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557 results for “demand”
entsoe__electric_demand_2012__20200414T121230Z__20200414T113100Z
<p>Historical electricity demand for 2012</p>
Recording of video on demand traffic
<p>The recording was created with the General Purpose Network Testing Framework:</p> <p><a href="https://github.com/vs-uulm/General-Purpose-Network-Testing-Framework">https://github.com/vs-uulm/General-Purpose-Network-Testing-Framework</a></p>
Adjustment costs and factor demand: New evidence from firms' real estate - Replication packages
<p>This is the replication package associated with the article "Adjustment costs and factor demand. New evidence from firms’ real estate" published in Economic Journal.</p>
A Conceptual Model Framework for Analyzing, Simulating, Planning and Controlling of Supply-Demand Matching of Ecosystem Services in Agricultural Landscapes
<p>The video gives an explanation of our conceptual approach for the investigation of the adaptation of societal demands for ecosystem services in agricultural landscapes.</p>
Data from: The demanding grey zone: sport indices by cardiac magnetic resonance imaging differentiate hypertrophic cardiomyopathy from athlete's heart
Background: We aimed to characterize gender specific left ventricular hypertrophy using a novel, accurate and less time demanding cardiac magnetic resonance (CMR) quantification method to differentiate physiological hypertrophy and hypertrophic cardiomyopathy based on a large population of highly trained athletes and hypertrophic cardiomyopathy patients. Methods and Results: Elite athletes (n=150,>18 training hours/week), HCM patients (n=194) and athletes with hypertrophic cardiomyopathy (n=10) were examined by CMR. CMR based sport indices such as maximal end-diastolic wall thickness to left ventricular end-diastolic volume index ratio (EDWT/LVEDVi) and left ventricular mass to left ventricular end-diastolic volume ratio (LVM/LVEDV) were calculated, established using both conventional and threshold-based quantification method. Whereas 47.5% of male athletes, only 4.1% of female athletes were in the grey zone of hypertrophy (EDWT 13-16mm). EDWT/LVEDVi discriminated between physiological and pathological left ventricular hypertrophy with excellent diagnostic accuracy (AUCCQ:0.998, AUCTQ:0.999). Cut-off value for LVM/LVEDVCQ<0.82 mm×m2/ml and for EDWT/LVEDViTQ<1.27 discriminated between physiological and pathological left ventricular hypertrophy with a sensitivity of 77.8% and 89.2%, a specificity of 86.7% and 91.3%, respectively. LVM/LVEDV evaluated using threshold-based quantification performed significantly better than conventional quantification even in the male subgroup with EDWT between 13-16mm (p<0.001). Conclusions: Almost 50% of male highly trained athletes can reach EDWT of 13 mm. CMR based sport indices provide an important tool to distinguish hypertrophic cardiomyopathy from athlete's heart, especially in highly trained athletes in the grey zone of hypertrophy.
Data from: C4 savanna grasses fail to maintain assimilation in drying soil under low CO2 compared with C3 trees despite lower leaf water demand
1) C4 photosynthesis evolved when grasses migrated out of contracting forests under a declining atmospheric CO2 concentration ([CO2]a) and drying climate around 30 million years ago. C4 grasses are hypothesised to benefit from improved plant–water relations in open habitats like savannas, giving advantages over C3 plants under low [CO2]a. But experimental evidence in a low CO2 environment is limited and comparisons with C3 trees are needed to understand savanna vegetation patterns. 2) To test whether stomatal conductance (gS) and CO2 assimilation (A) are maintained in drier soil for C4 grasses than C3 trees, particularly under low [CO2]a, we investigated photosynthesis and plant–water relations of three C3 tree and three C4 grass species grown at 800, 400 or 200 ppm [CO2]a over moderate wetting–drying cycles. 3) C4 grasses had a lower soil–to–leaf water potential gradient than C3 trees, especially at 200 ppm [CO2]a, indicating reduced leaf water demand relative to supply. Yet the dependence of gS and A on predawn leaf water potential (a measure of soil water availability) was greater for the C4 grasses than trees, particularly under low [CO2]a. 4) Our findings establish that gS and A are not maintained in drier soil for C4 grasses compared with C3 trees, suggesting that this mechanism was not prevailing in the expansion of C4–dominated grasslands under low [CO2]a. This inherent susceptibility to sudden decreases in soil water availability justifies why C4 grasses have not evolved a resistant xylem allowing operation under drought, but instead shut down below a water potential threshold and rapidly recover. We point to this capacity to respond to transient water availability as a key overlooked driver of C4 grass success under low [CO2]a.
