cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.x4ddzk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((101.278 1.615182, 101.207709 1.609614, 101.139151 1.593047, 101.074013 1.565889, 101.0139 1.528809, 100.960294 1.482722, 100.914513 1.428761, 100.877685 1.368256, 100.850716 1.302698, 100.83427 1.233701, 100.82875 1.162964, 100.834292 1.092229, 100.850758 1.023237, 100.877743 0.957686, 100.914581 0.897192, 100.960365 0.843242, 101.013968 0.797165, 101.074071 0.760095, 101.139193 0.732945, 101.207732 0.716383, 101.278 0.710816, 101.348268 0.716383, 101.416807 0.732945, 101.481929 0.760095, 101.542032 0.797165, 101.595635 0.843242, 101.641419 0.897192, 101.678257 0.957686, 101.705242 1.023237, 101.721708 1.092229, 101.72725 1.162964, 101.72173 1.233701, 101.705284 1.302698, 101.678315 1.368256, 101.641487 1.428761, 101.595706 1.482722, 101.5421 1.528809, 101.481987 1.565889, 101.416849 1.593047, 101.348291 1.609614, 101.278 1.615182))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002510-260120142942310/datasets/export for details.
DOI: 10.6084/m9.figshare.31099756
title: <b>Combined Effects of Blueberry Supplementation and Football Training on Muscle Damage, Liver Enzymes, and Respiratory Function in Adolescent Athletes</b>

description: Background: Blueberries contain bioactive compounds that may support exercise-related physiological adaptations. However, evidence regarding their effects on muscle damage, liver enzymes, and respiratory function in adolescent athletes is limited. This study investigated the effects of blueberry supplementation combined with football training on these parameters in adolescent football players. Methods: Thirty-six male adolescent players were randomly assigned to a control group (CG, n = 12), a blueberry supplementation group (EG, n = 12), or a blueberry supplementation plus additional exercise group (SEG, n = 12). All participants completed eight weeks of regular football training. The EG and SEG consumed 26 g/day of freeze-dried blueberry powder. Muscle damage and liver enzyme markers (CK, CK-MB, ALT, AST, LDH) and respiratory parameters (MIP, MEP, FVC, FEV₁, PEFmax, FEV₁/FVC) were assessed before and after the intervention. Results: No significant group × time effects were observed for muscle damage or liver enzyme markers (p &gt; 0.05). In contrast, the SEG showed significant improvements in all respiratory parameters compared with the CG and EG (p &lt; 0.05). Conclusion: Blueberry supplementation alone did not significantly affect muscle damage or liver enzyme responses in adolescent football players. When combined with structured football training and additional exercise, it may contribute to improved respiratory function and favorable functional adaptations.<br>
DOI: 10.6084/m9.figshare.31099756.v1
title: <b>Combined Effects of Blueberry Supplementation and Football Training on Muscle Damage, Liver Enzymes, and Respiratory Function in Adolescent Athletes</b>

description: Background: Blueberries contain bioactive compounds that may support exercise-related physiological adaptations. However, evidence regarding their effects on muscle damage, liver enzymes, and respiratory function in adolescent athletes is limited. This study investigated the effects of blueberry supplementation combined with football training on these parameters in adolescent football players. Methods: Thirty-six male adolescent players were randomly assigned to a control group (CG, n = 12), a blueberry supplementation group (EG, n = 12), or a blueberry supplementation plus additional exercise group (SEG, n = 12). All participants completed eight weeks of regular football training. The EG and SEG consumed 26 g/day of freeze-dried blueberry powder. Muscle damage and liver enzyme markers (CK, CK-MB, ALT, AST, LDH) and respiratory parameters (MIP, MEP, FVC, FEV₁, PEFmax, FEV₁/FVC) were assessed before and after the intervention. Results: No significant group × time effects were observed for muscle damage or liver enzyme markers (p &gt; 0.05). In contrast, the SEG showed significant improvements in all respiratory parameters compared with the CG and EG (p &lt; 0.05). Conclusion: Blueberry supplementation alone did not significantly affect muscle damage or liver enzyme responses in adolescent football players. When combined with structured football training and additional exercise, it may contribute to improved respiratory function and favorable functional adaptations.
DOI: 10.6084/m9.figshare.31099756.v2
title: <b>Combined Effects of Blueberry Supplementation and Football Training on Muscle Damage, Liver Enzymes, and Respiratory Function in Adolescent Athletes</b>

