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52 results for “Scientific Reports”
MCR LTER: Coral Reef: Growth-predation risk trade-offs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats; Data for Ladd et al., 2025, Scientific Reports.
This dataset is in support of the manuscript: Growth-predation risk tradeoffs constrain the local distribution of a thicket-forming staghorn coral to marginal reef habitats. These data were collected to 1) document how Acropora pulchra is distributed around the island of Moorea, and 2) to better understand the ecological processes that shape that distribution. Data include 1) results from surveys around the island of Moorea documenting the presence and size distribution of Acropora pulchra thickets, 2) results from an experiment measuring the growth and survivorship of Acropora pulchra fragments in the presence and absence of fish predators at nearshore fringing reef sites and adjacent sites in the mid lagoon (n = 20 sites in total), and 3) ancillary data on nitrogen content and dN15 in the tissue of the macroalgae Turbinaria ornata, sediment accumulation, and corallivore biomass at the experimental sites. All data were collected in 2016 and 2017.
Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)
<p>Summary statistics for genome-wide association studies reported in:</p> <p>Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>. </p> <p><strong>Abstract</strong></p> <p>Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.</p> <p> </p> <p>Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.</p>
MCR LTER: Coral Reef: Data in support of Edmunds 2018 Scientific Reports
These data are selected from the larger MCR LTER timeseries datasets knb-lter-mcr.4001 and knb-lter-mcr.4 which contain annual surveys of coral recruitment and coral cover and are formatted here in support of this publication: Edmunds, P.J., Implications of high rates of sexual recruitment in driving rapid reef recovery in Mo'orea, French Polynesia, Scientific Reports 8, Article number: 16615 (2018). DOI:10.1038/s41598-018-34686-z This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Data set for article Veto, P., Einhäuser, W., & Troje, N.F. (2017). Biological motion distorts size perception. Scientific Reports, 7, 42576.
<p>In this data set you find 3 files containing data from Experiments 1, 2 & 3 of Veto P, Einhauser W & Troje NF (2017) Biological motion distorts size perception. Scientific Reports, 7, 42576; doi: 10.1038/srep42576</p> <p><br> The data are freely available for academic use only. If you use these data for a publication, please cite the aforementioned article.<br> If you have any questions regarding the data, please do not hesitate to contact Peter Veto at vettop@gmail.com</p> <p>Each row of the files contain data from one trial.<br> Columns:</p> <p>Experiment 1<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Target orientation (1: Upright; -1: Inverted)<br> 5 - Stimulus width<br> 6 - Stimulus height<br> 7 - Response width<br> 8 - Response height</p> <p>Experiment 2<br> 1-8 Same as Experiment 1<br> 9 - Condition: dynamic (1) or static (2) target</p> <p>Experiment 3<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Walker orientation<br> (1: upper walker upright, lower walker inverted;<br> 2: upper walker inverted, lower walker upright)<br> 5 - Condition<br> (1: upper target larger (21%) than lower target;<br> 2: upper target larger (10.5%) than lower target;<br> 3: target sizes are identical;<br> 4: lower target larger (10.5%) than upper target;<br> 5: lower target larger (21%) than upper target)<br> 6 - Inter stimulus interval (from end of walker presentation to onset of target circles)<br> (1: 17ms; 2: 100ms)<br> 7 - Response<br> (1: upper target was larger;<br> 2: lower target was larger)</p>
Dataset accompanying Nölke et al. 2022. The choice of the white clover population alters overyielding of mixtures with perennial ryegrass and chicory and underlying processes. Scientific Reports
<p>This repository contains biomass and nitrogen yield data as well as data on diversity effects used by Nölke et al. in an article published in Scientific Reports (2022).</p> <p>Metadata are provided in the first excel worksheet ('explanation_overview'). For further details please see the original research article.</p>
Vortex input files - Carroll et al. Scientific Reports
<p>Vortex input files associated with the manuscript by Carroll et al. "Biological and sociopolitical sources of uncertainty in population viability analysis for endangered species recovery planning", published in Scientific Reports http://doi.org/10.1038/s41598-019-45032-2</p> <p>v2013nodd.xml: Scenario without density-dependent reproduction adapted from 2013 Mexican wolf PVA, as presented initially in Carroll et al. 2014. https://doi.org/10.1111/cobi.12156<br> v2013dd.xml: Scenario with density-dependent reproduction adapted from 2013 Mexican wolf PVA.<br> v2017.xml: Scenario adapted from 2017 Mexican wolf PVA (Miller 2017). <br> MXWlivNoMex.txt: Pedigree input file for above scenarios.</p>
Data used in the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan
This dataset includes modeled data describing the potential benefits of the Voluntary Agreements (VAs) from the Final Draft Scientific Basis Report Supplement in Support of Proposed Voluntary Agreements for the Sacramento River, Delta, and Tributaries Update to the San Francisco Bay/Sacramento-San Joaquin Delta Water Quality Control Plan.
