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67 results for “timing projections”
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Ensemble projections (+ uncertainties) of contemporary (2012-2031) and future (2081-2100) mean annual plankton/phytoplankton/zooplankton species diversity (and species turn-over in time) for the global surface open ocean.
<p><em><strong>Gridded spatial fields (raster objects) containing the species distribution models (SDMs) projections of mean annual plankton total plankton, phytoplankton and zooplankton species diversity from Benedetti et al. (2021). </strong></em></p> <p>The present .grd file ('rasterStack' object in R) contain the fields of mean annual surface plankton/phytoplankton/zooplankton species diversity for the contemporary (2012-2031) and future (2081-2100) conditions of the global open ocean (i.e., data underlying those maps in Figure 1 and Figure 3 of Benedetti et al., 2021). Layers quantifying the uncertainty (i.e., the variablity across models projections estimated through the standard deviation) in ensemble projections were also added (i.e., data underlying the maps in Supplementary Figure 4). See the Methods section of Benedetti et al. (2021) for a full description of the methodology and the ensemble SDMs forecasting framework. The raster layers follow the 1°x1° cell grid of the World Ocean Atlas (https://www.ncei.noaa.gov/).</p> <p>In short, we empirically modelled the monthly and mean annual diversity patterns stemming from the distribution of 860 plankton species (336 phytoplankton, 524 zooplankton) spanning 13 phyla, 71 orders and 324 genera through an ensemble approach based on SDMs. The considered species cover a wide range of traits and functions, representing 10 major plankton functional groups (PFGs; three phytoplankton and seven zooplankton groups). We compiled the species occurrence records from various data sources (available here: https://zenodo.org/record/5101349#.YO7Dqm469lM) and aggregated them onto a monthly-resolved 1°x1° grid, excluding observations from regions where the seafloor is shallower than 200 m. We matched these binned open ocean records with observation-based climatologies of environmental predictors (temperature, dissolved oxygen concentration, solar irradiance, macronutrients concentration, chlorophyll a concentration) that reflect the climatic and biogeochemical conditions of the surface open ocean. Four types of SDMs (generalized linear models, generalized additive models, artificial neural networks, and random forests) were fitted to model the species’ current environmental habitat suitability patterns. For each SDMs, we used four alternative pools of predictors. Assuming niche conservatism, we projected each of the 16 resulting species-level habitat suitability models into the future using outputs from five ESMs belonging to the Coupled Model Intercomparison Project 5 (CMIP5) that were forced by the Representative Concentration Pathway 8.5 (RCP8.5) scenario of high greenhouse gas concentrations. To this end, we first computed the modelled monthly climatologies of the selected predictors for the 2012-2031 and 2081-2100 periods, and derive the future monthly anomalies from the differences between these two time periods. These anomalies were added to the observation-based monthly climatologies (i.e., those used to train the SDMs) to estimate the future environmental conditions of the ocean, and projected the SDMs in these future conditions. Finally, we estimated the mean annual present and future alpha diversity (species richness; SR) and beta diversity (species turnover through time) patterns for both trophic levels, for each cell, from the ensemble of SDMs. SR ensembles are estimated as the sum of all species’ habitat suitability patterns averaged across all 80 possible combinations (i.e., "ensemble members") of SDMs (n = 4), ESMs (n = 5) and predictor pools (n = 4). To assess the uncertainties of our diversity projections based on the ensemble members, we compute the interquartile range of the 80 ensemble members SR projections. We calculate species turnover as the change in mean annual species composition between present and future time based on Jaccard’s dissimilarity index and by decomposing this total turnover into the true species turnover (ST, also known as species replacement) and the nestedness (SR change) components. Numerous tests are conducted to ensure the robustness of the results with regard to the spatially and temporally highly uneven sampling effort as well as with regard to the relative role of different predictors.</p> <p><strong>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862923. This output reflects only the author’s view, and the European Union cannot be held responsible for any use that may be made of the information contained therein.</strong></p>
