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557 results for “demand”
Daily bus transportation demand - São Paulo-Brazil - Jan-2017 to Jun-2022
<p>The raw demand data was obtained from SPTRANS, the public company responsible for bus transportation in São Paulo.</p> <p>São Paulo's greatest public transportation network is the bus network, which is mostly managed by public company SPTRANS. SPTRANS provides bus transportation services during business hours and night shifts, along with specific services directed towards impaired and disabled citizens who need support in traveling to hospitals and health centers. SPTRANS makes available online daily mobility data for all bus lines in São Paulo, providing an enormously valuable resource for data mining, urban geography studies, and transportation planning. By using this data, it is possible to effectively study the impacts of pandemics on São Paulo's bus transportation network and gain insights into the regional differences and if different kinds of lines were affected differently. </p> <p>The data is distributed in xlxs format and contains information about line name, demand by kind of users, such as users who pay with money, users who use travel cards, elderly users (who do not need to pay for travel), and total demand. The line name is a string containing a code made of letters and numbers. </p> <p>Demand data between January/2017 and June/2022 were automatically downloaded and accessed by a script in python, using pandas library for creating dataframes. There were significant challenges in using SPTRANS data, such as the irregular formatting of row and column names through the years, inconsistent abbreviations of keywords, such as terminal stations and metro stations (i.e "term.","terminal","metr","m", etc). These issues were solved by capturing the code and crossing the data with General Transit Feed Specification (GTFS) data, which is provided by SPTRANS as well on an almost weekly basis and contains information about route and schedule of lines for a given time period. This step enriched the data with standardized names and route points.</p> <p>Another difficulty in obtaining the data through automatic scripts is that Links do not have regular naming patterns. A significant number of downloaded XLSX files came with a bad configuration so that empty rows are read by pandas as filled with content, triggering an error related to a prohibitively large number of rows. This bug was solved by simply individually opening it and re-saving it. The details about the origin of this error are not understood by the authors. </p> <p>All the bus lines were enriched with geospatial information, containing all points belonging to each route, extracted from GTFS. After all these steps, the data was organized in a matrix where each row represents a day and each column represents a line. The geospatial information was stored separately. And since bus lines are created or deactivated with some regularity in São Paulo, The choice was to keep in the sample only the lines that remained active throughout the observation period. </p>
Photoresponsive Nanocarriers Based on Lithium Niobate Nanoparticles for Harmonic Imaging and On-Demand Release of Anticancer Chemotherapeutics
<p>Data set associated with the following publication:</p> <p>Gheata, A., Gaulier, G., Campargue, G., Vuilleumier, J., Kaiser, S., Gautschi, I., Riporto, F., Beauquis, S., Staedler, D., Diviani, D., Bonacina, L., Gerber-Lemaire, S. Photoresponsive Nanocarriers Based on Lithium Niobate Nanoparticles for Harmonic Imaging and On-Demand Release of Anticancer Chemotherapeutics. <em>ACS Nanoscience Au</em> <strong>2022</strong>, <em>2</em>, 355-366.</p> <p>Raw data for characterization of molecules and nanoparticles: NMR, TEM, DLS.</p> <p>Raw data for cell assays, cell imaging, cytotoxicity experiments and EGFR quantification.</p> <p>Metadata file.</p> <p>Copy of labbooks.</p>
ENTSO-E Electricity Demand - 01/2016 - 09/2022
<p>Electricity demand for ten European countries downloaded from the ENTSO-E Transparency Platform the 1st October 2022. The data covers the period 01/01/2016 - 31/09/2022. Saved in Parquet format.</p>
Data for article: Mapping temporal variations in ecosystem services: a case study of European wood supply and demand between 2008 and 2018
<p>The data consists of the indicators for spatio-temporal analysis of wood Ecosystem Service (ES) potential, supply, and demand across Europe between 2008 and 2018. This dataset was used for the analysis of temporal trends of wood ES in the study "Mapping temporal variations in ecosystem services: a case study of European wood supply and demand between 2008 and 2018".</p> <p>The data are collected and compiled from open access statistical databases. They consist of three parts:</p> <p>1. The PDF file with a detailed description of the data and all the input sources from which it was derived.</p> <p>2. The Zip file with 3 separate Excel files containing the short description of the indicators for mapping spatio-temporal changes, namely wood ES potential, wood ES supply and wood ES demand and their values between 2008 and 2018 at three different levels: continental, national and regional. Note that for the regional level only supply and demand indicators are available.</p> <p>3. The tiff file representing the spatial resolution for visualisation and analysis of the data.