cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21954/ou.rd.12971648
title: Covid Chronicles: Diary reflection on Time

description: Vicky is an undocumented migrant currently living in the Netherlands. <br> Vicky's hand-written diary entry dated 21.06.2020 starts by asking us as readers to: think about what you really care about. She ends with: Now is the time and chance / It’s never too late / Don’t forget to always visit yourself. <br> <br> ----- This material is part of the Covid Chronicles from the Margins project, funded by The Open University and The Hague. The project aims to highlight the impact of the pandemic on refugees, asylum seekers & undocumented migrants. <br> This item can also be found on our website.
DOI: 10.21954/ou.rd.12971648.v1
title: Covid Chronicles: Diary reflection on Time

description: Vicky is an undocumented migrant currently living in the Netherlands. <br> Vicky's hand-written diary entry dated 21.06.2020 starts by asking us as readers to: think about what you really care about. She ends with: Now is the time and chance / It’s never too late / Don’t forget to always visit yourself. <br> <br> ----- This material is part of the Covid Chronicles from the Margins project, funded by The Open University and The Hague. The project aims to highlight the impact of the pandemic on refugees, asylum seekers & undocumented migrants. <br> This item can also be found on our website.
DOI: 10.6084/m9.figshare.21435660.v1
title: Investigation of the sealing performance of the gearbox sealing system of high-speed trains

description: Investigation of the sealing performance of the gearbox sealing system ofhigh-speed trains-data
DOI: 10.3886/d3ea-zg17
title: Morocco Compact - Small-Scale Fisheries (Final): Final Analysis Report
DOI: 10.6084/m9.figshare.21591675
title: Gonococcus RNA-seq raw count.xlsx

description: RNA-seq row counts of a clinical isolate of <em>N. gonorrhoeae</em>, after induction of the stringent response by serine hydroxamate.
DOI: 10.6084/m9.figshare.21591675.v1
title: Gonococcus RNA-seq raw count.xlsx

description: RNA-seq row counts of a clinical isolate of <em>N. gonorrhoeae</em>, after induction of the stringent response by serine hydroxamate.
DOI: 10.34740/kaggle/dsv/4557489
title: Bali Popular Destination for Tourist 2022
DOI: 10.15151/esrf-es-953231211
title: Microscopic view of the pressure induced magnetically ordered phases in the UTe2 superconductor by U- M4,5 XMCD

description: This proposal is a resubmission of HC 4373 whose only the XANES part under high pressure (HP) has been achieved due to the repair of the 8 T cryomagnet suitable for HP XMCD. Its main aims is to probe the magnetic phases that occur above 1.5 GPa using HP XMCD at the U – M4,5 edges.
DOI: 10.6084/m9.figshare.20801881
title: Diatom pore pattern analyzer

description: This project contains: python scripts croped images of diatom cell walls
DOI: 10.6084/m9.figshare.20801881.v4
title: Diatom pore pattern analyzer

description: This project contains: python scripts croped images of diatom cell walls
DOI: 10.6084/m9.figshare.21592170
title: ATP6 Alinhamento

description: Alignment of ATP6 region
DOI: 10.6084/m9.figshare.21592170.v1
title: ATP6 Alinhamento

description: Alignment of ATP6 region
DOI: 10.5517/ccdc.csd.cc28l5sv
title: CCDC 2102847: Experimental Crystal Structure Determination

description: Related Article: Yanbang Li, Thomas J. Emge, Antonio Moreno-Vicente, William P. Kopcha, Yue Sun, Iram F. Mansoor, Mark C. Lipke, Gene S. Hall, Josep M. Poblet, Antonio Rodríguez-Fortea, Jianyuan Zhang|2021|Angew.Chem.,Int.Ed.|60|25269|doi:10.1002/anie.202110881
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.cc26q5fk
title: CCDC 2047098: Experimental Crystal Structure Determination

