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1,630 results for “occupations”
WISCO occupations_ISCO08_5dgt_55languages_4000titles_with_mapping_surveycodings_20230425
<p>Occupation is a key variable in socio-economic research, used in a wide variety of studies, but its measurement is a major challenge. The national stocks of job titles are large with 10,000’s of job titles, they are unstructured with vague boundaries between job titles, and the stock has no fixed list but instead many entries and exits over time. Measuring occupations in a multi-country survey is even a larger challenge, because occupations with the same tasks have to be coded similarly across countries. Most surveys use an open-ended survey question to measure occupations. The challenge relates to time-consuming and expensive office-coding. Alternatively, web surveys and CAPI surveys allow using a look-up database with occupational titles. The Surveycodings team and WageIndicator Foundation provide a multilingual database of coded and translated occupational titles that allow for urvey respondents' self-identification of their occupational titles, thereby tackling the challenge for multi-country surveys to classify job titles into ISCO-08 classification of occupations and to do so consistently across countries. The database is gradually extended with more occupational titles and more languages. The current version, as of 20230202, holds 55 languages for at most 4,000 titles, though some languages have only half of the titles translated, among others because the occupations do not exist in the country at stake or because no translations were aavailable. Details about this and related databases as well as related publications can be found at https://www.surveycodings.org/articles/codings/occupation.</p>
Occupation teleworkability indices
<p>The occupational indices of teleworking, as described in <a href="https://joint-research-centre.ec.europa.eu/publications/teleworkability-and-covid-19-crisis-new-digital-divide_en">Teleworkability and the COVID-19 crisis: a new digital divide?</a>.</p><p>The <strong>Technical teleworkability index</strong> estimates which ISCO 3-digit occupations can potentially work remotely, based on the absence of physical constraints (namely, interacting substantially with tools, machinery, or people) measured in the European occupational context.</p><p>The <strong>Social interaction index </strong> estimates the extent of social interaction typical of an ISCO 3-digit occupation. On its own, it may not prevent an occupation from teleworking, but may make teleworking more difficult.</p>
Patch occupancy of stream fauna across a land cover gradient in the southern Appalachians, USA
Field sampling of four functionally important focal stream consumers within the Little Tennessee River Basin took place in thirty-seven stream reaches between May - July 2009. Sampled reaches all drained an area less than 17 km2, and land cover varied among these reaches. This data was used to model patch occupancy to examine factors that best predicted the prevalence of the four groups.
Brainport, Automated valet parking, RS camera parking spot occupancy
<p><strong>Scenario description</strong>:</p> <p>RS Camera parking spot occupancy detection and publication of the iot message from type AutoPilot.ParkingSpotDetection to the PMS via IoT platforms</p> <p><strong>Session description</strong>:</p> <p>A AD-car parks to the selected parking spot the rs camera detect the car at the parking spot and publish the occupancy information to the PMS for parking management purpose</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>Dataset Description This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_DroneAvpCommand</strong>: Data sent from drone</p> <p>Dataset Description This dataset contains route information for a vehicle to a designated parking spot</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>Dataset Description This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>Dataset Description Each sensor data submission is a Message. A Message has an Envelope, a Path, and optionally (but likely) Path Events and optionally Path Media. The envelope bears fundamental information about the individual sender (the vehicle) but not to a level that owner of the vehicle can be identified or different messages can be identified that originate from a single vehicle.</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_ParkingSpotDetection</strong>: Data sent from drone to parkingService</p> <p>Dataset Description This dataset contains informaton about detected parking spots</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_PositioningSystemResampled</strong>: Data from GPS on the vehicle</p> <p>Dataset Description This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpCommand</strong>: Data sent from ParkingService to vehicle</p> <p>Dataset Description This dataset contains route to parkingspot, and some other environmental information</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleAvpStatus</strong>: Data sent from vehicle to ParkingService</p> <p>Dataset Description This dataset contains information about the current status and parkingstatus of the vehicle</p> <p><strong>AUTOPILOT_BrainPort_AutomatedValetParking_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>Dataset Description This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Species occurrence and occupancy in protected areas of the Natura2000 network in Belgium
