According to a study conducted in 2023 among the most popular Android mobile apps available on the Google Play Store in the United States, 40 percent of apps conducted two or more A/B testings in the past year on the screenshots they displayed. A/B testings, also called as split testings, were not a popular App Store Optimization practice for testing what icons, videos, and feature graphics were more effective in onboarding users on the Google Play Store.

Newly released AB Testing Software Market analysis report by Future Market Insights reveals that global sales of AB Testing Software Market in 2023 are estimated at USD 1,211.3 million. With a 11.7% projected growth rate during 2023 to 2033, the market is expected to reach a valuation of USD 3,673.5 million by 2033.

Dataset

This dataset was created by Rami Ashraf

Contents

Dataset

This dataset was created by Nicole Marsh

Contents

In a survey concluded in March 2020, among marketers in the United States, respondents were asked about the type of tools they used to execute personalization across their channels. According to the findings, 67 percent of survey participants were using e-mail marketing solutions and the same share used an A/B testing tool. Some 30 percent of marketers indicated using a customer data platform (CDP).

Dataset

This dataset was created by Igor Kocic

Contents

The global AB Testing market in 2019 was approximately USD 570 million. The market is expected to grow at a CAGR of 9% and is anticipated to reach around USD 1040 million by 2026.

Dataset

This dataset was created by Tetiana Klimonova

Contents

Dataset

This dataset was created by Mohamed-El haddad

Contents

During a global 2024 survey, it was found that A/B and multivariate testing was the type of advanced analytics performed most often in-house, named by 55 percent of respodents. Ad platform optimization ranked second, mentioned by 42 percent of respondents.

Most scientists consider randomized experiments to be the best method available to establish causality. On the Internet, during the past twenty-five years, randomized experiments have become common, often referred to as A/B testing. For practical reasons, much A/B testing does not use pseudo-random number generators to implement randomization. Instead, hash functions are used to transform the distribution of identifiers of experimental units into a uniform distribution. Using two large, industry data sets, I demonstrate that the success of hash-based quasi-randomization strategies depends greatly on the hash function used: MD5 yielded good results, while SHA512 yielded less impressive ones.

The statistic shows the percentage of public high school students in the United States scoring 3 or higher on at least one Advanced Placement Calculus Exam in 2010 by state. Nationally, the share of the graduating class that demonstrated a mastery of Calculus AB by scoring a 3 or higher on the AP Exam was 3.5 percent in 2010.

Dataset

This dataset was created by Muharrem Görkem

Contents

Online experiments and specifically A/B testing are commonly used to identify whether a proposed change to a web page is in fact an effective one. This study focuses on basic settings in which a binary outcome is obtained from each user who visits the website and the probability of a response may be affected by numerous factors. We use Bayesian probit regression to model the factor effects and combine elements from traditional two-level factorial experiments and multiarmed bandits to construct sequential designs that embed attractive features of estimation and exploitation.

The data set is comprised of five Excel spreadsheets, one for each of the tests described in the research article. The data in the spreadsheets are the colony forming unit (CFU) data for each coupon material replicate and location. This dataset is associated with the following publication: Mickelsen, L., J. Wood, W. Calfee, S. Serre, S. Ryan, A. Touati, F. Delafield, and D. Aslett. Low‐concentration hydrogen peroxide decontamination for Bacillus spore contamination in buildings. Remediation Journal. John Wiley & Sons, Inc., Hoboken, NJ, USA, 30(1): 47-56, (2019).

Time to Update the Split-Sample Approach in Hydrological Model Calibration

Hongren Shen¹, Bryan A. Tolson¹, Juliane Mai¹

¹Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada

Corresponding author: Hongren Shen (hongren.shen@uwaterloo.ca)

Abstract

Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly-used split-sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large-sample SST assessment study empirically assesses how different data splitting methods influence post-validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of the continuous calibration sub-periods (CSPs). A full-period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35-year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split-sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split-sample approach in hydrological modeling.

Data description

This data was used in the paper entitled "Time to Update the Split-Sample Approach in Hydrological Model Calibration" by Shen et al. (2022).

