cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/3ybf9fk24c.1
title: Dynamic Phenotyping Reveals Highly Personalized, Integrated Host-Tumor Crosstalk During Exercise in Cancer Patients

description: Code for manuscript titled "Dynamic Phenotyping Reveals Highly Personalized, Integrated Host-Tumor Crosstalk During Exercise in Cancer Patients"
DOI: 10.17632/32v833trwy
title: Vertical stratification drives divergent spatial trade-offs among xylem cell types in angiosperm trees of a mountain forest in eastern China

description: Trade-offs among vessels, fibers, and parenchyma fractions shift with different forest vertical strata, driven by changes in the relative demands and limitations associated with light availability, water transport, and mechanical support. Specifically, we predicted that in the canopy stratum, where high demands exist for efficient hydraulic conductance and robust mechanical support, the primary trade-off involves balancing investment between vessels and fibers. Conversely, we expected that in the subcanopy and understory strata, characterized by greater resource limitation (particularly light), and heightened vulnerability to physical disturbances, pest, and pathogen attacks, the dominant trade-off occurs between fibers and parenchyma.
This dataset contains fractions of three different cell types (vessels, fibers, ray parenchyma) in the xylem for 119 individuals across 39 species from the mountainous areas of eastern China. The variable level represents the vertical strata classification, VF denotes vessel fraction, FF denotes fiber fraction, and RAPF denotes ray parenchyma fraction.
The code analyzes the trade-offs between vessel and fiber fractions, as well as between parenchyma and fiber fractions, and examines the differences in these trade-offs between different vertical strata.
DOI: 10.17632/32v833trwy.3
title: Vertical stratification drives divergent spatial trade-offs among xylem cell types in angiosperm trees of a mountain forest in eastern China

description: Trade-offs among vessels, fibers, and parenchyma fractions shift with different forest vertical strata, driven by changes in the relative demands and limitations associated with light availability, water transport, and mechanical support. Specifically, we predicted that in the canopy stratum, where high demands exist for efficient hydraulic conductance and robust mechanical support, the primary trade-off involves balancing investment between vessels and fibers. Conversely, we expected that in the subcanopy and understory strata, characterized by greater resource limitation (particularly light), and heightened vulnerability to physical disturbances, pest, and pathogen attacks, the dominant trade-off occurs between fibers and parenchyma.
This dataset contains fractions of three different cell types (vessels, fibers, ray parenchyma) in the xylem for 119 individuals across 39 species from the mountainous areas of eastern China. The variable level represents the vertical strata classification, VF denotes vessel fraction, FF denotes fiber fraction, and RAPF denotes ray parenchyma fraction.
The code analyzes the trade-offs between vessel and fiber fractions, as well as between parenchyma and fiber fractions, and examines the differences in these trade-offs between different vertical strata.
DOI: 10.15468/dl.ada8n2
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Suncus fellowesgordoni",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037568-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/hgzd72gcwc
title: Accelerometer Data during the performance of the Heel Rise Test

description: This dataset contains raw and processed accelerometer signals recorded during the performance of the Heel Rise Test, a clinical assessment commonly used to evaluate calf muscle strength, endurance, and functional performance. Wearable sensors were positioned on specific anatomical landmarks to capture tri-axial acceleration data throughout the test. The recordings include both the upward and downward phases of each heel rise cycle. Data were collected from participants performing repeated heel rises at a self-selected pace until fatigue or until completing a predefined number of repetitions. Along with accelerometry, the dataset includes basic metadata for each trial such as sampling rate, sensor placement, participant identifier codes, and trial number. No personally identifiable information is included. The dataset may support research in biomechanics, movement analysis, rehabilitation, strength assessment, and algorithm development for automated detection of heel rise cycles or muscular performance metrics. Researchers can use the signals for feature extraction, signal processing validation, and machine learning approaches aimed at functional assessment.
DOI: 10.17632/hgzd72gcwc.2
title: Accelerometer Data during the performance of the Heel Rise Test

description: This dataset contains raw and processed accelerometer signals recorded during the performance of the Heel Rise Test, a clinical assessment commonly used to evaluate calf muscle strength, endurance, and functional performance. Wearable sensors were positioned on specific anatomical landmarks to capture tri-axial acceleration data throughout the test. The recordings include both the upward and downward phases of each heel rise cycle. Data were collected from participants performing repeated heel rises at a self-selected pace until fatigue or until completing a predefined number of repetitions. Along with accelerometry, the dataset includes basic metadata for each trial such as sampling rate, sensor placement, participant identifier codes, and trial number. No personally identifiable information is included. The dataset may support research in biomechanics, movement analysis, rehabilitation, strength assessment, and algorithm development for automated detection of heel rise cycles or muscular performance metrics. Researchers can use the signals for feature extraction, signal processing validation, and machine learning approaches aimed at functional assessment.
DOI: 10.17632/gs8tnf5h5m
title: Data for: In Situ Experimental Study of the 10 Å phase in NaCl-H2O Fluids: Implications for the Deep Water Cycle

