An English brochure about water conservation

This artifact accompanies the SEET@ICSE article "Assessing the impact of hints in learning formal specification", which reports on a user study to investigate the impact of different types of automated hints while learning a formal specification language, both in terms of immediate performance and learning retention, but also in the emotional response of the students. This research artifact provides all the material required to replicate this study (except for the proprietary questionnaires passed to assess the emotional response and user experience), as well as the collected data and data analysis scripts used for the discussion in the paper.

Dataset

The artifact contains the resources described below.

Experiment resources

The resources needed for replicating the experiment, namely in directory experiment:

alloy_sheet_pt.pdf: the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment. The sheet was passed in Portuguese due to the population of the experiment.

alloy_sheet_en.pdf: a version the 1-page Alloy sheet that participants had access to during the 2 sessions of the experiment translated into English.

docker-compose.yml: a Docker Compose configuration file to launch Alloy4Fun populated with the tasks in directory data/experiment for the 2 sessions of the experiment.

api and meteor: directories with source files for building and launching the Alloy4Fun platform for the study.

Experiment data

The task database used in our application of the experiment, namely in directory data/experiment:

Model.json, Instance.json, and Link.json: JSON files with to populate Alloy4Fun with the tasks for the 2 sessions of the experiment.

identifiers.txt: the list of all (104) available participant identifiers that can participate in the experiment.

Collected data

Data collected in the application of the experiment as a simple one-factor randomised experiment in 2 sessions involving 85 undergraduate students majoring in CSE. The experiment was validated by the Ethics Committee for Research in Social and Human Sciences of the Ethics Council of the University of Minho, where the experiment took place. Data is shared the shape of JSON and CSV files with a header row, namely in directory data/results:

data_sessions.json: data collected from task-solving in the 2 sessions of the experiment, used to calculate variables productivity (PROD1 and PROD2, between 0 and 12 solved tasks) and efficiency (EFF1 and EFF2, between 0 and 1).

data_socio.csv: data collected from socio-demographic questionnaire in the 1st session of the experiment, namely:

participant identification: participant's unique identifier (ID);

socio-demographic information: participant's age (AGE), sex (SEX, 1 through 4 for female, male, prefer not to disclosure, and other, respectively), and average academic grade (GRADE, from 0 to 20, NA denotes preference to not disclosure).

data_emo.csv: detailed data collected from the emotional questionnaire in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID) and the assigned treatment (column HINT, either N, L, E or D);

detailed emotional response data: the differential in the 5-point Likert scale for each of the 14 measured emotions in the 2 sessions, ranging from -5 to -1 if decreased, 0 if maintained, from 1 to 5 if increased, or NA denoting failure to submit the questionnaire. Half of the emotions are positive (Admiration1 and Admiration2, Desire1 and Desire2, Hope1 and Hope2, Fascination1 and Fascination2, Joy1 and Joy2, Satisfaction1 and Satisfaction2, and Pride1 and Pride2), and half are negative (Anger1 and Anger2, Boredom1 and Boredom2, Contempt1 and Contempt2, Disgust1 and Disgust2, Fear1 and Fear2, Sadness1 and Sadness2, and Shame1 and Shame2). This detailed data was used to compute the aggregate data in data_emo_aggregate.csv and in the detailed discussion in Section 6 of the paper.

data_umux.csv: data collected from the user experience questionnaires in the 2 sessions of the experiment, namely:

participant identification: participant's unique identifier (ID);

user experience data: summarised user experience data from the UMUX surveys (UMUX1 and UMUX2, as a usability metric ranging from 0 to 100).

participants.txt: the list of participant identifiers that have registered for the experiment.

Analysis scripts

The analysis scripts required to replicate the analysis of the results of the experiment as reported in the paper, namely in directory analysis:

analysis.r: An R script to analyse the data in the provided CSV files; each performed analysis is documented within the file itself.

requirements.r: An R script to install the required libraries for the analysis script.

normalize_task.r: A Python script to normalize the task JSON data from file data_sessions.json into the CSV format required by the analysis script.

normalize_emo.r: A Python script to compute the aggregate emotional response in the CSV format required by the analysis script from the detailed emotional response data in the CSV format of data_emo.csv.

Dockerfile: Docker script to automate the analysis script from the collected data.

Setup

To replicate the experiment and the analysis of the results, only Docker is required.

If you wish to manually replicate the experiment and collect your own data, you'll need to install:

A modified version of the Alloy4Fun platform, which is built in the Meteor web framework. This version of Alloy4Fun is publicly available in branch study of its repository at https://github.com/haslab/Alloy4Fun/tree/study.

If you wish to manually replicate the analysis of the data collected in our experiment, you'll need to install:

Python to manipulate the JSON data collected in the experiment. Python is freely available for download at https://www.python.org/downloads/, with distributions for most platforms.

