cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13829061
title: Explainable few-shot learning workflow for detecting invasive and exotic tree species

description: This is the supporting dataset of research work: Explainable few-shot learning workflow for detecting invasive and exotic tree species. (Link to be added after the publication) In this research, we presents a workflow that tackles both challenges by proposing an explainable few-shot learning workflow for detecting invasive and exotic tree species in the Atlantic Forest of Brazil using Unmanned Aerial Vehicle (UAV) images. By integrating a Siamese network with explainable AI (XAI), the workflow enables the classification of tree species with minimal labeled data while providing visual, case-based explanations for the predictions.

The workflow is accessible in this GitHub repository (Link to be added after the publication). The required dataset of this workflow in provided in this Zenodo repository.

This dataset repository has the following contents





uav_img.zip: the UAV orthomosaic image (.tif) of the study area used in this research, with related metadata


tree_labels.zip: the labels of trees created by expert, available in .shp and .gpkg



cutouts.zip: tree cutouts used in this study. They are two sub-directories:



all_cutouts: all the candidated cutouts from three sources. See the README.md file insisde this folder for more information

selected cutout: the manually selected cutouts from all cutouts used for training.





training_pairs.zip: training data created for base network traning. It is created by pairing the selected cutouts.


netflora.zip: Netflora workflow prediction results
DOI: 10.5281/zenodo.13829957
title: Explainable few-shot learning workflow for detecting invasive and exotic tree species

description: This is the supporting dataset of research work: Explainable few-shot learning workflow for detecting invasive and exotic tree species. (Link to be added after the publication) In this research, we presents a workflow that tackles both challenges by proposing an explainable few-shot learning workflow for detecting invasive and exotic tree species in the Atlantic Forest of Brazil using Unmanned Aerial Vehicle (UAV) images. By integrating a Siamese network with explainable AI (XAI), the workflow enables the classification of tree species with minimal labeled data while providing visual, case-based explanations for the predictions.

The workflow is accessible in this GitHub repository (Link to be added after the publication). The required dataset of this workflow in provided in this Zenodo repository.

This dataset repository has the following contents





uav_img.zip: the UAV orthomosaic image (.tif) of the study area used in this research, with related metadata


tree_labels.zip: the labels of trees created by expert, available in .shp and .gpkg



cutouts.zip: tree cutouts used in this study. They are two sub-directories:



all_cutouts: all the candidated cutouts from three sources. See the README.md file insisde this folder for more information

selected cutout: the manually selected cutouts from all cutouts used for training.





training_pairs.zip: training data created for base network traning. It is created by pairing the selected cutouts.


netflora.zip: Netflora workflow prediction results

optimized_model.zip: Optimized base models (shallow and deep) and refined models with different shots/fold setup.
DOI: 10.5281/zenodo.13833540
title: Explainable few-shot learning workflow for detecting invasive and exotic tree species

description: This is the supporting dataset of research work: Explainable few-shot learning workflow for detecting invasive and exotic tree species. (Link to be added after the publication) In this research, we presents a workflow that tackles both challenges by proposing an explainable few-shot learning workflow for detecting invasive and exotic tree species in the Atlantic Forest of Brazil using Unmanned Aerial Vehicle (UAV) images. By integrating a Siamese network with explainable AI (XAI), the workflow enables the classification of tree species with minimal labeled data while providing visual, case-based explanations for the predictions.

The workflow is accessible in this GitHub repository (Link to be added after the publication). The required dataset of this workflow in provided in this Zenodo repository.

