cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7274/28792457
title: Evaluation of Methods for Concentration and Analysis of Viral Sewage Metagenomes

description: Sewage is a valuable repository for tracking human pathogens in the environment. The detection and identification of viruses from sewage is crucial for wastewater-based epidemiology (WBE) and microbial source tracking (MST). This dissertation investigates the impacts of various concentration and processing methods on PCR and sequencing outcomes from the sewage virome. The goals of this research are (1) to observe how the concentration method impacts the recovery of different viruses, (2) to understand the presence and abundance of species within the sewage virome, and (3) to determine the feasibility of detecting pathogens via WBE and other innovative methods. 
The first study of this dissertation assesses the impacts of virus concentration method on the PCR recovery of common viral fecal indicators and pathogens and compares the native recovery to spike-in recovery. Amicon ultrafiltration resulted in the highest average PCR recoveries and the prefiltration step caused significant decreases in bacteria signal for all concentration methods. Spiked-in controls inconsistently reflected the concentration efficiency of the assayed targets, indicating that spike-ins should be chosen carefully to demonstrate recovery accurately. The next study explored the temporal fluctuations of the sewage virome over a short window to assess the impact of sampling frequency on target detection. Nanotrap particles were used to concentrate samples, dPCR was used to quantify viral fecal indicators and pathogens, and targeted sequencing was employed to help resolve low-titer pathogens from the bulk metagenome. Adenoviruses and astroviruses were the most prevalent species and were detected consistently throughout the sampling window, but multiple clinically-relevant targets were less abundant. It was determined that the frequency of sampling for WBE can significantly impact the recovery of low-titer targets. The following chapter assessed the impact of two concentration methods, Nanotrap particles and Amicon ultrafiltration, and observed geographic trends on sewage virome composition. Nanotrap-concentrated samples resulted in higher alpha diversities, and the concentration method was observed to have a greater impact on virome diversity than the geographic origin of the sample. Both concentration methods were effective for different pathogens, highlighting the need to optimize methods for specific targets. The final portion of this dissertation explores the use of Capillary Zone Electrophoresis (CZE) for separating viruses from mixed samples into distinct fractions. CZE was shown to effectively separate species into distinct migration windows using a curated phage mixture. Separating sewage into fractions increased the resolution of low-titer pathogens compared to a bulk metagenome.Overall, this work demonstrates the need for specific and targeted methods for processing wastewater for MST and WBE applications and contributes supporting data for multiple potential protocols.
DOI: 10.7274/28792457.v1
title: Evaluation of Methods for Concentration and Analysis of Viral Sewage Metagenomes

description: Sewage is a valuable repository for tracking human pathogens in the environment. The detection and identification of viruses from sewage is crucial for wastewater-based epidemiology (WBE) and microbial source tracking (MST). This dissertation investigates the impacts of various concentration and processing methods on PCR and sequencing outcomes from the sewage virome. The goals of this research are (1) to observe how the concentration method impacts the recovery of different viruses, (2) to understand the presence and abundance of species within the sewage virome, and (3) to determine the feasibility of detecting pathogens via WBE and other innovative methods. 
The first study of this dissertation assesses the impacts of virus concentration method on the PCR recovery of common viral fecal indicators and pathogens and compares the native recovery to spike-in recovery. Amicon ultrafiltration resulted in the highest average PCR recoveries and the prefiltration step caused significant decreases in bacteria signal for all concentration methods. Spiked-in controls inconsistently reflected the concentration efficiency of the assayed targets, indicating that spike-ins should be chosen carefully to demonstrate recovery accurately. The next study explored the temporal fluctuations of the sewage virome over a short window to assess the impact of sampling frequency on target detection. Nanotrap particles were used to concentrate samples, dPCR was used to quantify viral fecal indicators and pathogens, and targeted sequencing was employed to help resolve low-titer pathogens from the bulk metagenome. Adenoviruses and astroviruses were the most prevalent species and were detected consistently throughout the sampling window, but multiple clinically-relevant targets were less abundant. It was determined that the frequency of sampling for WBE can significantly impact the recovery of low-titer targets. The following chapter assessed the impact of two concentration methods, Nanotrap particles and Amicon ultrafiltration, and observed geographic trends on sewage virome composition. Nanotrap-concentrated samples resulted in higher alpha diversities, and the concentration method was observed to have a greater impact on virome diversity than the geographic origin of the sample. Both concentration methods were effective for different pathogens, highlighting the need to optimize methods for specific targets. The final portion of this dissertation explores the use of Capillary Zone Electrophoresis (CZE) for separating viruses from mixed samples into distinct fractions. CZE was shown to effectively separate species into distinct migration windows using a curated phage mixture. Separating sewage into fractions increased the resolution of low-titer pathogens compared to a bulk metagenome.Overall, this work demonstrates the need for specific and targeted methods for processing wastewater for MST and WBE applications and contributes supporting data for multiple potential protocols.
DOI: 10.15468/dl.qumkzh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Milvago chimango (Vieillot, 1816)",
 "Country is Germany",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030804-250426092105405/datasets/export for details.
DOI: 10.48577/jpl.gq9i8x
title: Dataset and software to support the paper “Orbiting Carbon Observatory-3: Preflight Spectral Calibration and Performance Evaluation”

