cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21197135
title: Novelty and Primacy: A Long-Term Estimator for Online Experiments

description: Online experiments are the gold standard for evaluating impact on user experience and accelerating innovation in software. However, since experiments are typically limited in duration, observed treatment effects are not always stable, sometimes revealing increasing or decreasing patterns over time. There are multiple causes for a treatment effect to change over time. In this article, we focus on a particular cause, user-learning, which is primarily associated with novelty or primacy. Novelty describes the desire to use new technology that tends to diminish over time. Primacy describes the growing engagement with technology as a result of adoption of the innovation. Estimating user-learning is critical because it holds experimentation responsible for trustworthiness, empowers organizations to make better decisions by providing a long-term view of expected impact, and prevents user dissatisfaction. In this article, we propose an observational approach, based on difference-in-differences technique to estimate user-learning at scale. We use this approach to test and estimate user-learning in many experiments at Microsoft. We compare our approach with the existing experimental method to show its benefits in terms of ease of use and higher statistical power, and to discuss its limitation in presence of other forms of treatment interaction with time.
DOI: 10.6084/m9.figshare.13708310
title: Analyzing Nonparametric Part-to-Part Variation in Surface Point Cloud Data

description: Surface point cloud data from three-dimensional optical scanners provide rich information about the surface geometry of scanned parts and potential variation in the surfaces from part-to-part. It is challenging, however, to make full use of these data for statistical process control purposes to identify sources of variation that manifest in a more complex nonparametric manner than variation in some prespecified set of geometric features of each part. We develop a framework for identifying nonparametric variation patterns that uses dissimilarity representation of the data and dissimilarity-based manifold learning, which helps discover a low-dimensional implicit manifold parameterization of the variation. Visualizing how the parts change as the manifold parameters are varied helps build an understanding of the physical characteristic of the variation. We also discuss using the nominal surface of parts when it is accessible to improve the computational expense and visualization aspects of the framework. Our approaches clearly reveal the nature of the variation patterns in a real cylindrical-part machining example and a simulated square head bolt example.
DOI: 10.6084/m9.figshare.21131405.v3
title: TSEC: A Framework for Online Experimentation under Experimental Constraints

description: Thompson sampling is a popular algorithm for tackling multi-armed bandit problems, and has been applied in a wide range of applications, from website design to portfolio optimization. In such applications, however, the number of choices (or arms) <i>N</i> can be large, and the data needed to make adaptive decisions require expensive experimentation. One is then faced with the constraint of experimenting on only a small subset of K≪N arms within each time period, which poses a problem for traditional Thompson sampling. We propose a new Thompson Sampling under Experimental Constraints (TSEC) method, which addresses this so-called “arm budget constraint.” TSEC makes use of a Bayesian interaction model with effect hierarchy priors, to model correlations between rewards on different arms. This fitted model is then integrated within Thompson sampling, to jointly identify a good subset of arms for experimentation and to allocate resources over these arms. We demonstrate the effectiveness of TSEC in two applications with arm budget constraints. The first is a simulated website optimization study, where TSEC shows considerable improvements over industry benchmarks. The second is a portfolio optimization application on industry-based exchange-traded funds, where TSEC provides more consistent and greater wealth accumulation over standard investment strategies.
DOI: 10.6084/m9.figshare.13708310.v2
title: Analyzing Nonparametric Part-to-Part Variation in Surface Point Cloud Data

description: Surface point cloud data from three-dimensional optical scanners provide rich information about the surface geometry of scanned parts and potential variation in the surfaces from part-to-part. It is challenging, however, to make full use of these data for statistical process control purposes to identify sources of variation that manifest in a more complex nonparametric manner than variation in some prespecified set of geometric features of each part. We develop a framework for identifying nonparametric variation patterns that uses dissimilarity representation of the data and dissimilarity-based manifold learning, which helps discover a low-dimensional implicit manifold parameterization of the variation. Visualizing how the parts change as the manifold parameters are varied helps build an understanding of the physical characteristic of the variation. We also discuss using the nominal surface of parts when it is accessible to improve the computational expense and visualization aspects of the framework. Our approaches clearly reveal the nature of the variation patterns in a real cylindrical-part machining example and a simulated square head bolt example.
DOI: 10.6084/m9.figshare.21197135.v3
title: Novelty and Primacy: A Long-Term Estimator for Online Experiments

