cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17876/data/2024_3
title: Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost

description: With Gaia Data Release 3 (DR3), new and improved astrometric, photometric, and spectroscopic measurements for 1.8 billion stars
 are available. Alongside this wealth of new data, however, come challenges in finding increasingly efficient and accurate computational
 methods to use for analysis. In this paper we explore the feasibility of using machine-learning regression as a method of extracting
 basic stellar parameters and line-of-sight extinctions, given spectro-photometric data. To this end, we build a stable gradient-boosted
 random-forest regressor (xgboost), trained on spectroscopic data, capable of producing output parameters with reliable uncertainties
 from Gaia DR3 data (most notably the low-resolution XP spectra) without ground-based spectroscopic observations. Using Shapley
 additive explanations, we are also able to interpret how the predictions for each star are influenced by each data feature. For the
 training and testing of the network, we use high-quality parameters obtained from the StarHorse code for a sample of around eight
 million stars observed by major spectroscopic surveys (APOGEE, GALAH, LAMOST, RAVE, SEGUE, and GES), complemented
 by curated samples of hot stars, very metal-poor stars, white dwarfs, and hot sub-dwarfs. The training data cover the whole sky, all
 Galactic components, and almost the full magnitude range of the Gaia DR3 XP sample of more than 217 million objects that also
 have parallaxes. We achieve median uncertainties (at G ≈ 16) of 0.20 mag in V-band extinction, 0.01 dex in logarithmic effective temperature, 0.20 dex in surface gravity, 0.18 dex in metallicity, and 12% in mass (over the full Gaia DR3 XP sample, with considerable
 variations in precision as a function of magnitude and stellar type). We succeed in predicting competitive results based on Gaia DR3
 XP spectra compared to classical isochrone or spectral-energy distribution fitting methods we employed in earlier work, especially
 for the parameters AV , Teff, and metallicity. Finally, we showcase some potential applications of this new catalogue (e.g. extinction
 maps, metallicity trends in the Milky Way, extended maps of young massive stars, metal-poor stars, and metal-rich stars).
DOI: 10.17630/d7ce6517-b2b6-49b1-81c5-1e99057fe45b
title: Data from: Is mating failure caused by cryptic male choice in the seed bug Lygaeus simulans?

description: Data set for the publication:
Balfour, V.L., Armand, M. and Shuker, D.M. (2024). Is mating failure caused by cryptic male choice in the seed bug Lygaeus simulans? Ecology and Evolution.
DOI: 10.15468/dl.2rphz4
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Anoura cadenai Mantilla-Meluk & Baker, 2006"
 ]
}

 The dataset includes 30 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006126-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cxn2wq
title: Occurrence Download

description: A dataset containing 411526 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Seasearch Marine Surveys in England"
 ]
}

 The dataset includes 411526 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006116-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3cnex7
title: Occurrence Download

description: A dataset containing 100 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is China",
 "Geometry POLYGON((-246.49821 22.79051,-246.40266 22.79051,-246.40266 22.87267,-246.49821 22.87267,-246.49821 22.79051))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 100 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006127-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5518/396
title: Data associated with 'Multi-modal millimeter-wave sensors for plastic polymer material characterization'

description: This paper presents, for the first time, a multimodal sensor for characterizing relative permittivity of plastic polymers by integrating in a single sensor (1) frequency-reconfigurable resonance technique at 98 and 100 GHz, and (2) 80-100-GHz broadband modified transmission-line technique. The sensor is designed based on a custom-made WR-10 waveguide featuring dual rectangular Complementary Split-Ring Resonators (CSRRs). By loading the CSRRs with a Material-Under-Test (MUT), the reflected and transmitted electromagnetic waves propagating inside the waveguide are changed depending on the dielectric properties of the material. Various plastic polymer materials, e.g. Polytetrafluoroethylene (PTFE), Polymethylmethacrylate (PMMA) and High-Density Polyethylene (HDPE), are characterized. The sensor in this paper offers various key advantages over any state-of-the-art material characterization techniques at millimeter-wave frequencies, e.g. multiple characterization techniques integrated in a single device, miniaturization, much higher tolerance to changes in the measurement environment, ease of design and fabrication, and better cost effectiveness.
DOI: 10.15468/dl.de5qx6
title: Occurrence Download

description: A dataset listing the 278 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mauritania",
 "TaxonKey is Perciformes"
 ]
}

 The dataset's 278 records were derived from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006114-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jsfaw2
title: Occurrence Download

description: A dataset listing the 1598 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mauritania",
 "TaxonKey is Chordata"
 ]
}

 The dataset's 1598 records were derived from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006115-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.chh8b2
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mongolia",
 "PublishingOrg is f5fc4e61-b7a0-414b-ab68-b41cb23e7fdc",
 "TaxonKey is Agropyron cristatum (L.) Gaertn."
 ]
}

