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Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts (Instructor Materials)
This EDI data package contains instructional materials necessary to teach Macrosystems EDDIE Module 6: Understanding Uncertainty in Ecological Forecasts, a ~3-hour educational module for undergraduates. Ecological forecasting is an emerging approach that provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Forecast uncertainty is derived from multiple sources, including model parameters and driver data, among others. Knowing the uncertainty associated with a forecast enables forecast users to evaluate the forecast and make more informed decisions. This module will guide students through an exploration of the sources of uncertainty within an ecological forecast, how uncertainty can be quantified, and steps that can be taken to reduce the uncertainty in a forecast that students develop for a lake ecosystem, using data from the National Ecological Observatory Network (NEON). Students will visualize data, build a model, generate a forecast with uncertainty, and then compare the contributions of various sources of forecast uncertainty to total forecast uncertainty. The flexible, three-part (A-B-C) structure of this module makes it adaptable to a range of student levels and course structures. There are two versions of the module: an R Shiny application which does not require students to code, and an RMarkdown version which requires students to read and alter R code to complete module activities. The R Shiny application is published to shinyapps.io and is available at the following link: https://macrosystemseddie.shinyapps.io/module6/. GitHub repositories are available for both the R Shiny (https://github.com/MacrosystemsEDDIE/module6) and RMarkdown versions (https://github.com/MacrosystemsEDDIE/module6_R) of the module, and both code repositories have been published with DOIs to Zenodo (R Shiny version at https://zenodo.org/doi/10.5281/zenodo.10380759 and RMarkdown versi
A database for benchmarking organ dose estimates and uncertainties in CT
<p>This database includes patient images and associated verified Monte Carlo based estimates of organ doses that may be used for benchmarking different organ dose estimation techniques against a reference standard.</p>
Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1
<p>Data used in the work "Representing Model Uncertainty for Global Atmospheric CO2 Flux Inversions Using ECMWF-IFS-46R1" - McNorton et al. (2020)</p> <p>All data generated using version 46R1 of the Integrated Forecast System based at the European Centre for Medium-Range Weather Forecasts, with work funded as part of the European Commission CO2 Human Emissions Project.</p> <p>Data includes global total standard errors for the total column CO2 mixing ratios at 3 hourly intervals for 2015 and both total column and surface transport errors at hourly intervals for January and July 2015, derived from a 50 member ensemble. It is suggested that the data are used by the inverse modelling community to account for transport model errors.</p> <p>Please view the README.txt file for a full description.</p> <p> </p> <p>###########################<br> ## EXPERIMENTAL SETUP ##<br> ###########################</p> <p># FLUXES #</p> <p>CHE-EDGAR-2015 EMISSIONS<br> CHE-TIER-2-FIRE/OCEAN<br> ONLINE CHTESSEL BIOGENIC FLUXES (FOR TRANSPORT ERROR THESE USE THE CONTROL MEMBER FLUXES)</p> <p># MODEL #</p> <p>IFS-CYCLE 46R1<br> RESOLUTION TCO399 (~25km)<br> 137 VERTICAL LEVELS<br> ALL DATA PROVIDED HERE ARE EITHER COLUMN INTEGRATED MIXING RATIO (XCO2) OR SURFACE (LEVEL 137)<br> ALL DATA PROVIDED HERE ARE STANDARD DEVIATION ACROSS 50 ENSEMBLE MEMBERS<br> </p>
QCD Uncertainties in Particle Spectra from Dark Matter Annihilation (updated data can be found in GitHub: https://github.com/ajueid/qcd-dm.github.io.git)
