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970 results for “Risk Assessment”
Contamination pattern and risk assessment of polar compounds in snow melt: an integrative proxy of road runoffs
<p><strong>Abstract</strong></p> <p>To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for more than 500 chemicals using LC-HRMS. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig WWTP during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). A toxic unit-based assessment was performed to investigate the risk of adverse environmental effects in the receiving water.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset contains the list of sampling points, the target compounds, the chemical findings, the results of the toxic unit assessment, the underlying ecotoxicity data, and the estimated compound removal rates in WWTP. The data is provided in xlsx and ods formats.</p>
MiRoR7-P1- Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design
<p>dataset referring to </p> <p><strong>Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design</strong></p> <p> </p> <p> </p> <p>Lorenzo Bertizzolo<sup>1</sup>, Patrick M Bossuyt<sup>2</sup>, Ignacio Atal<sup>1, 5</sup>, Philippe Ravaud<sup>1, 3-6</sup>, Agnès Dechartres<sup>7</sup></p> <p> </p> <p><sup>1</sup> INSERM, U1153 Epidemiology and Biostatistics Sorbonne Paris Cité Research Center (CRESS), Methods of therapeutic evaluation of chronic diseases Team (METHODS), Paris, F-75004 France; Paris Descartes University, Sorbonne Paris Cité, France.</p> <p><sup>2</sup> Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Netherlands.</p> <p><sup>3</sup> Centre d’Épidémiologie Clinique, Hôpital Hôtel Dieu, AP-HP (Assistance Publique des Hôpitaux de Paris), Paris, France.</p> <p><sup>4</sup> Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.</p> <p><sup>5</sup> Cochrane France, Paris, France</p> <p><sup>6</sup> Columbia University, Mailman School of Public Health, Department of Epidemiology, New York, USA</p> <p><sup>7</sup> Sorbonne Université, INSERM, Institut Pierre Louis de Santé Publique, Département Biostatistique, Santé Publique et Information Médicale, AP-HP, Hôpitaux Universitaires Pitié Salpêtrière – Charles Foix, Paris, France</p>
Risk assessment on Glycoalkaloids in feed and food: Occurrence data in food and feed submitted to EFSA and dietary exposure assessment for humans
<p><strong>UPDATE to version 2 of this upload:</strong></p> <p>Also the raw (no data cleaning applied to it) occurrence dataset as extracted from EFSA DWH is provided <em>in csv format</em>. 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. delete record or update values in specific fields).</p> <p><strong>Description - Version 1</strong></p> <p><strong>Annex: Tables on GAs on occurrence data in food and feed, and dietary exposure assessment for humans</strong></p> <p>Table A.1. Dietary surveys used for the estimation of acute dietary exposure to GA</p> <p>Table A.2. Number of results and samples per food category submitted to EFSA through the continuous call for data</p> <p>Table A.3. Analytical results excluded from the final dataset used to estimate dietary exposure and the criteria applied for exclusion</p> <p>Table A.4. Occurrence of alpha-chaconine and alpha-solanine (UB mg/kg) in the samples included in the final dataset (left censored results highlighted in yellow)</p> <p>Table A.5. European Starch Association data on feed and potatoes for starch</p> <p>Table A.6. Details acute assessment across surveys (consumption days only)</p> <p>Table A.7. Comparison of exposure summary results obtained using the uniform vs the normal distribution for reduction factors</p>
Exposure to pesticides data for residents and bystanders, and for environmental risk assessment
<p>In 2014, EFSA has commissioned a study to review and evaluate all published data related to the exposure to pesticides for residents and bystanders and for environmental risk assessment. The aim was to conduct a literature review and to produce a database containing all published data (predominately peer-reviewed publications supplemented by grey-literature) for the last 25-years, which will support the non-dietary exposure assessment to pesticides for bystanders and residents, as well as daily air concentration (vapours and aerosols) of pesticides, drift values from spray, seed and granular applications, and dislodgeable foliar residues.</p> <p>The data has been collated via a systematic and extensive literature review defined and managed according to a pre-defined 'review protocol'. The data was also exported in a format that meets the requirements of the EFSA Data Collection Framework (DCF).