R software for the analysis scripts. R is freely available for download at https://cran.r-project.org/mirrors.html, with binary distributions available for Windows, Linux and Mac.

Usage

Experiment replication

This section describes how to replicate our user study experiment, and collect data about how different hints impact the performance of participants.

To launch the Alloy4Fun platform populated with tasks for each session, just run the following commands from the root directory of the artifact. The Meteor server may take a few minutes to launch, wait for the "Started your app" message to show.

cd experimentdocker-compose up

This will launch Alloy4Fun at http://localhost:3000. The tasks are accessed through permalinks assigned to each participant. The experiment allows for up to 104 participants, and the list of available identifiers is given in file identifiers.txt. The group of each participant is determined by the last character of the identifier, either N, L, E or D. The task database can be consulted in directory data/experiment, in Alloy4Fun JSON files.

In the 1st session, each participant was given one permalink that gives access to 12 sequential tasks. The permalink is simply the participant's identifier, so participant 0CAN would just access http://localhost:3000/0CAN. The next task is available after a correct submission to the current task or when a time-out occurs (5mins). Each participant was assigned to a different treatment group, so depending on the permalink different kinds of hints are provided. Below are 4 permalinks, each for each hint group:

Group N (no hints): http://localhost:3000/0CAN

Group L (error locations): http://localhost:3000/CA0L

Group E (counter-example): http://localhost:3000/350E

Group D (error description): http://localhost:3000/27AD

In the 2nd session, likewise the 1st session, each permalink gave access to 12 sequential tasks, and the next task is available after a correct submission or a time-out (5mins). The permalink is constructed by prepending the participant's identifier with P-. So participant 0CAN would just access http://localhost:3000/P-0CAN. In the 2nd sessions all participants were expected to solve the tasks without any hints provided, so the permalinks from different groups are undifferentiated.

Before the 1st session the participants should answer the socio-demographic questionnaire, that should ask the following information: unique identifier, age, sex, familiarity with the Alloy language, and average academic grade.

Before and after both sessions the participants should answer the standard PrEmo 2 questionnaire. PrEmo 2 is published under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons licence (CC BY-NC-ND 4.0). This means that you are free to use the tool for non-commercial purposes as long as you give appropriate credit, provide a link to the license, and do not modify the original material. The original material, namely the depictions of the diferent emotions, can be downloaded from https://diopd.org/premo/. The questionnaire should ask for the unique user identifier, and for the attachment with each of the depicted 14 emotions, expressed in a 5-point Likert scale.

After both sessions the participants should also answer the standard UMUX questionnaire. This questionnaire can be used freely, and should ask for the user unique identifier and answers for the standard 4 questions in a 7-point Likert scale. For information about the questions, how to implement the questionnaire, and how to compute the usability metric ranging from 0 to 100 score from the answers, please see the original paper:

Kraig Finstad. 2010. The usability metric for user experience. Interacting with computers 22, 5 (2010), 323–327.

Analysis of other applications of the experiment

This section describes how to replicate the analysis of the data collected in an application of the experiment described in Experiment replication.

The analysis script expects data in 4 CSV files,

Notes: DMC, data collection method; MCOD, medical certification of death; VA, verbal autopsy; COD, cause-of-death.Data collection methods for vital statistics.

The Health Information National Trends Survey (HINTS) is a biennial, cross-sectional survey of a nationally-representative sample of American adults that is used to assess the impact of the health information environment. The survey provides updates on changing patterns, needs, and information opportunities in health; Identifies changing communications trends and practices; Assesses cancer information access and usage; Provides information about how cancer risks are perceived; and Offers a testbed to researchers to test new theories in health communication.

The City of Rochester and its staff use data about individuals in our community to inform decisions related to policies and programs we design, fund, and carry out. City staff must understand and be accountable to best practices and standards to guide the appropriate use of this information in an ethical and accurate manner that furthers the public good. With these disaggregated data standards, the City seeks to establish useful, uniform standards that guide City staff in their collection, stewardship, analysis, and reporting of information about individuals and their demographic characteristics.This internal guide provides recommended standards and practices to City of Rochester staff for the collection, analysis, and reporting of data related to following characteristics of an individual: Race & Ethnicity; Nativity & Citizenship Status; Language Spoken at Home & English Proficiency; Age; Sex, Gender, & Sexual Orientation; Marital Status; Disability; Address / Geography; Household Income & Size; Housing Tenure; Computer & Internet Use; Employment Status; Veteran Status; and Education Level. This kind of data that describes the characteristics of individuals in our community is disaggregated data. When we summarize data about these individuals and report the data at the group level, it becomes aggregated data. These disaggregated data standards can help City staff in different roles understand how to ask individuals about various demographic traits that may describe them, the collection of which may be useful to inform the City’s programs and policies. Note that this standards document does not mandate the collection of every one of these demographic factors for all analyses or program data intake designs – instead, it prompts City staff to intentionally design surveys and other data intake tools/applications to collect the right level of data to inform the City’s decision-making while also respecting the privacy of the individuals whose information the City seeks to gather. When a City team does choose to collect any of the above-mentioned demographic information about individuals in our community, we advise that they adhere to these standards.

