Find research datasets worth reusing

Bulgarian results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample is contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2022 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2021 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2020 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2019 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2018 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2017 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2016 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2015 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2014 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2013 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2012 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain MOPER_2011 - Bulgarian Food Safety Agency - Risk Assesment Center on Food Chain   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

Austrian results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample is contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - Austrian Agency for Health and Food Safety MOPER_2022 - Austrian Agency for Health and Food Safety MOPER_2021 - Austrian Agency for Health and Food Safety MOPER_2020 - Austrian Agency for Health and Food Safety MOPER_2019 - Austrian Agency for Health and Food Safety MOPER_2018 - Austrian Agency for Health and Food Safety MOPER_2017 - Austrian Agency for Health and Food Safety MOPER_2016 - Austrian Agency for Health and Food Safety MOPER_2015 - Austrian Agency for Health and Food Safety MOPER_2014 - Austrian Agency for Health and Food Safety MOPER_2013 - Austrian Agency for Health and Food Safety MOPER_2012 - Austrian Agency for Health and Food Safety MOPER_2011 - Austrian Agency for Health and Food Safety   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

Lithuanian results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - National Food and Veterinary Risk Assessment Institute MOPER_2022 - National Food and Veterinary Risk Assessment Institute MOPER_2021 - National Food and Veterinary Risk Assessment Institute MOPER_2020 - National Food and Veterinary Risk Assessment Institute MOPER_2019 - National Food and Veterinary Risk Assessment Institute MOPER_2018 - National Food and Veterinary Risk Assessment Institute MOPER_2017 - National Food and Veterinary Risk Assessment Institute MOPER_2016 - National Food and Veterinary Risk Assessment Institute MOPER_2015 - National Food and Veterinary Risk Assessment Institute MOPER_2014 - National Food and Veterinary Risk Assessment Institute MOPER_2013 - National Food and Veterinary Risk Assessment Institute MOPER_2012 - National Food and Veterinary Risk Assessment Institute MOPER_2011 - National Food and Veterinary Risk Assessment Institute   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

Irish results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - Department of Agriculture, Food and the Marine MOPER_2022 - Department of Agriculture, Food and the Marine MOPER_2021 - Department of Agriculture, Food and the Marine MOPER_2020 - Department of Agriculture, Food and the Marine MOPER_2019 - Department of Agriculture, Food and the Marine MOPER_2018 - Department of Agriculture, Food and the Marine MOPER_2017 - Department of Agriculture, Food and the Marine MOPER_2016 - Department of Agriculture, Food and the Marine MOPER_2015 - Department of Agriculture, Food and the Marine MOPER_2014 - Department of Agriculture, Food and the Marine MOPER_2013 - Department of Agriculture, Food and the Marine MOPER_2012 - Department of Agriculture, Food and the Marine MOPER_2011 - Department of Agriculture, Food and the Marine   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

Dutch results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - Netherlands Food and Consumer Product Safety Authority MOPER_2022 - Netherlands Food and Consumer Product Safety Authority MOPER_2021 - Netherlands Food and Consumer Product Safety Authority MOPER_2020 - Netherlands Food and Consumer Product Safety Authority MOPER_2019 - Netherlands Food and Consumer Product Safety Authority MOPER_2018 - Netherlands Food and Consumer Product Safety Authority MOPER_2017 - Netherlands Food and Consumer Product Safety Authority MOPER_2016 - Netherlands Food and Consumer Product Safety Authority MOPER_2015 - Netherlands Food and Consumer Product Safety Authority MOPER_2014 - Netherlands Food and Consumer Product Safety Authority MOPER_2013 - Netherlands Food and Consumer Product Safety Authority MOPER_2012 - Netherlands Food and Consumer Product Safety Authority MOPER_2011 - Netherlands Food and Consumer Product Safety Authority   We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