</p> <p>Based on quality and relevance criteria, articles and related studies have been selected. For dislodgeable foliar residues the assessment includes 27 articles (containing 49 discrete studies); for air concentrations, 26 articles (containing 84 discrete studies); for resident and bystander exposure, 5 articles (containing 8 discrete studies); and for drift values 55 articles (containing 275 discrete studies). </p> <p>For dislodgeable foliar residues the data retained covered 17 crops (including grass, glasshouse crops, lucerne, and citrus) and 29 pesticides; for air concentrations the data retained covered 21 crops (including fruit, glasshouse crops, ornamentals, grass, vegetables and cereals) and 39 pesticides. For drift values, the data covers a range of crops and landscapes from cereals, grass and turf, orchards, vineyards and regenerated forestry. The vast majority of the data retrieved applies to field studies for liquid spray drift, measured either as ground deposits or collected at various heights and were conducted using fluorescent tracers rather than pesticides. No data was found for microbials (biopesticides). For resident and bystander exposure, many articles were rejected due to the applied inclusion/exclusion criteria.</p>
C2D2: An Open-Source, Pan-European, Harmonised Crop Development Database for Use in Regulatory Pesticide Exposure Modelling and Risk Assessment.
<p>There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonised, pan-European, CropLife Europe Crop Development Database, C2D2, that is fully aligned with this regulatory requirement utilising efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU) 1107/2009. Evaluation of C2D2 against an independent dataset showed good agreement for equivalent time periods, crop growth stages and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large dataset compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being under-represented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modelling framework (using the AppDate tool) are often indicated over some/many growth stages suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons licence to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection.</p>
Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes
<p>This is the dataset for the publication "Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes".</p>
Row sequcenes data for assessing the risks of potential pathogens and antibiotic resistance genes among heterogeneous habitats in a temperate estuary wetland
<p>The study included 118 usable samples within three different habitats (water, soil, and sediment) across the Liaohe River basin to the Red Beach wetland collected from seven papers, and all of the sequence files were uploaded for availability.</p>
Risk assessment (susceptibility) of thaw slumps and thermokarst lakes in the Yangtze River source region
<p>Due to the influence of climate warming, the degradation of permafrost on the Qinghai-Tibet Plateau (QTP) has become evident. The formation of thermokarst hazards induced by the degradation of ice-rich permafrost has a significant impact on infrastructure construction and local ecology; therefore, it is necessary to assess its risk. In this study, a novel multiple thermokarst hazards risk assessment framework was proposed by combining stacking machine learning and potential environmental factors (vegetation factors, terrain factors, climate factors, and soil factors) to assess the risk of thermokarst hazards in the Yangtze River source region (YRSR). The results show the risk assessment (susceptibility) of thermokarst hazards in the YRSR from 2000 to 2016 at 500 m spatial resolution. This study divided the risk into 5 levels: very low (0.0-0.2), low (0.2-0.4), moderate (0.4-0.6), high (0.6-0.8), and very high (0.8-1.0) </p>
Dataset: Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assessments
<p>The Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assements is a database that contains fragility and vulnerability curves that can be used to evaluate the expected or potential damages to infrastructure assets due to flooding, earthquakes, windstorms and landslides. The database consists of three Excel-spreadsheets:</p> <ul> <li><em>Table_D1_Summary_CI_Vulnerability_Data:</em> summary table with information on hazard, exposure, and vulnerability characteristics as well as a number of details regarding reliability and reference purposes.</li> <li><em>Table_D2_Hazard_Fragility_and_Vulnerability Curves:</em> collection of fragility and vulnerability curves</li> <li><em>Table_D3_Costs:</em> cost values that can be used in combination with the curves for the estimation of asset damages</li> </ul> <p>Please consult the following publication for detailed information: Nirandjan, S., Koks, E. E., Ye, M., Pant, R., van Ginkel, K. C. H., Aerts, J. C. J. H., and Ward, P. J.: Review article: Physical Vulnerability Database for Critical Infrastructure Multi-Hazard Risk Assessments – A systematic review and data collection, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2023-208, in review, 2024.</p>
