cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5061/dryad.kwh70rzjd
title: Friction data from: Directional hydration lubrication underwater: Enabled by TA/PAAm submicron particles

description: Achieving durable hydration lubrication underwater is challenging due to
 interference from interfacial hydration layers. This study developed
 tannic acid/polyacrylamide (TA/PAAm) nanoparticles, synergistically
 combining TA's adhesive pyrogallol groups with PAAm's
 hydrophilic chains to enhance aqueous lubrication. Tests showed TA/PAAm
 suspensions improved copper/ultra-high molecular weight polyethylene
 tribo-pair wettability by 42% (contact angle reduction), lowered friction
 coefficients by 29%, and reduced wear volume by 56% versus water.
 Mechanistically, TA immobilizes nanoparticles onto surfaces via
 catechol-mediated adhesion to form a protective film, whereas PAAm
 disrupts the hydration layer via its hydrophilicity, establishing a
 hydrogen-bonded lubrication network and thereby enhancing lubrication
 performance. This strategy provides an environmentally friendly
 directional lubrication solution for marine applications such as
 water-lubricated bearings and subsea mechanical seals.
description: # Friction data from: Directional hydration lubrication underwater:
 Enabled by TA/PAAm submicron particles Dataset DOI:
 [10.5061/dryad.kwh70rzjd](https://doi.org/10.5061/dryad.kwh70rzjd) ##
 Description of the data and file structure The friction experimental data
 and parameters related to the paper 'Directional Hydration
 Lubrication Underwater: Enabled by TA/PAAm Nanoparticles'. ### Files
 and variables #### File: Time_-_Coefficient_of_friction-50N.txt
 **Description:** Friction data under a 50N load ##### Variables * The
 friction time under a 50N load * Water:Friction data under 50N load in
 water environment * TA:Friction data under 50N load in TA environment *
 PAAm:Friction data under 50N load in PAAm environment * TA/PAAm-1:Friction
 data under 50N load in TA/PAAm-1 environment * TA/PAAm-2:Friction data
 under 50N load in TA/PAAm-2 environment * TA/PAAm-3 :Friction data under
 50N load in TA/PAAm-3 environment #### File:
 Time_-_Coefficient_of_friction-100N.txt **Description:** Friction data
 under a 100N load ##### Variables * The friction time under a 100N load *
 Water:Friction data under 100N load in water environment * TA:Friction
 data under 100N load in TA environment * PAAm:Friction data under 100N
 load in PAAm environment * TA/PAAm-1:Friction data under 100N load in
 TA/PAAm-1 environment * TA/PAAm-2:Friction data under 100N load in
 TA/PAAm-2 environment * TA/PAAm-3: Friction data under 100N load in
 TA/PAAm-3 environment #### File: Time_-_Coefficient_of_friction-150N.txt
 **Description:** Friction data under a 150N load ##### Variables * The
 friction time under a 150N load * Water: Friction data under 150N load in
 water environment * TA: Friction data under 150N load in TA environment *
 PAAm: Friction data under 150N load in PAAm environment * TA/PAAm-:
 Friction data under 150N load in TA/PAAm-1 environment * TA/PAAm-2:
 Friction data under 150N load in TA/PAAm-2 environment * TA/PAAm-3:
 Friction data under 150N load in TA/PAAm-3 environment
DOI: 10.15468/dl.cd6q4q
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Perca fluviatilis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1870",
 "Year is less than or equal to 1874"
 ]
}

 The dataset includes 9 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004539-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6/lvrrae/vwqzty
title: drone_0021_03.mat

description: File uploaded via API
DOI: 10.15468/dl.zpssya
title: Occurrence Download

description: A dataset containing 664 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Macroptilium bracteatum",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 664 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004540-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6/lvrrae/st0bgo
title: drone_0210_01.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/bx5dfp
title: drone_0403_02.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/ru3ewn
title: drone_3040_00.mat

description: File uploaded via API
DOI: 10.15468/dl.pw5she
title: Occurrence Download

description: A dataset containing 336 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Amphorina linensis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 336 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004541-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6/lvrrae/9hyhe8
title: drone_4003_04.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/0sm2tu
title: drone_3001_00.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/s2hxkb
title: drone_0320_00.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/e6zzn1
title: drone_0401_01.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/kti7rv
title: drone_0041_03.mat

description: File uploaded via API
DOI: 10.15468/dl.q557pu
title: Occurrence Download

description: A dataset containing 98 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is one of (Ceratodontiformes, Coelacanthiformes)"
 ]
}

 The dataset includes 98 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004542-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jbh9zf
title: Occurrence Download

description: A dataset containing 3176 records in GBIF matching the query:
SELECT
 '523' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= 51.21395264452012
 AND decimalLatitude &lt;= 56.135291543055025
 AND decimalLongitude &gt;= 106.90300826225503
 AND decimalLongitude &lt;= 122.32367320199155
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 3176 records were derived from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004524-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6/lvrrae/egj8ya
title: drone_4100_02.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/gbp8kv
title: drone_3100_02.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/qtphcz
title: drone_4030_02.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/fkevsm
title: drone_0023_02.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/kch2od
title: drone_0021_04.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/avxv3q
title: drone_1040_00.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/d3mvnw
title: drone_3020_01.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/mewlqm
title: drone_0010_00.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/pt9gbb
title: drone_0003_00.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/yi6u4q
title: drone_0300_05.mat

description: File uploaded via API
DOI: 10.5061/dryad.6q573n6c2
title: Does ecology predict taxonomy? How ecological differentiation can be used to spatially infer intra-specific diversity

description: Assessing the true dimension of biodiversity is a major challenge. Many
 species hide within them a diversity that is now being uncovered using
 molecular data. However, population genetic studies tend to be
 resource-consuming and more difficult to apply to a broader range of taxa,
 limiting scalability. Moreover, the growing shortage of trained
 taxonomists makes it difficult to rely on comparative morphological
 studies to assess divergence and speciation processes for the vast
 majority of species-rich taxonomic groups. Here, we explore the usefulness
 of the “ecological speciation” concept and explore how these hidden
 lineages tend to occupy a distinct environmental niche that can be used to
 identify natural groups in the geographical space. From a total of 298
 species complexes, we assess the accuracy of five clustering methods and
 Random Forest models for correctly classifying the occurrences of the
 different subspecies based only on environmental data. For the best
 performing clustering method (Gaussian Mixture Models), we obtained that
 species can be predicted above random classification with a median
 Adjusted Rand Index of 0.37, only by their environmental profile. Random
 Forest, on the other hand, showed high accuracy for most of the species
 (&gt;0.75). We believe accuracy could be further improved by using
 species-specific climatic variables, although this study focuses on a
 widely applicable method. Our goal is to demonstrate that clustering
 methods can be used on a large scale to reveal the true diversity hidden
 within taxonomic complexes, thereby reducing the time and budget required
 for exploratory analysis. We also aim to demonstrate the extent to which
 different taxa are determined and delimited by the environment.
description: # Does ecology predict taxonomy? How ecological differentiation can be
 used to spatially infer intra-specific diversity Dataset DOI:
 [10.5061/dryad.6q573n6c2](https://doi.org/10.5061/dryad.6q573n6c2) ##
 Description of the data and file structure The code
 ClusterStabilityFunction.R is used to clusterize environmental data
 extracted for each occurrence point (see OccDataAnalysisWithVars.RData) to
 check correspondence with recognized taxonomic units. The code is designed
 to perform 500 replicates and is intended to run in parallel. taxonKey:
 Identifier for each unique group of taxa, used to differentiate between
 subspecies.\ speciesKey: Identifier at the species level, which can
 include multiple subspecies (taxonKey). ## Code/software The file is
 developed for the R programming language.
DOI: 10.25346/s6/lvrrae/lsyvoc
title: drone_0001_02.mat

description: File uploaded via API
DOI: 10.5061/dryad.6m905qgdk
title: Egg characteristics of female common terns are repeatable, and vary with maternal age and laying order

