Research Dataset Catalog and ShareScore Ratings | ScienceDex

zenodo48/100

Temperature-related mortality exposure-response functions for 854 cities in Europe

This repository contains data to reconstruct the exposure-response functions (ERF) of temperature-related mortality by five 5 age groups in 854 cities in Europe.These ERFs have been derived in the study by Masselot et al. 2023, Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health (<a href="https://protect-eu.mimecast.com/s/zqg2Cg204i4ZMYKf3NUKN?domain=doi.org">https://doi.org/10.1016/S2542-5196(23)00023-2</a>). An associated semi-replicable GitHub repository is available at <a href="https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM">https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM</a> to reproduce part of the analysis and the full results, as well as to provide technical details on the derivation of these ERFs.Note: This updated version contains revised data after the correction of an error in the code related to the computation of the age-specific baseline mortality rates. Details about the error can be found in the GitHub repository linked above. This correction only affects the figures of excess mortality (found in the `results.zip` archive) while the ERFs are negligibly affected. The originally published results can be found in V1.0.0 of this repository.Extraction of the ERFsThe ERFs are provided as coefficients of B-spline functions that can be used to reconstruct the ERFs, along with variance-covariance matrices and quantiles from location-specific temperature distributions. The parametrisation associated with these coefficients is a quadratic B-spline (degree 2), with knots located at the 10th, 75th and 90th percentiles of the temperature distribution. In R, the associated basis can be constructed using the dlnm package, with a temperature series x, as follows:<blockquote>library(dlnm) basis <- onebasis(x, fun = "bs", degree = 2, knots = quantile(x, c(.1, .75, .9)))</blockquote>The main files associated with ERFs are the following:coefs.csv: The B-spline coefficients for each age group and city.vcov.csv: The variance-covariance matrix of the coefficients in each city and age group. It is provided here as the lower triangular part of the matrix with names indicating the position of each value (v[row][column]). In R, assuming x is a row of this file, the matrix can be reconstructed using xpndMat(x) after loading the mixmeta package.coef_simu.csv: 1000 simulations from the distribution of each city and age-specific coefficients. Useful to derive empirical confidence intervals for derived measures such as excess deaths or attributable fractions.tmean_distribution.csv: The city-specific temperature percentiles representing the distribution of the data derived from the ERA5-Land dataset.Health impact assessment resultsresults.zip: A summary of the results from the health impact assessment reported in the analysis. The dataset includes several impact measures provided in files representing different geographical levels, including city, country and regional level. Different files are also provided for age-group specific or all age results.Additional dataWe provide additional data that are useful to reproduce or extend the analysis. Please note that due to restrictive data-sharing agreements for the mortality series, only a part of the code is reproducible. See the <a href="https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM">associated GitHub repository</a> for more details.metadata.csv: City-specific metadata used to create the ERFs and perform the health impact assessment.additional_data.zip: contains further data used to replicate the second stage of the analysis and the final health impact assessment. It includes the full city-level daily temperature series (era5series.csv), the detail of extracted metadata for available years (metacityyear.csv), a description of the city-level characteristics (metadesc.csv), and the first-stage ERF coefficients for all available city and age-groups (stage1res.csv). Additionally, the file meta-model.RData contains R object defining the second-stage model that can be used to predict new ERFs.

