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32 results for “near-term”
Towards near-term quantum simulation of materials
<p><strong>Overview of data provided in support of <em>Towards near-term quantum simulation of materials</em></strong></p> <p>Contents of this folder:</p> <ul> <li>`analyse_materials_results.py`: script used to generate the summary tables and figures presented in the manuscript. </li> <li>`towards_quantum_simulation_data`: raw data used and produced when studying various 3D materials.</li> <li>`towards_quantum_simulation_analysis`: tables (in `.tex` format) and figures (as PDFs) presented in the manuscript.</li> </ul> <p>The contents of `towards_quantum_simulation_analysis` were generated by `analyse_materials_results.py`, <br> i.e. the user need not run the script to produce the output files. <br> Users can generate these analyses directly by running ```python analyse_materials_results.py```, <br> though note the following packages will first need to be installed:</p> <ul> <li>`pandas >= 1.2.3`</li> <li>`numpy >= 1.23`</li> <li>`seaborn >= 0.11.1`</li> <li>`lfig >= 0.1.3`</li> <li>`matplotlib > 3.7.0`</li> </ul> <p><strong>Included data</strong><br> Within `towards_quantum_simulation_data`, there are subfolders <br> for each of the materials described in the manuscript, i.e. `SrVO3`, `GaAs`, `H3S`, `Si`, `Li2CuO2`.</p> <p>Within each material's folder are further subfolders for the `hamiltonian` and `encoding` used to represent the material, as well as subfolders for each of the `algorithms` studied. </p> <ul> <li>`hamiltonian`: files which specify the Hamiltonian for the material under study. There are a number of files <ul> <li>`interactions.json` Hamiltonian terms in terms of Majorana monomials.</li> <li>`map_majorana_to_mode.json` keys are Majorana indices; values are the mode index to which they are associated.</li> <li>`map_mode_to_group.json` keys are mode indices; values are the group (or site) index to which they are associated. </li> <li>`map_group_to_position.json` keys are group indices; values are the corresponding 3D Cartesian coordinates of the lattice used to represent the material. </li> <li>`stage_data.json` contains key/value pairs of any other fields of interest.</li> </ul> </li> <li>`encoding` files which specify the fermionic encoding which is customised for the material under study. <ul> <li>`encoding.json` which details the edges of the hybrid compact encoding described in Section VI of the supplementary material.</li> <li>`precompiler.json` contains all the information which permits the encoding construction, including the Hamiltonian terms (`_interactions`) which match those in `hamiltonian/interactions.json`. <ul> <li>The same mappings as present in the Hamiltonian data(a.g. `map_group_to_position`).</li> </ul> </li> <li>`stage_data.json` contains key/value pairs of any other fields of interest.</li> </ul> </li> <li>`algorithms` contains subfolders for each of the algorithms desribed in the manuscript <ul> <li>Those explored for circuits depths: <ul> <li>`TDSSplitTermsPriorityCircuitDepth` (TDS in the manuscript)</li> <li>`TDSSplitTermsPriorityCircuitDepthNoSwapNetwork` (TDS\*)</li> <li>`VQESplitTerms` (VQE)</li> <li>`VQESplitTermsNoSwapNetwork` (VQE*)</li> </ul> </li> <li>each of which contain the files, inside the `circuitry` folder: <ul> <li>`circuit_terms.csv`, which lists each individual term, together with their Pauli string and rotation angle, required to construct the corresponding quantum circuit </li> <li>`circuit_layers_to_implement.csv` groups the same terms into layers to achieve parallelism in the circuit.</li> <li>`stage_data.json` contains key/value pairs of any other fields of interest.</li> </ul> </li> <li>and those used to compose measurement layers, as outlined in Section VII D of the supplementary material: <ul> <li>`MeasurementCommutativity` </li> <li>`MeasurementNaiveQubitwise`</li> <li>`MeasurementNonCrossing`</li> </ul> </li> <li>each of which contain the files, inside the `compilation` folder: <ul> <li>`layers.csv` lists the terms which may be measured simultaneously to achieve the measurement strategies shown in Table S14.</li> <li>`stage_data.json` contains key/value pairs of any other fields of interest.</li> </ul> </li> </ul> </li> </ul> <p><strong>CSV files</strong></p> <p>In `towards_quantum_simulation_data`, there are unified CSV files containing the results of applying the procedures described in the manuscript to the target materials.</p> <ul> <li>`circuit_costs.csv`: results of running the circuit compiler described in the manuscript.</li> <li>`measurements.csv`: results of running the measurement compiler described in the manuscript.</li> </ul> <p>These CSVs are used in the analysis script `analyse_materials_results.py` to produce the figures and tables presented in the manuscript.</p>
