cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.60712/si-id398532.2
title: StrainInfo SI-ID 398532.2

description: StrainInfo dataset 398532 about a strain of Streptomyces spiramenti. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398533.3
title: StrainInfo SI-ID 398533.3

description: StrainInfo dataset 398533 about a strain of Paenibacillus pseudetheri. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398534.2
title: StrainInfo SI-ID 398534.2

description: StrainInfo dataset 398534 about a strain of Streptomyces. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398535.2
title: StrainInfo SI-ID 398535.2

description: StrainInfo dataset 398535 about a strain of Acrodontium. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398536.2
title: StrainInfo SI-ID 398536.2

description: StrainInfo dataset 398536 about a strain of Terrabacter. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398537.2
title: StrainInfo SI-ID 398537.2

description: StrainInfo dataset 398537 about a strain of Longispora urticae. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398538.2
title: StrainInfo SI-ID 398538.2

description: StrainInfo dataset 398538 about a strain of Streptomyces. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398539.2
title: StrainInfo SI-ID 398539.2

description: StrainInfo dataset 398539 about a strain of Pseudomonas. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398540.3
title: StrainInfo SI-ID 398540.3

description: StrainInfo dataset 398540 about a strain of Geodermatophilus normandii. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398541.2
title: StrainInfo SI-ID 398541.2

description: StrainInfo dataset 398541 about a strain of Trichophyton violaceum. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398542.3
title: StrainInfo SI-ID 398542.3

description: StrainInfo dataset 398542 about a strain of Lentzea atacamensis. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398543.3
title: StrainInfo SI-ID 398543.3

description: StrainInfo dataset 398543 about a strain of Effusibacillus lacus. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398544.2
title: StrainInfo SI-ID 398544.2

description: StrainInfo dataset 398544 about a strain of Paraburkholderia. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398545.3
title: StrainInfo SI-ID 398545.3

description: StrainInfo dataset 398545 about a strain of Aliidiomarina maris. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398546.2
title: StrainInfo SI-ID 398546.2

description: StrainInfo dataset 398546 about a strain of Georgenia. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398548.2
title: StrainInfo SI-ID 398548.2

description: StrainInfo dataset 398548 about a strain of Roseisolibacter agri. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398549.2
title: StrainInfo SI-ID 398549.2

description: StrainInfo dataset 398549 about a strain of Nocardiopsis. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.60712/si-id398550.3
title: StrainInfo SI-ID 398550.3

description: StrainInfo dataset 398550 about a strain of Alistipes ihumii. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data.
DOI: 10.15468/dl.f3y359
title: Occurrence Download

description: A dataset containing 703890 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bellis perennis L.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 703890 records from 3011 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004970-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18111141
title: GWAS summary statistics for Obesity, abdominal obesity, waist circumference and body mass index in Mexican population

description: In this study, we applied a candidate gene association study (CGAS) meta-analysis of obesity and related traits in four independent Mexican cohorts, focusing on 356 genes previously associated with the relevant phenotypes.
DOI: 10.5281/zenodo.18111142
title: GWAS summary statistics for Obesity, abdominal obesity, waist circumference and body mass index in Mexican population

description: In this study, we applied a candidate gene association study (CGAS) meta-analysis of obesity and related traits in four independent Mexican cohorts, focusing on 356 genes previously associated with the relevant phenotypes.
DOI: 10.24403/jp.1504438
title: Editorial / Dr. Steffen Raßloff, Redakteur der MVGAE
DOI: 10.15468/dl.6vyhxn
title: Occurrence Download

description: A dataset containing 6451 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Natural History Museum (London) Collection Specimens",
 "CollectionCode is PAL",
 "Country is one of (Argentina, Bolivia (Plurinational State of), Brazil, Colombia, French Guiana, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela (Bolivarian Republic of), Ecuador, Chile)"
 ]
}

