cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34724/casd.431.3484.v1
title: Système d'Information et de Traitement Automatisé des Données Élémentaires sur les Logements et les locaux - 1972

description: Le Système d’Information et de Traitement Automatisé des Données Elémentaires sur les Logements et les locaux (SITADEL) est une base de données qui recense l’ensemble des opérations de construction à usage d’habitation (logement) et à usage non résidentiel (locaux) soumises à la procédure d’instruction du permis de construire. Les données de SITADEL sont alimentées par l’ensemble des informations recueillies dans les mairies concernant les actes d’urbanisme. Il s’agit principalement des informations du permis de construire, mais également celles des déclarations de travaux, des permis de démolir ou de lotir. SITADEL a pour objectif de mesurer la construction afin de répondre aux besoins variés d’études, de prévision et d’orientation. Il permet de localiser finement l’implantation des logements neufs et des locaux d’activité. Il sert à estimer précisément l’offre de logements par type d’habitat et à prévoir son évolution. Il sert également à mesurer et prévoir l’activité du secteur de la construction et à orienter les politiques publiques menées par les ministères chargés de l’urbanisme et du logement. SITADEL fournit des informations sur les principales caractéristiques des opérations de construction neuve; les logements, en nombre et surfaces autorisées ou commencées, selon le type de construction, le mode d’utilisation, le maître d’ouvrage..., et les locaux autres qu’habitation, en surface autorisée ou commencée, selon le maître d’ouvrage et le type d’ouvrage… Pour les permis de construire, tous les événements de la vie du projet sont suivis depuis le dépôt de la demande à l’achèvement des travaux. Les informations de SITADEL sont disponibles à un niveau individuel (listes de permis) ou agrégées à la commune, la zone d’emploi, l’EPCI, le département. C’est le niveau individuel qui est mis à disposition.
DOI: 10.6084/m9.figshare.21968807
title: Data for the paper: Environmental co-benefits of maintaining natural vegetation with solar photovoltaic infrastructure on agricultural lands

description: Data for the paper "Environmental co-benefits of maintaining natural vegetation with solar photovoltaic infrastructure on agricultural lands" <strong>Chisago_Nutrients.xls:</strong> detailed soil nutrient profile from the bare PV treatment, veg PV treatment, and the control. <strong>Chisago_particlesize_CSC.csv:</strong> particle size distribution of the soil samples from the bare PV treatment, veg PV treatment, and the control. <strong>chisagoCN_1.csv</strong>: Total carbon and nitrogen percentage of the soil samples from the bare PV treatment, veg PV treatment, and the control. <strong>chi_bare_2019.csv</strong>: Sensor data from the bare PV treatment. <strong>chi_veg_2019.csv</strong>: Sensor data from the veg PV treatment. <strong>chi_con_2019.csv</strong>: Sensor data from the control. <strong>chi_con_slr_2019</strong>: Hourly sensor data from the control that are related to solar irradiance. <strong>chi_con_dly_2019.csv</strong>: Daily summary of the sensor data from the control <strong>Inverter_summary_chisago_2019.csv</strong>: Electricity production data from the vegetated PV and the bare PV treatments at the study site.
DOI: 10.6084/m9.figshare.21968807.v1
title: Data for the paper: Environmental co-benefits of maintaining natural vegetation with solar photovoltaic infrastructure on agricultural lands

description: Data for the paper "Environmental co-benefits of maintaining natural vegetation with solar photovoltaic infrastructure on agricultural lands" <strong>Chisago_Nutrients.xls:</strong> detailed soil nutrient profile from the bare PV treatment, veg PV treatment, and the control. <strong>Chisago_particlesize_CSC.csv:</strong> particle size distribution of the soil samples from the bare PV treatment, veg PV treatment, and the control. <strong>chisagoCN_1.csv</strong>: Total carbon and nitrogen percentage of the soil samples from the bare PV treatment, veg PV treatment, and the control. <strong>chi_bare_2019.csv</strong>: Sensor data from the bare PV treatment. <strong>chi_veg_2019.csv</strong>: Sensor data from the veg PV treatment. <strong>chi_con_2019.csv</strong>: Sensor data from the control. <strong>chi_con_slr_2019</strong>: Hourly sensor data from the control that are related to solar irradiance. <strong>chi_con_dly_2019.csv</strong>: Daily summary of the sensor data from the control <strong>Inverter_summary_chisago_2019.csv</strong>: Electricity production data from the vegetated PV and the bare PV treatments at the study site.
DOI: 10.5281/zenodo.7577002
title: [Replication package] Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning

description: Anonymized replication package of submission #1917: "Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning".
DOI: 10.6084/m9.figshare.21742247
title: Derepression may masquerade as activation in ligand-gated ion channels

description: Raw data for Nature Communications publication, "Derepression may masquerade as activation in ligand-gated ion channels"
DOI: 10.6084/m9.figshare.21742247.v1
title: Derepression may masquerade as activation in ligand-gated ion channels

description: Raw data for Nature Communications publication, "Derepression may masquerade as activation in ligand-gated ion channels"
DOI: 10.5281/zenodo.7577017
title: Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification

description: These are companion data and models of manuscript "Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification" that was submitted to Frontiers in Plant Science. The data is organized into three main folders: 'class_full_imgs', 'seg_full_imgs', and 'unlabeled'. The 'class_full_imgs' folder contains labeled data used to train the classification model, which is divided into train, validation, and test subfolders. Each of these subfolders contains 'oriented' and 'non-oriented' images. The 'seg_full_imgs' folder contains labeled data used to train the segmentation model. The 'InputImages' subfolder contains raw images, and the 'OutputImages' subfolder contains the segmented images. The 'unlabeled' folder contains images without any labels. These images were used for self-supervised pretraining of classification and segmentation models. The trained models can be found in Trained_models.zip. There are four zip files:<br> - "simclr_pretrained_bb.zip" contains the selected pretrained backbone trained via SimCLR.<br> - "nnclr_pretrained_bb.zip" contains the selected pretrained backbone trained via NNCLR.<br> - "finetuned_classification" contains classification models which have undergone end-to-end finetuning, split into supervised and self-supervision-pretrained models.<br> - "segmentation" contains image segmentation models, where the names refer to the pretraining method.
description: This work was partially supported by the AI Institute for Resilient Agriculture (USDA-NIFA #2021-67021-35329), COALESCE: COntext Aware LEarning for Sustainable CybEr-Agricultural Systems (CPS Frontier # 1954556), and support from a PSI faculty fellowship.
DOI: 10.5281/zenodo.7576660
title: Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification

description: These are companion data and models of manuscript "Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification" that was submitted to Frontiers in Plant Science. The data is organized into three main folders: 'class_full_imgs', 'seg_full_imgs', and 'unlabeled'. The 'class_full_imgs' folder contains labeled data used to train the classification model, which is divided into train, validation, and test subfolders. Each of these subfolders contains 'oriented' and 'non-oriented' images. The 'seg_full_imgs' folder contains labeled data used to train the segmentation model. The 'InputImages' subfolder contains raw images, and the 'OutputImages' subfolder contains the segmented images. The 'unlabeled' folder contains images without any labels. These images were used for self-supervised pretraining of classification and segmentation models. The trained models can be found in Trained_models.zip. There are four zip files:<br> - "simclr_pretrained_bb.zip" contains the selected pretrained backbone trained via SimCLR.<br> - "nnclr_pretrained_bb.zip" contains the selected pretrained backbone trained via NNCLR.<br> - "finetuned_classification" contains classification models which have undergone end-to-end finetuning, split into supervised and self-supervision-pretrained models.<br> - "segmentation" contains image segmentation models, where the names refer to the pretraining method.
description: This work was partially supported by the AI Institute for Resilient Agriculture (USDA-NIFA #2021-67021-35329), COALESCE: COntext Aware LEarning for Sustainable CybEr-Agricultural Systems (CPS Frontier # 1954556), and support from a PSI faculty fellowship.
DOI: 10.6084/m9.figshare.21970565
title: SupplementaryDataset1.tar.gz

description: Supplementary Dataset 1: unprocessed single gene trees from IQ-TREE (n=40951) in newick format.
DOI: 10.6084/m9.figshare.21970565.v1
title: SupplementaryDataset1.tar.gz

description: Supplementary Dataset 1: unprocessed single gene trees from IQ-TREE (n=40951) in newick format.
DOI: 10.5066/p97cfup6
title: Model Archive Data for Suspended-Sediment Regression at Station 07355860, Board Camp Creek near Wolf Pen Gap Recreation Area near Nunley, AR

