From 2004 to 2024, the net revenue of Amazon e-commerce and service sales has increased tremendously. In the fiscal year ending December 31, the multinational e-commerce company's net revenue was almost *** billion U.S. dollars, up from *** billion U.S. dollars in 2023.Amazon.com, a U.S. e-commerce company originally founded in 1994, is the world’s largest online retailer of books, clothing, electronics, music, and many more goods. As of 2024, the company generates the majority of it's net revenues through online retail product sales, followed by third-party retail seller services, cloud computing services, and retail subscription services including Amazon Prime. From seller to digital environment Through Amazon, consumers are able to purchase goods at a rather discounted price from both small and large companies as well as from other users. Both new and used goods are sold on the website. Due to the wide variety of goods available at prices which often undercut local brick-and-mortar retail offerings, Amazon has dominated the retailer market. As of 2024, Amazon’s brand worth amounts to over *** billion U.S. dollars, topping the likes of companies such as Walmart, Ikea, as well as digital competitors Alibaba and eBay. One of Amazon's first forays into the world of hardware was its e-reader Kindle, one of the most popular e-book readers worldwide. More recently, Amazon has also released several series of own-branded products and a voice-controlled virtual assistant, Alexa. Headquartered in North America Due to its location, Amazon offers more services in North America than worldwide. As a result, the majority of the company’s net revenue in 2023 was actually earned in the United States, Canada, and Mexico. In 2023, approximately *** billion U.S. dollars was earned in North America compared to only roughly *** billion U.S. dollars internationally.

Conversational AI (e.g., Google Assistant or Amazon Alexa) is present in many people’s everyday life and, at the same time, becomes more and more capable of solving more complex tasks. However, it is unclear how the growing capabilities of conversational AI affect people’s disclosure towards the system as previous research has revealed mixed effects of technology competence. To address this research question, we propose a framework systematically disentangling conversational AI competencies along the lines of the dimensions of human competencies suggested by the action regulation theory. Across two correlational studies and three experiments (N total = 1453), we investigated how these competencies differentially affect users’ and non-users’ disclosure towards conversational AI. Results indicate that intellectual competencies (e.g., planning actions and anticipating problems) in a conversational AI heighten users’ willingness to disclose and reduce their privacy concerns. In contrast, meta-cognitive heuristics (e.g., deriving universal strategies based on previous interactions) raise privacy concerns for users and, even more so, for non-users but reduce willingness to disclose only for non-users. Thus, the present research suggests that not all competencies of a conversational AI are seen as merely positive, and the proposed differentiation of competencies is informative to explain effects on disclosure.

Original provider: Happywhale

Dataset credits: Happywhale and contributors

Abstract: Happywhale.com is a resource to help you know whales as individuals, and to benefit conservation science with rich data about individual whales.

Supplemental information: Sightings and images were submitted to Happywhale by contributors. A portion of the Happywhale data were transferred to OBIS-SEAMAP upon the agreement between Happywhale and OBIS-SEAMAP.

There may be duplicate records among Happywhale datasets and other OBIS-SEAMAP datasets. The precision of date/time vary per record. Some records have date accuracy up to year only.

This dataset includes sightings and photos from the following 2 contributors in alphabetic order:

Olaf Pignataro; Per Nikolaj Bukh

In 2024, Amazon's net revenue from subscription services segment amounted to 44.37 billion U.S. dollars. Subscription services include Amazon Prime, for which Amazon reported 200 million paying members worldwide at the end of 2020. The AWS category generated 107.56 billion U.S. dollars in annual sales. During the most recently reported fiscal year, the company’s net revenue amounted to 638 billion U.S. dollars. Amazon revenue segments Amazon is one of the biggest online companies worldwide. In 2019, the company’s revenue increased by 21 percent, compared to Google’s revenue growth during the same fiscal period, which was just 18 percent. The majority of Amazon’s net sales are generated through its North American business segment, which accounted for 236.3 billion U.S. dollars in 2020. The United States are the company’s leading market, followed by Germany and the United Kingdom. Business segment: Amazon Web Services Amazon Web Services, commonly referred to as AWS, is one of the strongest-growing business segments of Amazon. AWS is a cloud computing service that provides individuals, companies and governments with a wide range of computing, networking, storage, database, analytics and application services, among many others. As of the third quarter of 2020, AWS accounted for approximately 32 percent of the global cloud infrastructure services vendor market.