 The dataset includes 6451 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071259-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y7mevs
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "Geometry POLYGON((-4.13104 51.20842,-4.14154 51.20674,-4.20291 51.20033,-4.22714 51.18818,-4.21691 51.18278,-4.17761 51.18582,-4.14259 51.18754,-4.13104 51.20842))",
 "HasGeospatialIssue is false",
 "TaxonKey is Basidiomycota",
 "Year 1000-2026"
 ]
}

 The dataset includes 31 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071260-251120083545085/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.30833293
title: Smartphone-assisted upconversion nanoparticle assay for rapid multiplex detection of H5, H7, and H10 avian influenza viruses

description: Avian influenza viruses (AIVs) of the H5, H7, and H10 subtypes pose substantial threats to global public health owing to their high pathogenicity, cross-species transmissibility, and potential to spark epidemics. Rapid and accurate detection is essential for outbreak control and zoonotic risk mitigation. Here, we report the development of a multiplex lateral flow immunoassay (LFA) based on core-shell upconversion nanoparticles (UCNPs) conjugated with subtype-specific monoclonal antibodies targeting the haemagglutinin proteins of H5, H7, and H10 AIVs. The assay achieved limits of detection of 0.0313, 0.0156, and 0.0625 ng/mL for recombinant HA proteins and 2<sup>−4</sup>, 2<sup>−4</sup>, and 2<sup>−3</sup> haemagglutination units for viral titres of H5, H7, and H10, respectively. No cross-reactivity was observed with other AIV subtypes or respiratory pathogens, and intra- and inter-assay variation remained below 6%, demonstrating high specificity and reproducibility. Validation with 135 avian and 125 human clinical samples showed complete concordance with real-time RT-PCR results. Integration with a smartphone-based analytical platform enabled rapid readout, automated quantification, and cloud-based data sharing, providing results within 10 min. This intelligent UCNPs-LFA system combines ultra sensitivity, multiplexing, and field-deployable usability, representing a significant advance over conventional methods. By enabling timely and reliable detection of H5, H7, and H10 AIVs in both animal and human samples, this platform offers a practical tool for early warning, surveillance, and control of emerging zoonotic influenza, thereby contributing to global preparedness against avian influenza outbreaks.
DOI: 10.6084/m9.figshare.30833293.v1
title: Smartphone-assisted upconversion nanoparticle assay for rapid multiplex detection of H5, H7, and H10 avian influenza viruses

description: Avian influenza viruses (AIVs) of the H5, H7, and H10 subtypes pose substantial threats to global public health owing to their high pathogenicity, cross-species transmissibility, and potential to spark epidemics. Rapid and accurate detection is essential for outbreak control and zoonotic risk mitigation. Here, we report the development of a multiplex lateral flow immunoassay (LFA) based on core-shell upconversion nanoparticles (UCNPs) conjugated with subtype-specific monoclonal antibodies targeting the haemagglutinin proteins of H5, H7, and H10 AIVs. The assay achieved limits of detection of 0.0313, 0.0156, and 0.0625 ng/mL for recombinant HA proteins and 2<sup>−4</sup>, 2<sup>−4</sup>, and 2<sup>−3</sup> haemagglutination units for viral titres of H5, H7, and H10, respectively. No cross-reactivity was observed with other AIV subtypes or respiratory pathogens, and intra- and inter-assay variation remained below 6%, demonstrating high specificity and reproducibility. Validation with 135 avian and 125 human clinical samples showed complete concordance with real-time RT-PCR results. Integration with a smartphone-based analytical platform enabled rapid readout, automated quantification, and cloud-based data sharing, providing results within 10 min. This intelligent UCNPs-LFA system combines ultra sensitivity, multiplexing, and field-deployable usability, representing a significant advance over conventional methods. By enabling timely and reliable detection of H5, H7, and H10 AIVs in both animal and human samples, this platform offers a practical tool for early warning, surveillance, and control of emerging zoonotic influenza, thereby contributing to global preparedness against avian influenza outbreaks.
DOI: 10.6084/m9.figshare.30741829
title: Decoding renewable shift: a threshold analysis of environmental policy strategies and innovative technologies in OECD countries

