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6,410 results for “Results”
Inter-Chemical Correlation results for the study: HHEARx2017-1839 (Zika Virus Congenital Health Outcomes and the Impact of Maternal Environmental Exposures)
Title: Zika Virus Congenital Health Outcomes and the Impact of Maternal Environmental Exposures <br>Species: Homo sapiens <br>Number of samples: 2705 <br>Number of named analytes: 10 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=61 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1449 (Environmental phenols and pesticide levels in relationship to autism)
Title: Environmental phenols and pesticide levels in relationship to autism <br>Species: Homo sapiens <br>Number of samples: 842 <br>Number of named analytes: 28 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=7 <br>
Inter-Chemical Correlation results for the study: HHEARx2016-1407 (Pediatric Inner-City Environmental Exposures at School and Home and Asthma Study)
Title: Pediatric Inner-City Environmental Exposures at School and Home and Asthma Study <br>Species: Homo sapiens <br>Number of samples: 157 <br>Number of named analytes: 28 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=2 <br>
Simulation results: Radiative cooling induced coherent maser emission in relativistic plasmas
<p>This repository contains some of the simulation data presented in the recent article titled <em>"Radiative cooling induced coherent maser emission in relativistic plasmas"</em> (<a href="https://arxiv.org/abs/2409.18955" target="_new" rel="noopener">https://arxiv.org/abs/2409.18955</a>). The data available are from 2D particle-in-cell (PIC) simulations, which investigate the effects of radiative cooling in relativistic plasmas and its role in inducing coherent maser emission. The simulations were performed using OSIRIS, a massively parallel and fully-relativistic PIC code.</p> <p>The electric field data in the third direction (E3) included here has been spatially averaged by a factor of 8 in both directions, resulting in a dataset that reflects a resolution 64 times lower than the actual simulation. Additionally, the raw data includes only one two-thousandth of the simulated electron macro-particles. Also included is the phase space data in the x2, p2, and p3 dimensions.</p> <p>These datasets represent key aspects of the simulation results discussed in the paper, where the focus is on understanding the interplay between radiative losses and coherent emission mechanisms.</p> <p>More details on the simulations and the analysis of these results can be found in the corresponding article.</p>
CATCO2NVERS Market Assessment Questionnaire Results
<p><span>This dataset contains the results of a market assessment questionnaire conducted as part of the CATCO2NVERS project, focused on exploring the potential markets, competitors, customer segments, and state-of-the-art trends for the Key Exploitable Results (KERs) of the project. The survey was completed by various project partners and provides valuable insights into geographical markets, customer segments, and industry trends relevant to the commercialization of the KERs. The data also includes information on potential competitors and the current landscape of the targeted sectors.</span></p>
Results for 'Health and sustainability of glaciers in High Mountain Asia'
<p>Summary table updated relative to prior version to provide more useful outputs and units according to the description below.</p> <p>Contains 1 .csv file including the glacier health metrics used in Miles and others (2021) for all RGI glacier outlines larger than 2km2 in High Mountain Asia (regions 13/14/15). The following attributes are provided in the table:</p> <ul> <li>RGIID: unique identifier from the RGI6.0</li> <li>VALID: flag to indicate if the data quality of the inputs and results was acceptable (see study Supplementary Material)</li> <li>CenLat: Latitude of glacier outline centroid from the RGI6.0</li> <li>CenLon: Longitude of glacier outline centroid from the RGI6.0</li> <li>meanSMB: Glacier mean mass balance (m w.e./year) derived by this study</li> <li>ELA: Equilibrium Line Altitude (m a.s.l.) estimated by this study</li> <li>ELAsig: Uncertainty of ELA based on 1000 Monte Carlo simulations using the derived surface mass balance uncertainty</li> <li>AAR: Accumulation Area Ration (unitless) estimated by this study</li> <li>AARsig: Uncertainty of AAR as for ELA</li> <li>totAbl: Volume of annual ablation, glacier-wide (m3/year)</li> <li>totAblsig: Uncertainty of total ablation, glacier-wide (m3/year)</li> <li>balAbl: Volume of 'balance' annual ablation, ie