cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7330951
title: Dataset on UAV RGB videos acquired over a vineyard property of Bodegas Terras Gauda at an early stage of Botrytis cinerea infection in 2021

description: The videos were collected in a vineyard owned by Bodegas Terras Gauda, in June 2021. The videos were collected with a DJI Matrice 210 RTK UAV, which had a DJI Zenmuse X5S sensor onboard. A total of 4 rows were recorded with side videos. The flights were carried out on a sunny day with wind velocity lower than 0.5 m/s. Annotations of the grape clusters in the MOTS style are provided.
description: {"references": ["Ariza-Sent\u00eds, M., V\u00e9lez, S., Baja, H., & Valente, J. (2022). IPPS 2022 Conference Book. 231."]}
DOI: 10.6084/m9.figshare.16766346
title: Impact of smoking cannabidiol (CBD)-rich marijuana on driving ability

description: To investigate effects of smoking cannabidiol (CBD)-rich marijuana on driving ability and determine free CBD and Δ<sup>9</sup>-tetrahydrocannabinol (THC) concentrations in capillary blood samples, a randomised, double-blind, placebo-controlled, two-way crossover pilot study was conducted with 33 participants. Participants smoked a joint containing 500 mg of tobacco and either 500 mg of CBD-rich marijuana (16.6% total CBD; 0.9% total THC) or 500 mg of a placebo substance, then performed three different dimensions of the Vienna Test System TRAFFIC examining reaction time, behaviour under stress, and concentration performance. For further assessment of participants’ fitness to drive, three tests of balance and coordination were evaluated and vital signs (blood pressure and pulse) were measured. Dried blood spot samples of capillary blood were taken after smoking and after completion of the tests to determine the cannabinoid concentrations (CBD, THC and THC-metabolites). The results revealed no significant differences between the effects of smoking CBD-rich marijuana and placebo on reaction time, motor time, behaviour under stress, or concentration performance. Maximum free CBD and THC concentrations in capillary blood were detected shortly after smoking, ranging between 2.6–440.0 ng/mL and 6.7–102.0 ng/mL, respectively. After 45 min, capillary blood concentrations had already declined and were in the range of 1.9–135.0 ng/mL (free CBD) and 0.9–38.0 ng/mL (free THC). Although the observed levels of free THC concentrations have been reported to cause symptoms of impairment in previous studies in which THC-rich marijuana was smoked, no signs of impairment were found in the current study. This finding suggests that higher CBD concentrations cause a negative allosteric effect in the endocannabinoid system, preventing the formation of such symptoms. Nevertheless, it is recommended that consumers refrain from driving for several hours after smoking CBD-rich marijuana, as legal THC concentration limits may be exceeded. Supplemental data for this article is available online at https://doi.org/10.1080/20961790.2021.1946924 .
DOI: 10.6084/m9.figshare.23611500
title: Bayesian Heterogeneous Hidden Markov Models with an Unknown Number of States

description: Hidden Markov models (HMMs) are valuable tools for analyzing longitudinal data due to their capability to describe dynamic heterogeneity. Conventional HMMs typically assume that the number of hidden states (i.e., the order of HMMs) is known or predetermined through criterion-based methods. However, prior knowledge about the order is often unavailable, and a pairwise comparison using criterion-based methods becomes increasingly tedious and computationally demanding when the model space enlarges. A few studies have considered simultaneously performing order selection and parameter estimation under the frequentist framework. Still, they focused only on homogeneous HMMs and thus cannot accommodate situations where potential covariates affect the between-state transition. This study proposes a Bayesian double-penalized (BDP) procedure to conduct a simultaneous order selection and parameter estimation for heterogeneous HMMs. We develop a novel Markov chain Monte Carlo algorithm coupled with an efficient adjust-bound reversible jump scheme to address the challenges in updating the order. Simulation studies show that the proposed BDP procedure considerably outperforms the commonly used criterion-based methods. An application to the Alzheimer’s Disease Neuroimaging Initiative study further confirms the utility of the proposed method. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.16766346.v1
title: Impact of smoking cannabidiol (CBD)-rich marijuana on driving ability