<p><strong>Context</strong></p> <p>Invasive alien species have been pointed out as an important driver of biodiversity loss. Many policy responses are being developed to address this threat. Protected areas often represent and preserve hotspots of biological diversity and ensure the maintenance of ecosystem services crucial to human livelihoods. The impact of biological invasions can be particularly severe in protected areas and their occurrence and impact in such areas is an important element of the risk they pose. To address this, there is a need for data on the occurrence and extent of alien species invasions in protected areas.</p> <p><strong>Description</strong></p> <p>This dataset contains species occurrence and occupancy in protected areas of the Natura2000 network in Belgium (Special Conservation Areas sensu Habitat Directive and Special Protection Areas sensu Bird Directive). The dataset was generated using the <a href="https://doi.org/10.5281/zenodo.3637911">Belgian occurrence cube at species level</a> and the <a href="https://doi.org/10.5281/zenodo.3635510">Belgian occurrence cube for non-native taxa</a> (both containing GBIF data aggregated using Oldoni et al. 2020), the 1x1km <a href="https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2">EEA reference grid</a> and the <a href="https://www.eea.europa.eu/data-and-maps/data/natura-11/natura-2000-spatial-data/natura-2000-shapefile-1">Natura2000 protected areas shapefiles</a> from the European Environment Agency.</p> <p>Data are grouped by protected area (<code>SITECODE</code>), year (<code>year</code>) and (infra)species (<code>taxonKey</code>, <code>speciesKey</code>). For each group, it provides the number of occurrences found in GBIF (<code>n</code>), the area of occupancy (<code>aoo</code>: number of 1 km<sup>2</sup> squares), the coverage (<code>coverage</code>: % of 1 km<sup>2</sup> squares), the minimum <a href="http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters">coordinateUncertaintyInMeters</a> (<code>min_coord_uncertainty</code>), and the alien status (<code>is_alien</code>) based on the <a href="https://doi.org/10.15468/xoidmd">Global Register of Introduced and Invasive Species - Belgium</a>. For infraspecific taxa in the latter, the <a href="https://github.com/trias-project/indicators/blob/00e1ae72df3fb98b2a215c3af8769e53fbcd0182/reference/species_of_infraspecific_alien_taxa.tsv">alien status of the species</a> is looked up and included.</p> <p>The dataset is built on open science principles and intended to be completely reproducible:</p> <ul> <li>The input data are publicly available on Zenodo, with the download DOIs listed in the related identifiers of this dataset package.</li> <li>The <a href="https://trias-project.github.io/indicators/10_species_observations_occupancy_in_protected_areas.html">code</a> to process the data is publicly available and documented on GitHub.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>protected_areas_species_occurrence.csv</strong>: number of occurrences (<code>n</code>), area of occupancy (<code>aoo</code>) and <code>coverage</code> of taxa (<code>taxonKey</code>) in Natura2000 areas of Belgium (<code>SITECODE</code>). Other columns included: <code>speciesKey</code> (for species is <code>speciesKey</code> = <code>taxonKey</code>), <code>SITETYPE</code> containing the site type of the Natura2000 area (one of <code>A</code>, <code>B</code> or <code>C</code>), <code>min_coord_uncertainty</code> with the lowest coordinate uncertainty in meters, <code>is_alien</code> containing the alien status (<code>TRUE</code> or <code>FALSE</code>) and <code>remarks</code> containing, if present, the infraspecific alien taxa whose occurrences contribute to the calculated <code>aoo</code> (only for species).</li> <li><strong>protected_areas_species_info.csv</strong>: taxonomic information of taxa in <code>protected_areas_species_occurrence.csv</code> as retrieved from <a href="https://www.gbif.org/dataset/d7dddbf4-2cf0-4f39-9b2a-bb099caae36c">GBIF Backbone Taxonomy</a>. Columns: <code>taxonKey</code>, <code>speciesKey</code>, <code>scientificName</code>, <code>kingdom</code>, <code>phylum</code>, <code>order</code>, <code>class</code>, <code>genus</code>, <code>family</code>, <code>species</code>, <code>rank</code> and <code>includes</code>. The latter contains the infraspecific taxa and synonyms whose occurrences contribute to the number of occurrences at species level.</li> <li><strong>protected_areas_metadata.csv</strong>: protected area information for areas included in <code>protected_areas_species_occurrence.csv</code>. Columns: <code>SITECODE</code> as in <code>protected_areas_species_occurrence.csv</code> (<code>BE*******</code>), <code>SITENAME</code> containing the name of the protected area, <code>SITETYPE</code> as in <code>protected_areas_species_occurrence.csv</code>, <code>flanders</code>, <code>wallonia</code> and <code>brussels</code> containing whether the area is situated respectively in Flanders, Wallonia or Brussels-Capital Region (<code>TRUE</code> or <code>FALSE</code>). Field codes are in line with <a href="https://www.eea.europa.eu/data-and-maps/data/natura-11/natura-2000-tabular-data-12-tables">EEA element definitions</a> for Natura 2000 sites.