cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29266862
title: RC1.xlsx

description: Here, we collected tree-ring cores of<i> Picea likiangensis</i> and <i>Abies georgei </i>from Southwest China and established a regional composite (RC) chronology.
DOI: 10.6084/m9.figshare.29266862.v1
title: RC1.xlsx

description: Here, we collected tree-ring cores of<i> Picea likiangensis</i> and <i>Abies georgei </i>from Southwest China and established a regional composite (RC) chronology.
DOI: 10.15468/dl.54c3n7
title: Occurrence Download

description: A dataset containing 1110 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Typha"
 ]
}

 The dataset includes 1110 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053194-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vuzhe3
title: Occurrence Download

description: A dataset containing 260165 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((3.96239 43.32023,4.36625 43.32023,4.36625 43.6669,3.96239 43.6669,3.96239 43.32023))",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves",
 "Year 2020-2025"
 ]
}

 The dataset includes 260165 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053181-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17422410
title: Data supplement to "Advancing regional water quality modelling: Integrating spatial machine learning with large-scale catchment data"

description: Advancing regional water quality modelling: Integrating spatial machine learning with large-scale catchment data

Marta Jemeļjanova, Holger Virro, Ilga Kokorīte, Alexander Kmoch, Marie Annusver, and Evelyn Uuemaa

 

Intensified agriculture increases nutrient loads in waterbodies, threatening aquatic ecosystems and human health. Estimating nutrient concentrations is difficult due to the limited spatial and temporal coverage of national monitoring networks. Explainable machine learning can address this by linking nutrient concentrations to upstream catchment characteristics.We trained Random Forest models to predict total nitrogen (TN) and total phosphorus (TP) concentrations in streams at almost 900 in-stream locations using upstream catchment-scale covariates. To characterise each of the upstream catchment areas, we used openly accessible global and local environmental datasets. As a novel approach, we additionally incorporated spatial covariates, including coordinates and buffers, and we tested how models would perform with fewer but meaningful covariates.TN predictions were accurate, while TP predictions were poor. Models with reduced covariates achieved similar accuracy to baseline models and decreased overfitting. The inclusion of spatial covariates provided only minimal improvement of the prediction accuracy scores themselves; however, they demonstrate potential in capturing spatial structure and regionalisation, and in some cases, they outranked their corresponding full-catchment covariate versions in SHAP feature importance. In conclusion, utilising catchment characteristics and machine learning can yield a robust regional model for TN, enabling the reliable estimation of TN loads in streams.

 

These folders contain the input and output data to reproduce the results and figures of Jemeljanova et al. "Advancing regional water quality modelling: Integrating spatial machine learning with large-scale catchment data".
DOI: 10.5281/zenodo.17422409
title: Data supplement to "Advancing regional water quality modelling: Integrating spatial machine learning with large-scale catchment data"

description: Advancing regional water quality modelling: Integrating spatial machine learning with large-scale catchment data

Marta Jemeļjanova, Holger Virro, Ilga Kokorīte, Alexander Kmoch, Marie Annusver, and Evelyn Uuemaa

 

Intensified agriculture increases nutrient loads in waterbodies, threatening aquatic ecosystems and human health. Estimating nutrient concentrations is difficult due to the limited spatial and temporal coverage of national monitoring networks. Explainable machine learning can address this by linking nutrient concentrations to upstream catchment characteristics.We trained Random Forest models to predict total nitrogen (TN) and total phosphorus (TP) concentrations in streams at almost 900 in-stream locations using upstream catchment-scale covariates. To characterise each of the upstream catchment areas, we used openly accessible global and local environmental datasets. As a novel approach, we additionally incorporated spatial covariates, including coordinates and buffers, and we tested how models would perform with fewer but meaningful covariates.TN predictions were accurate, while TP predictions were poor. Models with reduced covariates achieved similar accuracy to baseline models and decreased overfitting. The inclusion of spatial covariates provided only minimal improvement of the prediction accuracy scores themselves; however, they demonstrate potential in capturing spatial structure and regionalisation, and in some cases, they outranked their corresponding full-catchment covariate versions in SHAP feature importance. In conclusion, utilising catchment characteristics and machine learning can yield a robust regional model for TN, enabling the reliable estimation of TN loads in streams.

