cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5534383
title: Tabular summary of global drivers of fire

description: CSV file containing global drivers of fire. It is a global data set with monthly data on a 0.25 degree grid from the years 2003 to 2016. (Note that for some parameters data for the year 2016 is missing). It is generally understood that fire regimes are closely linked to vegetation, climate and human activities. Thus we used the following variables (sources are in brackets[]): <br> #Climate:<br> #Max Temperature in degree C (TMax) [Climatic Research Unit (CRU)]<br> #Diurnal temperature range in degree C (DTR) [Climatic Research Unit (CRU)]<br> #Number of wet days (NrWetDays) in days [Climatic Research Unit (CRU)]<br> #Potential evapotranspiration (PET) [Climatic Research Unit (CRU)]<br> #Precipitation in mm [Global Precipitation Climatology Centre (GPCC)] #Land cover / Vegetation (all fractional data (0-1) except LAI):<br> #Fraction of absorbed Photosynthetic Active Radiation (fPAR) [Climate change initiative ESA (CCI)]<br> #Leaf area index in m^2/m^2 (LAI) [Climate change initiative ESA (CCI)]<br> #Fraction of tree land cover (LC_Tree) [Climate change initiative ESA (CCI)]<br> #Fraction of shrub land cover (LC_Shrub) [Climate change initiative ESA (CCI)]<br> #Fraction of crop land cover (LC_Crop) [Climate change initiative ESA (CCI)]<br> #Fraction of herbs land cover (LC_Herbs) [Climate change initiative ESA (CCI)]<br> #Fraction of bare land cover (LC_Bare) [Climate change initiative ESA (CCI)] #Anthropogenic influences:<br> #Road density in m/km^2 [Global Roads Inventory Project (GRIP)]<br> #Population density in inhabitants/km^2 (PopDensity) [History database of the Global Environment (HYDE)]<br> #Distance to urban population in m (DistUrbPop) [History database of the Global Environment (HYDE)]<br> #Indigenous Peoples land (IDL) as categorical variable IDL, No IDL (IDL_Layer) [Garnett et al. 2018: "A spatial overview of the global importance of Indigenous lands for conservation"]. Not included!! #Response variables:<br> #Presence/Absence of fire Global fire atlas [Global Fire Emissions Database (GFED4), Global fire atlas]. Not included, can be derived from the data set.<br> #Fire size in km^2 [Global Fire Emissions Database (GFED4), Global fire atlas]<br> #Fire duration in days [Global Fire Emissions Database (GFED4), Global fire atlas]<br> #Fire ignitions [Global Fire Emissions Database (GFED4), Global fire atlas] #regional filters<br> #Biomes [Olson et al. 2001: "Terrestrial Ecoregions of the World: A New Map of Life on Earth]<br> #Continents [Environmental Systems Research Institute (ESRI) from ArcGIS]<br> #GFED [Global Fire Emissions Database (GFED4)] #All data except the data on Indigenous Peoples land is freely available and was uploaded. Data on Indigenous Peoples land can be requested by the authorship team of Garnett et al. (2018. Nature Sustainability, 1, 369, doi.org/10.1038/s41893-018-0100-6)
DOI: 10.5281/zenodo.5534382
title: Tabular summary of global drivers of fire