State-of-the-art review of near-term freshwater forecasting literature published between 2017 and 2022
This data publication includes code and results from a systematic literature review on the current state of near-term forecasting of freshwater quality. The review aimed to address the following questions: (1) Freshwater variables, scales, models, and skill: Which freshwater variables and temporal scales are most commonly targeted for near-term forecasts, and what modeling methods are most commonly employed to develop these forecasts? How is the accuracy of freshwater quality forecasts assessed, and how accurate are they? How is uncertainty typically incorporated into water quality forecast output? (2) Forecast infrastructure and workflows: Are iterative, automated workflows commonly employed in near-term freshwater quality forecasting? How are forecasts validated and archived? (3) Human dimensions: What is the stated motivation for development of most near-term freshwater quality forecasts, and who are the most common end users (if any)? How are end users engaged in forecast development? An initial search was conducted for published papers presenting freshwater quality forecasts from 1 January 2017 to 17 February 2022 in the Web of Science Core Collection. Results were subsequently analyzed in three stages. First, paper titles were screened for relevance. Second, an initial screen was conducted to assess whether each paper presented a near-term freshwater quality forecast. Third, papers that passed the initial screen were analyzed using a standardized matrix to assess the state of near-term freshwater quality forecasting and identify areas of recent progress and ongoing challenges. Additional details regarding the systematic literature search and review are presented in the Methods section of the metadata.
Dataset for the paper "Combining near-term benefits of climate adaptation with long-term benefits of emissions abatement"
<p>This repo archives all data used in Duan et al. (2024), including model codes, raw model outputs, and post-processing scripts. </p> <p>A Readme file describes the data and structure included here. If you have any questions, please contact the lead author (Lei Duan: leiduan@carnegiescience.edu). </p> <p>We have updated the post-process codes to reflect changes in the revised manuscript</p> <p>==</p> <p>Paper associated with this dataset can be found at: https://www.nature.com/articles/s43247-024-01976-6#:~:text=Adaptation%20deployed%20in%20conjunction%20with,adaptation%20reducing%20near%2Dterm%20damage. </p>
Lake Sunapee Gloeotrichia echinulata density near-term hindcasts from 2015-2016 and meteorological model driver data, including shortwave radiation and precipitation from 2009-2016
Hindcasts were generated for density of Gloeotrichia echinulata, a toxin-producing cyanobacterium, at a nearshore site (South Herrick Cove) in Lake Sunapee, NH, USA, from May-October in 2015 and 2016 using several different Bayesian state-space models as part of a Global Lake Ecological Observatory Network working group project (Lofton et al. 20XX). Hindcasts were produced for one-week to four-week forecast horizons. Models ranged in complexity from a random walk to dynamic linear models with up to two environmental covariates. A subset of the model meteorological driver data for calibration and hindcasting was downloaded from the North American Land Data Assimilation System (NLDAS-2; https://ldas.gsfc.nasa.gov/nldas/) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM; http://www.prism.oregonstate.edu/) for Lake Sunapee, New Hampshire, USA. The model driver data derived from NLDAS-2 data are daily summaries of solar radiation on G. echinulata sampling days from 2009-2016. The model driver data derived from PRISM data are daily sums of precipitation on G. echinulata sampling days from 2009-2016. All other model driver data are also published on the Environmental Data Initiative repository and are specified in the Notes and Comments of this data publication. All code to import data, calibrate models, and generate and analyze hindcasts are available on Github at https://github.com/GLEON/Bayes_forecast_WG/tree/eco_apps_release.