description: The Organisation for Economic Co-operation and Development (OECD) countries remain heavily dependent on fossil fuels, which still account for over 80% of their primary energy supply. This persistent reliance raises critical questions about the effectiveness of policies aimed at accelerating the renewable energy transition. To address this, the current study investigates the nonlinear effects of environmental and innovation policies on the renewable energy transition across 37 OECD countries from 1995 to 2023, using a dynamic panel threshold regression model. The results show that environmental policy stringency significantly enhances the renewable energy transition, but its impact is nonlinear: after exceeding a critical threshold, the effect rises from 0.448 in the lower regime to 0.516 in the upper regime. Similarly, public fiscal R&D spending and the diffusion of environmental technologies exhibit statistically significant positive effects only above their respective thresholds. In contrast, the impact of low-carbon technology exports shifts from a significant negative influence in the lower regime to a positive, albeit statistically insignificant, effect in the upper regime. These findings underscore the necessity of targeted investment to push R&D spending and technology diffusion beyond their critical thresholds. Furthermore, the study highlights the importance of policy coordination to address structural inefficiencies and maximize synergies among various actors.
DOI: 10.6084/m9.figshare.30741829.v1
title: Decoding renewable shift: a threshold analysis of environmental policy strategies and innovative technologies in OECD countries

description: The Organisation for Economic Co-operation and Development (OECD) countries remain heavily dependent on fossil fuels, which still account for over 80% of their primary energy supply. This persistent reliance raises critical questions about the effectiveness of policies aimed at accelerating the renewable energy transition. To address this, the current study investigates the nonlinear effects of environmental and innovation policies on the renewable energy transition across 37 OECD countries from 1995 to 2023, using a dynamic panel threshold regression model. The results show that environmental policy stringency significantly enhances the renewable energy transition, but its impact is nonlinear: after exceeding a critical threshold, the effect rises from 0.448 in the lower regime to 0.516 in the upper regime. Similarly, public fiscal R&D spending and the diffusion of environmental technologies exhibit statistically significant positive effects only above their respective thresholds. In contrast, the impact of low-carbon technology exports shifts from a significant negative influence in the lower regime to a positive, albeit statistically insignificant, effect in the upper regime. These findings underscore the necessity of targeted investment to push R&D spending and technology diffusion beyond their critical thresholds. Furthermore, the study highlights the importance of policy coordination to address structural inefficiencies and maximize synergies among various actors.
DOI: 10.15468/dl.xqxgdg
title: Occurrence Download

description: A dataset containing 7369 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oxydendrum arboreum",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7369 records from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071261-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18134113
title: dendropsophus_manonegra_spades_01.fasta.gz
DOI: 10.6084/m9.figshare.30991617
title: Cultural context and the development of students’ naïve and scientific knowledge: an example from volcanic processes

description: Despite extensive research on naïve knowledge/conceptions, few studies examine the socio-economic and cultural influence of students, particularly their perceptual experience, on knowledge construction. This study explores how students’ everyday environment shapes their understanding, using embodied cognition, analogy, and knowledge development, including naïve knowledge. It investigates factors influencing students’naïve and scientific knowledge of volcanic processes and risks, focusing on socio-economic and cultural status, and life experiences related to volcanoes and earthquakes (perceptual experience, social environment, school learning). It examines the extent to which these forms of knowledge are shared or differ based on these factors. Conducted among 1,059 Tanzanian students (832 in secondary schools) from two geologically distinct regions exposed to different levels of risk but following the same school curriculum, the study employs a questionnaire-based approach and statistical analysis. Results show that certain perceptual experiences linked to students’ interactions with their daily environment influence the activation of specific naïve knowledge. When perceptual experiences are widely shared, students’ naïve knowledge tends to be similar; conversely, when such experiences are less shared due to differing material and cultural contexts, naïve knowledge are expressed in more varied ways. These differences highlight the potential impact of some pedagogical approaches based on perceptual experience.
DOI: 10.6084/m9.figshare.30991617.v1
title: Cultural context and the development of students’ naïve and scientific knowledge: an example from volcanic processes

