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3,472 results for “Balance”
Net Ecosystem Carbon Balance of Grazing Lands across the continental United States, 2013-2023
Grazing lands underpin U.S. beef production, store roughly one-third of global soil organic carbon, deliver multiple ecosystem services, and are closely tied to the prosperity and resilience of rural communities. In this study, we calculated net ecosystem carbon balance (NECB), the net status of grazing lands as a carbon sink or source, by integrating carbon uptake from photosynthesis, and carbon loss through ecosystem respiration, enteric fermentation and manure from livestock. Our objective was to synthesize multiple years of annual NECB of grazing lands measured by eddy covariance towers from 16 pastures across seven USDA Long-term Agroecosystem Research Network (LTAR) sites and enteric fermentation and manure emissions derived from the stocking rates. We evaluated annual NECB against mean annual precipitation (MAP), mean annual temperature (MAT), vegetation, soil, fire history, grazing pressure index (GPI) and fertilization history. We found: (1) grazing lands were a carbon sink or neutral in most sites, and NECB was not significantly different between grasslands and shrublands, mesic and xeric conditions, and fertilized and unfertilized sites; (2) NECB increased with precipitation and temperature, but decreased with a higher GPI; and (3) precipitation, temperature, and GPI interacted such that temperature had a positive effect when MAP was greater than 700 mm and GPI had a negative effect when MAP was less than 1000 mm. Thus, most grazing lands in our study function as a carbon sink unless coupled with water deficit, low temperature, or heavy grazing. NECB is most sensitive to precipitation when water was limited with high interannual variability. Future work to improve our understanding of NECB on grazing lands should directly measure enteric fermentation and ecosystem emissions in different systems and add measurements on carbon loss through wind erosion and leaching.
Seasonal Ice Mass-balance Buoy (SIMB) measurements from sites along the Beaufort Sea Coast, Alaska, 2018-ongoing
Measurements of the thickness of sea ice and the depth of its snow cover allow us to calculate how their mass changes in response to the varying fluxes of heat between the ocean and atmosphere over the course of a season. Repeated drill measurements are not ideal for this purpose since each drill hole disturbs the ice and its insulating snow cover. Also, spatial variability in ice thickness can mask temporal changes if holes are not drilled in the same place each time. Hence, methods that do not require re-drilling are preferred. Automated systems such as the Seasonal Ice Mass-balance Buoy (SIMB; Planck et al, 2019) provide high temporal resolution for capturing sub-daily variations and typically include sensor strings to measure the vertical temperature profile from the air to the ocean, which can be used to infer other properties of the ice cover such as strength and porosity. Under the Beaufort Lagoon Ecosystems LTER (BLE LTER) research program, several SIMBs are deployed at sites along the Beaufort Sea coast and record a suite of parameters including but not limited to snow depth, ice thickness, position of ice surface and bottom, water/air temperature, and vertical profiles of temperature. Planck, C. J., J. Whitlock, C. Polashenski, and D. Perovich (2019), The evolution of the seasonal ice mass balance buoy, Cold Regions Science and Technology, 165, 102792, doi: https://doi.org/10.1016/j.coldregions.2019.102792.
