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1,751 results for “Future”
Vulnerability of estuarine systems in the contiguous United States to water quality change under future climate and land-use
<p>Changes in climate and land-use and land-cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (1) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios to altered nutrient loads, (2) sensitivity (i.e., how responsive estuaries are to altered nutrient loads), and (3) adaptive capacity (i.e., how the socio-ecological system can use existing resources to reduce the impacts associated with increased exposure). The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increase is expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Our findings illustrate the benefits of integrating natural and socio-ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries. A<a href="https://lisemontefiore.shinyapps.io/estuarine_vulnerability/"> web-based application</a> has been developed to visualize and download the data.</p>
Data from: Potential effects of future climate change on global reptile distributions and diversity
<p class="first-paragraph"><span><strong>Aim:</strong></span><span> Until recently, complete information on global reptile distributions has not been widely available. Here, we provide the first comprehensive climate impact assessment for reptiles on a global scale.</span></p> <p class="western"><span><strong>Location:</strong></span><span> Global, excluding Antarctica</span></p> <p class="western"><span><strong>Time period:</strong></span><span> 1995, 2050, 2080</span></p> <p class="western"><span><strong>Major taxa studied:</strong></span><span> Reptiles</span></p> <p class="western"><span><strong>Methods:</strong></span><span> We modelled the distribution of 6,296 reptile species and assessed potential global as well as realm-specific changes in species richness, the change in global species richness across climate space, and species-specific changes in range extent, overlap and position under future climate change. To assess the future climatic impact on 3,768 range-restricted species, which could not be modelled, we compared the future change in climatic conditions between both modelled and non-modelled species.</span></p> <p class="western"><span><strong>Results:</strong></span><span> Reptile richness was projected to decline significantly over time, globally but also for most zoogeographic realms, with the greatest decrease in Brazil, Australia and South Africa. Species richness was highest in warm and moist regions, with these regions being projected to shift further towards climate extremes in the future. Range extents were projected to decline considerably in the future, with a low overlap between current and future ranges. Shifts in range centroids differed among realms and taxa, with a dominating global poleward shift. Non-modelled species were significantly stronger affected by projected climatic changes than modelled species.</span></p> <p class="western"><span><strong>Main conclusions:</strong></span><span> With ongoing future climate change, reptile richness is likely to decrease significantly across most parts of the world. This effect as well as considerable impacts on species' range extent, overlap, and position were visible across lizards, snakes and turtles alike. Together with other anthropogenic impacts, such as habitat loss and harvesting of species, this is a cause for concern. Given the historical lack of global reptile distributions, this calls for a re-assessment of global reptile conservation efforts, with a specific focus on anticipated future climate change.</span></p>
Future role of the car
<p><em><strong>In the fourth Rebalance Dialogue, <a href="https://rebalancemobility.eu/andrea-ricci/">Andrea Ricci</a> moderated the dialogue between <a href="https://rebalancemobility.eu/jens-schade/">Jens Schade</a> and <a href="https://rebalancemobility.eu/stefan-gossling/">Stefan Gössling</a> about the role of the car in the future and how private car use is recognized as having significant negative impacts on environment, health, and safety. The car generates congestion, consumes significant urban space, and affects the quality of life in general. Access regulation, initiatives to foster active mobility and micro-mobility, and to make public transport more attractive address some of these concerns. But private car ownership and use is deeply ingrained in our culture, and car sales keep growing.