 The dataset includes 60 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006129-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3bd7p6
title: Occurrence Download

description: A dataset containing 323 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Bottlenose dolphins off Outer Banks 2007-2012",
 "Geometry POLYGON((-90.07372 30.17362,-60.92628 30.17362,-60.92628 41.82638,-90.07372 41.82638,-90.07372 30.17362))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 323 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006130-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x8k9ys
title: Occurrence Download

description: A dataset containing 234 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gynaephora qinghaiensis"
 ]
}

 The dataset includes 234 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006128-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zthfvb
title: Occurrence Download

description: A dataset containing 247 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Element content, bulk density, and pH in living and dead wood of 8 tree species in a mesic broad-leaved forest in the East European Plain"
 ]
}

 The dataset includes 247 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006131-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13912325
title: 9H0Q  N terminal domain of BC2L-C lectin in complex with N-(beta-L-Fucopyranosyl)-biphenyl-3-carboxamide

description: X-ray diffraction images collected on proxima 2 Soleil  the 5th of july 2024 at SOLEIL synchrotron, Saint Aubin, France for PDB ID 9H0Q  using a DECTRIS EIGER X 9M detector. X-ray dataset and xdsme processing for the structure of the N terminal domain of BC2L-C lectin in complex with N-(beta-L-Fucopyranosyl)-biphenyl-3-carboxamide. Rsolution was dut at 2.55 angstrom
DOI: 10.5281/zenodo.13912326
title: 9H0Q  N terminal domain of BC2L-C lectin in complex with N-(beta-L-Fucopyranosyl)-biphenyl-3-carboxamide

description: X-ray diffraction images collected on proxima 2 Soleil  the 5th of july 2024 at SOLEIL synchrotron, Saint Aubin, France for PDB ID 9H0Q  using a DECTRIS EIGER X 9M detector. X-ray dataset and xdsme processing for the structure of the N terminal domain of BC2L-C lectin in complex with N-(beta-L-Fucopyranosyl)-biphenyl-3-carboxamide. Rsolution was dut at 2.55 angstrom
DOI: 10.15468/dl.cdh2nz
title: Occurrence Download

description: A dataset containing 187 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Cladonia portentosa (Dufour) Coem."
 ]
}

 The dataset includes 187 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006132-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2hyzk0
title: CCDC 2322723: Experimental Crystal Structure Determination

description: Related Article: Ivana Predarska, Wieland Körber, Peter Lönnecke, Dmitri Gelman, Evamarie Hey-Hawkins|2024|Dalton Trans.|53|16159|doi:10.1039/D4DT01247J
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.cc2hyzl1
title: CCDC 2322724: Experimental Crystal Structure Determination

description: Related Article: Ivana Predarska, Wieland Körber, Peter Lönnecke, Dmitri Gelman, Evamarie Hey-Hawkins|2024|Dalton Trans.|53|16159|doi:10.1039/D4DT01247J
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.13912258
title: Dataset_Sanchez-Gomez et al._2024_nematode
DOI: 10.5281/zenodo.13912259
title: Dataset_Sanchez-Gomez et al._2024_nematode
DOI: 10.5517/ccdc.csd.cc2hyzm2
title: CCDC 2322725: Experimental Crystal Structure Determination

description: Related Article: Ivana Predarska, Wieland Körber, Peter Lönnecke, Dmitri Gelman, Evamarie Hey-Hawkins|2024|Dalton Trans.|53|16159|doi:10.1039/D4DT01247J
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.cc2hyzn3
title: CCDC 2322726: Experimental Crystal Structure Determination

description: Related Article: Ivana Predarska, Wieland Körber, Peter Lönnecke, Dmitri Gelman, Evamarie Hey-Hawkins|2024|Dalton Trans.|53|16159|doi:10.1039/D4DT01247J
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.cc2hyzp4
title: CCDC 2322727: Experimental Crystal Structure Determination

description: Related Article: Ivana Predarska, Wieland Körber, Peter Lönnecke, Dmitri Gelman, Evamarie Hey-Hawkins|2024|Dalton Trans.|53|16159|doi:10.1039/D4DT01247J
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.cc2hyzq5
title: CCDC 2322728: Experimental Crystal Structure Determination

description: Related Article: Ivana Predarska, Wieland Körber, Peter Lönnecke, Dmitri Gelman, Evamarie Hey-Hawkins|2024|Dalton Trans.|53|16159|doi:10.1039/D4DT01247J
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.cc2hyzr6
title: CCDC 2322729: Experimental Crystal Structure Determination

description: Related Article: Ivana Predarska, Wieland Körber, Peter Lönnecke, Dmitri Gelman, Evamarie Hey-Hawkins|2024|Dalton Trans.|53|16159|doi:10.1039/D4DT01247J
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.cc2hyzs7
title: CCDC 2322730: Experimental Crystal Structure Determination

description: Related Article: Ivana Predarska, Wieland Körber, Peter Lönnecke, Dmitri Gelman, Evamarie Hey-Hawkins|2024|Dalton Trans.|53|16159|doi:10.1039/D4DT01247J
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.cc2hyzt8
title: CCDC 2322731: Experimental Crystal Structure Determination

