cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11978505
title: Additional file 10 of New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters

description: Additional file 10. MAGs having KEGG modules. Number of MAGs having each KEGG module are reported. Only MAGs having complete and 1 block missing KEGG modules are considered. In column B is reported the total number of MAGs per each phylum (considering only high quality and medium-high quality MAGs). KEGG modules are reported in columns.
DOI: 10.6084/m9.figshare.11978508
title: Additional file 11 of New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters

description: Additional file 11. KEGG pathways abundance in different microbial samples. Abundance of all HQ and MHQ MAGs having a complete or "one block missing" KEGG module were summed and the value is reported here as an estimate of the abundance of this pathway in each sample.
DOI: 10.6084/m9.figshare.11978505.v1
title: Additional file 10 of New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters

description: Additional file 10. MAGs having KEGG modules. Number of MAGs having each KEGG module are reported. Only MAGs having complete and 1 block missing KEGG modules are considered. In column B is reported the total number of MAGs per each phylum (considering only high quality and medium-high quality MAGs). KEGG modules are reported in columns.
DOI: 10.6084/m9.figshare.11978508.v1
title: Additional file 11 of New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters

description: Additional file 11. KEGG pathways abundance in different microbial samples. Abundance of all HQ and MHQ MAGs having a complete or "one block missing" KEGG module were summed and the value is reported here as an estimate of the abundance of this pathway in each sample.
DOI: 10.6084/m9.figshare.11978478
title: Additional file 4 of In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output

description: Additional file 4. A detailed complete comparison of the models iDU1756 and iJB1325.
DOI: 10.6084/m9.figshare.11978475
title: Additional file 2 of Biomarker-defined pathways for incident type 2 diabetes and coronary heart disease—a comparison in the MONICA/KORA study

description: Additional file 2: Figure S4 (a). Pearson’s correlation coefficients r between the age, sex, and survey adjusted biomarker residuals, calculated in the subcohort of the T2D case-cohort study. Figure S4 (b). Pearson’s correlation coefficients r between the age, sex, and survey adjusted biomarker residuals, calculated in the subcohort of the CHD case-cohort study.
DOI: 10.6084/m9.figshare.11978475.v1
title: Additional file 2 of Biomarker-defined pathways for incident type 2 diabetes and coronary heart disease—a comparison in the MONICA/KORA study

description: Additional file 2: Figure S4 (a). Pearson’s correlation coefficients r between the age, sex, and survey adjusted biomarker residuals, calculated in the subcohort of the T2D case-cohort study. Figure S4 (b). Pearson’s correlation coefficients r between the age, sex, and survey adjusted biomarker residuals, calculated in the subcohort of the CHD case-cohort study.
DOI: 10.6084/m9.figshare.11978478.v1
title: Additional file 4 of In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output

description: Additional file 4. A detailed complete comparison of the models iDU1756 and iJB1325.
DOI: 10.6084/m9.figshare.11978469
title: Additional file 3 of In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output

description: Additional file 3. The iDU1756 model in SBML format.
DOI: 10.6084/m9.figshare.11978463
title: Additional file 2 of In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output

description: Additional file 2. The iDU1756 model presented as a spreadsheet.
DOI: 10.6084/m9.figshare.11978463.v1
title: Additional file 2 of In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output

description: Additional file 2. The iDU1756 model presented as a spreadsheet.
DOI: 10.6084/m9.figshare.11978469.v1
title: Additional file 3 of In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output

description: Additional file 3. The iDU1756 model in SBML format.
DOI: 10.6084/m9.figshare.11978445
title: Additional file 1 of The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017)

description: Additional file 1 Data set sheet contains the climate data in Bangkok form 2003-2017. Table S1 is mean temperature. Table S2 is rainfall. Table S3 is humidity. Table S4 is Dengue incidence number in Bangkok from 2003 to 2017. Correlation sheet contains the correlation between dengue case number and climate data in Bangkok.
DOI: 10.6084/m9.figshare.11978445.v1
title: Additional file 1 of The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017)