<p>************************************************************************************************</p> <p>QCD Uncertainties on Particle Spectra from Dark Matter Annihilation</p> <p><strong>Please check the updated data at GitHub: https://github.com/ajueid/qcd-dm.github.io.git</strong></p> <p>Authors: Simone Amoroso, Sascha Caron, Adil Jueid, Roberto Ruiz de Austri, and Peter Skands</p> <p>If you use these tables, please cite:</p> <p>S. Amoroso et al. arXiv: 1812.07424 [hep-ph], JCAP05(2019)007</p> <p>************************************************************************************************</p> <p>We provide the spectra of stable particles in dark matter annihilation, in the galactic region or beyond, in a tabulated form using PYTHIA8 version 8235. In addition to the central prediction, we estimate for the first time the QCD uncertainties both due to hadronization as well as to showering. The uncertainties on the spectra are provided in separate tables. A wide range of dark matter masses from 10 GeV to 100 TeV is covered. We consider 11 primary annihilation channels:</p> <p>DM DM -> e+e-, mu+ mu-, tau tau, qq (q=u,d,s), cc, bb, tt, WW, ZZ, gg, and hh.</p> <p>Each file contains 13 columns: the dark matter mass, the fraction x -- defined as the kinetic energy of the particle divided by the DM mass -- in the logarithmic scale, and dN/dLog_10(x) for 11 primary channels. The provided tables correspond to the dN/dLog_10(x) of Standard Model stable particles, i.e. of photons, positrons, electron anti-neutrinos, muon anti-neutrinos and tau anti-neutrinos.</p> <p>The work on the spectra of anti-protons is ongoing (please come back soon). </p> <p>For each particle species, we provide twelve tables which can be found in zip format. The notation of the different tables is given below:</p> <p> 1) The table corresponding to the central prediction for the spectra is denoted by 'AtProduction-Hadronization1-$TYPE.dat' with $TYPE=Nuel, Numu, Nuta, Ga which refers to the three flavours of neutrinos, and photons respectively.</p> <p> 2) There are nine tables corresponding to the different variations of the light quark fragmentation function's parameters. These tables are denoted by 'AtProduction-Hadronization$h-$TYPE.dat' with h=2,..,10.</p> <p> 3) The particle spectra corresponding to the variations of the shower evolution scale (mu_R) are denoted by 'AtProduction-Shower-Var$s-$TYPE.dat' with s=1,2 corresponds to 1/2 mu_R and 2 mu_R. </p> <p><em><strong>IMPORTANT:</strong></em></p> <p> i) Uncertainty on the spectra, from hadronization, is obtained from the envelope of all the variations (including the central prediction).</p> <p> ii) In the variations of the parton shower evolution scale, the parameters of the hadronization function are fixed to their central value.</p> <p> iii) In principle, showering uncertainties are uncorrelated to hadronization uncertainties. To obtain the full uncertainty, one might combine those uncertainties in quadrature.</p> <p><em><strong>If you use the data on the site, please cite:</strong></em></p> <p>Simone Amoroso, Sascha Caron, Adil Jueid, Roberto Ruiz de Austri, Peter Skands, "Estimating QCD uncertainties in Monte Carlo event generators for gamma-ray dark matter searches," <strong>JCAP 05 (2019) 007</strong>, arXiv: 1812.07424.</p> <p><em><strong>In addition, if you use the data corresponding to shower uncertainties, please cite:</strong></em></p> <p>S. Mrenna and P. Skands, "Automated Parton-Shower Variations in Pythia 8,'' <strong>Phys. Rev. D 94 (2016) no.7</strong>, 074005, arXiv:1605.08352 [hep-ph].</p> <p><em><strong>Finally, please cite the paper of M. Cirelli et al. if you use their data for comparison or other tasks:</strong></em></p> <p>M.Cirelli, G.Corcella, A.Hektor, G.Hütsi, M.Kadastik, P.Panci, M.Raidal, F.Sala, A.Strumia, "PPPC 4 DM ID: A Poor Particle Physicist Cookbook for Dark Matter Indirect Detection'', <strong>JCAP 1103 (2011) 051</strong>, arXiv 1012.4515, Erratum: <strong>JCAP 1210 (2012) E01</strong>.</p> <p>Contact: <em>Adil Jueid</em> <adil.hep@gmail.com></p>
Seafloor Density Measurements, Prediction, and Associated Uncertainty for "Predicting global marine sediment density using the random forest regressor machine learning algorithm"
<p>Global seafloor density prediction results using the random forest regressor machine learning algorithm. </p> <p>Dataset S1. Seafloor density measurements. Columns are labeled with a header and include associated drilling project and measurement type for each sample. File format: CSV text file</p> <p>Dataset S2. Seafloor density prediction results from the random forest regressor machine learning algorithm at 5×5-arc minute resolution. Units are g/cm^3. File format: netCDF (.nc)</p> <p>Dataset S3. Seafloor density prediction standard deviation from the random forest regressor machine learning algorithm at 5×5-arc minute resolution. Units are g/cm^3. File format: netCDF (.nc)</p>
Brexit Uncertainty and Trade Disintegration
