cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/icpsr25984.v2
title: County Business Patterns, 1962, 1964-1970: U.S. Summary, State, and County Data
title: Version 2

description: This dataset was created from County Business Pattern data ranging from 1962 to 1969 excluding data from 1963. The investigators of these data did their best to infer structure and meaning from the individual datasets to create this dataset. The 1962 data do not contain all counties. The investigators observed that multiple counties were combined together into single observations for the states with the most counties, so as to save storage space.
description: Datasets: DS1: County Business Patterns, 1962, 1964-1970: U.S. Summary, State, and County Data
description: All business establishments in the 50 states and the District of Columbia with one or more paid employees, excluding agriculture production, railroad, government, and household employment.
description: record abstractsThis dataset was created from raw data, as the principal investigators received it, for the County Business Patterns for 1962,1964-1969. To the best of their knowledge, no County Business Patterns data exists for 1963. The raw data did not come with any documentation. However, the principal investigators found that the documentation on ICPSR for later years of the data was adequate to infer the structure and meaning of these files. The 1962 data does not contain all counties. It appears that for the states with the most counties, so as to save storage space, multiple counties were combined together into single observations.The dataset only contains the two-digit SICs, plus a few other SICs that the principal investigators were particularly interested in. The meaning of the variables within the file should be clear from the naming conventions explained in the accompanying stata log file.The principal investigators found that many of the observations within the County Business Patterns data failed internal consistency checks. For example, the two-digit SIC codes should sum to totals, and the different establishment size categories should sum to the total number of establishments. The stata log file contains the coding for changes that we made as a result of these inconsistencies. The principal investigators made these changes by checking against hard copies of the county business patterns. In cases where both sources failed consistency checks, it was often clear, on visual inspection, what the error was. This is because the data should sum along two dimensions. In cases where there was only an inconsistency along one dimension, the totals were changed so that they summed along two dimensions.The file pkg25984-0001.zip contains the original county business pattern data files used to create this dataset. The PIs provided the Stata code, file doc25984-0001_Stata.txt, they used to read in the original county business pattern data files. This is only available as a Stata file.
DOI: 10.3886/icpsr26382.v2
title: National Crime Victimization Survey, 2008 [Record-Type Files]
title: Version 2

description: The National Crime Victimization Survey (NCVS) Series, previously called the National Crime Surveys (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1973. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. The survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual attack, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, motor vehicle theft, and vandalism. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected, to enable analysis of crime by various subpopulations.
description: The data files include several weight variables used to calculate national estimates of: households, persons, victimizations, and incidents. The codebook describes how to use the weights.
description: Stratified multistage cluster sample.
description: Datasets: DS0: Study-Level Files DS1: Address Record-Type File DS2: Household Record-Type File DS3: Person Record-Type File DS4: Incident Record-Type File DS5: 2008 Incident-Level Extract File
description: All persons in the United States aged 12 and over. Smallest Geographic Unit: region
description: computer-assisted telephone interview (CATI); face-to-face interview; telephone interviewThe 2008 Incident-Level Extract File was created from the record-type files and includes information on victims of crime. Nonvictims are not included. Records were extracted from the Incident Record-Type File and bounded by the year that the incident occurred. These records were then merged to their parent records from the Person Record-Type and Household Record-Type files.The data were collected by the United States Department of Commerce, Bureau of the Census.
DOI: 10.34740/kaggle/dsv/4984906
title: Video Game Sales Dataset Updated -Extra Feat
DOI: 10.5281/zenodo.6758723
title: SHM - Structural Health Monitoring Dataset

description: Synthetic and Real-World Data as described in C. Kralovec, B. Lehner, M. Kirchmayr, M. Schagerl, "Sandwich Face Layer Debonding Detection and Size Estimation<br> by Machine Learning-based evaluation of Electromechanical Impedance Measurements", Sensors, 2023 The implementation of the experiments can be found here (Python): https://github.com/berni-lehner/structural_health_monitoring
description: {"references": ["C. Kralovec, B. Lehner, M. Kirchmayr, M. Schagerl,\u00a0\"Sandwich Face Layer Debonding Detection and Size Estimation by Machine Learning-based evaluation of Electromechanical Impedance Measurements\", Sensors, 2023"]}
DOI: 10.5281/zenodo.6758722
title: SHM - Structural Health Monitoring Dataset