This data set contains a cross-country survey in 15 European countries on public perceptions of big-tech companies. The purpose of the dataset is to map out European citizens' perceptions of big-tech companies together with additional information that would allow to analyse how such perceptions are related to political and socio-demographic characteristics of the respondents. The survey contains questions about how people perceive the political and social role of GAFAM (Google, Apple, Facebook, Amazon, Microsoft), how much they trust these companies and how they perceive the need to regulate such companies. Furthermore, it contains questions on the political and ideological preferences of the respondents, how much they are satisfied with the political situation in their countries, how much they trust political institutions in their countries and what their attitudes are about the EU.

This data set is a supplement for:

Longo, M., S. S. Saatchi, M. Keller, K. W. Bowman, A. Ferraz, P. R. Moorcroft, D. Morton, D. Bonal, P. Brando, B. Burban, G. Derroire, M. N. dos-Santos, V. Meyer, S. R. Saleska, S. Trumbore, and G. Vin- cent, 2020: Impacts of degradation on water, energy, and carbon cycling of the Amazon tropical forests. J. Geophys. Res.-Biogeosci., 125 (8), e2020JG005 677, doi:10.1029/2020JG005677.

This data set contains the following files (which should be all downloaded and uncompressed in the same root directory):

• 00_SiteLidar.zip – R scripts to process forest inventory plots and Airborne LiDAR point clouds. Sub-directories contains a directory Template, which should be copied for each site for which data are to be processed.
• 01_LidarSynthesis.zip – R scripts to fit the statistical models of aggregated properties, and to evaluate both the statistical model and the prediction of Airborne LiDAR profiles to be used to initialize ED-2.2.
• 02_model_eval.zip – R scripts to compare the ED-2.2 model output and evaluate the model against tower observations.
• 03_degrad_mtr – R scripts to visualize the ED-2.2 simulation results.
• InputData – Miscellaneous data to be used by the scripts.
• Util – Additional R scripts
  • Rsc – Mostly R functions, which may be called by other R scripts
  • OutsideLAS – List of plots that were not fully overlapped by the Airborne LiDAR surveys
  • GenMERRA2_ED2 – Utility scripts to process MERRA-2 to generate the met drivers needed by ED-2.2
  • GenMSWEP2_ED2 – Utility scripts to process MSWEP-2.2 to generate the met drivers needed by ED-2.2
• ED2IN_Config – list of ED2IN files used in the runs.

To see the input data used for this analysis, load any of the objects available in 01_LidarSynthesis/01_eval_multivar, and look for the following structures:

    <ul>
      <li><strong>115:</strong> Km 115 of BR-163 highway, PA, BRA</li>
      <li><strong>ana:</strong> Anambé, PA, BRA</li>
      <li><strong>and:</strong> Fazenda Andiroba, PA, BRA</li>
      <li><strong>bon:</strong> Fazenda Bonal, AC, BRA</li>
      <li><strong>cau:</strong> Fazenda Cauaxi, PA, BRA</li>
      <li><strong>duc:</strong> Reserva Ducke, AM, BRA</li>
      <li><strong>fc2:</strong> Feliz Natal (zone C, area 2), MT, BRA</li>
      <li><strong>fd1:</strong> Feliz Natal (zone D, area 1), MT, BRA</li>
      <li><strong>fd2:</strong> Feliz Natal (zone D, area 2), MT, BRA</li>
      <li><strong>fd3:</strong> Feliz Natal (zone D, area 3), MT, BRA</li>
      <li><strong>fn2:</strong> Feliz Natal (long transect 2), MT, BRA</li>
      <li><strong>fna:</strong> Feliz Natal (zone A), MT, BRA</li>
      <li><strong>fst:</strong> Saracá-Taquera National Forest, PA, BRA</li>
      <li><strong>gf1:</strong> Paracou (Guyaflux plots), GUF</li>
      <li><strong>gf2:</strong> Paracou (Logging experiment plots), GUF</li>
      <li><strong>hum:</strong> Fazenda Humaitá, AC, BRA</li>
      <li><strong>jm2:</strong> Jamari National Forest (area 2), RO, BRA</li>
      <li><strong>jm3:</strong> Jamari National Forest (area 3), RO, BRA</li>
      <li><strong>par:</strong> Fazenda Nova Neonita, PA, BRA</li>
      <li><strong>sbe:</strong> </li>
    </ul>
    </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">local</th>
    <td>census[[1]], rlidar[[1]], tchdat[[1]]</td>
    <td>
    <p>Region identifier (used for regional cross-validation):</p>

