cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bstgf
title: CCDC 2168805: Experimental Crystal Structure Determination

description: Related Article: Chonghe Zhang, Junyi Wang, Zhenyang Lin, Qing Ye|2022|Inorg.Chem.|61|18275|doi:10.1021/acs.inorgchem.2c03111
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.cc2bsthg
title: CCDC 2168806: Experimental Crystal Structure Determination

description: Related Article: Chonghe Zhang, Junyi Wang, Zhenyang Lin, Qing Ye|2022|Inorg.Chem.|61|18275|doi:10.1021/acs.inorgchem.2c03111
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.cc2bstjh
title: CCDC 2168807: Experimental Crystal Structure Determination

description: Related Article: Chonghe Zhang, Junyi Wang, Zhenyang Lin, Qing Ye|2022|Inorg.Chem.|61|18275|doi:10.1021/acs.inorgchem.2c03111
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.cc2bzfh8
title: CCDC 2174200: Experimental Crystal Structure Determination

description: Related Article: Kang Liu, Xiao-Wang Chi, Yan Guo, Qun-Yan Wu, Kong-Qiu Hu, Lei Mei, Zhi-Fang Chai, Ji-Pan Yu, Wei-Qun Shi|2022|Inorg.Chem.|61|17993|doi:10.1021/acs.inorgchem.2c02263
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.cc2c01yd
title: CCDC 2174803: Experimental Crystal Structure Determination

description: Related Article: Rahul P, Veena S, Jubi John|2022|J.Org.Chem.|87|13708|doi:10.1021/acs.joc.2c01361
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.cc2bzqln
title: CCDC 2174482: Experimental Crystal Structure Determination

description: Related Article: Kang Liu, Xiao-Wang Chi, Yan Guo, Qun-Yan Wu, Kong-Qiu Hu, Lei Mei, Zhi-Fang Chai, Ji-Pan Yu, Wei-Qun Shi|2022|Inorg.Chem.|61|17993|doi:10.1021/acs.inorgchem.2c02263
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: 10.5281/zenodo.4294603
title: Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning (Open Data and Open Materials)

description: <strong>Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. <em>NeuroImage</em>, <em>226</em>, 117569.</strong> Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces <em>evolve</em> over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two different neutral faces on different background colors which serve as CS+ and CS-, respectively. Thereby, this design provides sufficient trials for EEG analyses while tripling the relative amount of trials that tap into more transient neurobiological processes. Consistent with prior studies on ERP components, data-driven topographic EEG analyses revealed that ERP amplitudes were potentiated during time periods from 33–60 ms, 108–200 ms, and 468–820 ms indicating that fear conditioning prioritizes early sensory processing in the brain, but also facilitates neural responding during later attentional and evaluative stages. Importantly, averaging across the three CS+/CS- sets allowed us to probe the temporal evolution of neural processes: Responses during each of the three time windows gradually increased from early to late fear conditioning, while long-latency (460–730 ms) electrocortical responses diminished throughout fear extinction. Our novel paradigm demonstrates how short-, mid-, and long-latency EEG responses change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.
description: Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. NeuroImage, 226, 117569.
DOI: 10.5281/zenodo.4294604
title: Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning (Open Data and Open Materials)

description: <strong>Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. <em>NeuroImage</em>.</strong> Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces <em>evolve</em> over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two different neutral faces on different background colors which serve as CS+ and CS-, respectively. Thereby, this design provides sufficient trials for EEG analyses while tripling the relative amount of trials that tap into more transient neurobiological processes. Consistent with prior studies on ERP components, data-driven topographic EEG analyses revealed that ERP amplitudes were potentiated during time periods from 33–60 ms, 108–200 ms, and 468–820 ms indicating that fear conditioning prioritizes early sensory processing in the brain, but also facilitates neural responding during later attentional and evaluative stages. Importantly, averaging across the three CS+/CS- sets allowed us to probe the temporal evolution of neural processes: Responses during each of the three time windows gradually increased from early to late fear conditioning, while long-latency (460–730 ms) electrocortical responses diminished throughout fear extinction. Our novel paradigm demonstrates how short-, mid-, and long-latency EEG responses change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.
description: <b>Important note: There is a newer version of this record available. <br> The latest version of this dataset is version 1.0.0.</b><br> Please click here to access the latest version:<br> https://doi.org/10.5281/zenodo.4294603
DOI: 10.5281/zenodo.4308063
title: Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning (Open Data and Open Materials)

