cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc20fslv
title: CSD 1860297: Experimental Crystal Structure Determination

description: Related Article: Matthew J. Cliffe, Evan N. Keyzer, Matthew T. Dunstan, Shahab Ahmad, Michael F. L. De Volder, Felix Deschler, Andrew J. Morris, Clare P. Grey|2019|Chemical Science|10|793|doi:10.1039/C8SC04082F
DOI: 10.5517/ccdc.csd.cc20fsmw
title: CSD 1860298: Experimental Crystal Structure Determination

description: Related Article: Matthew J. Cliffe, Evan N. Keyzer, Matthew T. Dunstan, Shahab Ahmad, Michael F. L. De Volder, Felix Deschler, Andrew J. Morris, Clare P. Grey|2019|Chemical Science|10|793|doi:10.1039/C8SC04082F
DOI: 10.5517/ccdc.csd.cc20fsnx
title: CSD 1860299: Experimental Crystal Structure Determination

description: Related Article: Matthew J. Cliffe, Evan N. Keyzer, Matthew T. Dunstan, Shahab Ahmad, Michael F. L. De Volder, Felix Deschler, Andrew J. Morris, Clare P. Grey|2019|Chemical Science|10|793|doi:10.1039/C8SC04082F
DOI: 10.5517/ccdc.csd.cc20fspy
title: CSD 1860300: Experimental Crystal Structure Determination

description: Related Article: Matthew J. Cliffe, Evan N. Keyzer, Matthew T. Dunstan, Shahab Ahmad, Michael F. L. De Volder, Felix Deschler, Andrew J. Morris, Clare P. Grey|2019|Chemical Science|10|793|doi:10.1039/C8SC04082F
DOI: 10.25505/fiz.icsd.cc20gf42
title: CSD 1860903: Experimental Crystal Structure Determination

description: Related Article: Md Saiduzzaman, Hikaru Yoshida, Takahiro Takei, Sayaka Yanagida, Nobuhiro Kumada, Masanori Nagao, Hisanori Yamane, Masaki Azuma, Mirza H. K. Rubel, Chikako Moriyoshi, Yoshihiro Kuroiwa|2019|Inorg.Chem.|58|11997|doi:10.1021/acs.inorgchem.9b01768
DOI: 10.25505/fiz.icsd.cc20hbf9
title: CSD 1861780: Experimental Crystal Structure Determination

description: Related Article: Igor OSHCHAPOVSKY, Sven LIDIN, Volodymyr PAVLYUK, Emil THORSELIUS|2018|ICSD Communication|||
description: Related Article: Igor Oshchapovsky, Sven Lidin, Volodymyr Pavlyuk|2019|Inorg.Chem.|58|12590|doi:10.1021/acs.inorgchem.9b01266
DOI: 10.25505/fiz.icsd.cc1zjx2h
title: CSD 1833497: Experimental Crystal Structure Determination

description: Related Article: J. Zhao, D. Zhao, Y.-L. Xue, Q. Zhong, S.-R. Zhang, B.-Z. Liu|2018|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|74|1045|doi:10.1107/S2053229618011877
DOI: 10.25505/fiz.icsd.cc20l6c6
title: CSD 1864537: Experimental Crystal Structure Determination

description: Related Article: Fatimah Alahmari, Somnath Dey, Abdul-Hamid Emwas, Bambar Davaasuren, Alexander Rothenberger|2019|New J.Chem.|43|10814|doi:10.1039/C9NJ01440C
DOI: 10.25505/fiz.icsd.cc20l6d7
title: CSD 1864538: Experimental Crystal Structure Determination

description: Related Article: Fatimah Alahmari, Somnath Dey, Abdul-Hamid Emwas, Bambar Davaasuren, Alexander Rothenberger|2019|New J.Chem.|43|10814|doi:10.1039/C9NJ01440C
DOI: 10.25505/fiz.icsd.cc20l6f8
title: CSD 1864539: Experimental Crystal Structure Determination

