These department codes are maintained in the City's financial system of record. Department Groups, Divisions, Sections, Units, Sub Units and Departments are nested in the dataset from left to right. Each nested unit has both a code and an associated name.

The dataset represents a flattened tree (hierarchy) so that each leaf on the tree has it's own row. Thus certain rows will have repeated codes across columns.

Data changes as needed.

These are the land use codes for the City of Austin as of April 2024.

City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

Neighborhood code reference dataset for the Assessor Historical Secured Property Tax Rolls dataset

Author Siming Zheng. Experiment CODES data(3 in 1).

This dataset contains the ICD-10 code lists used to test the sensitivity and specificity of the Clinical Practice Research Datalink (CPRD) medical code lists for dementia subtypes. The provided code lists are used to define dementia subtypes in linked data from the Hospital Episode Statistic (HES) inpatient dataset and the Office of National Statistics (ONS) death registry, which are then used as the 'gold standard' for comparison against dementia subtypes defined using the CPRD medical code lists. The CPRD medical code lists used in this comparison are available here: Venexia Walker, Neil Davies, Patrick Kehoe, Richard Martin (2017): CPRD codes: neurodegenerative diseases and commonly prescribed drugs. https://doi.org/10.5523/bris.1plm8il42rmlo2a2fqwslwckm2 Complete download (zip, 3.9 KiB)

Creator Nicolas Mejia-Petit My Kofi

  Code-Preference-Pairs Dataset





  Overview

This dataset was created while created Open-critic-GPT. Here is a little Overview: The Open-Critic-GPT dataset is a synthetic dataset created to train models in both identifying and fixing bugs in code. The dataset is generated using a unique synthetic data pipeline which involves:

Prompting a local model with an existing code example. Introducing bugs into the code. While also having the model… See the full description on the dataset page: https://huggingface.co/datasets/Vezora/Code-Preference-Pairs.

This dataset contains data from California resident tax returns filed with California adjusted gross income and self-assessed tax listed by zip code. This dataset contains data for taxable years 1992 to the most recent tax year available.

Housing code enforcement activities, including inspections and violations.

This dataset includes soil wet aggregate stability measurements from the Upper Mississippi River Basin LTAR site in Ames, Iowa. Samples were collected in 2021 from this long-term tillage and cover crop trial in a corn-based agroecosystem. We measured wet aggregate stability using digital photography to quantify disintegration (slaking) of submerged aggregates over time, similar to the technique described by Fajardo et al. (2016) and Rieke et al. (2021). However, we adapted the technique to larger sample numbers by using a multi-well tray to submerge 20-36 aggregates simultaneously. We used this approach to measure slaking index of 160 soil samples (2120 aggregates). This dataset includes slaking index calculated for each aggregates, and also summarized by samples. There were usually 10-12 aggregates measured per sample. We focused primarily on methodological issues, assessing the statistical power of slaking index, needed replication, sensitivity to cultural practices, and sensitivity to sample collection date. We found that small numbers of highly unstable aggregates lead to skewed distributions for slaking index. We concluded at least 20 aggregates per sample were preferred to provide confidence in measurement precision. However, the experiment had high statistical power with only 10-12 replicates per sample. Slaking index was not sensitive to the initial size of dry aggregates (3 to 10 mm diameter); therefore, pre-sieving soils was not necessary. The field trial showed greater aggregate stability under no-till than chisel plow practice, and changing stability over a growing season. These results will be useful to researchers and agricultural practitioners who want a simple, fast, low-cost method for measuring wet aggregate stability on many samples.

Local authorities must publish the information about their counter fraud work under the Local Government Transparency Code.

Dataset contains counts of individuals certified eligible for Medi-Cal, by Month of Eligibility, Zip Code, and Sex, from Calendar Year 2005 to the most recent reportable month. Due to the amount of data presented, below the dataset has been split into three files. All datasets are derived from the most recent reportable months information.

A self-hosted location dataset containing all administrative divisions, cities, and zip codes for Isle of Man. All geospatial data is updated weekly to maintain the highest data quality, including coverage of complex regions within the country.

