cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc27d9pn
title: CCDC 2067411: Experimental Crystal Structure Determination

description: Related Article: Liping Guo, Changjia Guo, Shuqi Peng, Renyuan Song|2022|New J.Chem.|46|2465|doi:10.1039/D1NJ05548H
DOI: 10.5517/ccdc.csd.cc27d9qp
title: CCDC 2067412: Experimental Crystal Structure Determination

description: Related Article: Liping Guo, Changjia Guo, Shuqi Peng, Renyuan Song|2022|New J.Chem.|46|2465|doi:10.1039/D1NJ05548H
DOI: 10.5517/ccdc.csd.cc27d9rq
title: CCDC 2067413: Experimental Crystal Structure Determination

description: Related Article: Liping Guo, Changjia Guo, Shuqi Peng, Renyuan Song|2022|New J.Chem.|46|2465|doi:10.1039/D1NJ05548H
DOI: 10.5517/ccdc.csd.cc27d9sr
title: CCDC 2067414: Experimental Crystal Structure Determination

description: Related Article: Liping Guo, Changjia Guo, Shuqi Peng, Renyuan Song|2022|New J.Chem.|46|2465|doi:10.1039/D1NJ05548H
DOI: 10.5517/ccdc.csd.cc27d9ts
title: CCDC 2067415: Experimental Crystal Structure Determination

description: Related Article: Liping Guo, Changjia Guo, Shuqi Peng, Renyuan Song|2022|New J.Chem.|46|2465|doi:10.1039/D1NJ05548H
DOI: 10.5517/ccdc.csd.cc27d9vt
title: CCDC 2067416: Experimental Crystal Structure Determination

description: Related Article: Liping Guo, Changjia Guo, Shuqi Peng, Renyuan Song|2022|New J.Chem.|46|2465|doi:10.1039/D1NJ05548H
DOI: 10.5517/ccdc.csd.cc27d9wv
title: CCDC 2067417: Experimental Crystal Structure Determination

description: Related Article: Liping Guo, Changjia Guo, Shuqi Peng, Renyuan Song|2022|New J.Chem.|46|2465|doi:10.1039/D1NJ05548H
DOI: 10.5517/ccdc.csd.cc27d9xw
title: CCDC 2067418: Experimental Crystal Structure Determination

description: Related Article: Liping Guo, Changjia Guo, Shuqi Peng, Renyuan Song|2022|New J.Chem.|46|2465|doi:10.1039/D1NJ05548H
DOI: 10.5517/ccdc.csd.cc27d9yx
title: CCDC 2067419: Experimental Crystal Structure Determination

description: Related Article: Liping Guo, Changjia Guo, Shuqi Peng, Renyuan Song|2022|New J.Chem.|46|2465|doi:10.1039/D1NJ05548H
DOI: 10.5517/ccdc.csd.cc28504m
title: CCDC 2090179: Experimental Crystal Structure Determination

description: Related Article: Chaochao Fan, Yue Wang, Jie Zhao, Yanxia Zhao, Dong Yang, Boyang Li, Le Yu, Xiao-Juan Yang, Biao Wu|2022|Inorg.Chem.|61|2198|doi:10.1021/acs.inorgchem.1c03392
DOI: 10.5517/ccdc.csd.cc28505n
title: CCDC 2090180: Experimental Crystal Structure Determination

description: Related Article: Chaochao Fan, Yue Wang, Jie Zhao, Yanxia Zhao, Dong Yang, Boyang Li, Le Yu, Xiao-Juan Yang, Biao Wu|2022|Inorg.Chem.|61|2198|doi:10.1021/acs.inorgchem.1c03392
DOI: 10.5517/ccdc.csd.cc28506p
title: CCDC 2090181: Experimental Crystal Structure Determination

description: Related Article: Chaochao Fan, Yue Wang, Jie Zhao, Yanxia Zhao, Dong Yang, Boyang Li, Le Yu, Xiao-Juan Yang, Biao Wu|2022|Inorg.Chem.|61|2198|doi:10.1021/acs.inorgchem.1c03392
DOI: 10.5066/p9h2ndwu
title: Nutrient Load Data used to Quantify Regional Effects of Agricultural Best Management Practices: An application of the 2012 SPARROW models for the Midwest, Northeast, and Southeast United States

