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40 results for “auxiliary data”
Little Rock Lake Experiment at North Temperate Lakes LTER: Secchi Disk Depth; Other Auxiliary Sample Data 1983 - 2000
The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Secchi Disk data were collected from the treatment and reference basins of Little Rock Lake at one station in the deepest part of each basin. Auxiliary data associated with each sampling event include time of day, air temperature, wind direction and speed, wave height, and cloud cover at the time of sampling. Sampling Frequency: varies - Number of sites: 2
Auxiliary Euro-Calliope datasets: Spatio-temporal data representing national cooking demand and electric vehicle characteristic profiles in Europe
<p>Output generated by the <a href="https://github.com/RAMP-project/">RAMP engine</a> for use in the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope model</a>. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope.</p> <ul> <li>Cooking energy demand profiles (<em>ramp-cooking-profiles</em>): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP">RAMP model</a> [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**.</li> <li>Electric vehicle plug-in profiles (<em>ramp-ev-plugin-profiles</em>): Profiles of the percentage of parked electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*.</li> <li>Electric vehicle energy consumption profiles (<em>ramp-ev-consumption-profiles</em>): Profiles of the electricity consumption of electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*.</li> </ul> <p>* AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE</p> <p>** (*) + BGR, SRB</p> <p>*** ALB, MKD, GRC, CYP, BIH, MNE, ISL</p> <p>[1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. <em>Energy</em> 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097.</p> <p>[2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. <em>Applied Energy</em> 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676.</p>
MesoLF demo data and auxiliary files
<p>Demo data and auxiliary files accompanying the article:</p> <p>Nöbauer, T., Zhang, Y., Kim, H. & Vaziri, A.<br> Mesoscale volumetric light-field (MesoLF) imaging of neuroactivity across cortical areas at 18 Hz.<br> <em>Nature Methods</em> 1–10 (2023). doi:<a href="https://doi.org/10.1038/s41592-023-01789-z">10.1038/s41592-023-01789-z</a><br> <br> The files provided here are required for running a demo of the MesoLF pipeline. These files will be downloaded automatically by the Matlab live notebook "mesolf_demo.mlx" that was published as part of "Supplementary Software 1" with the associated article. For installation instructions, see file "README.md" in "Supplementary Software 1". For future software updates, check <a href="https://github.com/vazirilab">https://github.com/vazirilab</a></p>
North Temperate Lakes LTER: Secchi Disk Depth; Other Auxiliary Base Crew Sample Data 1981 - current
Secchi disk depth is measured in the deepest part of each lake for the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout lakes, unnamed lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Fish, Mendota, Monona and Wingra). The disk is circular, 20 cm in diameter, and has alternating black and white quadrants. It is lowered using a calibrated Kevlar rope to minimize stretching. Readings are made on the shaded side of the boat both with (secview) and without (secnview) the aid of a plexiglass viewer. The points at which the disk disappears while being lowered and reappears while being raised are averaged to determine Secchi depth. Auxiliary data include time of day, air temperature, cloud cover, wave height, wind speed and direction and whether the lake was ice covered on the sampledate. Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season for the northern lakes. The southern lakes are similar except that sampling occurs monthly during the fall and typically only once during the winter (depending on ice conditions). Number of sites: 11 More information on NTL’s primary study lakes can be found at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-ntl&identifier=434
Auxiliary files and data to generate eddy flux and validate 2D model for MALTA
<p>This repository contains the following directories to accompany the manuscript 'A Zonally-Averaged Global Atmospheric Transport Model for Long-lived Trace Gases', submitted to JAMES:</p><p>1) <strong>GEOSChem </strong>This directory contains the run directory template and (slurm) runscript to generate the tracer fields used to generate the eddy fluxes. The GEOSChem model will have to be installed locally to run this, and the run directory built to your local area. It may be easiest to just copy the relevant bits in /Tracer_2D_template/ (i.e., the .rc files, /RestartFiles/, input.geos, reset_restart.py and species_database.yml) into a GEOSChem Transport run directory and change the directories in the copied files. If using slurm on an HPC, just change the directories in the runtracers_inputs.sh script to match that of your own HPC. Else, a different script will have to be written copying the slurm functionality.