cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7207856
title: Full-coverage high-resolution (Daily, 1-km) PM2.5 dataset for China (2000-2020)

description: We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 in China using a random forest-based hindcast modeling method which incorporates historical information into modeling. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The annual average data during 2000-2020 are released here and free for non-commercial use. This manuscript is under review. If you want use our dataset, please cite the following article at this moment. If you want more data (e.g.daily/monthly data) or further collaborate with us, please contact us via qqhe@whut.edu.cn. If you have any question regarding the dataset, feel free to contact me via qqhe@whut.edu.cn. -He, Qingqing, et al. “Satellite-Derived 1-Km Estimates and Long-Term Trends of PM2.5 Concentrations in China from 2000 to 2018.” Environment International, vol. 156, Nov. 2021, p. 106726, doi:10.1016/j.envint.2021.106726. <em>Please note that "0" in TIFF files should be regarded as invalid value!!!</em> If you want to use other datasets, please go to version_3km (10.5281/zenodo.4569557)
DOI: 10.5281/zenodo.7208480
title: Full-coverage high-resolution (Daily, 1-km) PM2.5 dataset for China (2000-2020)

description: We have estimated full-coverage, daily 1-km PM2.5 data from 2000 to 2020 in China using a random forest-based hindcast modeling method which incorporates historical information into modeling. Multiple sources were used as inputs, including MAIAC AOD, meteorological data from CMA, reanalysis data from ERA-5, and other land-related data. The annual average data during 2000-2020 are released here and free for non-commercial use. This manuscript is under review. If you want use our dataset, please cite the following article at this moment. If you want more data (e.g.daily/monthly data) or further collaborate with us, please contact us via qqhe@whut.edu.cn. If you have any question regarding the dataset, feel free to contact me via qqhe@whut.edu.cn. -He, Qingqing, et al. “Satellite-Derived 1-Km Estimates and Long-Term Trends of PM2.5 Concentrations in China from 2000 to 2018.” Environment International, vol. 156, Nov. 2021, p. 106726, doi:10.1016/j.envint.2021.106726. <em>Please note that "0" in TIFF files should be regarded as invalid value!!!</em> If you want to use other datasets, please go to version_3km (10.5281/zenodo.4569557)
DOI: 10.6084/m9.figshare.23601204
title: PRELIMINARY LAB PLATE VIBRATION RESULTS.docx

