Australian Standard Geographic Classification (ASGC) coding indexes from 1981-2011 in numerous formats.

Data of Experiment 3

Comprehensive YouTube channel statistics for Learn Code With Durgesh, featuring 346,000 subscribers and 67,055,116 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Technology category and is based in IN. Track 1,553 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

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.

containing sample expression dataframes for both stage and metastasis analysis, mapping of lncRNA ids, lncRNA's features as extracted from lncBook, statistical enrichment statistics in Tr-lncRNAs

The Woodland Carbon Code is a voluntary standard, initiated in July 2011, for woodland creation projects that make claims about the carbon they sequester (take out of the atmosphere).

Woodland Carbon Code statistics are used to monitor the uptake of this new voluntary standard, and are published quarterly since January 2013.

We recently reported that correlations between the activities of peripheral afferents mediate a phase invariant representation of natural communication stimuli that is refined across successive processing stages thereby leading to perception and behavior in the weakly electric fish Apteronotus leptorhynchus (Metzen et al., 2016). Here, we explore how phase invariant coding and perception of natural communication stimuli are affected by changes in the sinusoidal background over which they occur. We found that increasing background frequency led to phase locking, which decreased both detectability and phase invariant coding. Correlated afferent activity was a much better predictor of behavior as assessed from both invariance and detectability than single neuron activity. Thus, our results not only provide further evidence that correlated activity likely determines perception of natural communication signals, but also provide a novel explanation as to why these preferentially occur on top ...

A moving picture coding apparatus includes a motion estimation unit (101) for performing motion estimation by fixing the one of two reference pictures as a reference picture indicated by an inputted default reference picture number DefRefNo and a variable length coding unit (107) for performing variable length coding on coded residual data ERes, a prediction type PredType, a reference picture number RefNo2 and motion vectors MV1, MV2 on a block-by-block basis, and outputting them as coded moving picture data Str.

Data and statistical analysis scripts for manuscript on wheat root response to nitrate using X-ray CT and OpenSimRoot

X-ray CT reveals 4D root system development and lateral root responses to nitrate in soil - [https://doi.org/10.1002/ppj2.20036]

The ZIP file contains:

• MCT1_Rcode.R - Statistics script for candidate single-timepoint experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.
• MCT1... .csv - 3 CSV data files required by the R script.
• MCT2_Rcode.R - Statistics script for time-series experiment. Requires all CSV data files in the directory. User needs to set working directory to location of this script and the CSV data files before running.
• MCT2... .csv - 3 CSV data files required by the R script.
• R_RooThProcessing.R - R code for aggregating root traits from RooTh software.
• Modelling folder - OpenSimRoot with model parameters and root data used in manuscript.

The dataset consists of the interview protocols and codebook used in the design and data analysis steps of our study entitled "Co-Creation in Fully Remote Software Teams" accepted into the 2024 IEEE/ACM 46th International Confernce on Software Engineering (ICSE), The study is available at https://www.computer.org/csdl/proceedings-article/icse/2024/021700a593/1RLIWAH9Cco The interview-based study consisted of a first round of interviews with 25 software professionals with a subsequent follow-up interview with 5 of these original participants. The interview transcripts were coded inductively resulting in the final code book shared here., The data was produced by the researchers as part of the design and data analysis phases of the study., , # Data from: Co-creation in fully remote software teams

This dataset consists of two files that contains the interview protocols and codebook generated during the research for the "Co-creation in Fully Remote Software Teams" paper. This paper is available here

The interview-based study consisted of a first round of interviews with 25 software professionals with a subsequent follow-up interview with 5 of these original participants. The interview transcripts were coded inductively resulting in the final code book shared here.

Description of the data and file structure

The dataset consists of two files. The PDF file contains the interview protocols for both the first and second round of interviews. The spreadsheet contains the codebook.

Sharing/Access information

Data was derived from the following sources:

• The design of the interview study
• Subsequent data analysis of the transc...

