cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/e735df68b214f75132179f39a04fb16e
title: Hubbard Brook Experimental Forest: Daily Precipitation Rain Gage Measurements, 1956 - present

description: Precipitation has been measured at the Hubbard Brook Experimental Forest using rain gauges located in or around each watershed since 1956. Three types of rain gauges have been used: standard, mechanical weight recording, and electronic weight recording. Between 1956 and 2014, precipitation was measured weekly at standard gages located at 24 stations in or near gauged watersheds and at the headquarters building. Weight-recording gauges were located at 7 of the 24 stations and capture a continuous strip-chart record. Weekly totals were prorated using daily totals from the nearest recording gauges. Beginning in 2011, electronic weighing rain gauges were implemented to measure 15-minute precipitation. The number of precipitation stations was reduced to 10, when each station was fully converted to an electronic gauge for measuring 15-minute and daily precipitation beginning in 2015. These data were gathered at the Hubbard Brook Experimental Forest in Thornton, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
DOI: 10.6084/m9.figshare.13043522
title: Chemical production of oxygenated volatile organic compounds strongly enhances near-surface oxidation chemistry and ozone production

description: Modelling results of MCM 3.3.1 and GCM (GEOS-Chem v10-02), box and 1-D.
DOI: 10.6084/m9.figshare.13043522.v1
title: Chemical production of oxygenated volatile organic compounds strongly enhances near-surface oxidation chemistry and ozone production

description: Modelling results of MCM 3.3.1 and GCM (GEOS-Chem v10-02), box and 1-D.
DOI: 10.25345/c5jn35
title: MassIVE MSV000086230 - GNPS - Paenibacillus spp with coffee extracts Pilot 1
title: Cultures of Paenibacillus spp (Zengler Lab UCSD) on Nutrient Broth and BHI enriched with coffee extracts. Extracts obtained by SPE C18 100% MeOH. LC-MS/MS performed in an UltiMate 3000 UPLC system (Thermo Scientific) using a Kinetex 1.7 um C18 reversed phase UHPLC column (50 X 2.1 mm) and Maxis Impact Q-TOF mass spectrometer (Bruker Daltonics) equipped with ESI source. Data was acquired in positive ion mode.
DOI: 10.5281/zenodo.4064089
title: RotconML: A theoretical dataset for machine learning of spectroscopic parameters

description: This dataset comprises ~83,000 small organic molecules containing [H,C,O,N], with structures and harmonic frequency calculations performed at the ωB97X-D/6-31+G(d) level of theory with Gaussian '16. The purpose of this dataset is for training machine learning models—in particular, for use in rotational spectroscopy and identifying unknown molecules from spectroscopic parameters. Details of the model and the data can be found in this paper. This particular combination of electronic structure method and basis set was benchmarked in earlier work to provide relatively low uncertainties in the predicted rotational constants, and through a cancellation of errors, provides equilibrium constants that are extremely close to the vibrationally averaged (experimental) values. More details can be found in this paper. The dataset is included as a comma-separated value (CSV) file, which can be a little difficult to parse as plain text; I recommend using the `pandas` Python package to parse and manipulate as a Dataframe instead. The columns of this dataset include: rotational constants, moments of inertia and derived values (such as inertial defect and asymmetry parameter), harmonic frequencies and intensities, dipole moments, zero-point energy, the electronic energy, the cartesian coordinates, the SMILES identifier, the final energy difference after optimization, and the molecular mass. For more details, users are referred to our papers above and/or contact the author. If you are using this dataset for your research/work, please cite this Zenodo entry, and this reference: McCarthy, M.; Lee, K. L. K. Molecule Identification with Rotational Spectroscopy and Probabilistic Deep Learning. <em>J. Phys. Chem. A</em> <strong>2020</strong>, <em>124</em> (15), 3002–3017. https://doi.org/10.1021/acs.jpca.0c01376.
description: {"references": ["McCarthy, M.; Lee, K. L. K. Molecule Identification with Rotational Spectroscopy and Probabilistic Deep Learning. J. Phys. Chem. A 2020, 124 (15), 3002\u20133017. https://doi.org/10.1021/acs.jpca.0c01376.", "Lee, K. L. K.; McCarthy, M. Bayesian Analysis of Theoretical Rotational Constants from Low-Cost Electronic Structure Methods. J. Phys. Chem. A 2020, 5, 898\u2013910. https://doi.org/10.1021/acs.jpca.9b09982."]}
DOI: 10.5281/zenodo.4064088
title: RotconML: A theoretical dataset for machine learning of spectroscopic parameters

