cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2bfv0n
title: CCDC 2158251: Experimental Crystal Structure Determination

description: Related Article: Jin Tong, Li-Rong Zhao, Jin Zhang, Xia-Yan Wang, Yan-Min Yu, Shu-Yan Yu|2023|New J.Chem.|47|4374|doi:10.1039/D2NJ05408F
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.cc2dnjk5
title: CCDC 2224267: Experimental Crystal Structure Determination

description: Related Article: Jan Alfuth, Katarzyna Kazimierczuk, Tadeusz Połoński, Teresa Olszewska|2023|CrystEngComm|25|1403|doi:10.1039/D2CE01710E
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.cc2dnjqb
title: CCDC 2224272: Experimental Crystal Structure Determination

description: Related Article: Jan Alfuth, Katarzyna Kazimierczuk, Tadeusz Połoński, Teresa Olszewska|2023|CrystEngComm|25|1403|doi:10.1039/D2CE01710E
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.cc2d6dz0
title: CCDC 2210702: Experimental Crystal Structure Determination

description: Related Article: Tingwei Wang, Shu Bu, Zujia Lu, Baolong Kuang, Zhenxin Yi, Zhiming Xie, Chao Zhang, Yan Li, Jianguo Zhang |2023|Chem.Eng.Journal|457|141267|doi:10.1016/j.cej.2022.141267
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.cc2dnjm7
title: CCDC 2224269: Experimental Crystal Structure Determination

description: Related Article: Jan Alfuth, Katarzyna Kazimierczuk, Tadeusz Połoński, Teresa Olszewska|2023|CrystEngComm|25|1403|doi:10.1039/D2CE01710E
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.22140407
title: foehrdenbarl_with_forecast.csv

description: Hourly weather and gauge data for the catchment area Föhrden-Barl in Schleswig-Holstein, Germany. Contains artificial precipitation forecast. Used for training neural networks for flood prediction
DOI: 10.6084/m9.figshare.22140407.v2
title: foehrdenbarl_with_forecast.csv

description: Hourly weather and gauge data for the catchment area Föhrden-Barl in Schleswig-Holstein, Germany. Contains artificial precipitation forecast. Used for training neural networks for flood prediction
DOI: 10.6084/m9.figshare.22141697.v1
title: poetrau_with_forecast.csv

description: Hourly weather and gauge data for the catchment area Pötrau in Schleswig-Holstein, Germany.<br> Contains artificial precipitation forecast.<br> Used for training neural networks for flood prediction
DOI: 10.6084/m9.figshare.22141700.v1
title: poetrau.csv

description: Hourly weather and gauge data for the catchment area Pötrau in Schleswig-Holstein, Germany.<br> Used for training neural networks for flood prediction
DOI: 10.6084/m9.figshare.22141703
title: willenscharen_with_forecast.csv

description: Hourly weather and gauge data for the catchment area Willenscharen in Schleswig-Holstein, Germany.<br> Contains artificial precipitation forecast.<br> Used for training neural networks for flood prediction
DOI: 10.6084/m9.figshare.22141706.v1
title: willenscharen.csv

description: Hourly weather and gauge data for the catchment area Willenscharen in Schleswig-Holstein, Germany..<br> Used for training neural networks for flood prediction
DOI: 10.6084/m9.figshare.22141697
title: poetrau_with_forecast.csv

description: Hourly weather and gauge data for the catchment area Pötrau in Schleswig-Holstein, Germany.<br> Contains artificial precipitation forecast.<br> Used for training neural networks for flood prediction
DOI: 10.6084/m9.figshare.22141700
title: poetrau.csv

description: Hourly weather and gauge data for the catchment area Pötrau in Schleswig-Holstein, Germany.<br> Used for training neural networks for flood prediction
DOI: 10.6084/m9.figshare.22141703.v1
title: willenscharen_with_forecast.csv

description: Hourly weather and gauge data for the catchment area Willenscharen in Schleswig-Holstein, Germany.<br> Contains artificial precipitation forecast.<br> Used for training neural networks for flood prediction
DOI: 10.6084/m9.figshare.22141706
title: willenscharen.csv

description: Hourly weather and gauge data for the catchment area Willenscharen in Schleswig-Holstein, Germany..<br> Used for training neural networks for flood prediction
DOI: 10.4121/22128674.v1
title: Dataset: Biocompacting livestock accelerate drowning of tidal salt marshes with sea level rise.

