cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc27506n
title: CCDC 2060390: Experimental Crystal Structure Determination

description: Related Article: Mahesha, K. J. Pampa, C. S. Karthik, M. K. Hema, P. Mallu, N. K. Lokanath|2021|CrystEngComm|23|4344|doi:10.1039/D1CE00304F
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.cc26ymyr
title: CCDC 2054275: Experimental Crystal Structure Determination

description: Related Article: Elena Melnic, Victor Ch. Kravtsov, Elena Lucenti, Elena Cariati, Alessandra Forni, Nikita Siminel, Marina S. Fonari|2021|New J.Chem.|45|9040|doi:10.1039/D1NJ00909E
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.cc27914r
title: CCDC 2064263: Experimental Crystal Structure Determination

description: Related Article: Valery G. Vlasenko, Anatolii S. Burlov, Yurii V. Koshchienko, Alexandra A. Kolodina, Bogdan V. Chaltsev, Yan V. Zubavichus, Victor N. Khrustalev, Tatyana N. Danilenko, Alexander A. Zubenko, Leonid N. Fetisov, Alexander I. Klimenko|2021|Inorg.Chim.Acta|523|120408|doi:10.1016/j.ica.2021.120408
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.cc26ymzs
title: CCDC 2054276: Experimental Crystal Structure Determination

description: Related Article: Elena Melnic, Victor Ch. Kravtsov, Elena Lucenti, Elena Cariati, Alessandra Forni, Nikita Siminel, Marina S. Fonari|2021|New J.Chem.|45|9040|doi:10.1039/D1NJ00909E
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.cc26yn1w
title: CCDC 2054278: Experimental Crystal Structure Determination

description: Related Article: Elena Melnic, Victor Ch. Kravtsov, Elena Lucenti, Elena Cariati, Alessandra Forni, Nikita Siminel, Marina S. Fonari|2021|New J.Chem.|45|9040|doi:10.1039/D1NJ00909E
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.cc26yn3y
title: CCDC 2054280: Experimental Crystal Structure Determination

description: Related Article: Elena Melnic, Victor Ch. Kravtsov, Elena Lucenti, Elena Cariati, Alessandra Forni, Nikita Siminel, Marina S. Fonari|2021|New J.Chem.|45|9040|doi:10.1039/D1NJ00909E
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.cc26yn4z
title: CCDC 2054281: Experimental Crystal Structure Determination

description: Related Article: Elena Melnic, Victor Ch. Kravtsov, Elena Lucenti, Elena Cariati, Alessandra Forni, Nikita Siminel, Marina S. Fonari|2021|New J.Chem.|45|9040|doi:10.1039/D1NJ00909E
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.cc280b2q
title: CCDC 2085682: Experimental Crystal Structure Determination

description: Related Article: Xiaohui Huang, Kang Wang, Jing Han, Junfang Xie, Licun Li, Jean-Pascal Sutter|2021|Dalton Trans.|50|11992|doi:10.1039/D1DT02006D
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.cc280b3r
title: CCDC 2085683: Experimental Crystal Structure Determination

description: Related Article: Xiaohui Huang, Kang Wang, Jing Han, Junfang Xie, Licun Li, Jean-Pascal Sutter|2021|Dalton Trans.|50|11992|doi:10.1039/D1DT02006D
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.cc280b4s
title: CCDC 2085684: Experimental Crystal Structure Determination

description: Related Article: Xiaohui Huang, Kang Wang, Jing Han, Junfang Xie, Licun Li, Jean-Pascal Sutter|2021|Dalton Trans.|50|11992|doi:10.1039/D1DT02006D
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.7158149
title: Supplementary Simulation Data for "Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation"

description: Additional simulation data for "Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation", which has been submitted as a preprint to ChemRxiv.
DOI: 10.5281/zenodo.7158148
title: Supplementary Simulation Data for "Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation"

description: Additional simulation data for "Conformational Selection of a Tryptophan Side Chain Drives the Generalized Increase in Activity of PET Hydrolases Through a Ser/Ile Double Mutation", which has been submitted as a preprint to ChemRxiv.
DOI: 10.6084/m9.figshare.16635400
title: FLUX sample data - MEG recording (anon)

description: FLUX data
DOI: 10.6084/m9.figshare.20749729
title: MitoNet automatic instance segmentation of mitochondria in the OpenOrganelle Mouse Kidney dataset

