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1,118 results for “Time series”
plan4res - public dataset for case study 1 part MIM-1: time series used for multi-modal investment pathway modelling
<p>Public data set which is used within the plan4res project for performing case study 1 "Multi-modal European energy concept for achiving COP21" - Multi-modal Investment modelling (MIM) Part 1: Time series for the reference year 2015</p> <p>The related documentation is included in plan4res' deliverable D4.5 chapter 3.2 (see 10.5281/zenodo.3785010) </p> <p>The data set includes the following data:</p> <p>a) characteristic annual load profiles for large industrial heat demand for chemical, iron & steel, food & beverage and pulp & paper industries for the reference year 2015</p> <p>HOTMAPS__TD_OUT_D_CHEM__20200608T160653__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_FOOD__20200608T160724__20200422T120000Z__v01.csv <br> HOTMAPS__TD_OUT_D_IRON__20200608T160705__20200422T120000Z__v01.csv HOTMAPS__TD_OUT_D_PAPER__20200608T160715__20200422T120000Z__v01.csv </p> <p>b) characteristic demand profiles for road-side car passenger transport and availability of cars for charging while (home) parking for the reference year 2015 </p> <p>SIEMENS__TD_OUT_D_RoadCar__20200608T160627__20200401T120000Z__v01.csv SIEMENS__TD_CAP_CarPark__20200608T160637__20200401T120000Z__v01.csv </p> <p>c) load profiles for exogeneous demand of electricity for the reference year 2015. The exogenous demand includes all electricity consumptions not explicitly modeled within MIM modeling.</p> <p>HRE4__TD_OUT_ElectricityExo__20200608T160732__20200401T120000Z__v01.csv </p> <p>c) regionally resolved demand profiles for (individual) space heating and space cooling for the reference year 2015</p> <p>HRE4__TRD_CAP_Cool_2015__20200608T160051__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_HeatInd_2015__20200608T155849__20200401T120000Z__v01.csv</p> <p>d) regionally resolved generation profiles of electricity from photovoltaic, wind onshore, wind offshore, hydro run-of-river, and for heat generation from solar thermal for the reference year 2015</p> <p>NINJA__TRD_CAP_PV_2015__20200608T160440__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindOFF_2015__20200608T155422__20191104T120000Z__v01.csv <br> NINJA__TRD_CAP_WindON_2015__20200608T155251__20191104T120000Z__v01.csv HRE4__TRD_CAP_HydroRoR_2015__20200608T155550__20200401T120000Z__v01.csv <br> HRE4__TRD_CAP_SolarThermal_2015__20200608T155718__20200401T120000Z__v01.csv </p> <p>e) regionally resolved generation profile of electricity from wind offshore transformed in a way to represent potential capacity factors in future as stated by doi:10.2760/041705. Data based on reference year 2015</p> <p>SIEMENS__TRD_CAP_WindOFF_2040__20200608T155127__20200401T120000Z__v01.csv </p> <p>x) A list of geographical description of the zone hierarchy data used in MIM for the EU33 region set.:</p> <p>SIEMENS__ZoneHierarchy_MIM_EU33__20181231T120000Z___20200131T1200000Z__v001.csv </p> <p>Further info:</p> <p>Time series are based on historical data for the reference year 2015. </p> <p>Values are normalized over one reference year in a way that either the maximum = 1 (CAP) or the integral = 1 (OUT).</p> <p>All values are listed in arbitrary units. </p> <p>All country names are according to ISO 3166-1 alpha-2.</p>
InSAR Time-series of Jakobshavn and Petermann from Sentinel-1 Data
<p>Dataset 1: Sentinel-1 ascending track 90, descending track 127</p> <p>Study areas: Jakobshavn glacier in Greenland. We separate Jakobshavn into three individual areas (N, NE, and S) based on different reference locations.</p> <p>Date: Ascending: April 2016 to March 2020; Descending: July 2017 to April 2020.</p> <p>Processor: ISCE/topsStack + MintPy</p> <p>Dataset 2: Sentinel-1 ascending track 90, descending track 26</p> <p>Study areas: Petermann glacier in Greenland. </p> <p>Date: Ascending: April 2017 to April 2020; Descending: January 2017 to April 2020.</p> <p>Processor: ISCE/topsStack + MintPy</p>
Data from: Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis
<p><strong>Background</strong>. We evaluated the impact of a pharmacist-led Safety Medication dASHboard (SMASH) intervention on medication safety in primary care.<br> <strong>Methods and findings</strong>. SMASH comprised: (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedback; and (3) pharmacists reviewing individual at-risk patients, and initiating remedial actions or advising general practitioners on doing so. It was implemented in forty-three general practices covering a population of 235,595 people in Salford (Greater Manchester), UK. All practices started receiving the intervention between 18 April 2016 and 26 September 2017. We used an interrupted time series analysis of rates of potentially hazardous prescribing and inadequate blood-test monitoring, comparing observed rates post-intervention to extrapolations from a 24-month pre-intervention trend. The number of people registered to participating practices and having one or more risk factors for being exposed to hazardous prescribing or inadequate blood-test monitoring at the start of the intervention was 47,413 (males: 23,073 [48.7%]; mean age: 60 [standard deviation: 21]). At baseline, 95% of practices had rates of potentially hazardous prescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% confidence interval [CI], 20.3% to 36.8%) at 24 weeks and by 40.7% (95% CI, 29.1% to 54.2%) at twelve months after introduction of SMASH. The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI, 0.2% to 50.7%) at 24 weeks and by 23.5% (95% CI, -4.5% to 61.6%) at 12 months. After 12 months, 95% of practices had rates of potentially hazardous prescribing between 0.74% and 3.02%. We did not randomise practices but enrolled them in a naturalistic fashion. All our measurements were based on routinely kept electronic health records.<br> <strong>Conclusions</strong>. The SMASH intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices. This reduction was sustained over 12 months after start of the intervention for prescribing but not for monitoring of medication. There was a marked reduction in the variation in rates of high-risk prescribing between practices.</p>
Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series
<p>The datasets contain extreme time series obtained from the post-processed 3D wave fields simulated using HOS-Ocean, a high-order spectral model (HOSM) that solves the deterministic propagation of nonlinear wave fields in deep water (Ducrozet et al., 2016).</p> <p>Voermans. (2020). Data for "Wave anomaly detection in wave buoy measurements" - Phase-Resolving Time Series [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4028014</p> <p> </p>
Simulated time series and indicators for the Zambezi River Basin case study
<p>The archive includes:</p> <ul> <li>monthly time series over 2020-2059 time horizon of different physical variables, simulated by the Decision Analytic Framework (DAF) Strategic model of the Zambezi River Basin for the 7 interesting pathways identified in the Negotiation Simulation Lab meeting;</li> <li>a set of evaluation indicators, aggregated at different spatial and temporal scales, for the 7 interesting pathways.</li> </ul> <p>More details on physical variables and indicators included are available in the DAFNE Deliverable D7.3.</p> <p>Files are provided in the <em>csv </em>format, one per each physical variable or indicator: the first 13 rows contain metadata, while data starts at row 14 and use as many columns as the pathways considered. Metadata are the following:</p> <ul> <li><em>name</em>, time series or indicaror label</li> <li><em>author, </em>reference person or team for the specific time series</li> <li><em>created,</em> timestamp of the file creation</li> <li><em>description, </em>description of file content</li> <li><em>unit, </em>unit of measurement</li> <li><em>origin, </em>description of the simulation</li> <li><em>time_sampling,</em> whether the variable is sampled at a given point in time (instantaneous) or represents a time interval (interval)</li> <li><em>frequency, </em>time step of the time series</li> <li><em>scenario, </em>label of scenario</li> <li><em>marker_id, </em>label of the related model component</li> <li><em>id_type, </em>type of time series</li> <li><em>timestart,timeend,P1,P2,P3,P4,P5,P6,P7 : </em>header row of the data, with time information (in the matlab datenum format) at the beginning and pathways on each column</li> </ul>
