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AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Cyprus
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Bulgaria
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Hungary
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Slovenia
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Poland
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Latvia
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - The Netherlands
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Slovakia
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Norway
<p>This dataset contains the results of the surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Iceland
<p>This dataset contains the results of the EU co-funded surveillance activities conducted in 2021, which consisted of:</p> <ul> <li>Serological surveys to monitor the circulation of AIV subtypes H5 and H7 in poultry (active surveillance). These surveys should preferentially target poultry species or production systems with increased risk for introduction of avian influenza (AI).</li> <li>Passive surveillance aiming at the virological detection of AI in wild birds found dead or moribund.</li> </ul>
Annual report on surveillance for avian influenza in poultry and wild birds in Member States of the European Union in 2021 – monthly maps
<p>Here you can find the monthly maps referred to in the 'Annual report on surveillance for avian influenza in poultry and wild birds in Member States of the European Union in 2021' by EFSA. Figure legend for all figures: Monthly observations and samples from wild birds on the EFSA list of target species for 2021 by NUTS3 region. The green colour scale represents the number of wild bird observations from the target species, as per data provided by the EuroBirdPortal project. The black dots represent the number of wild bird samples from target species tested within the countries' avian influenza passive surveillance programmes. Wild bird samples reported at NUTS2 level are not shown on these maps.</p>
Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity
<p># Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity</p> <p>## Author<br> Marieke Scheel, Lund University, Sweden, marieke.scheel@gmail.com</p> <p>## Description<br> Data underlying analysis in:<br> Marieke Scheel, Mats Lindeskog, Benjamin Smith, Susanne Suvanto, Thomas A. M. Pugh<br> Increased Central European forest mortality explained by higher harvest rates driven by enhanced productivity<br> Scripts underlying analysis:<br> https://github.com/mariekesche/harvest_driven_canopy_mortality</p> <p>Folder: harvest_checks<br> - NFI_Germany.txt, National Forest Index data from Germany as difference between inventories in 200-2003 and 2011-2013, values are given as fraction, n: number of NFI plots in a grid cell, HARVEST_ALL: clear-cut harvest, HARVEST_PARTIAL: thinning harvest, NATDEAD: natural dead (not harvested)</p> <p>Folder: manag_climfix (S_man,clim)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_co2fix (S_man,CO2)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_ndepfix (S_man,N)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: manag_nofix (S_man)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - closs.txt, biomass loss in kg [C]/m^2 year split into DBH classes<br> - closs_harv.txt, biomass loss due to harvest in kg [C]/m^2 year split into DBH classes<br> - cpool_forest_rel.txt, biomass of forests in kg [C]/m^2 year<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - crownloss_age.txt, m^2 of crown/m^2 of ground lost per year due to age<br> - crownloss_dist.txt, m^2 of crown/m^2 of ground lost per year due to disturbance<br> - crownloss_fire.txt, m^2 of crown/m^2 of ground lost per year due to fire disturbance<br> - crownloss_greff.txt, m^2 of crown/m^2 of ground lost per year due to growth efficiency<br> - crownloss_harv.txt, m^2 of crown/m^2 of ground lost per year due to harvest<br> - crownloss_other.txt, m^2 of crown/m^2 of ground lost per year due to other reasons<br> - crownloss_thin.txt, m^2 of crown/m^2 of ground lost per year due to natural thinning<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes<br> - diam_dens.txt, total number of trees/m^2 year split into DBH classes<br> - stemloss.txt, number of trees/m^2 year lost in respective cells<br> - stemloss_harv.txt, number of trees/m^2 year lost in respective cells due to harvest</p> <p>Folder: manag_nothin (S_nothin)<br> - cflux_forest_rel.txt, Net Primary Production (NPP) values of forests in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year<br> - lai.txt, leaf area index (LAI) for simulated species and plant functional types (PFTs)</p> <p>Folder: PNV (S_PNV)<br> - cflux.txt, Net Primary Production (NPP) values in kg [C]/ m^2 year (column 4)<br> - crownloss.txt, m^2 of crown/m^2 of ground lost per year<br> - crownloss_age.txt, m^2 of crown/m^2 of ground lost per year due to age<br> - crownloss_dist.txt, m^2 of crown/m^2 of ground lost per year due to disturbance<br> - crownloss_fire.txt, m^2 of crown/m^2 of ground lost per year due to fire disturbance<br> - crownloss_greff.txt, m^2 of crown/m^2 of ground lost per year due to growth efficiency<br> - crownloss_other.txt, m^2 of crown/m^2 of ground lost per year due to other reasons<br> - crownloss_thin.txt, m^2 of crown/m^2 of ground lost per year due to natural thinning<br> - diam_cmass_wood.txt, wooden biomass in kg [C]/m^2 year split into DBH classes<br> - diam_crownarea.txt, total m^2 of crown/m^2 of ground per year split into DBH classes</p> <p>Folder: dependencies<br> - gridlist.txt, coordinates of 0.5°x0.5° grid cells that simulations were run on; longitude, latitude, FAO number, size in m^2<br> - landcover_eu.txt, input file LPJ-GUESS model showing changes from natural to forest (harvest); longitude, latitude, year, natural, forest, barren</p> <p>## Dependencies<br> - canopy mortality rates published in "Senf C Pflugmacher D Zhiqiang Y Sebald J Knorn J Neumann M Hostert P and Seidl R 2018 Canopy mortality has doubled in Europe’s temperate forests over the last three decades Nature Communications 9 4978 10.1038/s41467-018-07539-6"<br> - harvest removal rates published in "Ceccherini G Duveiller G Grassi G Lemoine G Avitabile V Pilli R and Cescatti A 2020<br> Abrupt increase in harvested forest area over Europe after 2015 Nature 583 72-77 10.1038/s41586-020-2438-y"<br> - FAO forest removal area data data retrieved from https://www.fao.org/faostat/en/#data/GF (24.07.2021), modified for overview (1st column: Area Code, 2nd column: Year, 3rd column: Area in 1000 ha)</p>
Processed model output of the climate simulation in the study: The effects of diachronous surface uplift of the European Alps on regional climate and the isotopic composition of precipitation (δ18Op) [Boateng et al.]
<p><strong>The geodynamic evolution of the Alps suggests that the Alps did not rise monotonically due to the different post-collisional processes such as slab break-off. However, understanding such subsurface dynamics would require adequate knowledge about its surface uplift history. Stable isotope paleoaltimetry methods are widely used to infer past surface elevation using geologic archives. However, its accurate interpretation relies on attributing the extracted isotopic signal from proxies to surface uplift despite other influences such as climate. To resolve this issue, topographic sensitivity experiments across the Alps are used to investigate the impacts of the diachronous surface uplift on regional climate and δ18Op. The Atmospheric General Circulation Model ECHAM5 with water isotope tracking capabilities (ECHAM5-wiso) is used to simulate the climate with varied topographic scenarios. We present the processed (long-term means) model output of the relevant climate variables (i.e δ18Op, near-surface temperature, precipitation amount, near-surface meridional and zonal winds, mean sea level pressure, and elevation) in response to the changes in topography. The file names are representative of the topographic scenarios used for the simulations. For example, the file “W2E1.nc” is the model output produced by a topographic scenario in which the topography across the west-central Alps was set to 200% of its modern height, and the Eastern Alps were kept at 100%. The “CTL.nc” file contains model output from the control simulation that uses present-day topography. The datasets for instance can be used to select far-field sampling points for the δ-δ paleoaltimetry method that are not significantly affected by the topographic changes.</strong></p>
Data from: Macro-evolutionary trade-offs as the basis for the distribution of European bats
