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1,600 results for “input”
Vortex input file -- Humboldt penguin PVA
<p>Vortex project input file for PVA of Humboldt penguins, completed by workshop participants in Lima, Peru, 2019. Report of the full PHVA and PVA workshop is provided in:</p> <p>McGill, P., A. Baker, R. Lacy, R. Paredes, J. Reyes, J. Rodriguez, A. Tieber, & R. Wallace, (eds.) 2021. Humboldt penguin (Spheniscus humboldti) Population and Habitat Viability Assessment Workshop Final Report. IUCN SSC Conservation Planning Specialist Group, Apple Valley, MN, USA. A PDF of this workshop report can be downloaded at: www.cpsg.org.<br> </p>
Data input for the RegMex model experiment on the power system and flexible sector coupling
<p>This file provides the input data used in the power system flexibility model experiment performed within the RegMex project. Comprehensive information about the project can be found in the project report [Lechtenböhmer2018] (in German, see link in the file). In the experiment performed with the data documented here, three scenarios were considered, labelled "Import", "Decentralized" and "Offshore". This file contains the input for all scenarios. All further information on the model and scenario configuration is available from the project report. Many technology parameter have been derived as own assumptions within previous projects, relying on different sources. Details can be found in the cited PhD and masters theses. In the experiment, Germany was modelled with 18 regions reflecting the transmission grid operator zones (see map in the file).</p>
Suggested Taxonomy: Tracking Technologies to Effectively Capture and Input Key Data on the Blockchain
<p>Within the paper titled "Transparency with Blockchain and Physical Tracking Technologies: Enabling Traceability in Raw Material Supply Chains" (Mater. Proc. 2021, 5(1), 1; <a href="https://doi.org/10.3390/materproc2021005001">https://doi.org/10.3390/materproc2021005001</a>), we consider the majority of tracking technologies to be part of the IoT ecosystem and suggest a taxonomy with their key features, benefits and use cases in the mining industry. Although technologies such as markers and QR/Barcodes are not necessarily electronic devices, they can integrate with other IoT objects and provide or qualify a digital identity. The common element connecting all these technologies is that they include functionalities that can capture and communicate granular, timely, relevant and accurate data, which can be automatically or manually entered into the blockchain. </p> <p>We have analysed the following most common physical tracking methods which will be described and exemplified in more detail below:</p> <ul> <li> <p>Video monitoring (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t001">Table 1</a>)</p> </li> <li> <p>Bar and QR codes (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t002">Table 2</a>)</p> </li> <li> <p>Markers and taggants (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t003">Table 3</a>)</p> </li> <li> <p>Cellular, near range and low power network tracking tools (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t004">Table 4</a>)</p> </li> <li> <p>Satellite network tracking tools (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t005">Table 5</a>)</p> </li> </ul>
Lateralization of CA1 assemblies in the absence of CA3 input
<p>Raw data (sorted spikes, local field potentials, behavior) for the paper "Lateralization of CA1 assemblies in the absence of CA3 input" Guan et al., <em>Nat Commun</em>, (2021). Each zip file contain data recorded from single mouse. Mice HX384, HX385, HX388, HX443 constitute the 'control' group. Mice HX1582, HX1584, HX386, HX387, HX400 constitute the 'mutants' (CA3-TeTX) group.</p>
Vortex input files -- Lacy et al. Assessing the viability of the Sarasota Bay community of bottlenose dolphins
<p>Vortex PVA input files for analyses presented in Lacy et al. "Assessing the viability of the Sarasota Bay community of bottlenose dolphins", Frontiers in Marine Science. </p>
Input data and analyzed data of "Topology of synaptic connectivity constrains neuronal stimulus representation (...)"