Simulated time series and indicators for the Omo-Turkana Basin case study
<p>The archive includes:</p> <ul> <li>daily time series over 2021-2099 time horizon of different physical variables, simulated by the Water Energy Food Integrated Model on the Omo Turkana River Basin for the 6 interesting pathways identified in the Negotiation Simulation Lab meeting;</li> <li>a set of evaluation indicators, aggregated at different spatial and temporal scales, for the 6 interesting pathways.</li> </ul> <p>More details on physical variables and indicators included are available in the DAFNE Deliverable D7.3.</p> <p>Files are provided in the <em>csv </em>format, one per each physical variable or indicator: the first 13 rows contain metadata, while data starts at row 14 and use as many columns as the pathways considered. Metadata are the following:</p> <ul> <li><em>name</em>, time series or indicaror label</li> <li><em>author, </em>reference person or team for the specific time series</li> <li><em>created,</em> timestamp of the file creation</li> <li><em>description, </em>description of file content</li> <li><em>unit, </em>unit of measurement</li> <li><em>origin, </em>description of the simulation</li> <li><em>time_sampling,</em> whether the variable is sampled at a given point in time (instantaneous) or represents a time interval (interval)</li> <li><em>frequency, </em>time step of the time series</li> <li><em>scenario, </em>label of scenario</li> <li><em>marker_id, </em>label of the related model component</li> <li><em>id_type, </em>type of time series</li> <li><em>timestart,timeend,P1,P2,P3,P4,P5,P6 : </em>header row of the data, with time information (in the matlab datenum format) at the beginning and pathways on each column</li> </ul>
Sliding velocity, water discharge and basal shear stress time series at Argentière Glacier
<p>The data set contains all data presented in:</p> <p>Gimbert, F., Gilbert, A., Gagliardini, O., Vincent, C., & Moreau, L. (2021). Do Existing Theories Explain Seasonal to Multi-Decadal Changes in Glacier Basal Sliding Speed? <em>Geophysical Research Letters</em>, <em>48</em>(15), e2021GL092858. <a href="https://doi.org/10.1029/2021GL092858">https://doi.org/10.1029/2021GL092858</a></p> <p>and also in:</p> <p>Gilbert, A., Gimbert, F., Thøgersen, K., Schuler, T. V., & Kääb, A. (2022). A Consistent Framework for Coupling Basal Friction with Subglacial Hydrology on Hard-bedded Glaciers. <em>Geophysical Research Letters</em>, <em>49</em>, e2021GL097507. <a href="https://doi.org/10.1029/2021GL097507">https://doi.org/10.1029/2021GL097507</a></p> <p>Files Description:</p> <p>==================================<br> SlidingVelocities1989_2019.csv :<br> ==================================</p> <p>Contains daily values of recorded sliding velocities at the wheel.</p> <p>Column 1 = Date<br> Column 2 = Daily Values (cm/day)</p> <p>================================<br> BasalShearStress1980_2019.csv :<br> ================================</p> <p>Contains daily values of inferred basal shear stress at the wheel.</p> <p>Column 1 = Date<br> Column 2 = Daily Values (MPa)</p> <p>================================<br> WaterDischarge1985_2019.csv :<br> ================================</p> <p>Contains daily values of recorded water discharge at the glacier outlet</p> <p>Column 1 = Date<br> Column 2 = Daily Values (m3/s)</p>
Data from: Disturbance detection in Landsat time series is influenced by tree mortality agent and severity, not by prior disturbance