Catchment, meteorological forcing and streamflow data are provided for hydrological modeling use. Specifically, the forcing and streamflow data are archived in the Raven hydrological modeling required format. The GR4J and HMETS model building results in the paper, i.e., reference KGE and KGE metrics in calibration, validation and testing periods, are provided for replication of the split-sample assessment performed in the paper.

Data content

The data folder contains a gauge info file (CAMELS_463_gauge_info.txt), which reports basic information of each catchment, and 463 subfolders, each having four files for a catchment, including:

(1) Raven_Daymet_forcing.rvt, which contains Daymet meteorological forcing (i.e., daily precipitation in mm/d, minimum and maximum air temperature in deg_C, shortwave in MJ/m2/day, and day length in day) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.

(2) Raven_USGS_streamflow.rvt, which contains daily discharge data (in m3/s) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.

(3) GR4J_metrics.txt, which contains reference KGE and GR4J-based KGE metrics in calibration, validation and testing periods.

(4) HMETS_metrics.txt, which contains reference KGE and HMETS-based KGE metrics in calibration, validation and testing periods.

Data collection and processing methods

Data source

• Catchment information and the Daymet meteorological forcing are retrieved from the CAMELS data set, which can be found here.
• The USGS streamflow data are collected from the U.S. Geological Survey's (USGS) National Water Information System (NWIS), which can be found here.
• The GR4J and HMETS performance metrics (i.e., reference KGE and KGE) are produced in the study by Shen et al. (2022).

Forcing data processing

• A quality assessment procedure was performed. For example, daily maximum air temperature should be larger than the daily minimum air temperature; otherwise, these two values will be swapped.
• Units are converted to Raven-required ones. Precipitation: mm/day, unchanged; daily minimum/maximum air temperature: deg_C, unchanged; shortwave: W/m2 to MJ/m2/day; day length: seconds to days.
• Data for a catchment is archived in a RVT (ASCII-based) file, in which the second line specifies the start time of the forcing series, the time step (= 1 day), and the total time steps in the series (= 12784), respectively; the third and the fourth lines specify the forcing variables and their corresponding units, respectively.
• More details of Raven formatted forcing files can be found in the Raven manual (here).

Streamflow data processing

• Units are converted to Raven-required ones. Daily discharge originally in cfs is converted to m3/s.
• Missing data are replaced with -1.2345 as Raven requires. Those missing time steps will not be counted in performance metrics calculation.
• Streamflow series is archived in a RVT (ASCII-based) file, which is open with eight commented lines specifying relevant gauge and streamflow data information, such as gauge name, gauge ID, USGS reported catchment area, calculated catchment area (based on the catchment shapefiles in CAMELS dataset), streamflow data range, data time step, and missing data periods. The first line after the commented lines in the streamflow RVT files specifies data type (default is HYDROGRAPH), subbasin ID (i.e., SubID), and discharge unit (m3/s), respectively. And the next line specifies the start of the streamflow data, time step (=1 day), and the total time steps in the series(= 12784), respectively.

GR4J and HMETS metrics

The GR4J and HMETS metrics files consists of reference KGE and KGE in model calibration, validation, and testing periods, which are derived in the massive split-sample test experiment performed in the paper.

• Columns in these metrics files are gauge ID, calibration sub-period (CSP) identifier, KGE in calibration, validation, testing1, testing2, and testing3, respectively.
• We proposed 50 different CSPs in the experiment. "CSP_identifier" is a unique name of each CSP. e.g., CSP identifier "CSP-3A_1990" stands for the model is built in Jan 1st 1990, calibrated in the first 3-year sample (1981-1983), calibrated in the rest years during the period of 1980 to 1989. Note that 1980 is always used for spin-up.
• We defined three testing periods (independent to calibration and validation periods) for each CSP, which are the first 3 years from model build year inclusive, the first 5 years from model build year inclusive, and the full years from model build year inclusive. e.g., "testing1", "testing2", and "testing3" for CSP-3A_1990 are 1990-1992, 1990-1994, and 1990-2014, respectively.
• Reference flow is the interannual mean daily flow based on a specific period, which is derived for a one-year period and then repeated in each year in the calculation period.
  • For calibration, its reference flow is based on spin-up + calibration periods.
  • For validation, its reference flow is based on spin-up + calibration periods.
  • For testing, its reference flow is based on spin-up +calibration + validation periods.
• Reference KGE is calculated based on the reference flow and observed streamflow in a specific calculation period (e.g., calibration). Reference KGE is computed using the KGE equation with substituting the "simulated" flow for "reference" flow in the period for calculation. Note that the reference KGEs for the three different testing periods corresponds to the same historical period, but are different, because each testing period spans in a different time period and covers different series of observed flow.