</li> </ul> <p><strong>Potential use of the dataset</strong></p> <p>Currently, there is no comprehensive reporting system for invasive alien species in Natura 2000 sites. This dataset provides a baseline as to which species occur in which protected area. We envisage this dataset can be an interesting starting point for various types of analyses on alien species in protected areas in Belgium, but that it can also be used in complement to other data on alien species in protected areas to study more general patterns. Some examples of research questions:</p> <ul> <li>Which protected areas are most invaded by alien species</li> <li>Which alien species are most distributed in protected areas and which traits do they have</li> <li>How does the proportion of alien species in protected areas change in time</li> <li>How does the occurrence/occupancy of alien species in protected areas match lists of regulated species (e.g. Union List, EPPO lists)</li> <li>To what extent can the network of protected areas contribute to providing safe refuge to native species from the impacts of invasive alien species</li> <li>How widespread are the impacts of certain alien species on protected areas</li> </ul> <h2>Acknowledgements</h2> <p>This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS), the European Union's LIFE program (LIFE19 NAT/BE/000953 - LIFE RIPARIAS).</p>
Data from: Occupancy patterns and upper range limits of lowland Bornean birds along an elevational gradient
<p>Aim: The traditional view of species' distributions is that they are less abundant near the edges of their ranges and more abundant toward the center. Testing this pattern is difficult because of the complexity of distributions across wide geographical areas. An alternative strategy, however, is to measure species' distributional patterns along elevational gradients. We applied this strategy to examine whether lowland forest birds are indeed less common near their upper range limits on a Bornean mountain, and tested co-occurrence patterns among species for potential causes of attenuation, including signatures of habitat selection and competition at the periphery of their ranges.</p> <p>Location: Mt. Mulu, Borneo</p> <p>Taxon: Rain forest birds Methods: We surveyed lowland forest birds on Mt. Mulu (2,376 m), classified their elevation-occupancy distributions using Huisman – Olff – Fresco (HOF) models, and examined co-occurrence patterns of species pairs for signatures of shared habitat patches and interspecific competition.</p> <p>Results: For 39 of 50 common species, occupancy was highest at sea level then gradually declined near their upper range edges, in keeping with a 'rare periphery' hypothesis. With respect to habitat selection, lowland species do not appear to cluster together at sites of patchy similar habitat near their upper range limits; neither are most lowland species segregated from potential montane competitors where ranges overlap.</p> <p>Main conclusions: High relative abundance at sea level implies that species inhabit 'truncated niches' and are not currently near the limits of their fundamental niche, unless unknown critical response thresholds exist. However, indirect effects of increasing temperature predicted under climate change scenarios could still influence lower range limits of lowland species indirectly by altering habitat, precipitation regimes, and competitive interactions. The lack of non-random co-occurrence patterns implies that patchy habitat and simple pairwise species interactions are unlikely to be responsible for upper range limits in most species; diffuse competition across diverse rain forest bird communities could still play a role.</p>
Figure 1 in Conservation in a changing landscape: habitat occupancy of the critically endangered Tennent's leaf-nosed lizard (Ceratophora tennentii) in Sri Lanka
Figure 1. Location of Knuckles forest reserve within Kandy and Matale Districts (left) and the four study sites [two at Riverston (1 and 2), Hunasgiriya (3) and Deanston (4)] within the reserve (right).
Figure 3 in Conservation in a changing landscape: habitat occupancy of the critically endangered Tennent's leaf-nosed lizard (Ceratophora tennentii) in Sri Lanka
Figure 3. Comparison of climatic and structural parameters among the four habitat types during the dry (dashed line) and wet (solid line) seasons. Data from both locations with lizards and random locations are considered in combination. (C = Cardamom plantations, M = Mixed cardamom forests, N = Natural forests, P = Pine plantations.)
Figure 2 in Conservation in a changing landscape: habitat occupancy of the critically endangered Tennent's leaf-nosed lizard (Ceratophora tennentii) in Sri Lanka
Figure 2. Mean number of sightings of Ceratophora tennentii within three habitat types at Knuckles Range, Sri Lanka.
