cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18164/dcacc028-69c4-4599-a4d7-9f4d817c030e
title: Lake Winnipeg Water Temperature 2007
title: Lac Winnipeg Température de l'eau 2007

description: Water temperature data were collected by Environment and Climate Change Canada at selected locations in Lake Winnipeg.Thermograph moorings were deployed in the north basin at station 505 and 506, in the south basin at station 500, in the Lake Winnipeg Narrows at station 502, northwest of Berens Island at station 503 and north of Reindeer Island at station 504. This data was used in the hydrodynamic Estuary, Lake and Coastal Model (ELCOM) to predict water movement patterns and then coupled with the Computational Aquatic Ecosystem Dynamics Model (CAEDYM) to predict response to potential nutrient loading reduction actions to improve water quality in Lake Winnipeg.
description: La température de l’eau des données ont été recueillies par Environnement et Changement climatique Canada à certains endroits du lac Winnipeg. Thermographe ancrages ont été déployées dans le bassin nord à la station 505 et 506, dans le bassin sud, à la station de 500, dans le lac Winnipeg Narrows à la station 502, au nord-ouest de l'île Berens à la station 503 et au nord de l’île de rennes à la station 504.Ces données ont été utilisées dans le modèle hydrodynamique d'estuaire, de lac et de côte (ELCOM) pour prédire les modèles de mouvement de l'eau, puis couplées au modèle informatique de dynamique des écosystèmes aquatiques (CAEDYM) pour prévoir la réponse à des actions potentielles de réduction de la charge en éléments nutritifs afin d'améliorer la qualité de l'eau du lac Winnipeg .
DOI: 10.18164/d8c9a158-475e-4d7d-92c3-03fa28ac386e
title: Basin averaged CanRCM4 variables for Western Canada
title: Variables CanRCM4 moyennes par bassin pour l'Ouest canadien

description: This dataset was extracted from the 50-member Large-Ensemble Canadian Regional Climate Model (CanRCM4-LE) (Scinocca et al., 2015), North American region 0.44° horizontal grid resolution (NAM-44) model runs. Basin averaged means for nine river basins in Western Canada consisting of three variables: mean daily temperature (tas: K); precipitation (pr: kg m-2 s-1); and surface snow amount (snw: kg m-2) are provided.
description: Le présent ensemble de données a été extrait du grand ensemble du Modèle régional canadien du climat comprenant 50 membres (CanRCM4-LE) (Scinocca et al., 2015), au moyen de cycles passés sur l’Amérique du Nord avec une résolution de la grille horizontale de 0,44 (NAM-44). Les moyennes par bassin ont été calculées pour neuf bassins hydrographiques de l’Ouest du Canada et trois variables : la température quotidienne moyenne (tas : K), les précipitations (pr : kg m-2 s-1) et la quantité de neige à la surface (snw : kg m-2).
DOI: 10.18164/d7a08bc2-8740-4700-b48b-370a2d493334
title: Lake Simcoe Water and Sediment Quality 2008 to 2011
title: Qualité de l’eau et des sédiments du lac Simcoe de 2008 à 2011

description: Lake Simcoe is located North of Toronto in Ontario, Canada and is a premier destination for tourists generating $200 million in revenue annually. Lake Simcoe is the 4th largest lake in Ontario with a surface area of 722 square kilometers and a watershed of 2900 square kilometers. Lake Simcoe has been negatively impacted by anthropogenic activity since the 1930's. In 2007, the Lake Simcoe Cleanup Fund was created to address increased phosphorus inputs, reproductive failure of the cold water fishery, nuisance aquatic plant growth and invasive species.Here we present nutrient and sediment data that was collected to inform decisions pertaining to the restoration of Lake Simcoe.
description: Le lac Simcoe se trouve au nord de la ville de Toronto en Ontario (Canada) et est une destination de choix pour les touristes, générant 200 millions de dollars en revenus annuellement. Il est le quatrième plus grand lac se trouvant entièrement en Ontario avec une surface de 722 kilomètres carrés et un bassin versant de 2 900 kilomètres carrés. Le lac Simcoe subit les effets néfastes des activités anthropiques depuis les années 1930. En 2007, le fonds de nettoyage du lac Simcoe a été mis sur pied afin de faire face aux apports accrus de phosphore, aux échecs de reproduction des poissons d’eau froide, à la croissance excessive des plantes aquatiques nuisibles et aux espèces envahissantes. Dans ce document, vous trouverez les données sur les sédiments et les nutriments recueillies pour la prise de décisions éclairées relatives à la restauration du lac Simcoe.
DOI: 10.5281/zenodo.6773581
title: River plume simulation for Qu et al. JPO