description: Additional file 1 Data set sheet contains the climate data in Bangkok form 2003-2017. Table S1 is mean temperature. Table S2 is rainfall. Table S3 is humidity. Table S4 is Dengue incidence number in Bangkok from 2003 to 2017. Correlation sheet contains the correlation between dengue case number and climate data in Bangkok.
DOI: 10.6084/m9.figshare.11977938
title: The simulated monthly Sea Surface Temperature over tropical Pacific on 1x1 grid derived from 43 CMIP5 and 36 CMIP6 models

description: This dataset contains the simulated historical (1870-2005) sea surface temperature (SST) monthly data derived from the first realization of 43 CMIP5 (r1i1p1) and 36 CMIP6 (r1i1p1f1) historical simulations. The data are interpolated to a 1x1 grid from the native model grid and then subtracted the tropical Pacific (100<sup>o</sup>E-60<sup>o</sup>W, 25<sup>o</sup>S-25<sup>o</sup>N) with the period of 1870-2005. All the CMIP5 and CMIP6 data were downloads from the ESGF website. The name lists of the CMIP5 and CMIP6 models are in the files named <i>cmip5_model_name.txt</i> and <i>cmip6_model_name.txt</i>, respectively. <br>
DOI: 10.6084/m9.figshare.11977938.v2
title: The simulated monthly Sea Surface Temperature over tropical Pacific on 1x1 grid derived from 43 CMIP5 and 36 CMIP6 models

description: This dataset contains the simulated historical (1870-2005) sea surface temperature (SST) monthly data derived from the first realization of 43 CMIP5 (r1i1p1) and 36 CMIP6 (r1i1p1f1) historical simulations. The data are interpolated to a 1x1 grid from the native model grid and then subtracted the tropical Pacific (100<sup>o</sup>E-60<sup>o</sup>W, 25<sup>o</sup>S-25<sup>o</sup>N) with the period of 1870-2005. All the CMIP5 and CMIP6 data were downloads from the ESGF website. The name lists of the CMIP5 and CMIP6 models are in the files named <i>cmip5_model_name.txt</i> and <i>cmip6_model_name.txt</i>, respectively. <br>
DOI: 10.6084/m9.figshare.11977938.v1
title: The simulated monthly Sea Surface Temperature over tropical Pacific on 1x1 grid derived from CMIP5 and CMIP6 models

description: This dataset contains the simulated historical (1870-2005) sea surface temperature (SST) monthly data derived from the first realization of 43 CMIP5 (r1i1p1) and 36 CMIP6 (r1i1p1f1) historical simulations. The data are interpolated to a 1x1 grid from the native model grid and then subtracted the tropical Pacific (100<sup>o</sup>E-60<sup>o</sup>W, 25<sup>o</sup>S-25<sup>o</sup>N) with the period of 1870-2005. All the CMIP5 and CMIP6 data were downloads from the ESGF website. The name lists of the CMIP5 and CMIP6 models are in the files named <i>cmip5_model_name.txt</i> and <i>cmip6_model_name.txt</i>, respectively. <br>
DOI: 10.6084/m9.figshare.11977884
title: FTICR-MS Data.pdf

description: FT-ICR-MS data of DON in effluent from a biological nitrogen removal process.
DOI: 10.6084/m9.figshare.11977884.v1
title: FTICR-MS Data.pdf

description: FT-ICR-MS data of DON in effluent from a biological nitrogen removal process.
DOI: 10.6084/m9.figshare.11977881
title: Impact Factor volatility to a single paper: A comprehensive analysis

