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1,751 results for “Future”
Data from: Predicting range shifts of the giant pandas under future climate and land use scenarios
<p><span><strong>Aim</strong>:</span><span> Understanding and predicting how species will respond to global environmental change (i.e., climate and land use change) is essential to efficiently inform conservation and management strategies for authorities and managers. Here, we assessed the combined effect of future climate and land use change on the potential range shifts of the giant pandas (<em>Ailuropoda melanoleuca</em>). </span></p> <p><span><strong>Location</strong>:</span><span> Sichuan Province, China.</span></p> <p><span><strong>Methods</strong>: </span><span>We used ensemble species distribution models (SDMs) to forecast range shifts of the giant pandas by the 2050s and 2070s under four combined climate and land use change scenarios. We also</span><span> compared the differences in </span><span>distributional changes of giant pandas among the five mountains in the study area. </span></p> <p><span><strong>Results</strong>: </span><span>Our ensemble SDMs exhibited good model performance in terms of both AUC (0.931) and TSS (0.747), and suggested that precipitation seasonality, annual mean temperature, the proportion of forest cover and total annual precipitation are the most important factors in shaping the current distribution patterns for the giant pandas. Our projections of future species distribution also suggested a range expansion under an optimistic greenhouse gas emission, while suggesting a range contraction under a pessimistic greenhouse gas emission. Moreover, we found that there is considerable variation in the projected range change patterns among the five mountains in the study area. Especially, the suitable habitat of the giant panda is predicted to increase under all scenarios in Minshan mountains, while is predicted to decrease under all scenarios in Daxiangling and Liangshan mountains, indicating the vulnerability of the giant pandas at low latitudes. </span></p> <p><span><strong>Main conclusions</strong>: </span><span>Our findings highlight the importance of an integrated approach that combines climate and land use change to predict the future species distribution and the need for a spatial explicit consideration of the projected range change patterns of target species for guiding conservation and management strategies. </span></p>
Towards future directions in data-integrative supervised prediction of human aging-related genes
<p><strong>Corresponding author:</strong> <a href="http://www.nd.edu/~tmilenko">Prof. Tijana Milenković</a>, tmilenko AT nd DOT edu.</p> <p><strong>Detailed descriptions of the data and program</strong>: check <a href="https://nd.edu/~cone/DirectionsAging/">our paper's website</a>.</p> <p><strong>Reference</strong>: Qi Li, Khalique Newaz, and Tijana Milenković (2022). <strong>Towards future directions in data-integrative supervised prediction of human aging-related genes.</strong><em> under review</em></p>
Peak refreezing in the Greenland firn layer under future warming scenarios
<p>This data set includes the materials required to reproduce the figures and tables presented in the study: "Peak refreezing in the Greenland firn layer under future warming scenarios". The data consist of:</p> <p>1. Time series of annual Greenland ice sheet (GrIS) integrated <strong>SMB components</strong> (Gigatons or Gt per year). These time series are available in ASCII format for all simulations presented in the manuscript.</p> <p><strong>RACMO2.3</strong><strong>p2-ERA: </strong>1 member</p> <ul> <li><strong>SMB-components_RACMO2.3p2-ERA_1958-2020_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the ERA-forced RACMO2.3p2 simulation at 5.5 km, statistically downscaled to 1 km spatial resolution (1958-2020).</li> </ul> <p><strong>RACMO2.3</strong><strong>p2</strong><strong>-CESM2</strong>: 3 members</p> <ul> <li><strong>SMB-components_RACMO2.3p2-CESM2-HIST-12_1950</strong><strong>-</strong><strong>2014_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 historical reconstruction (HIST) at 11 km, statistically downscaled to 1 km spatial resolution (1950-2014). RACMO2.3p2 was forced by member 12 of the CESM2-HIST ensemble (HIST-12, see <strong>CESM2-HIST</strong> below).