Online Real-Time Delphi Survey for the research project "MENARA" - Compilation of all Comments to Closed and Open Questions
<p><strong>Looking into the Futures: Delphi Survey about the MENA region</strong></p> <p>In order to get a more realistic overview of the situation and trends, of the potentials, problems and potentials of the countries of the MENA region a Real Time Delphi survey was conducted. This is an important tool of modern future research. It was managed by the IZT- Institute for Future Studies in Berlin. A group of 139 experts and researchers from different institutes and organizations were invited to participate at the Online Real-Time Delphi Survey (RTD) about possible and likely futures of the MENA region. The experts were asked to answer questions and provide their opinions on twelve topics such as social unrest, youth unemployment, urbanization, gender equality, security etc. In this dataset all comments to the closed and the open questions are compiled.</p> <p>The output was one of the basic material used for the creation of future regional scenarios for mid-term (2025) and long-term (2050) time horizons. Focus scenarios were produced in order to exemplify selected characteristic and important future options, in terms of chances and risks (e.g. energy futures).</p>
Demographic factors and the environmental Kuznets curve: global plastic pollution by 2050 could be 2 to 4 times worse than projected
<p>These data are made of two files. One file provides the observed data we collected and cleaned from the World Bank database. The second file provides the simulation results from the STIRPAT model we designed based on the observed data abovementioned. Our results can be summarised as follows:</p> <p>Since 2015, the detrimental effects of plastic pollution have attracted media, public, and governmental attention. Considering economic growth is inevitable and a key driver of plastic contamination, it is worthwhile to analyze the environmental Kuznets curve (EKC) relationship between economic development and plastic pollution. To this end, we contribute by being the first to (i) use the Stochastic Impacts by Regression on Population, Affluence, and technology model (STIRPAT model) to investigate this EKC relationship; (ii) provide a comprehensive analysis of how demographic factors affect plastic pollution; and (iii) use panel model techniques to examine the drivers of plastic pollution. Our empirical results support an inverted U-shaped relationship between plastic pollution and income. They show that at current trends, global plastic pollution (that is, annual discard of inadequately managed plastic waste) is expected to grow from 52 million tons per year in 2020 to 257 million tons per year in 2050.</p>
Time projections of Sea Surface Temperature, for RCP 4.5 and RCP 8.5, for decades 2020, 2030, 2040 and 2040
<p>The CMIP5 Sea Surface Temperature models projections correspond to the Coupled atmosphere-ocean general circulation models’ output named ‘tos’ (Temperature Of Surface) with a monthly time-step (12 values per year, from 2006 to 2100), for RCP 4.5 and RCP 8.5. For a given RCP, some models can have different sets of input parameters (called input ensemble), numbered r1i1p1, r1i1p2, etc., corresponding to different settings, resulting is an output for each rXiYpZ input. Variable ‘tos’ is provided by 86 combinations of models and input ensembles (see list in Annex). To compute an ensemble mean with equal weight for each model, the different outputs of a single model are first averaged. The resulting averaged models outputs, 1 average per model, are then regridded to a common grid, defined as a regular grid, with a spatial resolution of ½ ° in latitude per ½ ° in longitude, from 0° to 360° in longitude, and -85° to 85° in latitude. Then, the regridded averages are averaged all together with the same weight.</p> <p>The averaging operations are grid-cell and time independent, which means that the averaging operator is not applied along the space and time dimensions, only in-between the different models values for the same place and time.</p> <p><br /> The result of the operation is a time series of ocean surface temperature, from 2020 to 2050, at a grid resolution of 0.5°. Because of the difference in the spatial gridding, and difference in the land mass representation, some grid points did not used the same number of models averages to compute the final average: the number of model averages per grid cell is given in the final product, as well as the min-max amplitude between model averages.</p>
Data, plotting scripts, and figures for "A Projective Method for Solving the Single-Group Space-Time Neutron Kinetics Equations with Precursor Advection"