description: <strong>Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. <em>NeuroImage</em>, <em>226</em>, 117569.</strong> Electrophysiological studies in rodents allow recording neural activity during threats with high temporal and spatial precision. Although fMRI has helped translate insights about the anatomy of underlying brain circuits to humans, the temporal dynamics of neural fear processes remain opaque and require EEG. To date, studies on electrophysiological brain signals in humans have helped to elucidate underlying perceptual and attentional processes, but have widely ignored how fear memory traces <em>evolve</em> over time. The low signal-to-noise ratio of EEG demands aggregations across high numbers of trials, which will wash out transient neurobiological processes that are induced by learning and prone to habituation. Here, our goal was to unravel the plasticity and temporal emergence of EEG responses during fear conditioning. To this end, we developed a new sequential-set fear conditioning paradigm that comprises three successive acquisition and extinction phases, each with a novel CS+/CS- set. Each set consists of two different neutral faces on different background colors which serve as CS+ and CS-, respectively. Thereby, this design provides sufficient trials for EEG analyses while tripling the relative amount of trials that tap into more transient neurobiological processes. Consistent with prior studies on ERP components, data-driven topographic EEG analyses revealed that ERP amplitudes were potentiated during time periods from 33–60 ms, 108–200 ms, and 468–820 ms indicating that fear conditioning prioritizes early sensory processing in the brain, but also facilitates neural responding during later attentional and evaluative stages. Importantly, averaging across the three CS+/CS- sets allowed us to probe the temporal evolution of neural processes: Responses during each of the three time windows gradually increased from early to late fear conditioning, while long-latency (460–730 ms) electrocortical responses diminished throughout fear extinction. Our novel paradigm demonstrates how short-, mid-, and long-latency EEG responses change during fear conditioning and extinction, findings that enlighten the learning curve of neurophysiological responses to threat in humans.
description: Open Data and Open Materials of: Sperl, M. F. J., Wroblewski, A., Mueller, M., Straube, B., & Mueller, E. M. (2021). Learning Dynamics of Electrophysiological Brain Signals During Human Fear Conditioning. NeuroImage, 226, 117569.
DOI: 10.5281/zenodo.6635877
title: Towards the Third Millennium Changes in Siberian Triple Tree-Ring Stable Isotopes

description: Triple (d13C, d18O, d2H) Tree-Ring Stable Isotopes from Siberian subarctic.
description: This research was funded by the Russian Science Foundation (RSF) grant number 21-17- 00006 titled "Hydrogen isotopes and deuterium excess (d-excess) in conifer tree-ring cellulose as indicator of extreme eco-hydrological changes in boreal forests (ECO-HYDROTREE)".
description: {"references": ["Churakova (Sidorova) OV, Fonti MV, Barinov VV, Zharkov MS, Taynik AV, Trushkina TV, Kirdyanov AV, Arzac A, Saurer M. (2022) Towards the Third Millennium Changes in Siberian Triple Tree-Ring Stable Isotopes. Forests, 13(6), 934; https://doi.org/10.3390/f13060934"]}
DOI: 10.5281/zenodo.6635876
title: Towards the Third Millennium Changes in Siberian Triple Tree-Ring Stable Isotopes

description: Triple (d13C, d18O, d2H) Tree-Ring Stable Isotopes from Siberian subarctic.
description: This research was funded by the Russian Science Foundation (RSF) grant number 21-17- 00006 titled "Hydrogen isotopes and deuterium excess (d-excess) in conifer tree-ring cellulose as indicator of extreme eco-hydrological changes in boreal forests (ECO-HYDROTREE)".
description: {"references": ["Churakova (Sidorova) OV, Fonti MV, Barinov VV, Zharkov MS, Taynik AV, Trushkina TV, Kirdyanov AV, Arzac A, Saurer M. (2022) Towards the Third Millennium Changes in Siberian Triple Tree-Ring Stable Isotopes. Forests, 13(6), 934; https://doi.org/10.3390/f13060934"]}
DOI: 10.5281/zenodo.7338204
title: amazon_100bestsellers.csv

