cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr32453.v1
title: Survey of Consumer Attitudes and Behavior, December 2003
title: Survey of Consumers
title: Version 1

description: This survey was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. This type of information is essential for forecasting changes in aggregate consumer behavior. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2003 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Additional questions inquired about buying intentions for automobiles and computers, and the respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey were respondents' types of savings and financial investments, loan use, family income and sources of income, and respondents' ownership, lease, and use of automobiles. Other topics typically include respondents' use of personal computers at home and in the office, respondents' familiarity with and use of the Internet, electronic banking, and information on informed consent and confidentiality regarding the survey. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Standardized missing values.; Created online analysis version with question text..
description: Datasets: DS0: Study-Level Files DS1: Public-Use Data DS2: Restricted-Use Data
description: Survey of Consumer Attitudes and Behavior Series
description: computer-assisted telephone interview (CATI)
description: Persons aged 18 years or older living in households with telephones within the coterminous United States. Smallest Geographic Unit: FIPS code
description: National sample of dwelling units selected by area probability sampling.
DOI: 10.3886/icpsr32453.v3
title: Survey of Consumer Attitudes and Behavior, December 2003
title: Version 3

description: This survey was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. This type of information is essential for forecasting changes in aggregate consumer behavior. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2003 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Additional questions inquired about buying intentions for automobiles and computers, and the respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey were respondents' types of savings and financial investments, loan use, family income and sources of income, and respondents' ownership, lease, and use of automobiles. Other topics typically include respondents' use of personal computers at home and in the office, respondents' familiarity with and use of the Internet, electronic banking, and information on informed consent and confidentiality regarding the survey. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: This collection contains four weight variables; WT (HOUSEHOLD HEAD WEIGHT), WT_HH (HOUSEHOLD WEIGHT), WT_ADHD (ADULT HEAD WEIGHT), and WT_AD (ADULT WEIGHT) that must be used in any analysis. For more information on weights and sampling please visit the Surveys of Consumers Web site and refer to the documentation on Sample Design, and the section on Sample Weights.
description: National sample of dwelling units selected by area probability sampling.
description: Datasets: DS0: Study-Level Files DS1: Public-Use Data DS2: Restricted-Use Data
description: Persons aged 18 years or older living in households with telephones within the coterminous United States. Smallest Geographic Unit: FIPS code
description: computer-assisted telephone interview (CATI)Information on the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations and how they were created can be found in the P.I. Codebook.Additional information on the Surveys of Consumers can be found by visiting the Surveys of Consumers Web site.
DOI: 10.3886/icpsr32453.v2
title: Survey of Consumer Attitudes and Behavior, December 2003
title: Version 2

description: This survey was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. This type of information is essential for forecasting changes in aggregate consumer behavior. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2003 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Additional questions inquired about buying intentions for automobiles and computers, and the respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey were respondents' types of savings and financial investments, loan use, family income and sources of income, and respondents' ownership, lease, and use of automobiles. Other topics typically include respondents' use of personal computers at home and in the office, respondents' familiarity with and use of the Internet, electronic banking, and information on informed consent and confidentiality regarding the survey. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: This collection contains four weight variables; WT (HOUSEHOLD HEAD WEIGHT), WT_HH (HOUSEHOLD WEIGHT), WT_ADHD (ADULT HEAD WEIGHT), and WT_AD (ADULT WEIGHT) that must be used in any analysis. For more information on weights and sampling please visit the Surveys of Consumers Web site and refer to the documentation on Sample Design, and the section on Sample Weights.
description: National sample of dwelling units selected by area probability sampling.
description: Datasets: DS0: Study-Level Files DS1: Public-Use Data DS2: Restricted-Use Data
description: Persons aged 18 years or older living in households with telephones within the coterminous United States. Smallest Geographic Unit: FIPS code
description: computer-assisted telephone interview (CATI)Information on the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations and how they were created can be found in the P.I. Codebook.Additional information on the Surveys of Consumers can be found by visiting the Surveys of Consumers Web site.
DOI: 10.5517/ccdc.csd.cc20677q
title: CCDC 1853032: Experimental Crystal Structure Determination