opencc-by-4.0Mar 2023View details →

zenodo48/100

Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach

Contact details: wasim.shoman at chalmers.se  waahh7 at gmail com Abstract of the research: Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO2 emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions. However, they require sufficient charging infrastructure to support long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operation (travel times over 4.5 hours or over 360 km distance traveled) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25´25 km2 square with each square that could include multiple charging stations and parking lots of multiple charging points) needs to have four to five times more overnight than megawatt charging points. We estimate that about 40,000 overnight charging points (50-100 kW, combined charging system, CCS) and about 9,000 megawatt charging system (MCS, 0.7 – 1.2 MW) points are required for 15% of trucks as BETs in long-haul operation. On average, 8 and 2 CCS and MCS chargers are required per charging area, and each MCS and CCS serve, on average, 11 and 2 BETs daily, respectively. Public charging entails about 110 GWh daily electricity demand in each charging area. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization. The methodology: We develop a method to place charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by HDV is identified using the ETISplus dataset. We develop a travel pattern for the HDV to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving HDV. Break and rest locations for all moving HDVs are aggregated to suggest energy requirements if assuming these HDVs are BETs. The aggregated energy to charge stopped BETs is used to identify the number and type of chargers within each suggested charging station. Datasets details The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers, and Planned transport infrastructure.  The first dataset titled 'ChargerLocations' contains information about the locations of suggested charging stations, the number and type of chargers, and the number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields: <table> <tbody> <tr> <td>Name</td> <td>Description</td> <td>Data Type</td> <td>Unit</td> </tr> <tr> <td>DTN30/MainDTN</td> <td> number of electrified trucks in 2030</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChE30</td> <td> charged energy in Mega watt-hour from all charging (fast and slow)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>ChERM</td> <td> charged energy in Megawatt hour with slow charging only (rest)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_R</td> <td> number of electrified trucks using slow chargers (rest)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>ChEBM</td> <td> charged energy in Megawatt hour with fast charging only (break)</td> <td>float</td> <td> Mega watt-hour</td> </tr> <tr> <td>MDTN_B</td> <td> number of electrified trucks using fast chargers (break)</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NSCh2pD</td> <td> number of slow chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>NFCh30m</td> <td> number of fast chargers</td> <td>integer </td> <td>number</td> </tr> <tr> <td>TotCha</td> <td> Total number of chargers</td> <td>integer </td> <td>number</td> </tr> </tbody> </table>   The second dataset titled (RestandBreaksPoints.shp) with information about the rest and break point locations. The dataset includes detailes about stop type, number of stopped trucks, and required charged energy. The dataset is a shapefile with "shp" format.  <table> <tbody> <tr> <td> Name </td> <td> Description </td> <td> Data Type </td> <td> Unit </td> </tr> <tr> <td> ID_origin_region </td> <td> Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 </td> <td> Integer (9digits) </td> <td> - </td> </tr> <tr> <td> Name_origin_region </td> <td> National name of NUTS-3 region of origin </td> <td> String </td> <td> - </td> </tr> <tr> <td> ID_destination_region </td> <td> Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 </td> <td> Integer (9digits) </td> <td> - </td> </tr> <tr> <td> Name_destination_ region </td> <td> National name of NUTS-3 destination region </td> <td> String </td> <td> - </td> </tr> <tr> <td> Rest </td> <td> A value of ”1” indicates a rest stop </td> <td> Boolean </td> <td> - </td> </tr> <tr> <td> Break </td> <td> A value of ”1” indicates a break stop </td> <td> Boolean </td> <td> - </td> </tr> <tr> <td> ChaDisKM </td> <td> Charged range within a trip for stopped the truck </td> <td> Float </td> <td> km </td> </tr> <tr> <td> ChaEnekWh </td> <td> Charged energy within a trip for stopped the truck </td> <td> Float </td> <td> KWh </td> </tr> <tr> <td> MainDTN </td> <td> Number of stopped trucks for the main electrification scenario (15%) </td> <td> Float </td> <td> number </td> </tr> <tr> <td> ChE30M </td> <td> Charged energy for all stopped trucks </td> <td> Float </td> <td> MWh </td> </tr> <tr> <td> ChERM </td> <td> Charged energy for the trucks stopping for rest </td> <td> Float </td> <td> MWh </td> </tr> <tr> <td> MDTN_R </td> <td> Number of trucks stopping for rest </td> <td> Float </td> <td> number </td> </tr> <tr> <td> ChEBM </td> <td> Charged energy for the trucks stopping for break </td> <td> Float </td> <td> MWh </td> </tr> <tr> <td> MDTN_B </td> <td> Number of trucks stopping for break </td> <td> Float </td> <td> number </td> </tr> <tr> <td> geometry </td> <td> X, Y coordinates </td> <td> geometry </td> <td> - </td> </tr> </tbody> </table>     The following dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X): <table> <tbody><tr> <th> Name </th> <th> Description </th> <th> Data Type </th> <th> Unit </th> </tr> </tbody><tbody> <tr> <td> ID_origin_region </td> <td> Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 </td> <td> Integer (9digits) </td> <td> - </td> </tr> <tr> <td> Name_origin_region </td> <td> National name of NUTS-3 region of origin </td> <td> String </td> <td> - </td> </tr> <tr> <td> ID_destination_region </td> <td> Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3 </td> <td> Integer (9digits) </td> <td> - </td> </tr> <tr> <td> Name_destination_ region </td> <td> National name of NUTS-3 destination region </td> <td> String </td> <td> - </td> </tr> <tr> <td> Edge_path_E_road </td> <td> List of the network edge IDs of the shortest path between the O-D pair, determined with Dijkstra's algorithm </td> <td> String </td> <td> - </td> </tr> <tr> <td> Distance_from_origin_ region_to_E_road </td> <td> Distance from the geometric centre of the origin region to the closest network node </td> <td> Float </td> <td> Kilometres [km] </td> </tr> <tr> <td> Distance_within_E_ road </td> <td> Distance of the shortest edge path between the O-D pair </td> <td> Float </td> <td> Kilometres [km] </td> </tr> <tr> <td> Distance_from_E_ road_to_destination_ region </td> <td> Distance from the geometric centre of the destination region to the closest network node </td> <td> Float </td> <td> Kilometres [km] </td> </tr> <tr> <td> Total_distance </td> <td> Sum of Distance_from_origin_region_to_E_road, Distance_within_E_road and Distance_from_E_road_to_destination_region </td> <td> Float </td> <td> Kilometres [km] </td> </tr> <tr> <td> Traffic_flow_trucks_ 2010 </td> <td> Number of trucks that drive between the O-D pair in 2010 </td> <td> Float </td> <td> Number of trucks </td> </tr> <tr> <td> Traffic_flow_trucks_ 2019 </td> <td> Number of trucks that drive between the O-D pair after they had been scaled to 2019 </td> <td> Float </td> <td> Number of trucks </td> </tr> <tr> <td> Traffic_flow_trucks_ 2030 </td> <td> Number of trucks that drive between the O-D pair according to the forecast for 2030 </td> <td> Float </td> <td> Number of trucks </td> </tr> <tr> <td> Traffic_flow_tons_ 2010 </td> <td> Number of tons that are transported between the O-D pair in 2010 according to ETISplus </td> <td> Integer </td> <td> Tons [t] </td> </tr> <tr> <td> Traffic_flow_tons_ 2019 </td> <td> Number of tons that are transported between the O-D pair after they had been scaled to 2019 </td> <td> Integer </td> <td> Tons [t] </td> </tr> <tr> <td> Traffic_flow_tons_ 2030 </td> <td> Number of tons that are transported between the O-D pair according to the forecast for 2030 </td> <td> Integer </td> <td> Tons [t] </td> </tr> </tbody> </table> Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X)) <table> <tbody><tr> <th> Name </th> <th> Description </th> <th> Data Type </th> <th> Unit </th> </tr> </tbody><tbody> <tr> <td> Network_Node_ID </td> <td> Unique network node ID </td> <td> Integer (6 digits) </td> <td> - </td> </tr> <tr> <td> Network_Node_X </td> <td> Longitude of the location of network node </td> <td> Float </td> <td> Degrees </td> </tr> <tr> <td> Network_Node_Y </td> <td> Latitude of the location of network node </td> <td> Float </td> <td> Degrees </td> </tr> <tr> <td> ETISplus_Zone_ID </td> <td> ID of the NUTS-3 region in which the network node is located </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> Country </td> <td> Unique country code of the country in which the network node is located (country codes are defined by ETISplus) </td> <td> String </td> <td> - </td> </tr> </tbody> </table> Description of variables used in the network edges list (Updated_04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X)) <table> <tbody><tr> <th> Name </th> <th> Description </th> <th> Data Type </th> <th> Unit </th> </tr> </tbody><tbody> <tr> <td> Network_Edge_ID </td> <td> Unique edge ID </td> <td> Integer (7 digits) </td> <td> - </td> </tr> <tr> <td> Manually_Added </td> <td> Determines whether an edge had been manually added to the network (1) or not (0) </td> <td> Binary-integer </td> <td> - </td> </tr> <tr> <td> Distance </td> <td> Length of the network edge </td> <td> Float </td> <td> Kilometres [km] </td> </tr> <tr> <td> Network_Node_A_ID </td> <td> Unique ID of the network node that defines one end point of the network edge </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> Network_Node_B_ID </td> <td> Unique ID of the network node that defines one end point of the network edge </td> <td> Integer </td> <td> - </td> </tr> <tr> <td> Traffic_flow_trucks_2019 </td> <td> Number of trucks that drive on the edge in 2019 (both highway directions combined) </td> <td> Float </td> <td> Number of trucks </td> </tr> <tr> <td> Traffic_flow_trucks_2030 </td> <td> Number of trucks that drive on the edge in 2030 (both highway directions combined) </td> <td> Float </td> <td> Number of trucks </td> </tr> </tbody> </table>

