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1,751 results for “futures”
Historical and future irrigation water demand for the STARS4Water river basins
<p>Dataset contains data on historical and future irrigation water demand for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average summer net irrigation requirement [mm/year] for each combination GCM model (5 models)/time window (2 windows) was calculated within the boundaries of the project river basin hubs. The difference between the future and historical period was also calculated for each GCM. In addition, ensemble mean values for both horizons and ensemble mean differences were calculated. This dataset was prepared based on the data available in the "Net irrigation requirement under different climate scenarios using AquaCrop over Europe" repository (Busschaert et al., 2022, DOI: 10.5281/zendo.6760976).</p>
Historical and future land use and land cover data for the STARS4Water river basins
<p>Dataset contains data on historical and future land use and land cover for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average area fraction of five general land use classes (crop, forest, grass, urban and other) within the project river basins was calculated at five-year intervals starting in 2016 and ending in 2051. This dataset was prepared based on the data available in the "LUCAS LUC future land use and land cover change dataset for Europe (Version 1.1)" repository (Hoffmann et al., 2022, DOI: 10.26050/WDCC/LUC_future_EU_v1.1).</p>
Survey Data on Current Open Access Terms and Future Trends (2024)
<p><strong>Description:</strong><br>This dataset contains the analysis, codebook, and raw survey data from the 2024 survey <em>"Open Access – Current Terms and Future Areas of Focus"</em>. The survey aimed to gather perspectives from Open Access experts in the German-speaking region, focusing on the evaluation of current Open Access terminology, concepts, and emerging trends.</p> <p>The survey highlights how Open Access terminology has evolved over the past two decades and explores current perceptions regarding key terms in the Open Access discourse, as well as the anticipated future developments in this field. A total of 131 complete responses (<em>N=131</em>) were collected, providing valuable insights into the views of professionals working in Open Access publishing, information infrastructures, and scientific publishing houses.</p> <p><strong>Contents:</strong></p> <ol> <li><strong>codebook_oa_2024_2024-11-21.xlsx</strong>: The codebook, including detailed explanations of the variables, codes, and definitions used in the survey.</li> <li><strong>survey_results_oa_2024_2024-11-21.xlsx</strong>: Anonymized raw data from the survey, including both quantitative and qualitative responses from the participants.</li> <li><strong>values_oa_2024_2024-11-21.csv</strong>: CSV file containing the key terms and concepts identified by participants in response to the question on Open Access terminology.</li> <li><strong>values_oa_2024_2024-11-21.csv</strong>: An additional CSV file with detailed classification and analysis of the terms related to Open Access, including their frequency and significance based on participant responses.</li> </ol> <p><strong>Methodology:</strong><br>The survey was conducted via an online questionnaire distributed from September 7 to October 15, 2024, to professionals working in Open Access, both within information infrastructures (e.g., libraries) and in academic publishing houses. The survey gathered both qualitative and quantitative data, focusing on how Open Access terminology is understood and its future developments. The data were cleaned, anonymized, and analyzed using appropriate statistical and content analysis methods.</p> <p><strong>Purpose and Use:</strong><br>This dataset is valuable for researchers and professionals studying Open Access terminology, trends, and future developments. It provides insights into the current understanding of Open Access within the academic community and can be used for comparative studies, policy analysis, and future Open Access research.</p>
Historical and future water demand for households and industry for the STARS4Water river basins