Tree mortality risks under climate change in Europe: assessment of silviculture practices and genetic conservation networks
<p>General context: Climate change can positively or negatively affect abiotic and biotic drivers of tree mortality. Process-based models integrating these climatic effects are only seldom used at species distribution scale.</p> <p>Objective: The main objective of this study was to investigate the multi-causal mortality risk of five major European forest tree species across their distribution range from an ecophysiological perspective, to quantify the impact of forest management practices on this risk and to identify threats on the genetic conservation network.</p> <p><br> Methods: We used the process-based ecophysiological model CASTANEA to simulate the mortality risk of \textit{Fagus sylvatica}, \textit{Quercus petraea}, \textit{Pinus sylvestris}, \textit{Pinus pinaster} and \textit{Picea abies} under current and future climate conditions, while considering local silviculture practices. The mortality risk was assessed by a composite risk index \textit{(CRIM)} integrating the risks of carbon starvation, hydraulic failure and frost damage. We took into account extreme climatic events with the \textit{CRIM$_{max}$}, computed as the maximum annual value of the \textit{CRIM}.</p> <p><br> Results: The physiological processes' contributions to \textit{CRIM} differed among species: it was mainly driven by hydraulic failure for \textit{P. sylvestris} and \textit{Q. petraea}, by frost damage for \textit{P. abies}, by carbon starvation for \textit{P. pinaster}, and by a combination of hydraulic failure and frost damage for \textit{F. sylvatica}. Under future climate, projection showed an increase of \textit{CRIM} for \textit{P. pinaster} but a decrease for \textit{P. abies}, \textit{Q. petraea} and \textit{F. sylvatica}, and little variation for \textit{P. sylvestris}. Under the harshest future climatic scenario, forest management decreased the mean \textit{CRIM} for \textit{P. sylvestris}, increased it for \textit{P. abies} and \textit{P. pinaster} and had no major impact for the two broadleaved species. By the year 2100, 38\% to 90\% of the conservation units are at extinction threat (\textit{CRIM$_{max}$}=1), depending on the species.</p> <p><br> Conclusions: Using a process-based ecophysiological model allowed us to disentangle the multiple drivers of tree mortality under current and future climate. Taking into account the positive effect of increased CO$_2$ on fertilization and water use efficiency, the average risks may increase or decrease in the future depending on species and sites. However, considering extreme climatic events, future projections are as pessimistic than those obtained with bioclimatic niche models.</p> <p> </p> <p>Abbreviation for column:</p> <p>X Longitude<br> Y Latitude<br> LAImax Leaf area index max reach<br> Nha Density per hectar<br> Vha Volume per hectar<br> NEE Net ecosystem exchange<br> NPP net primary production<br> Reco Respiration ecosystem<br> GPP Gross primary production<br> Etveg Evapotranspiration canopy<br> Etsol Evapotranspiration sol<br> TR tree transpiration<br> ETP evapotranspiration potentiel<br> BiomassOfReserves Biomass of reserve<br> rw ring width<br> dbh diameter at breast heast<br> height height<br> BBday Budburst date<br> rFD risk of frost<br> CRIM_max Maximum combined risk index of mortality reach<br> rNSC risk of carbon starvation<br> rPLC risk of embolism<br> rPLC_max Maximum risk of embolism reach<br> CRIM combined risk index of mortality<br> Climate Climatic model<br> rNSC_max maximum risk of carbon starvation reach<br> rFD_max Maximum risk of frost reach<br> Scenario_Sylvicol null means no silvulcture simulated<br> species species<br> Country Country<br> alt_watch altitude of climate simulated<br> grid_watch number of the pixel point of WATCH<br> grid_eurocordex number of the pixel point of Eurocordex<br> Pinus_sylvestris 0 abscence ; 1 presence<br> Fagus_sylvatica 0 abscence ; 1 presence<br> Quercus_petraea 0 abscence ; 1 presence<br> Picea_abies 0 abscence ; 1 presence<br> Pinus_pinaster 0 abscence ; 1 presence</p> <p> </p>
Data to support the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia"