description: Related Article: Hashini N. Munasinghe, Marcos R. Imer, Regina G. Szlag, Leopoldo Suescun, Federico A. Rabuffetti|2022|Dalton Trans.|51|18224|doi:10.1039/D2DT02822K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25jpv8
title: CCDC 2012050: Experimental Crystal Structure Determination

description: Related Article: Soumalya Roy, Pooja Shukla, Naushad Ahmed, Ming-Hao Du, Ibtesham Tarannum, Xiang-Jian Kong, Tulika Gupta, Saurabh Kumar Singh, Sourav Das|2022|Dalton Trans.|51|18187|doi:10.1039/D2DT03117E
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6073/pasta/9de314bdd31af265b0db8b1768f6dbda
title: Lake Mendota at North Temperate Lakes LTER: Snow and Ice Depth 2009-2010

description: Ice core data collected by Yi-Fang (Yvonne) Hsieh and collaborators for her PhD project, “Modeling Ice Cover and Water Temperature of Lake Mendota.” Part of the project was the development of a 3D hydrodynamic-ice model that simulated both temporal and spatial distributions of ice cover on Lake Mendota for the winter 2009-2010. The parameters from these ice core data were used as model inputs to run model simulations. Parameters measured include: blue ice, white ice, snow depth, and total ice. On February 13, 2009, ice cores were taken on Lake Mendota at four different stations. From January 14, 2010 through March 3, 2010 ice cores were taken on Lake Mendota at 31 different stations. In addition, ice cores were taken on other Yahara Lakes during February of 2009: Lake Kegonsa (4 stations_February 6), Lake Waubesa (4 stations_February 7), Lake Wingra (2 stations_February 8), and Lake Monona (4 stations_February 8). Only total ice measurements are reported for 2009. Included in this data set are the ice core data, and geospatial information for ice coring stations. Documentation: Hsieh, Y.-F., 2012a. Modeling ice cover and water temperature of Lake Mendota. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 157.
DOI: 10.5281/zenodo.7374404
title: Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars

description: The datasets here correspond to a paper by Dawkins et al., “Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars”, originally submitted in November 2022. The following datasets are sufficient to produce Figure 2 and 3 in the main manuscript. Figure 2: Please use the 12 individual files with filenames, “Dawkins_et_al_2022__meteor_peak_altitude__*_data.txt”. Here the asterisk should be replaced by one of the following station abbreviations: CAR, COL, CPa, DAV, KIR, KSS, ROT, SMa, SOD, SVA, TdF, and TRO. Each file contains 5 columns: Column 1 is year (from 1999 to 2022), Column 2 is the time series of the annual peak altitude residuals (no units), Column 3 is the corresponding standard error, Column 4 is the multilinear model fit, and Column 5 is the normalized annual solar flux (F10.7) in arbitrary units. Figure 3: Please use “Dawkins_et_al_2022__meteor_peak_altitude_trends.txt”. For ease, a description of the different columns is included within this file.
DOI: 10.5281/zenodo.7374405
title: Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars

description: The datasets here correspond to a paper by Dawkins et al., “Solar cycle and long-term trends in the observed peak of the meteor altitude distributions by meteor radars”, originally submitted in November 2022. The following datasets are sufficient to produce Figure 2 and 3 in the main manuscript. Figure 2: Please use the 12 individual files with filenames, “Dawkins_et_al_2022__meteor_peak_altitude__*_data.txt”. Here the asterisk should be replaced by one of the following station abbreviations: CAR, COL, CPa, DAV, KIR, KSS, ROT, SMa, SOD, SVA, TdF, and TRO. Each file contains 5 columns: Column 1 is year (from 1999 to 2022), Column 2 is the time series of the annual peak altitude residuals (no units), Column 3 is the corresponding standard error, Column 4 is the multilinear model fit, and Column 5 is the normalized annual solar flux (F10.7) in arbitrary units. Figure 3: Please use “Dawkins_et_al_2022__meteor_peak_altitude_trends.txt”. For ease, a description of the different columns is included within this file.
DOI: 10.6073/pasta/2085dfc5ff5911fc6f586b7eb814cb1e
title: Fluxes project at North Temperate Lakes LTER: Spatial Metabolism Study 2007

