Hecht Museum Case Study Dataset

Hecht Museum Case Study Dataset

Datasets used in the article "Sea-level and monsoonal control on the Maldives carbonate platform (Indian Ocean) over the last 1.3 million years" by M. Alonso-Garcia et al. (2023) In this study, we used elemental geochemical compositional records, obtained by X-ray fluorescence (XRF) core-scanning, from IODP Site U1467, in the Maldives Sea (Indian Ocean), to investigate how sea-level and coupled ocean-atmosphere dynamics affected the production and export of carbonate platform sediments to the Maldives Inner Sea over the last 1.3 Ma. The Sr/Ca ratio has been interpreted as a proxy for neritic carbonate production at the Maldives platform and its export to the periplatform sediments. The record of the Sr/Ca ratio has been combined with the Br normalized record, as a proxy for organic matter content linked to pelagic primary productivity and water column mixing, and with other proxies from Site U1467 that indicate variations in the monsoon dynamics, such as the Fe/K ratio, as a proxy for summer monsoon intensity, and the Fe input for winter monsoon intensity (Kunkelova et al., 2018). The combination of all those proxies suggests that during the last 1.3 Ma changes in the carbonate production and export in the Maldives region responded to sea-level variations but also to climate fluctuations related to monsoon dynamics. Moreover, the long-term patterns observed in the records can be related to the MPT and MBE events.

Datasets used in the article "Sea-level and monsoonal control on the Maldives carbonate platform (Indian Ocean) over the last 1.3 million years" by M. Alonso-Garcia et al. (2023) In this study, we used elemental geochemical compositional records, obtained by X-ray fluorescence (XRF) core-scanning, from IODP Site U1467, in the Maldives Sea (Indian Ocean), to investigate how sea-level and coupled ocean-atmosphere dynamics affected the production and export of carbonate platform sediments to the Maldives Inner Sea over the last 1.3 Ma. The Sr/Ca ratio has been interpreted as a proxy for neritic carbonate production at the Maldives platform and its export to the periplatform sediments. The record of the Sr/Ca ratio has been combined with the Br normalized record, as a proxy for organic matter content linked to pelagic primary productivity and water column mixing, and with other proxies from Site U1467 that indicate variations in the monsoon dynamics, such as the Fe/K ratio, as a proxy for summer monsoon intensity, and the Fe input for winter monsoon intensity (Kunkelova et al., 2018). The combination of all those proxies suggests that during the last 1.3 Ma changes in the carbonate production and export in the Maldives region responded to sea-level variations but also to climate fluctuations related to monsoon dynamics. Moreover, the long-term patterns observed in the records can be related to the MPT and MBE events.

This dataset relates to the paper: Mining Fork-Including Development Traces (abstract below)
Authors: Iris Reinhartz-Berger and Amir Tomer
Starting point: readme.txt Open-source software development is a common practice that encourages collaborative development and reuse across projects. Forking is a way to make a copy of an existing project and explore it for different purposes. Two types of forks are commonly mentioned in the literature: contributing forks which continue the development lines of the forked projects and aim at merging the contribution back to the forked projects; and independently developed forks which open new lines of development deviating from the forked projects. In this study, we aim to explore characteristics of fork-involving software development traces. Analyzing 880 Java projects and their related action and observation events, with process mining and statistical techniques, we found that the occurrence of certain event types may predict the fork type, while the creation of certain fork types increase the involvement of users in the forked projects.

This dataset relates to the paper: Mining Fork-Including Development Traces (abstract below)
Authors: Iris Reinhartz-Berger and Amir Tomer
Starting point: readme.txt Open-source software development is a common practice that encourages collaborative development and reuse across projects. Forking is a way to make a copy of an existing project and explore it for different purposes. Two types of forks are commonly mentioned in the literature: contributing forks which continue the development lines of the forked projects and aim at merging the contribution back to the forked projects; and independently developed forks which open new lines of development deviating from the forked projects. In this study, we aim to explore characteristics of fork-involving software development traces. Analyzing 880 Java projects and their related action and observation events, with process mining and statistical techniques, we found that the occurrence of certain event types may predict the fork type, while the creation of certain fork types increase the involvement of users in the forked projects.