description: Dataset of published paper Elschot, K., Baptist, M., van Puijenbroek, M.E.B. (2023) Biocompacting livestock accelerate drowning of tidal salt marshes with sea level rise. Frontiers in Marine Science. <br> Datasets included: 1. Final dataset on sedimentation and vegetation measurement in the field 2. Dataset of the model outcome for the different sea level rise scenario and suspended sediment supply. <br> Futher information about the datasets is in the readme file.
DOI: 10.6084/m9.figshare.22140407.v1
title: FoehrdenBarl3.csv

description: Hourly weather and gauge data for the catchment area Föhrden-Barl in Schleswig-Holstein, Germany, Used for training neural networks for flood prediction
DOI: 10.6084/m9.figshare.22141694
title: foehrdenbarl.csv

description: Hourly weather and gauge data for the catchment area Föhrden-Barl in Schleswig-Holstein, Germany.<br> Used for training neural networks for flood prediction
DOI: 10.4121/22128674
title: Dataset: Biocompacting livestock accelerate drowning of tidal salt marshes with sea level rise.

description: Dataset of published paper Elschot, K., Baptist, M., van Puijenbroek, M.E.B. (2023) Biocompacting livestock accelerate drowning of tidal salt marshes with sea level rise. Frontiers in Marine Science. <br> Datasets included: 1. Final dataset on sedimentation and vegetation measurement in the field 2. Dataset of the model outcome for the different sea level rise scenario and suspended sediment supply. <br> Futher information about the datasets is in the readme file.
DOI: 10.6084/m9.figshare.22141694.v1
title: foehrdenbarl.csv

description: Hourly weather and gauge data for the catchment area Föhrden-Barl in Schleswig-Holstein, Germany.<br> Used for training neural networks for flood prediction
DOI: 10.5281/zenodo.7681321
title: Ulysses Fossae X,Y Topographic Profile Locations

description: X,Y coordinates for topographic profile cross-sections in Ulysses Fossae, Mars
DOI: 10.5281/zenodo.7681320
title: Ulysses Fossae X,Y Topographic Profile Locations

description: X,Y coordinates for topographic profile cross-sections in Ulysses Fossae, Mars
DOI: 10.6084/m9.figshare.22134434
title: Study of brain regions and neural mechanisms involved with respiratory responses in situations of hypercapnia during sleep and wakefulness.

description: Central chemoreception is a fundamental homeostatic mechanism by which the brain detects even the most subtle changes in tissue pCO2/pH and promotes the necessary regulatory adjustments to ensure maintenance of the acid-base balance. Dysfunction in its regulation, especially during sleep, has been related to several sleep-related respiratory pathologies, which are increasingly prevalent in the current population. Central chemoreceptors are widely distributed throughout the brain, and the contribution of each chemosensitive site or the relative importance of each region in situations of hypercapnia seems to be variable, depending on the state of alert as well as other physical or behavioral conditions. In fact, evidence suggests that throughout the sleep-wake cycle there is a variation of the central chemoreflex, as well as the predominance of chemosensitive sites and neurochemical switches involved, however such mechanisms are still poorly known. Thus, the present project had as its general objective to study the participation of suprabulbar regions in the control of the hypercapnic ventilatory response, and to provide unprecedented information on the neural switches underlying the control of the central chemoreflex during the sleep-wake cycle. Therefore, the objectives of this project were: 1) To investigate the possible participation of melatonin in the lateral hypothalamus and perifornital area (LH/PFA) in the modulation of CO2 chemosensitivity and hypercapnic ventilatory response during the sleep-wake cycle. 2) To study the possible role of melanin-concentrated hormones (MCH) in the Lateral Hypothalamus/Perifornitial Area and in the Locus Coeruleus in modulating the ventilatory response to hypercapnia during sleep and wakefulness. 3) To investigate whether melatonin and MCH are involved in body temperature control during central chemoreflex activation during sleep and wakefulness. 4) Evaluate the role of orexinergic neurotransmission in the nucleus tractus solitarius (NTS) and in the nucleus tegmental peduncle pontine (PPTg) on breathing under conditions of normocapnia and hypercapnia during sleep and wakefulness. For this purpose, we used techniques such as microinjections in the CNS, reverse central microdialysis, recording of pulmonary ventilation, EEG, EMG and body temperature in non-anesthetized Wistar rats.
DOI: 10.6084/m9.figshare.22134434.v1
title: Study of brain regions and neural mechanisms involved with respiratory responses in situations of hypercapnia during sleep and wakefulness.

