cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25451/flinders.16881904
title: South East Regional Groundwater Flow Model- LEACHM Recharge Model

description: LEACHM Recharge Model produced as a result of the GOYDER INSTITUTE FOR WATER RESEARCH Project No.E.2.6.<br><br>The LEACHM model was used to create the recharge input files for the regional groundwater flow model developed as part of the same project.<br><br>LEACHM is a one-dimensional soil water and chemical fate and transport model (Hutson, 2003). Water flow is described either by a capacity (tipping-bucket) model or by a mechanistic (Darcy-based Richard’s) model. The mechanistic model was used in this work. It was used in a previous project to assess the risk of nitrogen and pesticide contamination in the Lower South East (Fleming and Hutson, 2014).<br><br>The GIS-linked version of the LEACHM model (LEACHG) aims to assess regional behaviour. The stand-alone LEACHM input data file consists of sections describing the simulation period, profile geometry and boundary conditions, soil, vegetation, chemical properties, irrigation and chemical management and weather. In LEACHG, each of these data components vary spatially, defined by GIS rasters based on state soil and land use maps, along with spatially interpolated weather data. In addition, features such as water table depth and land surface slope class etc. may also be applied to the model in GIS format. In LEACHG, each of the data file components are selected from a library of input data files linked to raster ID values. For example, if a land use raster cell has an ID of 23, then LEACHG will read data from a library file named Crops.023. Initially, the model reads all relevant rasters, identifies unique combinations of soil, land use and weather, and performs a single simulation of each combination for the defined time period. A complete set of LEACHM output files are generated for each simulation, identified by the raster ID values that are used to name the files. Post-processing generates rasters of any desired output variable, such as drainage from the soil profile, actual evapotranspiration or irrigation applications, produces summaries of water mass balance components both in terms of water depths and volumes, and generates input data files for the groundwater model MODFLOW.<br><br>The LEACHM code is written in the FORTRAN language. The code is continually improved and adapted to the requirements of specific projects and post-processing requirements; contact Dr John Hutson (John.Hutson@flinders.edu.au). The time period of the LEACHM model simulations can be extended, however this requires modifying the input data files and obtaining or generating the required climate and land use datasets.<br><br>All datasets used in the model development are stored with the model.<br><br><br>Location: The model domain includes the Lower Limestone Coast Prescribed Wells Area in the Lower South East of SA (South Australia), and part of the Border Designated Area, and the encompassing groundwater flow system
DOI: 10.5517/ccdc.csd.cc26l3c9
title: CCDC 2043190: Experimental Crystal Structure Determination

description: Related Article: fu tsfmmiao|2020|CSD Communication|||
description: Related Article: Miao-Miao Fu, Ce Liu, Gui-Ying Dong|2021|CrystEngComm|23|7397|doi:10.1039/D1CE01128F
DOI: 10.5517/ccdc.csd.cc2769lc
title: CCDC 2061642: Experimental Crystal Structure Determination

description: Related Article: Gintarė Grybauskaitė-Kaminskienė, Vygailė Dudkaitė, Gintautas Bagdžiūnas|2021|New J.Chem.|45|19746|doi:10.1039/D1NJ02149D
DOI: 10.5517/ccdc.csd.cc2769pg
title: CCDC 2061645: Experimental Crystal Structure Determination

description: Related Article: Gintarė Grybauskaitė-Kaminskienė, Vygailė Dudkaitė, Gintautas Bagdžiūnas|2021|New J.Chem.|45|19746|doi:10.1039/D1NJ02149D
DOI: 10.5517/ccdc.csd.cc27d2tk
title: CCDC 2067198: Experimental Crystal Structure Determination

description: Related Article: Siphelele S. P. Malaza, Gift Mehlana, Ocean Cheung, Roger Hunter, Banothile C. E. Makhubela|2021|CrystEngComm|23|7418|doi:10.1039/D1CE00999K
DOI: 10.5517/ccdc.csd.cc27k0c6
title: CCDC 2071927: Experimental Crystal Structure Determination