<p>We have compiled a dataset of life history traits and distribution characteristics of 30 European bat species, based on a literature study of a total of 56 primary and secondary sources. These life history traits are grouped into morphological, physiological and ecological adaptations. </p> <p><em>Physiological adaptations:</em></p> <p>Neonatal mass: the average weight (g) of a newborn pup, measured within five days after birth.</p> <p>Average litter size: the average size of a full-term litter (including stillborn pups) per female.</p> <p>Weaning mass: the weight (g) of a juvenile during its first flight outside the roost.</p> <p>Adult body mass: the average weight (g) of adult bats during the summer (between 1 May and 1 July), excluding pregnant females.</p> <p>Litter mass: neonatal mass * average litter size.</p> <p>Relative mass of neonatal to adult: neonatal mass*100 / adult weight</p> <p>Relative mass of litter to adult: litter mass*100 / adult weight</p> <p>Gestation: the length of gestation period (in days), from fertilisation to birth. When mated during autumn or winter, the sperm (or fertilised egg in <em>M. schreibersii</em>) is stored throughout the winter. On arousal from hibernation in the spring, around mid March, female bats ovulate and gestation begins. In accordance with other researchers (e.g. Altringham 1996, Entwisle <em>et al</em>. 1998), 15 March was used as the start of the gestation period, for statistical reasons we also included <em>M. schreibersii</em>.</p> <p>Weaning: the length of the lactation period (in days) until offspring are fully independent. After the juveniles are capable of flight, mothers continue to give their young nourishment until they are fully independent. Only when no extra nourishment is provided are the offspring considered fully weaned.</p> <p>Reproductive period: gestation + weaning (in days).</p> <p>Average age at first reproduction: The age (in days) at which 75% of the female population becomes sexual mature. Many species reproduce just before or during their first winter (at approximately 80 days old), but in some species the majority of the population postpone their sexual development. Individuals are stated to have become sexual mature if they participate in mating, have been found to be pregnant or inseminated.</p> <p>Observed average age: observed average age of adults in a population at a given time (in years).</p> <p>Longevity: the age (in years) of the oldest observed individual. The longevity can only be obtained by marking and later recapturing individuals. Most recapture data are collected in summer roosts or hibernacula. As not all species show the same fidelity to summer roost sites or can be found in hibernacula that are accessible to humans, this measure is sensitive to the chance of recapture.</p> <p>Minimum hibernation temperature: the minimum temperature (degrees Celsius) at which each species is observed.</p> <p> </p> <p><em>Morphological adaptations</em></p> <p>Length of forearm at birth: the length of the forearm (mm) of a newborn bat, measured between the elbow to the wrist of a folded wing. This is widely accepted as a measurement of size. Although it is not the best reflection of the length of an individual, it can be measured rapidly and accurately under field conditions.</p> <p>Length of forearm adult: The length of the forearm (mm) of an adult bat.</p> <p>Relative length of forearm of a newborn to an adult: (length of the forearm at birth*100)/ Length of forearm adult.</p> <p>Wing span: the length of the wings (m). The distance between the wingtips of a bat with wings extended so the leading edge is straight (including body width).</p> <p>Wing area: The combined area of the two wings (m<sup>2</sup>) including the entire tail membrane and the portion of the body between the wings.</p> <p>Wing loading: the relation between body weight, wing size and gravity (Nm<sup>-2</sup>). This measurement is related to the mean pressure on the wings. Wing loading is the weight (mass, in kg, times gravitational acceleration) divided by the wing area, i.e Wing loading = (weight adult*9.81)/ wing area. The wing load can vary significantly between geometrically similar bats. Because of such allometry, large bats have a higher wing load than smaller bats.</p> <p>Wing aspect ratio: the square of the wingspan divided by the wing area, i.e. Wing aspect ratio = (wingspan)<sup>2</sup> / wing area. This ratio can be interpreted as a measure of the aerodynamic efficiency of flight. A higher aspect ratio usually corresponds with greater aerodynamic efficiency (i.e. a streamlined body) and lower energy use in flight.</p> <p>Flight speed: The speed of flight (m/s). The speed of flight is usually measured in wind tunnel experiments or during radio-tracking.