Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

European Exposure Model Data Repository

A repository of the exposure data used to develop the ESRM20 exposure models. More information available here: <a href="https://eu-risk.eucentre.it/exposure/">https://eu-risk.eucentre.it/exposure/</a>

opencc-by-4.0Dec 2021View details →

Unexposed populations and potential COVID-19 burden in European countries as of 21st November 2021

Estimates of numbers of SARS-CoV-2 infections by country and age group over time, current proportions in different immune states, and potential remaining burden of hospitalisations and deaths for 19 European countries from article "Unexposed populations and potential COVID-19 burden in European countries as of 21st November 2021"

How does moving Public Engagement with Research Online Change Audience Diversity? Comparing Inclusion Indicators for 2019 & 2020 European Researchers' Night events

Taking place annually in more than 400 cities, European Researchers’ Night is a pan- European synchronized event that aims to bring researchers closer to the public. In this paper audience profiles are compared from events in 2019 and 2020. In 2019, face-to-face events reached an estimated 1.6 million attendees, while in 2020, events shifted online due to the COVID-19 pandemic and reached an estimated 2.3 million attendees. Focusing on social inclusion metrics, survey data is analyzed across two national contexts (Ireland and Malta) in 2019 (n=656) and 2020 (n=506). The results from this exploratory, descriptive study shed light on how moving public engagement with research online shifted audience profiles. Based on prior research about the digital divide in access and use of online media, hypotheses were proposed that online European Researchers’ Night events would attract audiences with higher educational attainment levels and greater self-reported, subjective economic well-being. While changes were observed from 2019 to 2020, results for each hypothesis show a mixed picture. The first hypothesis was upheld for the highest education levels but failed for the lowest levels suggesting that the pivot to online events simultaneously attracted participants with no formal education and those with postgraduate qualifications, while attracting less of those with undergraduate or lower levels of education. The second hypothesis was not upheld, with online European Researchers’ Night events attracting audiences with slightly higher levels of economic well-being compared to face-to-face events. The findings of this study indicate that European Researchers’ Night events present a clear opportunity to measure the effects of the digital divide in relation to public engagement with research across Europe.

MAR-ERA5 European Alps (1981-2020)

This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the ERA-5 reanalysis. The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr) The simulations cover the period covered by ERA5 reanalysis : 1981-2020 (1979-1980=Spin-up years) Data are available at the daily frequency, with one variable (10 years of data) per file. The available variables in this deposit are : LWD: Surface downward longwave radiation, [W/m2] LWU: Surface upward longwave radiation, [W/m2] MB:  Total snow water equivalent, [mm.We] MBrr: Daily rainfall, [mm.We] (5) MBsf: Daily snowfall, [mm.We] (5) QQz:  Near-surface specific humidity at constant height, [g/kg]  (2) SWD: Surface downward shortwave radiation, [W/m2] SWU: Surface upward shortwave radiation, [W/m2] TTmax: Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C] TTmin: Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C] TTz: Near-surface mean air temperature at constant-height, [C] UUz: Near-surface zonal component of wind speed at constant height, [m/s] VVz: Near surface Meridional component of wind speed at constant height, [m/s] ZN3: Total snow height, [m] Other variables are available upon request (see email above). AL : Surface albedo, [0-1] CC: Cloud cover, [0-1] CD: Low level Cloud cover, [0-1] CM: Middle level Cloud cover, [0-1] CU: High level Cloud cover, [0-1] SP:  Surface pressure, [hPa] ST:  Surface temperature, [C] TT:  Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1) TTp: Constant pressure-level mean air temperature, [C] (4) ZZ:  Surface geopotential for the first three model level above the surface (constant sigma), [m] SHF: Surface sensible heat flux, [W/m2]  LHF: Surface latent heat flux, [W/m2] * Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUl.nc file (1) For variable TT, ZZ model constant sigma level of 0.9997479 (2) For variables TTz, QQz constant height level at 2m (3) For variables UUz, VVz constant height level at 10m (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results. Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION   1:CROPS_LOW   2:CROPS_MEDIUM   3:CROPS_HIGH   4:GRASS_LOW   5:GRASS_MEDIUM   6:GRASS_HIGH   7:BROADLEAF_LOW   8:BROADLEAF MEDIUM   9:BROADLEAF_HIGH  10:NEEDLELEAF_LOW  11:NEEDLELEAF MEDIUM  12:NEEDLELEAF_HIGH  13:City

