cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24847749
title: <b>Data from: </b><b>Anticipating honeybee winter mortality by tracking their social thermoregulation</b>

description: This dataset result from an experiment with a robotic honeycomb system under real-world field conditions in commercial hives and measured thermoregulation throughout the three-dimensional (3D) colony structure of honey bees (Apis mellifera). We used a total of 36 temperature sensors, evenly distributed over three frames, installed at positions 3, 5 and 7 within the 10-frame hive. Instead of using printed circuit boards, we fixed the sensors between two layers of wax to facilitate their acceptance by the bees. We equipped four colonies in France with this adapted robotic honeycomb system during the winter of 2022-2023, along with a temperature sensor outside each hive to measure <i>T</i><sub>ext</sub>, ranging from -18.6°C to 25.7°C. Temperature data were collected every 15 minutes and then averaged per hour. We calculated <i>T</i><sub>amb</sub>, <i>T</i><sub>core</sub> and <i>T</i><sub>cen</sub> following Barmack et al. (2023) method from a total of 2,010,358 raw in-nest temperature data recorded in the four hives over the winter. We found that a 3D-adapted robotic honeycomb system is needed to capture key metrics of social thermoregulation in honeybees under field conditions. Moreover, based on post-winter assessment of the colonies, we investigated whether social thermoregulation differed between surviving and collapsing colonies over the winter. Interestingly, we found four potential early warning signals of colony health and winter mortality. We conclude that the development of 3D monitoring of honey bee in-nest temperature could help beekeepers to assess colony survival chances in order to reduce winter colony mortality.The data are related to the scientific paper "Requier, F., et al. (under review) Anticipating honeybee winter mortality by tracking their social thermoregulation. Science Robotics".Data are available as four csv files titled "hiveF261_collapsingColony1.csv" (67,500 obs.) and "hiveF262_collapsingColony2.csv" (70,687 obs.) for the two examples of collapsing colonies, and "hiveF279_survivingColony1.csv" (73,807 obs.) and "hiveF280_survivingColony2.csv" (73,880 obs.) for the two examples of surviving colonies. Data represent the average temperatures per hour per day.# METADATA#'data.frame': 5 variables:# $ timestamp : Factor variable ; the date of the data in the format YYYY-MM-DD and the time in hours (from 01 for 1am to 23 for 11pm)# $ Tcore : Numeric variable; the core temperature of the whole bee cluster in °C# $ Tcen : Numeric variable; the temperature of the centroid of the bee cluster in °C# $ Tamb : Numeric variable; the average ambient temperature outside the bee cluster in °C# $ Text : Numeric variable; the external temperature in °C
DOI: 10.6084/m9.figshare.24847749.v1
title: <b>Data from: </b><b>Anticipating honeybee winter mortality by tracking their social thermoregulation</b>

description: This dataset result from an experiment with a robotic honeycomb system under real-world field conditions in commercial hives and measured thermoregulation throughout the three-dimensional (3D) colony structure of honey bees (Apis mellifera). We used a total of 36 temperature sensors, evenly distributed over three frames, installed at positions 3, 5 and 7 within the 10-frame hive. Instead of using printed circuit boards, we fixed the sensors between two layers of wax to facilitate their acceptance by the bees. We equipped four colonies in France with this adapted robotic honeycomb system during the winter of 2022-2023, along with a temperature sensor outside each hive to measure <i>T</i><sub>ext</sub>, ranging from -18.6°C to 25.7°C. Temperature data were collected every 15 minutes and then averaged per hour. We calculated <i>T</i><sub>amb</sub>, <i>T</i><sub>core</sub> and <i>T</i><sub>cen</sub> following Barmack et al. (2023) method from a total of 2,010,358 raw in-nest temperature data recorded in the four hives over the winter. We found that a 3D-adapted robotic honeycomb system is needed to capture key metrics of social thermoregulation in honeybees under field conditions. Moreover, based on post-winter assessment of the colonies, we investigated whether social thermoregulation differed between surviving and collapsing colonies over the winter. Interestingly, we found four potential early warning signals of colony health and winter mortality. We conclude that the development of 3D monitoring of honey bee in-nest temperature could help beekeepers to assess colony survival chances in order to reduce winter colony mortality.The data are related to the scientific paper "Requier, F., et al. (under review) Anticipating honeybee winter mortality by tracking their social thermoregulation. Science Robotics".Data are available as four csv files titled "hiveF261_collapsingColony1.csv" (67,500 obs.) and "hiveF262_collapsingColony2.csv" (70,687 obs.) for the two examples of collapsing colonies, and "hiveF279_survivingColony1.csv" (73,807 obs.) and "hiveF280_survivingColony2.csv" (73,880 obs.) for the two examples of surviving colonies. Data represent the average temperatures per hour per day.# METADATA#'data.frame': 5 variables:# $ timestamp : Factor variable ; the date of the data in the format YYYY-MM-DD and the time in hours (from 01 for 1am to 23 for 11pm)# $ Tcore : Numeric variable; the core temperature of the whole bee cluster in °C# $ Tcen : Numeric variable; the temperature of the centroid of the bee cluster in °C# $ Tamb : Numeric variable; the average ambient temperature outside the bee cluster in °C# $ Text : Numeric variable; the external temperature in °C
DOI: 10.5281/zenodo.10390265
title: ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset

description: ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations. 


ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD. 


Zip file has 2 main folders: 1. dataset_final_phase , 2. dataset_phase_1


Structure of dataset_final_phase:


2 folders:


1. test cases stenosis with 300 images with annotations


2. test case segmentation with 300 images with annotations


Structure of dataset_phase_1:


1. segmentation_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images and annotations folder, where 200 XCA images are provided. 


2. stenosis_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images and annotations folder, where 200 XCA images are provided. 


 


The corresponding Dataset Article will be provided later. 


[1] Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions
DOI: 10.5281/zenodo.7981245
title: ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1

description: ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD. Zip file has 2 main folders: 1. segmentation_dataset , 2. stenosis_dataset 1. segmentation_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images folder, where 200 XCA images are provided. 2. stenosis_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images folder, where 200 XCA images are provided. The corresponding Dataset Article will be provided later. [1] Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions
DOI: 10.5281/zenodo.8248931
title: ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1

description: ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD. Zip file has 2 main folders: 1. segmentation_dataset , 2. stenosis_dataset 1. segmentation_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images folder, where 200 XCA images are provided. 2. stenosis_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images folder, where 200 XCA images are provided. The corresponding Dataset Article will be provided later. [1] Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions
DOI: 10.5281/zenodo.8378645
title: ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1

description: ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD. Zip file has 2 main folders: 1. segmentation_dataset , 2. stenosis_dataset 1. segmentation_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images folder, where 200 XCA images are provided. 2. stenosis_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images folder, where 200 XCA images are provided. The corresponding Dataset Article will be provided later. [1] Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions
DOI: 10.5281/zenodo.8386059
title: ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset

description: ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 2 consist of two folders with 300 images in each of them as well as annotations. 


ARCADE: Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs Dataset Phase 1 consists of two datasets of XCA images for each of two tasks of ARCADE challenge. The first task includes in total 1200 coronary vessel tree images, which are divided into train(1000) and validation(200) groups, images for training are followed with annotations, depicting the division of a heart into 26 different regions based on the Syntax Score methodology[1]. Similarly, the second task includes a different set of 1200 images with same train-val division proportion with annotated regions containing atherosclerotic plaques. This dataset, carefully annotated by medical experts, enables scientists to actively contribute towards the advancement of an automated risk assessment system for patients with CAD. 


Zip file has 2 main folders: 1. dataset_final_phase , 2. dataset_phase_1


Structure of dataset_final_phase:


2 folders:


1. test cases stenosis with 300 images with annotations


2. test case segmentation with 300 images with annotations


Structure of dataset_phase_1:


1. segmentation_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images and annotations folder, where 200 XCA images are provided. 


2. stenosis_dataset consists of seg_train and seg_val folders. Seg_train folder has images folder, where 1000 XCA images are provided, and annotations folder, where annotation of 1000 images in COCO format is provided. Seg_val folder has images and annotations folder, where 200 XCA images are provided. 


 


The corresponding Dataset Article will be provided later. 


