The data contains 4 kinds of files. Files are organized in folders for easy interpretation:

1) An Excel file. This has all the data collected from the measurement. This file can be opened using Microsoft excel.

2) Minitab project files (MPJ) . These files can be opened using the statistical software Minitab version 17. They include the data, analyses and plots used to interpret the results of the research.

3) A PDF document. This has all the plots related obtained through the research data to determine the optimal settings. This can be opened in any PDF reader.

4) Original TIF and BMP images obtained from the CT scan. Only one relevant image from each data-set is shown because it contains hundreds of images. These can be opened using most image viewing applications such as windows photo viewer.

This thesis-mpc-dataset-public-readme.txt file was generated on 2020-10-20 by Masud Petronia

GENERAL INFORMATION
1. Title of Dataset: Data underlying the thesis: Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data
2. Author Information A. Principal Investigator Contact Information Name: Masud Petronia Institution: TU Delft, Faculty of Technology, Policy and Management Address: Mekelweg 5, 2628 CD Delft, Netherlands Email: masud.petronia@gmail.com ORCID: https://orcid.org/0000-0003-2798-046X
3: Description of dataset: This dataset contains perceptual data of firms' willingness to contribute protected data through multi party computation (MPC). Petronia (2020, ch. 6) draws several conclusions from this dataset and provides recommendations for future research Petronia (2020, ch. 7.4).
4. Date of data collection: July-August 2020
5. Geographic location of data collection: Netherlands
6. Information about funding sources that supported the collection of the data: Horizon 2020 Research and Innovation Programme, Grant Agreement no 825225 – Safe Data Enabled Economic Development (SAFE-DEED), from the H2020-ICT-2018-2

SHARING/ACCESS INFORMATION
1. Licenses/restrictions placed on the data: CC 0
2. Links to publications that cite or use the data: Petronia, M. N. (2020). Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data (Master's thesis). Retrieved from http://resolver.tudelft.nl/uuid:b0de4a4b-f5a3-44b8-baa4-a6416cebe26f
3. Was data derived from another source? No
4. Citation for this dataset: Petronia, M. N. (2020). Multiparty Computation: The effect of multiparty computation on firms' willingness to contribute protected data (Master's thesis). Retrieved from https://data.4tu.nl/. doi:10.4121/13102430

DATA & FILE OVERVIEW
1. File List: thesis-mpc-dataset-public.xlsxthesis-mpc-dataset-public-readme.txt (this document)
2. Relationship between files: Dataset metadata and instructions
3. Additional related data collected that was not included in the current data package: Occupation and role of respondents (traceable to unique reference), removed for privacy reasons.
4. Are there multiple versions of the dataset? No

METHODOLOGICAL INFORMATION
1. Description of methods used for collection/generation of data: A pre- and post test experimental design. For more information; see Petronia (2020, ch. 5)
2. Methods for processing the data: Full instructions are provided by Petronia (2020, ch. 6)
3. Instrument- or software-specific information needed to interpret the data: Microsoft Excel can be used to convert the dataset to other formats.
4. Environmental/experimental conditions: This dataset comprises three datasets collected through three channels. These channels are Prolific (incentive), LinkedIn/Twitter (voluntarily), and respondents in a lab setting (voluntarily). For more information; see Petronia (2020, ch. 6.1)
5. Describe any quality-assurance procedures performed on the data: A thorough examination of consistency and reliability is performed. For more information; see Petronia (2020, ch. 6).
6. People involved with sample collection, processing, analysis and/or submission: See Petronia (2020, ch. 6)

DATA-SPECIFIC INFORMATION
1. Number of variables: see worksheet experiment_matrix of thesis-mpc-dataset-public.xlsx
2. Number of cases/rows: see worksheet experiment_matrix of thesis-mpc-dataset-public.xlsx
3. Variable List: see worksheet labels of thesis-mpc-dataset-public.xlsx
4. Missing data codes: see worksheet comments of thesis-mpc-dataset-public.xlsx
5. Specialized formats or other abbreviations used: Multiparty computation (MPC) and Trusted Third Party (TTP).

INSTRUCTIONS
1. Petronia (2020, ch. 6) describes associated tests and respective syntax.