<p>This dataset contains the input data, as well as the analyzed data that our <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">preprint</a></p> <p><em><strong>Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies</strong></em></p> <p>to be found on <a href="https://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">bioRxiv</a> is based on. The input data (<em>input_data.zip</em>) contains everything that is needed to run the full <a href="https://github.com/BlueBrain/topological_sampling/">analysis pipeline</a> from start to the generation of the figures found in the manuscript. However, some of the analysis steps can be computationally heavy, so we also provide the output of these expensive steps, that can be simply used in conjunction with jupyter notebooks (<em>notebooks.zip)</em> to generate the figures.</p> <p><strong>Overview</strong></p> <p>An overview image can be found <a href="https://raw.githubusercontent.com/BlueBrain/topological_sampling/master/toposampling_pipeline_overview.png"><strong>here</strong></a></p> <p>Blue squares denote input / output files (that are part of this dataset). Grey circles denote steps of the analysis pipeline (that are implemented in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>). Red rectangles denote configuration files (that are part of this dataset and also in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>).</p> <p>This Dataset can also be browsed, downloaded and accessed as linked open data from the <a href="https://bbp.epfl.ch/nexus/web/studios/public/topological-sampling/studios/data:a7cc7e9f-53c5-4940-929c-95f4c4f57728?workspaceId=data:165e54c5-e8f6-4d85-ac94-53bc3dfe5cd4">BBP knowledge Graph based Data studios</a>.</p> <p><strong>Contained file types and their structure</strong></p> <p>Here, we provide four types of files. Configuration files specify analysis parameters and define the expected locations of the data files. Input files are the inputs into the analysis pipeline. Analyzed files are the outputs of said pipeline. Finally, we provide a number of jupyter notebooks that use the analyzed files to generate the manuscript figures. If you want to re-run the entire analysis pipeline, you need the code and configuration files from the <a href="https://github.com/BlueBrain/topological_sampling/">repository</a>, the input files and notebooks; the analyzed files will be generated as you run the pipeline. For information how to run this, refer to the <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a>. If you only want to generate the figures, you still need the code and configuration files from the repository, as it contains a package related to reading the result files; further, you need the analyzed files in addition to the input files. Of course, you can also run parts of the analysis pipeline and download the outputs for the rest.</p> <p>To run everything smoothly, the files have to be placed into the expected file structure. You can look up and configure the file structure in the configuration files. Below, we describe the default layout, which is very simple (<em>root</em> is where you placed the code from our <a href="https://github.com/BlueBrain/topological_sampling/">repository</a> and can be any location on your file system):</p> <ul> <li>Configuration files<em>: </em>Part of the <a href="https://github.com/BlueBrain/topological_sampling/">repository.</a> Placed into <em>root/working_dir/configs</em></li> <li>Input data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>input_data.zip</em> -- Input data. Contains details on the model used in the manuscript and the output (spike times) of the simulation described in the manuscript. Within the file: <ul> <li>For details, see <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a></li> </ul> </li> </ul> </li> <li>Analyzed data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>classifier_features_results.zip </em>-- Output of the "classifier" step. Results of stimulus classification on the data in <em>features.zip</em></li> <li><em>classifier_manifold_result</em>s.zip -- Output of the "classifier" step. Results of stimulus classification on the data in <em>extracted_components.zip</em></li> <li><em>community_database.zip</em> -- Output of "gen_topo_db". Various topological parameters related to the close neighborhood of neurons in the model</li> <li><em>extracted_components.zip </em>-- Output of "manifold_analysis". Results of factor analysis on the spike times in the <em>input_data</em></li> <li><em>features.zip</em> -- Output of "topological_featurization". A new dimensionality reduction method we introduce in the <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">manuscript</a></li> <li><em>split_spike_trains.zip -- </em>Output of "split_time_windows". The spike trains, split into time windows that are the responses to individual stimuli injected in the simulation</li> <li><em>structural_parameters.zip</em><em> -- </em>Output of "Structural tribe analysis". Values for the topological parameters in <em>community_database.zip</em> associated with the neuron samples specified in <em>tribes.zip</em></li> <li><em>structural_parameters_vol.zip</em> -- Output of "Structural tribe analysis". Same as above, but for volumetric neuron samples.