Find research datasets worth reusing

opencc-by-4.0May 2018View details →

Data providers package for reporting Chemical Contaminants (official data reporting phase) SSD1

In the framework of Articles 23 and 33 of Regulation (EC) No 178/2002 EFSA has received from the European Commission a mandate (<a href="http://registerofquestions.efsa.europa.eu/roqFrontend/mandateLoader?mandate=M-2010-0374">M-2010-0374</a>) to collect all available data on the occurrence of chemical contaminants in food and feed. These data are used in EFSA’s scientific opinions and reports on contaminants in food and feed. This data providers package provides the data collection configuration and supporting materials for reporting Chemical Contaminants in SSD1. These are to be used for the official data reporting phase. The package includes: The Standard Sample Description Version 2 XSD schema definition for CONTAMINANTS reporting. The general and CONTAMINANTS SSD1 specific business rules applied for the automatic validation of the submitted datasets. Excel Mapping tool to convert excel files after mapping into XML document. Please follow the instructions below for the correct use of the mapping tool to avoid compromising its functionalities: <ol> <li>Download and save the MS Excel® Standard Sample Description file to your computer (do not open the file before saving and do not change the file name)</li> <li>Download and save the file MS Excel® Simplified Reporting Format (do not open the file before saving)</li> <li>Keep both Excel files in the same folder</li> <li>Open both Excel files and enable the macros</li> <li>Keep both files open in the same Excel instance when filling in the data</li> </ol> Guidance on how to run the validation report after submitting data to the DCF.

opencc-by-4.0Jan 2018View details →

Data providers package for reporting monitoring results for veterinary medicinal product residues (official data reporting phase)

This data providers package provides the data collection configuration and supporting materials for reporting veterinary medicinal product residues (VMPR) results according to Council Directive 96/23/EC of 29 April 1996 on measures to monitor certain substances and residues thereof in live animals and animal products and repealing Directives 85/358/EEC and 86/469/EEC and Decisions 89/187/EEC and 91/664/EEC. These are to be used for the official data reporting phase. The package includes: Advice on the values to be reported for the mandatory fields specified for this data collection. The Standard Sample Description Version 2 XML schema definition for VMPR reporting. The STX transformation file which automatically assigns sampEventId and sampAnId when this information is not provided. The general and VMPR specific business rules applied for the automatic validation of the submitted datasets. The VMPR specific terminologies to be used for reporting analytical methods and the residues included in the scope of the analytical methods. An example of a reportable dataset following the Quick Start Reporting guide. Excel Mapping tool to convert excel files after mapping into XML document. Guidance on how to use the Excel Mapping tool. Guidance on how to run the validation report after submitting data to the DCF.

opencc-by-4.0Jul 2018View details →

Final Report on Data Management - Raw data of DC-TRNG for D2.4 statistical testing

Collected Raw & Post-processed data from DC-TRNG for both AIS-31 and NIST800-90B tests suites for Final Report on Data Management The purpose of the final report on data management is to provide an update of the analysis of the main elements of the data management policy used by the applications with regards to all the datasets that were generated by the project. Most important aspects regarding data management, like metadata generation, data preservation, and responsibilities, were updated compared to the initial report D5.2 (Data Management Plan) according to the outcome of the project.

Annex B to the technical report on the raw primary commodity (RPC) model - Summary statistics of the output data

The raw primary commodity model: Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs. Annex B to the technical report on the raw primary commodity model: Annex B is an excel file which presents summary statistics of the output data generated by the RPC model. The following tables are included in Annex B: Table B.1 :Summary statistics of chronic RPC consumption expressed in g/kg bw per day (total population) Table B.2 :Summary statistics of chronic RPC consumption expressed in g/day (total population) Table B.3 :Summary statistics of acute RPC consumption expressed in g/kg bw (consumers only) Table B.4 :Summary statistics of acute RPC consumption expressed in g (consumers only) Table B.5 :Comparison of the RPC consumption data with RPC consumption data used in EFSA's Pesticides Residues Intake Model (PRIMo) Table B.6 :Contribution of processed products to the average chronic RPC consumption

