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British American Tobacco stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.
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British American Tobacco reported GBP54.47B in Equity Capital and Reserves for its fiscal semester ending in June of 2024. Data for British American Tobacco | BATS - Equity Capital And Reserves including historical, tables and charts were last updated by Trading Economics this last July in 2025.
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This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.
Historical daily stock prices (open, high, low, close, volume)
Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)
Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)
Feature engineering based on financial data and technical indicators
Sentiment analysis data from social media and news articles
Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
Researchers investigating the effectiveness of machine learning in stock market prediction
Analysts developing quantitative trading Buy/Sell strategies
Individuals interested in building their own stock market prediction models
Students learning about machine learning and financial applications
The dataset may include different levels of granularity (e.g., daily, hourly)
Data cleaning and preprocessing are essential before model training
Regular updates are recommended to maintain the accuracy and relevance of the data
SpottedBatOverallRange is an ESRI SDE Feature Class encompassing the observed and predicted range of a population of Spotted Bats in Colorado. This information was derived from species experts. A variety of data capture techniques were used including implementation of the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing at various scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Various sources were referenced in developing these data including areas delineated as 50% or higher predicted occupancy as modeled in MaxEnt using various Colorado bat site collection records, telemetry, historic records noted in Armstrong et al. 1994, and CPW Scientific Collection data.This generalized graphic representation of species range data is provided for informational purposes only and has not been prepared for, nor is it suitable for, any type of legal, regulatory, or site specific planning purposes. These data are subject to errors and change. Users of the information displayed in this map service are strongly cautioned to verify all information and contact local CPW Biologists before making any decisions.These data were last updated in January 2019.
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Real and up to date stock market exchange of cryptocurrencies can be quite expensive and are hard to get. However, historical financial data are the starting point to develop algorithm(s) to analyze market trend and why not beat the market by predicting market movement.
Data provided in this dataset are historical data from the beginning of BAT-XBT pair market on Kraken exchange up to the present (2021 December). This data comes frome real trades on one of the most popular cryptocurrencies exchange.
Historical market data, also known as trading history, time and sales or tick data, provides a detailed record of every trade that happens on Kraken exchange, and includes the following information: - Timestamp - The exact date and time of each trade. - Price - The price at which each trade occurred. - Volume - The amount of volume that was traded.
In addition, OHLCVT data are provided for the most common period interval: 1 min, 5 min, 15 min, 1 hour, 12 hours and 1 day. OHLCVT stands for Open, High, Low, Close, Volume and Trades and represents the following trading information for each time period: - Open - The first traded price - High - The highest traded price - Low - The lowest traded price - Close - The final traded price - Volume - The total volume traded by all trades - Trades - The number of individual trades
Don't hesitate to tell me if you need other period interval 😉 ...
This dataset will be updated every quarter to add new and up to date market trend. Let me know if you need an update more frequently.
Can you beat the market? Let see what you can do with these data!
According to our latest research, the global connected baseball bat sensor market size reached USD 128.5 million in 2024, with a robust year-on-year growth trajectory. The market is projected to expand at a CAGR of 13.2% during the forecast period, reaching an estimated USD 357.1 million by 2033. This remarkable growth is driven by increasing adoption of smart sports technologies, a rising focus on sports analytics, and the growing popularity of baseball at both professional and amateur levels. As per our analysis, the market’s upward momentum is further propelled by technological advancements in sensor accuracy and connectivity, as well as an expanding consumer base seeking data-driven athletic improvement.
One of the primary growth factors for the connected baseball bat sensor market is the surging demand for performance analytics in sports. Coaches, athletes, and teams are increasingly leveraging data-driven insights to enhance player performance, minimize injury risks, and optimize training regimens. These sensors, which can be easily attached to baseball bats, provide real-time feedback on swing speed, angle, impact force, and other critical metrics. The integration of artificial intelligence and machine learning algorithms into sensor platforms has further amplified their value, enabling personalized coaching and in-depth analytics. As sports organizations and individual athletes become more aware of the competitive edge offered by such technologies, the adoption rate of connected bat sensors is expected to accelerate significantly.