description: Related Article: Mao-Long Chen, Zhen-Li Qi, Wan-Ting Jin, Zhou Xu, Yun-Hui Cheng, Zhao-Hui Zhou|2021|CrystEngComm|23|7442|doi:10.1039/D1CE00931A
DOI: 10.5517/ccdc.csd.cc27kq0k
title: CCDC 2072598: Experimental Crystal Structure Determination

description: Related Article: Jorge García Ponce, Mariana L. Díaz-Ramírez, Saidulu Gorla, Chanaka Navarathna, Gabriela Sanchez-Lecuona, Bruno Donnadieu, Ilich A. Ibarra, Virginia Montiel-Palma|2021|CrystEngComm|23|7479|doi:10.1039/D1CE01076J
DOI: 10.5517/ccdc.csd.cc27px73
title: CCDC 2076635: Experimental Crystal Structure Determination

description: Related Article: Miao-Miao Fu, Ce Liu, Gui-Ying Dong|2021|CrystEngComm|23|7397|doi:10.1039/D1CE01128F
DOI: 10.5517/ccdc.csd.cc27tb2h
title: CCDC 2079916: Experimental Crystal Structure Determination

description: Related Article: Darya O. Prima, Malena Madiyeva, Julia V. Burykina, Mikhail E. Minyaev, Daniil A. Boiko, Valentine P. Ananikov|2021|Cat.Sci.Tech.|11|7171|doi:10.1039/D1CY01601F
DOI: 10.5517/ccdc.csd.cc27tb3j
title: CCDC 2079917: Experimental Crystal Structure Determination

description: Related Article: Darya O. Prima, Malena Madiyeva, Julia V. Burykina, Mikhail E. Minyaev, Daniil A. Boiko, Valentine P. Ananikov|2021|Cat.Sci.Tech.|11|7171|doi:10.1039/D1CY01601F
DOI: 10.5517/ccdc.csd.cc27tb4k
title: CCDC 2079918: Experimental Crystal Structure Determination

description: Related Article: Darya O. Prima, Malena Madiyeva, Julia V. Burykina, Mikhail E. Minyaev, Daniil A. Boiko, Valentine P. Ananikov|2021|Cat.Sci.Tech.|11|7171|doi:10.1039/D1CY01601F
DOI: 10.5517/ccdc.csd.cc27tb5l
title: CCDC 2079919: Experimental Crystal Structure Determination

description: Related Article: Darya O. Prima, Malena Madiyeva, Julia V. Burykina, Mikhail E. Minyaev, Daniil A. Boiko, Valentine P. Ananikov|2021|Cat.Sci.Tech.|11|7171|doi:10.1039/D1CY01601F
DOI: 10.5517/ccdc.csd.cc27tb6m
title: CCDC 2079920: Experimental Crystal Structure Determination

description: Related Article: Darya O. Prima, Malena Madiyeva, Julia V. Burykina, Mikhail E. Minyaev, Daniil A. Boiko, Valentine P. Ananikov|2021|Cat.Sci.Tech.|11|7171|doi:10.1039/D1CY01601F
DOI: 10.5517/ccdc.csd.cc27tb7n
title: CCDC 2079921: Experimental Crystal Structure Determination

description: Related Article: Darya O. Prima, Malena Madiyeva, Julia V. Burykina, Mikhail E. Minyaev, Daniil A. Boiko, Valentine P. Ananikov|2021|Cat.Sci.Tech.|11|7171|doi:10.1039/D1CY01601F
DOI: 10.5517/ccdc.csd.cc2841zg
title: CCDC 2089275: Experimental Crystal Structure Determination

description: Related Article: Akhtaruzzaman, Baishakhi Pal, Samim Khan, Basudeb Dutta, Sanobar Naaz, Suvendu Maity, Prasanta Ghosh, Partha Pratim Ray, Mohammad Hedayetullah Mir|2021|CrystEngComm|23|7525|doi:10.1039/D1CE01197A
DOI: 10.5517/ccdc.csd.cc2894yp
title: CCDC 2094172: Experimental Crystal Structure Determination

description: Related Article: Ya Liu, Li Ren, Guang-Hua Cui|2021|CrystEngComm|23|7485|doi:10.1039/D1CE01169C
DOI: 10.5517/ccdc.csd.cc2894zq
title: CCDC 2094173: Experimental Crystal Structure Determination