</li> <li><em>triads.zip</em> -- Output of "Triad-counts". Over- and under-expression of triad motifs in the samples in <em>tribes.zip</em>.</li> <li><em>tribes.zip</em><em> -- </em>Output of "sample_tribes". Specific neuron samples that are then analyzed further.</li> </ul> </li> <li>Notebooks: Place into <em>root/notebooks</em> and unzip in place <ul> <li><em>notebooks.zip</em><em> -- </em>Jupyter notebooks. Run them to generate the figures in the manuscript.</li> </ul> </li> </ul> <p> </p> <p><strong>Updates:</strong></p> <p>v1.1.0 (2020/12/11): Added some additional control cases to the results for figure 7. These results will probably not be updated on bioRxiv, but go into the submission to a journal.</p> <p>v1.2.0 (2021/10/05): Updated the notebooks.zip with changes we made in response to reviewers' feedback.</p>
Social vulnerability to flooding in Ecuador : input variables, PCA vs Expert composite indices
<p><strong>Social vulnerability indices are used to better understand and predict the consequences of disasters, and support the development of improved disaster management policies. This research specifically supports the Ecuadorian Red Cross in generating a flood-specific social vulnerability index to inform flash flood early action protocol.</strong></p> <p>The dataset presents the results from the analysis of the social vulnerability to flooding in Ecuador, from individual input variables to the composite indices outputs. The results are available at the Parroquia level in Ecuador (admin level 3), for 1032 Parroquia excluding the Galapagos Islands.</p> <ul> <li>The dataset comprises, for each Parroquia, the estimation of <strong>15 variables characterizing the social vulnerability to flooding specific to Ecuador context</strong>. The variables are selected from literature review and consultation with Ecuadorian Red Cross disaster practitioners : <em>Disability, Poverty incidence, Gini Index, Agricultural labor share, Vectorborne disease incidence, Waterborne disease incidence, Social Security affiliation, Education level, Sanitation, Driking water access, Power access, Road travel time, Wall structure, Mobile access and Internet access.</em> All variables are normalized from 0 to 1, directed toward increasing vulnerability, and renamed accordingly.</li> <li>In addition, the <strong>Administrative level names, PCODE, calculated Area, population density,</strong> as well as related <strong>sub-regions</strong> are also referenced.</li> <li>Individual variables are integrated into <strong>composite vulnerability indices</strong>, using two different approaches: i) the Principal Component Analysis approach, using the first component <strong>PCA(n=1) </strong>and the first 5 components <strong>PCA(n=5)</strong> separately ; ii) the <strong>expert judgement weighting</strong> of the variables. The output composite indices, normalized from 0 to 1 are presented in 3 separated columns.</li> </ul> <p> </p>
OnSSET processed GIS input data for 20 Sub Sahara Africa countries
<p>This repository contains the processed geospatial data files needed to run the Open-Source Spatial Electrification Tool (OnSSET), for 20 countries in Sub Sahara Africa. These data files were created by the KTH team for the Global Electrification Platform (GEP) model (https://electrifynow.energydata.info/). To access result files and geospatial population clusters, please go to https://energydata.info/dataset/?q=gep.</p>
Inputs folder contents for DETECT model v1.0
<p>These files contain the inputs for running the soil CO2 and transport DETECT model v1.0. The model is described in the paper:</p> <p>Ryan, E. M., K. Ogle, H. Kropp, Y. Carrillo, K. E. Samuels-Crow, E. Pendall (in review) Modelling soil CO2 production and transport with dynamic source and diffusion terms: Testing the steady-state assumption using DETECT v1.0<em>. </em>In Geoscientific Model Development.</p>
Input data for performing a model evaluation of the sectional aerosol module SALSA embedded to PALM model system 6.0
<p>This dataset includes the input information applied to perform a model evaluation study of the PALM model system together with the sectional aerosol module SALSA. </p> <p>The content:</p> <ul> <li>PIDS_STATIC: building height and leaf area density data</li> <li>PIDS_AERO_<simulation time>_<number of aerosol size bins>: aerosol emission data as size bin specific surface emissions (level of detail 2) and aerosol background concentrations</li> <li>PIDS_CHEM_<simulation time>: emission data and background concentrations of gaseous compounds</li> </ul> <p>PIDS_STATIC contains static data and is therefore the same for all simulations.</p> <p>See the model documentation https://palm.muk.uni-hannover.de/trac/wiki/doc for further details.</p>