description: Central chemoreception is a fundamental homeostatic mechanism by which the brain detects even the most subtle changes in tissue pCO2/pH and promotes the necessary regulatory adjustments to ensure maintenance of the acid-base balance. Dysfunction in its regulation, especially during sleep, has been related to several sleep-related respiratory pathologies, which are increasingly prevalent in the current population. Central chemoreceptors are widely distributed throughout the brain, and the contribution of each chemosensitive site or the relative importance of each region in situations of hypercapnia seems to be variable, depending on the state of alert as well as other physical or behavioral conditions. In fact, evidence suggests that throughout the sleep-wake cycle there is a variation of the central chemoreflex, as well as the predominance of chemosensitive sites and neurochemical switches involved, however such mechanisms are still poorly known. Thus, the present project had as its general objective to study the participation of suprabulbar regions in the control of the hypercapnic ventilatory response, and to provide unprecedented information on the neural switches underlying the control of the central chemoreflex during the sleep-wake cycle. Therefore, the objectives of this project were: 1) To investigate the possible participation of melatonin in the lateral hypothalamus and perifornital area (LH/PFA) in the modulation of CO2 chemosensitivity and hypercapnic ventilatory response during the sleep-wake cycle. 2) To study the possible role of melanin-concentrated hormones (MCH) in the Lateral Hypothalamus/Perifornitial Area and in the Locus Coeruleus in modulating the ventilatory response to hypercapnia during sleep and wakefulness. 3) To investigate whether melatonin and MCH are involved in body temperature control during central chemoreflex activation during sleep and wakefulness. 4) Evaluate the role of orexinergic neurotransmission in the nucleus tractus solitarius (NTS) and in the nucleus tegmental peduncle pontine (PPTg) on breathing under conditions of normocapnia and hypercapnia during sleep and wakefulness. For this purpose, we used techniques such as microinjections in the CNS, reverse central microdialysis, recording of pulmonary ventilation, EEG, EMG and body temperature in non-anesthetized Wistar rats.
DOI: 10.6084/m9.figshare.22182982.v1
title: The risk of radiation-induced neurocognitive impairment and the impact of sparing the hippocampus during pediatric proton cranial irradiation

description: Hippocampus is a central component for neurocognitive function and memory. We investigated the predicted risk of neurocognitive impairment of craniospinal irradiation (CSI) and the deliverability and effects of hippocampal sparing. The risk estimates were derived from published NTCP models. Specifically, we leveraged the estimated benefit of reduced neurocognitive impairment with the risk of reduced tumor control. For this dose planning study, a total of 504 hippocampal sparing intensity modulated proton therapy (HS-IMPT) plans were generated for 24 pediatric patients whom had previously received CSI. Plans were evaluated with respect to target coverage and homogeneity index to target volumes, maximum and mean dose to OARs. Paired t-tests were used to compare hippocampal mean doses and normal tissue complication probability estimates. The median mean dose to the hippocampus could be reduced from 31.3 Gy<sub>RBE</sub> to 7.3 Gy<sub>RBE</sub> (<i>p</i> &lt; .001), though 20% of these plans were not considered clinically acceptable as they failed one or more acceptance criterion. Reducing the median mean hippocampus dose to 10.6 Gy<sub>RBE</sub> was possible with all plans considered as clinically acceptable treatment plans. By sparing the hippocampus to the lowest dose level, the risk estimation of neurocognitive impairment could be reduced from 89.6%, 62.1% and 51.1% to 41.0% (<i>p</i> &lt; .001), 20.1% (<i>p</i> &lt; .001) and 29.9% (<i>p</i> &lt; .001) for task efficiency, organization and memory, respectively. Estimated tumor control probability was not adversely affected by HS-IMPT, ranging from 78.5 to 80.5% for all plans. We present estimates of potential clinical benefit in terms of neurocognitive impairment and demonstrate the possibility of considerably reducing neurocognitive adverse effects, minimally compromising target coverage locally using HS-IMPT.
DOI: 10.25403/upresearchdata.22153367
title: Nurse managers' explanation of Ubuntu characteristics