description: Related Article: Fatimah Alahmari, Somnath Dey, Abdul-Hamid Emwas, Bambar Davaasuren, Alexander Rothenberger|2019|New J.Chem.|43|10814|doi:10.1039/C9NJ01440C
DOI: 10.25505/fiz.icsd.cc20nrs7
title: CSD 1866999: Experimental Crystal Structure Determination

description: Related Article: J.-L. Song, W.-X. Lu, T.-S. Guo, Y.-H. Zhou|2018|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|74|1146|doi:10.1107/S2053229618012792
DOI: 10.25505/fiz.icsd.cc20nrt8
title: CSD 1867000: Experimental Crystal Structure Determination

description: Related Article: J.-L. Song, W.-X. Lu, T.-S. Guo, Y.-H. Zhou|2018|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|74|1146|doi:10.1107/S2053229618012792
DOI: 10.25505/fiz.icsd.cc20tz72
title: CSD 1872004: Experimental Crystal Structure Determination

description: Related Article: R. Al-Oweini, B.S. Bassil, M. Itani, D.B. Emiroğlu, U. Kortz|2018|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|74|1390|doi:10.1107/S2053229618014183
DOI: 10.6084/m9.figshare.17742551.v1
title: Saul_et_al_2022_Figure 5.zip

description: Saul et al (2022) raw data for Figure 5 and supplements
DOI: 10.6084/m9.figshare.17742551
title: Saul_et_al_2022_Figure 5.zip

description: Saul et al (2022) raw data for Figure 5 and supplements
DOI: 10.6084/m9.figshare.17991278.v1
title: data for FigureA.1

description: The data table is the supplement for Figure A.1.
DOI: 10.6084/m9.figshare.17991278
title: data for FigureA.1

description: The data table is the supplement for Figure A.1.
DOI: 10.6084/m9.figshare.17742509
title: Saul_et_al_2022_Figure 4.zip

description: Saul et al (2022) raw data for Figure 4 and supplements
DOI: 10.6084/m9.figshare.17742509.v1
title: Saul_et_al_2022_Figure 4.zip

description: Saul et al (2022) raw data for Figure 4 and supplements
DOI: 10.6084/m9.figshare.17742470.v1
title: Saul_et_al_2022_Figure 3.zip

description: Saul et al (2022) raw data for Figure 3 and supplements
DOI: 10.6084/m9.figshare.17742470
title: Saul_et_al_2022_Figure 3.zip

description: Saul et al (2022) raw data for Figure 3 and supplements
DOI: 10.6084/m9.figshare.17701688
title: data for Figure3(a).csv

description: The data table is the supplement for Figure3(a).
DOI: 10.6084/m9.figshare.17701688.v1
title: data for Figure3(a).csv

description: The data table is the supplement for Figure3(a).
DOI: 10.6084/m9.figshare.17742428
title: Saul_et_al_2022_Figure 2.zip

description: Saul et al (2022) raw data for Figure 2 and supplements
DOI: 10.6084/m9.figshare.17742428.v1
title: Saul_et_al_2022_Figure 2.zip

description: Saul et al (2022) raw data for Figure 2 and supplements
DOI: 10.6084/m9.figshare.17742362
title: Saul_et_al_2022_Figure 1.zip

description: Saul et al (2022) raw data for Figure 1 and supplements
DOI: 10.6084/m9.figshare.17742362.v1
title: Saul_et_al_2022_Figure 1.zip

description: Saul et al (2022) raw data for Figure 1 and supplements
DOI: 10.6084/m9.figshare.14394653
title: TableS1_Paper_Election_Cell_Adv2020

description: <b>Table S1. List of cells chosen by the students</b>
DOI: 10.6084/m9.figshare.14394653.v1
title: TableS1_Paper_Election_Cell_Adv2020

description: <b>Table S1. List of cells chosen by the students</b>
DOI: 10.6084/m9.figshare.18131408
title: 102120clone7-1_mStr_MJPG_VtV_grnlist_rewrite.csv

description: HFDPC_clone 7-1_nuclear-cytoplasm_ratio
DOI: 10.6084/m9.figshare.18131408.v1
title: 102120clone7-1_mStr_MJPG_VtV_grnlist_rewrite.csv