description: Dataset and software to support the paper “Orbiting Carbon Observatory-3: Preflight Spectral Calibration and Performance Evaluation”
DOI: 10.6084/m9.figshare.28171241
title: Dataset S1.zip

description: This file is Dataset S1 in the supplementary information of the submitted manuscript to PNAS: 2024-27090 (Structure and evolution of Photosystem I in the early-branching cyanobacterium <i>Anthocerotibacter panamensis</i>).Dataset S1 (separate file). Phylogenetic trees and sequence alignments used for Figs. 6 and S16.
DOI: 10.15468/dl.5g7vbr
title: Occurrence Download

description: A dataset containing 22729 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gladiolus italicus Mill."
 ]
}

 The dataset includes 22729 records from 530 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030805-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15412090
title: BEST-CROP Variant Call Data for the barley cultivar Dorinka

description: Variant call data for variety Dorinka in "vcf" format from the University of Milan Genomic DNA from individuals was sequenced to approximately 40x coverage using short (2*150nt) paired end reads. Reads were trimmed using FastP and mapped to the MorexV3 assembly with Bowtie2. PCR duplicate reads were marked with Samtools and variants were called using FreeBayes. Any questions about this data please contact David Horner (University of Milan) david.horner@unimi.it. This data is released under a CC BY-NC licence. In the case of reuse of this data, please acknowledge the BestCrop project (https://www.bestcrop.eu/index.html).

Data is permanently hosted and available from https://germinate.hutton.ac.uk/best-crop/#/data/data-resources
DOI: 10.5281/zenodo.15412091
title: BEST-CROP Variant Call Data for the barley cultivar Dorinka

description: Variant call data for variety Dorinka in "vcf" format from the University of Milan Genomic DNA from individuals was sequenced to approximately 40x coverage using short (2*150nt) paired end reads. Reads were trimmed using FastP and mapped to the MorexV3 assembly with Bowtie2. PCR duplicate reads were marked with Samtools and variants were called using FreeBayes. Any questions about this data please contact David Horner (University of Milan) david.horner@unimi.it. This data is released under a CC BY-NC licence. In the case of reuse of this data, please acknowledge the BestCrop project (https://www.bestcrop.eu/index.html).

Data is permanently hosted and available from https://germinate.hutton.ac.uk/best-crop/#/data/data-resources
DOI: 10.7910/dvn/qhjysr
title: Dataset Submission

description: Benchmarking the environmental impact of AI models. (2025-05-02)
DOI: 10.5281/zenodo.15412296
title: CODES Dataset: lv_parametric_no_params

description: Dataset for the CODES benchmark (https://github.com/robin-janssen/CODES-Benchmark). This dataset was designed to test variations of the architectures that can take additional ODE parameters as inputs. It is related to lv_parametric, which contains the same trajectories, and additionally the parameter alpha used to generate them. This enables comparison between providing the architecture with the additional information that is the value of the parameter, and leaving out this additional information (as is the case in this dataset).

Lotka-Volterra predator-prey model with three predators (p1, p2, p3) and three prey (q1, q2, q3), as well as a parameter alpha, which modifies reproduction and predation coefficients.

The system is defined by its gradients as follows:











# --- reproduction rates (prey) ---

# baseline: 0.5, 0.6, 0.4

rep_p1 = 0.5 * (1.0 - alpha) * p1

rep_p2 = 0.6 * (1.0 - alpha) * p2

rep_p3 = 0.4 * (1.0 - alpha) * p3


# --- predation terms ---

# baseline coefficients: 0.02, 0.01, 0.03, 0.015, 0.01, 0.025

pred1 = (0.02 * (1.0 + alpha)) * p1 * q1 + (0.01 * (1.0 + alpha)) * p1 * q2


pred2 = (0.03 * (1.0 + alpha)) * p2 * q1 + (0.015 * (1.0 + alpha)) * p2 * q3


pred3 = (0.01 * (1.0 + alpha)) * p3 * q2 + (0.025 * (1.0 + alpha)) * p3 * q3


# --- predator death rates (unchanged) ---

death_q1 = 0.1 * q1

death_q2 = 0.08 * q2

death_q3 = 0.12 * q3


# --- predator gains (unchanged) ---

gain_q1 = 0.005 * p1 * q1 + 0.007 * p2 * q1

gain_q2 = 0.006 * p1 * q2 + 0.009 * p3 * q2

gain_q3 = 0.008 * p2 * q3 + 0.01 * p3 * q3


# --- derivatives ---

dp1_dt = rep_p1 - pred1

dp2_dt = rep_p2 - pred2

dp3_dt = rep_p3 - pred3


dq1_dt = -death_q1 + gain_q1

dq2_dt = -death_q2 + gain_q2

dq3_dt = -death_q3 + gain_q3


return np.array([dp1_dt, dp2_dt, dp3_dt, dq1_dt, dq2_dt, dq3_dt])





101 timesteps in the interval [0, 100], solved with scipy.integrate.solve_ivp with the DOP853 method (atol = rtol = 1e-8).