 

These folders contain the input and output data to reproduce the results and figures of Jemeljanova et al. "Advancing regional water quality modelling: Integrating spatial machine learning with large-scale catchment data".
DOI: 10.15468/dl.54rdaw
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is one of (France, Spain, Italy)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Testudo hermanni",
 "Year 1000-1970"
 ]
}

 The dataset includes 19 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053195-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rqju5s
title: Occurrence Download

description: A dataset containing 288 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lamprohaminoea ovalis"
 ]
}

 The dataset includes 288 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053196-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jp6ruv
title: Occurrence Download

description: A dataset containing 472 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is Thailand",
 "TaxonKey is Acridotheres tristis tristis"
 ]
}

 The dataset includes 472 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053197-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s8dvcz
title: Occurrence Download

description: A dataset containing 3223 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Asio otus",
 "Country is Germany",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year 2023-2024"
 ]
}

 The dataset includes 3223 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053198-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.58132/xc1omb
title: Quantum mechanical-like approach with non-Hermitian effective Hamiltonians in spin-orbit coupled optical cavities

description: Numerical data in Python format generated to plot figures 3,4,5 and 6 in Phys. Rev. Research 6, 013324 (2024).We present a comprehensive analytical model of resonant states in birefringent microcavities with permeable mirrors. We derive an effective, non-Hermitian photonic Hamiltonian describing cavity mode dispersion and modal lifetimes by applying the Green's function technique based on the Mittag-Leffler expansion with respect to the resonant states and the 𝒌·𝒑 perturbation theory known from semiconductor physics. Using this formalism we obtained results which can be interpreted as effective cavity mode spin-orbit coupling. We use this method to derive the two-mode Hamiltonian to describe the properties of light in the cavity, which significantly reduces the computational effort and properly captures the polarization of the eigenmodes. This is done by introducing the necessary corrections to the Hamiltonian matrix and modifying the basis modes resulting from the coupling with other optical modes present in the system. This simplified Hamiltonian allows us to determine the positions of exceptional points in momentum space. These points are connected by Fermi arcs and appear due to non-Hermiticity and 𝒫𝒯 symmetry
DOI: 10.17617/3.tdgyhs
title: Rewiring of chromatin regulation underlies the evolution of brown algal multicellularity

description: Chromatin structure plays a central role in regulating transcription, genome stability, and epigenetic inheritance in eukaryotes. Much of our understanding of chromatin architecture and histone post-translational modifications (hPTMs) comes from a narrow set of animal and plant models, but emerging data from non-model lineages are challenging canonical views of how chromatin functions across the tree of life. Brown algae are complex multicellular eukaryotes that provide a unique perspective on chromatin evolution given their independent origin of complex multicellularity. Here, we compile the chromatin toolkit of brown algae and show that canonical silencing systems involving DNA cytosine methylation and PRC2-mediated H3K27 methylation were lost early in their evolution. By generating hPTM profiles from diverse brown algal clades, we resolve the nature and regulatory roles of chromatin states in this lineage and show how H3K79 methylation emerged and diversified as a repressive system. We further uncover sex-specific reconfigurations in species with varying degrees of sexual dimorphism and reconstruct the ancestral regulatory landscape that likely preceded the emergence of brown algae. Together, our findings illuminate the dynamic evolution of chromatin regulation in a distinct multicellular lineage and challenge assumptions about the universality of chromatin-based mechanisms across eukaryotes.
DOI: 10.15468/dl.wyneyt
title: Occurrence Download

description: A dataset containing 11162 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Geometry POLYGON((-16.3148 -36.44796,52.41564 -36.44796,52.41564 18.86037,-16.3148 18.86037,-16.3148 -36.44796))",
 "TaxonKey is Adansonia digitata"
 ]
}

 The dataset includes 11162 records from 142 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053200-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.11588/duerer.online.693fd5758fc36
title: Prospekt des Platzes bei dem Tiergärtnertor in Nürnberg, 189380
DOI: 10.15468/dl.bphpyw
title: Occurrence Download

description: A dataset containing 382707 records in GBIF matching the query:
SELECT "month","year",speciesKey,countrycode,datasetKey,gbifid
 ,decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters
 