description: HFDPC_clone 7-1_nuclear-cytoplasm_ratio
DOI: 10.6084/m9.figshare.17706062
title: Saul_et_al_2022_Plasmid Maps.zip

description: Saul et al (2022) plasmid maps
DOI: 10.6084/m9.figshare.17706059
title: Saul_et_al_2022_Figure 10.zip

description: Saul et al (2022) raw data for Figure 10
DOI: 10.6084/m9.figshare.17706059.v1
title: Saul_et_al_2022_Figure 10.zip

description: Saul et al (2022) raw data for Figure 10
DOI: 10.6084/m9.figshare.18131417
title: 102620clone7-2_mStr_MJPG_VtV_grnlist_rewrite.csv

description: HFDPC_clone_7-2_nuclear_cytoplasm_ratio
DOI: 10.6084/m9.figshare.18131417.v1
title: 102620clone7-2_mStr_MJPG_VtV_grnlist_rewrite.csv

description: HFDPC_clone_7-2_nuclear_cytoplasm_ratio
DOI: 10.6084/m9.figshare.17706056
title: Saul_et_al_2022_Figure 9.zip

description: Saul et al (2022) raw data for Figure 9
DOI: 10.6084/m9.figshare.17706056.v1
title: Saul_et_al_2022_Figure 9.zip

description: Saul et al (2022) raw data for Figure 9
DOI: 10.6084/m9.figshare.17706053
title: Saul_et_al_2022_Figure 8.zip

description: Saul et al (2022) raw data for Figure 8
DOI: 10.6084/m9.figshare.17706053.v1
title: Saul_et_al_2022_Figure 8.zip

description: Saul et al (2022) raw data for Figure 8
DOI: 10.6084/m9.figshare.18131414
title: 110220clone7-3_mStr_MJPG_VtV_grnlist_rewrite.csv

description: HFDPC_clone7-3_nuclear_cytoplasm_ratio
DOI: 10.6084/m9.figshare.18131414.v1
title: 110220clone7-3_mStr_MJPG_VtV_grnlist_rewrite.csv

description: HFDPC_clone7-3_nuclear_cytoplasm_ratio
DOI: 10.6084/m9.figshare.17701691
title: data for Figure3(c).csv

description: The data table is the supplement for Figure3(c).
DOI: 10.6084/m9.figshare.17701691.v1
title: data for Figure3(c).csv

description: The data table is the supplement for Figure3(c).
DOI: 10.6084/m9.figshare.18131411.v1
title: 111320clone7-4_mStr_MJPG_VtV_grnlist_rewrite.csv

description: HFDPC_clone7_4_nuclear_cytoplasm_ratio
DOI: 10.6084/m9.figshare.18131411
title: 111320clone7-4_mStr_MJPG_VtV_grnlist_rewrite.csv

description: HFDPC_clone7_4_nuclear_cytoplasm_ratio
DOI: 10.6084/m9.figshare.17701694
title: downscaled basin water footprint under 13 SSP-RCP scenarios.csv

description: Estimated water footprints in all 22 basins and the nation mean based on 4 Global Climate Models under 13 scenarios
DOI: 10.6084/m9.figshare.17701697
title: data for Figure3(b).csv

description: The data table is the supplement for Figure 3(b).
DOI: 10.6084/m9.figshare.17701697.v1
title: data for Figure3(b).csv

description: The data table is the supplement for Figure 3(b).
DOI: 10.6084/m9.figshare.17701700
title: data for Figure4.csv

description: The data table is the supplement for Figure 4.
DOI: 10.6084/m9.figshare.17701700.v1
title: data for Figure4.csv

description: The data table is the supplement for Figure 4.
DOI: 10.6084/m9.figshare.17701706
title: data for Figure5.csv

description: The data table is the supplement for Figure5.
DOI: 10.6084/m9.figshare.17701706.v1
title: data for Figure5.csv

description: The data table is the supplement for Figure5.
DOI: 10.5281/zenodo.4560812
title: Mink fecal microbiomes are influenced by sex, temperature and time post-defecation

