cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/2g6vzzkzn5.1
title: Supplementary Material to "Advances in unsupervised parameterization of the seasonal-diurnal surface wind vector" (https://doi.org/10.3390/meteorology4030021)

description: Supplementary Material to the paper "Advances in unsupervised parameterization of the seasonal-diurnal surface wind vector" published in MDPI Meteorology. including summaries of results for the locations of the study and R scripts to reproduce the analyses and results and apply the to other locations.
ABSTRACT
The Offset Elliptical Normal (OEN) mixture model represents the seasonal–diurnal surface wind vector for wind engineering design applications. This study upgrades the parameterization of OEN by accounting for changes in format of the global database of surface observations, improving performance by eliminating manual supervision and extending the scope of the model to include skewness. The previous coordinate trans-formation of binned speed and direction, used to evaluate the joint probability distri-butions of the wind vector, is replaced by direct kernel density estimation. The slow process of sequentially adding additional components is replaced by initializing all components together using fuzzy clustering. The supervised process of sequencing each mixture component through time is replaced by a fully automated unsupervised pro-cess using pattern matching. Previously reported departures from normal in the tails of the fuzzy-demodulated OEN orthogonal vectors are investigated by directly fitting the bivariate skew generalized t distribution, showing that the small observed skew is likely real but that the observed kurtosis is an artefact of the demodulation process, leading to a new Offset Skew Normal mixture model. The supplied open-source R scripts fully automate parametrization for locations in the NCEI Integrated Surface Hourly global database of wind observations.
DOI: 10.3535/8hn-4kg-2ea
title: Xanthoparmelia stenophylla (Ach.) Ahti & D. Hawksw.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/0c8-8p7-jks
title: Lopholejeunea subfusca (Nees) Schiffn.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/qee-1k4-r1g
title: Metalejeunea cucullata (Reinw., Blume & Nees) Grolle

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/txpvt2zcry
title: Intelligent cross-linking in polymer simulations: SuSi’s approach to complex 3D networks

description: Cross-linked polymers play a vital role in the materials science due to their mechanical strength, chemical resistance, and thermal stability, making them invaluable in biomedical devices, coatings, and electronics. However, constructing realistic molecular models of these systems remains a challenge due to their complex cross-linked networks. This study introduces SuSi, a Python-based program designed to generate both linear and cross-linked polymer systems for molecular simulations. SuSi uses artificial intelligence tree search algorithms to optimize the cross-linking process, ensuring efficient and collision-free network formation. The program is compatible with the AMBER force field and supports a wide variety of polymer architectures, including homopolymers, block copolymers, and complex 3D-network structures. To demonstrate its capabilities, SuSi was employed to generate three distinct cross-linked systems: silane-cross-linked polyethylene (Si-XLPE), thermosensitive poly(NIPAAm-co-MBA), and the complex unsaturated polyesteramide hydrogel made of phenylalanine, butenediol, and fumarate, cross-linked with polyethylene glycol (UPEA-PEG). The generated structures were successfully parametrized for molecular dynamics simulations and validated through experimental observables, showing that SuSi is a versatile tool for accurately modeling complex polymeric systems and advancing polymer simulations.
DOI: 10.3535/kkt-vqv-kyz
title: Frullania brasiliensis Raddi

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/zkx-vh3-h8r
title: Cyclolejeunea peruviana (Lehm. & Lindenb.) A. Evans

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/3kq-y40-1sq
title: Lopholejeunea eulopha (Taylor) Schiffn.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/m5p-mhn-v3b
title: Cyclolejeunea convexistipa (Lehm. & Lindenb.) A. Evans