description: MitoNet is a generalist model for the instance segmentation of mitochondria in electron microscopy images. This dataset is the automatically generated result from applying MitoNet to the large OpenOrganelle Mouse Kidney FIB-SEM dataset at 16 nm voxel size. It contains nearly 220,000 segmented objects stored in a chunked Zarr array with chunk size of (512, 512, 512). <br> This version of MitoNet predicted both the semantic segmentation, to distinguish mitochondria from background, and contours of each instance. Predictions were hardened at a confidence threshold of 0.5 for the semantic segmentation and 0.3 for the contour segmentation. Connected components and the watershed algorithm were then applied to create an instance segmentation. Instances that were found to be smaller than 4,000 voxels were removed as likely false positives. <br> Inference took 4 hours on a single Biowulf compute node equipped with a A100 GPU, 64 GB RAM, and 32 CPUs cores. <br> The data file contains a Zarr directory that can be loaded in Python for further analysis or Napari for visualization.
DOI: 10.6084/m9.figshare.20749729.v1
title: MitoNet automatic instance segmentation of mitochondria in the OpenOrganelle Mouse Kidney dataset

description: MitoNet is a generalist model for the instance segmentation of mitochondria in electron microscopy images. This dataset is the automatically generated result from applying MitoNet model to the large OpenOrganelle Mouse Kidney FIB-SEM dataset at 16 nm voxel size. It contains over 220,000 segmented objects stored in a chunked Zarr array with chunk size of (512, 512, 512). <br> This version of MitoNet predicted both a semantic segmentation, to distinguish mitochondria from background, and contours of each instance. These predications were hardened at a confidence threshold of 0.5 for the semantic segmentation and 0.3 for the contour segmentation. Connected components and the watershed algorithm were then applied to create an instance segmentation. Instances that were found to be smaller than 4,000 voxels were removed as likely false positives. <br> The data file contains a Zarr directory that can be loaded in Python for further analysis or Napari for visualization.
DOI: 10.5281/zenodo.6303634
title: Data from the 8-fjords study What does value pluralism mean in practice?

description: For details about this data, see Isacs et al. (2022), What does value pluralism mean in practice? An empirical demonstration from a deliberative valuation. This data consists of photos of citizens' 32 individual value-maps, their four group value-maps (from September 2016) and the (one) group value-map of the seven politicians (from November 2017). The seven individual value-maps of the politicians can be held upon request from the first author. Inspired by Q methodology, they were used in factor analysis as well as "manual analysis".
description: {"references": ["Isacs et al. (2022). What does value pluralism mean in practice? An empirical demonstration from a deliberative valuation."]}
DOI: 10.5281/zenodo.6306022
title: Data from the 8-fjords study

description: For details about this data, see Isacs, L., Kenter, J. O., Wetterstrand, H. & Katzeff, C. (2022). What does value pluralism mean in practice? An empirical demonstration from a deliberative valuation. <em>People and Nature</em>, 00, 1–19. https://doi.org/10.1002/pan3.10324. The data set here consists of photos of the participating citizens' 32 individual value-maps (Files named "15" to "51"), their four group value-maps (from September 2016) and the (one) group value-map of the seven politicians (from November 2017). The seven individual value-maps of the politicians can be held upon request from the first author. Inspired by Q methodology, they were used in factor analysis (see the file ALLGROUP) as well as "manual analysis" (counts and visual comparisons of the maps). The file "Statements_ÖVERSÄTTNING till 1-29" contains the (translation) keys to the statements' numbers in the photos of the value-maps (15-42) to their numbers (1-29) used in the factor analysis (and later in the published article Isacs et al., see Table 2). Further data from the factor analysis can be held upon request from the first author. Qualitative data (transcripts of group discussions from the workshops) are thus far only accessible upon request from the first author.
description: {"references": ["Isacs et al. (2022). What does value pluralism mean in practice? An empirical demonstration from a deliberative valuation. Forthcoming in People and Nature."]}
DOI: 10.5281/zenodo.6303633
title: Data from the 8-fjords study

description: For details about this data, see Isacs, L., Kenter, J. O., Wetterstrand, H. & Katzeff, C. (2022). What does value pluralism mean in practice? An empirical demonstration from a deliberative valuation. <em>People and Nature</em>, 00, 1–19. https://doi.org/10.1002/pan3.10324. The data set here consists of photos of the participating citizens' 32 individual value-maps (Files named "15" to "51"), their four group value-maps (from September 2016) and the (one) group value-map of the seven politicians (from November 2017). The seven individual value-maps of the politicians can be held upon request from the first author. Inspired by Q methodology, they were used in factor analysis (see the file ALLGROUP) as well as "manual analysis" (counts and visual comparisons of the maps). The file "Statements_ÖVERSÄTTNING till 1-29" contains the (translation) keys to the statements' numbers in the photos of the value-maps (15-42) to their numbers (1-29) used in the factor analysis (and later in the published article Isacs et al., see Table 2). Further data from the factor analysis can be held upon request from the first author. Qualitative data (transcripts of group discussions from the workshops) are thus far only accessible upon request from the first author.
description: {"references": ["Isacs et al. (2022). What does value pluralism mean in practice? An empirical demonstration from a deliberative valuation. Forthcoming in People and Nature."]}
DOI: 10.5281/zenodo.7019410
title: Transcriptional and metabolic changes in Candida albicans during yeast-to-hypha transition

