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3,472 results for “Balance”
Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis
<div>This dataset contains key analysis and plotting scripts, data, and sample images.</div> <div> </div> <div>Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis</div> <div> </div> <div>Magnetic Resonance in Medicine Journal | DOI: 10.1002/mrm.30206</div> <div> </div> <div>§ Swetha Pala(1), § Antti Paajanen(1), Aapo Ristaniemi(1), Ervin Nippolainen(1), Isaac O. Afara(1), Olli Nykänen (1), Mikko J. Nissi (1*)</div> <div> </div> <div>1Department of Technical Physics, University of Eastern Finland</div> <div>§Shared authorship </div> <div> </div> <div> </div> <div> </div> <div>*Corresponding author</div> <div>Mikko J. Nissi</div> <div>Department of Technical Physics</div> <div>University of Eastern Finland, Kuopio Finland</div> <div>POB 1627</div> <div>70211 Kuopio</div> <div>mikko.nissi@uef.fi</div> <div>+358-50-5955517</div> <div> </div> <div> </div> <div>Keywords: Quantitative MRI, T1ρ relaxation, T1ρ dispersion, Compressed-sensing, radial acquisition.</div> <div> </div> <div>Included folders and files are:</div> <div>- Article_figures: all figures published in the manuscript (.eps format)</div> <div>- Data: MRI data files from 27 human cadaver samples with subfolders and files:</div> <div>- Human samples data: raw data files, along with generic analysis ROIs, zone divison inside specific samples folder, and within the parameter related data folder there are smaple specific analysis ROIs, computed profiles per spin lock amplitude. </div> <div>- CS reconstructed data files: </div> <div>- DataTables_used_for_analysis: Contains data tables per AF and reference data used for data analysis </div> <div>- Scripts: Matlab functions used for data processing and T1ρ computation, aedes plugins, and data analysis with subfolders and files:</div> <div> - Aedes_plugins: Plugins for aedes (http://aedes.uef.fi) for calculation of profiles from ROI. </div> <div> - Data analysis: Key scripts used for analysis and plotting.</div> <div> - Common functions: Some common functions that are required by the scripts/plugins. </div> <div> </div> <div>- README.txt: this file describing the contents of the dataset.</div> <div> </div> <div> </div> <div>See more info in separate readme files included in sub-folders.</div> <div> </div> <div> </div> <div>(Swetha Pala, 02 July 2024)</div> <p> </p>
Balancing the oceanic Zn isotope budget: the key role of deep-sea pelagic sediments
<p><span>Supplementary</span><span> i</span><span>nformation</span><span> present analytical methods, figures, and tables.</span></p> <p><span><span>Supplementary Table present geochmical data of deep-sea sediments from the Indian and Pacific.</span></span></p>
Ammo Inc Balance Sheet
<p>AMMO, Inc. (NASDAQ: POWW) owns two businesses in the US firearms industry. Its most valuable unit is Gunbroker.com (“GB”), the largest online platform for firearms and related products in the United States. The second division produces and sells ammunition and munition components for civilian, military, and law enforcement markets. AMMO has just launched an update to its GB platform which finally allows buyers to purchase more than one item at a time, which should drive significant growth and profitability over the coming quarters and years. It has also been experiencing improving profitability in the past few quarters, driven by its new management team. AMMO has also invested in expanding its production capacity and enhancing its operational efficiency, which will enable it to continue the ongoing recovery in its ammunition business. That is why Ammo is a strong hold of our former Speaker of the House, based on data of <a href="https://valueinvesting.io/nancy-pelosi-stock-trades-tracker">Nancy Pelosi Stock Trades Tracker</a>.</p> <p>Despite its solid performance and improving outlook, AMMO's stock price has been trading at a significant discount to its peers and industry averages. One of the factors that has weighed on the stock is the ongoing litigation with the current board member who sold Gunbroker.com to AMMO in 2019. The dispute involves allegations of misrepresentation, and the seller is seeking to rescind the transaction and regain control of the online platform. AMMO has denied the claims and filed counterclaims against the seller, seeking damages and injunctive relief. The litigation is expected to be resolved by the end of this year or early next year. We view the possibility of recission of the deal as non-existent.</p> <p>We believe that the market has overreacted to the litigation risk and overlooked the underlying value and potential of AMMO's business. We estimate that Gunbroker.com alone is potentially worth more than $400 million, based on its revenue and earnings. This implies that the market is assigning a negative value to AMMO's core ammunition and munition business, which is highly profitable and growing at a double-digit pace. There currently is significant merger and acquisition activity in this sector driven by world-wide demand for ammunition due to the conflicts in the Ukraine and Middle East. We think the low value currently assigned to this business is unjustified and irrational, given the strong fundamentals and competitive advantages of AMMO's business model.</p> <p>Below is a summary of AMMO’s highlights:</p> <p></p> <p>Gunbroker.com</p> <p>In 2021, AMMO, Inc. acquired GunBroker.com, the world’s largest online auction and sales marketplace dedicated to firearms, hunting, shooting, and related products for $244 Million using a combination of cash, stock and debt. GunBroker.com allows buyers to find new and used firearms, accessories, and other shooting sports equipment from federally licensed firearms dealers (“FFL” or “FFL’s”). Each transaction for the purchase of a gun requires the seller to ship the firearm to the FFL dealer of the buyer’s choice where the gun can then be picked up. The majority of FFL’s are independent shops located across the US, according to the indexing information of <a href="https://instaindex.io/">Google indexer</a>. Historically the GB site had only allowed the buyer to put in and purchase one item at a time in the shopping cart. Recently, GunBroker.com introduced several features including a multi-item cart and a single payment portal to enhance the customer experience. Here are the features:</p> <p>Multi-Item Cart: GunBroker.com now offers a multi-item cart, allowing users to add multiple items to their cart before making a purchase. This streamlines the buying process and improves convenience for buyers. Single Payment Portal: The platform also launched a single payment portal, simplifying the payment process for users. With this feature, buyers can complete their transactions more efficiently.</p> <p> </p> <p> </p> <p> POWW will receive increased revenue with little to no additional costs from these new features. Early indications are that both have been well received.