that compensated by net annual accumulation, glacier-wide (m3/year)</li> <li>balAblsig: Uncertainty of 'balanced' ablation, glacier-wide (m3/year)</li> <li>imbalAbl: Volume of 'imbalance' annual ablation, glacier-wide (m3/year)</li> <li>imbalAblsig: Uncertainty of imbalance ablation, glacier-wide (m3/year)</li> <li>balAblPct: Portion of annual ablation balanced by accumulation (unitless)</li> <li>balAblPctsig: Uncertainty of balance portion of ablation (unitless)</li> <li>Vol2100: Simulated glacier volume in the year 2100 under repeated application of current mass balace (m3)</li> <li>Vol2100sig: Uncertainty in Vol2100 based on current mass balance uncertainty (m3)</li> <li>PctVol2100: Simulated volume at 2100 expressed as a fraction of volume at 2000 (unitless)</li> <li>PctVol2100sig: Uncertainty in PctVol2100 based on current mass balance uncertainty (unitless)</li> </ul> <p>Also contains 1 .zip file with principal regridded inputs and results for continuity-derived glacier specific mass balances of High Mountain Asia, 2000-2016. A subdirectory contains the following for each glacier, identified by its Randolph Glacier Inventory identification number (RGIID), all in geotiff format and at the same resolution:</p> <ul> <li>'*_AW3D.tif': regridded digital elevation model based on the ASTER GDEM3 (apologies for misleading name)</li> <li>'*_debris.tif': binary rasterized debris-cover map based on the results of Scherler et al (2018)</li> <li>'*_dH.tif': regridded elevation change rate from Brun et al (2017), in m per year</li> <li>'*_dHe.tif': regridded elevation change rate uncertainty from Brun et al (2017), in m per year</li> <li>'*_FDIV.tif': raster of flux divergence, in m per year</li> <li>'*_FDIVe.tif': raster of flux divergence uncertainty, in m per year</li> <li>'*_Hdensity.tif': raster of estimated density of dH signal, in 1000 kg per m3</li> <li>'*_SMB.tif': raster of specific mass balance, in m w.e. per year</li> <li>'*_SMBe.tif': raster of specific mass balance uncertainty, in m w.e. per year</li> <li>'*_Smean.tif': raster of column-average surface speed based on regridded data from ITS_LIVE (Gardner et al, 2019), in m per year</li> <li>'*_THX.tif': raster of glacier thickness from consensus estimate of Farinotti et al (2019), in m </li> <li>'*_zFDIV.tif': raster of zonally-aggregated flux divergence, in m per year</li> <li>'*_zFDIVe.tif': raster of zonally-aggregated flux divergence uncertainty, in m per year</li> <li>'*_zones.tif': raster of elevation-based zonal segmentation for each glacier</li> <li>'*_zSMB.tif': raster of zonally-aggregated specific mass balance, in m w.e. per year</li> <li>'*_zSMBe.tif': raster of zonally-aggregated specific mass balance uncertainty, in m w.e. per year</li> </ul>
ESD Study Results
<p>Data and R Scripts necessary to reproduce results of ESD study at NUIG conducted and analysed in 2020-2021. The study aimed to investigate whether Systems Thinking (ST) and/or System Dynamics simulation (Sim) increased the effectiveness of Sustainability Education. In the study, participants were randomly allocated to one of four groups, and interacted with an online learning tool which contained embedded quizzes. Their performance in these quizzes formed the basis for comparison between groups. An open access of the learning tool is available here: https://exchange.iseesystems.com/public/carolineb/sustainability-learning-tool/.</p> <p>The R Scripts folder contains 4 R scripts used for data analysis. Run the script function_qualitative.R first.</p> <p>The data folder contains the anonymised research data. Create a folder called data beneath the folder containing your R scripts.</p> <p>The model folder contains the System Dynamics deer model (.stmx) file, used for the simulation exercises.</p> <p>The surveys folder is for reference only. It contains the surveys and quizzes used to collect the data, together with quiz marking schemes, code books and quiz answers.</p> <p>In the data, group members are identified by a number from 0 to 3: group 0 means control group, group 1 means ST group, group 2 means Sim group and group 3 means ST + Sim group.</p> <p>For more details see README.md and the readme.txt files in each folder.</p>
Financing conditions of renewable energy projects – results from an EU wide survey