This data release (version 5.0, February 2022) consists of a Microsoft® Access database and Microsoft® Excel workbook that contain water-level data and other hydrologic information for wells on and near the Nevada Test Site (currently the Nevada National Security Site). The three worksheets in the Microsoft® Excel workbook also are provided as individual comma-separated values (CSV) files. The data release supports U.S. Geological Survey Data Series 533 (https://pubs.usgs.gov/ds/533/). The Microsoft® Access database contains water levels measured from 930 wells in and near areas of underground nuclear testing at the Nevada Test Site. The water-level measurements were collected from 1941 to 2021. All water levels in the Microsoft® Access database are stored in the USGS National Water Information System (NWIS) database available at https://waterdata.usgs.gov/nv/nwis. The Microsoft® Access database also provides information for each well (well construction, borehole lithology, units contributing water to the well, and general site remarks) and water-level measurement (measurement source, status, method, accuracy, and specific water-level remarks). Additionally, the database provides hydrograph descriptions (hereinafter hydrograph narratives) that document the water-level history and describe and interpret the water-level hydrograph for each well. Multiple condition flags were assigned to each water‑level measurement to describe the hydrologic conditions at the time of measurement. The condition flags describe the general quality (accuracy), temporal variability, regional significance, and hydrologic conditions of the measurements. The Microsoft® Excel workbook contains hydrographs and locations for the 930 wells, which are interactively presented in the workbook as an interface to the Microsoft® Access database. This workbook is designed to be an easy-to-use tool to obtain the water-level history for any well in the study area. Water-level data can be restricted to certain wells, dates, or hydrologic conditions by using the Microsoft® Excel built-in AutoFilter. Additional information provided in the workbook includes selected well-site information, water-level information, contributing units, the hydrograph narratives, and hyperlinks to the NWISWeb (http://waterdata.usgs.gov/nv/nwis/) site home page for each well. Information presented in the workbook for all water levels in the database also includes measurement source, status, method, accuracy, remarks, and hydrologic condition flags. Interpretations for individual water-level measurements and for the period of record for the wells have been incorporated into the water-level remarks, flags, or hydrograph narratives.

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

This document explain how data were generated and how to interpret them.

LICENSE: CC0 But if you want to combine data with other datasets, feel free to use them as if they were published under CC0 license.
Data were published in February 2017. At that time, Zenodo only provided CC BY, CC BY-SA, CC BY-NC, CC BY-ND and CC BY-NC-ND. No CC0 option was available.

HOW DATA WERE COLLECTED The 21 recorded sessions took place between February 2013 and December 2016.
Data were collected using Turning Technologies' remote controls (called clickers) and TurningPoint software.

The 4 versions of the quiz used during these 4 years are provided in the 'quizzes' folder for information purpose (in PDF and Powerpoint formats).

Turning Technologies records data in a closed format (.tpzx) that can be exported and converted them into 3 formats provided here (these 3 files contain the same data):

• Excel (.xslx)
• Comma-spearated values (.csv)
• SQLite (.sqlite)

The first one was directly exported from TurningPoint and is provided for Excel users who can't read CSV correctly.
CSV was converted from Excel and is provided for non-Excel users.
Finally, SQLite is provided in order to apply different sorting and filters to the data. It can be read using SQLite manager for Firefox (https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/).

CODEBOOK Here is the name, the meaning and the possible values of the columns (name - meaning [possible values]). If students didn't answer the question, the value is '-'.

Session - session number (chronological) [1 to 21] AcademicYear - academic year [12-13, 13-14, 14-15, 15-16, 16-17] Year - calendar year [2013, 2014, 2015, 2016] Month - month (number) [1 to 12] Day - day (number) [1 to 31] Section - section abbreviation [CH, ESC, GM, IF, SIE, SV] Level - students' level [BA2, BA3, MA] Language - course's language [FR or EN] DeviceID - clicker's ID [(unique ID within a session)] Q1 - answers to question 1 [A, B, C, D, E] Q2 - answers to question 2 [A, B, C, D] Q3 - answers to question 3 [A or B] Q4 - answers to question 4 [A or B] Q5 - answers to question 5 [A or B] Q6 - answers to question 6 [A or B] Q7 - answers to question 7 [A or B] Q8 - answers to question 8 [A or B] Q9 - answers to question 9 [A or B] Q8-9 - answers to the question 8-9 (merge) [A or B] Q10 - answers to question 10 [1, 2] Q11 - answers to question 11 [A or B] Q12 - answers to question 12 [A, B]

Section abbreviation meaning * CH: chemistry * ESC: school of criminal justice (Unil) * GM: mechanical engineering * IF: financial engineering * SIE: environmental engineering * SV: life sciences

Level meaning
* BA2: 2nd year of Bachelor * BA3: 3rd year of Bachelor * MA: Master level

Question types For some questions, multiple answers were allowed: Q1, Q2, Q10 & Q12.
Half of the questions have only one correct answer, true or false: Q3, Q5, Q6, Q7, Q8, Q9 & Q8-9.
Finally, for 2 questions only one answer was accepted, but there is not only one correct answer: Q4 & Q11.

