cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10909614
title: Data from: BioEncoder: a metric learning toolkit for comparative organismal biology

description: BioEncoder: a metric learning toolkit for comparative organismal biology

Abstract - In the realm of biological image analysis, deep learning (DL) has been a transformative force.  Tasks like finding areas of interest within an image (segmentation) and/or sorting images into groups (classification), can now be automated and achieve unprecedented efficiency and accuracy. However, conventional DL methods are challenged by large-scale biodiversity datasets, which are characterized by unbalanced classes and small phenotypic differences between them. Here we present BioEncoder, a user-friendly toolkit for metric learning, which overcomes the aforementioned challenges by focussing on learning relationships between individual data points rather than on the separability of classes. This approach simplifies training of robust image classification models for biologists, democratizing access to advanced DL techniques. BioEncoder is encapsulated in a Python package, crafted for ease of use and flexibility across diverse datasets. It features taxon-agnostic data loaders, custom augmentation options, and streamlined hyperparameter adjustments through text-based configuration files. The toolkit's significance lies in its potential to unlock new possibilities in biological image analysis - from phenomics and disease diagnosis to species identification and ecosystem monitoring. BioEncoder focuses on the urgent need for toolkits bridging the gap between complex DL methodologies and practical applications in biological research.

Dataset - This data repository includes two things: a snapshot of the BioEncoder package (BioEncoder-main.zip, version 0.1.1, downloaded from https://github.com/agporto/BioEncoder on 2024-04-02 at 16:42), and the damselfly dataset used for the case study presented in the paper (bioencoder_data.zip). The dataset archive also encompasses the configuration files and the final model checkpoints from the case study, as well as a script to reproduce the results and figures presented in the paper.

How to use - Get started by consulting the GithHub repository for information on how to install BioEncoder, then download the data archive and run the script. Some parts of the script can be executed using the model checkpoints, for orther parts the training rountine needs to be run.
DOI: 10.5281/zenodo.12786244
title: Data from: BioEncoder: a metric learning toolkit for comparative organismal biology

description: BioEncoder: a metric learning toolkit for comparative organismal biology

Abstract - In the realm of biological image analysis, deep learning (DL) has become a core toolkit, e.g., for segmentation and classification. However, conventional DL methods are challenged by large biodiversity datasets characterized by unbalanced classes and hard-to-distinguish phenotypic differences between them. Here we present BioEncoder, a user-friendly toolkit for metric learning, which overcomes these challenges by focussing on learning relationships between individual data points rather than on the separability of classes. BioEncoder is released as a Python package, created for ease of use and flexibility across diverse datasets. It features taxon-agnostic data loaders, custom augmentation options, and simple hyperparameter adjustments through text-based configuration files. The toolkit's significance lies in its potential to unlock new research avenues in biological image analysis while democratizing access to advanced deep metric learning techniques. BioEncoder focuses on the urgent need for toolkits bridging the gap between complex DL pipelines and practical applications in biological research.

Dataset - This data repository includes two things: a snapshot of the BioEncoder package (BioEncoder-main.zip, version 1.0.0, downloaded from https://github.com/agporto/BioEncoder on 2024-07-19 at 17:20), and the damselfly dataset used for the case study presented in the paper (bioencoder_data.zip). The dataset archive also encompasses the configuration files and the final model checkpoints from the case study, as well as a script to reproduce the results and figures presented in the paper.

