cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2c80wk
title: CCDC 2182458: Experimental Crystal Structure Determination

description: Related Article: Bharti Singh, Natasha A. Chernova, Dibyajyoti Ghosh, Arunachalam Ramanan|2022|Cryst.Growth Des.|22|7090|doi:10.1021/acs.cgd.2c00791
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cm1qs
title: CCDC 2193055: Experimental Crystal Structure Determination

description: Related Article: Paola Paoli, Martina Lippi, Stella Milazzo, Patrizia Rossi, Jacopo Ceccarelli, Laura Chelazzi, Andrea Ienco, Luca Conti|2022|Cryst.Growth Des.|22|7176|doi:10.1021/acs.cgd.2c00849
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cm1rt
title: CCDC 2193056: Experimental Crystal Structure Determination

description: Related Article: Paola Paoli, Martina Lippi, Stella Milazzo, Patrizia Rossi, Jacopo Ceccarelli, Laura Chelazzi, Andrea Ienco, Luca Conti|2022|Cryst.Growth Des.|22|7176|doi:10.1021/acs.cgd.2c00849
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cm1sv
title: CCDC 2193057: Experimental Crystal Structure Determination

description: Related Article: Paola Paoli, Martina Lippi, Stella Milazzo, Patrizia Rossi, Jacopo Ceccarelli, Laura Chelazzi, Andrea Ienco, Luca Conti|2022|Cryst.Growth Des.|22|7176|doi:10.1021/acs.cgd.2c00849
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2cm1tw
title: CCDC 2193058: Experimental Crystal Structure Determination

description: Related Article: Paola Paoli, Martina Lippi, Stella Milazzo, Patrizia Rossi, Jacopo Ceccarelli, Laura Chelazzi, Andrea Ienco, Luca Conti|2022|Cryst.Growth Des.|22|7176|doi:10.1021/acs.cgd.2c00849
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7455415
title: Influenza phenotype-genotype database beta version

description: Beta version of a data base of genetic mutations in influenzaA viruses linked to phenotypic changes in the literature with references. Not exclusive and sources should be checked before use.
DOI: 10.5281/zenodo.7455414
title: Influenza phenotype-genotype database beta version

description: Beta version of a data base of genetic mutations in influenzaA viruses linked to phenotypic changes in the literature with references. Not exclusive and sources should be checked before use.
DOI: 10.5281/zenodo.7030030
title: Arctic specimens in the NHMO DNA bank Bird collection 2022

description: All Arctic specimens in the NHMO DNA bank Bird collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.
description: {"references": ["Johannessen et al. 2023. Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)"]}
DOI: 10.5281/zenodo.7429848
title: Arctic specimens in the NHMO DNA bank Bird collection 2022

description: All Arctic specimens in the NHMO DNA bank Bird collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.
description: {"references": ["Johannessen et al. 2023. Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)"]}
DOI: 10.5281/zenodo.7455413
title: Arctic specimens in the NHMO DNA bank Bird collection 2022

description: All Arctic specimens in the NHMO DNA bank Bird collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.
description: {"references": ["Johannessen et al. 2023. Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)"]}
DOI: 10.5281/zenodo.7030029
title: Arctic specimens in the NHMO DNA bank Bird collection 2022

description: All Arctic specimens in the NHMO DNA bank Bird collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.
description: {"references": ["Johannessen et al. 2023. Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)"]}
DOI: 10.34740/kaggle/dsv/4741518
title: Synthetic Faces High Quality (SFHQ) part 3
DOI: 10.5281/zenodo.7455528
title: Impact of larval high density on the performance of Anastrepha ludens: competition or feeding facilitation?