opencc-by-4.0Oct 2022View details →

zenodo48/100

Sustainable Agricultural Pathways in Europe (SIPATH) – land use data

The published land use data were part of the project “What is Sustainable Intensification? Operationalizing Sustainable Agricultural Pathways in Europe (SIPATH)”, which was funded by the Swiss National Science Foundation (grant no. CRSII5_183493). The overall objective of this project was to assess short-term trajectories in agriculture land use and landscape structure over a period of 20 years for 16 individual study sites located in 11 countries, ranging from the Mediterranean to the boreal zone. The following datasets illustrate land use data that were generated for the different study sites at two points in time between 2000 and 2020, depending on data availability. The data collection process encompassed land use mapping through visual image interpretation of orthorectified aerial photographs using geographic information systems (ESRI ArcGIS Pro). Land use digitalization was conducted by two scientists in Switzerland, who were in contact with the respective project partners in the study countries to exchange expert knowledge. Minimal mapping unit was 25m2 for areal elements. Land cover was classified following the European Nature Information System (EUNIS) habitat classification (EEA 2019). The broadest habitat classes were systematically mapped in the study sites, with each covering an area of approximately 25 km². While EUNIS focuses on habitat types, the study, however, targeted the intensity of agricultural land-use, several EUNIS classes were complemented with levels of land-use intensities. This was applied for grassland, olive groves and fruit orchards. The spatial resolution of the orthophotos ranged from 25cm to 2m. Accordingly, there were situations in which the spatial resolution was insufficient to accurately determine the land use. In such cases, either orthophotos from about the same year were consulted, possibly showing different phenological stages, or land use statistics and expert judgement based on local expert knowledge of the respective study area were applied.   Reference: EEA, 2019. EUNIS habitat classification 2007 (Revised descriptions 2012) amended 2019. Copenhagen (<a href="https://www.eea.europa.eu/ds_resolveuid/27788ca43d9e4f2e9477b15df88d20be" target="_blank" rel="noopener">Permalink</a>)

opencc-by-4.0Nov 2024View details →

zenodo48/100

BioVars - bioclimatic datasets for Europe based on a large regional climate ensemble for periods between 1971 to 2098

