cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.y4k7hp
title: Occurrence Download

description: A dataset containing 45893 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Euterpe broadwayi Becc. ex Broadway, Euterpe catinga var. catinga, Euterpe edulis Mart., Euterpe oleracea Mart., Euterpe precatoria var. longivaginata (Mart.) A.J.Hend., Euterpe precatoria var. precatoria, Hyospathe elegans Mart., Hyospathe frontinensis A.J.Hend., Hyospathe macrorhachis Burret, Hyospathe peruviana A.J.Hend., Hyospathe pittieri Burret, Hyospathe wendlandiana Dammer ex Burret, Neonicholsonia watsonii Dammer, Oenocarpus bacaba Mart., Oenocarpus balickii F.Kahn, Oenocarpus bataua var. bataua, Oenocarpus distichus Mart., Oenocarpus mapora H.Karst., Oenocarpus minor Mart., Oenocarpus simplex R.Bernal, Galeano & A.J.Hend., Prestoea acuminata var. dasystachys (Burret) A.J.Hend. & Galeano, Prestoea acuminata var. montana (Graham) A.J.Hend. & Galeano, Prestoea carderi (W.Bull) Hook.f., Prestoea decurrens H.E.Moore, Prestoea ensiformis (Ruiz & Pav.) H.E.Moore, Prestoea longipetiolata var. cuatrecasasii (H.E.Moore) A.J.Hend. & Galeano, Prestoea pubens var. semispicata (de Nevers & A.J.Hend.) A.J.Hend. & Galeano, Prestoea schultzeana (Burret) H.E.Moore, Prestoea tenuiramosa (Dammer) H.E.Moore)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 45893 records from 237 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048931-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qdexx8
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Matelea oaxacana (Standl.) W.D.Stevens"
 ]
}

 The dataset includes 15 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031634-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v4tpkw
title: Occurrence Download

description: A dataset containing 2030 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "MediaType is Image",
 "TaxonKey is Colias eurytheme Boisduval, 1852"
 ]
}

 The dataset includes 2030 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0265150-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qzgkz9
title: Occurrence Download

description: A dataset containing 374018 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Amborellales, Nymphaeales)"
 ]
}

 The dataset includes 374018 records from 1620 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020543-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u2g6c2
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Allium atroviolaceum",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005552-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b2gdqb
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Allium atroviolaceum",
 "Country is Uzbekistan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005553-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/42xmy5j64g
title: Dataset of Common Papaya Diseases: Anthracnose, Mealybug & Whitefly, Mosaic, and Leaf Curl Virus

description: The Papaya Disease Dataset consists of images and related information on major diseases affecting papaya plants. The dataset is designed to support the development of machine learning and computer vision models for disease identification, classification, and early detection in papaya crops.

It includes four main disease categories:

Anthracnose of Papaya – A fungal disease causing dark, sunken spots on fruits and leaves.

Mealybug and Whitefly Infestation – Caused by insect pests that damage leaves and fruits by sucking sap, leading to curling and yellowing.

Mosaic of Papaya Leaf – A viral disease characterized by mottled, patchy patterns of light and dark green areas on leaves.

Papaya Leaf Curl Virus – A serious viral disease that leads to upward curling, distortion, and thickening of leaves.
DOI: 10.18150/djfalm
title: 3D-printed chitosan-based scaffolds with Scutellariae baicalensis extract for dental applications

description: Uniform_of_fibers.xlsx - the columns indicate the next measurement, while the rows represent printouts containing different extract concentrations. Data are presented in mm. Data were examined using a microscope (ZEISS SteREO Discovery.V20 Motorized Stereo Microscope, Jena, Germany) and ImageJ.Pore_factor.xlsx - columns indicate the area of ​​the next three holes in the frame, rows represent printouts containing different extract concentrations. Data are presented in mm2. Data were examined using a microscope (ZEISS SteREO Discovery.V20 Motorized Stereo Microscope, Jena, Germany) and ImageJ.Viscosity.xlsx - the data represent the viscosity of the mixtures. The columns indicate the viscosity as a function of extract concentration, while the rows indicate the different shear rates. Data are presented in cps. The viscosity values were evaluated at shear rate from 5 to 20 1/s using a LV-3 spindle Brookfield DV2T viscometer at 25 °C, assessed using Rheocalc T software.Dissolution.xlsx - data represent the percentage of baicalin released. Columns indicate the systems tested, rows the corresponding time points. Data are presented as a percentage. Tests were performed using the Agilent 708-DS dissolving equipment (Agilent Technologies; Santa Clara, CA, USA). A typical basket method was used, with 50 rpm and stirring at 37 ± 0.5 °C.
DOI: 10.17632/42xmy5j64g.1
title: Dataset of Common Papaya Diseases: Anthracnose, Mealybug & Whitefly, Mosaic, and Leaf Curl Virus