description: A critical point in diet management is maximizing density as a strategy for reducing costs. Artificial diets elaborated with large particle sizes have increased volume, possess high porosity and aeration capacity, are more penetrable and facilitate the movement of larvae, and increase the bioavailability of nutrients. The present experiment consisted in demonstrating that the bulking agent facilitates feeding and could have more capacity to support a high density of larvae with a minimal effect of competition on the life-history traits of <em>Anastrepha ludens</em>. The results indicate that density affected larval and pupal weight, but not pupation at 24 h, adult emergence, and flier percentage, which remained unchanged. However, there was an increase in yield and bioconversion. Larvae in high density conditions aggregated to increase the effect of regurgitation of amylases and proteases as a strategy to metabolize the food prior to ingestion through enzymes secreted in the saliva, contributing thus to feeding facilitation and the uptake of ingested food, which decreased the negative impact of competition in high density conditions. High density leads to an increase in food consumption and a consequent increase in digestive enzyme activity, contributing to the bioavailability of macronutrients ─ proteins, carbohydrates, and lipids ─ in the diet. Identifying the properties of bulking agents and other ingredients and their interaction under high-density conditions is essential to develop novel artificial diets and improve the mass-rearing strategies for the SIT.
DOI: 10.5281/zenodo.7455527
title: Impact of larval high density on the performance of Anastrepha ludens: competition or feeding facilitation?

description: A critical point in diet management is maximizing density as a strategy for reducing costs. Artificial diets elaborated with large particle sizes have increased volume, possess high porosity and aeration capacity, are more penetrable and facilitate the movement of larvae, and increase the bioavailability of nutrients. The present experiment consisted in demonstrating that the bulking agent facilitates feeding and could have more capacity to support a high density of larvae with a minimal effect of competition on the life-history traits of <em>Anastrepha ludens</em>. The results indicate that density affected larval and pupal weight, but not pupation at 24 h, adult emergence, and flier percentage, which remained unchanged. However, there was an increase in yield and bioconversion. Larvae in high density conditions aggregated to increase the effect of regurgitation of amylases and proteases as a strategy to metabolize the food prior to ingestion through enzymes secreted in the saliva, contributing thus to feeding facilitation and the uptake of ingested food, which decreased the negative impact of competition in high density conditions. High density leads to an increase in food consumption and a consequent increase in digestive enzyme activity, contributing to the bioavailability of macronutrients ─ proteins, carbohydrates, and lipids ─ in the diet. Identifying the properties of bulking agents and other ingredients and their interaction under high-density conditions is essential to develop novel artificial diets and improve the mass-rearing strategies for the SIT.
DOI: 10.7910/dvn/t2wfez
title: TopSharing – a JobSharing for leadership positions

description: Pandemic-related digitalization has a lot of positive effects on the professional parquet, despite the difficult circumstances we all experienced. It also brought a home office and a mind-shifting regarding the new working arrangements. One of the models is TopSharing, which refers to JobSharing in positions with a high level of responsibility. This model is a north star of flexible working arrangements and could be a solution to many challenges that companies, but also employees, are facing.
DOI: 10.26188/21747848.v1
title: Luo_Shiraz quality model.xlsx

description: Composition of grapes and wine collected by a variety of analytical methods. Details in prepring: <strong>https://doi.org/10.31220/agriRxiv.2022.00162</strong>
DOI: 10.26188/21747848
title: Luo_Shiraz quality model.xlsx

description: Composition of grapes and wine collected by a variety of analytical methods. Details in prepring: <strong>https://doi.org/10.31220/agriRxiv.2022.00162</strong>
DOI: 10.25400/lincolnuninz.21358455
title: Flood mitigation capability assessment and classification

description: The maximum flood mitigation capability (FMC) of each zone is evaluated by comparing the zone’s collectable runoff volumes with the amounts of catchment runoff reduction required to maintain flood protection objectives under climate change (<em>FMC</em> = 100∀<sub><em>C</em></sub> /<em>C</em><sub><em>R</em></sub><em>)</em>. The required catchment runoff volume reductions (<em>C</em><sub><em>R</em></sub>) are calculated by subtracting the amount of current flood protection capacity from the future catchment runoff volumes corresponding to each scenario and future period without GSI implementation. The collectable runoff volume (∀<sub><em>C</em></sub> ) is equal to the total amount of runoff from a zone drainage area if its potential in-ground storage capacity is not reached. However, if the total runoff volume from a drainage area exceeds the storage capacity, the surplus is assumed to discharge into the primary drainage system. The results of FMC assessment are used to identify FMC classes grouping industrial SWM zones capable of mitigating the similar severity of climate change impact to below the current flood protection capacity. FMC sub-classes categorise SWM zones within each class by the key factor that limits their FMC. Sub-classes include the size of the drainage area (<em>d</em>), the high groundwater level (<em>w</em>), and the amount of potential GSI area (<em>a</em>).
DOI: 10.5517/ccdc.csd.cc2c55lb
title: CCDC 2179721: Experimental Crystal Structure Determination

