cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.g7j65x
title: Occurrence Download

description: A dataset containing 2500 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Agama aculeata Merrem, 1820"
 ]
}

 The dataset includes 2500 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045837-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ufab4u
title: Occurrence Download

description: A dataset containing 1187 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-121.64048 34.36238,-118.96868 34.36238,-118.96868 36.15188,-121.64048 36.15188,-121.64048 34.36238))",
 "TaxonKey is Chiroptera"
 ]
}

 The dataset includes 1187 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045838-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13174335
title: Binary black-hole simulation SXS:BBH:3669

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13174336
title: Binary black-hole simulation SXS:BBH:3669

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13174357
title: Binary black-hole simulation SXS:BBH:3663

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13174358
title: Binary black-hole simulation SXS:BBH:3663

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.34770/gjb8-8f04
title: Natural Movie - Grass Stalks

description: A raw .avi file used as a stimulus in some of our experiments. This is a
 recording of grass stalks swaying in the wind (60Hz frame rate, 8 bit
 depth, gray scale, ~7 minutes in length)
DOI: 10.5281/zenodo.13174365
title: Binary black-hole simulation SXS:BBH:3643

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13174366
title: Binary black-hole simulation SXS:BBH:3643

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.6084/m9.figshare.26485783
title: Inflorescence Color Variations of the three Royal Irises in Palestine

description: This Supplementary file depicts the inflorescence coloration variety within the three studied Royal Irises in Palestine, namely: <i>Iris haynei</i> (endemic), <i>Iris loretii</i> (sub-endemic), and <i>Iris atrofusca</i> (sub-endemic). This data is part of a comprehensive study that was conducted between the years 2020 and 2023. Collected data was summarized in a manuscript submitted to PLANT SOCIOLOGY under the title “Shedding light on the geographical distribution, population size and inflorescence coloration variety of Iris Sect. Oncocyclus”, which was accepted for publication on the 31st of July 2024.
DOI: 10.34770/p1jg-0p78
title: Complementary learning systems within the hippocampus: A neural network modeling approach to reconciling episodic memory with statistical learning

description: Download the README.txt file for a description of the simulation project file.
DOI: 10.15468/dl.tqehsn
title: Occurrence Download

description: A dataset containing 91447 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyanopica cyanus (Pallas, 1776)"
 ]
}

 The dataset includes 91447 records from 155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045842-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13174377
title: Binary black-hole simulation SXS:BBH:3646

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13174378
title: Binary black-hole simulation SXS:BBH:3646

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.25345/c59p2wj0w
title: MassIVE MSV000095510 - Non-canonical Metabolic and Molecular Effects of Calorie Restriction Are Revealed by Varying Temporal Conditions
title: Calorie restriction (CR) extends lifespan and healthspan in diverse species. However, comparing ad libitum (AL) and CR-fed mice is challenging due to their significantly different feeding patterns. CR-fed mice consume their daily meal in approximately 2 hours, subjecting themselves to a prolonged self-imposed fast each day. To gain deeper insights into the effects of CR, we conducted a comprehensive examination of how AL and CR-fed mice respond to tests performed at various times relative to the completion of their once-daily CR meal. Our findings reveal that many well-known effects of CR, including its impact on insulin sensitivity, result from the specific temporal conditions. CR animals exhibit a divergent response to insulin, and this response varies based on the time elapsed since the CR-fed mice consumed their food. Utilizing an unbiased metabolomics approach, we discovered that the effects of CR on circulating metabolites are heavily dependent upon the time-of-day and feeding regimen. Finally, while it is widely believed that CR functions in part by reducing activity of the kinase mTORC1, our study suggests that the observed differences in mTORC1 activity between AL and CR-fed mice are dependent upon both fasting duration and the specific tissue examined. Furthermore, we find that the metabolic effects of CR are independent of hepatic mTORC1. Our results shed new light on the physiological, metabolic, and molecular effects of a CR diet, and highlight that much of our understanding of the effects of CR are related to when, relative to feeding, we choose to examine the mice.
DOI: 10.5281/zenodo.13174401
title: Binary black-hole simulation SXS:BBH:3667