description: Nitrogen and phosphorus losses from agricultural areas have impacted the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown but assessments of their effectiveness at large spatial scales have been sparse. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW) models developed for the Midwest, Northeast, and Southeast regions of the United States to quantify regional effects of BMPs on nutrient losses from agricultural lands. These models were used because they account for specific BMPs in the prediction of instream nutrient loads. This data release accompanies the journal article "Quantifying regional effects of best management practices on nutrient losses from agricultural lands" Roland and others (2022, https://doi.org/10.2489/jswc.2022.00162), and it contains the input and output data for the modeling scenarios that were evaluated relative to the 2012 regional SPARROW models.
DOI: 10.6084/m9.figshare.19127210
title: DATASET [Thia 2022 Pest Man Sci] Halotydeus destructor thermal variation in chemical tolerance to pesticide

description: Dose–response data for redlegged earth mites, <i>Halotydeus destructor</i>, exposed to pesticides under different thermal conditions.
DOI: 10.6084/m9.figshare.19127210.v1
title: DATASET [Thia 2022 Pest Man Sci] Halotydeus destructor thermal variation in chemical tolerance to pesticide

description: Dose–response data for redlegged earth mites, <i>Halotydeus destructor</i>, exposed to pesticides under different thermal conditions.
DOI: 10.6084/m9.figshare.19119737
title: Annotation files for pale flax genome

description: These are the annotation files for the genome assembly of pale flax (Linum bienne Mill). The assembled genome has been deposited in NCBI database under assession QMEG00000000.<br>
DOI: 10.6084/m9.figshare.19119737.v1
title: Annotation files for pale flax genome

description: These are the annotation files for the genome assembly of pale flax (Linum bienne Mill). The assembled genome has been deposited in NCBI database under assession QMEG00000000.<br>
DOI: 10.5517/ccdc.csd.cc29gjcp
title: CCDC 2129122: Experimental Crystal Structure Determination

description: Related Article: Marc Paul Beller, Sergei Ivlev, Ulrich Koert|2022|Org.Lett.|24|912|doi:10.1021/acs.orglett.1c04261
DOI: 10.5517/ccdc.csd.cc29gjdq
title: CCDC 2129123: Experimental Crystal Structure Determination

description: Related Article: Marc Paul Beller, Sergei Ivlev, Ulrich Koert|2022|Org.Lett.|24|912|doi:10.1021/acs.orglett.1c04261
DOI: 10.5517/ccdc.csd.cc29gjfr
title: CCDC 2129124: Experimental Crystal Structure Determination

description: Related Article: Marc Paul Beller, Sergei Ivlev, Ulrich Koert|2022|Org.Lett.|24|912|doi:10.1021/acs.orglett.1c04261
DOI: 10.5517/ccdc.csd.cc29hqtc
title: CCDC 2130283: Experimental Crystal Structure Determination

description: Related Article: Milenko Korica, Ivana Balić, Lisa M. van Wyk, Dewald P. van Heerden, Varvara I. Nikolayenko, Leonard J. Barbour, Tomislav Jednačak, Ivica Đilović, Tomislav Balić|2022|Microporous and Mesoporous Materials|332|111708|doi:10.1016/j.micromeso.2022.111708
DOI: 10.5517/ccdc.csd.cc2988vq
title: CCDC 2123123: Experimental Crystal Structure Determination

description: Related Article: Morena Nocchetti, Anna Donnadio, Eleonora Vischini, Tamara Posati, Cristiano Albonetti, Davide Campoccia, Carla Renata Arciola, Stefano Ravaioli, Valentina Mariani, Lucio Montanaro, Riccardo Vivani|2022|Inorg.Chem.|61|2251|doi:10.1021/acs.inorgchem.1c03565
DOI: 10.5517/ccdc.csd.cc2988wr
title: CCDC 2123124: Experimental Crystal Structure Determination

description: Related Article: Morena Nocchetti, Anna Donnadio, Eleonora Vischini, Tamara Posati, Cristiano Albonetti, Davide Campoccia, Carla Renata Arciola, Stefano Ravaioli, Valentina Mariani, Lucio Montanaro, Riccardo Vivani|2022|Inorg.Chem.|61|2251|doi:10.1021/acs.inorgchem.1c03565
DOI: 10.6084/m9.figshare.19127288
title: cyg01.ddmi.s20190101-000000-e20190101-235959.l1.power-brcs.a21.d21.nc