description: Data from an optical turbidity sensor deployed at the stream station were recorded at 15-minute intervals by a data logger and uploaded every hour to the U.S. Geological Survey (USGS) database (Anderson, 2005; Wagner, 2006). Suspended-sediment samples were collected using a suction pump autosampler, equal width increments, or grab sampling techniques (Edwards, 1999). The use of an optical sensor to continuously monitor turbidity provided an accurate estimate of sediment fluctuations without the collection and analysis costs associated with intensive sampling (Office of Surface Water Memorandum 2016.07; Rasmussen et al., 2009). Turbidity was used as a surrogate for suspended-sediment concentration (SSC), which is a measure of sedimentation and siltation. Regression models were developed between SSC and turbidity using turbidity data from the optical sensor and the SSC data collected from the suspended-sediment samples. For Board Camp creek near Nunley, AR, a total of 62 suspended-sediment samples were collected beginning on June 25, 2018 and ranged from 1 to 1800 milligrams per liter across a range of turbidity from 0.5 to 341 formazin nephelometric units and streamflow from 1.25 to 2060 cubic feet per second. There were 37 suspended-sediment samples used in the final model after removing quality assurance/quality control samples, samples corresponding with gaps in turbidity data, samples associated with sampling/fouling errors, and extraneous samples from the same storm event which would result in overfitting of the model. Contacts
DOI: 10.57687/fk2/haiqpm/jy4bhr
title: y_test.tab

description: Y_Test has the dependent variable for the testing sample.
DOI: 10.57687/fk2/haiqpm/tolwhj
title: y_train.tab

description: Y_train hast the dependent variable for the training sample
DOI: 10.57687/fk2/haiqpm/rh8215
title: X_test.tab

description: X_Test has the independent variables for the testing sample
DOI: 10.57687/fk2/haiqpm/fjsu8n
title: X_train.tab

description: X_train has the independent variables for the training sample.
DOI: 10.57687/fk2/haiqpm
title: Replication Data for: Factors to improve online education: A study on the impact of COVID-19 on Delhi students

description: This dataset was adapted from: Chaturvedi, K., Vishwakarma, D., Singh, N., 2020a. Covid-19 and its impact on education, social life and mental health of students: A survey. Children and Youth Services Review 121. doi: 10.1016/j.childyouth.2020.105866.
DOI: 10.25919/aj6g-t793
title: Parkes observations for project P1101 semester 2022OCTS_13

description: We propose to carry out fortnightly monitoring of a very active repeater, FRB~190520, with the Parkes UWL system. Our current observations show that FRB~190520 is not only the most active FRB that can be observed with Parkes, but also shows extreme rotation measure (RM) variations and unique behavior at 2 to 3 GHz frequency. This makes UWL the ideal instrument to carry out in-depth studies of this repeating FRB. With this program, we expect to detect a large sample of bursts from FRB~190520, and in collaboration with the FAST telescope, we will study the local environment, emission properties, fluence distributions, and polarisation properties over an extremely wide bandwidth with high sensitivity.
DOI: 10.5517/ccdc.csd.cc25wlsg
title: CCDC 2022526: Experimental Crystal Structure Determination

description: Related Article: Yassine Laamari, Aziz Auhmani, Mohamed Saadi, Lahcen El Ammari, Mostafa Khouili, My Youssef Ait Itto, Abdelwahed Auhmani, El Mostafa Ketatni|2020|CSD Communication|||
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.cc267vkw
title: CCDC 2033338: Experimental Crystal Structure Determination

description: Related Article: Matthias Zeller, P. Veeraraghavan Ramachandran, Henry J. Hamann|2020|CSD Communication|||
description: Related Article: P. Veeraraghavan Ramachandran, Henry J. Hamann, Shivani Choudhary|2020|Org.Lett.|22|8593|doi:10.1021/acs.orglett.0c03184
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.cc26fkjr
title: CCDC 2038824: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fkks
title: CCDC 2038825: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fklt
title: CCDC 2038826: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fkmv
title: CCDC 2038827: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fknw
title: CCDC 2038828: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fkpx
title: CCDC 2038829: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fkqy
title: CCDC 2038830: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fkrz
title: CCDC 2038831: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fks0
title: CCDC 2038832: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fkt1
title: CCDC 2038833: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fkv2
title: CCDC 2038834: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fkw3
title: CCDC 2038835: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fky5
title: CCDC 2038837: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fkz6
title: CCDC 2038838: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.cc26fl08
title: CCDC 2038839: Experimental Crystal Structure Determination

description: Related Article: Shoko Kikkawa, Misaki Okayasu, Hidemasa Hikawa, Isao Azumaya|2021|Cryst.Growth Des.|21|1148|doi:10.1021/acs.cgd.0c01469
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.