description: This dataset comprises ~83,000 small organic molecules containing [H,C,O,N], with structures and harmonic frequency calculations performed at the ωB97X-D/6-31+G(d) level of theory with Gaussian '16. The purpose of this dataset is for training machine learning models—in particular, for use in rotational spectroscopy and identifying unknown molecules from spectroscopic parameters. Details of the model and the data can be found in this paper. This particular combination of electronic structure method and basis set was benchmarked in earlier work to provide relatively low uncertainties in the predicted rotational constants, and through a cancellation of errors, provides equilibrium constants that are extremely close to the vibrationally averaged (experimental) values. More details can be found in this paper. The dataset is included as a comma-separated value (CSV) file, which can be a little difficult to parse as plain text; I recommend using the `pandas` Python package to parse and manipulate as a Dataframe instead. The columns of this dataset include: rotational constants, moments of inertia and derived values (such as inertial defect and asymmetry parameter), harmonic frequencies and intensities, dipole moments, zero-point energy, the electronic energy, the cartesian coordinates, the SMILES identifier, the final energy difference after optimization, and the molecular mass. For more details, users are referred to our papers above and/or contact the author. If you are using this dataset for your research/work, please cite this Zenodo entry, and this reference: McCarthy, M.; Lee, K. L. K. Molecule Identification with Rotational Spectroscopy and Probabilistic Deep Learning. <em>J. Phys. Chem. A</em> <strong>2020</strong>, <em>124</em> (15), 3002–3017. https://doi.org/10.1021/acs.jpca.0c01376.
description: {"references": ["McCarthy, M.; Lee, K. L. K. Molecule Identification with Rotational Spectroscopy and Probabilistic Deep Learning. J. Phys. Chem. A 2020, 124 (15), 3002\u20133017. https://doi.org/10.1021/acs.jpca.0c01376.", "Lee, K. L. K.; McCarthy, M. Bayesian Analysis of Theoretical Rotational Constants from Low-Cost Electronic Structure Methods. J. Phys. Chem. A 2020, 5, 898\u2013910. https://doi.org/10.1021/acs.jpca.9b09982."]}
DOI: 10.25387/g3.12276539
title: Supplemental Material for Sideli et al., 2020

description: All supplementary files from manuscript included. Additional SNP hapmap file format included. <b>FigS1 </b>contains images of walnut pellicle pigmentation variation in bi-parental population in study.<b>FigS2</b> contains PCA and scree plot used to assess the number of PC's to add to model in GWAS analysis.<b>FigS3</b> contains a diagram of genetic map for chromosome 16 in 'Chandler' with significant QTLs. <b>FigS4</b> contains a diagram of genetic map and QTL for chromosome 7 in 'Chandler'. <b>FigS5</b> contains a diagram of genetic map for chromosome 4 and QTL for 'Chandler'.<b>FigS6</b> contains a diagram of genetic map for chromosome 14 and QTLs for 'Idaho'.<b>FigS7</b> contains a diagram of genetic map for chromosome 1 and QTL for 'Idaho'.<b>FigS8</b> contains both Manhattan plots for FarmCPU and MLMM models with QQ plots for DFA phenotype. <b>File S1-S3</b> contains methods and Fiji software code for computer vision analysis of walnut pellicle pigmentation.<br><b>File S4</b> ANOVA tables for L*, a*, b*, DFA scores in the GWAS population.<br><b>File S5</b> contains both 'Chandler' and 'Idaho' genetic maps and phenotypes for QTL mapping analysis.<b>File S6 </b>contains the phenotypes for GWAS analysis.<b>File S7</b> contains the first 6 principal components for GWAS analysis. <br><b>File S8</b> Raw color score phenotypic data for individuals in the GWAS and Chandler × Idaho population.<br><b>Table S1 </b>Estimated breeding values for color phenotypes. Samples collected from 2015, 2016 harvest on 528 individual trees and display the 20 individuals possessing the lightest color scores.<b>Table S2</b> Co-localization of marker trait associations with Marrano et al. 2019. LD for SNPs displayed.<b>Table S3</b> contains all gene functional annotations from BLAST analysis of 100kb regions around significant SNPs. <b>walnut_hapmap.txt</b> is the additional file added to submission to include the SNP genotypes for GWAS analysis. <br><br>
DOI: 10.25387/g3.12276539.v1
title: Supplemental Material for Sideli et al., 2020