Daily Activity and Nest Occupation Patterns of Fox Squirrels (Sciurus niger) Throughout the Year
<p>The daily distribution of activity has been studied in detail in ground squirrels in the field as well as in the laboratory, but studies of tree squirrels have been few and generally limited to the sampling of behavior of groups of animals. In this study, the authors investigated the general activity and nest occupation patterns of fox squirrels in a natural setting using temperature-sensitive data loggers that measure activity as changes in the microenvironment of the animal. Data were obtained from 25 distinct preparations, upon 13 unique squirrels, totaling 1385 recording days. Fox squirrels exhibited robust daily rhythmicity of locomotor activity, comparable to that of laboratory rats and gerbils. The animals were clearly diurnal, with a predominantly unimodal activity pattern, although individual squirrels occasionally exhibited bimodal patterns, particularly in the spring and summer. Even during the short days of winter (9 hours), the squirrels typically left the nest after dawn and returned before dusk, spending only about 7 hours out of the nest each day. Although the duration of the daily active phase did not change with the seasons, the squirrels exited the nest earlier in the day when the days became longer in the summer and exited the nest later in the day when the days became shorter in the winter, thus tracking dawn along the seasons. During the few hours each day spent outside the nest, fox squirrels seemed to spend most of the time sitting or lying. These findings suggest that fox squirrels may have adopted a slow life history strategy.</p>
Patterns in bird and pollinator occupancy and richness in a mosaic of urban office parks across scales and seasons
<p>Urbanization is a leading cause of global biodiversity loss, yet cities can provide resources required by many species throughout the year. In recognition of this, cities around the world are adopting strategies to increase biodiversity. These efforts would benefit from a robust understanding of how natural and enhanced features in urbanized areas influence various taxa. We explored seasonal and spatial patterns in occupancy and taxonomic richness of birds and pollinators among office parks in Santa Clara County, California, USA, where natural features and commercial landscaping have generated variation in conditions across scales. We surveyed birds and insect pollinators, estimated multi-species occupancy and species richness, and found that spatial scale, season, and urban sensitivity were all important for understanding how communities occupied sites. Features at the landscape- and local-scale (i.e., distance to streams or baylands and tree canopy, shrub, or impervious cover, respectively) were the strongest predictors of avian occupancy in all seasons. The pollinator richness index was influenced by local tree canopy and impervious cover in spring, and distance to baylands in early and late summer. We predicted relative contributions of different spatial scales to annual bird species richness by assigning values to simulated sites representing "good" and "poor" quality, based on influential covariates returned by models. Shifting from poor to good quality conditions locally increased annual avian richness by up to 6.8 species with no predicted effect of the quality of the neighborhood. Conversely, sites of poor local- and neighborhood-scale quality in good quality landscapes were predicted to harbor 11.5 more species than sites of good local- and neighborhood-scale quality in poor quality landscapes. Finally, more urban sensitive bird species were gained at good quality sites relative to urban tolerant species, suggesting that urban natural features at the local- and landscape-scales disproportionately benefited them.</p>
Intraguild interactions and abiotic conditions mediate occupancy of mammalian carnivores: co-occurrence of coyotes-fishers-martens
<p>The widespread eradication of large carnivores and subsequent expansion of top mesopredators have the potential to impact species and community interactions with ecosystem-wide implications. An example of these trophic dynamics is the widespread establishment of coyotes following the extirpation of wolves and mountain lions in eastern North America. Here, we examined the occupancy of three carnivores in northern New York considering both environmental/habitat factors and interspecific interactions. We estimated the co-occurrence of coyotes, fishers, and martens from a landscape-scale winter camera trap survey repeatedly annually for three years. Martens occurred independently of both coyotes and fishers, while fishers and coyotes displayed positive intraguild interactions that were constant across the landscape. Both marten and fisher first-order occupancy was driven by a combination of biotic and abiotic factors, with both species displaying positive associations with forest cover but antithetical responses to average snow depth. The integral and antithetical role of snow depth in driving the occurrence of martens (positive) and fishers (negative) in the landscape indicates that future