description: The Papaya Disease Dataset consists of images and related information on major diseases affecting papaya plants. The dataset is designed to support the development of machine learning and computer vision models for disease identification, classification, and early detection in papaya crops.

It includes four main disease categories:

Anthracnose of Papaya – A fungal disease causing dark, sunken spots on fruits and leaves.

Mealybug and Whitefly Infestation – Caused by insect pests that damage leaves and fruits by sucking sap, leading to curling and yellowing.

Mosaic of Papaya Leaf – A viral disease characterized by mottled, patchy patterns of light and dark green areas on leaves.

Papaya Leaf Curl Virus – A serious viral disease that leads to upward curling, distortion, and thickening of leaves.
DOI: 10.25574/26609
title: Chandra X-ray Observatory ObsId 26609

description: CHANDRA TOOs FOR FERMI GALACTIC PLANE TRANSIENTS
DOI: 10.17632/yg4r3w5yw7
title: PARSE: Physical attribute representativity and stationarity evaluator open-source library for 3D images using scalar and vector metrics

description: The concept of representative volume (REV, or RVE in material sciences) is the cornerstone of continuum scale models – one needs to get the volume big enough so that it could be represented with an averaged value (scalar, vector or tensorial, etc.). While REV should be established in all larger scale simulations, this is rarely done in practice despite widespread adoption of 3D imaging devices in all research areas starting from petroleum engineering, material sciences and spanning to biology. Sometimes REV analysis is performed in the form of very simple procedures, such as a check for convergence of porosity or surface area, which is technically identical to omitting it entirely. The main reason for this to happen is the poor understanding of REV concept in general (mainly its connection to spatial stationarity and necessity for vector metrics with high information content) and unavailability of open-source robust solutions. In this work we present PARSE library that solves exactly this problem – we developed an easy to use and well-documented code based on rigorous research carried out recently in explaining the “dark sides” of representativity. In addition to REV, our code allows spatial stationarity analysis and comparison of samples (with subsequent clusterization into different groups) based on vector metrics – correlation functions, persistence diagrams and pore-network statistics, that altogether possess high information content which is critical in establishing stationarity and REV. We test our library on images produced by known statistical processes, such as Poisson spheres. After verification, we show how to compare different samples and group them depending on their “structural DNA”. All solutions explained in the paper are represented by Jupiter notebooks that can be used to perform similar analysis, moreover, the class structure of PARSE library allows painless modifications to be implemented. We believe that such an open-source library will be useful in numerous fields and will become an invaluable tool for 3D image analysis.
DOI: 10.25574/26610
title: Chandra X-ray Observatory ObsId 26610

description: CHANDRA TOOs FOR FERMI GALACTIC PLANE TRANSIENTS
DOI: 10.7910/dvn/szqehp
title: Dataset for H3-MOSAIC: Multimodal Generative AI for Semantic Place Detection from High-Frequency GPS on H3 Grids in Mental Health Geomatics

description: &lt;p&gt;Dataset for H3-MOSAIC: Multimodal Generative AI for Semantic Place Detection from High-Frequency GPS on H3 Grids in Mental Health Geomatics.&lt;/p&gt;

&lt;p&gt;Background: Mental-health geomatics require reliable ways to convert high-frequency GPS trajectories into meaningful place types that support indicators such as homestay, location entropy, and spatial extent of daily activities. Raw coordinates are typically noisy and carry little semantic information. We introduce H3-MOSAIC, a multimodal generative framework that fuses OpenStreetMap (OSM) building text and satellite imagery on H3 grids to infer place semantics from high-frequency GPS.&lt;/p&gt;