description: Background: Blueberries contain bioactive compounds that may support exercise-related physiological adaptations. However, evidence regarding their effects on muscle damage, liver enzymes, and respiratory function in adolescent athletes is limited. This study investigated the effects of blueberry supplementation combined with football training on these parameters in adolescent football players. Methods: Thirty-six male adolescent players were randomly assigned to a control group (CG, n = 12), a blueberry supplementation group (EG, n = 12), or a blueberry supplementation plus additional exercise group (SEG, n = 12). All participants completed eight weeks of regular football training. The EG and SEG consumed 26 g/day of freeze-dried blueberry powder. Muscle damage and liver enzyme markers (CK, CK-MB, ALT, AST, LDH) and respiratory parameters (MIP, MEP, FVC, FEV₁, PEFmax, FEV₁/FVC) were assessed before and after the intervention. Results: No significant group × time effects were observed for muscle damage or liver enzyme markers (p &gt; 0.05). In contrast, the SEG showed significant improvements in all respiratory parameters compared with the CG and EG (p &lt; 0.05). Conclusion: Blueberry supplementation alone did not significantly affect muscle damage or liver enzyme responses in adolescent football players. When combined with structured football training and additional exercise, it may contribute to improved respiratory function and favorable functional adaptations.<br>
DOI: 10.15468/dl.z2j9uc
title: Occurrence Download

description: A dataset containing 4982530 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON (((-3.51734 48.11314, -0.92999 48.11314, -0.92999 50.09474, -3.51734 50.09474, -3.51734 48.11314)))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4982530 records from 1515 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002453-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xrg76z
title: Occurrence Download

description: A dataset containing 1067 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Abies religiosa (Kunth) Schltdl. & Cham."
 ]
}

 The dataset includes 1067 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011885-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/5662
title: medal
DOI: 10.6084/m9.figshare.28840580
title: Leukemia Classification

description: Classifying Acute Lymphoblastic Leukemia cells from normal cells using deep learning method
DOI: 10.15468/dl.6vw22e
title: Occurrence Download

description: A dataset containing 40021 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Solanum"
 ]
}

 The dataset includes 40021 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003223-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30683348
title: The assembly and annotation of <i>C</i><i>u</i> organelle genome.

description: The assembly and annotation of <i>C</i><i>u</i> organelle genome.
DOI: 10.6084/m9.figshare.30683348.v1
title: The assembly and annotation of <i>C</i><i>u</i> organelle genome.

description: The assembly and annotation of <i>C</i><i>u</i> organelle genome.
DOI: 10.6084/m9.figshare.30681350
title: Dataset for “Bimodal Social Capital and Its Psychosocial and Neurobiological Correlates.

description: Figure-Generating Dataset for the Study on Bimodal Social Capital and Brain Network Topology
DOI: 10.6084/m9.figshare.30681350.v2
title: Dataset for “Bimodal Social Capital and Its Psychosocial and Neurobiological Correlates.

description: Figure-Generating Dataset for the Study on Bimodal Social Capital and Brain Network Topology
DOI: 10.6084/m9.figshare.30681350.v1
title: Dataset for “Bimodal Social Capital and Its Psychosocial and Neurobiological Correlates

description: Figure-Generating Dataset for the Study on Bimodal Social Capital and Brain Network Topology
DOI: 10.15468/dl.vps3yz
title: Occurrence Download

description: A dataset containing 3546 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Indonesia, Papua New Guinea, Malaysia)",
 "Geometry POLYGON((94.58382 5.94064,94.423 4.06236,95.33432 2.13542,97.37139 -0.91034,99.24763 -3.64188,101.65995 -5.88545,104.28669 -7.74835,109.05771 -8.8093,111.47002 -8.96819,114.52562 -9.70873,118.27811 -10.60576,123.74602 -11.23739,125.51505 -10.55306,128.78507 -9.49731,131.73346 -8.91523,133.07363 -8.33222,135.27152 -9.60304,141.11468 -10.02562,141.11468 -8.12,141.16828 -6.0454,141.06107 -4.06977,141.06107 -2.08927,139.07761 -0.96394,136.45087 0.26897,130.92935 0.75141,130.44689 2.09114,129.16032 3.05508,125.78308 2.68031,123.90684 1.55534,121.2801 2.14471,119.88632 2.51965,119.99353 3.64375,120.04714 5.0869,119.18943 6.26046,117.68843 7.43138,116.24104 7.05913,114.3648 5.67398,113.56069 4.23203,110.66592 3.37622,108.95049 2.51965,108.36082 0.75141,105.51965 0.10815,102.46405 1.44817,100.10534 3.48324,98.9796 5.1403,96.29925 6.26045,94.58382 5.94064))",
 "HasGeospatialIssue is false",
 "TaxonKey is Bulbophyllum"
 ]
}

 The dataset includes 3546 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003224-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gb8f2a
title: Occurrence Download

description: A dataset listing the 32792 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Ethiopia",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 32792 records were derived from 1053 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003214-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17678298
title: Controls on productivity and bottom currents in the Pacific Antarctic seasonal sea-ice zone since 300 ka: implications for the dynamics of Southern Ocean overturning circulation

description: Understanding the mechanisms regulating productivity and bottom current variability in the Amundsen Sea is vital for constraining cryosphere-climate feedbacks in the South Pacific Seasonal Sea Ice Zone. To enhance our comprehension of the mechanisms underlying productivity variations in this region, we present an extensive set of multi-proxy records from gravity core ANT36/A3-08 (LATITUDE: -69.02 and LONGITUDE: -120.06), retrieved from the southernmost upwelling boundary of the modern Circumpolar Deep Water in the Amundsen Sea.