<p>The dataset contains data related to financing conditions and costs of capital for onshore wind, solar PV and offshore wind within the EU. It provides data on minimum, maximum and average country and technology-specific values on costs of debt, debt service coverage ratios, loan tenors, debt size, costs of equity and WACC values. The data was collected between September 2019 and April 2020.</p> <p>The data contains values for onshore wind in Austria, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Italy, Latvia, Lithuania, Netherlands, Poland, Portugal, Romania, Spain and Sweden. Furthermore, it contains values for solar PV in Czech Republic, Estonia, France, Greece, Hungary, Latvia, Portugal, Romania, Slovakia and Spain. Finally, it also contains values for offshore wind in Belgium, France, Germany and UK. </p> <p>The PDF files are survey questionnaires that were used for the data collection. This includes 1) a survey questionnaire used in an exploratory research phase, in which we identified the most relevant research aspects related to the impacts of auctions on costs of capital and financing 2) a survey questionnaire used for the focus-group countries (Germany, Denmark, Spain, Portugal and Greece), which includes a list of more extensive qualitative questions and 3) a survey questionnaire used for the focus-group countries (all other EU member states) and which focused only on collecting the quantitative data. </p>
Metal Intrusion Model Results
<p>Model results showing the percentage of geochem preserved and rounding facilitated in the pallasite formation region; all parameters used are included in csv results files.</p>
IMC Segmentation Pipeline results of example IMC data
<p>If you are working with these files, please cite them as follows:<br><br>Windhager, J., Zanotelli, V.R.T., Schulz, D. et al. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc (2023). <a href="https://doi.org/10.1038/s41596-023-00881-0">https://doi.org/10.1038/s41596-023-00881-0</a></p><p>This repository hosts the results of processing example imaging mass cytometry (IMC) data hosted at <a href="http://10.5281/zenodo.5949116">10.5281/zenodo.5949116</a> using the IMC Segmentation Pipeline available at <a href="https://github.com/BodenmillerGroup/ImcSegmentationPipeline">https://github.com/BodenmillerGroup/ImcSegmentationPipeline</a> (DOI: <a href="http://10.5281/zenodo.6402666">10.5281/zenodo.6402666</a>) v3.6. Please refer to <a href="https://github.com/BodenmillerGroup/steinbock">https://github.com/BodenmillerGroup/steinbock</a> as alternative processing framework and <a href="http://10.5281/zenodo.6043600">10.5281/zenodo.6043600</a> for the data generated by <i>steinbock</i>.</p><p>The following files are part of the <strong>analysis.zip</strong> folder when running the IMC Segmentation Pipeline:</p><ul><li><strong>cpinp</strong>: contains input files for the segmentation pipeline</li><li><strong>cpout</strong>: contains all final output files of the pipeline: <i>cell.csv</i> containing the single-cell features; <i>Experiment.csv</i> containing CellProfiler metadata; <i>Image.csv</i> containing acquisition metadata; <i>Object relationships.csv</i> containing an edge list indicating interacting cells; <i>panel.csv</i> containing channel information; <i>var_cell.csv</i> containing cell feature information; <i>var_Image.csv</i> containing acquisition feature information; <i>images </i>containing the hot pixel filtered multi-channel images and the channel order; <i>masks</i> containing the segmentation masks; <i>probabilities </i>containing the pixel probabilities.</li><li><strong>histocat</strong>: contains single channel .tiff files per acquisition for upload to histoCAT (<a href="https://bodenmillergroup.github.io/histoCAT/">https://bodenmillergroup.github.io/histoCAT/</a>)</li><li><strong>crops</strong>: contains upscaled image crops in .h5 format for ilastik (<a href="https://www.ilastik.org/">https://www.ilastik.org/</a>) training</li><li><strong>ometiff</strong>: contains .ome.tiff files per acquisition, .png files per panorama and additional metadata files per slide</li><li><strong>ilastik</strong>: multi channel images for ilastik pixel classification (<i>_ilastik.full</i>) and their channel order (<i>_ilastik.csv</i>); upscaled multi channel images for ilastik pixel prediction (<i>_ilastik_s2.h5</i>); upscaled 3 channel images containing ilastik pixel probabilities (<i>_ilastik_s2_Probabilities.tiff</i>).</li></ul><p>The remaining files are part of the root directory:</p><ul><li><strong>docs.zip: </strong>Documentation of the pipeline in markdown format</li><li>I<strong>MCWorkflow.ilp: </strong>Ilastik pixel classifier pre-trained on the example data</li><li><strong>resources.zip: </strong>The CellProfiler pipelines and CellProfiler plugins used for the analysis</li><li><strong>scripts.zip: </strong>Python notebooks used for pre-processing and downloading the example data</li><li><strong>src.zip: </strong>Scripts for the imcsegpipe python package</li></ul>