MCR LTER: Coral Reef: River chemistry dynamics in 2018 and 2019; data for Neumann et al., 2025 Scientific Reports
Rivers are the dominant avenue by which terrestrial efflux is transported to marine systems. Human activities on land, such as agriculture and infrastructure development, may impact coral reefs by disrupting historic biogeochemical processes of nearshore reef habitats. To identify how land use impacts rivers, and therefore nearshore lagoons, water chemistry was measured at a series of rivers around the island of Moorea, French Polynesia, in 2018 and 2019. Nitrate, Nitrite, Silicate, Phosphate, and Total Suspended Solids were evaluated at least weekly during sampling campaigns in rainy and dry seasons of both years. TSS was elevated following recent precipitation and negatively associated with watershed size. Phosphate was associated with Total Suspended Solids, presumably because of the phosphate-rich soils on Moorea. Dissolved Inorganic Nitrate was higher during the rainy season in watersheds with a high degree of land clearing. These results reinforce the importance of terrestrial factors for biogeochemistry of nearshore marine systems.
Sequence data for Claar et al. 2020 Scientific Reports
<p>Illumina Mi-Seq sequence data of the ITS2 marker (in .fasta format) associated with Claar et al. 2020 Scientific Reports "Chronic disturbance modulates symbiont (Symbiodiniaceae) beta diversity on a coral reef".</p>
Annexes A and B to EFSA Scientific report "Chronic dietary exposure to inorganic arsenic"
<p><strong>Annex A-</strong> Contains the raw occurrence dataset on arsenic as extracted from the EFSA DWH on 30 April 2020 (no data cleaning applied), with the food samples presented in the scientific report as described in its section 2.1. Occurrence data. The data are provided in csv format. This dataset is compliant with EFSA SSD model and contains two additional columns documenting issues identified in the cleaning process (column: issue) and the action taken (column: action) to address the issue (e.g. deleted record or updated values in specific fields). The link to the catalogues of controlled terminologies can be found under "Related identifiers”. </p> <p><strong>Annex B-</strong> Contains summary statistics on occurrence data, consumption data, and dietary exposure assessment results.</p> <p>Table B1. Dietary surveys per country and age group available in the EFSA Comprehensive Database considered in the chronic dietary exposure assessment to inorganic arsenic.</p> <p>Table B2. Analytical data on iAs after data cleaning and analysis steps (13,608 analytical results).<br> Table B3. Occurrence values of inorganic arsenic in food (µg/kg) as used for the exposure assessment.<br> Table B4. Occurrence values of total arsenic in food (µg/kg).<br> Table B5. Summary of the chronic dietary exposure assessment to inorganic arsenic (µg/kg bw per day).<br> Table B6. Detailed chronic dietary exposure assessment to inorganic arsenic (µg/kg bw per day) by European dietary surveys and age classes.</p> <p>Table B7. Main contributing food groups (%) to the mean LB and UB chronic dietary exposure to inorganic arsenic across European dietary surveys and age classes.</p> <p>Table B8. Detailed mean contribution of food groups (%) to the mean LB and UB chronic dietary exposure to inorganic arsenic across European dietary surveys and age classes. </p>
Data supplementing Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. Scientific Reports, 14, 8858.
