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

description: A dataset containing 432948 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Enneapterygius ziegleri, Enoplosus armatus, Entomacrodus cadenati, Entomacrodus caudofasciatus, Entomacrodus chapmani, Entomacrodus chiostictus, Entomacrodus corneliae, Entomacrodus cymatobiotus, Entomacrodus decussatus, Entomacrodus epalzeocheilos, Entomacrodus lemuria, Entomacrodus macrospilus, Entomacrodus marmoratus, Entomacrodus nigricans, Entomacrodus niuafoouensis, Entomacrodus randalli, Entomacrodus rofeni, Entomacrodus sealei, Entomacrodus solus, Entomacrodus strasburgi, Entomacrodus striatus, Entomacrodus textilis, Entomacrodus vermiculatus, Entomacrodus vomerinus, Entomacrodus williamsi, Ephippion guttifer, Epibulus brevis, Epibulus insidiator, Epinephelus adscensionis, Epinephelus aeneus, Epinephelus akaara, Epinephelus analogus, Epinephelus areolatus, Epinephelus awoara, Epinephelus bilobatus, Epinephelus bleekeri, Epinephelus bontoides, Epinephelus bruneus, Epinephelus chlorocephalus, Epinephelus chlorostigma, Epinephelus clippertonensis, Epinephelus coeruleopunctatus, Epinephelus coioides, Epinephelus corallicola, Epinephelus cyanopodus, Epinephelus daemelii, Epinephelus diacanthus, Epinephelus erythrurus, Epinephelus fasciatomaculosus, Epinephelus fasciatus, Epinephelus faveatus, Epinephelus flavocaeruleus, Epinephelus fuscoguttatus, Epinephelus gabriellae, Epinephelus guttatus, Epinephelus hexagonatus, Epinephelus howlandi, Epinephelus irroratus, Epinephelus itajara, Epinephelus labriformis, Epinephelus lanceolatus, Epinephelus latifasciatus, Epinephelus longispinis, Epinephelus macrospilos, Epinephelus maculatus, Epinephelus malabaricus, Epinephelus melanostigma, Epinephelus merra, Epinephelus miliaris, Epinephelus morio, Epinephelus multinotatus, Epinephelus ongus, Epinephelus polylepis, Epinephelus polyphekadion, Epinephelus quoyanus, Epinephelus retouti, Epinephelus rivulatus, Epinephelus sexfasciatus, Epinephelus socialis, Epinephelus spilotoceps, Epinephelus stoliczkae, Epinephelus striatus, Epinephelus summana, Epinephelus tauvina, Epinephelus timorensis, Epinephelus trimaculatus, Epinephelus tukula, Equetus punctatus, Erisphex pottii, Erosa erosa, Etelis oculatus, Eubalichthys bucephalus, Eubalichthys caeruleoguttatus, Eubalichthys cyanoura, Eubalichthys mosaicus, Eucinostomus argenteus, Eucinostomus gula, Eucinostomus jonesii, Eucinostomus melanopterus, Eupetrichthys angustipes)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 432948 records from 579 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008147-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.46yn88
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eichhornia crassipes",
 "Country is Nauru",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022123-250717081556266/datasets/export for details.
DOI: 10.15468/dl.h23ph6
title: Occurrence Download

description: A dataset containing 66 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ricinus communis",
 "Country is Madagascar",
 "HasCoordinate is true"
 ]
}

 The dataset includes 66 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022126-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16271356
title: Stoichiometric C/N shapes fungal and bacterial necromass accumulation across ecosystems
DOI: 10.5281/zenodo.16271357
title: Stoichiometric C/N shapes fungal and bacterial necromass accumulation across ecosystems
DOI: 10.21227/x7jn-bk95
title: "3-channel representation of highway and random vehicular networks scenario for DNN-based lookahead next-hop routing"

description: "This work presents a Deep Neural Network (DNN)-based routing approach for Vehicular Ad-hoc Networks (VANETs), addressing challenges in high-mobility environments. The method introduces a grid-based representation of the vehicle environment to support local, learning-based routing decisions without relying on global topology. A U-Net-style DNN is trained to minimize per-hop transmission delay and improve real-time routing efficiency. Our dataset includes spatial-temporal snapshots of vehicular movements and communication metrics, used to train and evaluate the model. "
DOI: 10.15468/dl.ddfrbn
title: Occurrence Download

description: A dataset containing 534 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Chinese Taipei)",
 "TaxonKey is Citrus trifoliata"
 ]
}