INFORMATION ABOUT THE SESSIONS Except otherwise stated below, all sessions were conducted like the original one: Q1 to Q12 (no Q8-9). The original French version of the quiz has been translated into English for a few sessions with Master students. For sessions 14 and 20, Q5 was removed and Q8 & Q9 were merged in Q8-9.
Session 18 was a short one with only 7 sevens questions: Q1, Q2, Q3, Q4, Q6, Q7 & Q9.

CONTACT INFORMATION If you have any question about these data, contact formations.bib@epfl.ch.

Electrospray ionization mass spectrometry is becoming an established tool for the investigation of lipids. As the methods for lipid analysis become more mature and their throughput increases, computer algorithms for the interpretation of such data will become a necessity. Toward this end, an algorithm dedicated to the analysis of Fourier transform mass spectral data from lipid extracts has been developed. The algorithm, Fatty Acid Analysis Tool, termed FAAT, has been successfully used to investigate complex lipid extracts containing thousands of components, from various species of mycobacteria including M. tuberculosis and M. abscessus. FAAT is rapid, generally taking tens of seconds to interpret multiple spectra, and accessible to most users as it is implemented in Microsoft Excel Visual Basic Software. In the reduction of data, FAAT begins by scaling spectra (i.e., to account for dilution factors), identifying monoisotopic ions, and assigning isotope packets. Unique features of FAAT include the following:  (1) overlapping saturated and unsaturated lipid species can be distinguished, (2) known ions are assigned from a user-defined library including species that possess methylene heterogeneity, (3) and isotopic shifts from stable isotope labeling experiments are identified and assigned (up to a user-defined maximum). In addition, abundance differences between samples grown under normal and stressed conditions can be determined. In the analysis of mycobacterial lipid extracts, FAAT has successfully identified isotopic shifts from incorporation of 15N in M. abscessus. Additionally, FAAT has been used to successfully determine differences in lipid abundances between M. tuberculosis wild-type and mutant strains.

These data accompanied with the paper "MULTISEGMENTAL KINEMATIC BEHAVIOR OF NORMAL AND PRONATED FEET DURING THE SQUAT PHASE OF THE ANTERIOR AND LATERAL STEP DOWN TESTS". The aim of the study was to compare the multisegmental kinematic behavior of neutral and pronated feet during the squat phase of the anterior and lateral step down tests. Our hypothesis is that neutral and pronated feet would exhibit different kinematic behaviors during the step down tests based on the analysis of the segments of the tibia, hindfoot and forefoot, as was the case in previous studies that analyzed gait The data were obtained through the kinematic analysis with a three-dimensional system during the execution of two functional tests: Anterior and Lateral Step Down Tests. Using a multisegmental foot model in pronated and neutral foot of health's individuals. The mean for the nine cycles of each variables for the two tasks was calculated and the data were save in excel format. In the data repository are three documents attached: - INSTRUCTIONS FOR EXCEL FILES (word): Instructions and legends of the data and how to interpret the excel files. - ANTERIOR STEP DOWN TEST (excel): There are five tabs with all the variables utilized for compare the two groups. - LATERAL STEP DOWN TEST (excel): There are five tabs with all the variables utilized for compare the two groups. - Anthropometrics Characteristics (excel): contains the data of anthropometrics characteristics of each volunteers. The variables were compared between groups using multivariate analysis of variance. The mean of each variables and segments were used to identify the movement realized by the groups. The findings of these study allowed identify that the most of the differences between groups were in the frontal plane and in the FFHFA segment. The pronated foot presented decreased movement for most variables of the foot.

Sample data for exercises in Further Adventures in Data Cleaning.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Reference data for the proatlas experiment. Includes screen caps of data traces, excel data sets and description of how to interpret the data.