More details of the split-sample test experiment and modeling results analysis can be referred to the paper by Shen et al. (2022).

Citation

Journal Publication

This study:

Shen, H., Tolson, B. A., & Mai, J.(2022). Time to update the split-sample approach in hydrological model calibration. Water Resources Research, 58, e2021WR031523. https://doi.org/10.1029/2021WR031523

Original CAMELS dataset:

A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan (2015). Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209-223, http://doi.org/10.5194/hess-19-209-2015

Data Publication

This study:

H. Shen, B.

NOTE: This dataset is no longer being updated as of 4/27/2023. It is retired and no longer included in public COVID-19 data dissemination.

See this link for more information https://imap.maryland.gov/pages/covid-data Summary The total number of COVID-19 tests administered and the 7-day average percent positive rate in each Maryland jurisdiction.

Description Testing volume data represent the total number of PCR COVID-19 tests electronically reported for Maryland residents; this count does not include test results submitted by labs and other clinical facilities through non-electronic means. The 7-day percent positive rate is a rolling average of each day’s posi"tivity percentage. The percentage is calculated using the total number of tests electronically reported to MDH (by date of report) and the number of positive tests electronically reported to MDH (by date of report). Electronic lab reports from NEDDSS. Upon reaching a limit to the Socrata Platform, we decided to break the data into two parts (now 3 parts). We now have "MD COVID-19 - Total Testing Volume by County" (for 2023), "MD COVID-19 - Total Testing Volume by County 2022 Archive", "MD COVID-19 - Total Testing Volume by County 2021 Archive", and "MD COVID-19 - Total Testing Volume by County 2020 Archive"

Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Datasets used for datatrove testing. Each split contains the same data: dst = [ {"text": "hello"}, {"text": "world"}, {"text": "how"}, {"text": "are"}, {"text": "you"}, ]

But based on the split name the data are sharded into n-bins

SDC-Scissor tool for Cost-effective Simulation-based Test Selection in Self-driving Cars Software

This dataset provides test cases for self-driving cars with the BeamNG simulator. Check out the repository and demo video to get started.

GitHub: github.com/ChristianBirchler/sdc-scissor

This project extends the tool competition platform from the Cyber-Phisical Systems Testing Competition which was part of the SBST Workshop in 2021.

Usage

Demo

YouTube Link

Installation

The tool can either be run with Docker or locally using Poetry.

When running the simulations a working installation of BeamNG.research is required. Additionally, this simulation cannot be run in a Docker container but must run locally.

To install the application use one of the following approaches:

Docker: docker build --tag sdc-scissor .

Poetry: poetry install

Using the Tool

The tool can be used with the following two commands:

Docker: docker run --volume "$(pwd)/results:/out" --rm sdc-scissor [COMMAND] OPTIONS

Poetry: poetry run python sdc-scissor.py [COMMAND] [OPTIONS]

There are multiple commands to use. For simplifying the documentation only the command and their options are described.

Generation of tests:

generate-tests --out-path /path/to/store/tests

Automated labeling of Tests:

label-tests --road-scenarios /path/to/tests --result-folder /path/to/store/labeled/tests

Note: This only works locally with BeamNG.research installed

Model evaluation:

evaluate-models --dataset /path/to/train/set --save

Split train and test data:

split-train-test-data --scenarios /path/to/scenarios --train-dir /path/for/train/data --test-dir /path/for/test/data --train-ratio 0.8

Test outcome prediction:

predict-tests --scenarios /path/to/scenarios --classifier /path/to/model.joblib

Evaluation based on random strategy:

evaluate --scenarios /path/to/test/scenarios --classifier /path/to/model.joblib

The possible parameters are always documented with --help.

Linting

The tool is verified the linters flake8 and pylint. These are automatically enabled in Visual Studio Code and can be run manually with the following commands:

poetry run flake8 . poetry run pylint **/*.py

License

The software we developed is distributed under GNU GPL license. See the LICENSE.md file.

