cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29344942
title: Efficacy and toxicity of <i>Tanacetum vulgare</i> and <i>Rosmarinus officinalis</i> essential oils novel biocontrol agents for sustainable management of <i>Phthorimaea operculella</i>, the potato moth

description: Phthorimaea operculella, a major pest of potato crops, causes significant yield losses, prompting farmers to rely on harmful pesticides. This study evaluates the insecticidal activity of essential oils from Tanacetum vulgare and Rosmarinus officinalis as sustainable alternatives. Chemical analysis revealed that Tanacetum vulgare oil is primarily composed of o-Phenylanisole (25.41%), β-pinene (20.91%), and comphor (18.35%), while Rosmarinus officinalis contains Camphor (18.58%) and camphene (8.78%). Toxicity assays showed that Tanacetum vulgare oil exhibited an LC50 range from 1.277 to 6.628 µL/L, with an LC99 from 3.22 to 3482.24 µL/L. For Rosmarinus officinalis, LC50 ranged from 1.999 to 10.426 µL/L, with LC99 exceeding 100,000 µL/L. Both oils demonstrated significant toxicity to adult P. operculella, supporting their potential as eco-friendly pesticide alternatives.
DOI: 10.6084/m9.figshare.29344942.v1
title: Efficacy and toxicity of <i>Tanacetum vulgare</i> and <i>Rosmarinus officinalis</i> essential oils novel biocontrol agents for sustainable management of <i>Phthorimaea operculella</i>, the potato moth

description: Phthorimaea operculella, a major pest of potato crops, causes significant yield losses, prompting farmers to rely on harmful pesticides. This study evaluates the insecticidal activity of essential oils from Tanacetum vulgare and Rosmarinus officinalis as sustainable alternatives. Chemical analysis revealed that Tanacetum vulgare oil is primarily composed of o-Phenylanisole (25.41%), β-pinene (20.91%), and comphor (18.35%), while Rosmarinus officinalis contains Camphor (18.58%) and camphene (8.78%). Toxicity assays showed that Tanacetum vulgare oil exhibited an LC50 range from 1.277 to 6.628 µL/L, with an LC99 from 3.22 to 3482.24 µL/L. For Rosmarinus officinalis, LC50 ranged from 1.999 to 10.426 µL/L, with LC99 exceeding 100,000 µL/L. Both oils demonstrated significant toxicity to adult P. operculella, supporting their potential as eco-friendly pesticide alternatives.
DOI: 10.25934/pr00011488
title: Data Challenge

description: Title: MIC creep in the shadows: Predictive modelling of resistance in newer antimicrobials against MDR pathogens

Antimicrobial resistance (AMR) continues to undermine the effectiveness of even our most potent, last-line antimicrobials. Newer drugs such as cefiderocol, sulbactam-durlobactam, and other β-lactam/β-lactamase inhibitor combinations are critical for treating multidrug-resistant (MDR) pathogens, particularly in ICUs. However, without proactive monitoring, their effectiveness may be compromised by emerging resistance. Forecasting minimum inhibitory concentration (MIC) trends of newer antimicrobials can serve as an early warning system against the rise of MDR pathogens. With this vision, we plan to develop an interactive, AI-powered dashboard that predicts MIC creep in critical WHO priority pathogens, such as Acinetobacter baumannii, Pseudomonas aeruginosa, and carbapenem-resistant Enterobacteriaceae. Our goal is to highlight global and country-level hotspots where MIC values are predicted to rise significantly, even before resistance thresholds are crossed. We believe that such predictive modelling can empower clinicians to make more informed empiric therapy decisions, strengthen antimicrobial stewardship by anticipating resistance trends, and inform public health responses with data-driven geographic and pathogen-specific insights. To build our models, we plan to use the Pfizer-ATLAS dataset, which offers extensive global coverage with isolates spanning multiple years and regions. In addition, we aim to incorporate supplementary datasets that include MIC data for newer antimicrobials, such as sulbactam-durlobactam and cefiderocol, and region-specific datasets containing Indian isolates, to enhance local relevance and model generalizability. By focusing on the subtle but significant creep in MICs for last-line agents, we aim to create a predictive, visual, and actionable resource to support global AMR containment efforts. We believe our project will fill a critical gap in current AMR surveillance efforts and promote more targeted interventions, especially in low-resource settings.
DOI: 10.6084/m9.figshare.29330072
title: WunDeeDB.jl: An easy to use, zero config, WAL, SQLite backend vector database