description: ROMS output of the idealized river plume simulations used in Qu, L., Hetland, R., and D. Schlichting, 2022: Mixing pathways in simple box models. <em>Journal of Physical Oceanography</em>. The simulation with a single river is 'RiverPlume_output.nc' and the simulation with two rivers is 'RiverPlume_2rivers_output.nc'.
DOI: 10.5281/zenodo.6385259
title: River plume simulation for Qu et al. JPO

description: ROMS output of the idealized river plume simulations used in Qu, L., Hetland, R., and D. Schlichting, 2022: Mixing pathways in simple box models. <em>Journal of Physical Oceanography</em>. The simulation with a single river is 'RiverPlume_output.nc' and the simulation with two rivers is 'RiverPlume_2rivers_output.nc'.
DOI: 10.18112/openneuro.ds004173.v1.0.0
title: Movement-related artefacts (MR-ART) dataset
DOI: 10.5517/ccdc.csd.cc297r5h
title: CCDC 2122606: Experimental Crystal Structure Determination

description: Related Article: Wei Wang, Xiao-Hong Xiong, Neng-Xiu Zhu, Zheng Zeng, Zhang-Wen Wei, Mei Pan, Dieter Fenske, Ji-Jun Jiang, Cheng-Yong Su|2022|Angew.Chem.,Int.Ed.|61|e202201766|doi:10.1002/anie.202201766
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.cc297r6j
title: CCDC 2122607: Experimental Crystal Structure Determination

description: Related Article: Wei Wang, Xiao-Hong Xiong, Neng-Xiu Zhu, Zheng Zeng, Zhang-Wen Wei, Mei Pan, Dieter Fenske, Ji-Jun Jiang, Cheng-Yong Su|2022|Angew.Chem.,Int.Ed.|61|e202201766|doi:10.1002/anie.202201766
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.cc297r7k
title: CCDC 2122608: Experimental Crystal Structure Determination

description: Related Article: Wei Wang, Xiao-Hong Xiong, Neng-Xiu Zhu, Zheng Zeng, Zhang-Wen Wei, Mei Pan, Dieter Fenske, Ji-Jun Jiang, Cheng-Yong Su|2022|Angew.Chem.,Int.Ed.|61|e202201766|doi:10.1002/anie.202201766
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.cc297rbn
title: CCDC 2122611: Experimental Crystal Structure Determination

description: Related Article: Wei Wang, Xiao-Hong Xiong, Neng-Xiu Zhu, Zheng Zeng, Zhang-Wen Wei, Mei Pan, Dieter Fenske, Ji-Jun Jiang, Cheng-Yong Su|2022|Angew.Chem.,Int.Ed.|61|e202201766|doi:10.1002/anie.202201766
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.cc297rcp
title: CCDC 2122612: Experimental Crystal Structure Determination

description: Related Article: Wei Wang, Xiao-Hong Xiong, Neng-Xiu Zhu, Zheng Zeng, Zhang-Wen Wei, Mei Pan, Dieter Fenske, Ji-Jun Jiang, Cheng-Yong Su|2022|Angew.Chem.,Int.Ed.|61|e202201766|doi:10.1002/anie.202201766
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.cc297r3f
title: CCDC 2122604: Experimental Crystal Structure Determination

description: Related Article: Wei Wang, Xiao-Hong Xiong, Neng-Xiu Zhu, Zheng Zeng, Zhang-Wen Wei, Mei Pan, Dieter Fenske, Ji-Jun Jiang, Cheng-Yong Su|2022|Angew.Chem.,Int.Ed.|61|e202201766|doi:10.1002/anie.202201766
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.cc29gv7w
title: CCDC 2129428: Experimental Crystal Structure Determination

description: Related Article: Xun Gong, Peng Deng, Jianhua Cheng|2022|ChemCatChem|14|e202200060|doi:10.1002/cctc.202200060
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.cc29gv8x
title: CCDC 2129429: Experimental Crystal Structure Determination

description: Related Article: Xun Gong, Peng Deng, Jianhua Cheng|2022|ChemCatChem|14|e202200060|doi:10.1002/cctc.202200060
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.cc29gv9y
title: CCDC 2129430: Experimental Crystal Structure Determination