description: CYGNSS raw data, 20190101
DOI: 10.6084/m9.figshare.19127288.v1
title: cyg01.ddmi.s20190101-000000-e20190101-235959.l1.power-brcs.a21.d21.nc

description: CYGNSS raw data, 20190101
DOI: 10.6084/m9.figshare.19126583
title: Data from: International Biological Flora: Ginkgo biloba

description: This dataset contains data from International Biological Flora: <i>Ginkgo biloba </i>(Journal of Ecology, in press). It comprises seven tables, including:Table 1: Global investigated trees of Ginkgo biloba (2516 trees with coordinates);Table 2: Ginkgo trees in Mt. Tianmu (397 trees with habitat and biological data, e.g., DBH, height, sex);Table 3: 27 ginkgo plots in Mt. Tianmu (vegetation type, coordinate, slope and aspect);Table 4: Tree species in 27 plots;Table 5: Shrub species in 27 plots;Table 6: Investigated tree in Guizhou (318 tree with DBH and height);Table 7: Investigated tree in Guangxi (73 tree with DBH and height).<br>See more data and information at the GinkgoDB web: https://ginkgo.zju.edu.cn/<br>
DOI: 10.6084/m9.figshare.19126583.v1
title: Data from: International Biological Flora: Ginkgo biloba

description: This dataset contains data from International Biological Flora: <i>Ginkgo biloba </i>(Journal of Ecology, in press). It comprises seven tables, including:Table 1: Global investigated trees of Ginkgo biloba (2516 trees with coordinates);Table 2: Ginkgo trees in Mt. Tianmu (397 trees with habitat and biological data, e.g., DBH, height, sex);Table 3: 27 ginkgo plots in Mt. Tianmu (vegetation type, coordinate, slope and aspect);Table 4: Tree species in 27 plots;Table 5: Shrub species in 27 plots;Table 6: Investigated tree in Guizhou (318 tree with DBH and height);Table 7: Investigated tree in Guangxi (73 tree with DBH and height).<br>See more data and information at the GinkgoDB web: https://ginkgo.zju.edu.cn/<br>
DOI: 10.5517/ccdc.csd.cc2979kf
title: CCDC 2122184: Experimental Crystal Structure Determination

description: Related Article: Udayan Mondal, Sourav Bej, Abhijit Hazra, Sukdeb Mandal, Tapan K. Pal, Priyabrata Banerjee|2022|Dalton Trans.|51|2083|doi:10.1039/D1DT03976H
DOI: 10.5517/ccdc.csd.cc293lhj
title: CCDC 2118617: Experimental Crystal Structure Determination

description: Related Article: Nannan Yuan, Jinhuan Yu, Yuanyuan Li, Ying Gao, Minghui Zuo, Shuxin Cui|2022|Inorg.Chem.Commun.|137|109214|doi:10.1016/j.inoche.2022.109214
DOI: 10.5517/ccdc.csd.cc27pn5s
title: CCDC 2076385: Experimental Crystal Structure Determination

description: Related Article: Bavita Kumari, Kiran Singh, Amit Sharma|2022|Chem. Data Collect.|38|100833|doi:10.1016/j.cdc.2022.100833
DOI: 10.5517/ccdc.csd.cc27dl3c
title: CCDC 2067672: Experimental Crystal Structure Determination

description: Related Article: Jingjing Sun, Lina Jia, Mengwei Wang, Yu Liu, Min Li, Dandan Han, Junbo Gong|2022|Cryst.Growth Des.|22|1005|doi:10.1021/acs.cgd.1c00810
DOI: 10.5517/ccdc.csd.cc27dl4d
title: CCDC 2067673: Experimental Crystal Structure Determination

description: Related Article: Jingjing Sun, Lina Jia, Mengwei Wang, Yu Liu, Min Li, Dandan Han, Junbo Gong|2022|Cryst.Growth Des.|22|1005|doi:10.1021/acs.cgd.1c00810
DOI: 10.5517/ccdc.csd.cc27dl5f
title: CCDC 2067674: Experimental Crystal Structure Determination