description: WunDeeDB.jl (v1.0.0) is a package written in Julia Lang that provides a SQLite disk-backed system for storing, searching, and managing embedding vectors at scale, influenced by disk oriented graph-based ANN techniques and the broader insights from hierarchical small-world graphs. By maintaining embeddings in an SQLite database, WunDeeDB.jl reduces in-memory overhead while supporting efficient similarity searches on commodity hardware. Its design also facilitates integration with common vector-database or ML pipelines that rely on embedding retrieval. In contrast to fully in-memory approaches, WunDeeDB.jl leverages disk-based storage and user-configurable adjacency (e.g., HNSW, LM-DiskANN, or fallback linear search), allowing large-scale data to be handled without saturating RAM. It supports incremental insertions and deletions, ensuring the index remains up-to-date as datasets evolve. By combining these disk-native strategies with tunable BFS expansions and adjacency pruning, WunDeeDB.jl enables robust nearest neighbor searches for high-dimensional embeddings
DOI: 10.6084/m9.figshare.29330072.v2
title: WunDeeDB.jl: An easy to use, zero config, WAL, SQLite backend vector database

description: WunDeeDB.jl (v1.0.0) is a package written in Julia Lang that provides a SQLite disk-backed system for storing, searching, and managing embedding vectors at scale, influenced by disk oriented graph-based ANN techniques and the broader insights from hierarchical small-world graphs. By maintaining embeddings in an SQLite database, WunDeeDB.jl reduces in-memory overhead while supporting efficient similarity searches on commodity hardware. Its design also facilitates integration with common vector-database or ML pipelines that rely on embedding retrieval. In contrast to fully in-memory approaches, WunDeeDB.jl leverages disk-based storage and user-configurable adjacency (e.g., HNSW, LM-DiskANN, or fallback linear search), allowing large-scale data to be handled without saturating RAM. It supports incremental insertions and deletions, ensuring the index remains up-to-date as datasets evolve. By combining these disk-native strategies with tunable BFS expansions and adjacency pruning, WunDeeDB.jl enables robust nearest neighbor searches for high-dimensional embeddings
DOI: 10.6084/m9.figshare.29344724
title: S1 - Lymph node dissection

description: Video recording of porcine lymph node dissection. After node is cut, endogenous bubbles formed by decompression from hyperbaric exposure can be see exiting.
DOI: 10.6084/m9.figshare.29344724.v1
title: S1 - Lymph node dissection

description: Video recording of porcine lymph node dissection. After node is cut, endogenous bubbles formed by decompression from hyperbaric exposure can be see exiting.
DOI: 10.6084/m9.figshare.29330072.v1
title: WunDeeDB.jl: A easy to use, zero config, WAL, SQLite backend vector database

description: WunDeeDB.jl (v1.0.0) is a package written in Julia Lang that provides a SQLite disk-backed system for storing, searching, and managing embedding vectors at scale, influenced by disk oriented graph-based ANN techniques and the broader insights from hierarchical small-world graphs. By maintaining embeddings in an SQLite database, WunDeeDB.jl reduces in-memory overhead while supporting efficient similarity searches on commodity hardware. Its design also facilitates integration with common vector-database or ML pipelines that rely on embedding retrieval. In contrast to fully in-memory approaches, WunDeeDB.jl leverages disk-based storage and user-configurable adjacency (e.g., HNSW, LM-DiskANN, or fallback linear search), allowing large-scale data to be handled without saturating RAM. It supports incremental insertions and deletions, ensuring the index remains up-to-date as datasets evolve. By combining these disk-native strategies with tunable BFS expansions and adjacency pruning, WunDeeDB.jl enables robust nearest neighbor searches for high-dimensional embeddings
DOI: 10.15468/dl.xpbv4q
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grammatotheca bergiana (Cham.) C.Presl",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.6372 -28.0032,16.2782 -28.5786,20.509 -28.5786,20.509 -24.0715,15.4933 -24.0715,15.4945 -24.1257,16.0509 -25.6631,15.6372 -28.0032))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052020-250525065834625/datasets/export for details.
DOI: 10.15468/dl.zqecbp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Enneacampus kaupi (Bleeker, 1863)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((45.7252 -9.5538,46.1382 -10.1057,46.1382 -8.8989,45.8555 -9.026,45.7252 -9.5538))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052021-250525065834625/datasets/export for details.
DOI: 10.6084/m9.figshare.29344727
title: S2 - Uncorrected data

description: Brightness analysis for porcine lymph node images collected before and after hyperbaric exposure. The original ultrasound image data was utilized without correcting for the varied ultrasound system settings. No significant difference is seen between the two different decompression sickness (DCS) outcome groups.
DOI: 10.6084/m9.figshare.29344727.v1
title: S2 - Uncorrected data

description: Brightness analysis for porcine lymph node images collected before and after hyperbaric exposure. The original ultrasound image data was utilized without correcting for the varied ultrasound system settings. No significant difference is seen between the two different decompression sickness (DCS) outcome groups.
DOI: 10.15468/dl.2k4ezh
title: Occurrence Download

description: A dataset containing 115710 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Luzula sudetica (Willd.) Schult., Gnaphalium norvegicum Retz., Argyrotegium poliochlorum (N.G.Walsh) J.M.Ward & Breitw., Phleum alpinum L., Poa alpina L.)",
 "Geometry POLYGON ((-180 45.53015, 180 45.53015, 180 83.62313, -180 83.62313, -180 45.53015))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "CoordinateUncertaintyInMeters is less than or equal to 50000"
 ]
}