description: Related Article: Rajib Moi, Arijit Ghorai, Kartik Maity, Susanta Banerjee, Kumar Biradha|2022|Cryst.Growth Des.|22|7215|doi:10.1021/acs.cgd.2c00890
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d316s
title: CCDC 2207454: Experimental Crystal Structure Determination

description: Related Article: Luka Fotović, Nikola Bedeković, Katarina Pičuljan, Vladimir Stilinović|2022|Cryst.Growth Des.|22|7508|doi:10.1021/acs.cgd.2c01055
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d317t
title: CCDC 2207455: Experimental Crystal Structure Determination

description: Related Article: Luka Fotović, Nikola Bedeković, Katarina Pičuljan, Vladimir Stilinović|2022|Cryst.Growth Des.|22|7508|doi:10.1021/acs.cgd.2c01055
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d318v
title: CCDC 2207456: Experimental Crystal Structure Determination

description: Related Article: Luka Fotović, Nikola Bedeković, Katarina Pičuljan, Vladimir Stilinović|2022|Cryst.Growth Des.|22|7508|doi:10.1021/acs.cgd.2c01055
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d319w
title: CCDC 2207457: Experimental Crystal Structure Determination

description: Related Article: Luka Fotović, Nikola Bedeković, Katarina Pičuljan, Vladimir Stilinović|2022|Cryst.Growth Des.|22|7508|doi:10.1021/acs.cgd.2c01055
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d31bx
title: CCDC 2207458: Experimental Crystal Structure Determination

description: Related Article: Luka Fotović, Nikola Bedeković, Katarina Pičuljan, Vladimir Stilinović|2022|Cryst.Growth Des.|22|7508|doi:10.1021/acs.cgd.2c01055
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d31cy
title: CCDC 2207459: Experimental Crystal Structure Determination

description: Related Article: Luka Fotović, Nikola Bedeković, Katarina Pičuljan, Vladimir Stilinović|2022|Cryst.Growth Des.|22|7508|doi:10.1021/acs.cgd.2c01055
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d31dz
title: CCDC 2207460: Experimental Crystal Structure Determination

description: Related Article: Luka Fotović, Nikola Bedeković, Katarina Pičuljan, Vladimir Stilinović|2022|Cryst.Growth Des.|22|7508|doi:10.1021/acs.cgd.2c01055
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d31f0
title: CCDC 2207461: Experimental Crystal Structure Determination

description: Related Article: Luka Fotović, Nikola Bedeković, Katarina Pičuljan, Vladimir Stilinović|2022|Cryst.Growth Des.|22|7508|doi:10.1021/acs.cgd.2c01055
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d31g1
title: CCDC 2207462: Experimental Crystal Structure Determination

description: Related Article: Luka Fotović, Nikola Bedeković, Katarina Pičuljan, Vladimir Stilinović|2022|Cryst.Growth Des.|22|7508|doi:10.1021/acs.cgd.2c01055
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d3k69
title: CCDC 2207950: Experimental Crystal Structure Determination

description: Related Article: Man Ding, Jiarui Chang, Jia-Xue Mao, Jie Zhang, Xuenian Chen|2022|Organometallics|41|3636|doi:10.1021/acs.organomet.2c00445
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d92ld
title: CCDC 2213263: Experimental Crystal Structure Determination

description: Related Article: Rüdiger Borrmann, Dominik Zetschok, Helma Wennemers|2022|Org.Lett.|24|8683|doi:10.1021/acs.orglett.2c03568
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7455590
title: Trajectory Analysis: Monocle3 in RStudio - Galaxy Training Material

