cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25592/uhhfdm.13470
title: Performance of Myopic Lot-sizing Heuristics and an Improvement Heuristic in the Case of Regular, Sporadic, and Close-To-Zero Demand

description: This dataset contains all research data from the article "Performance of Myopic Lot-sizing Heuristics and an Improvement Heuristic in the Case of Regular, Sporadic, and Close-To-Zero Demand". Included are the demands and heuristics used, as well as the data preparation and description for the article.
DOI: 10.18434/m3439
title: NIST Mugshot Identification Database (MID) - NIST Special Database 18
DOI: 10.5066/p95pgj0c
title: Streamflow, Dissolved Organic Carbon, and Nitrate Input Datasets and Model Results Using the Weighted Regressions on Time, Discharge, and Season (WRTDS) Model for Buck Creek Watersheds, Adirondack Park, New York, 2001 to 2021

description: This data release supports an analysis of changes in dissolved organic carbon (DOC) and nitrate concentrations in Buck Creek watershed near Inlet, New York 2001 to 2021. The Buck Creek watershed is a 310-hectare forested watershed that is recovering from acidic deposition within the Adirondack region. The data release includes pre-processed model inputs and model outputs for the Weighted Regressions on Time, Discharge and Season (WRTDS) model (Hirsch and others, 2010) to estimate daily flow normalized concentrations of DOC and nitrate during a 20-year period of analysis. WRTDS uses daily discharge and concentration observations implemented through the Exploration and Graphics for River Trends R package (EGRET) to predict solute concentration using decimal time and discharge as explanatory variables (Hirsch and De Cicco, 2015; Hirsch and others, 2010). Discharge and concentration data are available from the U.S. Geological Survey National Water Information System (NWIS) database (U.S. Geological Survey, 2016). The time series data were analyzed for the entire period, water years 2001 (WY2001) to WY2021 where WY2001 is the period from October 1, 2000 to September 30, 2001.�This data release contains 5 comma-separated values (CSV) files, one R script, and one XML metadata file. There are four input files (?Daily.csv?, ?INFO.csv?, ?Sample_doc.csv?, and ?Sample_nitrate.csv?) that contain site information, daily mean discharge, and mean daily DOC or nitrate concentrations. The R script (?Buck Creek WRTDS R script.R?) uses the four input datasets and functions from the EGRET R package to generate estimations of flow normalized concentrations. The output file (?WRTDS_results.csv?) contains model output at daily time steps for each sub-watershed and for each solute. Files are automatically associated with the R script when opened in RStudio using the provided R project file ("Files.Rproj").�All input, output, and R files are in the "Files.zip" folder.
DOI: 10.25390/caryinstitute.11801052.v59
title: Cary Institute Environmental Monitoring Program Current Year Data 2023

description: The Cary Institute of Ecosystem Studies Environmental Monitoring Program is a long-term data collection program designed to understand how the environment changes over time. The program includes monitoring of climate including temperature and precipitation, as well as variables related to air pollution, such as acid deposition and ozone, and water pollution and other streamwater chemistry. Our solar radiation monitoring includes diffuse and global photosynthetically active radiation (PAR), diffuse and global shortwave radiation, net radiation and UV. Long-term monitoring of solar radiation provides us with an understanding of atmospheric energy dynamics, which can affect natural and human systems. The Cary Institute of Ecosystem Studies, Environmental Monitoring Program furnishes data under the following conditions: The data have received quality assurance scrutiny by our program, and, although we are confident of the accuracy of these data, Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used. Data citation: please click on the Cite button in the Figshare repository.<br> Those wishing to publish data from Cary Institute of Ecosystem Studies, Environmental Monitoring Program are encouraged to contact data manager Vicky Kelly, kellyv@caryinstitute.org.<br> For complete data for previous years and for complete metadata, please see appropriate items for the Cary Institute Environmental Monitoring Program at https://caryinstitute.figshare.com/
DOI: 10.34740/kaggle/dsv/6687225
title: Trucks Detection
DOI: 10.6084/m9.figshare.24187461
title: ASAWEC