<p>This dataset supports the publication "Unknown risk: assessing refugee camp flood risk in Ethiopia". This dataset contains the delineated boundaries for 24 refugee camps in Ethiopia. Also included are refugee camp building footprint data (where available). All datasets are in shapefile format.</p>
Mobile Application Privacy Risk Assessments from User-authored Scenarios
<p>Mobile applications (apps) provide users valuable benefits at the risk of exposing users to privacy harms. Improving privacy in mobile apps faces several challenges, in particular, that many apps are developed by low resourced software development teams, such as end-user programmers or in startups. In addition, privacy risks are primarily known to users, which can make it difficult for developers to prioritize privacy for sensitive data. In this paper, we introduce a novel, lightweight method that allows app developers to elicit scenarios and privacy risk scores from users directly using only an app screenshot. The technique relies on named entity recognition (NER) to identify information types in user-authored scenarios, which are then fed in real-time to a privacy risk survey that users complete. The best-performing NER model predicts information types with a weighted average precision of 0.70 and recall of 0.72, after post-processing to remove false positives. The model was trained on a labeled 300-scenario corpus, and evaluated in an end-to-end evaluation using an additional 203 scenarios yielding 2,338 user-provided privacy risk scores. Finally, we discuss how developers can use the risk scores to prioritize, select and apply privacy design strategies in<br> the context of four user-authored scenarios.</p>
Dataset annexed to: "Testing ethical impact assessment for nano risk governance"
<p>To test the ethical impact assessment methodology guidelines and tools adapting CEN Workshop Agreement part 2 CWA 17145-2:2017 (E)) to support risk governance of nanomaterials, in the RiskGONE project (https://riskgone.eu/), feedback from stakeholders was requested. This dataset includes the responses of participants in several online meetings. The dataset is linked to the paper "Testing ethical impact assessment for nano risk governance".</p>
Climate Change Risk Assessment Dataset for Serbian Agriculture
<p>This dataset contains the assessment of observed climate change to the agriculture in Serbia. Data are given on a lat/lon gird with 0.01° horizontal resolution in geotif format. Climatic and bioclimatic indices, as well as risk occurrence frequencies are calculated using interpolated daily observations of temperature and precipitation over Serbia in the period 1998-2017. Statistical significance of the climate indices change, change in category of viticultural indices and increase in risk occurrence is given as well. Analyzed species are: vine grape, fruits (peaches, apricots, cherries, plums, apples, pears and quinces) and crops (corn, sunflower, sugar beet and winter wheat). Calculated climatic indices are: mean annual, vegetational and summer temperature and precipitation; bioclimatic indices: Winkler index, Huglin index, Dryness index, and Cool nights index; risks: drought, frost in the beginning of vegetation, low temeperature during winter, high temperature during summer and intensive rainfall.</p>
A protocol to assess the risk of dementia among patients with coronary artery diseases using CAIDE score-Extended Data
<p>The contents of this extended data file are-<br> 01. Consent form (English & Bengali Version)<br> 02. Interview Questionnaire (English & Bengali Version)<br> These contents will help to address the objectives of the study that attempted to identify the risk of long-term dementia among coronary artery disease patients in Bangladesh.</p>
Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning
<p>COVID-19 plasma samples spectrometry datasets for machine learning input. Used in the work of article Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning, currently under submittion.</p> <p>Abstract:</p> <p>COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) was enrolled from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules that are related to the disease’s pathophysiology and several discriminating features to patient’s health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings the disease’s pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using Machine Learning algorithms, transforming this screening approach in a tool with great potential for real-world application. </p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
Analyzing the sensitivity of a flood risk assessment model towards its input data, twelve damage scenarios
