cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7197524
title: Machine Learning the Tip of the Red Giant Branch

description: # Reproduction package for the Paper "Machine Learning the Tip of the Red Giant Branch"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> ## Citation Policy<br> If you use any of this reproduction package for independent work, we recommend you cite the following papers:<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019) For more information, see the Readme.md
DOI: 10.5281/zenodo.7197557
title: Machine Learning the Tip of the Red Giant Branch

description: # Reproduction package for the Paper "Machine Learning the Tip of the Red Giant Branch"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> ## Citation Policy<br> If you use any of this reproduction package for independent work, we recommend you cite the following papers:<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019) For more information, see the Readme.md
DOI: 10.5281/zenodo.7197580
title: Machine Learning the Tip of the Red Giant Branch

description: # Reproduction package for the Paper "Machine Learning the Tip of the Red Giant Branch"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> ## Citation Policy<br> If you use any part of this reproduction package for independent work, we recommend you cite the following papers: * This paper<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019) For more information, see the Readme.md
DOI: 10.5281/zenodo.7801550
title: Machine Learning the Tip of the Red Giant Branch

description: # Reproduction package for the Paper "Machine Learning the Tip of the Red Giant Branch"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> ## Citation Policy<br> If you use any part of this reproduction package for independent work, we recommend you cite the following papers: * This paper<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019) For more information, see the Readme.md
DOI: 10.5281/zenodo.7894181
title: Machine Learning the Tip of the Red Giant Branch

description: # Reproduction package for the Paper "Machine Learning the Tip of the Red Giant Branch"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> ## Citation Policy<br> If you use any part of this reproduction package for independent work, we recommend you cite the following papers: * This paper<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019) For more information, see the Readme.md
DOI: 10.5281/zenodo.7197149
title: Machine Learning the Tip of the Red Giant Branch

description: # Reproduction package for the Paper "Machine Learning the Tip of the Red Giant Branch"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> ## Citation Policy<br> If you use any part of this reproduction package for independent work, we recommend you cite the following papers: * This paper<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019) For more information, see the Readme.md
DOI: 10.5281/zenodo.7895972
title: Machine Learning the Tip of the Red Giant Branch

description: # Reproduction package for the Paper "Machine Learning the Tip of the Red Giant Branch"<br> ## Authors<br> Mitchell T. Dennis (mtde226@hawaii.edu)<br> Jeremy Sakstein (sakstein@hawaii.edu)<br> ## Software<br> MESA version 15140 (http://mesa.sourceforge.net/) <br> MESASDK version 20210401 (http://www.astro.wisc.edu/~townsend/static.php?ref=mesasdk) <br> GFORTRAN GCC version 9.2.0<br> ## Citation Policy<br> If you use any part of this reproduction package for independent work, we recommend you cite the following papers: * This paper<br> * Astrophys. J. Suppl. 192, 3 (2011)<br> * Astrophys. J. Suppl. 208, 4 (2013)<br> * Astrophys. J. Suppl. 234, 34 (2018)<br> * Astrophys. J. Suppl. 243, 10 (2019) For more information, see the Readme.md
DOI: 10.5519/qd.eesdrxtp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (70 records)
DOI: 10.25345/c56q1ss8p
title: MassIVE MSV000091859 - A crustacean neuropeptide spectral library for data-independent mass spectrometry applications
title: Neuropeptides have tremendous potential for application in modern medicine, including utility as biomarkers and therapeutics. To overcome the inherent challenges associated with neuropeptide identification and characterization, data-independent acquisition (DIA) is a fitting mass spectrometry (MS) method choice to achieve sensitive, accurate analysis. It is advantageous for preliminary neuropeptidomic studies to occur in less complex organisms, with crustacean models serving as a popular choice due to their relatively simple nervous system. With spectral libraries serving as a means to interpret DIA-MS output spectra, and crustaceans a choice model for neuropeptide analysis, we performed the first spectral library mapping of crustacean neuropeptides. Leveraging pre-existing data-dependent acquisition (DDA) spectra, a spectral library was built using PEAKS Online. The library is comprised of 333 unique neuropeptides. The identification results obtained through the use of this spectral library were compared with those achieved through library-free analysis of crustacean brain, pericardial organs (PO), and thoracic ganglia (TG) tissues. A statistically significant increase (Student s ttest, P value 0.05) in the number of identifications achieved from TG data was observed in the spectral library results. Furthermore, in each of the tissues, a distinctly different set of identifications were found in the library-search compared to the library-free search. This work highlights the necessity for use of spectral libraries in neuropeptide analysis, illustrating the advantage of spectral libraries for interpreting DIA spectra in a reproducible manner with greater neuropeptidomic depth.
DOI: 10.7281/t1/tnhloj/0jhdju
title: RWolf_2018_Cytokine Project Supplementary Table 1.zip