description: CSV file containing global drivers of fire. It is a global data set with monthly data on a 0.25 degree grid from the years 2003 to 2016. (Note that for some parameters data for the year 2016 is missing). It is generally understood that fire regimes are closely linked to vegetation, climate and human activities. Thus we used the following variables (sources are in brackets[]): <br> #Climate:<br> #Max Temperature in degree C (TMax) [Climatic Research Unit (CRU)]<br> #Diurnal temperature range in degree C (DTR) [Climatic Research Unit (CRU)]<br> #Number of wet days (NrWetDays) in days [Climatic Research Unit (CRU)]<br> #Potential evapotranspiration (PET) [Climatic Research Unit (CRU)]<br> #Precipitation in mm [Global Precipitation Climatology Centre (GPCC)] #Land cover / Vegetation (all fractional data (0-1) except LAI):<br> #Fraction of absorbed Photosynthetic Active Radiation (fPAR) [Climate change initiative ESA (CCI)]<br> #Leaf area index in m^2/m^2 (LAI) [Climate change initiative ESA (CCI)]<br> #Fraction of tree land cover (LC_Tree) [Climate change initiative ESA (CCI)]<br> #Fraction of shrub land cover (LC_Shrub) [Climate change initiative ESA (CCI)]<br> #Fraction of crop land cover (LC_Crop) [Climate change initiative ESA (CCI)]<br> #Fraction of herbs land cover (LC_Herbs) [Climate change initiative ESA (CCI)]<br> #Fraction of bare land cover (LC_Bare) [Climate change initiative ESA (CCI)] #Anthropogenic influences:<br> #Road density in m/km^2 [Global Roads Inventory Project (GRIP)]<br> #Population density in inhabitants/km^2 (PopDensity) [History database of the Global Environment (HYDE)]<br> #Distance to urban population in m (DistUrbPop) [History database of the Global Environment (HYDE)]<br> #Indigenous Peoples land (IDL) as categorical variable IDL, No IDL (IDL_Layer) [Garnett et al. 2018: "A spatial overview of the global importance of Indigenous lands for conservation"]. Not included!! #Response variables:<br> #Presence/Absence of fire Global fire atlas [Global Fire Emissions Database (GFED4), Global fire atlas]. Not included, can be derived from the data set.<br> #Fire size in km^2 [Global Fire Emissions Database (GFED4), Global fire atlas]<br> #Fire duration in days [Global Fire Emissions Database (GFED4), Global fire atlas]<br> #Fire ignitions [Global Fire Emissions Database (GFED4), Global fire atlas] #regional filters<br> #Biomes [Olson et al. 2001: "Terrestrial Ecoregions of the World: A New Map of Life on Earth]<br> #Continents [Environmental Systems Research Institute (ESRI) from ArcGIS]<br> #GFED [Global Fire Emissions Database (GFED4)] #All data except the data on Indigenous Peoples land is freely available and was uploaded. Data on Indigenous Peoples land can be requested by the authorship team of Garnett et al. (2018. Nature Sustainability, 1, 369, doi.org/10.1038/s41893-018-0100-6)
DOI: 10.34740/kaggle/dsv/2659630
title: COVID Vaccination in World (updated daily)
DOI: 10.3886/e141201
title: Data and Code for: Quantifying the Supply Response of Private Schools to Public Policies

description: School policies that cause a large demand shift between public and private schooling may cause some private schools to enter or exit the market. We study how the policy effects differ under a fixed versus changing market structure in the context of a public school funding reform in New York City. We find evidence of a reduction in private schools in response to the reform. Using a model of demand for and supply of private schooling, we estimate that 20% of the reform's effect on school enrollments came from increased private school exit and reduced private school entry.
description: Students, teachers, and schools in the New York City K-12 educational system.
DOI: 10.3886/e141201v1
title: Data and Code for: Quantifying the Supply Response of Private Schools to Public Policies

description: School policies that cause a large demand shift between public and private schooling may cause some private schools to enter or exit the market. We study how the policy effects differ under a fixed versus changing market structure in the context of a public school funding reform in New York City. We find evidence of a reduction in private schools in response to the reform. Using a model of demand for and supply of private schooling, we estimate that 20% of the reform's effect on school enrollments came from increased private school exit and reduced private school entry.
description: Students, teachers, and schools in the New York City K-12 educational system.
DOI: 10.34740/kaggle/dsv/2659444
title: Parts Revenue from Automotive Industry Dealer
DOI: 10.21227/vx0v-j232
title: Github Dataset for Authorship Attribution

description: This dataset is extracted from GitHub and contains 172,919 java source codes written by 3,128 authors. It can be used for authorship attribution.
DOI: 10.5281/zenodo.5534694
title: Engineering gene overlaps to sustain genetic constructs in vivo

description: Experimental data, simulation data, and code for data analysis and figure production for the paper "Engineering gene overlaps to sustain genetic constructs in vivo" (Decrulle, Frenoy, et al, PloS Computational Biology)
DOI: 10.5281/zenodo.3236451
title: Engineering gene overlaps to sustain genetic constructs in vivo

description: Experimental data, simulation data, and code for data analysis and figure production for the paper "Engineering gene overlaps to sustain genetic constructs in vivo" (Decrulle, Frenoy, et al, PloS Computational Biology)
DOI: 10.5517/ccdc.csd.cc25lq1k
title: CCDC 2013978: Experimental Crystal Structure Determination

description: Related Article: Friedrich W. Steuber, John J. Gough, Éadaoin Whelan, Lyubomyr Burtnyak, A. Louise Bradley, Wolfgang Schmitt|2020|Inorg.Chem.|59|17244|doi:10.1021/acs.inorgchem.0c02475
DOI: 10.5517/ccdc.csd.cc25lq2l
title: CCDC 2013979: Experimental Crystal Structure Determination