description: In this study hypha-associated transcriptional and metabolic changes were investigated in the opportunistich human pathogen <em>Candida albicans</em>. Three different strains were investigated the SC5314 wildtype and two filamentation-affected mutant strains <em>cph1</em>Δ<em>efg1</em>Δ and <em>hgc1</em>Δ. Three different hypha-inducing conditions were compared with two respective yeast conditions: SD minimal medium with 10% human serum or N-acetylglucosamin vs. SD minimal medium, and M199 pH7.4 vs. M199 pH4. Sampling time points were 0, 90 and 240 min. ON cultures were prepared in SD and re-inoculated in SD and grown to early log-phase prior to the experiment. Cells were collected, washed and used to inoculate the test medium at OD0.3. All cultures were generally maintained at 37°C. Obtained samples were submitted for transcriptional profiling (RNA-Seq with 10 million reads per sample and 150 bp paired-ends) and metabolomics. Media list: SD: 2% glucose, 0.17% YNB without amino acids, 0.5% ammonium sulfate, pH 4.5 HS - SD with 10 vol% human serum (Male, AB negative) GlcNAc - 2% <em>N</em>-acetylglucosamine, 0.17% YNB without amino acids, 0.5% ammonium sulfate, pH 4.5 Medium 199 with Earle's salts and L-Glu buffered to either pH 4 or 7.4 by addition of 1/5 volume of 0.1 M citric acid and 0.2 M Na<sub>2</sub>HPO<sub>4</sub> buffer
DOI: 10.5281/zenodo.7014350
title: Transcriptional and metabolic changes in Candida albicans during yeast-to-hypha transition

description: In this study hypha-associated transcriptional and metabolic changes were investigated in the opportunistich human pathogen <em>Candida albicans</em>. Three different strains were investigated the SC5314 wildtype and two filamentation-affected mutant strains <em>cph1</em>Δ<em>efg1</em>Δ and <em>hgc1</em>Δ. Three different hypha-inducing conditions were compared with two respective yeast conditions: SD minimal medium with 10% human serum or N-acetylglucosamin vs. SD minimal medium, and M199 pH7.4 vs. M199 pH4. Sampling time points were 0, 90 and 240 min. ON cultures were prepared in SD and re-inoculated in SD and grown to early log-phase prior to the experiment. Cells were collected, washed and used to inoculate the test medium at OD0.3. All cultures were generally maintained at 37°C. Obtained samples were submitted for transcriptional profiling (RNA-Seq with 10 million reads per sample and 150 bp paired-ends) and metabolomics. Media list: SD: 2% glucose, 0.17% YNB without amino acids, 0.5% ammonium sulfate, pH 4.5 HS - SD with 10 vol% human serum (Male, AB negative) GlcNAc - 2% <em>N</em>-acetylglucosamine, 0.17% YNB without amino acids, 0.5% ammonium sulfate, pH 4.5 Medium 199 with Earle's salts and L-Glu buffered to either pH 4 or 7.4 by addition of 1/5 volume of 0.1 M citric acid and 0.2 M Na<sub>2</sub>HPO<sub>4</sub> buffer
DOI: 10.6084/m9.figshare.20530066
title: A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets

description: Nonstationary non-Gaussian spatial data are common in many disciplines, including climate science, ecology, epidemiology, and social sciences. Examples include count data on disease incidence and binary satellite data on cloud mask (cloud/no-cloud). Modeling such datasets as stationary spatial processes can be unrealistic since they are collected over large heterogeneous domains (i.e., spatial behavior differs across subregions). Although several approaches have been developed for nonstationary spatial models, these have focused primarily on Gaussian responses. In addition, fitting nonstationary models for large non-Gaussian datasets is computationally prohibitive. To address these challenges, we propose a scalable algorithm for modeling such data by leveraging parallel computing in modern high-performance computing systems. We partition the spatial domain into disjoint subregions and fit locally nonstationary models using a carefully curated set of spatial basis functions. Then, we combine the local processes using a novel neighbor-based weighting scheme. Our approach scales well to massive datasets (e.g., 2.7 million samples) and can be implemented in nimble, a popular software environment for Bayesian hierarchical modeling. We demonstrate our method to simulated examples and two massive real-world datasets acquired through remote sensing.
DOI: 10.6084/m9.figshare.20530066.v1
title: A Scalable Partitioned Approach to Model Massive Nonstationary Non-Gaussian Spatial Datasets