</p> <p> </p> <p> </p> <p>Resolution:</p> <p>Data are available for 3 spatial levels, in the temporal dimension between 2008 and 2018 (annually). All 3 indicators are available at continental (European) and national scales. The study area covers 24 countries of the European Union (EU) and Switzerland. Supply and demand are also available at regional level. At the regional scale, we assessed supply and demand using the nomenclature of territorial units for statistics (NUTS 3; n = 1061) and local administrative units (LAU; n = 957) from the year 2016 (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units.">Eurostat, 2016</a>). The visualisation of the resolution is available in the tiff file attached to the data.</p> <p>The data are visualised and analysed in the ETRS 1989 LAEA projection.</p> <p> </p> <p>For more information on the indicators used, data collection and processing, see the supplementary files of the published article.</p> <p> </p> <p> </p> <p><strong>DEFINITIONS of ES mapping indicators used in the study: </strong></p> <p><em>ES potential</em> - the hypothetical maximum yield of services potentially available for supply.</p> <p><em>ES supply</em> – the amount of the mobilized service within the ecosystem capable to provide a service at a given location in a certain time (frequently referred in literature as ES flow).</p> <p><em>ES demand </em>- the need for the ecosystem-based service by the end users. In this study demand is analysed from the perspective of service end-user.</p>
Data and code for "Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"
<p>Data and code for <br>"Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"</p> <p>All R code used for the analysis is provided in the folder <em>code</em>. <br>Data and intermediate results are provided or stored in the folders <em>data </em>and <em>tmp_data</em>.<br>All results including figures will be stored in the folder <em>results</em>. </p> <p>R version: 4.3.1</p> <p>To carry out the entire analysis the code should be run in the provided order:</p> <p>1) Code to run non-metric multidimensional scaling (NMDS) for habitat groups and <br>produce Fig. 1b (habitat map and legend for Fig 1a: data/eunis_gl_habitat_ger_990m.tif,eunis_gl_habitat_ger_990m_legend.clr)<br> <br>2) Code to generate grassland vitality maps and time series from 1985 to 2021 (Fig. 3). <br>Grassland vitality maps on 30m for all grasslands in Germany provided in data/glv_1985-2021.zip.</p> <p>3) Code to model relation of grassland vitality to five drought indices (VPD, temperature, CWB, soil moisture, precipitation),<br>output are Fig. 4, Fig. S1, Tab. 1.</p> <p>4) Code for trend analysis of drought sensitivity based on 5-, 10-, and 15-year moving windows, output are Fig. 5, Fig. S2. </p> <p>5) Code to model drought sensitivity of grassland habitat groups and habitat types, output are Fig. 6 and table with sensitivity per habitat type. </p>
Case study input data set for article "Stochastic planning of energy system transformation pathways under uncertain industry demands"
<p>The data set contains input data for the model EMPRISE of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p>
Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks
<p>Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks</p>
Data Files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity"
<p>Data files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity" [https://doi.org/10.1016/j.euroecorev.2024.104848]. Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/AP_Solar/. Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.</p>
Raw data belonging to paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters"
<p>The Zip file contains the raw data used for drawing conclusions in the paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters" accepted for publication in Energy Informatics 2018.</p> <p>The raw data is the parameters of a sample of 50 domestic electric water heaters (DEWHs)<br> used for evaluating the algorithm in the paper. It is explained in the readme.txt.</p>
SET-NAV: WP5: Invert modelling output for the building sector final energy demand and cost data
<p>This data set contains the Invert modelling results for final energy demand for space heating, cooling and hot water in buildings; hourly data for district heating and electricity (for different technologies) for 3-4 building types; annual data for the other energy carriers.</p> <p>It also contains all annual cost (Annuity of investments, O&M, fuel cost ) of electricity generation and / or heat generation and considered efficiency measures.</p> <p>This data is also available and visualised in our dedicated SET-NAV open data platform: The SET-NAV Scenario Explorer: https://data.ene.iiasa.ac.at/set-nav/#/workspaces</p>
Global heat map of probable importance of terrestrial ecosystems on meeting local demand of freshwater services