Tongue Tip

## Overview

Tongue Tip is a dataset for object detection tasks - it contains Tongue Tip annotations for 1,652 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).

Analysis of ‘A Waiter's Tips’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jsphyg/tipping on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

One waiter recorded information about each tip he received over a period of a few months working in one restaurant. In all he recorded 244 tips.

Can you predict the tip amount?

Acknowledgements

The data was reported in a collection of case studies for business statistics.

Bryant, P. G. and Smith, M (1995) Practical Data Analysis: Case Studies in Business Statistics. Homewood, IL: Richard D. Irwin Publishing

The dataset is also available through the Python package Seaborn.

--- Original source retains full ownership of the source dataset ---

The global Guide Rail Data Collector market size was valued at USD 2.52 million in 2022 and is projected to grow at a CAGR of 10.1% during the forecast period, reaching a value of USD 4.95 million by 2030. The adoption of Industry 4.0 concepts, rising investments in smart manufacturing, and increasing demand for automated data collection systems are driving market growth. Guide Rail Data Collectors enable efficient and accurate data collection in various industries, including manufacturing, logistics, and automotive, to optimize operations and improve productivity. Asia Pacific is anticipated to be the most lucrative region in the market during the forecast period. The region's rapid industrialization, growing manufacturing sector, and government initiatives to promote smart manufacturing are contributing to market growth. Europe is also a significant market due to technological advancements and the presence of major automation companies. North America is expected to maintain a steady market share as industries adopt automation solutions to improve efficiency and reduce costs.

The yield curve, also called the term structure of interest rates, refers to the relationship between the remaining time-to-maturity of debt securities and the yield on those securities. Yield curves have many practical uses, including pricing of various fixed-income securities, and are closely watched by market participants and policymakers alike for potential clues about the markets perception of the path of the policy rate and the macroeconomic outlook. This page provides daily estimated real yield curve parameters, smoothed yields on hypothetical TIPS, and implied inflation compensation, from 1999 to the present. Because this is a staff research product and not an official statistical release, it is subject to delay, revision, or methodological changes without advance notice.

Overview: This document is a reference guide for users of the SAR Field Data Collection Form User Guide. The purpose is to provide a better understanding of how to use the form in the field.

The underlying technology used with this form is likely to evolve and change over time, therefore technical user guides will be provided as appendices to this document.

Background: The SAR Field Data Collection Form was created by an interdisciplinary group of first responders, decision-makers and technology specialists from across Federal, State, and Local Urban Search and Rescue Teams – the NAPSG Foundation SAR Working Group. If you have any questions or concerns regarding this document and associated materials, please send a note to comments@publicsafetygis.org.

Purpose: The SAR Field Data Collection Form is intended to provide a standardized approach to the collection of information during disaster response alongside resource management and tracking of assets.The primary goal of this approach is to obtain situational awareness (where, when, what) for SAR Teams in the field across four relevant themes: Victims that may need assistance or have already been helped. Hazards that must be avoided or mitigated. Damage that have been rapidly assessed for damage, when time and the mission permits. Other mission critical intelligence that vary based on mission type. The secondary goal of this approach is to provide essential elements of information to those not currently on-scene of the disaster. Using the themes above, information and maps can be shared based on “need to know”. If you are a technology specialist looking to deploy this application on your own see the Deployment Kit.

The Yelp Dataset is a valuable resource for academic research, teaching, and learning. It provides a rich collection of real-world data related to businesses, reviews, and user interactions. Here are the key details about the Yelp Dataset: Reviews: A whopping 6,990,280 reviews from users. Businesses: Information on 150,346 businesses. Pictures: A collection of 200,100 pictures. Metropolitan Areas: Data from 11 metropolitan areas. Tips: Over 908,915 tips provided by 1,987,897 users. Business Attributes: Details like hours, parking availability, and ambiance for more than 1.2 million businesses. Aggregated Check-ins: Historical check-in data for each of the 131,930 businesses.

Finansministeriet lämnar bifogade datasakerhetsrekommendation (nedan rekommendation) som gjorts upp av Ledningsgruppen för datasakerheten inom statsförvaltningen VAHTI, som tillsatts av och verkar under ledning av Finansministeriet. Rekommendationen completterar finansministeriets omfattande existerande datasakerhetsanvisningar och ersätter FM:s tidigare datasäsäkerhetsrekommendation “Datasäkerhetens resultatstyrning och verktyg för utveckling” (FM:s VAHTI-publikation 2/1997).