 
 ,GBIF_EEARGCode(100, 
 decimalLatitude, 
 decimalLongitude, 
 COALESCE(coordinateUncertaintyInMeters, 1000)) AS eeaCellCode FROM occurrence WHERE GBIF_Within('POLYGON ((5.086 50.513, 5.086 52.305, 4.097 52.305, 4.097 50.513, 5.086 50.513))', decimalLatitude, decimalLongitude) = TRUE
 AND "month" IS NOT NULL AND "year" IS NOT NULL 
 AND hasCoordinate = TRUE AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE') AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID') AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH') 
 AND taxonKey IN (5232437,1311477,3003979) 
 AND occurrenceStatus IN ('PRESENT', 'UNKNOWN') 

 The dataset's 382707 records were derived from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053185-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u34xhn
title: Occurrence Download

description: A dataset containing 340 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Observation.org, Nature data from around the World, iNaturalist Research-grade Observations, Inventaire de la flore de Charente-Maritime 2012-2020-Inventaire de la flore de Charente-Maritime 2019, Inventaire de la flore de Charente-Maritime 2012-2020-Inventaire de la flore de Charente-Maritime 2020, Données Oiseaux des Jardins, Données de l'inventaire forestier national de l'IGN-Relevés floristiques du protocole de l'Inventaire National Forestier de l'IGN, EOD – eBird Observation Dataset, Hotspots de biodiversité en Nouvelle-Aquitaine-Données issues du logiciel \"Faune Charente-Maritime\")",
 "Geometry POLYGON((-0.84151 45.60283,-0.8551 45.60418,-0.86641 45.60365,-0.87625 45.59516,-0.87783 45.57987,-0.86828 45.57114,-0.84678 45.56768,-0.83694 45.57547,-0.83443 45.59107,-0.84151 45.60283))",
 "OccurrenceStatus is Present",
 "Year 2015-2025"
 ]
}

 The dataset includes 340 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053202-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fh9n4t
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is ITA.15_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Asplenium obovatum"
 ]
}

 The dataset includes 33 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053203-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5284/1133043
title: Digital Archive from an Archaeological Excavation at Applebys, Park Lane, Langham, Colchester, July 2021

description: This collection includes text, images, vector graphics, and site records from an archaeological excavation at Applebys, Park Lane, Langham, Colchester. The project was undertaken by the Colchester Archaeological Trust in July 2021. The excavation, conducted in advance of creating a private fishing and wildlife pond, lies within an area of known cropmarks and prehistoric find spots. Excavation results indicated limited archaeological significance.
DOI: 10.15468/dl.agrczd
title: Occurrence Download

description: A dataset containing 10077 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Pteris"
 ]
}

 The dataset includes 10077 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053204-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17906712
title: Supporting Data and Code for "Discrepancies in High-Resolution Precipitation Products in Capturing the Sub-Daily Spatial-Temporal Evolution of Record-Breaking Rainfall Events"

description: This repository contains the datasets and code (R language) used in the study evaluating the spatial-temporal performance of seven high-resolution precipitation products (CMORPH, GPM, PERSIANN, PERSIANN-CCS, ERA5 single-level, ERA5-Land, and MSWEP) in capturing record-breaking extreme rainfall events (REREs). The analysis covers sub-daily (30 minutes to 3 hours) temporal scales and fine spatial resolutions (0.04° to 0.25°), focusing on four cities with varying climatic conditions (e.g., Beijing, Longnan, Zhengzhou, and Hong Kong).

Key findings reveal that CMORPH, GPM, and MSWEP generally outperform other datasets, though all show limitations in accurately representing storm center dynamics and rainfall duration. The performance of these precipitation products strongly depends on rainfall intensity, spatial patterns, and regional climate characteristics. This data and code release supports reproducibility and further research on selecting appropriate precipitation datasets for extreme rainfall event analysis.
DOI: 10.18150/3zt6gf
title: Dataset for: strong coupling in bulk nanoplasmonic nanoplatelet perovskite scintillators

description: Raw data from the following manuscript. The attached ZIP contains data of PL and XL characterization of CsPbBr3 nanoplatelet suspended in a polymer matrix with plasmonic nanoparticles - silver nanocubes (Ag CNPs) with varying side length (a). Data shows that by varying "a" in a bulk composite, one can obtain Rabi splitting under optical and ionizing radiation. It was the first practical approach to the strong coupling of the scintillation process.Please consult the measurement_info_readme.txt file for additional information.
DOI: 10.5281/zenodo.14723088
title: Anonymised dataset