<p><span>Landsat time series (LTS) and associated change detection algorithms are useful for monitoring the effects of global change on Earth's ecosystems. Because LTS algorithms can be easily applied across broad areas, they are commonly used to map changes in forest structure due to wildfire, insect attack, and other important drivers of tree mortality. But factors such as initial forest density, tree mortality agent, and disturbance severity (i.e., percent tree mortality) influence patterns of surface reflectance and may influence the accuracy of LTS algorithms. And while LTS algorithms are widely used in areas with a history of multiple disturbance events during the Landsat record, the effectiveness of LTS algorithms in these conditions is not well understood. We compared products from the LTS algorithm LandTrendr (<span>Landsat-based Detection of Trends in Disturbance and Recovery) with</span> a unique field dataset from a landscape heavily influenced by both wildfire and spruce beetles (<i>Dendroctonus rufipennis</i>) since c. 2000. We also compared LandTrendr to other common methods of mapping fire- and spruce beetle-affected areas. We found that LandTrendr more accurately detected wildfire than spruce beetle-induced tree mortality, and both mortality agents were more easily detected when they occurred at high severity. Surprisingly, prior spruce beetle outbreaks did not influence the detectability of subsequent wildfire. Compared to alternative disturbance mapping approaches, LandTrendr predicted a c. 40% lower area affected by wildfire or spruce beetle outbreaks. <span>Our findings indicate that disturbance type- and severity-specific differences in omission error may have broad implications for disturbance mapping efforts that utilize Landsat data. Gradual, low-severity disturbances (e.g., background tree mortality and non-stand replacing disturbance) are pervasive in forest ecosystems, yet they can be difficult to detect using automated LTS algorithms. Whenever possible, methods to account for these biases should be incorporated in LTS-based mapping efforts, including the use of multispectral ensembles and ancillary spatial data to refine predictions. However, our findings also indicate that LTS algorithms appear to be robust in areas with multiple disturbance events, which is important because these areas will increase as new acquisitions extend the length of the Landsat record. </span></span></p>
Establishing diversity in synthetic time series for prediction performance evaluation
<p>This dataset enables practitioners to evaluate their time series prediction algorithms on various types of time series</p>
The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings
<p>Submitted to The ISSI 2021 Conference. The conference is organised by KU Leuven in close collaboration with the university of Antwerp under the auspices of ISSI – the International Society for Informetrics and Scientometrics (<a href="http://www.issi-society.org/">http://www.issi-society.org/</a>). </p> <p>We present a forecasting analysis on the growth of scientific literature related to COVID-19 expected for 2021. Considering the paramount scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the ARIMA model and use two different data sources: the Dimensions and World Health Organization COVID-19 databases. These two sources have the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by NLM source (PubMed and PMC), and by domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings.</p>
High-frequency measurements of aeolian saltation flux: time series data
<p>High-frequency (25-50 Hz) coupled observations of wind speed and aeolian saltation flux (i.e, the wind-blown movement of sand) were measured at three field sites: Jericoacoara, Brazil; Rancho Guadalupe, California; and Oceano, California. The dataset provided here contains the full record of raw and processed time series of saltation flux and wind speed measured at multiple heights above the sediment surface.</p>
Scripts and data for "The adequacy of time-series reduction for renewable energy systems"
<p>This upload provides the scripts and data used for the computations in the aforementioned working paper. To run these files, you will need to adjust the directory in the files 'testTimeSeries.jl' and 'calli.bat' to your local directory.</p> <p>The subfolder 'reduceTimeSeries' contains all data and the script 'reduceTimeSeries.jl' to reduce the full time-series. Reduction using the 'Gerbaulet' method unfortunately requires a GAMS installation. The results of the reduction are already provided in the folder 'output'.</p> <p>The subfolder 'testTimeSeries' contains all data and the script 'testTimeSeries.jl' to test the reduced time-series with a capacity expansion model. The 'comment' and ‘source’ columns in the AnyMOD.jl input files provide further documentation on the used input parameters. The labels 'lowDem' and 'newDem' relate to what was referred to conventional demand and demand with sector integration in the paper, respectively.</p>