This dataset repository has the following contents





uav_img.zip: the UAV orthomosaic image (.tif) of the study area used in this research, with related metadata


tree_labels.zip: the labels of trees created by expert, available in .shp and .gpkg



cutouts.zip: tree cutouts used in this study. They are two sub-directories:



all_cutouts: all the candidated cutouts from three sources. See the README.md file insisde this folder for more information

selected cutout: the manually selected cutouts from all cutouts used for training.





training_pairs_20000.zarr.zip: training data created for base network traning. It is created by pairing the selected cutouts.


netflora.zip: Netflora workflow prediction results

optimized_models.zip: Optimized base models (shallow and deep) and refined models with different shots/fold setup.

n_fold_x_validation.zip: data pairs for refinement traing, with n fold and x valiation setup.
DOI: 10.5281/zenodo.13833791
title: Explainable few-shot learning workflow for detecting invasive and exotic tree species

description: This is the supporting dataset of research work: Explainable few-shot learning workflow for detecting invasive and exotic tree species. (Link to be added after the publication) In this research, we presents a workflow that tackles both challenges by proposing an explainable few-shot learning workflow for detecting invasive and exotic tree species in the Atlantic Forest of Brazil using Unmanned Aerial Vehicle (UAV) images. By integrating a Siamese network with explainable AI (XAI), the workflow enables the classification of tree species with minimal labeled data while providing visual, case-based explanations for the predictions.

The workflow is accessible in this GitHub repository (Link to be added after the publication). The required dataset of this workflow in provided in this Zenodo repository.

This dataset repository has the following contents





uav_img.zip: the UAV orthomosaic image (.tif) of the study area used in this research, with related metadata


tree_labels.zip: the labels of trees created by expert, available in .shp and .gpkg



cutouts.zip: tree cutouts used in this study. They are two sub-directories:



all_cutouts: all the candidated cutouts from three sources. See the README.md file insisde this folder for more information

selected cutout: the manually selected cutouts from all cutouts used for training.





training_pairs_20000.zarr.zip: training data created for base network traning. It is created by pairing the selected cutouts.


netflora.zip: Netflora workflow prediction results

optimized_models.zip: Optimized base models (shallow and deep) and refined models with different shots/fold setup.

n_fold_x_validation.zip: data pairs for refinement traing, with n fold and x valiation setup.
DOI: 10.5281/zenodo.15284157
title: Explainable few-shot learning workflow for detecting invasive and exotic tree species

description: This is the supporting dataset of research work: Explainable few-shot learning workflow for detecting invasive and exotic tree species. (Link to be added after the publication) In this research, we presents a workflow that tackles both challenges by proposing an explainable few-shot learning workflow for detecting invasive and exotic tree species in the Atlantic Forest of Brazil using Unmanned Aerial Vehicle (UAV) images. By integrating a Siamese network with explainable AI (XAI), the workflow enables the classification of tree species with minimal labeled data while providing visual, case-based explanations for the predictions.

The workflow is accessible in this GitHub repository (Link to be added after the publication). The required dataset of this workflow in provided in this Zenodo repository.

This dataset repository has the following contents





uav_img.zip: the UAV orthomosaic image (.tif) of the study area used in this research, with related metadata


tree_labels.zip: the labels of trees created by expert, available in .shp and .gpkg



cutouts.zip: tree cutouts used in this study. They are two sub-directories:



all_cutouts: all the candidated cutouts from three sources. See the README.md file insisde this folder for more information

selected cutout: the manually selected cutouts from all cutouts used for training.





training_pairs_20000.zarr.zip: training data created for base network traning. It is created by pairing the selected cutouts.


netflora.zip: Netflora workflow prediction results

optimized_models.zip: Optimized base models (shallow and deep) and refined models with different shots/fold setup.

n_fold_x_validation.zip: data pairs for refinement traing, with n fold and x valiation setup.
DOI: 10.7910/dvn/f4q8r6
title: Table5. Inequality decomposition results

description: Inequality decomposition results
DOI: 10.7910/dvn/7me5qg
title: Replication Data for JGR:Atmospheres

description: Replication Data for JGR:Atmospheres
DOI: 10.7910/dvn/89sben
title: A12_22670.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/899706c8-cd13-4423-ad1a-f060f0d1883a'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/899706c8-cd13-4423-ad1a-f060f0d1883a
DOI: 10.7910/dvn/v9c4zb
title: Supplementary Table (ST2) for the article "Relationship between severe housing damage and serum cortisol levels among survivors of the 2011 Great East Japan Earthquake: cross-sectional study"

