cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2nhgm8
title: CCDC 2457730: Experimental Crystal Structure Determination

description: Related Article:  I. A. Shentseva, A. N. Usol’tsev, N. A. Korobeinikov, M. N. Sokolov, S. A. Adonin|2025|Russ.J.Coord.Chem.|51|795|doi:10.1134/S1070328425600986
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: 10.18160/2e3t-wc0b
title: European Obspack compilation of atmospheric carbon monoxide data from ICOS stations for the period 2016-2026; obspack_co_466_ICOSFT2026.1_20260123

description: This data package contains high accuracy CO dry air mole fractions from 38 ICOS European observatories at in total 91 observation levels, collected by the ICOS Atmosphere Thematic Centre (ATC) and provided by the station contributors. The package includes the 2026.1 FastTrack update of the Globalview EU data product and is intended for use in carbon cycle inverse modeling, model evaluation, and satellite validation studies. Please report errors and send comments regarding this product to the ObsPack originators. Please read carefully the ObsPack Fair Use statement and cite appropriately. Please review the release notes for the associated Obspack product at www.esrl.noaa.gov/gmd/ccgg/obspack/release_notes.html. Metadata for this product are available at https://commons.datacite.org/doi.org/10.18160/2E3T-WC0B. Please visit http://www.gml.noaa.gov/ccgg/obspack/ for more information on Obspack.
DOI: 10.25824/redu/a5lymz
title: Modelo BIM de subestação em formato nativo do Revit

description: Os dados não podem ser disponibilizados por questões de proteção de informações sensíveis. Para mais informações, contatar os responsáveis.
DOI: 10.34740/kaggle/dsv/14593903
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.24435/materialscloud:37-c8
title: Electrostatic interactions in atomistic and machine-learned potentials for polar material: data

description: Long-range electrostatic interactions critically affect polar materials. However, state-of-the-art atomistic potentials, such as neural networks or Gaussian approximation potentials employed in large-scale simulations, often neglect the role of these long-range electrostatic interactions.This study introduces a novel model derived from first principles to evaluate the contribution of long-range electrostatic interactions to total energies, forces, and stresses. The model is designed to integrate seamlessly with existing short-range force fields without further first-principles calculations or retraining. The approach relies solely on physical observables, like the dielectric tensor and Born effective charges, that can be consistently calculated from first principles. We demonstrate that the model reproduces critical features, such as the LO-TO splitting and the long-wavelength phonon dispersions of polar materials, with benchmark results on the cubic phase of barium titanate (BaTiO3).

 

This dataset reports the raw dynamical matrices computed for BaTiO3 using DFPT, the bare short-range GAP potential, and the long-range corrected GAP potential.  A more accurate description of the methodology and all the parameters used to reproduce these data are discussed and reported in the referenced paper.
DOI: 10.24435/materialscloud:ep-z9
title: Electrostatic interactions in atomistic and machine-learned potentials for polar material: data

description: Long-range electrostatic interactions critically affect polar materials. However, state-of-the-art atomistic potentials, such as neural networks or Gaussian approximation potentials employed in large-scale simulations, often neglect the role of these long-range electrostatic interactions.This study introduces a novel model derived from first principles to evaluate the contribution of long-range electrostatic interactions to total energies, forces, and stresses. The model is designed to integrate seamlessly with existing short-range force fields without further first-principles calculations or retraining. The approach relies solely on physical observables, like the dielectric tensor and Born effective charges, that can be consistently calculated from first principles. We demonstrate that the model reproduces critical features, such as the LO-TO splitting and the long-wavelength phonon dispersions of polar materials, with benchmark results on the cubic phase of barium titanate (BaTiO3).

 

This dataset reports the raw dynamical matrices computed for BaTiO3 using DFPT, the bare short-range GAP potential, and the long-range corrected GAP potential.  A more accurate description of the methodology and all the parameters used to reproduce these data are discussed and reported in the referenced paper.
DOI: 10.15468/dl.e6bzwk
title: Occurrence Download

description: A dataset containing 1011919 species occurrences available in GBIF matching the query:
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 The dataset includes 1011919 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007170-260120142942310/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.30600707
title: Evaluation of Machine Learning Approaches for Estimating Individualized Treatment Regimens for Time-to-Event Outcomes in Observational Studies

description: In this paper we provide an overview and evaluation of machine learning methods for estimating individualized treatment regimens (ITR) for time-to-event outcomes through maximizing restricted mean survival time (RMST) in observational studies with non-randomized treatment assignment. We present extensive simulation studies that closely mimicked real-world data under a set of scenarios representing different degrees of alignment between the observed regimen, which reflects the actual prescribing practice, and the optimal ITR. The simulation results include performance characteristics of the candidate methods in terms of their ability to recover the optimal ITR and various empirical measures of RMST gain based on the comparison between the estimated ITR and the actual prescribing practice. Direct methods for estimating ITR for survival outcomes did not show advantages over indirect methods based on predicted potential outcomes under our implementation and simulation settings. Among indirect methods, gradient boosting for estimating potential survival outcomes has an advantage over random survival forests and parametric methods. Estimating the value of estimated ITRs and associated gains over the actual prescribing practice remains a challenging problem, especially under complex confounding scenarios when either most patients do not receive optimal treatment or when the actual treatment assignments are already close to optimal.
DOI: 10.6084/m9.figshare.30600707.v1
title: Evaluation of machine learning approaches for estimating individualized treatment regimens for time-to-event outcomes in observational studies

