Statistics of the experiments and datasets in the study.

This is digital research data corresponding to the manuscript, Reinhart, K.O., Vermeire, L.T. Precipitation Manipulation Experiments May Be Confounded by Water Source. J Soil Sci Plant Nutr (2023). https://doi.org/10.1007/s42729-023-01298-0 Files for a 3x2x2 factorial field experiment and water quality data used to create Table 1. Data for the experiment were used for the statistical analysis and generation of summary statistics for Figure 2. Purpose: This study aims to investigate the consequences of performing precipitation manipulation experiments with mineralized water in place of rainwater (i.e. demineralized water). Limited attention has been paid to the effects of water mineralization on plant and soil properties, even when the experiments are in a rainfed context. Methods: We conducted a 6-yr experiment with a gradient in spring rainfall (70, 100, and 130% of ambient). We tested effects of rainfall treatments on plant biomass and six soil properties and interpreted the confounding effects of dissolved solids in irrigation water. Results: Rainfall treatments affected all response variables. Sulfate was the most common dissolved solid in irrigation water and was 41 times more abundant in irrigated (i.e. 130% of ambient) than other plots. Soils of irrigated plots also had elevated iron (16.5 µg × 10 cm-2 × 60-d vs 8.9) and pH (7.0 vs 6.8). The rainfall gradient also had a nonlinear (hump-shaped) effect on plant available phosphorus (P). Plant and microbial biomasses are often limited by and positively associated with available P, suggesting the predicted positive linear relationship between plant biomass and P was confounded by additions of mineralized water. In other words, the unexpected nonlinear relationship was likely driven by components of mineralized irrigation water (i.e. calcium, iron) and/or shifts in soil pH that immobilized P. Conclusions: Our results suggest robust precipitation manipulation experiments should either capture rainwater when possible (or use demineralized water) or consider the confounding effects of mineralized water on plant and soil properties. Resources in this dataset: Resource Title: Readme file- Data dictionary File Name: README.txt Resource Description: File contains data dictionary to accompany data files for a research study. Resource Title: 3x2x2 factorial dataset.csv File Name: 3x2x2 factorial dataset.csv Resource Description: Dataset is for a 3x2x2 factorial field experiment (factors: rainfall variability, mowing seasons, mowing intensity) conducted in northern mixed-grass prairie vegetation in eastern Montana, USA. Data include activity of 5 plant available nutrients, soil pH, and plant biomass metrics. Data from 2018. Resource Title: water quality dataset.csv File Name: water quality dataset.csv Resource Description: Water properties (pH and common dissolved solids) of samples from Yellowstone River collected near Miles City, Montana. Data extracted from Rinella MJ, Muscha JM, Reinhart KO, Petersen MK (2021) Water quality for livestock in northern Great Plains rangelands. Rangeland Ecol. Manage. 75: 29-34.

Accurately extracting the Region of Interest (ROI) of a palm print was crucial for subsequent palm print recognition. However, under unconstrained environmental conditions, the user’s palm posture and angle, as well as the background and lighting of the environment, were not controlled, making the extraction of the ROI of palm print a major challenge. In existing research methods, traditional ROI extraction methods relied on image segmentation and were difficult to apply to multiple datasets simultaneously under the aforementioned interference. However, deep learning-based methods typically did not consider the computational cost of the model and were difficult to apply to embedded devices. This article proposed a palm print ROI extraction method based on lightweight networks. Firstly, the YOLOv5-lite network was used to detect and preliminarily locate the palm, in order to eliminate most of the interference from complex backgrounds. Then, an improved UNet was used for keypoints detection. This network model reduced the number of parameters compared to the original UNet model, improved network performance, and accelerated network convergence. The output of this model combined Gaussian heatmap regression and direct regression and proposed a joint loss function based on JS loss and L2 loss for supervision. During the experiment, a mixed database consisting of 5 databases was used to meet the needs of practical applications. The results showed that the proposed method achieved an accuracy of 98.3% on the database, with an average detection time of only 28ms on the GPU, which was superior to other mainstream lightweight networks, and the model size was only 831k. In the open-set test, with a success rate of 93.4%, an average detection time of 5.95ms on the GPU, it was far ahead of the latest palm print ROI extraction algorithm and could be applied in practice.