The GCEW herbicide data were collected from 1991-2010, and are documented at plot, field, and watershed scales. Atrazine concentrations in Goodwater Creek Experimental Watershed (GCEW) were shown to be among the highest of any watershed in the United States based on comparisons using the national Watershed Regressions for Pesticides (WARP) model and by direct comparison with the 112 watersheds used in the development of WARP. This 20-yr-long effort was augmented with a spatially broad effort within the Central Mississippi River Basin encompassing 12 related claypan watersheds in the Salt River Basin, two cave streams on the fringe of the Central Claypan Areas in the Bonne Femme watershed, and 95 streams in northern Missouri and southern Iowa. The research effort on herbicide transport has highlighted the importance of restrictive soil layers with smectitic mineralogy to the risk of transport vulnerability. Near-surface soil features, such as claypans and argillic horizons, result in greater herbicide transport than soils with high saturated hydraulic conductivities and low smectitic clay content. The data set contains concentration, load, and daily discharge data for Devils Icebox Cave and Hunters Cave from 1999 to 2002. The data are available in Microsoft Excel 2010 format. Sheet 1 (Cave Streams Metadata) contains supporting information regarding the length of record, site locations, parameters measured, parameter units, method detection limits, describes the meaning of zero and blank cells, and briefly describes unit area load computations. Sheet 2 (Devils Icebox Concentration Data) contains concentration data from all samples collected from 1999 to 2002 at the Devils Icebox site for 12 analytes and two computed nutrient parameters. Sheet 3 (Devils Icebox SS Conc Data) contains 15-minute suspended sediment (SS) concentrations estimated from turbidity sensor data for the Devils Icebox site. Sheet 4 (Devils Icebox Load & Discharge Data) contains daily data for discharge, load, and unit area loads for the Devils Icebox site. Sheet 5 (Hunters Cave Concentration Data) contains concentration data from all samples collected from 1999 to 2002 at the Hunters Cave site for 12 analytes and two computed nutrient parameters. Sheet 6 (Hunters Cave SS Conc Data) contains 15-minute SS concentrations estimated from turbidity sensor data for the Hunters Cave site. Sheet 7 (Hunters Cave Load & Discharge Data) contains daily data for discharge, load, and unit area loads for the Hunters Cave site. [Note: To support automated data access and processing, each worksheet has been extracted as a separate, machine-readable CSV file; see Data Dictionary for descriptions of variables and their concentration units.] Resources in this dataset:Resource Title: README - Metadata. File Name: LTAR_GCEW_herbicidewater_qual.xlsxResource Description: Defines Water Quality and Sediment Load/Discharge parameters, abbreviations, time-frames, and units as rendered in the Excel file. For additional information including site information, method detection limits, and methods citations, see Metadata tab. For Definitions used in machine-readable CSV files, see Data Dictionary.Resource Title: Excel data spreadsheet. File Name: c3.jeq2013.12.0516.ds1_.xlsxResource Description: Multi-page data spreadsheet containing data as well as metadata from this study. A direct download of the data spreadsheet can be found here: https://dl.sciencesocieties.org/publications/datasets/jeq/C3.JEQ2013.12.0516.ds1/downloadResource Title: Devils Icebox Concentration Data. File Name: DevilsIceboxConcData.csvResource Description: Concentrations of herbicides, metabolites, and nutrients (extracted from the Excel tab into machine-readable CSV data).Resource Title: Devils Icebox Load and Discharge Data. File Name: DevilsIceboxLoad&Discharge.csvResource Description: Discharge and Unit Area Loads for herbicides, metabolites, and suspended sediments (extracted from Excel tab as machine-readable CSV data)Resource Title: Devils Icebox Suspended Sediment Concentration Data. File Name: DevilsIceboxSSConcData.csvResource Description: Suspended Sediment Concentration Data (extracted from Excel tab as machine-readable CSV data)Resource Title: Hunters Cave Load and Discharge Data. File Name: HuntersCaveLoad&Discharge.csvResource Description: Discharge and Unit Area Loads for herbicides, metabolites, and suspended sediments (extracted from Excel tab as machine-readable CSV data)Resource Title: Hunters Cave Suspended Sediment Concentration Data. File Name: HuntersCaveSSConc.csvResource Description: Suspended Sediment Concentration Data (extracted from Excel tab as machine-readable CSV data)Resource Title: Data Dictionary for machine-readable CSV files. File Name: LTAR_GCEW_herbicidewater_qual.csvResource Description: Defines Water Quality and Sediment Load/Discharge parameters, abbreviations, time-frames, and units as implemented in the extracted machine-readable CSV files.Resource Title: Hunters Cave Concentration Data. File Name: HuntersCaveConcData.csvResource Description: Concentrations of herbicides, metabolites, and nutrients (extracted from the Excel tab into machine-readable CSV data)

Research data supporting the study "Probabilistic trade-offs analysis for sustainable and equitable management of climate-induced water risks"

This repository provides data of the Stochastic Dual Dynamic Programming (SDDP) model, and the output results of the simulations of the various policies and climate scenarios considered in this study, as well as the code used for postprocessing and visualizing the results.