Data from: Variability in commercial demand for tree saplings affects the probability of introducing exotic forest diseases
1. Several devastating forest pathogens are suspected or known to have entered the UK through imported planting material. The nursery industry is a key business of the tree trade network. Variability in demand for trees makes it difficult for nursery owners to predict how many trees to produce in their nursery. When in any given year, the demand for trees is larger than the production, nursery owners buy trees from foreign sources to match market demand. These imports may introduce exotic diseases. 2. We have developed a model of the dynamics of plant production linked to an economic model to quantify the effect of demand variability on the risk of introducing an exotic disease. 3. We find that - when the cost of producing a tree in a UK nursery is considerably smaller than the cost of importing a tree (in the example presented, less than half the importing cost), the risk of introducing an exotic disease is hardly affected by an increase in demand variability. - when the cost of producing a tree in the nursery is smaller than, but not very different from the cost of importing a tree, the risk of importing exotic diseases increases with increasing demand variability. 4. Synthesis and implications. Our results suggest that a balanced management of demand variability and costs can reduce the risk of importing an exotic forest disease according to the management strategy adopted.
Data from: Aggression supersedes individual oxygen demand to drive group air-breathing in a social catfish
1) Group-living is widespread among animals and comes with numerous costs and benefits. To date, research examining group-living has focused on trade-offs surrounding foraging, while other forms of resource acquisition have been largely overlooked. 2) Air breathing has evolved in many fish lineages, allowing animals to obtain oxygen in hypoxic aquatic environments. Breathing air increases the threat of predation, so some species perform group air breathing, to reduce individual risk. Within species, air breathing can be influenced by metabolic rate as well as personality, but the mechanisms of group air breathing remain unexplored. It is conceivable that keystone individuals with high metabolic demand or intrinsic tendency to breathe air may drive social breathing, especially in hypoxia. 3) We examined social air breathing in African sharptooth catfish Clarias gariepinus, to determine whether individual physiological traits and spontaneous tendency to breathe air influence the behaviour of entire groups, and whether such influences vary in relation to aquatic oxygen availability. 4) We studied eleven groups of four catfish in a laboratory arena and recorded air-breathing behaviour, activity, and agonistic interactions at varying levels of hypoxia. Bimodal respirometry was used to estimate individual standard metabolic rate (SMR) and the tendency to utilise aerial oxygen when alone. 5) Fish took more air breaths in groups as compared to when they were alone, regardless of water oxygen content, and displayed temporally clustere air-breathing behaviour, consistent with existing definitions of synchronous air breathing. However, groups displayed tremendous variability in surfacing behaviour. Aggression by dominant individuals within groups was the main factor influencing air breathing of the entire group. There was no association between individual SMR, or the tendency to obtain oxygen from air when in isolation, and group air breathing. 6) For C. gariepinus, synchronous air breathing is strongly influenced by agonistic interactions, which may expose subordinate individuals to risk of predation. Influential individuals exerted an overriding effect on risk-taking by the entire group, for reasons independent of their physiological oxygen requirements. Overall, this illustrates that social context can obscure interactions between an individual's physiological and behavioural traits and their tendency to take risks to obtain resources.