</strong></em></p> <p>REBALANCE explores the congruity of a perspective consisting of a radical shift away from cars and the “key values” that should be promoted in order to achieve it. Driving a car is perceived as an expression of freedom: the driver is “in control”, and can decide where, when, at what speed, and with what itinerary to move around. Although, this is sometimes a delusion as drivers suffer frustrating congestion, time losses to find parking spots, impotence in avoiding accidents provoked by others, along with high economic costs.</p> <blockquote> <p><strong>“Agency” is arguably a fundamental determinant of mobility choices. Nevertheless, it is worth researching how more and better evidence of the true costs and benefits, both individual and collective, could help deconstruct the current paradigm, and convince people that their real need is to move, not to drive.</strong></p> </blockquote> <p>It is often stated that the main reason behind the prevalence of private car use is the “status symbol” notion attached to the car, and the subsequent reticence to give up ownership. The emergence of shared vehicles and shared rides schemes can be optimistically interpreted as a promising signal in this regard. To become prevalent, “soft spots” in peoples’ values and perceptions must be addressed for this to happen, also considering the “generation gap” and differentiated perspectives across age groups. On the other hand, if it is sustained, the generalized acceptance of shared schemes could lead car use to become an integral part of more sustainable, multimodal, and seamless mobility patterns. This would then further rule out any perspective of phasing out cars as such! Ultimately, a radical shift to shared schemes could not be enough to curb the dramatic impacts of automobility.</p> <p>The participants agreed that the car is part of one’s personality, showing the real economic cost and impact will not change anything. Many costs do not steer behaviour because they are indirect and invisible. However, giving immediate feedback on your behaviour does have an effect. Possession has its value. Ownership will be more determinant than electric or autonomous. In this case, safety and self-protection discourses could help change minds.</p> <p>Powerful automotive industries in some countries (Germany, Italy) convey a message of car dependency, much more than the reality. Changes are easier in countries without a car industry (The Netherlands, Denmark). Car sharing of AVs could increase traffic and create additional problems. Perhaps the future of sharing will depend on convenience (and availability).</p> <p>The discussants agreed that societal change will not be fast, but resistance can grow quickly. To avoid that, awareness of wrong policy direction must be considered. Western societies do not accept restrictions well. Indirect measures (price, parking…) could work but will not lead to structural changes. The participants wondered why the vast majority of the population accepted COVID restrictions but show reluctance in other cases. Education is important to give information and awareness, including subsidies and internalisation of external costs and benefits and even the need to be open to addressing taboos like speed limits, driving licences renewal of elder people.</p> <p>The conversation addressed some issues related to urban space, mainly focused on cycling and how every bicycle creates space and needs improving by creating safe spaces to allow people (who are willing) to switch from cars to bicycles. This change could be based at first on progressive city dwellers and concentrate on the progressive part of big cities. Cities are the place to start implementing alternatives but politicians (and the electoral agenda) are one of the main barriers for car-free cities. Nevertheless, big cities are closer to achieving this change.</p> <p>Technological innovation is mostly presented as a promising pathway towards sustainability: cleaner propulsion, vehicle automation, “smart”, ICT-based mobility can undoubtedly bring – if only incremental – improvements in environmental and safety related performances of transport systems and services. This could simply mean a further step towards the consolidation of the car-based mobility culture, one that in the end definitively precludes a radical shift away from automobility. And even worse,</p> <blockquote> <p><strong>technological progress could generate perverse rebound effects that would ultimately mainly serve the interests of the automotive industry rather than those of society.