opencc-by-4.0Jan 2021View details →

Bosnia and Herzegovina results from the monitoring of pesticide residues in food

This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States, Iceland, Norway and three pre-accession countries: Bosnia-Herzegovina, Montenegro and North Macedonia.  A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL) A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.   REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION: MOPER_2023 - Food Safety Agency of Bosnia and Herzegovina MOPER_2022 - Food Safety Agency of Bosnia and Herzegovina MOPER_2021 - Food Safety Agency of Bosnia and Herzegovina MOPER_2020 - Food Safety Agency of Bosnia and Herzegovina MOPER_2019 - Food Safety Agency of Bosnia and Herzegovina MOPER_2018 - Food Safety Agency of Bosnia and Herzegovina MOPER_2017 - Food Safety Agency of Bosnia and Herzegovina We are seeking feedback on our open data please complete the survey at the link below: https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14

opencc-by-4.0Sep 2024View details →

Data set from ambient vibration monitoring and static loading of a steel stringer bridge subject to imposed damage

This data set contains structural response measurements acquired from the Route 345 Bridge over Big Sucker Brook in Waddington, NY prior to demolition and replacement.  This steel stringer bridge was instrumented with dual-axis accelerometers and strain transducers and response measurements were obtained prior to and following mechanically imposed damages.  Accelerometer measurements were obtained under vehicle passes and strain measurements were acquired during static loading of the span with a truck positioned at three prescribed locations.  All measurement data is contained in a single h5-file (hierarchical data format version 5).  The data is shared with the intent of promoting the advancement of structural health monitoring and vibration-based damage detection.  Version 2 Note: This version corrects the strain measurement data.  Due to an index counter error in the script that compiled the strain measurement data into the h5-file, the strain measurement data in the Version 1 data were incorrectly sourced from a single scenario.  The strain measurements are corrected in this new version.  No other changes were made in the h5-file.