<pre>This repository contains the data related to the deliverable D2.5 "Data sets on scenario narratives" prepared within the STARS4Water project ("Supporting STakeholders for Adaptive, Resilient and Sustainable Water Management").</pre> <p>The data spans historical years (2000-2020) and projections under different Shared Socioeconomic Pathways (SSP1-5) scenarios for the years 2020-2050.</p> <p>The repository contains historical and future water demand for households and industry for the STARS4Water river basins divided into two items packed in zip file:<br>1. STARS4Water_Domestic_and_Industrial_Water_Demands_historical.zip for years 2000-2020<br>2. STARS4Water_Domestic_and_Industrial_Water_Demands_projections.zip for years 2020-2050 (SSP1-SSP5)<br><br>The data in the repository was prepared based on Python scripts developed by Stephanie E. Lips and described in <em>Towards a global high </em><em>resolution water demand dataset. Effect of data quality and downscaling techniques - the case for Europe</em>, Utrecht University, 2020 as well as open source databases of WorldPop, WorldBank, UNCTADstat, EIA, Eurostat, Aquastat, UNEP an others. </p>
Current and future European potential vegetation types
<p>This dataset contains Potential Natural Vegetation (PNV) estimates for the European continent at 1km grain size. Estimates are made for six different vegetation types following the MAES Ecosystem classification at level 1. The predictions have been made through an ensemble of Bayesian Habitat distribution models available through the <em>ibis.iSDM</em> package <a href="https://doi.org/10.1016/j.ecoinf.2023.102127" target="_blank" rel="noopener">(Jung 2023)</a>. For more information on the methodology, original data and used covariates, please see the accompanying preprint (<a href="https://doi.org/10.31223/X59H71">Jung 2024</a>).<br><br><strong>Uploaded are:</strong></p> <ul> <li>The most likely current PNV transition (see screenshot) as categorical raster (and screenshot, see png)<br>(Classes: 1=Woodland.and.forest | 2=Heathland.and.shrub | 3=Grassland | 4=Sparsely.vegetated.areas | 5=Wetlands | 6=Marine.inlets.and.transitional.waters)</li> <li>Current PNV estimates as cloud-optimized geoTIFF ("COG") files (.tif)</li> <li>Future PNV estimates (zipped) for each considered SSP - GCM combination as geoTIFF (.tif).</li> </ul> <p><strong>Variable naming scheme:</strong><br>Current: "pnv_XX_laea_1km.tif"<br>where XX represents the vegetation type<br>Future: Here the hierachical organization scheme of Essential Biodiversity Variables (EBV) is followed where files are separated in folders by<br>Scenario | metric | entity | time, so for example "SSP126-GFDL-ESM4/suitability_mean/grassland/"<br>Filenames are labelled by the date (e.g. "2040.tif").<br><br><strong>Metrics and layers names and their interpretation:</strong><br>For current:<br>"mean" = Average Ensemble posterior prediction<br>"sd" = Standard deviation of posterior prediction<br>"q05" = Lower percentile (5%) of posterior prediction<br>"q50" = Median or 50% percentile of posterior prediction<br>"q95" = Upper percentile (95%) of posterior prediction<br>"mode" = Most commonly encountered value of posterior prediction<br>"cv" = Coefficient of variation of posterior prediction<br><br>For future:<br>"mean" = Average Ensemble posterior prediction<br>"q05" = Lower percentile (5%) of posterior prediction<br>"q50" = Median or 50% percentile of posterior prediction<br>"q95" = Upper percentile (95%) of posterior prediction</p> <p>---<br><strong>Data properties:</strong></p> <table> <tbody> <tr> <td>Shared Socioeconomic Pathways (SSP)</td> <td>SSP1-2.6, SSP2-4.5, SSP5-8.5</td> </tr> <tr> <td>General circulation models (GCMs)</td> <td>GFDL-ESM4, <p>IPSL-CM6A-LR, </p> <p>MPI-ESM1-2-HR,</p> <p>MRI-ESM2-0,</p> <p>UKESM1-0-LL</p> </td> </tr> <tr> <td>Spatial grain</td> <td>1 km²</td> </tr> <tr> <td>Geographic projection</td> <td>LAEA</td> </tr> <tr> <td>Temporal grain</td> <td>30 year climatologies</td> </tr> <tr> <td>Spatial extent</td> <td>Continental Europe including Turkey (see screenshot)</td> </tr> <tr> <td>Temporal extent</td> <td>1990 to 2020 (Current), 2020 - 2100 (Future)</td> </tr> <tr> <td>Number of variables/entities</td> <td>7</td> </tr> </tbody> </table> <p>All files are provided as is and the author takes no responsibility for errors or misuse and misinterpretation. </p>
