cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20484843.v1
title: Additional file 2 of Cluster randomised trials with a binary outcome and a small number of clusters: comparison of individual and cluster level analysis method

description: Additional file 2. Code to replicate illustrative example.
DOI: 10.6084/m9.figshare.20484925
title: Additional file 1 of Skin thickness affects the result of tuberculin skin test in systemic sclerosis

description: Additional file 1: Table S1. Data of systemic sclerosis patients with tuberculin skin test.
DOI: 10.6084/m9.figshare.20484925.v1
title: Additional file 1 of Skin thickness affects the result of tuberculin skin test in systemic sclerosis

description: Additional file 1: Table S1. Data of systemic sclerosis patients with tuberculin skin test.
DOI: 10.6084/m9.figshare.20484940
title: Additional file 1 of Mental health systems in six Caribbean small island developing states: a comparative situational analysis

description: Additional file 1. UKOTs mental health situational analysis data collection tool.
DOI: 10.6084/m9.figshare.20484940.v1
title: Additional file 1 of Mental health systems in six Caribbean small island developing states: a comparative situational analysis

description: Additional file 1. UKOTs mental health situational analysis data collection tool.
DOI: 10.6084/m9.figshare.20484942
title: Additional file 2 of Improved SARS-CoV-2 sequencing surveillance allows the identification of new variants and signatures in infected patients

description: Additional file 2: Supplementary tables 1-6. Extended table 1: Cost details for each solution. Extended table 2: Summary of all data produced. Extended table 3: List of the 6970 mutations found in this study, sorted by position along the Sars-CoV-2 genome. Extended table 4: List of the 194 mutations first detected in Campania, sorted by position along the Sars-CoV-2 genome. Extended table 5: List of lineages first detected in Campania. Extended table 6: World distribution of lineages first detected in Campania. Extended table 7: List of 161 genes correlated with viral load.
DOI: 10.6084/m9.figshare.20484942.v1
title: Additional file 2 of Improved SARS-CoV-2 sequencing surveillance allows the identification of new variants and signatures in infected patients

description: Additional file 2: Supplementary tables 1-6. Extended table 1: Cost details for each solution. Extended table 2: Summary of all data produced. Extended table 3: List of the 6970 mutations found in this study, sorted by position along the Sars-CoV-2 genome. Extended table 4: List of the 194 mutations first detected in Campania, sorted by position along the Sars-CoV-2 genome. Extended table 5: List of lineages first detected in Campania. Extended table 6: World distribution of lineages first detected in Campania. Extended table 7: List of 161 genes correlated with viral load.
DOI: 10.6084/m9.figshare.20484954
title: Additional file 2 of Identification and validation of stable reference genes for quantitative real time PCR in different minipig tissues at developmental stages

description: Additional file 2.
DOI: 10.6084/m9.figshare.20484954.v1
title: Additional file 2 of Identification and validation of stable reference genes for quantitative real time PCR in different minipig tissues at developmental stages

description: Additional file 2.
DOI: 10.5281/zenodo.6988541
title: Formation and transport of fluid mud triggered by typhoon events in front of the subaqueous Changjiang Delta

description: 1_SSC_calibration.xlsx is used for producing figure 2. 2_Wind_Wave.xlsx is used for producing figure 3. 3_SSC_contour_and_conducitivity.xlsx is used for producing figure 4. 4_SSC_Current_Profile.xlsx is used for producing figure 5. 5_Grain_Size.xlsx is used for producing figure 6. 6_Current_And_Fluid_Mud_Dynamics.xlsx is used for producing figures 1, 4 and 7 and tables 1 and 2. 7_Sediment_Flux.xlsx is used for producing figure 8.
DOI: 10.5281/zenodo.6988540
title: Formation and transport of fluid mud triggered by typhoon events in front of the subaqueous Changjiang Delta

description: 1_SSC_calibration.xlsx is used for producing figure 2. 2_Wind_Wave.xlsx is used for producing figure 3. 3_SSC_contour_and_conducitivity.xlsx is used for producing figure 4. 4_SSC_Current_Profile.xlsx is used for producing figure 5. 5_Grain_Size.xlsx is used for producing figure 6. 6_Current_And_Fluid_Mud_Dynamics.xlsx is used for producing figures 1, 4 and 7 and tables 1 and 2. 7_Sediment_Flux.xlsx is used for producing figure 8.
DOI: 10.5281/zenodo.6244758
title: Effects of Reanalysis Forcing Fields on Ozone Trends and Age of Air from a Chemical Transport Model