description: Related Article: Jingjing Sun, Lina Jia, Mengwei Wang, Yu Liu, Min Li, Dandan Han, Junbo Gong|2022|Cryst.Growth Des.|22|1005|doi:10.1021/acs.cgd.1c00810
DOI: 10.5517/ccdc.csd.cc29l738
title: CCDC 2132679: Experimental Crystal Structure Determination

description: Related Article: Zhanchao Meng, Alois Fürstner|2022|J.Am.Chem.Soc.|144|1528|doi:10.1021/jacs.1c12401
DOI: 10.5517/ccdc.csd.cc29lfv6
title: CCDC 2132888: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, Z. Yuan|2022|CSD Communication|||
DOI: 10.5281/zenodo.5977723
title: Global Impact of Heat from Wildfire on Convection and Aerosols

description: Dataset of model output in the work 'Global Impact of Heat from Wildfire on Convection and Aerosols'. Dataset File List File headed with 'CTL' : model output in the CTL run with aerosols but without heat from fires 1. CTL_Aerosols.nc : output of aerosol variables in the CTL run 2. CTL_Convection_Part1.nc : part1 output of convection variables in the CTL run 3. CTL_Convection_Part2.nc : part1 output of convection variables in the CTL run File headed with 'EXP' : model output in the EXP run with both aerosols and heat from fires 1. EXP_Aerosols.nc : output of aerosol variables in the EXP run 2. EXP_Convection_Part1.nc : part1 output of convection variables in the EXP run 3. EXP_Convection_Part2.nc : part1 output of convection variables in the EXP run
DOI: 10.5281/zenodo.5977722
title: Global Impact of Heat from Wildfire on Convection and Aerosols

description: Dataset of model output in the work 'Global Impact of Heat from Wildfire on Convection and Aerosols'. Dataset File List File headed with 'CTL' : model output in the CTL run with aerosols but without heat from fires 1. CTL_Aerosols.nc : output of aerosol variables in the CTL run 2. CTL_Convection_Part1.nc : part1 output of convection variables in the CTL run 3. CTL_Convection_Part2.nc : part1 output of convection variables in the CTL run File headed with 'EXP' : model output in the EXP run with both aerosols and heat from fires 1. EXP_Aerosols.nc : output of aerosol variables in the EXP run 2. EXP_Convection_Part1.nc : part1 output of convection variables in the EXP run 3. EXP_Convection_Part2.nc : part1 output of convection variables in the EXP run
DOI: 10.15151/esrf-es-682621131
title: Monitoring electrochemical absorption and adsorption processes on noble metal catalysts from XRD

description: In this experiment, we will investigate the electrochemical absorption and adsorption trends of various chemical species into and onto noble metal nanocatalysts. Both hydrogen absorption on Pd and adsorption on Pt, also with oxide surface formation will be monitored via the lattice strain variation detected from in situ XRD
DOI: 10.5281/zenodo.5981402
title: Misuse Case Modeling Experiment Dataset (red-green colorblindness)

description: Misuse Case Modeling Experiment Dataset (red-green colorblindness)
DOI: 10.5281/zenodo.5770384
title: gnomAD SQLite database V2.1.1

description: This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 34G for WGS v2.1.1 (261.942.336 variants) and 99G for WGS v3.1.1 (about 759.302.267 variants), and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - here you can find all selected features given the genome version). (A query containing 300.000 variants takes ~40s.) It extracts from a gnomAD vcf about 23 variant annotations. You can find further infromation about the exact fields here. gnomAD SQLite database V2.1.1
DOI: 10.5281/zenodo.5758554
title: gnomAD SQLite database V2.1.1

description: This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 34G for WGS v2.1.1 (261.942.336 variants) and 99G for WGS v3.1.1 (about 759.302.267 variants), and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - here you can find all selected features given the genome version). (A query containing 300.000 variants takes ~40s.) It extracts from a gnomAD vcf about 23 variant annotations. You can find further infromation about the exact fields here. gnomAD SQLite database V2.1.1
DOI: 10.5281/zenodo.5758663
title: gnomAD SQLite database V3.1.1

