cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6802656
title: Datasets for "Evaluating the performance of a Picarro G2207-i analyser for high-precision atmospheric O2 measurements"

description: These data files contain the data used in the manuscript "Evaluating the performance of a picarro G2207-i analyser for high-precision atmospheric O2 measurements" submitted to Atmospheric Measurement Techniques. WAO_G2207i_O2_calibrated_AM_all : Calibrated O2 measurements from the G2207-i during the no-drying, partial-drying, and full-drying periods at WAO, for both the water-corrected and non-water corrected outputs CRAM_lab_run1_noRT : calibrated O2 measurements for the first run of cylinder gases in the CRAM lab, UEA, without reference tank correction CRAM_lab_run1_wRT : calibrated O2 measurements for the first run of cylinder gases in the CRAM lab, UEA, with reference tank correction applied CRAM_lab_run2_noRT : calibrated O2 measurements for the second run of cylinder gases in the CRAM lab, UEA, without reference tank correction CRAM_lab_run2_wRT : calibrated O2 measurements for the second run of cylinder gases in the CRAM lab, UEA, with reference tank correction applied
DOI: 10.5281/zenodo.6672451
title: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Refinement dataset SPIRA

description: This dataset was collected over the internet and in hospital wards with the goal of detecting respiratory insufficiency (typically caused by COVID-19). This data collection is part of the SPIRA Project, whose goal is developing a system for recognizing respiratory insufficiency through speech analysis. The datasets presented here were used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger. The spira_trimmed_data file contains the original ~1 hour dataset collected over the internet (control) and in hospital wards (patients) by the SPIRA Project. This is as described in the paper: Deep learning against COVID-19: Respiratory insufficiency detection in Brazilian Portuguese Speech. We include it here for completeness. The spira_control_full_mp3 file contains the complete ~18 hours control data collected over the internet by the SPIRA project. While not useful for respiratory insufficiency detection, the dataset may be used for identifying age and gender as we mention in our paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers.
description: {"references": ["Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Gauy, Marcelo e Finger, Marcelo 2022", "Deep learning against COVID-19: Respiratory insufficiency detection in Brazilian Portuguese Speech - Casanova, Edresson e Gris, Lucas e Camargo, Augusto e Silva, Daniel e Gazzola, Murilo e Sabino, Ester e Levin, Anna e Candido Jr, Arnaldo e Aluisio, Sandra e Finger, Marcelo 2021", "Detecting Respiratory Insufficiency via Voice Analysis: the SPIRA Project - Aluisio, Sandra e Camargo, Augusto e Candido Jr, Arnaldo e Fernandes Jr, Ricardo e Casanova, Edresson e Gris, Lucas e Svartman, Flaviane e Silva, Daniel e Ferreira, Renato e Finger, Marcelo e Goldman, Alfredo e Spazzapan, Evelyn e Leyton, Pedro e Berti, Larissa e Levin, Anna e Gauy, Marcelo e Martins, Marcus e Quirino, Henrique e Queiroz, Marcelo e Raposo, Beatriz e Sabino, Ester 2022", "Detecting respiratory insufficiency by voice analysis: the SPIRA project (2021) - Finger, Marcelo e Aluisio, Sandra e Spazzapan, Evelyn e Berti, Larissa e Camargo, Augusto e Candido Jr, Arnaldo e Casanova, Edresson e Svartman, Flaviane e Ferreira, Renato e Fernandes Jr, Ricardo e Goldman, Alfredo e Gris, Lucas e Leyton, Pedro e Levin, Anna e Martins, Marcus e Queiroz, Marcelo e Quirino, Henrique e Medeiros, Beatriz e Sabino, Ester e Silva, Daniel"]}
DOI: 10.5281/zenodo.6672450
title: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Refinement dataset SPIRA

