cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/r96hjs8xv5.1
title: The efficiency of the microbial carbon pump as seen from the relationship between apparent oxygen utilization and fluorescent dissolved organic matter

description: This dataset related to the linear slope of the relationship between the relationship between apparent oxygen utilization and fluorescent dissolved organic matter. The excel dataset contain three sheets, including All location, All plotting data_Depth slope, All plotting data_global view. The sheet of "All location" showed the location of sampling sites related to this work. The sheet of "All plotting data_Depth slope" contained Depth slope, reference, depth range, FDOM component, literature reported information (unit of Fluorescent intensity, apparent oxygen untilization), the converted unit (Fluorescent intensity, apparent oxygen untilization), and converted slopes, as well the converision factors. The sheet of "All plotting data_global view" contained the same information to the sheet of  "All plotting data_Depth slope".
DOI: 10.5281/zenodo.6604815
title: LPL3 - May 2022

description: Measurements of LPL3 (Madeira East Coast) in May 2022 Madeira Light Pollution Laboratorie info
DOI: 10.17632/j4hzhpjct9.1
title: Data for: Plesiochorus irwinorum n. sp. (Trematoda: Gorgoderidae) from the urinary bladder of the hawksbill turtle, Eretmochelys imbricata (Testudines: Cheloniidae), off the east coast of Australia.

description: Aligned and trimmed ITS2, cox1 and 28S datasets, raw morphometric data, and r code used in this study.
DOI: 10.5281/zenodo.6604814
title: LPL3 - May 2022

description: Measurements of LPL3 (Madeira East Coast) in May 2022 Madeira Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604817
title: LPL4 - May 2022

description: Measurements of LPL4 (Ilha do Corvo) in May 2022 Corvo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604816
title: LPL4 - May 2022

description: Measurements of LPL4 (Ilha do Corvo) in May 2022 Corvo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604819
title: LPL5 - May 2022

description: Measurements of LPL5 (Tejeda) in May 2022 Gran Canaria Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604818
title: LPL5 - May 2022

description: Measurements of LPL5 (Tejeda) in May 2022 Gran Canaria Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604821
title: LPL7 - May 2022

description: Measurements of LPL7 (Archipielago Chinijo) in May 2022 Archipielago Chinijo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604820
title: LPL7 - May 2022

description: Measurements of LPL7 (Archipielago Chinijo) in May 2022 Archipielago Chinijo Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604823
title: LPL8 - May 2022

description: Measurements of LPL8 (Timanfaya NP) in May 2022 Lanzarote Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604822
title: LPL8 - May 2022

description: Measurements of LPL8 (Timanfaya NP) in May 2022 Lanzarote Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604825
title: LPL15 - May 2022

description: Measurements of LPL15 (Badajoz) in May 2022 Provincia de Badajoz Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604824
title: LPL15 - May 2022

description: Measurements of LPL15 (Badajoz) in May 2022 Provincia de Badajoz Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604827
title: LPL16 - May 2022

description: Measurements of LPL16 (Caceres) in May 2022 Provincia de Cáceres Light Pollution Laboratorie info
DOI: 10.5281/zenodo.6604826
title: LPL16 - May 2022

description: Measurements of LPL16 (Caceres) in May 2022 Provincia de Cáceres Light Pollution Laboratorie info
DOI: 10.6084/m9.figshare.19953746
title: Soil microbes under global changes-R codes

description: Soil microbes under global changes-R codes
DOI: 10.6084/m9.figshare.19953746.v3
title: Soil microbes under global changes-R codes

description: Soil microbes under global changes-R codes
DOI: 10.6084/m9.figshare.19953746.v2
title: Soil microbes under global changes

description: Soil microbial biomass dataset
DOI: 10.5281/zenodo.5510391
title: Machine Learning Quantum Reaction Rate Constants