Contents

1. Data: Model Inputs
   This folder contains the physical river network, reservoir and water demand, and economic data derived from the observed database.
   The key files are:
• Input_HydrologicalData
• Input_SystemData
2. Results: Model Output Analysis
   This folder includes outputs from the Stochastic Dual Dynamic Programming (SDDP) model under various policies and climate scenarios. The results showcase optimized sectoral water use, including irrigated areas, hydropower generation, and allocations for agriculture, energy, and urban demands across spatial locations (upstream and downstream).
   Key files include:
• SDDP Model Outputs (MATLAB format):
• EnergyPriority_Baseline.mat
• EnergyPriority_2070.mat
• EnergyPriority_2100.mat
• AgriculturePriority_Baseline.mat
• AgriculturePriority_2070.mat
• AgriculturePriority_2100.mat
• Extracted Model Results (Excel format):
• Organized for each policy and climate scenario to facilitate analysis.
3. Software: Data Analysis and Visualization
   Python scripts designed for outputs data analysis and visualization are included to reproduce the primary figures from the study.
   Scripts provided:
• CDF_outflow.py: Analyzes cumulative distribution functions for river discharge.
• CDF_sectors.py: Examines sectoral water use distributions.
• PCP_SI.py: Generates Parallel Coordinate Plots for trade-offs analysis.
4. Instructions: README File
   A comprehensive README file explains:
• Details of model input data.
• Instructions to interpret the SDDP model outputs.

Instructions:
The Python scripts process Excel files from the model output results folder to generate and visualize the figures for the paper. Each step is clearly documented within the scripts.

This data release (version 3.0, January 2020) consists of two files, a Microsoft® Access database and Microsoft® Excel workbook, that contain water-level data and other hydrologic information for wells on and near the Nevada Test Site. The data release supports U.S. Geological Survey Data Series 533 (https://pubs.usgs.gov/ds/533/). The Microsoft® Access database contains water levels measured from 919 wells in and near areas of underground nuclear testing at the Nevada Test Site. The water-level measurements were collected from 1941 to 2019. All water levels in the Microsoft® Access database are stored in the USGS National Water Information System (NWIS) database available at https://waterdata.usgs.gov/nv/nwis. The Microsoft® Access database also provides information for each well (well construction, borehole lithology, units contributing water to the well, and general site remarks) and water-level measurement (measurement source, status, method, accuracy, and specific water-level remarks). Additionally, the database provides hydrograph descriptions (hereinafter hydrograph narratives) that document the water-level history and describe and interpret the water-level hydrograph for each well. Multiple condition flags were assigned to each water‑level measurement to describe the hydrologic conditions at the time of measurement. The condition flags describe the general quality (accuracy), temporal variability, regional significance, and hydrologic conditions of the measurements. The Microsoft® Excel workbook contains hydrographs and locations for the 919 wells, which are interactively presented in the workbook as an interface to the Microsoft® Access database. This workbook is designed to be an easy-to-use tool to obtain the water-level history for any well in the study area. Water-level data can be restricted to certain wells, dates, or hydrologic conditions by using the Microsoft® Excel built-in AutoFilter. Additional information provided in the workbook includes selected well-site information, water-level information, contributing units, the hydrograph narratives, and hyperlinks to the NWISWeb (http://waterdata.usgs.gov/nv/nwis/) site home page for each well. Information presented in the workbook for all water levels in the database also includes measurement source, status, method, accuracy, remarks, and hydrologic condition flags. Interpretations for individual water-level measurements and for the period of record for the wells have been incorporated into the water-level remarks, flags, or hydrograph narratives.

Galatanet datasets 2009-2010 by Jean-Michel Balasque (jmbalasque@gsu.edu.tr) & Vincent Labatut(vlabatut@gsu.edu.tr)http://www.gsu.edu.tr

This dataset is open data: you can redistribute it and/or useit under the terms of the Creative Commons Zero license (see license.txt).