Dataset - Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks
<p>This data is complementary to the paper by Leijnse et al. 2022 "Generating reliable estimates of tropical cyclone induced coastal hazards along the Bay of Bengal for current and future climates using synthetic tracks" <br> https://doi.org/10.5194/nhess-2021-181</p> <p>This data is made available in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE</p> <p>For questions about the data ask: tim.leijnse@deltares.nl</p> <p>For more information about the tool to generate the used synthetic tracks TCWiSE see: <a href="https://www.deltares.nl/en/software/tcwise/">https://www.deltares.nl/en/software/tcwise/</a></p> <p> </p>
Calculated moisture sources for the Yangtse River Valley for past, present and future climate using a Lagrangian moisture source diagnostic
<p>This dataset contains calculated moisture sources for the Yangtse River Valley (110–122°E and 27–33°N, eastern China) for past, present and future climate using a Lagrangian moisture source diagnostic. The dataset comprises gridded monthly moisture source data files and monthly time series files for a Last Glacial Maximum (LGM) simulation and a Pre-Industrial reference simulation (PRE) with CAM5.1 using prescribed sea surface temperatures, and a control simulation (CTL, 2001-2010) and a climate scenario run with representative concentration pathway 6 (RCP, 2061-2070) with the coupled NorESM-1M model. Each file covers a 10-year time period, computed with the Lagrangian moisture source diagnostic WaterSip (Sodemann et al., 2008).</p>
NEON soil inorganic nitrogen measurements 2017-2020, derived data and code for Earth's Future manuscript
Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial-temporal scales. In a paper submitted for publication in Earth's future, we introduce a continentally distributed, publicly available dataset collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. To overcome methodological challenges and generate a standardized dataset, we produced a derived data version of soil inorganic N pools and net N transformation rate tables, which accounts for nitrite contamination in blanks. This derived dataset is then used to evaluate sources of variation within the NEON sampling design with mixed effects models, and we also compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2).
Future hourly wet-bulb globe temperature dataset for 842 cities in Japan
We provide projected hourly wet-bulb globe temperature (WBGT) data for 842 cities in Japan for April to October in the period 2030-2100. The projection is generated by applying prediction models built by learning the relationship between historical hourly WBGT and the daily weather indices using a machine learning method called eXtreme Gradient Boosting, to a future climate scenario data for Japan area called NIES2020. Projection data for the historical period 1980-2014 are also available.
Percent cover of under- and mid-story vegetation and seedling counts in the Future of Oak Forests project at Black Rock Forest, Cornwall, NY.
Black Rock Forest established a series of 12, 0.56 ha plots in 2005 to assess impacts of the loss of tree in the genus Quercus on the forest ecosystem (entitled the Future of Oak Forests experiment). Three trunk girdling treatments, with control plots were instituted in 2008. Each plot also contained an ~10m by ~15m deer exclosure to assess the impact of herbivory post-disturbance. In 2006 and 2008, before exclosures were erected, pre-treatment surveys were conducted in all unexclosed (n=120) quadrats. Surveys of all 240 understory quadrats were conducted annually in late summer (August to September) from 2009 to 2018 and then again in 2021. At each quadrat, trained observers identified all vascular plants to species and assigned each species a percent cover value. The percent cover of moss was also recorded but moss species were not identified. Counts of tree seedlings and some woody shrubs were also recorded in addition to percent cover values. Seedlings were considered saplings, and therefore not counted, once they reached 1.3 m tall (breast height).
Brain mechanisms underlying episodic future thinking of sustainable behaviors
Open the record for dataset details and reuse information.