Initial conditions for each species sampled in log-space between 1 and 10, while alpha was sampled linearly between 0 and 1. Initial conditions and the parameter were jointly Sobol-sampled (note that alpha is not included in the dataset).

 

350/50/100 train/test/val samples.
DOI: 10.5281/zenodo.15412297
title: CODES Dataset: lv_parametric_no_params

description: Dataset for the CODES benchmark (https://github.com/robin-janssen/CODES-Benchmark). This dataset was designed to test variations of the architectures that can take additional ODE parameters as inputs. It is related to lv_parametric, which contains the same trajectories, and additionally the parameter alpha used to generate them. This enables comparison between providing the architecture with the additional information that is the value of the parameter, and leaving out this additional information (as is the case in this dataset).

Lotka-Volterra predator-prey model with three predators (p1, p2, p3) and three prey (q1, q2, q3), as well as a parameter alpha, which modifies reproduction and predation coefficients.

The system is defined by its gradients as follows:











# --- reproduction rates (prey) ---

# baseline: 0.5, 0.6, 0.4

rep_p1 = 0.5 * (1.0 - alpha) * p1

rep_p2 = 0.6 * (1.0 - alpha) * p2

rep_p3 = 0.4 * (1.0 - alpha) * p3


# --- predation terms ---

# baseline coefficients: 0.02, 0.01, 0.03, 0.015, 0.01, 0.025

pred1 = (0.02 * (1.0 + alpha)) * p1 * q1 + (0.01 * (1.0 + alpha)) * p1 * q2


pred2 = (0.03 * (1.0 + alpha)) * p2 * q1 + (0.015 * (1.0 + alpha)) * p2 * q3


pred3 = (0.01 * (1.0 + alpha)) * p3 * q2 + (0.025 * (1.0 + alpha)) * p3 * q3


# --- predator death rates (unchanged) ---

death_q1 = 0.1 * q1

death_q2 = 0.08 * q2

death_q3 = 0.12 * q3


# --- predator gains (unchanged) ---

gain_q1 = 0.005 * p1 * q1 + 0.007 * p2 * q1

gain_q2 = 0.006 * p1 * q2 + 0.009 * p3 * q2

gain_q3 = 0.008 * p2 * q3 + 0.01 * p3 * q3


# --- derivatives ---

dp1_dt = rep_p1 - pred1

dp2_dt = rep_p2 - pred2

dp3_dt = rep_p3 - pred3


dq1_dt = -death_q1 + gain_q1

dq2_dt = -death_q2 + gain_q2

dq3_dt = -death_q3 + gain_q3


return np.array([dp1_dt, dp2_dt, dp3_dt, dq1_dt, dq2_dt, dq3_dt])





101 timesteps in the interval [0, 100], solved with scipy.integrate.solve_ivp with the DOP853 method (atol = rtol = 1e-8).

Initial conditions for each species sampled in log-space between 1 and 10, while alpha was sampled linearly between 0 and 1. Initial conditions and the parameter were jointly Sobol-sampled (note that alpha is not included in the dataset).

 

350/50/100 train/test/val samples.
DOI: 10.5281/zenodo.15411822
title: BEST-CROP Variant Call Data for the barley cultivar Carbona

description: Variant call data for variety Carbona in "vcf" format from the University of Milan. Genomic DNA from individuals was sequenced to approximately 40x coverage using short (2*150nt) paired end reads. Reads were trimmed using FastP and mapped to the MorexV3 assembly with Bowtie2. PCR duplicate reads were marked with Samtools and variants were called using FreeBayes. Any questions about this data please contact David Horner (University of Milan) david.horner@unimi.it. This data is released under a CC BY-NC licence. In the case of reuse of this data, please acknowledge the BestCrop project (https://www.bestcrop.eu/index.html).