</p> <p>The growth of online marketplaces like GunBroker.com has transformed how consumers buy firearms and ammunition. The convenience of online shopping and the ability to compare prices and products have contributed to this trend. This is reminiscent of how EBAY helped transform the sale of collectibles and other items sold by smaller independents.</p> <p>The three most similar publicly traded companies to GB are Shopify (“SHOP”), Etsy (“ETSY”) and EBAY (“EBAY”). These three companies are valued at a median EV/EBITDA multiple of 12 times 2024 and 11 times 2025 estimates. Table shown below:</p> <p></p> <p>Applying a 2-turn discount multiple for GB due to its smaller size would yield value for GB of between $388 million and $414 million, which is greater than the current $287 million enterprise value of POWW. Both sell-side analysts I spoke with gave GB have not accounted for the benefits of the site enhancements in their projections. The analysts’ main focus of fiscal 2025 growth is on centered on the 2024 is a presidential election year, which is a cycle that has historically spurred firearms and ammunition sales due to the fear that the incoming administration would limit availability of these items to buyers.</p> <p>Please note that gross merchandise moved over the GB site is over $1 billion and that GB only reports the fees it charges in its revenue line.</p> <p>GB financial table shown below:</p> <p></p> <p>Ammunition Manufacturing</p> <p>Ammo has invested ~$30 million during FY 2022 and 2023 in a new manufacturing facility that greatly increases its capacity. In particular, it has added a new capability for producing 50 caliber brass shell casings. This is widely recognized by the public and has led to the stock making it into <a href="https://valueinvesting.io/congress-stock-trades-tracker">Congress Stock Trades Tracker</a>. Manufacturing these larger calibers is exponentially more difficult than making more common calibers like 9mm pistol ammunition. The diagram below shows the components of making these products and the steps needed to make a high quality rifle shell.</p> <p> </p> <p>These products carry much higher margins due to the difficulty of the manufacturing process. For this reason, POWW management is focused on ramping up large caliber brass production while deemphasizing the production of pistol ammunition. This results in lower revenue but significantly higher profit margins as 9mm bullets are a commodity item that requires very large-scale productions and a large degree of vertical integration of ammunition components to be profitable. This has carried a very low or negative margin for AMMO over the last few years.</p> <p>In the third calendar quarter of 2023 AMMO experienced a manufacturing issue with one of its most important presses used in the production of large caliber brass. Due to supply chain issues at the time this caused production delays that lasted into late calendar 2023. This issue is now fully resolved and POWW has sufficient orders on hand to absorb all its current production. POWW has indicated that there is significant pent-up demand available for this work and expects to begin increasing sales as it ramps up production. The company expects to add shifts to increase this production during the next few quarters.</p> <p>The ammunition business at POWW was run poorly prior to the hiring of the current CEO, Jared Smith. Mr. Smith joined the Company from Fiocchi Munizioni, one of the world's leading ammunition manufacturers. Mr. Smith was the general manager of Fiocchi’s US business and has worked in the industry since 2010. He has successfully transitioned POWW away from low margin small caliber ammunition manufacturing and into higher margin products. This fact was masked in 2023 by the supply chain issues that delayed large scale production of the brass rifle casings. This issue has been solved and calendar 2024 and 2025 will show profitable production and increasing volumes.</p> <p>Ammunition EBITDA was negative $9 million and $9.5 million in fiscal years 2023 and 2024, respectively as the company struggled with high inventories of 9MM ammunition. Much of this production ended up being sold at a loss. Fiscal 2025 (3/25) will begin show the benefits of Mr. Smith’s clean up of the older business and shift to high brass shell casing production as the supply chain issues have been fixed in the early part of calendar 2024. One sell-side analyst has the ammunition segment generating $6.5 million in EBITDA for fiscal year 2025. This is despite the poor start of the year from the supply chain issue. In fiscal year 2026 POWW should be able to generate well in excess of $10 million in EBITDA. Vista Outdoor’s (“VSTO”) Kinetic Group (“KG”) has a $1.96 billion bid for its business. KG has trailing EBITDA of $100 million and is one of the leaders in the ammunition space. This values KG at 19.6 times twelve months EBITDA. </p> <p>The POWW ammunition segment has approximately $180 million in assets and is probably worth somewhere between that number on the high side and $50 million (5 times Fiscal 2026 EBITDA) on the low side. We think a $100 million valuation, just below the mid-point, is appropriate for this business.</p> <p> </p> <p>Ammunition Market Analysis</p> <p>AMMO, Inc. operates in a dynamic market influenced by various factors:</p> <p>Demand Surge: The US ammunition market has experienced increased demand due to various factors, including political uncertainty, social unrest, and concerns about personal safety. These factors have driven more people to purchase firearms and ammunition. Due to international conflicts in the Ukraine and Israel, the world has a significant deficit of rifle shells. Demand for high quality brass shell casings currently is running well ahead of supply. Even when these conflicts end, militaries will need to meaningfully restock their inventory.</p> <p>Supply Chain Challenges: Like many industries, the ammunition market faced supply chain disruptions during the pandemic. Shortages of raw materials, labor, and transportation impacted production and availability.</p> <p>Legislation and Regulation: Changes in gun laws and regulations at the federal and state levels directly impact the ammunition market. For instance, discussions around background checks, magazine capacity limits, and other restrictions affect consumer behavior.</p> <p>Competition: AMMO, Inc. competes with other ammunition manufacturers and distributors. The company’s ability to innovate, maintain quality, and meet market demands will determine its success.