How to use - Get started by consulting the GithHub repository for information on how to install BioEncoder, then download the data archive and run the script. Some parts of the script can be executed using the model checkpoints, for orther parts the training rountine needs to be run.
DOI: 10.5281/zenodo.10909613
title: Data from: BioEncoder: a metric learning toolkit for comparative organismal biology

description: BioEncoder: a metric learning toolkit for comparative organismal biology

Abstract - In the realm of biological image analysis, deep learning (DL) has become a core toolkit, e.g., for segmentation and classification. However, conventional DL methods are challenged by large biodiversity datasets characterized by unbalanced classes and hard-to-distinguish phenotypic differences between them. Here we present BioEncoder, a user-friendly toolkit for metric learning, which overcomes these challenges by focussing on learning relationships between individual data points rather than on the separability of classes. BioEncoder is released as a Python package, created for ease of use and flexibility across diverse datasets. It features taxon-agnostic data loaders, custom augmentation options, and simple hyperparameter adjustments through text-based configuration files. The toolkit's significance lies in its potential to unlock new research avenues in biological image analysis while democratizing access to advanced deep metric learning techniques. BioEncoder focuses on the urgent need for toolkits bridging the gap between complex DL pipelines and practical applications in biological research.

Dataset - This data repository includes two things: a snapshot of the BioEncoder package (BioEncoder-main.zip, version 1.0.0, downloaded from https://github.com/agporto/BioEncoder on 2024-07-19 at 17:20), and the damselfly dataset used for the case study presented in the paper (bioencoder_data.zip). The dataset archive also encompasses the configuration files and the final model checkpoints from the case study, as well as a script to reproduce the results and figures presented in the paper.

How to use - Get started by consulting the GithHub repository for information on how to install BioEncoder, then download the data archive and run the script. Some parts of the script can be executed using the model checkpoints, for orther parts the training rountine needs to be run.
DOI: 10.5281/zenodo.13017212
title: Data from: BioEncoder: a metric learning toolkit for comparative organismal biology

description: BioEncoder: a metric learning toolkit for comparative organismal biology

Abstract - In the realm of biological image analysis, deep learning (DL) has become a core toolkit, e.g., for segmentation and classification. However, conventional DL methods are challenged by large biodiversity datasets characterized by unbalanced classes and hard-to-distinguish phenotypic differences between them. Here we present BioEncoder, a user-friendly toolkit for metric learning, which overcomes these challenges by focussing on learning relationships between individual data points rather than on the separability of classes. BioEncoder is released as a Python package, created for ease of use and flexibility across diverse datasets. It features taxon-agnostic data loaders, custom augmentation options, and simple hyperparameter adjustments through text-based configuration files. The toolkit's significance lies in its potential to unlock new research avenues in biological image analysis while democratizing access to advanced deep metric learning techniques. BioEncoder focuses on the urgent need for toolkits bridging the gap between complex DL pipelines and practical applications in biological research.

Dataset - This data repository includes two things: a snapshot of the BioEncoder package (BioEncoder-main.zip, version 1.0.0, downloaded from https://github.com/agporto/BioEncoder on 2024-07-19 at 17:20), and the damselfly dataset used for the case study presented in the paper (bioencoder_data.zip). The dataset archive also encompasses the configuration files and the final model checkpoints from the case study, as well as a script to reproduce the results and figures presented in the paper.

How to use - Get started by consulting the GithHub repository for information on how to install BioEncoder, then download the data archive and run the script. Some parts of the script can be executed using the model checkpoints, for orther parts the training rountine needs to be run.
DOI: 10.15468/dl.asjmtq
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is United States of America",
 "GadmGid is one of (USA.43_1, USA.10_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is Prunus persica (L.) Stokes"
 ]
}

 The dataset includes 26 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038436-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.umbgec
title: Occurrence Download

description: A dataset containing 3048 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Origanum vulgare subsp. virens (Hoffmanns. & Link) Bonnier & Layens)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 3048 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038437-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9tsyx4
title: Occurrence Download

description: A dataset containing 373 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Europe, Asia)",
 "Country is Greece",
 "GadmGid is GRC.1_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ficus carica L."
 ]
}

 The dataset includes 373 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038439-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pmf4bx
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Origanum vulgare subsp. viridulum (Martrin-Donos) Nyman)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 118 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038438-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hufvss
title: Occurrence Download

description: A dataset containing 446 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Occurrence evidence, Specimen, Material citation)",
 "Geometry POLYGON((-120.05175 10.14643,-82.32955 10.14643,-82.32955 34.77725,-120.05175 34.77725,-120.05175 10.14643))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Passerella unalaschcensis (J.F.Gmelin, 1789)"
 ]
}