Potential near-term wetting of the Southwestern United States if the Eastern Pacific cooling trend reverses
<p>Data for the manuscript "Potential near-term wetting of the Southwestern United States if the Eastern Pacific cooling trend reverses."</p>
Fig. 7 in Gill dimensions in near-term embryos of Amazonian freshwater stingrays (Elasmobranchii: Potamotrygonidae) and their relationship to the lifestyle and habitat of neonatal pups
Fig. 7. (A) The relationship between total gill area (cm2) and Relative Opening of the spiracle. (B) Linear regression of the mass-specific gill area (cm2 g-1) and body surface area (cm2) of different potamotrygonid embryos: upsidedown triangle - Plesiotrygon iwamae; star - Paratrygon aiereba; diamond - Potamotrygon motoro (from Negro River); dot - Potamotrygon motoro (from Solimões River); triangle - Potamotrygon orbignyi; square - cururu ray Potamotrygon sp.
Fig. 6 in Gill dimensions in near-term embryos of Amazonian freshwater stingrays (Elasmobranchii: Potamotrygonidae) and their relationship to the lifestyle and habitat of neonatal pups
Fig. 6. The two main axes of a principal component analysis based on total gill area, mass-specific gill area, body mass, total length, Anatomical Diffusion Factor and Relative Opening of Spiracule (ROSp) in the different potamotrygonid embryos: black triangle - Plesiotrygon iwamae; open diamond - Paratrygon aiereba; circle - Potamotrygon motoro (from Negro River); open triangle - Potamotrygon motoro (from Solimões River); black square - Potamotrygon orbignyi; open square - Potamotrygon sp. (cururu ray).
Fig. 5 in Gill dimensions in near-term embryos of Amazonian freshwater stingrays (Elasmobranchii: Potamotrygonidae) and their relationship to the lifestyle and habitat of neonatal pups
Fig. 5. Triplot of log (Anatomic Diffusion Factor) versus log(water/blood barrier thickness) versus log(mass-specific gill area) of the potamotrygonid embryos.
Fig. 3 in Gill dimensions in near-term embryos of Amazonian freshwater stingrays (Elasmobranchii: Potamotrygonidae) and their relationship to the lifestyle and habitat of neonatal pups
Fig. 3. Mass-specific gill area of the anterior and posterior hemibranchs of the gill arches in different potamotrygonid embryos: (A) Potamotrygon orbignyi; (B) Potamotrygon sp.; (C) Potamotrygon motoro (from Negro River); (D) Potamotrygon motoro (from Solimões River); (E) Plesiotrygon iwamae; (F) Paratrygon aiereba.
Fig. 2 in Gill dimensions in near-term embryos of Amazonian freshwater stingrays (Elasmobranchii: Potamotrygonidae) and their relationship to the lifestyle and habitat of neonatal pups
Fig. 2. Total superficial gill area of the anterior and posterior hemibranchs of the gill arches in different potamotrygonid embryos: (A) Potamotrygon orbignyi; (B) Potamotrygon sp.; (C) Potamotrygon motoro (from Negro River); (D) Potamotrygon motoro (from Solimões River); (E) Plesiotrygon iwamae; (F) Paratrygon aiereba.
Fig. 1 in Gill dimensions in near-term embryos of Amazonian freshwater stingrays (Elasmobranchii: Potamotrygonidae) and their relationship to the lifestyle and habitat of neonatal pups
Fig. 1. The potamotrygonid embryos: (A) Potamotrygon orbignyi; (B) cururu ray Potamotrygon sp.; (C) Potamotrygon motoro (from Negro River); (D) Potamotrygon motoro (from Solimões River); (E) Plesiotrygon iwamae; (F) Paratrygon aiereba. Scale bars= 1 cm.
