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393 results for “pesticides”
Data for: Spillover effects of organic agriculture on pesticide use on nearby fields
<p><span>The environmental impacts of organic agriculture are only partially understood and </span><span>whether such practices have spillover effects </span><span>on pests or pest control activity </span><span>on nearby fields remains unknown</span><span>. Using </span><span>roughly 13,000 field observations per year from 2013-2019 in Kern County, CA , </span><span>we estimate that organic crop producers benefit from surrounding organic fields, decreasing overall pesticide use and pesticides targeting insect pests. </span><span><span>Conventional fields, in contrast, tend to increase pesticide use as the area of surrounding organic production increases.</span></span></p>
Curated dataset for analysis for the paper "Decision Support Systems Adoption in Pesticide Management"
<p>Dataset created from farmer responses to a survey on the decision support systems adoption for intergrated pest management in the framework of the EU funded project IPM Decisions.</p>
Estimated Annual Agricultural Pesticide Use for USA48 2000 to 2019 (gridded maps at 250-m resolution)
<p>Estimated low and high pescticide uses based on census data for USA48, provided for the cropland mask at 250-m spatial resolution. The original <a href="https://water.usgs.gov/nawqa/pnsp/usage/maps/">USGS maps</a> are only available as PNGs, so we have prepared here a code to rasterize the values and produce GeoTIFFs. For cropland mask we use the <a href="https://www.usgs.gov/centers/eros/science/national-land-cover-database">National Land Cover Database (NLCD)</a>. All processing is fully documented in: <a href="https://github.com/Envirometrix/pesticide-use-USA48">https://github.com/Envirometrix/pesticide-use-USA48</a>. All maps are projected in the <a href="https://epsg.io/5070-1252">EPSG:5070</a>. Description of the layers:</p> <ul> <li><code>croplands_20**_250m.tif</code> = cropland mask (0–100%) estimated based on NLCD.</li> <li><code>GLYPHOSATE_EPEST.HIGH.KG.KM2_20**_250m.tif</code> = estimated pesticide use per county (high estimate) expressed in kg/km-square.</li> <li><code>diff.GLYPHOSATE_EPEST.HIGH.KG.KM2_250m.tif</code> = annual difference in pesticide use from 2000 to 2019;</li> </ul> <p><strong>Disclaimer</strong>: this code is under construction and USGS makes is clearly available that there are some limitations to this data:</p> <ul> <li>These estimates are made by using projected county crop acres from the previous Census of Agriculture and are expected to be revised upon availability of updated crop acreages in the following Census of Agriculture.</li> <li>The files do not include pesticide use estimates for California. Data for California are obtained from the online Department of Pesticide Regulation-Pesticide Use Reporting (DPR-PUR) database and are typically not available at the time the preliminary pesticide use estimates are generated for the rest of the U.S.</li> </ul> <p>Pease have in mind these limitations when using these maps for further modeling.</p>
Predicted Transformation Products of 321 Pesticides
<p>This dataset contains transformation products predicted for 321 pesticides detected in European and Australian surface and ground waters. All predictions were done using the environmental microbial degradation module of BioTransformer3.0 version 2023-05-23. Five generations of transformation products were predicted.</p> <p><br>The files in this deposition include:<br><strong>pesticides_compounds.sql</strong> contains the all unique compounds and their identifiers (including both precursors and transformation products).<br><strong>pesticides_dead_end_tps.sql</strong> contains the IDs of all transformation products for which no further transformation products could be predicted as well as the original precursors.<br><strong>pesticides_input_pesticides.sql</strong> contains the IDs of the 321 original pesticides.<br><strong>pesticides_reaction_types.sql</strong> contains the unique enzyme biosystem and transformation rules.<br><strong>pesticides_reactions.sql</strong> contains the unique combinations of precursor IDs and transformation product IDs.<br><strong>pesticides_routines.sql </strong>contains query functions to obtain TPs and precursors as well as full transformation pathways.<br><strong>pesticides.sql</strong> is a self-contained file containing all the other files.</p> <p> </p>
EU Alerts/notifications on pesticides content in tea, vegetable and fruits
<p>The objective of this dataset is to establish a group of active substances/food products of interest and to know the importance of this risk (occurrence of pesticides) in the food trade between in EU and China. Data include active substances, concentrations, food product, notifying country, date of notification. All food products collected in this dataset come from China.</p> <p>The origin of the data is the RASFF portal.(since 2010 until January 2022). 294 entries have been collected.</p>