</strong></p> </blockquote> <p>The urge to avoid crowded transport solutions due to COVID-19 has at least partially overshadowed what was observed, only a few years ago, as a progressive disenchantment with car ownership and use, especially among the young. To minimize contact with others, many people are traveling in their own vehicles. Since the outbreak of COVID-19, the use of public transportation and carpools has declined significantly worldwide. Whether COVID-related concerns are an alibi to sticking to private car use, where all collective modes, but also shared cars, are perceived as a health risk or – especially among the younger generations – is the fall back on private car mobility only a “minor evil”, to get rid of as swiftly as possible has yet to be seen. However, the participants agreed that young people are becoming more conservative and could become more attached to cars and car ownership.</p>
Model output for "Enrichment of ammonium in the future ocean threatens diatom productivity"
<p>Each netcdf file (.nc) contains model output from simulations performed with the<br> NEMO-PISCES global ocean-biogeochemistry model. These simulations were<br> forced by physical output from the IPSL-CM5A Earth System Model, which <br> performed both the natural (no anthropogenic activities) and RCP8.5 scenarios.</p> <p>Variables in spin-up "ptrc" files are:</p> <p> name title I J K L<br> PHY (Nano)Phytoplankton Concentrati 1:360 1:180 1:31 1:12<br> PHY2 Diatoms Concentration 1:360 1:180 1:31 1:12<br> O2 Oxygen Concentration 1:360 1:180 1:31 1:12<br> PREO2 Abiotic Oxygen Concentration 1:360 1:180 1:31 1:12<br> FER Dissolved Iron Concentration 1:360 1:180 1:31 1:12<br> NO3 Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2 Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4 Ammonium Concentration 1:360 1:180 1:31 1:12<br> NO3_15 15N Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_15 15N Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4_15 15N Ammonium Concentration 1:360 1:180 1:31 1:12<br> O2_18 18O Dissolved Oxygen Concentrat 1:360 1:180 1:31 1:12<br> NO3_18 18O Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_18 18O Nitrite Concentration 1:360 1:180 1:31 1:12</p> <p> </p> <p>Variables in scenario "ptrc" files are:</p> <p> name title I J K L<br> PHY (Nano)Phytoplankton Concentrati 1:360 1:180 1:31 1:12<br> PHY2 Diatoms Concentration 1:360 1:180 1:31 1:12<br> ZOO (Micro)Zooplankton Concentratio 1:360 1:180 1:31 1:12<br> ZOO2 Mesozooplankton Concentration 1:360 1:180 1:31 1:12<br> O2 Oxygen Concentration 1:360 1:180 1:31 1:12<br> PREO2 Abiotic Oxygen Concentration 1:360 1:180 1:31 1:12<br> FER Dissolved Iron Concentration 1:360 1:180 1:31 1:12<br> NO3 Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2 Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4 Ammonium Concentration 1:360 1:180 1:31 1:12<br> DOC Dissolved organic Concentration 1:360 1:180 1:31 1:12<br> POC Small organic carbon Concentrat 1:360 1:180 1:31 1:12<br> GOC Big organic carbon Concentratio 1:360 1:180 1:31 1:12<br> NO3_15 15N Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_15 15N Nitrite Concentration 1:360 1:180 1:31 1:12<br> NH4_15 15N Ammonium Concentration 1:360 1:180 1:31 1:12<br> PHY_15 15N Nanophytoplankton Concentra 1:360 1:180 1:31 1:12<br> PHY2_15 15N Diatoms Concentration 1:360 1:180 1:31 1:12<br> DOC_15 15N Dissolved organic Concentra 1:360 1:180 1:31 1:12<br> POC_15 15N Small particulate Concentra 1:360 1:180 1:31 1:12<br> GOC_15 15N Large particulate Concentra 1:360 1:180 1:31 1:12<br> ZOO_15 15N Microzooplankton Concentrat 1:360 1:180 1:31 1:12<br> ZOO2_15 15N Mesozooplankton Concentrati 1:360 1:180 1:31 1:12<br> O2_18 18O Dissolved Oxygen Concentrat 1:360 1:180 1:31 1:12<br> NO3_18 18O Nitrate Concentration 1:360 1:180 1:31 1:12<br> NO2_18 18O Nitrite Concentration 1:360 1:180 1:31 1:12</p> <p>Variables in the scenario "diad" files are:</p> <p> name title I J K L<br> PH PH 1:360 1:180 1:31 1:12<br> HEUP Euphotic layer depth 1:360 1:180 ... 