description: Related Article: Ya Liu, Li Ren, Guang-Hua Cui|2021|CrystEngComm|23|7485|doi:10.1039/D1CE01169C
DOI: 10.5517/ccdc.csd.cc28bfvx
title: CCDC 2095409: Experimental Crystal Structure Determination

description: Related Article: Elizaveta A. Kvyatkovskaya, Kseniya K. Borisova, Polina P. Epifanova, Aleksey A. Senin, Victor N. Khrustalev, Mikhail S. Grigoriev, Alexander S. Bunev, Rovshan E. Gasanov, Kirill B. Polyanskii, Fedor I. Zubkov|2021|New J.Chem.|45|19497|doi:10.1039/D1NJ03991A
description: Related Article: Anh T. Le, Van T. T. Tran, Duan T. Le, Rosa M. Gomila, Antonio Frontera, Fedor I. Zubkov|2021|CrystEngComm|23|7462|doi:10.1039/D1CE01040A
DOI: 10.5517/ccdc.csd.cc28bfy0
title: CCDC 2095412: Experimental Crystal Structure Determination

description: Related Article: Elizaveta A. Kvyatkovskaya, Kseniya K. Borisova, Polina P. Epifanova, Aleksey A. Senin, Victor N. Khrustalev, Mikhail S. Grigoriev, Alexander S. Bunev, Rovshan E. Gasanov, Kirill B. Polyanskii, Fedor I. Zubkov|2021|New J.Chem.|45|19497|doi:10.1039/D1NJ03991A
description: Related Article: Anh T. Le, Van T. T. Tran, Duan T. Le, Rosa M. Gomila, Antonio Frontera, Fedor I. Zubkov|2021|CrystEngComm|23|7462|doi:10.1039/D1CE01040A
DOI: 10.5517/ccdc.csd.cc28bfz1
title: CCDC 2095413: Experimental Crystal Structure Determination

description: Related Article: Elizaveta A. Kvyatkovskaya, Kseniya K. Borisova, Polina P. Epifanova, Aleksey A. Senin, Victor N. Khrustalev, Mikhail S. Grigoriev, Alexander S. Bunev, Rovshan E. Gasanov, Kirill B. Polyanskii, Fedor I. Zubkov|2021|New J.Chem.|45|19497|doi:10.1039/D1NJ03991A
description: Related Article: Anh T. Le, Van T. T. Tran, Duan T. Le, Rosa M. Gomila, Antonio Frontera, Fedor I. Zubkov|2021|CrystEngComm|23|7462|doi:10.1039/D1CE01040A
DOI: 10.5517/ccdc.csd.cc28cl3c
title: CCDC 2096502: Experimental Crystal Structure Determination

description: Related Article: André Bütikofer, Peter Chen|2021|CSD Communication|||
DOI: 10.15468/wvh44s
title: Novel Mucor species (Mucoromycetes, Mucoraceae) from northern Thailand

description: This dataset contains the digitized treatments in Plazi based on the original journal article Hurdeal, Vedprakash G., Gentekaki, Eleni, Hyde, Kevin D., Nguyen, Thuong T. T., Lee, Hyang Burm (2021): Novel Mucor species (Mucoromycetes, Mucoraceae) from northern Thailand. MycoKeys 84: 57-78, DOI: http://dx.doi.org/10.3897/mycokeys.84.71530, URL: http://dx.doi.org/10.3897/mycokeys.84.71530
DOI: 10.5517/ccdc.csd.cc28gcbg
title: CCDC 2099175: Experimental Crystal Structure Determination

description: Related Article: Feng-Shun Xu, Chen Yan, Jing Sun, Chao-Guo Yan|2021|New J.Chem.|45|19666|doi:10.1039/D1NJ03772B
DOI: 10.5517/ccdc.csd.cc28gcch
title: CCDC 2099176: Experimental Crystal Structure Determination