Synthetic Data Experiment

## Overview

Synthetic Data Experiment is a dataset for object detection tasks - it contains Motorcycle Vehicles annotations for 4,137 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Control Experiment

## Overview

Control Experiment is a dataset for object detection tasks - it contains Splits annotations for 266 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

NIMS Experiment Data Record (EDR) files contain raw data from the Galileo Orbiter Near-Infrared Mapping Spectrometer (CARLSONETAL1992). This raw data requires considerable processing before it is readily amenable to science analysis. The EDRs comprise the base dataset from which spectral image cubes will be created by continually evolving software using successively more accurate calibration and geometry data.

This folder contains the data used for the following paper: "What Do Tanzanian Parents Want from Primary Schools—and What Can Be Done about It?"

Birthday Paradox Visitor Data

Exploring Probability and Patterns of Day of the Week Birthdays

By data.world's Admin [source]

About this dataset

This dataset contains daily visitor-submitted birthdays and associated data from an ongoing experimentation known as the Birthday Paradox. Be enlightened as you learn how many people have chosen the same day of their birthday as yours. Get a better perspective on how this phenomenon varies day-to-day, including recent submissions within the last 24 hours. This experiment is published under the MIT License, giving you access to detailed information behind this perplexing cognitive illusion. Find out now why the probability of two people in the same room having birthday matches is much higher than one might expect!

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For more datasets, click here.

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How to use the dataset

This dataset provides data on the Birthday Paradox Visitor Experiments. It contains information such as daily visitor-submitted birthdays, the total number of visitors who have submitted birthdays, the total number of visitors who guessed the same day as their birthday, and more. This dataset can be used to analyze patterns in visitor behavior related to the Birthday Paradox Experiment.

In order to use this dataset effectively and efficiently, it is important to understand its fields and variables:
- Updated: The date when this data was last updated
- Count: The total number of visitors who have submitted birthdays
- Recent: The number of visitors who have submitted birthdays in the last 24 hours
- binnedDay: The day of the week for a given visitor's birthday submission
- binnedGuess: The day of week that a given visitor guessed their birthday would fall on 6) Tally: Total number of visitors who guessed same day as their birthday 7) binnedTally: Total number of visitors grouped by guess day

To begin using this dataset you should first filter your data based on desired criteria such as date range or binnedDay. For instance, if you are interested in analyzing Birthady Paradox Experiment results for Monday submissions only then you can filter your data by binnedDay = 'Monday'. Then further analyze your filtered query by examining other fields such as binnedGuess and comparing it with tally or binnedTally results accordingly. For example if we look at Monday entries above we should compare 'Monday' tallies with 'Tuesday' guesses (or any other weekday). ` Furthermore understanding updates from recent field can also provide interesting insights into user behavior related to Birthady Paradox Experiment -- trackingt recent entries may yield valuable trends over time.

By exploring various combinations offields available in this dataset users will be ableto gain a better understandingof how user behaviordiffers across different daysofweek both within a singledayandover periodsoftimeaccordingtodifferent criteria providedbythisdataset

Research Ideas

• Analyzing the likelihood of whether a person will guess their own birthday correctly.
• Estimating which day of the week is seeing the most number of visitors submitting their birthdays each day and analyzing how this varies over time.
• Investigating how likely it is for two people from different regions to have the same birthday by comparing their respective submission rates on each day of the week

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: data.csv | Column name | Description | |:----------------|:-----------------------------------------------------------------------------------| | updated | The date and time the data was last updated. (DateTime) | | count | The total number of visitor submissions. (Integer) | | recent | The number of visitor submissions in the last 24 hours. (Integer) | | binnedDay | The day of the week the visitor submitted their birthday. (String) | | binnedGuess | The day of the week the visitor guessed their birthday. (String) | | tally | The total number of visitor guesses that matched their actual birthdays. (Integer) | | binnedTally | The day of the week the visitor guessed their birthday correctly. (String) |

Acknowledgement...