<p>The zip file contains the program to replicate the results in "Brexit Uncertainty and Trade Disintegration". It also contains the instructions (see README.pdf file).</p>
Replication code and data for: Recalculating ... How Uncertainty in Local Labor Market Definitions Affects Empirical Findings
<p>This repository contains the code and data to replicate all the analyses in our paper "Recalculating ... : How Uncertainty in Local Labor Market Definitions Affects Empirical Findings." Some of the data can also be used in other researchers' analyses to investigate the robustness of their results when they use commuting zones to aggregate or collect data.</p>
HiSS-Cube: A scalable framework for Hierarchical Semi-Sparse Cube that preserves uncertainties
<p>This dataset is used for our framework HiSS-Cube, available at <a href="https://github.com/nadvornikjiri/HiSS-Cube">GitHub</a>. </p> <p>It includes the data folder, the generated HDF5 file (SDSS_cube_gzip.h5) and a contiguous stream export in FITS that can be visualized for example in TOPCAT (SDSS_cutout_export.fits).</p> <p>The data folder contains spectra and images from the SDSS DR14. The documentation for these can be found on the <a href="https://data.sdss.org/datamodel/files/BOSS_PHOTOOBJ/frames/RERUN/RUN/CAMCOL/frame.html">Frame</a> and <a href="https://data.sdss.org/datamodel/files/BOSS_SPECTRO_REDUX/RUN2D/spectra/PLATE4/spec.html">Spectra</a> pages, respectively.</p> <p>The SDSS_cube_gzip.h5 file contains a copy of the data ingested from the data folder optimized for both visualization and stream-lined contiguous access required for example by machine learning algorithms. The purpose is to visualize or run machine learning on combined spectra and images.</p> <p>The SDSS_cube_export.fits contains joined spectra with their respective image cutouts flattened to a table where every row represents one image pixel or spectral "pixel". To visualize these in TOPCAT, choose the 3D Cube plot and RA for X axis, Dec for Z axis and Wavelength or Time for Y axis. Go to the Form tab and choose the "aux" where you can enter either the Mean or Sigma axis as auxiliary.</p> <p> </p>
Uncertainties associated with microwave link rainfall estimates in an urban environment
<p>These dataset were collected from a dedicated microwave link setup between Mt View Reservoir (T) and 33 Lakeside Burwood (R). There were two OTT1 disdrometers installed at both ends of the microwave link complemented by 3 tipping bucket rain gauges. </p>
Data from: A multilocus phylogeny of the fish genus Poeciliopsis: solving taxonomic uncertainties and preliminary evidence of reticulation
The fish genus Poeciliopsis constitutes a valuable research system for evolutionary ecology, whose phylogenetic relationships have not been fully elucidated. We conducted a multilocus phylogenetic study of the genus based on seven nuclear and two mitochondrial loci with a thorough set of analytical approaches, i.e., concatenated (also known as super-matrix), species trees, and phylogenetic networks. Although several relationships remain unresolved, the overall results uncovered phylogenetic affinities among several members of this genus. A population previously considered of undetermined taxonomic status could be unequivocally assigned to P. scarlli; revealing a relatively recent dispersal event across the Trans Mexican Volcanic Belt (TMVB) or Pacific Ocean, which constitute a strong barrier to north-south dispersal of many terrestrial and freshwater taxa. The closest relatives of P. balsas, a species distributed south of the TMVB, are distributed in the north; representing an additional north–south split in the genus. An undescribed species of Poeciliopsis, with a highly restricted distribution (i.e., a short stretch of the Rio Concepcion; just south of the US-Mexico border), falls within the Leptorhaphis species complex. Our results are inconsistent with the hypothesis that this species originated by "breakdown" of an asexual-hybrid lineage. On the other hand, network analyses suggest one or more possible cases of reticulation within the genus that require further evaluation with genome-wide marker representation and additional analytical tools. The most strongly supported case of reticulation occurred within the subgenus Aulophallus (restricted to Central America), and implies a hybrid origin for P. retropinna (i.e., between P. paucimaculata and P. elongata). We consider that P. balsas and P. new species are of conservation concern.
Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree
<p>Motivation: Sap flow sensors are crucial instruments to understand whole-tree water use. The lack of direct calibration of the available methods on large trees and the application of several data-processing procedures may jeopardize our understanding of water uptake dynamics by increasing the uncertainties around sensor-based estimates. We directly compared the heat ratio method (HRM) sap flow measurements to water uptake measured gravimetrically using the cut-tree method on a large mature aspen tree to quantify those uncertainties for ten consecutive days.</p> <p>Dataset: In this dataset, we provide sap flux density (ten-minutes intervals; g.cm-2.hr-1; corrected for wounding and sapwood thermal diffusivity) obtained from four HRM sap flow sensors installed at 2.5 m high on the focus tree (20 m tall, 60 years old trembling aspen in the boreal mixedwood region of Alberta) between July 18th and August 22nd 2017. We present the code and data (weather data from neighboring weather station) used to calculate whole-tree sap flux (L.hr-1) from each of the individual sensors using different methods of radial integration of sap flux density across the sapwood area estimated via different calculations, as well as different zero-flow corrections used. The cut-tree procedure was applied to the focus tree, and gravimetric measurements of water uptake (ten-minutes intervals) were made using a recording scale. We directly compared the different estimates of hourly, daily and cumulative sap flows obtained with gravimetric measurement of water uptake. We present the code providing the statistical analysis and results reported in the associated publication (Merlin, M., Solarik, K.A., Landhäusser, S.M. Quantification of uncertainties introduced by data-processing procedures of sap flow measurements using the cut-tree method on a large mature tree. 2020. Agricultural and Forest Meteorology, http://dx.doi.org/10.1016/j.agrformet.2020.107926)</p>
Data for the paper "Addressing Uncertainties in Modelling Cumulative Impacts within Maritime Spatial Planning in the Adriatic and Ionian Region"
<p>Data for the paper "Addressing Uncertainties in Modelling Cumulative Impacts within Maritime Spatial Planning in the Adriatic and Ionian Region."</p>
Saccade Adaptation and Visual Uncertainty
<p>Dataset from the following publication:</p> <p>Souto, D., Gegenfurtner, K. R., & Schütz, A. C. (2016). Saccade Adaptation and Visual Uncertainty.<em> Front. Hum. Neurosci.,10</em>(227), 1-12. <a>doi: 10.3389/fnhum.2016.00227 <span></span></a><a></a> .</p>
Reward draws the eye, uncertainty holds the eye: Associative learning modulates distracter interference in visual search.