1:12<br> PAR Photosynthetically Available Ra 1:360 1:180 1:31 1:12<br> PARDM Daily mean PAR 1:360 1:180 1:31 1:12<br> PPPHYN Primary production of nanophyto 1:360 1:180 1:31 1:12<br> PPPHYD Primary production of diatoms 1:360 1:180 1:31 1:12<br> PPNEWN New Primary production of nanop 1:360 1:180 1:31 1:12<br> PPNEWD New Primary production of diato 1:360 1:180 1:31 1:12<br> PPNO2N NO2 Primary production of nanop 1:360 1:180 1:31 1:12<br> PPNO2D NO2 Primary production of diato 1:360 1:180 1:31 1:12<br> NITRNH4 Ammonia-oxidation rate (NH4-->N 1:360 1:180 1:31 1:12<br> NITRNO2 Nitrite-oxidation rate (NO2-->N 1:360 1:180 1:31 1:12<br> MUAOA Growth rate of ammonia oxidiser 1:360 1:180 1:31 1:12<br> MUAOAMAX Max potential ammonia oxidation 1:360 1:180 1:31 1:12<br> LAOANH4 Substrate limitation of NH4 oxi 1:360 1:180 1:31 1:12<br> LAOAFER Iron limitation of NH4 oxidatio 1:360 1:180 1:31 1:12<br> LAOAPAR Light limitation of NH4 oxidati 1:360 1:180 1:31 1:12<br> LAOAPH pH limitation of NH4 oxidation 1:360 1:180 1:31 1:12<br> LNOBNO2 Substrate limitation of NO2 oxi 1:360 1:180 1:31 1:12<br> LNOBFER Iron limitation of NO2 oxidatio 1:360 1:180 1:31 1:12<br> LNOBPAR Light limitation of NO2 oxidati 1:360 1:180 1:31 1:12<br> NFIX Nitrogen fixation 1:360 1:180 1:31 1:12<br> RIVER_NO3<br> Nitrate added by rivers 1:360 1:180 ... 1:12<br> NDEP_NO3 Nitrate added by deposition 1:360 1:180 ... 1:12<br> REMIN Oxic remineralization of OM (DO 1:360 1:180 1:31 1:12<br> EXCR1 Excretion by microzooplankton 1:360 1:180 1:31 1:12<br> EXCR2 Excretion by mesozooplankton 1:360 1:180 1:31 1:12<br> DENITNO3 Denitrification rate (NO3-->NO2 1:360 1:180 1:31 1:12<br> DENITNO2 Denitrification rate (NO2-->N2) 1:360 1:180 1:31 1:12<br> ANAMMOX Anaerobic oxidation of NH4 (NH4 1:360 1:180 1:31 1:12<br> ALTREM Alternative anaerobic remin (DO 1:360 1:180 1:31 1:12<br> SDEN3D Sed denitrification of OM (NO3- 1:360 1:180 1:31 1:12<br> SREM3D Sed remineralisation of OM (DOC 1:360 1:180 1:31 1:12<br> <br> Files:</p> <ul> <li> ETOPO_nitr_kaoafer00_1m_ptrc.nc</li> <li> ETOPO_nitr_kaoafer00_1m_diad.nc</li> <li> ETOPO_nitr_kaoafer00_2ndpicontrol_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_2ndpicontrol_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acid_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acid_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_warm_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_warm_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_circ_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_circ_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_full_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_full_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_picontrolalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_picontrolalt_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acidalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_acidalt_1m_diad_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_fullalt_1m_ptrc_2081-2100_ave.nc</li> <li> ETOPO_nitr_kaoafer00_fullalt_1m_diad_2081-2100_ave.nc</li> </ul> <p> </p> <p>Naming convention:<br> "ETOPO" - refers to being on a regular 1x1 degree horizontal grid<br> "nitri" - refers to the developed PISCES model with explicit two-step nitrification<br> "kaoafer00" - refers to no iron limitation of AOA <br> "1m" - refers to the timestep resolution, here 1 month. Thus, all data presented here is monthly averaged values.<br> "2ndpicontrol" - refers to preindustrial control run<br> "acid" - refers to the control run + ocean acidification<br> "warm" - refers to the control run + warming<br> "circ" - refers to the control run + circulation change<br> "full" - refers to the control run + ocean acidification + warming + circulation change<br> "picontrolalt" - refers to preindustrial control run (alternative pH parameterisation)<br> "acidalt" - refers to the control run + ocean acidification (alternative pH parameterisation)<br> "fullalt" - refers to the control run + ocean acidification + warming + circulation change (alternative pH parameterisation)<br> <br> Contact: Pearse.Buchanan@liverpool.ac.uk or pbuchanan@carnegiescience.edu</p> <p> </p>
DEPRECATED body site-typical microbiome signatures - please see https://doi.org/10.5281/zenodo.7544549 for all future versions
<p><strong>DEPRECATED</strong> - this record is no longer being updated. Please see https://doi.org/10.5281/zenodo.7544549 for all future versions. </p> <p>Using the curatedMetagenomicData Bioconductor package (3.2.1) we calculated taxa prevalence of healthy control samples for each body site, at species and genus taxonomic levels, stratified by adult and child.</p>
PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS.
