cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.5f92hu
title: Occurrence Download

description: A dataset containing 194713 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Specimen)",
 "Country is Denmark",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Lutra lutra, Castor fiber, Canis lupus, Myotis daubentonii, Myotis dasycneme, Myotis mystacinus, Myotis brandtii, Myotis nattereri, Myotis bechsteinii, Vespertilio murinus, Nyctalus noctula, Barbastella barbastellus, Plecotus auritus, Pipistrellus pygmaeus, Pipistrellus nathusii, Pipistrellus pipistrellus, Muscardinus avellanarius, Sicista betulina, Emys orbicularis, Lacerta agilis, Coronella austriaca, Elaphe longissima, Triturus cristatus, Bombina bombina, Pelobates fuscus, Hyla arborea, Rana arvalis, Rana dalmatina, Bufo calamita, Bufo viridis, Coregonus oxyrinchus, Dytiscus latissimus, Graphoderus bilineatus, Osmoderma eremita, Maculinea arion, Aeshna viridis, Leucorrhinia pectoralis, Ophiogomphus cecilia, Unio crassus, Botrychium simplex, Cypripedium calceolus, Liparis loeselii, Luronium natans, Saxifraga hirculus, Apium repens, Eptesicus serotinus, Bufo calamita, Bufo calamita alpestris, Acipenser sturio, Coenonympha hero, Coregonus albula, Coronella austriaca, Epidalea calamita, Helosciadium repens, Lycaena dispar, Najas flexilis, Nymphalis vaualbum, Proserpinus proserpina, Zamenis longissimus, Nyctalus leisleri, Plecotus austriacus, Eptesicus nilssonii, Anser fabilis, Symphytum grandiflorum, Anemone nemorosa, Lathyrus linifolius, Anser fabalis, Dryocopus martius)",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 194713 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011878-260120142942310/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.31148601
title: Action research in operations management: methodological weaknesses and four improvement strategies

description: This study examines the design, application, and reporting of action research (AR) in operations management (OM) to identify recurring methodological weaknesses and propose strategies to strengthen AR as a practice-oriented, theory-generating methodology. A systematic review of 142 empirical AR studies published in leading OM journals was conducted using a coding framework grounded in methodological literature, focusing on four AR design dimensions: AR cycle completeness, clarification of the unit of analysis, use of comparison logic, and use of triangulation. The review reveals recurrent methodological weaknesses: incomplete cycles, inadequate specification of the unit of analysis, limited application of comparison logic, and limited use of researcher triangulation. To address these weaknesses, the study proposes four improvement strategies: extended cycle tracking, unit stratification, multi-context comparison, and AR–case study integration. Together, these strategies configure a design space for enhancing the rigour, transparency, and contribution of AR.
DOI: 10.6084/m9.figshare.31148601.v1
title: Action research in operations management: methodological weaknesses and four improvement strategies

description: This study examines the design, application, and reporting of action research (AR) in operations management (OM) to identify recurring methodological weaknesses and propose strategies to strengthen AR as a practice-oriented, theory-generating methodology. A systematic review of 142 empirical AR studies published in leading OM journals was conducted using a coding framework grounded in methodological literature, focusing on four AR design dimensions: AR cycle completeness, clarification of the unit of analysis, use of comparison logic, and use of triangulation. The review reveals recurrent methodological weaknesses: incomplete cycles, inadequate specification of the unit of analysis, limited application of comparison logic, and limited use of researcher triangulation. To address these weaknesses, the study proposes four improvement strategies: extended cycle tracking, unit stratification, multi-context comparison, and AR–case study integration. Together, these strategies configure a design space for enhancing the rigour, transparency, and contribution of AR.
DOI: 10.15468/dl.chepv2
title: Occurrence Download

description: A dataset containing 18916 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Abies alba, Acer campestre, Acer pseudoplatanus, Alnus incana, Alnus viridis, Betula pendula, Carpinus betulus, Castanea sativa, Corylus avellana, Fagus sylvatica, Fraxinus excelsior, Fraxinus ornus, Larix decidua, Ostrya carpinifolia, Picea abies, Pinus cembra, Pinus mugo, Pinus nigra, Pinus sylvestris, Populus tremula, Prunus avium, Quercus cerris, Quercus ilex, Quercus petraea, Quercus pubescens, Salix caprea, Rosaceae, Sorbus aucuparia, Tilia cordata)",
 "OccurrenceStatus is Present",
 "Year is greater than 2000",
 "Geometry POLYGON((10.45234 45.67312, 11.96268 45.67312, 11.96268 46.53302, 10.45234 46.53302, 10.45234 45.67312))"
 ]
}