We present 26 bio-climatic variables that are calculated based on a large ensemble consisting of 70 bias-adjusted GCM-RCM (Global Climate Model – Regional Climate Model) simulations for 1971 to 2098. Both, the historic and the projection periods were calculated using the same models to ensure consistency between the periods. The variables are validated against E-OBS observations from which we calculated the same bio-climatic variables. For projection periods we chose 20 year ranges between 2021 to 2098. Here, we offer two versions of them 1) variables separated into RCP 2.6, 4.5 and 8.5 including the 5th, 50th and 95th percentiles among the realisations and within the RCPS. And 2) variables per realisation separately. We then extracted the temporal 5th, 50th and 95th percentile per period as representing values. Each zipped file contains these 26 bio-climatic variables according to their aggregation. The variables and the units are explained within the data descriptor publication.    File descriptions <ul> <li>bioVars_1971-2000_met.tar.gz >> Projections per realisations for period 1971-2000</li> <li>bioVars_2021-2040_met.tar.gz >> Projections per realisations for period 2021-2040</li> <li>bioVars_2041-2060_met.tar.gz >> Projections per realisations for period 2041-2060</li> <li>bioVars_2061-2080_met.tar.gz >> Projections per realisations for period 2061-2080</li> <li>bioVars_2079-2098_met.tar.gz >> Projections per realisations for period 2079-2098</li> <li>bioVars_2021-2040_rcp.tar.gz >> Projections per RCP for period 2021-2040</li> <li>bioVars_2041-2060_rcp.tar.gz >> Projections per RCP for period 2041-2060</li> <li>bioVars_2061-2080_rcp.tar.gz >> Projections per RCP for period 2061-2080</li> <li>bioVars_2079-2098_rcp.tar.gz >> Projections per RCP for period 2079-2098</li> <li>validation.tar.gz >> Validation using E-OBS (v20.0) and Worldclim (version 2.1)</li> </ul>   References Reichmuth, A., Rakovec, O., Boeing, F. et al. BioVars - A bioclimatic dataset for Europe based on a large regional climate ensemble for periods in 1971–2098. Sci Data 12, 217 (2025). https://doi.org/10.1038/s41597-025-04507-w

opencc-by-4.0Aug 2024View details →

zenodo48/100

Turnover of the Radio Broadcasting Industry in Europe

Imputed and forecasted values of  the radio broadcasting industry from the <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=sbs_na_1a_se_r2&lang=en">Annual detailed enterprise statistics for services (NACE Rev. 2 H-N and S95)</a> Eurostat folder. We use backcasting, forecasting, approxmation, last observation carry forward and next observation carry backwards to impute missing values, and to create realistic forecasts up to three periods.  Compared to the Eurostat raw data we added value with     Increased number of observations: 65% Reduced missing values: -48.1% Increased non-missing subset for regression or AI: +66.67%

opencc-by-4.0Nov 2021View details →

zenodo48/100

Record Label & Music Publishing Turnover in Europe

Imputed and forecasted values of  the recording and music publishing industry from the <a href="https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=sbs_na_1a_se_r2&lang=en">Annual detailed enterprise statistics for services (NACE Rev. 2 H-N and S95)</a> Eurostat folder.