InSAR time series analysis results of ALOS-2/PALSAR-2 data for the post-eruptive displacement of the 2015 phreatic eruption of Hakone volcano, Japan
<p>This repository contains the InSAR products used in Doke et al., GRL (submitted).</p> <p> </p> <p><strong>Dataset 1</strong>: Surface velocity data estimated by InSAR time series analysis with NetCDF grid format.</p> <ol> <li>surface_velocity_p126.nc</li> <li>surface_velocity_p18.nc</li> </ol> <p> </p> <p><strong>Dataset 2</strong>: Time-series of LOS displacements in selected locations with text format.</p> <ol> <li>time_series_p126.txt</li> <li>time_series_p18.txt</li> </ol> <p> </p> <p><strong>Dataset 3</strong>: Inputs and results of model inversion with shapefile.</p> <p>Subsampled observation data, modeled (simulated) displacements, and other parameters are shown in attribute tables in shapefiles. Shapefiles that show the location of the estimated models are also included in ZIP files.</p> <ol> <li>point_source_deflation.zip</li> <li>sill_deflation.zip</li> </ol>
Subsidence of Beijing (China) mapped by Copernicus Sentinel-1 time series interferometry
<p><strong>RESULTS DESCRIPTION</strong></p> <p>Recent reports from scientific and mainstream media have indicated that the city of Beijing, together with its surroundings, is subsiding at fast and alarming rate as result of the overexploitation of groundwater. The depletion of groundwater causes underlying soil to compact, creating a phenomenon called subsidence. The Beijing region has been experiencing this phenomenon since 1935, but in last years the rate of sinking has significantly increased.</p> <p>A team of researchers, within ESA sponsored, SEOM InSARap project performed an interferometric analysis of Copernicus Sentinel-1 data which confirms the reported findings also with current data. While the results speak for themselves, we can just once more reiterate on the usefulness of the Copernicus Programme, in this case for deformation monitoring applications.</p> <p><strong>ANALYSIS SUMMARY</strong></p> <ul> <li>Data overview: <ul> <li>Sentinel-1 IW</li> <li>Track 47 descending</li> <li>Observation window December 2014 - June 2016</li> <li>Data download via Scientific Data Hub</li> </ul> </li> <li>Processing overview: <ul> <li>Time series analysis performed with Small Baseline Subset (SBAS) methodology</li> <li>Interferometric combinations of up to 96 days used</li> </ul> </li> </ul> <p><em>More information and context available at insarap.org</em></p> <p><em>Terms and Conditions:</em> All Sentinel-1 results that are available for download are Derived Works of Copernicus data (2014-2016), subject to the "<em>TERMS AND CONDITIONS FOR THE USE AND DISTRIBUTION OF SENTINEL DATA AND SERVICE INFORMATION</em>".</p> <p><em>Acknowledgments: </em> ESA SEOM InSARap project - Sentinel-1 InSAR Performance Study with TOPS Data, contract number 4000110680/14/I-BG-InSARap</p>
Results from monitoring changes in NDVI time series over southwest Ethiopia based on 11 years of Landsat 7 imagery and BFAST
<p>(see README.md)</p>
Load and renewable generation time series for selected regions in Africa and Eurasia
<p>The data set consist of three groups of yearly time series for 2003-2012 used in grid modelling. All time series are presented in .csv format. The data covers wind and PV power generation and load of 12 regions. The regions have 2 letter identifier and include the following:</p> <p>EU - EU countries</p> <p>NA - Algeria, Egypt, Libya, Morocco, Tunisia</p> <p>WA - Cameroon, Ghana, Nigeria</p> <p>EA - Ethiopia, Kenya, Tanzania, Uganda</p> <p>SA - South Africa</p> <p>RU - European part of Russia, Belarus, Ukraine</p> <p>MS - Arabic countries, Israel, Turkey</p> <p>IP - Bangladesh, India, Pakistan, Sri Lanka</p> <p>SB - Asian part of Russia, Kazakhstan, Turkmenistan, Uzbekistan</p> <p>IC - Indonesia, Malaysia, Philippines, Thailand, Vietnam</p> <p>CN - China</p> <p>FE - South Korea, Japan</p> <p> </p> <p>Wind power time series are derived from MERRA wind speed data for Enercon E-126 wind turbine.