description: Supplementary Table (ST1) for the article "Relationship between severe housing damage and serum cortisol levels among survivors of the 2011 Great East Japan Earthquake: cross-sectional study"
DOI: 10.7910/dvn/bmofvz
title: Temporary versus permanent teams in team-based learning

description: JEEHP-24-329 (Temporary 20240367)
DOI: 10.15468/dl.jwtvwk
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-120.0",
 "HasCoordinate is True",
 "HasGeospatialIssue is False",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 {
 "or" : [
 "GadmGid is U",
 "GadmGid is S",
 "GadmGid is A"
 ]
 },
 "Year 2020-2021"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002208-250424135625364/datasets/export for details.
DOI: 10.5281/zenodo.15284100
title: Lake depth and winter hydroclimate drive year-round dissolved oxygen dynamics in small mountain lakes

description: Data and code needed to replicate analysis and figures for "Lake depth and winter hydroclimate drive year-round dissolved oxygen dynamics in small mountain lakes" submitted to Journal of Geophysical Research - Biogeosciences.

This version may be updated after manuscript review.
DOI: 10.5281/zenodo.15284101
title: Lake depth and winter hydroclimate drive year-round dissolved oxygen dynamics in small mountain lakes

description: Data and code needed to replicate analysis and figures for "Lake depth and winter hydroclimate drive year-round dissolved oxygen dynamics in small mountain lakes" submitted to Journal of Geophysical Research - Biogeosciences.

This version may be updated after manuscript review.
DOI: 10.7910/dvn/86zuja
title: EFC anode-gate connection

description: Raw data
DOI: 10.7910/dvn/faal6l
title: A90_3634.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/8b092447-5bae-4611-3914-2539a7c11a2d'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/8b092447-5bae-4611-3914-2539a7c11a2d
DOI: 10.7910/dvn/fgfykr
title: A12_22542.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/21e536cb-3c1b-46a8-b7a2-44af4ddb8c08'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/21e536cb-3c1b-46a8-b7a2-44af4ddb8c08
DOI: 10.7910/dvn/fer4za
title: A11_18800.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/b471232f-7bd6-46a7-b0c2-11fe155c10a9'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/b471232f-7bd6-46a7-b0c2-11fe155c10a9
DOI: 10.7910/dvn/paszsz
title: A14_26983.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/3ecbf401-68ad-4925-aec2-915806b01837'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/3ecbf401-68ad-4925-aec2-915806b01837
DOI: 10.15468/dl.wehpmu
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Butia exilata Deble & Marchiori"
 ]
}

 The dataset includes 16 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002210-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/11567647
title: Bitcoin BTC, 7 Exchanges, 1m Full Historical Data
DOI: 10.7910/dvn/n3fldq
title: A14_27073.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/e79350b1-c2b4-44c9-ab02-071a0c8198b8'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/e79350b1-c2b4-44c9-ab02-071a0c8198b8
DOI: 10.7910/dvn/bdkycj
title: A10_18396.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/75886f62-019c-413f-8500-ef42f295d0e8'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/75886f62-019c-413f-8500-ef42f295d0e8
DOI: 10.7910/dvn/n1jcyj
title: A10_18600.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/6819bb66-df76-4756-a327-dfe1e24c6765'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/6819bb66-df76-4756-a327-dfe1e24c6765
DOI: 10.15468/dl.56km9z
title: Occurrence Download