Climate Forcing due to Future Ozone Changes: An intercomparison of metrics and methods
<p>The data provided in this repository relates to a paper on ozone radiative forcing submitted for publication in Atmos. Chem. Phys., as part of the TOAR-II special issue (<a href="https://acp.copernicus.org/articles/special_issue1256.html">ACP – Special issue – Tropospheric Ozone Assessment Report Phase II (TOAR-II) Community Special Issue (ACP/AMT/BG/GMD inter-journal SI)</a>). The paper is entitled "<span>Climate Forcing due to Future Ozone Changes</span><span>: An intercomparison of metrics and methods" by authors <span><span>William J. Collins</span></span><span><span>,</span> <span>Fiona M. O’Connor</span></span><span><span>, </span><span>Connor R. Barker</span></span><span><span>, </span><span>Rachael E. Byrom</span></span><span><span>, </span><span>Sebastian D. Eastham</span></span><span><span>,</span> <span>Øivind Hodnebrog</span></span><span><span>, Patrick Jöckel</span></span><span><span>, </span><span>Eloise A. Marais</span></span><span><span>, </span><span>Mariano Mertens</span></span><span><span>, Gunnar Myhre</span></span><span><span>, Matthias Nützel</span></span><span><span>, Dirk Olivié</span></span><span><span>, Ragnhild </span><span>Bieltvedt</span><span> Skeie</span></span><span><span>5</span></span><span><span>, Laura Stecher</span></span><span><span>, Larry W. Horowitz</span></span><span><span>, Vaishali Naik</span></span><span><span>, Gregory Faluvegi</span></span><span><span>, Ulas Im</span></span><span><span>, Lee T. Murray</span></span><span><span>, Drew Shindell</span></span><span><span>, Kostas Tsigaridis</span></span><span><span>, Nathan Luke Abraham</span></span><span><span>, James Keeble.</span></span></span></p>
Supplementary data for the article: Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges
<p>This repository provides the supplementary data to the paper titled <a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener"><em>"Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges"</em></a>, published 2024 in <em>Resources, Conservation and Recycling</em>.</p> <h4><strong>Contents</strong></h4> <p>The repository is split in 3 parts and comprises the following files (more details are provided in the <em>README.md</em>):</p> <p><strong>A_Database of reviewed studies:</strong></p> <ul> <li>contains the detailed review data, meant for readers to use as an overview file to gather studies relevant to them. It also includes an overview of all data sources that the reviewed studies used.</li> </ul> <p><strong>B_Scientific supplement to paper:</strong></p> <ul> <li>Contains all data relevant to the related publication Harpprecht et al. (2024), such as studies screened , FAIR data analysis, or analyzed impact trends.</li> </ul> <p><strong>C_Data for figures in paper:</strong></p> <ul> <li>This file contains all the data for Figures 3, 4 and 5 in tabular form, representing impact trends, scenario variables, scenario modelling approaches and data sources used.</li> </ul> <h4><strong>Summary</strong></h4> <p>These files allow to reproduce the results of our study. In this work, we systematically reviewed studies which assessed future environmental impacts of metal supply chains. Our review yielded 40 publications covering 15 metals: copper, iron, aluminium, nickel, zinc, lead, cobalt, lithium, gold, manganese, neodymium, dysprosium, praseodymium, terbium, and titanium. We evaluated their results regarding future impact trends, and their methods, i.e., modelling approaches, scenario variables, and data sources of scenario variables. We identified 15 scenario variables. The most common variables are background electricity mix, ore grade, recycling shares, demand, and energy efficiency. We identified 229 unique data sources for the reviewed scenario variables.</p> <h4><strong>Related publication</strong></h4> <p>More details on the data and its interpretation as well as the scientific context are provided in the publication itself:</p> <p><a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener">Harpprecht, C., Miranda Xicotencatl, B., van Nielen, S., van der Meide, M., Li, C. , Li, Z., Tukker, A., Steubing, B. (2024). <em>Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges.</em> Resources, Conservation and Recycling.</a></p> <h4><strong>Funding </strong></h4> <p>Carina Harpprecht received funding from the Energy Program of the German Aerospace Center in 2022. Zhijie Li received funding from the European Institute of Innovation and Technology (EIT) under the project Valomag (Project No. 14049).</p> <h4><strong>License</strong></h4> <p>CC-BY 4.0 license for DLR (German Aerospace Center)</p>
Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration
<p>Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration</p> <p>Data contains the georeferenced map plate of our geologic map that can be used in any geoinformation system (GIS).</p> <p><strong>If you use these data, please cite BOTH the Planetary Science Journal publication and the Zenodo dataset.</strong></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>The Planetary Science Journal</em>, <em>5</em>(6), 147. <a href="https://iopscience.iop.org/article/10.3847/PSJ/ad2c04">https://iopscience.iop.org/article/10.3847/PSJ/ad2c04</a></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Supplementary Data for Wueller et al. (2024): Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>Zenodo Dataset</em>. <a href="https://doi.org/10.5281/zenodo.10693820" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10693820</a></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>Mapping Scale is 1:100,000</p> <p>Print Scale is 1:1,000,000</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact lwueller@uni-muenster.de</p> <p>Lukas Wueller, Institut für Planetologie, Universität Münster, Germany, June 2024</p>
Co-design – Part 2: Workshop with professionals, early-adopters, and late/non-adopters to design interventions for a more responsible and just future with smart home technologies
<h3>Description</h3> <p>This qualitative dataset is the <strong>second part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a series of two <strong>in-person workshops</strong>: one with professionals developing smart technology and its early-adopters, and a second one with late/non-adopters of smart technology. The first workshop had four groups of participants and the second three groups. The data is divided by each group. The data collected during the previous and subsequent parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from workshop with professionals and early-adopters</h3> <ul> <li><strong>P2_WSP-PA-G1-TRANSCR_R00.docx</strong> (transcription of group 1 audio recordings) <ul> <li><strong>P2_WSP-PA-G1-VIS_000 </strong>to <strong>_005</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G2-TRANSCR_R00.docx </strong>(transcription of group 2 audio recordings) <ul> <li><strong>P2_WSP-PA-G2-VIS_000 </strong>to<strong> _008</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G3-TRANSCR_R00.docx </strong>(transcription of group 3 audio recordings) <ul> <li><strong>P2_WSP-PA-G3-VIS_000 </strong>to<strong> _004</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G4-TRANSCR_R00.docx </strong>(transcription of group 4 audio recordings) <ul> <li><strong>P2_WSP-PA-G4-VIS_000 </strong>to<strong> _002</strong> (participant-generated visual data)</li> </ul> </li> </ul> <p> </p> <h3>Documents from workshop with late/non-adopters</h3> <ul> <li><strong>P2_WSP-LN-G1-TRANSCR_R00</strong> (transcription of group 1 audio recordings) <ul> <li><strong>P2_WSP-LN-G1-VIS_000 </strong>and<strong> _001</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-LN-G2-TRANSCR_R00</strong> (transcription of group 2 audio recordings) <ul> <li><strong>P2_WSP-LN-G2-VIS_000 </strong>to<strong> _003</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-LN-G3-TRANSCR_R00</strong> (transcription of group 3 audio recordings) <ul> <li><strong>P2_WSP-LN-G3-VIS_000 </strong>to<strong> _002</strong> (participant-generated visual data)</li> </ul> </li> </ul> <p> </p> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Side-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the side-looking orientation, where the installation angle of radar is 90 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Out1_240522_160925.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Corner_160925.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesCornner_160925.mat' contains the timestamped velocity for the frames that are synchronised with the frames of front-looking radar.</p> <p>(The dataset for the front-looking radar is stored in another repository with DOI: 10.5281/zenodo.14215115)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesCornner_160925.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Corner_240522_160925_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
STREAM - Sub-THz Radar sensing of the Environment for future Autonomous Marine platforms: Multi-Perspective Sensing - Maritime Environment - Front-looking Perspective
<p>This dataset contains the files corresponding to which results have been included in the journal paper titled 'High-Resolution Multi-Modal Sensing of Distributed Radar Network'. The full description of the conducted trials and data structure is mentioned in the attached PDF document.</p> <p>The trials were conducted at the Gosport Marina, Portsmouth, UK with a sea state of approximately 3 according to the Douglas Scale.</p> <p>The experiments were performed with automotive radars operating in the 79 GHz band to investigate the Doppler and imaging capabilities of these radars. A multi-sensory suite distributed around Valkyrie VI was mounted in front, corner, side and backward-looking orientations.