description: Related Article: Yanbang Li, Thomas J. Emge, Antonio Moreno-Vicente, William P. Kopcha, Yue Sun, Iram F. Mansoor, Mark C. Lipke, Gene S. Hall, Josep M. Poblet, Antonio Rodríguez-Fortea, Jianyuan Zhang|2021|Angew.Chem.,Int.Ed.|60|25269|doi:10.1002/anie.202110881
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.cc26q5gl
title: CCDC 2047099: Experimental Crystal Structure Determination

description: Related Article: Yanbang Li, Thomas J. Emge, Antonio Moreno-Vicente, William P. Kopcha, Yue Sun, Iram F. Mansoor, Mark C. Lipke, Gene S. Hall, Josep M. Poblet, Antonio Rodríguez-Fortea, Jianyuan Zhang|2021|Angew.Chem.,Int.Ed.|60|25269|doi:10.1002/anie.202110881
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.cc26q5hm
title: CCDC 2047100: Experimental Crystal Structure Determination

description: Related Article: Yanbang Li, Thomas J. Emge, Antonio Moreno-Vicente, William P. Kopcha, Yue Sun, Iram F. Mansoor, Mark C. Lipke, Gene S. Hall, Josep M. Poblet, Antonio Rodríguez-Fortea, Jianyuan Zhang|2021|Angew.Chem.,Int.Ed.|60|25269|doi:10.1002/anie.202110881
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.cc2cb5c8
title: CCDC 2184519: Experimental Crystal Structure Determination

description: Related Article: D.P. Ganesha, Syed Nizamuddin, N.R. Sreenatha, C.R. Gnanendra, B.N. Lakshminarayana|2022|J.Mol.Struct.|1274|134462|doi:10.1016/j.molstruc.2022.134462
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.cc28y4n1
title: CCDC 2113383: Experimental Crystal Structure Determination

description: Related Article: Maheswar Reddy Gowri, Gurunath Ramanathan|2022|J.Mol.Struct.|1274|134481|doi:10.1016/j.molstruc.2022.134481
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.cc28z25j
title: CCDC 2114267: Experimental Crystal Structure Determination

description: Related Article: Maheswar Reddy Gowri, Gurunath Ramanathan|2022|J.Mol.Struct.|1274|134481|doi:10.1016/j.molstruc.2022.134481
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.21592188
title: ITS alignment

description: ITS alignment
DOI: 10.6084/m9.figshare.21592188.v1
title: ITS alignment

description: ITS alignment
DOI: 10.6084/m9.figshare.21592221
title: LSU alignment

description: LSU alignment
DOI: 10.6084/m9.figshare.21592221.v1
title: LSU alignment

description: LSU alignment
DOI: 10.23728/b2share.44219e37ca9045779ede02ac4feb418a
title: TERENOEifelLowerRhineValley - Phenological Annual Summary Statistics - 2019/2020

description: Phenological Annual Summary Statistics based on Ecosystem Functional Attribute framework. The result are based on S2 interpolated with a bayesian implementation of Harmonic Model.
description: Details of the method can be found in Vicario S, Adamo M, Alcaraz-Segura D, Tarantino C (2019) Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sens 12:83 . doi: 10.3390/rs12010083
description: The phenology is not a scalar variable but it is an ensamble of sub-variables all based on MCARI2 vegetation index and for each one two statistics are given: expected value (mean) and a mask for all pixel with standard deviation of uncertianities larger than 10% the mean (CVmask) within the general name rule proposed: locality_variable_timestamp.extension variable formed in: Phenology-SubvariableStatistics The subvariables are: mean: mean value across the year - values range between 0-0.5 stdintra: standard deviation of the value across the year - values range between 0-0.05 maxpos: day of the year of the maximum value - values range between 0-0.5 sdinter: standard deviation across years - values range between 0-0.05 The statistics are: mean: Expected value of the subvariable across 100 simulation CVmask: 0-1 mask with value 1 for pixel with less than 10% of standard deviation compared to the mean The timestamp refer to a year or to two years
DOI: 10.6084/m9.figshare.21592251
title: ITS-LSU-ATP6 alignment