</p> <p> </p> <p>Valuation</p> <p>We are bullish on AMMO's stock and have a target price of over $4.37 per share. This represents an 71% upside from the current price of $2.55 per share. We expect that AMMO can generate about $45 million in adjusted EBITDA in FY 2025 (end 3/31/25). With a market cap of $302 million, net cash of $19 million the company has an enterprise value of $283 million which implies a 6.2 times EBITDA multiple. Ammunition maunfacturer mulitples are currently well above that EBITDA multiple as VSTO’s Kinetic Group has a 19.6 times EBITDA offer outstanding. The worldwide shortage of rifle ammunition is not ending anytime soon and we believe that POWW’s ammunition business could fetch a 10 times EBITDA multiple for a $100 million value. We believe that the GunBroker marketplace business with its +85% gross margins deserves a high multiple. Given GunBroker’s growth rate, margin and cashflow contribution we believe that a 10x multiple for this unit is appropriate. This would imply a value of about $400 million for the GB segment and this combines for a $500 million enterprise value at a stock price of $4.37</p> <p>In conclusion, AMMO, Inc.'s acquisition of GunBroker.com positions it well in the online firearms marketplace. The company’s focus on innovation and customer experience, along with the recent platform enhancements, will likely contribute to its continued success. We believe that AMMO is an attractive investment opportunity that offers a compelling combination of growth, profitability, and value. The company has a diversified portfolio of products and services that cater to a large and loyal customer base across various segments and geographies. The company also has a strong balance sheet and cash flow generation that allow it to invest in its growth and reward its shareholders. We think that the current stock price does not reflect the true worth and potential of AMMO, and that the litigation issue is a temporary and manageable challenge that will not materially affect the long-term prospects of the company. </p>
THE ORTHODOX CHURCH AND THE PRESERVATION OF THE ECOLOGICAL BALANCE OF THE POPULATION IN RUSSIA
<p> </p> <p>The article analyzes ecological problems associated with religion and faith, which is especially noticeable among the inhabitants of megacities. It is shown that in order to preserve nature, it is necessary to include environmental education in the priority tasks of universal education. Particular attention is drawn to different approaches to environmental problems.</p>
Paleohydrology on Mars constrained by mass balance and mineralogy of pre‐Amazonian sodium chloride lakes
<p>Included are the raw results obtained by running the numerical models and analyzing the remote sensing maps. Also included are the input data and output data for all CHIM-XPT and SOLVEQ-XPT geochemical models, so the user may reproduce the results from the paper. Programs CHIM-XPT and SOLVEQ-XPT ((c) Mark H. Reed, University of Oregon) are required to run the geochemical models. Please refer to the paper for details on methodology, and get in touch if clarification is required ("Paleohydrology on Mars constrained by mass balance and mineralogy of pre-Amazonian sodium chloride lakes", by M. Melwani Daswani and E. S. Kite, published in the Journal of Geophysical Research: Planets Volume 122, Issue 9 (2017), https://doi.org/10.1002/2017JE005319).</p> <p>Please get in touch if you notice any mistakes too. All data are backed up by M. Melwani Daswani. A lot of data were generated for this project, and it is possible something may have been missed while uploading.</p> <p>Mohit Melwani Daswani, 20 June 2018<br> melwani.mohit@gmail.com</p> <p>EOM</p>
Dataset: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model
<p>This dataset is a companion to the submitted WRR publication entitled ‘Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model’. The file structure is organized as follows:</p> <ul> <li><strong>ASO_50m_depth_surfaces</strong> - This folder contains the Airborne Snow Observatory lidar-derived snow depth products aggregated to 50m gridded spatial resolution. Each file is titled with a date such as ‘TB<em>YYYYMMDD</em>_SUPERsnow_depth.asc’. The coordinates are in UTM zone 11N and use the WGS84 coordinate system.</li> <li><strong>static_grids</strong> <ul> <li>Static grids are used in each of the subsequent folders and are not changed between years.</li> <li>init0000.ipw <ul> <li>Initialization file to begin the model run. Contains the digital elevation model in band 1, surface roughness raster in band 2, and zeroed images of snow properties in bands 3-7.</li> </ul> </li> <li>maxus.nc <ul> <li>netCDF file of 72 separate images of maximum upwind slope for all upwind directions from 0 (north) to 355 degrees in 5-degree increments. Derived using Adam Winstral’s Sx algorithm.</li> </ul> </li> <li>tuolx_dem_50m.ipw <ul> <li>Digital elevation model from ASO snow-free acquisition aggregated to 50m gridded spatial resolution. Same information as band 1 in the init0000.ipw file.</li> </ul> </li> <li>tuolx_hetchy_mask_50m.ipw <ul> <li>Basin mask of the Tuolumne River Basin above Hetch Hetchy Reservoir. Out-of-basin cells denoted as 0, and in-basin cells denoted as 1.</li> </ul> </li> <li>tuolx_vegheight_50m.ipw <ul> <li>Vegetation height raster in meters. Derived from NLCD dataset of vegetation type..</li> </ul> </li> <li>tuolx_vegk_50m.ipw <ul> <li>Emissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> <li>tuolx_vegnlcd_50m.ipw <ul> <li>Vegetation type from the National Land Cover Database.</li> </ul> </li> <li>tuolx_vegtau_50m.ipw <ul> <li>Fractional transmissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type.</li> </ul> </li> </ul> </li> <li><strong>level1_raw_data</strong> <ul> <li>{Hourly data interpolated to nearest hour from downloaded raw data (CDEC/MesoWest)}</li> <li>air_temp_level1.csv</li> <li>precip_accum_level1.csv</li> <li>relative_humidity_level1.csv</li> <li>solar_radiation_level1.csv</li> <li>wind_direction_level1.csv</li> <li>wind_speed_level1.csv</li> </ul> </li> </ul> <p>The directories for each water year contain the configuration file for that year along with the vector meteorological data from measurement sites and site metadata in .csv format.</p> <ul> <li><strong>wy2013</strong></li> <li><strong>wy2014</strong></li> <li><strong>wy2015</strong></li> <li><strong>wy2016</strong> <ul> <li> <ul> <li>backup_config.ini {Initialization file used to distribute station data over a regular grid for each water year.}</li> <li>air_temp.csv</li> <li>cloud_factor.csv</li> <li>metadata.csv</li> <li>precip.csv</li> <li>vapor_pressure.csv</li> <li>wind_direction.csv</li> <li>wind_speed.csv</li> <li><strong>data/</strong> <ul> <li>[subdirectory containing all future created forcing grid files]</li> </ul> </li> <li><strong>runs/</strong> <ul> <li>[subdirectory containing all <em>iSnobal</em> output files in addition to reinitialization scripts for ASO snow depth updates]</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Data for : Social equity shapes zone-selection: Balancing aquatic biodiversity conservation and ecosystem services delivery in the transboundary Danube River Basin