description: Dataset of 1,517,419 quantum reaction rate constant products \(k^{\text {QM}}(T)Q_{\text R}(T)\) computed from the transmission coefficient for model single and double barrier minimum energy paths. Here \(k^{QM}(T) \) is the quantum reaction rate constant at temperature \(T\) and \(Q_\text{R}(T)\) is the reactant partition function computed with the rigid rotor and harmonic oscillator approximations.This dataset was created for Ref [1] where it was used to train and test a DNN to predict \(\log{k^{\text{QM}}(T)Q_\text{R}(T)}\). <strong>Cite as</strong> Please cite the following references when using this dataset: [1] E. Komp and S. Valleau, Machine Learning Quantum Reaction Rate Constants, <em>J. Phys. Chem. A</em>, 124:8607–8613, 2020, doi: 10.1021/acs.jpca.0c05992. [2] E. Komp and S.Valleau, Machine Learning Quantum Reaction Rate Constants (1.0.0) [Data set], 2020, <em>Zenodo</em>, https://doi.org/10.5281/zenodo.5510392 <strong>Contents</strong> Descriptions of entries in the tabular dataset file `QM_kQ.csv`. Please refer to the publication [1] for details. `mass_au`: Mass of the reactants in atomic units. `width_1_au`: Width of first potential energy barrier in atomic units. `width_2_au`: Width of second (if present) potential barrier in atomic units. For single barriers width_2_au = 0.0. `height_1_au`: Activation energy of first potential energy barrier in atomic units. `height_2_au`: Activation energy of second (if present) potential energy barrier in atomic units. For single barriers height_2_au = 0.0. `dist_au`: For double barriers, absolute value of the difference between the position of the two potential energy maxima along the reaction coordinate in atomic units. For single barriers dist_au = 0.0. `alpha_symm`: Symmetry constant for single barriers, defined as the difference between product and reactant energies in atomic units. `alpha_double`: Symmetry constant for double barriers, defined as the sum of the normalized difference between barrier heights and the normalized difference between barrier widths, unitless. `slope`: Slope of the first reaction barrier along the reaction coordinate in atomic units. Slope values were evaluated numerically from the type of potential energy barrier see Ref [1] SI. `temp_K`: Temperature in Kelvin. `kQ_rate`: Quantum reaction rate constant times reactant partition function in units of 1/ps. `log_kQ_rate`: Natural logarithm of the quantum reaction rate constant times the reactant partition function in units of log(1/ps).
DOI: 10.5281/zenodo.6604700
title: Machine learning the quantum flux-flux correlation function for catalytic surface reactions

description: This dataset contains information on each of the 14 reactions used in the paper, the geometries for these reactions, the product of the quantum reaction rate constant and canonical reactant partition function and the flux-flux correlation function time series values for each reaction-temperature combination. <strong>reaction_details.csv</strong> This is a .csv file containing additional details on the reactions used in this paper. Each row contains one reaction/temperature combination, of which there are 55. Column descriptions: reaction_number: Reaction identifier number used in this work reaction: The chemical reaction equation metal_surface: atomic symbol of metal surface facet_number: Miller indices of surface reactants: Python dictionary object of reactants and their quantities products: Python dictionary object of products and their quantities reaction_energy [eV]: reaction energy in electron-volts activation_energy [eV]: activation energy of reaction in electron-volts temperature [K]: The randomly assigned temperature a calculation was run for kQ_Cff [1/au]: The calculated integrated reaction rate product at corresponding temperature {1,2,3,4} in units 1/(au time). reaction_split: Train/test placement of that reaction/temperature combination for reaction split temperature_split:<strong> </strong>Trian/test placement of that reaction/temperature combination for temperature split catalysishub_reactionID: Catalysis Hub reaction ID identifier for referencing catalysis hub database doi:<strong> </strong>digital object identifier of original publication for which DFT calculations were performed <strong>Flux_flux_correlation_functions:</strong> Directory containing flux-flux correlation function time series values for each reaction temperature combination. Values are organized in subdirectories, one for each of the 14 reaction. In each subdirectory .csv files are labeled by reaction number and temperature in Kelvin. Each csv file contains a column with time points [au of time] and the corresponding flux-flux correlation function value in units [1/(au of time)<sup>2</sup>]. <strong>Geometries:</strong> Directory containing geometry files for each reaction. Geometries of reactants on the surface were shifted respect to those supplied by catalysis hub to create continuous reaction pathways where necessary. Geometry files are organized in subdirectories for each reaction. When complete nudged elastic band (NEB) minimum energy paths (MEP) were not available ,subdirectories contain a products.xyz, reactants.xyz, and TSstar.xyz file (reactions 1 to 11) otherwise the complete set of NEB MEP images labeled neb{n}.xyz is given (reactions 12, 13, 14).
DOI: 10.5281/zenodo.6604699
title: Machine learning the quantum flux-flux correlation function for catalytic surface reactions