opencc-by-4.0Nov 2021View details →

zenodo48/100

Resilience of pig producers in Europe

This dataset collected for the organic core POWER project to assess resilience capacities of organic pig prodcuers in Austria, Danemark, Italy, Sweden and Switzerland. These datasets have been anonymized.    The resilience farm data are all data that where observed at farm level, and contain farm characterisitcs, namely <table> <tbody> <tr> <td>variable</td> <td>description</td> <td>values</td> </tr> <tr> <td>farm id</td> <td>unique identifier of the farm</td> <td>characters, including country code based on ISO2</td> </tr> <tr> <td>breeding type</td> <td>type of pig entreprise on the found on the farm</td> <td>breeding, finishing or both</td> </tr> <tr> <td>entrerprise_x</td> <td>description of other entreprises found on the farm</td> <td>feed production, cash crop, chicken, sheep, dairy, beef, direct marketing, tourism, on-farm processing, horse housing.</td> </tr> <tr> <td>number non-pig entreprise</td> <td>number of entreprise describes</td> <td>integer</td> </tr> <tr> <td>structure</td> <td>type of pig housing structure</td> <td>permanent, temporary, both</td> </tr> <tr> <td>outdoor area</td> <td>type of oudoor access for pig</td> <td>concrete, shifting arable land, permanent pasture</td> </tr> <tr> <td>LSU</td> <td>livstock standard units computed following Eurostat standards</td> <td>numeric</td> </tr> <tr> <td>pig/ha</td> <td>intensity of production as LSU/UAApig</td> <td>numeric</td> </tr> <tr> <td>self-sufficiency</td> <td>percentage of pig feed produced on farm</td> <td>numeric</td> </tr> <tr> <td>UAA pig</td> <td>utilized agricultural area for the pig production</td> <td>numeric</td> </tr> <tr> <td>UAA total</td> <td>utilized agricultural area of the farm</td> <td>numeric</td> </tr> </tbody> </table>   The resilience data is the result of the interpretation of farmers' resilience narratives, which were interpreted been interpreted using the Meuwissen et al, 2019 farming systems framework. The data is in long fromat and represents a particular resilience capacity related to a specific shock. More particularly, the data contains the follwing information     <table> <tbody> <tr> <td>variables name</td> <td>description</td> <td>values</td> </tr> <tr> <td>farm id</td> <td>unique identifier of the farm</td> <td>characters, including country code based on ISO2</td> </tr> <tr> <td>country</td> <td>country code</td> <td>based on ISO2</td> </tr> <tr> <td>question related to shocks</td> <td>shocks to which the resilience narrative related to</td> <td>input cost, price, outbreak, climate, legislation, labour, general</td> </tr> <tr> <td>narratives (a= first, b=second)</td> <td>identifier of the narrative within a question</td> <td>a, b</td> </tr> <tr> <td>capacity</td> <td>resilience capacity following the Meuwissen et al (2019) framework</td> <td>robustness, adaptability, transformability, non-resilience</td> </tr> <tr> <td>resilience attribute type</td> <td>resilience attribute based on an expanded interpretation the Meuwissen et al (2019) framework (see paper)</td> <td> functional diversity, response diversity, modularity, tighness of feedback, social capital, attitude, system reserve (physical captial -inherent), system reserve (physical capital -use), system reserve (natural capital -inherent) system reserve (human capital - use) </td> </tr> <tr> <td>resilience attribute</td> <td>description of the attribute that led to the resilience attribute type classification</td> <td> ability to convert to cash crop, ability to offer good working conditions, ability to switch brand, access to financial services, access to technical solutions, adapted crops, adding finishing section, adjust feed production, adjust volume of pig production, adjusting paddock size to enable double fencing, advisory and veterinary services, believe in organic, brand building with social media, build temporary shelter, build up savings, by-product through partnership, capacity to access more land, change external feed, change feed ratio, conservable end product, create microclimates, create new brand, created a young farmer network, customer relation, decrease pig, decrease pig production, direct marketing, diverse farm, diverse sale channels, do something else, double fencing, efficiency, entrepreneurship, excess cereal production, exploring governance model as no successor, family labour, farmer owned value chain, fencing, financial lock-in, flexible infrastructure (enabling), flexible pig keeping system, forest system, good indoor infrastructure, good infrastructure, good relation to customers, governmental support, habit, has margin, home feed production, inadequate salary, increase cash crop, increase own work, increase own working time, independent feed ratio, indoor keeping, indoor production, innovator, innovator (one welfare) , insurance, margins, mechanisation, mobile mode of production, neighbor network, neighborhood early warning, neighborhood network, new cooling infrastructure, niche production, no competition, no fencing option, no own farm, land or