opencc-by-nc-nd-3.0Nov 2024View details →

NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe

This is the dataset presented in the paper "NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe", which can be found here: <a href="https://arxiv.org/pdf/2412.03633">https://arxiv.org/pdf/2412.03633</a>.   Update 2025, June 13th - A metadata file can now be found alongside the dataset ("metadata.csv"): it contains the recording date for original NBM files whenever available, and date and location for all but two of the files originating from Xeno-Canto. All audio samples in the train_nbm_orig folder were recorded in France, but no precise location is available for this collection. - Irregularities due to unconstrained text input in several annotation files have been fixed; in particular all annotated parasitic and background noise are now gathered in two classes: 0: Background and 0: Other biophonia. Unidentified bird vocalizations fall under the Other category.   File description Files come in three directories: train_nbm_orig, train_nbm_xc and test. Each is composed of a list of .wav files with its .txt annotations file. All bird vocalizations are linked to a species and are annotated both in time and frequency. The train dataset is composed of a total of  2,077 audio files and 13,359 annotations, for a total of ~38 hours of recording. Among these, 883 files were downloaded from Xeno-canto and finely hand-annotated.   Citation (Bibtex) <pre>@article{airale2024nbm, title={NBM: an Open Dataset for the Acoustic Monitoring of Nocturnal Migratory Birds in Europe}, author={Airale, Louis and Pajot, Adrien and Linossier, Juliette}, journal={arXiv preprint arXiv:2412.03633}, year={2024} } </pre> License This dataset is distributed under a non-commercial, non-derivative license (CC BY-NC-ND 3.0).   Acknowledgments Special thanks to all contributors to the initial NBM database: Adrien Pajot, Aymeric Mousseau, Christophe Mercier, Frédéric Cazaban, Gaëtan Mineau, Ghislain Riou, Guillaume Bigayon, Hervé Renaudineau, Kévin Leveque, Lionel Manceau, Mathurin Aubry, Maxence Pajot, Nidal Issa, Willy Raitière, and equal thanks to all anonymous further contributors. Acknowledgments also go to all contributors of the Xeno-Canto database whose recordings where used to build this dataset: Joost van Bruggen, Ricardo Hevia, Paul Kelly, Franck Hollander, Frédéric Cazaban, Ireneusz Oleksik, Irish Wildlife Sounds, Stanislas Wroza, Cedric Mroczko, Will Scott, Sergi, Chris Batty, Martin Billard, Juha Saari, Julien Bottinelli, Mark Pearson, Uku Paal, Christophe Mercier, Thierry THOMAS, Samuel Levy, Paul Coiffard, Lluís Brotons, Jorge Leitão, Alan Dalton, David Tattersley, Xavier Riera, Lars Mogensen, Feliu López i Gelats, José Manuel Reyes Páez, Jon Sparshott, Sergi Carreras, Marta Celej, julien Rochefort, Chèvremont Fabian, MILLON Xavier, Ed Stubbs, Niels Van Doninck, Jarek Matusiak, Anthony ROUX, Dan Lombard, C PAL, Ray Tsu 诸仁, Martijn Verdoes, Pierrick Devoucoux, Manceau Lionel, Lionel Manceau, Vandousselaere Patrick, Volker Arnold, Lars Edenius, Krzysztof Deoniziak, Albert Noorlander, Robert Ekman, Oriol Baena, Mr Mark Shorten, Beatrix Saadi-Varchmin, Simon Kiesé, Robert Manzano, Martin Fousert, Simon Gillings, Lisette, Marco Dragonetti, Cédric PEIGNOT, Adrien CHARBONNEAU, Gosse Hoekstra, Christian Kerihuel, Koen Lepla, Daniele Baroni, Peter van Vlaardingen, Albert Subirà, Alain Malengreau, Julien Piette, Calum Mckellar, Mikael Litsgård, Martin Grienenberger, Sven Kransel, Oliwier Myka, Susanne Kuijpers, Pierre Foulquier, Paolo Zucca, Maties, Geoffrey Monchaux, Jean COURTIN, Pere Josa, Stein Ø. Nilsen, florent yvert, John Sirrett, Toby Carter, Grzegorz Lorek, Tom Jordan, Miguel Tirado, Helder Cardoso, Ignaas Robbe, James P, Corentin Rivière, Romuald Mikusek, David Santamaría Urbano, Diego Fernandez Martinez, Tanguy Loïs, Camille Vacher, Frank Pierik, Testaert Dominique, Piotr Szczypinski, Rafael Costas, Patrick Franke, Ad Hilders, J. Veeken, Thijs Calu, David Pennington, Nic Hallam, Albert Lastukhin, Dominique Guillerme, W. Agster, Nittert van de Water, Marcos Prada Arias, Francesco Barberini, Enrico Hübner, Luca Forneris, Graham Clarke, Mike Douglas, Johan Willner, Sébastien Arriubergé, Rafał Szczerbik, Alessandro Pavesi, Jarred Johnson, Tomasz Wałachowski, Cédric JOUVE, Tom Gheskiere, jesus carrion, David Darrell-Lambert, Graham Sparshott, Théo Hervé, Martin Sutherland, Quentin Giraud, Kévin Giraudin, LE ROY Renaud, August Thomasson, Marcin Sołowiej, Filippo Ceccolini, BirdingAlbufera, Armin Kreusel, Robert Thorpe, Sannier, Oscar Vilches, Paweł Szymański, Jerome Fischer, Matt Slaymaker, Gil Lissens, Sidney M, Matthias Feuersenger, JC Paniagua, Anita Rakitić, david m, Maxime Pirio, brickegickel, Mark Newsome, Sophie REVERDIAU, Oriol, Michaël Bridoux, Sonothèque ADVL, Kieran Nixon, Jochem verweij, Sjouke Scholten, Michael Brunhøj Hansen, Peter Stronach, Pere Josa Anguera, Esperanza Poveda, Johannes Dag Mayer, Pablo Valverde, Friedemann Arndt, Tristan Guillebot de Nerville, Adam Cross, Richard Drew, Cindie Arlaud, Petr Večeřa, Domagoj Tomičić, Guillaume Petitjean, riou, Peter Mattsson, Michał Jezierski, SCECB NATURA-Z, Karol Łanocha, Twan Mols, Klaus Fink, Dawid Jablonski, Arnold Meijer, Paul Ehlers, José Carlos Sires, manuel Grosselet, Birding The Strait, Charlie Bodin, Juan Carlos Paniagua, Nabholz Benoit, Fergus Crystal, Agris Celmins, Albert Cama, Iván Vega, John Muddeman, Adrien Mauss, Gerard Troost, Serge Hoste, Stefano Miceli, Joan Balfagon, Ace, Frank A. Roos, Tom Wulf, Vincent Palomares, Marcel Tenhaeff, Gabriel Hasan, James Lidster, Frank van de Weijer, Herman van Oosten, Alain Verneau, Herman van der Meer, Romain SPELLER, Johan Lorentzon, Thomas ARMAND, Chris Beach, Jacob Spinks, András Schmidt, Jonathan van Erkel, James Spencer, Rowan Wakefield, LEPAGE Frédéric, Itziar Gutiérrez, Severin Racky, NEVILLE MADON, B Whyte, Bodhuin Maxime, Benoit Paepegaey, Ruysschaert, Soulier Pierrick, Frederik Fluyt, Thomas Roux, Eduardo Realinho, Simon Elliott, Oscar Vilches Mendoza, Juan Pita-Romero Caamaño, Gary Elton, Marc Hughes, Raul Pascual, Nicolas Selosse, Arnaud Hedel, Gaëtan Mineau, Peter Boesman, Steve Flynn, Gerry p oneill, Andy Hultberg, Helmut Schaffer, Luca Giussani, edouard dansette, Gavia Stellata, Robert Schouw, Sławomir Karpicki-Ignatowski, mvallespirc, Mark Plummer, Chacron, Seynaeve Adriaan, Kristian Whittaker, Andreas Pettersson, Rafał Kurowski, Delpit, Anja van Halbeek, Lars Burnus, Benjamin Schedl, Sreekumar Chirukandoth, Mathias Götz, Mikhail Velikanov, Paul Bourdin, Török Tamás, Mehmet Ali Demiral, Manuel Grosselet, Olivier Swift, Yoann Blanchon, Jacob Bosma, Alexis Bukowski, Götz Ellwanger, Guillaume Bigayon, Charlie BODIN, Olivier SWIFT, Manuel Grosselet, Tero Linjama, Ad Postma, Eetu Paljakka, Jan Hein van Steenis, Alwin van Lubeck, Jacobo Ramil MIllarengo, Andrew Cobley, guus van duin, Meinolf Ottensmann, Steve Thorpe, Antoine Salmon, Alain Beuget, Simon Busuttil, Bertrand Dallet, Bill Haines, Robbin van Dijk, Jacopo Barchiesi, Johan Jordaans, Simon BAUDOUIN, Nelson Conceição, Teet Sirotkin, Dean McDonnell, Jocce Ekstrom, Michael John O Mahony, Fred Prak, Joachim Pintens, Christophe Legrand, Daniel Beuker, G Berger, Steve Blain, Boris Delahaie, Will Langdon, David Melichar, Emmanuel Roy, Jonas Brüggeshemke, YvesDS, FRIEDRICH Richard