Annex A to the technical report on the raw primary commodity (RPC) model - Input data

The raw primary commodity model: Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs. Annex A to the technical report on the raw primary commodity model: Annex A is an excel file which presents input data tables used by the RPC model. The annex contains the following tables: Table A.1 (Survey table) - An overview of the food consumption surveys incorporated in the RPC model Table A.2 (FoodEx table) - An outline of the food classification system used in the RPC model (EFSA's FoodEx classification system with additional codes) Table A.3 (Probability table) - Manages foods coded at food group level (example, breakfast cereals) Table A.4 (Disaggregation table) - Disassembles composite foods into their single components (RPC derivatives and/or RPCs) Table A.5 (Conversion table) - Converts amounts of RPC derivatives into corresponding amounts of RPC Table A.6 (Component table) - Overview of the search strings used for the probability analysis of components

Tsunamis due to ice masses: Different calving mechanisms and linkage to landslide-tsunamis - Data storage report

Land ice melt and retreat is one of the most visible effects of climate change and contributes ≈1.5 mm/year to global sea-level rise (SLR) of a total of ≈2.7 mm/year. Global warming results in the shrinking of ice masses in most ice covered regions in the World, particularly in the Alps and in Greenland and the Greenlandic mass loss is estimated at –269 ±51 Gt/year. A significant part of this mass loss is through the detachment of icebergs at glacier fronts in a mechanism called iceberg calving. Such iceberg impacting into a water body generate tsunamis, such called "iceberg-tsunamis". Such an iceberg-tsunami reached a height of 50 m at the Eqip Sermia outlet glacier in 2014. These tsunamis pose a considerable hazard for the local community, the fishing industry and the increasing number of tourists in ice covered areas. Several iceberg calving mechanisms have been proposed including fall, over-turning and capsizing. Reliable guidance on the upper limit of iceberg-tsunami heights are currently unavailable. A main reason for this limited understanding is that reliable field data are rare, such that laboratory tests complemented with numerical simulations are important to advance this research field. This was the aim of this HYDRALAB+ funded study. The wave features (height, length, velocity) caused by icebergs in function of the iceberg calving mechanisms (fall, over-turning, capsizing), as well as the mass volume and kinematics, were modelled in unique large-scale experiments. This minimised both scale effects and wave reflection. The attached file is an HYDRALAB+ standard Data Storage Report about these experiments.

opencc-by-4.0May 2019View details →

Data for dissemination and communication reporting

This data was collected through the periodic monitoring of the project's miscellaneous dissemination activities, such as publications in relevant journals, posts, etc. The data consist of a list that depicts the number of publications, posts, events organized or attended by the consortium partners, etc. as well as the number of different type of stakeholders reached by the project's dissemination activities. The purpose of collecting this data is to assess the outreach and efficiency of the dissemination activities during the implementation of the project.

SparBOFWEC Spar Buoy for Offshore Floating Wind Energy Conversion - Data Storage Report

The present work describes the experiences gained from the design methodology and operation of a 3D physical model experiment aimed to investigate the dynamic behaviour of a spar buoy (SB) off-shore floating wind turbine (WT) under different wind and wave conditions. The physical model tests have been performed at Danish Hydraulic Institute (DHI) off-shore wave basin within the European Union-Hydralab+ Initiative, in April 2019. The floating WT model has been subjected to a combination of regular and irregular wave attacks and wind loads.

opencc-by-4.0Jul 2019View details →

Data Storage Report. RODBreak - Wave run-up, overtopping and damage in rubble-mound breakwaters under oblique extreme wave conditions due to climate change scenarios

Wave breaking / run-up / overtopping and their impact on the stability of rubble-mound breakwaters (both at trunk and roundhead) are not adequately characterized yet for climate change scenarios. The same happens with the influence of high-incidence angles on such phenomena. To study these phenomena a stretch of a rubble-mound breakwater (head and part of the adjoining trunk, with a slope of 1(V):2(H)) was built in the wave basin of the LUH, The trunk of the breakwater was 7.5 m long and the head had the same cross section as the exposed part of breakwater. The model was 9.0 m long, 0.82 m high and 3.0 m wide. The angle between the longitudinal axis of the breakwater and the tank wall was 70º. Two types of armour elements (rock and Antifer cubes) were tested. 60 tests were carried out in this experiment to assess, under extreme wave conditions (wave steepness of 0.055) with different incidence wave angles (from 40º to 90º), the structure behaviour in what concerns wave run-up, wave overtopping and damage progression of the armour layer. The report describes the data collected in those tests as well as how such data is stored.