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13174402
title: Binary black-hole simulation SXS:BBH:3667

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.dk2q5n
title: Occurrence Download

description: A dataset containing 132 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Tanzania, United Republic of",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Canis mesomelas Schreber, 1775"
 ]
}

 The dataset includes 132 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045844-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13174407
title: Binary black-hole simulation SXS:BBH:3673

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13174408
title: Binary black-hole simulation SXS:BBH:3673

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.yxk4sp
title: Occurrence Download

description: A dataset containing 11132 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((31.76015 -0.97895,31.2403 -1.00527,31.0692 -1.39352,31.0034 -1.84099,31.02314 -2.11736,31.18765 -2.47928,31.31268 -2.79514,31.52983 -2.91359,31.8786 -2.98598,32.15497 -2.96623,32.33264 -2.82147,32.25368 -2.58457,32.0102 -2.61089,31.89834 -2.73592,31.68118 -2.40032,31.59564 -2.15026,31.54299 -1.88705,31.74041 -1.58435,31.83911 -1.26849,31.76015 -0.97895))",
 "OccurrenceStatus is Present",
 "TaxonKey is Chordata"
 ]
}

 The dataset includes 11132 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045845-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13174442
title: Binary black-hole simulation SXS:BBH:3652

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13174443
title: Binary black-hole simulation SXS:BBH:3652

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.6084/m9.figshare.26485852
title: Ignatz_qPCR_RQ

description: Defense-relevant gene expression differences in hatchlings among wild Newfoundland and farmed European and North American Atlantic salmon and their hybrids. Eric H. Ignatz, Xi Xue, Jennifer R. Hall, Shahinur S. Islam, Matthew L. Rise, Ian A. FlemingqPCR results.<br><br>
DOI: 10.5281/zenodo.13174456
title: Binary black-hole simulation SXS:BBH:3656

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13174457
title: Binary black-hole simulation SXS:BBH:3656

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.es396f
title: Occurrence Download

description: A dataset containing 4866177 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Guatemala"
 ]
}

 The dataset includes 4866177 records from 1435 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045839-240626123714530/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.30184042.v2
title: <b>Emergence of a chicken-source </b><b><i>Riemerella anatipestifer</i></b><b> </b><b>h</b><b>arboring the</b><b>novel</b><b> virulence plasmid pRASD with the </b><b>apxIB-vapD</b><b> virulence cluster</b>

description: <i>Riemerella anatipestifer</i> (<i>R. </i><i>anatipestifer</i>), a well-known waterfowl pathogen, increasingly threatens Chinese poultry by spreading to chickens. The genetic differentiation and host adaptation following cross-host transmission remain unclear. Here, we isolated and characterized a highly virulent, multidrug-resistant(MDR) <i>R. </i><i>anatipestifer</i> strain (SDAU-RA1) in chickens and performed comparative genomics with global R. anatipestifer strains to elucidate population structure and evolutionary dynamics. SNP phylogeny revealed significant geographic clustering and dominant clonal groups. Strains from different hosts showed a pattern of “overall mixing with local clustering,” confirming cross-host transmission rather than strict host-specific evolution. In terms of virulence, certain virulence genes are enriched specifically in chicken-source strains. Notably, SDAU-RA1 contains a novel virulence plasmid pRASD, the first report in chicken strains, with a core functional region of the apxIB-vapD virulence cluster tightly associated with the mobile element ISRa1, suggesting the potential for horizontal gene transfer(HGT) and enhanced pathogenicity. Analysis of the geographical distribution and temporal dynamics of antibiotic resistance genes(ARGs) reveals high heterogeneity among R. anatipestifer strains from different hosts. Pangenome analysis revealed that <i>R. anatipestifer</i> possesses an open pangenome, conferring high genetic plasticity. In conclusion, our study shows <i>R. anatipestifer</i> transmits to chickens without strict host-specific adaptation, though incipient genetic differentiation has emerged. The discovery of pRASD suggests HGT drives the emergence of highly virulent, MDR strains capable of adapting to new hosts. These findings enhance our understanding of cross-host transmission and underscore the importance of continuous surveillance of chicken-source <i>R. anatipestifer</i> and its novel mobile genetic elements.
DOI: 10.15468/dl.kduhsx
title: Occurrence Download