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/txpvt2zcry.1
title: Intelligent cross-linking in polymer simulations: SuSi’s approach to complex 3D networks

description: Cross-linked polymers play a vital role in the materials science due to their mechanical strength, chemical resistance, and thermal stability, making them invaluable in biomedical devices, coatings, and electronics. However, constructing realistic molecular models of these systems remains a challenge due to their complex cross-linked networks. This study introduces SuSi, a Python-based program designed to generate both linear and cross-linked polymer systems for molecular simulations. SuSi uses artificial intelligence tree search algorithms to optimize the cross-linking process, ensuring efficient and collision-free network formation. The program is compatible with the AMBER force field and supports a wide variety of polymer architectures, including homopolymers, block copolymers, and complex 3D-network structures. To demonstrate its capabilities, SuSi was employed to generate three distinct cross-linked systems: silane-cross-linked polyethylene (Si-XLPE), thermosensitive poly(NIPAAm-co-MBA), and the complex unsaturated polyesteramide hydrogel made of phenylalanine, butenediol, and fumarate, cross-linked with polyethylene glycol (UPEA-PEG). The generated structures were successfully parametrized for molecular dynamics simulations and validated through experimental observables, showing that SuSi is a versatile tool for accurately modeling complex polymeric systems and advancing polymer simulations.
DOI: 10.3535/g3f-bey-a1e
title: Melanohalea exasperatula (Nyl.) O. Blanco, A. Crespo, Divakar, Essl., D. Hawksw. & Lumbsch

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/waz-n7w-1n3
title: Lopholejeunea subfusca (Nees) Schiffn.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/3jf-mc5-cjy
title: Plagiochila vincentina Lindenb.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/3l1-js5-y6j
title: Lepraria lobificans Nyl.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/rh5-vdy-bv2
title: Melanohalea elegantula (Zahlbr.) O. Blanco, A. Crespo, Divakar, Essl., D. Hawksw. & Lumbsch

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/9gj-32r-pz9
title: Aspicilia desertorum (Kremp.) Mereschk.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/f4w-30m-avh
title: Aspicilia

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/cex-dm2-hr6
title: Melanohalea exasperatula (Nyl.) O. Blanco, A. Crespo, Divakar, Essl., D. Hawksw. & Lumbsch

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/fl9-tw6-w5g
title: Frullania subtilissima (Nees ex Mont.) Lindenb.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/j3j-54c-r3z
title: Lecidea atrobrunnea (DC.) Schaer.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/n9y-sbd-rqw
title: Lecanora chlarotera Nyl.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/vtfzjnc6cr
title: Machine learning assisted canonical sampling (MLACS)

description: The acceleration of material property calculations while maintaining ab initio accuracy (1 meV/atom) is one of the major challenges in computational physics. In this paper, we introduce a Python package enhancing the computation of (finite temperature) material properties at the ab initio level using machine learning interatomic potentials (MLIP). The Machine Learning Assisted Canonical Sampling (Mlacs) method, grounded in a self-consistent variational approach, iteratively trains a MLIP using an active learning strategy in order to significantly reduce the computational cost of ab initio simulations.
Mlacs offers a modular and user-friendly interface that seamlessly integrates Density Functional Theory (DFT) codes, MLIP potentials, and molecular dynamics packages, enabling a wide range of applications, while maintaining a near-DFT accuracy. These include sampling the canonical ensemble of a system, performing free energy calculations, transition path sampling, and geometry optimization, all by utilizing surrogate MLIP potentials, in place of ab initio calculations.
This paper provides a comprehensive overview of the theoretical foundations and implementation of the Mlacs method. We also demonstrate its accuracy and efficiency through various examples, showcasing the capabilities of the Mlacs package.
DOI: 10.3535/cw6-36p-t6e
title: Digitalis viridiflora Lindl.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/avj-xtj-f2g
title: Radula javanica Gottsche

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/ttb-mjq-87y
title: Frullania intumescens (Lehm. & Lindenb.) Lehm. & Lindenb.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/vtfzjnc6cr.1
title: Machine learning assisted canonical sampling (MLACS)

description: The acceleration of material property calculations while maintaining ab initio accuracy (1 meV/atom) is one of the major challenges in computational physics. In this paper, we introduce a Python package enhancing the computation of (finite temperature) material properties at the ab initio level using machine learning interatomic potentials (MLIP). The Machine Learning Assisted Canonical Sampling (Mlacs) method, grounded in a self-consistent variational approach, iteratively trains a MLIP using an active learning strategy in order to significantly reduce the computational cost of ab initio simulations.
Mlacs offers a modular and user-friendly interface that seamlessly integrates Density Functional Theory (DFT) codes, MLIP potentials, and molecular dynamics packages, enabling a wide range of applications, while maintaining a near-DFT accuracy. These include sampling the canonical ensemble of a system, performing free energy calculations, transition path sampling, and geometry optimization, all by utilizing surrogate MLIP potentials, in place of ab initio calculations.
This paper provides a comprehensive overview of the theoretical foundations and implementation of the Mlacs method. We also demonstrate its accuracy and efficiency through various examples, showcasing the capabilities of the Mlacs package.
DOI: 10.3535/wys-nst-9a8
title: Linaria genistifolia subsp. genistifolia