description: Nonstationary non-Gaussian spatial data are common in many disciplines, including climate science, ecology, epidemiology, and social sciences. Examples include count data on disease incidence and binary satellite data on cloud mask (cloud/no-cloud). Modeling such datasets as stationary spatial processes can be unrealistic since they are collected over large heterogeneous domains (i.e., spatial behavior differs across subregions). Although several approaches have been developed for nonstationary spatial models, these have focused primarily on Gaussian responses. In addition, fitting nonstationary models for large non-Gaussian datasets is computationally prohibitive. To address these challenges, we propose a scalable algorithm for modeling such data by leveraging parallel computing in modern high-performance computing systems. We partition the spatial domain into disjoint subregions and fit locally nonstationary models using a carefully curated set of spatial basis functions. Then, we combine the local processes using a novel neighbor-based weighting scheme. Our approach scales well to massive datasets (e.g., 2.7 million samples) and can be implemented in nimble, a popular software environment for Bayesian hierarchical modeling. We demonstrate our method to simulated examples and two massive real-world datasets acquired through remote sensing.
DOI: 10.5281/zenodo.6572948
title: lguillier/SACADA_Database: v1.0_Dataset_submission

description: Release v1.0 This release is associated with the submission of a dataset paper.
DOI: 10.6075/j0gx493p
title: Atmospheric Aerosol Submicron Particle Composition including Organic Functional Group Concentrations at Arctic Locations

description: [test] This collection includes separate data sets of atmospheric aerosol submicron particle composition that include organic functional group concentrations at locations within the Arctic. Measurements include analysis of ambient aerosol particles sampled on Teflon filters that were analyzed by Fourier transform infrared (FTIR, Bruker Tensor 27) spectroscopy in a temperature and humidity controlled clean room. The FTIR spectrum for each filter was analyzed with an automated algorithm that baselined the spectra. Absorptivity and molar masses were used to convert peak area at specific wavenumbers to mass for the organic functional groups: hydroxyl, alkane, amine, carboxylic acid, and carbonyl. The total organic mass (OM) is calculated as the sum of the concentrations of the organic functional groups. Some filters were also analyzed by Chester laboratories for X‐ray fluorescence (XRF) spectroscopy to quantify elemental concentrations.
DOI: 10.5281/zenodo.7158204
title: Flight demonstration of a miniature atomic scalar magnetometer based on a microfabricated rubidium vapor cell

description: Data acquired by the Miniature Absolute Scalar Magnetometer (MASM) on the low-flying sounding rocket of the Twin Rockets to Investigate Cusp Electrodynamics 2 (TRICE-2) mission. The data columns are as follows: epoch time: CDF Epoch Time time: year, month, day, hour, minute, second lfreq [Hz]: Larmor frequency in Hertz Bt [nT]: Measured total magnetic field in nano-Tesla Btm [nT]: Total magnetic field of the IGRF model in nano-Tesla Bxm [nT]: Magnetic field Bx-component of the IGRF model in ECEF coordinates and nano-Tesla Bym [nT]: Magnetic field By-component of the IGRF model in ECEF coordinates and nano-Tesla Bzm [nT]: Magnetic field Bz-component of the IGRF model in ECEF coordinates and nano-Tesla flight time: Flight time with respect to launch in seconds ecef x pos [m]: ECEF X coordinate in meters ecef y pos [m]: ECEF Y coordinate in meters ecef z pos [m]: ECEF Z coordinate in meters ecef x vel [m/s]: ECEF X velocity in meters per second ecef y vel [m/s]: ECEF Y velocity in meters per second ecef z vel [m/s]: ECEF Z velocity in meters per second lat [deg]: Geopgraphic latitude in degrees lon [deg]: Geographics longitude in degrees alt [km]: Altitude in kilometers vcsel temp: VCSEL temperature in engineering units gas cell temp: Rubidium vapor cell temperature in engineering units bx mag: Magnetic field Bx-component measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla by mag: Magnetic field By-component measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla bz mag: Magnetic field Bz-component measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla bt mag: Total magnetic field measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla btf mag: Filtered total magnetic field measured by the mission's science magnetometer in ECEF coordinates and nano-Tesla