description: This dataset contains information on each of the 14 reactions used in the paper, the geometries for these reactions, the product of the quantum reaction rate constant and canonical reactant partition function and the flux-flux correlation function time series values for each reaction-temperature combination. <strong>reaction_details.csv</strong> This is a .csv file containing additional details on the reactions used in this paper. Each row contains one reaction/temperature combination, of which there are 55. Column descriptions: reaction_number: Reaction identifier number used in this work reaction: The chemical reaction equation metal_surface: atomic symbol of metal surface facet_number: Miller indices of surface reactants: Python dictionary object of reactants and their quantities products: Python dictionary object of products and their quantities reaction_energy [eV]: reaction energy in electron-volts activation_energy [eV]: activation energy of reaction in electron-volts temperature [K]: The randomly assigned temperature a calculation was run for kQ_Cff [1/au]: The calculated integrated reaction rate product at corresponding temperature {1,2,3,4} in units 1/(au time). reaction_split: Train/test placement of that reaction/temperature combination for reaction split temperature_split:<strong> </strong>Trian/test placement of that reaction/temperature combination for temperature split catalysishub_reactionID: Catalysis Hub reaction ID identifier for referencing catalysis hub database doi:<strong> </strong>digital object identifier of original publication for which DFT calculations were performed <strong>Flux_flux_correlation_functions:</strong> Directory containing flux-flux correlation function time series values for each reaction temperature combination. Values are organized in subdirectories, one for each of the 14 reaction. In each subdirectory .csv files are labeled by reaction number and temperature in Kelvin. Each csv file contains a column with time points [au of time] and the corresponding flux-flux correlation function value in units [1/(au of time)<sup>2</sup>]. <strong>Geometries:</strong> Directory containing geometry files for each reaction. Geometries of reactants on the surface were shifted respect to those supplied by catalysis hub to create continuous reaction pathways where necessary. Geometry files are organized in subdirectories for each reaction. When complete nudged elastic band (NEB) minimum energy paths (MEP) were not available ,subdirectories contain a products.xyz, reactants.xyz, and TSstar.xyz file (reactions 1 to 11) otherwise the complete set of NEB MEP images labeled neb{n}.xyz is given (reactions 12, 13, 14).
DOI: 10.6084/m9.figshare.19783168
title: Super-matrix for A NEAR-COMPLETE PHYLOGENY OF THE OLD WORLD FLYCATCHERS, ROBINS AND CHATS (AVES, MUSCICAPIDAE)

description: Accession numbers for nucleotide sequences used in this study.
DOI: 10.6084/m9.figshare.19783168.v1
title: Super-matrix for A NEAR-COMPLETE PHYLOGENY OF THE OLD WORLD FLYCATCHERS, ROBINS AND CHATS (AVES, MUSCICAPIDAE)

description: Accession numbers for nucleotide sequences used in this study.
DOI: 10.6084/m9.figshare.19953944
title: Oligosoma activity recorded by trail cameras

description: Observations of skinks per hour generated by time-lapse trail cameras. Temperature recordings are generated by Thermochron iButton data loggers that were placed in copper models and secured in potential basking locations. Wind and weather observations were generated from the photographs.
DOI: 10.6084/m9.figshare.19953944.v1
title: Oligosoma activity recorded by trail cameras

description: Observations of skinks per hour generated by time-lapse trail cameras. Temperature recordings are generated by Thermochron iButton data loggers that were placed in copper models and secured in potential basking locations. Wind and weather observations were generated from the photographs.
DOI: 10.5281/zenodo.6604847
title: Elemental and S isotopic composition data for "Sulfur isotopic fractionation of the youngest Chang'e-5 basalts: Constraints on the magma degassing and geochemical features of the mantle source"