description: This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 34G for WGS v2.1.1 (261.942.336 variants) and 99G for WGS v3.1.1 (about 759.302.267 variants), and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - here you can find all selected features given the genome version). (A query containing 300.000 variants takes ~40s.) It extracts from a gnomAD vcf about 23 variant annotations. You can find further infromation about the exact fields here. gnomAD SQLite database V3.1.1
DOI: 10.5281/zenodo.5758662
title: gnomAD SQLite database V3.1.1

description: This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 34G for WGS v2.1.1 (261.942.336 variants) and 99G for WGS v3.1.1 (about 759.302.267 variants), and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - here you can find all selected features given the genome version). (A query containing 300.000 variants takes ~40s.) It extracts from a gnomAD vcf about 23 variant annotations. You can find further infromation about the exact fields here. gnomAD SQLite database V3.1.1
DOI: 10.5281/zenodo.5981074
title: The Global Navigation Satellite System (GNSS) data came from the Crustal Movement Observation Network of China

description: The dataset reports the estimated vertical Total Electron Content (TEC) from 52 GPS receivers came from the Crustal Movement Observation Network of China on 4 March 2014. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.
DOI: 10.5281/zenodo.5284172
title: The Global Navigation Satellite System (GNSS) data came from the Crustal Movement Observation Network of China

description: The dataset reports the estimated vertical Total Electron Content (TEC) from 52 GPS receivers came from the Crustal Movement Observation Network of China on 4 March 2014. Every receiver's data is saved in a TXT file, whose time resolution is thirty seconds.
DOI: 10.3886/e112518v1-108420
title: Replication data for: Measuring Economic Growth from Outer Space

description: We develop a statistical framework to use satellite data on night lights to augment official income growth measures. For countries with poor national income accounts, the optimal estimate of growth is a composite with roughly equal weights on conventionally measured growth and growth predicted from lights. Our estimates differ from official data by up to three percentage points annually. Using lights, empirical analyses of growth need no longer use countries as the unit of analysis; we can measure growth for sub- and supranational regions. We show, for example, that coastal areas in sub-Saharan Africa are growing slower than the hinterland. (JEL E01, E23, O11, 047, 057)
DOI: 10.6084/m9.figshare.19127291
title: GVC_Data_F1000.csv

description: Primary data collected for the Green Value Chain study
DOI: 10.6084/m9.figshare.19127291.v1
title: GVC_Data_F1000.csv

description: Primary data collected for the Green Value Chain study
DOI: 10.34740/kaggle/dsv/3146166
title: Omicron daily cases by country (COVID-19 variant)
DOI: 10.5517/ccdc.csd.cc24tg5m
title: CCDC 1990670: Experimental Crystal Structure Determination

description: Related Article: R.K. Tiwari, M.K. Saini, J.N. Behera, F. Hussain|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|116|doi:10.1107/S2053229622000419
DOI: 10.5517/ccdc.csd.cc22gn9j
title: CCDC 1920707: Experimental Crystal Structure Determination

description: Related Article: A.M. Grześkiewicz, T. Stefański, Z. Dutkiewicz, M. Kubicki|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|107|doi:10.1107/S2053229622000420
DOI: 10.5517/ccdc.csd.cc22gnbk
title: CCDC 1920708: Experimental Crystal Structure Determination

description: Related Article: A.M. Grześkiewicz, T. Stefański, Z. Dutkiewicz, M. Kubicki|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|107|doi:10.1107/S2053229622000420
DOI: 10.5517/ccdc.csd.cc22gncl
title: CCDC 1920709: Experimental Crystal Structure Determination

description: Related Article: A.M. Grześkiewicz, T. Stefański, Z. Dutkiewicz, M. Kubicki|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|107|doi:10.1107/S2053229622000420
DOI: 10.5517/ccdc.csd.cc22gndm
title: CCDC 1920710: Experimental Crystal Structure Determination

description: Related Article: A.M. Grześkiewicz, T. Stefański, Z. Dutkiewicz, M. Kubicki|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|107|doi:10.1107/S2053229622000420
DOI: 10.5517/ccdc.csd.cc29w8vb
title: CCDC 2141382: Experimental Crystal Structure Determination

description: Related Article: A.M. Grześkiewicz, T. Stefański, Z. Dutkiewicz, M. Kubicki|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|107|doi:10.1107/S2053229622000420
DOI: 10.5517/ccdc.csd.cc2382m3
title: CCDC 1944184: Experimental Crystal Structure Determination

