cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/ny2-92g-7l9
title: Asyneuma limonifolium (L.) Janch.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/3gr-4a0-6rl
title: Teucrium chamaedrys L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/tym-28h-9sz
title: Teucrium chamaedrys L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/73s-n14-a92
title: Saponaria officinalis L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/lav-j2f-6a7
title: Saponaria officinalis L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/w4v-wzr-8cf
title: Leontodon hispidus subsp. hispidus

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/s8w-zmk-y6t
title: Saponaria officinalis L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/xd8-0h5-nxn
title: Teucrium chamaedrys L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/8ay-q56-mvb
title: Holcus lanatus L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/lre-e6c-4se
title: Holcus lanatus L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/075-e4y-2b3
title: Holcus lanatus L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/wz0-vjx-kle
title: Anthemis pindicola Heldr. ex Halácsy

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/tbt-d44-pbc
title: Campanula rapunculoides L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/vwg-bxb-4zq
title: Campanula rapunculoides L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/1hy-jtn-3d9
title: Campanula rapunculoides L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/ksv-baa-mhz
title: Leontodon hispidus subsp. hispidus

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/9a7-852-7vw
title: Campanula rapunculoides L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/mat-0zw-vyy
title: Marrubium vulgare L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/yls-8m4-pj1
title: Campanula rapunculoides L.

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/5lc-7c7-qg8
title: Medicago sativa subsp. sativa

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/8w1-87h-f58
title: Leontodon hispidus subsp. hispidus

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.3535/x6y-kef-cch
title: Medicago sativa subsp. sativa

description: Digital Specimen for the physical specimen hosted at Botanic Garden and Botanical Museum Berlin.
DOI: 10.15468/dl.943uj7
title: Occurrence Download

description: A dataset containing 156595 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is The Himalayan Uplands Plant database (HUP Version 1)",
 "Geometry POLYGON((101.03828 43.33662,46.13898 43.62107,110.80448 5.22,101.03828 43.33662))"
 ]
}

 The dataset includes 156595 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033952-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2ap795
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Hippolais icterina)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033973-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xdstwf
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eichhornia crassipes",
 "Country is Australia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033972-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21220/v5p14f
title: GIS Data: Westmoreland County, Virginia Shoreline Management Model
DOI: 10.15468/dl.uqq78w
title: Occurrence Download

description: A dataset containing 948 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Hippolais icterina)",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 948 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033974-250717081556266/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.29627081.v1
title: XL-MS results of all purified FtsH-YidC samples

description: XL-MS analysis results of all FtsH-YidC purifications
DOI: 10.6084/m9.figshare.29627081
title: XL-MS results of all purified FtsH-YidC samples

description: XL-MS analysis results of all FtsH-YidC purifications
DOI: 10.5281/zenodo.16366822
title: Dataset to generate figures to "Dual-domain coherent emission: Photon-magnon laser in a hybrid quantum system"

description: Repository containing the datasets and LaTeX code to reproduce the figures of the manuscript "Dual-domain coherent emission: Photon-magnon laser in a hybrid quantum system"
DOI: 10.5281/zenodo.16366821
title: Dataset to generate figures to "Dual-domain coherent emission: Photon-magnon laser in a hybrid quantum system"

description: Repository containing the datasets and LaTeX code to reproduce the figures of the manuscript "Dual-domain coherent emission: Photon-magnon laser in a hybrid quantum system"
DOI: 10.15468/dl.qucz85
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phalaris canariensis",
 "Country is Albania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033976-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s54h3g
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Hippolais icterina)",
 "Country is one of (Anguilla, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Bouvet Island, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), South Georgia and the South Sandwich Islands, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguay, Venezuela (Bolivarian Republic of), Virgin Islands (British), Virgin Islands (U.S.))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033975-250717081556266/datasets/export for details.
DOI: 10.15468/dl.xk5xe7
title: Occurrence Download

description: A dataset containing 248 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phalaris canariensis",
 "Country is Australia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 248 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033977-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5065/c9v1-aq81
title: ICARUS Chamber Experiment: Ziemann_20160607_Methyl nitrite/Nitric oxide/6-Undecanol_Hydroxyl radical_DOS