description: Online experiments are the gold standard for evaluating impact on user experience and accelerating innovation in software. However, since experiments are typically limited in duration, observed treatment effects are not always stable, sometimes revealing increasing or decreasing patterns over time. There are multiple causes for a treatment effect to change over time. In this article, we focus on a particular cause, user-learning, which is primarily associated with novelty or primacy. Novelty describes the desire to use new technology that tends to diminish over time. Primacy describes the growing engagement with technology as a result of adoption of the innovation. Estimating user-learning is critical because it holds experimentation responsible for trustworthiness, empowers organizations to make better decisions by providing a long-term view of expected impact, and prevents user dissatisfaction. In this article, we propose an observational approach, based on difference-in-differences technique to estimate user-learning at scale. We use this approach to test and estimate user-learning in many experiments at Microsoft. We compare our approach with the existing experimental method to show its benefits in terms of ease of use and higher statistical power, and to discuss its limitation in presence of other forms of treatment interaction with time.
DOI: 10.6084/m9.figshare.13708310.v1
title: Analyzing Nonparametric Part-to-part Variation in Surface Point Cloud Data

description: Surface point cloud data from three-dimensional optical scanners provide rich information about the surface geometry of scanned parts and potential variation in the surfaces from part-to-part. It is challenging, however, to make full use of these data for statistical process control purposes to identify sources of variation that manifest in a more complex nonparametric manner than variation in some pre-specified set of geometric features of each part. We develop a framework for identifying nonparametric variation patterns that uses dissimilarity representation of the data and dissimilarity-based manifold learning, which helps discover a low-dimensional implicit manifold parameterization of the variation. Visualizing how the parts change as the manifold parameters are varied helps build an understanding of the physical characteristic of the variation. We also discuss utilizing the nominal surface of parts when it is accessible to improve the computational expense and visualization aspects of the framework. Our approaches clearly reveal the nature of the variation patterns in a real cylindrical-part machining example and a simulated square head bolt example.
DOI: 10.5281/zenodo.10199104
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85cooler 2040-2059

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.10199103
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85cooler 2040-2059

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.10214522
title: The structure and dynamics of scientific collaborations in Europe (PhD. Dataset)

description: The dataset comprises articles used in the qualitative analysis within the doctoral thesis entitled The Structure and Dynamics of Scientific Collaborations in Europe (Pințoiu-Mihăilă Bianca-Elena, 2023). These articles capture networks of authors and co-authors who have published on COVID-19 between 2020 and 2021. They also encompass information regarding the relationship between the number of prevention measures recommended by researchers and those implemented by the Romanian, Italian, or Swedish governments. The articles were found on the Web of Science using a search algorithm incorporating keywords such as COVID-19 and [country], publication year (for 2020 and 2021), document type (articles), Web of Science Category (Multidisciplinary Science, Sociology, and all other categories containing the term "policy"), sorted by country.
DOI: 10.5281/zenodo.10214523
title: The structure and dynamics of scientific collaborations in Europe (PhD. Dataset)

description: The dataset comprises articles used in the qualitative analysis within the doctoral thesis entitled The Structure and Dynamics of Scientific Collaborations in Europe (Pințoiu-Mihăilă Bianca-Elena, 2023). These articles capture networks of authors and co-authors who have published on COVID-19 between 2020 and 2021. They also encompass information regarding the relationship between the number of prevention measures recommended by researchers and those implemented by the Romanian, Italian, or Swedish governments. The articles were found on the Web of Science using a search algorithm incorporating keywords such as COVID-19 and [country], publication year (for 2020 and 2021), document type (articles), Web of Science Category (Multidisciplinary Science, Sociology, and all other categories containing the term "policy"), sorted by country.
DOI: 10.6084/m9.figshare.24651720
title: Phyloseq_Object_16s

description: Phyloseq object used in the corresponding 16S R scripts
DOI: 10.6084/m9.figshare.24651720.v1
title: Phyloseq_Object_16s

description: Phyloseq object used in the corresponding 16S R scripts
DOI: 10.5281/zenodo.10199074
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85cooler 2020-2039