</p> <p>PV power time series are derived from MERRA solar surface irradiance data.</p> <p>Load time series are generated with the periodical function.</p> <p> </p> <p>Data for EU was derived within the project RESTORE 2050 as described in</p> <p>Kies, A., Chattopadhyay, K., von Bremen, L., Lorenz, E., & Heinemann, D. (2016). Simulation of renewable feed-in for power system studies.</p> <p>Data for all other nodes was derived as described in</p> <p>Krutova, M. et al., The smoothing effect for renewable resources in an Afro-Eurasian power grid, Adv. Sci. Res., 2017</p> <p>Load for EU is obtained from ENTSO-E and is not included in this data set.</p>
BioTIME 2.0: expanding and improving a database of biodiversity time series
<p>Here we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies in the database, and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.</p> <p>We include here:</p> <ul> <li>SQL file of the database - biotime_v2_sql_15April25.sql</li> <li>RDS file of the query combining raw data with species names - biotime_v2_query_15April25.rds</li> <li>csv file for metadata information - biotime_v2_metadata_15April25.csv</li> <li>csv file of citations - references_biotime_v2_15April25.csv</li> <li>text file of BIB text citations - BIB_biotime_v2_15April25.csv.txt</li> </ul> <p>for issue of version 2.0 of the BioTIME database</p>
Geospatial and time series dataset for hydrologic analyses within South Asia (GHSA)
<p>Summary of changes:</p> <ul> <li>v2504, GHSA <ul> <li>1,702 stations from 5 countries in South Asia (Bhutan, China, India, Nepal and Pakistan);</li> <li>districts and basin states from 7 countries in South Asia (Afghanistan, Bangladesh, Bhutan, China, India, Nepal and Pakistan)</li> <li>land use change, vegetation index (NDVI, NDVI-crop), precipitation, evaporation (E-total, E-crop and E-irrigation), streamflow, surface and root zone soil moisture, terrestrial water storage change, snow water equivalent and snow cover fraction; 1950-2023</li> <li>publication:</li> </ul> </li> <li>v2409 <ul> <li>test version</li> </ul> </li> <li>v2301, GHI <ul> <li>645 stations, limited to Peninsular India</li> <li>precipitation, evaporation and streamflow; 1950-2020</li> <li>publication: Goteti (2023), Earth Syst. Sci. Data, https://doi.org/10.5194/essd-15-4389-2023</li> </ul> </li> </ul> <p> </p>
Daily activity time series of ants, Camponotus japonicus
<p>Social insects often share tasks among individuals. In this study, we analyzed the foraging activity of ants (<em>Camponotus japonicus</em>) and recorded the daily passage event counts of individual workers between a nest chamber and a foraging arena using five monodomous colonies. We proposed two hypotheses on the time series of foraging frequency by individual worker ants:</p> <p>(i) Regarding the time series of foraging frequency by individual worker ants, the foraging frequency on a certain day could be expressed by the product of the foraging frequency on the previous day and the exponential of a random number.<br>(ii) The random numbers are correlated between some pairs of worker ants.</p> <p>The results for the five tested ant colonies showed that the probability of total daily passage counts (the sum of an individual's passage count) were characterized by a log-normal distribution. The worker ants behaved differently in terms of active days and foraging frequency. However, for >54% of the worker ants, the probability of the daily passage count was characterized by a log-normal distribution, and these worker ants performed >72% of the tasks in each colony. Furthermore, for >73% of the worker ants, the time development of passage count was confirmed to be mathematically modelled; the logarithmic first difference between the passage counts on a certain day and those on the previous day was a random normal variable. These results support hypothesis (i). Additionally, the random numbers, that were equivalent to the logarithmic first difference, were correlated for some pairs of worker ants. These results support hypothesis (ii).</p>
GODEEEP Light Duty Vehicle (LDV) Hourly Time Series Loads by County