description: Related Article: Xun Gong, Peng Deng, Jianhua Cheng|2022|ChemCatChem|14|e202200060|doi:10.1002/cctc.202200060
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.cc29gvbz
title: CCDC 2129431: Experimental Crystal Structure Determination

description: Related Article: Xun Gong, Peng Deng, Jianhua Cheng|2022|ChemCatChem|14|e202200060|doi:10.1002/cctc.202200060
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.cc29gvc0
title: CCDC 2129432: Experimental Crystal Structure Determination

description: Related Article: Xun Gong, Peng Deng, Jianhua Cheng|2022|ChemCatChem|14|e202200060|doi:10.1002/cctc.202200060
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: 10.25921/ypfy-6w22
title: EK60 Water Column Sonar Data Collected During EX2103
DOI: 10.5281/zenodo.6773531
title: YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines

description: <strong>D</strong><strong>ata abstract:</strong><br> The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.) <br> <strong>Paper abstract:</strong><br> The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%.
DOI: 10.5281/zenodo.6773530
title: YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines

description: <strong>D</strong><strong>ata abstract:</strong><br> The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.) <br> <strong>Paper abstract:</strong><br> The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%.
DOI: 10.26071/ogsl-af60180b-72ec
title: Bathymétrie multifaisceaux des pentes deltaïques dans l'estuaire du Saint-Laurent (2020)
title: Multibeam bathymetry of deltaic slopes in the lower St. Lawrence Estuary (2020)

description: Le projet est issue d’un financement du Réseau Québec Maritime (RQM) et MEOPAR (https://meopar.ca/meopar-and-rqms-first-joint-projects-explore-risk-assessment-and-response-in-the-st-lawrence-system/) et porte sur les risques naturels associés à la remobilisation des sédiments sur la Côte-Nord du Saint-Laurent. Le projet vise principalement à évaluer la distribution des risques naturels sous-marins sur les deltas marins de la Côte-Nord. Il s’agit d’une collaboration entre l’Université du Québec à Rimouski, l’Université du Nouveau-Brunswick, la Commission géologique du Canada, l’Université Laval et l'Université Dalhousie.

Des images bathymétriques pour l'année 2020 sont disponibles via ce jeu de données. Ces geotiffs peuvent être utilisés avec un système d’information géographique (SIG). La bathymétrie multifaisceaux fut effectuée dans 5 secteurs possédant des pentes deltaïques dans le bas estuaire du Saint-Laurent (Betsiamites, Manicouagan, Pointe-aux-Outardes, Godbout et Franquelin). Une mosaïque du secteur de Pointe-des-Monts est aussi disponible. Les données ont été collectées avec un EM-240 (Échosondeur Multifaisceaux) installé sur le Navire de recherche (N/R) Coriolis II.

Les rapports de mission pour ce jeu de données sont accessibles :
- https://semaphore.uqar.ca/1729/1/Jean-Carlos_Montero-Serrano_et_al_octobre2020-.pdf
- https://semaphore.uqar.ca/id/eprint/1731
description: The project is the result of funding from Réseau Québec Maritime and MEOPAR (https://meopar.ca/meopar-and-rqms-first-joint-projects-explore-risk-assessment-and-response-in-the-st-lawrence-system/) and focuses on the natural risks associated with the remobilization of sediments on the North Shore of the St. Lawrence. The main objective of the project is to assess the distribution of underwater natural hazards on the marine deltas of the North Shore. It is a collaboration between the Université du Québec à Rimouski, the University of New Brunswick, the Geological Survey of Canada, Laval University and Dalhousie University.

Bathymetric images for the year 2020 are available via this dataset. These geotiffs can be used with a geographic information system (GIS). Multibeam bathymetry was performed in 5 sectors with deltaic slopes in the lower St. Lawrence Estuary (Betsiamites, Manicouagan, Pointe-aux-Outardes, Godbout and Franquelin). A mosaic of the Pointe-des-Monts area is also available. The data was collected with an EM-240 (Multibeam Echosounder) installed on the Research Vessel (R/V) Coriolis II.