MAR-ERA-20C European Alps (1902-2010)

This folder contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the ERA-20C reanalysis The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr) The simulations cover the period covered by ERA-20C reanalysis : 1902-2010 (1901=Spin-up year) Data are available at the daily frequency, with one variable per file (10 years of data per file). The available variables are : LWD: Surface downward longwave radiation, [W/m2] LWU: Surface upward longwave radiation, [W/m2] MB:  Total snow water equivalent, [mm.We] MBrr: Daily rainfall, [mm.We] (5) MBsf: Daily snowfall, [mm.We] (5) QQz:  Near-surface specific humidity at constant height, [g/kg]  (2) SWD: Surface downward shortwave radiation, [W/m2] SWU: Surface upward shortwave radiation, [W/m2] TTmax: Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C] TTmin: Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C] TTz: Near-surface mean air temperature at constant-height, [C] UUz: Near-surface zonal component of wind speed at constant height, [m/s] VVz: Near surface Meridional component of wind speed at constant height, [m/s] ZN3: Total snow height, [m] Other variables are available upon request (see email above). AL : Surface albedo, [0-1] CC: Cloud cover, [0-1] CD: Low level Cloud cover, [0-1] CM: Middle level Cloud cover, [0-1] CU: High level Cloud cover, [0-1] LHF : Surface latent heat flux, [W/m2] SHF : Surface sensible heat flux, [W/m2] SP:  Surface pressure, [hPa] ST:  Surface temperature, [C] TT:  Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1) TTp: Constant pressure-level mean air temperature, [C] (4) ZZ:  Surface geopotential for the first three model level above the surface (constant sigma), [m] * Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUe.nc file (1) For variable TT, ZZ model constant sigma level of 0.9997479 (2) For variables TTz, QQz constant height level are : 2m (3) For variables UUz, VVz constant height level are : 10m (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results. Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION   1:CROPS_LOW   2:CROPS_MEDIUM   3:CROPS_HIGH   4:GRASS_LOW   5:GRASS_MEDIUM   6:GRASS_HIGH   7:BROADLEAF_LOW   8:BROADLEAF MEDIUM   9:BROADLEAF_HIGH  10:NEEDLELEAF_LOW  11:NEEDLELEAF MEDIUM  12:NEEDLELEAF_HIGH  13:City

MAR-EC-Earth3 HIST (1961-2014) and SSP245 European Alps (2015-2100)

This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the EC-EARTH3 GCM (CMIP6 version) The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr) The realization used is r25i1p1f1 and simulation was done for the historial (1961-2014), SSP245 scenario (2015-2100). For information about the EC-EARTH3 simulation, contact Eduardo Moreno-Chamarro (eduardo.moreno@bsc.es). Data are available at the daily frequency, with one variable per file (10 years of data per file) CC: Cloud cover, [0-1] MB:  Total snow water equivalent, [mm.We] MBrr: Daily rainfall, [mm.We] (5) MBsf: Daily snowfall, [mm.We] (5) QQz:  Near-surface specific humidity at constant height, [g/kg]  (2) TTmax: Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C] TTmin: Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C] TTz: Near-surface mean air temperature at constant-height, [C] UUz: Near-surface zonal component of wind speed at constant height, [m/s] VVz: Near surface Meridional component of wind speed at constant height, [m/s] ZN3: Total snow height, [m] Other variables are available upon request (see email above). AL : Surface albedo, [0-1] CD: Low level Cloud cover, [0-1] CM: Middle level Cloud cover, [0-1] CU: High level Cloud cover, [0-1] SP:  Surface pressure, [hPa] ST:  Surface temperature, [C] TT:  Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1) TTp: Constant pressure-level mean air temperature, [C] (4) ZZ:  Surface geopotential for the first three model level above the surface (constant sigma), [m] LWD: Surface downward longwave radiation, [W/m2] LWU: Surface upward longwave radiation, [W/m2] SWD: Surface downward shortwave radiation, [W/m2] SWU: Surface upward shortwave radiation, [W/m2] SHF: Surface sensible heat flux, [W/m2]  LHF: Surface latent heat flux, [W/m2] * Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUe.nc file (1) For variable TT, ZZ model constant sigma level of 0.9997479 (2) For variables TTz, QQz constant height level are : 2m (3) For variables UUz, VVz constant height level are : 50m (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respective fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results. Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION   1:CROPS_LOW   2:CROPS_MEDIUM   3:CROPS_HIGH   4:GRASS_LOW   5:GRASS_MEDIUM   6:GRASS_HIGH   7:BROADLEAF_LOW   8:BROADLEAF MEDIUM   9:BROADLEAF_HIGH  10:NEEDLELEAF_LOW  11:NEEDLELEAF MEDIUM  12:NEEDLELEAF_HIGH  13:City