This bundle contains ASCII tables containing corrected, reduced, and concatenated versions of all available calibrated data from the Apollo 15 and 17 Heat Flow Experiment, along with supporting documentation and source data. These tables are based on other data in the PDS and the published literature, specifically (1) transcriptions of data sent by the original instrument team to the NSSDC and (2) data not archived by the instrument team and recovered much later from ARCSAV tapes. The data here correct several errors in (1), and furthermore place (1) and (2) into a standardized format for ease of use.

The csv file contains aggregated data on the results of the experiment. The task is to analyze the results of the experiment and write your recommendations.

The effect of microgravity on gene expression in C.elegans was comprehensively analysed by DNA microarray. This is the first DNA microarray analysis for C.elegans grown under microgravity. Hyper gravity and clinorotation experiments were performed as reference against the flight experiment.

Dataset

This dataset was created by Abdelrahman Youssef

Contents

This dataset records Forest Research Experiment sites on the National Forest Estate and private land. Objective is to avoid accidental damage to Forest Research experiments and sample plots during Forest Enterprise operations and to provide Forest Enterprise Districts with contact details for experiments and sample plots so that they can enquire as to suitability of operations in surrounding forest. Field explanations EXPT NAME Text Local name used for experiment CONTACT Text Name of TSU staff member responsible for experiment Caps & Lower case ADDRESS Text Address of fieldstation where contact works, including telephone number. Caps & Lower case PROJ LEADER Text Name of Project Leader/s for experiment/sample. May be more than one name. Caps & Lower case DIVISION Text Division of Project Leader/s again me be more than one. Caps & Lower case YEAR TAG Text Date as used in local experiment name. P year (e.g. p2006) For planted experiments /year (e.g. /2006) For experiments imposed on existing crop or open ground. P year or /year STATUS Text Status of Experiment · O (Open) · REA (retained as part of experimental area) · RD (retained as demonstration) · C closed O, REA, RD or C DESC Text Brief description of experiment type Caps & Lower case VALIDATION Text Method of validation · NIL · G (validated by GPS) · V (visual validation from OS map or SCDB) NIL,G or V Attribution Statement: Contains OS data © Crown copyright [and database right] [year].

Stimulation data from Experiment 1 of EGS Collab, which occurred on the 4850 ft level of the Sanford Underground Research Facility (SURF). A detailed description of the stimulation data is provided in the StimulationDataNotes.docx and is also available on the EGS Collab Wiki. A Meta Data Cheat Sheet, which describes all of the channels in the Raw CSV files, is available as well. Note that this cheat sheet is a comprehensive meta data descriptor and channels were added as the experiment evolved. This means that some columns may not be populated in early data. Additionally, we have included the chat logs from these experiments. The experiments were broadcast over teleconferencing software and real-time data displays were available to remote observers. The logs contain important observations from those personnel performing the experiment and the remote contributors. Finally, we have included summary and individual plots of all of the data for the user to compare to.

Floating structures have a wide range of application and shapes. This experimental investigations observes a hexagonal floating structure under wave conditions for three different draft configurations. Regular waves as well as a range of white noise tests were conducted to quantify the response amplitude operator (RAO). Further irregular waves focused on the survivability of the floating structure. The presented dataset includes wave gauge data as well as 6 degree of freedom motion measurement to quantify the response only restricted by a soft mooring system. Additional analysis include the measurement of the mass properties of the individual configuration, natural frequency of the mooring system as well as the comparison between requested and measured wave heights. This allow to use the provided dataset as a validation experiment. This research was carried out at the FloWave Ocean Energy Research Facility of the Institute for Energy Systems, University of Edinburgh.

