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540 results for “Denmark”
FULFILL dataset - diet policy acceptability - efficacy and acceptability framing Denmark
<p>This dataset represents survey data on sufficiency-oriented policy acceptability in regard to dietary consumption. The study was part of the second round surveys in Denmark in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from two countries: Denmark and Germany, with representative sampling (age, income, gender, current region). In this survey on the acceptability of sufficiency-oriented diet policies we recruited a representative sample with approximately 800 participants from Denmark and Germany, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The central part of the survey includes a framing experiment including three groups with participants being randomly assigned to. We were interested in peoples' acceptability on three majorly discussed and sufficiency-relevant policies, i.e. meat tax, carbon label or meat-free day at public canteens. We investigated if an information on either the efficacy of the measures or a combination of information with acceptance information or none of these information could influence people's acceptability (overall, self vs. others perspective). We measured several control variables (socio-economics such as age, gender, income, education, household size, life stage, ideological measures such as political orientation or attitudinal measures such as sufficiency orientation and climate change denial). A quantitative assessment of the carbon footprint in the food consumption domain was also included.</p>
FULFILL dataset round 1 Denmark
<p>This dataset and codebook correspond to the initial round of survey data gathered in Denmark in 2022, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. </p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. In the first round of the survey, we recruited a representative sample of approximately 2000 households in each country, taking into account both the individual and household perspectives. The survey includes a quantitative assessment of the carbon footprint in various domains of life, such as housing, mobility, and diet. In addition to this, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort.</p>
FULFILL dataset - housing policy acceptability - framing experiment Denmark
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Denmark in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
Potential forest conservation value rasters for Denmark from Assmann et al. "LiDAR data fusion and machine learning identify temperate forests of high conservation value"
<p>Potential forest conservation value (high / low) rasters for Denmark based on a remote sensing data fusion approach. Please see manuscript (below) for a detailed description of the methods and data products. </p> <p><br>Jakob J. Assmann, Pil B. M. Pedersen, Jesper E. Moeslund, Cornelius Senf, Urs A. Treier, Derek Corcoran, Zsófia Koma, Thomas Nord-Larsen, Signe Normand. In prep. LiDAR data fusion and machine learning identify temperate forests of high conservation value.</p> <p><br>When using the data, please cite the above manuscript. </p> <p><br>Files description:</p> <ul> <li>Compressed and cloud optimised rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:3857 <ul> <li>forest_quality_ranger_biowide_10m_cog_epsg3857.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m_cog_epsg3857.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m_cog_epsg3857.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m_cog_epsg3857.tif GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul> <p> </p> <ul> <li>Aggregated rasters of potential forest conservation value projections for Denmark (100 m res.) in EPSG:25832 <ul> <li>forest_quality_ranger_biowide_100m.tif RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_100m.tif RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_100m.tif GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_100m.tif GBM model projections based on SustainScapes stratification </li> </ul> </li> </ul> <p> </p> <ul> <li>Uncompressed and tiled rasters of potential forest conservation value projections for Denmark (10 m res.) in EPSG:25832<br>Please note: the archives contain approx. 