description: Synthetic and Real-World Data as described in C. Kralovec, B. Lehner, M. Kirchmayr, M. Schagerl, "Sandwich Face Layer Debonding Detection and Size Estimation<br> by Machine Learning-based evaluation of Electromechanical Impedance Measurements", Sensors, 2023 The implementation of the experiments can be found here (Python): https://github.com/berni-lehner/structural_health_monitoring
description: {"references": ["C. Kralovec, B. Lehner, M. Kirchmayr, M. Schagerl,\u00a0\"Sandwich Face Layer Debonding Detection and Size Estimation by Machine Learning-based evaluation of Electromechanical Impedance Measurements\", Sensors, 2023"]}
DOI: 10.5281/zenodo.7686564
title: An Exploratory Study on the Usage and Readability of Messages Within Assertion Methods of Test Cases

description: This is the code and dataset that accompanies the study: "<strong>An Exploratory Study on the Usage and Readability of Messages Within Assertion Methods of Test Cases</strong>." This study has been accepted for publication at the 2023 International Workshop on Natural Language-based Software Engineering. <strong><em>Following is the abstract of the study:</em></strong> Unit testing is a vital part of the software development process and involves developers writing code to verify or assert production code. Furthermore, to help comprehend the test case and troubleshoot issues, developers have the option to provide a message that explains the reason for the assertion failure. In this exploratory empirical study, we examine the characteristics of assertion messages contained in the test methods in 20 open-source Java systems. Our findings show that while developers rarely utilize the option of supplying a message, those who do, either compose it of only string literals, identifiers, or a combination of both types. Using standard English readability measuring techniques, we observe that a beginner's knowledge of English is required to understand messages containing only identifiers, while a 4th-grade education level is required to understand messages composed of string literals. We also discuss shortcomings with using such readability measuring techniques and common anti-patterns in assert message construction. We envision our results incorporated into code quality tools that appraise the understandability of assertion messages.
DOI: 10.5281/zenodo.7686565
title: An Exploratory Study on the Usage and Readability of Messages Within Assertion Methods of Test Cases

description: This is the code and dataset that accompanies the study: "<strong>An Exploratory Study on the Usage and Readability of Messages Within Assertion Methods of Test Cases</strong>." This study has been accepted for publication at the 2023 International Workshop on Natural Language-based Software Engineering. <strong><em>Following is the abstract of the study:</em></strong> Unit testing is a vital part of the software development process and involves developers writing code to verify or assert production code. Furthermore, to help comprehend the test case and troubleshoot issues, developers have the option to provide a message that explains the reason for the assertion failure. In this exploratory empirical study, we examine the characteristics of assertion messages contained in the test methods in 20 open-source Java systems. Our findings show that while developers rarely utilize the option of supplying a message, those who do, either compose it of only string literals, identifiers, or a combination of both types. Using standard English readability measuring techniques, we observe that a beginner's knowledge of English is required to understand messages containing only identifiers, while a 4th-grade education level is required to understand messages composed of string literals. We also discuss shortcomings with using such readability measuring techniques and common anti-patterns in assert message construction. We envision our results incorporated into code quality tools that appraise the understandability of assertion messages.
DOI: 10.5281/zenodo.7672273
title: Rename Chains: An Exploratory Study on the Occurrence and Characteristics of Identifiers Undergoing Multiple Renamings