 The dataset includes 115710 records from 253 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052004-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00011493
title: Anticipating Resistance Risks to Cefiderocol in MDR Pathogens Data Challenge

description: Cefiderocol remains one of the last-resort treatment options against multidrug-resistant (MDR) Gram-negative bacteria. As resistance to older agents like carbapenems and colistin rises, the clinical value of cefiderocol is increasingly vital. Yet, early indicators of declining cefiderocol susceptibility are poorly understood, leaving clinicians and policymakers with little guidance on when and where this drug may begin to lose effectiveness.

This project aims to identify phenotypic early warning signs of emerging cefiderocol resistance by analyzing trends in susceptibility to cefiderocol and other last-line antibiotics. Using the SIDERO-WT dataset, which includes MIC values for cefiderocol and comparators across North America and Europe, we will examine whether resistance to certain antibiotics reliably precedes reduced susceptibility to cefiderocol.

To improve generalizability, we will request access to the Pfizer ATLAS dataset, which includes global MIC data for comparator antibiotics but not cefiderocol. While ATLAS cannot directly inform cefiderocol trends, it can help test whether phenotypic signatures found in SIDERO-WT also appear in other regions, thereby flagging settings where cefiderocol resistance could plausibly emerge.

Our analytical approach will be exploratory and flexible; combining descriptive analyses, resistance clustering, and machine learning where appropriate to uncover consistent phenotypic patterns across datasets.

The ultimate goal is to support antimicrobial stewardship by pinpointing geographic or pathogen-specific “blind spots” where cefiderocol may be at risk, even before widespread resistance is observed. This work could also inform treatment prioritization frameworks, guiding the responsible deployment of cefiderocol in regions with constrained options.

Our approach is scalable and adaptable, and the insights gained could shape how future antimicrobials are monitored and preserved across diverse global contexts.
DOI: 10.15468/dl.btvd87
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grammatotheca bergiana (Cham.) C.Presl",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((15.4784 -23.4046,15.4933 -24.0715,20.509 -24.0715,20.509 -24.0455,20.0952 -23.5072,19.9968 -23.017,19.7843 -22.5405,19.1988 -21.8785,18.5972 -21.5447,17.8859 -21.3993,17.1626 -21.4805,16.5332 -21.7596,16.1257 -22.0882,15.8234 -22.4662,15.606 -22.8993,15.4784 -23.4046))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052023-250525065834625/datasets/export for details.
DOI: 10.15468/dl.f8dnz4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Enneacampus kaupi (Bleeker, 1863)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((45.5611 -26.0728,46.1382 -25.8059,46.1382 -24.9401,43.4152 -24.9401,43.6209 -25.3534,43.9532 -25.6605,44.5932 -25.8012,45.0156 -26.0824,45.5611 -26.0728))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052024-250525065834625/datasets/export for details.
DOI: 10.15468/dl.arspss
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Grammatotheca bergiana (Cham.) C.Presl",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((25.5395 -28.5786,20.509 -28.5786,20.509 -32.9007,25.5395 -32.9007,25.5395 -28.5786))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052025-250525065834625/datasets/export for details.
DOI: 10.25934/pr00011496
title: Vivli Data Challenge: Genomic-Phenotypic Association for AMR Drug Target Discovery

description: GEPHARD: Genomic-Phenotypic Association for AMR Drug Target Discovery