Datasakers ökar kvaliteten, effektiviteten och productionviteten av förvaltningens tjänster. En tillräcklig nivå på data collections är en nödvändig förutsättning för organisationernas kontinuerliga verksamhet och en tryggad verksamhetsförmåga. Utvecklingen av datasakerhetens resultatstyrning är högt prioriterad inom den totala utvecklingen av statens data collections, vilken beskrivits bl.a. i utvecklingsprogrammet för statsförvaltningens (FMs VAHTI-publikation 1/2004).

I rekommendationen har i KONCIS form framförts de Centrala principerna för utvecklandet av data collections samt Deras förbindelse med resultatstyrningen, ledningen av ämbetsverken och utvärderingen av verksamheten. Är en viktig del av den normala utvecklingen av tjänsterna och verksamheten, varför den bör Ingå även i resultatstyrningen. Väsentligt är att data collection i Fråga om resultatavtal och -styrning binds samman med organisationernas verksamhetsmässiga mål.

The dataset comprises cleaned records of yellow taxi rides in a specified time frame, covering essential details such as pickup and drop-off dates, number of passengers, distance traveled, fare, tips, tolls, total payment, taxi color, and payment method. Detailed statistics on ride durations, distances, fares, and payments are included.

Source

• This Dataset is From Seaborn Data_Set Collection. Taxis Dataset.
• The Original Data Contains Many Missing Values.
• I Imputed The Missing values on Some Research Idea's and Some With The Help Of Iterative_Imputer.
• Complete Notebook Also Attached in Data Code Section.

Column Description: - pickup: Pickup date and time. - dropoff: Drop-off date and time. - passengers: Number of passengers. - distance: Distance traveled in miles. - fare: Fare amount. - tip: Extra tip amount. - tolls: Toll tax amount. - total: Total payment including fare, tip, and tolls. - color: Color of the taxi. - payment: Payment method (e.g., credit card, cash). - 03/28/2019 - 03/31/2019: Ride counts aggregated by date range. - 2019-03-01 - 2019-04-01: Ride counts aggregated by date. - Label: Categorized ranges for distances, fares, tips, tolls, and totals. - yellow: Percentage breakdown of yellow taxi rides. - green: Percentage breakdown of green taxi rides.

This Data Guide is aimed at those engaged in the cycle of social science research, from applying for a research grant, through the data collection phase, and ultimately to preparation of the data for deposit in the DANS data archive, or any other data repository.

This publication is an adaption of the 4th edition of the Guide to social science data preparation and archiving of 2009, published by the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan in the United States.
The publication is intended to help researchers manage and document their data to prepare them for archival deposit, as well as think more broadly about the types of digital content that should be deposited in an archive.

Place Profiles have been prepared to help people understand how Planning relates to the places where people live, work and spend their time.Based on the development planning themes of sustainability, liveability and productivity, they tell a story about the environmental, social and economic characteristics of each community council area. They have been created to help people engage in the preparation of Stirling’s next Local Development Plan (LDP3), and with the planning system more generally. They have also been produced to assist communities in preparing Local Place Plans. Place Profiles for all community council areas covered by the Stirling Local Development Plan will be published in spring 2024._StoryMaps offer a way to interact with our data, allowing a user the ability to move around an area of interest, click on a shape to view its pop-up information, or view an embedded map in full-screen. Simply scroll up and down through the Story, just like a normal webpage. If you want to view a map full-screen, click on the double arrows in the top right of that map. Images, simply click on an image within the StoryMaps to view a larger version. Note; some of our graphs are embedded images too.Click "Get Started" to view the first StoryMap.

D'Amico, Kim, and Wei use a no-arbitrage term structure model to decompose TIPS inflation compensation into three components: inflation expectation, inflation risk premium, and TIPS liquidity premium over the 1983-present period. The model is also used to decompose nominal yields or forward rates into four components: expected real short rate, expected inflation, inflation risk premium, and real term premium.

The HELP dataset is an automatically created natural language inference (NLI) dataset that embodies the combination of lexical and logical inferences focusing on monotonicity (i.e., phrase replacement-based reasoning). The HELP (Ver.1.0) has 36K inference pairs consisting of upward monotone, downward monotone, non-monotone, conjunction, and disjunction.

This guide will provide resources on Leadership and Communication. This research guide is not a comprehensive listing of sources, but is intended to be a starting point from which employees can begin their research according to their specific needs. https://dhs-gov.libguides.com/c.php?g=1047434

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Seek Help

## Overview

Seek Help is a dataset for object detection tasks - it contains Hand Gesture annotations for 918 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).