climatic warming could reduce the availability of current spatial refuges for martens created by severe winter conditions. Climate-driven alterations to established competitive interactions and co-existence patterns between marten and fishers have critical implications for the species' survival and conservation. We provide correlational evidence consistent with the potential for positive top-down effects of dominant mesocarnivores on subordinate species, with fisher occupancy increasing conditional on the presence of coyotes across the landscape. These findings align with the hypothesis that under certain conditions, coyotes may facilitate certain subordinate carnivores. The evidence produced here is consistent with hypotheses on the dynamic nature of trophic niches. We demonstrate the need to consider the interplay between climate, habitat, and interspecific interactions to understand wildlife occupancy patterns and inform wildlife management in a rapidly changing world.</p>
Annual occupancy estimates for butterflies, grasshoppers and dragonflies in Bavaria (Germany), 1980-2019
<p>Recent climate and land-use changes are having substantial impacts on biodiversity, including population declines, range shifts, and changes in community composition. However, few studies have compared these impacts among multiple taxa, particularly because of a lack of standardized time series data over long periods. Existing datasets are typically of low resolution or poor coverage, both spatially and temporally, thereby limiting the inferences that can be drawn from such studies. Here, we compare climate and land-use driven occupancy changes in butterflies, grasshoppers, and dragonflies using an extensive dataset of highly heterogeneous observation data collected in the central European region of Bavaria (Germany) over a 40-year period. Using occupancy models, we find occupancies (the proportion of sites occupied by a species in each year) of 37% of species have decreased, 30% have increased and 33% showed no significant trend. Butterflies and grasshoppers show strongest declines with 41% of species each. By contrast, 52% of dragonfly species increased. Temperature preference and habitat specificity appear as significant drivers of species trends. We show that cold-adapted species across all taxa have declined, while warm-adapted species have increased. In butterflies, habitat specialists have decreased, while generalists increased or remained stable. The trends of habitat generalists and specialists both in grasshoppers and semi-aquatic dragonflies however did not differ. Our findings indicate strong and consistent effects of climate warming across insect taxa. The decrease of butterfly specialists could hint towards a threat from land-use change, as especially butterfly specialists' occurrence depends mostly on habitat quality and area. Our study not only illustrates how these taxa showed differing trends in the past, but also provides hints on how we might mitigate the detrimental effects of human development on their diversity in the future.</p>
Data from: Improving inferences and predictions of species environmental responses with occupancy data
<p>Occupancy models represent a useful tool to estimate species distribution throughout the landscape. Among them, MacKenzie et al.'s model (2002, MC), is frequently used to infer species environmental responses. However, the assumption that detection probability is homogeneous or fully explained by covariates may limit its performance. Species should be more easily observed at sites with a higher number of individuals. We simulated data following Royle and Nichols (2003) occupancy model (RN) that accounts for abundance-driven heterogeneous detection and two variants with overdispersion in the detection probability and local abundances. Then, we compared the performance of the MC model against that of RN.</p> <p> In addition to model misspecifications, insufficient information in data (i.e. infrequent detections) can limit our ability to detect existing effects with affordable sampling designs. To deal with this source of error, we extended RN approach to a community-level joint species model (RN-JSM), where species responses and detectability depended on their traits and phylogeny. Then, we tested RN-JSM performance in simulated and out-of-sample field data.</p> <p>High abundance-driven heterogeneity in detection (i.e. common and secretive species) limited the ability of the MC model to quantify covariate effects; especially, when the number of visits was low. Both models (MC and RN), often failed to detect existing effects when data were overdispersed. Moreover, the RN model consistently lacked sufficient power when analyzing data from uncommon species (even when simulations and model specifications perfectly matched). This problem was solved by our RN-JSM, which yielded more precise and accurate estimates of species environmental responses. Increased accuracy in rare species held when the RN-JSM was tested with real and out-of-sample datasets.</p> <p>In the light of our results, we propose: (i) for common and secretive species analyze occupancy data with the RN model and prioritize revisiting sites; (ii) for species that may have overdispersed detectability or local abundances (e.g. with correlated behaviors or occurring in clusters), apply RN extensions that account for this extra variation (e.g. Poisson-beta or zero-inflated models). Finally, (iii) for uncommon species (mean abundances < 1), whenever possible, gather data at the community level and apply joint-species modeling techniques.</p>