Data from: Estimating mobile traffic demand using Twitter
In this letter, the authors show that structured social media data can act as an accurate predictor for wireless data demand patterns at a high spatial-temporal resolution. A casestudy is performed on Greater London covering a 5000 km2 area. The data used includes over 0.6 million geo-tagged Twitter data, over 1 million mobile phone data demand records, and U.K. census data. The analysis shows that social media activity (Tweets/s n) can accurately predict the long-term traffic demand for both the uplink and downlink channels. The relationship between social media activity and traffic demand obeys a power law and the model explains for over 71%-79% of the variance in real traffic demand. This is a significant improvement over existing methods of long-term traffic prediction such as census population data (R2 = 0.57). The authors also show that social media data can also forward predict short-term traffic demand for up to 2 h on the same day and for the same time in the following 2-3 days.
Ecological significance of marcescence in Himalayan plants: Why is standing dead phytomass more important in demanding, resource-limited environments?
<ol> <li>Understanding mechanisms allowing plants to thrive in challenging conditions is critical for predicting their responses to global environmental change. An often overlooked ecological adaptation is marcescence, where leaves and stems are retained beyond their typical shedding time, with implications for nutrient recycling and carbon sequestration. Marcescence may be common in plants with conservative resource-use strategies, especially in environments with limited resources and marked seasonality, such as deserts and alpine regions. However, the extent to which marcescence occurs in different taxa across different habitats and its relationship to seasonality and plant functioning as reflected in ecophysiological traits remains understudied.</li> <li>We studied 600 individuals across 40 Himalayan herb species found in desert, steppe, alpine, and subnival habitats, spanning 3000 to 5400 m elevation. Marcescence was observed in 37 out of 40 species evaluated, with 57% of the sampled individuals displaying dead phytomass, indicating the widespread nature of this phenomenon in Himalayan ecosystems.</li> <li>Environment and plant traits emerged as significant factors influencing the amount of dead-standing biomass. These findings remained robust after accounting for intraspecific variation and phylogenetic inertia. Desert and steppe species exhibited greater marcescent biomass, characterized by tenfold more dead stem biomass than alpine and subnival plants. In contrast, the proportion of dead phytomass increased with elevation due to increased dead leaf fraction.</li> <li>Tall desert species with heavily lignified stems and limited nitrogen and phosphorus content had high levels of stem marcescence, which may aid litter photodegradation and nutrient recycling but also deter herbivory and promote seed dispersal (tumbleweed). Conversely, slow-growing alpine and subnival plants with higher water-use efficiency, foliar C:N ratios, and parenchymatic storage tissue rich in mobile carbohydrates had a high proportion of marcescent leaves, which may aid survival in these resource-limited environments by providing delayed nutrients through moisture-conserving mulch that also provides insulation and protection from frost.</li> <li>This study provides new insights into the complex interplay of habitat, seasonality, and plant traits in dead phytomass retention in Himalayan plants. Marcescence is prevalent in resource-conservative species in highly seasonal, resource-limited habitats and may significantly shape the functioning and biodiversity of Himalayan ecosystems.</li> </ol>
Demand-side flexibility enables cost savings in a Reversible pH-swing electrochemical process for Oceanic Co2 Removal
<p>Raw datasets and codes used in our manuscript of the same title. Figure numbers correspond with the figure numbers in the manuscript.</p> <p><strong>Figure 2: </strong>Process Overview(a) Steps of the proposed, reversible, oceanic CDR process, starting with acidification of the ocean water, followed by vacuum stripping of the CO2, basification of the oceanwater, and return to the ocean to equilibrate with the atmosphere. (b) Corresponding effects on pH and dissolved inorganic carbon (DIC) at each step in the oceanic CDR process. Oceanwater pH decreases from 8.1 during acidification to produce a shift in speciation from HCO3 todissolved CO2, which is phase-separated between step 2-3 to capture pure CO2 which decreases the DIC. Basification negates these changes through the addition of the same concentration of OH− ions as H+ ions added to ocean water during acidification, resulting in pH > 8.1 at the end of basification. CO2-starved ocean water will re-equilibrate with the atmosphere and get buffered back to a neutral-pH to re-enter acidification and the cycle continues.