description: Mink metadata, QIIME2 artifacts and demultiplexed EMP-paired end sequences from Argonne National laboratory, R code for statistical analyses and figure generation, and QIIME2 pipeline for Lafferty et al. 2021.
DOI: 10.5281/zenodo.4560811
title: Mink fecal microbiomes are influenced by sex, temperature and time post-defecation

description: Mink metadata, QIIME2 artifacts and demultiplexed EMP-paired end sequences from Argonne National laboratory, R code for statistical analyses and figure generation, and QIIME2 pipeline for Lafferty et al. 2021.
DOI: 10.6084/m9.figshare.14080155
title: Waterfowl hunting wetlands as habitat for two New Zealand eel species

description: New Zealand’s native shortfin eel (<i>Anguilla australis</i>) and endemic longfin eel (<i>Anguilla dieffenbachii</i>) have been negatively affected by wetland habitat loss. However, in Southland, open water wetland habitat has been created by hunters in the form of waterfowl hunting wetlands (duck ponds), habitat which can be utilised by eels. The aim of this study was to estimate the number and biomass of eels supported by Southland duck ponds to highlight the value of hunter-created wetlands as eel habitat. Eel population surveys were conducted in 56 duck ponds located on private agricultural land across Southland. Shortfin eels were found in 28 ponds with an average population size of 22 and biomass of 9.3 kg. Longfin eels were found in 26 ponds with an average population size of nine and biomass of 7.6 kg. Estimates indicate there are 7,013 ± (1761) duck ponds in Southland and they collectively support 36,000 shortfin eels weighing 15,500 kg and 60,000 longfin eels weighing 53,000 kg. Results from this study show that duck hunting ponds are utilised by shortfin and longfin eels.
DOI: 10.6084/m9.figshare.14415009
title: Contests between male New Zealand sheet-web spiders, <i>Cambridgea plagiata</i> (Araneae: Desidae)

description: Male competition for females can often take the form of direct physical contests. The outcome of a given contest or the decision to persist is determined by the resource holding potential (RHP) of rivals, which often corresponds to body size or weapon size. Males of the endemic New Zealand sheet-web spider <i>Cambridgea plagiata</i> Forster & Wilton 1973 have exaggerated chelicerae that are used in contests to gain access to females. We staged male–male contests in the field to determine the factors contributing to contest duration and outcome. The current models which describe how individuals make decisions to persist in a contest fall broadly into (i) energetic war of attrition or pure self-assessment, (ii) mutual assessment, and (iii) cumulative assessment. Our results showed that male body size was the key predictor in determining the winner of a given contest. The durations of contests were more consistent with predictions from either mutual or cumulative assessment models. We highlight that staging contests in the field may result in more realistic results and natural behaviours. Similar to the congener <i>C. foliata</i>, it appears that further development of current contest models may be required to understand male contests in sheet-web spiders.
DOI: 10.6084/m9.figshare.14415009.v1
title: Contests between male New Zealand sheet-web spiders, <i>Cambridgea plagiata</i> (Araneae: Desidae)

description: Male competition for females can often take the form of direct physical contests. The outcome of a given contest or the decision to persist is determined by the resource holding potential (RHP) of rivals, which often corresponds to body size or weapon size. Males of the endemic New Zealand sheet-web spider <i>Cambridgea plagiata</i> Forster & Wilton 1973 have exaggerated chelicerae that are used in contests to gain access to females. We staged male–male contests in the field to determine the factors contributing to contest duration and outcome. The current models which describe how individuals make decisions to persist in a contest fall broadly into (i) energetic war of attrition or pure self-assessment, (ii) mutual assessment, and (iii) cumulative assessment. Our results showed that male body size was the key predictor in determining the winner of a given contest. The durations of contests were more consistent with predictions from either mutual or cumulative assessment models. We highlight that staging contests in the field may result in more realistic results and natural behaviours. Similar to the congener <i>C. foliata</i>, it appears that further development of current contest models may be required to understand male contests in sheet-web spiders.
DOI: 10.6084/m9.figshare.14080155.v1
title: Waterfowl hunting wetlands as habitat for two New Zealand eel species