description: This data was gathered through transcribed verbatim. Once the data was generated, the researcher conducted data analysis using thematic analysis (TA) to identify, analyse and report on themes within the data. Research findings were based on open ended questions posed to the nurse managers during the semi structured interviews. Three (3) themes and eight categories emerged from the collected data. The following themes emerged “Nurse Managers' understanding of Ubuntu”, “Nurse Managers' experiences of mentoring newly graduated professional nurses” and “Nurse Managers' recommendations to improve Ubuntu in mentoring of newly graduated professional nurses.
DOI: 10.25403/upresearchdata.22153367.v1
title: Nurse managers' explanation of Ubuntu characteristics

description: This data was gathered through transcribed verbatim. Once the data was generated, the researcher conducted data analysis using thematic analysis (TA) to identify, analyse and report on themes within the data. Research findings were based on open ended questions posed to the nurse managers during the semi structured interviews. Three (3) themes and eight categories emerged from the collected data. The following themes emerged “Nurse Managers' understanding of Ubuntu”, “Nurse Managers' experiences of mentoring newly graduated professional nurses” and “Nurse Managers' recommendations to improve Ubuntu in mentoring of newly graduated professional nurses.
DOI: 10.5281/zenodo.7670394
title: Species distribution and abundance modelling with dynamicSDM: a case study analysis of the red-billed quelea (Quelea quelea).

description: <strong>GBIF_all_aves_2000_2020.csv</strong><br> A dataset containing e-Bird sampling events for all bird species across southern Africa between 2000-2020 (Fink et al., 2021, GBIF, 2021). <br> <br> Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, O. Robinson, S. Ligocki, W. Hochachka, L. Jaromczyk, C. Wood, I. Davies, M. Iliff, L. Seitz. 2021. eBird Status and Trends, Data Version: 2020; Released: 2021. Cornell Lab of Ornithology, Ithaca, New York. \doi{10.2173/ebirdst.2020}<br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q} <strong>RBQ_full_analysis.R</strong> An R script for the generation of dynamic species distribution and abundance models for nomadic bird, the red-billed quelea (<em>Quelea quelea</em>) using dynamicSDM package functions. <strong>Unfiltered_quelea_occurrence.csv</strong><br> A dataset containing species occurrence and abundance records for the bird species, the red-billed quelea (<em>Quelea quelea</em>) between 1976-2021 (GBIF 2021 & GBIF 2022 & sources listed in Table 1). <br> <br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}<br> <br> GBIF.org (25 July 2022) GBIF Occurrence Download \doi{10.15468/dl.k2kftv}<br> <strong>Table S1. </strong>Red-billed quelea (<em>Quelea quelea</em>) occurrence and abundance data sources. <strong>Data type</strong> <strong>Sources</strong> <strong>Control operation </strong> Information Core for Southern African Migrant Pests (ICOSAMP, 2001-2005). Centre for Overseas Pest Research (COPR), Natural History Museum, Tring. Botswana Ministries of Agriculture. Mozambique Ministry of Agriculture <strong>Citizen science</strong> Global Biodiversity Information Facility, including iNaturalist, eBird, South Africa Bird Atlas Project (SABAP) and South Africa Bird Ringing Unit (SAFRING) sources. <strong>Independent research</strong> EXCEL File "NfA-yearposRAC" (unpublished data set complied by R. A. Cheke, 2010).
DOI: 10.5281/zenodo.7681366
title: Species distribution and abundance modelling with dynamicSDM: a case study analysis of the red-billed quelea (Quelea quelea).