description: Raw data for figures in the manuscript.
DOI: 10.17632/gs8tnf5h5m.1
title: Data for: In Situ Experimental Study of the 10 Å phase in NaCl-H2O Fluids: Implications for the Deep Water Cycle

description: Raw data for figures in the manuscript.
DOI: 10.17632/4354pnf2x3
title: Food Fortification Data on LSFF

description: We conducted a situation analysis using key informant qualitative interviews with stakeholders working in large scale food fortification programs in East Central Africa Health Community (ECSA-HC) and the Southern Africa Development Community (SADC). A total of nineteen in-depth interviews were conducted global regional and country stakeholders inclusive of key fortification experts from United States, non-government organizations (NGO), and multilateral organizations, as well as countries that are members of SADC and ECSA-HC – specifically Botswana, Eswatini, Ethiopia, Kenya, Lesotho, Madagascar, Malawi, Mozambique, and Tanzani. Key informants were purposefully identified resource according to selection criteria: 1) direct experiences in or familiarity with the history of food fortification in the region/individual member states and/or 2) knowledgeability of current implementation of food fortification and/or universal salt iodization policies and programs in the region and individual member states. The in-depth key informant interviews (KIIs) were conducted remotely using Microsoft Teams in English, Portuguese or French.
DOI: 10.17632/4354pnf2x3.1
title: Food Fortification Data on LSFF

description: We conducted a situation analysis using key informant qualitative interviews with stakeholders working in large scale food fortification programs in East Central Africa Health Community (ECSA-HC) and the Southern Africa Development Community (SADC). A total of nineteen in-depth interviews were conducted global regional and country stakeholders inclusive of key fortification experts from United States, non-government organizations (NGO), and multilateral organizations, as well as countries that are members of SADC and ECSA-HC – specifically Botswana, Eswatini, Ethiopia, Kenya, Lesotho, Madagascar, Malawi, Mozambique, and Tanzani. Key informants were purposefully identified resource according to selection criteria: 1) direct experiences in or familiarity with the history of food fortification in the region/individual member states and/or 2) knowledgeability of current implementation of food fortification and/or universal salt iodization policies and programs in the region and individual member states. The in-depth key informant interviews (KIIs) were conducted remotely using Microsoft Teams in English, Portuguese or French.
DOI: 10.17632/8wt48bjmx3
title: BD-Food33: A 1.2K Image Dataset of 33 Bangladeshi Local Food Categories

description: BD-Food33 is a curated image dataset containing 1,200+ high-quality images of 33 categories of Bangladeshi local food. The dataset was collected from multiple publicly accessible online sources, restaurant websites, food blogs, and manually captured samples. Only selected portions from each source were used to ensure diversity and visual clarity.

All images were cleaned, resized, checked for quality, and organized into category-specific folders. The dataset provides a representative visual catalog of Bangladeshi cuisine—covering traditional, regional, snacks, sweets, and main dishes.

This dataset is suitable for research in computer vision, image classification, food recognition, fine-grained visual categorization, and deep learning–based food analysis. It can be used for training convolutional neural networks, benchmarking image recognition models, building food-recommendation systems, and developing automated nutrition/food-identification pipelines.
DOI: 10.17632/8wt48bjmx3.1
title: BD-Food33: A 1.2K Image Dataset of 33 Bangladeshi Local Food Categories

description: BD-Food33 is a curated image dataset containing 1,200+ high-quality images of 33 categories of Bangladeshi local food. The dataset was collected from multiple publicly accessible online sources, restaurant websites, food blogs, and manually captured samples. Only selected portions from each source were used to ensure diversity and visual clarity.

All images were cleaned, resized, checked for quality, and organized into category-specific folders. The dataset provides a representative visual catalog of Bangladeshi cuisine—covering traditional, regional, snacks, sweets, and main dishes.