description: These are the results of the laboratory test results from the excitations of plate vibrator.
DOI: 10.48529/1e2h-vy53
title: Global Financial Inclusion (Global Findex) Database 2021
title: VNM_2022_FINDEX_v01_M
DOI: 10.48529/smph-e664
title: Global Financial Inclusion (Global Findex) Database 2021
title: TCD_2022_FINDEX_v01_M
DOI: 10.48529/10py-nv58
title: Global Financial Inclusion (Global Findex) Database 2021
title: YEM_2022_FINDEX_v01_M
DOI: 10.48529/jq97-aj70
title: Global Financial Inclusion (Global Findex) Database 2021
title: WLD_2021_FINDEX_v03_M
DOI: 10.48529/q53p-en90
title: Global Financial Inclusion (Global Findex) Database 2021
title: SWZ_2022_FINDEX_v01_M
DOI: 10.48529/p102-z404
title: Global Financial Inclusion (Global Findex) Database 2021
title: MEX_2022_FINDEX_v01_M
DOI: 10.48529/1gj7-jv76
title: High-Frequency Phone Survey 2020-2023
title: MWI_2020_HFPS_v15_M
DOI: 10.48529/rszj-ez23
title: Global Financial Inclusion (Global Findex) Database 2021
title: MRT_2022_FINDEX_v01_M
DOI: 10.48529/cc4f-h861
title: Global Financial Inclusion (Global Findex) Database 2021
title: NER_2022_FINDEX_v01_M
DOI: 10.48529/pgjj-vx72
title: Global Financial Inclusion (Global Findex) Database 2021
title: LSO_2022_FINDEX_v01_M
DOI: 10.48529/96gx-7485
title: COVID-19 LAC High Frequency Phone Survey 2023, Wave 4
title: HTI_2023_HFPS-W4_v01_M
DOI: 10.48529/t96a-wz89
title: Global Agriculture and Food Security Project Impact Evaluation 2017
title: LBR_2017_GAFSPIE-BL_v01_M
DOI: 10.48529/vc74-5v86
title: High-Frequency Monitoring of COVID-19 Impacts Rounds 1-8, 2020-2023
title: IDN_2020_HFMCI_v06_M
DOI: 10.48529/sc8e-5g93
title: Global Financial Inclusion (Global Findex) Database 2021
title: MDG_2022_FINDEX_v01_M
DOI: 10.48529/sx83-a749
title: Global Financial Inclusion (Global Findex) Database 2021
title: ETH_2022_FINDEX_v01_M
DOI: 10.48529/tg65-a945
title: Global Financial Inclusion (Global Findex) Database 2021
title: GTM_2022_FINDEX_v01_M
DOI: 10.48529/an98-bf03
title: Global Financial Inclusion (Global Findex) Database 2021
title: GMB_2022_FINDEX_v01_M
DOI: 10.48529/dj1x-q178
title: COVID-19 LAC High Frequency Phone Survey 2022, Wave 3
title: HTI_2022_HFPS-W3_v01_M
DOI: 10.48529/aecv-x671
title: Global Financial Inclusion (Global Findex) Database 2021
title: COD_2022_FINDEX_v01_M
DOI: 10.48529/b0w6-4w42
title: Smallholder Rice Farmers in Ghana’s Kpong and Weta Irrigation Projects
title: GHA_2013-2014_KIP_v01_M
DOI: 10.48529/fthr-7t74
title: Global Financial Inclusion (Global Findex) Database 2021
title: BWA_2022_FINDEX_v01_M
DOI: 10.48529/90yr-2m57
title: Global Financial Inclusion (Global Findex) Database 2021
title: COM_2022_FINDEX_v01_M
DOI: 10.48529/f28w-ks66
title: High Frequency Phone Survey 2020-2023
title: ETH_2020-2023_HFPS_v10_M
DOI: 10.48529/39hy-cx11
title: High Frequency Phone Survey 2020-2023
title: BFA_2020-2023_HFPS_v16_M
DOI: 10.48529/xc65-9v80
title: Global Financial Inclusion (Global Findex) Database 2021
title: AZE_2022_FINDEX_v01_M
DOI: 10.48529/6a8a-ek81
title: The Effects of Childcare on Women and Children: Evidence from a Randomized Evaluation, 2019
title: BFA_2019_PEJDCIE-BL_v01_M
DOI: 10.5281/zenodo.8096622
title: A new lithospheric density and magnetic susceptibility model of Iran, starting from high resolution seismic tomography

description: The Iranian collisional belt formed through different geological events, some of which are still in progress, such as the convergence between Eurasian and Arabian plates, that led to the formation of a complex structure throughout the entire area. To better investigate these structures, we realize a 3D model of the lithosphere in Iran showing the density and the magnetic susceptibility distribution, obtained from a Bayesian joint gravity and magnetic field inversion, starting from a high-resolution seismic tomography (Kaviani et al., 2020). With these models we also calculate the rigidity distribution of the area. The Data Cube uploaded contains the density, magnetic susceptibility and shear modulus volumes, the Bouguer gravity field and the magnetic field, and the Moho, Curie depth and sediment base depth surfaces.
description: {"references": ["Kaviani, A., A. Paul, A. Moradi, P.M. Mai, S. Pilia, L. Boschi, G. Rumpker, Y. Lu, Z. Tang, and E. Sandvol (2020), Crustal and uppermost mantle shear wave velocity structure beneath the Middle East from surface wave tomography. Geophysical Journal International 221 (2): 1349\u20131365. https://doi.org/10.1093/gji/ggaa075 ."]}
DOI: 10.48366/r599914
title: Comparison of KG-enhanced LLM methods for the pre-training of KG-enhanced LLM

description: This comparison has summarized 17 KG-enhanced large scale pre-trained language models for the pre-training of KG-enhanced LLM. This comparison have modeled with 10 different properties and explored the datasets, experiments, methodologies code source for 17 Large language Models.
DOI: 10.5281/zenodo.8095056
title: Genomic Signatures of Disease Resistance in Endangered Staghorn Corals