description: ITS-LSU-ATP6 alignment
DOI: 10.6084/m9.figshare.21592251.v1
title: ITS-LSU-ATP6 alignment

description: ITS-LSU-ATP6 alignment
DOI: 10.23728/b2share.b9a0acc25ca74ec9ace2bdc9a6799e97
title: Donana - Phenological Annual Summary Statistic - 2019/2020

description: Phenological Annual Summary Statistics based on Ecosystem Functional Attribute framework. The result are based on S2 interpolated with a bayesian implementation of Harmonic Model.
description: Details of the method can be found in Vicario S, Adamo M, Alcaraz-Segura D, Tarantino C (2019) Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sens 12:83 . doi: 10.3390/rs12010083
description: The phenology is not a scalar variable but it is an ensamble of sub-variables all based on MCARI2 vegetation index and for each one two statistics are given: expected value (mean) and a mask for all pixel with standard deviation of uncertianities larger than 10% the mean (CVmask) within the general name rule proposed: locality_variable_timestamp.extension variable formed in: Phenology-SubvariableStatistics The subvariables are: mean: mean value across the year - values range between 0-0.5 stdintra: standard deviation of the value across the year - values range between 0-0.05 maxpos: day of the year of the maximum value - values range between 0-0.5 sdinter: standard deviation across years - values range between 0-0.05 The statistics are: mean: Expected value of the subvariable across 100 simulation CVmask: 0-1 mask with value 1 for pixel with less than 10% of standard deviation compared to the mean The timestamp refer to a year or to two years
DOI: 10.5281/zenodo.4425812
title: Data of publication: Defect engineering for Quantum Grade Rare-Earth Nanocrystals

description: Data corresponding to the figures of the publication "Defect Engineering for Quantum Grade Rare-Earth Nanocrystals" by S. Liu et al. (hhttps://pubs.acs.org/doi/10.1021/acsnano.0c02971). A text file describes data in each compressed folder, please refer to the publication for more details.
DOI: 10.5281/zenodo.4425813
title: Data of publication: Defect engineering for Quantum Grade Rare-Earth Nanocrystals

description: Data corresponding to the figures of the publication "Defect Engineering for Quantum Grade Rare-Earth Nanocrystals" by S. Liu et al. (hhttps://pubs.acs.org/doi/10.1021/acsnano.0c02971). A text file describes data in each compressed folder, please refer to the publication for more details.
DOI: 10.23728/b2share.536114446997472eb390c5c2770271d5
title: KerkiniLake-Phenological Annual Summary Statistics-2019/2020

description: Phenological Annual Summary Statistics based on Ecosystem Functional Attribute framework. The result are based on S2 interpolated with a bayesian implementation of Harmonic Model.
description: Details of the method can be found in Vicario S, Adamo M, Alcaraz-Segura D, Tarantino C (2019) Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sens 12:83 . doi: 10.3390/rs12010083
description: The phenology is not a scalar variable but it is an ensamble of sub-variables all based on MCARI2 vegetation index and for each one two statistics are given: expected value (mean) and a mask for all pixel with standard deviation of uncertianities larger than 10% the mean (CVmask) within the general name rule proposed: locality_variable_timestamp.extension variable formed in: Phenology-SubvariableStatistics The subvariables are: mean: mean value across the year - values range between 0-0.5 stdintra: standard deviation of the value across the year - values range between 0-0.05 maxpos: day of the year of the maximum value - values range between 0-0.5 sdinter: standard deviation across years - values range between 0-0.05 The statistics are: mean: Expected value of the subvariable across 100 simulation CVmask: 0-1 mask with value 1 for pixel with less than 10% of standard deviation compared to the mean The timestamp refer to a year or to two years
DOI: 10.23728/b2share.2854520d9e4947e68ddb6a0a270a4b5f
title: KalkalpenNationalPark-Phenological Annual Summary Statistics-2019/2020