description: A specialized corpus of scientific research articles written by Arab scholars in the field of English Applied linguistics and literature. This corpus contains approximately 200 research articles and more than 1 million words in its initial version. The project is regularly updated to ensure that it remains current and relevant.
DOI: 10.6084/m9.figshare.24187461.v1
title: ASAWEC

description: A specialized corpus of scientific research articles written by Arab scholars in the field of English Applied linguistics and literature. This corpus contains approximately 200 research articles and more than 1 million words in its initial version. The project is regularly updated to ensure that it remains current and relevant.
DOI: 10.3886/e190721
title: Data and Code for: Socioeconomic Disparities in Privatized Pollution Remediation: Evidence from Toxic Chemical Spills

description: Replication code and data accompanying the research article.<br><b>Article Abstract:</b> Governments often privatize the administration of regulations to third-party specialists paid for by the regulated parties. We study how the resulting conflict of interest can have unintended consequences for the distributional impacts of regulation. In Massachusetts, the party responsible for hazardous waste contamination must hire a licensed contractor to quantify the environmental severity. We find that contractors' evaluations favor their clients, exhibiting substantial score bunching just below thresholds that determine government oversight of the remediation. Client favoritism is more pronounced in socioeconomically disadvantaged neighborhoods and is associated with inferior remediation quality, highlighting a novel channel for inequities in pollution exposure.<br>
DOI: 10.3886/e190721v1
title: Data and Code for: Socioeconomic Disparities in Privatized Pollution Remediation: Evidence from Toxic Chemical Spills

description: Replication code and data accompanying the research article.<br><b>Article Abstract:</b> Governments often privatize the administration of regulations to third-party specialists paid for by the regulated parties. We study how the resulting conflict of interest can have unintended consequences for the distributional impacts of regulation. In Massachusetts, the party responsible for hazardous waste contamination must hire a licensed contractor to quantify the environmental severity. We find that contractors' evaluations favor their clients, exhibiting substantial score bunching just below thresholds that determine government oversight of the remediation. Client favoritism is more pronounced in socioeconomically disadvantaged neighborhoods and is associated with inferior remediation quality, highlighting a novel channel for inequities in pollution exposure.<br>
DOI: 10.5519/qd.hbjun0ez
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (26 records)
DOI: 10.6084/m9.figshare.24309378.v1
title: Controlling Cumulative Adverse Risk in Learning Optimal Dynamic Treatment Regimens

description: Dynamic treatment regimen (DTR) is one of the most important tools to tailor treatment in personalized medicine. For many diseases such as cancer and type 2 diabetes mellitus (T2D), more aggressive treatments can lead to a higher efficacy but may also increase risk. However, few methods for estimating DTRs can take into account both cumulative benefit and risk. In this work, we propose a general statistical learning framework to learn optimal DTRs that maximize the reward outcome while controlling the cumulative adverse risk to be below a pre-specified threshold. We convert this constrained optimization problem into an unconstrained optimization using a Lagrange function. We then solve the latter using either backward learning algorithms or simultaneously over all stages based on constructing a novel multistage ramp loss. Theoretically, we establish Fisher consistency of the proposed method and further obtain non-asymptotic convergence rates for both reward and risk outcomes under the estimated DTRs. The finite sample performance of the proposed method is demonstrated via simulation studies and through an application to a two-stage clinical trial for T2D patients.
DOI: 10.6084/m9.figshare.24309378
title: Controlling Cumulative Adverse Risk in Learning Optimal Dynamic Treatment Regimens

description: Dynamic treatment regimen (DTR) is one of the most important tools to tailor treatment in personalized medicine. For many diseases such as cancer and type 2 diabetes mellitus (T2D), more aggressive treatments can lead to a higher efficacy but may also increase risk. However, few methods for estimating DTRs can take into account both cumulative benefit and risk. In this work, we propose a general statistical learning framework to learn optimal DTRs that maximize the reward outcome while controlling the cumulative adverse risk to be below a pre-specified threshold. We convert this constrained optimization problem into an unconstrained optimization using a Lagrange function. We then solve the latter using either backward learning algorithms or simultaneously over all stages based on constructing a novel multistage ramp loss. Theoretically, we establish Fisher consistency of the proposed method and further obtain non-asymptotic convergence rates for both reward and risk outcomes under the estimated DTRs. The finite sample performance of the proposed method is demonstrated via simulation studies and through an application to a two-stage clinical trial for T2D patients.
DOI: 10.15784/601742
title: Distribution of blue ice areas in Antarctica derived from Landsat ETM+ and Modis images