Occupancy model for Rattus spp. in high and low human human refuse supplementation conditions
<p>Globally, the genus <em>Rattus </em>is one of the most influential exotic species due to its high rates of competitive exclusion and large dietary breadth. However, the specific foraging strategies of urban and urban-adjacent populations remain largely unknown. We examined <em>Rattus </em>spp. dependency on human food supplementation in a peri-urban population. Through a natural experiment made possible by the COVID-19 shelter in place order in Santa Cruz California, USA, we measured changes in activity between invasive rats and native rodents with and without human supplementation. We measured invasive rat presence in normal (pre-COVID) conditions near dining halls and similar waste sources, and again under COVID lockdown conditions where all sources of human supplementation were removed. We found a decrease in <em>Rattus </em>presence after the removal of human refuse (p < 0.001), while native small mammal presence remained unchanged. These results have strong conservation implications, as they suggest that proper waste management is an effective, targeted, and less-invasive form of population control over conventional forms of poison. </p>
Data from: Occupancy winners in tropical protected forests: a pantropical analysis
<p class="MsoNormal"><span>The structure of forest mammal communities appears surprisingly consistent across the continental tropics<span>, presumably due to convergent evolution in similar environments. W</span>hether such consistency extends to mammal occupancy, despite variation in species characteristics and context, remains unclear. Here we ask whether we can predict occupancy patterns and, if so, whether these relationships are consistent across biogeographic regions. Specifically, we assessed how mammal feeding guild, body mass and ecological specialization relate to occupancy in protected forests across the tropics. We used standardized camera-trap data (</span><span>1,002 camera-trap locations and 2-10 years of data)</span><span> and a hierarchical Bayesian occupancy model. We found that occupancy varied by regions, and </span><span>certain species characteristics</span><span> explained much of this variation. Herbivores consistently had the highest occupancy. However, only in the Neotropics did we detect a significant effect of body mass on occupancy: large mammals had lowest occupancy. Importantly, habitat specialists generally had higher occupancy than generalists, though this was reversed in the Indo-Malayan sites. We conclude that </span><span>habitat specialization is key for understanding variation in mammal occupancy across regions, and that habitat specialists often benefit more from protected areas, than do generalists. </span><span>The contrasting examples seen in the Indo-Malayan region likely reflect distinct anthropogenic pressures.</span></p>
Predicting potential distributions of large carnivores in Kenya: An occupancy study to guide conservation
<p><span><strong>Aim</strong>:</span><span> Species distribution maps are frequently the foundation upon which species-specific conservation strategies are developed, however, mapping species distribution is challenging, especially across large spatial extents. Our aim was to use a novel empirical approach to predict the national distribution for all six large carnivore species </span><span>found in Kenya to guide conservation and management decisions by identifying knowledge and conservation gaps.</span></p> <p><span><strong>Location</strong>:</span><span> Kenya</span></p> <p><span><strong>Methods</strong>:</span><span> Data on carnivore presence and absence were collected through questionnaires and sightings-based surveys. These data were combined and analysed using single-season false-positive occupancy models, which account for imperfect detections and false positives. </span><span>To inform conservation strategies, </span><span>we used the occupancy outputs to make predictions for unsampled areas and create occupancy-based distribution </span><span>maps, where ψ>0.50, </span><span>to</span><span> (1) quantify differences with IUCN Red List range maps, (2) quantify overlap with wildlife areas and (3) </span><span>identify areas of high carnivore richness</span><span>.</span></p> <p><span><strong>Results</strong>:</span><span> Large carnivore occupancy was associated with land conversion, habitat, and prey availability. Our results suggest that all six species are widely distributed across Kenya and reveal substantial differences in distribution maps compiled by the IUCN Red List. </span><span>More specifically, our occupancy-based distribution maps predict a </span><span>much larger distribution for African wild dog (5.09X), lion (4.77X), and leopard (1.46X), similar distribution for cheetah, and smaller distribution for spotted hyaena (0.84X) and striped hyaena (0.65X). For all large carnivores, the vast majority (~80%) of their predicted distribution falls outside wildlife areas and northern Kenya is predicted to have the highest large carnivore richness.