[1] Syntax score segment definitions. https://syntaxscore.org/index.php/tutorial/definitions/14-appendix-i-segment-definitions
DOI: 10.6084/m9.figshare.23609643.v2
title: dataset_labels.csv

description: Dataset labels consist of : Normal (1), Pre-Plus (2) and Plus (3)
DOI: 10.5281/zenodo.10396932
title: myiothlypis_coronata_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.10396933
title: myiothlypis_coronata_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.24847698
title: Tree_mortality_sites_Sourcedata_Fig1.csv

description: 1,303 georeferenced tree mortality sites (https://www.iufro.org/science/task-forces/tree-mortality-patterns)
DOI: 10.6084/m9.figshare.24847737
title: All-day cloud property every 3 hours from September 6, 2022 to September 18, 2022

description: Retrieval of all-day cloud property based on the satellites Himawari-8/9
DOI: 10.6084/m9.figshare.24847737.v3
title: All-day cloud property every 3 hours from September 6, 2022 to September 18, 2022

description: Retrieval of all-day cloud property based on the satellites Himawari-8/9
DOI: 10.6084/m9.figshare.24847737.v2
title: All-day cloud property every 3 hours from September 6, 2022 to September 18, 2022

description: Retrieval of all-day cloud property based on the satellites Himawari-8/9
DOI: 10.6084/m9.figshare.24847731
title: All-day cloud property every 10 days at 6:00 (UTC+0) from July 7, 2015 to December 31, 2022

description: Retrieval of all-day cloud property based on the satellites Himawari-8/9
DOI: 10.6084/m9.figshare.24847731.v3
title: All-day cloud property every 10 days at 6:00 (UTC+0) from July 7, 2015 to December 31, 2022

description: Retrieval of all-day cloud property based on the satellites Himawari-8/9
DOI: 10.6084/m9.figshare.24847731.v2
title: All-day cloud property every 10 days at 6:00 (UTC+0) from July 7, 2015 to December 31, 2022

description: Retrieval of all-day cloud property based on the satellites Himawari-8/9
DOI: 10.5281/zenodo.10396942
title: internoretia_fryeana_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.10396943
title: internoretia_fryeana_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.24845838.v1
title: <b>Retrieval of all-day cloud property based on the satellites Himawari-8/9</b>

description: 2015.07.07~2022.12.31 at 06:00 every 10d
DOI: 10.5281/zenodo.10396950
title: microcambeva_barbata_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.10396951
title: microcambeva_barbata_zanfona_1.fasta.gz

description: .
DOI: 10.6084/m9.figshare.24846543
title: test

description: <b>Retrieval of all-day cloud property based on the satellites Himawari-8/9</b>
DOI: 10.6084/m9.figshare.24846543.v2
title: test

description: <b>Retrieval of all-day cloud property based on the satellites Himawari-8/9</b>
DOI: 10.6084/m9.figshare.24846543.v1
title: <b>Retrieval of all-day cloud property based on the satellites Himawari-8/9</b>

description: 2022.09.06 00:00~2022.09.18 00:00 every 3h
DOI: 10.6084/m9.figshare.24845838
title: test

description: <b>Retrieval of all-day cloud property based on the satellites Himawari-8/9</b>
DOI: 10.6084/m9.figshare.24845838.v3
title: test

description: <b>Retrieval of all-day cloud property based on the satellites Himawari-8/9</b>
DOI: 10.6084/m9.figshare.24845838.v2
title: All-day cloud property every 10 days at 6:00 (UTC+0) from July 7, 2015 to December 31, 2022

description: <b>Retrieval of all-day cloud property based on the satellites Himawari-8/9</b>
DOI: 10.6084/m9.figshare.24847614
title: test

description: All-day cloud property every 10 days at 6:00 (UTC+0) from July 7, 2015 to December 31, 2022
DOI: 10.6084/m9.figshare.24847614.v3
title: test

description: All-day cloud property every 10 days at 6:00 (UTC+0) from July 7, 2015 to December 31, 2022
DOI: 10.6084/m9.figshare.24847614.v2
title: All-day cloud property every 10 days at 6:00 (UTC+0) from July 7, 2015 to December 31, 2022

description: All-day cloud property every 10 days at 6:00 (UTC+0) from July 7, 2015 to December 31, 2022
DOI: 10.6084/m9.figshare.24847617
title: test

description: <b>Retrieval of all-day cloud property based on the satellites Himawari-8/9</b>
DOI: 10.6084/m9.figshare.24847617.v2
title: test