Dataset

This dataset was created by Vikram

Contents

The primary article (cited below under "Related works") introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. The article includes an online supplement with a worked example demonstrating DCE design and analysis with realistic simulated data. The worked example focuses on caregivers' priorities in choosing treatment for children with attention deficit hyperactivity disorder. This dataset includes the scripts (and, in some cases, Excel files) that we used to identify appropriate experimental designs, simulate population and sample data, estimate sample size requirements for the multinomial logit (MNL, also known as conditional logit) and random parameter logit (RPL) models, estimate parameters using the MNL and RPL models, and analyze attribute importance, willingness to pay, and predicted uptake. It also includes the associated data files (experimental designs, data generation parameters, simulated population data and parameters, ..., In the worked example, we used simulated data to examine caregiver preferences for 7 treatment attributes (medication administration, therapy location, school accommodation, caregiver behavior training, provider communication, provider specialty, and monthly out-of-pocket costs) identified by dosReis and colleagues in a previous DCE. We employed an orthogonal design with 1 continuous variable (cost) and 12 dummy-coded variables (representing the levels of the remaining attributes, which were categorical). Using the parameter estimates published by dosReis et al., with slight adaptations, we simulated utility values for a population of 100,000 people, then selected a sample of 500 for analysis. Relying on random utility theory, we used the mlogit package in R to estimate the MNL and RPL models, using 5,000 Halton draws for simulated maximum likelihood estimation of the RPL model. In addition to estimating the utility parameters, we measured the relative importance of each attribute, esti..., , # Data from: How to Use Discrete Choice Experiments to Capture Stakeholder Preferences in Social Work Research

Access this dataset on Dryad

This dataset supports the worked example in:

Ellis, A. R., Cryer-Coupet, Q. R., Weller, B. E., Howard, K., Raghunandan, R., & Thomas, K. C. (2024). How to use discrete choice experiments to capture stakeholder preferences in social work research. Journal of the Society for Social Work and Research. Advance online publication. https://doi.org/10.1086/731310

The referenced article introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. In a DCE, researchers ask participants to complete a series of choice tasks: hypothetical situations in which each participant is presented with alternative scenarios and selects one or more. For example, social work researchers may want to know how parents and other caregivers pr...

A field experiment was undertaken to measure yield loss in narrowleaf lupin due to Sclerotinia disease. Treatments included a complete disease control, nil control and sclerote inoculated treatments. Measurements were made in season of disease progress and yield loss due to disease. Design; Harvest Results; Field operations.

Dopaminergic action prediction errors serve as a value-free teaching signalAnimals’ choice behavior is characterized by two main tendencies: taking actions that led to rewards and repeating past actions. Theory suggests these strategies may be reinforced by different types of dopaminergic teaching signals: reward prediction error to reinforce value-based associations and movement-based action prediction errors to reinforce value-free repetitive associations. Here we use an auditory-discrimination task in mice to show that movement-related dopamine activity in the tail of the striatum encodes the hypothesized action prediction error signal. Causal manipulations reveal that this prediction error serves as a value-free teaching signal that supports learning by reinforcing repeated associations. Computational modelling and experiments demonstrate that action prediction errors alone cannot support reward-guided learning but when paired with the reward prediction error circuitry they serve to consolidate stable sound-action associations in a value-free manner. Together we show that there are two types of dopaminergic prediction errors that work in tandem to support learning, each reinforcing different types of association in different striatal areas.This is the record of all fiber photometry recording experiments (except those in ED Fig 5O-Y and ED FIg 12). It contains mouse ID, date, experiment type as well as any annotated notes. It should be used in conjunction with 'Processed striatal dopamine fiber photometry data, required to reproduce all photometry figures (except EDfig5pqrstwvxy and EDfig12dfg)'.

Using the MM methodology detailed in the supplementary information of https://www.nature.com/articles/s41586-018-0637-6, we implemented code for generating Moral Machine scenarios. After generating the MM scenarios, responses from GPT-3.5, GPT-4, PaLM 2, and Llama 2 were collected using the application programming interface (API) and relevant code. We applied the conjoint analysis framework to evaluate the relative importance of the nine preferences.