</p> <p>This dataset contains data from the front-looking orientation, where the installation angle of radar is 0 degrees respective to the platform velocity vector.</p> <p><strong>Radar Data:</strong></p> <p>The radar data is stored in the file 'GM2_Lab_240522_160943.h5'. The methodology to process the data in MATLAB is presented in the attached pdf. document.</p> <p><strong>Inertial Measurement Unit:</strong></p> <p>Three xSens 680G IMU were mounted on the roof, front and back of the boat. They have been included in the corresponding zip folders.</p> <p>PC3_Corner_RLG: IMU at the corner of the boat.</p> <p>PC4_Forward_RLG: IMU at the roof of the boat.</p> <p>PC5_Backward_RLG: IMU at the back of the boat.</p> <p>The IMU data is converted to .txt files that can be directly loaded into MATLAB.</p> <p><strong>Timestamped Velocity:</strong></p> <p>The file 'Front_160943.mat' contains the time-stamped velocity for each radar frame. Here, the integration interval is 128 ms with 512 radar chirps.</p> <p>The file 'CommonFramesFront_160943.mat' contains the timestamped velocity for the frames that are synchronised with the frames of side-looking radar.</p> <p>(The dataset for the side-looking radar is stored in another repository with DOI: 10.5281/zenodo.14174138)</p> <p><strong>Camera:</strong></p> <p>Each radar also has a camera for ground truth. The time-stamped camera frames for each radar frame are stored in 'CommonFramesFront_160943.mat'.</p> <p>Processed camera frames and video of the scene are available in: 'GM2_Front_240522_160943_CameraFrames.zip'.</p> <p> </p> <p>For more information, please contact:</p> <p>Anum Pirkani: a.a.a.pirkani@bham.ac.uk, anum.apirkani@gmail.com</p> <p>Marina Gashinova: m.s.gashinova@bham.ac.uk</p>
Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean's biological carbon pump
<p>This repository contains the post-processed model outputs underlying the main figures in the paper "Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean’s biological carbon pump" by Oziel et al. in Nature Climate Change (https://doi.org/10.1038/s41558-024-02233-6). The repository also contains the jupyter notebooks (python) scripts used to produce the figures, the custom model code as well as the mesh informations to reproduce the model run.</p>
UB2030 | The Future of Research Libraries as Knowledge Hubs | Interviews
<p>UB2030 is a podcast about the innovation of the university library through technological changes and shifts in research and education. In this podcast, David Oldenhof and Maurice Vanderfeesten discuss different subjects. They will be accompanied by guests who bring in an outside perspective.</p> <p>This release contains 11 episodes.</p> <p>Follow for more at <a href="https://ubvu.github.io/ub2030/">https://ubvu.github.io/ub2030/</a></p> <p>📖 <a href="https://doi.org/10.5281/zenodo.14615659"><strong>CLICK TO READ THE REPORT</strong></a></p> <p>🎧<strong>Listen on</strong> <a href="https://soundcloud.com/vu-library-live/sets/ub2030-the-future-of-research-libraries"><strong>SoundCloud</strong></a>, <a href="https://open.spotify.com/show/7dgTKn69lE3cnvs7CKv59v"><strong>Spotify</strong></a>, or your favorite <a href="https://antennapod.org/">(open)</a> podcast app.</p> <ul> <li>Authors: Maurice Vanderfeesten, David Oldenhof</li> <li>Client: Joeri Both</li> <li>Organization: <a href="https://vu.nl/nl/over-de-vu/diensten/universiteitsbibliotheek">University Library, Vrije Universiteit Amsterdam</a></li> <li>Date: 2023-05-19</li> </ul> <p><a href="https://doi.org/10.5281/zenodo.14615659" target="_blank" rel="noopener">DOI:10.5281/zenodo.14615659</a> (Rapport)</p> <p><a href="https://doi.org/10.5281/zenodo.10666049" target="_blank" rel="noopener">DOI:10.5281/zenodo.10666049</a> (Data)</p> <p><a href="https://ubvu.github.io/ub2030/">Project Page</a> | <a href="https://soundcloud.com/vu-library-live/sets/ub2030-the-future-of-research-libraries">Listen on SoundCloud</a> | <a href="https://feeds.soundcloud.com/users/soundcloud:users:527805591/sounds.rss">Podcast RSS</a> | <a href="https://forms.office.com/e/KX08BEenpu">Listener Feedback</a></p> <h2>Reason (Why Now)</h2> <p>The world is rapidly changing technologically, around AI, blockchain (NFTs), and linked data. As a library, you want to remain relevant for state-of-the-art research and education. We need to not only implement existing projects but also explore the horizon of opportunities and threats that await us. The client for this project is Joeri Both.</p> <h2>Project Goal (Why and How)</h2> <ul> <li>This project provides input for the next multi-year plan, creating a roadmap with a horizon up to 2030.</li> <li>With this project, we aim to increase the knowledge level of the UB by identifying key innovative/disruptive developments, to become a full-fledged partner for providers of (new) technological applications.</li> <li>From there, we translate innovative developments/trends into UB practice in broad terms, offering suggestions for workable/realistic pilot projects that contribute to the UB ambitions for researchers.