description: Data from a lake spatial metabolism study by Matthew C. Van de Bogert for his Phd project, "Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The goal of this study was to capture the spatial heterogeneity of within-lake processes in effort to make robust estimates of daily metabolism metrics such as gross primary production (GPP), respiration (R), and net ecosystem production (NEP). In pursuing this goal, multiple sondes were placed at different locations and depths within two stratified Northern Temperate Lakes, Sparkling Lake (n=35 sondes) and Peter Lake (n=27 sondes), located in the Northern Highlands Lake District of Wisconsin and the Upper Peninsula of Michigan, respectively.Dissolved oxygen and temperature measurements were made every 10 minutes over a 10 day period for each lake in July and August of 2007. Dissolved oxygen measurements were corrected for drift. In addition, conductivity, temperature compensated specific conductivity, pH, and oxidation reduction potential were measured by a subset of sondes in each lake. Two data tables list the spatial information regarding sonde placement in each lake, and a single data table lists information about the sondes (manufacturer, model, serial number etc.). Documentation:Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 156. Also see Van de Bogert, M.C., Bade, D.L., Carpenter, S.R., Cole, J.J., Pace, M.L., Hanson, P.C., Langman, O.C., 2012. Spatial heterogeneity strongly affects estimates of ecosystem metabolism in two north temperate lakes. Limnology and Oceanography 57, 1689-1700.
DOI: 10.6073/pasta/9abc8580cbf29fc212dd1af3ba6e1e3a
title: Fluxes project at North Temperate Lakes LTER: Hydrology Scenarios Model Output

description: A spatially-explicit simulation model of hydrologic flow-paths was developed by Matthew C. Van de Bogert and collaborators for his PhD project, " Aquatic ecosystem carbon cycling: From individual lakes to the landscape." The model is coupled with an in-lake carbon model and simulates hydrologic flow paths in groundwater, wetlands, lakes, uplands, and streams. The goal of this modeling effort was to compare aquatic carbon cycling in two climate scenarios for the North Highlands Lake District (NHLD) of northern Wisconsin: one based on the current climate and the other based on a scenario with warmer winters where lakes and uplands do not freeze, hereinafter referred to as the "no freeze" scenario. In modeling this "no freeze" scenario the same precipitation and temperature data as the current climate model was used, however temperature inputs were artificially floored at 0 degrees Celsius. While not discussed in his dissertation, Van de Bogert considered two other climate scenarios each using the same precipitation and temperature data as the current climate scenario. These scenarios involved running the model after artificially raising and lowering the current temperature data by 10 degrees Celsius. Thus, four scenarios were considered in this modeling effort, the current climate scenario, the "no freeze" scenario, the +10 degrees scenario, and the -10 degrees scenario. These data are the outputs of the model under the different scenarios and include average monthly temperature, average monthly rainfall, average monthly snowfall, total monthly precipitation, daily evapotranspiration, daily surface runoff, daily groundwater recharge, and daily total runoff. Note that the results of how temperature inputs influence aquatic carbon cycling under these different scenarios is not included in this data set, refer to Van de Bogert (2011) for this information.Documentation: Van de Bogert, M.C., 2011. Aquatic ecosystem carbon cycling: From individual lakes to the landscape. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 156.
DOI: 10.3886/e172261v1-131092
title: Data and Code for: Measuring Human Capital

description: There are many reasons to want measures of countries’ investments in human capital and especially their investments in formal education. We review the existing literature on the measurement of human capital. Broadly speaking, economists have proposed three approaches to the measurement of human capital—the indicator approach, the cost approach and the income approach. Studies employing the indicator approach have used single measures such as average years of schooling or created indexes of multiple measures as human capital proxies. The cost approach values human capital investments based on spending. The income approach values human capital investments by looking forward to the increment to expected future earnings they produce. The latter two approaches have the significant advantage of consistency with national income accounting practices and measures of other types of capital, but there are also challenges to their implementation. Measures based on the income approach typically yield far larger estimates of the value of human capital than measures based on the cost approach. We outline possible reasons for this discrepancy and show how changes in assumptions can reconcile estimates based on the two approaches.
DOI: 10.6073/pasta/5f353d9114aa9f3f13986e19b1cfcf5a
title: North Temperate Lakes LTER Northern Highland Lake District Bathymetry