description: This dataset was collected over the internet and in hospital wards with the goal of detecting respiratory insufficiency (typically caused by COVID-19). This data collection is part of the SPIRA Project, whose goal is developing a system for recognizing respiratory insufficiency through speech analysis. The datasets presented here were used in the paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers by Marcelo Gauy and Marcelo Finger. The spira_trimmed_data file contains the original ~1 hour dataset collected over the internet (control) and in hospital wards (patients) by the SPIRA Project. This is as described in the paper: Deep learning against COVID-19: Respiratory insufficiency detection in Brazilian Portuguese Speech. We include it here for completeness. The spira_control_full_mp3 file contains the complete ~18 hours control data collected over the internet by the SPIRA project. While not useful for respiratory insufficiency detection, the dataset may be used for identifying age and gender as we mention in our paper: Acoustic models of Brazilian Portuguese Speech based on Neural Transformers.
description: {"references": ["Acoustic models of Brazilian Portuguese Speech based on Neural Transformers - Gauy, Marcelo e Finger, Marcelo 2022", "Deep learning against COVID-19: Respiratory insufficiency detection in Brazilian Portuguese Speech - Casanova, Edresson e Gris, Lucas e Camargo, Augusto e Silva, Daniel e Gazzola, Murilo e Sabino, Ester e Levin, Anna e Candido Jr, Arnaldo e Aluisio, Sandra e Finger, Marcelo 2021", "Detecting Respiratory Insufficiency via Voice Analysis: the SPIRA Project - Aluisio, Sandra e Camargo, Augusto e Candido Jr, Arnaldo e Fernandes Jr, Ricardo e Casanova, Edresson e Gris, Lucas e Svartman, Flaviane e Silva, Daniel e Ferreira, Renato e Finger, Marcelo e Goldman, Alfredo e Spazzapan, Evelyn e Leyton, Pedro e Berti, Larissa e Levin, Anna e Gauy, Marcelo e Martins, Marcus e Quirino, Henrique e Queiroz, Marcelo e Raposo, Beatriz e Sabino, Ester 2022", "Detecting respiratory insufficiency by voice analysis: the SPIRA project (2021) - Finger, Marcelo e Aluisio, Sandra e Spazzapan, Evelyn e Berti, Larissa e Camargo, Augusto e Candido Jr, Arnaldo e Casanova, Edresson e Svartman, Flaviane e Ferreira, Renato e Fernandes Jr, Ricardo e Goldman, Alfredo e Gris, Lucas e Leyton, Pedro e Levin, Anna e Martins, Marcus e Queiroz, Marcelo e Quirino, Henrique e Medeiros, Beatriz e Sabino, Ester e Silva, Daniel"]}
DOI: 10.5281/zenodo.6802680
title: Lagrangian particles in turbulence: An experimental data set

description: This data set contains the coordinates of Lagrangian trajectories in a quasi-homogeneous isotropic turbulent flow. The trajectories were measured in a water tank experiment using the 3D-PTV method through the MyPTV open-source software (https://github.com/ronshnapp/MyPTV). The total duration of this data set corresponds to 20 seconds of recording at a rate of 500 Hz, and it holds roughly 575,000 trajectories. <br> The data is stored in two text files in a tab-separated format, where each file coresponds to 10 seconds of recording. Each row in the files outlines a single measurement point from the experiment. The various columns correspond to the following information:<br> 1 - trajectory_id <br> 2 - x [mm] <br> 3 - y [mm]<br> 4 - z [mm]<br> 5 - vx [mm/frame]<br> 6 - vy [mm/frame]<br> 7 - vz [mm/frame]<br> 8 - ax [mm/frame^2]<br> 9 - ay [mm/frame^2]<br> 10 - az [mm/frame^2]<br> 11 - time [frame]<br> where trajectory_id uniquely marks samples that correspond to the same physical trajectory; x, y, and z are the position components in the three orthogonal space directions; vx, vy, and vz correspond to the velocity components; ax, ay, and az that correspond to the acceleration components. <br> The flow in the experiment was forced using a system of 8 propellers, powered by DC motors that were positioned at the corners of the cylindrical, octagonally shaped, water tank. The propellers were changing their direction of rotation at random time intervals with an average interval duration of 0.1 seconds. The root mean square of the turbulent flow fluctuations is about 100 millimeters per second. There is a time-averaged secondary circulation with a magnitude of roughly 66% of the root mean squared fluctuation strength. The Taylor microscale Reynolds number is estimated as about 188.
DOI: 10.5281/zenodo.6802679
title: Lagrangian particles in turbulence: An experimental data set