</span></p> <p><span><strong>Main conclusions</strong>:</span> <span>Our results are encouraging as large carnivores may be widely distributed across Kenya, in some cases potentially more so than previously acknowledged. However, much of this range lies outside wildlife areas and represents areas of concern both for conservation and human livelihoods illustrating the challenges of conserving large carnivores across their range.</span></p>
Synthetic Indoor Climate and Occupancy Data from Office and Meeting Room Simulations
<p>This is the dataset used for the publication "Coddora: CO2-based Occupancy Detection model<br>trained via DOmain RAndomization". The goal is to provide training data for occupancy detection.<br><br>The dataset contains one million days of data including 10 occupied days for each of 100,000 randomized room models (50,000 rooms considering office activity and 50,000 meeting room activity). Data were generated in EnergyPlus simulations according to the methodology described in the paper.<br><br>When using the dataset, please cite:</p> <blockquote> <p><em>Manuel Weber, Farzan Banihashemi, Davor Stjelja, Peter Mandl, Ruben Mayer, and Hans-Arno Jacobsen. 2024. Coddora: CO2-Based Occupancy Detection Model Trained via Domain Randomization. In International Joint Conference on Neural Networks (IJCNN). June 30 - July 5, 2024, Yokohama, Japan.</em></p> </blockquote> <h2>Dataset Structure</h2> <p>The following files are provided:<br><br> 1. dataset_office_rooms.h5 (provided as zip file)<br> 2. dataset_meeting_rooms.h5 (provided as zip file)<br> 3. simulated_occupancy_office_rooms.csv<br> 4. simulated_occupancy_meeting_rooms.csv</p> <p>Please use an archiving tool such as 7zip to unzip the hdf5 files.<br>Both hdf5 files contain two datasets with the following keys:<br><br> 1. "<em>data</em>": contains the simulated indoor climate and occupancy data<br> 2. "metadata": contains the metadata that were used for each simulation</p> <p>The csv files contain the time series of occupancy that were used for the simulations.<br><br></p> <h2>Data</h2> <p><em>Data</em> includes the following fields:</p> <p><em>Datetime:</em> day of the year (may be relevant due to seasonal differences) and time of the day<br><em>Zone Air CO2 Concentration:</em> CO2 level in ppm<br><em>Zone Mean Air Temperature:</em> temperature in °C<br><em>Zone Air Relative Humidity: </em>relative humidity in %<br><em>Occupancy: </em>level of occupancy relative to the maximum capacity of the room (in the range [0-1])<br><em>Ventilation:</em> fraction of window opening in the range [0.01, 1]<br><em>SimID:</em> foreign key to reference the room properties the simulation was based on<br><em>BinaryOccupancy:</em> 0 or 1 denoting absence or presence (for binary classification)</p> <p> </p> <p>Example row:</p> <table> <tbody> <tr> <th><em>Datetime</em></th> <th><em>Zone Air CO2 Concentration</em></th> <th><em>Zone Mean Air Temperature</em></th> <th><em>Zone Air Relative Humidity</em></th> <th><em>Occupancy</em></th> <th><em>Ventilation</em></th> <th><em>simID</em></th> <th><em>BinaryOccupancy</em></th> </tr> <tr> <td> <p>10/09 11:21:00</p> </td> <td> <p>1084.5624647371608</p> </td> <td> <p>24.545635909907148</p> </td> <td> <p>41.18393114737054</p> </td> <td> <p>0.7</p> </td> <td> <p>0.0</p> </td> <td>99</td> <td>1</td> </tr> </tbody> </table> <pre> </pre> <h2>Metadata</h2> <p><em>Metadata</em> includes the following fields. <br>Underscores denote that the field was not selected during randomization but calculated from the other values.</p> <p>width: room width in m<br>length: room length in m<br>height: hoom height in m<br>infiltration: infiltration per exterior area in m³/m²s<br>outdoor_co2: co2 concentration in the outdoor air in ppm (set to a random value between [300, 500])<br>orientation: angle between the room's facade orientation and the north direction in degrees<br>maxOccupants: room occupation limit, i.e. the maximum number of occupants<br>_floorArea: floor area in m² (calculated from room dimensions)<br>_volume: room volume in m³ (calculated from room dimensions)<br>_exteriorSurfaceArea: surface area of the facade wall (calculated from room dimensions)<br>_winToFloorRatio: ratio between total window area and floor area (calculated from room model)<br>firstDayUsedOfOccupancySequence: selected starting day in the sequence of occupancy data for rooms with the respective maxOccupants value<br>simID: unique identifier of the simulation to relate between simulation metadata and resulting simulated data</p> <p> </p> <p>Example row:</p> <table> <tbody> <tr> <th>width</th> <th>length</th> <th>height</th> <th>infiltration</th> <th>outdoor_co2</th> <th>orientation</th> <th>maxOccupants</th> <th>_floorArea</th> <th>_volume</th> <th>_exteriorSurfaceArea</th> <th>_winToFloorRatio</th> <th>firstDayOfUsedOccupancySequence</th> <th>simID</th> </tr> <tr> <td>5.481</td> <td>5.190</td> <td>3.264</td> <td>0.000214</td> <td>438.0</td> <td>316.0</td> <td>4.0</td> <td>28.446</td> <td>92.849</td> <td>16.940</td> <td>0.216</td> <td>192</td> <td>0</td> </tr> </tbody> </table> <p> </p> <h2>Occupancy Data</h2> <p>The occupancy data provided through the separate csv files contain the data from the upfront occupancy simulations that the climate simulation was based on. For each level of considered room occupancy limit (maxOccupants), the datasets provide minute values of occupancy throughout 1000 days.