description: Phenological Annual Summary Statistics based on Ecosystem Functional Attribute framework. The result are based on S2 interpolated with a bayesian implementation of Harmonic Model.
description: Details of the method can be found in Vicario S, Adamo M, Alcaraz-Segura D, Tarantino C (2019) Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sens 12:83 . doi: 10.3390/rs12010083
description: The phenology is not a scalar variable but it is an ensamble of sub-variables all based on MCARI2 vegetation index and for each one two statistics are given: expected value (mean) and a mask for all pixel with standard deviation of uncertianities larger than 10% the mean (CVmask) within the general name rule proposed: locality_variable_timestamp.extension variable formed in: Phenology-SubvariableStatistics The subvariables are: mean: mean value across the year - values range between 0-0.5 stdintra: standard deviation of the value across the year - values range between 0-0.05 maxpos: day of the year of the maximum value - values range between 0-0.5 sdinter: standard deviation across years - values range between 0-0.05 The statistics are: mean: Expected value of the subvariable across 100 simulation CVmask: 0-1 mask with value 1 for pixel with less than 10% of standard deviation compared to the mean The timestamp refer to a year or to two years
DOI: 10.23728/b2share.69b11c4e7a3b4762b80951728b030ba5
title: GesauseNationalPark - Phenological Annual Summary Statistics - 2019/2020

description: Phenological Annual Summary Statistics based on Ecosystem Functional Attribute framework. The result are based on S2 interpolated with a bayesian implementation of Harmonic Model.
description: Details of the method can be found in Vicario S, Adamo M, Alcaraz-Segura D, Tarantino C (2019) Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sens 12:83 . doi: 10.3390/rs12010083
description: The phenology is not a scalar variable but it is an ensamble of sub-variables all based on MCARI2 vegetation index and for each one two statistics are given: expected value (mean) and a mask for all pixel with standard deviation of uncertianities larger than 10% the mean (CVmask) within the general name rule proposed: locality_variable_timestamp.extension variable formed in: Phenology-SubvariableStatistics The subvariables are: mean: mean value across the year - values range between 0-0.5 stdintra: standard deviation of the value across the year - values range between 0-0.05 maxpos: day of the year of the maximum value - values range between 0-0.5 sdinter: standard deviation across years - values range between 0-0.05 The statistics are: mean: Expected value of the subvariable across 100 simulation CVmask: 0-1 mask with value 1 for pixel with less than 10% of standard deviation compared to the mean The timestamp refer to a year or to two years
DOI: 10.6084/m9.figshare.19522048
title: Speech intelligibility of children with an auditory brainstem implant: a triple-case study

description: Auditory brainstem implantation (ABI) is a relative recent development in paediatric hearing restoration. Consequently, young-implanted children’s productive language has not received much attention. This study investigated speech intelligibility of children with ABI (N = 3) in comparison to children with cochlear implants (CI) and children with typical hearing (TH). Spontaneous speech samples were recorded from children representing the three groups matched on cumulative vocabulary level. Untrained listeners (N = 101) rated the intelligibility of one-word utterances on a continuous scale and transcribed each utterance. The rating task yielded a numerical score between 0 and 100, and similarities and differences between the listeners’ transcriptions were captured by a relative entropy score. The speech intelligibility of children with CI and children with TH was similar. Speech intelligibility of children with ABI was well below that of the children with CI and TH. But whereas one child with ABI’s intelligibility approached that of the control groups with increasing lexicon size, the intelligibility of the two other children with ABI did not develop in a similar direction. Overall, speech intelligibility was only moderate in the three groups of children, with quite low ratings and considerable differences in the listeners’ transcriptions, resulting in high relative entropy scores.
DOI: 10.6084/m9.figshare.19522048.v1
title: Speech intelligibility of children with an auditory brainstem implant: a triple-case study