description: This data set contains the coordinates of Lagrangian trajectories in a quasi-homogeneous isotropic turbulent flow. The trajectories were measured in a water tank experiment using the 3D-PTV method through the MyPTV open-source software (https://github.com/ronshnapp/MyPTV). The total duration of this data set corresponds to 20 seconds of recording at a rate of 500 Hz, and it holds roughly 575,000 trajectories. <br> The data is stored in two text files in a tab-separated format, where each file coresponds to 10 seconds of recording. Each row in the files outlines a single measurement point from the experiment. The various columns correspond to the following information:<br> 1 - trajectory_id <br> 2 - x [mm] <br> 3 - y [mm]<br> 4 - z [mm]<br> 5 - vx [mm/frame]<br> 6 - vy [mm/frame]<br> 7 - vz [mm/frame]<br> 8 - ax [mm/frame^2]<br> 9 - ay [mm/frame^2]<br> 10 - az [mm/frame^2]<br> 11 - time [frame]<br> where trajectory_id uniquely marks samples that correspond to the same physical trajectory; x, y, and z are the position components in the three orthogonal space directions; vx, vy, and vz correspond to the velocity components; ax, ay, and az that correspond to the acceleration components. <br> The flow in the experiment was forced using a system of 8 propellers, powered by DC motors that were positioned at the corners of the cylindrical, octagonally shaped, water tank. The propellers were changing their direction of rotation at random time intervals with an average interval duration of 0.1 seconds. The root mean square of the turbulent flow fluctuations is about 100 millimeters per second. There is a time-averaged secondary circulation with a magnitude of roughly 66% of the root mean squared fluctuation strength. The Taylor microscale Reynolds number is estimated as about 188.
DOI: 10.34683/sr2574
title: StoneRounds Case: SR2574
title: AR Stargardt Disease

description: StoneRounds Case SR2574 was diagnosed with AR Stargardt Disease likely caused by mutations in the gene: ABCA4.
DOI: 10.6084/m9.figshare.20239050.v1
title: catalogA

description: Data for ECML22 paper " A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks".
DOI: 10.6084/m9.figshare.20239044.v1
title: catalogC

description: Data for ECML22 paper " A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks".
DOI: 10.5281/zenodo.6802655
title: A Dataset of Synthetic Images of Outdoor Scenes Taken from Sidewalks, for Temporal Semantic Segmentation Applications

description: This dataset has been generated using the CARLA simulator (release 0.9.11), an open-source 3D simulator for experiments in autonomous vehicle, based on the Unreal Engine game engine. It comes with pre-made city environment maps. CARLA is distributed with several integrated maps as well as parameters to increase the variety in the dataset. In the release that we have used, there are 13 semantic segmentation classes: None, Building, Fence, Other, Pedestrian, Pole, Lane-marking, Road, Sidewalk, Vegetation, Vehicle, Wall, and Traffic sign. The "None" category corresponds to textures that are not part of an object, such as lawns which are not part of "Vegetation", or sky. In the “Other” category are found objects that are not included in the other classes like plant and flower pots. For smart mobility applications, the “Sidewalks” and “Road” classes are of particular importance to find the way forward, as well as “Buildings” and “Poles” for obstacle avoidance. Sequences are made of 4 images. The dataset is composed of 46436 frames (11609 sequences) partitioned in 41024 frames (10256 sequences) for train, 2696 frames (674 sequences) for validation, and 2716 for test (679 sequences). The size of the images is 800 x 600 (resp. width x height). Additionaly, we have generated another smaller dataset with images taken from 2 different viewpoints: one located on the road and the other located on the sidewalk. The number of frames for train/validation/test is respectively 7288 (1822 sequences) partitioned in 6344 (1687 sequences) for train, 416 frames (104 sequences) for validation, and 424 for test (106 sequences). This smaller dataset is aimed at showing the importance of the viewpoint in the result of semantic segmentation. This can be done by cross-validation: learning on images taken from a viewpoint located on the road and test on images with a viewpoint located on the sidewalk, and vice versa.
DOI: 10.6084/m9.figshare.20239044
title: none