description: Related Article: A.M. Grześkiewicz, T. Stefański, Z. Dutkiewicz, M. Kubicki|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|107|doi:10.1107/S2053229622000420
DOI: 10.5517/ccdc.csd.cc22gnhq
title: CCDC 1920713: Experimental Crystal Structure Determination

description: Related Article: A.M. Grześkiewicz, T. Stefański, Z. Dutkiewicz, M. Kubicki|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|107|doi:10.1107/S2053229622000420
DOI: 10.5517/ccdc.csd.cc22gngp
title: CCDC 1920712: Experimental Crystal Structure Determination

description: Related Article: A.M. Grześkiewicz, T. Stefański, Z. Dutkiewicz, M. Kubicki|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|107|doi:10.1107/S2053229622000420
DOI: 10.5517/ccdc.csd.cc2382n4
title: CCDC 1944185: Experimental Crystal Structure Determination

description: Related Article: A.M. Grześkiewicz, T. Stefański, Z. Dutkiewicz, M. Kubicki|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|107|doi:10.1107/S2053229622000420
DOI: 10.5517/ccdc.csd.cc294ym1
title: CCDC 2119923: Experimental Crystal Structure Determination

description: Related Article: V.V. Mossine, S.P. Kelley, T.P. Mawhinney|2022|IUCrData|7|x220061|doi:10.1107/S241431462200061X
DOI: 10.5517/ccdc.csd.cc29y55m
title: CCDC 2143190: Experimental Crystal Structure Determination

description: Related Article: E.A. Mikhalyova, M. Zeller, J.P. Jasinski, M. Kaur, A.W. Addison|2022|Acta Crystallogr.,Sect.E:Cryst.Commun.|78|103|doi:10.1107/S2056989022000676
DOI: 10.5517/ccdc.csd.cc29y62k
title: CCDC 2143218: Experimental Crystal Structure Determination

description: Related Article: J.A. Kaduk, N.C. Boaz, E.L. Markun, A.M. Gindhart, T.N. Blanton|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|101|doi:10.1107/S2053229622000687
DOI: 10.6084/m9.figshare.19127327
title: G/G/1: Model and Data Produced

description: This excel contains a generic model of a G/G/1 with failure and corrective maintenance repair
DOI: 10.6084/m9.figshare.19127327.v2
title: G/G/1: Model and Data Produced

description: This excel contains a generic model of a G/G/1 with failure and corrective maintenance repair
DOI: 10.6084/m9.figshare.19127327.v1
title: G/G/1: Model and Data Produced

description: This excel contains a generic model of a G/G/1 with failure and corrective maintenance repair
DOI: 10.5281/zenodo.5982182
title: Misuse Case Modeling Experiment Dataset (red-green colorblindness)

description: Misuse Case Modeling Experiment Dataset (red-green colorblindness)
DOI: 10.5281/zenodo.5981401
title: Misuse Case Modeling Experiment Dataset (red-green colorblindness)

description: Misuse Case Modeling Experiment Dataset (red-green colorblindness)
DOI: 10.6084/m9.figshare.17171171
title: Plant speciation in the Quaternary

description: There are conflicting views between palaeobotanists and plant systematists/evolutionary biologists regarding the occurrence of plant speciation in the Quaternary. Palaeobotanists advocate that Quaternary speciation was rare despite opposing molecular phylogenetic evidence, the extent of which appears underappreciated. To document, describe and discuss evidence for Quaternary plant speciation across different geographical regions based on dated molecular phylogenies and related studies. From a search of the literature, we compiled a selection mainly of dated molecular phylogenies from all continents (except Antarctica) and from all major climate zones. Molecular phylogenetic analyses and related studies show that Quaternary plant speciation and radiations occurred frequently and that in many instances Quaternary climatic oscillations were likely important drivers of them. In all geographical regions studied, Quaternary plant speciation and radiations were particularly evident in mountainous areas and arid regions, and were also prevalent on all major oceanic archipelagos. Based on our survey of the molecular phylogenetic and related literature we propose there is now overwhelming evidence that plant speciation and radiations were ubiquitous during the Quaternary. We therefore reject the view of palaeobotanists that plant speciation was rare during this period and briefly discuss possible reasons for this discrepancy.