 The dataset includes 534 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022124-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a7z6f9
title: Occurrence Download

description: A dataset containing 16005 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Erynnis horatius"
 ]
}

 The dataset includes 16005 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022125-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.drxfur
title: Occurrence Download

description: A dataset containing 80307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rosa multiflora, Rosa multiflora roseiflora, Rosa damascena brachyacantha, Rosa multiflora adenophora, Rosa quelpaertensis, Rosa multiflora adenophylla, Rosa polyantha roseipetala, Rosa diffusa, Rosa multiflora legitima, Rosa multiflora pilosissima, Rosa thyrsiflora, Rosa multiflora taoyuanensis, Rosa thunbergii, Rosa multiflora villosula, Rosa thoryi, Rosa glabrifoliolata, Rosa roxburghii, Rosa multiflora thunbergiana, Rosa multiflora carnea, Rosa multiflora hiburiensis, Rosa multiflora pilosissima, Rosa polyantha pilosissima, Rosa multiflora glabrifoliolata, Rosa wichurae, Rosa multiflora formosana, Rosa intermedia, Rosa multiflora roseiflora, Rosa franchetii paniculigera, Rosa polyantha inermis, Rosa dawsoniana, Rosa multiflora thunbergii, Rosa polyanthus, Rosa hiburiensis, Rosa blinii, Rosa multiflora platyphylla, Rosa prolifera, Rosa japonica, Rosa multiflora poecilostyla, Rosa platyphylla, Rosa thyrsiflora, Rosa multiflora praegeri, Rosa multiflora microphylla, Rosa multiflora trichogyna, Rosa dawsoniana, Rosa watsoniana, Rosa polyantha, Rosa polyanthos, Rosa multiflora trichogyma, Rosa franchetii, Rosa multiflora momienensis, Rosa lebrunei, Rosa polyantha glabrescens, Rosa polyantha pilosissima, Rosa multiflora uchiyamana, Rosa multiflora nanningensis, Rosa prolifera, Rosa florida, Rosa multiflora inermis, Rosa polyantha erubescens, Rosa multiflora brachyacantha, Rosa multiflora quelpaetensis, Rosa polyantha quelpaertensis, Rosa mokanensis quelpaertensis, Rosa multiflora quelpaertensis, Rosa multiflora rosipetala, Rosa calva, Rosa thoryi carnea, Rosa polyantha hiburiensis, Rosa polyantha glabrifoliolata, Rosa calva formosana, Rosa formosana, Rosa multiflora roseiflora, Rosa multiflora inermis, Rosa platyphylla, Rosa multiflora platyphylla, Rosa multiflora watsoniana, Rosa multiflora watsoniana, Rosa trichogyna, Rosa multiflora glabrescens, Rosa multiflora pilosissima, Rosa uchiyamana, Rosa multiflora erubescens)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 80307 records from 340 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022105-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16271908
title: GZIP dataset for CoreProfiler

description: This dataset is associated with the tool CoreProfiler.
DOI: 10.5281/zenodo.16271909
title: GZIP dataset for CoreProfiler

description: This dataset is associated with the tool CoreProfiler.
DOI: 10.15468/dl.c9a982
title: Occurrence Download

description: A dataset containing 339 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ricinus communis",
 "Country is Malawi",
 "HasCoordinate is true"
 ]
}

