cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc6dm8g
title: CCDC 190875: Experimental Crystal Structure Determination

description: Related Article: D.L.J.Clive, S.P.Fletcher|2002|Chem.Commun.||1940|doi:10.1039/b205753k
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/cc6bh77
title: CCDC 188828: Experimental Crystal Structure Determination

description: Related Article: J.D.Harvey, C.J.Ziegler|2002|Chem.Commun.||1942|doi:10.1039/b205345d
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/cc68cph
title: CCDC 186796: Experimental Crystal Structure Determination

description: Related Article: J.D.Harvey, C.J.Ziegler|2002|Chem.Commun.||1942|doi:10.1039/b205345d
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/cc69wp1
title: CCDC 188253: Experimental Crystal Structure Determination

description: Related Article: A.P.Henderson, E.Mutlu, A.Leclercq, C.Bleasdale, W.Clegg, R.A.Henderson, B.T.Golding|2002|Chem.Commun.||1956|doi:10.1039/b205079j
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/cc69wq2
title: CCDC 188254: Experimental Crystal Structure Determination

description: Related Article: A.P.Henderson, E.Mutlu, A.Leclercq, C.Bleasdale, W.Clegg, R.A.Henderson, B.T.Golding|2002|Chem.Commun.||1956|doi:10.1039/b205079j
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.17287048
title: SolPOC v0.9.7 correct output of exemples

description: This archive two folder

1. Folder "SolPOC_v097_correct_ouput" contains a modified version of the “Example” folder from SolPOC v0.9.7.

It includes one output folder per script, providing examples of the expected results when the code runs correctly.

In some cases, the output corresponds to the console output (for instance, for scripts such as solpoc_and_tmm_with_nevergrad or solpoc_and_tmm_with_scipy). The text printed in the terminal has been saved in a .txt file.

In other cases, the output is a result folder generated by the script itself (e.g., by “template” scripts such as template_AR or template_selective_coating). These folders are originally named automatically based on the execution date and time, but have been renamed for clarity to better reflect their content.

Starting from version 0.9.7, these example outputs are no longer included in the main SolPOC package, as they significantly increase package size and are not aligned with Python’s best packaging practices. They are instead provided here as a separate Zenodo archive for reference and reproducibility purposes.

2. Folder "paper_example" contains the 3 differents exemples presented in the paper "Solar Performance Optimization Code for the Optical Response of Multilayer Stacks in Python: SolPOC", section 5.1.
DOI: 10.5281/zenodo.17340621
title: SolPOC v0.9.7 correct output of exemples

description: This archive two folder

1. Folder "SolPOC_v097_correct_ouput" contains a modified version of the “Example” folder from SolPOC v0.9.7.

It includes one output folder per script, providing examples of the expected results when the code runs correctly.

In some cases, the output corresponds to the console output (for instance, for scripts such as solpoc_and_tmm_with_nevergrad or solpoc_and_tmm_with_scipy). The text printed in the terminal has been saved in a .txt file.

In other cases, the output is a result folder generated by the script itself (e.g., by “template” scripts such as template_AR or template_selective_coating). These folders are originally named automatically based on the execution date and time, but have been renamed for clarity to better reflect their content.

Starting from version 0.9.7, these example outputs are no longer included in the main SolPOC package, as they significantly increase package size and are not aligned with Python’s best packaging practices. They are instead provided here as a separate Zenodo archive for reference and reproducibility purposes.

2. Folder "paper_example" contains the 3 differents exemples presented in the paper "Solar Performance Optimization Code for the Optical Response of Multilayer Stacks in Python: SolPOC", section 5.1.
DOI: 10.5281/zenodo.17010402
title: Data for the manuscript 'Large-scale GWAS meta-analysis of serum antibody levels reveals distinct genetic architectures'
DOI: 10.5281/zenodo.15545977
title: Data and Codes for "Tropical forest carbon offsets deliver partial gains amid persistent over-crediting"

description: These are data and codes associalte with a paper entitled "Tropical forest carbon offsets deliver partial gains amid persistent over-crediting", which was published in Science (DOI: 10.1126/science.adw4094).
DOI: 10.5281/zenodo.15475924
title: Data and Codes for "Tropical forest carbon offsets deliver partial gains amid persistent over-crediting"

description: These are data and codes associalte with a paper entitled "Tropical forest carbon offsets deliver partial gains amid persistent over-crediting", which was published in Science (DOI: 10.1126/science.adw4094).
DOI: 10.6084/m9.figshare.30137563.v3
title: jawm data

description: Input data for testing jawm workflows.
DOI: 10.6084/m9.figshare.30344629
title: Park et al__SSC based fruit maturity effects on fruit quality__raw dataset

description: This dataset provides the processed results from untargeted metabolomic analysis of ‘Autumn Sense’ hardy kiwifruit obtained using GC-MS/MS. The available data include mean, standard deviation (SD), standard error (SE), and %RSD values of identified metabolites across different harvest maturities and storage durations. The original instrumental raw files were not available from the analytical service provider; therefore, only the processed quantitative results are made publicly accessible in accordance with FAIR data principles to ensure transparency and reusability.
DOI: 10.6084/m9.figshare.30344629.v1
title: Park et al__SSC based fruit maturity effects on fruit quality__raw dataset

description: This dataset provides the processed results from untargeted metabolomic analysis of ‘Autumn Sense’ hardy kiwifruit obtained using GC-MS/MS. The available data include mean, standard deviation (SD), standard error (SE), and %RSD values of identified metabolites across different harvest maturities and storage durations. The original instrumental raw files were not available from the analytical service provider; therefore, only the processed quantitative results are made publicly accessible in accordance with FAIR data principles to ensure transparency and reusability.
DOI: 10.6084/m9.figshare.30344623
title: Compound class and environmental data for Reframing natural organic matter research through compositional data analysis

description: Supplementary data for the Review paper by Kida et al. "Reframing natural organic matter research through compositional data analysis", to be used in the supplementary R demo code. These data were originally produced and published in the following paper, which is freely available under the license CC-BY 4.0: https://doi.org/10.1021/acs.est.3c00249
DOI: 10.6084/m9.figshare.30344623.v1
title: Compound class and environmental data for Reframing natural organic matter research through compositional data analysis

description: Supplementary data for the Review paper by Kida et al. "Reframing natural organic matter research through compositional data analysis", to be used in the supplementary R demo code. These data were originally produced and published in the following paper, which is freely available under the license CC-BY 4.0:https://doi.org/10.1021/acs.est.3c00249
DOI: 10.6084/m9.figshare.30344623.v2
title: Compound class and environmental data for Reframing natural organic matter research through compositional data analysis

description: Supplementary data for the Review paper by Kida et al. "Reframing natural organic matter research through compositional data analysis", to be used in the supplementary R demo code. These data were originally produced and published in the following paper, which is freely available under the license CC-BY 4.0: https://doi.org/10.1021/acs.est.3c00249
DOI: 10.5287/ora-00xxnmb7z-1
title: Proline enhances repulsive double-layer forces between charged particles (data)

description: Measurements of interaction Gibbs free energies across proline-KCl aqeuous solutions as discussed and analysed in the associated manuscript.
DOI: 10.5287/ora-00xxnmb7z-2
title: The influence of proline on surface interactions in aqueous solutions (data)

description: Measurements of interaction Gibbs free energies across proline-KCl aqeuous solutions as discussed and analysed in the associated manuscript.
DOI: 10.5281/zenodo.17340669
title: Immune cell clustering and Immune Environment in RIF
DOI: 10.5281/zenodo.17340670
title: Immune cell clustering and Immune Environment in RIF
DOI: 10.48623/aperta.286296
title: A Cross-Sectional Study

description: The dataset supporting the findings of this study was deposited in Aperta TÜBİTAK Open Archive for transparency and reproducibility purposes. No preprint version of the manuscript exists.
DOI: 10.5281/zenodo.17340726
title: Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland

description: Der Datensatz "Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland" des Robert Koch-Instituts dokumentiert die tägliche intensivmedizinische Versorgungslage seit der COVID-19-Pandemie. Basierend auf Meldungen aller intensivbettenführenden Krankenhäuser in Deutschland erfasst das DIVI-Intensivregister Echtzeitdaten zu belegten und freien Intensivbetten. Die Erhebung differenziert nach Altersgruppen, Regionen und Versorgungsstufen. COVID-19-Fälle auf Intensivstationen werden gesondert ausgewiesen. Die Daten stehen aggregiert auf Bundes-, Landes- und Kreisebene zur Verfügung. Damit bildet der Datensatz eine Grundlage für die Überwachung von Kapazitäten, die Koordination von Behandlungskapazitäten und politische Entscheidungsprozesse während der Pandemie und darüber hinaus.
DOI: 10.17030/uclan.data.00000632
title: Effects of peppermint (Mentha piperita L.) oil in cardiometabolic outcomes in participants with pre and stage 1 hypertension: A placebo randomized controlled trial
DOI: 10.15161/oar.it/h4ke1-dnf86
title: The harmonised geological map of Italy 1:100,000 scale

description: The INSPIRE Directive establishes a European infrastructure for spatial information to support the environmental policies of the European Union. Thirty-four different themes representing various environmental information have been identified. Geology theme is modelled conforming specific data structure that is Geology, Geophysic, Hydrogeology, which is further divided into three subthemes. Geology is in "priority dataset" list  as it provides fundamental knowledge about the physical properties and composition of rocks and sediments, their structure, and their age, as depicted in geological maps, including geomorphological features and it represents the base map for geological phenomenon monitoring.

In the INSPIRE data model, specific codelists have been reused in the different field types to satisfy the compliance to the geological information content. Some of the codelists fully comply with the features contained in the 1:100,000 geological map database are integrated in the semantic harmonization process.

In 2021-22, the dataset was further harmonized in accordance with the INSPIRE and GeoSciML data models.

The Geological Map of Italy at a scale of 1:100,000 is currently the most comprehensive and detailed geological map available for Italy. It consists of a compilation of 277 sheets and was created over a period exceeding 100 years, with certain sheets having two editions. In the late 1990s, the map was converted into a vector database through the digitization of raster format data. The dataset underwent revision, integration, and correction by the Geological Survey of Italy between 2005 and 2009.

The dataset is served through a specific online resource allows the visualization of the Geologic Map of Italy 1:100,000 scale, in a digital format. The geology has been grouped based on the lower age of the geological units. Additionally, the service allows for the visualization of tectonic features.

The map has been harmonized in the framework of the activity of the Geological Survey of Italy - ISPRA with a support by the Joint Research Unit - EPOS Italia (2020-2022).
DOI: 10.15161/oar.it/jn2s3-8yg30
title: The harmonised geological map of Italy 1:100,000 scale

description: The INSPIRE Directive establishes a European infrastructure for spatial information to support the environmental policies of the European Union. Thirty-four different themes representing various environmental information have been identified. Geology theme is modelled conforming specific data structure that is Geology, Geophysic, Hydrogeology, which is further divided into three subthemes. Geology is in "priority dataset" list  as it provides fundamental knowledge about the physical properties and composition of rocks and sediments, their structure, and their age, as depicted in geological maps, including geomorphological features and it represents the base map for geological phenomenon monitoring.

In the INSPIRE data model, specific codelists have been reused in the different field types to satisfy the compliance to the geological information content. Some of the codelists fully comply with the features contained in the 1:100,000 geological map database are integrated in the semantic harmonization process.

In 2021-22, the dataset was further harmonized in accordance with the INSPIRE and GeoSciML data models.

The Geological Map of Italy at a scale of 1:100,000 is currently the most comprehensive and detailed geological map available for Italy. It consists of a compilation of 277 sheets and was created over a period exceeding 100 years, with certain sheets having two editions. In the late 1990s, the map was converted into a vector database through the digitization of raster format data. The dataset underwent revision, integration, and correction by the Geological Survey of Italy between 2005 and 2009.

The dataset is served through a specific online resource allows the visualization of the Geologic Map of Italy 1:100,000 scale, in a digital format. The geology has been grouped based on the lower age of the geological units. Additionally, the service allows for the visualization of tectonic features.

The map has been harmonized in the framework of the activity of the Geological Survey of Italy - ISPRA with a support by the Joint Research Unit - EPOS Italia (2020-2022).
DOI: 10.6084/m9.figshare.30344857
title: Bearing capacity of plate anchors under out-of-plane loading

description: In the Excel file, one can find z, ex,ey, theta, beta and nc. The loading on the plate anchor is expressed with a force T, two position parameters (ex and ey) and two angle parameters (β and θ). ex and ey represent the eccentricities in the x- and y-directions, respectively, while β and θ are the loading and eccentric angles, which describes the direction of the applied load. z is the embedment depth of the plate anchor. Nc is the normalized bearing capacity.
DOI: 10.6084/m9.figshare.30344857.v1
title: Bearing capacity of plate anchors under out-of-plane loading

description: In the Excel file, one can find z, ex,ey, theta, beta and nc. The loading on the plate anchor is expressed with a force T, two position parameters (ex and ey) and two angle parameters (β and θ). ex and ey represent the eccentricities in the x- and y-directions, respectively, while β and θ are the loading and eccentric angles, which describes the direction of the applied load. z is the embedment depth of the plate anchor. Nc is the normalized bearing capacity.
DOI: 10.25824/redu/2yrto3
title: Dados de magnetização, espectroscopia Raman e difração de raios-X Sincrotron em função da temperatura e de espectroscopia de raios-X por dispersão de energia das manganitas La5/8-yPryCa3/8MnO3 (y = 0.35, 0.40, 0.45)