</li> <li>The approach is to deliver an innovation sub-report each month, including a podcast episode, with the aim of making innovation an actively discussed topic within the UB.</li> <li>This gives the management team insight into the wide range of possibilities and developments, allowing them to make strategic choices for starting pilots and better embedding the innovation process in the organization.</li> </ul> <h2>Project Scope (What is and isn't included)</h2> <p>Through interviews, we gather information and ideas from each department and external experts. This information is linked to the ambition themes in the multi-year plan, focusing mainly on technological developments and their impact on our work processes and product/service offerings. The collected information is available in the form of an audio recording/podcast and interview report. For the Research Support department, these ideas are further developed into pilot proposals on three implementation levels: short, medium, and long term. The interviews are scheduled per department, divided into the themes of the ambitions in the multi-year plan.</p> <h2>Episodes</h2> <ul> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-01-introductie/"><strong>Episode 01 -- Introduction</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-02-rdm/"><strong>Episode 02 -- Future of Research Data Management and Research Software Management</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-03-research_intelligence/"><strong>Episode 03 -- Future of Research Intelligence</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-04-open_science/"><strong>Episode 04 -- Future of Open Science</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-05-research_support/"><strong>Episode 05 -- Future of Research Support</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-06-digital_services_and_infrastructures/"><strong>Episode 06 -- Future of Digital Services and Infrastructures</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-07-education_support/"><strong>Episode 07 -- Future of Education Support</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-08-special_collections/"><strong>Episode 08 -- Future of Special Collections</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-09-information_services/"><strong>Episode 09 -- Future of Information Services (not public)</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-10-library_desk_services/"><strong>Episode 10 -- Future of Library Desk Services</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-11-aquisition_and_metadata/"><strong>Episode 11 -- Future of Acquisition and Metadata (canceled)</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-12-society/"><strong>Episode 12 -- Future of the Library in Society</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-bonus-01-desci/"><strong>Episode 13 -- BONUS DeSci: Future of Open Science Ecosystems</strong></a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/ubvu/ub2030/compare/v1.1...v1.6">https://github.com/ubvu/ub2030/compare/v1.1...v1.6</a></p>
Bicycle Mobility Data: Current Use and Future Potential. An International Survey of Domain Professionals
<p>Active mobility, especially cycling, is an essential building block for sustainable urban mobility. Public and private stakeholders are striving to improve conditions for cycling and subsequently increase its modal share. Data are regarded as key for different measures to become efficient and targeted. There is extensive evidence for an increasing amount of mobility data, availability of new data sources and potential usage scenarios for such data. However, little is known about the current use of these data in policy making, planning and related fields. To the best of our knowledge, it has not been investigated yet to which degree professionals in the broader field of cycling promotion benefit from an increasing amount of cycling-related data. Thus, we conducted a multi-lingual online survey among domain professionals and acquired data on their perspectives on current data availability, use and suitability as well as the potential they see for the use of cycling data in the future. In total, we received 325 complete responses from 32 countries, with the vast majority of 241 valid responses originating from Germany, Austria and Italy. Key findings are: 84% of domain professionals attribute high importance to data, and 89% state that they currently cannot or only partly solve their tasks with the data available to them. Results emphasize the need for making more and better suited data available to professionals in cycling-related positions, in both the private and public sector.</p> <p>Read the full publication: <a href="https://doi.org/10.3390/data6110121">https://doi.org/10.3390/data6110121 </a></p>
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