Data is permanently hosted and available from https://germinate.hutton.ac.uk/best-crop/#/data/data-resources
DOI: 10.5281/zenodo.15411823
title: BEST-CROP Variant Call Data for the barley cultivar Carbona

description: Variant call data for variety Carbona in "vcf" format from the University of Milan. Genomic DNA from individuals was sequenced to approximately 40x coverage using short (2*150nt) paired end reads. Reads were trimmed using FastP and mapped to the MorexV3 assembly with Bowtie2. PCR duplicate reads were marked with Samtools and variants were called using FreeBayes. Any questions about this data please contact David Horner (University of Milan) david.horner@unimi.it. This data is released under a CC BY-NC licence. In the case of reuse of this data, please acknowledge the BestCrop project (https://www.bestcrop.eu/index.html).

Data is permanently hosted and available from https://germinate.hutton.ac.uk/best-crop/#/data/data-resources
DOI: 10.15468/dl.4kpuvk
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gladiolus ×hybridus C.Morren"
 ]
}

 The dataset includes 32 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030806-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11066664
title: Results from national testing programs on the occurrence of chemical contaminants in food and feed - The Netherlands

description: In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed.  

The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. 

Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below:  



Commission Regulation (EU) 625/2017, on the application of food and feed law

Commission Delegated Regulation (EU) 2022/931

Commission Implementing Regulation (EU) 2022/932

Commission Regulation (EU) 2023/915, on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006


These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2023, 2022, 2021, and 2020, split by sampling year (data element ‘sampY’). 

More details are available in last year's finalised call for data ‘Call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA (europa.eu)’.

REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: 

OCC-CHEMMON2020 – Institute for Public Health and the Environment

OCC-CHEMMON2021 – Institute for Public Health and the Environment

OCC-CHEMMON2022 – Institute for Public Health and the Environment

OCC-CHEMMON2023 – Institute for Public Health and the Environment
description: NL; CSV; data.collection@efsa.europa.eu
DOI: 10.5281/zenodo.15412464
title: Results from national testing programs on the occurrence of chemical contaminants in food and feed - The Netherlands

description: In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed.  

The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. 

Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below:  



Commission Regulation (EU) 625/2017, on the application of food and feed law

Commission Delegated Regulation (EU) 2022/931

Commission Implementing Regulation (EU) 2022/932

Commission Regulation (EU) 2023/915, on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006


These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2024, 2023, 2022, 2021, and 2020, split by sampling year (data element ‘sampY’). 

More details are available in last year's finalised call for data ‘Annual call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA’.

REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: 

OCC-CHEMMON2020 – Institute for Public Health and the Environment

OCC-CHEMMON2021 – Institute for Public Health and the Environment

OCC-CHEMMON2022 – Institute for Public Health and the Environment

OCC-CHEMMON2023 – Institute for Public Health and the Environment

OCC-CHEMMON2024 – Institute for Public Health and the Environment
description: NL; CSV; data.collection@efsa.europa.eu
DOI: 10.5281/zenodo.7034475
title: Results from national testing programs on the occurrence of chemical contaminants in food and feed - The Netherlands

description: In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed.  

The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. 

Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below:  



Commission Regulation (EU) 625/2017, on the application of food and feed law

Commission Delegated Regulation (EU) 2022/931

Commission Implementing Regulation (EU) 2022/932

Commission Regulation (EU) 2023/915, on maximum levels for certain contaminants in food and repealing Regulation (EC) No 1881/2006


These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2024, 2023, 2022, 2021, and 2020, split by sampling year (data element ‘sampY’). 

More details are available in last year's finalised call for data ‘Annual call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA’.

REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: 

OCC-CHEMMON2020 – Institute for Public Health and the Environment

OCC-CHEMMON2021 – Institute for Public Health and the Environment

OCC-CHEMMON2022 – Institute for Public Health and the Environment

OCC-CHEMMON2023 – Institute for Public Health and the Environment

OCC-CHEMMON2024 – Institute for Public Health and the Environment
description: NL; CSV; data.collection@efsa.europa.eu
DOI: 10.5281/zenodo.7034476
title: Results from national testing programs on the occurrence of chemical contaminants in food and feed - The Netherlands

description: In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (M-2010-0374) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed. The presence of unauthorised substances or chemical contaminants in food may pose a risk factor for public health and can cause a negative impact on the quality of food. Commission Recommendations and Regulations on occurrence monitoring are in place for several contaminants of interest, some of which can be found here below: Commission Regulation (EU) 625/2017, on the application of food and feed law Commission Regulation (EC) 1881/2006, on maximum levels for certain contaminants in foodstuffs Commission Regulation (EC) 2017/2158 establishing mitigation measures and benchmark levels for the reduction of the presence of acrylamide in food These datasets contain the results of sampling that was designed according to national testing programs for a variety of contaminants in food and feed, as reported under the Chemical Monitoring Data Collection 2020, split by sampling year (data element ‘sampY’). More details are available in the ‘Call for continuous collection of chemical contaminants occurrence data in food and feed | EFSA (europa.eu)’. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: OCC-CHEMMON2020 – Institute for Public Health and the Environment
description: NL; CSV; data.collection@efsa.europa.eu