</p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div><strong>Figure 3:</strong> Predicted Performance from 0-D Equivalent Circuit Modeling (Sec. Prediction of current-volatge behavior) for (a,c) acidification and (b,d) basification to determine (a,b) polarization behavior and (c,d) Faradaic efficiencies; including (c) power density produced during acidification.<br>Different mass-transfer conditions and therefore limiting currents are modeled by varying the liquid-side boundary layer thicknesses, (δl). Two different pH-shift conditions are modeled for the low and high pH values at the end of acidification and basification, that are respectively constrained by the concentration of H+ and OH− additions to be equal during both steps – (pHlow, pHhigh): (4,10.7) and (6,9) as described in Sec. S3. Baseline parameter values listed in Table S1 are used in the equivalent circuit models with consideration of desired and competing reactions. Note the differences in the current and potential ranges plotted between (a) acidification and (b) basification steps.</div> <div> </div> <div><strong>Figure 4: </strong>Energy Intensity Comparisons (a-c) Predictions for electrochemical energy intensity normalized by the amount of CO2 captured (Eq. (14)) in (GJ/tCO2 ) as a function of the current density when jbase = jacid (a) without and (b) with competing reactions modeled. Dashed lines on the plot indicates a baseline cell resistance of 2.18 Ω cm2 and the shaded areas are indica-tive of this ohmic resistance in the range of 0 - 5 Ω cm2; the shades of blue are indicative of theliquid-side boundary layer thicknesses, δl, modeled. Electrochemical energy intensities from (a)and (b) where δl = 50 μm are shown in (c), and compared with state-of-the-art ocean water CDRprocess intensities reported by Digdaya et al.,15 Kim et al.,23 and Yan et al.22 The gray filled areashows possible operational points with varying extents of competing reactions with the maximumbounded by competing reactions occurring at mass-transfer limited rates. (d) Overall energy inten-sity (GJ/tCO2 ) including electrochemical and parasitic loads (as described in Sec. Energy IntensityCalculations) for the proposed process without competing reactions, calculated at an industrially-relevant current density of 100 mAcm−2, the baseline cell resistance, and pH shift of 6 to 9.6,compared with electrochemical and thermally-driven direct air capture (DAC) processes with data obtained from Singh et al.;24 error bars for the thermal DAC processes are from Viebahn et al.</div> <div> </div> <div><strong>Figure 5: </strong>Operating Energy Profiles and Effects of Load Shifting (a) Integration of solar and wind electricity production in California, CAISO 2022-2023,40 with predicted energy demand for the proposed oceanic CO2 removal process as a function of varying extents of yearly CO2 captured, 0 – 16 MtCO2,year. (b) Hourly power demand to capture 1 tCO2 per day. The gray curve indicates the process shown in (a), while the red curves shows a constant power demand process occurring at an equivalent energy intensity, i.e., the area underneath both curves are the same. (c) Hourly cost to capture 1 tCO2 per day. The gray curve indicates the hourly cost associated with variable energy process shown in (b) if all produced electricity is sold at market value, while the red curve compares it to the hourly cost associated with constant power consumption process shown in (b). Operating conditions of the proposed, variable process shown in (a-c) include jacid = 75 mA cm−2 and jbase = 150 mA cm−2, while still constraining the same volume of ocean water being treated over a 24-hour day in the individual steps (i.e. tbase = 0.5tacid). The process switches between acidification (occurring early mornings and evenings/nights when electricity price is high) and basification (occurring mid-day when electricity price is low) based on the supply and the cost of electricity purchased from CAISO (Sec S8.5). (d) Electricity cost savings for the proposed process operation compared to a process with constant energy usage at different ratios of tbase to tacid, assuming a time-weighted average operating current density of 100 mA cm−2. Different line styles indicate different proportions of market value at which produced electricity can be sold (0%, 50%, and 100%). For all cases: competing reactions are not considered, a thin boundary layer is assumed (δl = 10 μm), and the pH shift is from 6 to 9.6. Parasitic energy costs are included for CO2 phase separation, but not for pumping as we assume co-location with a desalination plant. Parasitics are assumed to only operate during the basification process, at high enough power to remove all CO2 treated over the day.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Future electricity demand time series for European Countries from 2023 to 2100