opencc-by-4.0Apr 2024View details →

GEOGLAM Crop Monitor Sub-National Crop Calendars

The GEOGLAM Crop Monitor Sub-National Crop Calendars are based on a combination of the best available data from different international agencies, national ministries, and expert knowledge of the country partners from GEOGLAM Crop Monitors. The crops covered are wheat, maize, rice, soybeans, sorghum, millet, beans, and teff. In locations where there are multiple cropping seasons, the seasons are numbered with the first being the largest producing season followed by the next largest, and so on. The crop calendars consist of five different phenological stages which are broadly applicable across most crops; Planting through Early Vegetative, Vegetative through Reproductive, Ripening through Harvest, End of Harvest, and Out of Season. The sub-national regions were built using the administrative level 1 FAO GAUL units as the basic building blocks since crop production statistics are typically readily available for most countries at this level and the crop calendars were designed to link crop conditions to potential yield and production within the GEOGLAM Crop Monitors. Agroecological zones, subnational statistics, and national partner inputs were then used to inform the formation process. This was done so that areas with similar phenological development timelines were grouped, >90% of national production was captured in the calendars, and the national production was relatively distributed among the sub-national regions, accounting for the multiple crops monitored per country. To provide a finer temporal detailed product, the crop calendars are displayed in bi-monthly steps.