42k tiles, each 10 x 10 km, as well as a VRT file for covenient loading. <ul> <li>forest_quality_ranger_biowide_10m.zip RandomForest model projections based on BIOWIDE stratification (!! best performing model !!)</li> <li>forest_quality_ranger_sustainscapes_10m.zip RandomForest model projections based on SustainScapes stratification</li> <li>forest_quality_gbm_biowide_10m.zip GBM model projections based on BIOWIDE stratification</li> <li>forest_quality_gbm_sustainscapes_10m.zip GBM model projections based on SustainScapes stratification</li> </ul> </li> </ul>
Dataset - paper: Parental feeding practices and parental involvement in child feeding in Denmark: gender differences and predictors
<p>Dataset corresponding to a paper that has been accepted for publication in Appetite (Philippe, K., Chabanet, C., Issanchou, S., Grønhøj, A., Aschemann-Witzel, J., & Monnery-Patris, S. (2022, in press). <em>Parental feeding practices and parental involvement in child feeding in Denmark: gender differences and predictors</em>. Appetite).</p> <p>The objectives of the study were (1) to examine possible differences between Danish mothers and fathers with regard to their involvement in child feeding and their feeding practices, and (2) to identify possible parent-related predictors of parental feeding practices and of parental involvement in child feeding at home.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-Denmark.docx".</p>
High resolution land cover 2015 Aarhus, Denmark
<p><strong>Description</strong></p> <p>This dataset provides land-cover and land-use information at a 20cm resolution for the municipality of Aarhus, Denmark. It depicts the status for the year 2015, containing 23 thematic classes.</p> <p><strong>Spatial reference</strong><br> All data is projected in ETRS 1989 UTM Zone 32N (EPSG:25832)</p> <p><strong>Related publication</strong><br> J. M. Knopp, G. Levin and E. Banzhaf, "Aerial Data Analysis for Integration Into a Green Cadastre—An Example From Aarhus, Denmark," in <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, vol. 16, pp. 6545-6555, 2023, doi: <a href="https://ieeexplore.ieee.org/document/10168752">10.1109/JSTARS.2023.3289218</a>.</p> <p><strong>Class Codec</strong></p> <table> <tbody> <tr> <td> <p><strong>Class</strong></p> </td> <td> <p><strong>Vector </strong></p> <p><strong>NumCodec</strong></p> <p><strong>(16bit)</strong></p> </td> <td> <p><strong>Raster</strong></p> <p><strong>NumCodec</strong></p> <p><strong>(8bit)</strong></p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Building</p> </td> <td> <p>100</p> </td> <td> <p>10</p> </td> </tr> <tr> <td> <p>0 Lowest rise building</p> </td> <td> <p>110</p> </td> <td> <p>11</p> </td> </tr> <tr> <td> <p>1 Low rise building</p> </td> <td> <p>120</p> </td> <td> <p>12</p> </td> </tr> <tr> <td> <p>2 Mid rise building</p> </td> <td> <p>130</p> </td> <td> <p>13</p> </td> </tr> <tr> <td> <p>3 High rise building</p> </td> <td> <p>140</p> </td> <td> <p>14</p> </td> </tr> <tr> <td> <p>4 Highest rise building</p> </td> <td> <p>150</p> </td> <td> <p>15</p> </td> </tr> <tr> <td> <p>Mineral surface</p> </td> <td> <p>210</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p>Bare soil</p> </td> <td> <p>220</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>Artificial grass</p> </td> <td> <p>230</p> </td> <td> <p>23</p> </td> </tr> <tr> <td> <p>Grass</p> </td> <td> <p>310</p> </td> <td> <p>31</p> </td> </tr> <tr> <td> <p>Shrub round</p> </td> <td> <p>410</p> </td> <td> <p>41</p> </td> </tr> <tr> <td> <p>Shrub linear</p> </td> <td> <p>420</p> </td> <td> <p>42</p> </td> </tr> <tr> <td> <p>Evergreen</p> </td> <td> <p>510</p> </td> <td> <p>51</p> </td> </tr> <tr> <td> <p>Deciduous</p> </td> <td> <p>520</p> </td> <td> <p>52</p> </td> </tr> <tr> <td> <p>Lake</p> </td> <td> <p>610</p> </td> <td> <p>61</p> </td> </tr> <tr> <td> <p>River</p> </td> <td> <p>620</p> </td> <td> <p>62</p> </td> </tr> <tr> <td> <p>Sea</p> </td> <td> <p>630</p> </td> <td> <p>63</p> </td> </tr> <tr> <td> <p>Undergrowth</p> </td> <td> <p>710</p> </td> <td> <p>71</p> </td> </tr> <tr> <td> <p>Agriculture, intensive