description: New Zealand’s native shortfin eel (<i>Anguilla australis</i>) and endemic longfin eel (<i>Anguilla dieffenbachii</i>) have been negatively affected by wetland habitat loss. However, in Southland, open water wetland habitat has been created by hunters in the form of waterfowl hunting wetlands (duck ponds), habitat which can be utilised by eels. The aim of this study was to estimate the number and biomass of eels supported by Southland duck ponds to highlight the value of hunter-created wetlands as eel habitat. Eel population surveys were conducted in 56 duck ponds located on private agricultural land across Southland. Shortfin eels were found in 28 ponds with an average population size of 22 and biomass of 9.3 kg. Longfin eels were found in 26 ponds with an average population size of nine and biomass of 7.6 kg. Estimates indicate there are 7,013 ± (1761) duck ponds in Southland and they collectively support 36,000 shortfin eels weighing 15,500 kg and 60,000 longfin eels weighing 53,000 kg. Results from this study show that duck hunting ponds are utilised by shortfin and longfin eels.
DOI: 10.4121/16782613.v1
title: Acoustic Surface Elevation Dynamics data Marconi salt marsh

description: At the semi-natural constructed Marconi salt marsh, bed level change data were collected using state-of-the-art Acoustic Surface Elevation Dynamics (A-SED) instruments. This recently developed instrument measures the distance between the bed and the instrument through the echo of an acoustic signal. The instrument is set-up to be able to collect measurements when the sensor of the instrument is fully inundated. A burst of eight samples (sampling frequency of 5 Hz) was stored at a measurement interval of 5 to 15 minutes. The instruments were installed downward looking, with the sensor head aimed vertically at the bottom. The instruments were serviced, cleaned and data were downloaded every two to three months. A post-processing script was developed in Python to convert the acoustic return signals to bed level change data. The measurements were validated with manual readings of the height above bed (HAB) of the sensors every time the instruments were serviced. <br>
DOI: 10.4121/16782613
title: Acoustic Surface Elevation Dynamics data Marconi salt marsh

description: At the semi-natural constructed Marconi salt marsh, bed level change data were collected using state-of-the-art Acoustic Surface Elevation Dynamics (A-SED) instruments. This recently developed instrument measures the distance between the bed and the instrument through the echo of an acoustic signal. The instrument is set-up to be able to collect measurements when the sensor of the instrument is fully inundated. A burst of eight samples (sampling frequency of 5 Hz) was stored at a measurement interval of 5 to 15 minutes. The instruments were installed downward looking, with the sensor head aimed vertically at the bottom. The instruments were serviced, cleaned and data were downloaded every two to three months. A post-processing script was developed in Python to convert the acoustic return signals to bed level change data. The measurements were validated with manual readings of the height above bed (HAB) of the sensors every time the instruments were serviced. <br>
DOI: 10.4121/14992029.v1
title: Data supplementary to the paper 'Tracing Long-term Value Change in (Energy) Technologies'

description: This dataset and notebook corresponds to the article 'Tracing long-term value change in (energy) technologies - Opportunities of probabilistic topic models using large datasets'
DOI: 10.4121/14992029
title: Data supplementary to the paper 'Tracing Long-term Value Change in (Energy) Technologies'

description: This dataset and notebook corresponds to the article 'Tracing long-term value change in (energy) technologies - Opportunities of probabilistic topic models using large datasets'
DOI: 10.4121/18065285.v1
title: Weather data for Netatmo personal weather stations around the Hague (2021)

description: Data for the outdoor modules of the Netatmo personal weather stations that have selected to share their data publicly, which were obtained by querying the Netatmo public API every 15 minutes. One CSV (comma-separated values) file per queried time. There is one weather station per line. The attributes available are: ID (MAC address of station), longitude, latitude, altitude, temperature, humidity and pressure. For those weather stations that have a rain gauge, there is also rainfall data available.
DOI: 10.4121/18065285
title: Weather data for Netatmo personal weather stations around the Hague (2021)