description: To investigate effects of smoking cannabidiol (CBD)-rich marijuana on driving ability and determine free CBD and Δ<sup>9</sup>-tetrahydrocannabinol (THC) concentrations in capillary blood samples, a randomised, double-blind, placebo-controlled, two-way crossover pilot study was conducted with 33 participants. Participants smoked a joint containing 500 mg of tobacco and either 500 mg of CBD-rich marijuana (16.6% total CBD; 0.9% total THC) or 500 mg of a placebo substance, then performed three different dimensions of the Vienna Test System TRAFFIC examining reaction time, behaviour under stress, and concentration performance. For further assessment of participants’ fitness to drive, three tests of balance and coordination were evaluated and vital signs (blood pressure and pulse) were measured. Dried blood spot samples of capillary blood were taken after smoking and after completion of the tests to determine the cannabinoid concentrations (CBD, THC and THC-metabolites). The results revealed no significant differences between the effects of smoking CBD-rich marijuana and placebo on reaction time, motor time, behaviour under stress, or concentration performance. Maximum free CBD and THC concentrations in capillary blood were detected shortly after smoking, ranging between 2.6–440.0 ng/mL and 6.7–102.0 ng/mL, respectively. After 45 min, capillary blood concentrations had already declined and were in the range of 1.9–135.0 ng/mL (free CBD) and 0.9–38.0 ng/mL (free THC). Although the observed levels of free THC concentrations have been reported to cause symptoms of impairment in previous studies in which THC-rich marijuana was smoked, no signs of impairment were found in the current study. This finding suggests that higher CBD concentrations cause a negative allosteric effect in the endocannabinoid system, preventing the formation of such symptoms. Nevertheless, it is recommended that consumers refrain from driving for several hours after smoking CBD-rich marijuana, as legal THC concentration limits may be exceeded. Supplemental data for this article is available online at https://doi.org/10.1080/20961790.2021.1946924 .
DOI: 10.6084/m9.figshare.16766346.v2
title: Impact of smoking cannabidiol (CBD)-rich marijuana on driving ability

description: To investigate effects of smoking cannabidiol (CBD)-rich marijuana on driving ability and determine free CBD and Δ<sup>9</sup>-tetrahydrocannabinol (THC) concentrations in capillary blood samples, a randomised, double-blind, placebo-controlled, two-way crossover pilot study was conducted with 33 participants. Participants smoked a joint containing 500 mg of tobacco and either 500 mg of CBD-rich marijuana (16.6% total CBD; 0.9% total THC) or 500 mg of a placebo substance, then performed three different dimensions of the Vienna Test System TRAFFIC examining reaction time, behaviour under stress, and concentration performance. For further assessment of participants’ fitness to drive, three tests of balance and coordination were evaluated and vital signs (blood pressure and pulse) were measured. Dried blood spot samples of capillary blood were taken after smoking and after completion of the tests to determine the cannabinoid concentrations (CBD, THC and THC-metabolites). The results revealed no significant differences between the effects of smoking CBD-rich marijuana and placebo on reaction time, motor time, behaviour under stress, or concentration performance. Maximum free CBD and THC concentrations in capillary blood were detected shortly after smoking, ranging between 2.6–440.0 ng/mL and 6.7–102.0 ng/mL, respectively. After 45 min, capillary blood concentrations had already declined and were in the range of 1.9–135.0 ng/mL (free CBD) and 0.9–38.0 ng/mL (free THC). Although the observed levels of free THC concentrations have been reported to cause symptoms of impairment in previous studies in which THC-rich marijuana was smoked, no signs of impairment were found in the current study. This finding suggests that higher CBD concentrations cause a negative allosteric effect in the endocannabinoid system, preventing the formation of such symptoms. Nevertheless, it is recommended that consumers refrain from driving for several hours after smoking CBD-rich marijuana, as legal THC concentration limits may be exceeded. Supplemental data for this article is available online at https://doi.org/10.1080/20961790.2021.1946924 .
DOI: 10.6084/m9.figshare.23611500.v2
title: Bayesian Heterogeneous Hidden Markov Models with an Unknown Number of States