Power Balance Characteristics for Multirotor- and Fixed-Wing-Type UAV-BSs Equipped with RES and RISs
<h2><strong>Overview</strong></h2> <p>The following dataset presents the power balance characteristics for Unmanned Aerial Vehicle Base Stations (UAV-BSs) equipped with Renewable Energy Sources (RES) and Reconfigurable Intelligent Surfaces (RISs). The dataset has been prepared for two different types of UAVs, i.e., multirotor and fixed-wing ones.</p> <h2><strong>Scenario</strong></h2> <p>The considered scenario includes 2 UAV-BSs (each of a different type) equipped with a single RF transceiver and an RIS device and RES — a single photovoltaic panel (PV) and a single wind turbine (WT). The UAV-BSs are placed within the city of Poznan and hover (multirotor) or follow a circular route (fixed-wing) above a single mobile user with fixed traffic demand (100 Mbps downlink — DL, and 50 Mbps uplink — UL). The simulation runs have been performed for 4 dates (vernal equinox, summer solstice, autumn equinox, winter solstice), each one from a different season of the year. The aim of such an approach was to highlight the impact of the time of the day and the year on the energy gain obtained thanks to enabling RES generators as well as on the power consumption of the hardware of each UAV-BS type. The weather conditions assumed within the simulation are typical for the climate in Poland.</p> <h2><strong>Methodology</strong></h2> <p>The power-balance calculations (UAV-BSs' power consumption, renewable energy production) have been based on the mathematical formulas from the scientific literature and performed within the digital simulation runs by using dedicated software developed in Python programming language.</p> <h2><strong>Simulation setup</strong></h2> <p>The setup of the input parameters for used mathematical models (power consumption, energy generation) has been done in accordance with the values attached within the literature positions (cited within the publication included in the <em>Related works</em> section of the following dataset) and adjusted to the considered study. Furthermore, the data used to predict weather conditions are the real data (for the year 2022) collected by the weather stations placed in Poznan. A single simulation run has been performed (which takes into account 2 types of UAV-BS simultaneously and estimates their power balance for 4 seasons of the year), where the time step has been set to 1 hour of the day.</p> <h2><strong>Results</strong></h2> <p>The results of the aforementioned investigations have been included in the attached files (<em>_power_balance_multirotor.csv</em> & <em>_power_balance_fixed_wing.csv</em>). The first column denotes the hour of a particular day. Next, 4 multicolumns have been presented for the following variants — No RES enabled, only PV enabled, only WT enabled, and both types of RES generators enabled. In addition, each multicolumn consists of 4 columns, each of which represents a UAV-BS's hardware power balance (in W) for a different date (season of the year).</p> <h2><strong>Acknowledgment</strong></h2> <p>More details about the conducted study have been described within the attached paper (<em>Related works</em> section). The work (including the following dataset preparation) was realized within project no. 2021/43/B/ST7/01365 funded by the National Science Center in Poland.</p>
Dataset of "Balancing Activity and Stability through Compositional Engineering of Ternary PtNi–Au Alloy ORR Catalysts"
<p>A systematic comparative analysis of the activity-stability relationship for compositionally tuned PtNi-Au model layers, prepared by magnetron co-sputtering, was conducted using a diverse range of complementary characterization techniques and electrochemistry, supported by density functional theory calculations. Our study reveals that progressively increasing the Au concentration in the Pt50Ni50 alloy from 3 to 15 at.% leads to opposing catalyst activity and stability trends. Specifically, we observe a decrease in ORR activity accompanied by an increase in catalyst stability, manifested in the suppression of both Pt and Ni dissolution. Despite the reduced activity compared to PtNi, the PtNi–Au alloy with 15 at.% Au still exhibits nearly three times the activity of monometallic Pt. It also demonstrates a significantly improved dissolution stability relative to the PtNi alloy and even monometallic Pt. These findings provide valuable insights into the intricate balance between activity and stability in multimetallic ORR catalysts, paving the way for the design of cost-effective and durable materials for PEMFCs.</p>
Performance of users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool
<p>This dataset contains data generated by users of GABLE platform. The data shows the performance of some users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool. More information about GABLE project can be found at: www.projectgable.eu</p>
Dataset for "Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections"
<p>This dataset is used to reproduce the results presented in the following publication:</p> <p>Goelzer, H., Noel, B. P. Y., Edwards, T. L., Fettweis, X., Gregory, J. M., Lipscomb, W. H., van de Wal, R. S. W., and van den Broeke, M. R.: Remapping of Greenland ice sheet surface mass balance anomalies for large ensemble sea-level change projections, The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-188, in review, 2019.</p> <p> </p>
Heat balance of selected Brayton cycle
<p>The dataset provides the heat balance of the selected Brayton cycle among the 10 cycles considered. Simulations of several cases defined by a different supercritical CO2 cycle type were performed with Ebsilon software in order to assess the net power block efficiency of the cycle and the Levelized Cost of Electricity (LCOE) of the plant. Due to its highest efficiency among the 10 envisaged Brayton cycle options, it is the Partial Cooling with Intercooling and Reheating cycle that is selected.</p> <p>The datasets could help other people design a sCO2 Brayton cycle.</p> <p>For detailed analysis, please refer to Deliverable 1.1 (Process Parameters of Solar sCO2 Brayton Cycle) to be downloaded at: <a href="https://www.compassco2.eu/wp-content/uploads/2021/02/D1.1_Process-parameters-of-solar-sCO2-Brayton-cycle.pdf">https://www.compassco2.eu/wp-content/uploads/2021/02/D1.1_Process-parameters-of-solar-sCO2-Brayton-cycle.pdf</a></p>
Data from: Carbon and Water Balances in a Watermelon Crop Mulched with Biodegradable Films in Mediterranean Conditions at Extended Growth Season Scale
<p><span>Abstract</span></p> <p><span>The uploaded data are relative to the investigation around (i) the carbon source/sink nature and, further, (ii) the water and carbon balances, of a drip-irrigated and mulched watermelon. The crop was cultivated under the semi-arid climate of the Apulia region, in south Italy.</span></p> <p><span>The used mulching films were biodegradable as indicate by the producer; plants and some non-standard fruits were left on the soil as green manure after harvesting, thus, the experiment spanned from planting to the subsequent crop (6 months of continuous measurement from June to November 2023). </span></p> <p><span>The results detailed in the original publication indicate that mulching films contribute to carbon sequestration in the soil (+19.3 gC m<sup>−2</sup>). However, this mulched watermelon represents a net carbon source, with a net biome exchange, as loss from ecosystems, equal to +230 gC m<sup>−2</sup>. This is primarily due to the substantial amount of carbon exported through marketable fruits. Fixed water scheduling led to water waste through deep percolation (approximately 1/6 of the water supplied), which also contributed to the loss of organic carbon via leaching (−4.3 gC m<sup>−2</sup>). </span></p> <p><span> </span></p> <p><span>Methods</span></p> <p><span>Site and crop</span></p> <p><span>The field site was at the CREA-AA Research Unit experimental farm located in southern Italy (Rutigliano–Bari, 41 01’ N, 17°01’ E, altitude 147 m a.s.l.)., characterized by a Mediterranean semi-arid climate (average annual rainfall of 535 mm). The soil is classified as Lithic Rhodoxeralf, with a clay texture, stable structure, shallow profile (0.6–1.1 m) and rapid drainage due to an underlying cracked limestone subsoil. The SOC content averages around 12.0 g kg<sup>−1</sup>. The field capacity and the permanent wilting point volumetric water contents are 0.36 and 0.21 m<sup>3</sup> m<sup>−3</sup>, respectively; with a bulk density of 1.15 Mg m<sup>−3</sup>, the available soil water ranges from 80 to 140 mm.</span></p> <p><span>The studied watermelon crop (seedless var. Lion king), followed a broccoli cabbage crop harvested in April and partially incorporated (0.81 kg m<sup>−2</sup> of fresh biomass in a soil layer depth of 0.30 m, corresponding to 0.69 kgH2O m<sup>−2</sup>) as green manure on 25 May 2023. Main tillage at medium depth ploughing (0.30 m) and seedbed preparation were performed between 25 and 30 May 2023; the biodegradable film mulch (model PC 100 d8, BASF, Italy, 1 m width) was applied on 1 June 2023. On the same day, driplines (2.1 Lh<sup>−1</sup> emitters, 0.60 m apart) and the main organic fertilization (Orga-Kem 6.11.8 + 11CaO, 300 kg ha<sup>−1</sup>) were also applied. The watermelon plants were transplanted on 9 June at a spacing of 2.70 m between rows and 1 m between plants, covering an area of about 4.0 ha, with a density of approximately 3200 plants ha<sup>−1</sup>. Every 6 rows, the inter-row distance was 