description: This dataset is based on the global off-line 3-D chenmical transport model (TOMCAT/SLIMCAT) forced with ECMWF reanalyses (ERA-Interim and ERA5) to compare the performance of the stratospheric ozone simulations. Each field is separately saved as NETCDF file. Each field is show on geographic coordinates, which can be longitude, latitude, vertical hybrid-pressure level (for zonal mean fields, such as ozone, temperature and age-of-air). The dimensions in each field are: lat --&gt; latitude lon --&gt; longitude lev --&gt; hydrid pressure level time --&gt; months of the simulation The output of the total column ozone from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 1-4 and Figure S2 in the supplement are in files: toz_A_ERAI.nc toz_B_ERA5.nc The output of the stratospheric column ozone (SCO) in Figure S1 in the supplement are in the file (levels1-3 are SWOOSH, B_ERA5 and A_ERAI SCO data, respectively): sco_SWOOSH_A_ERAI_B_ERA5.nc The output of zonal mean ozone profiles from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 5-7, 9 and Figures S3-4 are in files: O3_mm_A_ERAI.nc O3_mm_B_ERA5.nc The output of zonal mean temperature from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figure 8 are in files: te_mm_A_ERAI.nc te_mm_B_ERA5.nc The output of zonal mean age-of-air from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 10-12 are in files: Age_mm_A_ERAI.nc Age_mm_B_ERA5.nc The output of the zonal mean ozone, temperature and age-of-air from the ERA5.1 reanalysis corrected simulations during the period from 2000 to 2006 in all Figures above using ERA5 are in files: ERA5_1_O3_2000_18.nc ERA5_1_te_2000_18.nc ERA5_1_Age_2000_18.nc
DOI: 10.5281/zenodo.6207784
title: Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"

description: <strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong> <strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> - "Manual Validation": It report all row data concerning the manual analysis (validation done by the different evaluators, the reviewers, etc.) <br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> <em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis <em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.
description: ACKNOWLEDGMENTS We gratefully acknowledge the Horizon 2020 (EU Commission) support for the projects COSMOS (DevOps for Complex Cyberphysical Systems), Project No. 957254-COSMOS.
DOI: 10.5281/zenodo.6320297
title: Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"

description: <strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong> <strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> - "Manual Validation": It report all row data concerning the manual analysis (validation done by the different evaluators, the reviewers, etc.) <br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> <em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis <em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.
description: ACKNOWLEDGMENTS We gratefully acknowledge the Horizon 2020 (EU Commission) support for the projects COSMOS (DevOps for Complex Cyberphysical Systems), Project No. 957254-COSMOS.
DOI: 10.5281/zenodo.6330888
title: Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"

description: <strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong> <strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> - "Manual Validation": It report all row data concerning the manual analysis (validation done by the different evaluators, the reviewers, etc.) <br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> <em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis <em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.
description: ACKNOWLEDGMENTS We gratefully acknowledge the Horizon 2020 (EU Commission) support for the projects COSMOS (DevOps for Complex Cyberphysical Systems), Project No. 957254-COSMOS.
DOI: 10.5281/zenodo.6331034
title: Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"

description: <strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong> <strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> - "Manual Validation": It report all row data concerning the manual analysis (validation done by the different evaluators, the reviewers, etc.) <br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> <em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis <em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.
description: ACKNOWLEDGMENTS We gratefully acknowledge the Horizon 2020 (EU Commission) support for the projects COSMOS (DevOps for Complex Cyberphysical Systems), Project No. 957254-COSMOS.
DOI: 10.34740/kaggle/dsv/4065935
title: YouTube Daily
DOI: 10.21227/6nky-vs45
title: Julia Source Code: Transfer Function Synthesis with Real Poles

description: The code synthesizes a transfer function with real poles from tabulated frequency response data. The reproducible run of this capsule will synthesize the impedance matrix of a 500-kV double-circuit overhead power transmission line and also that of a 250-kV dc submarine power cable.
DOI: 10.6084/m9.figshare.20289123
title: Data for "Widespread irreversible changes in surface temperature and precipitation in response to CO2 forcing"

description: Data for "Widespread irreversible changes in surface temperature and precipitation in response to CO2 forcing"
DOI: 10.6084/m9.figshare.20289123.v1
title: Data for "Widespread irreversible changes in surface temperature and precipitation to CO2 forcing"

description: Data for "Widespread irreversible changes in surface temperature and precipitation to CO2 forcing"
DOI: 10.6084/m9.figshare.16681900.v4
title: MSA (SARS-CoV-2)