description: Here are the data for papers [1,2]. The 1st excel sheet ("theory") has data for Figures 1, 2 of [1]. The 2nd sheet ("JCR data") has data for Figures 3, 4, 5, 6 of [1], and Figure 1 of [2].<br>A. Data in the "theory" sheet:<br><br><b>2nd row</b>: Citation count, <i>c</i>, of a single paper published by a journal of Impact Factor <i>f</i><sub>1</sub>=10 and biennial size <i>N</i><sub>1</sub>. We have chosen <i>c</i> to range from 0 to 1000 in our data. <br><b>2nd column</b>: biennial size <i>N</i><sub>1</sub> of journal. We have chosen <i>N</i><sub>1</sub> to range from 10 to 2500 in our data. <br>The data in the cells from C3 to EC283 in the sheet are calculations of the volatility, Δ<i>f</i>(<i>c</i>), as defined in Eq. (4) of [1].<br>B. Data in the "JCR data" sheet:<br><br>The publication and citation data below are from each journal's <i>individual</i> Journal Citation Report for 2017. <b><br></b><b>Impact Factor</b>. These are data from the 2017 Journal Citation Reports (JCR).<br><b>Journal biennial size</b>, <i>N</i><sub>2Y</sub>. This is the number of articles & reviews published in 2015-2016 by each journal.<br><b>Citation average</b>, <i>f</i>. This is the average number of citations received in 2017 by the articles & reviews published in 2015-2016.<br><b>Volatility</b>, Δ<i>f</i>(<i>c</i>*): This is defined as <i>f - </i><i>f</i>* (see below for <i>f</i>*)<br><b>Relative volatility</b>, Δ<i>f<sub>r</sub></i>(<i>c</i>*): This is defined as (<i>f - </i><i>f</i>*)/<i>f</i>* (see below for <i>f</i>*)<br><b>Top-cited paper</b>, <i>c</i>*: This is the citation count of the top-cited paper in each journal, in the year 2017. <br><b>Citation average excluding top-cited paper</b>, <i>f</i>*: This is the average number of citations received in 2017 by the articles & reviews published in 2015-2016, once we exclude the top-cited paper (article or review). <br><br><b>Acknowledgment</b><br>This work uses data, accessed through Columbia University, from the Web of Science and Journal Citation Reports (2017) with explicit consent from Clarivate Analytics.<br><br><br><b>References </b><br>[1] M. Antonoyiannakis,<i> Impact Factor volatility to a single paper: A comprehensive analysis</i>, Quantitative Science Studies (2020, accepted), https://arxiv.org/abs/1911.02533<br>[2] M. Antonoyiannakis, <i>How a single paper affects the Impact Factor: Implications for Scholarly Publishing</i>, Proceedings of the 17th Conference of the International Society on Scientometrics & Informetrics, vol. II, 2306-2313 (2019), https://arxiv.org/abs/1906.02660
DOI: 10.6084/m9.figshare.11977881.v1
title: Antonoyiannakis_volatility.xlsx