5 m to facilitate machinery passage. The first irrigation was performed the day before planting. Crop management adhered to the usual treatments in the area including mechanical weed removal every 4 weeks, irrigation around three times per week to maintain optimal soil water conditions and monthly fertigation (ammonium sulphate 50 kg ha<sup>−1</sup>, magnesium nitrate 30 kg ha<sup>−1</sup>, calcium nitrate 60 kg ha<sup>−1</sup>, mycorrhizae 20 kg ha<sup>−1</sup>). The scalar harvest of marketable fruits occurred between 28 and 31 August 2023. After harvesting, on 25 September 2023, the fresh plant residues (0.6 kg m<sup>−2</sup> of fresh biomass, corresponding to 0.49 kgH2O m<sup>−2</sup>), unharvested fruits (4.0 kg m<sup>−2</sup> of fresh material, corresponding to 3.7 kgH2O m<sup>−2</sup>) and the mulching film were chopped by a tractor shredder and ploughed in two steps, on 2 and 13 October 2023, to a soil depth of 0.30 m. Measurements concluded at the end of November 2023, when tillage for the new winter crop commenced.</span></p> <p><span> </span></p> <p><span>Measurements of H<sub>2</sub>O and CO<sub>2</sub> fluxes; partitioning in evaporation, transpiration, photosynthesis and respiration</span></p> <p><span>The eddy covariance technique was employed to monitor water vapor (H<sub>2</sub>O) and carbon dioxide (CO<sub>2</sub>) fluxes. The equipment comprised a three-dimensional sonic anemometer (uSonic 3 Scientific, Metek GmbH, 25337 Elmshorn, Germany) and a fast response open-path infrared gas analyzer (LI-7500, Li-COR Inc., Lincoln, NE, USA). The three wind components, sonic temperature and atmospheric concentrations of CO<sub>2</sub> and H<sub>2</sub>O were continuously measured at 1.5 m above the crop canopy, with the sensor height adjusted to follow crop growth, reaching a maximum of 1.75 m. </span></p> <p><span>Data were recorded at a frequency of 10 Hz on a dedicated computer using the MeteoFlux software (Servizi Territorio, S.n.c., Cinisello Balsamo, Italy) and were stored on an hourly scale. Post-processing and computation of hourly fluxes of H<sub>2</sub>O (mmol m<sup>−2</sup> s<sup>−1</sup>) and CO<sub>2</sub> (</span>μ<span>mol m<sup>−2</sup> s<sup>−1</sup>) were conducted using EddyPro software, v7.0.9 (</span><a href="http://www.licor.com/eddypro"><span>http://www.licor.com/eddypro</span></a><span>), applying 60 min block averaging, double coordinate rotation, the statistical test, the maximum cross-covariance method, and the WPL density correction.</span></p> <p><span>H<sub>2</sub>O and CO<sub>2</sub> fluxes were partitioned into transpiration, evaporation, photosynthesis and respiration, respectively, using the flux variance similarity method. This method utilizes the Monin–Obukhov similarity theory to separate stomatal (photosynthesis, Fp, and transpiration, Ft) from non-stomatal (respiration, Fr, and evaporation, Fe) processes (Palatella et al., 2014). the H<sub>2</sub>O and CO<sub>2</sub> EC fluxes were partitioned using an adaptation of the code in Phyton provided by (Skaggs et al., 2018) and downloaded from <span> </span></span><a href="https://github.com/usda-arsussl/fluxpart"><span>https://github.com/usda-arsussl/fluxpart</span></a><span> (V0.2.10).</span></p>
Thermal balance of forests in the Mediterranean-temperate ecotone
<p><strong>Thermal balance of forests in the Mediterranean-temperate ecotone.</strong></p> <p>This dataset comprises the data used in the manuscript "Disentangling the role of Forest structure and functional traits for the thermal balance in the Mediterranean–Temperate Ecotone", to be submitted to a scientific journal shortly after the publication date of this dataset. Data comprise 54 variables including case categorization, meteorological, climatic and forest structural variables and the thermal balance of the forest estimated from <a href="https://ecostress.jpl.nasa.gov">ECOSTRESS</a> remote sensing measurements.</p> <p> </p>
Monthly water balance data for southern Taylor Slough Watershed (FCE LTER) from January 2001 to December 2011
The following abstract is from Sandoval (2013). The purpose of this research was to investigate the water balance, flushing time, and water chemistry of Taylor Slough; one of the main natural waterways of the coastal Everglades, during its early stages of restoration. Watershed flushing times were estimated as the surface water volume divided by the total water outputs. Both the water balance and water residence times were calculated on monthly from 2001 – 2011. Flushing times varied between 3 and 78 days, with the highest values occurring in December and the lowest in May. Flushing times were negatively correlated with evapotranspiration (ET), but were longer when surface water volume exceeded ET and shorter when ET exceeded water volume.