<p>Freshwater biodiversity is declining, despite national and international efforts to manage and protect <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/freshwater-ecosystem">freshwater ecosystems</a>. Ecosystem-based management (EBM) has been proposed as an approach that could more efficiently and adaptively <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/ecological-balance">balance ecological</a> and societal needs. However, this raises the question of how social and ecological objectives can be included in an <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/integrated-management">integrated management</a> plan. Here, we present a generic model-coupling framework tailored to address this question for freshwater ecosystems, using three components: biodiversity, ecosystem services (ESS), and a spatial prioritisation that aims to balance the spatial representation of biodiversity and <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/evolutionarily-stable-strategy">ESS</a> supply and demand. We illustrate this model-coupling approach within the Danube River Basin using the spatially explicit, potential distribution of (i) 85 fish species as a surrogate for biodiversity as modelled using hierarchical Bayesian models, and (ii) four estimated ESS layers produced by the <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/artificial-intelligence">Artificial Intelligence</a> for Ecosystem Services (ARIES) platform (with ESS supply defined as <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/carbon-sequestration">carbon storage</a> and flood regulation, and demand specified as recreation and water use). These are then used for (iii) a joint spatial prioritisation of biodiversity and ESS employing Marxan with Zones, laying out the spatial representation of multiple management zones. Given the transboundary setting of the Danube River Basin, we also run comparative analyses including the country-level <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/purchasing-power-parity">purchasing power parity</a> (PPP)-adjusted gross domestic product (GDP) and each country's percent cover of the total basin area as potential cost factors, illustrating a scheme for balancing the share of establishing specific zones among countries. We demonstrate how emphasizing various biodiversity or ESS targets in an EBM model-coupling framework can be used to cost-effectively test various spatially explicit management options across a multi-national case study. We further discuss possible limitations, future developments, and requirements for effectively managing a balance between biodiversity and ESS supply and demand in freshwater ecosystems.</p>
Transportation Electrification Load Profiles by Balancing Authority and State-Level Electrification Rates in the Western United States for GODEEEP
<p>Time-series hourly electric charging load profiles for the transportation sector across Balancing Authorities (BAs) in the Western Electricity Coordinating Council (WECC) interconnect, annual fleet sizes by state and vehicle type, annual transportation sector energy usage by state and fuel, and annual transportation fuel usage by state. The data is provided for three different socioeconomic pathways and two different climate pathways, resulting in four total scenarios. The socioeconomic pathways--Net-Zero (<code>nz_climate</code>), Net-Zero allowing for Carbon Capture Sequestration (CCS) technology (<code>nz_ccs_climate</code>), and Net-Zero allowing for CCS with Inflation Reduction Act (IRA) policies (<code>nz_ira_ccs_climate</code>)--are described by <a href="https://doi.org/10.5281/zenodo.10642507">https://doi.org/10.5281/zenodo.10642507</a>. The climate pathways--Representative Concentration Pathway (RCP) 4.5 cooler (<code>rcp45cooler</code>) and RCP 8.5 hotter (<code>rcp85hotter</code>)--are described by <a href="https://doi.org/10.57931/1885756">https://doi.org/10.57931/1885756</a>. The climate influence is only considered for Light Duty Vehicles (LDVs).</p> <p>For additional details please consult the paper Acharya et al 2024, Impact of the Inflation Reduction Act and Carbon Capture on Transportation Electrification for a Net-Zero Western U.S. Grid, submitted, and the code repository <a href="https://github.com/GODEEEP/transportation_electrification">https://github.com/GODEEEP/transportation_electrification</a>.</p> <p>A brief summary of the files and directories in this data package is provided below. Text within chevrons implies a multiplicity of files, one for each actual value.</p> <ul> <li>nz_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>nz_ira_ccs_climate <ul> <li>rcp45cooler <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> <li>rcp85hotter <ul> <li><balancing authority>_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> </ul> </li> </ul> </li> <li>WECC_hourly_transportation_load_<socioeconomic pathway>_<climate scenario>_<year>.csv</li> <li>EV_electric_and_total_energy.csv</li> <li>LDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>MDV_fleet_size_all_fuel_types_state_wise.csv</li> <li>HDV_fleet_size_all_fuel_types_state_wise.csv</li> </ul> <p> </p> <p><strong>Hourly transportation load:</strong></p> <ul> <li><code>time</code> - ISO 8601 timestamp representing the end of the hourly timestep; values are reported as the summation over the preceding hour</li> <li><code>balancing_authority</code> - Acronym of the balancing authority for this data point</li> <li><code>LDV_load_MWh</code> - Energy consumed by the charging of Light Duty Vehicles (LDVs) during the previous hour in Megawatt hours</li> <li><code>MDV_load_MWh</code> - Energy consumed by the charging of Medium Duty Vehicles (MDVs) during the previous hour in Megawatt hours</li> <li><code>HDV_load_MWh</code> - Energy consumed by the charging of Heavy Duty Vehicles (HDVs) during the previous hour in Megawatt hours</li> <li><code>passenger_rail_load_MWh</code> - Energy consumed by the charging of passenger rail vehicles during the previous hour in Megawatt hours</li> <li><code>freight_rail_load_MWh</code> - Energy consumed by the charging of freight rail vehicles during the previous hour in Megawatt hours</li> <li><code>aviation_load_MWh</code> - Energy consumed by the charging of aviation vehicles during the previous hour in Megawatt hours</li> <li><code>ship_load_MWh</code> - Energy consumed by the charging of ships during the previous hour in Megawatt hours</li> <li><code>transportation_load_MWh</code> - Total energy consumed by the charging of vehicles during the previous hour in Megawatt hours (summation of the other columns)</li> </ul> <p>The WECC files provide summations of all BAs for each scenario, with the same columns as above excepting <code>balancing_authority</code></p> <p><strong>State-wise fleet sizes by vehicle type:</strong></p> <ul> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>technology</code> - fuel type such as BEV (battery electric vehicle), FCEV (fuel cell electric vehicle), hybrid liquids and liquids (refined liquids)</li> <li><code>veh_type</code> - one of LDV, MDV, or HDV (Light, Medium, or Heavy Duty Vehicle)</li> <li><code>fleet_size</code> - the number of vehicles</li> </ul> <p>To calculate an electrification rate in terms of fleet size for a given scenario, state, year, and veh<em>type, we divide the fleet</em>size for BEV technology by the summation of fleet_size for all technologies.