description: Data for "Sulfur isotopic fractionation of the youngest Chang'e-5 basalts: Constraints on the magma degassing and geochemical features of the mantle source".
DOI: 10.5281/zenodo.6604846
title: Elemental and S isotopic composition data for "Sulfur isotopic fractionation of the youngest Chang'e-5 basalts: Constraints on the magma degassing and geochemical features of the mantle source"

description: Data for "Sulfur isotopic fractionation of the youngest Chang'e-5 basalts: Constraints on the magma degassing and geochemical features of the mantle source".
DOI: 10.24431/rw1k6cf
title: Pacific Sleeper shark movement behavior, health, and bioenergetic data collected in Gulf of Alaska, 2018-2019

description: Data in this collection include Pacific sleeper shark (Somniosus pacifcus) behavior, health, and bioenergetics data collected from sharks in Resurrection Bay, and sharks temporarily brought to the Alaska SeaLife Center (ASLC) during summers 2018 & 2019. Bioenergetics data were generated from respirometry trials conducted at the ASLC, and behavioral data were generated from telemetry tagging of sharks (free-ranging as well as temporarily captive individuals). Data are presented as thirteen CSV files: PSS_Catch_effort_log_daysoaks_Mar2020.csv and PSS_Catch_effort_log_overnight_Mar2020.csv contain records of date, location, bait, soak time, and catch for daytime (&lt;12hr) fishing efforts in summer 2018-2019 in Resurrection Bay. PSS-animal-overview-log_Mar2020.csv contains the capture date, handling, sex, morphometrics, sampling and tagging efforts for each of the sharks in our study. PSS_Blood_Data_log_Mar2020.csv contains the results of blood panels (i-STAT, chemistry, hematology) for 11 immature Pacific sleeper sharks caught in Resurrection Bay, AK. Samples from two individuals include blood taken in the field and while in temporary captivity. PSS_respo_data_summary_Mar2020.csv summarizes the respirometry trials conducted in 2019 at the Alaska SeaLife Center. The raw data generated from these trials, consisting of temperature and dissolved oxygen levels, are in files “SP1904.T1.csv, SP1904.T2.csv, SP1904.T2.csv, SP1908.T1.csv, SP1908.T2.csv. One shark had a tri-axial accelerometer attached during respirometry trials to identify periods of activity and rest. Data generated from that accelerometer is contained in files Axy01-SP1908-T1.csv and Axy01-SP1908-T2.csv. Original raw data from telemetry tag sensors (miniPAT, e.g. temperature, depth, light levels) and location data is housed on the Wildlife Computers Portal. Data can be processed through the DAP processor and downloaded in .csv file format from the portal. Access to files on the portal will be provided upon request.
DOI: 10.5517/ccdc.csd.cc1zvf7h
title: CCDC 1842647: Experimental Crystal Structure Determination

description: Related Article: Yun Ji Park, Marconi N. Peñas-Defrutos, Michael J. Drummond, Zachary Gordon, Oscar R. Kelly, Ian J. Garvey, Kelly L. Gullett, Max García-Melchor, Alison R. Fout|2022|Inorg.Chem.|61|8182|doi:10.1021/acs.inorgchem.2c00462
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.cc1zvf9k
title: CCDC 1842649: Experimental Crystal Structure Determination

description: Related Article: Yun Ji Park, Marconi N. Peñas-Defrutos, Michael J. Drummond, Zachary Gordon, Oscar R. Kelly, Ian J. Garvey, Kelly L. Gullett, Max García-Melchor, Alison R. Fout|2022|Inorg.Chem.|61|8182|doi:10.1021/acs.inorgchem.2c00462
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.cc1zvfbl
title: CCDC 1842650: Experimental Crystal Structure Determination