description: Related Article: Feng-Shun Xu, Chen Yan, Jing Sun, Chao-Guo Yan|2021|New J.Chem.|45|19666|doi:10.1039/D1NJ03772B
DOI: 10.5517/ccdc.csd.cc28gytj
title: CCDC 2099748: Experimental Crystal Structure Determination

description: Related Article: G. Tamás Gál, Nóra V. May, László Trif, Judith Mihály, Petra Bombicz|2021|CrystEngComm|23|7425|doi:10.1039/D1CE00995H
DOI: 10.5517/ccdc.csd.cc28gyvk
title: CCDC 2099749: Experimental Crystal Structure Determination

description: Related Article: G. Tamás Gál, Nóra V. May, László Trif, Judith Mihály, Petra Bombicz|2021|CrystEngComm|23|7425|doi:10.1039/D1CE00995H
DOI: 10.5517/ccdc.csd.cc28gyxm
title: CCDC 2099751: Experimental Crystal Structure Determination

description: Related Article: G. Tamás Gál, Nóra V. May, László Trif, Judith Mihály, Petra Bombicz|2021|CrystEngComm|23|7425|doi:10.1039/D1CE00995H
DOI: 10.5517/ccdc.csd.cc28gyyn
title: CCDC 2099752: Experimental Crystal Structure Determination

description: Related Article: G. Tamás Gál, Nóra V. May, László Trif, Judith Mihály, Petra Bombicz|2021|CrystEngComm|23|7425|doi:10.1039/D1CE00995H
DOI: 10.5517/ccdc.csd.cc28gyzp
title: CCDC 2099753: Experimental Crystal Structure Determination

description: Related Article: G. Tamás Gál, Nóra V. May, László Trif, Judith Mihály, Petra Bombicz|2021|CrystEngComm|23|7425|doi:10.1039/D1CE00995H
DOI: 10.15468/rr4vwk
title: New species of Labiobaetis Novikova & Kluge from Southeast Asia and New Guinea (Ephemeroptera, Baetidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Kaltenbach, Thomas, Gattolliat, Jean-Luc (2021): New species of Labiobaetis Novikova & Kluge from Southeast Asia and New Guinea (Ephemeroptera, Baetidae). ZooKeys 1067: 159-208, DOI: http://dx.doi.org/10.3897/zookeys.1067.72251, URL: http://dx.doi.org/10.3897/zookeys.1067.72251
DOI: 10.15468/6w7e6a
title: Two new calcicolous caloplacoid lichens from South Korea, with a taxonomic key to the species of Huriella and Squamulea

description: This dataset contains the digitized treatments in Plazi based on the original journal article Lee, Beeyoung Gun, Hur, Jae-Seoun (2021): Two new calcicolous caloplacoid lichens from South Korea, with a taxonomic key to the species of Huriella and Squamulea. MycoKeys 84: 35-55, DOI: http://dx.doi.org/10.3897/mycokeys.84.71227, URL: http://dx.doi.org/10.3897/mycokeys.84.71227
DOI: 10.5281/zenodo.5634207
title: Swift-BAT Response Files for NITRATES

description: The response files used in the NITRATES search. resp_tabs.tar.gz - contains the photoelectric and Compton response files that were generated using detectors only in the mass model. The direct response. resp_tabs_ebins.tar.gz - same as resp_tabs.tar.gz except it uses only the 9 analysis energy bins comp_flor_resps.tar.gz - contains the Compton and fluorescence response files that were generated using the whole mass model. The indirect response. hp_flor_resps.tar.gz - contains the fluorescence only response files that were generated using the whole mass model. The indirect response. solid_angle_dpi.npy - file with the solid angle each detector is exposed to through the holes in the mask. Used for the diffuse model. rate_resps.tar.gz - contains a grid of spectral models folded through the response for the split rates analysis for a spectral norm of 1. This is for a grid of points in imx and imy. rate_resps_outFoV.tar.gz - the same as rate_resps.tar.gz except for the out of FoV grid points.
DOI: 10.5281/zenodo.5634206
title: Swift-BAT Response Files for NITRATES