description: Hidden Markov models (HMMs) are valuable tools for analyzing longitudinal data due to their capability to describe dynamic heterogeneity. Conventional HMMs typically assume that the number of hidden states (i.e., the order of HMMs) is known or predetermined through criterion-based methods. However, prior knowledge about the order is often unavailable, and a pairwise comparison using criterion-based methods becomes increasingly tedious and computationally demanding when the model space enlarges. A few studies have considered simultaneously performing order selection and parameter estimation under the frequentist framework. Still, they focused only on homogeneous HMMs and thus cannot accommodate situations where potential covariates affect the between-state transition. This study proposes a Bayesian double-penalized (BDP) procedure to conduct a simultaneous order selection and parameter estimation for heterogeneous HMMs. We develop a novel Markov chain Monte Carlo algorithm coupled with an efficient adjust-bound reversible jump scheme to address the challenges in updating the order. Simulation studies show that the proposed BDP procedure considerably outperforms the commonly used criterion-based methods. An application to the Alzheimer’s Disease Neuroimaging Initiative study further confirms the utility of the proposed method. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.23611500.v1
title: Bayesian heterogeneous hidden Markov models with an unknown number of states

description: Hidden Markov models (HMMs) are valuable tools for analyzing longitudinal data due to their capability to describe dynamic heterogeneity. Conventional HMMs typically assume that the number of hidden states (i.e., the order of HMMs) is known or predetermined through criterion-based methods. However, prior knowledge about the order is often unavailable, and a pairwise comparison using criterion-based methods becomes increasingly tedious and computationally demanding when the model space enlarges. A few studies have considered simultaneously performing order selection and parameter estimation under the frequentist framework. Still, they focused only on homogeneous HMMs and thus cannot accommodate situations where potential covariates affect the between-state transition. This study proposes a Bayesian double-penalized (BDP) procedure to conduct a simultaneous order selection and parameter estimation for heterogeneous HMMs. We develop a novel Markov chain Monte Carlo algorithm coupled with an efficient adjust-bound reversible jump scheme to address the challenges in updating the order. Simulation studies show that the proposed BDP procedure considerably outperforms the commonly used criterion-based methods. An application to the Alzheimer’s Disease Neuroimaging Initiative study further confirms the utility of the proposed method.
DOI: 10.5281/zenodo.8158464
title: Cipher Machine Elcrotel 1 (Inv. 2020-390)

description: This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine. object details:<br> name: Elcrotel 1<br> Inv.-No. of the Deutsches Museum: 2020-390 file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.670<br> Y pixel spacing: 0.670<br> Z pixel spacing: 0.600<br> grid size x: 476<br> grid size y: 587<br> grid size z: 967<br> color depth: 16Bit ownership & image acquisition:<br> project: 3D-Cipher<br> collection: Deutsches Museum<br> IP holder: Deutsches Museum<br> license: Creative Commons BY-SA 4.0<br> scanning facility: Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS<br> scanning device: High Energy CT XXL-CT<br> Funding attribution: German Federal Ministry of Education and Research<br> acknowledgement: CryptoMuseum (as main source for informations about the cipher machines)
description: Funding: German Federal Ministry of Education and Research (BMBF), grant programme: eHeritage.
DOI: 10.5281/zenodo.8158463
title: Cipher Machine Elcrotel 1 (Inv. 2020-390)