MAR-MPI-ESM1-2-HR SSP585 European Alps (2015-2100)

This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the MPI-ESM1-2-HR GCM (CMIP6 version) for the SSP585 projection (2015 to 2100). The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr) The realization used is r1i1p1f1. Data are available at the daily frequency, with one variable per file (10 years of data per file). The available variables in this deposit are : CC: Cloud cover, [0-1] MB:  Total snow water equivalent, [mm.We] MBrr: Daily rainfall, [mm.We] (5) MBsf: Daily snowfall, [mm.We] (5) QQz:  Near-surface specific humidity at constant height, [g/kg]  (2) TTmax: Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C] TTmin: Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C] TTz: Near-surface mean air temperature at constant-height, [C] UUz: Near-surface zonal component of wind speed at constant height, [m/s] VVz: Near surface Meridional component of wind speed at constant height, [m/s] ZN3: Total snow height, [m] Other variables are available upon request (see email above). AL : Surface albedo, [0-1] CD: Low level Cloud cover, [0-1] CM: Middle level Cloud cover, [0-1] CU: High level Cloud cover, [0-1] SP:  Surface pressure, [hPa] ST:  Surface temperature, [C] TT:  Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1) TTp: Constant pressure-level mean air temperature, [C] (4) ZZ:  Surface geopotential for the first three model level above the surface (constant sigma), [m] LWD: Surface downward longwave radiation, [W/m2] LWU: Surface upward longwave radiation, [W/m2] SWD: Surface downward shortwave radiation, [W/m2] SWU: Surface upward shortwave radiation, [W/m2] SHF: Surface sensible heat flux, [W/m2]  LHF: Surface latent heat flux, [W/m2] * Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUy.nc file (1) For variable TT, ZZ model constant sigma level of 0.9997479 (2) For variables TTz, QQz constant height level is : 2m (3) For variables UUz, VVz constant height level are : 10m (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results. Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION   1:CROPS_LOW   2:CROPS_MEDIUM   3:CROPS_HIGH   4:GRASS_LOW   5:GRASS_MEDIUM   6:GRASS_HIGH   7:BROADLEAF_LOW   8:BROADLEAF MEDIUM   9:BROADLEAF_HIGH  10:NEEDLELEAF_LOW  11:NEEDLELEAF MEDIUM  12:NEEDLELEAF_HIGH  13:City ~

opencc-by-4.0Dec 2021View details →

Dataset for: Multi-scale approach to biodiversity proxies of biological control service in European farmlands

Dataset for the BiodivERsA COFUND Woodned project. Information on which spatio-temporal factors are simultaneously affecting crop pests and their natural enemies is required to improve conservation biological control practices. The study was conducted in 80 winter wheat crop fields distributed in three regions of North-western Europe (Brittany, Hauts-de-France and Wallonia), along intra-regional gradients of landscape complexity. Five taxa : aphids, slugs, spiders, carabids, and parasitoids were sampled for two consecutive years. We analysed the influence of regional, landscape and local factors on the abundance and species richness of crop-dwelling organisms, as proxies of the service/disservice they provide. Firstly, there was higher biocontrol potential in areas with mild winter climatic conditions. Secondly, natural enemy communities were less diverse and had lower abundances in landscapes with high crop and wooded continuities, contrary to slugs and aphids. Finally, field boundaries with grass strips were more favourable to spiders and carabids than boundaries formed by hedges, while the opposite was found for crop pests, with the latter being less abundant towards the centre of the fields. These results are quite unexpected because they show that hedgerows and woodlots should not be the unique cornerstones of agro-ecological landscape design strategies. We point out that combining woody and grassy habitats to take full advantage of the features and ecosystem services they both provide may promote sustainable agricultural ecosystems. It may be possible to both reduce pest pressure and promote natural enemies by accounting for taxa-specific antagonistic responses to multi-scale environmental characteristics.

opencc-by-4.0Jul 2021View details →

The European Natural Gas Demand database (ENaGaD)