<p>This is the data for "PREDICTING THE HABITAT SUITABILITY OF ASIAN ELEPHANTS UNDER FUTURE CLIMATE SCENARIOS."</p>
VCF file from: Signatures of local adaptation to current and future climate in phenology-related genes in natural populations of Quercus robur
<p>Qrob_seqcap_87_18799.vcf - containing 18,799 single nucleotide variants (SNPs) in phenology-related genes in 87 individuals of Quercus robur from 6 populations, described in Meger et al.</p> <p> </p>
Past, present and future rainfall erosivity in Central Europe
<p>Past, present and future rainfall erosivity in central Europe calculated from convection-permitting climate simulations in COSMO-CLM using emission scenario RCP 8.5. A description of the dataset and methodology is given in the article "Past, present and future rainfall erosivity in central Europe based on convection-permitting climate simulations" by Magdalena Uber et al. (2024) in Hydrology and Earth System Sciences (https://doi.org/10.5194/hess-28-87-2024).</p> <p>This work was funded by the German Federal Ministry for Digital and Transport Network of Experts.</p> <p> </p> <p> </p>
FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 2: Essays, Finland.
<p><strong>Version 1.0.</strong></p> <p><strong>Related to </strong>https://zenodo.org/record/4734161</p> <p><strong>Changes: README </strong>added to this description page.</p> <p> </p> <p>Description</p> <p>This matrix, presented in two formats (.xlsx and .csv), contains an English-language dataset (translated from original Finnish). The data relate to a research article <em>Agency and transformative potential of technology in students’ images of the future: Futures thinking as critical scientific literacy, </em>accepted to be published in Science & Education.</p> <p>As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Here, excerpts from students' essays (the context of which is given in the article) are given. The excerpts are the ones that have been used in analysis for the article identified above. Further details will be available in the published article.</p> <p>A number of excerpts are given, originating in 57 essays in which upper-secondary students imagine the year 2035 or 2040 and the technological environment in which they would like to live at that time. This overlaps with another dataset (see link above); a numbering scheme was used to group codes for the analysis: type of technology (1), effect of technology (1E), and positive/negative framing (2A). The 1-codes are omitted. While these are unrelated to the article of this analysis, the 2B-2D codes correspond to the categories in the article. Due to some unfortunate redundancies, some excerpts are separated in this version. However, the data should provide transparency for the analysis.</p> <p>The dataset is intended for providing transparency, but it may also be used for further research, in which case some processing is needed. Assistance (clarification) may be available from the authors at reasonable request. Please note that the dataset presented here contains redundancies and may contain a few additional codes that were not used in the analysis. The redundant quotations from the essays were not duplicated in the analysis, but were not removed from this spreadsheet export. Apologies for any inconvenience.</p> <p>To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the quotations are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters, as these can easily be inferred.</p> <p>The related research article gives a fuller description of the dataset and analysis.</p> <p>Please contact the corresponding author for more information.</p> <p> </p> <p>---</p> <p><a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:</p> <p> </p> <p><strong>README</strong></p> <p><strong>Data Set Title:</strong> “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland"</p> <p><strong>Data Set Author/s:</strong> Antti Laherto, Tapio Rasa, Jari Lavonen (University of Helsinki)</p> <p><strong>Data Set Contact Person/s</strong>: Tapio Rasa<strong> </strong>(University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi;</p> <p><strong>Data Set License</strong>: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>Publication Year</strong>: 2023</p> <p><strong>Project Info</strong>: FEDORA<strong> </strong>(Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty<strong> , </strong>funded by European Union, Horizon 2020 Programme. Grant Agreement num.<strong> </strong>872841,<br> www.fedora-project.eu)</p> <p> </p> <p><strong>Data set Contents</strong></p> <p>The data set consists of:</p> <p>One spreadsheet file, provided in two alternative formats (CSV and XLSX).</p> <p>Students_images_of_tech_futures_agency_DATA_Zenodo_csv.csv</p> <p>Students_images_of_tech_futures_agency_DATA_Zenodo_xlsx.xlsx</p> <p> </p> <p><strong>Data set Documentation</strong></p> <p><em>Given above this README, on the ZENODO repository. </em><em>https://zenodo.org/record/6397196</em></p> <p> </p>
Fig. 4 in Current and future suitable habitats of a range-restricted species group (Cyrtodactylus chauquangensis) in Vietnam
Fig. 4. The potential distribution of the C. chauquangensis species group under climate change scenarios.