Another significant driver is the proliferation of connected devices and advancements in wireless connectivity. The widespread adoption of Bluetooth and Wi-Fi technologies has made it easier for users to sync bat sensors with smartphones, tablets, and cloud platforms. This seamless connectivity allows for instant data transmission, in-depth analysis, and easy sharing of performance metrics with coaches or teammates. The convenience offered by these wireless solutions has broadened the market appeal, attracting not only professional players but also amateur athletes and youth leagues. Furthermore, the integration with mobile applications and cloud-based dashboards provides users with a holistic view of their progress, fostering a culture of continuous improvement and engagement within the baseball community.
The growing emphasis on youth sports development and grassroots training programs is also fueling market expansion. Training academies, sports schools, and community organizations are increasingly incorporating connected baseball bat sensors into their curricula to provide young athletes with cutting-edge tools for skill development. These sensors enable coaches to deliver data-backed feedback, track progress over time, and tailor drills to individual needs. The democratization of performance analytics, once reserved for elite athletes, is now empowering a wider spectrum of players to achieve their full potential. This trend is particularly pronounced in regions where baseball is gaining popularity, such as Asia Pacific and Latin America, further boosting market growth.
From a regional perspective, North America continues to dominate the connected baseball bat sensor market, accounting for the largest revenue share in 2024. This leadership position is attributed to the region’s strong baseball culture, high disposable incomes, and early adoption of sports technology. However, Asia Pacific is emerging as a high-growth market, driven by increasing investments in sports infrastructure, rising participation in baseball, and growing awareness of performance analytics. Europe and Latin America are also witnessing steady growth, supported by expanding youth sports programs and the introduction of connected sports equipment in training academies. As the market matures, manufacturers are expected to focus on product localization and strategic partnerships to capture untapped opportunities in these regions.
As per our latest research, the global Smart Baseball Bat Swing Chip market size reached USD 188.7 million in 2024, reflecting robust growth driven by the increasing integration of advanced sensor technologies in sports equipment. The market is projected to expand at a CAGR of 13.9% from 2025 to 2033, reaching a forecasted value of USD 549.2 million by 2033. This significant growth trajectory is propelled by heightened demand for data-driven performance analytics among both professional and amateur athletes, as well as growing adoption in training and coaching environments.
One of the primary growth factors for the Smart Baseball Bat Swing Chip market is the rising emphasis on sports analytics and performance improvement across all levels of baseball. Modern athletes and coaches are increasingly leveraging technology to gain deeper insights into swing mechanics, bat speed, and impact angles. The integration of smart chips—either as attachable devices or embedded within bats—enables real-time data capture and analysis, facilitating personalized training regimens. This data-centric approach is transforming the landscape of baseball training, fostering higher adoption rates among both elite professionals and grassroots players. The growing awareness about injury prevention and recovery further amplifies demand, as these chips can help identify improper swing techniques that may lead to strain or injury, making them an invaluable tool for athlete health management.
Another crucial driver for market expansion is the technological advancement in connectivity and data processing. The advent of Bluetooth and Wi-Fi-enabled chips has made it easier than ever to sync bat swing data with mobile devices and cloud platforms. This seamless integration allows for instant feedback, remote coaching, and even gamification of training sessions, which is particularly appealing to younger, tech-savvy athletes. Moreover, the proliferation of user-friendly mobile applications and analytics dashboards is democratizing access to sophisticated performance metrics, once reserved for top-tier professionals. As a result, the market is witnessing a surge in product innovation, with manufacturers introducing chips with enhanced battery life, improved accuracy, and expanded compatibility with various bat models.
Furthermore, the expanding ecosystem of sports technology partnerships and collaborations is fueling the commercial momentum of the Smart Baseball Bat Swing Chip market. Equipment manufacturers, sports analytics firms, and professional leagues are increasingly joining forces to develop comprehensive solutions that integrate hardware, software, and coaching expertise. These collaborations are not only enhancing product offerings but also driving market penetration through joint marketing efforts and bundled solutions. The growing popularity of baseball in emerging markets, coupled with increasing investments in sports infrastructure and youth development programs, adds another layer of opportunity for market participants. As baseball continues to globalize, the demand for advanced training tools like smart swing chips is expected to rise, further accelerating market growth.
From a regional standpoint, North America remains the dominant market for Smart Baseball Bat Swing Chips, accounting for the largest share in 2024 due to the strong presence of baseball culture, advanced sports technology adoption, and a mature ecosystem of professional and amateur leagues. However, significant growth is also anticipated in the Asia Pacific region, where rising disposable incomes, increasing interest in baseball, and government initiatives to promote sports are fostering rapid market expansion. Europe and Latin America are also emerging as promising markets, driven by grassroots development programs and growing consumer awareness of sports technology. The Middle East & Africa, while currently a smaller market, is expected to witness steady growth as baseball gains traction and technology adoption increases.
