cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/5ft-nqa-8bv
title: Plantae

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/82k-se9-7ql
title: †Limipecten Girty, 1904

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qmp-kxb-m9l
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.25574/23722
title: Chandra X-ray Observatory ObsId 23722

description: Quantifying the Rate of Nearby Dual AGN
DOI: 10.3535/22z-bvw-3rv
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/gdx-b88-2vy
title: Plantae

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0pa-wcb-xqy
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/z9w-nvt-ae6
title: Brachiopoda Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/06g-ycs-bgj
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/4bg-vxn-hbh
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.25574/23750
title: Chandra X-ray Observatory ObsId 23750

description: An X-ray view of the most powerful galactic-wide outflows
DOI: 10.3535/jka-gej-58l
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/89y-qr3-pb9
title: Cervus elaphus Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/pd8-cs8-8k0
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.34740/kaggle/dsv/12810346
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.3535/2lq-d41-qeg
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/vj8-fsq-vfp
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.25574/23757
title: Chandra X-ray Observatory ObsId 23757

description: An X-ray view of the most powerful galactic-wide outflows
DOI: 10.3535/cbh-5tt-e3s
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/f4p-fve-sv4
title: Cardium Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/gzx-5fd-qv4
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.zgsfg4
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mesembryanthemum crystallinum L.",
 "Country is Namibia"
 ]
}

 The dataset includes 16 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008231-250525065834625/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.29194808
title: Seed Info Sheet - SLAGC NPS Seedbank

description: Seeds used in the research project provided by the National Park Services
DOI: 10.6084/m9.figshare.29194808.v1
title: Seed Info Sheet - SLAGC NPS Seedbank

description: Seeds used in the research project provided by the National Park Services
DOI: 10.5281/zenodo.15450395
title: Supplementary material and data from: Climatic disequilibrium modulates canopy service across abiotic stress gradients

description: Canopy service (CS) is defined as the extent to which established (canopy) plants affect the recruitment of new individuals in the community. This service, when positive, is known as facilitation, and is mainly driven by the modification of the microenvironment that plants induce under their canopies, often by buffering the macroclimate. The importance of canopy service in maintaining diversity may increase in the context of climate change. Climate change may also push species away from their niche optima, increasing their so-called climatic disequilibrium (CD). This process can provoke poorer performance, defoliation, and decay of the canopy species, jeopardizing the canopy service they provide. Therefore, canopy CD is expected to modulate canopy service under current climate conditions. In this study, we test (1) whether canopy species experiencing greater CD will exhibit diminished canopy services, and (2) whether the negative impact of CD on canopy service will be more pronounced under increasing abiotic stress conditions (i.e. aridity and continentality). To address these questions, we used a database of 54 communities, mainly distributed across the Mediterranean basin, which includes a total of 316 canopy species involved in 35,941 recruiting interactions. The CD of the species in a given community resulted in contrasted effects on their canopy service depending on the level of climatic stress. The maximum Canopy service occurs at intermediate aridity levels, following a humped-shaped pattern when considering this single climatic stressor. However, when continentality is also considered, a greater intensity of both stressors together increase a positive canopy service linearly, following the Stress Gradient Hypothesis. Our results indicate that abiotic stressors, combined with CD, modulates the canopy service. Consequently, under future climate change scenarios that may increase stress and CD, canopy service will become crucial for the maintenance of the recruiting community.
description: The R script (modelling.R) performs data integration, visualization, and modeling to assess how canopy service (CS) is affected by climatic disequilibrium (CD) across abiotic stress gradients, particularly aridity and continentality. The analysis is part of a study using data from the RecruitNet network.

Com_biomes.csv: Contains geographic information (latitude, longitude, biome) for each community in the dataset.

Cannopy_biovar.csv: Contains bioclimatic variables (BIOCLIM) for Recruitnet plots.

table_canopy.csv: Main table combining facilitation interaction data and canopy species information for modelling.