description: 30,229 SARS-CoV-2 sequences with the collection date ranging from 31 December 2019 to 22 March 2021 were retrieved from the GISAID database (https://www.gisaid.org/). Multiple sequence alignment (MSA) of the sequences was performed using MAFFT with the parameters left as default and the results are split into four smaller files for the ease of identifying mutations.
DOI: 10.6084/m9.figshare.20484204
title: idea

description: Identification of Drug-Side Effect Association
DOI: 10.5441/001/1.f4r24r5r/1
title: Death's-head hawkmoth migration Europe

description: Menz MHM, Scacco M, Bürki-Spycher H-M, Williams HJ, Reynolds DR, Chapman JW, Wikelski M. 2022. Individual tracking reveals long-distance flightpath control in a nocturnally-migrating moth. Science. 377(6607):764-768. doi:10.1126/science.abn1663
description: Each year, trillions of insects make long-range seasonal migrations. These movements are relatively well understood at a population level, but how individual insects achieve them remains elusive. Behavioral responses to conditions en route are little studied, primarily owing to the challenges of tracking individual insects. Using a light aircraft and individual radio tracking, we show that nocturnally migrating death’s-head hawkmoths maintain control of their flight trajectories over long distances. The moths did not just fly with favorable tailwinds; during a given night, they also adjusted for head and crosswinds to precisely hold course. This behavior indicates that the moths use a sophisticated internal compass to maintain seasonally beneficial migratory trajectories independent of wind conditions, illuminating how insects traverse long distances to take advantage of seasonal resources.
DOI: 10.5441/001/1.f4r24r5r/2
title: Death's-head hawkmoth migration Europe-reference-data

description: Menz MHM, Scacco M, Bürki-Spycher H-M, Williams HJ, Reynolds DR, Chapman JW, Wikelski M. 2022. Individual tracking reveals long-distance flightpath control in a nocturnally-migrating moth. Science. 377(6607):764-768. doi:10.1126/science.abn1663
description: Each year, trillions of insects make long-range seasonal migrations. These movements are relatively well understood at a population level, but how individual insects achieve them remains elusive. Behavioral responses to conditions en route are little studied, primarily owing to the challenges of tracking individual insects. Using a light aircraft and individual radio tracking, we show that nocturnally migrating death’s-head hawkmoths maintain control of their flight trajectories over long distances. The moths did not just fly with favorable tailwinds; during a given night, they also adjusted for head and crosswinds to precisely hold course. This behavior indicates that the moths use a sophisticated internal compass to maintain seasonally beneficial migratory trajectories independent of wind conditions, illuminating how insects traverse long distances to take advantage of seasonal resources.
DOI: 10.5441/001/1.f4r24r5r/3
title: moths_windVariables_trajVariables_allAtmLev_2018-2019

description: Menz MHM, Scacco M, Bürki-Spycher H-M, Williams HJ, Reynolds DR, Chapman JW, Wikelski M. 2022. Individual tracking reveals long-distance flightpath control in a nocturnally-migrating moth. Science. 377(6607):764-768. doi:10.1126/science.abn1663
description: Each year, trillions of insects make long-range seasonal migrations. These movements are relatively well understood at a population level, but how individual insects achieve them remains elusive. Behavioral responses to conditions en route are little studied, primarily owing to the challenges of tracking individual insects. Using a light aircraft and individual radio tracking, we show that nocturnally migrating death’s-head hawkmoths maintain control of their flight trajectories over long distances. The moths did not just fly with favorable tailwinds; during a given night, they also adjusted for head and crosswinds to precisely hold course. This behavior indicates that the moths use a sophisticated internal compass to maintain seasonally beneficial migratory trajectories independent of wind conditions, illuminating how insects traverse long distances to take advantage of seasonal resources.
DOI: 10.5517/ccdc.csd.cc27r9z9
title: CCDC 2077991: Experimental Crystal Structure Determination

description: Related Article: Gao-Xiang Wang, Wang Chen, Xuechao Yan, Fei Xie, Junnian Wei, Shengfa Ye, Zhenfeng Xi|2022|Organometallics|41|2227|doi:10.1021/acs.organomet.2c00304
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.cc27rb0c
title: CCDC 2077992: Experimental Crystal Structure Determination

description: Related Article: Gao-Xiang Wang, Wang Chen, Xuechao Yan, Fei Xie, Junnian Wei, Shengfa Ye, Zhenfeng Xi|2022|Organometallics|41|2227|doi:10.1021/acs.organomet.2c00304
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.