Fig. 3 in Current and future suitable habitats of a range-restricted species group (Cyrtodactylus chauquangensis) in Vietnam
Fig. 3. The response curves of the top three highest contribution predictors for the C. chauquangensis species group model. The red line shows the mean response of 25 replicates in the Maxent model, and the blue band illustrates the standard deviation. A, Karst distance; B, Bio2 ‒ Mean diurnal range; C, Bio 14 ‒ Precipitation of Driest Month.
Fig. 2 in Current and future suitable habitats of a range-restricted species group (Cyrtodactylus chauquangensis) in Vietnam
Fig. 2. The potential distribution of the C. chauquangensis species group under present conditions generated by MaxEnt, A, Minimum training presence logistic threshold; B, 10 percentile training presence logistic threshold.
Predicting the Future of AI with AI
<p>This dataset augments the paper "Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network" by Mario Krenn, Lorenzo Buffoni, Bruno Coutinho, Sagi Eppel, Jacob Gates Foster, Andrew Gritsevskiy, Harlin Lee, Yichao Lu, Joao P. Moutinho, Nima Sanjabi, Rishi Sonthalia, Ngoc Mai Tran, Francisco Valente, Yangxinyu Xie, Rose Yu, Michael Kopp.</p> <p>GitHub: https://github.com/artificial-scientist-lab/FutureOfAIviaAI</p>
Global gridded GDP under the historical and future scenarios
<p>We have extended the time series of global GDP based on Version 5 at https://zenodo.org/record/5880037#.Yyx4lsi5fRQ, which makes the following changes:</p> <p>a) includes annual global GDP from 2000 - 2020, the unit is PPP 2005 international dollars. </p> <p>b) updates the GDP projections for the period 2025 - 2100 at five-year intervals under five SSPs, and the unit is PPP 2005 international dollars, which allows for comparsion against the historical values mention above.</p> <p>This dataset consists of a total of 101 tif images with spatial resolutions of 1 km (in 7 zip files) and 0.25-degree, respectively. The gridded GDP are distributed over land, with Antarctica, oceans, and some non-illuminated or depopulated areas marked as zero. The spatial extents are 90S - 90N and 180E - 180W in standard WGS84 coordinate system.</p> <p>For more details, please refer to the article: Global gridded GDP data set consistent with the shared socioeconomic pathways that is consistent with Version 5 (GDP unit is PPP 2005 U.S. dollars).</p>
Auralizations of Current and Future Aircraft Concepts
<p>The noise of the four flyovers in this video are purely synthetic sound - so called auralizations.</p> <p>In the Horizon 2020 research project ARTEM (Aircraft noise Reduction Technologies and related Environmental iMpact: <a href="https://cordis.europa.eu/project/id/769350">https://cordis.europa.eu/project/id/769350</a>) funded by the European Union, these four presented and 40 more auralizations were used in a psychoacoustic laboratory experiment, conducted at Empa Dübendorf, to investigate the noise annoyance to aircraft flyovers of a future aircraft design compared to a current commercial aircraft.</p> <p> </p> <p>[1] R. Pieren, I. LeGriffon, L. Bertsch, A. Heusser, F. Centracchio, D. Weinstraub, C. Lavandier, and B. Schäffer, "Perception-based noise assessment of a future blended wing body aircraft concept using synthesized flyovers in an acoustic VR environment – the ARTEM study", Aerosp. Sci. Technol., vol. 144, 2024, doi.org/10.1016/j.ast.2023.108767</p> <p>[2] B. Schäffer, L. Bertsch, I. Le Griffon, A. Heusser, C. Lavandier, and R. Pieren, "Evaluation of flyover auralizations of today's and future long-range aircraft concepts", International Congress and Exposition on Noise Control Engineering (InterNoise), Glasgow, 21-24 August 2022.</p> <p>[3] R. Pieren and D. Lincke, "Auralization of aircraft flyovers with turbulence-induced coherence loss in ground effect", J. Acoust. Soc. Am., vol. 151, no. 4, pp. 2453-2460, 2022.</p> <p>[4] R. Pieren, L. Bertsch, D. Lauper, and B. Schäffer, "Improving future low-noise aircraft technologies using experimental perception-based evaluation of synthetic flyovers", Sci. Total Environ., vol. 692, pp. 68-81, 2019.</p>