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/zng-yfy-tfx
title: Lecidea

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/eal-r7l-3pj
title: Linaria alpina subsp. filicaulis (Leresche & Levier) Lainz

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/zqx-1na-6y8
title: Lecidella

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/610-lfk-5sk
title: Lecidea

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/hbs-nh7-wv1
title: Odontites longiflorus (Vahl) Webb

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/bll-ngs-zvy
title: Orobanche caesia Rchb.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/rw5-8va-c7w
title: Linaria elegans Cav.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/5js-gkm-mds
title: Frullania intumescens (Lehm. & Lindenb.) Lehm. & Lindenb.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/krg-cbq-xsm
title: Macrocolura sagittistipula (Spruce) R.M. Schust.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/j8j3f9rx2j
title: Data for Self-Passivated Coal-Derived Carbon Nanotubes Enable Defect-Engineered Interfaces for High-Performance Carbon-Based Perovskite Solar Cells

description: This dataset is derived from the research paper "Self-Passivated Coal-Derived Carbon Nanotubes Enable Defect-Engineered Interfaces for High-Performance Carbon-Based Perovskite Solar Cells". It includes J-V curve data of the best devices corresponding to self-passivated coal-derived carbon nanotubes (C-CNTs) and commercial carbon nanotubes at different doping concentrations , as well as J-V curve data of control devices and devices doped with activated carbon (a-C) a. The data can be used to calculate the photoelectric conversion efficiency.
DOI: 10.3535/c2a-dee-gnc
title: Saxifraga granulata L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.9wpu2a
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Digitaria ciliaris",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 35 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068386-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/hhy-hgy-wgb
title: Scrophulariaceae Juss.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/jlc-pnj-wnm
title: Digitalis obscura subsp. obscura

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/j8j3f9rx2j.1
title: Data for Self-Passivated Coal-Derived Carbon Nanotubes Enable Defect-Engineered Interfaces for High-Performance Carbon-Based Perovskite Solar Cells

description: This dataset is derived from the research paper "Self-Passivated Coal-Derived Carbon Nanotubes Enable Defect-Engineered Interfaces for High-Performance Carbon-Based Perovskite Solar Cells". It includes J-V curve data of the best devices corresponding to self-passivated coal-derived carbon nanotubes (C-CNTs) and commercial carbon nanotubes at different doping concentrations , as well as J-V curve data of control devices and devices doped with activated carbon (a-C) a. The data can be used to calculate the photoelectric conversion efficiency.
DOI: 10.3535/3r8-dwz-4df
title: Linaria vulgaris subsp. vulgaris

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/k25-w01-pxz
title: Linaria clementei Haens. ex Boiss.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/7y6-t68-l37
title: Veronica dillenii Crantz

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/qja-1cm-t52
title: Rutaceae Juss.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/7vx-kkz-kva
title: Staurothele

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/tt4-fwq-r0m
title: Xanthoria elegans (Link) Th. Fr.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/b3h-d1b-s9b
title: Eugeniella leucocheila (Tuck.) Lücking, Sérus. & Kalb

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/pbf-5sp-4zr
title: Pseudevernia

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/2nd-5e9-6wz
title: Linaria elegans Cav.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/2wd-eqf-eme
title: Pertusaria

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/slz-zs0-ltq
title: Sargassum C.Agardh, 1820

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.kmzfk3
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Digitaria ciliaris",
 "Country is Nicaragua",
 "HasCoordinate is true"
 ]
}

 The dataset includes 15 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068388-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/1yh-ewh-8w8
title: Orobanche gracilis Sm.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/c75-hhn-8qd
title: Arthoniaceae

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.sqb5vc
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acacia mearnsii",
 "Country is Cook Islands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068387-250717081556266/datasets/export for details.
DOI: 10.3535/9d9-d2y-9rr
title: Candelariella xanthostigma (Ach.) Lettau