description: This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine. object details:<br> name: Elcrotel 1<br> Inv.-No. of the Deutsches Museum: 2020-390 file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.670<br> Y pixel spacing: 0.670<br> Z pixel spacing: 0.600<br> grid size x: 476<br> grid size y: 587<br> grid size z: 967<br> color depth: 16Bit ownership & image acquisition:<br> project: 3D-Cipher<br> collection: Deutsches Museum<br> IP holder: Deutsches Museum<br> license: Creative Commons BY-SA 4.0<br> scanning facility: Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS<br> scanning device: High Energy CT XXL-CT<br> Funding attribution: German Federal Ministry of Education and Research<br> acknowledgement: CryptoMuseum (as main source for informations about the cipher machines)
description: Funding: German Federal Ministry of Education and Research (BMBF), grant programme: eHeritage.
DOI: 10.5281/zenodo.8178921
title: Supporting Information for the article "Exploring Photoredox-Catalyzed (Re)functionalizations with Core-Modified Benziodoxolones"

description: Complete supporting information for the article: "Exploring Photoredox-Catalyzed (Re)functionalizations with Core-Modified Benziodoxolones" published on 21.11.2022 in Helvetica Chimica Acta https://doi.org/10.1002/hlca.202200161 This file contain all the data on general methods, reaction optimization and compound synthesis and characterization.
DOI: 10.5281/zenodo.8178920
title: Supporting Information for the article "Exploring Photoredox-Catalyzed (Re)functionalizations with Core-Modified Benziodoxolones"

description: Complete supporting information for the article: "Exploring Photoredox-Catalyzed (Re)functionalizations with Core-Modified Benziodoxolones" published on 21.11.2022 in Helvetica Chimica Acta https://doi.org/10.1002/hlca.202200161 This file contain all the data on general methods, reaction optimization and compound synthesis and characterization.
DOI: 10.6084/m9.figshare.20789203
title: Data used in the article <em>Different methods in assessing El Niño flavors lead to opposite results</em> (https://doi.org/10.1029/2023GL104558)

description: Data used in the article <i>Different methods in assessing El Niño flavors lead to opposite results </i>(https://doi.org/10.1029/2023GL104558).The Jupyter notebook needed to reproduce the figures in the article is there https://github.com/iadicarlo/elnino-flavors-paper (or there https://doi.org/10.5281/zenodo.7938040).
DOI: 10.6084/m9.figshare.20789203.v4
title: Data used in the article <em>Different methods in assessing El Niño flavors lead to opposite results</em> (https://doi.org/10.1029/2023GL104558)

description: Data used in the article <i>Different methods in assessing El Niño flavors lead to opposite results </i>(https://doi.org/10.1029/2023GL104558).The Jupyter notebook needed to reproduce the figures in the article is there https://github.com/iadicarlo/elnino-flavors-paper (or there https://doi.org/10.5281/zenodo.7938040).
DOI: 10.6084/m9.figshare.20789203.v3
title: Data used in the article <em>Different methods in assessing El Niño flavors lead to opposite results</em> (https://doi.org/10.1029/2023GL104558).

description: Data used in the article <i>Different methods in assessing El Niño flavors lead to opposite results </i>(https://doi.org/10.1029/2023GL104558).
DOI: 10.25345/c58k7567n
title: MassIVE MSV000092636 - GNPS Ocotea cymbarum essential oil GCMS
title: Analysis of the essential oil from bark, leaves and fruits of the species Ocotea cymbarum. These sample were obtained in two periods (dry and flood) on the Amazonian forest.
DOI: 10.5281/zenodo.8158475
title: Cipher Machine Elcrobit 1 (Inv. 2020-391)