TemoaProject: Databases used in Sinha et al. (2024), Diverse Decarbonization Pathways Under Near Cost-Optimal Futures
<p>Contains modeling to generate alternative databases for the U.S. energy system used as part of the Open Energy Outlook. Databases were created using the logic outlined in: https://github.com/adityasinha1992/temoa/tree/mga_parallelized</p>
Interviews on the future of digital editing and publishing
<p>Transcriptions of 46 interviews with theorists and practitioners in the field of digital scholarly editing on topics relating to the future of digital scholarly editions</p>
CROVER model output data for paper "Future Warming Decreases the Benefits of Irrigation in Global Food Production"
<p>This data is output data simulated by the CROVER crop-river coupled model for the scientific paper entitled: “Future Warming Decreases the Benefits of Irrigation in Global Food Production”. Simulations are based on 120-year simulations (1981-2100) using 20 climate projections (four Representative Concentration Pathways (RCPs) (2.6, 4.5, 6.0, and 8.5) × five General Circulation Models (GCMs) (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M)). The dataset includes crop yield of the modeled five crops (maize, rice, soybean, spring wheat, and winter wheat) each for irrigated and rainfed with a spatial resolution of 1.125 degree.</p> <p>The file name consists of a series of identifiers, separated by underscore, according to the following pattern.</p> <p>crover_<gcm>_<rcp>_yield_<crop>_<irrigated|rainfed>.grd</p> <p>crover_<gcm>_<rcp>_yield_<crop>_<irrigated|rainfed>.ctl</p> <p>Format: grads binary (global, 1.125 degree)</p> <p>These files were compressed as a zip file for each GCM and RCP.</p> <p> </p>
Data, tomatoes and ecological futures
<p>Exhibited complementary artefact for author's research presentation at the NORDES Doctoral Consortium </p>
Apulian Aqueduct demo site: daily time series of simulated system behavior for future inflows conditions (RCP4.5)
<p>This dataset contains the daily time series obtained from the strategic model simulation, considering net estimated inflows (natural springs and main reservoirs of Apulian aqueduct - demo site 1) considering climate projection RCP 4.5 and two decades, in the medium (2050-2059) and long-term future (2090-2099). In all simulations, a modified version of the drinking water demand is considered, following a different distribution of the populations, an increase in density in coastal areas also due to investments in the tourism sector, to the detriment of density in inland areas.</p> <p>For each decade, two simulations are performed, with or without the rehabilitation of some well-fields making the water withdrawn drinkable with innovative purification techniques.</p> <p>. More precisely, the dataset contains:</p> <ul> <li>the daily level of the main reservoirs;</li> <li>the water supplied to all users (drinking water users, irrigation, and industrial districts) from each reservoir;</li> <li>the corresponding single irrigation and industrial deficit;</li> <li>the total drinking water deficit;</li> <li>the aggregated distribution cost.</li> </ul> <p>Two simulations are performed, with or without the environmental flow constraint acting on each reservoir release activated.</p> <ul> <li>Temporal coverage: 2050-2059; 2090-2099</li> <li>Spatial coverage: <ul> <li>Springs: Sele, Calore;</li> <li>Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo;</li> <li>Users: drinking water users, irrigation, and industrial districts supplied by Apulian aqueduct.</li> </ul> </li> <li>Unit of measure: <em>m</em>, <em>m3/s</em> depending on the variable</li> </ul> <p>More information and details on the content of this dataset can be found in Project Ô <a href="https://zenodo.org/record/7576611">Deliverable D4.4</a>.</p>
ScienceDex guides
Understand access before you commit
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.