 The dataset includes 446 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038440-240626123714530/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.26384305
title: Figure 1: Bactericidal activity of Peptoids TM1, TM5, and TM6

description: Relative luminescence (A) and CFUs/mL (B) for P. aeruginosa Xen41 treated with 2-fold dilutions of TM1, TM5, and TM6. (A) TM1, TM5, and TM6 were all significantly different from the no treatment control with respect to their relative luminescence at concentrations of 1 μg/ml and higher. (B) TM1 was significantly different from the no treatment control for CFUs/mL at concentrations of 0.5 μg/mL and above, while TM5 and TM6 were significantly different at 2 and 4 μg/ml respectively. Data points are represented as means (n = replicates). Error is shown in ± standard deviation (SD). Statistics were performed using 2-way ANOVA, comparing antimicrobial to no treatment control. P values are: &lt;0.001 = ***, between 0.001 and 0.01 = **, and between 0.01 and 0.05 = *.
DOI: 10.15468/dl.fw6cmv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Origanum vulgare var. prismaticum Gaudin)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038442-240626123714530/datasets/export for details.
DOI: 10.6084/m9.figshare.26384311
title: Figure 2: Bactericidal of standard antibiotics

description: Relative OD600 (A) and CFUs/ml (B) for P. aeruginosa Xen41 with 2-fold dilutions of ciprofloxacin, ceftazidime, kanamycin, and meropenem. (A) Ciprofloxacin was significantly different above concentrations of 0.0625 μg/ml and above. Ceftazidime and meropenem were significantly different at concentrations above 0.125 μg/ml, while kanamycin was significantly different at concentrations of 8 μg/ml and above. (B) Ciprofloxacin showed a significant decrease compared to the no treatment control for CFUs/ml at all concentrations, while ceftazidime and meropenem were significantly different at 0.25 μg/ml and above. Kanamycin didn’t show any significant difference at any concentrations. Data points are represented as means using four replicates. Error is shown in ± standard deviation (SD). Statistics were performed using 2-way ANOVA, comparing antimicrobial to no treatment control. P values are: &lt;0.0001 = ****, between 0.0001 and 0.001 = ***, between 0.001 and 0.01 = **, and between 0.01 and 0.05 = *.
DOI: 10.6084/m9.figshare.26384311.v1
title: Figure 2: Bactericidal of standard antibiotics

description: Relative OD600 (A) and CFUs/ml (B) for P. aeruginosa Xen41 with 2-fold dilutions of ciprofloxacin, ceftazidime, kanamycin, and meropenem. (A) Ciprofloxacin was significantly different above concentrations of 0.0625 μg/ml and above. Ceftazidime and meropenem were significantly different at concentrations above 0.125 μg/ml, while kanamycin was significantly different at concentrations of 8 μg/ml and above. (B) Ciprofloxacin showed a significant decrease compared to the no treatment control for CFUs/ml at all concentrations, while ceftazidime and meropenem were significantly different at 0.25 μg/ml and above. Kanamycin didn’t show any significant difference at any concentrations. Data points are represented as means using four replicates. Error is shown in ± standard deviation (SD). Statistics were performed using 2-way ANOVA, comparing antimicrobial to no treatment control. P values are: &lt;0.0001 = ****, between 0.0001 and 0.001 = ***, between 0.001 and 0.01 = **, and between 0.01 and 0.05 = *.
DOI: 10.6084/m9.figshare.26384314
title: Figure 3: Comparative time-kill experiments of Peptoid TM5 and ciprofloxacin

description: Time course experiments for 2-fold dilutions of TM5 (A) and ciprofloxacin (B) mixed with <i>P. aeruginosa </i>Xen41 were measured for luminescence at the following time points: 0, 5, 10, 15, 20, 30, 45, 60, 90, and 120 minutes. In addition to 2-fold dilutions, a control with PBS was also measured (<i>n=</i> 4 replicates).
DOI: 10.6084/m9.figshare.26384314.v1
title: Figure 3: Comparative time-kill experiments of Peptoid TM5 and ciprofloxacin