description: The response files used in the NITRATES search. resp_tabs.tar.gz - contains the photoelectric and Compton response files that were generated using detectors only in the mass model. The direct response. resp_tabs_ebins.tar.gz - same as resp_tabs.tar.gz except it uses only the 9 analysis energy bins comp_flor_resps.tar.gz - contains the Compton and fluorescence response files that were generated using the whole mass model. The indirect response. hp_flor_resps.tar.gz - contains the fluorescence only response files that were generated using the whole mass model. The indirect response. solid_angle_dpi.npy - file with the solid angle each detector is exposed to through the holes in the mask. Used for the diffuse model. rate_resps.tar.gz - contains a grid of spectral models folded through the response for the split rates analysis for a spectral norm of 1. This is for a grid of points in imx and imy. rate_resps_outFoV.tar.gz - the same as rate_resps.tar.gz except for the out of FoV grid points.
DOI: 10.25451/flinders.16881907
title: South East Regional Water Balance Model Historical Groundwater Extraction Dataset 1970-2013

description: As part of the Goyder Institute for Water Research funded South East Regional Water Balance project, a historical groundwater extraction dataset was produced for a large portion of the South East of South Australia. The objective of the dataset was to provide inputs for a regional groundwater flow model.<br><br>The basis for the dataset was a metered groundwater extraction dataset for 2009-2013, provided by DEWNR. The metered dataset contained 3,812 metered groundwater extraction records. A quality check removed obvious errors and issues with inconsistent reading dates. Meter records were merged where they related to the same extraction well but different time periods. The extraction records were then assigned locations. Geographical coordinates were assigned to the meter records based on meter position data provided by DEWNR. Meter position data was not available for 1,032 wells, so the extraction volumes for these wells were distributed evenly across those wells with geographical coordinates in the corresponding groundwater management areas. This ensured that water balances were correct at least at the management area scale, but provides a source of error, particularly if this dataset is to be used in local scale groundwater models.<br><br>Extraction commencement dates were assigned to the meter records using a variety of methods with varying uncertainty associated with them. Historical groundwater extraction from 1970 to 2014 was then estimated using the average annual groundwater extraction rate for an individual bore was assumed to be constant over time. Records for the metered period (2009/10 – 2012/13) were applied to preceding years since extraction commenced at each location.<br>This study was supported by The Goyder Institute for Water Research.<br><br>Date coverage: 1970 - 2013<br>Location:<br>- South East of South Australia.<br>- GDA94 MGA Zone 54 Approx. E377,300 m, N5,770,000 m in the south-west and E565,300 m, N5,994,000 m in the north-east.
DOI: 10.25451/flinders.16881907.v1
title: South East Regional Water Balance Model Historical Groundwater Extraction Dataset 1970-2013

description: As part of the Goyder Institute for Water Research funded South East Regional Water Balance project, a historical groundwater extraction dataset was produced for a large portion of the South East of South Australia. The objective of the dataset was to provide inputs for a regional groundwater flow model.<br><br>The basis for the dataset was a metered groundwater extraction dataset for 2009-2013, provided by DEWNR. The metered dataset contained 3,812 metered groundwater extraction records. A quality check removed obvious errors and issues with inconsistent reading dates. Meter records were merged where they related to the same extraction well but different time periods. The extraction records were then assigned locations. Geographical coordinates were assigned to the meter records based on meter position data provided by DEWNR. Meter position data was not available for 1,032 wells, so the extraction volumes for these wells were distributed evenly across those wells with geographical coordinates in the corresponding groundwater management areas. This ensured that water balances were correct at least at the management area scale, but provides a source of error, particularly if this dataset is to be used in local scale groundwater models.<br><br>Extraction commencement dates were assigned to the meter records using a variety of methods with varying uncertainty associated with them. Historical groundwater extraction from 1970 to 2014 was then estimated using the average annual groundwater extraction rate for an individual bore was assumed to be constant over time. Records for the metered period (2009/10 – 2012/13) were applied to preceding years since extraction commenced at each location.<br>This study was supported by The Goyder Institute for Water Research.<br><br>Date coverage: 1970 - 2013<br>Location:<br>- South East of South Australia.<br>- GDA94 MGA Zone 54 Approx. E377,300 m, N5,770,000 m in the south-west and E565,300 m, N5,994,000 m in the north-east.
DOI: 10.5519/qd.hsx24xtm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (285 records)
DOI: 10.25451/flinders.16881913.v1
title: River Murray Floodplain Groundwater Model 2015: Salinity dynamics of the River Murray floodplain in South Australia