opencc-by-4.0Dec 2023View details →

The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept - Supplementary Material

<h3>Abstract</h3> Field monitoring plays a crucial role in understanding insect dynamics within ecosystems. It facilitates pest distribution assessment, control measure evaluation, and prediction of pest outbreaks. Additionally, it provides important information on bioindicators with which the state of biodiversity and ecological integrity in specific habitats and ecosystems can be accurately assessed. However, traditional monitoring systems can present various difficulties, leading to a limited temporal and spatial resolution of the obtained information. Despite recent advancements in automatic insect monitoring traps, also called e-traps, most of these systems focus exclusively on studying agricultural pests, rendering them unsuitable for monitoring diverse insect populations. To address this issue, we introduce the Field Automatic Insect Recognition (FAIR)-Device, a novel non-lethal field tool that relies on semi-automatic image capture and species identification using artificial intelligence via the iNaturalist platform. Our objective was to develop an automatic, cost-effective, and non-specific monitoring solution capable of providing high-resolution data for assessing insect diversity. During a 26-day proof-of-concept evaluation, the FAIR-Device recorded 24.8 GB of video, identifying 431 individuals from 9 orders, 50 families, and 69 genera. While improvements are possible, our device demonstrated potential as a cost-effective, non-lethal tool for monitoring insect biodiversity. Looking ahead, we envision new monitoring systems such as e-traps as valuable tools for real-time insect monitoring, offering unprecedented insights for ecological research and agricultural practices. <h3>Description of the data and file structure</h3> This repository complements the publication <a href="https://www.biorxiv.org/content/10.1101/2024.03.22.586299v2" target="_blank" rel="noopener">"The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept".</a> It contains the result data from the proof of concept field test of V1.0 of the FAIR-Device, conducted between July and August 2021 at the Thünen Institute of Agricultural Technology in Braunschweig. The repository comprises three compressed files (.zip): <ul> <li>FAIR-D_captures.zip: <ul> <li>Video captures from the field tests organized by recording day.</li> <li>Filenames indicating recording time (hh-mm-ss).</li> </ul> </li> </ul> <ul> <li>FAIR-D_Tables&Code.zip: <ul> <li>Processed results from the obtained image captures, organized into:  <ul> <li>Monitoring2021_TotalPeriod.xlsx: Result table with taxonomic classifications, data analysis, and charts.</li> <li>iNat_observations.xlsx: iNaturalist reviews analysis table.</li> <li>R: code for generating article graphics.</li> </ul> </li> </ul> </li> </ul> <ul> <li>FAIR-D_V1.0_3D_Models.zip: <ul> <li>Complete 3D design of the FAIR-D V1.0 in .stl format, organized into: <ul> <li>3D_print_parts: 3D-printable parts with spatial coordinates for correct positioning in CAD software.</li> <li>other_3Dparts: Non-printable parts, also in .stl format with spatial coordinates.</li> </ul> </li> </ul> </li> </ul>   NOTE: For visualizing the videos, we recommend VLC media player - <a href="https://www.videolan.org/vlc/">https://www.videolan.org/vlc/</a>

opencc-zeroOct 2023View details →

Below-ground pitfall traps for standardised monitoring of soil mesofauna: Design and comparison to Berlese/Tullgren funnels

The attached CSV file supports the article Below-ground pitfall traps for standardised monitoring of soil mesofauna: Design and comparison to Berlese/Tullgren funnels (Pedobiologia, Volume 101, 2023, 150911, ISSN 0031-4056, <a href="https://doi.org/10.1016/j.pedobi.2023.150911)">https://doi.org/10.1016/j.pedobi.2023.150911)</a> relative to its comparison part. It presents the specimen count of the deployed pitfall traps and Berlese/Tullgren extractions across five environments at different positions along transects.