Aridity RecruitNet.csv: Provides aridity indices for the communities, based on climatic data.
DOI: 10.5281/zenodo.15450396
title: Supplementary material and data from: Climatic disequilibrium modulates canopy service across abiotic stress gradients

description: Canopy service (CS) is defined as the extent to which established (canopy) plants affect the recruitment of new individuals in the community. This service, when positive, is known as facilitation, and is mainly driven by the modification of the microenvironment that plants induce under their canopies, often by buffering the macroclimate. The importance of canopy service in maintaining diversity may increase in the context of climate change. Climate change may also push species away from their niche optima, increasing their so-called climatic disequilibrium (CD). This process can provoke poorer performance, defoliation, and decay of the canopy species, jeopardizing the canopy service they provide. Therefore, canopy CD is expected to modulate canopy service under current climate conditions. In this study, we test (1) whether canopy species experiencing greater CD will exhibit diminished canopy services, and (2) whether the negative impact of CD on canopy service will be more pronounced under increasing abiotic stress conditions (i.e. aridity and continentality). To address these questions, we used a database of 54 communities, mainly distributed across the Mediterranean basin, which includes a total of 316 canopy species involved in 35,941 recruiting interactions. The CD of the species in a given community resulted in contrasted effects on their canopy service depending on the level of climatic stress. The maximum Canopy service occurs at intermediate aridity levels, following a humped-shaped pattern when considering this single climatic stressor. However, when continentality is also considered, a greater intensity of both stressors together increase a positive canopy service linearly, following the Stress Gradient Hypothesis. Our results indicate that abiotic stressors, combined with CD, modulates the canopy service. Consequently, under future climate change scenarios that may increase stress and CD, canopy service will become crucial for the maintenance of the recruiting community.
description: The R script (modelling.R) performs data integration, visualization, and modeling to assess how canopy service (CS) is affected by climatic disequilibrium (CD) across abiotic stress gradients, particularly aridity and continentality. The analysis is part of a study using data from the RecruitNet network.

Com_biomes.csv: Contains geographic information (latitude, longitude, biome) for each community in the dataset.

Cannopy_biovar.csv: Contains bioclimatic variables (BIOCLIM) for Recruitnet plots.

table_canopy.csv: Main table combining facilitation interaction data and canopy species information for modelling.

Aridity RecruitNet.csv: Provides aridity indices for the communities, based on climatic data.
DOI: 10.34740/kaggle/dsv/11857206
title: Forex Exchange Rates Since 2004 (Updated Daily)
DOI: 10.15468/dl.t4jnrp
title: Occurrence Download

description: A dataset containing 154144 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 Lantana L."
 ]
}

 The dataset includes 154144 records from 949 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004519-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hata6v
title: Occurrence Download

description: A dataset containing 27121 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (United States of America, Italy, Spain)",
 "OccurrenceStatus is Present",
 "TaxonKey is Cynodon dactylon (L.) Pers."
 ]
}

 The dataset includes 27121 records from 340 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004525-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2ezqqt
title: Occurrence Download

description: A dataset containing 74528 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Larus cachinnans Pallas, 1811",
 "HasCoordinate is true",
 "Year is greater than 1989",
 "Year is less than 2026",
 {
 "not" : {
 "OccurrenceStatus" : [
 "is Absent"
 ]
 }
 },
 "CoordinateUncertaintyInMeters is less than or equal to 1000"
 ]
}

 The dataset includes 74528 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004521-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14975338
title: TrialBench: Benchmarking Multi-Modal Artificial-Intelligence-Ready Clinical Trial Prediction

description: Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to predict key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development.
DOI: 10.5281/zenodo.14975339
title: TrialBench: Benchmarking Multi-Modal Artificial-Intelligence-Ready Clinical Trial Prediction

description: Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to predict key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development.
DOI: 10.5281/zenodo.15358396
title: TrialBench: Benchmarking Multi-Modal Artificial-Intelligence-Ready Clinical Trial Prediction

description: Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to predict key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development.
DOI: 10.5281/zenodo.15426202
title: TrialBench: Benchmarking Multi-Modal Artificial-Intelligence-Ready Clinical Trial Prediction

description: Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to predict key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development.
DOI: 10.5281/zenodo.15455785
title: TrialBench: Benchmarking Multi-Modal Artificial-Intelligence-Ready Clinical Trial Prediction