description: White Band Disease (WBD) has caused unprecedented declines in the Caribbean <em>Acropora</em> corals, which are now listed as critically endangered species. Highly disease resistant <em>Acropora</em> <em> cervicornis</em> genotypes exist, but we lack understanding of the genetic underpinnings of disease resistance. Using transmission experiments, a newly assembled genome, and whole genome resequencing of 76 <em>A. cervicornis</em> genotypes from Florida and Panama, we identified ten genomic regions and 73 single-nucleotide polymorphisms (SNPs) that are strongly associated with disease resistance and that include functional, protein coding changes in four genes involved in coral immunity and pathogen detection. Polygenic scores calculated from 10 genomic loci indicate that simple genetic screens can predict <em>A. cervicornis</em> disease resistance and that adaptive polygenic variation can be used to improve disease resistance in wild and nursery stocks of <em>A. cervicornis</em> across the Caribbean.
description: NSF OCE-1924145
DOI: 10.5281/zenodo.8095055
title: Genomic Signatures of Disease Resistance in Endangered Staghorn Corals

description: White Band Disease (WBD) has caused unprecedented declines in the Caribbean <em>Acropora</em> corals, which are now listed as critically endangered species. Highly disease resistant <em>Acropora</em> <em> cervicornis</em> genotypes exist, but we lack understanding of the genetic underpinnings of disease resistance. Using transmission experiments, a newly assembled genome, and whole genome resequencing of 76 <em>A. cervicornis</em> genotypes from Florida and Panama, we identified ten genomic regions and 73 single-nucleotide polymorphisms (SNPs) that are strongly associated with disease resistance and that include functional, protein coding changes in four genes involved in coral immunity and pathogen detection. Polygenic scores calculated from 10 genomic loci indicate that simple genetic screens can predict <em>A. cervicornis</em> disease resistance and that adaptive polygenic variation can be used to improve disease resistance in wild and nursery stocks of <em>A. cervicornis</em> across the Caribbean.
description: NSF OCE-1924145
DOI: 10.6073/pasta/9feeb0ac58ad4940a6140109e6da4066
title: Hubbard Brook Experimental Forest: 15-minute Precipitation by Watershed, 2017 - present

description: From 2011 to 2017, ten electronic weighing rain gauges were progressively implemented at the Hubbard Brook Experimental Forest to measure precipitation at 15-minute intervals. 15-minute resolution watershed precipitation values for nine research watersheds are calculated as a weighted average of precipitation using Inverse Distance Weighting. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
DOI: 10.6073/pasta/a746aca47b33590f106e92d5cabe9677
title: Chemistry of rainfall and throughfall from El Verde and Bisley

description: Rain, throughfall, and stream water are collected weekly at the LEF sites listed below. Samples are collected by USDA Forest Service technicians Carlos Estrada in the field filtered in the lab by Miriam Salgado. These data sets begin as early as 1983; LTER sampling began in 1988. Rain and throughfall samples are the total catch for the week, and are exposed to field conditions for that time. No event sampling is conducted on a routine basis. Rain samples from WDEV are wet only from an automatically-closing collector that prevents any dry deposition (Aerochem Metrics NADP collector). RCEV and RCB are bulk or always-open collectors that receive dry deposition by sedimentation. All samples are measured for pH and conductivity, and then filtered (pre-combusted Whatman GF/F glass fiber filter) prior to further analysis. From 1983-1994 samples were cooled and returned to the San Juan chemistry laboratory for analysis. During those years, samples for NH4 and NO3 analyses were refrigerated continuously until analysis. Subsamples for NH4 analysis were also preserved with 1 molar H2SO4. From 1994 on, samples for NH4 and NO3 were frozen until analysis, were not acidified, and all analyses were conducted at the University of New Hampshire. Rain and Throughfall Sampling SitesDescriptions of LTER LUQ rain and throughfall weekly sample chemistry data from 1988 onwards. Chemical concentrations are recorded as mg/L or ug/L as appropriate. Values below detection limits are recorded as 1/2 the detection limit. Site Abbreviation Description Comments Rain collector Bisley. RCB Bulk collector. Rain collector El Verde RCEV Bulk collector. Wet/dry El Verde. WDEV. Wet only collector. Throughfall Bisley. TFB=TCB TF bulk 10-collector composite Bisley gap= BGAP, TF bulk 10-collector composite Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
DOI: 10.5281/zenodo.8095299
title: smithsoniella_earleae_spades_01.fasta.gz