<p>Contains all the files necessary for figure reproduction.</p>
Projections of the Timing of Decreasing Coastal Flood Protection
<p>This data set contains projections of the timing of decreasing coastal flood protection (i.e., projections of the timing of frequency amplifications of estimated flood protection standards) associated with Hermans et al. (in revision), The Timing of Decreasing Coastal Flood Protection Due to Sea-Level Rise. It contains the output of extreme value analysis of high-frequency GESLA3 tide gauge observations (daily maxima, generalized Pareto distribution fits and return curves), total AR6 sea-level projections interpolated to GESLA3 tide gauge locations, and the required sea-level rise for and the timing of frequency amplifications relative to estimated coastal flood protection standards (FLOPROS) or to the historical centennial event. Results are included for both automatically selected extremes thresholds and for a constant threshold of 98.8% at all locations.</p> <p>The manuscript that this data accompanies can be found at <a href="https://www.nature.com/articles/s41558-023-01616-5">https://www.nature.com/articles/s41558-023-01616-5</a>.</p> <p>The code used to produce this data can be found at <a href="https://github.com/Timh37/TimingAFs">https://github.com/Timh37/TimingAFs</a>.</p> <p><strong>Required Acknowledgements and Citations</strong></p> <p>Users of this dataset are asked to cite:</p> <ul> <li>The manuscript that this dataset accompanies: Hermans, T.H.J., Malagón-Santos, V., Katsman, C.A. <em>et al.</em> The timing of decreasing coastal flood protection due to sea-level rise. <em>Nat. Clim. Chang.</em> <strong>13</strong>, 359–366 (2023). https://doi.org/10.1038/s41558-023-01616-5</li> <li>Garner, G. G., T.H.J. Hermans, R. E. Kopp, A. B. A. Slangen, T. L. Edwards, A. Levermann, S. Nowikci, M. D. Palmer, C. Smith, B. Fox-Kemper, H. T. Hewitt, C. Xiao, G. Aðalgeirsdóttir, S. S. Drijfhout, T. L. Edwards, N. R. Golledge, M. Hemer, G. Krinner, A. Mix, D. Notz, S. Nowicki, I. S. Nurhati, L. Ruiz, J-B. Sallée, Y. Yu, L. Hua, T. Palmer, B. Pearson, 2021. IPCC AR6 Global Mean Sea-Level Rise Projections. Version 20210809. Dataset accessed [YYYY-MM-DD] at <a href="https://doi.org/10.5281/zenodo.5914709">https://doi.org/10.5281/zenodo.5914709</a></li> <li>Tiggeloven, T., de Moel, H., Winsemius, H. C., Eilander, D., Erkens, G., Gebremedhin, E., Diaz Loaiza, A., Kuzma, S., Luo, T., Iceland, C., Bouwman, A., van Huijstee, J., Ligtvoet, W., and Ward, P. J.: Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures, Nat. Hazards Earth Syst. Sci., 20, 1025–1044, <a href="https://doi.org/10.5194/nhess-20-1025-2020">https://doi.org/10.5194/nhess-20-1025-2020</a>, 2020</li> </ul> <pre><code>R.E.K. was supported by the National Science Foundation (NSF) as part of the Megalopolitan Coastal Transformation Hub (MACH) under NSF award ICER-2103754. T.H.J.H., V.M.-S. and A.B.A.S. were supported by PROTECT. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869304, PROTECT contribution number TBD.</code></pre> <p> </p>
Data and Code for "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" Paper Submission to AGU GRL
<p>This contains the emergent constraint code, the offline CMIP6 hydrology data, and the shapefiles for each region used in the paper "A Novel Emergent Constraint Approach for Refining Regional Climate Model Projections of Flood Timing" submitted to AGU GRL.</p>
Real-Time Dataset of Metheorological Measurements Collected During the Project
<p>This dataset contains a collection of real-time meteorological data during the Resisto project. This information has been recorded and is structured into different fields, each representing a specific meteorological variable of the deployed sensors in the Doñana National Park. </p>
Real-Time Dataset of Fire Sensor Measurements Collected During the Resisto Project
<p>This dataset contains real-time environmental measurements from fire detection sensors across multiple locations. These sensors has been deployed on diferent locations, principally on the Doñana National Park. </p>
SINGER-inferred targets for exceptional population differentiation in coalescence times in African populations in 1000 Genomes Project