</li> <li><strong>SMB-components_RACMO2.3p2-CESM2-SSP126-4_2015-2099_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 SSP1-2.6 projection (SSP126) at 11 km, statistically downscaled to 1 km spatial resolution (2015-2099). RACMO2.3p2 was forced by member 4 of the CESM2-SSP126 ensemble (SSP126-4, see <strong>CESM2-SSP126</strong> below).</li> <li><strong>SMB-components_RACMO2.3p2-CESM2-SSP</strong><strong>585</strong><strong>-</strong><strong>3</strong><strong>_2015-2099_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the CESM2-forced RACMO2.3p2 SSP5-8.5 projection (SSP585) at 11 km, statistically downscaled to 1 km spatial resolution (2015-2099). RACMO2.3p2 was forced by member 3 of the CESM2-SSP585 ensemble (SSP585-3, see <strong>CESM2-SSP585</strong> below).</li> </ul> <p><strong>CESM2</strong><strong>-IND: </strong>11 members</p> <ul> <li><strong>SMB-components_CESM2-IND-</strong><strong>X</strong><strong>_1850-1949_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 pre-industrial reconstructions (IND) at ~111 km, statistically downscaled to 1 km spatial resolution (1850-1949). The term “X” in the filename above represents the CESM2 member ranging from 1 to 11.</li> </ul> <p><strong>CESM2</strong><strong>-HIST: </strong>12 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>HIST</strong><strong>-</strong><strong>X</strong><strong>_1</strong><strong>9</strong><strong>50-</strong><strong>2014</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 historical reconstructions (HIST) at ~111 km, statistically downscaled to 1 km spatial resolution (1950-2014). The term “X” in the filename above represents the CESM2 member ranging from 1 to 12.</li> </ul> <p><strong>CESM2</strong><strong>-SSP126: </strong>6 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP126</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP1-2.6 projections (SSP126) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 5.</li> <li><strong>SMB-components_CESM2-</strong><strong>SSP126</strong><strong>-</strong><strong>6</strong><strong>_</strong><strong>2100</strong><strong>-</strong><strong>2299</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2 SSP1-2.6 projection (SSP126) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299).</li> </ul> <p><strong>CESM2</strong><strong>-SSP245: </strong>6 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP245</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP2-4.5 projections (SSP245) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 6.</li> </ul> <p><strong>CESM2</strong><strong>-SSP370: </strong>5 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP370</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP3-7.0 projections (SSP370) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 5.</li> </ul> <p><strong>CESM2</strong><strong>-SSP585: </strong>8 members</p> <ul> <li><strong>SMB-components_CESM2-</strong><strong>SSP585</strong><strong>-</strong><strong>X</strong><strong>_</strong><strong>2015</strong><strong>-</strong><strong>2099</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native CESM2 SSP5-8.5 projections (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2015-2099). The term “X” in the filename above represents the CESM2 member ranging from 1 to 6.</li> <li><strong>SMB-components_CESM2-</strong><strong>SSP585</strong><strong>-</strong><strong>7</strong><strong>_</strong><strong>2100</strong><strong>-</strong><strong>2299</strong><strong>_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2 SSP5-8.5 projection (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299). This run (member 7) does not include ice dynamics.</li> <li><strong>SMB-components_CESM2-CISM2-SSP585-8_2100-2299_GrIS_1km.txt</strong>: time series of annual GrIS-integrated SMB, total precipitation (snow + rain), snowfall, runoff, total melt (ice + snow), refreezing and retention (Gt per year) from the native long-term CESM2-CISM2 SSP5-8.5 projection (SSP585) at ~111 km, statistically downscaled to 1 km spatial resolution (2100-2299). This run (member 8) includes ice dynamics.</li> </ul> <p>2. Time series of annual GrIS-wide <strong>runoff line altitude</strong> in meters above sea-level (m a.s.l.). These time series are available in ASCII format for the RACMO2.3p2-ERA simulation (ERA), and as an ensemble mean for all pre-industrial (IND) and historical reconstructions (HIST), and projections (SSP) including both native CESM2 and RACMO2.3p2-CESM2, statistically downscaled to 1 km.