Find research datasets worth reusing

Bees and Butterflies Monitoring

Biodiversity monitoring surveys of selected pollinator species (bees and butterflies) performed according to specific protocols adapted to NBS and observers, along fixed transects, once a week during the pollinators’ season, for 3 years. Reference methods: Underwood, Darwin, Gerritsen, (2017), Pollinator initiatives in EU Member States: Success factors and gaps. Report for European Commission under contract for provision of technical support related to Target of the EU Biodiversity Strategy to 2020 – maintaining and restoring ecosystems and their services. ENV.B.2/SER/2016/0018. Institute for European Environmental Policy, Brussels; Potts et al. (2020), Proposal for an EU Pollinator Monitoring Scheme, EUR 30416 EN, Publications Office of the European Union, Luxembourg.

Dataset for Monitoring and Visualizing Stroke Rehabilitation Progress using Wearable Sensors (IMU)

<div> This dataset is associated with a manuscript that is currently under peer review.   Article Abstract: Stroke is one of the leading causes of death and disability worldwide, and recovering mobility is an important goal during post-stroke rehabilitation. In this work, we present a study to verify the feasibility of monitoring and visualizing longitudinal stroke gait rehabilitation progress using wearable sensors. Wearable devices such as inertial measurement units (IMUs) are easy-to-use and cost-effective tools for quantifying mobility. However, there is a need for research on longitudinal monitoring of stroke rehabilitation progress with wearables, as well as generating clinically relevant insights using appropriate visualizations. To this aim, we recruited ten stroke patients in their early rehabilitation stage. We collected and analyzed the IMU-derived gait features across two visits, and presented visualizations of the foot movement trajectories as well as the spatio-temporal gait parameters in the average, symmetry, and variation domains to quantify changes in gait. Our visualization and quantification methods are evaluated and validated by clinical experts, and prove to be promising in aiding clinicians to monitor rehabilitation progression.   Data description: The dataset consists data from ten stroke patients who completed both visits. The "raw" data folder contains tri-axial acceleration and angular velocity data from the IMUs. In addition, information about the participants such as demographics (e.g., body height and body weight), FAC scores at both visits, and evaluations of gait improvement are documented in the file "participant_info.csv". The “interim” folder contains IMU data that has been manually segmented to remove irrelevant movements before and after each walking session during a visit, based on visual inspection of raw IMU signals. For quality control, the segmented accelerometer and gyroscope data of each sensor were plotted, and the plots were saved in the same folder as the IMU signals. In addition, during the first execution of gait parameter extraction, calculated 3D feet trajectories were cached in the "interim" folder, so that for future executions, the cached trajectories can be loaded directly, reducing the computational efforts for re-calculation. The file "stance_magnitude_thresholds_manual.csv" documents the angular velocity thresholds used to identify stance phases for the gait analysis algorithm for each participant. The threshold values were determined manually by observing the angular velocity signals.  The “processed” folder contains stride-by-stride spatio-temporal gait parameters extracted for each of the four walking conditions, and aggregated gait parameters in terms of coefficients of variation and symmetry for all walking conditions for each participant.    </div>

Temperature and Relative Humidity monitoring

Temperature and relative humidity has been monitored before and after the installation of nature-based solutions during the proGIreg project. Specifically, NBS3 in Dortmund, NBS2, NBS3 and NBS5 in Turin, and NBS5 in Zagreb have been monitored. Continuous measurements have been made inside the NBS and in a control site over three years; for each monitoring site, 6 temperature sensors are used (3 for the NBS site and 3 for the control site). For NBS5 outdoor green walls in Turin and Zagreb, the temperature has been measured inside the building and only for one year, and the temperature measured by a reference weather station in the same district has been used as control. More details are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.

PM Monitoring

The species-specific PM removal potential has been determined during the proGIreg project for the plant species present in different type of nature-based solutions implemented during the project, in four cities. Namely, this study has been conducted for NBS1 and NBS3 in Dortmund, NBS2, NBS3 and NBS5 in Turin, for NBS3 in Ningbo and for NBS3 and NBS5 in Zagreb. Particulate matter (PM) abatement has been detected by monitoring its deposition on leaves, at different particle size fractions. PM abatement is measured by selecting young leaves, from trees of the same species, sampled within the different NBSs. Three replicate branches have been collected per plant, and two leaves per branch have been analysed by Scanning Electron Microscopy combined with Energy Dispersed X-Ray (SEM/EDX) microanalysis, obtaining a quanti-qualitative characterization of the deposited particles, as a function of their size and elemental composition. In addition, the amount of atmospheric PM removed by deposition per unit leaf area is obtained. These data result to be useful for the upscaled evaluation of each NBS potential towards the removal of this specific atmospheric pollutant.