<p>Each file contains projected light duty vehicle (LDV) load by county for a particular U.S. state, GCAM-USA scenario, and climate pathway as specified in the file name. For the full discussion of the methodology, please see <a href="https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf">https://godeeep.pnnl.gov/pubs/EV_Load_Shapes_GODEEEP_Arxiv.pdf</a>. For the balancing authority level timeseries, please see <a href="https://doi.org/10.5281/zenodo.7888568">10.5281/zenodo.7888568</a>. The code used to produce this data is available at <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>. Note that fleet sizes at the state scale are derived from the GCAM-USA scenario output. Downscaling to the county scale uses the electric vehicle penetration rates found in the appendix of <a href="https://www.pnnl.gov/sites/default/files/media/file/EV-AT-SCALE_1_IMPACTS_final.pdf">M. Kintner-Meyer, S. Davis, S. Sridhar, D. Bhatnagar, S. Mahserejian and M. Ghosal, "Electric vehicles at scale-phase I analysis: High EV adoption impacts on the western US power grid", Tech. Rep., 2020</a>. To harmonize the state scale LDV energy use from the GCAM-USA scenarios with the LDV load calculated with EV-Pro Lite, a scale factor was applied to the county level loads, so note that if the reported scale factor is much different than 1.0 there is potentially some disagreement between the load and the fleet size. This scale factor has already been applied to the loads reported in these files (but has NOT been applied to the fleet sizes).</p><h4><strong>GCAM-USA scenarios</strong></h4><p>See <a href="https://doi.org/10.5281/zenodo.7838871">10.5281/zenodo.7838871</a> and <a href="https://doi.org/10.5281/zenodo.8377778">10.5281/zenodo.8377778</a> for more details</p><ul><li>BAU_Climate - a business-as-usual scenario without IRA incentives</li><li>business_as_usual_ira_ccs_climate - a business-as-usual scenario with IRA incentives for CCS technology</li><li>NetZeroNoCCS_Climate - a scenario targeting net-zero by 2050 without IRA incentives, disallowing CCS technology</li><li>net_zero_ira_ccs_climate - scenario targeting net-zero by 2050 with IRA incentives for CCS technology</li></ul><h4><strong>Climate pathways</strong></h4><p>See <a href="https://doi.org/10.1038/s41597-023-02485-5">10.1038/s41597-023-02485-5</a> for more details</p><ul><li>rcp45cooler - historical weather patterns projected into the future with a warming signal applied commensurate with a cooler ensemble of RCP4.5 CMIP6 models</li><li>rcp85hotter - historical weather patterns projected into the future with a warming signal applied commensurate with a hotter ensemble of RCP4.5 CMIP6 models</li></ul><h4><strong>Fields in the data files:</strong></h4><ul><li>time - hourly timestamp in UTC representing the preceding hour of data</li><li>county - the county name</li><li>State - the state abbreviation for this county</li><li>FIPS - FIPS code for the county</li><li>balancing_authority - the balancing authority responsible for the load reported in this row; note that some counties span multiple balancing authorities and their load is divided between those balancing authorities proportional to the population residing within that balancing authority</li><li>load_MWh - load on the grid caused by the charging of LDVs during this hour within this county and balancing authority in megawatt hours</li><li>temperature_celsius - mean temperature within this county and balancing authority in degrees Celsius</li><li>fleet_size - number of electrified LDV cars within this county and balancing authority</li><li>daily_miles - average number of miles traveled per day per LDV within this county and balancing authority in miles/day</li><li>scale_factor - the values in the load_MWh field have been scaled by this multiplier in order to harmonize the state scale LDV loads with the GCAM-USA scenarios</li></ul><h4><strong>Changelog</strong></h4><ul><li>v1.0.1 - added fleet_size, daily_miles, and scale_factor to the output, and updated the README accordingly</li></ul><h4><strong>Acknowledgements</strong></h4><p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p><p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</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.