 The dataset includes 339 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022127-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18150/zg3jgt
title: Bioactive Copper-Doped Natural Hydroxyapatite Quantum Dots/Graphene Oxide Nanocomposites in 3D-Printed PCL Scaffolds for Superior Osteogenic and Angiogenic Performance in Bone Tissue Engineering

description: This study introduces a sustainable scaffold designed by integrating copper-doped natural hydroxyapatite quantum-dots (Cu-HA QDs) and graphene oxide (GO) into a polycaprolactone (PCL) matrix using 3D printing technology, to address the dual requirements of osteogenesis and angiogenesis in large bone defects. HR-TEM and synchrotron SAXS/WAXS investigations of the Cu-HA QDs exhibited a highly crystalline hexagonal structure with distinct QD architecture, and core-level HR-XPS analysis confirmed Cu²⁺ substitution for Ca²⁺ within the HA lattice. Incorporating Cu-HA–GO nanocomposites significantly improved the physicochemical properties of the PCL scaffolds, including enhanced wettability, accelerated hydrolytic degradation, and enhanced mechanical stiffness. Under basal culture conditions, the PCL/Cu-HA–GO scaffolds significantly promoted mesenchymal stem cell proliferation, extracellular matrix mineralization, and differentiation. Furthermore, robust expression of key osteogenic genes ALP, RUNX2, and OCN increased by 28-, 33-, and 21-fold, respectively, and angiogenic genes HIF-1α and Ang1 increased by 45- and 18-fold, respectively. Immunofluorescence staining demonstrated strong osteocalcin signals and widespread formation of CD31-positive capillary-like structures throughout the scaffold, highlighting its unique potential to enhance bone regeneration and vascularization concurrently. These results present the 3D-printed PCL/Cu-HA–GO scaffolds as a promising, sustainable, and superior dual-functional osteogenic and angiogenic performances, offering an effective alternative for large/critical-size bone defect regeneration.
DOI: 10.15468/dl.fj7dff
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ricinus communis",
 "Country is Cook Islands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022132-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vuftc9
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eichhornia crassipes",
 "Country is Nepal",
 "HasCoordinate is true"
 ]
}

 The dataset includes 23 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022130-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16270250
title: Experiment fault recovery 2025 - data
DOI: 10.5281/zenodo.16270251
title: Experiment fault recovery 2025 - data
DOI: 10.15468/dl.m4k2sa
title: Occurrence Download

description: A dataset containing 45 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eichhornia crassipes",
 "Country is New Zealand",
 "HasCoordinate is true"
 ]
}

 The dataset includes 45 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022131-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16269000
title: SANT:SANT:n#84409
DOI: 10.5281/zenodo.16269001
title: SANT:SANT:n#84409
DOI: 10.15468/dl.phu59k
title: Occurrence Download

description: A dataset containing 2142 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Material citation, Material sample)",
 "GadmGid is one of (IND.4_1, IND.3_1, IND.24_1, IND.21_1, IND.23_1, IND.33_1, IND.22_1, IND.30_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is Squamata"
 ]
}

 The dataset includes 2142 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022135-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16271316
title: Dataset for journal paper called "Spatial structure of yeast biofilms and the role of cell adhesion across different media"

description: The dataset represents data for journal paper called "Spatial structure of yeast biofilms and the role of cell adhesion across different media"
DOI: 10.5281/zenodo.16271315
title: Dataset for journal paper called "Spatial structure of yeast biofilms and the role of cell adhesion across different media"

description: The dataset represents data for journal paper called "Spatial structure of yeast biofilms and the role of cell adhesion across different media"
DOI: 10.13012/b2idb-4207757_v1
title: Slide scans, annotated pollen counts, and trained pollen detection models for fossil pollen samples from Laguna Pallcacocha, El Cajas National Park, Ecuador

description: This dataset includes image stacks, annotated counts, and ground-truth masks from two high-resolution sediment cores extracted from Laguna Pallcacocha, in El Cajas National Park, Ecuadorian Andes by Moy et al. (2002) and Hagemans et al. (2021). The first core (PAL 1999, from Moy et al. (2002)) extends through the Holocene (11,600 cal. yr. BP - present). There are a total of 900 annotated image stacks and masks in the PAL 1999 domain. The second core (PAL IV, from Hagemans et al. (2021)) captures the 20th century. There are 2986 annotated image stacks and masks in the PAL IV domain.

Different microscopes and annotations tools were used to image and annotate each core and there are corresponding differences in naming conventions and file formats. Thus, we organized our data separately for the PAL 1999 and the PAL IV domains. The three letter codes used to label our pollen annotations are in the file: “Pollen_Identification_Codes.xlsx”.