 The dataset includes 18916 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011896-260120142942310/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.31148557
title: SRL in STEM Education dataset

description: This data is a database of the article "Self-Regulated Learning in STEM Education: A Systematic Review of Conceptualizations, Equity, and Technologies."
DOI: 10.6084/m9.figshare.31148557.v1
title: SRL in STEM Education dataset

description: This data is a database of the article "Self-Regulated Learning in STEM Education: A Systematic Review of Conceptualizations, Equity, and Technologies."
DOI: 10.15468/dl.44s8wc
title: Occurrence Download

description: A dataset containing 6022 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((37.36358 12.51178,37.42647 12.51178,37.58368 12.56844,37.74572 12.67465,37.84489 12.79968,37.93679 12.96001,37.97549 13.10139,37.92228 13.16498,37.81828 13.16498,37.71186 13.11788,37.57884 13.0095,37.49177 12.90815,37.41679 12.82327,37.33456 12.73128,37.30312 12.63689,37.29828 12.54956,37.36358 12.51178))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 6022 records from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011897-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9kdtce
title: Occurrence Download

description: A dataset containing 451 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is FRA.1.11.1_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Parus major",
 "Year 2024-2024"
 ]
}

 The dataset includes 451 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011898-260120142942310/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.cc2qlc8z
title: CCDC 2520091: Experimental Crystal Structure Determination

description: Related Article: Yangxin Mao|2026|CSD Communication|||
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: 10.34740/kaggle/dsv/14622582
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.4119/unibi/3013315
title: PhD Thesis Katja Prager pages 26-27

description: Supporting information for the publication "Partial Conversion of an Animal-Like Cryptochrome Into a Plant Cryptochrome" authored by Sabine Oldemeyer, Robin Held, Corinna Strothenke, Jan Petersen, Maria Mittag, Tilman Kottke in the Journal of Physical Chemistry B.
Enclosed are two pages from the PhD thesis of Katja Prager, which was carried out in the group of Maria Mittag at Friedrich Schiller University Jena, Germany.
DOI: 10.6084/m9.figshare.29152733
title: <b>Integrative multi-omics profiling of microbiome architecture and pathogen dynamics in ecologically dominant rodents across Guizhou's karst ecosystems</b>

description: Karst ecosystems in the Guizhou Plateau harbor unique wildlife reservoirs for zoonotic pathogens, yet their microbiome architecture and pathogen dynamics remain poorly characterized. Here, we integrate metagenomics, 16S rRNA amplicon sequencing, and culturomics to profile gut and lung microbiomes of three ecologically dominant rodents—Apodemus chevrieri, Rattus losea, and Niviventer confucianus—across Guizhou’s heterogeneous habitats.
DOI: 10.6084/m9.figshare.29152733.v2
title: <b>Integrative multi-omics profiling of microbiome architecture and pathogen dynamics in ecologically dominant rodents across Guizhou's karst ecosystems</b>

description: Karst ecosystems in the Guizhou Plateau harbor unique wildlife reservoirs for zoonotic pathogens, yet their microbiome architecture and pathogen dynamics remain poorly characterized. Here, we integrate metagenomics, 16S rRNA amplicon sequencing, and culturomics to profile gut and lung microbiomes of three ecologically dominant rodents—Apodemus chevrieri, Rattus losea, and Niviventer confucianus—across Guizhou’s heterogeneous habitats.
DOI: 10.6084/m9.figshare.29152733.v3
title: <b>Integrative multi-omics profiling of microbiome architecture and pathogen dynamics in ecologically dominant rodents across Guizhou's karst ecosystems</b>

description: Karst ecosystems in the Guizhou Plateau harbor unique wildlife reservoirs for zoonotic pathogens, yet their microbiome architecture and pathogen dynamics remain poorly characterized. Here, we integrate metagenomics, 16S rRNA amplicon sequencing, and culturomics to profile gut and lung microbiomes of three ecologically dominant rodents—Apodemus chevrieri, Rattus losea, and Niviventer confucianus—across Guizhou’s heterogeneous habitats.
DOI: 10.15468/dl.8695yf
title: Occurrence Download

description: A dataset containing 11122 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lacerta agilis",
 "Country is Germany",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year 2005-2020"
 ]
}