description: A dataset containing 246 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ophioglossum polyphyllum)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 246 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008101-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16792854
title: Longitudinal Dataset of Indeterminate Lung Nodules from CT Scans

description: This dataset is derived from the National Lung Screening Trial (NLST) database, accessed under a data transfer agreement with the National Cancer Institute (Bethesda, MD, USA). The NLST includes a large repository of lung cancer screening data with imaging from over 50,000 high-risk individuals. Participants were current or former smokers aged 55 to 74 years, with a smoking history of at least 30 pack-years and no prior lung cancer.

The original NLST dataset contains radiologist annotations marking screening exams as positive if non-calcified nodules or masses ≥4 mm were detected or other suspicious abnormalities identified.

Our dataset focuses on indeterminate pulmonary nodules observed during longitudinal low-dose CT (LDCT) screening. We selected nodules that underwent follow-up with at least two LDCT scans during the 3-year screening period, excluding nodules with malignancy diagnosed beyond this period or nodules confirmed malignant at baseline.

The dataset contains nodules from 443 subjects (235 non-cancer and 208 cancer cases), with up to three annual LDCT scans per subject (baseline T0, follow-ups T1 and T2). Non-cancer nodules were confirmed benign by follow-up, while malignant nodules were biopsy-confirmed with anatomical locations annotated from NLST records. To maintain consistency, we used images reconstructed with soft kernel filters, which are standard for nodule detection and assessment.

A total of 703 nodules are included in this dataset, with longitudinal position data (x, y, z) and malignancy status, enabling research on temporal modeling and malignancy prediction.

Usage note:Please cite the related publication when using this dataset:Farina, B., Bermejo Peláez, D., Montalvo García, D., Carbajo Benito, R., Seijo Maceiras, L., & Ledesma Carbayo, M. J. (2025). Spatio-Temporal Deep Learning with Temporal Attention for Indeterminate Lung Nodule Classification. Computers in Biology and Medicine. 10.1016/j.compbiomed.2025.110813.
DOI: 10.5281/zenodo.16792855
title: Longitudinal Dataset of Indeterminate Lung Nodules from CT Scans

description: This dataset is derived from the National Lung Screening Trial (NLST) database, accessed under a data transfer agreement with the National Cancer Institute (Bethesda, MD, USA). The NLST includes a large repository of lung cancer screening data with imaging from over 50,000 high-risk individuals. Participants were current or former smokers aged 55 to 74 years, with a smoking history of at least 30 pack-years and no prior lung cancer.

The original NLST dataset contains radiologist annotations marking screening exams as positive if non-calcified nodules or masses ≥4 mm were detected or other suspicious abnormalities identified.

Our dataset focuses on indeterminate pulmonary nodules observed during longitudinal low-dose CT (LDCT) screening. We selected nodules that underwent follow-up with at least two LDCT scans during the 3-year screening period, excluding nodules with malignancy diagnosed beyond this period or nodules confirmed malignant at baseline.

The dataset contains nodules from 443 subjects (235 non-cancer and 208 cancer cases), with up to three annual LDCT scans per subject (baseline T0, follow-ups T1 and T2). Non-cancer nodules were confirmed benign by follow-up, while malignant nodules were biopsy-confirmed with anatomical locations annotated from NLST records. To maintain consistency, we used images reconstructed with soft kernel filters, which are standard for nodule detection and assessment.