Surface productivity is reconstructed using a suite of biogeochemical proxies, including Ti-normalized bromine, biogenic opal, total nitrogen, and excess barium normalized to Ti. These proxies reflect marine production. Bottom current strength is inferred from sedimentological and geochemical indicators such as mean sortable silt, sortable silt percentage, zirconium-to-rubidium, and zirconium-to-aluminum ratios.
DOI: 10.5281/zenodo.13363878
title: Controls on productivity and bottom currents in the Pacific Antarctic seasonal sea-ice zone since 300 ka: implications for the dynamics of Southern Ocean overturning circulation

description: Understanding the mechanisms regulating productivity and bottom current variability in the Amundsen Sea is vital for constraining cryosphere-climate feedbacks in the South Pacific Seasonal Sea Ice Zone. To enhance our comprehension of the mechanisms underlying productivity variations in this region, we present an extensive set of multi-proxy records from gravity core ANT36/A3-08 (LATITUDE: -69.02 and LONGITUDE: -120.06), retrieved from the southernmost upwelling boundary of the modern Circumpolar Deep Water in the Amundsen Sea.

Surface productivity is reconstructed using a suite of biogeochemical proxies, including Ti-normalized bromine, biogenic opal, total nitrogen, and excess barium normalized to Ti. These proxies reflect marine production. Bottom current strength is inferred from sedimentological and geochemical indicators such as mean sortable silt, sortable silt percentage, zirconium-to-rubidium, and zirconium-to-aluminum ratios.
DOI: 10.6084/m9.figshare.30446918.v1
title: Drift-corrected fluctuation data

description: Drift-corrected fluctuation data of "Virtual modulation based high-throughput fluorescence fluctuation super-resolution imaging (VSOFI)".
DOI: 10.34740/kaggle/dsv/13824905
title: World Weather Repository ( Daily Updating )
DOI: 10.15468/dl.kawyhx
title: Occurrence Download

description: A dataset containing 58304 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Bulbophyllum"
 ]
}

 The dataset includes 58304 records from 357 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003217-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.by5n86
title: Occurrence Download

description: A dataset containing 731043 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is FishBase Database"
 ]
}

 The dataset includes 731043 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003220-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57967/hf/7056
title: BUBBLES
DOI: 10.15468/dl.dm86x3
title: Occurrence Download

description: A dataset listing the 109286 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (Z06.6.1.6_1, CHN.30_1, CHN.26_1, IND.30_1, IND.36_1, PHL.59_1, MYS.14_1, IND.13_1)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 109286 records were derived from 2564 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003222-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/w8sr775pjb.1
title: MoringaLeafNet: A Multi-Class Leaf Disease Dataset for Precision Agriculture and Deep Learning Research

description: This dataset comprises high-resolution leaf images of Moringa Oleifera collected from Sumi Nursery, located in Madhupur, Tangail, Bangladesh. It was designed to support the development and evaluation of machine learning, deep learning, and computer vision models aimed at early and accurate disease diagnosis in Moringa leaves. The image data focuses on four key classes and leads to a total of 2,203 original images.

Primary Image: 
1. Healthy Leaf: 593 images
2. Yellow Leaf: 555 images
3. Bacterial Leaf Spot; 533 images
4. Cercospora Leaf Spot: 522 images

To address class imbalance and enhance model robustness, image augmentation techniques were applied to expand the dataset. The augmentation process included operations such as rotation, scaling, flipping, brightness adjustment, and noise addition. This resulted in a significantly enriched dataset with the following class distributions:

Healthy Leaf: 2,372 images
Cercospora Leaf Spot: 2,132 images
Yellow Leaf: 2, 220 images
Bacterial Leaf Spot: 2,132 images

Bringing the total number of images to 8,812.

For computer vision applications, this dataset enables segmentation, localization, and classification tasks. It can be used to develop mobile and embedded vision systems for real-time leaf disease detection in smart agriculture, thereby assisting farmers in early diagnosis and timely treatment. Moreover, the dataset supports image preprocessing research, including denoising, edge detection, and contrast enhancement tailored for leaf texture analysis.