</p> <p> </p> <p><em>Ecological adaptations </em></p> <p>Maximum migration distance: the maximum observed distance (km) between the summer and winter habitat. In contrast to birds, the direction of migration in bats is not determined by the change of the seasons, but by the locations of the hibernacula. This migration distance can only be obtained by capturing, marking and later recapturing individuals. Bats often migrate across national boundaries and gathering recapture data requires international cooperation. The chance of recapture is sensitive to sample effort and local observation methods.</p> <p>Average migration distance: the average distance (km) between the summer and winter habitat. Most species of bats migrate both short and long distances. The same restrictions described for maximum migration distance also apply to this parameter.</p> <p>Echolocation type: the predominant echolocation type used by each bat species. European bats use one or sometimes a combination of the following four types of echolocations: fm-CF-fm, fm-QCF (with the QCF part dominant), FM-qcf (with the FM part dominant) and FM. For statistical reasons both FM-qcf and FM are clustered in the group FM. The FM-qcf and fm-QCF echolocations are both often loud and used to detect distant prey. FM and fm-CF-fm echolocations are softer and bats using these types of echolocation receive more detailed knowledge of their surroundings. Bats primarily use only one type of echolocation, although many can make some slight adjustments to this.</p> <p>Echolocation range: the maximum distance that an echolocating bat can detect a structure or object.</p> <p>Echolocation minimum frequency: the minimum echolocation frequency (MHz) used by each bat species.</p> <p>Echolocation maximum frequency: the maximum echolocation frequency (MHz) used by each bat species.</p> <p>Duration call (ms): the average duration (in ms) of one complete call cycle.</p> <p> </p> <p><em>Distribution parameters</em></p> <p>Northern limit of range: the most northerly observation (in latitude) of each bat species. This measurement includes anecdotal observations and observations of male bats.</p> <p>Northern limit of reproduction range: the most northerly observation (in latitude) of a maternity group. Note: confusion is possible between summer roosts and maternity roosts. Summer roosts are often inhabited by both males and females and less than 70% of the adult females participate in reproduction. Maternity roosts are predominantly occupied by females, and more than 70% of the adult females participate in reproduction.</p> <p>Southern limit of range: the most southerly observation (in latitude) of each bat species. This measurement includes anecdotal observations and observations of male bats.</p> <p>Southern limit of reproduction range: the most southerly observation (in latitude) of a maternity group. The same restrictions described for northern limit of reproduction range also apply to this parameter.</p> <p>Western limit of range: the most western observation (in longitude) of each bat species</p> <p>Eastern limit of range: the most eastern observation (in longitude) of each bat species</p> <p>Night length: The average night length (in hours) during midsummer (21<sup>st</sup> June) at the northern limit of the reproduction range.</p> <p> </p> <p>Sources: 1. Jones et al. 2009, 2. Krapp 2011, 3. Schober & Grimmberger 1997, 4. Norberg & Rayner 1987, 5. Hutterer et al. 2005, 6. Dietz et al. 2009, 7. Supplementary data from Barclay et al. 2004, 8. Wilkinson & South 2002, 9 Jones & Rydell 1994, 10. Norberg 1986, 11. Jones 1994, 12. Baagøe 1987, 13.Fleming & Eby 2003, 14. Neuweiler 2000, 15. Hayssen et al. 1993, 16. Kunz & Kurta 1987, 17. Russo & Jones 2002, 18. Brunet-Rossinni & Austad 2004, 19. Aldridge 1987, 20. Urbańczyk 1991, 21. Nagel & Nagel 1991, 22. Masing & Lutsar 2007, 23. Masing 1983, 24. Gaisler 1970, 25. Norberg 1987, 26. Baydemür & Albayrak 2006, 27. Dietz et al. 2006, 28. Sharifi 2004, 29. Kerth et al. 2001, 30. Schmidt 2005, 31. Smirnov et al. 2008, 32. Verbeek 1998, 33. Pandurkska & Beshkov 1998, 34. Harmata 1969, 35. Sachanowicz & Zub 2002, 36. Arlettaz et al. 2001, 36. Ibáñez et al. 2001, 37. Estók 2007, 38. Lohrl 1936, 39. Kunz & Hood 2000, 40. Happold & Happold 1990, 41. Rydell 1990, 42. Reiter 2004, 43. Ransome 1990, 44. Zahn 1999, 45. Deanesly & Warwick 1939, 46. Racey 1969, 47. Racey & Swift 1981, 48. Racey 1974, 49. Masing 1982, 50. Boyd & Stebbings 1989, 51. Lesiñski 1986, 52. Barak & Yom-tov 1991, 53. Arlettaz et al. 2000, 54. Gaisler et al. 1997, 55, Heise 1989, 56. Papadatou et al. 2009, 57. Unpublished data: own measurements.</p> <p> </p>