description: This is the dataset that accompanies the study: "<strong>Rename Chains: An Exploratory Study on the Occurrence and Characteristics of Identifiers Undergoing Multiple Renamings</strong>." This study has been accepted for publication at the 2022 International Workshop on Refactoring. <strong><em>Following is the abstract of the study:</em></strong> Identifier names play a significant role in program comprehension activities, with high-quality names improving developer productivity and system quality. To correct poor-quality names, developers rename identifiers to reflect their intended purpose better. However, renames do not always result in high-quality, long-lasting names; in many cases, developers perform multiple rename operations on the same identifier throughout the system's lifetime. In this paper, we report on a large-scale empirical study that examines the occurrence of identifiers undergoing multiple renames (i.e., rename chains). Our findings show the presence of rename chains in almost every project, with methods typically having more rename chains than other identifier types. Furthermore, it is usually the same developer responsible for creating all renames within a chain, with most names maintaining the same grammatical structure. Understanding rename chains can help us provide stronger advice, and targeted research, on how to craft high-quality, long-lasting identifiers.
DOI: 10.5281/zenodo.7672274
title: Rename Chains: An Exploratory Study on the Occurrence and Characteristics of Identifiers Undergoing Multiple Renamings

description: This is the dataset that accompanies the study: "<strong>Rename Chains: An Exploratory Study on the Occurrence and Characteristics of Identifiers Undergoing Multiple Renamings</strong>." This study has been accepted for publication at the 2022 International Workshop on Refactoring. <strong><em>Following is the abstract of the study:</em></strong> Identifier names play a significant role in program comprehension activities, with high-quality names improving developer productivity and system quality. To correct poor-quality names, developers rename identifiers to reflect their intended purpose better. However, renames do not always result in high-quality, long-lasting names; in many cases, developers perform multiple rename operations on the same identifier throughout the system's lifetime. In this paper, we report on a large-scale empirical study that examines the occurrence of identifiers undergoing multiple renames (i.e., rename chains). Our findings show the presence of rename chains in almost every project, with methods typically having more rename chains than other identifier types. Furthermore, it is usually the same developer responsible for creating all renames within a chain, with most names maintaining the same grammatical structure. Understanding rename chains can help us provide stronger advice, and targeted research, on how to craft high-quality, long-lasting identifiers.
DOI: 10.6084/m9.figshare.22219030
title: Supplementary Material for: Evaluation of MicroRNAs expression pattern (miR-28, miR-181a, miR-34a, and miR-31) in Patients with COVID-19 Admitted to ICU, and Diabetic COVID-19 patients

description: Introduction: MicroRNAs, or miRNAs, with regulatory performance in inflammatory responses and infection are the prevalent manifestations of severe Coronavirus disease (COVID-19). This study aimed to evaluate whether PBMC miRNAs are diagnostic biomarkers to screen the ICU COVID-19 and diabetic-COVID-19 subjects. Methods: Candidate miRNAs were selected through previous studies, and then the PBMC levels of selected miRNAs (miR-28, miR-31, miR-34a, and miR-181a) were measured via quantitative reverse transcription PCR. The diagnostic value of miRNAs was determined by the receiver operating characteristic (ROC) curve. The bioinformatics analysis was utilized to predict the DEMs genes and relevant bio-functions. Results: The COVID-19 patients admitted to the ICU had significantly greater levels of selected miRNAs compared to non-hospitalized COVID-19 and healthy people. Besides, the mean miR-28 and miR-34a expression levels in the diabetic-COVID-19 group were upregulated considerably when compared with the non-diabetic COVID-19 group. ROC analyses demonstrated the role of miR-28, -miR-34a, and -181a as new biomarkers to discriminate the non-hospitalized COVID-19 group from the COVID-19 patients admitted to ICU samples, and also miR-34a can probably act as a useful biomarker for screening diabetic COVID-19 patients. Using bioinformatics analyses, we found the performance of target transcripts in many bio-processes and diverse metabolic routes such as regulating multiple inflammatory parameters. Discussion: The difference in miRNA expression patterns between the studied groups suggested that miR-28, miR-34a, and miR181a could be helpful as potent biomarkers for diagnosing and controlling COVID-19.
DOI: 10.6084/m9.figshare.22219040
title: Proteotranscriptomic data from You are what you eat—ecological niche and microhabitat influence venom activity and composition in aquatic bugs