    <ul>
      <li><strong>bte:</strong> Belterra, PA, BRA</li>
      <li><strong>duc:</strong> Manaus (Reserva Ducke), AM, BRA</li>
      <li><strong>fst:</strong> Saracá-Taquera National Forest, PA, BRA</li>
      <li><strong>fzn:</strong> Feliz Natal, MT, BRA</li>
      <li><strong>gyf:</strong> Paracou, GUF</li>
      <li><strong>jam:</strong> Jamari National Forest, RO, BRA</li>
      <li><strong>prg:</strong> Paragominas, PA, BRA</li>
      <li><strong>rib:</strong> Rio Branco, AC, BRA</li>
      <li><strong>sfx:</strong> São Félix do Xingu, PA, BRA</li>
      <li><strong>tan:</strong> Tanguro, MT, BRA</li>
      <li><strong>sbe:</strong> Southeastern Belterra, PA, BRA</li>
      <li><strong>sx1:</strong> São Félix do Xingu (area 1), PA, BRA</li>
      <li><strong>sx2:</strong> São Félix do Xingu (area 2), PA, BRA</li>
      <li><strong>tac:</strong> Tomé-Açu, PA, BRA</li>
      <li><strong>tal:</strong> Fazenda Talismã, AC, BRA</li>
      <li><strong>tn1:</strong> Fazenda Tanguro (Sustainable Landscapes transects), MT, BRA</li>
      <li><strong>tn2:</strong> Fazenda Tanguro (fire experiment transects), MT, BRA</li>
      <li><strong>tp1:</strong> Tapajós National Forest, PA, BRA</li>
      <li><strong>tp2:</strong> São Jorge (area 2), PA, BRA</li>
      <li><strong>tp3:</strong> São Jorge (area 3), PA, BRA</li>
    </ul>
    </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">poi</th>
    <td>census[[1]], rlidar[[1]], tchdat[[1]]</td>
    <td>Nominal size of each plot</td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">when</th>
    <td>census[[1]], rlidar[[1]], tchdat[[1]]</td>
    <td>Date of measurement</td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">col</th>
    <td>census[[1]], rlidar[[1]], tchdat[[1]]</td>
    <td>Colour associated with plot (for plotting only)</td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">pch</th>
    <td>census[[1]], rlidar[[1]], tchdat[[1]]</td>
    <td>Symbol associated with plot (for plotting only)</td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">dist.key</th>
    <td>census[[1]], rlidar[[1]], tchdat[[1]]</td>
    <td>
    <p>Disturbance flag:</p>