This data release includes video files and image-processing results used to conduct the analyses of hibernation patterns in groups of bats reported by Hayman et al. (2017), "Long-term video surveillance and automated analyses reveal arousal patterns in groups of hibernating bats.” Thermal-imaging surveillance video cameras were used to observe little brown bats (Myotis lucifugus) in a cave in Virginia and Indiana bats (M. sodalis) in a cave in Indiana during three winters between 2011 and 2014. There are 740 video files used for analysis (‘Analysis videos’), organized into 7 folders by state/site and winter. Total size of the video data set is 14.1 gigabytes. Each video file in this analysis set represents one 24-hour period of observation, time-lapsed at a rate of one frame per 30 seconds of real time (video plays at 30 frames per second). A folder of illustrative videos is also included, which shows all of the analysis days for one winter of monitoring merged into a single video clip, time-lapsed at a rate of one frame per two hours of real time. The associated image-processing results are included in 7 data files, each representing computer derived values of mean pixel intensity in every 10th frame of the 740 time-lapsed video files, concatenated by site and winter of observation. Details on the format of these data, as well as how they were processed and derived are included in Hayman et al. (2017) and with the project metadata on Science Base. Hayman, DTS, Cryan PM, Fricker PD, Dannemiller NG. 2017. Long-term video surveillance and automated analyses reveal arousal patterns in groups of hibernating bats. Methods Ecol Evol. 2017;00:1-9. https://doi.org/10.1111/2041-210X.12823
HoaryBatOverallRange is an ESRI SDE Feature Class encompassing the observed and predicted range of a population of Hoary Bats in Colorado. This information was derived from species experts. A variety of data capture techniques were used including implementation of the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing at various scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Various sources were referenced in developing these data including areas delineated as 50% or higher predicted occupancy as modeled in MaxEnt using various Colorado bat site collection records, telemetry, historic records noted in Armstrong et al. 1994, and CPW Scientific Collection data.This generalized graphic representation of species range data is provided for informational purposes only and has not been prepared for, nor is it suitable for, any type of legal, regulatory, or site specific planning purposes. These data are subject to errors and change. Users of the information displayed in this map service are strongly cautioned to verify all information and contact local CPW Biologists before making any decisions.These data were last updated in January 2019.
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Seven decades of research on the “cognitive map”, the allocentric representation of space, have yielded key neurobiological insights, yet we still lack field evidence from free-ranging wild animals. Using a system capable of tracking dozens of animals simultaneously at high accuracy and resolution, we assembled a large dataset of 172 foraging Egyptian fruit bats comprising >18M localizations collected over 3,449 bat-nights across 4 years. Detailed track analysis, combined with translocation experiments, revealed that wild bats seldom exhibit random search but instead repeatedly forage in goal-directed, long and straight flights that include frequent shortcuts. Alternative non-map-based strategies were ruled out by simulations, time-lag embedding and other trajectory analyses. Our results are consistent with expectations from cognitive map-like navigation and support previous neurobiological evidence from captive bats.
Methods All bat procedures were approved by the Hebrew University of Jerusalem Animal Care and Use Committee (permit NS-15-14660-2). Bats were mist-netted on fruit trees or cave entrances and tagged with ATLAS in 38 capture sessions spanning all seasons between 2015-2019. Bats were tagged with ATLAS – a reverse-GPS system that localizes extremely light-weight, low-cost tags. Each ATLAS tag transmits a unique radio signal detected by a base-station network distributed in the study area. Tag localization is computed using nanosecond-scale differences in signal time-of-arrival to each station, enabling nearly real-time tracking and alleviating the need to retrieve the tag or to have some power-consuming remote-download capability. Bats were tagged by gluing the tag to their back (138 individuals) or by a custom-made collar (34 individuals). We applied a simple 10-second median filter to eliminate localization errors and smoothen the data.