Data from: Male long-distance migrant turned sedentary; The West European pond bat (Myotis dasycneme) alters their migration and hibernation behaviour
<p>Winter survey data, temperature data and mark recapture data of <em>Myotis dasycneme</em>. This study aimed to better understand the migration, mating and hibernation choices of the pond bat.</p> <p> </p> <p>The study area covered the whole of the Netherlands, Belgium and East Frisia (northwest Germany). We defined two study periods, data collected between 1930 and 1980 (Sluiter and van Heerdt) and data between 1980 and 2015 (Haarsma). All available mark and recovery data (ringing) of both the historical and recent migration research were digitized. Observations include location and date of capture, species, sex and ring number. The latest observations in the recent dataset (Haarsma) also include biometric measurements (forearm length, body mass) and information about age and reproductive status. These biometric measurements show that male pond bats are on average smaller and lighter than females (body mass (g)/ forearm length (mm) females: 18.9/47.1, males: 16.4/46.4). The dataset shows changes in the fat mass of both sexes during a year.</p> <p>This study also compares migration data with winter monitoring survey data. We selected winter roosts with three or more records of three or more pond bats in one or both of the study periods. Only data from sites with long-term data series (from the hibernacula in the Dutch provinces of Zuid-Holland, Gelderland and Limburg) were used to analyse trends and annual abundance. Our selection included 59 limestone mines in the province of Limburg and 16 WOII bunkers in Gelderland and 38 in Zuid-Holland. We divided the sites into 'core' and 'satellite' sites depending on the timing of first colonization.</p> <p> </p> <p><strong>Bunker limestone mine microclimate</strong></p> <p> </p> <p>Radiation temperature: radiation temperature of the wall, measured with a non-contact infrared thermometer</p> <p>How many bats: the group size of each bat/ group of bats observed, categorized as alone and group.</p> <p>Where: the hanging location of the observed bat, categorized as hidden (in crevice) or free (free on ceiling or wall)</p> <p>Date: date of the observation</p> <p>Xy-coord: The coordinates of the entrance of the bunker or limestone mine. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p>Type: Bunker or limestone</p> <p>Location description: description of the name of the site</p> <p> </p> <p><strong>Bunker monitoring core and satellite</strong></p> <p> </p> <p>Date: date</p> <p>Winter: the period between September and April is defined as the winter of the year starting in January.</p> <p>Location description: description of the name of the site</p> <p>N of pond bats: total number of observed pond bats</p> <p>Province: the province</p> <p>Type: hibernacula categorized as a core or satellite site, sites occupied by pond bats since 1977 and 1997 respectively.</p> <p>XY-coord: The coordinates of the entrance of the bunker or limestone mine. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p> </p> <p> </p> <p><strong>Supporting information (as referenced in the published paper, hence also available with plos one)</strong></p> <p><br> <strong>S1 Fig. The range of the West European pond bat population (TIF).</strong> The shaded areas indicate the<br> areas where the bulk of the surveys were carried out.</p> <p><br> <strong>S2 Fig. The distribution of the pond bat in Europe (country boundaries are only indicative) (JPG).</strong> Within the whole range of the species distribution seven groups can be separated.<br> A The Netherlands, Belgium and Northwest Germany (~the West European population),<br> B Jutland Peninsula,<br> C Central European lakelands,<br> D The Baltic States,<br> E Ural Mountains (hibernacula),<br> F Volga Valley (summer nurseries),<br> G Hungary and Romania.<br> <br> <strong>S3 Fig. The distribution of hibernacula used by the western pond bat population (TIF). </strong>These are<br> sites with three or more records of pond bats in one or both study periods. We identified four<br> roost categories: Roosts which have been used ever since 1900 (= green squares), roosts used<br> only between 1900–1980 (= open black squares), roosts occupied after 1980 (= purple circles),<br> roosts occupied after 1997 (= blue asterisks). Detailed maps, all with the same enlargement, of<br> the clusters in the provinces of Zuid-Holland (1), Gelderland (1) and Limburg (3) are provided.