description: Here are the data for papers [1,2]. The 1st excel sheet ("theory") has data for Figures 1, 2 of [1]. The 2nd sheet ("JCR data") has data for Figures 3, 4, 5, 6 of [1], and Figure 1 of [2].<br>A. Data in the "theory" sheet:<br><br><b>2nd row</b>: Citation count, <i>c</i>, of a single paper published by a journal of Impact Factor <i>f</i><sub>1</sub>=10 and biennial size <i>N</i><sub>1</sub>. We have chosen <i>c</i> to range from 0 to 1000 in our data. <br><b>2nd column</b>: biennial size <i>N</i><sub>1</sub> of journal. We have chosen <i>N</i><sub>1</sub> to range from 10 to 2500 in our data. <br>The data in the cells from C3 to EC283 in the sheet are calculations of the volatility, Δ<i>f</i>(<i>c</i>), as defined in Eq. (4) of [1].<br>B. Data in the "JCR data" sheet:<br><br>The publication and citation data below are from each journal's <i>individual</i> Journal Citation Report for 2017. <b><br></b><b>Impact Factor</b>. These are data from the 2017 Journal Citation Reports (JCR).<br><b>Journal biennial size</b>, <i>N</i><sub>2Y</sub>. This is the number of articles & reviews published in 2015-2016 by each journal.<br><b>Citation average</b>, <i>f</i>. This is the average number of citations received in 2017 by the articles & reviews published in 2015-2016.<br><b>Volatility</b>, Δ<i>f</i>(<i>c</i>*): This is defined as <i>f - </i><i>f</i>* (see below for <i>f</i>*)<br><b>Relative volatility</b>, Δ<i>f<sub>r</sub></i>(<i>c</i>*): This is defined as (<i>f - </i><i>f</i>*)/<i>f</i>* (see below for <i>f</i>*)<br><b>Top-cited paper</b>, <i>c</i>*: This is the citation count of the top-cited paper in each journal, in the year 2017. <br><b>Citation average excluding top-cited paper</b>, <i>f</i>*: This is the average number of citations received in 2017 by the articles & reviews published in 2015-2016, once we exclude the top-cited paper (article or review). <br><br><b>Acknowledgment</b><br>This work uses data, accessed through Columbia University, from the Web of Science and Journal Citation Reports (2017) with explicit consent from Clarivate Analytics.<br><br><br><b>References </b><br>[1] M. Antonoyiannakis,<i> Impact Factor volatility to a single paper: A comprehensive analysis</i>, Quantitative Science Studies (2020, accepted), https://arxiv.org/abs/1911.02533<br>[2] M. Antonoyiannakis, <i>How a single paper affects the Impact Factor: Implications for Scholarly Publishing</i>, Proceedings of the 17th Conference of the International Society on Scientometrics & Informetrics, vol. II, 2306-2313 (2019), https://arxiv.org/abs/1906.02660
DOI: 10.6084/m9.figshare.11977830.v1
title: Urban birds of Lima, Peru: A 15-year dataset

description: Birds in the cities are usually one of the first ways people get in contact with nature. Birds are vertebrates that sometimes are very diverse in urban areas; monitoring them offers opportunities for education and scientific research. Capture and recapture of birds give important information on morphology and if it is done on a long-term basis with a census, on population dynamics. Here I present a dataset of birds that were counted by sight and also captured, banded and released with mist-nets through a monitoring program in an urban area of the city of Lima, Peru; from 1995 until 2010. It also includes records of re-sightings of banded individuals. The censuses were each week and the captures every month, most of the time. The study area had 0.5 Ha and was a private park that had exotic vegetation in different forms: pastures, shrubs, and trees. The data shows the bird censuses, captured individuals and banded individuals resighted. The presence of captured individuals is shown by band number (not all individuals and species were banded or measured), body condition and measurements. The body condition recorded were sex, molt and breeding status. The body measurements were weight, wing, tarsus, beak (total and exposed culmen) and tail. The sampling effort for the bird census was 296 days; half an hour per day. For the bird-banding was 4724.74 Hours-net, with a range of 4 to 107 Hours-net per month. The resighted individuals were 1438. I recorded 43 species in this park, the most common species sighted were <i>Columbina cruziana, Zenaida meloda</i> and <i>Coereba flaveola</i>. There were 1827 captures of 25 bird species, from 12 families, within the order Passeriformes, Columbiformes, Apodiformes, Psittaciformes, and Falconiformes. Individual records per species range between 1 to 395, the median was 16 individuals per species. Most of the birds that were captured were <i>Coereba flaveola</i> (21.6%), <i>Passer domesticus</i> (17.4%) and <i>Volatinia jacarina</i> (12.1%). The longest life spans recorded during this project were individuals of <i>Volatinia jacarina</i> (10Y,7M), <i>Pyrocephalus rubinus</i> (9Y,7M) and <i>Coereba flaveola</i> (8Y,10M). I expect this database to be helpful for researchers in population ecology and bird morphology.
DOI: 10.6084/m9.figshare.11977680
title: Role of pharmacogenetics and clinical parameters on nevirapine plasma concentration among HIV-1 patients receiving antiretroviral therapy in Kenya