opencc-zeroFeb 2024View details →

dryad40/100

Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal Passive Acoustic Monitoring datasets

Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches, and indices are best suited for characterizing marine and terrestrial acoustic environments. Here, we describe the application of multiple machine-learning techniques to the analysis of a large PAM dataset. We combine pre-trained acoustic classification models (VGGish, NOAA & Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labelled sounds in the 8 kHz range, however, low and high-frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow for establishing a link between ecologically relevant information and PAM recordings at multiple scales. The datasets and scripts provided in this repository allow replicating the results presented in the publication.

opencc-zeroJan 2024View details →

Dataset for "Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank's Solution by Multisine Impedimetric Monitoring"

This dataset contains raw data of the publication “Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank’s Solution by Multisine Impedimetric Monitoring”. Corrosion Science, Volume 231, 2024, 111979, ISSN 0010-938X, <a href="https://doi.org/10.1016/j.corsci.2024.111979">https://doi.org/10.1016/j.corsci.2024.111979</a>. The data was collected at Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences (Poland) and Gdansk University of Technology (Poland). All data are provided under CC0 license. The dataset includes Scanning electron and optical microscopy images (.tiff and ,jpg format); X-ray photoelectron spectroscopy spectra (.vms format); Electrochemical impedance spectra (.txt format). Dzmitry Kharytonau gratefully acknowledges the financial support of this study by the National Science Centre (Poland) under research Grant Sonatina no. 2021/40/C/ST5/00266. This work was also supported by Gdansk University of Technology under the Aurum Supporting International Research Team Building – ‘Excellence Initiative – Research University’ [grant number: 2/2021/IDUB/II.1.3].

dryad40/100

Automatic monitoring of neural activity with single-cell resolution in behaving Hydra

The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, we report coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. We imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron's calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.

opencc-zeroMar 2024View details →

opencc-by-4.0Feb 2019View details →

Operating diagram of hatching module, this module consists of two clearly separated sections, each consisting of two long tanks (2 × 0.2 × 0.2 m) designed to accommodate hatching boxes, a filtration tank and an independent water circulation pump with a cooling unit and UV sterilizer. This allows simultaneous monitoring of 16 batches of eggs. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum

Operating diagram of hatching module, this module consists of two clearly separated sections, each consisting of two long tanks (2 × 0.2 × 0.2 m) designed to accommodate hatching boxes, a filtration tank and an independent water circulation pump with a cooling unit and UV sterilizer. This allows simultaneous monitoring of 16 batches of eggs.

opencc-by-4.0Feb 2019View details →

Bottom of DR1 tank, the sides of the module are fitted with glass panels which allow natural light from a window to enter the tank, and the observer to view the behaviour of the broodstock. This device ensures easy viewing and checking of the broodstock, facilitates management of feeding and allows effective monitoring of reproduction. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum

Bottom of DR1 tank, the sides of the module are fitted with glass panels which allow natural light from a window to enter the tank, and the observer to view the behaviour of the broodstock. This device ensures easy viewing and checking of the broodstock, facilitates management of feeding and allows effective monitoring of reproduction.

opencc-by-4.0Mar 2024View details →

One year of high frequency monitoring of groundwater physico-chemical parameters in the Weierbach Experimental Catchment, Luxembourg.

We present a novel dataset from the Weierbach Experimental Catchment in Luxembourg, derived from a year-long high-frequency monitoring campaign focused on groundwater physico-chemical parameters. Through meticulous data collection and rigorous quality control, parameters such as electrical conductivity, dissolved oxygen, oxidation-reduction potential, pH and cations/anion concentrations were measured, offering new insights into the CZ's hydrological and biogeochemical dynamics. The dataset highlights the intricate interplay between redox reactions, pH, and ion exchange processes, as well as the influence of seasonal variability and flowline interactions on solute transport. By providing a detailed view of the catchment's response to hydrological changes, this dataset fills a significant gap in CZ research, offering a valuable resource for advancing our understanding of hydro-biogeochemical catchment processes. This contribution is not only a step forward in CZ science but also serves as a critical tool for researchers and practitioners aiming to refine models, inform land management practices, and foster a more holistic understanding of catchment biogeochemistry.