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.5281/zenodo.10199075
title: Hourly Wind and Solar Generation Profiles at 1/8th Degree Resolution - wind 80m rcp85cooler 2020-2039

description: Please see the parent record at https://doi.org/10.5281/zenodo.10214348.
DOI: 10.20394/kaxsduex
title: Test dataset

description: test update
DOI: 10.3886/e100699v5-98020
title: Uniform Crime Reporting (UCR) Program Data: Supplementary Homicide Reports, 1976-2016

description: <b>Version 5 release notes:</b>Adds 2016 dataStandardizes the "group" column which categorizes cities and counties by population.Arrange rows in descending order by year and ascending order by ORI. <br><b>Version 4 release notes: <b><br></b></b>Fix bug where Philadelphia Police Department had incorrect FIPS county code. <b>Version 3 Release Notes:</b>Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Change column names for relationship variables from offender_n_relation_to_victim_1 to victim_1_relation_to_offender_n to better indicate that all relationship are victim 1's relationship to each offender. <br>Reorder columns.<br><br>This is a single file containing all data from the Supplementary Homicide Reports from 1976 to 2015. The Supplementary Homicide Report provides detailed information about the victim, offender, and circumstances of the murder. Details include victim and offender age, sex, race, ethnicity (Hispanic/not Hispanic), the weapon used, circumstances of the incident, and the number of both offenders and victims. <br><br>All the data was downloaded from NACJD as ASCII+SPSS Setup files and cleaned using R. The "cleaning" just means that column names were standardized (different years have slightly different spellings for many columns). Standardization of column names is necessary to stack multiple years together. Categorical variables (e.g. state) were also standardized (i.e. fix spelling errors, have terminology be the same across years). <br><br>The following is the summary of the Supplementary Homicide Report copied from ICPSR's 2015 page for the data.<br><br>The Uniform Crime Reporting Program Data: Supplementary Homicide Reports (SHR) provide detailed information on criminal homicides reported to the police. These homicides consist of murders; non-negligent killings also called non-negligent manslaughter; and justifiable homicides. UCR Program contributors compile and submit their crime data by one of two means: either directly to the FBI or through their State UCR Programs. State UCR Programs frequently impose mandatory reporting requirements which have been effective in increasing both the number of reporting agencies as well as the number and accuracy of each participating agency's reports. Each agency may be identified by its numeric state code, alpha-numeric agency ("ORI") code, jurisdiction population, and population group. In addition, each homicide incident is identified by month of occurrence and situation type, allowing flexibility in creating aggregations and subsets.<br>
description: Victims of homicide in the United States between 1976 and 2015. Smallest Geographic Unit: Police agency
DOI: 10.3886/e100699v4-9981
title: Uniform Crime Reporting Program Data: Supplementary Homicide Reports, 1976-2015

description: Version 4 release notes: <br>Fix bug where Philadelphia Police Department had incorrect FIPS county code. Version 3 Release Notes:Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Change column names for relationship variables from offender_n_relation_to_victim_1 to victim_1_relation_to_offender_n to better indicate that all relationship are victim 1's relationship to each offender. <br>Reorder columns.<br><br>This is a single file containing all data from the Supplementary Homicide Reports from 1976 to 2015. The Supplementary Homicide Report provides detailed information about the victim, offender, and circumstances of the murder. Details include victim and offender age, sex, race, ethnicity (Hispanic/not Hispanic), the weapon used, circumstances of the incident, and the number of both offenders and victims. <br><br>All the data was downloaded from NACJD as ASCII+SPSS Setup files and 
cleaned using R. The "cleaning" just means that column names were 
standardized (different years have slightly different spellings for many
 columns). Standardization of column names is necessary to stack 
multiple years together. Categorical variables (e.g. state) were also 
standardized (i.e. fix spelling errors, have terminology be the same across years). <br><br>The following is the summary of the Supplementary Homicide Report copied from ICPSR's 2015 page for the data.<br><br>The Uniform Crime Reporting Program Data: Supplementary Homicide Reports (SHR) provide detailed information on criminal homicides reported to the police. These homicides consist of murders; non-negligent killings also called non-negligent manslaughter; and justifiable homicides. UCR Program contributors compile and submit their crime data by one of two means: either directly to the FBI or through their State UCR Programs. State UCR Programs frequently impose mandatory reporting requirements which have been effective in increasing both the number of reporting agencies as well as the number and accuracy of each participating agency's reports. Each agency may be identified by its numeric state code, alpha-numeric agency ("ORI") code, jurisdiction population, and population group. In addition, each homicide incident is identified by month of occurrence and situation type, allowing flexibility in creating aggregations and subsets.<br><br><br><br>
description: Victims of homicide in the United States between 1976 and 2015.Smallest Geographic Unit: Police agency
DOI: 10.3886/e100699v6-10327
title: Uniform Crime Reporting (UCR) Program Data: Supplementary Homicide Reports, 1976-2017