description: These data provide clinical outcome in nevirapine plasma concentration, pharmacogenetics and clinical data
DOI: 10.6084/m9.figshare.11977635
title: Antiviral

description: Antiviral in TXT format<br>
DOI: 10.6084/m9.figshare.11977464
title: Data Journal of Animal Ecology

description: <b>Movement data of spiders acquired with the software Ethovision to test how they behave when exposed to the cues of the ant Lasius niger</b><br>
DOI: 10.6084/m9.figshare.11977464.v1
title: Data Journal of Animal Ecology

description: <b>Movement data of spiders acquired with the software Ethovision to test how they behave when exposed to the cues of the ant Lasius niger</b><br>
DOI: 10.6084/m9.figshare.11974533
title: Supplemental Files GI-00076-2020Supplemental Data and Figures

description: Supplemental Figure Legends (three)Supplemental Figures-S 1, S 2, S 3 Mass Spec Supplemental data file
DOI: 10.6084/m9.figshare.11974533.v1
title: Supplemental Files GI-00076-2020

description: Supplemental Figure Legends (three)Supplemental Figures-S 1, S 2, S 3 Mass Spec Supplemental data file
DOI: 10.6084/m9.figshare.11974485
title: Data.Final.sav

description: The data for the journal article
DOI: 10.6084/m9.figshare.11974485.v1
title: Data.Final.sav

description: The data for the journal article
DOI: 10.6084/m9.figshare.11974479
title: Supplementary Table 1

description: PCR primer sequences to detect the expression of <i>PLA2G16</i> in PAD and healthy participants.
DOI: 10.6084/m9.figshare.11974479.v1
title: Supplementary Table 1

description: PCR primer sequences to detect the expression of <i>PLA2G16</i> in PAD and healthy participants.
DOI: 10.6084/m9.figshare.11974368
title: Supplementary Figures S1-S2 from The <i>fruitless</i> gene affects female receptivity and species isolation

description: Supporting figures, as cited in manuscript.
DOI: 10.6084/m9.figshare.11974368.v1
title: Supplementary Figures S1-S2 from The <i>fruitless</i> gene affects female receptivity and species isolation

description: Supporting figures, as cited in manuscript.
DOI: 10.6084/m9.figshare.11974356
title: Detailed Search Strategy

description: Search Strategy for Medline
DOI: 10.6084/m9.figshare.11974356.v1
title: Detailed Search Strategy

description: Search Strategy for Medline
DOI: 10.6084/m9.figshare.11974260
title: Review Protocol

description: Research protocol for Diagnostic test accuracy of Xpert MTB/RIF for tuberculous pericarditis: A systematic review and meta-analysis
DOI: 10.6084/m9.figshare.11974203
title: Trowbridge_MMSF_soilBVOC.csv

description: This file contains measurements of the flux of biogenic volatile organic compounds (bVOCs) between the soil and atmosphere in plots with trees dominated by arbuscular mycorrhizal (AM) or ectomycorrhizal (ECM) associations as described in Trowbridge et al. (2020) Journal of Geophysical Research.
DOI: 10.6084/m9.figshare.11974260.v1
title: Review Protocol

description: Research protocol for Diagnostic test accuracy of Xpert MTB/RIF for tuberculous pericarditis: A systematic review and meta-analysis
DOI: 10.6084/m9.figshare.11974203.v1
title: Trowbridge_MMSF_soilBVOC.csv

description: This file contains measurements of the flux of biogenic volatile organic compounds (bVOCs) between the soil and atmosphere in plots with trees dominated by arbuscular mycorrhizal (AM) or ectomycorrhizal (ECM) associations as described in Trowbridge et al. (2020) Journal of Geophysical Research.
DOI: 10.6084/m9.figshare.11974068
title: Alcalase effects on lumpfish length, survival and chorion thickness