A total of 703 nodules are included in this dataset, with longitudinal position data (x, y, z) and malignancy status, enabling research on temporal modeling and malignancy prediction.

Usage note:Please cite the related publication when using this dataset:Farina, B., Bermejo Peláez, D., Montalvo García, D., Carbajo Benito, R., Seijo Maceiras, L., & Ledesma Carbayo, M. J. (2025). Spatio-Temporal Deep Learning with Temporal Attention for Indeterminate Lung Nodule Classification. Computers in Biology and Medicine. 10.1016/j.compbiomed.2025.110813.
DOI: 10.15468/dl.jgqmnt
title: Occurrence Download

description: A dataset containing 267 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Thalictrum venulosum",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 267 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008102-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.27346
title: 16s sequencing dataset of feces from male Burmese pythons in Hainan
title: 海南雄性缅甸蟒粪便16s测序数据集

description: This research dataset analyzed 24 fecal samples of captive pythons obtained from the Python Technology Python Farm in Wenchang City, Hainan Province. In addition, fecal samples of 10 wild pythons were obtained from Datian National Nature Reserve in Dongfang City, Hainan Province. Systematic analysis of the impact of captivity on the gut microbiota of pythons in Hainan Island using 16S rRNA sequencing technology 
description: This research dataset analyzed 24 fecal samples of captive pythons obtained from the Python Technology Python Farm in Wenchang City, Hainan Province. In addition, fecal samples of 10 wild pythons were obtained from Datian National Nature Reserve in Dongfang City, Hainan Province. Systematic analysis of the impact of captivity on the gut microbiota of pythons in Hainan Island using 16S rRNA sequencing technology
DOI: 10.15468/dl.9x7y4z
title: Occurrence Download

description: A dataset containing 1319 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Livistona chinensis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1319 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008103-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.weaqky
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008104-250920141307145/datasets/export for details.
DOI: 10.15468/dl.cdwdgw
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008105-250920141307145/datasets/export for details.
DOI: 10.15468/dl.ttwsek
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Heteranthemis viscidehirta",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008106-250920141307145/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.30186337
title: <b>DECARBONISING SUPPLY CHAINS IN EMERGING ECONOMIES: A MULTILEVEL ANALYSIS OF REGULATION, ESG, AND DIGITISATION</b>

description: This dataset underpins a cross-sector survey of supply chain professionals in Nigeria, examining how regulatory conditions, ESG practices, and digital transformation jointly drive supply chain decarbonization. The analytic file contains 306 de-identified respondent records drawn from firms operating in manufacturing, construction, energy, and logistics, which align with the study’s stratified sampling of high-impact, carbon-intensive sectors. Variables include respondent role and firm descriptors (anonymised), alongside the study’s core constructs measured on 5-point Likert scales. These constructs are: Regulatory Stringency (RS), Regulatory Enforcement (RE), Regulatory Inconsistencies (RI), ESG Awareness (ESG), Financial Constraints (FC), Institutional Pressures (IP), Digital Technology Adoption (DTA: blockchain/AI/IoT), Digitalisation & ESG Compliance (DEC), Digital Infrastructure Constraints (DI), and Supply Chain Decarbonisation (SCD), operationalised via carbon-reduction initiatives, energy-efficiency practices, and circular-economy integration. The dataset supports replication of the reported measurement model (reliability/validity) and structural analyses (direct, mediated, and moderated effects).Accompanying documentation (codebook and survey instrument) defines item wording, construct mappings, and scoring conventions, with derived indices clearly labelled for reproducibility. Personally identifiable information and firm names have been removed; categorical groupings are provided only at safe aggregation levels to protect confidentiality while permitting robust secondary analysis (e.g., sectoral comparisons and tests of digital infrastructure moderation). Researchers can reuse the data to probe alternative specifications (e.g., different enforcement or financing measures), conduct robustness checks on ESG-technology interactions, or extend the model to additional institutional or operational outcomes relevant to decarbonization in emerging economy supply chains.