description: All supplementary files from manuscript included. Additional SNP hapmap file format included. <b>FigS1 </b>contains images of walnut pellicle pigmentation variation in bi-parental population in study.<b>FigS2</b> contains PCA and scree plot used to assess the number of PC's to add to model in GWAS analysis.<b>FigS3</b> contains a diagram of genetic map for chromosome 16 in 'Chandler' with significant QTLs. <b>FigS4</b> contains a diagram of genetic map and QTL for chromosome 7 in 'Chandler'. <b>FigS5</b> contains a diagram of genetic map for chromosome 4 and QTL for 'Chandler'.<b>FigS6</b> contains a diagram of genetic map for chromosome 14 and QTLs for 'Idaho'.<b>FigS7</b> contains a diagram of genetic map for chromosome 1 and QTL for 'Idaho'.<b>FigS8</b> contains both Manhattan plots for FarmCPU and MLMM models with QQ plots for DFA phenotype. <b>File S1-S3</b> contains methods and Fiji software code for computer vision analysis of walnut pellicle pigmentation.<br><b>File S4</b> ANOVA tables for L*, a*, b*, DFA scores in the GWAS population.<br><b>File S5</b> contains both 'Chandler' and 'Idaho' genetic maps and phenotypes for QTL mapping analysis.<b>File S6 </b>contains the phenotypes for GWAS analysis.<b>File S7</b> contains the first 6 principal components for GWAS analysis. <br><b>File S8</b> Raw color score phenotypic data for individuals in the GWAS and Chandler × Idaho population.<br><b>Table S1 </b>Estimated breeding values for color phenotypes. Samples collected from 2015, 2016 harvest on 528 individual trees and display the 20 individuals possessing the lightest color scores.<b>Table S2</b> Co-localization of marker trait associations with Marrano et al. 2019. LD for SNPs displayed.<b>Table S3</b> contains all gene functional annotations from BLAST analysis of 100kb regions around significant SNPs. <b>walnut_hapmap.txt</b> is the additional file added to submission to include the SNP genotypes for GWAS analysis. <br><br>
DOI: 10.21227/sh59-fs52
title: Annotated image dataset of household objects from the RoboFEI@Home team

description: Annotated image dataset of household objects from the RoboFEI@Home teamThis data set contains two sets of pictures of household objects, created by the RoboFEI@Home team to develop object detection systems for a domestic robot.The first data set was created with objects from a local supermarket. Product brands are typical from Brazil. The second data set is composed of objects from the RoboCup@Home 2018 OPL competition.The data set contains the basic resources for the creation of custom object detection systems. Users can use the provided annotated images to train their own models and validate them in a set of test images, or they can analyze the inference time of the proposed methods in the provided videos.Example videos of detection systems trained with this data set can be found in the following links:https://www.youtube.com/watch?v=Py6_qG52EYQhttps://www.youtube.com/watch?v=fXJYmJOaSxQ
DOI: 10.6069/5v4j-vp07
title: Low- and Middle-Income Country Under-5 Diarrhea Incidence, Prevalence, and Mortality Geospatial Estimates 2000-2017

description: "Annual estimates were produced for the prevalence and incidence of diarrhea and diarrhea-related mortality among children younger than 5 years of age at the 5x5 km-level for 94 low- and middle-income countries (LMICs) between 2000-2017. These estimates were produced using geo-positioned data from 466 household surveys. Survey sources used include the Demographic and Health Survey (DHS) and UNICEF Multiple Indicator Cluster Survey (MICS) series, and other country‐specific surveys.

This dataset includes the following:

- GeoTIFF raster files for pixel-level estimates of under-5 diarrhea prevalence, incidence, and diarrhea-related mortality
- CSV files of aggregated under-5 diarrhea prevalence, incidence, and diarrhea-related mortality for each country at zero, first and second administrative divisions
- Code files used to generate the estimates"
DOI: 10.6069/42y8-wx60
title: Sub-Saharan Africa Male Circumcision Geospatial Estimates 2000-2017

description: "Annual estimates were produced for adult male circumcision (MC) prevalence and the number of circumcised and uncircumcised males ages 15-49 at the 5x5 km-level for 38 countries in sub-Saharan Africa between 2000 and 2017. These estimates were produced using a geo-positioned dataset created from 109 household surveys. Survey sources used include the Demographic and Health Survey (DHS), AIDS Indicator Survey (AIS), Multiple Indicator Cluster Survey (MICS), Core Welfare Indicators Questionnaire Survey (CWIQ), Population-based HIV Impact Assessment Survey (PHIA), and other country‐specific surveys.