</p> <ul> <li><strong>Runoff-line-altitude_ERA_1958-2020_GrIS_1km.txt</strong>: time series of GrIS-wide runoff line altitude (m a.s.l.) derived from the ERA-forced RACMO2.3p2 simulation at 1 km (1958-2020).</li> <li><strong>Runoff-line-altitude_IND-EnsembleMean_1850-1949_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all pre-industrial reconstructions at 1 km (1850-1949, 11 IND members).</li> <li><strong>Runoff-line-altitude_HIST-EnsembleMean_1950-2014_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all historical reconstructions at 1 km (1950-2014, 13 HIST members).</li> <li><strong>Runoff-line-altitude_SSP126-EnsembleMean_2015-2299_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP1-2.6 projections at 1 km (2015-2299, 7 SSP126 members).</li> <li><strong>Runoff-line-altitude_SSP245-EnsembleMean_2015-2099_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP2-4.5 projections at 1 km (2015-2099, 6 SSP245 members).</li> <li><strong>Runoff-line-altitude_SSP370-EnsembleMean_2015-2099_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP3-7.0 projections at 1 km (2015-2099, 5 SSP245 members).</li> <li><strong>Runoff-line-altitude_SSP585-EnsembleMean_2015-2299_GrIS_1km.txt</strong>: time series of ensemble mean GrIS-wide runoff line altitude (m a.s.l.) derived from all SSP5-8.5 projections at 1 km (2015-2299, 9 SSP585 members).</li> </ul> <p>3. Time series of annual <strong>500hPa global temperature anomalies</strong> (ºC). These time series are available in ASCII format for the ERA5 reanalysis (ERA5-reanalysis), and as an ensemble mean for all pre-industrial (IND) and historical reconstructions (HIST), and projections (SSP) from the native CESM2 model at ~111 km spatial resolution.</p> <ul> <li><strong>Tglobal-500hPa-anomaly_ERA5-reanalysis_1950-2020.txt</strong>: time series of annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from the ERA5 climate reanalysis (1950-2020). Anomalies are estimated relative to the reference period 1950-1990.</li> <li><strong>Tglobal-500hPa-anomaly_IND-EnsembleMean_1850-1949.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 pre-industrial reconstructions (1850-1949, 11 IND members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_HIST-EnsembleMean_1950-2014.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 historical reconstructions (1950-2014, 12 HIST members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP126-EnsembleMean_2015-2299.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 short/long term SSP1-2.6 projections (2015-2299, 6 SSP126 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP245-EnsembleMean_2015-2099.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 SSP2-4.5 projections (2015-2099, 6 SSP245 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP370-EnsembleMean_2015-2099.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 SSP3-7.0 projections (2015-2099, 5 SSP370 members). Anomalies are estimated relative to the reference period 1850-1949.</li> <li><strong>Tglobal-500hPa-anomaly_SSP585-EnsembleMean_2015-2299.txt</strong>: time series of ensemble mean annual anomalies in upper atmospheric (500hPa) global temperature (ºC) derived from all native CESM2 short/long term SSP5-8.5 projections (2015-2299, 8 SSP585 members). Anomalies are estimated relative to the reference period 1850-1949.</li> </ul> <p>The gridded, daily downscaled SMB data sets from the ERA-forced RACMO2.3p2 simulation, and the CESM2-forced RACMO2.3p2 projections under a low-end SSP1-2.6 and high-end SSP5-8.5 warming scenario, as well as gridded, monthly downscaled SMB data sets from native CESM2 under pre-industrial (IND), historical (HIST), and short/long term climate projections (SSPs) are freely available from the authors upon request and without conditions (contact: b.p.y.noel@uu.nl). Besides SMB, the data sets include total precipitation (snow and rain), snowfall, total melt (snow and ice), runoff, refreezing and retention and total sublimation (surface and drifting snow) at 1 km horizontal resolution. </p> <p><strong>Abstract</strong>: Firn (compressed snow) covers approximately 90% of the Greenland ice sheet (GrIS) and currently retains about half of rain and meltwater through refreezing, reducing runoff and subsequent mass loss. The loss of firn could mark a tipping