<p>These files supplement the publication <br>Einhäuser, W., Neubert, C. R., Grimm, S., & Bendixen, A. (2024). High visual salience of alert signals can lead to a counterintuitive increase of reaction times. <em>Scientific Reports, </em>14, 8858. https://doi.org/10.1038/s41598-024-58953-4</p> <p>The files data_expX.mat, where X is the experiment number (1-4), contain the data as described below. </p> <p>The files dataTraining_expX.mat contain the data of the first (training) block of each experiment. They are needed only for the supplemental material. </p> <p>To exemplify the usage, the functions figure2and3.m, figure4.m, figure5.m, figure6.m and Table1.m output the paper's figures and the data of Table 1, respectively; figureS2.m, figureS3.m, figureS4.m and figureS5.m output the figures of the supplemental material (figure S1 needs substantial amounts of external source code to compute the salience maps and is therefore not included).</p> <p><br>data_exp1.mat contains the following variables<br>For alert trials, variable of dimensions subjects x blocks x alert trials (20x10x64); note that only used participants and blocks with alert trials (2 through 11) are included in the data set:<br>alert_aud - the salience level of the alert tone (1-8, corresponding to 54dB(A) through 89 dB(A))<br>alert_vis - the salience level of the alert frame (1-8, corresponding to 0.10 to 8.50 Weber contrasts in logarithmic steps)<br>alert_side - the side on which the alert frame and the tone were presented (1-left, 2-right)<br>alert_fixOk - derived from eye movement data, was the first fixation closer to the alert square than to the center?<br>alert_primaryRT - primary-task reaction time (for alert trials)<br>alert_alertRT - alert-task reaction time <br>alert_correctAlert - was the response (up/down) to the alert correct?<br>alert_intrusionAlert - was there an intrusion (left/right pressed before up or down)?<br>alert_correctPrimary - was the primary task conducted correctly?<br>alert_intrusionPrimary - was there an intrusion for the primary task?<br>alert_timeToFixation - time to first fixation on alert square <br>alert_fixationToResp - time from beginning of fixation to response to the alert <br>alert_fixDur - duration of first fixation after trial onset</p> <p>For no-alert trials, variable of dimensions subjects x blocks x no-alert trials (20x10x448):<br>noalert_correctPrimary - was the primary task conducted correctly?<br>noalert_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?</p> <p>For all trials, variable of dimensions subjects x blocks x no-alert trials (20x10x512):<br>all_correctPrimary - was the primary task conducted correctly?<br>all_intrusionPrimary - was there an intrusion for the primary task? (i.e., up/down pressed before left/right)?<br>all_RT - reaction time in the primary task<br>all_isAlertTrial - was the trial an alert trial? (useful to map no-alert trials and alert trials on all trials)</p> <p>In addition, there are some raw eye movement data for the alert blocks:<br>alert_eyeX, alert_eyeY - dimension 20 x 10 x 64 x 6000; x and y position in pixel coordinates relative to trial (and alert) onset, 1ms/sample, ends at conclusion of trials, filled up with NaN if duration was less than 6000ms <br>alert_eyeFixX, alert_eyeFixY, alert_eyeFixTon, alert_eyeFixDur - 20 x 10 x 64 x 15; x and y position, onset (in ms relative to trial onset) and duration of fixations during the trial (from onset to primary-task response), filled with NaN when less than 15 fixations were made. Note that the first entry of alert_eyeFixDur along the forth dimension will usually equal the alert_fixDur</p> <p><br>data_exp2.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_vis - contains only two levels (1 and 2) corresponding to Weber contrasts of 0.10 and 2.39, respectively<br>alert_dur - the level of duration of the alert frame (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_aud is not included (all tones were at 54 dB(A))<br>there are only 19 participants; hence the variables are of size 19 x ...