&lt;p&gt;Methods: Raw GPS was smoothed by minute-level speed filtering, then assigned to Level 10 H3 hexagons. Cells were retained if the mean speed was ≤1.2 m/s and the cumulative duration was ≥15 minutes, contiguous cells were merged, and home was defined as the cell with the longest dwell from 23:45 to 06:00. We compared text-only OSM classification with image-based and fused approaches across open-source models (DeepSeek, CLIP, LLaVA, Qwen-VL) and proprietary models (GPT-4o-mini, Gemini-2.5-flash-lite). Performance was assessed by accuracy, Cohen’s kappa, precision, recall, F-measure, and confusion matrices. Day level associations between H3 semantic exposures and stress were examined by a random forest model and explainable methods.&lt;/p&gt;

&lt;p&gt;Results: Multimodal methods outperformed single-modality baselines. In the 11-class task, accuracies were: CLIP 0.179, LLaVA 0.269, Qwen-VL 0.565, GPT-4o-mini 0.779, and Gemini-2.5-flash-lite 0.790. In the 5-class consolidation, accuracies rose to 0.702 (Qwen-VL), 0.849 (GPT-4o-mini), and 0.858 (Gemini-2.5-flash-lite). Text-only OSM baselines were lower (≈0.60–0.68). Across 3,845 hexagons with OSM text, closed-source models agreed on 79% of labels; disagreements concentrated in mixed-use, office, and green classes. Error modes reflected area-dominant versus keyword-triggered reasoning, hybrid-parcel ambiguity, tag sparsity, and symbolic artifacts. Stabilized semantics support more robust computation of homestay, entropy, and activity space and are suitable for privacy-aware, cross-city reuse. In a day-level case study, minutes at Home related to lower stress, Green showed a U-shaped pattern.&lt;/p&gt;

&lt;p&gt;Conclusions: H3-MOSAIC provides a scalable, auditable pipeline for semantic place detection from high-frequency GPS. Multimodal fusion markedly improves accuracy and consistency, proprietary models are most robust on hard classes, and open-source models are practical for coarse taxonomies. H3 day level exposures show stress patterns consistent with established mental health pathways, supporting face validity. The framework enables downstream exposure analyses with reduced misclassification and improved interpretability.&lt;/p&gt;
DOI: 10.17632/yg4r3w5yw7.1
title: PARSE: Physical attribute representativity and stationarity evaluator open-source library for 3D images using scalar and vector metrics

description: The concept of representative volume (REV, or RVE in material sciences) is the cornerstone of continuum scale models – one needs to get the volume big enough so that it could be represented with an averaged value (scalar, vector or tensorial, etc.). While REV should be established in all larger scale simulations, this is rarely done in practice despite widespread adoption of 3D imaging devices in all research areas starting from petroleum engineering, material sciences and spanning to biology. Sometimes REV analysis is performed in the form of very simple procedures, such as a check for convergence of porosity or surface area, which is technically identical to omitting it entirely. The main reason for this to happen is the poor understanding of REV concept in general (mainly its connection to spatial stationarity and necessity for vector metrics with high information content) and unavailability of open-source robust solutions. In this work we present PARSE library that solves exactly this problem – we developed an easy to use and well-documented code based on rigorous research carried out recently in explaining the “dark sides” of representativity. In addition to REV, our code allows spatial stationarity analysis and comparison of samples (with subsequent clusterization into different groups) based on vector metrics – correlation functions, persistence diagrams and pore-network statistics, that altogether possess high information content which is critical in establishing stationarity and REV. We test our library on images produced by known statistical processes, such as Poisson spheres. After verification, we show how to compare different samples and group them depending on their “structural DNA”. All solutions explained in the paper are represented by Jupiter notebooks that can be used to perform similar analysis, moreover, the class structure of PARSE library allows painless modifications to be implemented. We believe that such an open-source library will be useful in numerous fields and will become an invaluable tool for 3D image analysis.
DOI: 10.13145/bacdive122956.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122956.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0114642.10fu
title: SH0114642.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive122957.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122957.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122958.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122958.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0114643.10fu
title: SH0114643.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.13145/bacdive122959.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122959.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122960.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122960.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122961.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122961.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122962.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122962.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122963.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122963.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122964.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122964.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122965.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122965.20251217.10
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive122966.20241212.9.2
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15156/bio/sh0114644.10fu
title: SH0114644.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.