description: ....
DOI: 10.6084/m9.figshare.20239044.v3
title: none

description: ....
DOI: 10.6084/m9.figshare.20239044.v2
title: catalogC

description: ....
DOI: 10.6084/m9.figshare.20239050
title: none

description: ....
DOI: 10.6084/m9.figshare.20239050.v3
title: none

description: ....
DOI: 10.6084/m9.figshare.20239050.v2
title: catalogA

description: ....
DOI: 10.6084/m9.figshare.20239017
title: none

description: ....
DOI: 10.6084/m9.figshare.20239017.v2
title: none

description: ....
DOI: 10.6084/m9.figshare.20239017.v1
title: catalogB

description: Data for ECML22 paper " A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks"
DOI: 10.35077/ace-ban
title: fMOST_cells_192774

description: This dataset comes from combining genetic labeling with fMOST to achieve a genetically targeted single neuron saturation screen of major classes of cortical projection neurons in the sensorimotor system.
description: Imaging: LIMO; Virus: AAV
description: DATASET METADATA: title:'fMOST_cells_192774', GeneralModality:'cell morphology', Technique:'fMOST; neuron morphology reconstruction' SPECIMEN METADATA: LocalID:'192774', Species:'mouse', NCBITaxonomy:'NCBI:txid10090', Age:'51', Ageunit:'Weeks', Sex:'Male', Genotype:'PlxnD1-2A-creER', OrganName:'Brain', Locations:'M' INSTRUMENT METADATA: MicroscopeType:'fMOST' IMAGE METADATA: yAxis:'inferior-to-superior', zAxis:'anterior-to-posterior', Number:'1, gray', displayColor:'(0.8, 0.8, 0.8)', stepSizeX:'0.35 micron/pixel', stepSizeY:'0.35 micron/pixel', stepSizeZ:'1 micron/pixel'
DOI: 10.6084/m9.figshare.17063990
title: Göpel & Burggren 2022 - Supplementary material

description: Supplementary material for: Göpel & Burggren 2022 - Cores of Reproducibility in Physiology: Insufficient Reporting of Experimental Variables as a Cause for Non-Reproducibility in Animal Physiology? A Case Study
DOI: 10.6084/m9.figshare.17063990.v1
title: Göpel & Burggren 2022 - Supplementary material

description: Supplementary material for: Göpel & Burggren 2022 - Cores of Reproducibility in Physiology: Insufficient Reporting of Experimental Variables as a Cause for Non-Reproducibility in Animal Physiology? A Case Study
DOI: 10.5517/ccdc.csd.cc2409cv
title: CCDC 1966496: Experimental Crystal Structure Determination

description: Related Article: Mahesh M. Parsutkar, Curtis E. Moore, T. V. RajanBabu|2022|Dalton Trans.|51|10148|doi:10.1039/D2DT01484J
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.cc2409fx
title: CCDC 1966498: Experimental Crystal Structure Determination

description: Related Article: Mahesh M. Parsutkar, Curtis E. Moore, T. V. RajanBabu|2022|Dalton Trans.|51|10148|doi:10.1039/D2DT01484J
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.cc2409gy
title: CCDC 1966499: Experimental Crystal Structure Determination

description: Related Article: Mahesh M. Parsutkar, Curtis E. Moore, T. V. RajanBabu|2022|Dalton Trans.|51|10148|doi:10.1039/D2DT01484J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.17701127
title: EEG Dataset for RSVP and P300 Speller Brain-Computer Interfaces

description: EEG dataset for subject 24
DOI: 10.5517/ccdc.csd.cc25nxnf
title: CCDC 2016105: Experimental Crystal Structure Determination

description: Related Article: Mahesh M. Parsutkar, Curtis E. Moore, T. V. RajanBabu|2022|Dalton Trans.|51|10148|doi:10.1039/D2DT01484J
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.cc2brrql
title: CCDC 2167790: Experimental Crystal Structure Determination

description: Related Article: Sen Zhang, Zhang-Hong Luo, Wen-Tao Wang, Linghui Qian, Jia-Yu Liao|2022|Org.Lett.|24|4645|doi:10.1021/acs.orglett.2c01761
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.19727212
title: Dataset for the study submitted to BJLS