<p>This dataset represents the future time series of electricity demand for European countries from 2023 to 2100, aligning with the findings presented in our paper 'Future Electricity Demand for Europe: Unraveling the Dynamics of the Temperature Response Function,' published in Applied Energy. To cite this dataset, please cite the published paper <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.apenergy.2024.123387" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.apenergy.2024.123387</a></p> <p>This dataset includes electricity demand data for 36 European countries, with each year being presented as a distinct .CSV file. Data for all years in each country are then compressed in a single .ZIP file. </p> <p>The column explanation is as below:</p> <ul> <li>'country_code': the country code in 2 digits</li> <li>'year': the projection year</li> <li>'month': month of the year</li> <li>'day': day of the month</li> <li>'S0_RCP26_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S0_RCP26_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S0_RCP45_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S0_RCP45_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S0_RCP85_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S0_RCP85_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S1_RCP26_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S1_RCP26_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S1_RCP45_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S1_RCP45_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S1_RCP85_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S1_RCP85_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S2_RCP26_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S2_RCP26_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S2_RCP45_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S2_RCP45_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S2_RCP85_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S2_RCP85_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S3_RCP26_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S3_RCP26_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S3_RCP45_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S3_RCP45_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S3_RCP85_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S3_RCP85_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S4_RCP26_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S4_RCP26_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.</li> <li>'S4_RCP45_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S4_RCP45_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.</li> <li>'S4_RCP85_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> <li>'S4_RCP85_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.</li> </ul>
Borrowing Constraints and Demand for Remedial Education: Evidence from Tanzania
<p>Replication package for “Borrowing Constraints and Demand for Remedial Education: Evidence from Tanzania” by Konrad Burchardi, Jonathan de Quidt, Selim Gulesci, and Munshi Sulaiman.</p>
A Refined Supply-demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals
<p>This database relies on the article entitled "A Refined Supply-demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals " to be published in Earth's Future. The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang. The file named "Supplementary Data" contains the necessary Water Bulletin and Statistical Yearbook data.</p>
AWESOME Water and food demand (WATNEEDS crop model)
<p><span>In this record, we provide the results in terms of crop water requirements, calculated by the hydrological model WATNEEDS, and crop harvested area under the implemented crop replacement scenarios, that have been developed to optimize water use, crop production and agricultural value in present and future climate scenarios in the Nile Basin. These scenarios were designed, modeled and generated for the deliverable D2.3 of AWESOME WP2: <em>Dynamic and spatially distributed crop water use modeling.</em></span></p> <p><span>The analyses have been conducted at the mesoscale, with a specific focus has been reserved for Egypt, as it is particularly relevant for the AWESOME project because the demo sites of the innovative technologies are being build there. </span>In addition, we investigated present and future reallocation scenarios also for Ethiopia and Sudan, to cover the main cropland along the Nile River Basin.</p> <p><span>A baseline scenario is evaluated to quantify blue and green crop water requirements for the current climate conditions and current crop distribution (averaged for the period 2011-2016). </span>Future scenarios have been implemented using future climatic data for the average period around year 2050 (2048-2052) and year 2100 (2096-2100) under three different Representative Concentration Pathways (RCPs): RCP 4.5, RCP 2.6 and RCP 8.5.</p> <p> </p> <p><span>This record contains:</span></p> <p><span>- The Deliverable D2.3, where the optimization model and the calculation of the crop water requirements under different climatic scenarios are presented.</span></p> <p><span>- The results for each implemented scenario, in terms of harvested area and irrigation water consumption (.tif files and excel files), for the Nile River Basin and at the national level for each focus country (Ethiopia, Sudan and Egypt).</span></p> <p><span>- Description of the data (excel file and pdf file)</span></p> <p><span> </span></p>
Heat demand profiles from the When2Heat project repository
Open the record for dataset details and reuse information.