Trajectory-Aware Rate Adaptation for Aerial Networks Simulation Results
<p><strong>Introduction</strong></p> <p>Even though the concept of ubiquitous wireless connectivity is becoming a reality, there are scenarios where wireless communications coverage is insufficient or does not exist. Considering natural and man-made disaster scenarios, communications infrastructures may be damaged and become unavailable. In temporary crowded events, the existing infrastructure may not have been designed to cope with the additional traffic demand, resulting in overload. In maritime scenarios, environmental monitoring activities using autonomous vehicles will take place in offshore zones, typically not in range of existing onshore communications infrastructures.</p> <p>Flying networks, composed of Unmanned Aerial Vehicles (UAV), are emerging as a flexible and cost-effective solution to provide on-demand wireless connectivity in such scenarios. UAVs have the possibility to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware, playing the role of mobile base stations, access points or relay nodes. A flying network may typically be composed of a fleet of UAVs, organized in a multi-tier topology with so-called Flying Edge Nodes (FENs) and Flying Gateways (FGWs) <a href="https://doi.org/10.1016/j.adhoc.2022.103000">[1]</a>. FENs can play the role of Flying Access Points that provide the access network to the users on the ground, or the role of Flying Sensor Nodes that can perform video surveillance missions. The FENs forward the traffic to the FGWs, that act as relay nodes and are responsible for forwarding the traffic to/from the backhaul (BKH) network and ultimately to/from the Internet.</p> <p>The flying network concept brings up new challenges. The flying nodes need to be properly positioned and their wireless link configuration dynamically adjusted in order to ensure the Quality of Service (QoS) expected by the end users. In addition, these scenarios are typically highly unpredictable due to the varying locations as well as the concentration/dispersion of end-users and their movements regarding direction and velocity - e.g., vehicles or pedestrians. Therefore, a static wireless link configuration and UAV positioning are not adequate. State of the art work has been mainly focused on the optimal positioning of the flying nodes, having most of the wireless link parameters statically configured with default values. The Rate Adaptation challenge is well-known in fixed or low mobility IEEE 802.11 networks, and Minstrel High Throughput (HT) <a href="https://lwn.net/Articles/376765">[2]</a> is the default Wi-Fi rate adaptation algorithm used in the Linux kernel since the IEEE 802.11n version. However, few works propose solutions designed to consider the characteristics of other communications environments, such as flying and vehicular networks <a href="https://doi.org/10.1007/s11276-020-02295-2">[3]</a>. To the best of our knowledge, solutions that use the node trajectory information to predict the wireless channel conditions and perform rate adaptation are yet to be developed.</p> <p>The main contribution of this paper is the Trajectory-Aware Rate Adaptation (TARA) algorithm. TARA takes advantage of knowing the trajectory of all nodes in the flying network to estimate future changes in the wireless link quality and perform rate adaptation accordingly. The network performance improvement achieved with TARA was evaluated using ns-3 <a href="https://doi.org/10.1007/978-3-642-12331-3_2">[4]</a>. The simulation results presented in this dataset show significant throughput gains when compared with conventional rate adaptation algorithms.</p> <p><strong>Folder Organization</strong></p> <p>The following dataset presents the results of the TARA Paper, organized in different folders for each Rate Adaptation Algorithm, as well as the random seeds that were used to obtain such results:</p> <p><strong>Naming Convention:</strong></p> <ul> <li>Rate Adaptation Algorithm<strong> </strong> <ul> <li><strong>tara </strong>– Trajectory-Aware Rate Adaptation</li> <li><strong>min </strong>– MinstrelHTWifiManager</li> <li><strong>id </strong>– IdealWifiManager</li> </ul> </li> </ul> <p><strong>Folder Content: </strong></p> <ul> <li><em>distances.csv - </em><strong>Distances between nodes</strong> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Distance between BKH and FGW</strong> (meters)</li> <li>Column 3 – <strong>Distance between FEN and FGW </strong>(meters)</li> </ul> </li> <li><em>positions.csv</em> <em>- </em><strong>Current 3D position of nodes</strong> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>BKH x </strong>(meters)</li> <li>Column 3 – <strong>BKH y </strong>(meters)</li> <li>Column 4 – <strong>BKH z </strong>(meters)</li> <li>Column 5 – <strong>FEN x </strong>(meters)</li> <li>Column 6 – <strong>FEN y </strong>(meters)</li> <li>Column 7 – <strong>FEN z </strong>(meters)</li> <li>Column 8 – <strong>FGW x </strong>(meters)</li> <li>Column 9 – <strong>FGW y </strong>(meters)</li> <li>Column 10 – <strong>FGW z </strong>(meters)</li> </ul> </li> <li><em>throughput.csv</em> - <strong>Link Specific Throughput, at MAC layer level</strong> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Relay Link (BKH - FGW), Throughput measured in BKH </strong>(Mbit/second)</li> <li>Column 3 – <strong>Access Link (FEN - FGW), Throughput measured in FEN </strong>(Mbit/second)</li> <li>Column 4 – <strong>Relay Link (BKH - FGW), Throughput measured in FGW </strong>(Mbit/second)</li> <li>Column 5 – <strong>Access Link (FEN - FGW), Throughput measured in FGW </strong>(Mbit/second)</li> </ul> </li> </ul>
Datasets and results from: "Random Forest Classification and Solar Flares Data: Analysis and Validation"
<p><strong>Instructions for the data and code repository</strong></p> <p>Results, post-processing workflow, and datasets for the research paper titled "Random Forest Classification and Solar Flares Data: Analysis and Validation".</p> <p>The folder contains three .csv files: the complete dataset (dataset.csv), the balanced training dataset (train_dataset.csv), and the testing dataset (test_dataset.csv).</p> <p>The folder also contains the result files from the research (.csv output files with predictions and .html files with evaluation metrics, etc.) exported from the JASP software. The number in each file name corresponds to the number of trees utilized in Random Forest modelling.</p> <p>In addition, the Python script for the post-processing workflow is provided, with comments located in the script.</p> <p>The soft range X-ray irradiance and VLF amplitude data were obtained from:<br> National Centers for Environmental Information (NCEI) Available online: https://www.ncei.noaa.gov/. Accessed on: 24th June 2023. <br> Worldwide archive of low-frequency data and observations (WALDO) Available online: https://waldo.world/. Accessed on: 24th June 2023.</p>
Results files for Land-free Bioenergy From Circular Agroecology -- A Diverse Option Space and Trade-offs
<p>This is the open data repository to support and reproduce results in the paper "<em>Land-free Bioenergy From Circular Agroecology -- A Diverse Option Space and Trade-offs</em>." There are <strong>three types </strong>of files here:</p> <p> </p> <p> </p> <p><strong>1. Ready-to-use final results files of all strategies and scenarios referred to in the paper. </strong>They can be downloaded and used directly without running any codes. They all have the same naming format for strategies/scenarios: `Org` = organic share, `ConcRed` = concentrate feeding reduction share, `WasteRed` = waste reduction share, and numbers refer to the share. E.g., `Org0_ConcRed50_WasteRed75` is a strategy with 0% organic share, 50% concentrate feeding reduction, and 75% waste reduction.</p> <p> </p> <ul> <li>`NationalAncillaryBioenergyPotential_EJ.csv`: The national potential of ancillary bioenergy in 2050 from all scenarios. (Units: EJ). Same in both pathways.</li> <li>`GlobalPotentialEnvironmentalImpacts_NutrientFirst.csv`: Environmental impacts of all scenarios from the pathway `<em>NutrientFirst</em>.` The first three rows refer to the combination of agroecological practices in places, which allow you to explore environmental impacts grouped by, e.g., different organic shares.</li> <li>`GlobalPotentialEnvironmentalImpacts_NegFirst.csv`: Same structure as the file above, but from another pathway, `<em>NegativeFirst</em>`.</li> </ul> <p> </p> <p><strong>2. `SOLmOutputs` contains all original output files from our model <a href="https://orgprints.org/id/eprint/38778/">SOLmV6</a>. </strong></p> <p> </p> <p><strong>3. `DataCleaningKit` has the Python codes and additional dataset of heat values to process 2. `SOLmOutputs` and spit 1. </strong>(Tip: One should adjust the `input_path` and `output_path` before running `DataCleaning.py.`)</p> <p> </p> <p> </p> <p>Fei Wu (fei.wu@usys.ethz.ch)</p> <p>Delft, August, 2023</p> <p> </p>
Simulation results of adaptive multicast streaming for videoconferences in software-defined networks
