. This dataset is an extract from this database.Data Triage Considerations:Granularity:We decided to share as individual results at the lowest level of granularity.Anonymisation:It is a requirement that this data cannot be used to identify a singular person or household. We discussed many options for aggregating the data to a specific geography to ensure this requirement is met. The following geographical aggregations were discussed: Water Supply Zone (WSZ) - Limits interoperability with other datasets Postcode – Some postcodes contain very few households and may not offer necessary anonymisation Postal Sector – Deemed not granular enough in highly populated areas Rounded Co-ordinates – Not a recognised standard and may cause overlapping areas MSOA – Deemed not granular enough LSOA – Agreed as a recognised standard appropriate for England and Wales Data Zones – Agreed as a recognised standard appropriate for Scotland Data Specifications:Each dataset will cover a calendar year of samplesThis dataset will be published annuallyThe Determinands included in the dataset are as per the list that is required to be reported to the Drinking Water Inspectorate Context:Many UK water companies provide a search tool on their websites where you can search for water quality in your area by postcode. The results of the search may identify the water supply zone that supplies the postcode searched. Water supply zones are not linked to LSOAs which means the results may differ to this dataset. Some sample results are influenced by internal plumbing and may not be representative of drinking water quality in the wider area. Some samples are tested on site and others are sent to scientific laboratories.Prior to undertaking analysis on any new instruments or utilising new analytical techniques, the laboratory undertakes validation of the equipment to ensure it continues to meet the regulatory requirements. This means that the limit of quantification may change for the method either increasing or decreasing from the previous value. Any results below the limit of quantification will be reported as < with a number. For example, a limit of quantification change from <0.68 mg/l to <2.4 mg/l does not mean that there has been a deterioration in the quality of the water supplied. Data Publishing Frequency:AnnuallySupplementary information:Below is a curated selection of links for additional reading, which provide a deeper understanding of this dataset: Drinking Water Inspectorate Standards and Regulations Description for LSOA boundaries by the ONS: Census 2021 geographies - Office for National Statistics Postcode to LSOA lookup tables: Postcode to 2021 Census Output Area to Lower Layer Super Output Area to Middle Layer Super Output (February 2024)Legislation history: Legislation - Drinking Water InspectorateInformation about lead pipes: Lead pipes and lead in your water - United UtilitiesDataset Schema:SAMPLE_ID: Identity of the sampleSAMPLE_DATE: The date the sample was takenDETERMINAND: The determinand being measuredDWI_CODE: The corresponding DWI code for the determinandUNITS: The expression of resultsOPERATOR: The measurement operator for limit of detectionRESULT: The test resultsLSOA: Lower Super Output Area (population weighted centroids used by the Office for National Statistics (ONS) for geo-anonymisation)

GHCN

• The Global Historical Climatology Network (GHCN) is an integrated database of climate summaries from land surface stations across the globe.
• GHCN-Daily contains records from over 100,000 stations in 180 countries and territories.
• The data are obtained from more than 20 sources. Some data are more than 175 years old.
• NCEI provides numerous daily variables, including maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about one half of the stations report precipitation only

Data description

https://www.ncdc.noaa.gov/ghcn-daily-description

Collection

The data can be collected from S3 buckets. Here I collected it for 2019.
For detail information the link is as below:
https://docs.opendata.aws/noaa-ghcn-pds/readme.html
Question for data quality should be addressed at noaa.bdp@noaa.gov.

Summary of Date format

ID = 11 character station identification code. Please see ghcnd-stations section below for an explantation
YEAR/MONTH/DAY = 8 character date in YYYYMMDD format (e.g. 19860529 = May 29, 1986)
ELEMENT = 4 character indicator of element type
DATA VALUE = 5 character data value for ELEMENT
M-FLAG = 1 character Measurement Flag
Q-FLAG = 1 character Quality Flag
S-FLAG = 1 character Source Flag
OBS-TIME = 4-character time of observation in hour-minute format (i.e. 0700 =7:00 am)

The fields are comma delimited and each row represents one station-day.

Variable and feature details

These variables have the following definitions:

• ID = the station identification code.
  The first two characters denote the FIPS country code. Details for FIPS country code https://www.geodatasource.com/resources/tutorials/international-country-code-fips-versus-iso-3166/
  The third character is a network code that identifies the station numbering system used
  0 = unspecified (station identified by up to eight alphanumeric characters)
  1 = Community Collaborative Rain, Hail,and Snow (CoCoRaHS) based identification number. To ensure consistency with with GHCN Daily, all numbers in the original CoCoRaHS IDs have been left-filled to make them all four digits long. In addition, the characters “-” and “_” have been removed to ensure that the IDs do not exceed 11 characters when preceded by “US1”. For example, the CoCoRaHS ID “AZ-MR-156” becomes “US1AZMR0156” in GHCN-Daily
• LATITUDE = latitude of the station (in decimal degrees).
• LONGITUDE = longitude of the station (in decimal degrees).
• STATE = U.S. postal code for the state (for U.S. and Canadian stations only).
• NAME = name of the station.
• GSN FLAG = flag that indicates whether the station is part of the GCOS Surface Network (GSN).
• HCN/CRN FLAG = flag that indicates whether the station is part of the U.S. Historical Climatology Network (HCN). T
• WMO ID is the World Meteorological Organization (WMO) number for the station. If the station has no WMO number (or one has not
  yet been matched to this station), then the field is blank.

GHCND inventory master data

This is the periods of record for each station and element

Data structure

• ID = the station identification code. Please see “ghcnd-stations.txt” for a complete list of stations and their metadata.
• LATITUDE = the latitude of the station (in decimal degrees).
• LONGITUDE = the longitude of the station (in decimal degrees).
• ELEMENT = the element type. See section III for a definition of elements.
• FIRSTYEAR = the first year of unflagged data for the given element.
• LASTYEAR = the last year of unflagged data for the given element.

Acknowledgements

Referenced from AWS open source data storage in S3 and NOAA data domain.

Inspiration

NOAA weather stations, weather transaction data