Data assimilation experiments inform monitoring needs for near-term ecological forecasts in a eutrophic reservoir: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Beaverdam Reservoir forecasting data assimilation experiments using the FLARE (Forecasting Lake And Reservoir Ecosystems) system: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations and meteorological data, forecasts.zip contains forecast parquet files generated from the BVR FLARE DA experiment workflow, and scores.zip contains forecast skill metrics required for analysis. Within the forecasts and scores folders, there are four runs that were conducted with different parameter tuning and uncertainty quantification. The "all_UC" folder includes forecasts run with process, driver, parameter, and initial condition uncertainty quantification. The "IC_off" folder includes forecasts run without initial conditions uncertainty included (i.e., only process, driver, and parameter uncertainty). The "constant_bad_pars" folder includes forecasts run with constant parameters (but daily updating of initial conditions) that were not tuned for Beaverdam Reservoir before forecasts were generated. Finally, the "tuned_bad_pars" folder includes forecasts that were run with daily updating of initial conditions and parameters, but the parameters started out at random values that were not tuned for Beaverdam Reservoir.</p>
Systematic review of near-term ecological forecasting literature published between 1932 and 2020
This data publication includes results and code from a systematic review of near-term ecological forecasting literature. The study had two primary goals: (1) analyze the state of near-term ecological forecasting literature, and (2) compare forecast skill across ecosystems and variables. We began by conducting a Web of Science search for “forecast*” in the title, abstract, and keywords of all papers published in ecological journals, then screened all papers from this search to identify near-term ecological forecasts. We defined a near-term ecological forecast as future predictions of community, population, or biogeochemical variables ≤ 10 years from the forecast date. To more broadly survey the literature, we then searched all papers that cited or were cited by the near-term ecological forecasts we identified. We performed an in-depth review of all near-term ecological forecasting papers identified through this search process, and recorded forecast skill data for all papers that reported R or R2. Our results indicate that the rate of publication of near-term ecological forecasts is increasing over time and the field is becoming increasingly open and automated. Across published forecasts, we find that forecast skill decreases in predictable patterns and these patterns differ between forecast variables. This data publication includes three products from this analysis: (1) a database of all papers identified in the two searches, including our assessment of whether they included an ecological focal variable, included a forecast, and whether the forecast was near-term (≤10 years), (2) a matrix of all data collected on the near-term ecological forecasts we identified, and (3) a database of R2 values for papers that reported R or R2.
Fig. 4 in Gill dimensions in near-term embryos of Amazonian freshwater stingrays (Elasmobranchii: Potamotrygonidae) and their relationship to the lifestyle and habitat of neonatal pups
Fig. 4. Mass-specific surface area of the gills of different potamotrygonid embryos.
Code for Manuscript - Near-term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species -
<p>Code for Manuscript - Near-term lake water temperature forecasts can be used to anticipate the ecological dynamics of freshwater species -</p>
Data and GrADS scripts needed to reproduce the figures in the article "Probabilistic forecasts of near-term climate change: verification for temperature and precipitation changes from years 1971-2000 to 2011-2020"
<p>Data and GrADS scripts needed to reproduce the figures in the article "Probabilistic forecasts of near-term climate change: verification for temperature and precipitation changes from years 1971-2000 to 2011-2020", submitted for publication in Climate Dynamics.</p> <p>Please see the file README for further details.</p> <p> </p>
Data from: Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States
<p>The combination of increasing fire-caused tree mortality and warmer, drier post-fire conditions is making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a novel dataset of post-fire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Post-fire regeneration is sensitive to high-severity fire, which limits seed availability, and post-fire climate, which influences seedling establishment and survival. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected impacts of climate change for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in post-fire regeneration. Across 40–42% of the study area, we project post-fire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031–2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981–2000 to 26–31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support post-fire conifer regeneration.</p>
A multi-model ensemble of baseline and process-based models improves the predictive skill of near-term lake forecasts: data, forecasts, and scores
<p>This data publication contains zipped parquet from the Falling Creek Reservoir multi-model ensemble (MME) forecasting work using the FLARE (Forecasting Lake And Reservoir Ecosystems) system and baseline models: drivers.zip contains NOAA driver forecast files, targets.zip contains in-situ water temperature observations, forecasts.zip contains forecast parquet files generated from the MME workflow (FLARE & baseline models), and scores.zip contains forecast skill metrics required for analysis.</p>
Data from: Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States
Open the record for dataset details and reuse information.
Data from: Seasonal difference in temporal transferability of an ecological model: near-term predictions of lemming outbreak abundances
Ecological models have been criticized for a lack of validation of their temporal transferability. Here we answer this call by investigating the temporal transferability of a dynamic state-space model developed to estimate season-dependent biotic and climatic predictors of spatial variability in outbreak abundance of the Norwegian lemming. Modelled summer and winter dynamics parametrized by spatial trapping data from one cyclic outbreak were validated with data from a subsequent outbreak. There was a distinct difference in model transferability between seasons. Summer dynamics had good temporal transferability, displaying ecological models' potential to be temporally transferable. However, the winter dynamics transferred poorly. This discrepancy is likely due to a temporal inconsistency in the ability of the climate predictor (i.e. elevation) to reflect the winter conditions affecting lemmings both directly and indirectly. We conclude that there is an urgent need for data and models that yield better predictions of winter processes, in particular in face of the expected rapid climate change in the Arctic.
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