<p>Eye tracking data and statistical analysis of:</p> <p>Koenig, S., Kadel, H., Uengoer, M., Schubö, A., & Lachnit, H. (2017). Reward draws the eye, uncertainty holds the eye: Associative learning modulates distracter interference in visual search. <em>Frontiers in Behavioral Neuroscience</em>. doi: 10.3389/fnbeh.2017.00128.</p> <p> Abstract: Stimuli in our sensory environment differ with respect to their physical salience, but moreover may acquire motivational salience by association with reward. If we repeatedly observed that reward is available in the context of a particular cue, but absent in the context of another cue, the former typically attracts more attention than the latter. However, we also may encounter cues uncorrelated with reward. A cue with 50% reward contingency may induce an average reward expectancy, but at the same time induces high reward uncertainty. In the current experiment we examined how both values, reward expectancy and uncertainty, affected overt attention. Two different colors were established as predictive cues for low reward and high reward respectively. A third color was followed by high reward on 50% of the trials and thus induced uncertainty. Colors then were introduced as distractors during search for a shape target and we examined the relative potential of the color distractors to capture and hold the first fixation. We observed that capture frequency corresponded to reward expectancy while capture duration corresponded to uncertainty. The results may suggest that within trial, reward expectancy is represented at an earlier time window than uncertainty.</p>
Taxonomic Uncertainty on Range Size and Niche Estimation in a Southern Ocean Cryptic Species Complex
<p>R code and data related to assessing the effect of taxonomic uncertainty on range size and environmental niche estimates for a Southern Ocean invertebrate. </p> <p>Clarke, D.A., Wilson, N.G. and McGeoch, M.A. (2025) ‘Effects of Taxonomic Uncertainty on Range Size and Niche Estimation in a Southern Ocean Cryptic Species Complex’, Journal of Biogeography, n/a(n/a), p. e15182. Available at: https://doi.org/10.1111/jbi.15182.</p>
Data for "Early and Widespread Emergence of Regional Warming is Robust to Observational and Model Uncertainty"
<p>These data can be used to reproduce all figures in "Early and Widespread Emergence of Regional Warming is Robust to Observational and Model Uncertainty". Figure code is hosted at https://github.com/jshaw35/RegionalToE_ShawAndLenssen/releases/tag/v1.0</p>
Data for: When Correlation Matters: On Uncertainty Propagation In The Case Of Data Disaggregation
<p>This is the data repository for our study on “When Correlation Matters: On Uncertainty Propagation In The Case Of Data Disaggregation” submitted to the Journal of Industrial Ecology (JIE).</p> <p>I contains:</p> <ul> <li>The data behind the all numeric plots</li> <li>The data needed to reproduce the case-study results in the Supplementary Information (check out V1)</li> </ul> <p> </p> <p>To reproduce our results you need to download the files from this repo, our code from Github (https://github.com/simschul/uncertainty_disaggregation) and put the data into the `./data` folder. </p> <p>The data is an intermediate output from an earlier study: https://essd.copernicus.org/articles/16/2669/2024/essd-16-2669-2024.html </p> <p>For more information how those intermediate results were created please refer to the paper and the code (https://github.com/simschul/uncertainty_GHG_accounts)</p> <p> </p> <p> </p>
Dataset for Bukovsky et al. (2021): "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections"
<p>This dataset contains derived data and model data necessary for reproducing the results found in "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections" by Melissa S. Bukovsky, Jing Gao, Linda O. Mearns, and Brian C. O'Neill. This dataset contains data not otherwise available in other public archives, as noted in Bukovsky et al. (2021, Earth's Future; preprint available at https://doi.org/10.1002/essoar.10504141.2). That is, this dataset contains data from the land-use change simulations that are not part of NA-CORDEX (na-cordex.org), but which are complementary to those published in the NA-CORDEX archive.</p>
Dataset for the paper entitled "Implications of Uncertainty in Technology Cost Projections for Least-Cost Decarbonized Electricity Systems"
<p>This dataset contains model codes, post-process scripts, and model output data used to support findings in the paper entitled "Implications of Uncertainty in Technology Cost Projections for Least-Cost Decarbonized Electricity Systems". </p><p>Please contact Lei Duan (leiduan@carnegiescience.edu) for any questions. </p>
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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.