SilverHairedBatOverallRange is an ESRI SDE Feature Class encompassing the observed and predicted range of a population of Silver-haired Bats in Colorado. This information was derived from species experts. A variety of data capture techniques were used including implementation of the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing at various scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Various sources were referenced in developing these data including areas delineated as 50% or higher predicted occupancy as modeled in MaxEnt using various Colorado bat site collection records, telemetry, historic records noted in Armstrong et al. 1994, and CPW Scientific Collection data.This generalized graphic representation of species range data is provided for informational purposes only and has not been prepared for, nor is it suitable for, any type of legal, regulatory, or site specific planning purposes. These data are subject to errors and change. Users of the information displayed in this map service are strongly cautioned to verify all information and contact local CPW Biologists before making any decisions.These data were last updated in January 2019.
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Myosin VI (encoded by the Myo6 gene) is highly expressed in the inner and outer hair cells of the ear, retina, and polarized epithelial cells such as kidney proximal tubule cells and intestinal enterocytes. The Myo6 gene is thought to be involved in a wide range of physiological functions such as hearing, vision, and clathrin-mediated endocytosis. Bats (Chiroptera) represent one of the most fascinating mammal groups for molecular evolutionary studies of the Myo6 gene. A diversity of specialized adaptations occur among different bat lineages, such as echolocation and associated high-frequency hearing in laryngeal echolocating bats, large eyes and a strong dependence on vision in Old World fruit bats (Pteropodidae), and specialized high-carbohydrate but low-nitrogen diets in both Old World and New World fruit bats (Phyllostomidae). To investigate what role(s) the Myo6 gene might fulfill in bats, we sequenced the coding region of the Myo6 gene in 15 bat species and used molecular evolutionary analyses to detect evidence of positive selection in different bat lineages. We also conducted real-time PCR assays to explore the expression levels of Myo6 in a range of tissues from three representative bat species. Molecular evolutionary analyses revealed that the Myo6 gene, which was widely considered as a hearing gene, has undergone adaptive evolution in the Old World fruit bats which lack laryngeal echolocation and associated high-frequency hearing. Real-time PCR showed the highest expression level of the Myo6 gene in the kidney among ten tissues examined in three bat species, indicating an important role for this gene in kidney function. We suggest that Myo6 has undergone adaptive evolution in Old World fruit bats in relation to receptor-mediated endocytosis for the preservation of protein and essential nutrients.
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British American Tobacco reported GBP119.37B in Assets for its fiscal semester ending in June of 2024. Data for British American Tobacco | BATS - Assets including historical, tables and charts were last updated by Trading Economics this last July in 2025.
RedBatOverallRange is an ESRI SDE Feature Class encompassing the observed and predicted range of a population of Red Bats in Colorado. This information was derived from species experts. A variety of data capture techniques were used including implementation of the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing at various scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Various sources were referenced in developing these data including areas delineated as 50% or higher predicted occupancy as modeled in MaxEnt using various Colorado bat site collection records, telemetry, historic records noted in Armstrong et al. 1994, and CPW Scientific Collection data.This generalized graphic representation of species range data is provided for informational purposes only and has not been prepared for, nor is it suitable for, any type of legal, regulatory, or site specific planning purposes. These data are subject to errors and change. Users of the information displayed in this map service are strongly cautioned to verify all information and contact local CPW Biologists before making any decisions.These data were last updated in January 2019.
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Linear features can benefit wildlife by assisting animal movement. We captured bats along barbed-wire and live-tree fences connecting Tropical Dry Forest patches in Nicaragua. Bat species richness and captures were higher along live fences but we noted differences in sex ratios, richness, and species composition compared to surrounding natural forests.
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Numbers indicate the cycle threshold (Ct). ND, not done because of missing samples.Undet, Ct undetermined.Results of real-time PCR on organs from Coleura afra individuals.