Victorian Coastal Monitoring Program: (1) VCMP Sites; (2) VCMP Coastal Erosion Indicators

This dataset was produced by the Victorian Coastal Monitoring Program (VCMP), Department of Energy, Environment and Climate Action, Victoria State Government, Australia.<a href="https://www.marineandcoasts.vic.gov.au/marine-and-coastal-knowledge/victorian-coastal-monitoring-program">https://www.marineandcoasts.vic.gov.au/marine-and-coastal-knowledge/victorian-coastal-monitoring-program</a> <a href="https://www.marineandcoasts.vic.gov.au/__data/assets/pdf_file/0023/625352/VCMP_Erosion-Indicators_April_2023.pdf">https://www.marineandcoasts.vic.gov.au/__data/assets/pdf_file/0023/625352/VCMP_Erosion-Indicators_April_2023.pdf</a>[ADD LINK TO VCMP SITES METADATA REPORT - ONCE PUBLISHED TO VCMP WEBSITE]Products include: (1) Complete processed shorelines dataset ('VCMP Sites'), including drone and satellite data, with transects, shorelines, time series, and cross-sections for drone surveys(2) Summary outputs ('VCMP Coastal Erosion Indicators'), including the Erosion Warning Indicator (EWI) summary statistics and Erosion Hotspot Detector (EHD) outputs. Survey data in this repository were updated to the start of 2023. Regularly updated outputs for all VCMP products, including pre-processed VCMP drone data (digital surface models and orthomosaics) can be accessed through the Victorian government (contact <a href="mailto:vcmp@delwp.vic.gov.au">vcmp@delwp.vic.gov.au</a>; <a href="https://www.marineandcoasts.vic.gov.au/marine-and-coastal-knowledge/victorian-coastal-monitoring-program">https://www.marineandcoasts.vic.gov.au/marine-and-coastal-knowledge/victorian-coastal-monitoring-program</a>). 'VCMP Sites' and 'VCMP Coastal Erosion Indicator' outputs and a wide array of marine and coastal data are viewable through the decision support portal CoastKit (<a href="https://mapshare.vic.gov.au/coastkit/">https://mapshare.vic.gov.au/coastkit/</a>). Select outputs are also downloadable in spatial file format through DataShare Victoria (<a href="https://datashare.maps.vic.gov.au/search?q=vcmp">https://datashare.maps.vic.gov.au/search?q=vcmp</a>).

opencc-zeroNov 2023View details →

Smoke designations for eastern Kansas monitoring sites during March-May 2022

Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM2.5). PM2.5 can penetrate deep into the lungs and harm cardiovascular and respiratory systems. The Flint Hills region of Kansas experiences extensive prescribed burning each spring (March - May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM2.5 concentrations to the prescribed burning in the Flint Hills. To determine PM2.5 increases from local burning, we used low-cost PM2.5 sensors (PurpleAir) and satellite observations. The Flint Hills were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24-hour median PM2.5 increased by 5.2 µg m-3 on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. We found the Flint Hills to be the most smoke PM2.5 impacted region compared to the surrounding area across satellite products and in-situ measurements.

opencc-zeroNov 2023View details →

AERPAW helikite spectrum monitoring at Packapalooza festival in Aug 2022

In this dataset, we conducted a spectrum monitoring experiment in an urban environment. The helikite flies up to an altitude of 400 feet throughout the day from noon to 9 p.m. during NC State's Packapalooza festival in August, 2022. The spectrum is swept up to 6 GHz. Every sweep takes around 1 minute, while after every 4 measurements, the 5th measurement takes close to 5 minutes due to another data collection activity running in parallel. The photo of a flying Helikite at the experiment site is shown in the dataset image. The helikite goes up and stays at an altitude of around 400 feet and goes down around an altitude of around 70 feet four times during the whole measurement period.