description: Despite extensive research on naïve knowledge/conceptions, few studies examine the socio-economic and cultural influence of students, particularly their perceptual experience, on knowledge construction. This study explores how students’ everyday environment shapes their understanding, using embodied cognition, analogy, and knowledge development, including naïve knowledge. It investigates factors influencing students’naïve and scientific knowledge of volcanic processes and risks, focusing on socio-economic and cultural status, and life experiences related to volcanoes and earthquakes (perceptual experience, social environment, school learning). It examines the extent to which these forms of knowledge are shared or differ based on these factors. Conducted among 1,059 Tanzanian students (832 in secondary schools) from two geologically distinct regions exposed to different levels of risk but following the same school curriculum, the study employs a questionnaire-based approach and statistical analysis. Results show that certain perceptual experiences linked to students’ interactions with their daily environment influence the activation of specific naïve knowledge. When perceptual experiences are widely shared, students’ naïve knowledge tends to be similar; conversely, when such experiences are less shared due to differing material and cultural contexts, naïve knowledge are expressed in more varied ways. These differences highlight the potential impact of some pedagogical approaches based on perceptual experience.
DOI: 10.5281/zenodo.18134112
title: dendropsophus_manonegra_spades_01.fasta.gz
DOI: 10.6084/m9.figshare.29910315
title: Causality-Oriented Robustness: Exploiting General Noise Interventions

description: Since distribution shifts are common in real-world applications, there is a pressing need to develop prediction models that are robust against such shifts. Existing frameworks, such as empirical risk minimization or distributionally robust optimization, either lack generalizability for unseen distributions or rely on postulated distance measures. Alternatively, causality offers a data-driven and structural perspective to robust predictions. However, the assumptions necessary for causal inference can be overly stringent, and the robustness offered by such causal models often lacks flexibility. In this article, we focus on causality-oriented robustness and propose Distributional Robustness via Invariant Gradients (DRIG), a method that exploits general noise interventions in training data for robust predictions against unseen interventions, and naturally interpolates between in-distribution prediction and causality. In a linear setting, we prove that DRIG yields predictions that are robust among a data-dependent class of distribution shifts. Furthermore, we show that our framework includes anchor regression as a special case, and that it yields prediction models that protect against more diverse perturbations. We establish finite-sample results and extend our approach to semi-supervised domain adaptation to further improve prediction performance. Finally, we empirically validate our methods on synthetic simulations and on single-cell and intensive health care datasets. 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.29910315.v2
title: Causality-Oriented Robustness: Exploiting General Noise Interventions

description: Since distribution shifts are common in real-world applications, there is a pressing need to develop prediction models that are robust against such shifts. Existing frameworks, such as empirical risk minimization or distributionally robust optimization, either lack generalizability for unseen distributions or rely on postulated distance measures. Alternatively, causality offers a data-driven and structural perspective to robust predictions. However, the assumptions necessary for causal inference can be overly stringent, and the robustness offered by such causal models often lacks flexibility. In this article, we focus on causality-oriented robustness and propose Distributional Robustness via Invariant Gradients (DRIG), a method that exploits general noise interventions in training data for robust predictions against unseen interventions, and naturally interpolates between in-distribution prediction and causality. In a linear setting, we prove that DRIG yields predictions that are robust among a data-dependent class of distribution shifts. Furthermore, we show that our framework includes anchor regression as a special case, and that it yields prediction models that protect against more diverse perturbations. We establish finite-sample results and extend our approach to semi-supervised domain adaptation to further improve prediction performance. Finally, we empirically validate our methods on synthetic simulations and on single-cell and intensive health care datasets. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.