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Social structure can emerge from hierarchically embedded scales of movement, where movement at one scale is constrained within a larger scale (e.g., among branches, trees, forests). In most studies of animal social networks, some scales of movement are unobserved, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual’s social connectedness? Using existing data from common vampire bats (Desmodus rotundus), we created an agent-based model of how three nested scales of movement—among roosts, clusters, and grooming partners—each influence a bat’s grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically-derived rates that were either fixed or individually variable and either independent or correlated across scales. We found the number of partners groomed per bat was driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks. Methods Empirical analyses We analyzed existing published data to estimate how often common vampire bats switched roosts, clusters, and partners. To estimate individual rates of roost-switching, we used 1,336 observations of 81 free-ranging bats of both sexes (38 males and 43 females) that were observed >25 times across 11 tree roosts along the Rio Corobici in Guanacaste, Costa Rica (1, 2). We also made grooming networks using 1,761 grooming interactions among 29 of these bats (3). To estimate individual rates of cluster switching and partner switching, we used 4,092 observations of clusters (defined as bats roosting in the same corner of a flight cage) and 22,836 observations of grooming from 31 vampire bats of both sexes (5 males and 26 females) at a captive colony in Panama (4). Individuals in both studies were identified visually using unique combinations of distinctive wing bands. To estimate roost-switching rates, we only used observations of the same bat or roost on consecutive days, because roost switching would be underestimated when a bat moved away and then returned to the same roost between observations (see supplement in associated paper for details). To calculate cluster-switching and partner-switching rates, we counted consecutive observations of the same bat where a switch occurred, then divided that count by the total time elapsed between those observations (see supplement for details). We only considered consecutive cluster-switching and partner-switching observations that occurred within a sampled hour. To calculate within-cluster partner-switching rates, we did not count partner switches and the associated time lapse that occurred due to partner switches between clusters. To create co-roosting and co-clustering networks, we defined edge weights in the co-roosting and co-clustering networks as the ‘simple ratio index’ of association (5–7). To create grooming networks, we defined edge weights as total minutes of grooming. To assess within-bat correlations between movement types, we used a linear model to test if cluster-switching rates predicted within-cluster partner-switching rates. To determine how well roost, cluster, and partner-switching rates predict the overdispersed counts of the number of bats groomed (outdegree centrality), we fit a quasi-Poisson generalized linear mixed-effects model with each of the three rates as single predictors, and bat as random intercept. We used nonparametric bootstrapping to create a 95% confidence interval (CI) around the standardized coefficient (b). Agent-based model We created a model using NetLogo 6.2.0 and used it to simulate movements of virtual vampire bats that lacked preferences for roosts, clusters, or partners. Each of 11 roosts contained 4 locations for potential clusters. We randomly assigned each virtual bat to a starting roost and cluster location. For each spatial scale, each bat had a switching propensity randomly sampled with replacement from empirical estimates of the probabilities of movement. Switching probabilities at every scale were conditional on the time since the last switch (see supplement). We initially ran all the simulations with populations of 200 virtual bats, the approximate number of bats encountered and banded by Wilkinson along the Rio Corobici between 1978 and 1983 (1, 2). To explore how our results would change with fewer bats and limited partner choice, we later ran the simulation with 100 virtual bats to explore how our results would change with fewer bats, leading to smaller group sizes and limited partner choice (2.3 bats per cluster, or an average of 1.3 partners per cluster). To isolate the effects of movement, we fixed the probability of grooming per minute for all virtual bats at 1.8% (the mean probability that a captive vampire bat groomed another bat during the sampled hours from empirical observations of captive vampire bats (4)). We included a synchronous 200-minute foraging period where bats left all roosts to forage outside the roosts. The simulations recorded observations of behaviors every minute for 15 days. When in a roost, virtual bats randomly decided every minute whether to groom a partner and whether they would switch partners based on an increasing probability related to the time since last switch at that scale. The decision was solely determined by the groomer initiating the exchange; the receiver did not decide whether to accept grooming. Each bat could only groom one partner at any particular minute, but multiple bats could groom the same bat during that minute. Virtual bats decided whether to switch clusters within their roost once every hour. Additionally, they decided whether to switch roosts once per day after returning from foraging. If a bat changed its partner as a result of cluster or roost switching, we did not count this event as partner switching. Similarly, if a bat changed clusters due to roost switching, we did not count this event as cluster switching. We took this approach to test the effects of a bat’s decisions at each scale rather than the effect of what it experiences. Although we measured within-roost cluster switching and within-cluster partner switching, for brevity, these are simply referred to as ‘cluster switching’ and ‘partner switching.’ Simulations using agent-based model We ran five types of simulation, each 100 times, and we ran those five simulation types across two different population sizes, once for 100 bats and again for 200 bats. Each of the five simulation types had switching propensities that were either fixed or individually-variable and either correlated or uncorrelated. In simulation 1, virtual bats were assigned a random propensity of roost, cluster, and partner switching; these propensities were uncorrelated within each individual bat because they were drawn independently from empirical distributions. The resulting standardized coefficients of the switching rates from this simulation measured how well each movement type predicted grooming outdegree when controlling for the other movement types. In simulations 2-4, one type of movement varied among bats while the two others were fixed (to the mean observed from the empirical data). In simulation 2, only partner-switching propensity varied across individuals. In simulation 3, only cluster-switching propensity varied across individuals. In simulation 4, only roost-switching propensity varied across individuals. Using simulations 2-4, we estimated the reference effects, defined as the median standardized coefficients of the switching rate when switching propensity was not variable between bats. The reference effects measure how well one movement type predicts grooming outdegree when it lacks individual variation in switching propensity. We estimated the isolated effects of individual variation in each switching propensity, defined as the difference between the standardized coefficient of the switching rate when only it was variable between bats and the reference effect. The isolated effect measures how well individual variation in only one movement propensity predicts grooming outdegree while accounting for the reference effect. Simulation 5 was similar to simulation 1 except that the three switching propensities were positively correlated, such that virtual bats that moved most frequently at one scale also moved most frequently at other scales (see supplement). By comparing the results of simulations 1 and 5, we could therefore assess the effect of switching propensities being correlated (simulation 5) or uncorrelated (simulation 1). In sum, our model allowed us to ‘switch on and off’ the existence of realistic individual variation in movement at each spatial scale to isolate the social consequences for the individuals, while eliminating the confounding effects of social and spatial preferences found in real vampire bats. By adding or removing individual variation in movement at each scale or across all scales, and by making these movements correlated or not across scales, these simulations allowed us to isolate the causal effects of individually variable movement on grooming network centrality. Note that a bat’s assigned probability of switching (its switching propensity) is not the same as the number of times it actually switched during the simulation (its switching rate). When switching propensity was fixed, all bats with the same time since last switch also had the same probability of switching at that time step. However, as the model was randomized, the realized number of switching events
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Bat acoustic libraries are important tools that assemble echolocation calls to allow the comparison and discrimination to confirm species identifications. The Sonozotz project represents the first nation-wide library of bat echolocation calls for a megadiverse country. It was assembled following a standardized recording protocol that aimed to cover different recording habitats, recording techniques, and call variation inherent to individuals. The Sonozotz project included 69 species of echolocating bats, a high species richness that represents 50% of bat species found in the country. We include recommendations on how the database can be used and how the sampling methods can be potentially replicated in countries with similar environmental and geographic conditions. To our knowledge, this represents the most exhaustive effort to date to document and compile the diversity of bat echolocation calls for a megadiverse country. This database will be useful to address a range of ecological questions including the effects of anthropogenic activities on bat communities through the analysis of bat sound.
Methods Geographic and environmental coverage
We selected multiple localities scattered across Mexico to maximize the number of species included in the database. We divided the Mexican territory into eight study regions based on topography, environmental complexity and the collaboration of bat experts working in each region: 1) Californian region (Baja California, Baja California Sur and Sonora); 2) Northeast region (Durango, Sinaloa and Chihuahua); 3) West region (Colima, Nayarit and Jalisco); 4) East region (Puebla, Tlaxcala and Veracruz): 5) North center region (Aguascalientes, Guanajuato, San Luis Potosí, Nuevo León and Zacatecas); 6) South center region (Estado de México, Morelos, Hidalgo and Querétaro); 7) Southeast region (Campeche, Quintana Roo and Yucatán); and 8) Southwest region (Chiapas, Oaxaca and Tabasco). Number of individuals recorded per species in each region is presented in Table 1. Based on this organization, we sampled in 27/32 (84%) of the Mexican states and six out of the seven ecoregions (defined as geographically distinctive areas containing a group of natural communities that share most of their species, environmental conditions and ecological dynamics- Challenger & Soberón, 2008) that have been defined for the Mexican territory by the National Commission for the Knowledge and Use of Biodiversity (CONABIO) (Figure 2, INEGI, CONABIO & INE, 2008). The localities sampled covered an altitudinal gradient ranging from sea level to 3600 m.a.s.l., and a great variety of ecosystems ranging from the northern xerophytic shrub lands to the southeastern tropical forests.