description: A dataset containing 872 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Geometry POLYGON((30.51153 50.39676,30.48405 50.38256,30.48405 50.35869,30.50895 50.34863,30.51399 50.35315,30.52418 50.35186,30.53244 50.35805,30.54573 50.35727,30.54624 50.35263,30.55837 50.35069,30.55824 50.35831,30.54869 50.3694,30.54637 50.36979,30.54495 50.36695,30.53592 50.36708,30.53269 50.36889,30.5274 50.36863,30.52663 50.36682,30.51992 50.36708,30.51257 50.36669,30.50779 50.36889,30.51476 50.37405,30.51811 50.37173,30.52598 50.37521,30.52908 50.38205,30.52869 50.38811,30.51153 50.39676))",
 "Geometry POLYGON((30.56399 50.33081,30.55366 50.3052,30.52919 50.31284,30.54066 50.27671,30.57202 50.26218,30.59052 50.20357,30.64295 50.16445,30.67424 50.16445,30.74232 50.16366,30.6406 50.27087,30.56399 50.33081))",
 "Geometry POLYGON((30.43989 50.35874,30.44058 50.35442,30.45593 50.35434,30.46395 50.36267,30.45292 50.36544,30.43989 50.35874))",
 "Geometry POLYGON((30.30054 50.55273,30.29523 50.5334,30.27977 50.52471,30.25996 50.51263,30.27058 50.49958,30.26285 50.48943,30.25464 50.49088,30.24836 50.47494,30.25754 50.46817,30.24111 50.45175,30.24353 50.43918,30.23435 50.42517,30.24498 50.42227,30.25947 50.42517,30.2759 50.43,30.28025 50.44401,30.32035 50.44015,30.34451 50.4503,30.32373 50.46914,30.33388 50.4788,30.35562 50.4788,30.36867 50.48267,30.35224 50.49185,30.34693 50.48847,30.32422 50.48508,30.31359 50.49571,30.34209 50.49668,30.37688 50.50393,30.34548 50.53727,30.33147 50.55031,30.31552 50.55515,30.30054 50.55273))",
 "Geometry POLYGON((30.4355 50.56107,30.45649 50.54949,30.47423 50.54985,30.47929 50.56939,30.44925 50.57229,30.4355 50.56107))"
 ]
 },
 "TaxonKey is Aves",
 "Year 2024-2025"
 ]
}

 The dataset includes 872 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002211-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/prkkrm
title: MADRS_MaddenDissertation_Chapter4_TaxLawSampled+Population

description: This spreadsheet contains case names, links, and citations for the full population of Canadian tax law decisions (other than those from Quebec) from 2022, and identifies the 106 decisions from within this population that were randomly selected for study within Chapter 4 of Mike Madden's doctoral dissertation.
DOI: 10.7910/dvn/5lsop0
title: A13_24016.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/8d538232-7046-4f76-8f2f-440fa84f3b5d'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/8d538232-7046-4f76-8f2f-440fa84f3b5d
DOI: 10.7910/dvn/pubeqi
title: Surface tension (in N/m) for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth.

description: Data Set S7. Surface tension (in N/m) for a 90-K methene-ethane-nitrogen liquid as a function of methane–alkane fraction and depth. The header row across the top specifies the value of methane–alkane fraction (see text for definition and use), and a header column along the left side specifies the depth below the surface in meters.
DOI: 10.7910/dvn/j7cwsu
title: Global 2050 Anopheles stephensi suitability for Plasmodium vivax transmission at 97.5% CI (5 arc minutes), RCP 4.5, HadGEM2-ES

description: This dataset contains the 2050 global distributed predicted number of months suitable for transmission of Plasmodium vivax malaria by Anopheles stephensi mosquitoes per pixel (5 arc minutes - approximately 10km sq at the equator), under RCP 4.5, HadGEM2-ES. Please refer to the accompanying publication for details.
DOI: 10.7910/dvn/figt2a
title: A12_22722.jpg

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/023dd903-5c7d-41bc-9217-1c71e3f5aa16'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/023dd903-5c7d-41bc-9217-1c71e3f5aa16
DOI: 10.7910/dvn/8f4ufo
title: Replication Data for: How prejudice shapes public perceptions of minority-organized spaces: the case of community education

description: This repository serves as a comprehensive resource for replicating the findings presented in the research paper titled "How prejudice shapes public perceptions of minority-organized spaces: the case of community education," authored by Julia Steenwegen and Maurits J. Meijers and published in the Journal of Ethnic and Migration Studies.