We collected data of 3197 users' fairness perception regarding various configurations of a AI-based system in the recruitment domain, as well as, the demographic and personally characteristics of the participants. The dataset includes the following columns: :System characteristics :Certification Uncertificated system (U) Certificated system (C) :Input data High quality input data (H) Low quality input data (L) :Output Positive outcome (P) Borderline outcome (B) Negative outcome (N) :Explanation style Control- no explanation (CON) Case-based (CAS) Certification-based (CER) Demographic-based (DEM) Input influence-based (INP) Sensitivity-based (SEN) :Demographic characteristics :Gender Female Male :Age 18-34 35-50 50+ :Residence Unites states of America India Other :Education level High school degree or less Bachelor's degree Master's or doctoral degree :Employment status Not employed Employed :Income level Above average Average Below average :Personality characteristics (TIPI questionnaire) Extraverted, enthusiastic 1-7 (1= disagree strongly up to 7= agree strongly) Critical, quarrelsome 1-7 (1= disagree strongly up to 7= agree strongly) Dependable, self-disciplined 1-7 (1= disagree strongly up to 7= agree strongly) Anxious, easily upset 1-7 (1= disagree strongly up to 7= agree strongly) Open to new experiences, complex 1-7 (1= disagree strongly up to 7= agree strongly) Reserved, quiet 1-7 (1= disagree strongly up to 7= agree strongly) Sympathetic, warm 1-7 (1= disagree strongly up to 7= agree strongly) Disorganized, careless 1-7 (1= disagree strongly up to 7= agree strongly) Calm, emotionally stable 1-7 (1= disagree strongly up to 7= agree strongly) Conventional, uncreative 1-7 (1= disagree strongly up to 7= agree strongly) :Participants responses :Fairness evaluation The participants were requested to report their level of perceived fairness (their view about the fairness of the system - at what level they consider the system as a fair system) on a 6-point Likert scale, from "Extremely fair" (represented as 3) to "Extremely unfair" (represented as -3). The option of "neither fair or unfair" (represented as 0) was excluded from the scale. :Transparency evaluation the participants were requested to report their level of perceived transparency (their understanding why the system produced the specific output - at what level they understand why this output was given) on a 6-point Likert scale, from " Thoroughly understand" (represented as 3) to " Thoroughly don't understand" (represented as -3). The option of "neither understand or don't understand" (represented as 0) was excluded from the scale. :Output Expectation The participants were requested to report their expectation for the specific output based on the input they received according to the system's scale, 5-point Likert scale from "Strongly recommended" (represented as 2) to "Strongly not recommended" (represented as -2).

We collected data of 3197 users' fairness perception regarding various configurations of a AI-based system in the recruitment domain, as well as, the demographic and personally characteristics of the participants. The dataset includes the following columns: :System characteristics :Certification Uncertificated system (U) Certificated system (C) :Input data High quality input data (H) Low quality input data (L) :Output Positive outcome (P) Borderline outcome (B) Negative outcome (N) :Explanation style Control- no explanation (CON) Case-based (CAS) Certification-based (CER) Demographic-based (DEM) Input influence-based (INP) Sensitivity-based (SEN) :Demographic characteristics :Gender Female Male :Age 18-34 35-50 50+ :Residence Unites states of America India Other :Education level High school degree or less Bachelor's degree Master's or doctoral degree :Employment status Not employed Employed :Income level Above average Average Below average :Personality characteristics (TIPI questionnaire) Extraverted, enthusiastic 1-7 (1= disagree strongly up to 7= agree strongly) Critical, quarrelsome 1-7 (1= disagree strongly up to 7= agree strongly) Dependable, self-disciplined 1-7 (1= disagree strongly up to 7= agree strongly) Anxious, easily upset 1-7 (1= disagree strongly up to 7= agree strongly) Open to new experiences, complex 1-7 (1= disagree strongly up to 7= agree strongly) Reserved, quiet 1-7 (1= disagree strongly up to 7= agree strongly) Sympathetic, warm 1-7 (1= disagree strongly up to 7= agree strongly) Disorganized, careless 1-7 (1= disagree strongly up to 7= agree strongly) Calm, emotionally stable 1-7 (1= disagree strongly up to 7= agree strongly) Conventional, uncreative 1-7 (1= disagree strongly up to 7= agree strongly) :Participants responses :Fairness evaluation The participants were requested to report their level of perceived fairness (their view about the fairness of the system - at what level they consider the system as a fair system) on a 6-point Likert scale, from "Extremely fair" (represented as 3) to "Extremely unfair" (represented as -3). The option of "neither fair or unfair" (represented as 0) was excluded from the scale. :Transparency evaluation the participants were requested to report their level of perceived transparency (their understanding why the system produced the specific output - at what level they understand why this output was given) on a 6-point Likert scale, from " Thoroughly understand" (represented as 3) to " Thoroughly don't understand" (represented as -3). The option of "neither understand or don't understand" (represented as 0) was excluded from the scale. :Output Expectation The participants were requested to report their expectation for the specific output based on the input they received according to the system's scale, 5-point Likert scale from "Strongly recommended" (represented as 2) to "Strongly not recommended" (represented as -2).

This folder contains the data and all necessary files to replicate all results from the article “
"Risk and protective factors for psychological distress during Covid-19 in Israel”

This folder contains the data and all necessary files to replicate all results from the article “
"Risk and protective factors for psychological distress during Covid-19 in Israel”