infrastructure, no qualified staff required, offering jobs to young people, other livestock, part time worker, partnership with other farmers, producing more home grown feed, profit, reduced pig production, rely on sectoral organization, resistant breed, robust animals, robust breed, sectoral power, sectoral response, short term feed contracts, social media, soil health, split production on other farms, staffing agency (through advisory services), sufficient outdoor space, sufficient pasture, sufficient space, sufficient space (enabling), switch to indoor production, switch to other livestock, tiredness in the sector, Too big to fail, training, unique pig keeping system, up-to-date infrastructure, volunteer networks, wallow, work with nature </td> </tr> </tbody> </table>   To compute the resilience capacity score (Cscore) assign 0 to lack of resilience, 1 to robustness, 2 to adpababilty and 3 to transformability. If there is more than one narrative with a different capacity, the average score between mentionned capacities was taken. Use following R code in dplyr mydata<- ResilienceDataPreProcessed %>%   mutate(code = ifelse(capacity=="robustness", 1,ifelse(capacity=="adaptability",10,ifelse(capacity=="transformability",100,ifelse(capacity =="no resilience capacity",1000,ifelse(capacity=="no long term resilience capacity",10000,ifelse(capacity=="no short term resilience",1000,NA)))))))%>%   group_by(farm, question)%>%   summarise(Ccode=sum(code))%>%   mutate(Cscore=ifelse(Ccode==1|Ccode==2| Ccode==3, 1,ifelse(Ccode==20|Ccode==10|Ccode==111,2, ifelse(Ccode==100|Ccode==200,3, ifelse(Ccode==11|Ccode==21, 1.5,ifelse(Ccode==110|Ccode==120, 2.5,ifelse(Ccode==101,3,ifelse(Ccode==1000,0,ifelse(Ccode==10001|Ccode==1001,0.5,NA)) ))) ))))   Resilience questionnaire Farm number: Farm name or ID: Country:   System descriptors <table> <tbody> <tr> <td> Breeding or finishing (or both) </td> <td>          </td> </tr> <tr> <td> Indoor or outdoor (or a mix) </td> <td>   </td> </tr> <tr> <td> Organic or conventional </td> <td>    </td> </tr> <tr> <td> Number of years organic </td> <td>      </td> </tr> </tbody> </table>   1) Has your farm experienced significant challenges in the last 5 years? <table> <tbody> <tr> <td> Yes or no? </td> <td> Yes / No </td> </tr> <tr> <td> If "no", what factars (farm/external) created this resilience? </td> <td>         </td> </tr> <tr> <td> If "yes", please describe the 1st challenge </td> <td>         </td> </tr> <tr> <td> What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)? </td> <td>           </td> </tr> <tr> <td> Did this change your management or farm structure subsequently (and how)? </td> <td>         </td> </tr> <tr> <td> If "yes", please describe a 2nd challenge </td> <td>         </td> </tr> <tr> <td> What was the impact on the farm (production, animal health/welfare, work load, work life quality etc)? </td> <td>           </td> </tr> <tr> <td> Did this change your management or farm structure subsequently (and how)? </td> <td>         </td> </tr> </tbody> </table>     2) In the future, how do you feel your pig system would cope with these challenges: a) Decreasing or negative margins due to increased feed or other input costs? <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>               </td> </tr> </tbody> </table>   b). Decreasing or negative margins due to reduced pig prices? <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>               </td> </tr> </tbody> </table>       c) Wide spread disease outbreak such as African Swine Fever <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>               </td> </tr> </tbody> </table>   d) Climate change impact, e.g. severe storms, flooding, drought, hot seasons <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td> </td> <td> </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>                 </td> </tr> </tbody> </table>       e) Changing legislation impact, e.g. increased floor space allowance indoors, mandatory access to pasture, more land required (lower stocking densities to reduce nutrient loads from pasture systems or in general for the whole farm) <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>                 </td> </tr> </tbody> </table>   f) Shortage in qualified ‘work-mass’ (difficulties in recruiting qualified employees) <table> <tbody> <tr> <td> Very severely (e.g. bankruptcy) </td> <td>   </td> </tr> <tr> <td> Severely (e.g. closure of pig enterprise) </td> <td>   </td> </tr> <tr> <td> Strong impact (e.g. large reduction in production) </td> <td>   </td> </tr> <tr> <td> Short term impact (e.g. reduced production) </td> <td>   </td> </tr> <tr> <td> Little impact (e.g. change ration) </td> <td>   </td> </tr> <tr> <td>   </td> <td>   </td> </tr> <tr> <td> Why?   </td> <td>           </td> </tr> <tr> <td> How are you prepared for this potential challenge? (what are the characteristics of your farm or management that make you more or less prepared for this challenge) </td> <td>                 </td> </tr> </tbody> </table> 3) Any other comments on resilience of their system? 4) General comments/system description?