This dataset is suitable for research in computer vision, image classification, food recognition, fine-grained visual categorization, and deep learning–based food analysis. It can be used for training convolutional neural networks, benchmarking image recognition models, building food-recommendation systems, and developing automated nutrition/food-identification pipelines.
DOI: 10.17632/j2vmfbg9p7
title: HumSensE32: A low-cost infrasound sensor for volcano monitoring

description: Acoustic infrasound is the fingerprint of a volcano and carries vital information that characterizes its activities. In conjunction with seismic monitoring, it can help in studying the characteristics of volcanic activity, potentially reducing casualties and saving lives. The International Monitoring System (IMS) infrasound network, the only global infrasound network of its kind, indirectly plays an essential role in volcano monitoring and is relatively scattered in the Southeast Asia region, which is not ideal for infrasound volcano study in this region. This paper presented an open source, low-cost, Internet-of-Things (IoT) enabled infrasound sensor, HumSensE32, which enables mass deployment for localized infrasound monitoring and study. It features data logging and data forwarding capability using optional wireless or wired communication (cellular/Wi-Fi/Ethernet), enabling both local storage and remote access for real time monitoring. Its low-power consumption enables long-term deployment with minimal maintenance required, making it well suited for extended field monitoring missions. Its small footprint allows easy handling and quick installation with minimal effort. It can be easily programmed with PlatformIO for custom operations, whichever suits the monitoring mission. To validate HumSensE32, field deployment has been conducted alongside with scientific grade infrasound sensor, Seismo Wave MB3d as reference and had shown comparable performance.

This repository includes hardware, software and design files required to build and run HumSensE32 infrasound sensor.
DOI: 10.17632/j2vmfbg9p7.2
title: HumSensE32: A low-cost infrasound sensor for volcano monitoring

description: Acoustic infrasound is the fingerprint of a volcano and carries vital information that characterizes its activities. In conjunction with seismic monitoring, it can help in studying the characteristics of volcanic activity, potentially reducing casualties and saving lives. The International Monitoring System (IMS) infrasound network, the only global infrasound network of its kind, indirectly plays an essential role in volcano monitoring and is relatively scattered in the Southeast Asia region, which is not ideal for infrasound volcano study in this region. This paper presented an open source, low-cost, Internet-of-Things (IoT) enabled infrasound sensor, HumSensE32, which enables mass deployment for localized infrasound monitoring and study. It features data logging and data forwarding capability using optional wireless or wired communication (cellular/Wi-Fi/Ethernet), enabling both local storage and remote access for real time monitoring. Its low-power consumption enables long-term deployment with minimal maintenance required, making it well suited for extended field monitoring missions. Its small footprint allows easy handling and quick installation with minimal effort. It can be easily programmed with PlatformIO for custom operations, whichever suits the monitoring mission. To validate HumSensE32, field deployment has been conducted alongside with scientific grade infrasound sensor, Seismo Wave MB3d as reference and had shown comparable performance.

This repository includes hardware, software and design files required to build and run HumSensE32 infrasound sensor.
DOI: 10.15468/dl.yy2h5j
title: Occurrence Download

description: A dataset containing 12686 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Harmothoe imbricata",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 12686 records from 207 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037569-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/h5c-0x6-gwy
title: Leveillula verbasci (Jacz.) Golovin

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/cqz-nnp-xts
title: Balladyna sydowii Petr.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/m0m-3d2-t18
title: Brunnipila longipila Nograsek & Matzer

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/7sa-wmp-3zw
title: Aulacostroma palawanense Syd. & P. Syd.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/fzj-e7p-bdw
title: Rhopalomyces strangulatus Thaxt.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/mfm-xd5-r2c
title: Achorodothis poasensis Syd.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.60893/figshare.jcp.29941280
title: data for replecation of calculations

description: data and instructions
DOI: 10.60893/figshare.jcp.29941280.v1
title: data for replecation of calculations

description: data and instructions
DOI: 10.3535/559-er9-phk
title: Achorodothis rapaneae Syd.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/pyv-p7s-gjl
title: Aulacostroma nigrificans Petr.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.afkfej
title: Occurrence Download

description: A dataset containing 93993135 species occurrences available in GBIF matching the query:
(Query is too complex. Can be viewed on the landing page)