description: Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to predict key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development.
DOI: 10.5281/zenodo.15285004
title: Surface Rupture Database of the 2025 M7.7 Mandalay Earthquake along the Sagaing Fault in Myanmar
title: Surface Rupture Mapping of the 2025 M7.7 Mandalay Earthquake on the Sagaing Fault

description: This work presents a preliminary earthquake geology study and surface rupture database associated with the 2025 M7.7 Mandalay earthquake along the Sagaing Fault in Myanmar. The purpose of this work is to document coseismic surface deformation, understand fault behavior, and support seismic hazard assessment in the region. Field investigations were conducted seven days after the mainshock, focusing on three main zones: Sagaing–Mandalay, Yamethin–Pyawbwe, and Naypyidaw. Surface ruptures and deformation features were identified through comparison of pre- and post-earthquake satellite imagery and verified by field observations. Data collection was carried out using the Field Move mobile application, and the results were processed and visualized in ArcGIS Pro. This is part of an ongoing project, and some areas remain unmapped due to fieldwork limitations. The expected outcome is to provide a detailed record of surface deformation associated with the 2025 event, contributing valuable information for earthquake geology research and risk reduction strategies in Myanmar.

ArcGIS dashboard: https://www.arcgis.com/apps/dashboards/776f994899c64f9da56cb49b105f80c3
DOI: 10.5281/zenodo.15455738
title: Surface Rupture Database of the 2025 M7.7 Mandalay Earthquake along the Sagaing Fault in Myanmar
title: Surface Rupture Mapping of the 2025 M7.7 Mandalay Earthquake on the Sagaing Fault

description: This work presents a preliminary earthquake geology study and surface rupture database associated with the 2025 M7.7 Mandalay earthquake along the Sagaing Fault in Myanmar. The purpose of this work is to document coseismic surface deformation, understand fault behavior, and support seismic hazard assessment in the region. Field investigations were conducted seven days after the mainshock, focusing on three main zones: Sagaing–Mandalay, Yamethin–Pyawbwe, and Naypyidaw. Surface ruptures and deformation features were identified through comparison of pre- and post-earthquake satellite imagery and verified by field observations. Data collection was carried out using the Field Move mobile application, and the results were processed and visualized in ArcGIS Pro. This is part of an ongoing project, and some areas remain unmapped due to fieldwork limitations. The expected outcome is to provide a detailed record of surface deformation associated with the 2025 event, contributing valuable information for earthquake geology research and risk reduction strategies in Myanmar.

ArcGIS dashboard: https://www.arcgis.com/apps/dashboards/776f994899c64f9da56cb49b105f80c3
DOI: 10.15468/dl.zheeej
title: Occurrence Download

description: A dataset containing 21846 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 Laportea Gaudich."
 ]
}

 The dataset includes 21846 records from 446 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004529-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ryvrgm4k8z.1
title: Synchronization of transcription steps defines the major bottlenecks in HIV-1 latency reactivation

description: Western blot images for manuscript entitled "Synchronization of transcription steps defines the major bottlenecks in HIV-1 latency reactivation" by Hyder, Challa et al.
DOI: 10.15468/dl.ccrc5k
title: Occurrence Download

description: A dataset containing 17890 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Floristic diversity of potential exploitable ranches in the Zou and Collines departments of Benin"
 ]
}

 The dataset includes 17890 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002266-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nu8by7
title: Occurrence Download

description: A dataset containing 359 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cydonia vulgaris Pers."
 ]
}

 The dataset includes 359 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002260-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17589358
title: River discharge estimates for 18 major Atlantic rivers using the Water Mass Balance approach

description: This work has been carried out as part of the Copernicus Marine Service WAMBOR and F3O projects. Copernicus Marine Service is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union.
DOI: 10.5281/zenodo.17589359
title: River discharge estimates for 18 major Atlantic rivers using the Water Mass Balance approach

description: This work has been carried out as part of the Copernicus Marine Service WAMBOR and F3O projects. Copernicus Marine Service is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union.