opencc-by-4.0Apr 2024View details →

Understanding the risks of co-exposures in a changing world: A case study of dual monitoring of the biotoxin domoic acid and Vibrio spp. in Pacific oyster

<div> Assessing the co-occurrence of multiple health risk factors in coastal ecosystems is challenging due to the complexity of multi-factor interactions and limited availability simultaneously collected data. Understanding co-occurrence is particularly important for risk factors that may be associated with or occur in similar environmental conditions. In marine ecosystems, the co-occurrence of harmful algal bloom toxins and bacterial pathogens within the genus Vibrio may impact both ecosystem and human health. This study examined the co-occurrence of Vibrio spp. and domoic acid (DA) produced by the harmful algae Pseudonitzschia by 1) analyzing existing California Department of Public Health monitoring data for V. parahaemolyticus and DA in oysters; 2) and conducting a one-year seasonal monitoring of these risk factors across two Southern California embayments. Existing public health monitoring efforts in the state were robust for individual risk factors; however, it was difficult to evaluate the co-occurrence of these risk factors in oysters due to low number of co-monitoring instances between 2015 and 2020. Seasonal co-monitoring of that DA and Vibrio spp. (V. vulnificus or V. parahaemolyticus) at two embayments revealed the co-occurrence of these health risk factors in 35% of sampled oysters in most seasons. Interestingly, both the overall detection frequency and co-occurrence of these risk factors was considerably less frequent in water samples. These findings may in part suggest the slow depuration of Vibrio spp. and DA in oysters as residual levels may be retained. This study expanded our understanding of the simultaneous presence of DA and Vibrio spp. in bivalves and demonstrate the feasibility of co-monitoring different risk factors from the same sample. Individual programs monitoring for different risk factors from the same sample matrix may consider combining efforts to reduce cost, streamline the process, and better understand the prevalence of co-occurring health risk factors. </div>

opencc-by-4.0Nov 2024View details →

AdaptFerm: Bioprocess Monitoring Using FTIR spectroscopy: Insights into Substrate Effects and Domain Adaptation