description: .
DOI: 10.5281/zenodo.8095298
title: smithsoniella_earleae_spades_01.fasta.gz

description: .
DOI: 10.3886/icpsr04186.v1
title: American Time Use Survey (ATUS), 2003
title: Version 1

description: The American Time Use Survey (ATUS) collects information on how people living in the United States spend their time. Estimates show the kinds of activities people engage in and the time they spend involved in these activities by age, sex, educational attainment, labor force status, and other characteristics, as well as by weekday and weekend day. Data about the quality of life in the United States include how much time people spend working, sleeping, caring for children, volunteering, participating in religious activities, commuting, or relaxing, as well as with whom they spend their time. The ATUS sample is drawn from households that have completed their final month of interviews for the Current Population Survey, the federal survey that is the source of the nation's unemployment rate.
description: The ATUS sample is drawn from households that have completed their final month of interviews for the Current Population Survey, the federal survey that is the source of the nation's unemployment rate. Households are selected to ensure that estimates can be made across major demographic groups. One individual from each selected household is chosen to participate in the ATUS, and this person is interviewed only once about his or her time use. In 2003, the sample consisted of approximately 39,000 cases, which yielded about 21,000 completed interviews. In 2004, the sample was reduced to approximately 27,000 cases, which yielded about 14,000 completed interviews.
description: Datasets: DS0: Study-Level Files DS1: Current Population Survey DS2: Respondent DS3: Roster DS4: Activity DS5: Activity Summary DS6: Who DS7: Replicate Weights DS8: Case History
description: ATUS covers all residents living in households in the United States who are at least 15 years of age, with the exception of active military personnel and people residing in institutions such as nursing homes and prisons.
description: computer-assisted telephone interview (CATI)The methodology used to weight the 2004 data was different from that used to weight the 2003 data. In 2003, the ATUS weights added up to the number of person-days in the month only for the population as a whole. In 2004, the methodology was changed so that ATUS weights add up to the number of person-days for selected subpopulations as well as for the general population. Users who wish to pool 2003 and 2004 data in order to increase the number of observations should use weights calculated according to the 2004 methodology. On September 20, 2005, these new weights were added to the 2003 Respondent file, as variable TU04FWGT. Thus, users who combine the 2003 and 2004 datasets should use TU04FWGT (on the 2003 Respondent file) to weight the 2003 data and TUFINLWGT (on the 2004 Respondent file) to weight the 2004 data. These weights were created using identical methodologies.
DOI: 10.6073/pasta/5f43dd06d7e5f6c7a6cd9b27e6d915e4
title: Marsh to Upland: Vegetation Monitoring in Coastal Virginia, 2018-2022

description: This dataset includes data from vegetation plots along a marsh to upland gradient in three salt-marshes along the Eastern Shore of Virginia. Coastal wetlands serve as vital habitats and provide various ecosystems services. These ecosystems are experiencing novel abiotic conditions driven by anthropogenic change, including changes in salinity and moisture due to sea level rise. A key component of wetland ecosystems are their vegetation communities, which often show a transition from non-woody marsh plants like grasses and sedges to woody shrubs and larger trees in the upland reaches of the wetland. This dataset was collected from three coastal wetlands: Boxtree Preserve (Boxtree), Cushmans Landing (Cushmans), and Mockhorn Wildlife Management Area GATR Tract (GATR). Measurements of emergent vegetation percent cover and tree and shrub characteristics are taken annually at maximum biomass for the plant community, which occurs in August in this system (Moore 2013). At each site sampling occurs along four transects which run through the low marsh, high marsh, transitional area, lowest elevation upland forest, and slightly higher elevation upland forest. Each transect is segmented into five zones based on these plant communities, which roughly correspond to elevation bins of 1m. Each zone contains one sampling location.
DOI: 10.5066/p95ij3zb
title: Vegetation and Water Monitoring Datasets for selected locations in the Republic of the Marshall Islands from 2017-2022