Mission reports for this dataset are available:

- https://semaphore.uqar.ca/1729/1/Jean-Carlos_Montero-Serrano_et_al_octobre2020-.pdf
- https://semaphore.uqar.ca/id/eprint/1731
DOI: 10.18164/d1849da9-82ff-49c8-bebf-070db9b3a62a
title: Nutrients in Great Lakes Priority Tributaries Data
title: Données nutritionnelles sur les affluents prioritaires des Grands Lacs

description: Water quality monitoring data collected in priority tributaries to provide nutrient concentration data to estimate nutrient loads to the waters of the Great Lakes. Data is collected to advance the science to understand and address the complex problem of recurrent toxic and nuisance algae in the Great Lakes. The majority of the data is focused on Lake Erie, the smallest, shallowest of the Great Lakes, and most susceptible to nearshore water quality issues. Water quality monitoring is conducted to establish current nutrient loadings from selected Canadian tributaries; to enhance the knowledge of the factors that affect tributary and nearshore water quality, ecosystem health, and algae growth; to establish binational lake ecosystem objectives, phosphorus objectives, and phosphorous load reduction targets, and to support the development of a binational nearshore assessment and management framework.
description: Données de surveillance de la qualité de l'eau recueillies dans les affluents prioritaires afin de fournir des données sur la concentration en éléments nutritifs afin d'estimer les charges d'éléments nutritifs dans les eaux des Grands Lacs. Des données sont recueillies pour faire avancer la science afin de comprendre et de résoudre le problème complexe des algues toxiques et nuisibles récurrentes dans les Grands Lacs. La majorité des données portent sur le lac Érié, le plus petit et le moins profond des Grands Lacs, et le plus vulnérable aux problèmes de qualité des eaux littorales. Une surveillance de la qualité de l'eau est effectuée pour établir les charges actuelles d'éléments nutritifs provenant de certains affluents canadiens. améliorer la connaissance des facteurs qui affectent la qualité des eaux tributaires et littorales, la santé des écosystèmes et la croissance des algues; établir des objectifs binationals pour les écosystèmes lacustres, des objectifs en matière de phosphore et des objectifs de réduction de la charge en phosphore, et appuyer l'élaboration d'un cadre binational d'évaluation et de gestion des zones côtières.
DOI: 10.5281/zenodo.6773643
title: [IO Islamic 2271] Tarjuma-i-Khulâṣat-almafâkhir

description: <strong>Tarjuma-i-Khulâṣat-almafâkhir.</strong> <strong>This manuscript is now IO Islamic 643 in the India Office collections.</strong> <strong>[metadata:</strong> <strong>Hermann Ethé</strong><strong><em>, </em></strong><strong><em>Catalogue of Persian Manuscripts in the Library of the India Office,</em></strong> <strong>2 vols. (Oxford: India Office, 1903): volume 1,</strong> <strong>number 2271 here with notations and hyperlinks]</strong>. 643 Tarjuma-i-Khulâṣat-almafâkhir ( ترجمۀ خلاصة المفاخر ) . A Persian translation of the supplement to the preceding work, usually denoted by the simple name of تكملة ; see fol. 2<sup>a</sup>, l. 4, and fol. 228<sup>b</sup>, last line; the author of the Arabic original is the same ‘Abdallâh bin As’ad alyâfi’î alyamanî; comp. O. Loth, loc. cit., and W. Pertsch, Berlin Cat., p. 66. It adds 200 stories to those in the روض الرياحين , most of which refer to the great Shaikh ‘Abd-alḳâdir (see above). The name of the Persian translator is not mentioned, but as the first of two other copies of the same work in the Bodleian Library (Bodleian Cat., Nos. 332 and 333) was made before A.H.910 (A.D. 1504), and as moreover the anonymous author mentions as his spiritual guide the great Shaikh Sayyid Jalâl-aldîn Makhdûm-i-Jahâniyân, who died A.H. 785 (A.D. 1384), the translation of this تكمله must have been made at least 200 years before that of the روض الرياحين . The Persian poet ‘Abdî made a poetical version of the same تكملة in A.H. 1051 (A.D.1641, 1642), entitled ترجمۀ تكملة ; see A. Sprenger, Catal., p. 307. Beginning: الحمد لله ربّ العالمين ...... ميگويد اضعف العباد أَصْلَحَ الله شانه كه چون كتاب خلاصة المفاخر فى مناقب غوث الملكوت قطب الصمد الخ . Dated the 14th of Ṣafar, A.H. 1089 (A.D. 1678, April 7). College of Fort William, 1825. No. 2271, ff. 228, written in different styles on Naskhî, mixed with Shikasta, by various hands, the oldest part, ll. 15, the more modern ones, ll. 17; very much damaged, but carefully mended; size, 8<sup>5/8</sup> in. by 5<sup>1/2</sup> in.