description: Alcalase effects on lumpfish length, survival and chorion thickness
DOI: 10.6084/m9.figshare.11974068.v1
title: Alcalase effects on lumpfish length, survival and chorion thickness

description: Alcalase effects on lumpfish length, survival and chorion thickness
DOI: 10.6084/m9.figshare.11973693.v1
title: Coronal mass ejection in situ data for creating catalogs and statistics (MESSENGER, VEX, Wind, STEREO-A/B, MAVEN, PSP) 2007-2019

description: Data for https://github.com/cmoestl/heliocats<br>work in progress
DOI: 10.6084/m9.figshare.11973591
title: Internally heated porous convection: an idealised model for Enceladus' hydrothermal activity

description: This archive contains the source codes to simulate porous convection with internal heating. It also contains the output data of many simulations. Jupyter notebooks are provided to plot the output data.<br>
DOI: 10.6084/m9.figshare.11973693.v2
title: Solar wind in situ data for creating catalogs and statistics of coronal mass ejections (MESSENGER, VEX, Wind, Ulysses, STEREO-A/B, MAVEN, PSP) 2007-2019

description: Data for https://github.com/cmoestl/heliocats<br>work in progress
DOI: 10.6084/m9.figshare.11973588
title: bulk.h5

description: test
DOI: 10.6084/m9.figshare.11973591.v2
title: Internally heated porous convection: an idealised model for Enceladus' hydrothermal activity

description: This archive contains the source codes to simulate porous convection with internal heating. It also contains the output data of many simulations. Jupyter notebooks are provided to plot the output data.<br>
DOI: 10.6084/m9.figshare.11973693.v3
title: Solar wind in situ data for creating catalogs and statistics of coronal mass ejections (MESSENGER, VEX, Wind, Ulysses, STEREO-A/B, MAVEN, PSP) 2007-2019

description: Data for https://github.com/cmoestl/heliocats<br>work in progress
DOI: 10.6084/m9.figshare.11973591.v1
title: Internally heated porous convection: an idealised model for Enceladus' hydrothermal activity

description: This archive contains the source codes to simulate porous convection with internal heating. It also contains the output data of many simulations. Jupyter notebooks are provided to plot the output data.<br>
DOI: 10.6084/m9.figshare.11973693.v4
title: Solar wind in situ data for creating catalogs and statistics of interplanetary coronal mass ejections (MESSENGER, VEX, Wind, Ulysses, STEREO-A/B, MAVEN, PSP) 2007-2019

description: These are solar wind in situ data arrays in python pickle format suitable for creating catalogs, plots and movies of interplanetary coronal mass ejections.<br><br>See AAREADME_insitu.txt for more explanation.<br>If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, A. Isavnin).<br>Made with https://github.com/cmoestl/heliocats
DOI: 10.6084/m9.figshare.11973693.v5
title: Solar wind in situ data for creating catalogs and statistics of interplanetary coronal mass ejections (MESSENGER, VEX, Wind, Ulysses, STEREO-A/B, MAVEN, Parker Solar Probe) 2007-2019

description: These are solar wind in situ data arrays in python pickle format suitable for creating catalogs, plots and movies of interplanetary coronal mass ejections.<br><br>See AAREADME_insitu.txt for more explanation.<br>If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, A. Isavnin).<br>Made with https://github.com/cmoestl/heliocats
DOI: 10.6084/m9.figshare.11973693.v6
title: Solar wind in situ data for creating catalogs and statistics of interplanetary coronal mass ejections (MESSENGER, VEX, Wind, Ulysses, STEREO-A/B, MAVEN, Parker Solar Probe) 2007-2019

description: These are solar wind in situ data arrays in python pickle format suitable for creating catalogs, plots and movies of interplanetary coronal mass ejections.<br><br>See AAREADME_insitu.txt for more explanation.<br>If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, A. Isavnin).<br>Made with https://github.com/cmoestl/heliocats