description: Amazon 100 libros más vendidos día 9/11/22
DOI: 10.5281/zenodo.7338203
title: amazon_100bestsellers.csv

description: Amazon 100 libros más vendidos día 9/11/22
DOI: 10.6084/m9.figshare.21587040
title: Sistema de códigos - 1ra ronda de entrevistas

description: This is the code system and code frequency generated by MAXQDA software after the segments' codification for the 9 interviews with beginners and experimented teachers of the Universidad Nacional Autónoma de Honduras, as part of the doctoral thesis: "Análisis de las Prácticas Pedagógicas de los Profesores Principiantes y Experimentados en el Contexto del Desarrollo Profesional del Profesorado Universitario: El Caso de la Universidad Nacional Autónoma de Honduras".
DOI: 10.6084/m9.figshare.21587040.v1
title: Sistema de códigos - 1ra ronda de entrevistas

description: This is the code system and code frequency generated by MAXQDA software after the segments' codification for the 9 interviews with beginners and experimented teachers of the Universidad Nacional Autónoma de Honduras, as part of the doctoral thesis: "Análisis de las Prácticas Pedagógicas de los Profesores Principiantes y Experimentados en el Contexto del Desarrollo Profesional del Profesorado Universitario: El Caso de la Universidad Nacional Autónoma de Honduras".
DOI: 10.5517/ccdc.csd.cc2cyd0r
title: CCDC 2202984: Experimental Crystal Structure Determination

description: Related Article: Kang Liu, Xiao-Wang Chi, Yan Guo, Qun-Yan Wu, Kong-Qiu Hu, Lei Mei, Zhi-Fang Chai, Ji-Pan Yu, Wei-Qun Shi|2022|Inorg.Chem.|61|17993|doi:10.1021/acs.inorgchem.2c02263
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.21280152.v4
title: Raw Data

description: the data contains raw data as values of depth of sealer penetration into the dentinal tubules measured by an in built ruler in mm in Confocal Laser Scanning Microscopy. <br> The data contains an image of coronal, middle and apical third sections of the root specimen.
DOI: 10.6084/m9.figshare.19440434
title: Recreational Therapy

description: Recreational Therapy Data<br>
DOI: 10.6084/m9.figshare.19440434.v1
title: Recreational Therapy With Veterans.sav

description: This data provides a variety of outcome measures for veterans in a recreational therapy program<br>
DOI: 10.21227/v023-0v92
title: A modified IEEE 118 case

description: The case contains 118 lines, 99 loads, 27 generators, and an external grid, which is set to the slack generator. The generators on buses 14, 31,41, 54,73, 84, 89, and 106 are solar farms, and off-shore wind farms are only integrated into one area, i.e., at buses 71 and 72, to represent the real-world integration of offshore wind resources.The dataset is to incorporate dynamic line rating into the optimal power flow analysis.The zip folder contains 4 files, a json file is used to generate the case using Pandapower, three csv files are time series data for generators' maximum P output limits, lines' maximum capacities, and loads.
DOI: 10.5281/zenodo.7338342
title: Ofertas de H&M en Europa: tipo de oferta, nombre del producto y precio.

description: El dataset contiene información sobre todos los productos de H&M que se encuentran en la sección de ofertas en todas las páginas web de la marca en países de Europa. El dataset consta de los siguientes campos: <strong>main page</strong> - la dirección URL de la oferta donde puedes encontrar el producto (cadena de caracteres). <strong>name</strong> - el nombre del producto (cadena de caracteres). <strong>url</strong> - la dirección URL del producto (cadena de caracteres). <strong>price</strong> - el precio del producto en moneda local (cadena de caracteres). El dataset contiene 42910 registros, extraidos de las páginas de H&M en Europa
DOI: 10.5281/zenodo.7338341
title: Ofertas de H&M en Europa: tipo de oferta, nombre del producto y precio.