This dataset includes the following:

- GeoTIFF raster files for pixel-level estimates of male circumcision (MC) prevalence and the number of circumcised and uncircumcised males ages 15-49
- CSV files of aggregated circumcision estimates for each country at zero, first and second administrative divisions
- Code files used to generate the estimates"
DOI: 10.6069/mdrr-2a80
title: Low- and Middle-Income Country Oral Rehydration Therapy Coverage Geospatial Estimates 2000-2017

description: "Annual estimates were produced for oral rehydration therapy (ORT) coverage for children under 5 years of age who had diarrhea at the 5x5 km-level for 94 low- and middle-income countries (LMICs) between 2000-2017. These estimates were produced using a geo-positioned dataset created from 385 household surveys. Survey sources used include the Demographic and Health Survey (DHS) and UNICEF Multiple Indicator Cluster Survey (MICS) series, and other country‐specific surveys.

This dataset includes the following:

- GeoTIFF raster files for pixel-level estimates of oral rehydration therapy percent (percent of children with diarrhea who received treatment) and number (number of children with diarrhea who received treatment)
- CSV files of aggregated oral rehydration therapy coverage percent and number for each country at zero, first and second administrative divisions
- Code files used to generate the estimates
"
DOI: 10.6069/anr1-9s72
title: Development Assistance for Health Database 1990-2019

description: "This version of the Development Assistance for Health (DAH) Database includes estimates for 1990-2019, which are based on project databases, financial statements, annual reports, IRS 990s, and correspondence with agencies. The DAH Database enables comprehensive analysis of trends in international disbursements of grants and loans for health projects in low- and middle-income countries from key agencies. The data are disaggregated by source of funds, channel of funding, country and geographic region, health focus areas, and program areas. New in 2019: The current estimates of DAH incorporated improvements in methodology such as leveraging additional project-level descriptions from the Creditor Reporting System for the allocation of disbursements channeled through non-governmental organizations (NGOs) and ongoing refinement of the project’s keyword search list.

To better understand the data and how to use it, please refer to the IHME DAH Database 2019 User Guide."
DOI: 10.6073/pasta/66d0c53429a2b9f20b5104e891a44c1f
title: Above- and belowground biomass of S. patens in elevational marsh planter experiments, Plum Island, MA, PIE LTER.

description: Spartina patens is grown in experimental elevation planters placed in draining ponds on the salt marsh platform. The planters are constructed from PVC pipes of varying lengths. All of the PVC pipes are open at the bottom, and flush to the mudflat surface of the pond, resulting in the tops of the pipes being situated at various relative elevations within the tidal range. Plants growing in the shorter PVC pipes are flooded for a longer duration of each tidal cycle than plants growing in the taller PVC pipes. The growth response of S. patens when exposed to different flood frequencies is monitored by destructive 'harvesting' of all above- and belowground plant material at the end of the growing season.
DOI: 10.5066/p9n8x0zy
title: Management of remnant tallgrass prairie by grazing or fire in western Minnesota, 2016-2017

description: We studied the impacts of using cattle grazing or prescribed fire to manage remnant prairies on plant communities and soil characteristics in the prairie parkland province of western Minnesota. These datasets consist of nested frequency plot plant survey data, study site and soil characteristics, and management information taken from 73 sites owned and managed by the U.S. Fish and Wildlife Service, Minnesota Department of Natural Resources, The Nature Conservancy and private land owners. Because management effects can take many years to become evident, we evaluated response variables on remnant prairies that had been subject to either fire or grazing for at least 10 years prior to our surveys, between 2005-2015.
DOI: 10.6069/0pwc-pv84
title: Global Expected Health Spending 2018-2050

description: Research by the Global Burden of Disease Health Financing Collaborator Network produced projected health spending estimates for 2018-2050 for 195 countries and territories. The estimates cover total health spending, health spending disaggregated by source into three domestic financing source categories (government, out-of-pocket, and prepaid private), and development assistance for health (DAH). Retrospective health spending estimates for 1995-2017 and key covariates (including GDP per capita, total government spending, total fertility rate, and fraction of the population older than 65 years) were used to forecast GDP and health spending through 2050. Estimates are reported in constant 2019 US dollars, constant 2019 purchasing-power parity-adjusted (PPP) dollars, and as a percent of gross domestic product.
DOI: 10.5517/ccdc.csd.cc1pw54w
title: CCDC 1575238: Experimental Crystal Structure Determination