description: In this paper we provide an overview and evaluation of machine learning methods for estimating individualized treatment regimens (ITR) for time-to-event outcomes through maximizing restricted mean survival time (RMST) in observational studies with non-randomized treatment assignment. We present extensive simulation studies that closely mimicked real-world data under a set of scenarios representing different degrees of alignment between the observed regimen, which reflects the actual prescribing practice, and the optimal ITR. The simulation results include performance characteristics of the candidate methods in terms of their ability to recover the optimal ITR and various empirical measures of RMST gain based on the comparison between the estimated ITR and the actual prescribing practice. Direct methods for estimating ITR for survival outcomes did not show advantages over indirect methods based on predicted potential outcomes under our implementation and simulation settings. Among indirect methods, gradient boosting for estimating potential survival outcomes has an advantage over random survival forests and parametric methods. Estimating the value of estimated ITRs and associated gains over the actual prescribing practice remains a challenging problem, especially under complex confounding scenarios when either most patients do not receive optimal treatment or when the actual treatment assignments are already close to optimal.
DOI: 10.6084/m9.figshare.30600707.v2
title: Evaluation of Machine Learning Approaches for Estimating Individualized Treatment Regimens for Time-to-Event Outcomes in Observational Studies

description: In this paper we provide an overview and evaluation of machine learning methods for estimating individualized treatment regimens (ITR) for time-to-event outcomes through maximizing restricted mean survival time (RMST) in observational studies with non-randomized treatment assignment. We present extensive simulation studies that closely mimicked real-world data under a set of scenarios representing different degrees of alignment between the observed regimen, which reflects the actual prescribing practice, and the optimal ITR. The simulation results include performance characteristics of the candidate methods in terms of their ability to recover the optimal ITR and various empirical measures of RMST gain based on the comparison between the estimated ITR and the actual prescribing practice. Direct methods for estimating ITR for survival outcomes did not show advantages over indirect methods based on predicted potential outcomes under our implementation and simulation settings. Among indirect methods, gradient boosting for estimating potential survival outcomes has an advantage over random survival forests and parametric methods. Estimating the value of estimated ITRs and associated gains over the actual prescribing practice remains a challenging problem, especially under complex confounding scenarios when either most patients do not receive optimal treatment or when the actual treatment assignments are already close to optimal.
DOI: 10.5281/zenodo.18348234
title: Replication package for Analyzing System Requirements with ChatGPT

description: This replication package contains 8 xlsx files used in the study Analyzing System Requirements with ChatGPT
DOI: 10.5281/zenodo.18348233
title: Replication package for Analyzing System Requirements with ChatGPT

description: This replication package contains 8 xlsx files used in the study Analyzing System Requirements with ChatGPT
DOI: 10.24435/materialscloud:fw-wq
title: Enhanced Climbing Image Nudged Elastic Band method with Hessian Eigenmode Alignment

description: Accurate determination of transition states is central to an understanding of reaction kinetics. Double-endpoint methods where both initial and final states are specified, such as the climbing image nudged elastic band (CI-NEB), identify the minimum energy path between the two and thereby the saddle point on the energy surface that is relevant for the given transition, thus providing an estimate of the transition state within the harmonic approximation of transition state theory. Such calculations can, however, incur high computational costs and may suffer stagnation on exceptionally flat or rough energy surfaces. Conversely, methods that only require specification of an initial set of atomic coordinates, such as the minimum mode following (MMF) method, offer efficiency but can converge on saddle points that are not relevant for transition of interest. Here, we present an adaptive hybrid algorithm that dynamically switches between the CI-NEB with the MMF method so as to get faster convergence to the relevant saddle point. The method is benchmarked for the Baker-Chan (BC) saddle point test set using the PET-MAD machine-learned potential as well as 59 transitions of a heptamer island on Pt(111) from the OptBench benchmark set. A Bayesian analysis of the performance shows a median change in energy and force calculations of -46% [95% CrI: -55%, -37%] relative to CI-NEB for the BC set, while a 28\% reduction is found for the transitions of the heptamer island. These results establish this hybrid method as a highly effective tool for high-throughput automated chemical discovery of atomic rearrangements.