Input data to model multiple effects of large-scale deployment of grass in crop-rotations at European scale
<p>This is the input dataset to a Python script (<a href="https://github.com/oskeng/MF-bio-grass">https://github.com/oskeng/MF-bio-grass</a>) used to model the effects of widespread deployment of grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts.</p> <p>For more information about the dataset and the study, see the original article:</p> <p>Englund, O., Mola-Yudego, B., Börjesson, P., Cederberg, C., Dimitriou, I., Scarlat, N., Berndes, G. Large-scale deployment of grass in crop rotations as a multifunctional climate mitigation strategy. GCB Bioenergy</p>
Homeostatic regulation through strengthening of neuronal network correlated synaptic inputs
<p>Data from "Homeostatic regulation through strengthening of neuronal network correlated synaptic inputs" by Barnes SJ, Keller GB & Keck T.</p> <p> </p>
Solanum pimpinellifolium input data collected from the TPA - used for GWAS
<p>The GWAS input data used for mapping the candidate genes in S. pimpinellifolium collection, exposed to salt stress in The Plant Accelerator experiment (TPA). The phenotypic data was collected in an experiment was performed by Mitchell Morton while being a PhD student in the group of Prof. Mark Tester at KAUST. The genotypic data was collected by Magdalena Julkowska, and used for sequencing. The SNPs were called by Elodie Ray, and subsequently curated by Magdalena Julkowska for GWAS analysis. The GWAS was conducted by Magdalena Julkowska. </p>
Alpine range by species input to simulations that reveal climate and legacy effects
<p>Whether the distribution and assembly of plant species are adapted to current climates or legacy effects poses a problem for their conservation during ongoing climate change. The alpine regions of southern and central Europe (SACEU) are compared to those of the western US and Canada (WUSAC) because they differ in their geographies and histories. Individual-based simulation experiments disentangled the role of geography in species adaptations and legacy effects in four combinations: approximations of observed alpine geographies vs. regular lattices with the same number of regions (realistic and null representations), and virtual species with responses to either climatic or simple spatial gradients (adaptations or legacy effects). Additionally, dispersal distances were varied using five Gaussian kernels. Because the similarity of pairs of regional species pools indicated the processes of assembly at extensive spatiotemporal scales and is a measure of beta diversity, this output of the simulations was correlated to observed similarity for Europe and North America. In North America, correlations were highest for simulations with approximated geography and location-adapted species; those in Europe had their highest correlation with the lattice pattern and climate-adapted species. Only SACEU correlations were sensitive to dispersal limitation. The southern and central European alpine areas are more isolated and with more distinct climates to which species are adapted. In the western US and Canada, less isolation and more mixing of species from refugia have caused location to mask climate adaptation. Among continents, the balance of explanatory factors for the assembly of regional species pools will vary with their unique historical biogeographies, with isolation lessening disequilibria.</p>
Input data - Wasteaware Cities Benchmark Indicators - WABI 2023 - Global data analytics
<p>This is the input dataset for the research publication "<em>Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning</em>". It features </p> <ul> <li>Metadata info used by R codes</li> <li>Full data set for the WABI, used by the R codes</li> <li>Data required for plotting the map in Figure 1</li> </ul> <p>The independent variables data set refers to specific indicators of the WABI methodology (<a href="https://www.sciencedirect.com/science/article/pii/S0956053X14004905">https://www.sciencedirect.com/science/article/pii/S0956053X14004905</a>) which generates solid waste management and resource recovery profiles for cities. It is applied here for 40 cities around the world. The data set contains also values for a series of explanatory variables, which are measures of the level of socioeconomic development at country level.</p> <p> </p> <p> </p> <p> </p>
Improving Robustness of Deep Neural Networks for Aerial Navigation by Incorporating Input Uncertainty