description: Blue-ice areas (BIAs) and their geographical distribution in Antarctica were mapped using Landsat-7 ETM+ images with 15 m spatial resolution obtained during the 1999–2003 austral summers and covering the area north of 82.5° S, and a snow grain-size image of the MODIS-based Mosaic of Antarctica (MOA) dataset with 125 m grid spacing acquired during the 2003/04 austral summer from 82.5°S to the South Pole. A map of BIAs was created with algorithms of thresholds based on band ratio and reflectance for ETM+ data and thresholds based on snow grain size for the MOA dataset. The underlying principle is that blue ice can be separated from snow or rock by their spectral discrepancies and by different grain sizes of snow and ice. We estimate the total area of BIAs in Antarctica during the data acquisition period is 234 549 km2, or 1.67% of the area of the continent. Blue ice is scattered widely over the continent but is generally located in coastal or mountainous regions. The BIA dataset presented in this study is the first map covering the entire Antarctic continent sourced solely from ETM+ and MODIS data. Support by National Natural Science Foundation of China (grant No. 41106157) and NASA grant NNX10AL42G (nsidc0549_hui_V0).
DOI: 10.34740/kaggle/dsv/6687350
title: Electric Vehicle Population
DOI: 10.3886/e192846v2-142005
title: Data and Code for: The COVID-19 Baby Bump in the United States

description: This is the code replication archive for the paper, "The COVID-19 Baby Bump in the United States," published in the Proceedings of the National Academy of Sciences. The underlying natality microdata are restricted, so this archive contains only the code to replicate our analysis.<br><br>We use natality microdata covering the universe of U.S. births for 2015-2021 and California births from 2015 through February 2023 to examine childbearing responses to the COVID-19 pandemic. We find that 60% of the 2020 decline in U.S. fertility rates was driven by sharp reductions in births to foreign-born mothers although births to this group comprised only 22% of all U.S. births in 2019. This decline started in January 2020. In contrast, the COVID-19 recession resulted in an overall “baby bump” among U.S.-born mothers which marked the first reversal in declining fertility rates since the Great Recession. Births to U.S.-born mothers fell by 31,000 in 2020 relative to a pre-pandemic trend but increased by 71,000 in 2021. The data for California suggest that U.S. births remained elevated through February 2023. The baby bump was most pronounced for first births and women under age 25, suggesting that the pandemic led some women to start families earlier. Above age 25, the baby bump was most pronounced for women ages 30-34 and women with a college education. The 2021-2022 baby bump is especially remarkable given the large declines in fertility rates that would have been projected by standard statistical models.<br>
DOI: 10.3886/e192846v2-142074
title: Data and Code for: The COVID-19 Baby Bump in the United States

description: This is the code replication archive for the paper, "The COVID-19 Baby Bump in the United States," published in the Proceedings of the National Academy of Sciences. The underlying natality microdata are restricted, so this archive contains only the code to replicate our analysis.<br><br>We use natality microdata covering the universe of U.S. births for 2015-2021 and California births from 2015 through February 2023 to examine childbearing responses to the COVID-19 pandemic. We find that 60% of the 2020 decline in U.S. fertility rates was driven by sharp reductions in births to foreign-born mothers although births to this group comprised only 22% of all U.S. births in 2019. This decline started in January 2020. In contrast, the COVID-19 recession resulted in an overall “baby bump” among U.S.-born mothers which marked the first reversal in declining fertility rates since the Great Recession. Births to U.S.-born mothers fell by 31,000 in 2020 relative to a pre-pandemic trend but increased by 71,000 in 2021. The data for California suggest that U.S. births remained elevated through February 2023. The baby bump was most pronounced for first births and women under age 25, suggesting that the pandemic led some women to start families earlier. Above age 25, the baby bump was most pronounced for women ages 30-34 and women with a college education. The 2021-2022 baby bump is especially remarkable given the large declines in fertility rates that would have been projected by standard statistical models.<br>
DOI: 10.3886/e192846v2-142161
title: Data and Code for: The COVID-19 Baby Bump in the United States