<h2> </h2> <h2>1. Introduction</h2> The AdaptFerm dataset is designed to support the development of a monitoring framework for lactic acid production fermentation using Fourier Transform Infrared (FTIR) spectroscopy. Its primary goal is to facilitate the control strategies for continuous fermentation processes to maximize the lactic acid production. The AdaptFerm encompasses data from two distinct batch fermentation environments: one employing simple sugar (glucose) as the substrate and the other utilizing complex sugars derived from bio-waste. The study focuses on developing accurate predictive models for glucose and lactic acid concentrations, with an emphasis on applying classical machine learning techniques and enhancing domain generalization capabilities. <h2>2. Prediction Model for Different Substrate Environments</h2> The chemical composition of substrates are presented in <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Table 1 [1]</a>. The dataset is utilized to train and test models within the same substrate domain. For instance, data from a single fermentation environment (e.g., glucose substrate) is used for both training and testing phases. The applied machine learning models showed accurate prediction within the same domain <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">[1]</a>. For more details on the methods applied, please refer to the following link: <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">https://doi.org/10.1016/j.heliyon.2024.e38791</a>. In this study, the MIR results correspond to the AdaptFerm dataset. The spectra of the glucose and biowaste hydrolysate fermentation process are presented in <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Figure 3</a> and <a title="Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates" href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">Figure 4</a>. <h2>3. Domain Adaptation</h2> The dataset was also used to address the challenge posed by shifts in FTIR data when substrates change. Transitioning from simple sugar (glucose) to complex sugar (bio-waste) causes significant variations in the FTIR spectra, making it difficult for models trained on glucose fermentation data to maintain prediction accuracy in the complex sugar fermentation environment. This results in reduced robustness and performance when applied to out-of-distribution data. To address these challenges, we explore methods that improve the generalization ability and robustness of models in such scenarios without using labels from complex sugar fermentation <a title="Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation" href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">[2]</a>. It shows the application of machine learning interpretation to find domain invariant features for glucose and lactic acid. For more details on the methods applied, please refer to the following link: <a title="Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation" href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">https://dx.doi.org/10.2139/ssrn.5012080</a>. The code is available at <a title="ShapFS" href="https://github.com/shl-shawn/ShapFS" target="_blank" rel="noopener">https://github.com/shl-shawn/ShapFS</a>. <h2>4. Real-World Use Cases</h2> <h3>4.1. Regression Task</h3> AdaptFerm serves as a benchmark for machine learning model applications in fermentation processes, specifically for predicting glucose and lactic acid concentrations, measured in g/L (grams per liter), while considering issues of out-of-distribution generalization. <h3>4.2. Domain Adaptation Regression Task</h3> The dataset is also suitable for evaluating different  domain adaptation methods. In particular, the glucose substrate fermentation data can be used as the source domain, while the complex sugar fermentation data from bio-waste serves as the target domain. For semi-supervised domain adaptation approaches, it is recommended to use the initial data points (i.e., those collected at the beginning of the fermentation process) from the target domain, as the dataset is organized chronologically by collection day. These approaches aim to improve the robustness of models by transferring knowledge across domains and mitigating the effects of out-of-distribution data. <h3>4.3. Anomaly Detection</h3> The dataset can be used to train anomaly detection models to identify outliers or deviations from normal fermentation behavior. This could be valuable in industrial bioprocessing, where early detection of issues like contamination or process failure is crucial. Techniques like Isolation Forests, One-Class SVM, or Autoencoders could be applied to identify unusual patterns in FTIR spectra. <h3>4.4. Classification Task</h3> Although the main task is regression, the dataset could also be used in classification tasks by discretizing the concentrations of glucose and lactic acid into categories (e.g., low, medium, high). This would allow for the application of classification algorithms like Support Vector Machines (SVM), Random Forests, or Neural Networks for predicting the fermentation phase or identifying specific operational conditions. <h3>4.5. Transfer Learning</h3> Given the nature of the domain adaptation approach in this dataset, transfer learning models can be explored. Models pre-trained on glucose fermentation data can be fine-tuned on complex sugar fermentation data, enabling quicker model convergence and improved performance in data-scarce environments. <h3>4.6. Multi-Task Learning</h3> In a multi-task learning scenario, models could simultaneously predict both glucose and lactic acid concentrations from the same FTIR data. This could help in improving model accuracy by leveraging shared representations across the two tasks. <h3>4.7. Feature Selection</h3> The FTIR spectral data contains a large number of features (wavelengths), and feature selection techniques such as Recursive Feature Elimination (RFE), Lasso regression, or mutual information could be applied to identify the most relevant wavelengths for predicting glucose and lactic acid concentrations, improving model performance and interpretability. <h2>5. Dataset Structure and Meta Information</h2> The dataset is organized into four Excel files, corresponding to two main fermentation domains (different substrates) and two key process variables: a) Simple Sugar Substrate This domain contains data for the fermentation process using glucose as the substrate to produce lactic acid. It includes two files—one for glucose concentrations and one for lactic acid concentrations. Both are measured in g/L. b) Complex Sugar Substrate This doman contains data for the fermentation process using bio-waste as the substrate to produce lactic acid. Similar to the previous domain, it includes two files—one for glucose concentrations and one for lactic acid concentrations. Both are measured in g/L. Each file is structured as follows: <ul> <li>The first column contains the sample ID, which serves as the timeline of measurements (Sample ID 1 represents the first measurement in the fermentation process).</li> <li>From the second column onwards, the FTIR data is provided, covering the spectral range from 549.6 cm-1 to 3999.6 cm-1 comprising 3,579 features.</li> <li>The final column contains the ground truth data, the chemical measurements of fermentation variables such as glucose and lactic acid concentrations, both measured in g/L.</li> </ul> <h2>6. Conclusion</h2> The AdaptFerm features FTIR spectra data from two distinct fermentation environments: simple sugar (glucose) and complex sugar (bio-waste). The dataset is designed to be used in regression tasks, including domain adaptation, and can be applied in machine learning model development for fermentation process monitoring, with a focus on enhancing model robustness and handling out-of-distribution data. This dataset provides a valuable resource for exploring domain shift and improving the robustness of machine learning models in bioengineering and fermentation processes. It enables further research into domain generalization techniques and offers a wide range of possibilities for machine learning applications. <h2>References</h2>  [1] Arman Arefi, Barbara Sturm, Majharulislam Babor, Michael Horf, Thomas Hoffmann, Marina Höhne, Kathleen Friedrich, Linda Schroedter, Joachim Venus, Agata Olszewska-Widdrat, Digital model of biochemical reactions in lactic acid bacterial fermentation of simple glucose and biowaste substrates, Heliyon, Volume 10, Issue 19, 2024, e38791, ISSN 2405-8440, DOI: 10.1016/j.heliyon.2024.e38791, <a href="https://doi.org/10.1016/j.heliyon.2024.e38791" target="_blank" rel="noopener">https://doi.org/10.1016/j.heliyon.2024.e38791</a>. [2]  Majharulislam Babor, Shanghua Liu, Arman Arefi, Agata Olszewska-Widdrat,  Barbara Sturm, Joachim Venus, and Marina M.-C. Höhne, Domain-Invariant Monitoring for Lactic Acid Production: Transfer Learning from Glucose to Bio-Waste Using Machine Learning Interpretation. Available at <a href="https://dx.doi.org/10.2139/ssrn.5012080" target="_blank" rel="noopener">http://dx.doi.org/10.2139/ssrn.5012080.</a>