temporary crops</p> </td> <td> <p>810</p> </td> <td> <p>81</p> </td> </tr> <tr> <td> <p>Agriculture, intensive permanent crops</p> </td> <td> <p>820</p> </td> <td> <p>82</p> </td> </tr> <tr> <td> <p>Agriculture, extensive</p> </td> <td> <p>830</p> </td> <td> <p>83</p> </td> </tr> <tr> <td> <p>unclassified</p> </td> <td> <p>999</p> </td> <td> <p>99</p> </td> </tr> <tr> <td> <p>NonAOI</p> </td> <td> <p>999</p> </td> <td> <p>99</p> </td> </tr> </tbody> </table>
NACLIM - Fluxes: Denmark Strait overflow transport
<p><strong>Description: </strong>Daily average of overflow transport on Denmark Strait </p> <p><strong>Period: </strong>September 1996 – September 2014 </p> <p><strong>Location:</strong> 66° N 28° W (map) </p> <p><strong>Instruments: </strong>Moored ADCPs </p> <p><strong>Variables:</strong> Overflow volume transport. </p> <p><strong>Updated on</strong>: 18 December, 2014</p>
Lemming Mesocosm (Denmark): in-situ fluorescence chlorophyll-a calibration, underlying data
<p>These data files include 2 years (2018-2020) of high-frequency in-situ chlorophyll-a and phycocyanin fluorescence sensor (Turner Designs, Cyclops 7F) data, together with data from various tests conducted with these sensors. Morever, in-vitro chlorophyll-a data for 2018-2020 period is also included.</p> <p>These data are collected in Lemming mesocosm site, Denmark, where there are 24 tanks with 2 nutrient and 3 temperature treatments (2x3 factorial design). There is data from 24 in-situ chlorophyll-a and 12 in-situ phycocyanin fluorescence sensors.</p> <p>In this Dataset folder, there are:</p> <ul> <li>one <strong>Metadata </strong>(*.xlsx)<strong> </strong>file with 3 sheets; <ul> <li>"<em>Descriptive</em>": comprises information on location, authors, study period and the main instrument used, </li> <li>"<em>Structural</em>": includes detailed information on each dataset.</li> <li>"<em>Relational</em>": includes a figure showing the relations between the datasets</li> </ul> </li> <li>thirteen files (*.csv) in LemCP_DataORE that were used to; <ul> <li>conduct in-situ fluorescence sensor tests (i.e. blank variation, linearity check, DOC effect check), </li> <li>calibrate 24 in-situ fluorescence chlorophyll-a sensors</li> <li>plot various figures</li> </ul> </li> </ul>
Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Denmark
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_DK: Danish Veterinary and Food Administration (DVFA)</li> <li>TSE_2022_DK: Danish Veterinary and Food Administration (DVFA)</li> <li>TSE_2021_DK: Danish Veterinary and Food Administration (DVFA)</li> <li>TSE_2020_DK: Danish Veterinary and Food Administration (DVFA)</li> <li>TSE_2019_DK: Danish Veterinary and Food Administration (DVFA)</li> </ul>
Lemming Mesocosm (Denmark): in-situ fluorescence chlorophyll-a calibration, extended data
<p>These files include a scheme showing the sensor and tank (mesocosm) system, plots from high frequency in-situ chlorophyll-a and phycocyanin fluorescence data and in-vitro chlorophyll-a data collected in Lemming mesocosm site, Denmark, where there are 24 tanks with 2 nutrient and 3 temperature treatments (2x3 factorial design). These plots are based on 24 in-situ chlorophyll-a and 12 in-situ phycocyanin fluorescence sensors from 24 tanks/mesocosms. There is also a table showing the steps for cleaning the high-frequency data.</p>
Estimated prevalence of chronic hepatitis B in Denmark on December 31, 2016 – an update based on nationwide registers
<p>Anonymised dataset analysed in the study "Estimated prevalence of chronic hepatitis B in Denmark on December 31, 2016 – an update based on nationwide registers". </p>
Cereal aphids monitored in 2,110 fields in Denmark 2002-2019