description: Avian eggs exhibit striking variability in size, shape, colour, and
 maculation, not only among, but also within species. Technical and
 analytical advances in image analysis offer the opportunity to understand
 the factors underpinning this variability, especially when
 individual-based longitudinal data are available. Making use of such data,
 collected over four years, we investigated sources of variation in eight
 egg characteristics capturing the colour, spottiness, shape, and size of
 1589 eggs from 687 clutches produced by 330 female common terns (Sterna
 hirundo) of known age. We found a high repeatability of the eight egg
 traits, both within clutches (range 0.48 – 0.77), and among clutches of
 the same female laid in different years (range 0.48 – 0.73). We also
 observed a within-female increase in egg size and spottiness with age, and
 evidence for selective disappearance of females producing spottier eggs,
 suggesting that egg maculation could reveal female quality. We also found
 that the size and the shape of eggs were affected by their laying order
 within the clutch, suggesting that these traits may mediate intra-brood
 competition. We suggest further studies to identify the specific agents of
 selection that shape variation in egg size and morphology, to fully
 understand the eco-evolutionary significance of this extended female
 phenotype and its potential consequences for reproductive success and
 offspring survival.
description: READ ME FOR Dryad_data_rep_eggs.csv Data used for analyses presented in:
 Egg Characteristics of Female Common Terns Are Repeatable, and Vary With
 Maternal Age and Laying Order by Bichet, C., Moiron, M., Kürten N., Vedder
 O., & Bouwhuis S. published in Ecology and Evolution for information
 regarding the data or analyses, please contact
 [coraline.bichet@univ-lr.fr](mailto:coraline.bichet@univ-lr.fr)
 and [sandra.bouwhuis@ifv-vogelwarte.de](mailto:sandra.bouwhuis@ifv-vogelwarte.de) these data are part of those collected in an ongoing individual-based longitudinal population study additional data are likely to be available, and we are often very happy to collaborate provided that none of our own staff or students is working on a conflicting project * Female_ID: identity of the females * age: age in years * year: breeding season * nest_id: Identity of the nest * CSLO: CS for clutch size and LO for a laying order * The next columns are the egg parameters related to maculation, colouration, and shape analyzed in the related study.
DOI: 10.25346/s6/lvrrae/wnbihx
title: drone_4000_04.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/znlowf
title: drone_0300_03.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/qdkwrr
title: drone_0300_01.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/cle6bf
title: drone_0200_00.mat

description: File uploaded via API
DOI: 10.15468/dl.w2fth3
title: Occurrence Download

description: A dataset containing 1561 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dysmicoccus brevipes)"
 ]
}

 The dataset includes 1561 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004543-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6/lvrrae/dh0chk
title: drone_0100_03.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/3cmxfk
title: drone_4000_05.mat

description: File uploaded via API
DOI: 10.5061/dryad.zgmsbccs4
title: Data from: Surface elevation trends in natural and restored coastal forested wetlands reveal vulnerability to saltwater intrusion and sea level rise

description: Accelerating sea level rise and increasing frequency of storms are
 impacting coastal wetlands. Similar to salt marshes, coastal freshwater
 wetlands provide important flood protection and storm abatement services,
 but their capacity to keep up with sea level rise and associated saltwater
 intrusion remains unclear. Long-term monitoring of wetland soil surface
 elevation using surface elevation table and marker horizons (SET-MH) has
 been conducted in salt marshes, while forested wetlands have not received
 as much attention. In 2015, we installed 18 SET-MHs in one restored and
 two mature forested wetlands on the coastal plain of North Carolina, all
 of which have experienced saltwater intrusion in recent years. This
 dataset includes individual pin measurements for the 18 SETs from 2016 to
 2022. Trends are reported in the manuscript, but we provide the raw data
 for future use. 
description: # Data from: Surface elevation trends in natural and restored coastal
 forested wetlands reveal vulnerability to saltwater intrusion and sea
 level rise Dataset DOI:
 [10.5061/dryad.zgmsbccs4](https://doi.org/10.5061/dryad.zgmsbccs4) ##
 Description of the data and file structure Surface elevation tables (SETs)
 were installed in three sites: Timberlake Observatory for Wetland
 Restoration, Palmetto Peartree Preserve, and Goose Creek State Park. These
 are mature forested wetlands in coastal North Carolina. SETs were
 monitored annually from 2016 to 2022. Pin measurements included in the
 dataset can be used to calculate elevation change trends.  ### Files and
 variables #### File: set_data_public.xlsx **Description:**  #####
 Variables * SET ID: identification code of each SET * Year: year of
 measurement * Month: Month of measurement * Day: day of the month of
 measurement * Arm position: Numbers 1-4 indicating cardinal direction of
 pins. 1: North, 2: South, 3: East, 4: West * Pin number: Numbers 1-9
 indicating pins in the arm. * Height: Height of the pin measured in mm
 above the arm. * qaqc_code: Code (1) indicating presence of holes, or wood
 that prevented accurate measurement of soil elevation. * Site: Name of
 site of SET location. TL= Timberlake Observatory for Wetland Restoration,
 PP= Palmetto Peartree Preserve, GS= Goose Creek State Park.
DOI: 10.25346/s6/lvrrae/2hvhyw
title: drone_0040_05.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/bgv0in
title: drone_0001_01.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/4votpi
title: drone_0200_05.mat