description: Data for the outdoor modules of the Netatmo personal weather stations that have selected to share their data publicly, which were obtained by querying the Netatmo public API every 15 minutes. One CSV (comma-separated values) file per queried time. There is one weather station per line. The attributes available are: ID (MAC address of station), longitude, latitude, altitude, temperature, humidity and pressure. For those weather stations that have a rain gauge, there is also rainfall data available.
DOI: 10.4121/17998085.v1
title: Data underlying the research of Cataclastic Flow deformation and consequent hydro-mechanical evolution in high porosity rock

description: Abaqus output file: Triaxial compression test response under 300 MPa confinement of high porous Bentheim sandstone using Finite Elements. A damage-breakage model proposed by was used for this analysis. Besides flow analysis is also carried out to understand the change in hydraulic response during localized deformation. A detailed analysis can be found in Nanda et al. 2020.<br>PFC3D output file and input file.<br><br>Nanda, Kamal, et al. "Hydro-mechanical response in porous rocks during localized deformation: finite element analysis." <i>Journal of structural geology</i> 130 (2020): 103909.<br>
DOI: 10.4121/17998085
title: Data underlying the research of Cataclastic Flow deformation and consequent hydro-mechanical evolution in high porosity rock

description: Abaqus output file: Triaxial compression test response under 300 MPa confinement of high porous Bentheim sandstone using Finite Elements. A damage-breakage model proposed by was used for this analysis. Besides flow analysis is also carried out to understand the change in hydraulic response during localized deformation. A detailed analysis can be found in Nanda et al. 2020.<br>PFC3D output file and input file.<br><br>Nanda, Kamal, et al. "Hydro-mechanical response in porous rocks during localized deformation: finite element analysis." <i>Journal of structural geology</i> 130 (2020): 103909.<br>
DOI: 10.25911/61dcf40c85855
title: Supporting data files for: "Scholarly communication competencies: An analysis of confidence among Australasian library staff"

description: Four data files: 1. Survey instrument 2. The raw responses to the survey 3. Analysis of number of responses for each question 4. File detailing how charts and tables in the paper "Scholarly communication competencies: An analysis of confidence among Australasian library staff" were generated.
description: For the purposes of this study, we define scholarly communication roles as roles which include: Institutional repository management, Publishing services, Research practice, Copyright services, Open access policies and the scholarly communication landscape, Data management services, and Assessment & impact metrics. The focus of this research is on aspects of the level of confidence that librarians working in scholarly communication have in their current competencies. The focus is on questions of confidence in the core competencies of scholarly communication and the education and training background of the respondents, the latter in order to understand how respondents develop confidence in these core competencies. The research team undertook a comparative analysis of the NASIG and COAR (Confederation of Open Access Repositories) competencies to inform their assessment of the competencies for inclusion in this study. Our analysis of the comparison is online https://openresearch-repository.anu.edu.au/handle/1885/251609. The survey used Qualtrics software. The ANU ethics panel required that no question would be compulsory, thus not all respondents answered all questions. As a result, a pre-analysis was undertaken for each of the questions to determine the number of responses for each question. Participants were recruited through communication mediums used by the target cohort. The first completed response was received on 21 October 2020; the last was submitted on 3 December 2020. One hundred and sixty valid responses were received and analyzed using Excel and descriptive statistics for the quantitative questions, and NVivo and manual thematic coding for the qualitative questions. Each member of the team took responsibility for analysis of different quantitative questions and coding of the qualitative questions, after which the team came together and confirmed each other’s work. Initial analysis of the Confidence in Competency Areas included a set of specific Tasks for each Competency Area from matrix questions. The results from these questions were initially individually analyzed for each Competency Area. These charts demonstrated the depth of information available from these sections of the survey. We analyzed the level of confidence the responses showed across a Competency Area by adding together all of the Tasks listed in each Competency Area and then charting the total Confidence for each Competency Area.
DOI: 10.5517/ccdc.csd.cc1nx2m8
title: CCDC 1546330: Experimental Crystal Structure Determination

description: Related Article: P.I. Nkoe, H.G. Visser, C. Swart, A. Brink, M. Schutte-Smith|2018|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|74|1116|doi:10.1107/S205322961801207X
description: Related Article: P.I. Nkoe, A. Brink, M. Schutte-Smith, Chantelle Swarta, H.G Visser|2017|CSD Communication|||