    <ul>
      <li><strong>bnm:</strong> Burnt multiple times</li>
      <li><strong>bno:</strong> Burnt once</li>
      <li><strong>cvl:</strong> Conventional logging</li>
      <li><strong>int:</strong> Intact (minimally disturbed) forest</li>
      <li><strong>lbn:</strong> Logged and burnt once</li>
      <li><strong>lth:</strong> Logged and thinned</li>
      <li><strong>ril:</strong> Reduced-impact logging</li>
      <li><strong>sbn:</strong> Secondary growth then burnt</li>
      <li><strong>sec:</strong> Secondary growth</li>
      <li><strong>ukn:</strong> Unknown/Unclassified</li>
    </ul>
    </td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">dist.age</th>
    <td>census[[1]], rlidar[[1]], tchdat[[1]]</td>
    <td>Age since last disturbance</td>
    <td>yr</td>
  </tr>
  <tr>
    <th scope="row">dist.col</th>
    <td>census[[1]], rlidar[[1]], tchdat[[1]]</td>
    <td>Colour associated with disturbance (for plotting only)</td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">dist.pch</th>
    <td>census[[1]], rlidar[[1]], tchdat[[1]]</td>
    <td>Symbol associated with disturbance (for plotting only)</td>
    <td> </td>
  </tr>
  <tr>
    <th scope="row">agb.std</th>
    <td>census[[1]]</td>
    <td>Above-ground biomass of individuals with DBH ≥ 10 cm</td>
    <td>kgC m<sup>−2</sup></td>
  </tr>
  <tr>
    <th scope="row">ba.std</th>
    <td>census[[1]]</td>
    <td>Basal area of individuals with DBH ≥ 10 cm</td>
    <td>cm<sup>2</sup> m<sup>−2</sup></td>
  </tr>
  <tr>
    <th scope="row">lai.std</th>
    <td>census[[1]]</td>
    <td>Potential (allometry-based) leaf area index of individuals with DBH ≥ 10 cm</td>
    <td>m<sup>2</sup> m<sup>−2</sup></td>
  </tr>
  <tr>
    <th scope="row">nplant.std</th>
    <td>census[[1]]</td>
    <td>Stem number density of individuals with DBH ≥ 10 cm</td>
    <td>m<sup>−2</sup></td>
  </tr>
  <tr>
    <th scope="row">elev.mean</th>
    <td>rlidar[[1]]</td>
    <td>Mean elevation of point cloud return distribution (all returns)</td>
    <td>m</td>
  </tr>
  <tr>
    <th scope="row">elev.sdev</th>
    <td>rlidar[[1]]</td>
    <td>Standard deviation of point cloud return distribution (all returns)</td>
    <td>m</td>
  </tr>
  <tr>
    <th scope="row">elev.skew</th>
    <td>rlidar[[1]]</td>
    <td>Skewness of point cloud return distribution (all returns)</td>
    <td>m</td>
  </tr>
  <tr>
    <th scope="row">elev.kurt</th>
    <td>rlidar[[1]]</td>
    <td>Kurtosis of point cloud return distribution (all returns)</td>
    <td>m</td>
  </tr>
  <tr>
    <th scope="row">elev.p01</th>
    <td>rlidar[[1]]</td>
    <td>1<sup>st</sup> percentile of the point cloud return distribution (all returns)</td>
    <td>m</td>
  </tr>
  <tr>
    <th scope="row">elev.p05</th>
    <td>rlidar[[1]]</td>
    <td>5<sup>th</sup> percentile of the point cloud return distribution (all returns)</td>
    <td>m</td>
  </tr>
  <tr>
    <th scope="row">elev.p10</th>
    <td>rlidar[[1]]</td>
    <td>10<sup>th</sup> percentile of the point cloud return distribution (all returns)</td>
    <td>m</td>
  </tr>
  <tr>
    <th scope="row">elev.p25</th>
    <td>rlidar[[1]]</td>
    <td>25<sup>th</sup> percentile of the point cloud return distribution (all returns)</td>
    <td>m</td>
  </tr>
  <tr>
    <th scope="row">elev.p50</th>
    <td>rlidar[[1]]</td>
    <td>50<sup>th</sup> percentile (median) of the point cloud return distribution (all returns)</td>
    <td>m</td>
  </tr>
  <tr>
    <th

This data shows healthcare utilization for asthma by Allegheny County residents 18 years of age and younger. It counts asthma-related visits to the Emergency Department (ED), hospitalizations, urgent care visits, and asthma controller medication dispensing events.

The asthma data was compiled as part of the Allegheny County Health Department’s Asthma Task Force, which was established in 2018. The Task Force was formed to identify strategies to decrease asthma inpatient and emergency utilization among children (ages 0-18), with special focus on children receiving services funded by Medicaid. Data is being used to improve the understanding of asthma in Allegheny County, and inform the recommended actions of the task force. Data will also be used to evaluate progress toward the goal of reducing asthma-related hospitalization and ED visits.

Regarding this data, asthma is defined using the International Classification of Diseases, Tenth Revision (IDC-10) classification system code J45.xxx. The ICD-10 system is used to classify diagnoses, symptoms, and procedures in the U.S. healthcare system.

Children seeking care for an asthma-related claim in 2017 are represented in the data. Data is compiled by the Health Department from medical claims submitted to three health plans (UPMC, Gateway Health, and Highmark). Claims may also come from people enrolled in Medicaid plans managed by these insurers. The Health Department estimates that 74% of the County’s population aged 0-18 is represented in the data.

Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time. Missing from the data are the uninsured, members in participating plans enrolled for less than 90 continuous days in 2017, children with an asthma-related condition that did not file a claim in 2017, and children participating in plans managed by insurers that did not share data with the Health Department.

Data users should also be aware that diagnoses may also be subject to misclassification, and that children with an asthmatic condition may not be diagnosed. It is also possible that some children may be counted more than once in the data if they are enrolled in a plan by more than one participating insurer and file a claim on each policy in the same calendar year.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Air quality data is collected from the Allegheny County Health Department monitors throughout the county. This data must be verified by qualified individuals before it can be considered official. The following data is unverified. This means that any electrical disruption or equipment malfunction can report erroneous monitored data.

For more information about the Health Department's Air Quality Program or to view a live version of the dashboard, please visit the ACHD website: https://alleghenycounty.us/Health-Department/Programs/Air-Quality/Air-Quality.aspx

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

The PELD FORR site is compound by five locations represented by two protected areas (Viruá National Park, hereafter, PNV, and Maracá Ecological Station, hereafter EEM), which are part of the Program for Research in Biodiversity (PPBio / MCTI), and three private forest areas. The selected protected areas are important for the conservation of several tree species (such as Centrolobium paraense), medium and large mammals, and several species of birds of conservation concern. Each of the protected areas selected as our study sites has a network of 30 permanent plots of 1 hectare, which are systematically distributed over an area of ~25 km2, and where ~20,000 trees are marked, mapped, and being monitored at intervals of five years since 2006. The forest areas outside protected areas surround PNV and are Brazil nut stands (Berthollethia excelsa) where rural population collect nuts for own consume or to sell. Working with the surrounding community is vital in proposing alternatives that combine economic return with forest conservation and the diffusion of sustainable agricultural practices is very important to hold the advance of deforestation and the threat of fire within the park boundaries. Being located at the northern edge of the Amazon, the PELD FORR site presents climatic and geological characteristics unique in the Amazonian scenario. Rainfall patterns, for example, are very different from other Amazonian active LTER sites, with a much longer dry season and a precipitation gradient that ranges between 1700-2300 mm/year. In addition, the selected areas represent forest ecosystems that are rare elsewehere in the Amazon and represent two important ecotones (white-sand / humid forest, and savanna / seasonal forest), which are sensitive to droughts, which should become increasingly frequent in the Amazon in predicted future climate scenarios. Extreme climatic events, like El Niño have affected Roraiman forest dramatically in previous years (e.g, 1997-98, 2010 and 2015-16), resulting in wild fires of great proportions. The Viruá National Park (PNV) has a total area of 214,950.52 hectares and was created in 1998 to protect samples of the ecosystems of the south-central region of Roraima, where the Campinaranas predominate, a term that designates a phytophysiognomy that occurs in areas of sandy and often hydromorphic soils. In the regional context, the Park is part of the Campinaranas Ecological Region, which extends through the Negro, Branco and Orinoco river basins, covering areas of the Brazilian, Colombian and Venezuelan Amazon. Recently the park received the title of a Ramsar site, showing its importance as a wetland area. PNV is marked by high environmental heterogeneity, with a great variety of physiognomies associated to topography, hydrology and soils. The predominant vegetation is the ecotone campinarana (white-sand forest) with extensive alluvial forest areas (floodplain and igapó forests) and mainland forest enclaves that include Open Lowland Ombrophilous Forests, as well as small enclaves of Open Ombrophylous Submontane Forests on isolated residual hills. PNV presents high floristic diversity, with 1262 registered plant species, including 1149 angiosperms and 110 pteridophytes (ICMBio, 2014). The soil types that predominate in the PNV are sandy, nutrient poor, poorly drained and developed over extensive sedimentary plains (Hydromorphic Quartzic Neosols and Hydromorphic Humic Spodosols). The low altitude of the land (between 45 and 60m) causes periodic flooding due to elevation of the water table or the accumulation of rainwater, whose flow is hampered by the presence in depth of layers cemented by iron oxides, aluminum oxides and organic matter (Mendonça et al., 2014). The low water storage capacity of these soils causes extreme water deficits during dry seasson. Parna Viruá is in a climatic transition zone (Aw-Am by the Köppen classification system) with the dry period peak between January-March and the rainy season between May-August (Schaefer et al., 2008). The Maracá Ecological Station (EEM) is a river island, about 830 km2 (60 km long x 25 km wide), located on the river Uraricoera, one of the main tributaries of Rio Branco, the main river of Roraima. Most of the island (90%) is covered by forests, although in the southern and eastern portions savannah patches are found. The forest phytophysiognomies present on the island include Ombrophilous Forest and Seasonal Forest. The height of the canopy varies from 25 to 40m, with occasional emergent trees reaching 50m in height. The diversity of tree species is low, compared to other areas of the Amazon (~ 80 species with dap ≥10 cm in 1.5 hectares), with occurrences of monodominant forest patches of Peltogyne gracilipes (Milliken & Ratter, 1998). Esec Maracá is home to populations of endangered tree species such as Albizia glabripetala (CNCFlora, 2016a) and Centrolobium paraense (CNCFlora, 2016b). Podzolic soils, mostly dystrophic and containing low base saturation, predominate on the island (Nortclif & Robison, 1991). In areas where the soil has low depth of root penetration, temporary flooding on the surface, and a high Mg: Ca ratio, monodominant forest patches are observed. Esec Maracá is located in the area described for the "Aw" climate (tropical climate with dry winter season). In the period from 1986 to 2004, the average annual rainfall registered was 2091 mm (ICMBio / RR). In general, the wettest month is July and the driest month, February (Nascimento, 1997). For the same period, from April to September (rainy season), the average rainfall was 1635 mm, and in the months of October to March (dry season), the average was 456 mm. Brazil nut stands: The permanent plots located in forests with natural occurrence of Brazil nut (Bertholletia excelsa) were established by Embrapa Roraima between 2005 and 2008, with the objective of studying population dynamics (growth, mortality and recruitment) and fruit production and seeds in natural populations of the species. Each plot has a total area of nine hectares (300 x 300 m), subdivided into 144 subplots of 25 x 25 m, in which all Brazil nut trees with DBH (diameter at 1,30 m from the soil) equal to or greater than 10 cm were marked, mapped and measured (stem and crown diameter and total height). All plots have been monitored annually since its establishment (2005-2008). In total, there are five plots, one of which is in the municipality of São João da Baliza, 313 km from the capital Boa Vista, and four in the municipality of Caracaraí, 135 km from the capital. The plot located in São João da Baliza was installed in 2006 and has 34 marked individuals (average density of 3.8 Brazil nut trees/hectare). The region is characterized by Dense Ombrophylous Forest vegetation and the climate is Awi type (tropical humid with small dry season during the year), with a dry period between November and March (Barbosa, 1997). The plots installed in the municipality of Caracaraí are located around the Viruá National Park. Two plots were installed between 2006 and 2007 in a 400 ha forest fragment where the average density of Brazil nut trees was estimated as 13.2 trees/hectare, totalizing 238 individuals. Other two plots were installed in 2008 in an area with density of 6.3 trees/hectare, totalizing 113 individuals. The vegetation in the region was classified as Open Tropical Rainforest with Palms and the climate of the region as Ami (rainy tropical, with small dry season), with average annual precipitation between 1700-2000 mm, being the rainy period between April and August.