The ``European Natural Gas Demand'' (ENaGaD) database is composed of daily time series of the national demand of natural gas for each of the 25 European Member States with a transmission system and some European Countries. The series are compiled and presented from 2015 to 2020 in energy unit of measurement. Values are mainly collected from the transparency platform of National Transmission System Operators in compliance to Regulation (EC) No 715/2009. Whenever possible, the daily demand is further divided in consumption by electricity and heat producers, consumption by industrial users and by households. The ENaGaD database is also  available from the Joint Research Centre Data Catalogue at <a href="https://data.jrc.ec.europa.eu/">https://data.jrc.ec.europa.eu</a>. A newer public version of ENaGad (superseding version 1.1)  is now available at <a href="https://data.jrc.ec.europa.eu/collection/id-00372">https://data.jrc.ec.europa.eu/collection/id-00372</a> .

opencc-by-4.0Mar 2022View details →

A refined method for studying foraging behaviour and body mass in group-housed European starlings.

Datasets and R script corresponding to the following manuscript: A refined method for studying foraging behaviour and body mass in group-housed European starlings. Laboratory experiments on passerine birds have been important for testing hypotheses regarding the effects of environmental variables on the adaptive regulation of body mass. However, previous work in this area has suffered from poor ecological validity and animal welfare due to the requirement to house birds individually in small cages to facilitate behavioural measurement and frequent catching for weighing. Here we describe the social foraging system, a novel technology that permits continuous collection of individual-level data on operant foraging behaviour and body mass from group-housed European starlings (Sturnus vulgaris). We demonstrate rapid acquisition of operant key pecking, followed by foraging and body mass data from two groups of six birds maintained on a fixed-ratio operant schedule under closed economy for 11 consecutive days. Birds gained 6.0 ± 1.2 g (mean ± sd) between dawn and dusk each day and lost an equal amount overnight. Individual daily mass gain trajectories were non-linear, with the rate of gain decelerating between dawn and dusk. Within-bird variation in daily foraging effort (key pecks) positively predicted within-bird variation in dusk mass. However, between-bird variation in mean foraging effort was uncorrelated with between-bird variation in mean mass, potentially indicative of individual differences in daily energy requirements. We conclude that the social foraging system delivers refined data collection and offers potential for improving our understanding of mass regulation in starlings and other species.

opencc-by-4.0Mar 2022View details →

AMASS European Testbed Artistic Experiment Evaluations

PURPOSE The data set of 36 AMASS European Testbed case studies fulfils a specific purpose: • Build a research database for good practices in the field of arts-based social interventions in AMASS partner countries. • Construct a solid foundation for the development of new interventions with similar objectives. • To present methods of evaluation for arts-based social interventions, avoiding obstacles that made many previous efforts in this field unsustainable and unadaptable. • To offer a valid and authentic knowledge repository for policy makers, developers of future projects and researchers to identify motivations, philosophies, modes of engagement and impact of arts-based social interventions.

opencc-by-4.0Aug 2021View details →

Motion Capture Benchmark of Industrial Tasks for Ergonomic Assessment and European Historic Crafts