Use cases for the Global Zip Code Database (Geospatial data) - Address capture and validation - Map and visualization - Reporting and Business Intelligence (BI) - Master Data Management - Logistics and Supply Chain Management - Sales and Marketing

Data export methodology Our location data packages are offered in variable formats, including .csv. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features - Fully and accurately geocoded - Administrative areas with a level range of 0-4 - Multi-language support including address names in local and foreign languages - Comprehensive city definitions across countries

For additional insights, you can combine the map data with: - UNLOCODE and IATA codes - Time zones and Daylight Saving Times

Why do companies choose our location databases - Enterprise-grade service - Reduce integration time and cost by 30% - Weekly updates for the highest quality

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

This dataset contains all data and code necessary to reproduce the analysis presented in the manuscript: Winzeler, H.E., Owens, P.R., Read Q.D.., Libohova, Z., Ashworth, A., Sauer, T. 2022. 2022. Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization. Land 11:2018. DOI: 10.3390/land11112018. There are several steps to this analysis. The relevant scripts for each are listed below. The first step is to use the raw digital elevation data (DEM) to produce different versions of the topographic wetness index (TWI) for the study region (Calculating TWI). Then, these TWI output files are processed, along with soil moisture (volumetric water content or VWC) time series data from a number of sensors located within the study region, to create analysis-ready data objects (Processing TWI and VWC). Next, models are fit relating TWI to soil moisture (Model fitting) and results are plotted (Visualizing main results). A number of additional analyses were also done (Additional analyses). Input data The DEM of the study region is archived in this dataset as SourceDem.zip. This contains the DEM of the study region (DEM1.sgrd) and associated auxiliary files all called DEM1.* with different extensions. In addition, the DEM is provided as a .tif file called USGS_one_meter_x39y400_AR_R6_WashingtonCO_2015.tif. The remaining data and code files are archived in the repository created with a GitHub release on 2022-10-11, twi-moisture-0.1.zip. The data are found in a subfolder called data. 2017_LoggerData_HEW.csv through 2021_HEW.csv: Soil moisture (VWC) logger data for each year 2017-2021 (5 files total). 2882174.csv: weather data from a nearby station. DryPeriods2017-2021.csv: starting and ending days for dry periods 2017-2021. LoggerLocations.csv: Geographic locations and metadata for each VWC logger. Logger_Locations_TWI_2017-2021.xlsx: 546 topographic wetness indexes calculated at each VWC logger location. note: This is intermediate input created in the first step of the pipeline. Code pipeline To reproduce the analysis in the manuscript run these scripts in the following order. The scripts are all found in the root directory of the repository. See the manuscript for more details on the methods. Calculating TWI TerrainAnalysis.R: Taking the DEM file as input, calculates 546 different topgraphic wetness indexes using a variety of different algorithms. Each algorithm is run multiple times with different input parameters, as described in more detail in the manuscript. After performing this step, it is necessary to use the SAGA-GIS GUI to extract the TWI values for each of the sensor locations. The output generated in this way is included in this repository as Logger_Locations_TWI_2017-2021.xlsx. Therefore it is not necessary to rerun this step of the analysis but the code is provided for completeness. Processing TWI and VWC read_process_data.R: Takes raw TWI and moisture data files and processes them into analysis-ready format, saving the results as CSV. qc_avg_moisture.R: Does additional quality control on the moisture data and averages it across different time periods. Model fitting Models were fit regressing soil moisture (average VWC for a certain time period) against a TWI index, with and without soil depth as a covariate. In each case, for both the model without depth and the model with depth, prediction performance was calculated with and without spatially-blocked cross-validation. Where cross validation wasn't used, we simply used the predictions from the model fit to all the data. fit_combos.R: Models were fit to each combination of soil moisture averaged over 57 months (all months from April 2017-December 2021) and 546 TWI indexes. In addition models were fit to soil moisture averaged over years, and to the grand mean across the full study period. fit_dryperiods.R: Models were fit to soil moisture averaged over previously identified dry periods within the study period (each 1 or 2 weeks in length), again for each of the 546 indexes. fit_summer.R: Models were fit to the soil moisture average for the months of June-September for each of the five years, again for each of the 546 indexes. Visualizing main results Preliminary visualization of results was done in a series of RMarkdown notebooks. All the notebooks follow the same general format, plotting model performance (observed-predicted correlation) across different combinations of time period and characteristics of the TWI indexes being compared. The indexes are grouped by SWI versus TWI, DEM filter used, flow algorithm, and any other parameters that varied. The notebooks show the model performance metrics with and without the soil depth covariate, and with and without spatially-blocked cross-validation. Crossing those two factors, there are four values for model performance for each combination of time period and TWI index presented. performance_plots_bymonth.Rmd: Using the results from the models fit to each month of data separately, prediction performance was averaged by month across the five years of data to show within-year trends. performance_plots_byyear.Rmd: Using the results from the models fit to each month of data separately, prediction performance was averaged by year to show trends across multiple years. performance_plots_dry_periods.Rmd: Prediction performance was presented for the models fit to the previously identified dry periods. performance_plots_summer.Rmd: Prediction performance was presented for the models fit to the June-September moisture averages. Additional analyses Some additional analyses were done that may not be published in the final manuscript but which are included here for completeness. 2019dryperiod.Rmd: analysis, done separately for each day, of a specific dry period in 2019. alldryperiodsbyday.Rmd: analysis, done separately for each day, of the same dry periods discussed above. best_indices.R: after fitting models, this script was used to quickly identify some of the best-performing indexes for closer scrutiny. wateryearfigs.R: exploratory figures showing median and quantile interval of VWC for sensors in low and high TWI locations for each water year. Resources in this dataset:Resource Title: Digital elevation model of study region. File Name: SourceDEM.zipResource Description: .zip archive containing digital elevation model files for the study region. See dataset description for more details.Resource Title: twi-moisture-0.1: Archived git repository containing all other necessary data and code . File Name: twi-moisture-0.1.zipResource Description: .zip archive containing all data and code, other than the digital elevation model archived as a separate file. This file was generated by a GitHub release made on 2022-10-11 of the git repository hosted at https://github.com/qdread/twi-moisture (private repository). See dataset description and README file contained within this archive for more details.