<p>This dataset contains the output shapefiles of twelve different risk assessment scenarios for the case study of Annotto Bay, Jamaica. These assessments were performed in the context of the research 'Analyzing the sensitivity of a flood risk assessment model towards its input data', published in the journal Natural Hazards and Earth System Sciences. More information on the input data and methodology can be found in this paper.</p>
Updated list of QPS-recommended microorganisms for safety risk assessments carried out by EFSA
<p>The European Food Safety Authority (EFSA) asked the Panel on Biological Hazards (BIOHAZ) to deliver a scientific Opinion on the maintenance of the list of qualified presumption of safety (QPS) biological agents. The QPS approach was developed by the EFSA Scientific Committee to provide a harmonised generic pre-evaluation to support safety risk assessments of biological agents intentionally introduced into the food and feed chain, in support of the concerned scientific Panels and Units in the frame of market authorisations.</p> <p>The taxonomic identity, body of knowledge, the safety concerns in relation to pathogenicity and virulence, and the safety for the environment of those microbiological agents are assessed. Safety concerns identified for a respective taxonomic unit (TU) are, where possible and reasonable in number, reflected as ‘qualifications’ that are assessed at the strain level by the EFSA’s scientific Panels.</p> <p>The<strong> “list of microorganisms with QPS status”</strong> first established in 2007, has been revised and updated annually until 2014 via <strong>QPS</strong> <strong>Opinions</strong>; since 2014 the updates are carried out and published every 3 years. If new information is retrieved from extended literature searches (ELS) that would change the QPS status of a TU or its qualifications, this is also published in the Panel Statement covering the previous 6-months period. The <strong>ELS </strong><strong>protocol</strong> can be found at <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Fdoi%2F10.5281%2Fzenodo.3607188&data=05%7C02%7C%7Ca719c81b95134433fddc08dc112814e1%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C638404111040390688%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=FaY3tbLnn%2BB%2BDu3PrtEzJR0zPa14z%2FpoKWUBBOaHk34%3D&reserved=0">https://zenodo.org/doi/10.5281/zenodo.3607188</a> and the <strong>Search</strong><strong> strategies</strong> are available at: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.3607193&data=02%7C01%7C%7Ca3e4d3a851c84defc2a008d7aa44f9e5%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637165085527347066&sdata=tCzKMQLR2I%2BZBr%2F0sOXPDoPitdjQyqKdlVqDqwigA%2Bo%3D&reserved=0">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.3607192">10.5281/zenodo.3607192</a>.</p> <p> </p> <p>The <strong>QPS Panel Statement</strong> also includes the evaluation of microbiological agents notified to EFSA within the 6-month period for an assessment for feed additives, food enzymes, food additives and flavourings, and novel foods or plant protection products for a possible QPS status. The new QPS status recommendations are incorporated into the 2022 updated<strong> “list of microorganisms with QPS status</strong>” is available in this upload. The list of “<strong>Microbi</strong><strong>ological agents </strong><strong>as notified to EFSA</strong><strong>”</strong> from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment can be found at <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.3607184&data=02%7C01%7C%7Ca3e4d3a851c84defc2a008d7aa44f9e5%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637165085527357024&sdata=%2Fe7qiRXiuKQi%2FBvK5S%2F7undYHv7ibxBlLEHPcSlRz24%3D&reserved=0">https://doi.org/10.5281/zenodo.</a><a href="https://doi.org/10.5281/zenodo.3607183">3607183</a>.</p> <p> </p> <p><strong>Useful links on EFSA QPS</strong></p> <p>EFSA topic on QPS: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.efsa.europa.eu%2Fen%2Ftopics%2Ftopic%2Fqualified-presumption-safety-qps&data=02%7C01%7C%7Ca3e4d3a851c84defc2a008d7aa44f9e5%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637165085527366985&sdata=TlcglouTWhBY24K5ApAoawNpADKgyl%2FefxVUEbxjQo0%3D&reserved=0">https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps</a></p> <p>Link to the virtual issue on QPS on Wiley Online Library: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2Ftoc%2F10.1002%2F(ISSN)1831-4732.QPS&data=02%7C01%7C%7C7f88eba744034d83233b08d7a8cd3c1c%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637163471731831002&sdata=AiprIUek%2B%2But46VabBGk0IMIgLR98CDmxoINYPkKBbs%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)1831-4732.QPS</a></p> <p><strong>Versions history:</strong></p> <p>Versions 1 and 2 were substituted by version 3 – all are associated with the QPS Panel