description: Groundwater models produced from GOYDER INSTITUTE FOR WATER RESEARCH Project No. E1.11.<br><br>A series of numerical groundwater models have been designed to represent generic floodplain conditions that exist along the River Murray in SA, from the border to Lock 1. The aim is to improve understanding and modelling capability of flow and salinity dynamics, to assist with management and engineering decisions.<br><br>Multiple base case scenarios are considered, varying by floodplain topography, the presence or local absence of a lock, and whether the river conditions are gaining, throughflow or losing. Model variants were developed to investigate how results depended on the methods used to simulate key processes, including river level changes, floodplain inundation, groundwater evapotranspiration, and solute transport.<br><br>The MODFLOW and MT3DMS simulations were developed using Groundwater Vistas version 6. Some data inputs were pre-processed using scripts in Python 2.7.6.0. Post-processing was done with both Groundwater Vistas and Python.<br><br>Model parameters are informed by an amalgamation of data from the study area, but are not specific to any one location. Where there is a wide range of possible parameters, values from the Pike Floodplain have been adopted. Where no local field data exists, values were used from the literature and/or from DEWNR’s Salinity Register regional models.<br><br>As the Goyder Floodplain Model is a synthetic model, in that it does not model a specific location, no detailed calibration was possible. However, the models were constrained based on the current knowledge of floodplains along the SA River Murray, such as in-stream geophysics, estimated actual evapotranspiration, and Run of River estimates of groundwater salt flux to the river.<br><br><br>Location:<br>- South Australia, Australia<br>- The models simulate a generic domain representing conditions in the River Murray floodplain between the SA Border and Lock 1. Conditions are similar upstream of the SA Border as far as Nangiloc-Colignan.
DOI: 10.25451/flinders.16881913
title: River Murray Floodplain Groundwater Model 2015: Salinity dynamics of the River Murray floodplain in South Australia

description: Groundwater models produced from GOYDER INSTITUTE FOR WATER RESEARCH Project No. E1.11.<br><br>A series of numerical groundwater models have been designed to represent generic floodplain conditions that exist along the River Murray in SA, from the border to Lock 1. The aim is to improve understanding and modelling capability of flow and salinity dynamics, to assist with management and engineering decisions.<br><br>Multiple base case scenarios are considered, varying by floodplain topography, the presence or local absence of a lock, and whether the river conditions are gaining, throughflow or losing. Model variants were developed to investigate how results depended on the methods used to simulate key processes, including river level changes, floodplain inundation, groundwater evapotranspiration, and solute transport.<br><br>The MODFLOW and MT3DMS simulations were developed using Groundwater Vistas version 6. Some data inputs were pre-processed using scripts in Python 2.7.6.0. Post-processing was done with both Groundwater Vistas and Python.<br><br>Model parameters are informed by an amalgamation of data from the study area, but are not specific to any one location. Where there is a wide range of possible parameters, values from the Pike Floodplain have been adopted. Where no local field data exists, values were used from the literature and/or from DEWNR’s Salinity Register regional models.<br><br>As the Goyder Floodplain Model is a synthetic model, in that it does not model a specific location, no detailed calibration was possible. However, the models were constrained based on the current knowledge of floodplains along the SA River Murray, such as in-stream geophysics, estimated actual evapotranspiration, and Run of River estimates of groundwater salt flux to the river.<br><br><br>Location:<br>- South Australia, Australia<br>- The models simulate a generic domain representing conditions in the River Murray floodplain between the SA Border and Lock 1. Conditions are similar upstream of the SA Border as far as Nangiloc-Colignan.
DOI: 10.6084/m9.figshare.5990905
title: Effects of species traits, motif profiles, and environment on spatial variation in multi-trophic antagonistic networks

description: This file set includes a read me file, R code file and data set files associated with the manuscript: Effects of species traits, motif profiles, and environment on spatial variation in multi-trophic antagonistic networks