This project contains the datasets relative to the Galatanet survey, conducted in 2009 and2010 at the Galatasaray University in Istanbul (Turkey). The goal of this survey was toretrieve information regarding the social relationships between students, their feelingregarding the university in general, and their purchase behavior. The survey was conductedduring two phases: the first one in 2009 and the second in 2010. For the moment, only thedata corresponding to the first phase are available here, because those from the secondphase will be used in some publication to come. The dataset includes two kinds of data. First, the answers to most of the questions arecontained in a large table, available under both CSV and MS Excel formats. An explicativefile allows understanding the meaning of each field appearing in the table. Note thesurvey form is also contained in the archive, for reference (it is in french and turkishonly, though). Second, the social network of students is available under both Pajek andGraphml formats. having both individual (nodal attributes) and relational (links)information in the same dataset is, to our knowledge, rare and difficult to find in publicsources, and this makes (to our opinion) this dataset interesting and valuable. All data are completely anonymous: students' names have been replaced by random numbers.Note the survey is not exactly the same between the two phases: some small adjustmentswere applied thanks to the feedback from the first phase (but the datasets have beennormalized since then). Also, the electronic form was very much improved for the secondphase, which explains why the answers are much more complete than in the first phase. If you use this data, please cite the following article: Labatut, V. & Balasque, J.-M.(2010). Business-oriented Analysis of a Social Network of University Students. In:International Conference on Advances in Social Network Analysis and Mining, 25-32. Odense,DK : IEEE. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5562794 Note the data was used in other publications, too:* An extended version of the original article: Labatut, V. & Balasque, J.-M. (2013).Informative Value of Individual and Relational Data Compared Through Business-OrientedCommunity Detection. Özyer, T.; Rokne, J.; Wagner, G. & Reuser, A. H. (Eds.), TheInfluence of Technology on Social Network Analysis and Mining, Springer, 2013, chap.6,303-330. http://link.springer.com/chapter/10.1007/978-3-7091-1346-2_13* A more didactic article using some of these data just for illustration purposes:Labatut, V. & Balasque, J.-M. (2012). Detection and Interpretation of Communities inComplex Networks: Methods and Practical Application. Abraham, A. & Hassanien, A.-E.(Eds.), Computational Social Networks: Tools, Perspectives and Applications, Springer,chap.4, 81-113. http://link.springer.com/chapter/10.1007/978-1-4471-4048-1_4

This dataset was collected as part of a study exploring high school mathematics teachers’ perceptions and use of artificial intelligence, with a particular focus on the perceived usefulness and perceived ease of use of artificial intelligence (AI) in teaching. ChatGPT was used as the artificial intelligence technology used in this study. The study employed a sequential explanatory mixed-methods design, guided by the Technology Acceptance Model 3 as a theoretical framework. Quantitative data were gathered through an online survey, in which structured Technology Acceptance Model 3 questionnaires were adapted and administered to examine participants' perceived usefulness and perceived ease of use of artificial intelligence, as well as the determinants. The quantitative data were analysed using the Statistical Package for the Social Sciences version 26. Descriptive statistics was used to interpret the data.Qualitative data were obtained through classroom observations and semi-structured interviews. Observations focused on how participants use artificial intelligence in their teaching, while the interviews provided deeper insights into their experiences and perspectives. All observations and interviews were recorded and subsequently transcribed for the dissertation. In order to open this data, Microsoft Excel, an MP4 video player, an audio player, and a portable document format reader will be needed.

The data are the number and proportion of female weevils (Ceratapion basicorne) that oviposited after exposure to three different environmental hibernation conditions for three different durations (4, 8 and 11 weeks). The conditions were Greenhouse [ambient temperature and photoperiod], glass door Refrigerator [5°C and ambient photoperiod], and incubator [5°C and 24 h dark]). Resources in this dataset:Resource Title: Data from: Conditions to terminate reproductive diapause of a univoltine insect: Ceratapion basicorne (Coleoptera: Apionidae), a biological control agent of yellow starthistle. File Name: Ceba_diapause.csvResource Description: The data are the number and proportion of female weevils (Ceratapion basicorne) that oviposited after exposure to three different environmental hibernation conditions for three different durations (4, 8 and 11 weeks). The conditions were Greenhouse [ambient temperature and photoperiod], glass door Refrigerator [5°C and ambient photoperiod], and incubator [5°C and 24 h dark]).Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel Resource Title: Meta data for: Conditions to terminate reproductive diapause of a univoltine insect: Ceratapion basicorne (Coleoptera: Apionidae), a biological control agent of yellow starthistle. File Name: Ceba_diapause_meta.csvResource Description: Description of the meaning of the variables.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/microsoft-365/excel