description: Goals: Determine SOA yield of OH radical oxidation of the VOCSummary: Experiments were conducted in an 8.0 m^3 FEP Teflon environmental chamber in Boulder, CO, that was filled with clean, dry air [less than 5 ppb of hydrocarbons and less than 1% relative humidity (RH)] from two Aadco clean air systems and operated at ambient temperature (23 C) and pressure (?630 Torr). The VOC was added to the chamber by evaporating a measured amount of the compound from a heated glass bulb into a stream of ultrahigh-purity (UHP) N2. Approximately 200 ?g m?3 dioctyl sebacate (DOS) seed particles in UHP N2 were added from an evaporation?condensation apparatus. Known amounts of methyl nitrite and NO were prepared on a vacuum manifold using a calibrated glass bulb and absolute pressure gauge and then flushed into the chamber with UHP N2. Reactions were initiated by turning on blacklights that covered two walls of the chamber at 50% intensity (JNO2 = 0.37 h?1) for one hour. Photolysis of methyl nitrite created OH radicals with an average concentration of 4 x 107 molecules cm^-3, as determined from the measured decay of the VOC and estimated OH reaction rate constants. The VOC, methyl nitrite, and NO concentrations were 1, 5, and 5 ppm.Organization: Ziemann Lab Affiliation: University of Colorado, Boulder, Boulder, CO, USA Chamber: Janet (Chamber 1)Experiment Category: Gas phase chemical reaction, Photolysis, any phase, Aerosol formation Oxidant: Hydroxyl radical Reactants: Methyl nitrite, Nitric oxide, 6-Undecanol Reaction Type: Photooxidation Relative Humidity: less than1 Temperature: 23 Seed Name: DOS Pressure: 630
DOI: 10.15468/dl.ysxyky
title: Occurrence Download

description: A dataset containing 14101 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Calidris alba)",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 14101 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033978-250717081556266/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.29626943
title: Documentos - Dissertação

description: Planilhas e rotinas utilizadas na dissertação
DOI: 10.6084/m9.figshare.29626943.v2
title: Documentos - Dissertação

description: Planilhas e rotinas utilizadas na dissertação
DOI: 10.6084/m9.figshare.29626943.v1
title: Documentos - Dissertação

description: Planilhas e rotinas utilizadas na dissertação
DOI: 10.15468/dl.6ftj8h
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033979-250717081556266/datasets/export for details.
DOI: 10.15468/dl.jxgn9n
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rubus malvaceus"
 ]
}

 The dataset includes 19 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033980-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rwhh8s
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erodium moschatum",
 "Country is Argentina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033981-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nymgt8
title: Occurrence Download

description: A dataset containing 1261656 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Calidris alba)",
 "Country is one of (Bermuda, Canada, Greenland, Saint Pierre and Miquelon, United States of America)"
 ]
}

 The dataset includes 1261656 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033948-250717081556266/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.29626184
title: Multispectral UAV imagery and machine learning for morphological differentiation of two tropical C4-forage grasses in an integrated crop-livestock system in the State of Goias, Brazil

description: <b>Context.</b> Mixed tropical pastures containing both bunchgrasses (<i>Megathyrsus maximus</i>) and lightly sod-forming grasses (<i>Urochloa bryzantha</i>) present significant management challenges due to their contrasting growth habits and grazing requirements. Traditional visual assessment methods are inadequate for large-scale monitoring of grass growth forms. <b>Aims.</b> To develop and evaluate an automated system for detecting and mapping bunchgrass distribution in tropical pastures using multispectral UAV imagery and machine learning algorithms, enabling precision grazing management. <b>Methods.</b> Multispectral imagery (green, red, red edge, NIR bands) was acquired using a DJI Mavic 3M drone over 6.30 hectares of mixed pasture. We extracted 65 features, including spectral, vegetation indices, height, and metrics for Gray-Level Co-occurrence matrix (GLCM) texture. Three machine learning algorithms (Random Forest – RF, SVM, XGBoost) were compared using 200,000 annotated pixels. Feature selection experiments evaluated accuracy-efficiency trade-offs. <b>Key results.</b> XGBoost achieved superior performance (F1-score = 0.69), representing 9.73 % improvement over RF. Red edge-based GLCM texture features were the most important predictors. Feature-optimized models using only 15 features retained 91.3 % of full model performance while reducing processing time by 77 %. Fast spectral-only models achieved 400× speedup with 67.9 % performance retention. Spatial analysis revealed 28.14 % bunchgrass coverage across the study area. <b>Conclusions.</b> Machine learning, particularly XGBoost with texture features, effectively discriminates between tropical grass growth forms. Feature selection enables practical deployment without substantial accuracy loss. <b>Implications.</b> This methodology enables objective assessment of pasture composition, supporting precision grazing strategies, targeted fertilization, and the sustainable intensification of tropical livestock systems. The approach is scalable for operational deployment across extensive ranching operations.<br>For instructions download attached word files above.
DOI: 10.6084/m9.figshare.29626184.v2
title: Multispectral UAV imagery and machine learning for morphological differentiation of two tropical C4-forage grasses in an integrated crop-livestock system in the State of Goias, Brazil