<p>Real-time applications, such as video conferences, have strong Quality of Service requirements for ensuring a decent Quality of Experience. Nowadays, most of these conferences are performed over wireless devices. Thus, an appropriate management of both heterogeneous mobile devices and network dynamics is necessary. Software Defined Networking enables the use of multicasting and stream layering inside the network nodes, two techniques able to enhance the quality of live video streams. In this paper, we propose two algorithms for building and maintaining multicast sessions in a software-defined network. The first algorithm sets up the initial multicast trees for a given call. It optimally places the stream layer adaptation function inside the core network in order to minimize the bandwidth consumption. This algorithm has two versions: the first one, based on shortest path trees is minimizing the latency, while the second one, based on spanning trees is minimizing the bandwidth consumption. The second algorithm adapts the multicast trees according to the network changes occurring during a call. It does not recompute the trees, but only relocates the stream layer adaptation functions. It requires very low computation at the controller, thus making our proposal fast and highly reactive. Extensive simulation results confirm the efficiency of our solution in terms of processing time and bandwidth savings compared to existing solutions such as multiple unicast connections, Multipoint Control Unit solutions and application layer multicast.</p>
GR4SP Suite: Additional Data and Simulation Results
<p>This entry is for the GR4SP's Suite, a model and simulation tool used to analyse the Victorian electricity system's history and potential future transition pathways. It includes additional input data and simulation results. Most of these files were used or generated with Jupyter Notebook scripts shared in the author's <a href="https://github.com/gr4sp/simulationEngine">GitHub repository</a>. </p> <p>Input data in YAML files include VIC.yaml with input settings for the business-as-usual scenario. Some of these inputs can be changed to achieve different future trajectories. For example, try increasing 25% the <em>basePrice </em>of Brown Coal and check how this could impact future emission trajectories, electricity production from renewable energy, and spot prices.</p> <p>This entry includes Sobol's sensitivity indices, the Elementary Effects Test (EET) mu* and sigma. The Morris and Sobol sampling results are saved as .tar.gz files with corresponding names. </p> <p>The input data and results for the Energy Vulnerability (EV) assessment quantifying the LIHC indicator and the data used for the Sectoral Network Analysis (SNA) (i.e. ActorActorRelV0.8.csv). </p> <p>This also includes the results from exploring future pathways of the electricity system using EMA workbench's PRIM, FS and other tools for open exploration (https://emaworkbench.readthedocs.io/en/latest/). The exploration was guided by three scenarios: Low-Carbon Transition (LCT), Just Transition (JT), and Sustainability Transition (ST). Any additional file with BAU in the name includes the results for the Business-as-usual scenario.</p> <p>This version of the dataset includes a few results of policy mixes to achieve any of the three transition scenarios generated in the Jupyter Notebook <a href="https://github.com/gr4sp/experiments/blob/main/notebooksGr4sp/ScenariosAnalysis.ipynb">ScenariosAnalysis</a>. The zip file "policyMixesResults" contains the .csv file with the outputs for different input changes. These details of the input changes for each file are in the "ScenarioAnalysisOutputDetailsv0.2.xlsx". The outputs of the model for these policy mixes can be found in the zip file "policyMixesFigs".</p>
Flexibility market results
<p>The SLO_ACTIVATION_DATA dataset includes data about requested activation energy, delivered activation energy and price of delivered energy (monthly aggregates). The data set in CIM XML contains flexibility market results of Slovenian pilot in OneNet project. It is intended to enable o<span><span>bserving effectiveness </span><span>of flexibility services activation and ratio between requested and activated flexibility.</span></span><span> </span></p> <p>More about the Slovenian demo in the One Net deliverable 10.4 (<a href="https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D10.4_V1.0.pdf">OneNet_D10.4_V1.0.pdf (onenet-project.eu)</a>)</p>
Competence Centres and User Support Centres Landscaping Results
<p>The dataset contains the data collected from the landscaping activity related to the Competence Centre and user support network for the D7.1 Report on Competence Centres landscape and user support activities.</p>