COVID-19 Vaccination data as reported by the County's health department. On May 19, 2023, with the rescinding of the COVID-19 public health emergency, changes in data and reporting mechanisms prompted a change in the municipal counts. Data attributes listed as 'Archive Only' within the below description are reflected only in data prior to May 19, 2023. These files are maintained as 'Archive' files within this repository.

This dataset contains 3 tables:

1. Allegheny County COVID-19 Vaccine Individual Data: Contains vaccination information on an individual level for all three vaccination statuses
2. Allegheny County COVID-19 Vaccine Municipal Data: Contains counts of bivalent booster vaccinations for all neighborhoods in Allegheny County
3. Allegheny County COVID-19 Vaccine Historical Municipal Data: Contains historical counts for each vaccination status for all neighborhoods in Allegheny County up through May 2023, then limited to counts of bivalent booster vaccinations following this cut-off.

Types of Vaccination Status:

1. Partially Vaccinated [Individual and Archive Only]: If an individual has not completed their primary series with either 1 of 2-dose series or 2 of 3-dose series for children under the age of 5 years
2. Completed Primary Series (formerly Fully Vaccinated) [Individual and Archive Only]: If an individual has completed their primary series with the 1-dose J&J series, 2-dose series, or 3-dose series for children under 5 years.
3. Bivalent Booster: If an individual has obtained their bivalent booster dose.