point for sustained GrIS mass loss, since decades to centuries of cold summers would be required to rebuild the firn buffer. Here we estimate the warming required for GrIS firn to reach peak refreezing, using 51 climate simulations statistically downscaled to 1 km resolution, that project the long-term firn layer evolution under multiple emission scenarios (1850–2300). We predict that refreezing stabilises under low warming scenarios, whereas under extreme warming, refreezing could peak and permanently decline starting in southwest Greenland by 2100, and further expanding GrIS-wide in the early 22<sup>nd</sup> century. After passing this peak, the GrIS contribution to global sea level rise would increase over twenty-fold compared to the last three decades.</p> <p> </p>
Flexibility solutions - making the power grid fit for the future
<p><b>Abstract</b></p><p class="dhik-abstract-content">The competence center Business Engineering presents different projects. QualyGridS: flexible hydrogen production, business ecosystem design; PACE: optimized operation of FC µCHPs, operations research, market analysis; - Multiple Benefits: valuation of MB of energy efficiency measures; smart services, CE, LCA</p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (DOI:<a href="https://zenodo.org/record/7123781">10.5281/zenodo.7123781</a>)</li><li>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (DOI:<a href="https://zenodo.org/record/7123783">10.5281/zenodo.7123783</a>)</li><li>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (DOI:<a href="https://zenodo.org/record/7123785">10.5281/zenodo.7123785</a>)</li><li><b>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (<a href="#collapseTwo">Video</a>)</b></li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (DOI:<a href="https://zenodo.org/record/7123796">10.5281/zenodo.7123796</a>)</li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (DOI:<a href="https://zenodo.org/record/7123800">10.5281/zenodo.7123800</a>)</li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (DOI:<a href="https://zenodo.org/record/7123814">10.5281/zenodo.7123814</a>)</li><li>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (DOI:<a href="https://zenodo.org/record/7123816">10.5281/zenodo.7123816</a>)</li><li>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (DOI:<a href="https://zenodo.org/record/7123818">10.5281/zenodo.7123818</a>)</li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
Datasets for "Compositions and Interior Structures of the Large Moons of Uranus and Implications for Future Spacecraft Observations"
<p>Files used to build Figures 3, 4, 5, 7, 9, 10, 13 in manuscript entitled "Compositions and Interior Structures of the Large Moons of Uranus and Implications for Future Spacecraft Observations" submitted with JGR.</p>
Schematic and adapted figures from IPBES Sustainable Use of Wild Species Assessment - Chapter 5. Future scenarios of sustainable use of wild species
<p>Schematic and adapted figures from Chapter 5 of the thematic assessment of the sustainable use of wild species of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.</p>
Back To The Future - Dutch Museum - ARCore Scan
Quick scan from my visit to the biggest Dutch BTTF museum. Scanned using Open Constructor and Note 8. Source: Objaverse 1.0 / Sketchfab
Week 2 - Technology based tourism in future
<p><strong>Video for Week 2 ICT Tourism & Hospitality</strong></p> <p>How techonology will affect the tourism in the future</p>
TWEETHER Future Generation W-band Backhaul and Access Network Technology
<p>Paper Presented at EUCNC 2017. Data of lens antenna simulated by 3D simulator and measured by Vector Network analyser. Measurements of chips on wafer.</p>
Adaptation required to preserve future high-end river flood risk at present levels
<p>Dataset accompanying the publication</p> <p>S.N. Willner, A. Levermann, F. Zhao, K. Frieler, Adaptation required to preserve future high-end river flood risk at present levels. Sci. Adv. 4, eaao1914 (2018).</p> <p>The dataset includes the increase in flood protection that is required to keep the observed high-end flood risk of the past constant in the next 25 years as well as the affected population in both periods used (1971-2004 and 2035-2044).</p>
Global Catastrophic Effects on Future Climate due to Increasing Total Solar Irradiance. A General Atmospheric Circulation Analysis.