<p>This repo saves the gene targets which shows the signal of population differentiation in coalescence times in African populations in 1000 Genomes Project. </p> <p>Here are the detailed explanations for the header of the files:</p> <p>chrom The chromosome where the genomic window is located (e.g., chr1, chrX).<br>window_start The 0-based start position of the genomic window being analyzed.<br>window_end The 1-based end position of the genomic window (exclusive).<br>score Ratio between the overall diversity and the population specific diversity, in the genomic window.<br>transcript_chrom The chromosome where the transcript is located.<br>transcript_start The 0-based start position of the transcript.<br>transcript_end The 1-based end position of the transcript (exclusive).<br>transcript_id The unique identifier for the transcript (e.g., Ensembl or RefSeq ID).<br>strand The strand of the transcript: "+" for forward, "-" for reverse.<br>coding_start The start position of the coding region of the transcript (if applicable).<br>coding_end The end position of the coding region of the transcript (if applicable).<br>RGB The RGB color used for visualization in genome browsers (format: R,G,B).<br>block_count The number of exons in the transcript.<br>block_sizes Comma-separated list of exon lengths (in base pairs).<br>block_starts Comma-separated list of exon start positions relative to transcript_start.</p>
Project files provided as supporting information to the manuscript "Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework"
<p><strong>Project files provided as supporting information to the manuscript "Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework"</strong></p> <p><br> GLE Optimization: Optimizator of GLE parameters. Uses Matlab</p> <p>MD_GLE: CG GLE and LE simulator. Uses Matlab</p> <p>Water_simulation: Folders for atomistic water system simulation with GROMACS. It requires to be run on Linux with GROMACS <br> and VOTCA packages installed.</p>
Rethinking Sea-Level Projections using Families and Timing Differences
<p>Dataset for publication "Rethinking Sea-Level Projections using Families and Timing Differences"</p>
Simulations dataset and pre-trained models of "Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory" Ph.D. project
<p>Ph.D. project datasets and models release, <br><em>Deep learning in real-time on the astrophysical data obtained from the Čerenkov CTA Observatory.</em></p>
Ferneyhough_dataset_PoD: Dataset containing results from the research project Points of Discontinuity concerning Brian Ferneyhough, Time and Motion Study I for bass clarinet (1971–77)
<p>The complete datasets resulting from the research project <em>Points of Discontinuity</em> contain 23 datasets for the musical works or excerpts that were part of the online listening experiment, with each dataset containing seven or eight files (all audio files are stored in a dataset with restricted access), as well as a dataset (PoD_general_dataset) with five additional files.</p> <p>This dataset<strong> Ferneyhough_dataset_PoD </strong>contains eight files:</p> <ul> <li>Ferneyhough_01_ReadMe.pdf</li> <li>Ferneyhough_02_data.xlsx (processed data for this work)</li> <li>Ferneyhough_03_individual_data.xlsx (raw data for each participant obtained from the experiment)</li> <li>Ferneyhough_04_audio.mp3 <strong>[non-public] </strong>(audio recording used in the experiment) [stored in the restricted dataset <a href="https://doi.org/10.5281/zenodo.13981214" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.13981214</a>]</li> <li>Ferneyhough_05_model_results.sv (graphical representation of results and the model in Sonic Visualiser) [requires audio file Ferneyhough_04_audio.mp3 to display correctly]</li> <li>Ferneyhough_06_SV-data_model+results.zip (text files with the marker locations for all layers in Sonic Visualiser)</li> <li>Ferneyhough_07_model+results_SV-screenshot.pdf (a screenshot of the full-screen display of the SV-file)</li> <li>Ferneyhough_08_annotated_score.pdf (model analysis annotated in the score)</li> </ul>
Apulian Aqueduct demo site: daily time series of estimated inflows (natural springs and reservoirs) for climate projections
<p>This dataset contains the daily time series of net estimated inflows (natural springs and main reservoirs of Apulian aqueduct - demo site 1) computed with a lumped rainfall-runoff model starting from climate projections of precipitation and temperature.</p> <p>This dataset considers two representative concentration pathways (RCP 4.5 and 8.5) and two decades, in the medium (2050-2059) and long-term future (2090-2099).</p> <ul> <li>Temporal coverage: 2050-2059; 2090-2099</li> <li>Spatial coverage: Springs: Sele, Calore; Reservoirs: Conza, Locone, Monte Cotugno, Occhito, Pertusillo</li> <li>Unit of measure: m3/s</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>
Prediction of Port Recovery Time after a Severe Storm Project