<br> <br> </p> <p><strong>S1 Table. Summary of the average weight of pond bats over the study period.</strong> The weight is averaged per week. The table gives average weight of females, males both adults and juveniles.</p> <p> </p> <p>Avg weight: average weight of pond bats of each sex, in a certain week</p> <p>Sex: male of female</p> <p>Week number: number of the week</p> <p>Age: juvenile (or young of the year). Defined as the from birth until the onset of first hibernation. Subadult or sexual immature, defined as individuals with no signs of (past) reproductive activity. Adult or sexual mature, defined as all individuals with signs of (previous) reproductive activity.</p> <p>N observations: number of observations within each subset.<br> </p> <p><strong>S2 Table. Mark and recapture data from the historical dataset.</strong><br> </p> <p>Ringnumber: the label of the ring</p> <p> Sex: male or female</p> <p>capture date: date of capture</p> <p>capture location: description of capture location</p> <p>x y coordinate: The coordinates of the capture location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p>recapture date: date of recapture</p> <p>recapture location: description of recapture location</p> <p>x y coordinate: The coordinates of the recapture location in RD. The RD (Rijks-Driehoek) system is the coordinate system used by the Dutch geographical service.</p> <p> </p> <p><strong>S3 Table. Mark and recapture data from the recent dataset.</strong></p> <p> </p> <p>Same dataset as the historical set, but now including age (see definition used in S1)<br> <br> </p>
Historical Annual Revenue of Energy Storage on European Electricity Markets
<p>This dataset provides the optimized annual revenue for 96 generic storage technologies (12 efficiency x 8 discharge duration). It covers 17 European electricity markets for up to 16 years (Austria, AT; Belgium, BE; Switzerland, CH; Czech Republic, CZ; German, DE; Spain, ES; France, FR; Italy, IT; Ireland, IR; Netherlands, NL; Nordpool which includes Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden, NP; Poland, PL; Portugal, PT; Romania, RO; Slovakia, SK; United Kingdom, UK). The optimization is an adaptation of the model presented in Gaudard et al. [2013]. It assumes perfect foresight assumption and a stochastic algorithm. Therefore, the results are an approximation of the maximum rather than the absolute optimum. Further information is provided in "Gaudard L. and Madani K., Energy storage race: Has the monopoly of pumped-storage in Europe come to an end?, forthcoming". </p> <p>The following information is provided:</p> <p>Country: Code of the specific market (also the filename)</p> <p>Currency: The currency in which the results are expressed</p> <p>Discharge duration [hours]: The time required to empty at full nominal power a device that is fully charged. </p> <p>Efficiency: Ratio between the amount of discharged and charged energy during a full cycle.</p> <p>Year: From January 1st to December 31st.</p> <p>The given numbers are in euros or GBP per year and normalized to 1kWh of energy storage. This means that for a specific device, the given figures must be multiplied by the volume of energy storage (in terms of kWh). As an example, for an energy device with the efficiency of 0.95, discharge duration of 6h and volume of energy storage of 2000kWh, the revenues in 2003 in Austria would be 9.26 x 2000=18520 euros. </p> <p>For any questions or further requirements, please feel free to get in touch with the authors.</p>
Simulated coherent diffraction from 2NIP (SPB-SFX instrument, 3 fs, 4.96 keV European XFEL pulses)
<p>Simulated diffraction from 2NIP</p> <p>Input: https://dx.doi.org/10.5281/zenodo.886061 (photon-matter interaction)</p> <p>Simulation code: singFEL</p>
TNO-CAMS European CO2 emissions 2000-2014 v1