Underlying data of the project "A survey exploring biomedical editors' perceptions of editorial interventions to improve adherence to reporting guidelines"

Survey dataset.xlsx contains the anonymised responses to the survey.

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

Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system:  <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> 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>). 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. 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). 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. The input and output data for the exposure assessment are reported in the following annexes: <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> 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>). 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.

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

Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid:  <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> 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>). 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. 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). 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. The input and output data for the exposure assessment are reported in the following annexes: <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> 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>). 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.

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

Retrospective dietary exposure assessments were conducted for two groups of pesticides that have chronic effects on the thyroid:  <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> 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>). 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. Exposure estimates were obtained with SAS® 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). 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. The input and output data for the exposure assessment are reported in the following annexes: <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> 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® software (<a href="https://doi.org/10.2903/j.efsa.2019.5763">here</a>). 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.

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

Retrospective dietary exposure assessments were conducted for two groups of pesticides that have acute effects on the nervous system:  <ol> <li>brain and/or erythrocyte acetylcholinesterase inhibition (CAG-NAN);</li> <li>functional alterations of the motor division (CAG-NAM).</li> </ol> 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>). 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. Exposure estimates were obtained with SAS® 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). 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. The input and output data for the exposure assessment are reported in the following annexes: <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> 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® software (<a href="https://doi.org/10.2903/j.efsa.2019.5764">here</a>). 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.

HY+_HSVA‐06_UNIS - Data Storage Report

Data Storage Report Bending rheology of floating saline ice and wave damping – Waves in Ice; BRWD-Waves in Ice Large Ice Model Basin (LIMB), Hamburg Ship Model Basin

opencc-by-4.0Oct 2019View details →

HY+_HSVA-02_UCL - Data Storage Report

Hydralab+ Data Storage Report Sea Ice Dynamics: The Role of Ice Rubble in Multi-Scale Deformation Hy+_HSVA-02-UCL HSVA ARCTECLAB – Large Ice Model Basin (LIMB) Author: Sally Scourfield, Institute for Risk and Disaster Reduction, University College London

opencc-by-4.0Oct 2019View details →

HY+_HSVA‐08_UNI ST.ANDREWS - Data Storage Report and Data Set

Data from the internal waves project according to data storage report

opencc-by-4.0Nov 2023View details →

Figure 2 in Data to the Heterocera (Insecta, Lepidoptera) fauna of East Kazakhstan: report on a summer expedition in 2018

Figure 2. Landscapes of collecting points in Eastern Kazakhstan: Kara-Kaba, 29.VI.2018, photo by V.V. Ivonin.

opencc-by-4.0Nov 2023View details →

Figures 6-23 in Data to the Heterocera (Insecta, Lepidoptera) fauna of East Kazakhstan: report on a summer expedition in 2018

Figures 6-23. Adult specimens in Nature, Burkhat, 27-28.VI.2018, photos by S.A. Knyazev; 6 – Selenia tetralunaria; 7 – Odontopera bidentata; 8 – Plagodis pulveraria; 9 – Spargania luctuata; 10 – Colostygia aptata; 11 – Colostygia turbata; 12 – Xanthorhoe sajanaria; 13 – Heterothera serraria; 14 – Perizoma blandiata; 15 – Lasiocampa quercus; 16 – Cosmotriche lobulina; 17 – Furcula aeruginosa; 18 – Chelis dahurica; 19 – Hypena tristalis; 20 – Euchalcia renardi; 21 – Syngrapha ain; 22 – Acronicta auricoma; 23 – Acronicta psi.