Application of multi-analyte / multi-matrix screening method for pesticide residues in fruits and vegetables to Interaboratory Comparison Study on Pesticide Residues in Food (ILC)
<p>The suitability of multi-analyte / multi-matrix screening method for pesticide residues in fruits and vegetables and related products, developed within activities of task (Multi-analyte / multi-matrix screening method for pesticide residues in fruits and vegetables (including tea) and fruit juices), was evaluated by the Interaboratory Comparison Study on Pesticide Residues in Food (ILC). Test material (“Pesticide Residues in green tea”), prepared from the batch used for another proficiency test (PT), was provided by Fapas (Fera Science Ltd, York, UK).</p> <p>Data set obtains (i) information about performance characteristics of the analytical method and (ii) compilation of results of interlaboratory study.</p>
Pesticide metabolites database and occurrence data in wine
<p>Pesticide metabolites database is the overview of potential pesticide metabolites originated from selected parent compounds. These metabolites can be found in various source including JMPR (FAO/WHO) documents, EU pesticide database, EFSA etc. Based on their elemental formula respective ions (protonated / deprotonated molecules, their adducts) originated in ESI source can be derived for ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS).</p> <p>Occurrence data were obtained from determination of pesticide residues and their metabolites in samples of grapevine and wine, using UHPLC-HRMS, with the objective of supporting the possibility of the verification of the method of farming. It documents the identification of pesticide metabolites commonly used in conventional farming and provides a characterization of pesticide degradation during grapevine growth, maturation and during the wine-making process.</p> <p>The dataset is useful for anyone working on authentication of organic fruits and vegetables.</p>
United Kingdom results from the monitoring of pesticide residues in food
<p>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.</p> <p>A sample is considered <strong>free of quantifiable residues</strong> 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.</p> <p>If a sample <strong>contains quantifiable residues</strong> 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)</p> <p>A sample is considered <strong>non-compliant</strong> 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.</p> <p> </p> <p><strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong></p> <p>MOPER_2020 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2019 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2018 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2017 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2016 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2015 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2014 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2013 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2012 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p>MOPER_2011 - Chemicals Regulation Directorate, Health and Safety Executive</p> <p> </p> <p><strong>We are seeking feedback on our open data please complete the survey at the link below:<br> https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong></p>
Annexes for the 2020 EU report on pesticide residues
<p>Annex II: PRIMo rev.3.1 file containing the results of the exposure assessment </p> <p>Annex III: detailed information on the different control programmes, full list of samples exceeding the MRLs anonymised by sample code, analytical scope from the official laboratories reporting to EFSA data on pesticide residues, the HBGV and PF used in the risk assessment.</p>
Running the validation assistant tool on pesticide dossiers in IUCLID
<p>Before submitting a dossier, <strong>applicants</strong> are recommended to use the Validation Assistant tool to check the dossier is technically complete. It is important to resolve all validation assistant quality warnings as this will support the Admissibility Check of the RMS/EMS. The <strong>RMS/EMS</strong> should also run the validation assistant report before declaring the admissibility of a dossier to make sure the dossier is complete.</p> <p>If the report shows a business rule failure (anything starting with BR, e.g. BR_PPP_033) this will prevent the applicant from successfully submitting the dossier and therefore must be resolved.</p> <p>This demonstration is supporting RMS/EMS and applicants on how to run the validation assistant tool and extract the report. </p>
Appendices for the paper "Decision Support Systems Adoption in Pesticide Management"
Open the record for dataset details and reuse information.