Open Data Set for the article Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763
<p><span>The datasets available for open access from the article ‘Furió, D. and Moreno-del-Castillo, J.<em> Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763</em>’ are provided here.</span></p> <p><span>The datasets comprise time series data on wholesale market global prices and quantity demanded by reference suppliers and competing retailers, along with day-ahead market prices for each of the 24 hours of the day spanning from January 1, 2007, to March 31, 2022. These series have been downloaded from the CNMC website. Additionally, primary temperature data were obtained from the Spanish Meteorological Agency’s website. This dataset provided hourly weather information from a nationwide network of weather stations. Given the scope of the demand series data, which reflects the entire Spanish market, the temperature series were constructed to ensure national representativeness. These data were then aggregated to develop comprehensive national temperature series, aligning them with the demand series. <span>Finally, a dummy variable for each day t of the studied period was constructed to capture the business/non-business day effect on electricity demand. It takes the value of 1 if </span></span><span>𝑡</span><span> corresponds to a working day (non-holiday, Monday to Friday) and 0 otherwise. The national holidays considered are as follows: January 1<sup>st</sup>, January 6<sup>th</sup>, May 1<sup>st</sup>, August 15<sup>th</sup>, October 12<sup>th</sup>, November 1<sup>st</sup>, December 6<sup>th</sup>, December 8<sup>th</sup>, and December 25th. Additionally, the corresponding Good Friday for each year included in the study period was also considered.</span></p>
Dataset Used For Research On: Exploring Geographically Weighted Regression In Water Demand Forecasting For A Rapidly Developing City
<div> <p><span><span>Dataset Used For Research On: Exploring Geographically Weighted Regression In Water Demand Forecasting For A Rapidly Developing City</span></span></p> </div> <p> </p>
ATELIER. QoE and Net.KPIs for On-Demand video transmission in Mobile Networks (emu.) part 1
<p>This dataset contains all the raw (and processed) information for thousands of experiments that transfer a video on-demand from a core network to a UE emulated through SRSRan inside docker containers. The dataset contains both the network statistics but also the evaluation of the QoE through VMAF for each experiment. In total, 3 different videos has been used with different characteristics and different network impairments has been applied.</p> <p>First part. Please download both parts of raw logs before unzipping.</p> <p>The logs contain network KPI information collected during the transfer of a video-stream using FFMpeg from the core net to a UE using SRS-RAN.<br>Each experiment contains the full logs with the full network traces, for a total of more than 4000 experiments.<br>The transferred and received videos are excluded due to the total weight.</p> <p>For a more detailed explanation on how the dataset has been generated, please refer to the associated article and GitHub repository.<br>Title of the article: “ATELIER: Service Tailored and Limited-Trust Network Analytics Using Cooperative Learning”.</p>
ATELIER. QoE and Net.KPIs for On-Demand video transmission in Mobile Networks (emu.) part 2
<p>This dataset contains all the raw (and processed) information for thousands of experiments that transfer a video on-demand from a core network to a UE emulated through SRSRan inside docker containers. The dataset contains both the network statistics but also the evaluation of the QoE through VMAF for each experiment. In total, 3 different videos has been used with different characteristics and different network impairments has been applied.</p> <p>Second part. Please download both parts of raw logs before unzipping.</p> <p>The logs contain network KPI information collected during the transfer of a video-stream using FFMpeg from the core net to a UE using SRS-RAN.<br>Each experiment contains the full logs with the full network traces, for a total of more than 4000 experiments.<br>The transferred and received videos are excluded due to the total weight.</p> <p>For a more detailed explanation on how the dataset has been generated, please refer to the associated article and GitHub repository.<br>Title of the article: “ATELIER: Service Tailored and Limited-Trust Network Analytics Using Cooperative Learning”.</p>
ScienceDex guides
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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.