</p><p>2) <strong>GEOSChem_SF6 </strong>This directory contains the monthly mean SF6 mole fractions generated using GEOSChem used to validate the 2D model MALTA. Emissions come from the EDGAR v4.2 emissions inventory. Emissions after 2008 continue to use 2008 as the emissions value.</p><p>3) <strong>CFC11_inversion</strong> This directory contains the relevant script and files to quantify emissions of CFC-11 using an output mole fraction from the TOMCAT 3D model using MALTA, and compare these to the TOMCAT emissions used to generate the mole fractions. The directory paths at the beginning of the main script in CFC11_inversion.py must be changed to point to the remaining files in the /CFC11_inversion/ directory, and a save directory must be specified, before running locally. MALTA must be installed to run this.</p><p>4) <strong>singapore.dat </strong>This file contains the QBO winds above Singapore, taken from https://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/index.html</p><p> </p>
Auxiliary Euro-Calliope datasets: QTDIAN storyline-specific spatial data to represent a European energy system model at several spatial resolutions
<p>Custom output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with three additional land area scenarios.</p> <p>These scenarios are in line with three storylines from the <a href="https://zenodo.org/record/5834010">QTDIAN toolbox</a> and are based on updating the `possibility-for-electricity-autarky` workflow configuration to include the following parameters (also included in `config.yaml`):</p> <p> </p> <pre><code> scenarios: people-powered: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 0.2 # agro pv share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 0.1 share-rooftop-used: 1.0 government-directed: use-of-protected areas: false pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 0.1 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0 market-driven: use-of-protected areas: true pv-on-farmland: true share-farmland-used: 1.0 share-forest-areas-used: 1.0 share-other-land-used: 1.0 share-offshore-used: 1.0 share-rooftop-used: 1.0</code></pre> <p> </p> <p>This dataset includes different spatial resolutions of land availability. For more information on the `ehighways` resolution, see <a href="https://zenodo.org/record/6600619">https://zenodo.org/record/6600619</a>.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p> </p>
Auxiliary evaluation data of SmartReviews
<p>Contains two files related to the evaluation of SmartReviews:</p> <p>1. Evaluation of the Web Content Accessibility Guidelines (WCAG) for SmartReviews </p> <p>2. Comparison with existing authoring tools, includes a textual justification of the scores </p>
Auxiliary data release for "Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization"
<p>This release contains parameter estimation runs on synthetic injections, described in https://arxiv.org/abs/2404.02435 .</p> <p>Important note: The posterior samples provided are weighted, the weights are stored in a column named 'weights' . </p>
The Influence of Data Storytelling on the Ability to Recall Information — Auxiliary materials
<p>This repository contains auxiliary materials for the CHIIR 2022 paper "The Influence of Data Storytelling on the Ability to Recall Information" by Dominyk Zdanovic, Tanja Julie Lembcke and Toine Bogers (= corresponding author)</p> <p>Published in: <em>Proceedings of the 2022 Conference on Human Information Interaction and Retrieval (CHIIR '22)</em>, March 14--18, 2022, Regensburg, Germany.</p> <p>The paper presents the results of an experimental comparison of influence data storytelling and traditional data visualizations on the ability to recall information contained in the visualizations. There are two types of auxiliary materials:</p> <ol> <li> <p>Questions posed to participants in the two condition, both post-task and post-exeriment along with the correct answers.</p> </li> <li> <p>PDF versions of the six visualizations used in the experiment</p> </li> </ol>
Dataset of lake ozone flux measurements and auxiliary data
<p>Dataset of O<sub>3</sub> fluxes measured by the eddy covariance system over Lake Kuivajärvi during 23 days period in August and September 2012. The lake is located in the vicinity of the Hyytiälä Forestry Field Station and SMEAR II Station in Southern Finland. The dataset consists of post-processed and quality filtered fluxes and auxiliary measurements that were used in the analysis. The flux measurements were performed on the lake platform, by using an ultrasonic anemometer (Metek USA-1, GmbH, Elmshorn, Germany) to measure the three wind velocity components and sonic temperature and a fast chemiluminescent gas analyzer FOS (Sextant Technology Ltd, New Zealand) that measured O<sub>3</sub> concentration. </p>
Auxiliary data files for replication of "Augmenting the availability of historical GDP per capita estimates through machine learning"