Pesticide exposure triggers sex-specific inter- and trans-generational effects conditioned by past sexual selection
<p>Environmental variation often induces plastic responses in organisms that can trigger changes in subsequent generations through non-genetic inheritance mechanisms. Such transgenerational plasticity thus consists of environmentally-induced non-random phenotypic modifications that are transmitted through generations. Transgenerational effects may vary according to the sex of the organism experiencing the environmental perturbation, the sex of their descendants, or both, but whether they are affected by past sexual selection is unknown. Here we use experimental evolution on an insect model system to conduct a first test of the involvement of sexual selection history in shaping transgenerational plasticity in the face of rapid environmental change (exposure to pesticides). We manipulated evolutionary history in terms of the intensity of sexual selection for over 80 generations before exposing individuals to the toxicant. We found that sexual selection history constrained adaptation under rapid environmental change. We also detected intergenerational and transgenerational effects of pesticide exposure in the form of increased fitness and longevity. These cross-generational influences of toxicants were sex-dependent (they affected only male descendants), and intergenerational, but not transgenerational, plasticity was modulated by sexual selection history. Our results highlight the complexity of intragenerational, intergenerational, and transgenerational influences of past selection and environmental stress on phenotypic expression.</p>
Figura 1 in Using ( food a sentinel colony of Apis mellifera Hymenoptera: Apidae) to assess pesticides and sources
Figura 1. Imagen de la izquierda: mapa de Argentina y zona de estudio indicada con el círculo amarillo. Imagen de la derecha: rango de hogar de las abejas melíferas pecoreadoras en nuestro apiario experimental en la Escuela de Agricultura y Sacarotecnia, Argentina.
Figure 3. Chromatogram from the sample fairs MAC 02 and ARA 0 in Detection of enteropathogens and research of pesticide residues in Lactuca sativa from traditional and agroecological fairs
Figure 3. Chromatogram from the sample fairs MAC 02 and ARA 0, with the peaks of Diphenoconazole compared with the pattern.
Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using MCRA software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system: </p> <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (<a href="https://doi.org/10.2903/j.efsa.2019.5800">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-NAN</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-NAM</li> <li>Annex B.1 – Output data from the Tier II exposure assessment of CAG-NAN</li> <li>Annex B.2 – Output data from the Tier II exposure assessment of CAG-NAM</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using MCRA software (<a href="https://doi.org/10.2903/sp.efsa.2019.en-1708">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid: </p> <ol> <li>hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);</li> <li>hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (<a href="https://doi.org/10.2903/j.efsa.2019.5801">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with Monte Carlo Risk Assessment (MCRA) software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-TCP</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-TCF</li> <li>Annex B.1 – Output data from the Tier II exposure assessment of CAG-TCP</li> <li>Annex B.2 – Output data from the Tier II exposure assessment of CAG-TCF</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the external scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using MCRA software (<a href="https://doi.org/10.2903/sp.efsa.2019.en-1707">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS® software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid: </p> <ol> <li>hypertrophy, hyperplasia and neoplasia of C-cells, i.e. affecting the parafollicular cells or the calcitonin system of the thyroid (CAG-TCP);</li> <li>hypothyroidism, i.e. affecting the follicular cells and/or the hormone system of the thyroid (CAG-TCF).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the thyroid (<a href="https://doi.org/10.2903/j.efsa.2019.5801">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with SAS<sup>®</sup> software using a 2-dimensional probabilistic method, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-TCP</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-TCF</li> <li>Annex B.1 – Output data from the Tier I exposure assessment of CAG-TCP</li> <li>Annex B.2 – Output data from the Tier I exposure assessment of CAG-TCF</li> <li>Annex C.1 – Output data from the Tier II exposure assessment of CAG-TCP</li> <li>Annex C.2 – Output data from the Tier II exposure assessment of CAG-TCF</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have chronic effects on the thyroid using SAS<sup>®</sup> software (<a href="https://doi.org/10.2903/j.efsa.2019.5763">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the thyroid (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Annexes to the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS® software - Input and output data sets