</p> <p><strong>State-wise electric and total energy for LDVs, MDVs, and HDVs:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hdv_total</code> - energy in ExaJoules consumed by all HDVs irrespective of fuel type</li> <li><code>ldv_total</code> - energy in ExaJoules consumed by all LDVs irrespective of fuel type</li> <li><code>mdv_total</code> - energy in ExaJoules consumed by all MDVs irrespective of fuel type</li> <li><code>hdv_electric</code> - electric energy in ExaJoules consumed by HDVs</li> <li><code>ldv_electric</code> - electric energy in ExaJoules consumed by LDVs</li> <li><code>mdv_electric</code> - electric energy in ExaJoules consumed by MDVs</li> </ul> <p>To calculate the electrification rate in terms of EV energy for a given scenario, state, year, and veh_type, we divide electric energy by the total energy.</p> <p><strong>State-wise transportation fuel mix:</strong></p> <ul> <li><code>state</code> - two letter abbreviation of the state within the Western U.S. Interconnection</li> <li><code>year</code> - 5 year increments from 2020 to 2050</li> <li><code>scenario</code> - the socioeconomic pathway, one of <code>nz_climate</code>, <code>nz_ccs_climate</code>, or <code>nz_ira_ccs_climate</code></li> <li><code>hydrogen</code> - hydrogen energy in ExaJoules consumed by the transportation sector</li> <li><code>electricity</code> - electric energy in ExaJoules consumed by the transportation sector</li> <li><code>refined liquids</code> - refined liquid energy in ExaJoules consumed by the transportation sector</li> </ul> <p><br><br></p> <p><strong>Changelog:</strong></p> <ul> <li>v2.0.0 - new set of scenarios; fuel mix data added</li> </ul> <p> </p> <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>
Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling
<p>This dataset and the scripts accompany the manuscript "<strong>Unveiling the Future Water Pulse of Central Asia: A Comprehensive 21st Century Hydrological Forecast from Stochastic Water Balance Modeling</strong>". The manuscript is published in the Journal Climatic Change.</p>
EMS3D-KITTI-Synthetic: A Synthetic 3D Dataset in KITTI Format with Balanced EMS Vehicle Distribution for Autonomous Driving AI Model Training
<p>A 3D synthetic dataset in KITTI format, focused on emergency vehicles such as ambulances and police cars. The dataset was generated across 8 towns within the CARLA simulator and converted into the KITTI format, ensuring compatibility for direct use in AI model training for autonomous driving applications.</p>
Regional-scale assessment of groundwater recharge and the water balance for Austria
<p><strong>Version 1.0 - This version is the final revised one. <br></strong></p> <p>This dataset accompanies the paper: Zeitfogel et al. (2025), Regional-scale assessmentof groundwater recharge and the water balance for Austria, published at Journal of Hydrology: Regional Studies.</p> <p>The dataset includes spatially distributed maps of groundwater recharge rates, precipitation, runoff, and actual evapotranspiration for Austria, resulting from an Austria-wide implementation of the rainfall-runoff model COSERO. By downloading these datasets, you acknowledge that neither the authors nor the providers of the source datasets can be held liable for the accuracy or completeness of the data.</p> <p>The model parameterization of soil information was based on regionalized soil hydraulic property maps.</p> <p>The basins_info file provides spatial information about the delinated model basins, the corresponding gauges, and the calibration stages. </p> <p>This study was funded by the Austrian Academy of Science (Project RechAUT), the Austrian Federal Ministry of Agriculture, Regions and Tourism (Project InfCapAT), and by the Austrian Climate Research Program (ACRP) - 14th call, under grant agreement No. KR21KB0K00001 (HyMELT-CC).</p>
A Factor Two Difference in 21st-Century Greenland Ice Sheet Surface Mass Balance Projections from Three Regional Climate Models for a Strong Warming Scenario (SSP5-8.5)
<p>1km regridded Greenland Ice Sheet SMB / Runoff / Melt projection until 2100. Projections from MAR, RACMO, HIRHAM forced by CESM2 (SSP5-8.5).</p>
Virtual Reality Balance Disturbance Dataset
<p><strong>Background and Purpose:</strong></p> <p>There are very few publicly available datasets on real-world falls in scientific literature due to the lack of natural falls and the inherent difficulties in gathering biomechanical and physiological data from young subjects or older adults residing in their communities in a non-intrusive and user-friendly manner. This data gap hindered research on fall prevention strategies. Immersive Virtual Reality (VR) environments provide a unique solution.</p> <p>This dataset supports research in fall prevention by providing an immersive VR setup that simulates diverse ecological environments and randomized visual disturbances, aimed at triggering and analyzing balance-compensatory reactions. The dataset is a unique tool for studying human balance responses to VR-induced perturbations, facilitating research that could inform training programs, wearable assistive technologies, and VR-based rehabilitation methods.</p> <p> </p> <p><strong>Dataset Content:</strong><br>The dataset includes:</p> <ul> <li><strong>Kinematic Data:</strong> Captured using a full-body Xsens MVN Awinda inertial measurement system, providing detailed movement data at 60 Hz.</li> <li><strong>Muscle Activity (EMG):</strong> Recorded at 1111 Hz using Delsys Trigno for tracking muscle contractions.</li> <li><strong>Electrodermal Activity (EDA)*:</strong> Captured at 100.21 Hz with a Shimmer GSR device on the dominant forearm to record physiological responses to perturbations.</li> <li><strong>Metadata:</strong> Includes participant demographics (age, height, weight, gender, dominant hand and foot), trial conditions, and perturbation characteristics (timing and type).</li> </ul> <p>The files are named in the format <strong>"<em>ParticipantX_labelled</em>"</strong>, where <strong><em>X</em></strong> represents the participant's number. Each file is provided in a <em><strong>.mat</strong> </em>format, with data already synchronized across different sensor sources. The structure of each file is organized into the following columns:</p> <ul> <li><strong>Column 1:</strong> Label indicating the visual perturbation applied. 0 means no visual perturbation.</li> <li><strong>Column 2:</strong> Timestamp, providing the precise timing of each recorded data point.</li> <li><strong>Column 3:</strong> Frame identifier, which can be cross-referenced with the MVN file for detailed motion analysis.</li> <li><strong>Columns 4 to 985:</strong> Xsens motion capture features, exported directly from the MVN file.</li> <li><strong>Columns 986 to 993:</strong> EMG data - Tibialis Anterior (R&L), Gastrocnemius Medial Head (R&L), Rectus Femoris (R), Semitendinosus (R), External Oblique (R), Sternocleidomastoid (R).</li> <li><strong>Columns 994 to 1008:</strong> Shimmer data: Accelerometer (x,y,z), Gyroscope (x,y,z), Magnetometer (x,y,z), GSR Range, Skin Conductance, Skin Resistance, PPG, Pressure, Temperature.</li> </ul> <p>In addition, we are also releasing the <strong>.MVN </strong>and <strong>.MVNA files</strong> for each participant (1 to 10), which provide comprehensive motion capture data and include the participants' body measurements, respectively. This additional data enables precise body modeling and further in-depth biomechanical analysis.