 

This record provides the reproduction data, transition state search logs, and statistical models for the Replica Optimization of Nudged Elastic Bands (RONEB) algorithm. It includes the complete outputs to accompany the Github repository of scripts which support the manuscript.
DOI: 10.15468/dl.h6876f
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
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 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007218-260120142942310/datasets/export for details.
DOI: 10.15468/dl.8y5b3a
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
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 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007219-260120142942310/datasets/export for details.
DOI: 10.5281/zenodo.18350459
title: DEVELOPINGSTUDENTS'CREATIVETHINKINGTHROUGHTHEAPPLICATIONOFTHESTEAMAPPROACHINPRIMARYEDUCATION

description: InThisarticleexaminesthedevelopmentofcreativethinkingamongprimaryschoolstudentsthroughtheimplementationoftheSTEAMapproach(Science,Technology,Engineering,Art,Mathematics)intheeducationalprocess.TheessenceandcontentofSTEAMeducation,itsdifferencesfromtraditionalteachingmethods,anditsimportanceforyounglearnersareanalyzed.Specialattentionisgiventointegratedlessons,project-basedlearning,problem-solvingactivities,andpracticaltasksthatenhancestudents’independence,creativity,initiative,andproblem-solvingskills.ThefindingsindicatethattheSTEAMapproachisaneffectivepedagogicaltechnologythatfostersnotonlyacademicknowledgebutalsocreativeandcriticalthinking.Theresultshighlighttherelevanceofintroducinginnovativeteachingmethodsintoprimaryeducationandprovidepracticalrecommendationsforteachers.
DOI: 10.5281/zenodo.18350460
title: DEVELOPINGSTUDENTS'CREATIVETHINKINGTHROUGHTHEAPPLICATIONOFTHESTEAMAPPROACHINPRIMARYEDUCATION

description: InThisarticleexaminesthedevelopmentofcreativethinkingamongprimaryschoolstudentsthroughtheimplementationoftheSTEAMapproach(Science,Technology,Engineering,Art,Mathematics)intheeducationalprocess.TheessenceandcontentofSTEAMeducation,itsdifferencesfromtraditionalteachingmethods,anditsimportanceforyounglearnersareanalyzed.Specialattentionisgiventointegratedlessons,project-basedlearning,problem-solvingactivities,andpracticaltasksthatenhancestudents’independence,creativity,initiative,andproblem-solvingskills.ThefindingsindicatethattheSTEAMapproachisaneffectivepedagogicaltechnologythatfostersnotonlyacademicknowledgebutalsocreativeandcriticalthinking.Theresultshighlighttherelevanceofintroducinginnovativeteachingmethodsintoprimaryeducationandprovidepracticalrecommendationsforteachers.
DOI: 10.3886/e241296v1
title: Multi-view Deep Learning Improves Detection of Major Cardiac Conditions from Echocardiography

description: This dataset provides a minimal, fully de-identified example of the multiview echocardiography inputs used in “Multi-view Deep Learning Improves Detection of Major Cardiac Conditions from Echocardiography.” It contains 30 echocardiography studies, each with three standard non-Doppler views (apical four-chamber, apical two-chamber, and parasternal long-axis) along with a CSV file specifying file paths and study-level labels for ventricular abnormality. Videos were preprocessed exactly as described in the manuscript, including cropping to the echo cone, removing burned-in text, resizing to 224×224 pixels, and selecting the first 64 frames.
This small dataset is intended only to illustrate the multiview data format and support reproducibility of model-loading and training code. It is not suitable for clinical modeling. All clips are fully de-identified and contain no protected health information.
DOI: 10.5066/p13qgezh
title: Groundwater tracing used to delineate recharge areas for subterranean streams at Oregon Caves National Monument and Preserve

description: Karst is a landscape where the hydrology of surface water systems is intricately connected to groundwater systems through the development of conduits and solutional opening in soluble bedrock, such as limestone, marble, or dolomite. This connectivity can present challenges to preserving water quality and quantity for karst aquifers, as activities that occur in karst areas can often have a direct impact to groundwater quality. Agency personnel responsible for managing karst areas must understand the karst landscapes that drain to springs and caves to properly preserve groundwater quality. Oregon Caves National Monument and Preserve is in Josephine County, Oregon approximately 11 miles east of the community of Cave Junction. The Monument and Preserve protect 4,554 acres of conifer-dominated forests, meadows, streams, and multiple cave systems.

From 2021 to 2024, the U.S. Geological Survey along with personnel from Oregon Caves National Monument and Preserve conducted a dye tracing investigation seeking to delineate recharge areas for two karst systems, Oregon Caves and Cave Next Door. Additionally, the study sought to determine if there were any hydrologic connections between these two karst systems. A total of eight dye injections were conducted, delineating a recharge area for each of the two karst systems, confirming hydrologic connections among different streams within the Oregon Caves system, and identifying previously undocumented resurgences for the stream in Oregon Caves.

This data release contains shapefiles that relate to dye injection locations, monitoring sites, dye traces, and delineated recharge areas. All files were created in ArcGIS Pro and each shapefile contains associated attributes for the features contained within. Layer files are included with the datasets to match symbology found in figures in the accompanying report. All shapefiles and layer files were created and modified in ArcGIS software. For a full description of the methods to create these files, see Process Steps in "ORCA_Metdata.xml" metadata file.