description: This is the code replication archive for the paper, "The COVID-19 Baby Bump in the United States," published in the Proceedings of the National Academy of Sciences. The underlying natality microdata are restricted, so this archive contains only the code to replicate our analysis.<br><br>We use natality microdata covering the universe of U.S. births for 2015-2021 and California births from 2015 through February 2023 to examine childbearing responses to the COVID-19 pandemic. We find that 60% of the 2020 decline in U.S. fertility rates was driven by sharp reductions in births to foreign-born mothers although births to this group comprised only 22% of all U.S. births in 2019. This decline started in January 2020. In contrast, the COVID-19 recession resulted in an overall “baby bump” among U.S.-born mothers which marked the first reversal in declining fertility rates since the Great Recession. Births to U.S.-born mothers fell by 31,000 in 2020 relative to a pre-pandemic trend but increased by 71,000 in 2021. The data for California suggest that U.S. births remained elevated through February 2023. The baby bump was most pronounced for first births and women under age 25, suggesting that the pandemic led some women to start families earlier. Above age 25, the baby bump was most pronounced for women ages 30-34 and women with a college education. The 2021-2022 baby bump is especially remarkable given the large declines in fertility rates that would have been projected by standard statistical models.<br>
DOI: 10.3886/e192846
title: Data and Code for: The COVID-19 Baby Bump in the United States

description: This is the code replication archive for the paper, "The COVID-19 Baby Bump in the United States," published in the Proceedings of the National Academy of Sciences. The underlying natality microdata are restricted, so this archive contains only the code to replicate our analysis.<br><br>We use natality microdata covering the universe of U.S. births for 2015-2021 and California births from 2015 through February 2023 to examine childbearing responses to the COVID-19 pandemic. We find that 60% of the 2020 decline in U.S. fertility rates was driven by sharp reductions in births to foreign-born mothers although births to this group comprised only 22% of all U.S. births in 2019. This decline started in January 2020. In contrast, the COVID-19 recession resulted in an overall “baby bump” among U.S.-born mothers which marked the first reversal in declining fertility rates since the Great Recession. Births to U.S.-born mothers fell by 31,000 in 2020 relative to a pre-pandemic trend but increased by 71,000 in 2021. The data for California suggest that U.S. births remained elevated through February 2023. The baby bump was most pronounced for first births and women under age 25, suggesting that the pandemic led some women to start families earlier. Above age 25, the baby bump was most pronounced for women ages 30-34 and women with a college education. The 2021-2022 baby bump is especially remarkable given the large declines in fertility rates that would have been projected by standard statistical models.<br>
DOI: 10.3886/e192846v1
title: Data and Code for: The COVID-19 Baby Bump in the United States

description: This is the code replication archive for the paper, "The COVID-19 Baby Bump in the United States," forthcoming in the Proceedings of the National Academy of Sciences. The underlying natality microdata are restricted, so this archive contains only the code to replicate our analysis.<br><br>We use natality microdata covering the universe of U.S. births for 2015-2021 and California births from 2015 through February 2023 to examine childbearing responses to the COVID-19 pandemic. We find that 60% of the 2020 decline in U.S. fertility rates was driven by sharp reductions in births to foreign-born mothers although births to this group comprised only 22% of all U.S. births in 2019. This decline started in January 2020. In contrast, the COVID-19 recession resulted in an overall “baby bump” among U.S.-born mothers which marked the first reversal in declining fertility rates since the Great Recession. Births to U.S.-born mothers fell by 31,000 in 2020 relative to a pre-pandemic trend but increased by 71,000 in 2021. The data for California suggest that U.S. births remained elevated through February 2023. The baby bump was most pronounced for first births and women under age 25, suggesting that the pandemic led some women to start families earlier. Above age 25, the baby bump was most pronounced for women ages 30-34 and women with a college education. The 2021-2022 baby bump is especially remarkable given the large declines in fertility rates that would have been projected by standard statistical models.<br>
DOI: 10.3886/e192846v2
title: Data and Code for: The COVID-19 Baby Bump in the United States