</p> <p> </p> <p><strong>Participants & VR Headset:</strong></p> <p>Twelve healthy young adults (average age: 25.09 ± 2.81 years; height: 167.82 ± 8.40 cm; weight: 64.83 ± 7.77 kg; 6 males, 6 females) participated in this study (<strong>Table 1</strong>). Participants met the following criteria: i) healthy locomotion, ii) stable postural balance, iii) age ≥ 18 years, and iv) body weight < 135 kg.</p> <p>Participants were excluded if they: i) had any condition affecting locomotion, ii) had epilepsy, vestibular disorders, or other neurological conditions impacting stability, iii) had undergone recent surgeries impacting mobility, iv) were involved in other experimental studies, v) were under judicial protection or guardianship, or vi) experienced complications using VR headsets (e.g., motion sickness).</p> <p>All participants provided written informed consent, adhering to the ethical guidelines set by the <strong>University of Minho Ethics Committee (CEICVS 063/2021)</strong>, in compliance with the <strong>Declaration of Helsinki and the Oviedo Convention</strong>.</p> <p>To ensure unbiased reactions, participants were kept unaware of the specific protocol details. Visual disturbances were introduced in a random sequence and at various locations, enhancing the unpredictability of the experiment and simulating a naturalistic response.</p> <p>The VR setup involved an <strong>HTC Vive Pro</strong> headset with two wirelessly synchronized base stations that tracked participants’ head movements within a 5m x 2.5m area. The base stations adjusted the VR environment’s perspective according to head movements, while controllers were used solely for setup purposes.</p> <p> </p> <p><strong>Table 1</strong> - Participants' demographic information</p> <table> <tbody> <tr> <td><strong>Participant</strong></td> <td><strong>Height (cm)</strong></td> <td><strong>Weight (kg)</strong></td> <td><strong>Age</strong></td> <td><strong>Gender</strong></td> <td><strong>Dom. Hand</strong></td> <td><strong>Dom. Foot</strong></td> </tr> <tr> <td>1</td> <td>159</td> <td>56.5</td> <td>23</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>2</td> <td>157</td> <td>55.3</td> <td>28</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>3</td> <td>174</td> <td>67.1</td> <td>31</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>4</td> <td>176</td> <td>73.8</td> <td>23</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>5</td> <td>158</td> <td>57.3</td> <td>23</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>6</td> <td>181</td> <td>70.9</td> <td>27</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>7</td> <td>171</td> <td>73.3</td> <td>23</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>8</td> <td>159</td> <td>69.2</td> <td>28</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>9</td> <td>177</td> <td>57.3</td> <td>22</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>10</td> <td>171</td> <td>75.5</td> <td>25</td> <td>M</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>11</td> <td>163</td> <td>58.1</td> <td>23</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> <tr> <td>12</td> <td>168</td> <td>63.7</td> <td>25</td> <td>F</td> <td>Right</td> <td>Right</td> </tr> </tbody> </table> <p> </p> <p><strong>Data Collection Methodology:</strong></p> <p>The experimental protocol was designed to integrate four essential components: (i) precise control over stimuli, (ii) high reproducibility of the experimental conditions, (iii) preservation of ecological validity, and (iv) promotion of real-world learning transfer.</p> <ul> <li><strong>Participant Instructions and Familiarization Trial: </strong>Before starting, participants were given specific instructions to (i) seek assistance if they experienced motion sickness, (ii) adjust the VR headset for comfort by modifying the lens distance and headset fit, (iii) stay within the defined virtual play area demarcated by a blue boundary, and (iv) complete a familiarization trial. During this trial, participants were encouraged to explore various virtual environments while performing a sequence of three key movements—walking forward, turning around, and returning to the initial location—without any visual perturbations. This familiarization phase helped participants acclimate to the virtual space in a controlled setting.</li> <li><strong>Experimental Protocol and Visual Perturbations: </strong>Participants were exposed to 11 different types of visual perturbations as outlined in <strong>Table 2</strong>, applied across a total of 35 unique perturbation variants (<strong>Table 3</strong>). Each variant involved the same type of perturbation, such as a clockwise Roll Axis Tilt, but varied in intensity (e.g., rotation speed) and was presented in randomized virtual locations. The selection of perturbation types was grounded in existing literature on visual disturbances. This design ensured that participants experienced a diverse range of visual effects in a manner that maintained ecological validity, supporting the potential for generalization to real-world scenarios where visual perturbations might occur spontaneously.</li> <li><strong>Protocol Flow and Randomized Presentation: </strong>Throughout the experimental protocol, each visual perturbation variant was presented three times, and participants engaged repeatedly in the familiarization activities over a nearly one-hour period. These activities—walking forward, turning around, and returning to the starting point—took place in a 5m x 2.5m physical space mirrored in VR, allowing participants to take 7–10 steps before turning. Participants were not informed of the timing or nature of any perturbations, which could occur unpredictably during their forward walk, adding a realistic element of surprise. After each return to the starting point, participants were relocated to a random position within the virtual environment, with the sequence of positions determined by a randomized, computer-generated order.