<p>Predicting the impact of incoming tropical cyclones on ports in terms of the number of days underperforming is crucial for the effective management of the ports. However, existing methods perform undesirably due to the limited data and the inherent uncertainty associated with cyclone trajectory forecasting. This study applies a recommendation algorithm to address these challenges by focusing on predicting port impact rankings instead of predicting the duration of port impacts, which is often inaccurate and unreliable. First, we have collected comprehensive features of ports and hurricanes in the Gulf of Mexican and employed a modular time-series regression model to determine the duration of port impacts due to tropical cyclones, leveraging vessel count data extracted from the Automatic Identification System (AIS). Inspired by the recommendation algorithm, we recast tropical cyclones and ports as “user” and “items,” respectively, while the duration of port impacts represents their “interaction,” offering an innovative approach to model and analyze cyclone effects on ports. The Factorization Machine (FM) is adopted to learn the relationship between features (i.e., ports and cyclones) and subsequently conduct the port impact ranking. Finally, utilizing the hurricanes Alex, Ian, and Nicole that happened in 2022 as testing cases, the FM-based model excels in prediction performance and robustness against uncertainties compared to the widely used distance-based method. This study aims to provide port authorities and other stakeholders with a trustworthy tool for informed disaster management decisions, thereby enhancing port resilience. </p>
Data from: Space-for-time substitution misleads projections of plant community and stand-structure development after disturbance in a slow-growing environment
Open the record for dataset details and reuse information.
MOTIVE - tiMe-OpTimized contextual Information flow on unmanned VEhicles project experimental results
<p>The experimentation data were collected during the 6th Fed4FIRE+ Open Call (https://www.fed4fire.eu/) using the mobile nodes of the w-iLab.t (link) testbed. During this Open Call we proposed to evaluate the performance of an optimization model for temporal control of the transmission of messages from an IoT mobile device. This mechanism is based on a network condition model that transits from favourable to adverse conditions and vice versa. All these transitions are monitored and validated through our system (change detection and optimal stopping). If a network is performing properly then the transmission control can be relaxed to exploit available resources.The main contribution of MOTIVE is to apply a sequential decision-making process (DMP) on IoT devices that leverages the on-line derived network statistics to efficiently control their telemetry measurements transmission.</p> <p>Our data collected from the experimentation using the testbed's devices consists of network related information, packet error rate and latency. We collected data from a completely functional mobile node that operates in a saturated network and five mobile nodes in the same circumstances. Saturated conditions were generated by data produced by twenty static sensors for the duration of the each experiment. On each run we collected more than 2 * 105 samples. The comparative assessment we did was based on five different policies of decision making: i) no-policy model, ii) the heuristic threshold based model in which the transmission of the messages is paused when a specific threshold of quality network is below a threshold, iii) TOCP model which overviews the quality of network in normal mode and gets in pausing mode when a change is detected; the pausing period lasts for a specific threshold and then it is activated again, iv) TOCP-DRP and the v) fair TOCP-DRP which enters in pausing mode if a change is indicated by TOCP decision making model.</p> <p>The datasets include the packet error rate and the latency of each device, for each experiment, and the critical areas found during the experiments (most saturated areas).</p> <p>The datatset includes for each scenario(experiment) the participating devices and the Latency and Packet Error Rate(PER) per policy( NoPolicy, Heuristic, TOCP, TOCPDRP, TOCPDRPFAIR), apart from scenarioA(single node) where only four policies are present, since the TOCPDRPFAIR applies with more that one participating nodes.</p>
Map-projected time-lapse of zebrafish left-right somite formation
<p>Zebrafish embryos in their chorions were imaged from 6 angles in a multiview light sheet microscope. The imaging was started 10 hours post fertilization and was performed for about 4 to 5 hours at a frame interval of 5 min. The resulting images were fused using FIJI, followed by nuclei detection in the fused images and map projection. This data set contains map projected time-lapses of 6 utr::mcherry transgenic embryos. Utrophin binds to actin filaments and was used as a somite boundary marker. The pixel size varies spatially in a map as well as across different projection layers in a single time-lapse. The corresponding Map_pixel_size mat file for each time-lapse contains respective pixel sizes.</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.