Capture, handling and sampling of bats
For each individual, we recorded sex, age (juvenile or adult), reproductive status (females: inactive, pregnant, or lactating; males: abdominal, inguinal or scrotal testes), and standard morphometric measurements (forearm length, head and body length, tail length, and body weight). Individual bats were photographed in standard formats and angles to provide support for posterior taxonomic identification and to create a photographic library of Mexican insectivorous bats. In addition, we obtained a small wing biopsy (diameter = 2 mm) stored in 96% ethanol to also serve as genetic reference material for future studies, in the case that intraspecific acoustic variation can hint to the presence of cryptic species.
When identification certainty was < 80% (based on the judgment of the most experienced collector), a voucher specimen was collected to confirm its identity based on cranial and postcranial characters and measurements. All handling and sampling procedures followed ethical recommendations provided by Sikes and the Animal Care and Use Committee of the American Society of Mammalogists (2016). This project had collection permits (SGPA/DGVS/05867/16, SGPA/DGVS/07291/17) issued by the Secretaría de Medio Ambiente y Recursos Naturales to M. Briones-Salas.
Individual data and biopsy samples were labeled with unique consecutive numbers which kept information on the region, site, and locality and will be freely available to researchers together with the acoustic material. Collected tissues were deposited in the Regional Collection of Durango (Mammalia), at CIDIIR-Durango, and voucher specimens were deposited at the Mammalogy Collection, CIB at Centro de Investigaciones Biológicas del Noroeste (CIBNOR).
Recording of bat echolocation calls
The ultimate purpose of the Sonozotz project was to build a reference call library that could be used to identify free-flying bats while foraging or commuting under natural conditions. Therefore, we aimed to record search calls under the conditions most commonly encountered by the species depending on their traits, habits, and behavior. Species-specific recommendations included recording mode: 1) hand release at ground level: bats were released from 1.5-2 m from the ground; 2) hand release at heights > 5 m; 3) flight cage: rooms or enclosures that allowed the bat to fly, we used this technique for species low intensity calls (e.g. Lampronycteris brachyotis, Lophostoma brasiliensis); 4) zip-lining: bats are attached to a 2-m length of small elastic cord by a loose-fitting loop of the cord pulled over the bat’s foot, the other end of the elastic cord is attached via a small snap swivel to 30–50 m of taut monofilament line about 1 m above the ground; 5) inside the bag; 6) take-off flight from perch, distance to microphone (0.5 m, 0.5-1 m, 5 m, or > 10 m). We also described the recording environment (stationary inside bag, closed, edge, or open). Bat echolocation calls were recorded immediately after processing individuals, which were afterwards released on site. All bats were recorded in real time with broad-band bat detectors (Avisoft UltraSoundGate 116H; Avisoft Bioacoustics, Glienicke, Germany), coupled with a sensitive condenser microphone (CM16/CMPA; Avisoft Bioacoustics, Glienicke, Germany) through a XLR-5 cable, and a laptop Dell Inspiron 7348 (Dell Inc.) running the software Avisoft-RECORDER (Avisoft Bioacoustics, Glienicke, Germany) through a USB cable. Recording settings were fixed on the software using standard parameters. We recorded in channel 1 without going over 15-sec per recording sequence employing a sampling rate of 300 kHz, a sample resolution of 16 bits, and a high-pass filtering of 4 kHz. We named files directly on the software window typing the unique codes previously assigned that preserved information on the region, site, locality, date, time, and individual. Complementary information was recorded on channel 2 (voice notes) to store information on environmental conditions, recording mode, and any other significant information for the recording output.
Sound analyses
We used BATSOUND PRO v.4.21 (Pettersson Elektronik AB, Uppsala, Sweden) to visually inspect all recorded sequences and remove those recordings that had: a) non-search-phase calls, b) calls not belonging to the targeted species, and c) low signal-to-noise ratio. We distinguished search phase calls from approach-phase and social calls by their duration, frequency and pattern of change over time.
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This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.
Historical daily stock prices (open, high, low, close, volume)
Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)
Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)
Feature engineering based on financial data and technical indicators
Sentiment analysis data from social media and news articles
Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
Researchers investigating the effectiveness of machine learning in stock market prediction
Analysts developing quantitative trading Buy/Sell strategies
Individuals interested in building their own stock market prediction models
Students learning about machine learning and financial applications
The dataset may include different levels of granularity (e.g., daily, hourly)
Data cleaning and preprocessing are essential before model training
Regular updates are recommended to maintain the accuracy and relevance of the data
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British American Tobacco stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.