description: The Republic of the Marshall Islands (RMI) is a sovereign Small Island State in the tropical central North Pacific Ocean. RMI is a nation of more than thirty atolls and islands, most of which are inhabited, dispersed across an exclusive economic zone (EEZ) over 2 million square kilometers. This data release contains raster datasets for vegetation and water monitoring including Normalized Difference Vegetation Index (NDVI), Landscape Water Requirement Satisfaction Index (L-WRSI), and Soil Moisture Index (SMI) for selected locations and precipitation (dekadal (10-day) sum) for the entire RMI from 2017-2022. These data were compiled to support a 2022-2023 U.S. Geological Survey project for developing methods to apply Earth Observation to monitor the health of vegetation in the RMI. The project was funded by the U.S. Geological Survey and the U.S. Agency for International Development Bureau for Humanitarian Assistance.
DOI: 10.3886/icpsr04148.v1
title: Current Population Survey, March/April 1996 Match File: Child Support
title: Version 1

description: Information for this file was collected for Current Population Surveys in March and April 1996. The March portion of this file, also known as the Annual Demographic File, provides the usual monthly labor force data, as well as supplemental data on work experience, income, noncash benefits, and migration. Comprehensive work experience information is given on the employment status, occupation, and industry of persons 15 years old and over, as well as data concerning weeks worked and hours per week worked, reason for not working full-time, total income and income components, and residence on March 1, 1996. This file also contains data covering nine noncash income sources: food stamps, school lunch programs, employer-provided group health insurance and pension plans, personal health insurance, Medicaid, Medicare, CHAMPUS or military health care, and energy assistance. Also included are demographic characteristics such as age, sex, race, household relationship, and Hispanic origin for each person in the household. The April portion of this file, the child support supplement, contains responses from all people 15 years of age and older, with children present in the household.
description: A probability sample is used in selecting housing units.
description: Datasets: DS1: Current Population Survey, March/April 1996 Match File: Child Support
description: Civilian noninstitutional population of the United States living in housing units, and members of the Armed Forces living in civilian housing units on a military base or in a household not on a military base.
description: face-to-face interviewIn this hierarchical dataset, there are three record types. The first is the Household with 124 variables for 44,859 records. The Family record type has 76 variables for 38,995 records. The Person record type has 718 variables for 89,972 records. There are a total of 173,826 records in this dataset.
DOI: 10.3886/icpsr04148
title: Current Population Survey, March/April 1996 Match File: Child Support
title: Archival Version

description: Information for this file was collected for Current Population Surveys in March and April 1996. The March portion of this file, also known as the Annual Demographic File, provides the usual monthly labor force data, as well as supplemental data on work experience, income, noncash benefits, and migration. Comprehensive work experience information is given on the employment status, occupation, and industry of persons 15 years old and over, as well as data concerning weeks worked and hours per week worked, reason for not working full-time, total income and income components, and residence on March 1, 1996. This file also contains data covering nine noncash income sources: food stamps, school lunch programs, employer-provided group health insurance and pension plans, personal health insurance, Medicaid, Medicare, CHAMPUS or military health care, and energy assistance. Also included are demographic characteristics such as age, sex, race, household relationship, and Hispanic origin for each person in the household. The April portion of this file, the child support supplement, contains responses from all people 15 years of age and older, with children present in the household.
description: A probability sample is used in selecting housing units.
description: Datasets: DS1: Current Population Survey, March/April 1996 Match File: Child Support
description: Civilian noninstitutional population of the United States living in housing units, and members of the Armed Forces living in civilian housing units on a military base or in a household not on a military base.
description: face-to-face interviewIn this hierarchical dataset, there are three record types. The first is the Household with 124 variables for 44,859 records. The Family record type has 76 variables for 38,995 records. The Person record type has 718 variables for 89,972 records. There are a total of 173,826 records in this dataset.
DOI: 10.3886/icpsr02810.v1
title: Current Population Survey, October 1998: School Enrollment
title: Version 1

description: Data on labor force activity for the week prior to the survey are supplied in this collection. Information is available on the employment status, occupation, and industry of persons 15 years old and over. Demographic variables such as age, sex, race, marital status, veteran status, household relationship, educational background, and Hispanic origin are included. In addition to providing these core data, the October survey also contains a special supplement on school enrollment for all persons surveyed aged 3 years and older. This supplement includes the following items: current grade attending at public or private school, whether attending college full- or part-time at a two- or four-year institution, year last attended a regular school, and year graduated from high school.
description: The probability sample selected to represent the universe consists of approximately 48,000 households.
description: Datasets: DS1: Current Population Survey, October 1998: School Enrollment
description: All persons in the civilian noninstitutional population of the United States living in households.