Antimicrobial resistance (AMR) poses a significant threat to global health, with multidrug-resistant (MDR) pathogens becoming increasingly prevalent across sub-Saharan Africa. While phenotypic surveillance (e.g., Vivli: Pfizer – ATLAS MIC data) captures clinical resistance trends, and independent whole-genome sequencing (WGS) studies catalogue antimicrobial resistance (AMR) genes, these datasets rarely intersect, limiting their translational value for drug discovery.
We hypothesize that population-level similarities in MIC resistance patterns across multiple antibiotics can be linked to specific AMR gene prevalence, allowing predictive modelling of resistance determinants and prioritization of potential drug targets.
This project, GEPHARD: Genomic-Phenotypic Association for AMR Drug Target Discovery, proposes an integrative computational approach that leverages Vivli’s large-scale phenotypic antimicrobial susceptibility data and available genomic data, combined with publicly available whole-genome sequence (WGS) data from African clinical isolates, to inform drug discovery targeting multidrug-resistant pathogens. Rather than requiring isolate-level genotype-phenotype pairing, we will perform ecological association analyses, combining resistance profiles with gene prevalence data from independently sequenced genomes. This predictive gene set will form the basis for downstream target nomination, evaluating druggability, functional roles, and cross-species conservation.
The project comprises three sequential objectives: (1) Surveillance Trend Analysis using Vivli’s MIC data to map MDR patterns geographically, and identify top-priority species; (2) Predictive Association integrating MIC profiles with gene prevalence to identify AMR drivers via machine learning and statistical association models; and (3) Target Nomination to prioritize AMR genes as druggable targets.

This framework allows us to derive predictive associations between MIC resistance profiles and AMR gene prevalence, uncover candidate drug targets across priority African pathogens, and provide exploratory leads for compound repurposing or optimization. The outputs are anticipated to contribute to the understanding of resistance mechanisms, assist future surveillance, and guide early-stage drug discovery focused on AMR drivers prevalent in Africa.
DOI: 10.15468/dl.94fhd8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Enneacampus kaupi (Bleeker, 1863)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((37.1382 2.0599,28.1382 2.0599,28.1382 -6.9401,37.1382 -6.9401,37.1382 2.0599))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052026-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15088430
title: Additional data for "Settling velocities of tire and road wear particles: Analyzing finely graded density fractions of samples from a road simulator and a highway tunnel"

description: This repository provides additional files for the publication "Settling velocities of tire and road wear particles: Analyzing finely graded density fractions of samples from a road simulator and a highway tunnel" by Stefan Dittmar, Steffen Weyrauch, Thorsten Reemtsma, Paul Eisentraut, Korinna Altmann, Aki Sebastian Ruhl and Martin Jekel (DOI: 10.1021/acs.est.5c04165).

It contains:- single particle raw data from settling experiments (1_settling_data.zip)- cumulative distributions of settling velocity and particle size for each investigated density fraction of both samples (2_measured_distributions.zip)- composed cumulative distributions of settling velocity and particle size for incorporated tire material/TRWP from both samples (3_composed_distributions.zip)- Python script to compute composed distributions (similar to 3) corrected for water properties differing from the experimental conditions (T=15 °C, salinity=0 g/kg)(4_correct_temperature_salinity.zip)

Please consider the included readme files (0_README{...}.txt) and revisit main publication and supplement information for further context on this data set, especially when you want to implement the data (e.g. from 3_composed_distributions) or generate corrected composed distributions via the Python script 4_correct_temperature_salinity.py yourself to be used in future studies.

Please contact Stefan Dittmar (stefan.dittmar@tu-berlin.de), if you have questions in that regard.
DOI: 10.5281/zenodo.15088431
title: Additional data for "Settling velocities of tire and road wear particles: Analyzing finely graded density fractions of samples from a road simulator and a highway tunnel"

description: This repository provides additional files for the publication "Settling velocities of tire and road wear particles: Analyzing finely graded density fractions of samples from a road simulator and a highway tunnel" by Stefan Dittmar, Steffen Weyrauch, Thorsten Reemtsma, Paul Eisentraut, Korinna Altmann, Aki Sebastian Ruhl and Martin Jekel (DOI: 10.1021/acs.est.5c04165).

It contains:- single particle raw data from settling experiments (1_settling_data.zip)- cumulative distributions of settling velocity and particle size for each investigated density fraction of both samples (2_measured_distributions.zip)- composed cumulative distributions of settling velocity and particle size for incorporated tire material/TRWP from both samples (3_composed_distributions.zip)- Python script to compute composed distributions (similar to 3) corrected for water properties differing from the experimental conditions (T=15 °C, salinity=0 g/kg)(4_correct_temperature_salinity.zip)

Please consider the included readme files (0_README{...}.txt) and revisit main publication and supplement information for further context on this data set, especially when you want to implement the data (e.g. from 3_composed_distributions) or generate corrected composed distributions via the Python script 4_correct_temperature_salinity.py yourself to be used in future studies.

Please contact Stefan Dittmar (stefan.dittmar@tu-berlin.de), if you have questions in that regard.
DOI: 10.15468/dl.cffegn
title: Occurrence Download

description: A dataset containing 45169 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is one of (Argentina, Brazil, Paraguay)",
 "TaxonKey is Paroaria capitata (d'Orbigny & Lafresnaye, 1837)"
 ]
}

 The dataset includes 45169 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052027-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.