description: Supplementary Table 1
DOI: 10.7281/t1/tnhloj/yrmevx
title: RWolf_2018_Cytokine Project Supplementary Figures.zip

description: Supplementary Figures 1 and 2
DOI: 10.5281/zenodo.7566670
title: Arena: Rapid and accurate reconstruction of full-atomic RNA structures from coarse-grained models

description: Arena-main.zip contains the source code, which is also available on GitHub. benchmarking_datasets.zip contains the datasets used for benchmarking Arena and the other RNA reconstruction programs: pdb (original files), pdb_input (original files with missing atoms added by Arena), pdb_input_C3_prime (input files with only the C3' atoms), pdb_input_glycoN (input files with only the glycosidic N1/N9 atoms), pdb_input_P (input files with only the P atoms), pdb_input_P_C1_base (input files with only the P, C1', and base atoms), pdb_input_P_ribose (input files with only the backbone atoms), NAST (files from NAST structure prediction), and SimRNA (files from SimRNA structure prediction). fasta.zip contains the nucleotide sequences of the RNAs in the benchmarking dataset, which is a required input for Rosetta rna_thread. lists.zip contains text files of the PDB IDs used for benchmarking. RNA_classes.zip contains tsv files of the benchmarking dataset split by type of RNA.
DOI: 10.5281/zenodo.7566671
title: Arena: Rapid and accurate reconstruction of full-atomic RNA structures from coarse-grained models

description: Arena-main.zip contains the source code, which is also available on GitHub. benchmarking_datasets.zip contains the datasets used for benchmarking Arena and the other RNA reconstruction programs: pdb (original files), pdb_input (original files with missing atoms added by Arena), pdb_input_C3_prime (input files with only the C3' atoms), pdb_input_glycoN (input files with only the glycosidic N1/N9 atoms), pdb_input_P (input files with only the P atoms), pdb_input_P_C1_base (input files with only the P, C1', and base atoms), pdb_input_P_ribose (input files with only the backbone atoms). fasta.zip contains the nucleotide sequences of the RNAs in the benchmarking dataset, which is a required input for Rosetta rna_thread. list is a text file of the PDB IDs in the benchmarking dataset.
DOI: 10.5281/zenodo.7897366
title: Arena: Rapid and accurate reconstruction of full-atomic RNA structures from coarse-grained models

description: Arena-main.zip contains the source code, which is also available on GitHub. benchmarking_datasets.zip contains the datasets used for benchmarking Arena and the other RNA reconstruction programs: pdb (original files), pdb_input (original files with missing atoms added by Arena), pdb_input_C3_prime (input files with only the C3' atoms), pdb_input_glycoN (input files with only the glycosidic N1/N9 atoms), pdb_input_P (input files with only the P atoms), pdb_input_P_C1_base (input files with only the P, C1', and base atoms), pdb_input_P_ribose (input files with only the backbone atoms), NAST (files from NAST structure prediction), and SimRNA (files from SimRNA structure prediction). fasta.zip contains the nucleotide sequences of the RNAs in the benchmarking dataset, which is a required input for Rosetta rna_thread. lists.zip contains text files of the PDB IDs used for benchmarking. RNA_classes.zip contains tsv files of the benchmarking dataset split by type of RNA
DOI: 10.5281/zenodo.7897374
title: Arena: Rapid and accurate reconstruction of full-atomic RNA structures from coarse-grained models