description: El dataset contiene información sobre todos los productos de H&M que se encuentran en la sección de ofertas en todas las páginas web de la marca en países de Europa. El dataset consta de los siguientes campos: <strong>main page</strong> - la dirección URL de la oferta donde puedes encontrar el producto (cadena de caracteres). <strong>name</strong> - el nombre del producto (cadena de caracteres). <strong>url</strong> - la dirección URL del producto (cadena de caracteres). <strong>price</strong> - el precio del producto en moneda local (cadena de caracteres). El dataset contiene 42910 registros, extraidos de las páginas de H&M en Europa
DOI: 10.5281/zenodo.7338367
title: DLS measurements and Zeta potential measurements

description: DLS measurements and Zeta potential measurements
DOI: 10.5281/zenodo.7338366
title: DLS measurements and Zeta potential measurements

description: DLS measurements and Zeta potential measurements
DOI: 10.5281/zenodo.7094287
title: Dataset for a machine learning tool to improve lymph node staging with FDG-PET/CT

description: This upload provides Open Data associated with the publication "A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [<sup>18</sup>F]FDG-PET/CT parameters" by Rogasch JMM <em>et al.</em> (2022). The upload contains the anonymized dataset with 10 features necessary for the final GBM model that was presented in the publication. However, the original full dataset with 40 features was excluded from this Open Data repository because it may not comply with strict rules of data anonymization. The full dataset can be obtained from the corresponding author (julian.rogasch@charite.de) upon reasonable request. Besides the dataset, this upload provides the original python and R scripts that were used as well as their output. A description of all files can be found in "content_description_2022_11_19.txt". A user-friendly web tool that implements the final machine learning model can be found here: PET_LN_calculator
DOI: 10.5281/zenodo.7094286
title: Dataset for a machine learning tool to improve lymph node staging with FDG-PET/CT

description: This upload provides Open Data associated with the publication "A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [<sup>18</sup>F]FDG-PET/CT parameters" by Rogasch JMM <em>et al.</em> (2022). The upload contains the anonymized dataset with 10 features necessary for the final GBM model that was presented in the publication. However, the original full dataset with 40 features was excluded from this Open Data repository because it may not comply with strict rules of data anonymization. The full dataset can be obtained from the corresponding author (julian.rogasch@charite.de) upon reasonable request. Besides the dataset, this upload provides the original python and R scripts that were used as well as their output. A description of all files can be found in "content_description_2022_11_19.txt". A user-friendly web tool that implements the final machine learning model can be found here: PET_LN_calculator
DOI: 10.6084/m9.figshare.3761562.v2407
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5281/zenodo.7338336
title: Mountain shelters and campsites in Spain, Andorra and South of France

description: This dataset contains information about shelters and campsites from Spain, Andorra and the south of France. The data was obtained from https://www.walkaholic.me/. Specifically from the sections of shelter and campsite, which contains a list of links with the information of each place. The attributes of this dataset are the following: <strong>Place type: </strong>Specifies if the place is a shelter or a campsite <strong>Name: </strong>Name of the place <strong>Place list: </strong>Geographic region list. Depening on the country the list items may vary. <strong>Capacity: </strong>Total number of beds <strong>Fee: </strong>Indicates if it is free to use or paid <strong>Altitude: </strong>Altitude in meters above the sea level <strong>Description: </strong>Description of the place <strong>Telephone: </strong>Contact telephone number <strong>Website: </strong>Place website <strong>Email: </strong>Contact email <strong>Hiking association: </strong>Hiking association in charge of the place <strong>Guard name(s): </strong>Guard/s name of the place <strong>Services: </strong>Services offered by the place. Includes: blankets, fireplace, internet, food and beverages, WC... <strong>Coordinates: </strong>Latitude and longitude coordinates of the place. <strong>Access: </strong>Instruccions on how to get to the place <strong>Zones: </strong>Zone inside the province where the place is found <strong>Emplacement: </strong>More specific instructions inside the zone to help find the place <strong>Nearby routes: </strong>List with the names of nearby hiking routes