statement EFSA 7: suitability of taxonomic units notified to EFSA until September 2017: <a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/j.efsa.2018.5131">https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/j.efsa.2018.5131</a> </p> <p>Version 4 is associated with the QPS Panel statement EFSA 8: suitability of taxonomic units notified to EFSA until March 2018: <a href="https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2018.5315">https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2018.5315</a> </p> <p>Versions 5 and 6 were substituted by version 7 – all are associated with the QPS Panel statement EFSA 9: suitability of taxonomic units notified to EFSA until September 2018: <a href="https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5555">https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5555</a> </p> <p>Version 8 is associated with the QPS Panel statement EFSA 10: suitability of taxonomic units notified to EFSA until March 2019: <a href="https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5753">https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5753</a> </p> <p>Version 9 is associated with the QPS Panel statement EFSA 11: suitability of taxonomic units notified to EFSA until September 2019: <a href="https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5965">https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5965</a> and with the Scientific Opinion on the update of the list of QPS-recommended biological agents intentionally added to food or feed as notified to EFSA (2017-2019): <a href="https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5966">https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5966</a> </p> <p>Version 10: updates version 9 following the update of the qualification of <em>Bacillus velezensis</em> as: ‘absence of toxigenic potential and absence of aminoglycoside production ability’ - applied in the QPS Statement part 11 (ON-5965) and in the 2019 Scientific Opinion (ON-5966).</p> <p>Version 11: updates version 10 following the addition of the <em>Bacillus circulans</em> ‘for production purposes only’ - applied in the QPS Statement part 13 (ON-6377) and in the 2019 Scientific Opinion (ON-5966).</p> <p>Version 12 updates version 11 following the addition of the <em>Bacillus paralicheniformis</em><strong> </strong>with the qualifications 'absence of toxigenic activity’ and ‘absence of genetic information to synthesize bacitracin';<strong> </strong>and adding <em>Schizochytrium limacinum</em>, which is a synomym for <em>Aurantiochytrium</em> <em>limacinum,</em> - applied in the QPS Statement part 14 (ON-6689)<strong> </strong>and in the 2019 Scientific Opinion (ON-5966).</p> <p>Version 13 updates version 12 following the addition of <em>Haematococcus lacustris</em> synonym <em>Haematococcus pluvialis</em>, recommended for QPS status with the qualification ‘for production purposes only’. Some other changes related to taxonomy and qualifications are described in the QPS Statement part 15 (ON-7045)<strong>.</strong></p> <p>Version 14, update of version 13, is related to QPS Statement part 16 (ON-7408)<strong>.</strong></p> <p>Version 15 is related to QPS Statement part 17 (ON-7746)<strong>.</strong></p> <p>Version 16 is related to QPS Statement part 18 (ON-8092)<strong>.</strong></p> <p>Version 17 is related to QPS Statement part 19 (ON-8517)<strong>.</strong></p> <p>Version 18 is also related to QPS Statement part 19 (ON-8517) with a small correction<strong>.</strong></p> <p>Version 19 and 20 are related to QPS Statement part 20 (ON-8882).</p> <p>Version 21 is related to QPS Statement part 21 (ON-9169)</p> <p>Version 22 is related to QPS Statement part 22 (ON-9510)</p> <p> </p> <p> </p> <p><strong>Note: </strong>As of January 2022 the updated list is provided only as excel file format.</p>
An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')
<p>These data were produced as part of the study "An empirical social vulnerability map for flood risk assessment at global scale ('GlobE-SoVI')" (in press in Earth's Future, https://doi.org/10.1029/2023EF003895). We provide raster data at 30 arc seconds spatial resolution (folder 'raster') and vector and table data per administrative unit (folder 'admin') of five social vulnerability variables and the final Global Empirical Social Vulnerability Index (GlobE-SoVI) calculated from the five variables. Please see 'overview_table.pdf' for names and units.</p> <p>The code for data processing and analysis is available at https://github.com/lena-reimann/GlobE-SoVI (https://doi.org/10.5281/zenodo.10671539).</p>
ScienceDex guides
Understand access before you commit
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.