</li> </ul> <p> </p> <p><strong>Table 2 - </strong>Visual perturbations' name and parameters (L - Lateral; B - Backward; F - Forward; S - Slip; T - Trip; CW- Clockwise; CCW - Counter-Clockwise)</p> <table> <tbody> <tr> <td><strong>Perturbation [Fall Category]</strong></td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td>Roll Axis Tilt - CW [L]</td> <td>[10º, 20º, 30º] during 0.5s</td> </tr> <tr> <td>Roll Axis Tilt – CCW [L]</td> <td>[10º, 20º, 30º] during 0.5s</td> </tr> <tr> <td>Support Surface ML Axis Translation - Bidirectional [L]</td> <td>Discrete Movement (static pauses between movements) – 1 m/s</td> </tr> <tr> <td>AP Axis Translation - Front [F]</td> <td>1 m/s</td> </tr> <tr> <td>AP Axis Translation - Backwards [B]</td> <td>1 m/s</td> </tr> <tr> <td>Pitch Axis Tilt [S]</td> <td>0º-25º, 60º/s</td> </tr> <tr> <td>Virtual object with lower height than a real object [T]</td> <td>Variable object height</td> </tr> <tr> <td>Roll-Pitch-Yaw Axis Tilt [Syncope]</td> <td>Sum of sinusoids drive each axis rotation</td> </tr> <tr> <td>Scene Object Movement [L]</td> <td>Objects fly towards the subject’s head. Variable speeds</td> </tr> <tr> <td>Vertigo Sensation [F/L]</td> <td>Walk at a comfortable speed. With and without avatar. House’s height</td> </tr> <tr> <td>Axial Axis Translation [F/B/L]</td> <td>Free fall</td> </tr> </tbody> </table> <p> </p> <p><strong>Table 3 </strong>- Label Encoding</p> <table> <tbody> <tr> <td><strong>Visual Perturbation</strong></td> <td><strong>Label</strong></td> <td><strong>Visual Perturbation</strong></td> <td><strong>Label</strong></td> <td><strong>Visual Perturbation</strong></td> <td><strong>Label</strong></td> </tr> <tr> <td>Roll Indoor 1 CW10</td> <td>1</td> <td>Roll Indoor 1 CW20</td> <td>2</td> <td>Roll Indoor 1 CW30</td> <td>3</td> </tr> <tr> <td>Roll Indoor 1 CCW10</td> <td>4</td> <td>Roll Indoor 1 CCW20</td> <td>5</td> <td>Roll Indoor 1 CCW30</td> <td>6</td> </tr> <tr> <td>Roll Indoor 2 CW10</td> <td>7</td> <td>Roll Indoor 2 CW20</td> <td>8</td> <td>Roll Indoor 2 CW30</td> <td>9</td> </tr> <tr> <td>Roll Indoor 2 CCW10</td> <td>10</td> <td>Roll Indoor 2 CCW20</td> <td>11</td> <td>Roll Indoor 2 CCW30</td> <td>12</td> </tr> <tr> <td>Roll Outdoor CW10</td> <td>13</td> <td>Roll Outdoor CW20</td> <td>14</td> <td>Roll Outdoor CW30</td> <td>15</td> </tr> <tr> <td>Roll Outdoor CCW10</td> <td>16</td> <td>Roll Outdoor CCW20</td> <td>17</td> <td>Roll Outdoor CCW30</td> <td>18</td> </tr> <tr> <td>ML-Axis Trans. - Kitchen</td> <td>19</td> <td>AP-Axis Trans. - Corridor Forward</td> <td>20</td> <td>AP-Axis Trans. - Corridor Backward</td> <td>21</td> </tr> <tr> <td>Pitch Indoor - Bathroom (wet floor)</td> <td>22</td> <td>Pitch Indoor - Near Fridge (wet floor)</td> <td>23</td> <td>Roof Beam Walking - Vertigo</td> <td>24</td> </tr> <tr> <td>Roof Beam Walking - Vertigo No Avatar</td> <td>25</td> <td>Simple Roof - Vertigo</td> <td>26</td> <td>Simple Roof - Vertigo No Avatar</td> <td>27</td> </tr> <tr> <td>Pitch Outdoor - Near Car Oil</td> <td>28</td> <td>Trip - Sidewalk / Trip Shock</td> <td>29/290</td> <td>Bedroom Syncope</td> <td>30</td> </tr> <tr> <td>Garden - Object Avoidance</td> <td>31</td> <td>Electricity Pole - Vertigo</td> <td>32</td> <td>Electricity Pole - No Avatar</td> <td>33</td> </tr> <tr> <td>Free Fall</td> <td>34</td> <td>Climbing Virtual Stairs</td> <td>35</td> <td> </td> <td> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>*</strong> Some data from Shimmer device was collected but not used or checked by the research team.</p>
Dataset and Analysis Software of locations of Pressure Balance in the Martian Ionosphere by S.R. Shaver
<p>Software used to identify locations of pressure balance in the Martian ionosphere using MAVEN data, and the subsequent analysis and plotting routines. Written in IDL. These programs also use routines from the space physics environment data analysis system (SPEDAS) (Angelopoulos et al., 2019) and data made available on the Planetary Data System (PDS). </p> <p>This dataset was used for the work done in "Shaver, S. R., L. Andersson, R. Ramstad, Bhagyashree Waghule, D. Brain, R. Lillis, T. Cravens, et al. (in review) “Piercing the Martian Veil: A Statistical Study of Interplanetary Magnetic Field Reach through Ionospheric Pressure Balance.” <em>JGR: Space Physics</em>."</p>
Elemental balances Ruditapes decussatus
<p><span><span>Dietary ratio of lipids to carbohydrates affects elemental stoichiometric relationships in different growth phenotypes of the clam <em>Ruditapes decussatus</em></span></span></p>
Temporally-balanced selection during development of larval Pacific oysters (Crassostrea gigas) inherently preserves genetic diversity within offspring
<p>Balancing selection is one of the mechanisms which has been proposed to explain the maintenance of genetic diversity in species across generations. For species with large populations and complex life histories, however, heterogeneous selection pressures may create a scenario in which the net effects of selection are balanced across developmental stages. With replicated cultures and a pooled sequencing approach, we show that genotype-dependent mortality in larvae of the Pacific oyster (Crassostrea gigas) is largely temporally dynamic and inconsistently in favor of a single genotype or allelic variant at each locus. Overall, the patterns of genetic change we observe to be taking place are more complex than what would be expected under classical examples of additive or dominant genetic interactions. They are also not easily explained by our current understanding of the effects of genetic load. Collectively, temporally heterogeneous selection pressures across different larval developmental stages may act to maintain genetic diversity in oysters, while also inherently sheltering genetic load within populations.</p>
Interplay of metal-acid balance and methylcyclohexane admixture effect on n-octane hydroconversion over Pt/HUSY
<p>This repository includes characterization data (H<sub>2</sub>-O<sub>2</sub> titration and N<sub>2</sub> adsorption t-plot) associated with the publication: N.Korica, P.S.F.Mendes, J.De Clercq, J.W.Thybaut "Interplay of metal-acid balance and methylcyclohexane admixture effect on <em>n</em>-octane hydroconversion over Pt/HUSY", submitted to Industrial & Engineering Chemistry Research journal in May 2021.</p> <p>The impact of methylcyclohexane admixture on <em>n</em>-octane hydroconversion (isomerization and cracking) has been studied by experiments performed on high-throughput setup over Pt/HUSY catalyst with four different Pt loadings. The dispersion of platinum over HUSY zeolite was determined by H<sub>2</sub>-O<sub>2</sub> titration, while the specific surface area was determined by t-plot N<sub>2</sub> adsorption. These data are classified based on the platinum loading and the characterization method.</p> <p>0.5wt.%Pt (H2-O2 titration) : The dispersion of 0.5wt.%Pt/HUSY</p> <p>0.5wt.%Pt (N2 ads t-plot) : The specific surface area of 0.5wt.%Pt/HUSY</p> <p>0.1wt.%Pt (H2-O2 titration) : The dispersion of 0.1wt.%Pt/HUSY</p> <p>0.1wt.%Pt (N2 ads t-plot) : The specific surface area of 0.1wt.%Pt/HUSY</p> <p>0.07wt.%Pt (H2-O2 titration) : The dispersion of 0.07wt.%Pt/HUSY</p> <p>0.07wt.%Pt (N2 ads t-plot) : The specific surface area of 0.07wt.%Pt/HUSY</p> <p>0.04wt.%Pt (H2-O2 titration) : The dispersion of 0.04wt.%Pt/HUSY</p> <p>0.04wt.%Pt (N2 ads t-plot) : The specific surface area of 0.04wt.%Pt/HUSY</p> <p> </p>
Interplay of metal-acid balance and methylcyclohexane admixture effect on n-octane hydroconversion over Pt/HUSY