<p>Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system: </p> <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> <p>The pesticides considered in this assessment were identified and characterised in the scientific report on the establishment of cumulative assessment groups of pesticides for their effects on the nervous system (<a href="https://doi.org/10.2903/j.efsa.2019.5800">here</a>).</p> <p>The exposure calculations used monitoring data collected by Member States under their official pesticide monitoring programmes in 2014, 2015 and 2016 and individual food consumption data from ten populations of consumers from different countries and from different age groups. Regarding the selection of relevant food commodities, the assessment included water, foods for infants and young children and 30 raw primary commodities of plant origin that are widely consumed within Europe.</p> <p>Exposure estimates were obtained with SAS<sup>®</sup> software using a 2-dimensional Monte Carlo simulation, which is composed of an inner-loop execution and an outer-loop execution. Variability within the population is modelled through the inner-loop execution and is expressed as a percentile of the exposure distribution. The outer-loop execution is used to derive 95% confidence intervals around those percentiles (reflecting the sampling uncertainty of the input data).</p> <p>Furthermore, calculations were carried out according to a tiered approach. While the first-tier calculations (Tier I) use very conservative assumptions for an efficient screening of the exposure with low risk for underestimation, the second-tier assessment (Tier II) includes assumptions that are more refined but still conservative. For each scenario, exposure estimates were obtained for different percentiles of the exposure distribution and the total margin of exposure (MOET, i.e. the ratio of the toxicological reference dose to the estimated exposure) was calculated at each percentile.</p> <p>The input and output data for the exposure assessment are reported in the following annexes:</p> <ul> <li>Annex A.1 – Input data for the exposure assessment of CAG-NAN</li> <li>Annex A.2 – Input data for the exposure assessment of CAG-NAM</li> <li>Annex B.1 – Output data from the Tier I exposure assessment of CAG-NAN</li> <li>Annex B.2 – Output data from the Tier I exposure assessment of CAG-NAM</li> <li>Annex C.1 – Output data from the Tier II exposure assessment of CAG-NAN</li> <li>Annex C.2 – Output data from the Tier II exposure assessment of CAG-NAM</li> </ul> <p>Further information on the data, methodologies and interpretation of the results are provided in the scientific report on the cumulative dietary exposure assessment of pesticides that have acute effects on the nervous system using SAS<sup>®</sup> software (<a href="https://doi.org/10.2903/j.efsa.2019.5764">here</a>).</p> <p>The results reported in this assessment only refer to the exposure and are not an estimation of the actual risks. These exposure estimates should therefore be considered as documentation for the final scientific report on the cumulative risk assessment of dietary exposure to pesticides for their effects on the nervous system (<a href="https://www.efsa.europa.eu/en/consultations/call/public-consultation-scientific-report-cumulative">here</a>). The latter combines the hazard assessment and exposure assessment into a consolidated risk characterisation, including all related uncertainties.</p>
Figure 1 in Organochlorine pesticide residues in feathers of four bird species from western part of Turkey
Figure 1. Specimen collecting locations from four geographical regions (A: Central Anatolia; B: Aegean; C: Mediterranean; D: Marmara; triangle: common buzzard, square; common kestrel; circle: common blackbird; ring: house sparrow) across Turkey.
Fig. 3 in The threat of pesticide and disease co-exposure to managed and wild bee larvae
Fig. 3. Pesticide research bias across bee genera depending on pesticide type based on Web of Science searches. The number of studies per search term is indicated for each genus with at least 10 studies across search terms; Understudied is a cumulative group including genera which contain less than 10 studies; Unexplored is a cumulative group including genera which have no published papers for any pesticide exposure category. Warmer colours are used to indicate a higher number of studies related to a genus for the corresponding pesticide search term, while colder colours indicate a lower number of studies found. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.