description: This dataset contains item-level responses to an adapted Japanese version of Oxford’s Strategy Inventory for Language Learning (SILL). As part of a two-stage adaptation and validation study, the same 50-item adapted instrument was administered in both Stage 1 (calibration) and Stage 2 (validation) to undergraduate students enrolled in English-as-a-foreign-language (EFL) courses at two private universities in Japan. Although the final validated scale reported elsewhere comprises 22 items, the present dataset preserves responses to all 50 adapted SILL items collected at both stages, thereby supporting transparency, secondary analyses, and potential revalidation or alternative model testing.
DOI: 10.5281/zenodo.14723089
title: Anonymised dataset

description: This dataset contains item-level responses to an adapted Japanese version of Oxford’s Strategy Inventory for Language Learning (SILL). As part of a two-stage adaptation and validation study, the same 50-item adapted instrument was administered in both Stage 1 (calibration) and Stage 2 (validation) to undergraduate students enrolled in English-as-a-foreign-language (EFL) courses at two private universities in Japan. Although the final validated scale reported elsewhere comprises 22 items, the present dataset preserves responses to all 50 adapted SILL items collected at both stages, thereby supporting transparency, secondary analyses, and potential revalidation or alternative model testing.
DOI: 10.15468/dl.6d23bn
title: Occurrence Download

description: A dataset containing 78832116 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 78832116 records from 1710 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053141-251120083545085/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.29868576
title: Ethical Challenges to the Adoption of AI in Healthcare: A Review

description: There are few comprehensive summaries of the ethical challenges associated with the adoption of artificial intelligence in healthcare. This review utilizes a systematic search focused on identifying the barriers and facilitators to the implementation of artificial intelligence in healthcare, highlighting the diversity of ethical challenges and the complex interactions between practical challenges and ethics issues. For example, the quality of the data upon which artificial intelligence models are developed relates to several ethics principles, as does the issue of gaining user trust. Importantly, there is also the difficulty of achieving the right balance between the discussed principles, since one might not be able to maximize one principle without having to sacrifice another. For example, maximizing privacy might require minimizing data collection from patients, which might negatively affect beneficence. As such, this review highlights the variety and complexity of ethical issues associated with artificial intelligence implementation in healthcare.
DOI: 10.6084/m9.figshare.29868576.v2
title: Ethical Challenges to the Adoption of AI in Healthcare: A Review

description: There are few comprehensive summaries of the ethical challenges associated with the adoption of artificial intelligence in healthcare. This review utilizes a systematic search focused on identifying the barriers and facilitators to the implementation of artificial intelligence in healthcare, highlighting the diversity of ethical challenges and the complex interactions between practical challenges and ethics issues. For example, the quality of the data upon which artificial intelligence models are developed relates to several ethics principles, as does the issue of gaining user trust. Importantly, there is also the difficulty of achieving the right balance between the discussed principles, since one might not be able to maximize one principle without having to sacrifice another. For example, maximizing privacy might require minimizing data collection from patients, which might negatively affect beneficence. As such, this review highlights the variety and complexity of ethical issues associated with artificial intelligence implementation in healthcare.
DOI: 10.6084/m9.figshare.29868576.v1
title: Ethical Challenges to the Adoption of AI in Healthcare: A Review

description: There are few comprehensive summaries of the ethical challenges associated with the adoption of artificial intelligence in healthcare. This review utilizes a systematic search focused on identifying the barriers and facilitators to the implementation of artificial intelligence in healthcare, highlighting the diversity of ethical challenges and the complex interactions between practical challenges and ethics issues. For example, the quality of the data upon which artificial intelligence models are developed relates to several ethics principles, as does the issue of gaining user trust. Importantly, there is also the difficulty of achieving the right balance between the discussed principles, since one might not be able to maximize one principle without having to sacrifice another. For example, maximizing privacy might require minimizing data collection from patients, which might negatively affect beneficence. As such, this review highlights the variety and complexity of ethical issues associated with artificial intelligence implementation in healthcare.
DOI: 10.34740/kaggle/dsv/13035717
title: Contrastive Language Image Pretraining by openai