This curated and augmented image dataset provides a valuable resource for advancing automated plant health monitoring systems, especially in the domain of sustainable agriculture and precision farming.
DOI: 10.15468/dl.cvkb6n
title: Occurrence Download

description: A dataset containing 3191 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "GadmGid is USA.2_1",
 "TaxonKey is Pandion haliaetus (Linnaeus, 1758)",
 "Year 2019-2025"
 ]
}

 The dataset includes 3191 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003860-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17603/ds2-6b5q-4w86
title: Research Experiences for Undergraduates (REU), NHERI 2024: Evaluating the Impacts of Flooding on Transportation Infrastructure in Coastal Areas

description: The project is focused on understanding and mitigating the impact of urban floods on transportation infrastructure in Atlantic City, New Jersey. It involves estimating inundation depths, analyzing how floods affect traffic flow, vehicle speeds, traffic flow capacity, and congestion by simulating a storm surge affected evacuation.
DOI: 10.17603/ds2-6ney-m619
title: OpenSees Building Model Catalog

description: The OpenSees Building Model Catalog provides information about and hyperlinks to OpenSees models created by researchers around the world. These models were originally developed to validate new OpenSees modeling tools and to investigate the nonlinear earthquake response of buildings. The models are archived and published with doi’s on github or via DesignSafe (Data Depot, NEES Data Repository, Community Data). These models i) represent buildings with a wide range of configurations, structural systems, and building materials, ii) employ a wide range of OpenSees element formulations, material models, and solution algorithms, and iii) simulate building response to a range of loading scenarios (e.g., gravity plus quasi-static monotonically increasing lateral, gravity plus dynamic lateral, and gravity plus earthquake). The models are cataloged here to facilitate reuse by the community in new research efforts such as quantification of uncertainty in building response simulation, optimization of building design, and assessment of regional earthquake risk. Currently, the catalog includes approximately 1000 models provided by approximately 17 research groups characterizing the response of concrete, steel and wood buildings that employ walls, moment-resisting frames, and braced frames to resist lateral loads. Most models are two-dimensional; some are three-dimensional.
DOI: 10.17603/ds2-my9w-2j38
title: Flash Flood Custom Named Entity Recognition Model: Dataset

description: This published dataset is a source for researchers and practitioners to improve upon the performances of the (Flash Flood) FF-NER model. FF-NER, a custom Named Entity Recognition (NER) model designed to mine relevant information from diverse and voluminous FF-related web-based texts. FF-NER extracts specific information for eight entities: Region, Street, Location, County, Water Body, Monetary Damage, Power Outages, and Infrastructure Services. To develop FF-NER, we curated a dataset of 2,670 FF-related web paragraphs and experimented with conventional and advanced NER techniques. The FF-related paragraphs are the ones labeled 1 under the Damage/Impact category in the Flash Flood BERT Text Classification Model: Dataset (https://doi.org/10.17603/ds2-p9jf-xe52 v1). Our final model outperforms the baseline, improving accuracy, precision, recall, and F1 score by 4.6%, 7.1%, 4.6%, 9.35%, respectively. The study is currently under publication. We have designed FF-NER to be jointly used with FF-IR (https://doi.org/10.1016/j.envsoft.2023.105734) and FF-BERT (https://doi.org/10.1016/j.aei.2023.102293). Engineers, researchers, and practitioners can use FF-NER to enhance existing databases, recognize patterns, and conduct detailed analysis of past FF events. This dataset contains six csv files, BIO_Dataset_Train.csv, BIO_Dataset_Test.csv, BIOES_Dataset_Train.csv, BIOES_Dataset_Test.csv, Prompt_FF_NER_Train.csv, and Prompt_FF_NER_Test.csv. These represent three different datasets (BIO-tagged, BIOES_tagged, and Prompt-based) and for each type of dataset, we have a Training file (2170 paragraphs) and a Testing file (500 paragraphs). We used the BIOES and BIO tagged datasets to train and test the encoder-based deep learning architectures, whereas we used the Prompt based dataset to fine-tune a decoder-based Large Language Model (i.e., LlaMA2-13B). We used the train file to train the models and the test file to compare the performances of the trained models using unseen test data. Details regarding the dataset are available in the readme file.
DOI: 10.17603/ds2-q34z-z160
title: East Africa Cyclone Flooding - Mozambique
title: in East African Cyclone Flooding - Mozambique

description: This study aims to assess disaster risk factors that leave low-income households vulnerable during extreme weather events such as extreme heat and flooding. The data generated can underpin the development of a decision support framework that can be used to promote equitable allocation of resources in disaster risk reduction planning efforts in the selected case study areas. The primary audience is the building regulatory and compliance community.
description: This study aims to assess disaster risk factors that leave low-income households vulnerable during extreme weather events such as extreme heat and flooding.