description: Auditory brainstem implantation (ABI) is a relative recent development in paediatric hearing restoration. Consequently, young-implanted children’s productive language has not received much attention. This study investigated speech intelligibility of children with ABI (N = 3) in comparison to children with cochlear implants (CI) and children with typical hearing (TH). Spontaneous speech samples were recorded from children representing the three groups matched on cumulative vocabulary level. Untrained listeners (N = 101) rated the intelligibility of one-word utterances on a continuous scale and transcribed each utterance. The rating task yielded a numerical score between 0 and 100, and similarities and differences between the listeners’ transcriptions were captured by a relative entropy score. The speech intelligibility of children with CI and children with TH was similar. Speech intelligibility of children with ABI was well below that of the children with CI and TH. But whereas one child with ABI’s intelligibility approached that of the control groups with increasing lexicon size, the intelligibility of the two other children with ABI did not develop in a similar direction. Overall, speech intelligibility was only moderate in the three groups of children, with quite low ratings and considerable differences in the listeners’ transcriptions, resulting in high relative entropy scores.
DOI: 10.25403/upresearchdata.21523905
title: Mathematics teachers' assessment practices on lesson study cycle

description: This data set consists of: four transcripts of direct observations and unstructured interviews in Microsoft Office Word format. two research instruments in Microsoft Office Word format. 15 figures, gathered from the initial data analysis. one table, describing the data collection method and documentation. A qualitative approach was used to collect a rich narrative of the participants interpretation and perspective of their formative assessmnet practices within the context of lesson study. The data collection methods included direct observations of two consecutive stages (Stage 1: Collaborative lesson planning and Stage 2: Lesson presentation and observation) within two in-school lesson studies. From the observations, questions were drafted for the unstructured interviews, which were conducted after the observations. The data was analysed using inductive and deductive analysis with the aid of Microsoft Offfice Word.
DOI: 10.25403/upresearchdata.21523905.v1
title: Mathematics teachers' assessment practices on lesson study cycle

description: This data set consists of: four transcripts of direct observations and unstructured interviews in Microsoft Office Word format. two research instruments in Microsoft Office Word format. 15 figures, gathered from the initial data analysis. one table, describing the data collection method and documentation. A qualitative approach was used to collect a rich narrative of the participants interpretation and perspective of their formative assessmnet practices within the context of lesson study. The data collection methods included direct observations of two consecutive stages (Stage 1: Collaborative lesson planning and Stage 2: Lesson presentation and observation) within two in-school lesson studies. From the observations, questions were drafted for the unstructured interviews, which were conducted after the observations. The data was analysed using inductive and deductive analysis with the aid of Microsoft Offfice Word.
DOI: 10.23728/b2share.34a1f1b96ab6404983a3da0f3e13713f
title: Gran Paradiso National Park - Phenological Annual Summary Statistics - 2019/2020

description: Phenological Annual Summary Statistics based on Ecosystem Functional Attribute framework. The result are based on S2 interpolated with a bayesian implementation of Harmonic Model.
description: Details of the method can be found in Vicario S, Adamo M, Alcaraz-Segura D, Tarantino C (2019) Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires. Remote Sens 12:83 . doi: 10.3390/rs12010083
description: The phenology is not a scalar variable but it is an ensamble of sub-variables all based on MCARI2 vegetation index and for each one two statistics are given: expected value (mean) and a mask for all pixel with standard deviation of uncertianities larger than 10% the mean (CVmask) within the general name rule proposed: locality_variable_timestamp.extension variable formed in: Phenology-SubvariableStatistics The subvariables are: mean: mean value across the year - values range between 0-0.5 stdintra: standard deviation of the value across the year - values range between 0-0.05 maxpos: day of the year of the maximum value - values range between 0-0.5 sdinter: standard deviation across years - values range between 0-0.05 The statistics are: mean: Expected value of the subvariable across 100 simulation CVmask: 0-1 mask with value 1 for pixel with less than 10% of standard deviation compared to the mean The timestamp refer to a year or to two years