description: Supplement table showing detailed information on the proteotranscriptomic identification of venom candidate proteins for each species
DOI: 10.6084/m9.figshare.22219052
title: Brassicaceae data from Neutral processes underlying the macro eco-evolutionary dynamics of mixed-ploidy systems

description: Raw data provided by a third party on speciation and extinction rates for 1333 species of the Brassicaceae family factored by ploidy level. See main manuscript for reference to the original work.
DOI: 10.6084/m9.figshare.22219052.v1
title: Brassicaceae data from Neutral processes underlying the macro eco-evolutionary dynamics of mixed-ploidy systems

description: Raw data provided by a third party on speciation and extinction rates for 1333 species of the Brassicaceae family factored by ploidy level. See main manuscript for reference to the original work.
DOI: 10.5281/zenodo.6821588
title: A high-resolution GPM IMERG precipitation dataset for China

description: The database of the paper: An attention mechanism based convolutional network for satellite precipitation downscaling over China
description: {"references": ["Jing, Y., Lin, L., Li, X., Li, T., Shen, H., An attention mechanism based convolutional network for satellite precipitation downscaling over China, Journal of Hydrology (2022), doi: https://doi.org/ 10.1016/j.jhydrol.2022.128388."]}
DOI: 10.5281/zenodo.6821587
title: A high-resolution GPM IMERG precipitation dataset for China

description: The database of the paper: An attention mechanism based convolutional network for satellite precipitation downscaling over China
description: {"references": ["Jing, Y., Lin, L., Li, X., Li, T., Shen, H., An attention mechanism based convolutional network for satellite precipitation downscaling over China, Journal of Hydrology (2022), doi: https://doi.org/ 10.1016/j.jhydrol.2022.128388."]}
DOI: 10.5281/zenodo.5765910
title: STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China

description: <strong>If you used our dataset, please cite our reference:</strong> <strong>Jing, Y., Li, X., and Shen, H.: STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) </strong> <strong>for China, Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, 2022.</strong> 1. A recent 20-year cloud-free MODIS normalized difference snow index (NDSI) collection for China (excluding sea area) is generated using a Spatio-Temporal Adaptive fusion method with erroR correction (STAR). 2. The STAR NDSI collection is derived from daily 1-km MODIS NDSI datasets (MOD10A1 and MYD10A1). 3. It is provided using a WGS_1984_UTM_48N projection, with the data format of TIFF images. 4. Each ZIP contains NDSI data for one hydrological year (August to next July). After uncompressing into the TIFF format, the files are named as "NDSI_ yyyymmdd_Daily_500M_ V01.tif" for NDSI data and "QA_yyyymmdd_Daily_500M_V01.tif" for quality assessment (QA) data. 5. The accuracy of this collection has been well validated by the in-situ snow depth observations and Landsat OLI NDSI maps. The detailed information can be found in the published paper. 6. The NDSI value ranges from 0, 1-100. The value of water is set to 255. The fill value is set to 127. 7. Disclaimer: The spatial scope of the dataset is for research convenience. Please refer to official release for a standard map of China, http://211.159.149.56/index.html. 8. The new version includes data from 2001 to 2020, while the old version includes data from 2012 to 2020 (https://doi.org/10.5281/zenodo.5644386). 9. In case any questions arise, please do not hesitate to contact us (yhjing@whu.edu.cn).
description: {"references": ["Jing, Y., Li, X., and Shen, H.: STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001\u20132020) for China, Earth Syst. Sci. Data, 14, 3137\u20133156, https://doi.org/10.5194/essd-14-3137-2022, 2022."]}
DOI: 10.5281/zenodo.5644385
title: STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China

description: <strong>If you used our dataset, please cite our reference:</strong> <strong>Jing, Y., Li, X., and Shen, H.: STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) </strong> <strong>for China, Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, 2022.</strong> 1. A recent 20-year cloud-free MODIS normalized difference snow index (NDSI) collection for China (excluding sea area) is generated using a Spatio-Temporal Adaptive fusion method with erroR correction (STAR). 2. The STAR NDSI collection is derived from daily 1-km MODIS NDSI datasets (MOD10A1 and MYD10A1). 3. It is provided using a WGS_1984_UTM_48N projection, with the data format of TIFF images. 4. Each ZIP contains NDSI data for one hydrological year (August to next July). After uncompressing into the TIFF format, the files are named as "NDSI_ yyyymmdd_Daily_500M_ V01.tif" for NDSI data and "QA_yyyymmdd_Daily_500M_V01.tif" for quality assessment (QA) data. 5. The accuracy of this collection has been well validated by the in-situ snow depth observations and Landsat OLI NDSI maps. The detailed information can be found in the published paper. 6. The NDSI value ranges from 0, 1-100. The value of water is set to 255. The fill value is set to 127. 7. Disclaimer: The spatial scope of the dataset is for research convenience. Please refer to official release for a standard map of China, http://211.159.149.56/index.html. 8. The new version includes data from 2001 to 2020, while the old version includes data from 2012 to 2020 (https://doi.org/10.5281/zenodo.5644386). 9. In case any questions arise, please do not hesitate to contact us (yhjing@whu.edu.cn).
description: {"references": ["Jing, Y., Li, X., and Shen, H.: STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001\u20132020) for China, Earth Syst. Sci. Data, 14, 3137\u20133156, https://doi.org/10.5194/essd-14-3137-2022, 2022."]}
DOI: 10.6084/m9.figshare.22219063
title: Tree and cavity Measurements_Luzon Hornbill Breeding Ecology Study_3Mar23.xlsx

description: Nest site desciptions and tree and cavity measurements of 7 Luzon Hornbill nests from the Subic Watershed Forest Reserve, Zambales, Luzon Island, Philippines. Data were acquired from February to July 2022.
DOI: 10.6084/m9.figshare.22219063.v1
title: Tree and cavity Measurements_Luzon Hornbill Breeding Ecology Study_3Mar23.xlsx

description: Nest site desciptions and tree and cavity measurements of 7 Luzon Hornbill nests from the Subic Watershed Forest Reserve, Zambales, Luzon Island, Philippines. Data were acquired from February to July 2022.
DOI: 10.6084/m9.figshare.22181932
title: FRAP data

description: Raw data_FRAP experiment
DOI: 10.6084/m9.figshare.22181932.v1
title: FRAP data_2021-02-01802D-Z

description: Raw data_FRAP experiment_ 2021-02-01802D-Z
DOI: 10.5281/zenodo.7680206
title: Nankai Trough FakeQuakes geodetic and waveform data

description: Supplementary data for the master thesis "A Study on Tsunami Damage Prediction Based on Pre-computed Scenarios<strong>" </strong>by Louise A. Hirao-Vermare, master's student at Tohoku University.<br> "nagahama_geodetic_data.tar.gz" gathers synthetic geodetic data, i.e., ground displacements, at several inland stations. "nagahama_wave_data.tar.gz" gathers synthetic wave elevations at several offshore gauges. <br> Not all data are used in the master thesis. <br> File formats are explained in READ_ME.txt. The location of the observation stations are provided in the .klm files.
DOI: 10.5281/zenodo.7680205
title: Nankai Trough FakeQuakes geodetic and waveform data

description: Supplementary data for the master thesis "A Study on Tsunami Damage Prediction Based on Pre-computed Scenarios<strong>" </strong>by Louise A. Hirao-Vermare, master's student at Tohoku University.<br> "nagahama_geodetic_data.tar.gz" gathers synthetic geodetic data, i.e., ground displacements, at several inland stations. "nagahama_wave_data.tar.gz" gathers synthetic wave elevations at several offshore gauges. <br> Not all data are used in the master thesis. <br> File formats are explained in READ_ME.txt. The location of the observation stations are provided in the .klm files.