description: <b>Context.</b> Mixed tropical pastures containing both bunchgrasses (<i>Megathyrsus maximus</i>) and lightly sod-forming grasses (<i>Urochloa bryzantha</i>) present significant management challenges due to their contrasting growth habits and grazing requirements. Traditional visual assessment methods are inadequate for large-scale monitoring of grass growth forms. <b>Aims.</b> To develop and evaluate an automated system for detecting and mapping bunchgrass distribution in tropical pastures using multispectral UAV imagery and machine learning algorithms, enabling precision grazing management. <b>Methods.</b> Multispectral imagery (green, red, red edge, NIR bands) was acquired using a DJI Mavic 3M drone over 6.30 hectares of mixed pasture. We extracted 65 features, including spectral, vegetation indices, height, and metrics for Gray-Level Co-occurrence matrix (GLCM) texture. Three machine learning algorithms (Random Forest – RF, SVM, XGBoost) were compared using 200,000 annotated pixels. Feature selection experiments evaluated accuracy-efficiency trade-offs. <b>Key results.</b> XGBoost achieved superior performance (F1-score = 0.69), representing 9.73 % improvement over RF. Red edge-based GLCM texture features were the most important predictors. Feature-optimized models using only 15 features retained 91.3 % of full model performance while reducing processing time by 77 %. Fast spectral-only models achieved 400× speedup with 67.9 % performance retention. Spatial analysis revealed 28.14 % bunchgrass coverage across the study area. <b>Conclusions.</b> Machine learning, particularly XGBoost with texture features, effectively discriminates between tropical grass growth forms. Feature selection enables practical deployment without substantial accuracy loss. <b>Implications.</b> This methodology enables objective assessment of pasture composition, supporting precision grazing strategies, targeted fertilization, and the sustainable intensification of tropical livestock systems. The approach is scalable for operational deployment across extensive ranching operations.<br>For instructions download attached word files above.
DOI: 10.6084/m9.figshare.29626184.v1
title: Multispectral UAV imagery and machine learning for morphological differentiation of two tropical C4-forage grasses in an integrated crop-livestock system in the State of Goias, Brazil

description: <b>Context.</b> Mixed tropical pastures containing both bunchgrasses (<i>Megathyrsus maximus</i>) and lightly sod-forming grasses (<i>Urochloa bryzantha</i>) present significant management challenges due to their contrasting growth habits and grazing requirements. Traditional visual assessment methods are inadequate for large-scale monitoring of grass growth forms. <b>Aims.</b> To develop and evaluate an automated system for detecting and mapping bunchgrass distribution in tropical pastures using multispectral UAV imagery and machine learning algorithms, enabling precision grazing management. <b>Methods.</b> Multispectral imagery (green, red, red edge, NIR bands) was acquired using a DJI Mavic 3M drone over 6.30 hectares of mixed pasture. We extracted 65 features, including spectral, vegetation indices, height, and metrics for Gray-Level Co-occurrence matrix (GLCM) texture. Three machine learning algorithms (Random Forest – RF, SVM, XGBoost) were compared using 200,000 annotated pixels. Feature selection experiments evaluated accuracy-efficiency trade-offs. <b>Key results.</b> XGBoost achieved superior performance (F1-score = 0.69), representing 9.73 % improvement over RF. Red edge-based GLCM texture features were the most important predictors. Feature-optimized models using only 15 features retained 91.3 % of full model performance while reducing processing time by 77 %. Fast spectral-only models achieved 400× speedup with 67.9 % performance retention. Spatial analysis revealed 28.14 % bunchgrass coverage across the study area. <b>Conclusions.</b> Machine learning, particularly XGBoost with texture features, effectively discriminates between tropical grass growth forms. Feature selection enables practical deployment without substantial accuracy loss. <b>Implications.</b> This methodology enables objective assessment of pasture composition, supporting precision grazing strategies, targeted fertilization, and the sustainable intensification of tropical livestock systems. The approach is scalable for operational deployment across extensive ranching operations.
DOI: 10.15468/dl.5gskwf
title: Occurrence Download

description: A dataset containing 155 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Jonian Dolphin Conservation di Taranto marine mammal sightings 2009-2012"
 ]
}

 The dataset includes 155 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033984-250717081556266/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.29627099
title: Refined XL-MS analysis results of FtsH-YidC purified samples

description: Refined XL-MS analysis results of FtsH-YidC purified samples
DOI: 10.6084/m9.figshare.29627099.v1
title: Refined XL-MS analysis results of FtsH-YidC purified samples

description: Refined XL-MS analysis results of FtsH-YidC purified samples