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)

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.

Zip Code; Median household income; Unemployed (ages GE 16); Families below 185% FPL; Children (ages 0-17) below 185% FPL; Children (ages 3-4) enrolled in preschool or nursery school; Less than high school; High school graduate; Some college or associates degree; College graduate or higher; High school graduate or less. Percentages unless otherwise noted. Source information provided at: https://www.sccgov.org/sites/phd/hi/hd/Documents/City%20Profiles/Methodology/Neighborhood%20profile%20methodology_082914%20final%20for%20web.pdf

Notes:

JSON data related to average statistics on children food habits and physical activities (per postcode)

View details of Coding Assembly imports shipment data in May with price, HS codes, major Indian ports, countries, importers, buyers in India, quantity and more.

Housing code enforcement activities, including inspections and violations.

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.

According to Cognitive Market Research, the global erasure coding market size will be USD 14.62 million in 2025. It will expand at a compound annual growth rate (CAGR) of 7.10% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 5.41 million in 2025 and will grow at a compound annual growth rate (CAGR) of 5.9% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 4.24 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 3.51 million in 2025 and will grow at a compound annual growth rate (CAGR) of 9.9% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 0.56 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.4% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 0.58 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.6% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 0.32 million in 2025 and will grow at a compound annual growth rate (CAGR) of 3.3% from 2025 to 2033.
Small & medium enterprises category is the fastest growing segment of the erasure coding industry

Market Dynamics of Erasure Coding Market

Key Drivers for Erasure Coding Market

Technological Advancements in Storage Solutions and Coding Algorithms Drives Market Growth

Technological advancements in storage solutions and coding algorithms are significantly contributing to the growth of the erasure coding market. Modern storage systems are increasingly integrating erasure coding to enhance data reliability, reduce redundancy, and optimize storage capacity. Innovations in coding algorithms have improved performance, enabling faster data recovery and reduced latency, even in large-scale environments. These advancements support efficient storage management in cloud, edge, and on-premises infrastructures. Additionally, the rise of AI and machine learning has enabled smarter data placement and error correction strategies. As a result, businesses are adopting erasure coding solutions to achieve cost-effective, scalable, and secure data storage, further accelerating market growth. For instance, in November 2022, Seagate launched its Next-Generation Exos X Storage Arrays, powered by its sixth-generation controller architecture. The new Exos X systems delivered up to double the performance of their predecessors and improved enterprise-grade durability. They incorporated ADAPT (Advanced Distributed Autonomic Protection Technology) erasure coding and Seagate’s ADR (Autonomous Drive Regeneration) self-healing storage technology to ensure robust data protection.

https://www.titandatasolutions.com/titan-news-exos-x-storage-arrays/

Enhanced Reliability and Fault Tolerance Offered by Erasure Coding Propels Market Growth

Enhanced reliability and fault tolerance offered by erasure coding propel the growth of the erasure coding market. As data volumes continue to rise, organizations seek efficient and secure storage solutions to prevent data loss and ensure business continuity. Erasure coding addresses these needs by breaking data into fragments, encoding them with redundant information, and distributing them across multiple storage nodes. This method enables data recovery even if some fragments become unavailable, offering superior fault tolerance compared to traditional methods. Industries such as BFSI, healthcare, and IT are adopting erasure coding to enhance data resilience. This growing reliance on reliable storage drives the expansion of the erasure coding market across regions.

Restraint Factor for the Erasure Coding Market

Dependence on Constant Internet Connectivity for Cloud-Based Storage Limits Market Growth

Dependence on constant internet connectivity for cloud-based storage is a significant restraint limiting the growth of the erasure coding market. Cloud-based storage solutions utilizing erasure coding require a stable and reliable internet connection to ensure continuous data access, transfer, and redundancy. In regions with inconsistent or limited internet infrastructure, this reliance can hinder the adoption of such technologies. Additionally, latency issues...

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