Due to minor discrepancies in the Municipal boundary and the City of Pittsburgh Neighborhood files individuals whose City Neighborhood cannot be identified are be counted as “Undefined (Pittsburgh)”.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

This page describes the datasets necessary to reproduce the results found in the study conducted by Pimenta et al. (2025), titled “Pollen niche of Amazonian stingless bees”, published in Biotropica.The available files from this study are: bioclim.txt, compover.txt, compunder.txt, global_data.txt, radam.csv, tree.txt, tree_codification.csv.bioclim.txt: Rows represent the colonies of Meliponini species studied. Columns represent variables as follows: Nest coordinates of Meliponini included in the study and values of bioclimatic variables from the WorldClim 2 database.• Long – Longitude in decimal degrees• Lat – Latitude in decimal degrees• Bio1 to Bio19 – Extracted values for each nest location (study areas) of the bioclimatic variables from the WorldClim 2 database, following the original measurement units.compover.txt and compunder.txt: Rows represent the colonies of Meliponini species studied. Columns represent botanical taxa collected by Meliponini bees. Pollen taxa lists were compiled from studies conducted in the Amazon following methods described in “Data assembly”. The lists of taxa collected by Meliponini contained pollen identified at different taxonomic levels, leading to the construction of two versions of a bee-pollen interaction matrix: Underestimated and Overestimated, corresponding to the compunder.txt and compover.txt files. The logic behind these two dataset versions is described in the “Data analysis” section.global_data.txt: Rows represent the colonies of a studied Meliponini species. Columns represent variables compiled from studies on bee-pollen interactions in the Amazon, including the addition of the average intertegular distance (DIT) of species included in this study. The DIT values were obtained from measurements of individuals deposited in the entomological collection of the National Institute for Amazonian Research.• references – Citation with author and year referring to the individual bee-pollen study data included in this study. The complete list of bee-pollen studies is located at the end of the article in the “Data Sources” section.• Species – Species included in this study. The names in the “Species” column do not follow the binomial system from Moure's Bee Catalogue, to facilitate phylogenetic tree codification using the tree.txt and tree_codification.csv files.• intertegular.distance – Intertegular distance values measured in specimens from the entomological collection of the National Institute for Amazonian Research. Corresponds to the average of three specimens per species.• Habitat – Environmental variable. Indicates whether the studied colony was located in a natural environment (“Wild”) or an urban environment (“Urban”).• latitude – Latitude of compiled studies in decimal degrees• longitude – Longitude of compiled studies in decimal degrees• sample.type – Substrate used for pollen sampling collected by Meliponini in the Amazon.• first.year – Year of pollen analysis studies of Meliponini. Some studies were conducted over multiple years. To construct a continuous variable, we opted to use the first year of each study.• n.corbicule – Binary variable (0 = No, 1 = Yes). Indicates whether the compiled dataset examined pollen found in the corbicula of a Meliponini specimen from the Amazon.• n.pollen.pot – Binary variable (0 = No, 1 = Yes). Indicates whether the compiled dataset examined pollen found in the pollen pot of a Meliponini colony from the Amazon.• n.honey.pot – Binary variable (0 = No, 1 = Yes). Indicates whether the compiled dataset examined pollen found in the honey pot of a Meliponini colony from the Amazon.• n.months – Number of months pollen sample collection occurred in compiled studies.• n.pollen.types – Count variable. Sum of pollen types found in Meliponini pollen collection studies in the Amazon. The logic used to construct this variable can be found in the “Data analysis” subsection of the “Methods” section.radam.csv: Rows represent forest inventory locations closest to the colonies of a studied Meliponini species. Columns represent tree species found in RADAM inventory areas. More information about the RADAM inventory can be found in the “Data assembly” section of the “METHODS” section.tree.txt: This file corresponds to the phylogeny used in this study. See figure s1 included in the supplementary material.tree_codification.csv: This file provides information on which rows in global_data.txt correspond to which species available in the phylogeny (tree.txt).