description: Arena-main.zip contains the source code, which is also available on GitHub. benchmarking_datasets.zip contains the datasets used for benchmarking Arena and the other RNA reconstruction programs: pdb (original files), pdb_input (original files with missing atoms added by Arena), pdb_input_C3_prime (input files with only the C3' atoms), pdb_input_glycoN (input files with only the glycosidic N1/N9 atoms), pdb_input_P (input files with only the P atoms), pdb_input_P_C1_base (input files with only the P, C1', and base atoms), pdb_input_P_ribose (input files with only the backbone atoms), NAST (files from NAST structure prediction), and SimRNA (files from SimRNA structure prediction). fasta.zip contains the nucleotide sequences of the RNAs in the benchmarking dataset, which is a required input for Rosetta rna_thread. lists.zip contains text files of the PDB IDs used for benchmarking. RNA_classes.zip contains tsv files of the benchmarking dataset split by type of RNA.
DOI: 10.6084/m9.figshare.22766516
title: Characteristics of children with type 1 diabetes mellitus in 2014 and 2020

description: The data of patients with DM1 in follow-up in a university service in the state of São Paulo, Brazil, aged 0–19 years, enrolled in the service in 2014 and 2020.
DOI: 10.6084/m9.figshare.22766516.v1
title: Characteristics of children with type 1 diabetes mellitus in 2014 and 2020

description: The data of patients with DM1 in follow-up in a university service in the state of São Paulo, Brazil, aged 0–19 years, enrolled in the service in 2014 and 2020.
DOI: 10.25345/c52z1304b
title: MassIVE MSV000091860 - A crustacean neuropeptide spectral library for DIA mass spectrometry applications
title: Neuropeptides have tremendous potential for application in modern medicine, including utility as biomarkers and therapeutics. To overcome the inherent challenges associated with neuropeptide identification and characterization, data-independent acquisition (DIA) is a fitting mass spectrometry (MS) method choice to achieve sensitive, accurate analysis. It is advantageous for preliminary neuropeptidomic studies to occur in less complex organisms, with crustacean models serving as a popular choice due to their relatively simple nervous system. With spectral libraries serving as a means to interpret DIA-MS output spectra, and crustaceans a choice model for neuropeptide analysis, we performed the first spectral library mapping of crustacean neuropeptides. Leveraging pre-existing data-dependent acquisition (DDA) spectra, a spectral library was built using PEAKS Online. The library is comprised of 333 unique neuropeptides. The identification results obtained through the use of this spectral library were compared with those achieved through library-free analysis of crustacean brain, pericardial organs (PO), and thoracic ganglia (TG) tissues. A statistically significant increase (Students t-test, P value&lt;0.05) in the number of identifications achieved from TG data was observed in the spectral library results. Furthermore, in each of the tissues, a distinctly different set of identifications were found in the library-search compared to the library-free search. This work highlights the necessity for use of spectral libraries in neuropeptide analysis, illustrating the advantage of spectral libraries for interpreting DIA spectra in a reproducible manner with greater neuropeptidomic depth.
DOI: 10.3886/icpsr38397.v1
title: Survey of Consumer Attitudes and Behavior, May 2019
title: Version 1

description: The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) 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. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2019 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding 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 retirement planning. Other topics in this series typically include economic attitudes, home price expectations, savings and financial investments, political affiliation, and opinion on the Trump administration. 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 survey is a rotating panel design. For each monthly sample, an independent cross-section sample of households is drawn. The respondents chosen in this drawing are then reinterviewed six months later. The result is a rotating panel design, and the total sample for any one survey is normally made up of 60 percent new respondents with 40 percent being interviewed for the second time.
description: The data are not weighted, however, this collection contains three weight variables; <b>WT</b> (HOUSEHOLD WEIGHT (WT_HH RENAMED)), <b>WT_HH</b> (HOUSEHOLD WEIGHT), and <b>WT_AD</b> (ADULT WEIGHT) that must be used in any analysis. For more information on weights and sampling, please refer to the documentation and/or visit the Surveys of Consumers Website.
description: National sample of telephone numbers from cell (RDD) sampling frame
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 Website.
DOI: 10.3886/icpsr38397
title: Survey of Consumer Attitudes and Behavior, May 2019
title: Archival Version