<p>This repository includes experimental data associated with the publication: N.Korica, P.S.F.Mendes, J.De Clercq, J.W.Thybaut "Interplay of metal-acid balance and methylcyclohexane admixture effect on <em>n</em>-octane hydroconversion over Pt/HUSY", submitted to Industrial & Engineering Chemistry Research journal in May 2021.</p> <p>The impact of methylcyclohexane admixture on <em>n</em>-octane hydroconversion (isomerization and cracking) has been studied by experiments performed on high-throughput setup over Pt/HUSY catalyst with four different Pt loadings. Furthermore, the feed to reactor was varied for all examined catalysts from pure <em>n</em>-octane, over equimolar mixture of <em>n</em>-octane and methylcyclohexane, to pure methylcyclohexane. </p> <p>The process conditions which were used for every Pt loading are further summarized:</p> <ul> <li>0.5wt.%Pt/HUSY <ul> <li>Feeds <em>n</em>-octane : methylcyclohexane, mol/mol – 1:0 ; 0:1; 1:1</li> <li>Temperature, K – 523 ; 543</li> <li>Pressure, bar – 10 ; 20</li> <li>Partial pressure of reactants, bar – 0.1 (pure) ; 0.05 (mix)</li> </ul> </li> <li>0.1wt.%Pt/HUSY <ul> <li>Feeds <em>n</em>-octane : methylcyclohexane, mol/mol – 1:0 ; 1:1; 3:1</li> <li>Temperature, K – 523 ; 543</li> <li>Pressure, bar – 10 ; 20</li> <li>Partial pressure of reactants, bar – 0.05 (in all feeds)</li> </ul> </li> <li>0.07wt.%Pt/HUSY <ul> <li>Feeds <em>n</em>-octane : methylcyclohexane, mol/mol – 1:0 ; 0:1; 1:1</li> <li>Temperature, K – 523 ; 543 ; 563 ; 583 ; 603 </li> <li>Pressure, bar – 10 ; 20</li> <li>Partial pressure of reactants, bar – 0.05 (in all feeds)</li> </ul> </li> <li>0.04wt.%Pt/HUSY <ul> <li>Feeds <em>n</em>-octane : methylcyclohexane, mol/mol – 1:0 ; 0:1; 1:1</li> <li>Temperature, K – 603 ; 623 </li> <li>Pressure, bar – 10 ; 20</li> <li>Partial pressure of reactants, bar – 0.05 (in all feeds)</li> </ul> </li> </ul> <p>The catalytic activity of tested catalysts was compared based on conversion of both reactants, yields of octane isomers, selectivities to mono- and dibranched octane isomers, and selectivities to isomers of methylcyclohexane. These data are classified based on figures in the Article were they are shown.</p> <p>Figure 2 : <em>n</em>-Octane conversion as a function of space time - experiments with pure <em>n</em>-octane</p> <p>Figure 3 : Octane isomer yields as a function of <em>n</em>-octane conversion - experiments with pure <em>n</em>-octane</p> <p>Figure 4 : Methylcyclohexane conversion as a function of space time - experiments with pure methylcyclohexane</p> <p>Figure 5 : <em>n</em>-Octane conversion as a function of space time - comparison of experiments with pure <em>n</em>-octane and 1:1 and 3:1 mixture of <em>n</em>-octane and methylcyclohexane (at 10 bar)</p> <p>Figure 6 : Octane isomer yields as a function of <em>n</em>-octane conversion - comparison of experiments with pure <em>n</em>-octane and 1:1 mixture of <em>n</em>-octane and methylcyclohexane</p> <p>Figure 7 : Monobranched and dibranched isomers selectivity as a function of <em>n</em>-octane conversion - comparison of experiments with pure <em>n</em>-octane and 1:1 mixture of <em>n</em>-octane and methylcyclohexane</p> <p>Figure 8 : Methylcyclohexane conversion as a function of space time - comparison of experiments with pure methylcyclohexane and 1:1 and 3:1 mixture of <em>n</em>-octane and methylcyclohexane (at 10 bar)</p> <p>Figure 9 : Methylcyclohexane isomer yields as a function of methylcyclohexane conversion - comparison of experiments with pure methylcyclohexane and 1:1 and 3:1 mixture of <em>n</em>-octane and methylcyclohexane</p> <p>Figure S9 : <em>n</em>-Octane conversion as a function of space time - comparison of experiments with pure <em>n</em>-octane and 1:1 and 3:1 mixture of <em>n</em>-octane and methylcyclohexane (at 20 bar)</p> <p>Figure S10 : <em>n</em>-Octane conversion as a function of space time - comparison of experiments with pure <em>n</em>-octane and 1:1 and 3:1 mixture of <em>n</em>-octane and methylcyclohexane</p> <p>Figure S11 : Methylcyclohexane conversion as a function of space time - comparison of experiments with pure methylcyclohexane and 1:1 and 3:1 mixture of <em>n</em>-octane and methylcyclohexane (at 20 bar)</p> <p>Figure S12 : Methylcyclohexane conversion as a function of space time - comparison of experiments with pure methylcyclohexane and 1:1 and 3:1 mixture of <em>n</em>-octane and methylcyclohexane</p>
Data and scripts from: Balanced polymorphism fuels rapid selection in an invasive crab despite high gene flow and low genetic diversity
<p><em>Carcinus maenas</em> is a globally invasive species which spreads and thrives across a range of temperate environments. In the northwestern Pacific, the species has spread across >12 degrees of latitude in 10 years from a single source, following its introduction <35 years ago. Using six locations spanning >1,500 km, we examined genetic structure and selection to temperature using 9,376 Single Nucleotide Polymorphisms (SNPs) derived from cardiac transcriptome sequencing.</p> <p>Data in this repository includes information on sequenced samples (*.csv, *.txt), a cleaned transcriptome assembly after expression filtering (*.fasta), transcriptome annotation from EnTAP (*.tsv), list of transcripts removed from analysis after mapping (*.txt), high-quality SNPs identified from the transcriptome sequencing with GATK (seven files representing different SNP sets used in the analysis; *.vcf), and four custom scripts used in processing SNP data (*.py and *.R).</p> <p>Raw sequence data is archived in GenBank's SRA. 2015-2016 samples: BioProject ID PRJNA690934 and BioSample IDs SAMN17267686–SAMN17267781. 2011 samples: BioProject ID PRJNA283611 and BioSample IDs SAMN03653390–SAMN03653413.</p>
Objective evaluation of Nintendo Wii Fit plus balance program training on postural stability in Multiple Sclerosis patients
<p>The use of the Nintendo Wii system has become a common tool for balance rehabilitation in patients with multiple sclerosis, but few studies verified the effectiveness of such an approach using quantitative measures of postural control. We aimed to evaluate the impact of rehabilitation treatment using the Nintendo Wii Fit Plus balance program on objective stabilometric parameters in multiple sclerosis patients. We enrolled 36 multiple sclerosis patients, with mild-moderate disability, referring to the multiple sclerosis Centre of the University of Catania from September 2013 to June 2014. Twenty participants underwent 20 individual sessions of balance exercise using the Nintendo Wii Fit Plus. They were assessed at baseline (T0) and at the end of rehabilitation program (T1) by Neurocom Balance Manager. Functional independence measure, Barthel index, and Berg balance scale were also administered. At T1, we found a significant improvement in total path length-open eyes, sway area-open eyes, and mean sway velocity-open eyes. Patients showed significant improvement in functional independence measure motor score, Barthel index, and in Berg balance scale. No significant differences between T0 and T1 in closed eyes trials were found. A significant correlation between delta values between T0 and T1 of sway area-open eyes and the Berg balance scale (r = -0.76; P < 0.0001) was found. This study confirmed that balance rehabilitation training performed using the Nintendo Wii with balance board significantly reduced some postural sway parameters in multiple sclerosis patients. It could be a good support to standard rehabilitation program in multiple sclerosis patients.</p>
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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.