<p>10-yr CESM run with standard TSI (BGCN_T31_g37.cam.h0*)</p> <p>10-yr CESM run with TSI +10% (BGCN_T31_g37_TSI10p.cam.h0*)</p>
MDM data for "Wind driven ocean circulation changes can amplify future cooling of the North Atlantic warming hole" - submitted to Journal of Climate
<p>Data files for MDM simulation used in Journal of Climate submission, "Wind driven ocean circulation changes can amplify future cooling of the North Atlantic warming hole"</p>
Effect of Soil Moisture on Future Heatwaves over Eastern China: Convection-Permitting Regional Climate Simulations
<p>Data used in the manuscript "Effect of Soil Moisture on Future Heatwaves Over Eastern China: Convection-Permitting Regional Climate Simulations" which will be submitted to Journal of Geophysical Research: Atmospheres.</p>
Data for: Planning for a future of changes: prioritizing areas for conservation of small mammals in the Caatinga, Brazil
<p>Human land use and climate change are two of the main threats affecting biodiversity, especially in arid/semiarid regions. The most effective way to protect the species in these ecosystems against these threats is through the delimitation of Protected Areas (PAs). However, such PAs need to be targeted cost-efficiently and consider future climate change. We identify priority areas to preserve small mammal species in the Caatinga in the present and in the future of climate change. We also evaluate how well these priority areas are protected by current PAs and identify ways forward to improve their protection. We use ecological niche models and Zonation spatial prioritisation software to identify the top 30% priority areas to preserve small mammal species under current climate and land use scenarios, besides considering optimistic and pessimistic scenarios of future climate change. We also evaluate how much these priority areas are covered by current PAs, identify ways to further improve their protection using hierarchical mask analysis, and evaluate species mean distribution coverage. The consequences of climate change will not hugely impact the distribution of priority areas for species conservation in the Caatinga. Around 13% of the identified priority areas overlap with current PAs, and planning the expansion of PAs considering integral protection areas increases the coverage of priority areas to more than 18% and captures more than 72% of species-suitable areas. Our prioritisations take into account climate change and provide low risk if conducted as a "no-regrets" conservation action. These priority areas are poorly supported by the Brazilian PA system, and need of further protection. One cost-effective option could be to upgrade some Sustainable Use PAs into more restrictive ones. Securing these priority areas helps preserve the long-term ecosystem functioning and to prevent biodiversity loss in a changing world.</p>
Replication Package: The Past, Present, and Future of Research on the Continuous Development of AI
<p>Replication package for the publication regarding the <strong>The Past, Present, and Future of Research on the Continuous Development of AI.</strong></p> <p> </p> <p> </p>
ARCHIMEDES Project's Animated Video 'ARCHIMEDES: Pioneering a Sustainable Future'
<div> <p><span>The ARCHIMEDES project, a leading initiative in sustainable innovation, has launched an animated video titled "ARCHIMEDES: Pioneering a Sustainable Future." This video offers a detailed look into the project’s groundbreaking efforts to integrate sustainability with advanced technology across various sectors.</span></p> </div> <div> </div> <div> </div> <div> <p><span>ARCHIMEDES stands at the intersection of innovation and sustainability, aiming to revolutionize the future of mobility, energy, and safety within the framework of Society 5.0. The project focuses on developing state-of-the-art components, models, and methodologies to significantly improve the efficiency and lifespan of propulsion systems, power components, and energy storage devices.</span></p> </div> <div> </div> <div> </div> <div> <p><span>The video highlights how the ARCHIMEDES project spans multiple key industries:</span></p> </div> <div> <ul> <li> <p><span><strong>Automotive:</strong> Pioneering new propulsion systems to make vehicles more efficient and environmentally friendly.</span></p> </li> <li> <p><span><strong>Aviation:</strong> Enhancing energy efficiency and sustainability in the aerospace sector.</span></p> </li> <li> <p><span><strong>Industry:</strong> Transforming industrial processes to be more sustainable and cost-effective.</span></p> </li> </ul> </div> <div> </div> <div> </div> <div> <p><span>The ARCHIMEDES project is committed to creating a sustainable future by leveraging the latest innovations and technologies. By adopting an interdisciplinary approach, the project aims to be at the forefront of advancements in mobility, energy, and industrial processes. The vision is to not only meet current demands but also to anticipate and address future challenges.</span></p> </div> <div> </div> <div> </div> <div> <p><span>The newly released animated video provides a comprehensive overview of how ARCHIMEDES is leading the way toward a more sustainable future. The project invites viewers to join in this transformative journey.</span></p> </div>