General Info: This benchmark provides motion capture (MoCap) files in .bvh form. The recordings were done in the span of May 2019 to January 2020 for the needs of the <a href="https://collaborate-project.eu/">CoLLaboratE</a> and <a href="http://www.mingei-project.eu/">MINGEI</a> H2020 projects funded by the European Commission. The tasks included are: <ul> <li>TV assembling</li> <li>Airplane component manufacturing</li> <li>High ergonomic hazard motions </li> <li>Silk-Weaving</li> <li>Glassblowing</li> <li>Mastic Cultivation</li> </ul> The TV assembly and airplane component manufacturing tasks were recorded in real-world conditions inside the factory during the actual production of the items. The high ergonomic hazard motions were recorded in a controlled lab environment and serve as baseline/prototype motions for ergonomic risk assessment. The silk-weaving, glassblowing, and mastic cultivation data sets were created, corresponding to movements performed by skilled craftsmen and mastic farmers. These data sets were produced in order to extract the expert's gestural knowledge and analyze their dexterity while doing their crafts. Naming Convention: All files in this benchmark follow a strict naming convention to allow for easier parsing by scripts. The names have a total of 12 or 13 characters that convey the following information: <ul> <li>The first three or fours characters label the recording session (e.g., LAB, PLN, GBBC, MCSN, etc.)</li> <li>The next three characters label the subject number (e.g., S01, S02, S03, etc.)</li> <li>The next three characters label the posture or gesture number (e.g., P01, P02, G01, G02, etc.)</li> <li>The final three characters label the repetition number (e.g., R01, R02, R03, etc.)</li> </ul> For example, LABS02P03R01 denotes a lab recording of the second subject, performing the third posture for the first time. Recording Sessions: There are six recording sessions in this benchmark, the ergonomic risk motion recorded in the lab (denoted as "LAB"), the construction of an airplane component (denoted as "PLN"), and the assembling and packaging of TVs (denoted as "TV*"), the silk weaving (denoted as "SW*"), glassblowing (denoted as "GB*"), and mastic cultivation (denoted as "MC*"). The postures are the following: LAB: <ul> <li>Standing: <ul> <li>P01: The subject stays in I-pose</li> <li>P02: The subject rotates his/her torso to the left as far the person can</li> <li>P03: The subject will laterally bend his/her torso to the left for 6 seconds</li> <li>P04: The subject bends more than 20° but less than 60°</li> <li>P05: The subject bends more than 20° but less than 60° while rotating and laterally bending the torso to the left</li> <li>P06: The subject stretches his/her arms, and bends forward more than 20° but less than 60° while rotating and laterally bending the torso to the left</li> <li>P07: The subject bends more than 60°</li> <li>P08: The subject bends more than 60° while rotating and laterally bending the torso to the left</li> <li>P09: The subject stretches his/her arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li>P10: The subject upright, raises the elbows above the shoulder level with the forearms bent 90° (</li> <li>P11: The subject raises the elbows above the shoulder level with the forearms bent 90° while rotating and laterally bending the torso to the left</li> <li>P12: The subject raises the elbows above the shoulder level with the arms stretched while rotating and laterally bending the torso to the left</li> <li>P13: The subject upright, raises the hands above the head</li> <li>P14: The subject raises the hands above the head with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> <li>Sitting on a chair: <ul> <li>P15: The subject sits upright</li> <li>P16: The subject bends forward more than 60°</li> <li>P17: The subject bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li>P18: The subject stretches the arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li>P19: The subject raises the hands above the head with arms stretched</li> <li>P20: The subject raises the hands above the head with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> <li>Kneeling: <ul> <li>P21: The subject stays upright</li> <li>P22: The subject rotates the torso to the left as far he/she can</li> <li>P23: The subject will laterally bend the torso to the left for 6 seconds</li> <li>P24: The subject bends more than 60°</li> <li>P25: The subject bends more than 60° while rotating and laterally bending the torso to the left</li> <li>P26: The subject stretches the arms, and bends forward more than 60° while rotating and laterally bending the torso to the left</li> <li>P27: The subject upright, raises the elbows to the shoulder level with the arms stretched</li> <li>P28: The subject raises the elbows to the shoulder level with the arms stretched while rotating and laterally bending the torso to the left</li> </ul> </li> </ul> The TV assembling tasks are further divided. The subtasks are: packing the TVs on a stack for shipping (denoted as "TVP" for medium-sized TVs and "TVL" for larger TVs), placing assembling and placing electronic circuit boards on the chassis (denoted as "TVB"), and screwing the boards on the TV chassis (denoted as "TV_"). Each task is comprised of a number of postures.   TV Assembling: <ul> <li>Assembling the board and placing it on the TV chassis (TVB): <ul> <li>P01: Reaching high, above the shoulder level, to pick one component</li> <li>P02: Reaching low, below the knee level, to pick up the second component</li> <li>P03: Connecting the components and placing the board on the chassis to be screwed</li> </ul> </li> <li>Screwing an electrical circuit board on the TV chassis (TV_) : <ul> <li>P01: A screw is placed on a power tool and it is being screwed on the chassis. The process is repeated four times</li> </ul> </li> <li>Preparing TVs for Shipping (TVP & TVL): <ul> <li>P01: Placing TVs on a wooden pallet (bottom level)</li> <li>P02: Preparing to wrap the bottom level with a membrane</li> <li>P03: Wrapping the bottom level</li> <li>P04: Placing TVs on top of the bottom level (second level)</li> <li>P05: Placing TVs on top of the second level (third level)</li> <li>P06: Wrapping the second level with a plastic membrane</li> <li>P07: Wrapping the third level with a plastic membrane</li> <li>P08: Placing TVs on top of the third level (fourth level)</li> <li>P09: Wrapping the fourth level with a plastic membrane</li> </ul> </li> </ul> Riveting of an airplane floater (PLN): <ul> <li>P01: Rivet with the pneumatic hammer.</li> <li>P02: Prepare the pneumatic hammer and grab rivets. </li> <li>P03: Place the bucking bar to counteract the incoming rivet.</li> </ul> The tasks recorded for silk weaving, glassblowing, and mastic cultivation data sets were segmented by gestures (e.g., G01, G02, etc.) . The tasks recorded for these three data sets are the following: Silk weaving (SW*): <ul> <li>The creation of the punch cards (SWPC).</li> <li>Preparation of the beam (SWPB).</li> <li>Wrapping of the beam (SWWB).</li> <li>Jacquard weaving with small loom (SWSL).</li> <li>Jacquard weaving with medium size loom (SWML).</li> <li>Jacquard weaving with large loom (SWLL).</li> </ul> Glassblowing (GB*): <ul> <li>Beak cutting (GBBC).</li> <li>Blowing and shaping (GBBS).</li> <li>Cervix refining (GBCR).</li> <li>Cord laying (GBCL).</li> <li>Finish details (GBFD).</li> <li>Handle laying (GBHL).</li> <li>Transfer to punty (GBTP).</li> <li>Leg and foot laying (GBLF).</li> </ul> Mastic Cultivation (MC*): <ul> <li>Scrapping with new tool (MCSN).</li> <li>Scrapping with old tool (MCSO).</li> <li>Sweeping (MCSW).</li> <li>Dusting (MCDU).</li> <li>Embroidery A (MCEA).</li> <li>Embroidery B (MCEB).</li> <li>Embroidery with an axe (MCEX).</li> <li>Gathering (MCGA).</li> <li>Harvesting (MCHA).</li> <li>Wiping (MCWI).</li> <li>Shifting A (MCSA).</li> <li>Shifting B (MCSB).</li> <li>Cleaning with the wind (MCCW).</li> </ul> The motion capture files were processed and segmented with a 3D character animation software (MotionBuilder, Autodesk Inc., San Rafael, CA. USA) and exported to Biovision Hierarchy (BVH) files.