Summarized glacier mass balance measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package contains total mass balance changes at each stake measured on six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys region of Antarctica. These values are the result of an analysis of the raw data presented in other data files (glacier stake heights, snow depths, and snow densities). Included here for each stake is the total water equivalent mass change. The standard deviation or the range for each total is given. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
Snow, ice, and total glacier mass balance measurements, McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor glacial mass balance and meltwater flow. This data package includes mass balance changes at each stake on six glaciers (Canada, Commonwealth, Hughes, Suess, Howard, and Taylor) in Taylor Valley and one glacier (Adams) in Miers Valley, all of which are located in the McMurdo Dry Valleys region of Antarctica. The values are the result of an analysis of the raw data presented in other data files (glacier stake heights, snow depths, and glacier snow densities). Included here for each stake are the change in ice and snow water equivalent (mass) values, and the total mass change. The standard deviation or the range for each total is also given. Most measurements began during the 93-94 field season. Adams measurements were established during the 14-15 field season. Measurements are ongoing except at Hughes and Suess Glaciers where monitoring ceased following the 08-09 field season. Monitoring the changes in these measurements over time provides a record of mass balance, and aids in determining the role of glaciers in the polar hydrologic cycle.
Standing Balance Experiment with Long Duration Random Pulses Perturbation
<p>Standing balance experiment and the measured data-set are fundamental for identifying postural feedback controllers. As the generalized feedback controllers can only be identified from long duration balance data (under random external perturbations), a standing balance experiment is conducted and the long duration motion data was recorded. The data-set includes the perturbation reaction data from eight subjects. Each subject performed four experiment trials, including two quiet standing and two perturbed trials. Each trial lasted five minutes. A total of 80 minutes quiet standing and 80 minutes perturbed standing data are included in this data-set. Recorded information including three dimensional trajectories of thirty-two markers (27 on subjects' trunk and legs and 5 on the treadmill frame), six dimensional ground reaction forces, and nine Electromyography signals (EMGs, on subjects' right leg). In addition, joint angles and torques were calculated using a human body model and inverse dynamics. Basic statistical analysis of the data is also included.</p> <p>Measured raw data for each subject in each experimental trial includes three files:</p> <ol> <li>Mocapxxxx.txt: contains motion capture marker data, ground reaction force, and 76 analog channels. Data was recorded at 100 Hz sampling rate.</li> <li>Mocapxxxx_Motion Analysis_analog.txt: contains 76 high sampling rate (1000Hz) analog channels' data. Analog data is consisted of the analog singal from the froce sensor on the treadmill, EMG signals in the Delsys EMG sensors, and 3 axises acceeleration signals of the Delsys EMG sensors.</li> <li>Recordxxxx.txt: contains the sway motion data of treadmill and the three-axis acceleration data of two Xsens MTi-10 series sensors.</li> </ol> <p>Measured raw data also includes two files of the unloaded trial, which is used for the inertia compensation.</p> <ol> <li>Mocap0000.txt: contains motion capture marker data (5 markers on the treadmill frame) and ground reaction forces.</li> <li>Record0000.txt: contains the treadmill sway motion data and the acceleration data (three-axis) of two Xsens MTi-10 series sensors.</li> </ol> <p>Processed data of each subject in each experimental trial contains four files:</p> <ol> <li>Mocapxxxx.txt: contains the gap filled motion capture marker data and the inertia compensated ground reaction force data.</li> <li>Motionxxxx.txt: contains the calculated the trajectories of three joints' (hip, knee, and ankle) angles, angular velocities, moments, and joint contact forces.</li> <li>Data_infoxxxx.txt: contains the quality of recorded raw marker data (percentage and biggest duration of missing marker data), and the percentage of removed inertia artifacts in ground reaction forces</li> <li>MotionAnalysis.fig: shows the mean and standard deviation of three joints' trajectories in four experimental trials.</li> </ol> <p>There are two more plots in the processed data folder which shows the joint motion/moment and the raw/compensated ground reaction forces of one example experimental trial (subject 07 trial 03).</p> <p>The processed data was generated using the code in the 'Processing_Code' folder. The code was wrote using Matlab and the main function is "Data_Processing_Main.m"</p> <p>More details of the standing balance experiment can be found in the document 'Standing_Balance_Experiment_with_Long_Duration_Random_Pulses_Perturbation.pdf'</p>