description: Related Article: Yun Ji Park, Marconi N. Peñas-Defrutos, Michael J. Drummond, Zachary Gordon, Oscar R. Kelly, Ian J. Garvey, Kelly L. Gullett, Max García-Melchor, Alison R. Fout|2022|Inorg.Chem.|61|8182|doi:10.1021/acs.inorgchem.2c00462
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.cc26fwp7
title: CCDC 2039139: Experimental Crystal Structure Determination

description: Related Article: Yonas Belay, Alfred Muller, D. Bradley G. Williams|2022|Inorg.Chem.|61|8226|doi:10.1021/acs.inorgchem.2c00648
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.cc29fhqz
title: CCDC 2128141: Experimental Crystal Structure Determination

description: Related Article: Po-Jung Huang, Kouji Taniguchi, Hitoshi Miyasaka|2022|Chem.Mater.|34|4428|doi:10.1021/acs.chemmater.2c00094
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.cc29fx7x
title: CCDC 2128529: Experimental Crystal Structure Determination

description: Related Article: Ilia Kochetygov, Anita Justin, Mehrdad Asgari, Shuliang Yang, Vikram Karve, Till Schertenleib, Dragos Stoian, Emad Oveisi, Mounir Mensi, Wendy L. Queen|2022|Chemical Science|13|6418|doi:10.1039/D2SC01041K
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.cc29fx8y
title: CCDC 2128530: Experimental Crystal Structure Determination

description: Related Article: Ilia Kochetygov, Anita Justin, Mehrdad Asgari, Shuliang Yang, Vikram Karve, Till Schertenleib, Dragos Stoian, Emad Oveisi, Mounir Mensi, Wendy L. Queen|2022|Chemical Science|13|6418|doi:10.1039/D2SC01041K
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.cc29g79b
title: CCDC 2128841: Experimental Crystal Structure Determination

description: Related Article: Aleksandar Kondinski, Maren Rasmussen, Sebastian Mangelsen, Nicole Pienack, Viktor Simjanoski, Christian Näther, Daniel L. Stares, Christoph A. Schalley, Wolfgang Bensch|2022|Chemical Science|13|6397|doi:10.1039/D2SC01004F
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.19947182
title: SourceCode.zip

description: <strong>Local features drive identity responses in macaque anterior face patches</strong> <br> <strong>The anterior macaque face patches have been assumed to represent face identity in a holistic manner. Here the authors show that the neural encoding of face identity in face patches AF and AM is instead driven principally by local features.</strong>
DOI: 10.6084/m9.figshare.19947188
title: SourceData.zip

description: <strong>Local features drive identity responses in macaque anterior face patches</strong> <br> <strong>The anterior macaque face patches have been assumed to represent face identity in a holistic manner. Here the authors show that the neural encoding of face identity in face patches AF and AM is instead driven principally by local features.</strong>
DOI: 10.6084/m9.figshare.19940162
title: ENCSR035OXA_ENCFF083AEY_hg38_CTCF_bigwig_hg19.bw

description: aaa a
DOI: 10.6084/m9.figshare.19940162.v1
title: ENCSR035OXA_ENCFF083AEY_hg38_CTCF_bigwig_hg19.bw

description: aaa a
DOI: 10.25493/42nk-ryf
title: Simple and complex spikes of cerebellar Purkinje cells and simultaneously tracked whisker voluntary movement (v1)

description: The cerebellum coordinates movement using many diverse types of inputs to compute a unique cerebellar cortical output consisting of Purkinje cell complex and simple spikes. To decode how such coordination takes place, electrophysiological Purkinje cell recordings and quantifiable movements are necessary, but very laborious to acquire. This dataset consists of 233 single units of Purkinje cells recorded extracellularly during freely whisking of awake, head-fixed restrained mice. The whisker movement, monitored with a camera, was quantified and averaged across whiskers, and is made available as a time series. Spike-sorting of Purkinje cell raw traces was performed using customized software, named SpikeTrain, and the result is given as spike times for each cell. Using this dataset, many cerebellar scientists can test their hypotheses on cerebellar functioning. Therefore this dataset allows revealing how the Purkinje cells affect movement and vice versa. Planning to reuse these data? We are interested to hear about your project. Please [send us a short message](mailto:v.romano@erasmusmc.nl).