Water Dawgs STEM Confidence Survey Results, 2023
This dataset originates from a STEM confidence survey conducted during the Water Dawgs program—a paid summer initiative hosted at the University of Georgia (UGA) in Athens, Georgia, USA. Held over 10 days in Summer 2023, the program engaged 16 high school students in a hands-on experience in freshwater science. Water Dawgs was designed to support students’ academic and professional development, with an emphasis on increasing access for individuals from populations historically excluded from STEM fields. The initiative was part of the broader impacts of two National Science Foundation-funded research projects and was shaped by three main objectives: (1) to expand access to university-led STEM opportunities by collaborating with a local public high school to recruit students from underrepresented backgrounds; (2) to highlight the connections between environmental science and students’ everyday lives and future career options, including non-STEM pathways; and (3) to foster greater self-efficacy in engaging with STEM subjects, particularly environmental science. The survey was administered at both the beginning and conclusion of the 10-day program to assess changes in participants’ confidence related to STEM. The survey measured STEM confidence using a Likert scale ranging from 1 to 5, with 1 indicating "not at all confident" and 5 indicating "totally confident." The included R code contains a script to generate a figure and summary statistics for questions related to general STEM confidence, specifically Questions 2, 6, 7, 9, and 12. One participant who only submitted a post-program survey was excluded from the dataset and analysis to ensure consistency across responses.
Plot-level field data and model simulation results, archived to accompany Turner et al. manuscript; reports data from summer 2017 sampling of short-interval fires that burned during summer 2016 in Greater Yellowstone.
Subalpine forests in the northern Rocky Mountains have been resilient to stand-replacing fires that historically burned at 100–300-yr intervals. Fire intervals are projected to decline drastically as climate warms, and forests that reburn before recovering from previous fire may lose their ability to rebound. We studied recent fires in Greater Yellowstone (Wyoming, USA) and asked whether short-interval (less than 30 yrs) stand-replacing fires can erode lodgepole pine (Pinus contorta var. latifolia) forest resilience via increased burn severity, reduced early postfire tree regeneration, reduced carbon stocks, and slower carbon recovery. During 2016, fires reburned young lodgepole pine forests that regenerated after wildfires in 1988 and 2000. During 2017, we sampled 0.25-ha plots in stand-replacing reburns (n=18) and nearby young forests that did not reburn (n=9). We also simulated stand development with and without reburns to assess carbon recovery trajectories. Nearly all prefire biomass was combusted ("crown fire plus") in some reburns in which prefire trees were dense and small (≤ 4 cm basal diameter). Postfire tree seedling density was reduced six-fold relative to the previous (long-interval) fire, and high-density stands (greater than 40,000 stems ha-1) were converted to sparse stands (less than 1,000 stems ha-1). In reburns, coarse wood biomass and aboveground carbon stocks were reduced by 65% and 62%, respectively, relative to areas that did not reburn. Increased carbon loss plus sparse tree regeneration delayed simulated carbon recovery by greater than 150 yrs. Forests did not transition to nonforest, but extreme burn severity and reduced tree recovery foreshadow an erosion of forest resilience.
Stream nitrate concentrations and discharge, stream nitrate uptake, and results of stream network nitrate model to determine lateral nitrate load from land to stream in Oak Creek, Arizona, USA
Data package associated with Handler et al. (2024) "Nitrate loads from land to stream are balanced by in-stream nitrate uptake across season in a dryland stream". The study describes the nitrate dynamics in Oak Creek watershed. Data include measurements from four seasonal synoptic sampling campaigns, nine seasonal stream nitrate uptake experiments on the main stem and tributaries, and the results of a network model that estimates the lateral load of nitrate from surrounding landscape to the stream network as well as network-level stream nitrate uptake and retention.
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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.