Both domain directories contain:
• Image stacks organized by subdirectory
• Annotations within each image stack directory, containing specimen identifications using a three letter code and coordinates defining bounding boxes or circles
• Ground-truth distance-transform masks for each image stack

The zip file "bestValModel_encoder.paramOnly.zip" is the trained pollen detection model produced from the images and annotations in this dataset.


Please cite this dataset as:

Feng, Jennifer T.; van den Berg, Thya; Donders, Timme H.; Kong, Shu; Puthanveetil Satheesan, Sandeep; Punyasena, Surangi W. (2025): Slide scans, annotated pollen counts, and trained pollen detection models for fossil pollen samples from Laguna Pallcacocha, El Cajas National Park, Ecuador . University of Illinois Urbana-Champaign. https://doi.org/10.13012/B2IDB-4207757_V1 

Please also include citations of the original publications from which these data are taken:

Feng, Jennifer T., Sandeep Puthanveetil Satheesan, Shu Kong, Timme H. Donders, and Surangi W. Punyasena. “Addressing the ‘Open World’: Detecting and Segmenting Pollen on Palynological Slides with Deep Learning.” bioRxiv, January 1, 2025. https://doi.org/10.1101/2025.01.05.631390. 

Feng, Jennifer T., Sandeep Puthanveetil Satheesan, Shu Kong, Timme H. Donders, and Surangi W. Punyasena. “Addressing the ‘Open World’: Detecting and Segmenting Pollen on Palynological Slides with Deep Learning.” Paleobiology, 2025 [in press]. 

Feng, J. T. (2023). Open-world deep learning applied to pollen detection (MS thesis, University of Illinois at Urbana-Champaign). https://hdl.handle.net/2142/120168
DOI: 10.6084/m9.figshare.29212960.v1
title: Comprehensive analysis of mRNA and miRNA expression profiles in follicular development in quail

description: 1. Ovarian follicle development is a key determinant of egg production in quail which is regulated by a dynamic and stage-specific genetic program. However, most follicle-related genes and miRNAs have not yet been identified in quail. 2. This study applied transcriptome sequencing to dissect mRNA and miRNA expression patterns in quail ovarian follicles at distinct developmental stages: the pre-selection (SYF), the post-selection (F5) and the pre-ovulatory follicles (F1) in egg-laying birds. Comparative analyses identified 362 differentially-expressed mRNAs and 13 miRNAs in the F1 <i>vs</i>. F5 comparison, 549 and 72 in the F5 <i>vs</i>. SYF comparison and 1833 and 55 in the F1 <i>vs</i>. SYF comparison, respectively. 3. Significant differentially-expressed mRNAs implicated in follicle development included <i>VCAN</i>, <i>FGFR3</i>, <i>ALDH1A1, NR0B1,ZP2</i> and <i>SMOC1</i>. Other significant miRNAs were represented by ga-miR-460b-5p, gga-miR-1552-5p, gga-miR-125b-5p, gga-miR-135a-5p, gga-miR-122-5p, gga-miR-29b-3p and ggga-miR-138-1-3p. 4. Enrichment analyses using GO and KEGG databases highlighted the influence of the extracellular matrix (ECM), cell adhesion molecules (CAMs), TGF-β and calcium signalling pathways and the steroid synthesis pathway on follicular development. 5. Additionally, the miRNA-mRNA interaction network pinpointed the regulatory roles of specific differentially expressed miRNAs (DE miRNAs), including gga-miR-1306-5p, gga-miR-133a-3p, gga-miR-133c-3p, gga-miR-214, gga-miR-214b-5p and gga-miR-34a-5p.
DOI: 10.6084/m9.figshare.25321317
title: A scoping review of artificial intelligence in medical education: BEME Guide No. 84

description: Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. This rapid scoping review, conducted over 16 weeks, employed Arksey and O’Malley’s framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.
DOI: 10.6084/m9.figshare.25321317.v2
title: A scoping review of artificial intelligence in medical education: BEME Guide No. 84

description: Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. This rapid scoping review, conducted over 16 weeks, employed Arksey and O’Malley’s framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.