description: dataset and results
DOI: 10.25422/azu.data.19755244
title: Development of human pituitary adenoma organoids to facilitate effective targeted treatments of Cushing's disease

description: Cushing's disease (CD) is a serious endocrine disorder caused by an unregulated adrenocorticotropic hormone (ACTH)-secreting pituitary tumor that stimulates the adrenal glands to overproduce cortisol. Chronic exposure to excess cortisol has detrimental effects on health, including increased stroke rates, diabetes, obesity, cognitive impairment, anxiety, depression, and death. The first-line treatment for CD is pituitary surgery, which is followed by disease remission in only 47% of patients. The lack of specificity of current standard of care treatments with low efficacy and tolerability makes CD a medical therapeutic challenge. One major limitation that hinders the development of specific medical therapies is the lack of human relevant model systems that recapitulate the cellular composition of pituitary adenomas. Human pituitary adenoma tissue was harvested during transsphenoidal surgery from CD patients to generate organoids (hPITOs). hPITOs generated from corticotroph, lactotroph, gonadotroph and somatotroph adenomas exhibited morphological diversity among the organoid lines between individual patients and amongst subtypes. The similarity in cell lineages between the organoid line and the patient’s tumor was validated by comparing the neuropathology report, to the expression pattern of pituitary adenoma specific markers using spectral flow cytometry and exome sequencing. A high-throughput drug screen demonstrated patient-specific drug responses of hPITOs amongst each adenoma subtype. Generation of induced pluripotent stem cells (iPSCs) from patients carrying germline mutations relevant to CD exhibited dysregulated cell lineage commitment. The development of the human pituitary adenoma organoids represent a paradigm shift in how we model complex pathologies in CD patients that may alter patient care. <em>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</em>
DOI: 10.6084/m9.figshare.19727212.v1
title: Dataset for the study submitted to BJLS

description: dataset and results
DOI: 10.25422/azu.data.19755244.v1
title: Development of human pituitary adenoma organoids to facilitate effective targeted treatments of Cushing's disease

description: Cushing's disease (CD) is a serious endocrine disorder caused by an unregulated adrenocorticotropic hormone (ACTH)-secreting pituitary tumor that stimulates the adrenal glands to overproduce cortisol. Chronic exposure to excess cortisol has detrimental effects on health, including increased stroke rates, diabetes, obesity, cognitive impairment, anxiety, depression, and death. The first-line treatment for CD is pituitary surgery, which is followed by disease remission in only 47% of patients. The lack of specificity of current standard of care treatments with low efficacy and tolerability makes CD a medical therapeutic challenge. One major limitation that hinders the development of specific medical therapies is the lack of human relevant model systems that recapitulate the cellular composition of pituitary adenomas. Human pituitary adenoma tissue was harvested during transsphenoidal surgery from CD patients to generate organoids (hPITOs). hPITOs generated from corticotroph, lactotroph, gonadotroph and somatotroph adenomas exhibited morphological diversity among the organoid lines between individual patients and amongst subtypes. The similarity in cell lineages between the organoid line and the patient’s tumor was validated by comparing the neuropathology report, to the expression pattern of pituitary adenoma specific markers using spectral flow cytometry and exome sequencing. A high-throughput drug screen demonstrated patient-specific drug responses of hPITOs amongst each adenoma subtype. Generation of induced pluripotent stem cells (iPSCs) from patients carrying germline mutations relevant to CD exhibited dysregulated cell lineage commitment. The development of the human pituitary adenoma organoids represent a paradigm shift in how we model complex pathologies in CD patients that may alter patient care. <em>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</em>
DOI: 10.17903/fk2/pwv3we
title: Autonomy index

description: The Autonomy Index quantifies the concept of Autonomy on the basis of indicators relating to Independence and Imagination as desirable child values, but not as such to Obedience. Together with the Choice Index, they form the two sub-dimensions of the Liberating orientation, one of the two dimensions of emancipatory values. The liaison dimension relates to the Egalitarian orientation. For a better understanding of the overall scheme of indicators and dimensions see the related figure in the data project resources.
DOI: 10.6084/m9.figshare.20239767
title: ECML22 GRAPE Data

description: Data for ECML22 paper "A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks".