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181 results for “outdoor”
Data on female oviposition behaviour of the Pararge aegeria butterfly in outdoor cages (2019–2020)
<p><span>We studied oviposition behavior in a butterfly (</span><em><span>Pararge aegeria</span></em><span>)</span><span> that used to be confined to forest, but recently colonized anthropogenic areas too. This provides an ideal study system when trying to understand the underlying processes of niche expansion and colonisation success. The dataset produced allows us to test to what extent ecotype origin (agricultural vs. forest landscape types) and larval developmental conditions (open field vs. canopy-covered forest floors) affect multiple features of oviposition behahaviour. We also performed multiple behavioural trials per individual and considered changes in oviposition site preference within and over trials. </span></p> <p>The two provided files contain all behavioral or independent variables that were obtained during behavioural tracking of the oviposition behaviour of the Speckled Wood butterfly (<em>Pararge aegeria</em>) in an outdoor cage in 2019 and 2020.</p> <p>In the<strong> 'femdata' </strong>file, each line contains all dependent (e.g., proportion of time spent active) and independent variables (e.g. ecotype) that apply for a single observation trial of 30 minutes of a single individual.In total, 211 observation trials were performed, by 110 different female individuals. </p> <p>In the <strong>'ovimdata' </strong>file, each line contains all variables for a single oviposition bout (i.e., if a butterfly curls its abdomen on a surface and lays eggs). Examples include number of eggs per bout, the height above ground where an egg was laid and temperature at the egg laying site. These data were for example used to track changes over consecutive bouts within a single trial. In total 275 oviposition bouts were observe, laid by 85 different female individuals.</p> <p>Data of the ovimdata file were also implemented in the femdata file. For example, in the femdata file information can be obtained about the properties of the first oviposition bout an individual made during that trial (starting with 'b1').</p>
Characterisation of night-time outdoor lighting in small urban centres using cluster analysis of remotely sensed light emissions (Dataset)
<p>Data used for the paper "Characterisation of night-time outdoor lighting in small urban centres using cluster analysis of remotely sensed light emissions". </p>
Timelapse of Outdoor Pre-experiment of Monitoring Framework
<p>The presented timelapse is part of an ongoing EU-funded project called Eco-Metabolistic Architecture at the Royal Danish Academy - CITA in Copenhagen, Denmark. The timelapse is a pre-experiment of the Outdoor Monitoring Framework developed for investigating the behaviour of bio-based materials to weathering. </p>
Academy Sports and Outdoors, Inc - Income Statement
<p>Academy Sports and Outdoors, Inc. (Ticker: ASO) is a mid-cap Texas-based sporting goods and outdoor recreational retailer trading at a P/E of 7x, and an EV/EBITDA yield of 17%, which places it among the cheapest 10% of stocks in our liquid, tradeable universe of stocks (mkt cap > ~$2 bn).</p><p>ASO employs approximately 22,000 people, and operates 269 retail locations in 18 states across the southeastern US, as well as three distribution centers located in Texas, Tennessee, and Georgia.</p><p>ASO was written up in 2021 by baileyb906, and we encourage VIC members to review that writeup for additional background.</p><p>Q1 Weakness and Retail Crime Fallout</p><p>There are some good recent reasons to be pessimistic about the stock. First, fiscal Q1, ending 4/29/23, showed a negative trend -- a YoY decline in quarterly revenues of 5.7%. The company has also indicated Q2 would also be challenging. With earnings coming out next week, we will soon see. Most of the Q1 softness was due to a 15% YoY decline in revenues in its Outdoor division, traditionally the company's largest. The company has explained that 1) it had very tough comps versus the prior year in hunting, camping, fitness and bikes, and 2) the company's products are designed to be enjoyed outside, and much of the weakness was due to unfavorable weather patterns, including cooler temperatures and rain. Second, Dick's missed earnings pretty dramatically few days ago due to inventory shrink due to a rise in retail crime, and sold off ~20%. ASO has been caught up in concerns about how this might affect the sector. But this current weakness comes at the tail end of a successful multi-year turnaround story, and is a reasonable entry point.</p><p>Turnaround Background</p><p>By way of background, KKR bought 20% the company in 2011, and the company did poorly from 2013-2018 during which time, despite aggressive store count growth, EBITDA/Store fell from ~$2.5 million to ~$1 million. Enter Ken Hicks, who was appointed CEO in 2018. Hicks had previously run a successful turnaround at Foot Locker, increasing Sales, and EBIT and net income margins while there.</p><p>At Academy Sports, Hicks pursued a successful store expansion plan, oversaw its IPO in 2020, and grew sales from $4.8 bn in 2018 to $6.4 bn in 2022. KKR sold its stake in ~2021. As of Q4 22, the company had increased its market cap by almost $4 bn since its IPO, and had returned $2 bn to stakeholders, including $900 million of repurchases. It's been a successful turnaround.</p><p>Merchandise and TAM</p><p>The company sells merchandise across four divisions: Outdoors (Camping, Fishing, Hunting at ~31% of sales), Sports and recreation (Fitness, Team sports, Recreation at ~28% of sales), Apparel (Outdoor, Youth and Athletic apparel at ~21% of sales), and Footwear (Casual, Work, Youth and Athletic footwear at ~20% of sales).</p><p>The company believes its total addressable market in the US is ~$175 bn, of which Dick's Sporting Goods, the largest sporting goods competitor, has less than a 10% share, but also has over 2x the revenues of ASO. This suggests there may be room to take share, and the market looks healthy. The US sporting goods market has/is expected to grow at a 7.9% CAGR from 2019 to 2025, according to a Morgan Stanley Outdoor and Active Living 2022 survey. Additionally, the Bureau of Economic Analysis reports that consumer spending on sporting equipment, supplies, guns and ammunition grew at 5.6% CAGR from 2000 to 2022. The industry appears to be fragmented, but growing.</p><p>Favorable Geographic/Demographic Tailwinds</p><p>Approximately 29% of the company's stores are in the top 5 fastest growing metropolitan statistical areas, including parts of Texas, Tennessee, and Florida. Of note, approximately 40% of the company's stores are in Texas, which last year surpassed 30 million people, and which the company estimates will see population growth of 17% between 2020-30.</p><p>The company believes there is ample opportunity for geographic expansion. Walmart has a store within 10 miles of 88% of Americans. For ASO, the figure is 17%, which means there's a good runway for further penetration.</p><p>Succession</p><p>On June 1, 2023, the company announced that as a result of a planned succession process, Steven Lawrence was promoted from Chief Merchandising Officer and became the new CEO. Lawrence has announced a 5-year plan to open 120-140 new stores and achieve $10 bn in revenue by 2027 (approximately a 10% CAGR), up from ~$6 bn today, with a net income margin of 10%, and ROIC of 30%, which we think are attainable goals. The blueprint is in place, and now it falls to the new CEO to execute. Longer-term, the company sees the opportunity for 800 additional stores.</p><p>According to the <a href="http://www.columbia.edu/~tmd2142/5-best-stock-research-websites.html">best stock research websites</a>, a key driver of the company's growth strategy is expansion of the store base by ~50% over the next few years, with new stores making up $2.4-$2.8 bn of the incremental revenue required to meet the goal of $10 bn.</p><p>Store Economics</p><p>The company's stores average approximately 70,000 SF and are highly profitable. The company seeks to lease all its stores via long-term lease agreements, ranging from 15 to 20 years, and executes sale-leaseback transactions for stores it is developing. ASO's average store delivers ~$4 million in EBIT, which is double the $2 million for Dick's. It costs approximately $4-$5 million to open a new store, which is expected in the first year to achieve $18 million in sales, be EBITDA positive, and have an ROIC of 20%. The stores ramp to $25 million in sales over 4-5 years. If the company can successfully open 120-140 new stores in the next 5 years, as planned, good things will happen to the stock price.</p><p>Omnichannel</p><p>ASO has built an e-commerce and mobile platform that allows enhanced consumer connection with stores. This includes buy-online-pickup-in-store program, and ship-to-store and curbside pickup programs. The company is also enhancing the customer experience through new features such as new site search capabilities, outfitting, express check-out, and biometric security.</p><p>The company expects its omnichannel efforts will be a continued driver of growth and gross margin. The company has stated that 75% of e-commerce sales are fulfilled in stores, and 60% of omnichannel customer spend came from within 10 miles of a store. Omnichannel customers spend more and purchase more frequently than the average customer. During 2022, stores facilitated approximately 95% of ASO's total sales.</p><p>Capex Budget and FCF</p><p>In support of its store expansion and growth goals, the company has unveiled a 5-year capex plan to invest $1.5 bn over the next five years. With $5.5 bn -$6 bn of anticipated adjusted EBIT projected over the next 5 years, the company anticipates the plan will be entirely self-funded through cash flow. Add an additional $0.5 bn to $ 1 bn required for WC and other factors, and that still leaves $3.5 bn in FCF available to stakeholders.</p><p>Return of Capital / Share Repurchases</p><p>The company has repurchased $440 million of stock in the past 12 months, and the company initiated a quarterly cash dividend in FY 22. We believe this is a shareholder friendly management team who will continue to return capital to shareholders when it makes sense. With the stock price as cheap as it is today relative to fundamentals, we would applaud additional buybacks at these prices.</p><ul><li><a href="https://valueinvesting.io/cost-of-equity-calculator">Cost of equity calculator</a></li><li><a href="https://valueinvesting.io/cost-of-debt-calculator">Cost of debt calculator</a></li><li><a href="http://www.columbia.edu/~tmd2142/wacc-calculator.html">WACC calculator</a></li></ul><p>Operational Momentum: Increasing Margins, Returns, Inventory Turns</p><p>The company's gross margins have improved from 29% in 2018 to 34% LTM. Similarly, EBIT margins have improved from 3.5% in 2018, to 11.9% LTM. ROIC has improved from 14% in 2018 to 34% LTM. Inventory turns are also up, from 2.7x in 2018 to 3.2x LTM. These metrics demonstrate that the business has positive operating momentum, and is increasing efficiencies. The company's current ratio has increased from 1.4x a year ago to 1.6x today, indicating increased liquidity. Additionally, the company's ROE is 37%, demonstrating that it is using its equity capital effectively.</p><p>The company also boasts a strong balance sheet. The company's net debt has declined from $1.5 bn in 2018 to $0.5 bn currently. Additionally, the company maintains a $1 bn credit facility, with no debt maturities until 2027. This outperforms all estimates of the <a href="http://www.columbia.edu/~tmd2142/5-best-stock-screeners.html">best stock screeners</a>.</p><p>Summary</p><p>ASO trades at a P/E of 7x, and has an EBITDA/EV yield of 17%, which we think is cheap for a company that has been successfully turned around from 5 years ago, and with good growth prospects for the next 5 years. ASO is positioned to benefit from positive growth trends in its core product areas, and has stores in metropolitan areas that are growing. Its stores have become increasingly profitable and are being run efficiently, and compare favorably to those of its competitors. The company's ROE of 37% and ROIC of 34% are both impressive, indicating that it has done a good job managing its capital. Margins have strengthened over recent years, and are reasonable today with EBITDA margins of 14.9% and net margins of 9%. It has an ambitious store count growth plan for the next 5 years, with self-funded capital and a conservative balance sheet available to pursue it. It is generating strong positive free cash flow, and is buying back stock and returning capital to shareholders via dividends, demonstrating its friendliness to shareholders.</p>
Data from: Odor source distance is predictable from time-histories of odor statistics for large scale outdoor plumes
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Hunting, but not outdoor recreation, modulates behavioural tolerance to human disturbance in Alpine marmots Marmota marmota
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Data from: High resolution outdoor videography of insects using fast lock-on tracking
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Data from: Parentage of 920 gray-sided voles (Myodes rufocanus) born in a 3-ha outdoor enclosure between September 1992 and May 1994
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Data from: Low levels of outdoor recreation alter wildlife behavior
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Data from: Temperature-related differences in hair cortisol among outdoor-housed Rhesus Macaques (Macaca mulatta)
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Cool birds: facultative use by an introduced species of mechanical air conditioning systems during extremely hot outdoor conditions
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Adaptive phenotypic evolution of Skeletonema costatum to ocean acidification and warming with trade-offs from a multi-year outdoor experiment
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Data on female oviposition behaviour of the Pararge aegeria butterfly in outdoor cages (2019–2020)
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RIBuild: Outdoor test stand with solid wall in combination with wooden beam ends at DTU
<p>The dataset consists of</p> <p>(1) measurement data from an outdoor test stand involving a number of different internal insulation systems and surface treatments. The placement outdoor makes it possible to take into consideration the wind driven rain on the behaviour of the different systems and treatments</p> <p>(2) files with risk prediction of fungal growth</p> <p>(3) test results from Mycometer tests and pH-measurements</p> <p>(4) Photo documentation for identification of fungal growth</p> <p>(5) Delphin 6 models and results of Delphin simulations</p> <p>(6) Appendics with background information for Delphin simulations</p> <p>Overview of data files to be found in ’RIBuild data WP3_DTU Outdoor test unit' as part of this dataset.</p> <p>Further details to be found in Jensen, N.F., Odgaard, T.R., Bjarløv, S.P., Andersen, B., Rode, C., Møller, E.B. (2020). Hygrothermal assessment of diffusion open insulation systems for interior retrofitting of solid masonry walls. Building and Environment (In Review)</p> <p>Data on hot box cold box experiments with a solid wall wooden beam end combination is to be found in the data sets 'RIBuild: Hotbox coldbox experiment with solid wall in combination with wooden beam ends at KU Leuven' (https://zenodo.org/deposit/3890050) and 'RIBuild: Hotbox coldbox experiment with solid wall in combination with wooden beam ends at TU Dresden' (https://zenodo.org/deposit/3904836)</p>
No water, no eggs: insights from a warming outdoor mesocosm experiment
<p>Insects are susceptible to dehydration and change in atmospheric humidity could affect their fitness. To understand the impacts of humidity changes on insect's reproductive fitness we released an outcrossed <i>Drosophila melanogaster</i> population to outdoor mesocosm units and tracked their fecundity over ninety days under progressively developing summer season. The study was carried out in a tropical urban garden. Often temperature has been found to be the key player in changes in reproductive output in a considerable number of laboratory based studies. Our work suggested that temperature and humidity interactions determine the physiological state of an organism which could untimely impact organismal fitness in a given environmental set-up. This work suggested that fecundity in <i>Drosophila</i> populations was significantly influenced by relative humidity and its interaction with temperature. This together suggested that while temperature was an important parameter for fecundity, relative humidity individually and in combination with temperature also played an important role for fecundity in <i>Drosophila</i>. Thus, the combination of temperature and relative humidity was a better metric to predict the fecundity in <i>Drosophila</i> populations than considering these parameters individually under natural conditions. It clearly indicated that future warming events could drastically impact insects' reproductive output.</p>
Data from: Using deep learning to quantify the beauty of outdoor places
Beautiful outdoor locations are protected by governments and have recently been shown to be associated with better health. But what makes an outdoor space beautiful? Does a beautiful outdoor location differ from an outdoor location that is simply natural? Here, we explore whether ratings of over 200 000 images of Great Britain from the online game Scenic-Or-Not, combined with hundreds of image features extracted using the Places Convolutional Neural Network, might help us understand what beautiful outdoor spaces are composed of. We discover that, as well as natural features such as 'Coast', 'Mountain' and 'Canal Natural', man-made structures such as 'Tower', 'Castle' and 'Viaduct' lead to places being considered more scenic. Importantly, while scenes containing 'Trees' tend to rate highly, places containing more bland natural green features such as 'Grass' and 'Athletic Fields' are considered less scenic. We also find that a neural network can be trained to automatically identify scenic places, and that this network highlights both natural and built locations. Our findings demonstrate how online data combined with neural networks can provide a deeper understanding of what environments we might find beautiful and offer quantitative insights for policymakers charged with design and protection of our built and natural environments.
A dataset for RSSI based outdoor localization using LoRaWAN in a harbor as a harsh and industrial environment
<p>Enabling precise device localization is a critical requirement for the future of industry. Leveraging signal features for location determination has emerged as a leading approach and good alternative for Global Navigation Satellite Systems (GNSS) because of their limitations (low accuracy for indoor environments, expensive chips, and high energy consumption). On this basis, to provide localization for IoT in an industry with a harsh environment, the adopted wireless networks should have a long range coverage area. LoRaWAN is one of the most common communication networks that can provide large coverage with low power consumption and low implementation cost. Between various signal features that can be used for localization, Received Signal Strength (RSS) received more attention because of their low-cost deployment. But, RSS is highly dependent and sensitive to environmental changes, such as temperature, humidity, and background noise. This sensitivity becomes more intensive in an industrial environment with a harsh and dynamic environment. In order to evaluate the environmental effects on RSS in the harsh and highly dynamic industry, we present a comprehensive repository of LoRaWAN Received Signal Strength Indicator (RSSI) measurements, collected in a harbor as a testbed featuring three LoRaWAN gateways and one mobile end node. During the data collecting process, the mobile device obtains its location via a GPS and transmits it as the LoRaWAN message. In addition, to provide more insight of the effect of dynamic environment on the RSSI, two end nodes are implemented in fixed locations. These end nodes transmit messages with fixed time intervals including their unique id. The collected dataset includes RSSI and SNR measurements recorded by multiple gateways for each transmitted packet by fixed or mobile end nodes, and timestamp. This dataset enables the development and evaluation of RSSI-based localization and allows researchers to explore the challenges and opportunities associated with localization in dynamic IoT deployments.</p>
Ljubljana Multi-Sensor Indoor and Outdoor PM Exposure, Environment, and Personal Health Dataset
<p>This dataset was compiled between February 16, 2019, and May 25, 2019, involving 82 participants residing in the municipality of Ljubljana. Data collected before March 12th represents the "heating season," while data after April 27th corresponds to the "non-heating season." This dataset is the cleaned/filtered version with outliers caused by software or hardware errors removed.</p><p>Derived from the larger ICARUS project in Ljubljana, this dataset integrates various data sources, including:</p><ul><li>Participant Questionnaires: Containing certain individual information, i.e., age, height, gender.</li><li>Time Activity Diaries: Providing hourly activity records for each participant.</li><li>Personal PM Monitors: Measuring indoor and outdoor PM concentrations.</li><li>Smart Activity Trackers: Recording heart rate and movement data.</li><li>Indoor Air Quality Station: Capturing indoor air quality parameters.</li></ul><p>The dataset includes the calculation of inhalation rate based on heart rate data, allowing for the determination of inhalation rate-adjusted exposure or intake dose.</p><p>This version of the dataset is in .csv format.</p>
Selected SSB-based RF-EMF Outdoor Measurement Campaigns data
<p>This presents the selected Synchronisation Signal Block (SSB) based radio frequency electromagnetic field (RF-EMF) measurement data acquired under measurement campaign in indoor environment. This data links to the findings shown in Section 4.2 of D1 report at http://empir.npl.co.uk/5grfex/wp-content/uploads/sites/55/2022/01/updated-EMPIR-18SIP02-5GRFEX-Deliverable-Report-D1.pdf. </p> <p>This work was supported by the EU project 5GRFEX entitled – ‘Metrology for RF exposure from Massive MIMO 5G base station: Impact on 5G network deployment’ (this project has received funding from the support for impact (SIP) programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme), under European Association of National Metrology Institutes (EURAMET) Reference 18SIP02.</p>
OUTDOOR OVEN
Enjoy cooking in the fresh air! Source: Objaverse 1.0 / Sketchfab
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.