description: Version 6 release notes:Adds 2017 data.<br><br>Version 5 release notes:Adds 2016 data.Standardizes the "group" column which categorizes cities and counties by population.Arrange rows in descending order by year and ascending order by ORI. <br>Version 4 release notes: <br>Fix bug where Philadelphia Police Department had incorrect FIPS county code. Version 3 Release Notes:Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Change column names for relationship variables from offender_n_relation_to_victim_1 to victim_1_relation_to_offender_n to better indicate that all relationship are victim 1's relationship to each offender. <br>Reorder columns.<br><br>This is a single file containing all data from the Supplementary Homicide Reports from 1976 to 2017. The Supplementary Homicide Report provides detailed information about the victim, offender, and circumstances of the murder. Details include victim and offender age, sex, race, ethnicity (Hispanic/not Hispanic), the weapon used, circumstances of the incident, and the number of both offenders and victims. <br><br>All the data was downloaded from NACJD as ASCII+SPSS Setup files and cleaned using R. The "cleaning" just means that column names were standardized (different years have slightly different spellings for many columns). Standardization of column names is necessary to stack multiple years together. Categorical variables (e.g. state) were also standardized (i.e. fix spelling errors, have terminology be the same across years). <br><br>The following is the summary of the Supplementary Homicide Report copied from ICPSR's 2015 page for the data.<br><br>The Uniform Crime Reporting Program Data: Supplementary Homicide Reports (SHR) provide detailed information on criminal homicides reported to the police. These homicides consist of murders; non-negligent killings also called non-negligent manslaughter; and justifiable homicides. UCR Program contributors compile and submit their crime data by one of two means: either directly to the FBI or through their State UCR Programs. State UCR Programs frequently impose mandatory reporting requirements which have been effective in increasing both the number of reporting agencies as well as the number and accuracy of each participating agency's reports. Each agency may be identified by its numeric state code, alpha-numeric agency ("ORI") code, jurisdiction population, and population group. In addition, each homicide incident is identified by month of occurrence and situation type, allowing flexibility in creating aggregations and subsets.<br>
description: Victims of homicide in the United States between 1976 and 2017.Smallest Geographic Unit: Police agency
DOI: 10.3886/e100699v7-12521
title: Jacob Kaplan's Concatenated Files: Uniform Crime Reporting (UCR) Program Data: Supplementary Homicide Reports, 1976-2017

description: For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.<br><br>Version 7 release notes:<br>Changes project name to avoid confusing this data for the ones done by NACJD.Version 6 release notes:Adds 2017 data.<br>Version 5 release notes:Adds 2016 data.Standardizes the "group" column which categorizes cities and counties by population.Arrange rows in descending order by year and ascending order by ORI. <br>Version 4 release notes: <br>Fix bug where Philadelphia Police Department had incorrect FIPS county code. Version 3 Release Notes:Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Change column names for relationship variables from offender_n_relation_to_victim_1 to victim_1_relation_to_offender_n to better indicate that all relationship are victim 1's relationship to each offender. <br>Reorder columns.<br><br>This is a single file containing all data from the Supplementary Homicide Reports from 1976 to 2017. The Supplementary Homicide Report provides detailed information about the victim, offender, and circumstances of the murder. Details include victim and offender age, sex, race, ethnicity (Hispanic/not Hispanic), the weapon used, circumstances of the incident, and the number of both offenders and victims. <br><br>All the data was downloaded from NACJD as ASCII+SPSS Setup files and cleaned using R. The "cleaning" just means that column names were standardized (different years have slightly different spellings for many columns). Standardization of column names is necessary to stack multiple years together. Categorical variables (e.g. state) were also standardized (i.e. fix spelling errors, have terminology be the same across years). <br><br>The following is the summary of the Supplementary Homicide Report copied from ICPSR's 2015 page for the data.<br><br>The Uniform Crime Reporting Program Data: Supplementary Homicide Reports (SHR) provide detailed information on criminal homicides reported to the police. These homicides consist of murders; non-negligent killings also called non-negligent manslaughter; and justifiable homicides. UCR Program contributors compile and submit their crime data by one of two means: either directly to the FBI or through their State UCR Programs. State UCR Programs frequently impose mandatory reporting requirements which have been effective in increasing both the number of reporting agencies as well as the number and accuracy of each participating agency's reports. Each agency may be identified by its numeric state code, alpha-numeric agency ("ORI") code, jurisdiction population, and population group. In addition, each homicide incident is identified by month of occurrence and situation type, allowing flexibility in creating aggregations and subsets.<br><br>
description: Victims of homicide in the United States between 1976 and 2017.Smallest Geographic Unit: Police agency