description: This data set represents the bathymetry of the seven NTL-LTER primary study lakes in the Northern Highland Lake District (aka Trout Lake Region). Bathymetry is encoded as vector contours with a varying contour interval depending on the depth of the lake.
DOI: 10.6084/m9.figshare.20301249
title: Supplementary Tables

description: Suppelemntary Table 1: Differential expressed genes in RNA-seq data of mpkDCT transfected with siRNAs targeting Hnf1b or non-targeting siRNAs Supplementary Table 2: HNF1β ChIP-sequencing peaks in mpkDCT cells annoted to closest gene. <br> For manuscript: <strong>Transcription factor HNF1β controls a transcriptional network regulating kidney cell structure and tight junction integrity </strong>
DOI: 10.6084/m9.figshare.20301249.v1
title: Supplementary Tables

description: Suppelemntary Table 1: Differential expressed genes in RNA-seq data of mpkDCT transfected with siRNAs targeting Hnf1b or non-targeting siRNAs Supplementary Table 2: HNF1β ChIP-sequencing peaks in mpkDCT cells annoted to closest gene. <br> For manuscript: <strong>Transcription factor HNF1β controls a transcriptional network regulating kidney cell structure and tight junction integrity </strong>
DOI: 10.6073/pasta/acf57c4a8c7ee617d53c32b772f926a1
title: Lake Shoreline in the Contiguous United States

description: There are millions of lakes, ponds and reservoirs in the United States. Existing datasets are large and unweildy. With this data product, derived from the US NHD (downloaded Jan 2013), we summarize at a high level the distribution of lakes across the US.
DOI: 10.21227/jjkz-7x74
title: CRAWDAD tum/proximityness

description: Dataset for evaluation of co-presence detection&lt;/oneline&gt;We conducted a study with 126 subjects, over three months, collecting data from various sensors, that resulted in a multimodal dataset for co-presence detection. We publish a subset of the original data set in the period between 01.06.2018 and 15.06.2018 including Wi-Fi scans as proximity verification set, magnetometer as sensor data, the positions of Wi-Fi access points, and magnetometer's sensor hardware.This study has been published at IEEE PerCrowd 2020, M. Haus, A. Y. Ding, J. Ott, "Multimodal Co-Presence Detection with Varying Spatio-Temporal Granularity", in Proceedings of IEEE 3rd International Workshop on Context-awareness for Multi-device Pervasive and Mobile Computing (PerCrowd), 2020. DOI: http://doi.org/10.15783/c7-x43g-h794Tracesettum/proximityness/traceset1file: proximity_verification_set_wifi.csv.gzdescription: proximity_verification_set_wifi.csv.gz contains the scans of Wi-Fi access points including mobile device's id, relative time, and BSSID. We have anonymized the device id and BSSID with substitute identifier. In addition, we converted the absolute timestamp to a relative time using a time reference point.measurement purpose: ARRAY(0x55bfb7a81de0)tum/proximityness/traceset1 Tracetrace1: first tracetum/proximityness/traceset2file: proximity_verification_set_wifi_positions.csv.gzdescription: proximity_verification_set_wifi_positions.csv.gz contains the positions of Wi-Fi access points including BBSID and relative position. We have anonymized the BSSID and coordinates of Wi-Fi access points with substitute identifier.measurement purpose: ARRAY(0x55bfb7a821e8)tum/proximityness/traceset2 Tracetrace2: second tracetum/proximityness/traceset3file: sensor_data_magnetometer.csv.gzdescription: sensor_data_magnetometer.csv.gz contains the magnetometer's sensor data including mobile device's id, relative time, and x, y, z of magnetometer sensor. We have anonymized the device id with substitute identifier and converted the absolute timestamp to a relative time using a time reference point.measurement purpose: ARRAY(0x55bfb7a825f0)tum/proximityness/traceset3 Tracetrace3: third tracetum/proximityness/traceset4file: sensor_hardware_magnetometer.csv.gzdescription: sensor_hardware_magnetometer.csv.gz contains the magnetometer's sensor hardware including mobile device's id and hardware of magnetometer sensor. We have anonymized the device id and sensor hardware with substitute identifier.measurement purpose: ARRAY(0x55bfb7a829f8)tum/proximityness/traceset4 Tracetrace4: fourth trace