<p>CEA covered the scenario of UAV navigation through a set of gates with unknown locations using a DNN-based navigation model. The implemented navigation model uses two DL components (perception and control), and uses (Bayesian) uncertainty estimation methods to capture the uncertainty (confidence) associated with the predictions of each component. The safety requirements in the UAV mission are related to the confidence (uncertainty) associated with the predictions from these components. CEA observed and analysed the uncertainty from each DNN under specific situations that can pose a risk to the UAV mission. Then, the observations were used to define STL rules to track the confidence of the DNN-based navigation system. Finally, mitigation behaviours (e.g., hover, land, DNN-based autonomous flight) are triggered depending on the satisfaction (or violation) of the STL rules. Moreover, the proposed ROS2-based architecture for safe navigation contributed to the definition and improvement of the COMP4DRONES reference architecture, showing in practice how the proposed safety monitoring architecture relates and integrates with the components from other system functions.</p>
Input data from: Mammalian forelimb evolution is driven by uneven proximal-to-distal morphological diversity
<p>Vertebrate limb morphology often reflects the environment due to variation in locomotor requirements. However, proximal and distal limb segments may evolve differently from one another, reflecting an anatomical gradient of functional specialization that has been suggested to be impacted by the timing of development. <span>Here we explore whether the temporal sequence of bone condensation predicts variation in the capacity of evolution to generate morphological diversity in proximal and distal forelimb segments across more than </span>600 species of mammals. Distal elements not only exhibit greater shape diversity, but also show stronger within-element integration and, on average, faster evolutionary responses than intermediate and upper limb segments. Results are consistent with the hypothesis that late-developing distal bones display greater morphological variation than more proximal limb elements. However, the higher integration observed within the autopod deviates from such developmental predictions, suggesting that functional specialization plays an important role in driving within-element covariation. Proximal and distal limb segments also show different macroevolutionary patterns, albeit not showing a perfect proximo-distal gradient. The high disparity of the mammalian autopod, reported here, is consistent with <span>the higher potential of development to generate variation in more distal limb structures, as well as functional specialization of the distal elements.</span></p>
Model setup code and input for internal tide-eddy simulation
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>The dataset includes the setup code and input files for simulations of internal tide-eddy interactions using MITgcm. Due to the large size of the model output data, it is not included but can be made available upon request at <a target="_new">yangwangow@gmail.com</a>. If you have any questions about using or testing these files, please feel free to reach out. Cheers!</p> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Input files to run free subduction model by Keum and So (2023), submitted to Tectonophysics.
<p>This release contains a series of Lagrangian free subduction models, and MATLAB script used to create the initial temperature condition.</p>
Intergenic RNAPII Atlas : input data
<p>This Zenodo record refers to the <strong>"input data" </strong>used in the manuscript titled "<a href="https://doi.org/10.1101/2023.03.24.534112">Characterising intergenic transcription at RNA polymerase II binding sites in normal and cancer tissues</a>" by de Langen <em>et al.</em> </p> <p>This Zenodo record allows to replicate the results presented in the manuscript, please refer to the instructions available on Github at <a href="https://github.com/benoitballester/Pol2Atlas">https://github.com/benoitballester/Pol2Atlas</a>. </p> <p>The results presented in the manuscript can be accessed at <a href="https://zenodo.org/record/7740073">https://zenodo.org/record/8091826</a>.</p> <p><strong>In short :</strong> </p> <ul> <li><strong>Data "in" :</strong> this record</li> <li><strong>Data "out"</strong> : <a href="https://zenodo.org/record/7740073">https://zenodo.org/record/8091826</a></li> <li><strong>Github Code</strong> : <a href="https://github.com/benoitballester/Pol2Atlas">https://github.com/benoitballester/Pol2Atlas</a></li> </ul> <pre><code class="language-bash"># uncompress the gz files $ cat repro_data.gz.part* | gunzip -c > repro_data.gz</code></pre> <p> </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.