 The dataset includes 93993135 records from 2038 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041561-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q34um6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is incertae sedis",
 "Year is greater than 2013",
 "Geometry MULTIPOLYGON (((12.957718 42.079306,12.956811 42.078436,12.956319 42.077364,12.956001 42.076551,12.955403 42.075192,12.955313 42.074611,12.95526 42.073923,12.955272 42.073081,12.95481 42.072846,12.954122 42.072615,12.954027 42.07241,12.954015 42.072069,12.953797 42.071786,12.953318 42.071466,12.952213 42.070831,12.9512 42.070385,12.950485 42.070262,12.950149 42.070148,12.949587 42.070132,12.949219 42.070146,12.948912 42.070213,12.948621 42.070523,12.948023 42.070876,12.947956 42.071262,12.947763 42.07145,12.947368 42.071572,12.947051 42.071549,12.943151 42.072277,12.940931 42.072138,12.937223 42.072572,12.935288 42.073073,12.934227 42.073083,12.934249 42.073724,12.934008 42.074087,12.933523 42.074257,12.933179 42.074501,12.933076 42.074713,12.932873 42.075067,12.932433 42.075309,12.93225 42.075455,12.93223 42.075953,12.932309 42.076423,12.932275 42.076868,12.932496 42.07716,12.932597 42.077779,12.933174 42.077953,12.93388 42.078458,12.934751 42.07873,12.935415 42.078846,12.935408 42.079201,12.935483 42.079529,12.935778 42.079959,12.93635 42.080277,12.937201 42.080681,12.937427 42.080938,12.93784 42.081225,12.938469 42.081231,12.939425 42.081619,12.939416 42.081904,12.940721 42.082648,12.941259 42.083054,12.941575 42.08308,12.942061 42.083391,12.942553 42.083907,12.943565 42.084635,12.944264 42.084815,12.945072 42.085044,12.945485 42.085354,12.945594 42.085589,12.946095 42.086063,12.94652 42.086285,12.947622 42.086965,12.947873 42.087453,12.948386 42.087927,12.948997 42.088331,12.950264 42.087061,12.950621 42.086967,12.95124 42.086518,12.951781 42.086145,12.952334 42.085948,12.952886 42.085715,12.953439 42.085436,12.953868 42.085246,12.954322 42.085042,12.954474 42.084833,12.954649 42.084647,12.956032 42.084159,12.956221 42.084,12.957462 42.083228,12.958584 42.081139,12.958319 42.080709,12.958209 42.080452,12.957718 42.079306)))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0034000-230530130749713/datasets/export for details.
DOI: 10.15468/dl.mcjzyq
title: Occurrence Download

description: A dataset containing 6338 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Panthera leo (Linnaeus, 1758)"
 ]
}

 The dataset includes 6338 records from 132 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079755-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-140400
title: Renewal, no. 96 (Dec. 1981 / Jan. 1982)

description: [description english/roman)] Renewal, no. 96 (Dec. 1981 / Jan. 1982). Published six times per year.
DOI: 10.15468/dl.pph38h
title: Occurrence Download

description: A dataset containing 683 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-84.4131801000000053 33.6484452999999988, -84.4131801000000053 33.6499742399999988, -84.4131801000000053 33.6513939700000009, -84.4131801000000053 33.6534689599999979, -84.4131801000000053 33.6549978999999979, -84.4131801000000053 33.6572913099999980, -84.4131801000000053 33.6593663000000021, -84.4102358300000049 33.6593663000000021, -84.4075098099999934 33.6593663000000021, -84.4054380299999991 33.6593663000000021, -84.4033662599999985 33.6593663000000021, -84.4002040799999946 33.6593663000000021, -84.3976961399999936 33.6593663000000021, -84.3949701199999964 33.6593663000000021, -84.3932254700000044 33.6593663000000021, -84.3908263999999946 33.6593663000000021, -84.3908263999999946 33.6583834099999990, -84.3908263999999946 33.6563084200000020, -84.3908263999999946 33.6544518500000009, -84.3908263999999946 33.6532505400000019, -84.3908263999999946 33.6512847600000029, -84.3908263999999946 33.6484452999999988, -84.3931164300000063 33.6484452999999988, -84.3946430000000021 33.6484452999999988, -84.3971509299999951 33.6484452999999988, -84.4004221600000051 33.6484452999999988, -84.4029301000000061 33.6484452999999988, -84.4046747499999981 33.6484452999999988, -84.4070736500000010 33.6484452999999988, -84.4084911799999986 33.6484452999999988, -84.4107810299999954 33.6484452999999988, -84.4131801000000053 33.6484452999999988))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 683 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0085048-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-140434
title: Renewal, no. 96 (Dec. 1981 / Jan. 1982)

description: [description english/roman)] Renewal, no. 96 (Dec. 1981 / Jan. 1982). Published six times per year.
DOI: 10.15468/dl.twpkrd
title: Occurrence Download

description: A dataset containing 106 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Corytophanes F.Boie, 1826"
 ]
}

 The dataset includes 106 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020980-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xd2b9g
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Plagiopholis styani (Boulenger, 1899))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 25 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024500-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-140404
title: Renewal, no. 96 (Dec. 1981 / Jan. 1982)

description: [description english/roman)] Renewal, no. 96 (Dec. 1981 / Jan. 1982). Published six times per year.
DOI: 10.15468/dl.98bmvz
title: Occurrence Download

description: A dataset containing 132 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Borreria verticillata (L.) G.Mey."
 ]
}

 The dataset includes 132 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0117948-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.