opencc-by-4.0Dec 2021View details →

zenodo48/100

Morphometric data from: Incongruent molecular and morphological variation in the crab spider Synema globosum (Araneae: Thomisidae) in Europe

Here we provide the complete set of files used by <a href="https://doi.org/10.3897/zookeys.1078.64116">Urfer et al. (2021</a>, see References section below for the complete citation of the publication) for the morphometric and the molecular analysis. In particular, we provide the following documents: PART 1: MORPHOMETRIC ANALYSIS - 1_Synema_data_multiple_imputation_mice.R: R-script used for replacing NAs. - 1_Synema_data_NA_imputed.csv: Dataset with raw values (in millimeters) of all 28 specimens used for the morphometric analysis. Each specimen was measured 4 times. NAs replaced using the R-script "Synema_multiple_imputation_mice.R" above. This is the datafile used for all morphometric analyses. - 1_Synema_data_with_NA.csv: Dataset with raw values (in millimeters) of all 28 specimens. Each specimen was measured 4 times. NAs not replaced.          - 1_Synema_Reliability.R: R-script for calculating reliability.      - 1_Synema_Reliability_supplementary_figure.pdf: Results of reliability analysis presented in a bar plot. - 1_Synema_Reliability_supplementary_table.txt: Results of reliability analysis presented in a table.      - 1_Synema_Shape_PCA_and_PCA_Ratio_Spectrum.R: R-script for calculating the shape PCA and the PCA Ratio Spectrum of the first shape PC. You may get the necessary MRA source script from http://doi.org/10.5281/zenodo.4250142         - Synema_globosum_AR9379_PV.jpg, Synema_globosum_AR9379_PV.jpg, Synema_globosum_AR9379_PV.jpg, etc.: Photographs taken with a LEICA M205 C stere-omicroscope.     1. Numbers after AR_ refer to the inventory number of the specimens in the Natural History Musuem Bern (NMBE). The specimen number was also used in the data file.     2. The photo named "Synema_globosum_AR9163_with_measurements" shows the position of the measurements. Otherwise, the measurements are not indicated in the raw photos. Example image                                                           Character name    Definition Synema_globosum_AR9163_with_measurements    cym.l                   Cymbium lenght    Distance of the anterior margin to the tip of the cymbium Synema_globosum_AR9163_with_measurements    cym.b                  Cymbium breadth    widest breadth of the cymbium Synema_globosum_AR9163_with_measurements    bul.b                    Bulb breadth    widest breadth of the genital bulbus Synema_globosum_AR9163_with_measurements    tib.b                     Tibia breadth    breadth of the tibia base at the patella joint

opencc-by-4.0Dec 2021View details →

zenodo48/100

Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362

Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for  the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe",  https://doi.org/10.3390/land10121362 from the the long term experiments  belonging in some of the SoilCare project partners.

opencc-by-4.0Dec 2021View details →

zenodo48/100

Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"

Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below). R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.

opencc-by-4.0Sep 2021View details →

zenodo48/100

Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe

Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale. The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10]; Soil properties were predicted at fixed depths:     Surface soil = s0..0cm,     Subsoil 1 = s30..30cm,     Subsoil 2 = s60..60cm,     Subsoil 3 = s100..100cm. To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula. Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020; The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.