description: This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine. object details:<br> name: Elcrobit 1<br> Inv.-No. of the Deutsches Museum: 2020-391 file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.670<br> Y pixel spacing: 0.670<br> Z pixel spacing: 0.600<br> grid size x: 551<br> grid size y: 587<br> grid size z: 1103<br> color depth: 16Bit ownership & image acquisition:<br> project: 3D-Cipher<br> collection: Deutsches Museum<br> IP holder: Deutsches Museum<br> license: Creative Commons BY-SA 4.0<br> scanning facility: Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS<br> scanning device: High Energy CT XXL-CT<br> Funding attribution: German Federal Ministry of Education and Research<br> acknowledgement: CryptoMuseum (as main source for informations about the cipher machines)
description: Funding: German Federal Ministry of Education and Research (BMBF), grant programme: eHeritage.
DOI: 10.5281/zenodo.8158474
title: Cipher Machine Elcrobit 1 (Inv. 2020-391)

description: This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine. object details:<br> name: Elcrobit 1<br> Inv.-No. of the Deutsches Museum: 2020-391 file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.670<br> Y pixel spacing: 0.670<br> Z pixel spacing: 0.600<br> grid size x: 551<br> grid size y: 587<br> grid size z: 1103<br> color depth: 16Bit ownership & image acquisition:<br> project: 3D-Cipher<br> collection: Deutsches Museum<br> IP holder: Deutsches Museum<br> license: Creative Commons BY-SA 4.0<br> scanning facility: Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS<br> scanning device: High Energy CT XXL-CT<br> Funding attribution: German Federal Ministry of Education and Research<br> acknowledgement: CryptoMuseum (as main source for informations about the cipher machines)
description: Funding: German Federal Ministry of Education and Research (BMBF), grant programme: eHeritage.
DOI: 10.5281/zenodo.8155233
title: Cipher Machine Elcrovox 1-0, SB 490-1 (Inv. 2020-388)

description: This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine. object details:<br> name: Elcrovox 1-0, SB 490-1<br> Inv.-No. of the Deutsches Museum: 2020-388 file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.670<br> Y pixel spacing: 0.670<br> Z pixel spacing: 0.600<br> grid size x: 702<br> grid size y: 486<br> grid size z: 1052<br> color depth: 16Bit ownership & image acquisition:<br> project: 3D-Cipher<br> collection: Deutsches Museum<br> IP holder: Deutsches Museum<br> license: Creative Commons BY-SA 4.0<br> scanning facility: Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS<br> scanning device: High Energy CT XXL-CT<br> Funding attribution: German Federal Ministry of Education and Research<br> acknowledgement: CryptoMuseum (as main source for informations about the cipher machines)
description: Funding: German Federal Ministry of Education and Research (BMBF), grant programme: eHeritage.
DOI: 10.5281/zenodo.8158421
title: Vocoder VOC 490 for Elcrovox 1-0 (Inv. 2020-389)

description: This dataset represents the computed tomography image acquisition of a historical cipher machine from the collection of the Deutsches Museum. It is composed of CT reconstructed image stacks in the DICOM (.dcm) format.<br> It can be imported in any free or propietary CT-Viewer that supports the DICOM standard to generated 2D and 3D imaging.<br> If segmentations/ROIs are availabe, they are uploaded in a seperate image stacks and are a subset of the scanned cipher machine. object details:<br> name: Vocoder VOC 490 for Elcrovox 1-0<br> Inv.-No. of the Deutsches Museum: 2020-389 file object details:<br> file format: image/dcm<br> pixel spacing unit: mm<br> X pixel spacing: 0.670<br> Y pixel spacing: 0.670<br> Z pixel spacing: 0.600<br> grid size x: 679<br> grid size y: 486<br> grid size z: 1063<br> color depth: 16Bit ownership & image acquisition:<br> project: 3D-Cipher<br> collection: Deutsches Museum<br> IP holder: Deutsches Museum<br> license: Creative Commons BY-SA 4.0<br> scanning facility: Fraunhofer Development Center X-ray Technology EZRT/Fraunhofer IIS<br> scanning device: High Energy CT XXL-CT<br> Funding attribution: German Federal Ministry of Education and Research<br> acknowledgement: CryptoMuseum (as main source for informations about the cipher machines)
description: Funding: German Federal Ministry of Education and Research (BMBF), grant programme: eHeritage.