cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2d94ql
title: CCDC 2213329: Experimental Crystal Structure Determination

description: Related Article: Guilin Hu, Jingyi He, Jing Chen, Yongjun Li|2023|J.Am.Chem.Soc.|145|5400|doi:10.1021/jacs.2c13604
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.cc2d94xs
title: CCDC 2213335: Experimental Crystal Structure Determination

description: Related Article: Guilin Hu, Jingyi He, Jing Chen, Yongjun Li|2023|J.Am.Chem.Soc.|145|5400|doi:10.1021/jacs.2c13604
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.cc2d9v1m
title: CCDC 2213990: Experimental Crystal Structure Determination

description: Related Article: Qing-Hong Meng, Hui Liu, Kai-Bo Wang, Shan-Shan Ling, Huan Yan, Hai-Yang Liu|2023|Fitoterapia|166|105459|doi:10.1016/j.fitote.2023.105459
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.cc2dc8bd
title: CCDC 2215363: Experimental Crystal Structure Determination

description: Related Article: Vadim Adamovich, Llorenç Benavent, Pierre-Luc T. Boudreault, Miguel A. Esteruelas, Ana M. López, Enrique Oñate, Jui-Yi Tsai|2023|Inorg.Chem.|62|3847|doi:10.1021/acs.inorgchem.2c04106
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.cc2dc8cf
title: CCDC 2215364: Experimental Crystal Structure Determination

description: Related Article: Vadim Adamovich, Llorenç Benavent, Pierre-Luc T. Boudreault, Miguel A. Esteruelas, Ana M. López, Enrique Oñate, Jui-Yi Tsai|2023|Inorg.Chem.|62|3847|doi:10.1021/acs.inorgchem.2c04106
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.cc2dcgv3
title: CCDC 2215565: Experimental Crystal Structure Determination

description: Related Article: Jifa Zhang, Lun Tan, Chengyong Wu, Yuyan Li, Hao Chen, Yinghuan Liu, Yuxi Wang|2023|Eur.J.Med.Chem.|248|115085|doi:10.1016/j.ejmech.2022.115085
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.cc2dd9sx
title: CCDC 2216369: Experimental Crystal Structure Determination

description: Related Article: Da-Hua Shi|2022|CSD Communication|||
description: Related Article: Jing-Pei Zou, Zhen Zhang, Jin-Yu Lv, Xiao-Qing Zhang, Zhao-Yuan Zhang, Shu-Tong Han, Yu-Wei Liu, Wei-Wei Liu, Jing Ji, Da-Hua Shi|2023|Tetrahedron|134|133293|doi:10.1016/j.tet.2023.133293
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: 10.6084/m9.figshare.22241197
title: Mechanism of MTO1 regulation of related gene expression in hypertrophic cardiomyopathy

description: <strong>The raw data of</strong><em><strong> Mechanism of MTO1 regulation of related gene expression in hypertrophic cardiomyopathy.</strong></em>
DOI: 10.6084/m9.figshare.22241197.v1
title: Mechanism of MTO1 regulation of related gene expression in hypertrophic cardiomyopathy

description: <strong>The raw data of</strong><em><strong> Mechanism of MTO1 regulation of related gene expression in hypertrophic cardiomyopathy.</strong></em>
DOI: 10.5281/zenodo.5767930
title: Food Pricing Data in Newfoundland and Labrador, Canada 2020-2021

description: This dataset contains data for food prices on several key food items in the Canadian province of Newfoundland and Labrador collected using citizen science, from October 2020 to December 2021. Data were collected in different places in the province on different timelines, with some locations receiving regular biweekly data and others having a single date inputted. The goal of this dataset was to gain high-resolution temporal data on food pricing to see variations over time, place, stores, online vs in-person shopping, sales, and food items. This data is the basis of the report: Liboiron, Max, Willa Neilsen, Morgan Davidson, Sarah Crocker, Amanda Asiamah, Kaitlyn Hawkins, Brittany Marie Schaefer, Patricia Johnson-Castle, Kerri Claire Neil, Lynn Blackwood, Corinne Neil, Sarah Sauvé, and Charlotte Florian. (2023). <em>Comparative Food Pricing in Newfoundland and Labrador using Citizen Science, 2020-2021</em>. Civic Laboratory for Environmental Action Research (CLEAR). St. John’s: Memorial University. Report, figures, etc are available at https://civiclaboratory.nl/nl-food-pricing-project/
DOI: 10.5281/zenodo.5767931
title: Food Pricing Data in Newfoundland and Labrador, Canada 2020-2021

description: This dataset contains data for food prices on several key food items in the Canadian province of Newfoundland and Labrador collected using citizen science. Data was collected in different places in the province on different timelines, with some locations receiving regular biweekly data and others having a single date inputted. The goal of this dataset was to gain high resolution temporal data on food pricing to see variations over time, place, stores, online vs inperson shopping, sales, and food items.
DOI: 10.5281/zenodo.7712681
title: Food Pricing Data in Newfoundland and Labrador, Canada 2020-2021

description: This dataset contains data for food prices on several key food items in the Canadian province of Newfoundland and Labrador collected using citizen science, from October 2020 to December 2021. Data were collected in different places in the province on different timelines, with some locations receiving regular biweekly data and others having a single date inputted. The goal of this dataset was to gain high-resolution temporal data on food pricing to see variations over time, place, stores, online vs in-person shopping, sales, and food items. This data is the basis of the report: Liboiron, Max, Willa Neilsen, Morgan Davidson, Sarah Crocker, Amanda Asiamah, Kaitlyn Hawkins, Brittany Marie Schaefer, Patricia Johnson-Castle, Kerri Claire Neil, Lynn Blackwood, Corinne Neil, Sarah Sauvé, and Charlotte Florian. (2023). <em>Comparative Food Pricing in Newfoundland and Labrador using Citizen Science, 2020-2021</em>. Civic Laboratory for Environmental Action Research (CLEAR). St. John’s: Memorial University. Report, figures, etc are available at https://civiclaboratory.nl/nl-food-pricing-project/
DOI: 10.5519/qd.c1osp3kh
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1456 records)
DOI: 10.15128/r2mw22v5475
title: Survey answers on the academic and professional profile of modern foreign languages' teachers in HE [dataset]

description: This paper deals with the role of the scholarship of teaching and learning (SoTL) among staff on teaching-only contracts in UK higher education, particularly in contrast with the activities of research-active staff. Drawing on the results of a quantitative study carried out during the summer of 2021 among modern foreign language teachers in 64 UK universities, the questions of the status and purpose of SoTL are addressed. The results of the survey show that, beyond the traditional two-tier division of academic labour based on the research vs. scholarship dichotomy, a third group of academic staff with no research or SoTL responsibilities has emerged in recent years. The paper concludes with recommendations to review the existing divisions between staff on research, SoTL and teaching-only contracts in order to create a more inclusive academic environment where individuals in different types of academic posts can fulfil their scholarly potential.
DOI: 10.34740/kaggle/dsv/5134823
title: COVID Vaccination in World (updated daily)
DOI: 10.15468/s2u4wd
title: List of animal species included into the Red Data Book of Ukraine

description: A core part of the national legislation of Ukraine for the protection of rare and threatened species of animals, plants, and fungi is the Red Data Book of Ukraine (RDB). RDB is governed by the corresponding Law and issued once a decade as a scientific edition (Vol. 1 — Animals, Vol. 2 — Plants, Fungi, and Algae). Species listed in the RDB of Ukraine and in the international conservation agreements valid in the territory of Ukraine, such as IUCN Red List or Appendices of Bern Convention, might partly overlap. Although the RDB of Ukraine, as an edition, was issued for the last time back in 2009 y., in 2021, the Ministry of Environmental Protection and Natural Resources of Ukraine issued two Orders establishing lists of animal, plant, fungal, and algae species that must be included into the next edition of the RDB of Ukraine. Since then, mentioned species have acquired legal conservation statuses throughout all the territory of Ukraine and are considered protected according to the Law of the Red Bada Book of Ukraine. Here we present the list of animal species approved to be included in the Red Data Book of Ukraine by Order of the Ministry of Environmental Protection and Natural Resources of Ukraine № 29 as of 19 January 2021.
DOI: 10.5281/zenodo.7648977
title: GIS Data for Optimising Vaccination Center Placement in Flanders and Brussels

description: This dataset collection contains geospatial data that was used in a study on optimising vaccination center placement in Flanders and Brussels Capital Region (Belgium). The collection includes five raster datasets, a road network dataset in vector format, and datasets of potential vaccination facilities in vector format. The raster datasets provide information on population density, mean age, proximity to the nearest N-road, travel time to the nearest hospital, and node value of collective transport. These datasets cover the region of Flanders and the Brussels Capital Region, and have been normalised on a scale of 0 to 1. The population density data was sourced from Statbel [1], while the road network and the hospital locations were queried from OpenStreetMap [2]. The node value of collective transport dataset was obtained from a study by Verachtert et al. [3]. The road network dataset is a multilinestring vector dataset that includes all of the roads in Belgium. This dataset can be used to analyse traffic flow and identify optimal locations for vaccine centers. The two point vector datasets contain the locations of potential vaccination facilities within the province of Antwerp, with one dataset including 14 facilities and the other including 7 facilities. These datasets can be used to evaluate the effectiveness of different vaccine center placement strategies. The geospatial data in this collection is stored in GeoJSON format for the road network and the potential vaccination facilities and GeoTIFF format for the raster datasets and can be accessed and analyzed using a variety of geospatial tools and software.
description: {"references": ["[1] Statbel, 2020. Population according to the km2 grid. Available from: https://statbel.fgov.be/en/open-data/population-according-km2-grid-2020", "[2] OpenStreetMap contributors. OpenStreetMap. 2023. Available from: https://www.openstreetmap.org/", "[3] Verachtert, E., Mayeres, I., Poelmans, L., der Meulen, M., Vanhulsel, M., and Engelen, G., 2016. Ontwikkelingskansen op basis van knooppuntwaarde en nabijheid voorzieningen--eindrapport."]}
DOI: 10.15128/r26q182k16s
title: Approaching STEP file analysis as a language processing task: A robust and scale-invariant solution for machining feature recognition [dataset]

description: Machining feature recognition is a key task in the intelligent analysis of 3D CAD models as it represents a bridge between a part design and the manufacturing processes required for manufacture and can, therefore, increase automation in the manufacturing process. As 3D model files do not naturally conform to the fixed size necessary as the input to most varieties of neural network, most existing solutions for machining feature recognition rely on either transforming CAD models into a fixed shape representation, accepting some loss of information in the process, or employ rigid rules-based feature extraction techniques prior to applying any learning-based algorithm, resulting in solutions which may display high performance for specific applications but which lack in the flexibility provided by a purely learning-based approach. In this paper, we present a novel machining feature recognition model, which is capable of interpreting the data present in a STEP (standard for the exchange of product data) file using purely learning-based algorithms, with no need for human input. Our model builds on the basic framework for feature extraction from STEP file data proposed in \cite{encoder}, with the design of a decoder network capable of using extracted features to perform the complex task of machining feature recognition. Model performance is evaluated based on accuracy at the task of identifying 24 classes of machining feature in CAD models containing between two and ten intersecting features. Results demonstrate that our solution achieves comparable performance with existing solutions when given data similar to that used during training and significantly increased robustness when compared to existing solutions when presented with CAD models which vary from those seen during training and contain small features.
DOI: 10.5281/zenodo.7648978
title: GIS Data for Optimising Vaccination Center Placement in Flanders and Brussels

description: This dataset collection contains geospatial data that was used in a study on optimising vaccination center placement in Flanders and Brussels Capital Region (Belgium). The collection includes five raster datasets, a road network dataset in vector format, and datasets of potential vaccination facilities in vector format. The raster datasets provide information on population density, mean age, proximity to the nearest N-road, travel time to the nearest hospital, and node value of collective transport. These datasets cover the region of Flanders and the Brussels Capital Region, and have been normalised on a scale of 0 to 1. The population density data was sourced from Statbel [1], while the road network and the hospital locations were queried from OpenStreetMap [2]. The node value of collective transport dataset was obtained from a study by Verachtert et al. [3]. The road network dataset is a multilinestring vector dataset that includes all of the roads in Belgium. This dataset can be used to analyse traffic flow and identify optimal locations for vaccine centers. The two point vector datasets contain the locations of potential vaccination facilities within the province of Antwerp, with one dataset including 14 facilities and the other including 7 facilities. These datasets can be used to evaluate the effectiveness of different vaccine center placement strategies. The geospatial data in this collection is stored in GeoJSON format for the road network and the potential vaccination facilities and GeoTIFF format for the raster datasets and can be accessed and analyzed using a variety of geospatial tools and software.
description: {"references": ["[1] Statbel, 2020. Population according to the km2 grid. Available from: https://statbel.fgov.be/en/open-data/population-according-km2-grid-2020", "[2] OpenStreetMap contributors. OpenStreetMap. 2023. Available from: https://www.openstreetmap.org/", "[3] Verachtert, E., Mayeres, I., Poelmans, L., der Meulen, M., Vanhulsel, M., and Engelen, G., 2016. Ontwikkelingskansen op basis van knooppuntwaarde en nabijheid voorzieningen--eindrapport."]}
DOI: 10.5281/zenodo.6411375
title: Overview of PASTA Questionnaires: Physical Activity through Sustainable Transport Approaches (PASTA): protocol for a multi-centre, longitudinal study

description: Overview of questionnaires used in the PASTA project (Physical Activity through Sustainable Transport Approaches (PASTA)) English version
description: https://doi.org/10.1186/s12889-015-2453-3
DOI: 10.5281/zenodo.6411374
title: Overview of PASTA Questionnaires: Physical Activity through Sustainable Transport Approaches (PASTA): protocol for a multi-centre, longitudinal study

description: Overview of questionnaires used in the PASTA project (Physical Activity through Sustainable Transport Approaches (PASTA)) English version
description: https://doi.org/10.1186/s12889-015-2453-3
DOI: 10.5281/zenodo.7692055
title: Cost Calculation Simulation for Optimising Vaccination Center Placement in Belgium

description: This Excel spreadsheet contains a cost calculation simulation for optimising vaccination center placement in Belgium. It consists of three tabs: the Last Mile Cost Calculation, the Mobility Cost Calculation, and the Total Cost Calculation summary. The Last Mile Cost Calculation tab calculates the cost of the last mile of transportation from a potential vaccination centre to the population, simulated at five scenarios (i.e., five different spatial levels). The Mobility Cost Calculation tab gives a cost estimation for the people to be vaccinated to the vaccination centres. The Total Cost Calculation summary tab provides an overview of the total cost for each of the five scenarios. These calculations can be used to optimise the placement of vaccination centers in Belgium, with the aim of achieving the most efficient and cost-effective vaccination strategy.
DOI: 10.5281/zenodo.7692056
title: Cost Calculation Simulation for Optimising Vaccination Center Placement in Belgium

description: This Excel spreadsheet contains a cost calculation simulation for optimising vaccination center placement in Belgium. It consists of three tabs: the Last Mile Cost Calculation, the Mobility Cost Calculation, and the Total Cost Calculation summary. The Last Mile Cost Calculation tab calculates the cost of the last mile of transportation from a potential vaccination centre to the population, simulated at five scenarios (i.e., five different spatial levels). The Mobility Cost Calculation tab gives a cost estimation for the people to be vaccinated to the vaccination centres. The Total Cost Calculation summary tab provides an overview of the total cost for each of the five scenarios. These calculations can be used to optimise the placement of vaccination centers in Belgium, with the aim of achieving the most efficient and cost-effective vaccination strategy.
DOI: 10.6084/m9.figshare.20270731
title: How significant a role can China’s forest sector play in decarbonizing its economy?

description: To assess the potential role of China’s forest sector in achieving its goals of carbon (C) emission reduction and neutralization, it is essential to project C sequestered by forest ecosystems (FESs) and stored in harvested wood products (HWPs). It is also crucial to put these actions into a coherent framework of C accounting. Building on these steps, we show that during 2006–2020, the sector’s biomass and soil C sequestration reached 2.589 petagrams (Pg) and 1.275 Pg respectively, and its C storage in HWPs added 0.491 Pg. These outcomes could have offset 11.76% of the country’s cumulative C emissions. As China’s emissions pass their peak in this decade, the proportion of carbon offset by FESs and HWPs will likely decrease below 10.00%. During 2006–2060, however, FES biomass and soil C sequestration would be 7.574 and 3.730 Pg, and C storage in HWPs would be 1.709 Pg, even under conservative assumptions. These estimates could lead to an overall offset ratio of 14.72%. In addition to highlighting the relevance of pursuing more aggressive nature-based solutions, this study can serve as an example of C accounting and tracking toward meeting countries’ targets as specified in their Nationally Determined Contributions under the Paris Agreement. China’s forest ecosystems and in-service wood products play an important role in removing and offsetting carbon (C) emissions.The forest sector actions are also cost-effective, especially if forest growth can be improved by adopting broad management practices.During the 2050s, these actions could more than offset the country’s total emissions, allowing it to achieve C neutrality earlier than originally planned.To enhance the efficiency and productivity of forestry, China needs to further restructure its institutional arrangements and public policies.To fully account for forest sector’s potentials, China must capture all possible C pools of the ecosystems, including in soil, urban trees/forests, and other trees outside of forests. China’s forest ecosystems and in-service wood products play an important role in removing and offsetting carbon (C) emissions. The forest sector actions are also cost-effective, especially if forest growth can be improved by adopting broad management practices. During the 2050s, these actions could more than offset the country’s total emissions, allowing it to achieve C neutrality earlier than originally planned. To enhance the efficiency and productivity of forestry, China needs to further restructure its institutional arrangements and public policies. To fully account for forest sector’s potentials, China must capture all possible C pools of the ecosystems, including in soil, urban trees/forests, and other trees outside of forests.