LGBTQIAphobia dataset (augmented and balanced)
<p><strong>Name: LGBTQIAphobia_dataset_augmented_balanced</strong><br><strong>Description</strong>: Labeled dataset with phrases retrieved from different digital sources (X/twitter, Instagram, TikTok) containing diverse messages directed towards the LGBTQIA+ community. It has 1000 phrases classified as {Non-LGBTQIAphobic (0), LGBTQIAphobic (1)} . It is the balanced version of <strong>LGBTQIAphobia_dataset_augmented.</strong><br><strong>Language: </strong>Spanish <br><strong>Format: </strong>CSV (UTF-8)<br><strong>Structure:</strong> id; phrase; class {0,1}<br><strong>Purpose</strong>: Be used for fine-tuned models that detect language offensive to Spanish or Latin LGBT communities in digital environments.<br><strong>Sources: </strong>X/Twitter, Instagram, TikTok, Youtube comments<br><strong>Size: </strong>20Kb <br><strong>Ethical considerations:</strong> This dataset was created strictly for academic and research purposes. We oppose any type of digital violence, in this case, against the LGBTQIA+ community. The person who was the target of the hate speech has been anonymised, and there is no intention to harm them in any way, either them or the person who delivered the speech. We prioritise the protection of the privacy and confidentiality of vulnerable individuals. To safeguard privacy, we carefully remove any identifying details, such as user IDs, phone numbers, and addresses, before sharing the data with our annotators. All the data we collect is from publicly available sources and does not contain any personal or sensitive information that may jeopardise anyone’s privacy. I request researchers to commit to abiding by ethical guidelines so as not to unnecessarily harm individuals.<br><strong>¿How was it created?<br></strong>- Starting recovery of discriminatory phrases for the LGBTQIA+ community from X/Twitter, Instagram, and Tiktok (197 phrases).<br>- Labelling by 3 raters as non-LGBTphobic (0) and LGBTphobic (1).<br>- Text augmentation was applied through backtranslation and random synonym replacement.<br>- Translating to Spanish part of <strong>McGiff, J., & Nikolov, N. S. (2024)</strong> dataset and was added under licence <strong>CC-BY-4.0<br>- </strong>To balance the majority class, we applied the undersampling technique.<strong><br></strong>- Finally, we obtained 1000 tagged phrases for version 1.0.2 of LGBTQIAphobia_augmented_balanced</p> <p><strong>Class distribution</strong><strong> </strong></p> <table style="border-collapse: collapse; height: 1%; width: 1%; border-width: 1px;"><colgroup><col style="width: 44.9675%;"><col style="width: 54.9128%;"></colgroup> <tbody> <tr style="height: 47.5868px;"> <td style="height: 47.5868px;"> <pre><strong>class</strong></pre> </td> <td style="height: 47.5868px;"> <pre><strong> instances</strong></pre> </td> </tr> <tr style="height: 24.5868px;"> <td style="height: 24.5868px;"> <pre><strong>0</strong></pre> </td> <td style="height: 24.5868px;"> <pre>513</pre> </td> </tr> <tr style="height: 47.5868px;"> <td style="height: 47.5868px;"> <pre><strong>1</strong></pre> </td> <td style="height: 47.5868px;"> <pre>487</pre> </td> </tr> </tbody> </table> <pre><strong>where class is</strong><br>0: non-lgbtphobic<br>1: lgbtphobic<br><br></pre>
CONUS-wide Balancing Authority Scale Hydropower Projections derived from 9505 Third Assessment
<p>This dataset provides historical and climate projection monthly hydropower generation timeseries for balancing authorities within the contiguous U.S. (CONUS). These data were developed as an extension to the Department of Energy Water Power Technologies Office's SECURE Water Act Section 9505 Third Assessment (9505) and include both federal and non-federal hydropower facilities. Additional modeling detail can be found in <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad6ceb" target="_blank" rel="noopener">Broman et al., 2024</a> and in the article's <a href="https://github.com/9505-PNNL/broman-etal_2024_erl">metarepository</a>. </p> <p>The dataset is provided in three separate formats to facilitate ease of use:</p> <p>1) Machine-readable csv in 'tidy' data format:</p> <table> <tbody> <tr> <td><strong>Short Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>class</td> <td>N/A</td> <td>simulation