The repository includes the following components:

README.txt: This text file serves as the main entry point for the repository. It provides an overview of the replication files, instructions for replication, and additional notes for users.

data.sav: This file contains the dataset utilized in the study. It is formatted for use with Stata statistical software.

exp_study.do: Stata script for the experimental study. This script contains the code used to analyze data and generate results for the experimental portion of the study.

obs_study.do: Stata script for the observational study. This script contains the code used to analyze data and generate results for the observational portion of the study.
DOI: 10.5281/zenodo.11126730
title: Potentiodynamic polarisation curves and pitting descriptors of 316L stainless steel in NaCl electrolyte

description: This deposit contains all data and code supporting the analysis in:

 

Identifying stable pitting pathways in 316L stainless steel via fractal-inspired PCA-based clustering

Coelho L.B., Amand T., Torres D., Olivier M., Ustarroz J. (accepted 21 April 2025, npj Materials Degradation)

 

Included files

 

calculated_epit_data.csv – the ML-estimated critical pitting potentials (Epit)

 

calculated_logipit_data.csv – the ML-estimated log(jₚᵢₜ) values corresponding to Epit

 

calculated_epass_data.csv – the ML-estimated passive potentials (Epass) and log(jₚₐₛₛ)

 

calculated_epit_df_meta_data.csv – manually curated Epit values just before stable pit growth

 

calculated_logipit_df_meta_data.csv – manually curated log(jₚᵢₜ) values just before stable pit growth

 

Critical-descriptor definitions

 

The critical pitting potentials (Epit) and passive potentials (Epass) were estimated using the machine learning (ML) model described previously [1].

The files calculated_epit_df_meta_data.csv and calculated_logipit_df_meta_data.csv contain the last metastable-pitting descriptors—manually identified—immediately prior to the onset of stable pit growth.

 

Experimental methods

The macro-scale potentiodynamic polarization (PP) tests were performed at varying NaCl concentrations (0.005 M, 0.01 M and 0.05 M). Using an SP-300 (Bio-Logic) potentiostat inside a Faraday cage, the cell comprised:

 

WE: 316L SS specimen (~1 cm² exposed area)

 

RE: Ag/AgCl/KCl_sat inside a Luggin capillary

 

CE: platinum foil

 

After 60 min immersion to stabilize the open-circuit potential (OCP), polarization scans ran from –30 mV to +900 mV vs. OCP at 0.5 mV/s, matching our previous macro PP study on the same sample [2]. Each NaCl concentration was tested 28 times (84 curves total).

 

References

 

[1] L.B. Coelho, D. Torres, V. Vangrunderbeek, M. Bernal, G.M. Paldino, G. Bontempi, J. Ustarroz, Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysis, Npj Mater Degrad 7 (2023) 82. https://doi.org/10.1038/s41529-023-00403-z.

 

[2] L. B. Coelho, S. Kossman, A. Mejias, X. Noirfalise, A. Montagne, A. Van Gorp, M. Poorteman, M. G. Olivier, “Mechanical and corrosion characterization of industrially treated 316L stainless steel surfaces,” Surf. Coat. Technol. 382 (2020) 125175. https://doi.org/10.1016/j.surfcoat.2019.125175
DOI: 10.5281/zenodo.11126731
title: Potentiodynamic polarisation curves of 316L stainless steel in NaCl electrolyte

description: The macro-scale potentiodynamic polarisation (PP) tests were performed at varying NaCl concentrations (0.005 M, 0.01 M and 0.05 M).

Using an SP-300 (Bio-logic) potentiostat, the electrochemical cell (inside a Faraday cage) comprised the stainless steel specimen as the working electrode (WE, ~1 cm² of exposed area), an Ag/AgCl/KClsat as the reference electrode (inside a Luggin capillary) and a platinum foil as the counter electrode (CE). The polarisation tests started from – 30 mV to +900 mV (Vs OCP) after 60 min of immersion to guarantee relatively stable OCP values.

The measurements were repeated 28 times for each NaCl electrolyte concentration, generating a complete dataset of 84 polarisation curves.A scan rate of 0.5 mV/s was chosen to match our previous Macro PP study on the same 316L SS sample[1].

[1] L.B. Coelho, S. Kossman, A. Mejias, X. Noirfalise, A. Montagne, A. Van Gorp, M. Poorteman, M.G. Olivier, Mechanical and corrosion characterization of industrially treated 316L stainless steel surfaces, Surf Coat Technol 382 (2020) 125175. https://doi.org/10.1016/j.surfcoat.2019.125175.