description: Related Article: Friedrich W. Steuber, John J. Gough, Éadaoin Whelan, Lyubomyr Burtnyak, A. Louise Bradley, Wolfgang Schmitt|2020|Inorg.Chem.|59|17244|doi:10.1021/acs.inorgchem.0c02475
DOI: 10.5517/ccdc.csd.cc25lq3m
title: CCDC 2013980: Experimental Crystal Structure Determination

description: Related Article: Friedrich W. Steuber, John J. Gough, Éadaoin Whelan, Lyubomyr Burtnyak, A. Louise Bradley, Wolfgang Schmitt|2020|Inorg.Chem.|59|17244|doi:10.1021/acs.inorgchem.0c02475
DOI: 10.5517/ccdc.csd.cc25lq4n
title: CCDC 2013981: Experimental Crystal Structure Determination

description: Related Article: Friedrich W. Steuber, John J. Gough, Éadaoin Whelan, Lyubomyr Burtnyak, A. Louise Bradley, Wolfgang Schmitt|2020|Inorg.Chem.|59|17244|doi:10.1021/acs.inorgchem.0c02475
DOI: 10.5517/ccdc.csd.cc2794h6
title: CCDC 2064367: Experimental Crystal Structure Determination

description: Related Article: Yuan‐Yuan Guo,  Clement Elliott,  Jason A. McNulty,  David B. Cordes,  Alexandra M. Z. Slawin,  Philip Lightfoot|2021|Eur.J.Inorg.Chem.|2021|3404|doi:10.1002/ejic.202100433
DOI: 10.5517/ccdc.csd.cc2794j7
title: CCDC 2064368: Experimental Crystal Structure Determination

description: Related Article: Yuan‐Yuan Guo,  Clement Elliott,  Jason A. McNulty,  David B. Cordes,  Alexandra M. Z. Slawin,  Philip Lightfoot|2021|Eur.J.Inorg.Chem.|2021|3404|doi:10.1002/ejic.202100433
DOI: 10.5517/ccdc.csd.cc27xrzx
title: CCDC 2083230: Experimental Crystal Structure Determination

description: Related Article: Pablo Ríos,  Francisco José Fernández‐de‐Córdova,  Javier Borge,  Natalia Curado,  Agustí Lledós,  Salvador Conejero|2021|Eur.J.Inorg.Chem.|2021|3528|doi:10.1002/ejic.202100428
DOI: 10.5517/ccdc.csd.cc27xs21
title: CCDC 2083233: Experimental Crystal Structure Determination

description: Related Article: Pablo Ríos,  Francisco José Fernández‐de‐Córdova,  Javier Borge,  Natalia Curado,  Agustí Lledós,  Salvador Conejero|2021|Eur.J.Inorg.Chem.|2021|3528|doi:10.1002/ejic.202100428
DOI: 10.5517/ccdc.csd.cc27xs32
title: CCDC 2083234: Experimental Crystal Structure Determination

description: Related Article: Pablo Ríos,  Francisco José Fernández‐de‐Córdova,  Javier Borge,  Natalia Curado,  Agustí Lledós,  Salvador Conejero|2021|Eur.J.Inorg.Chem.|2021|3528|doi:10.1002/ejic.202100428
DOI: 10.5517/ccdc.csd.cc27xs43
title: CCDC 2083235: Experimental Crystal Structure Determination

description: Related Article: Pablo Ríos,  Francisco José Fernández‐de‐Córdova,  Javier Borge,  Natalia Curado,  Agustí Lledós,  Salvador Conejero|2021|Eur.J.Inorg.Chem.|2021|3528|doi:10.1002/ejic.202100428
DOI: 10.5517/ccdc.csd.cc27xs0z
title: CCDC 2083231: Experimental Crystal Structure Determination

description: Related Article: Pablo Ríos,  Francisco José Fernández‐de‐Córdova,  Javier Borge,  Natalia Curado,  Agustí Lledós,  Salvador Conejero|2021|Eur.J.Inorg.Chem.|2021|3528|doi:10.1002/ejic.202100428
DOI: 10.5517/ccdc.csd.cc27xs10
title: CCDC 2083232: Experimental Crystal Structure Determination

description: Related Article: Pablo Ríos,  Francisco José Fernández‐de‐Córdova,  Javier Borge,  Natalia Curado,  Agustí Lledós,  Salvador Conejero|2021|Eur.J.Inorg.Chem.|2021|3528|doi:10.1002/ejic.202100428
DOI: 10.5517/ccdc.csd.cc24zsc9
title: CCDC 1995791: Experimental Crystal Structure Determination