description: Time course experiments for 2-fold dilutions of TM5 (A) and ciprofloxacin (B) mixed with <i>P. aeruginosa </i>Xen41 were measured for luminescence at the following time points: 0, 5, 10, 15, 20, 30, 45, 60, 90, and 120 minutes. In addition to 2-fold dilutions, a control with PBS was also measured (<i>n=</i> 4 replicates).
DOI: 10.15468/dl.p36bwq
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Reithrodontomys spectabilis Jones & Lawlor, 1965"
 ]
}

 The dataset includes 154 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038443-240626123714530/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.26384317
title: Figure 4: Resistance to Peptoid TM5 could not be derived through laboratory evolution

description: <i>P. aeruginosa </i>PA01was serially passaged in increased concentrations of either (A) ciprofloxacin or (B) peptoids TM5. After 3 weeks of successive passaging in concentrations doubling weekly from 0.5XMIC-2XMIC, resistance of ciprofloxacin had readily emerged with the MIC increasing from 0.125 µg/mL to 8 µg/mL. In contrast, no increase in MIC was observed for peptoid TM5. To evaluate whether TM5 was effective against drug resistant strains, the newly evolved ciprofloxacin-resistant isolates were tested against peptoids TM5, which was found to be equally effective with a MIC of 4 µg/mL.
DOI: 10.6084/m9.figshare.26384317.v1
title: Figure 4: Resistance to Peptoid TM5 could not be derived through laboratory evolution

description: <i>P. aeruginosa </i>PA01was serially passaged in increased concentrations of either (A) ciprofloxacin or (B) peptoids TM5. After 3 weeks of successive passaging in concentrations doubling weekly from 0.5XMIC-2XMIC, resistance of ciprofloxacin had readily emerged with the MIC increasing from 0.125 µg/mL to 8 µg/mL. In contrast, no increase in MIC was observed for peptoid TM5. To evaluate whether TM5 was effective against drug resistant strains, the newly evolved ciprofloxacin-resistant isolates were tested against peptoids TM5, which was found to be equally effective with a MIC of 4 µg/mL.
DOI: 10.6084/m9.figshare.26384320
title: Figure 5: Cytotoxicity of Peptoids TM1, TM5, and TM6 on mammalian cells

description: An alamarBlue assay was used to determine the effects of 24 hour treatment with 2-fold dilutions of each Peptoid (A) or standard frontline antibiotic (B) on the viability of A549 human lung epithelial cells <i>P. aeruginosa</i> Xen41. All concentrations and conditions were tested in triplicate, with error is shown as standard deviation.
DOI: 10.6084/m9.figshare.26384320.v1
title: Figure 5: Cytotoxicity of Peptoids TM1, TM5, and TM6 on mammalian cells

description: An alamarBlue assay was used to determine the effects of 24 hour treatment with 2-fold dilutions of each Peptoid (A) or standard frontline antibiotic (B) on the viability of A549 human lung epithelial cells <i>P. aeruginosa</i> Xen41. All concentrations and conditions were tested in triplicate, with error is shown as standard deviation.
DOI: 10.6084/m9.figshare.26384338
title: Figure 6: Cytotoxicity of Peptoid TM5 in three-dimensional models of the human lung