<p>This repository holds auxiliary data files needed for the replication "Augmenting the availability of historical GDP per capita estimates through machine learning". All further information and data is provided in the <a href="https://github.com/philmkoch/historicalGDPpc" target="_blank" rel="noopener">GitHub repository</a>.</p> <p>The data included in this auxiliary folder is based on the work by Laouenan et al. (https://www.nature.com/articles/s41597-022-01369-4). </p>
Auxiliary data for Moustakis et al. 2024 "Temperature overshoot responses to ambitious forestation in an Earth System Model"
<p>The netcdf file "Moustakis_et_al_2024_Data.nc" contains all the key variables presented in the figures of the manuscript of Moustakis et al. 2024: "Temperature overshoot responses to ambitious forestation in an Earth System Model".</p> <p>Please read the README.txt file for more information on the variables included.</p> <p>For any further queries please refer to the corresponding author, Yiannis Moustakis: <br>yiannis.moustakis@geographie.uni-muenchen.de</p> <p> </p>
Energy Balance Flanders quarterly and monthly data and related auxiliary data
<p>This data set is used in the VITO pilot study of the UNECE Machine Learning project 2019-2020. </p>
Raw data for the article "Enantioselective Carboetherification/Hydrogenation for the Synthesis of Amino Alcohols via a Catalytically-Formed Chiral Auxiliary"
<p>Raw NMR, MS, IR and HPLC data for the article "Enantioselective Carboetherification/Hydrogenation for the Synthesis of Amino Alcohols via a Catalytically-Formed Chiral Auxiliary" published in the Journal of the American Chemical Society, DOI: <a href="https://doi.org/10.1021/jacs.0c09177">https://doi.org/10.1021/jacs.0c09177</a></p> <p>The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.</p>
Nanomechanical topological insulators with an auxiliary orbital degree of freedom - source data
<p>This data set contains the source data and the matlab scripts applied to obtain the results published in "Nanomechanical topological insulators with an auxiliary orbital degree of freedom". For details of the experimental procedure please refer to the main text of the article, as well as the supplementary information. </p>
Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space, and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.
Conformational Ensembles Reveal the Origins of Serine Protease Catalysis - auxiliary data and code
<p>EnsemblePDB.zip - package version used to create pseudo-ensembles in the paper "Conformational Ensembles Reveal the Origins of Serine Protease Catalysis"</p> <p>serine_protease_ensembles.zip - data and code used to generate and analyze the data presented in the paper "Conformational Ensembles Reveal the Origins of Serine Protease Catalysis"</p>
Raw data for the article "Asymmetric Cyclopropanation and Epoxidation via a Catalytically Formed Chiral Auxiliary"
<p>Raw NMR, HPLC, IR and MS data for the article "Asymmetric Cyclopropanation and Epoxidation via a Catalytically Formed Chiral Auxiliary" published in Angewandte Chemie, International Edition, DOI: </p> <p><a href="https://doi.org/10.1002/anie.202113925 ">https://doi.org/10.1002/anie.202113925 </a> </p> <p>The number of the folders correspond to compounds numbers in the article. All details concerning conditions and equipment for measurements can be found in the supporting information of the article.</p>
Auxiliary Euro-Calliope datasets: Spatial data to represent a European energy system model at several spatial resolutions
<p>Main output generated with the <a href="https://github.com/brynpickering/possibility-for-electricity-autarky/tree/custom-regions">custom-region possibility-for-electricity-autarky</a> workflow.</p> <p>This output provides similar data to <a href="https://doi.org/10.5281/zenodo.3246302">https://doi.org/10.5281/zenodo.3246302</a> (technically eligible land area for renewables and other spatially disaggregated energy system data), but with two key differences:</p> <ol> <li>The spatial extent has been expanded to include Iceland.</li> <li>Two new spatial resolutions have been added: `ehighways` and `ehighways_disaggregated`.</li> </ol> <p>`ehighways` defines 98 regions based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. The regions cover 35 European countries; 19 are described at a national resolution and the rest at a subnational resolution. Those at a subnational resolution are aggregated from NUTS3-2006 statistical units. `ehighways_disaggregated` provides the data at the resolution of statistical units in Europe, which is then aggregated to produce the data at the `ehighways` resolution. The mapping from statistical units to ehighways regions is defined in `./ehighways/statistical_units_to_ehighways_regions.csv`. `./ehighways/units.png` shows a map of the resulting 98 `ehighways` regions. The region colours are used to help differentiate regions and have no other meaning.</p> <p>This dataset is used as an input to the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope workflow</a>.</p> <p>[1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)</p>
Data from "Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR" manuscript
<p>Data from "Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR" manuscript. Full results for applications 1 and 2.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.