opencc-by-4.0Nov 2024View details →

Supplentary materials for: "Long-term ecosystem monitoring along the Trabocchi coast (Chieti, Italy): insights from underwater visual surveys (2011-2024)"

This collection of photos and videos showcases the rich biodiversity of marine fauna and flora along the Trabocchi coast reefs in the Chieti district of Italy.

opencc-by-4.0Nov 2024View details →

Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden"

Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden" (Monitoring open data practices - challenges in finding data publications using the example of publications by researchers at TU Dresden) - Katharina Zinke, Institut für Bibliotheks- und Informationswissenschaften, Humboldt-Universität Berlin, 2023 This ZIP-File contains the data the thesis is based on, interim exports of the results and the R script with all pre-processing, data merging and analyses carried out. The documentation of the additional, explorative analysis is also available. The actual PDFs and text files of the scientific papers used are not included as they are published open access. The folder structure is shown below with the file names and a brief description of the contents of each file. For details concerning the analyses approach, please refer to the master's thesis (publication following soon). ## Data sources  Folder 01_SourceData/ - PLOS-Dataset_v2_Mar23.csv (PLOS-OSI dataset) - ScopusSearch_ExportResults.csv (export of Scopus search results from Scopus) - ScopusSearch_ExportResults.ris (export of Scopus search results from Scopus) - Zotero_Export_ScopusSearch.csv (export of the file names and DOIs of the Scopus search results from Zotero) ## Automatic classification  Folder 02_AutomaticClassification/ - (NOT INCLUDED) PDFs folder (Folder for PDFs of all publications identified by the Scopus search, named AuthorLastName_Year_PublicationTitle_Title)  - (NOT INCLUDED) PDFs_to_text folder (Folder for all texts extracted from the PDFs by ODDPub, named  AuthorLastName_Year_PublicationTitle_Title) - PLOS_ScopusSearch_matched.csv (merge of the Scopus search results with the PLOS_OSI dataset for the files contained in both) - oddpub_results_wDOIs.csv (results file of the ODDPub classification) - PLOS_ODDPub.csv (merge of the results file of the ODDPub classification with the PLOS-OSI dataset for the publications contained in both) ## Manual coding  Folder 03_ManualCheck/ - CodeSheet_ManualCheck.txt (Code sheet with descriptions of the variables for manual coding) - ManualCheck_2023-06-08.csv (Manual coding results file) - PLOS_ODDPub_Manual.csv (Merge of the results file of the ODDPub and PLOS-OSI classification with the results file of the manual coding) ## Explorative analysis for the discoverability of open data   Folder04_FurtherAnalyses  Proof_of_of_Concept_Open_Data_Monitoring.pdf (Description of the explorative analysis of the discoverability of open data publications using the example of a researcher) - in German ## R-Script  Analyses_MA_OpenDataMonitoring.R (R-Script for preparing, merging and analyzing the data and for performing the ODDPub algorithm)

opencc-by-nc-4.0Dec 2023View details →

DBFZ Resource Database: DE-Biomass Monitor. Biomass Potentials and Utilization of Biogenic Wastes and Residues in Germany 2010-2020

Excerpt from the<a href="https://datalab.dbfz.de/resdb?lang=en"> DBFZ Resource Database</a> (DE Biomass Monitor) as of 02.12.2024 (v6.3.2).

Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Croatia

This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Croatia. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health. For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005.  Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken. Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC. The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles. Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance. Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product. Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food. Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation. Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation. Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: VMPR_2023 – Croatian Food Agency VMPR_2022 – Croatian Food Agency VMPR_2021 – Croatian Food Agency VMPR_2020 – Croatian Food Agency VMPR_2019 – Croatian Food Agency VMPR_2018 – Croatian Food Agency VMPR_2017 – Croatian Food Agency

Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Ireland

This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Ireland. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health. For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005.  Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken. Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC. The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles. Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance. Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product. Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food. Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation. Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation. Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: VMPR_2023 – Department of Agriculture, Food and the Marine VMPR_2022 – Department of Agriculture, Food and the Marine VMPR_2021 – Department of Agriculture, Food and the Marine VMPR_2020 – Department of Agriculture, Food and the Marine VMPR_2019 – Department of Agriculture, Food and the Marine VMPR_2018 – Department of Agriculture, Food and the Marine VMPR_2017 – Department of Agriculture, Food and the Marine

Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Finland

This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Finland. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health. For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005.  Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken. Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC. The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles. Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance. Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product. Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food. Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation. Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation. Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: VMPR_2023 – Finnish Food Authority VMPR_2022 – Finnish Food Authority VMPR_2021 – Finnish Food Authority VMPR_2020 – Finnish Food Authority VMPR_2019 – Finnish Food Authority VMPR_2018 – Finnish Food Authority VMPR_2017 – Finnish Food Authority

Results from the monitoring of veterinary medicinal product residues and other substances in live animals and animal products - Greece

This dataset contains the monitoring results of veterinary medicinal product residues and other substances measured in live animals and animal products analysed by the national competent authority of Greece. The presence of unauthorised substances, residues of veterinary medicinal products in food may pose a risk factor for public health. For this reason and in order to ensure a high level of consumer protection, a comprehensive legislative framework has been established in the European Union (EU) which defines maximum limits permitted in food and monitoring programmes for the control of the presence of these substances in the food chain. Regulation (EU) No 37/2010 establishes maximum limits for residues of veterinary medicinal products in food-producing animals and animal products. Maximum residue levels for pesticides in or on food and feed of plant and animal origin are laid down in Regulation (EC) No 396/2005.  Commission Implementing Regulation (EU) 2022/1646 lays down practical arrangements for and specific content of official controls of the use of veterinary medicinal products in live animals and products of animal origin through three different official national control plans: a national risk-based control plan for production in the Member States, a national randomised surveillance plan for production in the Member States and a national risk-based control plan for third-country imports. Additionally, Commission Delegated Regulation (EU) 2022/1644 lays down the range of samples and stage of production, processing and distribution at which the samples are to be taken. Since 2018 until 2022, the data on the national residue monitoring plan were reported to EFSA in accordance with Council Directive 96/23/EC. The dataset contains the results of laboratory tests from samples taken from bovines, pigs, sheep, goats, horses, poultry, rabbits, farmed game, wild game aquaculture, milk, eggs and honey, and from 2023 also samples from casings, insects and reptiles. Targeted samples are taken with the aim of detecting illegal treatment or controlling compliance with the maximum levels laid down in the relevant legislation. This means that, in their national plans Member States target the groups of animals (species, gender, age) where the probability of finding residues is the highest. Conversely, the objective of random sampling is to collect significant data to evaluate, for example, consumer exposure to a specific substance. Suspect samples are taken as a consequence of i) non-compliant results on samples taken in accordance with the control plans, ii) possession or presence of prohibited substances at any point during manufacture, storage, distribution or sale through the food and feed production chain, or iii) suspicion or evidence of illegal treatment or non-compliance with the withdrawal period for an authorised medicinal veterinary product. Residues of pharmacologically active substances mean active substances, excipients or degradation products and their metabolites, which remain in food. Unauthorised substances mean substances that are not authorised as veterinary medicinal products or as a feed additive under European Union legislation. Prohibited substances mean substances which are prohibited for use in food producing animals according to the European Union legislation. Non-compliant sample is a sample that has been analysed for the presence of one or more substances and failed to comply with the legal provisions for at least one substance. Thus, a sample can be non-compliant for one or more substances. REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: VMPR_2023 – Ministry of Rural Development and Food VMPR_2022 – Ministry of Rural Development and Food VMPR_2021 – Ministry of Rural Development and Food VMPR_2020 – Ministry of Rural Development and Food VMPR_2019 – Ministry of Rural Development and Food VMPR_2018 – Ministry of Rural Development and Food VMPR_2017 – Ministry of Rural Development and Food