 The dataset includes 11122 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011899-260120142942310/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.25488550
title: CARE: Large Precision Matrix Estimation for Compositional Data

description: High-dimensional compositional data are prevalent in many applications. The simplex constraint poses intrinsic challenges to inferring the conditional dependence relationships among the components forming a composition, as encoded by a large precision matrix. We introduce a precise specification of the compositional precision matrix and relate it to its basis counterpart, which is shown to be asymptotically identifiable under suitable sparsity assumptions. By exploiting this connection, we propose a composition adaptive regularized estimation (CARE) method for estimating the sparse basis precision matrix. We derive rates of convergence for the estimator and provide theoretical guarantees on support recovery and data-driven parameter tuning. Our theory reveals an intriguing tradeoff between identification and estimation, thereby highlighting the blessing of dimensionality in compositional data analysis. In particular, in sufficiently high dimensions, the CARE estimator achieves minimax optimality and performs as well as if the basis were observed. We further discuss how our framework can be extended to handle data containing zeros, including sampling zeros and structural zeros. The advantages of CARE over existing methods are illustrated by simulation studies and an application to inferring microbial ecological networks in the human gut. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.25488550.v3
title: CARE: Large Precision Matrix Estimation for Compositional Data

description: High-dimensional compositional data are prevalent in many applications. The simplex constraint poses intrinsic challenges to inferring the conditional dependence relationships among the components forming a composition, as encoded by a large precision matrix. We introduce a precise specification of the compositional precision matrix and relate it to its basis counterpart, which is shown to be asymptotically identifiable under suitable sparsity assumptions. By exploiting this connection, we propose a composition adaptive regularized estimation (CARE) method for estimating the sparse basis precision matrix. We derive rates of convergence for the estimator and provide theoretical guarantees on support recovery and data-driven parameter tuning. Our theory reveals an intriguing tradeoff between identification and estimation, thereby highlighting the blessing of dimensionality in compositional data analysis. In particular, in sufficiently high dimensions, the CARE estimator achieves minimax optimality and performs as well as if the basis were observed. We further discuss how our framework can be extended to handle data containing zeros, including sampling zeros and structural zeros. The advantages of CARE over existing methods are illustrated by simulation studies and an application to inferring microbial ecological networks in the human gut. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.27260858
title: eDNAPlus: A Unifying Modeling Framework for DNA-based Biodiversity Monitoring

description: DNA-based biodiversity surveys, which involve collecting physical samples from survey sites and assaying them in the laboratory to detect species via their diagnostic DNA sequences, are increasingly being adopted for biodiversity monitoring and decision-making. The most commonly employed method, metabarcoding, combines PCR with high-throughput DNA sequencing to amplify and read “DNA barcode” sequences, generating count data indicating the number of times each DNA barcode was read. However, DNA-based data are noisy and error-prone, with several sources of variation, and cannot alone estimate the species-specific amount of DNA present at a surveyed site (<i>DNA biomass</i>). In this article, we present a unifying modeling framework for DNA-based survey data that allows estimation of <i>changes in DNA biomass within species, across sites</i> and their links to environmental covariates, while for the first time simultaneously accounting for key sources of variation, error and noise in the data-generating process, and for between-species and between-sites correlation. Bayesian inference is performed using MCMC with Laplace approximations. We describe a re-parameterization scheme for crossed-effects models designed to improve mixing, and an adaptive approach for updating latent variables, which reduces computation time. Theoretical and simulation results are used to guide study design, including the level of replication at different survey stages and the use of quality control methods. Finally, we demonstrate our new framework on a dataset of Malaise-trap samples, quantifying the effects of elevation and distance-to-road on each species, and produce maps identifying areas of high biodiversity and species DNA biomass. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.27260858.v2
title: eDNAPlus: A Unifying Modeling Framework for DNA-based Biodiversity Monitoring

description: DNA-based biodiversity surveys, which involve collecting physical samples from survey sites and assaying them in the laboratory to detect species via their diagnostic DNA sequences, are increasingly being adopted for biodiversity monitoring and decision-making. The most commonly employed method, metabarcoding, combines PCR with high-throughput DNA sequencing to amplify and read “DNA barcode” sequences, generating count data indicating the number of times each DNA barcode was read. However, DNA-based data are noisy and error-prone, with several sources of variation, and cannot alone estimate the species-specific amount of DNA present at a surveyed site (<i>DNA biomass</i>). In this article, we present a unifying modeling framework for DNA-based survey data that allows estimation of <i>changes in DNA biomass within species, across sites</i> and their links to environmental covariates, while for the first time simultaneously accounting for key sources of variation, error and noise in the data-generating process, and for between-species and between-sites correlation. Bayesian inference is performed using MCMC with Laplace approximations. We describe a re-parameterization scheme for crossed-effects models designed to improve mixing, and an adaptive approach for updating latent variables, which reduces computation time. Theoretical and simulation results are used to guide study design, including the level of replication at different survey stages and the use of quality control methods. Finally, we demonstrate our new framework on a dataset of Malaise-trap samples, quantifying the effects of elevation and distance-to-road on each species, and produce maps identifying areas of high biodiversity and species DNA biomass. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.25488550.v2
title: CARE: Large Precision Matrix Estimation for Compositional Data