description: Related Article: Ivana Predarska, Wieland Körber, Peter Lönnecke, Dmitri Gelman, Evamarie Hey-Hawkins|2024|Dalton Trans.|53|16159|doi:10.1039/D4DT01247J
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: 10.5518/397
title: Cells under stress: An inertial-shear microfluidic determination of cell behaviour - dataset

description: The deformability of a cell is the direct result of a complex interplay between the different constituent elements at the subcellular level, coupling a wide range of mechanical responses at different length-scales. Changes to the structure of these components can also alter cell phenotype, thus the critical importance of cell mechano-response for diagnostic applications. The response to mechanical stress depends strongly on the forces experienced by the cell. Here we use cell deformability in both shear-dominant and inertia-dominant microfluidic flow regimes to probe different aspects of the cell structure. In the inertial regime we follow cellular response from (visco-)elastic through plastic deformation to cell structural failure and show a significant drop in cell viability for shear stresses above &gt; 11.8 kN/m2. Comparatively, a shear-dominant regime requires lower applied stresses to achieve higher cell strains. From this regime, deformation traces as a function of time contain a rich source of information including; maximum strain, elastic modulus and cell relaxation times and thus provide a number of markers for distinguishing cell types and potentially disease progression. These results emphasise the benefit of multiple parameter determination for improving detection and will ultimately lead to improved accuracy for diagnosis. We present results for leukemia cells (HL60) as a model circulatory cell as well as for a colorectal cancer cell line SW480 derived from primary adenocarcinoma (Dukes stage B). SW480 were also treated with the actin disrupting drug Latrunculin A (LatA), to test the sensitivity of flow regimes to the cytoskeleton. We show that the shear regime is more sensitive to cytoskeletal changes, and that large strains in the inertial-regime cannot resolve changes to the actin cytoskeleton.
DOI: 10.15468/dl.8jche6
title: Occurrence Download

description: A dataset listing the 28 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Dataset of walvisstrandingen_nl"
 ]
}

 The dataset's 28 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006124-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.7639859
title: Spatial Variable Selection and An Application to Virginia Lyme Disease Emergence

description: Lyme disease is an infectious disease, that is, caused by a bacterium called <i>Borrelia burgdorferi</i> sensu stricto. In the United States, Lyme disease is one of the most common infectious diseases. The major endemic areas of the disease are New England, Mid-Atlantic, East-North Central, South Atlantic, and West North-Central. Virginia is on the front-line of the disease’s diffusion from the northeast to the south. One of the research objectives for the infectious disease community is to identify environmental and economic variables that are associated with the emergence of Lyme disease. In this article, we use a spatial Poisson regression model to link the spatial disease counts and environmental and economic variables, and develop a spatial variable selection procedure to effectively identify important factors by using an adaptive elastic net penalty. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations of disease counts. The performance of the proposed method is studied and compared with existing methods via a comprehensive simulation study. We apply the developed variable selection methods to the Virginia Lyme disease data and identify important variables that are new to the literature. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7639859.v3
title: Spatial Variable Selection and An Application to Virginia Lyme Disease Emergence

description: Lyme disease is an infectious disease, that is, caused by a bacterium called <i>Borrelia burgdorferi</i> sensu stricto. In the United States, Lyme disease is one of the most common infectious diseases. The major endemic areas of the disease are New England, Mid-Atlantic, East-North Central, South Atlantic, and West North-Central. Virginia is on the front-line of the disease’s diffusion from the northeast to the south. One of the research objectives for the infectious disease community is to identify environmental and economic variables that are associated with the emergence of Lyme disease. In this article, we use a spatial Poisson regression model to link the spatial disease counts and environmental and economic variables, and develop a spatial variable selection procedure to effectively identify important factors by using an adaptive elastic net penalty. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations of disease counts. The performance of the proposed method is studied and compared with existing methods via a comprehensive simulation study. We apply the developed variable selection methods to the Virginia Lyme disease data and identify important variables that are new to the literature. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7639859.v2
title: Spatial Variable Selection and An Application to Virginia Lyme Disease Emergence

description: Lyme disease is an infectious disease, that is, caused by a bacterium called <i>Borrelia burgdorferi</i> sensu stricto. In the United States, Lyme disease is one of the most common infectious diseases. The major endemic areas of the disease are New England, Mid-Atlantic, East-North Central, South Atlantic, and West North-Central. Virginia is on the front-line of the disease’s diffusion from the northeast to the south. One of the research objectives for the infectious disease community is to identify environmental and economic variables that are associated with the emergence of Lyme disease. In this article, we use a spatial Poisson regression model to link the spatial disease counts and environmental and economic variables, and develop a spatial variable selection procedure to effectively identify important factors by using an adaptive elastic net penalty. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations of disease counts. The performance of the proposed method is studied and compared with existing methods via a comprehensive simulation study. We apply the developed variable selection methods to the Virginia Lyme disease data and identify important variables that are new to the literature. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.