Dataset for "Multi-model evidence of future tropical Atlantic rainfall change modulated by AMOC decline"
<p>This dataset contains EC-Earth3 experiments used for the paper Multi-model evidence of future tropical Atlantic rainfall change modulated by AMOC decline (G. Cerato, K. Bellomo, R. D'Agostino, J. von Hardenberg, 2024) submitted to <em>Journal of Climate. </em>Data uploaded here allows the user to reproduce the figures related to the EC-Earth3 experiments from the journal article.</p>
Future sea level rise in northwest Mexico is projected to decrease the distribution and habitat quality of the endangered Calidris canutus roselaari (Red Knot)
<p>Sea level rise (SLR) is one of the most unequivocal consequences of climate change, yet the implications for shorebirds and their coastal habitats is not well understood, especially outside of the north temperate zone. Here, we show that by the year 2050, SLR has the potential to cause significant habitat loss and reduce the quality of the remaining coastal wetlands in Northwest Mexico—one of the most important regions for Nearctic breeding migratory shorebirds. Specifically, we used species distribution modelling and a moderate SLR static inundation scenario to assess the effects of future SLR on coastal wetlands in Northwest Mexico and the potential distribution of <em>Calidris canutus roselaari </em>(Red Knot), a threatened long-distance migratory shorebird. Our results suggest that under a moderate SLR scenario, 55% of the current coastal wetland extent in northwest Mexico will be at risk of permanent submergence by 2050, and the high-quality habitat areas that remain will be 20% less suitable for <em>C. c. roselaari</em>. What is more, 8 out of the 10 wetlands currently supporting the largest numbers of <em>C. c. roselaari</em> are predicted to lose — on average — 17.8% of their highly suitable habitat areas, with two sites completely losing all their highly suitable habitat. In combination with increasing levels of coastal development and anthropogenic disturbance in Northwest Mexico, these predicted changes suggest that the potential future distribution of <em>C. c. roselaari</em> (and other shorebirds) will likely contract, exacerbating their ongoing population declines. Our results also make clear that SLR will likely have profound effects on ecosystems outside the north temperate zones, providing a clarion call to natural resource managers. Urgent action is required to begin securing sufficient space to accommodate the natural capacity of wetlands to migrate inland and implement local-scale solutions that strengthen the resilience of wetlands and human populations to SLR.</p>
Data from: Future climatically suitable areas for bats in South Asia
<p>Climate change majorly impacts biodiversity in diverse regions across the world, including South Asia, a megadiverse area with heterogeneous climatic and vegetation regions. However, climate impacts on bats in this region are not well‐studied, and it is unclear whether climate effects will follow patterns predicted in other regions. We address this by assessing projected near‐future changes in climatically suitable areas for 110 bat species from South Asia. We used ensemble ecological niche modelling with four algorithms (random forests, artificial neural networks, multivariate adaptive regression splines and maximum entropy) to define climatically suitable areas under current conditions (1970–2000). We then extrapolated near future (2041–2060) suitable areas under four projected scenarios (combining two global climate models and two shared socioeconomic pathways, SSP2: middle‐of‐the‐road and SSP5: fossil‐fuelled development). Projected future changes in suitable areas varied across species, with most species predicted to retain most of the current area or lose small amounts. When shifts occurred due to projected climate change, new areas were generally northward of current suitable areas. Suitability hotspots, defined as regions suitable for >30% of species, were generally predicted to become smaller and more fragmented. Overall, climate change in the near future may not lead to dramatic shifts in the distribution of bat species in South Asia, but local hotspots of biodiversity may be lost. Our results offer insight into climate change effects in less studied areas and can inform conservation planning, motivating reappraisals of conservation priorities and strategies for bats in South Asia.</p>
Output data for: Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia
<p>This repository contains the output data associated with the publication "Flammable Futures – Storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia". It contains the FLAM modeled burned area and the GLOBIOM output, as well as the a downscaling grid.</p> <p>Descriptions of the results can be found in the publication (DOI will follow).</p>
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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.