type; control: historical, cc: climate scenario</td> </tr> <tr> <td>forcing</td> <td>N/A</td> <td>meteorological forcing used to drive hydrology model</td> </tr> <tr> <td>model</td> <td>N/A</td> <td>hydrology model</td> </tr> <tr> <td>hp</td> <td>N/A</td> <td>hydropower model</td> </tr> <tr> <td>gcm*</td> <td>N/A</td> <td>global climate model name</td> </tr> <tr> <td>ds*</td> <td>N/A</td> <td>downscaling method; DBCCA (statistical), RegCM (dynamical)</td> </tr> <tr> <td>balancing_authority</td> <td>N/A</td> <td>balancing authority code</td> </tr> <tr> <td>year</td> <td>N/A</td> <td>year</td> </tr> <tr> <td>month</td> <td>N/A</td> <td>month</td> </tr> <tr> <td>modeled_generation_MWh</td> <td>MWh per month</td> <td>simulated generation</td> </tr> </tbody> </table> <p>* only present in the climate projection (cc) files</p> <p>2) xlsx with balancing authority data by tab</p> <p>3) csv by balancing authority:</p> <p>for historical data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp</em> and with the units <em>MWh per month</em>.</p> <p>for climate projection (cc) data: year, <em>month</em>, and <em>HUC4_group</em> columns are the same as above. Data column headers are <em>class</em>_<em>forcing</em>_<em>model</em>_<em>hp_gcm_ds</em> and with the units <em>MWh per month</em>.</p>
Dataset: The effects of class balance on the training energy consumption of logistic regression models
<p>Two synthetic datasets for binary classification, generated with the Random Radial Basis Function generator from WEKA. They are the same shape and size (104.952 instances, 185 attributes), but the "balanced" dataset has 52,13% of its instances belonging to class c0, while the "unbalanced" one only has 4,04% of its instances belonging to class c0. Therefore, this set of datasets is primarily meant to study how class balance influences the behaviour of a machine learning model.</p>
Output from the Glacier Energy and Mass Balance (GEMB v1.0) forced with 3-hourly ERA5 fields and gridded to 10km, Greenland and Antarctica 1979-2024
<p>These model output of firn air content (FAC) and surface mass balance (SMB) are from version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB is a column model of ice sheet and glacier surface-atmospheric energy and mass exchange as well as firn state. GEMB has been integrated into the open-source Ice-Sheet and Sea-level System Model which can be downloaded at https://issm.jpl.nasa.gov/. Here, GEMB is forced with 3-hourly ERA5 output from 1979 through end of 2024. For Greenland and its periphery, the ERA5 surface temperature and downwelling longwave radiation forcing are spatially bias-corrected for each month. All values are adjusted by the difference between the RACMO2.3 and the ERA5 1980-2015 monthly means. The GEMB output is bilinearly interpolated onto a 10km grid, from the native ISSM grid, and the output is given as 5-day output or as monthly.</p>
Dataset to Study TSO-DSO Coordination Market Models for Flexibility Procurement to Balancing and Congestion Management
<p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line, which has capacity of 1.0. The interconnected system is fully represented in "Network.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_18, DN_69, DN_141);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 45 to 50. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected. A minimum value for the quantity is imposed as 0.01. The generated orderbook is presented in "OrderbookTN" (transmission system) and "OrderbookDN" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_18, DN_69, DN_141) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya (Data Sets and Codes)
<p>This repository contains AWS datasets for the modelling periods considered in the analysis presented in the research paper, together with ablation measurements, pre-processed forcing data, T&C model codes, outputs and scripts for analysing outputs. When previously published elsewhere, references and links to the full, original datasets are provided under References.</p> <p>Matlab scripts for executing the T&C model are provided and should work stand-alone on any machine with a Matlab version 2019b or later installed.</p>
Dataset: "Balancing consumer and business value of recommender systems: A simulation-based analysis"
<p>The data files in this directory contain to the results of the simulations reported in the paper: "Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysis" published in Electronic Commerce Research and Applications. The paper is available here: <a href="https://doi.org/10.1016/j.elerap.2022.101195">https://doi.org/10.1016/j.elerap.2022.101195</a></p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.