description: Aqui se encontram dados não tratados de curvas de magnetização em função da temperatura para diferentes valores de campos magnéticos aplicados (50 Oe - 5 kOe) e diferentes protocolos de variação de temperatura, espectros Raman em função da temperatura (15 K - 300 K), da potencia do laser analisador (10 mW - 150 mW) e em função de variações cíclicas da potência e difratogramas de Raios-X Sincrotron em temperatura ambiente e em baixas temperaturas (~115 K - 300 K) e, imagens HAADF e espectros de raios-X por dispersão de energia para as manganitas La5/8-yPryCa3/8MnO3 (y = 0.35, 0.40, 0.45)
DOI: 10.5286/isis.e.rb2520324-1
title: Towards increasingly complex model systems for the study of extracellular vesicle-like interactions with the endosomal membrane.

description: Extracellular vesicle (EVs) are lipid membrane-bound nanoparticles produced by cells, which measure 10s to 1000s of nm in diameter. Due to their provenance, they are innately biocompatible and have huge potential in therapeutics and diagnostics. While the roles of the proteins and nucleic acid cargoes are well studied in these processes and applications, less is known about the role of the lipids. Two crucial steps in delivery of nanoparticle-encapsulated cargo, such as EV-mediated delivery of nucleic acids, are cell uptake and subsequent endosomal escape. Both of these processes involve traversing a lipid membrane, although the physical and chemical environments vary between these two processes. In this NR experiment, we will study how vesicles mimicking the lipid composition of EVs interact with floating membrane bilayers representing the intracellular endosomal membrane at different stages of maturation, and study the fusion processes observed at a range of physiologically relevant pH. This beamtime represents the third in a series of neutron reflectometry experiments in which we will identify the role that the lipid composition and asymmetric distribution of EVs and their synthetic mimics play in cell uptake and endosomal release. It is a crucial step forward in the field of understanding the biophysical mechanisms behind such biological processes, and paves the way towards identifying new EV-mimicking drug delivery vehicles for nucleic acid therapies.
DOI: 10.5286/isis.e.rb2520324
title: Towards increasingly complex model systems for the study of extracellular vesicle-like interactions with the endosomal membrane.

description: Extracellular vesicle (EVs) are lipid membrane-bound nanoparticles produced by cells, which measure 10s to 1000s of nm in diameter. Due to their provenance, they are innately biocompatible and have huge potential in therapeutics and diagnostics. While the roles of the proteins and nucleic acid cargoes are well studied in these processes and applications, less is known about the role of the lipids. Two crucial steps in delivery of nanoparticle-encapsulated cargo, such as EV-mediated delivery of nucleic acids, are cell uptake and subsequent endosomal escape. Both of these processes involve traversing a lipid membrane, although the physical and chemical environments vary between these two processes. In this NR experiment, we will study how vesicles mimicking the lipid composition of EVs interact with floating membrane bilayers representing the intracellular endosomal membrane at different stages of maturation, and study the fusion processes observed at a range of physiologically relevant pH. This beamtime represents the third in a series of neutron reflectometry experiments in which we will identify the role that the lipid composition and asymmetric distribution of EVs and their synthetic mimics play in cell uptake and endosomal release. It is a crucial step forward in the field of understanding the biophysical mechanisms behind such biological processes, and paves the way towards identifying new EV-mimicking drug delivery vehicles for nucleic acid therapies.
DOI: 10.6084/m9.figshare.30344872
title: Supplementary material from Avian malaria and the overlooked metabolic pathways underlying mosquito-Plasmodium interactions

description: Tryptophan Degradation via the Kynurenine Pathway. The diagram shows the conversion of tryptophan through the kynurenine pathway. Tryptophan is converted to formylkynurenine by TDO, then to kynurenine by KFase. Kynurenine is metabolized to either kynurenic acid via KAT or to 3-hydroxykynurenine by KMO. 3-Hydroxykynurenine is further converted to xanthurenic acid by HKT or to ommochrome by PHS.
DOI: 10.6084/m9.figshare.30344872.v1
title: Supplementary material from Avian malaria and the overlooked metabolic pathways underlying mosquito-Plasmodium interactions

description: Tryptophan Degradation via the Kynurenine Pathway. The diagram shows the conversion of tryptophan through the kynurenine pathway. Tryptophan is converted to formylkynurenine by TDO, then to kynurenine by KFase. Kynurenine is metabolized to either kynurenic acid via KAT or to 3-hydroxykynurenine by KMO. 3-Hydroxykynurenine is further converted to xanthurenic acid by HKT or to ommochrome by PHS.