description: A549 cells were grown in three-dimensional models of the human lung in the form of (A) Spheroid cultures or (B) air-liquid interface (ALI) cultures. (C) Culturing A549 cells under conditions which mimic an in vivo lung environment result in decreased cytotoxicity following peptoid treatment as measured by MTS assay (D) Integrity of the barrier function of ALI monolayers following peptoid treatment as measured by dextran exclusion assay showing that the monolayers remain intact following peptoid treatment with no significant difference in dextran diffusion observed at 200 µM concentrations or below.. Dextran Alexa FluorTM 488 was used for the 3000 Da exclusion and Dextran Alexa FluorTM 647 was used for the 10,000 Da exclusion. Statistics were performed using 2-way ANOVA, using Šídák's multiple comparisons test to compare sample means, with standard error mean (SEM). P values represented as: &lt;0.0001 = ****, between 0.0001 and 0.001 = ***, between 0.001 and 0.01 = **, and between 0.01 and 0.05 = *
DOI: 10.6084/m9.figshare.26384338.v1
title: Figure 6: Cytotoxicity of Peptoid TM5 in three-dimensional models of the human lung

description: A549 cells were grown in three-dimensional models of the human lung in the form of (A) Spheroid cultures or (B) air-liquid interface (ALI) cultures. (C) Culturing A549 cells under conditions which mimic an in vivo lung environment result in decreased cytotoxicity following peptoid treatment as measured by MTS assay (D) Integrity of the barrier function of ALI monolayers following peptoid treatment as measured by dextran exclusion assay showing that the monolayers remain intact following peptoid treatment with no significant difference in dextran diffusion observed at 200 µM concentrations or below.. Dextran Alexa FluorTM 488 was used for the 3000 Da exclusion and Dextran Alexa FluorTM 647 was used for the 10,000 Da exclusion. Statistics were performed using 2-way ANOVA, using Šídák's multiple comparisons test to compare sample means, with standard error mean (SEM). P values represented as: &lt;0.0001 = ****, between 0.0001 and 0.001 = ***, between 0.001 and 0.01 = **, and between 0.01 and 0.05 = *
DOI: 10.6084/m9.figshare.26384341
title: Figure 7: <i>in vivo</i> Toxicity of Peptoid TM5 following intratracheal delivery

description: (A) BALB/c mice were treated with 50 µL of either 40 µg/mL (p) or 80 µg/mL (¡) doses of TM5 and monitored for survival at 0, 2, 4, 6, 8, 10, 24, 30, and 48 hours post-treatment (<i>n</i> = 2 mice per concentration). (B) BALB/c mice were treated with increasing doses of TM5 to determine a toxicity threshold and monitored for disease symptoms at 0, 2, 4, 6, 8, 10, 24, 30, and 48 hours post-treatment as indicated below the X-axis. Error bars represent SEM across <i>n = </i>4 mice, and timepoints marked with an X indicated animals humanely euthanized at that timepoint.
DOI: 10.6084/m9.figshare.26384341.v1
title: Figure 7: <i>in vivo</i> Toxicity of Peptoid TM5 following intratracheal delivery

description: (A) BALB/c mice were treated with 50 µL of either 40 µg/mL (p) or 80 µg/mL (¡) doses of TM5 and monitored for survival at 0, 2, 4, 6, 8, 10, 24, 30, and 48 hours post-treatment (<i>n</i> = 2 mice per concentration). (B) BALB/c mice were treated with increasing doses of TM5 to determine a toxicity threshold and monitored for disease symptoms at 0, 2, 4, 6, 8, 10, 24, 30, and 48 hours post-treatment as indicated below the X-axis. Error bars represent SEM across <i>n = </i>4 mice, and timepoints marked with an X indicated animals humanely euthanized at that timepoint.
DOI: 10.6084/m9.figshare.26384347
title: Figure 8: Disease scores of mice infected with <i>P. aeruginosa</i> Xen41 following peptoid or antibiotic treatment

description: BALB/c mice were infected with <i>P. aeruginosa</i> via the intratracheal route and treated with TM1, TM5, TM6, or ciprofloxacin. A modified Karnofsky score was used to score any adverse effects observed over the course of the infection. (A) Average overall Karnofsky scores for each group of mice tested from 0 hours to 72 hours, with variance represented as SD. (B) Average Karnofsky scores for each group of mice over time, split into various categories tested. Columns marked with an ‘X’ indicate where a mouse was sacrificed ahead of schedule.