description: Related Article: Catiúcia R. M. O. Matos, Rana Sanii, Shi-Qiang Wang, Célia M. Ronconi, Michael J. Zaworotko|2021|Dalton Trans.|50|12923|doi:10.1039/D1DT01839F
DOI: 10.5517/ccdc.csd.cc289r2f
title: CCDC 2094734: Experimental Crystal Structure Determination

description: Related Article: Michał Magott, Dawid Pinkowicz|2021|Chem.Commun.|57|9926|doi:10.1039/D1CC03870B
DOI: 10.5517/ccdc.csd.cc289r3g
title: CCDC 2094735: Experimental Crystal Structure Determination

description: Related Article: Michał Magott, Dawid Pinkowicz|2021|Chem.Commun.|57|9926|doi:10.1039/D1CC03870B
DOI: 10.5517/ccdc.csd.cc289r4h
title: CCDC 2094736: Experimental Crystal Structure Determination

description: Related Article: Michał Magott, Dawid Pinkowicz|2021|Chem.Commun.|57|9926|doi:10.1039/D1CC03870B
DOI: 10.5517/ccdc.csd.cc24zsfc
title: CCDC 1995793: Experimental Crystal Structure Determination

description: Related Article: Catiúcia R. M. O. Matos, Rana Sanii, Shi-Qiang Wang, Célia M. Ronconi, Michael J. Zaworotko|2021|Dalton Trans.|50|12923|doi:10.1039/D1DT01839F
DOI: 10.5517/ccdc.csd.cc24zs53
title: CCDC 1995785: Experimental Crystal Structure Determination

description: Related Article: Catiúcia R. M. O. Matos, Rana Sanii, Shi-Qiang Wang, Célia M. Ronconi, Michael J. Zaworotko|2021|Dalton Trans.|50|12923|doi:10.1039/D1DT01839F
DOI: 10.5517/ccdc.csd.cc24zshf
title: CCDC 1995795: Experimental Crystal Structure Determination

description: Related Article: Catiúcia R. M. O. Matos, Rana Sanii, Shi-Qiang Wang, Célia M. Ronconi, Michael J. Zaworotko|2021|Dalton Trans.|50|12923|doi:10.1039/D1DT01839F
DOI: 10.5517/ccdc.csd.cc28b55z
title: CCDC 2095140: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc28bsw9
title: CCDC 2095751: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc28cvh0
title: CCDC 2096762: Experimental Crystal Structure Determination

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DOI: 10.25346/s6/azma0v/fg9hpu
title: 11.81.jpg
DOI: 10.25346/s6/azma0v/bhrd4d
title: 11.35.jpg
DOI: 10.25346/s6/azma0v/jdmxds
title: 11.19.jpg
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title: 11.88.jpg
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title: 11.116.jpg
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title: 11.75.jpg
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title: 11.2.jpg
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title: 11.108.jpg
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title: 11.115.jpg
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title: 11.100.jpg
DOI: 10.25346/s6/azma0v/dqcy9z
title: 11.8.jpg
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title: 11.109.jpg
DOI: 10.25346/s6/azma0v/fxsm8c
title: 11.84.jpg
DOI: 10.25346/s6/azma0v/802j3j
title: 11.93.jpg
DOI: 10.25346/s6/azma0v/brqczg
title: 11.65.jpg
DOI: 10.25346/s6/azma0v/njdkp8
title: 11.34.jpg
DOI: 10.25346/s6/azma0v/glsn5i
title: 11.72.jpg
DOI: 10.25346/s6/azma0v/z9cwcq
title: 11.62.jpg
DOI: 10.25346/s6/azma0v/ezzwmo
title: 11.104.jpg
DOI: 10.25346/s6/azma0v/haz4j8
title: 11.106.jpg
DOI: 10.25346/s6/azma0v/psn86b
title: 11.32.jpg
DOI: 10.6084/m9.figshare.16692559.v1
title: FCB T3 Patient questionnaire.docxSurvey tools for the Frailty Care Bundle:an implementation science study promoting nutrition, mobilisation and cognitive engagement in Hospitalised older adults