The Illinois Lead and Copper Rule dataset was downloaded from http://163.191.83.31/dww/index.jsp for 2020 and the first half of 2021. It excludes water samples from source water and from water upstream of home/school sampling points. The Pennsylvania Lead and Copper Rule dataset for 2020 and the first half of 2021 were downloaded from http://www.drinkingwater.state.pa.us/. That dataset includes water samples from source water and water upstream of the home/school sampling points. The code was used to simulate water lead levels in 100,000 homes each sampled 15 times, under a variety of assumptions about baseline distribution of water lead levels and transitional probabilities. The tables are the output of that code.

151 Active Global Sic Codes buyers list and Global Sic Codes importers directory compiled from actual Global import shipments of Sic Codes.

Firm-product data provide information for various research questions in international trade or innovation economics. However, working with these data require harmonizing product classifications consistently over time to avoid internal validity issues. Harmonization is required because classification systems like the EU classifications Combined Nomenclature (CN) for goods or the Prodcom for the production of manufactured goods undergo several changes. We have addressed this problem and developed an approach to harmonize product codes. This approach tracks product codes from 1995 to 2022 for CN and 2001 to 2021 for Prodcom. Additional years can be conveniently added. We provide the harmonized product codes for CN and Prodcom in the selected period's last (or first) year. Our approach is summarized in an open-source R package so that researchers can consistently track product codes for their selected period. We demonstrate the importance of harmonization using the micro-level trade data for Croatia as a case study. Our approach facilitates working with firm-product data, allowing the analysis of important research questions.

Dataset gives an overview of the work in progress to harmonise a codification for the construction sector starting from CI/SfB and the original Belgian addition BB/SfB-plus (2024). The dataset contains a description in Dutch, French, English, German of building objects based on a purely functional definition. The database contains a text version in PDF and a database format in Excel

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

AutoTrain Dataset for project: goodreads_without_bookid

  Dataset Description

This dataset has been automatically processed by AutoTrain for project goodreads_without_bookid.

  Languages

The BCP-47 code for the dataset's language is en.

  Dataset Structure





  Data Instances

A sample from this dataset looks as follows: [ { "target": 5, "text": "This book was absolutely ADORABLE!!!!!!!!!!! It was an awesome light and FUN read. I loved… See the full description on the dataset page: https://huggingface.co/datasets/fernanda-dionello/autotrain-data-goodreads_without_bookid.

It provides city code, city, county, and contract type code, industry classification code, power generation source code, and welfare discount type code data used in APIs in the power data open portal provided by Korea Electric Power Corporation. This API can be used after registering as a member of the power data open portal API.