<br>note: block 8 for subject 6 contains only 450 trials (57 alert trials), the remainder is filled with NaN.</p> <p><br>data_exp3.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_dur - the level of duration of the alert tone (1 through 8, corresponding to 25ms, 50ms, 100ms, 200ms, 300ms, 400ms, 600ms, 800ms)<br>alert_vis is not included (all alert frames were at 0.10 contrast)</p> <p> </p> <p>data_exp4.mat contains the same variables as data_exp1.mat with the following exceptions:<br>alert_aud - contains only two levels (1 and 2) corresponding to sound levels of 54 dB(A) and 79 dB(A) respectively<br>alert_vis is not included and replaced by<br>alert_condBefore - alert frame contrast level before the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)<br>alert_condAfter - alert frame contrast level after the saccade (1 - 0.10 contrast, 2 - 2.39 contrast)</p> <p><br>dataTraining_expX.mat contains for the first (training) block of experiment X (X being 1, 2, 3 or 4) the following variables of size 20x512 (participant x trial) [19x512 in case of Experiment 2]:<br>all_correctPrimary - was the primary task conducted correctly?<br>all_RT - reaction time in the primary task<br>[Note that there are no alert trials in this block and these data are only used in the supplementary material (part 4)]</p>
Settings and data files from Silva et al (Scientific Reports 2022)
<p><strong>Neolithic Greece mtDNA sequences</strong></p> <p>The 47 mitochondrial sequences generated for this work are available in the .arp format for the program ARLEQUIN (http://cmpg.unibe.ch/software/arlequin35/).</p> <p><strong>Simulated Data and Simulation Program</strong></p> <p>This dataset permits to simulate the scenarios investigated in the article submitted by Silva et al, using the modified version of the program SPLATCHE2 provided here (http://www.splatche.com).</p> <p>There is a zipped folder Silva_et_al_SimulationSettings" that contains:</p> <p>i) SPLATCHE2 executable called "SPLATCHE2-VariableAdmixture".</p> <p>ii) a folder "DanubeRouteExpansion" including the settings used for the simulation of the four scenarios of the Neolithic expansion along the Danubian route.</p> <p>iii) a folder "GreeceContinuity" including the settings used to perform the structured population continuity test.</p> <p>A "ReadMe.txt" file is available in both settings folders with the instructions to launch the simulations.</p>
Data from: The Subantarctic Rayadito (Aphrastura subantarctica), a new bird species on the southernmost islands of the Americas. Scientific Reports
<p><strong>Description of the dataset</strong><br> This dataset contains morphological and genetic information of Aphrastura populations in different sample sites in Chile and Argentina. This dataset was analysed in: Rozzi R, Quilodrán CS, Botero-Delgadillo E, Napolitano C, Torres-Mura JC, Barroso O, Crego RD, Bravo C, Ippi S, Quirici V, Mackenzie R, Suazo CG, Rivero-de-Aguilar J, Goffinet B, Kempenaers B, Poulin E and RA Vásquez. 2022. The Subantarctic Rayadito (<em>Aphrastura subantarctica</em>), a new bird species on the southernmost islands of the Americas. Scientific Reports.</p> <p>There are three files: </p> <ul> <li>Subantarctic_Rayadito_Morphology.txt: morphological information used for differentiating <em>Aphrastura subantarctica</em> from <em>Aphrastura spinicauda</em>. The former was sampled in the Diego Ramirez archipelago. The date of sampling is included in the last column. </li> <li>Subantarctic_Rayadito_Microsatellite.txt: all captured and genotyped adults from five populations. The band (ring) number is used as an ID for each individual bird. The matrix includes information regarding the locality of origin (MA: Manquehue; BA: Bariloche; TF: Tierra del Fuego; NI: Navarino Island; DR: Diego Ramírez Archipelago), and allele size (number of repeats) at 12 polymorphic microsatellite loci.