Experimental Assessment of the Thermal Strain Distribution in Nb3Sn React & Wind Conductor Prototype for European DEMO

The measured data, processed data and metadata related to the publication "Experimental Assessment of the Thermal Strain Distribution in Nb3Sn React & Wind Conductor Prototype for European DEMO"  (doi: 10.1109/TASC.2022.3141699) are uploaded.  The raw data correspond to susceptibility measurement as a function of temperature.  Out of this measurement, strand distribution in the superconducting cable is determined by analysis. This work was supported by the Swiss National Science Foundation (SNF) under contract number 200021_179134.

Diversity of options to eliminate fossil fuels and reach carbon-neutrality across the entire European energy system

Sector-coupled Euro-Calliope model outputs The subdirectories found here cover cost-optimal and cost relaxation (SPORES) carbon-neutrality runs for a sector-coupled, sub-national resolution European energy system model. The underlying model to produce these results, <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-coupled Euro-Calliope</a>, is an extension of the power-sector only <a href="https://github.com/calliope-project/euro-calliope">Euro-Calliope model</a>. It incorporates all energy consuming sectors and includes a more detailed representation of transmission capacities between 98 model regions in Europe. The model runs here are based on specific Sector-Coupled Euro-Calliope minor releases: <ul> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/74f6a9b2e157b6147e155b556f521c03ef23246a">cost-opt</a></li> <li><a href="https://github.com/calliope-project/euro-calliope-2.0/commit/519a4fb26920114e451b8247b38ed86b93b6af89">slack-*</a></li> </ul> The models were optimised using the <a href="https://github.com/calliope-project/calliope">Calliope open energy system modelling framework</a>, again based on different minor releases: <ul> <li><a href="https://github.com/calliope-project/calliope/commit/1faed85eeddbe41c29d52982a6bfb147ef9001a3">cost-opt</a></li> <li><a href="https://github.com/calliope-project/calliope/commit/19460da2e23e752995a9a02ae6dca49379565d43">slack-*</a></li> </ul> <code>slack-*</code> results are for cost relaxation runs, where <code>*</code> refers to the percentage relaxation from the optimal cost of the 2018 energy system. All results use the <a href="https://github.com/sentinel-energy/friendly_data">friendly data</a> format. Data files are structured according to standardised sector-coupled Euro-Calliope output processing provided by the <a href="https://github.com/brynpickering/friendly-calliope">friendly-calliope</a> package + additional processing to produce data relevant to nine high-level metrics (see script <a href="https://github.com/calliope-project/sector-coupled-euro-calliope/blob/main/src/analyse/result_to_friendly.py">here</a>). Both cost optimal and SPORES results related to a projected demand scenario are given in the directories ending in "demand-update". To explore the data, please refer to the <a href="https://sentinel-energy.github.io/friendly_data/">friendly data documentation</a>.

Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions

Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow. This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios. These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):   <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre>   This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>. This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.

European Aerosol Phenomenology - 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets

Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013–2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30–240 min) and long-term data coverage (9–36 months), providing essential information to improve/validate air quality, health impact, and climate models.

opencc-by-4.0Jun 2022View details →

An updated map of GRCh38 linkage disequilibrium blocks based on European ancestry data

A map of approximately independent linkage disequilibrium (LD) blocks has many uses in statistical genetics. Current publicly available LD block maps are based on sparse recombination maps and are only available for GRCh37 (hg19) and prior genome assemblies. We generated LD blocks in GRCh38 for European (EUR) ancestry populations using a recent recombination map based on more than 115,000 individuals. This new map consists of 1,361 independent LD blocks across the 22 autosomal chromosomes and can be accessed at https://github.com/jmacdon/LDblocks_GRCh38

opencc-by-4.0Dec 2020View details →

Techno-economic details of fixed-bottom offshore wind projects deployed in the European markets

Version (with all files) - Updated version (research article is accepted). Publishing Date: July 10, 2022 This dataset describes the techno-economic information of fixed-bottom offshore wind projects deployed in the North Sea region (DK, NL, BE, DE, and the UK).  Contents:  1) Offshore wind farm project prices and technical characteristics (farm size, turbine rated power, water depth, etc.,) 2) Offshore wind farm capacity factor and cumulative energy generation 3) Monopile weight  4) Offshore wind farm installation duration  5) UK offshore wind farms' transmission system cost

opencc-by-4.0Dec 2016View details →

Gene annotation files for Fraxinus excelsior (European ash) genome assembly BATG-0.5

Gene annotation files for Fraxinus excelsior genome assembly v. BATG-0.5, published in Nature (doi:10.1038/nature20786). These files were previously hosted on the Ash Tree Genomes website (http://www.ashgenome.org/transcriptomes) and first made available for download via that site on 2016-02-08. The following annotation files are available: ### GFF file of all gene models (all isoforms) Fraxinus_excelsior_38873_TGAC_v2.gff3 ### FASTA file of all cDNA sequences (all isoforms) Fraxinus_excelsior_38873_TGAC_v2.gff3.cdna.fa ### FASTA file of all CDS DNA sequences (all isoforms) Fraxinus_excelsior_38873_TGAC_v2.gff3.cds.fa ### FASTA file of all peptide sequences (all isoforms) Fraxinus_excelsior_38873_TGAC_v2.gff3.pep.fa ### Functional annotation for each gene model (all isoforms) Fraxinus_excelsior_38873_TGAC_v2.gff3.functional_annotation.tsv ### GFF file of all gene models (longest isoform only) Fraxinus_excelsior_38873_TGAC_v2.longestCDStranscript.gff3 ### FASTA file of all cDNA sequences (longest isoform only) Fraxinus_excelsior_38873_TGAC_v2.longestCDStranscript.gff3.cdna.fa ### FASTA file of all CDS DNA sequences (longest isoform only) Fraxinus_excelsior_38873_TGAC_v2.longestCDStranscript.gff3.cds.fa ### FASTA file of all peptide sequences (longest isoform only) Fraxinus_excelsior_38873_TGAC_v2.longestCDStranscript.gff3.pep.fa ### GFF file for gene models identified as probable transposable element related sequences (excluded from the other files) Fraxinus_excelsior_38873_TGAC_v2.transposable_elements.gff3 NB: The annotation files include preliminary annotations for genes within the organellar scaffolds (gene models FRAEX38873_v2_000400370-FRAEX38873_v2_000401330), which were not reported in the publication of the BATG0.5 assembly (doi:10.1038/nature20786).