description: <strong>GBIF_all_aves_2000_2020.csv</strong><br> A dataset containing e-Bird sampling events for all bird species across southern Africa between 2000-2020 (Fink et al., 2021, GBIF, 2021). <br> <br> Fink, D., T. Auer, A. Johnston, M. Strimas-Mackey, O. Robinson, S. Ligocki, W. Hochachka, L. Jaromczyk, C. Wood, I. Davies, M. Iliff, L. Seitz. 2021. eBird Status and Trends, Data Version: 2020; Released: 2021. Cornell Lab of Ornithology, Ithaca, New York. \doi{10.2173/ebirdst.2020}<br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q} <strong>RBQ_full_analysis.R</strong> An R script for the generation of dynamic species distribution and abundance models for nomadic bird, the red-billed quelea (<em>Quelea quelea</em>) using dynamicSDM package functions. <strong>Unfiltered_quelea_occurrence.csv</strong><br> A dataset containing species occurrence and abundance records for the bird species, the red-billed quelea (<em>Quelea quelea</em>) between 1976-2021 (GBIF 2021 & GBIF 2022 & sources listed in Table 1). <br> <br> GBIF.org (12 July 2021) GBIF Occurrence Download \doi{10.15468/dl.ppcu6q}<br> <br> GBIF.org (25 July 2022) GBIF Occurrence Download \doi{10.15468/dl.k2kftv}<br> <strong>Table S1. </strong>Red-billed quelea (<em>Quelea quelea</em>) occurrence and abundance data sources. <strong>Data type</strong> <strong>Sources</strong> <strong>Control operation </strong> Information Core for Southern African Migrant Pests (ICOSAMP, 2001-2005). Centre for Overseas Pest Research (COPR), Natural History Museum, Tring. Botswana Ministries of Agriculture. Mozambique Ministry of Agriculture <strong>Citizen science</strong> Global Biodiversity Information Facility, including iNaturalist, eBird, South Africa Bird Atlas Project (SABAP) and South Africa Bird Ringing Unit (SAFRING) sources. <strong>Independent research</strong> EXCEL File "NfA-yearposRAC" (unpublished data set complied by R. A. Cheke, 2010).
DOI: 10.5281/zenodo.7267913
title: Supporting data for "Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics"

description: Supporting data for "Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics" by M. Bocus, R. Goeminne, A. Lamaire, M. Cools-Ceuppens, T. Verstraelen and V. Van Speybroeck, <em>Nature Communications</em>, <strong>2023</strong>, 14, 1008. This dataset contains examples of input files, submission and analysis scripts to train and use a machine learning potential based on the Schnet architecture for the proton hopping reaction in the H-CHA zeolite. The complete DFT training set, obtained by unbiasing the forces printed by CP2K (with PLUMED coupling), is stored as extended xyz files in the folders DFT/A-B/training_data.xyz where A=1-3 and A&lt;B&lt;5. More details on the folder architecture can be found in the README.md file.
DOI: 10.5281/zenodo.7267912
title: Supporting data for "Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics"

description: Supporting data for "Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics" by M. Bocus, R. Goeminne, A. Lamaire, M. Cools-Ceuppens, T. Verstraelen and V. Van Speybroeck, <em>Nature Communications</em>, <strong>2023</strong>, 14, 1008. This dataset contains examples of input files, submission and analysis scripts to train and use a machine learning potential based on the Schnet architecture for the proton hopping reaction in the H-CHA zeolite. The complete DFT training set, obtained by unbiasing the forces printed by CP2K (with PLUMED coupling), is stored as extended xyz files in the folders DFT/A-B/training_data.xyz where A=1-3 and A&lt;B&lt;5. More details on the folder architecture can be found in the README.md file.
DOI: 10.6084/m9.figshare.22182988
title: Forecast Pötrau

description: Water level forecast for Pötrau from January 2014 until November 2022. The first column is the observed water level, the columns 1-48 contain the predicted waterlevel in 1-48 hours.
DOI: 10.6084/m9.figshare.22182988.v1
title: Forecast Pötrau

description: Water level forecast for Pötrau from January 2014 until November 2022. The first column is the observed water level, the columns 1-48 contain the predicted waterlevel in 1-48 hours.
DOI: 10.34740/kaggle/dsv/5074839
title: COVID Vaccination in World (updated daily)