<p><strong>Introduction</strong></p> <p>This TNO_CAMS_CO2 emission dataset was prepared by TNO as a contribution to the H2020 project MACC-III and the subsequent Copernicus Atmospheric Monitoring Service. This model-ready historic emission inventory at high spatial resolution (~7x7 km) for UNECE-Europe for 15 consecutive years (2000–2014) providing CO<sub>2</sub> from fossil fuels and CO<sub>2</sub> from biofuels is intended to support modelling and sub-national scale identification of emissions. Where available and considered fit for purpose, we have used CO<sub>2</sub> estimates as reported by the Parties to UNFCCC. The data have been supplemented by other estimates, most notable from the IIASA GAINS model and the JRC EDGAR database to create a complete coverage. The approach to the spatial distribution of the dataset is similar to the TNO-MACC emission dataset for air pollutants ( see Kuenen et al., ACP, 2014).</p> <p>The emission grid consists of UNECE-Europe in WGS84 projection (lon-lat) with a spatial resolution of 1/8 x 1/16 degrees (lon x lat). The lower left of the grid is at lon = -60, lat = 30 and the upper right is at lon = 60, lat = 72.</p> <p>The grid files TXT (.csv) & netcdf (.nc) both contain annual total emissions per grid cell for the year 2000-2014. A separate file has been prepared for each year. </p> <p>The unit in the .csv files is Mg/gridcell/yr</p> <p>The unit in the .nc files is kg/gridcell/yr</p> <p>Sectoral breakdown uses the SNAP classification. Compared to the default SNAP1 sectors (1 to 10), a couple of refinements have been made to the sectors:</p> <ul> <li> <p>SNAP 3 and SNAP 4 are grouped as SNAP 34</p> </li> <li> <p>SNAP 7 is split in SNAP 71 to 75</p> </li> </ul> <p>The dataset is described in </p> <p>Denier van der Gon, H.A.C., J.J.P. Kuenen, G. Janssens-Maenhout, U. Döring, S. Jonkers, A.J.H. Visschedijk., TNO_CAMS high resolution European emission inventory for anthropogenic CO<sub>2</sub> for 2000-2014 and future years following two different pathways, ESSD, in preparation, 2017.</p> <p> </p>
Polidoc.net CODEBOOK: National and Regional Manifestos and other Political Documents Collected for the Research Projects "Representation in Europe: Congruence between Preferences of Elites and Voters" (REPCONG) and "The Impact of EU Cohesion Policy on European Identification" (COHESIFY)
<p>The Political Documents Archive http://www.polidoc.net/ contains election manifestos, coalition agreements, government declarations and various other documents of political actors from developed democracies. Currently, the archive builds on a stock of more than 3000 political documents from 20 European countries. The aim of the repository is to provide political texts in order to facilitate scholarly research in different areas of comparative politics such as party competition, coalition politics, legislative decision-making or electoral behavior.</p> <p>National electoral manifestos have been collected in the course of the REPCONG project ("Representation in Europe: Policy Congruence between Citizens and Elites"), and the archive includes party manifestos for regional elections in several European democracies. Because the process of European integration resulted in a strengthening of regions in EU member states and in countries that want to join the European Union, the relevance of the regional level for political decision-making has increased during the last decades. Therefore, also the policy profiles of regional parties are required to get a full picture of democratic responsiveness in European states across all levels of the political system. The collection of regional manifestos was supported by the COHESIFY project (www.cohesify.eu), funded under the Horizon 2020 Framework Programme for Research and Innovation. The aim of COHESIFY is to study whether the European Structural and Investment Funds affect people’s support for and identification with the European project.</p> <p>The archive is freely accessible (after a simple registration) and meant to foster rigorous research in these areas by enabling scholars to produce valid and reliable findings from empirical studies of textual data rather than unnecessarily struggling to obtain and process texts.</p>
Interactive map of distribution of gene fragments indicative of cyanotoxin biosynthesis and cyanotoxins in the European Alps
<p><span>Distribution of cyanotoxins and cyanotoxin biosynthesis genes in Alpine region determined by LC-MS/MS and (q)PCR. Cyanotoxins and cyanotoxin genes are mapped on separate layers, and two basemaps are available (simple and relief). Results can be filtered by location, sample type, water body type, cyanotoxins and cyanotoxin genes. Note that cyanotoxin analyses were not performed on all sampling points.</span></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.