description: This is the code replication archive for the paper, "The COVID-19 Baby Bump in the United States," published in the Proceedings of the National Academy of Sciences. The underlying natality microdata are restricted, so this archive contains only the code to replicate our analysis.<br><br>We use natality microdata covering the universe of U.S. births for 2015-2021 and California births from 2015 through February 2023 to examine childbearing responses to the COVID-19 pandemic. We find that 60% of the 2020 decline in U.S. fertility rates was driven by sharp reductions in births to foreign-born mothers although births to this group comprised only 22% of all U.S. births in 2019. This decline started in January 2020. In contrast, the COVID-19 recession resulted in an overall “baby bump” among U.S.-born mothers which marked the first reversal in declining fertility rates since the Great Recession. Births to U.S.-born mothers fell by 31,000 in 2020 relative to a pre-pandemic trend but increased by 71,000 in 2021. The data for California suggest that U.S. births remained elevated through February 2023. The baby bump was most pronounced for first births and women under age 25, suggesting that the pandemic led some women to start families earlier. Above age 25, the baby bump was most pronounced for women ages 30-34 and women with a college education. The 2021-2022 baby bump is especially remarkable given the large declines in fertility rates that would have been projected by standard statistical models.<br>
DOI: 10.3886/e192846v3
title: Data and Code for: The COVID-19 Baby Bump in the United States

description: This is the code replication archive for the paper, "The COVID-19 Baby Bump in the United States," published in the Proceedings of the National Academy of Sciences. The underlying natality microdata are restricted, so this archive contains only the code to replicate our analysis.<br><br>We use natality microdata covering the universe of U.S. births for 2015-2021 and California births from 2015 through February 2023 to examine childbearing responses to the COVID-19 pandemic. We find that 60% of the 2020 decline in U.S. fertility rates was driven by sharp reductions in births to foreign-born mothers although births to this group comprised only 22% of all U.S. births in 2019. This decline started in January 2020. In contrast, the COVID-19 recession resulted in an overall “baby bump” among U.S.-born mothers which marked the first reversal in declining fertility rates since the Great Recession. Births to U.S.-born mothers fell by 31,000 in 2020 relative to a pre-pandemic trend but increased by 71,000 in 2021. The data for California suggest that U.S. births remained elevated through February 2023. The baby bump was most pronounced for first births and women under age 25, suggesting that the pandemic led some women to start families earlier. Above age 25, the baby bump was most pronounced for women ages 30-34 and women with a college education. The 2021-2022 baby bump is especially remarkable given the large declines in fertility rates that would have been projected by standard statistical models.<br>
DOI: 10.25592/uhhfdm.13469
title: Dataset and Code: Sound Suppresses Earliest Visual Cortical Processing After Sight Recovery in Congenitally Blind Humans

description: Dataset and code for <em>Sound Suppresses Earliest Visual Cortical Processing After Sight Recovery in Congenitally Blind Humans.</em> The zip file ERP_RT_sLORETA contains three folders: ERP: ERP analysis scripts and data RT: RT analysis scripts and data sLORETA: Source analysis (sLORETA) protocol for brainstorm
description: The study was funded by the European Research Council grant ERC-2009-AdG 249425-CriticalBrainChanges and DFG Ro 2625/10-1 to Brigitte Röder.
DOI: 10.25592/uhhfdm.13468
title: Dataset and Code: Sound Suppresses Earliest Visual Cortical Processing After Sight Recovery in Congenitally Blind Humans

description: Dataset and code for <em>Sound Suppresses Earliest Visual Cortical Processing After Sight Recovery in Congenitally Blind Humans.</em> The zip file ERP_RT_sLORETA contains three folders: ERP: ERP analysis scripts and data RT: RT analysis scripts and data sLORETA: Source analysis (sLORETA) protocol for brainstorm
description: The study was funded by the European Research Council grant ERC-2009-AdG 249425-CriticalBrainChanges and DFG Ro 2625/10-1 to Brigitte Röder.