description: These survey instruments form the basis for the data collection within the Frailty Care Bundle Research project.The Frailty Care Bundle (FCB) project is an intervention which aims to implement and evaluate evidence-based practice on early mobilisation, enhanced nutrition and increased cognitive engagement to prevent functional and hospital associated decline in older patients.<br><br>Data is collected at both the patient and staff level.At the staff level contextual factors such as nurse staffing levels and skill mix are examined through surveys. Barriers to implementation and ideas for change are also exmained.<br><br>At the patient level, health and functional status and attitudes to nutrition, mobilisation, cognitive engagement and hospital care are examined.
DOI: 10.25346/s6/azma0v/drgg12
title: 11.27.jpg
DOI: 10.25346/s6/azma0v/xbzf79
title: 11.76.jpg
DOI: 10.25346/s6/azma0v/eczaze
title: 11.113.jpg
DOI: 10.25346/s6/azma0v/ymrr8y
title: 11.78.jpg
DOI: 10.25346/s6/azma0v/616ebx
title: 11.89.jpg
DOI: 10.25346/s6/azma0v/r3dwbk
title: 11.85.jpg
DOI: 10.25346/s6/azma0v/tkaqyg
title: 11.96.jpg
DOI: 10.25346/s6/azma0v/gauh1m
title: 11.102.jpg
DOI: 10.25346/s6/azma0v/v2bamq
title: 11.91.jpg
DOI: 10.25346/s6/azma0v/icqmtb
title: 11.57.jpg
DOI: 10.25346/s6/azma0v/udfqac
title: 11.118.jpg
DOI: 10.25346/s6/azma0v/voajgk
title: 11.87.jpg
DOI: 10.25346/s6/azma0v/0a0l70
title: 11.90.jpg
DOI: 10.5281/zenodo.5534698
title: BasqueRoads, a dataset to benchmark road selection algorithms

description: This dataset contains two road networks, one used for 1:25k scale maps (roads_ini), and one used at the 1:80k scale (roads_final). This dataset can be used to benchmark road selection algorithm that seek to transform the initial dataset into the final dataset.
DOI: 10.25346/s6/azma0v/84wupi
title: 11.58.jpg
DOI: 10.25346/s6/azma0v/edj4ea
title: 11.30.jpg
DOI: 10.25346/s6/azma0v/vzlntq
title: 11.117.jpg
DOI: 10.25346/s6/azma0v/eenjog
title: 11.48.jpg
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title: 11.45.jpg
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title: 11.105.jpg
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title: 11.94.jpg
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title: 11.67.jpg
DOI: 10.25346/s6/azma0v/7u32cg
title: 11.97.jpg
DOI: 10.25346/s6/azma0v/hmsto0
title: Catalog of Reconstructed Vessels, appendix to Chapter 11.tab
DOI: 10.25346/s6/azma0v/qdsvld
title: 11.79.jpg
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DOI: 10.25346/s6/azma0v/nfxbyp
title: 11.1.jpg
DOI: 10.25346/s6/azma0v/wmqwlj
title: 11.9.jpg
DOI: 10.25346/s6/azma0v/og9wpp
title: 11.44.jpg
DOI: 10.25346/s6/azma0v/zs9xxu
title: 11.110.jpg
DOI: 10.25346/s6/azma0v/3ro7wt
title: 11.41.jpg
DOI: 10.25346/s6/azma0v/jpfn21
title: 11.20.jpg
DOI: 10.25346/s6/azma0v/o21dlq
title: 11.6.jpg
DOI: 10.25346/s6/azma0v/vfdqe8
title: 11.38.jpg
DOI: 10.25346/s6/azma0v
title: Reconstructed Vessels Catalog

description: Reconstructed Vessels Catalog
DOI: 10.25346/s6/azma0v/wcthgw
title: 11.4.jpg
DOI: 10.5281/zenodo.5534665
title: IODP Expedition 346, Hole U1427A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 346: Asian Monsoon
DOI: 10.5281/zenodo.5534664
title: IODP Expedition 346, Hole U1427A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 346: Asian Monsoon
DOI: 10.3886/e151081
title: Male Teacher Assignment and Teacher Turnover in Elementary School

description: Parents and educators commonly assume that male teachers serve as more effective disciplinarians for boys than female teachers. Do schools tend to assign male teachers to teach boys with perceived behavioral issues? Our study uses administrative data in Indiana to investigate male teacher assignment in elementary school. We find that boys with at least one suspension record in the previous year are 12% more likely to be assigned to a male teacher than boys without suspensions, whereas girls’ suspension histories do not predict assignments to male teachers. In addition, teachers who have 10% or more of students with suspension histories are 19.5% more likely to migrate to another school and 16.2% more likely to leave the state’s teacher workforce. Our study suggests that male teachers have an elevated risk of being assigned to teach male students with suspension histories, which may contribute to teacher turnover.