description: File uploaded via API
DOI: 10.5061/dryad.msbcc2gbt
title: AVIRIS-NG-like smart virtual remote sensing via spectra-aware physics informed GANs

description: This paper aims to create a physics informed virtual replica of the
 hyperspectral image captured by NASA’s Airborne Visible InfraRed Imaging
 Spectrometer - Next Generation (AVIRIS-NG) sensor equipped on manned
 aircraft. Few image samples are selected from study site around New
 Mexico, USA from flight mission ran in 2019. Out of 425 bands, 8 bands are
 utilized. For each band, reflectance spectra are chosen from United States
 Geological Survey (USGS) based on site specific geographical features.
 These spectras are infused during the image generation process with the
 correction check using matched filters. Moreover, we include the methane
 plumes along with other closely related hydrocarbons during the image
 generation process. Generative Adversarial Networks (GANs) architecture is
 employed with physics informed loss function for generating realistic and
 physically plausible images. Additionally, we also propose a new light
 weight dataset for creating the virtual replica of the AVIRIS-NG sensor on
 selected 8 bands in the visible light spectrum and the short-wave infrared
 region.
description: # AVIRIS-NG-like smart virtual remote sensing via spectra-aware physics
 informed GANs Dataset DOI:
 [10.5061/dryad.msbcc2gbt](https://doi.org/10.5061/dryad.msbcc2gbt) ##
 Description of the data and file structure aviris-ng.zip: This is light
 weight 8 channel benchmark dataset for synthesis of hyperspectral image
 from NASA JPL , AVIRIS-NG sensor. The dataset consist of 3 channels from
 visible spectrum (RGB- 460 nm, 550 nm, 640 nm) and 5 channels (2004 nm,
 2109 nm, 2310 nm, 2350 nm, 2360 nm) in Short wave Infrared(SWIR) spectrum
 from paper title "AVIRIS-NG-like smart virtual remote sensing via
 spectra-aware physics informed GANS". "Train" and
 "test" directories consist of train and test data. Both of these
 directories have "Hyper" folder which contains the 5 channel
 SWIR in .tif format. Also, both train and test folders have
 "Multi" directory contains 3 channel RGB in .tif format. BibTeX
 for paper:
 [@inproceedings](https://www.kaggle.com/inproceedings){giri2025aviris,
 title={AVIRIS-NG-Like Smart Virtual Remote Sensing via Spectra-Aware
 Physics Informed GANs}, author={Giri, Sachin and Krzysiak, Rafal and
 Hollenbeck, Derek and Chen, YangQuan}, booktitle={International Design
 Engineering Technical Conferences and Computers and Information in
 Engineering Conference}, volume={89251}, pages={V005T07A001}, year={2025},
 organization={American Society of Mechanical Engineers} } CC/More details
 - [https://avirisng.jpl.nasa.gov/](https://avirisng.jpl.nasa.gov/) ##
 Code/software Contact at
 [sachingiri@ucmerced.edu](mailto:sachingiri@ucmerced.edu) if you need full
 code for the paper
DOI: 10.25346/s6/lvrrae/2khd5i
title: drone_3000_00.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/rl7p7t
title: drone_0010_02.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/w4ucrd
title: drone_0040_04.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/sxgbop
title: drone_0030_04.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/1xgfwz
title: drone_0001_03.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/u7g9i7
title: drone_1000_02.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/56qmgp
title: drone_0003_02.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/ahg2yw
title: drone_0010_01.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/wsyppy
title: drone_0004_03.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/azdsqb
title: drone_0001_05.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/9hvchs
title: drone_0004_05.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/mzy6zg
title: drone_3000_03.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/vyrfgl
title: drone_0400_01.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/bhpw6y
title: drone_0003_03.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/ayxhfk
title: drone_0002_04.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/soebuk
title: drone_0100_05.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/itbs8j
title: drone_0300_04.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/mijg0p
title: drone_0004_00.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/otf1zn
title: drone_0200_03.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/k2so9v
title: drone_0020_05.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/2q77pv
title: drone_0002_02.mat

description: File uploaded via API
DOI: 10.15468/dl.8xdchv
title: Occurrence Download

description: A dataset containing 94 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Perca fluviatilis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1875",
 "Year is less than or equal to 1879"
 ]
}

 The dataset includes 94 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004544-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25346/s6/lvrrae/0ogpds
title: drone_0030_03.mat

description: File uploaded via API
DOI: 10.25346/s6/lvrrae/w2luky
title: drone_2000_03.mat

description: File uploaded via API