<p>The aphids were accounted for visually as the percentage of tillers infested. The type of crop (winter wheat or spring barley) and its growth stage at the time of aphid assessment are included in the data set.</p> <p>The data is provided as both a tab-separated text file and a binary R data file. The R files provides code to read and plot the data. The two plots produced are also provided as PNG files.</p> <p>The data were collected by SEGES Innovation, Denmark.</p>
Trap catches of carrot fly (Psila rosae) and cutworm (Agrotis ipsilon) from 142 fields in Denmark and Southern Sweden 1997-2019
<p>The pests were caught in various vegetable crops in Denmark and Southern Sweden: Yellow sticky traps for carrot flies and pheromone traps for cutworm adults.</p> <p>The data is provided as both a tab-separated text file and a binary R data file. The R files provides code to read and plot the data. The two plots produced are also provided as PNG files.</p> <p>The data were collected by SEGES Innovation, Denmark.</p>
CoMix social contact data (Denmark)
<p>CoMix social contact data for Austria.</p> <p>We gratefully acknowledge the efforts of all teams involved in the implementation of the CoMix study in their country. More specifically: the team of Michael Bang Petersen at Aarhus University.</p>
AI results complementing the 2021 Annual Report on surveillance for Avian Influenza in poultry and wild birds in Member States of the European Union - Denmark
<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>
The BEV*ARV Project; the Preservation Conditions of Museum Collection Storages in Denmark.
<p>A national survey on the preservation condition in Danish state subsidised museums’ storages was conducted in 2022-23. The collected data has been anonymized and is open for further study and research.</p> <p>The survey consisted of 25 questions (<em>BEV.ARV_Spørgeskema</em>) responded during physical inspections of the storages. 103 museums participated in the survey, 350 buildings and more than 850 storage rooms were physically inspected, and the results recorded. Upon inspection, the preservation/degradation risks addressed in each question were rated according to an A-B-C-D scale. Character A is the best (no degradation risk), D is the lowest (high degradation risk). A guideline (<em>BEV.ARV_Svarvejledning</em>) was used to assist uniform evaluations of the storage conditions.</p> <p>The survey covered museums with art collections, cultural history collections and natural history collections. The indoor climate over one calendar year was recorded in around five hundred of the storage rooms.</p> <p>The collected and uploaded data contain information on the condition of the buildings used for storages, the condition of the storage rooms and the objects stored therein, and how the museums manage and control their collection storage rooms. The survey method has been developed for future inspections and comparative reports of the storage conditions of museum collections.</p> <p>The files included in the datasets have been used in the report to the Ministry of Culture. Furthermore, the data has been applied for making individual museum storage reports with scores and characters for each storage facility. The data is available in Danish only.</p> <p>Content of the folder <strong>Klimadata</strong>:</p> <ul> <li>The file BEV.ARV_2024.04_Dataoversigt provides information regarding type of museum and whether climate data from the storages have been collected or not. </li> <li>The Excel files (<em>M00X-files</em>), one per museum, provides the climate data (Relative Humidity, RH % and Temperature, T °C) from the individual storages, naming corresponding to those applied in the file BEV.ARV_2024.04_Anonymiseret_Raadata.csv</li> </ul> <p>Content of the folder<strong> Rapporter_Supp.Info</strong>:</p> <ul> <li>The file BEV.ARV_2024.04_Anonymiseret_Raadata.csv holds the complete inspection results for all museum storages (the buildings and their rooms).</li> <li>The file BEV.ARV_2024.04_Karaktermodel+Analyse contains analyses carried out and used in the overall report to the Ministry of Culture.</li> <li>For completeness, the report to the Ministry of Culture<em> (BEV.ARV_Slutrapport)</em>, an example of an individual museum storage report ( BEV.ARV_<em>Magasinrapport_Museum_X</em>) the 25 survey questions (<em>BEV.ARV_Spoergeskema</em>) and the response guidelines (<em>BEV.ARV_Svarvejledning</em>), all in Danish, are included. </li> </ul>
Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungary, Switzerland
<p><strong>Anonymised transcriptions (local and translated versions) of 18 Focus Groups with RWPP voters in Spain, UK, Denmark, Germany, Hungay, Switzerland</strong></p> <p>In the UNTWIST project, we have carried out a total of 18 focus groups in Denmark, Germany, Hungary, Spain, Switzerland and the United Kingdom. They explore RWPP voters’ subjective perceptions of their needs and demands, their horizon of expectations, and their level of ‘gender fatigue’. Groups’ design followed two minimum criteria: same-sex composition (with a minimum of two same sex -male and female- groups per country) and voting behaviour (current voters of RWPP who have previously voted for mainstream parties or abstained or have doubts about RWPP and mainstream or abstain in case of voting for the first time).</p> <p> The composition of the groups varied between 6 and 10 participants per group in all but one partner’s country. In Denmark, all focus groups experienced dropouts. These unforeseen issues led to conducting the focus groups with fewer participants than was initially designed.</p> <p> In 83% of countries, the empirical composition of focus groups was considered and controlled for participants’ age, social class position and level of education.</p> <p>Finally, groups were same-sex moderated.</p> <p>Two comprised folders are provided. One contains the anonymised transcriptions of 18 Focus Groups carried out for WP2 of the UNTWIST project in their local languages. The other contains the IA-translated (Deepl) version of the same focus groups. Please note that the translations have not been human-supervised. </p> <p>FG_CHE_1 Female <br>Female Group, Switzerland</p> <p>FG_CHE_2 Male<br>Male Group, Switzerland </p> <p>FG_DEN_1 Female <br>Female Groups, Denmakr</p> <p>FG_DEN_2 Male<br>Male Group, Denmark </p> <p>FG_DEN_3 Male <br>Male Group, Denmark</p> <p>FG_DEN_4 Mixed <br>Mix Male and Female Group, Switzerland</p> <p>FG_ESP_1 Male<br>Male Group, Spain</p> <p>FG_ESP_2 Male<br>Male Group, Spain </p> <p>FG_ESP_3 Female <br>Female Group, Spain</p> <p>FG_ESP_4 Female<br>Female Group, Spain</p> <p>FG_GBR_1 Female <br>Female Group, UK</p> <p>FG_GBR_2 Male <br>Male Group, UK</p> <p>FG_GER_1 Female <br>Female Group, Germany</p> <p>FG_GER_2 Male<br>Male Group, Germany</p> <p>FG_HUN_1 Female <br>Female Group, Hungary</p> <p>FG_HUN_2 Female <br>Female Group, Hungary</p> <p>FG_HUN_3 Male<br>Male Group, Hungary </p> <p>FG_HUN_4 Male<br>Male Group, Hungary </p>
ENERGISE Living Lab country report - Denmark
<p>The Danish ELLs were conducted in Roskilde, where ELL1 was situated in Viby Sjælland, and ELL2 was situated in Trekroner. In ELL1 18 participants were involved, and in ELL2 20 participants were involved. In ELL1, participants mainly lived in detached, privately owned houses, where as participants in ELL2 primarily lived in privately owned terraced hoses. The buildings in ELL1 are older than the buildings in ELL2, and the houses in ELL2 are slightly smaller than the houses in ELL1. There is a mix of household sizes and compositions in each ELL, where the average age of participants in ELL1 is slightly older than the average age of ELL2 participants. ELL1 can be considered a community of place, whereas ELL2 can be considered a community of interest, as ELL2 participants consider themselves to community-builders and to be slightly greener than the average population. This is, however, not necessarily so, as this report will also demonstrate.</p>
National Checklists 2017: Denmark Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Denmark collected using effechecka and geonames polygons
National Checklists 2019: Denmark Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Denmark collected using effechecka and geonames polygons
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.