description: The input dataset for the trajectory analysis tutorial using Monocle3 in RStudio, which is a follow-up to the case study tutorials that can be found on the Galaxy Training Network.
DOI: 10.5281/zenodo.7455589
title: Trajectory Analysis: Monocle3 in RStudio - Galaxy Training Material

description: The input dataset for the trajectory analysis tutorial using Monocle3 in RStudio, which is a follow-up to the case study tutorials that can be found on the Galaxy Training Network.
DOI: 10.25400/lincolnuninz.21358338
title: Runoff volume calculation

description: Runoff volume is calculated using the Rational Method to determine runoff flow at the outlet of the industrial SWM zone. The flow volume is calculated by multiplying the flow rate by the catchment time of concentration (16 hours for the Heathcote River catchment). The results of the calculation indicate the optimum runoff volume, which can be managed within a green stormwater infrastructure network in the SWM zone.
DOI: 10.25919/zs9w-hv85
title: Draft genome assembly for Aedes notoscriptus

description: The genome was generated using a paired end illumina reads.
DOI: 10.25400/lincolnuninz.21300507
title: The characteristics of industrial SWM zones

description: The industrial SWM zone is a unit of analysis which is a cluster of industrial properties sharing a common drainage outlet. This spatial unit is used for land capability assessment and classification for adaptive flood mitigation planning.
DOI: 10.25400/lincolnuninz.21300507.v1
title: The characteristics of industrial SWM zones

description: The industrial SWM zone is a unit of analysis which is a cluster of industrial properties sharing a common drainage outlet. This spatial unit is used for land capability assessment and classification for adaptive flood mitigation planning.
DOI: 10.25400/lincolnuninz.21300522
title: The estimation of in-ground storage capacity

description: Maximum in-ground storage capacity within the SWM zone is estimated from the measurement of potential GSI area and maximum in-ground storage depth associated with the high groundwater level.
DOI: 10.6084/m9.figshare.21747854
title: Data Set 1 and 2.xlsx

description: two datasets in one excel file to analyse a regression model for distance calculation
DOI: 10.25919/hp42-4379
title: Australian energy transition data

description: In order to explore the scale of the challenge in transition the electricity sector to meet the challenge of Net Zero Emissions a time series data set was created. This data set provides a continuous annual time series of historical and projected Australian electricity generation and capacity data from 1900 to 2050.
DOI: 10.26186/133822
title: Chloride Mass Balance recharge estimation, points dataset, McBride and Nulla basalt provinces

description: This dataset includes point estimates of groundwater recharge in mm/year. Recharge rates have been estimated at monitoring bore locations in the basaltic aquifers of the Nulla and McBride basalt provinces. Recharge estimates have been calculated using the “chloride mass balance” method. The chloride mass balance process assumes that the chloride ion is a conservative tracer in precipitation, evapotranspiration, recharge and runoff; and that all the chloride is from rainfall, instead of for example halite saturation or dissolution processes. So the volumetric water balance and the flux of chloride balance must both be true. Assuming that runoff and evapotranspiration are negligible (so approximated by zero), the equation is simplified: Water balance P=ET+R+Q Water balance multiplied by chloride concentrations (chloridefluxbalance) P∙Cl_ppt=ET∙Cl_ET+R∙Cl_gw+Q∙Cl_riv | ΔCl_reac≈0 Assumptions to simplify equation P∙Cl_ppt=R∙Cl_gw | Q≈0 & ET≈0 Rearranging for recharge rate (unknown) R=P∙(Cl_ppt)/(Cl_gw ) | Q≈0 & ET≈0 Where P = precipitation rate; ET = evapotranspiration rate; R = recharge rate; Q = runoff to streams; Clppt = concentration of Cl in precipitation; ClET = concentration of chloride in evapotranspiration; Clgw = concentration of Cl in groundwater; Clriv = concentration of chloride in river runoff; ΔClreac = change in chloride concentrations from reactions.