Contacts

Christian Birchler - Zurich University of Applied Science (ZHAW), Switzerland - birc@zhaw.ch

Nicolas Ganz - Zurich University of Applied Science (ZHAW), Switzerland - gann@zhaw.ch

Sajad Khatiri - Zurich University of Applied Science (ZHAW), Switzerland - mazr@zhaw.ch

Dr. Alessio Gambi - Passau University, Germany - alessio.gambi@uni-passau.de

Dr. Sebastiano Panichella - Zurich University of Applied Science (ZHAW), Switzerland - panc@zhaw.ch

References

Christian Birchler, Nicolas Ganz, Sajad Khatiri, Alessio Gambi, and Sebastiano Panichella. 2022. Cost-effective Simulation-based Test Selection in Self-driving Cars Software with SDC-Scissor. In 2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE.

If you use this tool in your research, please cite the following papers:

@INPROCEEDINGS{Birchler2022, author={Birchler, Christian and Ganz, Nicolas and Khatiri, Sajad and Gambi, Alessio, and Panichella, Sebastiano}, booktitle={2022 IEEE 29th International Conference on Software Analysis, Evolution and Reengineering (SANER), title={Cost-effective Simulationbased Test Selection in Self-driving Cars Software with SDC-Scissor}, year={2022}, }

This was a 2×2 randomized crossover control trial to compare the cardiovascular endurance of healthy volunteers using a 2-minute marching test (2MMT) and a 6-minute walk test (6MWT). This study included 254 participants of both sexes, aged 20–50 years, with a height and body mass index (BMI) of ≥150 cm and ≤25 kg/m2, respectively. Participants could perform activities independently and had normal annual chest radiographs and electrocardiograms. A group-randomized design was used to assign participants to Sequence 1 (AB) or 2 (BA). The tests were conducted over 2 consecutive days, with a 1-day washout period. On day 1, the participants randomly underwent either a 6MWT or 2MMT in a single-anonymized setup, and on day 2, the tests were performed in reverse order. We analyzed maximal oxygen consumption (VO2max) as the primary outcome and heart rate (HR), respiratory rate (RR), blood pressure (BP), oxygen saturation, dyspnea, and leg fatigue as secondary outcomes. Data were collected from 12..., Sample size The sample size required for the equivalence study was estimated using nQuery software and calculated using two one-sided equivalence tests for crossover design. To calculate the sample size, we set the alpha error probability, statistical power, the lower equivalence limit, and upper equivalence limit at 5%, 90%, -2.00, and +2.00, respectively, using the clinical margin (minimal clinically important difference [MCID] of VO2max from a previous study, which was 2 ml/kg/min [15], and standard deviation was 8.6 [16]. Based on these values, we needed 101 participants for the crossover design, allowing for a 20% dropout rate. Therefore, we decided to randomize 127 patients per arm, resulting in 254 participants. However, due to the COVID-19 pandemic, data collection was incomplete, and we could only analyze 127 data sets in this study. Inclusion and exclusion criteria The inclusion criteria were male and female healthy volunteers, aged 20–50 years, with height: ≥150 cm and, BMI ≤..., , # A comparison of new cardiovascular endurance test using the 2-minute marching test vs. 6-minute walk test in healthy volunteers: A crossover randomized controlled trial

https://doi.org/10.5061/dryad.31zcrjdv2

We have submitted data tables 1-3 for the description of (Fig 1 CONSORT diagram of the study_figure.TIFF), (Fig 2 The trial design_figure.TIFF, and(Fig 3 The study protocol_figure.TIFF), and submitted of data analysis (Table 1 Baseline characteristics_data.CVS),(Table 2 Equivalence test of VO2max between 2MMT and 6MWT_data.CVS), (Table 3 Mean and standard deviation of 6MWT and 2MMT_data.CVS), and (Table 4 Comparison of secondary outcomes between 6MWT and 2MMT_data.CVS)

Description

Fig 1 CONSORT diagram of the study\Figure

Materials and Methods

The trial protocol and supporting Consolidated Standards of Reporting Trials (CONSORT) checklist are available as supporting information (S1 File CONSORT Checklist) and the CO...