SeaPaCS graphic elaboration of the Protocol for marine micro-plastic collection and monitoring in citizen science and for building a L.A.D.I. trawling tool

This  is a graphic elaboration  (in Italian) of the protocol "SeaPaCS deliverable - protocol for plastic monitoring in citizen science" in English and Italian is a deliverable of the SeaPaCS project (Participatory Citizen Science Against Marine Pollution), funded by IMPETUS (project ID 101058677). The protocol  and the visual elaboration has been freely adapted from "LADI and the Trawl" by Coco Coyle with Melissa Novaceski, Emily Wells and Max Liboiron, as published by the Civic Laboratory for Environmental Action Research, August 2016.  The graphic elaboration (as the protocol) in both languages, consists of three parts: 1) how to build a DIY low cost manta trawl device (LADI - Low-Tech Aquatic Detection Debris Instrument) to monitor plastic pollution,  adjusted to materials availability and costs in Italy; 2) how to monitor (the sampling itself and towing procedure); and 3) how to categorize plastic debris back on land.

In-house CHO HCP Platform: a promising approach for HCP ELISA monitoring

Open the record for dataset details and reuse information.

Monitor predation on zebra finch nest

Here, we report a detailed predation sequence, captured on film, of a sand goanna (Varanus gouldii) predating six zebra finch nestlings from a single nestbox in arid western New South Wales, Australia. The sand goanna made nine attempts to reach the nestbox by first climbing an adjacent bush and jumping onto the nestbox and eventually also by climbing up the steel post on which the box was attached.

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

US National Native Bee Monitoring RCN Data Management Workshop: CC BY NC Videos

The US National Native Bee Monitoring Research Coordination Network (RCN) held a two-day workshop on data management best practices for native bee inventory, survey, and monitoring data on March 28 and 30, 2023. Videos in this data set were played at the workshop. These videos are released with a CC BY-NC license. These videos are for non-commercial use only. Please cite the presenter(s) of the video(s) you use. This data set includes the following videos: <ul><li>APHIS National Honey Bee Pests and Diseases Survey by Anne LeBrun</li><li>Importance of FAIR in Biodiversity Science and Research by Elizabeth R. Ellwood</li><li>Bugflow: A Community Driven Repository for Entomology Digitization Resources by Crystal Maier</li><li>A Primer on iNaturalist Bee Data by Keng-Lou James Hung, Paige Chesshire, Michael Orr, Alice Hughes, Jess Mullins, Katherine Parys, Patricia Simpson, Lindsie McCabe, Neil Cobb, and John Ascher</li></ul>

opencc-zeroDec 2023View details →

Peatland restoration in Norway – evaluation of ongoing monitoring and identification of plant indicators of restoration success

Norway launched a national action plan on wetland restoration in 2016. So far, 90% of the restoration effort has been on peatland restoration, with about 140 mires restored so far. There are three main restoration goals stated in the action plan: 1) Limit greenhouse gas (GHG) emissions, 2) climate adaptation, and 3) improve ecological condition. Quantifying the outcome of the restoration actions is necessary to evaluate whether the goals of the action plan are met. A vegetation monitoring protocol was suggested before restoration started and has been implemented at five restoration sites. As the peatland restoration effort in Norway is increasing, it is timely to evaluate if the data currently collected can measure peatland restoration outcomes. We evaluate the monitoring protocol based on statistical analyses of the data collected at two sites, describe how indicator species can be identified using generalized composition data used as the basis for classifying habitats in Norway (EcoSyst framework), and suggest the way forward for peatland restoration monitoring in Norway. Data collected according to the monitoring protocol can document changes in species composition at restoration sites but has limitations when the ecological complexity at the sites increases and reference sites are unavailable. We argue that adjusting the monitoring protocol will: 1) Facilitate alignment with existing peatland research; 2) connect better with monitoring programs where data is collected applying EcoSyst framework principles; and 3) enable upscaling to cover the wide variation emerging in peatland restoration.