description: <b>Retrieval of all-day cloud property based on the satellites Himawari-8/9</b>
DOI: 10.6084/m9.figshare.24847617.v1
title: All-day cloud property every 3 hours from September 6, 2022 to September 18, 2022

description: <b>Retrieval of all-day cloud property based on the satellites Himawari-8/9</b>
DOI: 10.5281/zenodo.10396940
title: Modified Quicksort : worst case made best
DOI: 10.5281/zenodo.10396941
title: Modified Quicksort : worst case made best
DOI: 10.5281/zenodo.10396968
title: monilaria_moniliformis_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.10396969
title: monilaria_moniliformis_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.10396956
title: Data from: Where consumers control plant reproduction in coastal wetlands: the environmental stress model in plants' versus consumers' perspectives

description: This is the data set for the paper entitled "Where consumers control plant reproduction in coastal wetlands: the environmental stress model in plants’ versus consumers’ perspectives" upcoming in the Journal of Ecology.

The metadata for interpreting the data set are included in the Excel file.
DOI: 10.5281/zenodo.10396957
title: Data from: Where consumers control plant reproduction in coastal wetlands: the environmental stress model in plants' versus consumers' perspectives

description: This is the data set for the paper entitled "Where consumers control plant reproduction in coastal wetlands: the environmental stress model in plants’ versus consumers’ perspectives" upcoming in the Journal of Ecology.

The metadata for interpreting the data set are included in the Excel file.
DOI: 10.5281/zenodo.10396977
title: monocelis_spectator_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.10396976
title: monocelis_spectator_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.10396974
title: ASYMPTOTIC PERFORMANCES OF POPULAR PROGRAMMING LANGUAGES FOR POPULAR SORTING ALGORITHMS
DOI: 10.5281/zenodo.10396975
title: ASYMPTOTIC PERFORMANCES OF POPULAR PROGRAMMING LANGUAGES FOR POPULAR SORTING ALGORITHMS
DOI: 10.6084/m9.figshare.24847806
title: Raw data for OHDCA study at Hok Tsui_autumn 2020

description: The dataset contains bi-hourly data of dozens of organic aerosol markers, other air pollutants, and several meteorological parameters.
DOI: 10.6084/m9.figshare.24847806.v1
title: Raw data for OHDCA study at Hok Tsui_autumn 2020

description: The dataset contains bi-hourly data of dozens of organic aerosol markers, other air pollutants, and several meteorological parameters.
DOI: 10.6084/m9.figshare.24847767
title: Data_all.zipInfluence of Cr(III) on the formation and transformation of corrosion products of steel in marine environments

description:  A fundamental approach is used to better understand the influence of Cr on the resistance of low alloy steel to marine corrosion. It focuses on the formation and transformation of corrosion products of steel in seawater-like electrolytes. Fe(OH)<sub>2</sub> precipitates are then prepared by mixing a solution of NaCl, Na<sub>2</sub>SO<sub>4</sub>·10H<sub>2</sub>O, FeCl<sub>2</sub>·4H<sub>2</sub>O or FeCl<sub>2</sub>·4H<sub>2</sub>O + CrCl<sub>3</sub>·6H<sub>2</sub>O (so that Cr = 8% and Fe = 92%), with a solution of NaOH. In a first approach, the obtained aqueous suspension is stirred, at a controlled temperature of 25±0.5°C, to be oxidized by air. The resulting oxidation product is characterized by XRD and Raman spectroscopy. In a second approach, the Fe(OH)<sub>2</sub> precipitates are aged one week in suspension in anoxic conditions, filtered, mixed as a wet paste with glycerol and set as a thin compact layer on the sample holder of a X-ray diffraction system. The oxidation process of Fe(OH)<sub>2 </sub>is then monitored in situ during the acquisition of the X-ray diffractogram. The obtained results demonstrate that Cr(III) hinders the formation of green rust compounds, hence hindering the formation of gamma-FeOOH, i.e., the main oxidation product of green rust compounds. The other oxidation pathways of Fe(OH)<sub>2</sub>, which lead to Fe<sub>3</sub>O<sub>4</sub> or alpha-FeOOH, are thus favored. Nevertheless, Cr(III) favors mainly the solid state transformation pathway of Fe(OH)<sub>2</sub>, thus promoting a-FeOOH via the formation of poorly ordered Fe(III) oxyhydroxides, namely feroxyhyte and ferrihydrite.