description: High-dimensional compositional data are prevalent in many applications. The simplex constraint poses intrinsic challenges to inferring the conditional dependence relationships among the components forming a composition, as encoded by a large precision matrix. We introduce a precise specification of the compositional precision matrix and relate it to its basis counterpart, which is shown to be asymptotically identifiable under suitable sparsity assumptions. By exploiting this connection, we propose a composition adaptive regularized estimation (CARE) method for estimating the sparse basis precision matrix. We derive rates of convergence for the estimator and provide theoretical guarantees on support recovery and data-driven parameter tuning. Our theory reveals an intriguing tradeoff between identification and estimation, thereby highlighting the blessing of dimensionality in compositional data analysis. In particular, in sufficiently high dimensions, the CARE estimator achieves minimax optimality and performs as well as if the basis were observed. We further discuss how our framework can be extended to handle data containing zeros, including sampling zeros and structural zeros. The advantages of CARE over existing methods are illustrated by simulation studies and an application to inferring microbial ecological networks in the human gut. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.27260858.v1
title: eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitoring

description: DNA-based biodiversity surveys, which involve collecting physical samples from survey sites and assaying them in the laboratory to detect species via their diagnostic DNA sequences, are increasingly being adopted for biodiversity monitoring and decision-making. The most commonly employed method, metabarcoding, combines PCR with high-throughput DNA sequencing to amplify and read ‘DNA barcode’ sequences, generating count data indicating the number of times each DNA barcode was read. However, DNA-based data are noisy and error-prone, with several sources of variation, and cannot alone estimate the species-specific amount of DNA present at a surveyed site (<i>DNA biomass</i>). In this paper, we present a unifying modelling framework for DNA-based survey data that allows estimation of <i>changes in DNA biomass within species, across sites</i> and their links to environmental covariates, whilst for the first time simultaneously accounting for key sources of variation, error and noise in the data-generating process, and for between-species and between-sites correlation. Bayesian inference is performed using MCMC with Laplace approximations. We describe a re-parameterisation scheme for crossed-effects models designed to improve mixing, and an adaptive approach for updating latent variables, which reduces computation time. Theoretical and simulation results are used to guide study design, including the level of replication at different survey stages and the use of quality control methods. Finally, we demonstrate our new framework on a dataset of Malaise-trap samples, quantifying the effects of elevation and distance-to-road on each species, and produce maps identifying areas of high biodiversity and species DNA biomass.
DOI: 10.15468/6a5zm5
title: A new species in the subgenus Wilhelmia (Diptera: Simuliidae: Simulium) from Sichuan, China

description: This dataset contains the digitized treatments in Plazi based on the original journal article An, Yuxin, Wang, Yuyu (2025): A new species in the subgenus Wilhelmia (Diptera: Simuliidae: Simulium) from Sichuan, China. Zootaxa 5590 (1): 146-150, DOI: 10.11646/zootaxa.5590.1.10, URL: https://doi.org/10.11646/zootaxa.5590.1.10AbstractSimulium (Wilhelmia) xichangense sp. nov. is described based on females from Sichuan province, China. It is the firstly recorded species of the subgenus Wilhelmia in Sichuan province. This species is assigned to the subgenus Wilhelmia and is morphologically most similar to S. (W.) pinnatum Chen, Zhang and Jiang, 2016 from Gansu province in China. A key to the female adults of the subgenus Wilhelmia of China is provided.
DOI: 10.23673/re-568
title: Retrospective Photogrammetry Models from the Salme Ship Burial Excavations

description: This dataset contains 3D models generated through retrospective photogrammetry using archival photo materials from the Salme ship burial excavations in Saaremaa, Estonia. The models include structure-from-motion reconstructions of excavation surfaces, burial contexts, artefact clusters, and selected stratigraphic exposures. They were produced as part of a methodological study evaluating the usability of non-systematic photographic documentation for archaeological spatial reconstruction. Outputs include textured meshes, point clouds, and associated metadata documenting image sources and processing parameters.