<p>This map (raster dataset, single layer) uses existing datasets to map globally “How important point x is likely to be for meeting the demand of a reliable & useable source of water on a scale of 0 to 1?” This relatively simple approach uses estimated water demand in a given basin as weight to identify pressure for flow regulation and water provisioning services. Precipitation and land cover estimates are then combined with it to give some insight into the hydrologic attributes of “location” and “timing” of flow that the ecosystems may influence. The underlying assumption here is that undisturbed ecosystems everywhere are performing the ecohydrological functions leading to freshwater services. The question is more (at the global scale): how dependent are the populations in the basin on the continued functioning of these services.</p> <p><strong>Input datasets:</strong></p> <ol> <li>Annual surface & groundwater (“blue”) water consumption estimates. URL: <a href="http://waterfootprint.org/en/resources/water-footprint-statistics/">http://waterfootprint.org/en/resources/water-footprint-statistics/</a></li> <li>HydroBasins watershed outline.</li> <li>European Space Agency (ESA) global land cover 2015.</li> <li>WorldClim annual average precipitation (Version 2.0).</li> </ol> <p><strong>Process:</strong></p> <p>Step 1: Calculate average annual water consumption estimates over HydroBasin outlines. This step spreads the demand laterally (in case of small basins) and upstream to the headwaters from (typically) downstream consumer concentration.</p> <p>Step 2: Normalize the demand globally and map the normalized values on to “natural” land cover classes from the land cover dataset [forests, grasslands, etc].</p> <p>Step 3: Normalize annual precipitation layer within basins on the scale 0-1 where 1 is the maximum annual precipitation in that basin. This is also mapped on the “natural” land cover. Precipitation is thus acting as ‘weight’ for importance within the basin. Example, upland headwaters will typically receive more rainfall and can be argued to be important for the flow regulation in the basin.</p> <p>Step 4: Combine the layers from 2 and 3.</p> <p><strong>Caveats:</strong></p> <ol> <li>Identification of what constitutes a “natural” land cover is not trivial, especially from global land cover maps. Example: Forests and plantations are hard to distinguish from these products.</li> <li>Improvement of quality of water is assumed to be implicit for functioning ecosystems.</li> </ol>
Mitigating Traffic Congestion on I-10 in Baton Rouge, LA: Supply- and Demand-Oriented Strategies & Treatments
<p>Corresponding data set for Tran-SET Project No. 17ITSLSU09. Abstract of the final report is stated below for reference:</p> <p>"The aim of this study is to develop a better understanding of the causes of traffic congestion on I-10 in the Baton Rouge, LA area, particularly at the I-10 Mississippi River Bridge, and to identify treatments and strategies to mitigate congestion at the bridge site. This study developed and calibrated a microsimulation model of I-10 (from Lobdell Highway in Port Allen to Highland Road, I-110 to Florida Street, and I-12 to Walker Road) and investigated several supply- and demand-oriented strategies. This includes: rehabilitation and utilization of the old Mississippi River Bridge on US-190 and the existing US-190/US-61 corridor, overall demand management of I-10 EB traffic, reduction in percent trucks traveling eastbound on I-10 during the A.M. peak period, and ramp metering at the on-ramp west of the I-10 Mississippi River Bridge. The majority of the tested strategies appear to be feasible and effective solutions; however, a combination of supply- and demand-oriented treatments must be implemented to fully relieve congestion on I-10 in Baton Rouge."</p>
Cooking demand data for clean cooking simulation.
<p>This will be a dataset of clean cooking data collected hourly to allow for clean cooking simulation. Currently this is dummy data but it will soon be uploaded with live working data.</p>
Global gridded scenarios of residential cooling energy demand to 2050
<p># ggACene (global gridded Air Conditioning energy) projections</p> <p>### Output AC and AC electricity gridded data</p> <p>This repository hosts output data for SSPs126, 245, 370 and 585 on the estimated and future projected ownership of residential air conditioning (% of households), the related energy consumption (TWh/yr.), and the underlying population counts (useful to quantify the per-capita average consumption or the headcount of people affected by the cooling gap). These data are contained in the multi-layer .nc (NCDF) files, which can be opened and processed in any GIS software/library, or visualised in softwares such as Panoply.</p> <p>### Input data and analysis replication</p> <p>The repository also hosts input data to replicate the entire data generating process. A twin Github repository hosts code (<a href="https://github.com/giacfalk/ggACene">https://github.com/giacfalk/ggACene</a>) to run the model generating the ggACene (global gridded Air Conditioning energy) projections dataset.</p> <p>## Instructions<br>To reproduce the model and generate the dataset from scratch, please refer to the following steps:<br>- Download input data "replication_package_input_data.7z" by cloning the repository<br>- Decompress the folder using 7-Zip (https://www.7-zip.org/download.html)<br>- Open RStudio and adjust the path folder in the sourcer.R script<br>- Run the sourcer.R script to train the ML model, make projections, and represent result files<br><br></p> <p>### Figures replication package</p> <p>Finally, the source_code_data_replication_figures.zip archive contains an R script and processed input data to replicate all the figures contained in the manuscript.<br><br></p> <p>### Reference<br><br>Falchetta, G., De Cian, E., Pavanello, F., & Wing, I. S. Inequalities in global residential cooling energy use to 2050. Nature Communications. https://www.nature.com/articles/s41467-024-52028-8</p>
Data from: Built structures influence patterns of energy demand and CO2 emissions across countries
Open the record for dataset details and reuse information.
Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study
<p>Este conjunto de dados foi gerado como parte do estudo "Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study". O estudo focou na avaliação do desempenho das ferramentas Copilot, ChatGPT e Gemini, utilizando problemas do LeetCode em quatro linguagens de programação: Python, Java, JavaScript e C.</p> <p>O conjunto de dados atualizado está organizado nas seguintes pastas:</p> <ol> <li> <p><strong>c_programs</strong>: Esta pasta contém os scripts Python utilizados para calcular a complexidade ciclomática e a complexidade cognitiva do código C gerado pelas ferramentas.</p> <ul> <li><code>calculate_cyclomatic_complexity.py</code>: Script para calcular a complexidade ciclomática.</li> <li><code>calculate_cognitive_complexity.py</code>: Script para calcular a complexidade cognitiva.</li> </ul> </li> <li> <p><strong>codes_suggested_by_the_tools</strong>: Esta pasta contém as sugestões de código geradas pelo Copilot, ChatGPT e Gemini para cada problema do LeetCode.</p> <ul> <li>Subpastas: <code>ChatGPT</code>, <code>Copilot</code>, <code>Gemini</code>, cada uma contendo as sugestões de código correspondentes nos formatos das linguagens.</li> </ul> </li> <li> <p><strong>complexity_of_codes</strong>: Esta pasta contém dois arquivos CSV que fornecem os resultados da análise de complexidade para o código gerado.</p> <ul> <li><code>AI analysis results table - Cognitive.csv</code>: Resultados da complexidade cognitiva do código gerado.</li> <li><code>AI analysis results table - Cyclomatic.csv</code>: Resultados da complexidade ciclomática do código gerado.</li> </ul> </li> </ol> <p>Este conjunto de dados atualizado oferece insights valiosos sobre o desempenho das ferramentas de geração de código com IA e pode ser utilizado para análises futuras ou estudos de replicação.</p>
Data to calculate metal demand from IAM energy projections and add flexibility to the technological mix
<p>In the "Data" folder, you’ll find all necessary data to run the <strong>"IAM_metal_optimisation.py"</strong> script, available on GitHub (<a href="https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master" target="_new" rel="noopener">https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master</a>).</p> <p>The <strong>"IAM_metal_optimisation.py"</strong> script is a tool designed to:</p> <ol> <li>Quantify metal supply and demand by sector through 2050, using energy projections from various IAMs and SSP-RCP scenarios.</li> <li>Optimize the IAM technological mix, minimizing adjustments needed to prevent metal demand from exceeding supply constraints.</li> </ol> <p>This tool supports all IAMs with power projections for SSP-RCP scenarios (except SSP3) provided in the IPCC’s Sixth Assessment Report (AR6), Working Group III, and available in the IIASA database.</p> <p>Data for RCP 2.6, SSPs 1, 2, 4, 5, of the SSP marker IAM models of the IPCC, are organized in the "Capacity Factor IAM," "GDP IAM," and "Power Capacity IAM" folders. See the GitHub README for more details.</p>
Idiothetic representations are modulated by availability of sensory inputs and task-demands in hippocampal-septal circuit
<p>This is the dataset underlying the results presented in the article titled: "Idiothetic representations are modulated by availability of sensory inputs and task-demands in hippocampal-septal circuit". Data is sub-divided per brain regions: CA1, CA3, and lateral septum (LS). Then each region is subdivided by mouse number, and finally by experiment (day). Each experiment contains two main files: ms.mat (miniscope data) and behav.mat (behavior data). These two streams are not sampled at the same frequency and should thus be interpolated during analysis (timestamps are available for both streams). Each .mat files can be opened with Matlab or with any HDF5 reader (e.g. in H5py Python).</p>
Energy demand and its temporal flexibility: approaches, criticalities and ways forward
<p>Data set of the reviewed documents in the contribution 'Energy demand and its temporal flexibility: approaches, criticalities and ways forward' under consideration for publication in 'Renewable & Sustainable Energy Reviews'</p>
Plasticity of the gastrocnemius elastic system in response to decreased work and power demand during growth
<p class="MsoBodyText">Elastic energy storage and release can enhance performance that would otherwise be limited by the force-velocity constraints of muscle. While functional influence of a biological spring depends on tuning between components of an elastic system (the muscle, spring, driven mass, and lever system), we do not know whether elastic systems systematically adapt to functional demand. To test whether altering work and power generation during maturation alters the morphology of an elastic system, we prevented growing guinea fowl (<i>Numida Meleagris</i>) from jumping. At maturity, we compared the jump performance of our treatment group to that of controls and measured the morphology of the gastrocnemius elastic system. We found that restricted birds jumped with lower jump power and work, yet there were no significant between-group differences in the components of the elastic system. Further, subject-specific models revealed no difference in energy storage capacity between groups, though energy storage was most sensitive to variations in muscle properties (most significantly operating length and least dependent on tendon stiffness). We conclude that the gastrocnemius elastic system in the guinea fowl displays little to no plastic response to decreased demand during growth and hypothesize that neural plasticity may explain performance variation.</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.