description: The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) 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. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter. The surveys conducted in 2019 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding 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 retirement planning. Other topics in this series typically include economic attitudes, home price expectations, savings and financial investments, political affiliation, and opinion on the Trump administration. 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 survey is a rotating panel design. For each monthly sample, an independent cross-section sample of households is drawn. The respondents chosen in this drawing are then reinterviewed six months later. The result is a rotating panel design, and the total sample for any one survey is normally made up of 60 percent new respondents with 40 percent being interviewed for the second time.
description: The data are not weighted, however, this collection contains three weight variables; <b>WT</b> (HOUSEHOLD WEIGHT (WT_HH RENAMED)), <b>WT_HH</b> (HOUSEHOLD WEIGHT), and <b>WT_AD</b> (ADULT WEIGHT) that must be used in any analysis. For more information on weights and sampling, please refer to the documentation and/or visit the Surveys of Consumers Website.
description: National sample of telephone numbers from cell (RDD) sampling frame
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 Website.
DOI: 10.5281/zenodo.7705446
title: The Case of the Missing Meleagris: Diet and Animal Management at Copán, Honduras

description: Senior thesis at Harvard College. Topic: Diet and Animal Management at Copán, Honduras
DOI: 10.5281/zenodo.7705447
title: The Case of the Missing Meleagris: Diet and Animal Management at Copán, Honduras

description: Senior thesis at Harvard College. Topic: Diet and Animal Management at Copán, Honduras
DOI: 10.5281/zenodo.7897400
title: The Case of the Missing Meleagris: Diet and Animal Management at Copán, Honduras

description: Senior thesis at Harvard College. Topic: Diet and Animal Management at Copán, Honduras
DOI: 10.7281/t1/zpgbjw/2b7umx
title: Mullany - 2018 - OHCS - Descriptive Analysis.zip

description: Tables and associated label lists and variable lists
DOI: 10.7281/t1/zpgbjw/dfsgos
title: Erchick - 2019 - BMC Oral Health - Hygiene, Gingivitis and risk factors among preg women in rural Nepal.pdf

description: Open Source Manuscript
DOI: 10.7281/t1/zpgbjw/h7zoxp
title: Mullany-Nepal-NNIPS-OHCS-Qualitative.zip

description: Field guides, English-language transcripts, and associated publications for qualitative research component.
DOI: 10.6084/m9.figshare.21407595
title: Epidemiology of canine round cell tumours on the Canary Archipelago in Spain

description: Source data for my new article
DOI: 10.6084/m9.figshare.21407595.v2
title: Epidemiology of canine round cell tumours on the Canary Archipelago in Spain

description: Source data for my new article
DOI: 10.6084/m9.figshare.21407595.v1
title: Epidemiology of canine round cell tumours on the Canary Archipelago in Spain

description: Source data for my new article
DOI: 10.6084/m9.figshare.22766609
title: Interview Transcriptions

description: Qualitative research based upon interpretive phenomenological analysis. Twelve subjects are interviewed who are veteran first responders with at least 10 years of continuous employment. The interviews are conducted using a semi structured format with 10 prepared questions.
DOI: 10.6084/m9.figshare.22766609.v1
title: Interview Transcriptions

description: Qualitative research based upon interpretive phenomenological analysis. Twelve subjects are interviewed who are veteran first responders with at least 10 years of continuous employment. The interviews are conducted using a semi structured format with 10 prepared questions.
DOI: 10.6084/m9.figshare.22766630
title: geneRIF pubmed count

description: the number of PubMed IDs (PMIDs) associated with GeneRIF records for a particular gene or gene product
DOI: 10.5517/ccdc.csd.cc2bmyvs
title: CCDC 2164136: Experimental Crystal Structure Determination

description: Related Article: Jia-Li Li, Xiao Xiong, Dong Luo, Yu-Bai Wei, Weigang Lu, Dan Li|2022|Chem.Commun.|58|6490|doi:10.1039/D2CC02041F
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.