</li> <li>Subantarctic_Rayadito_mtDNA.nex: mtDNA information for all <em>Aphrastura</em> individuals sequenced and used in our analysis. The labels denote the code number and location for each individual (DR: Diego Ramirez Archipelago; CH: Cape Horn Island ; Navarino : Navarino Island; TdelFuego : Tierra del Fuego; PtoNatales : Puerto Natales ; ElChalten : El Chalten; CalTortel : Caleta Tortel ; Coihayque : Coihayque ; Chaiten : Chaiten ; LosAlerces : Los Alerces ; Chiloe : Chiloé Island ; IslaMocha : Mocha Island ; Curacautin : Curacautin ; Rinihue : Rinihue ; Epulauquen : Epu Lauquen ; Constitucion : Constitución ; Manquehue : Manquehue ; FrayJorge : Fray Jorge National Park). </li> </ul> <p><strong>Acknowledgments </strong><br> This study was funded with Grants from the Sub-Antarctic Biocultural Conservation Program of the University of North Texas, University of Magallanes, the Cape Horn International Center (ANID CHIC-FB210018), the Institute of Ecology and Biodiversity of Chile (CONICYT PFB-23), and the Patagonia Mar y Tierra Working Group (The Pew Charitable Trust - Chile). We thank the support of the Omora Foundation, and FONDECYT 1140548 to RAV. C.N. thank support from ANID PAI 77190064, and ANID/BASAL FB210006. CSQ acknowledges support from the Swiss National Science Foundation (N° P400PB_183930 and P5R5PB_203169). We are grateful to Sylvia Kuhn and Alexander Girg from the Max Planck Institute for Ornithology for help in the laboratory, and to Jaime A. Cursach and Maximiliano Daigre during fieldwork and ornithological records in Gonzalo Island. Fieldwork in protected areas was possible thanks to people from Parque Nacional Bosque Fray Jorge, Minera Los Pelambres, Estación Biológica Senda Darwin, Parque Nacional Nahuel Huapi, and Parque Natural Karukinka. We also express our gratitude for logistical and personnel support from the 3rd Naval Zone of the Chilean Navy.</p> <p> </p>
OTU table - Joli et al. Scientific Reports - Janus Gateway
<p>This texte file represent the OTU table from the paper Joli et al. published in Scientific Reports as "Need for focus on microbial species following ice melt and changing freshwater regimes in a Janus Arctic Gateway". The first column contains the OTU number and each following columns contain the number of reads for each sample. The last column gives the taxonomy after confronting dataset to a eukaryotic 18S database.</p>
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>
Annexes to the EFSA external scientific report "Proposed prospective scenarios for cumulative risk assessment of pesticide residues"
<p>In the context of prospective cumulative risk assessment of pesticides, different options and scenarios for a tiered approach were investigated by means of 15 case studies for the cumulative assessment group associated with an effect on the motor division of the nervous system (CAG-NAM) and 15 case studies for the cumulative assessment group associated with an effect on hypothyroidism (CAG-TCF) (doi:10.2903/sp.efsa.2021.EN-6811). The results of the prospective exposure calculations are reported in the following annexes:</p> <p><strong>Annex A</strong>: Acute exposure calculations - CAG-NAM</p> <p><strong>Annex B</strong>: Chronic exposure calculations - CAG-TCF</p> <p><strong>Annex C</strong>: Results supporting the discussion on prospective acute scenarios - CAG-NAM</p> <p><strong>Annex D</strong>: Results supporting the discussion on prospective chronic scenarios - CAG-TCF</p> <p>The case studies reported above also include fictitious data, which were included for assessing the relevance of the various parameters in these calculations. The results of these case studies do not represent real estimates of exposure or risk, nor do they represent the formal outcome of a risk assessment.</p>
Fig. 1. A in Scientific Note Does the association of young fishes with jellyfishes protect from predation? A report on a failure case due to damage to the jellyfish
Fig. 1. A watchful comb grouper (Mycteroperca acutirostris) while following the jellyfish (Chrysaora lactea) tries to approach a small group of juvenile scads (Trachurus lathami), and induce them to leave the shelter on the top and among the tentacles of the jellyfish.
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.