description: Related Article: Matthias Hardy, Niklas Struch, Filip Topic, Gregor Schnakenburg, Kari Rissanen, Arne Lützen|2018|Inorg.Chem.|57|3507|doi:10.1021/acs.inorgchem.7b02516
DOI: 10.5517/ccdc.csd.cc1pzb55
title: CCDC 1578277: Experimental Crystal Structure Determination

description: Related Article: Olga N. Mazaleva, Ilya N. Ioffe, Fei Jin, Shangfeng Yang, Erhard Kemnitz, Sergey I. Troyanov|2018|Inorg.Chem.|57|4222|doi:10.1021/acs.inorgchem.7b02554
DOI: 10.5517/ccdc.csd.cc1pzb66
title: CCDC 1578278: Experimental Crystal Structure Determination

description: Related Article: Olga N. Mazaleva, Ilya N. Ioffe, Fei Jin, Shangfeng Yang, Erhard Kemnitz, Sergey I. Troyanov|2018|Inorg.Chem.|57|4222|doi:10.1021/acs.inorgchem.7b02554
DOI: 10.6069/hef2-0e04
title: Global Health Spending 1995-2017

description: Research by the Global Burden of Disease Health Financing Collaborator Network produced retrospective health spending estimates for 1995-2017 for 195 countries and territories. The estimates cover total health spending, health spending disaggregated by source into three domestic financing source categories (government, out-of-pocket, and prepaid private), and development assistance for health (DAH). Domestic health spending source data came primarily from the WHO’s Global Health Expenditure Database (GHED). DAH data came from a diverse set of sources, including program reports, budget data, national estimates, and National Health Accounts (NHAs). The resulting estimates were used to help produce prospective health spending estimates for 2018-2050. Estimates are reported in constant 2019 United States dollars, constant 2019 purchasing-power parity-adjusted (PPP) dollars, and as a percent of gross domestic product.
DOI: 10.6069/hhbb-e660
title: Global Malaria Spending 2000-2017

description: Research by the by the Global Burden of Disease Health Financing Collaborator Network estimated estimated malaria spending for 106 countries for 2000-2017. The estimates cover malaria spending disaggregated by source into three domestic financing source categories (government, out-of-pocket, and prepaid private) and development assistance for health (DAH). Domestic malaria spending estimates were produced from a diverse set of data, including the World Malaria Report, WHO National Health Accounts and sub-accounts, the Global Fund Price Quality Reporting, WHO Global Price Reporting Mechanism, Management Sciences for Health reference prices, the Malaria Atlas Project, and more. Development assistance for malaria data were drawn from IHME's 2019 Development Assistance for Health Database. This database is also informed by a diverse set of sources, including program reports, budget data, national estimates, and NHAs. Estimates are reported in constant 2019 United States dollars.
DOI: 10.6069/19pr-qe36
title: Low- and Middle-Income Drinking Country Water and Sanitation Facilities Access Geospatial Estimates 2000-2017

description: "Annual estimates were produced for access to drinking water and sanitation Facilities at the 5x5 km-level for 90 low- and middle-income countries (LMICs) for 2000-2017. These estimates were produced using a geo-positioned dataset created from 634 household surveys. Survey sources used include the Demographic and Health Survey (DHS) and UNICEF Multiple Indicator Cluster Survey (MICS) series, and other country‐specific surveys.

This dataset includes the following:

- GeoTIFF raster files for pixel-level estimates of drinking water and sanitation facility coverage percent (percent of people with the given type of access) and number (number of people with the given type of access)
- CSV files of aggregated estimates for each country at zero, first and second administrative divisions
- Code files used to generate the estimates"
DOI: 10.6069/8na6-6z25
title: Global Lymphatic Filariasis Prevalence Geospatial Estimates 2000-2018

description: "Estimates were produced for lymphatic filariasis (LF) all-age prevalence at the 5x5 km-level in endemic countries across Africa, Asia, and Hispaniola, annually between 2000 and 2018. Bayesian time series estimates were produced for 17 small area geographies in South America, the Indian Ocean, and Oceania. These estimates were produced using data on LF and geographical locations from endemicity mapping surveys, sentinel surveillance surveys, transmission assessment surveys (TAS), and other sources.

This dataset includes the following:

- GeoTIFF raster files for pixel-level estimates of LF prevalence rate, counts, and posterior probability that prevalence was lower than 2% in 2018
- CSV files of aggregated estimates of LF prevalence rate, count and posterior probability of prevalence below 2% (2018) for each country at the zero, first, and second administrative divisions
- Code files used to generate the estimates
"