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/d9g-sha-d5f
title: Catillaria nigroclavata (Nyl.) J. Steiner

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.17632/9pznfx98t4
title: Geophysical dataset across the Masoke Igneous Complex in Kanye, Botswana

description: The Masoke Igneous Complex, located in southeastern Botswana within the Kanye Basin, forms part of the larger Segwagwa–Masoke Igneous Ring Complex (SMIRC). It comprises steep-sided, elliptical intrusive bodies dominated by granite, diorite, syenite, and rhyolite. Radiometric dating places the crystallization of these rocks at approximately 2054 ± 9 million years ago, establishing a temporal link to the Bushveld Large Igneous Province in neighboring South Africa. The complex intrudes metasedimentary rocks of the upper Transvaal Supergroup and exhibits pronounced structural deformation, including E–W trending folds and N–S to NE–SW oriented fold systems. Geophysical modeling suggests that the Masoke intrusion extends to depths of nearly 5 kilometers and is associated with strong magnetic and gravity anomalies. These geophysical signatures point to magma emplacement along concentric fracture zones, likely formed during caldera collapse, reflecting a dynamic magmatic and tectonic history.
To investigate the subsurface architecture of the Masoke Igneous Complex, a comprehensive geophysical survey was conducted along a single North–South oriented profile. Data were acquired at uniform 4-meter station intervals using a suite of methods: Audio Magnetotellurics (AMT), gravity measurements with a CG5 gravimeter, ground magnetic survey, MaxMin electromagnetic (EM) profiling, and seismic refraction. The AMT survey was divided into nine segments, later merged into a continuous resistivity profile to enhance structural coherence and interpretability. Gravity data reveal subsurface density variations, while magnetic measurements delineate lithological contrasts and highlight zones of magnetic enrichment. MaxMin EM readings provide high-resolution insights into shallow conductivity anomalies, particularly sensitive to mineralized zones and fluid-bearing structures, complementing the deeper resistivity data from AMT. Seismic measurements contribute stratigraphic and lithological information, offering a layered view of subsurface composition. To support regional interpretation, satellite imagery and a gridded aeromagnetic map were integrated, providing broader spatial context and aiding in the identification of structural trends and geological boundaries.
DOI: 10.3535/ax8-ktw-pwr
title: Physcia dubia (Hoffm.) Lettau

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/clb-hpm-fdj
title: Protoparmeliopsis

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.96k5ye
title: Occurrence Download

description: A dataset containing 312 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Scylla paramamosain Estampador, 1950"
 ]
}

 The dataset includes 312 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003291-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12579180
title: Data for Robust Algorithms for the analysis of the FFC-NMRD dispersion curves

description: Data for article
DOI: 10.5281/zenodo.12579181
title: Data for Robust Algorithms for the analysis of the FFC-NMRD dispersion curves

description: Data for article
DOI: 10.17882/100853
title: Sea surface temperatures from the GFDL-ESM2M large ensemble at the University of Bern

description: We here present montly sea surface temperatures from a large ensemble of 30 ensemble members of the GFDL-ESM2M. These simulations were performed at the University of Bern and span the time from 1861 to 2100. Until 2005, the model was forced with CMIP5 historical forcing datasets. From 2006 onwards, the model was forced with forcing data from the high-emission scenario RCP8.5.
DOI: 10.5517/cc6bftr
title: CCDC 188784: Experimental Crystal Structure Determination

description: Related Article: K.S.Cook, W.E.Piers, P.G.Hayes, M.Parvez|2002|Organometallics|21|2422|doi:10.1021/om020145+
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/cc6bfvs
title: CCDC 188785: Experimental Crystal Structure Determination

description: Related Article: A.Asadi, C.Eaborn, M.S.Hill, P.B.Hitchcock, M.M.Meehan, J.D.Smith|2002|Organometallics|21|2430|doi:10.1021/om020106y
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/cc6bfwt
title: CCDC 188786: Experimental Crystal Structure Determination

description: Related Article: A.Asadi, C.Eaborn, M.S.Hill, P.B.Hitchcock, M.M.Meehan, J.D.Smith|2002|Organometallics|21|2430|doi:10.1021/om020106y
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.