List and location of all the businesses and social clubs who have received an exemption from the Pennsylvania Clean Indoor Air Act. “The Clean Indoor Air Act, Act 27 of 2008, was signed into law on June 13, 2008. The legislation prohibits smoking in a public place or a workplace and lists examples of what is considered a public place. The bill allows for some exceptions, including a private residence (except those licensed as a child care facility), a private social function where the site involved is under the control of the sponsor (except where the site is owned , leased, or operated by a state or local government agency) and a wholesale or retail tobacco shop. It also imposes penalties for those establishments in noncompliance, as well as those individuals smoking in prohibited areas.”

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Datasets used in the paper "Species-specific behaviour and environmental drivers of trap interactions in wild ornamental fishes" by Pineda et al. (2025) Published in the Journal of Fish Biology.

Uploaded data contains the main data set used for modelling, the data used for coefficients of dispersion, and the raw video files, which contain counts of all behaviours during a trial.

Paper Abstract: The harvest of animals from the wild is a pervasive selective force, especially in fisheries, where harvesting often targets individuals with specific traits. While most research has focussed on large-scale commercial or recreational fisheries, little attention has been paid to artisanal fisheries, particularly those targeting ornamental species. Furthermore, environmental factors such as temperature and oxygen levels influence behaviour of fishes, such as boldness and sociability, but their role in the harvesting process remains poorly understood. Here, we used underwater video to examine how two ornamental Amazonian fishes, Hemigrammus sp. and Copella nattereri, interact with artisanal trap gear. We quantified the number of passes, inspections, entries, and exits using latency to inspect and enter traps as proxies for boldness, and coefficients of dispersion (CD) to assess sociability and group coordination. We found that the majority of fish that inspected traps did not enter them, and a given trap typically caught one species over the other. Overall, Copella were captured more frequently, but within individual trials there was no statistical difference in catch numbers between species. While both species inspected traps, Hemigrammus exhibited significantly more passes and a higher rate of inspection. Latency to inspect and enter traps did not differ between species but decreased with increasing temperature for both. Hemigrammus also displayed greater group coordination, with higher CD values across behaviours. Notably, temperature had opposing effects on coordination: for Hemigrammus, CD of inspections increased with temperature and CD of exits decreased, whereas for Copella, inspection CD decreased and exit CD increased. These findings reveal that different species interact with fishing gear in behaviourally distinct ways, influenced by environmental conditions. This highlights the potential for selective pressures to vary not only by species, but also with ecological context. Understanding such dynamics is critical for predicting how artisanal fisheries may shape behavioural traits in wild populations.

Abstract INTRODUCTION Snakebites are considered a neglected tropical disease in many countries in Latin America, including Brazil. As few studies have assessed snakebites in the Amazon region and especially in the state of Acre, epidemiological studies are of great importance. The present study aimed to describe the epidemiological characteristics of snakebites in the Rio Branco region, observing their characteristics in rural and urban areas and their correlation with rainfall and river outflow. METHODS This retrospective, descriptive study analyzed epidemiological information obtained from snakebite notifications registered on the Information System for Notifiable Diseases that occurred from March, 2018 to February, 2019. The cases of snakebite were correlated with rainfall and flow. RESULTS A total of 165 cases of snakebite were registered in the period. Most cases were caused by Bothrops and affected mainly individuals of the male sex who were between 21 and 30 years old. Most of the snakebites occurred in Rio Branco (71.52%; 29 cases per 100,000 inhabitants). Of these, 60.2% occurred in the urban area and 39.8% in the rural area and the majority occurred during the rainy season. CONCLUSIONS Although studies have shown that a majority of cases occur in rural areas, in this study, urbanization of snakebites was observed. The Bothrops genus was responsible for the highest number of snakebites and, during the rainy season, bites occurred more frequently. Educational prevention campaigns, population advice, and first aid in case of snakebites for the population are thus suggested.

Context

There are a few companies that are considered to be revolutionary. These companies also happen to be a dream place to work at for many many people across the world. These companies include - Facebook,Amazon,Apple,Netflix and Google also known as FAANG! These companies make ton of money and they help others too by giving them a chance to invest in the companies via stocks and shares. This data wass made targeting these stock prices.

Content

The data contains information such as opening price of a stock, closing price, how much of these stocks were sold and many more things. There are 5 different CSV files in the data for each company.