</p> <p><em>Datetime, </em><em>Date, </em><em>Timestamp: fictive time of simulated occupancy record (sequences are in 1-minute resolution)</em><br><em>Occupants: number of present occupants</em><br><em>Occupancy: binary occupancy state (0=unoccupied, 1=occupied)</em><br><em>WindowState: binary state of ventilation (0=windows closed, 1=room is ventilated)</em><br><em>maxOccupants: maximum number of occupants considered for the simulated sequence</em><br><em>WindowOpeningFraction: fractional extent to which windows are opened, within the interval [0.01, 1]<br><br></em></p> <p>Example row:</p> <table> <tbody> <tr> <th>Datetime</th> <th>Date</th> <th>Timestamp</th> <th>Occupants</th> <th>Occupancy</th> <th>WindowState</th> <th>maxOccupants</th> <th>WindowOpeningFraction</th> </tr> <tr> <td>2023-01-01 00:00:00</td> <td>2023-01-01</td> <td>1.672531e+09</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0.0</td> </tr> </tbody> </table> <p> </p> <p> </p>
France under German occupation. The German and French administration 1940-1945 – full data
<p>At <a href="http://www.adresses-france-occupee.fr">www.adresses-france-occupee.fr</a>, the GHIP provides an interactive map showing the German and French authorities in France during the German occupation of France between 1940 and 1945. In addition to the historical and current address, the website provides information about the tasks, responsibilities and the structure of each department of the authorities, as well as photos if available. Depending on the type and scope of the query, it gives an impression of everyday life, the presence of the German occupation administration and forces and, last but not least, their cooperation with the French authorities. The information is based on a systematic evaluation of the telephone books of the German authorities and French administration directories from the time of the war. They were completed by research in German and French archives, in particular the <em>Archives municipales</em>, as well as press media, historical map collections and research works.</p> <p>With this entry we give access to the data of this database in four different tables (csv UTF-8 and excel).</p> <p>1) The table "services" relates to the departments (German: <em>Dienststellen</em>). The data is maintained in the state closest to that of the phone directories and address books: a department is valid in a hierarchy at a given address and at a given time. The table contains:</p> <p>· the ID number (id), its linking to its superior department (parent_service_id)</p> <p>· the name of the department (name),</p> <p>· the ID number of the source from which the information originates (source_id),</p> <p>· the ID number of the place where the departments office is located (place_id),</p> <p>· the status on the website (status: visible, pending, deleted),</p> <p>· the display of the hierarchy (d_breadcrumb) and</p> <p>· the last edit of the entry by the project team (last_edit_date).</p> <p>There are 68,044 entries in total.</p> <p> </p> <p>2) The table "service_bridge" is a cross table, relating the departments to each other by showing their hierarchy. IT contains the parent_service_id, the child_service_id, and the breadcrumb, that indicates the department's position in the hierarchy. There are 28,162 entries in total</p> <p> </p> <p>3) The table "places" is used to localise the departments. A place is a geographical point identified by its latitude and longitude. The address (in text format) of this place may have changed over time. The database contains both old and new names. The table contains the ID number of the place (id), the name of the building/accommodation (name), the status of the entry on the website (status: visible, pending, deleted), the current address (current_country, current_zip, current_city, current_street, current_house_number), former address (fromer_street, former_house_number) as well as the longitude and latitude of the place. There are 15,454 entries in total.</p> <p> </p> <p>4) The table "sources_details" relates to the sources used to build the database. By source we understand ‘data source’, i.e. any source used to obtain information on the German and French authorities of the time. We mostly used German and French telephone directories of the years 1940–1944. The table contains:</p> <p>· the ID number of the source (id),</p> <p>· the name of the source in short (name),</p> <p>· the country of publication or origin of the source (country),</p> <p>· the full edition date (edition_date) if available, otherwise approximate,</p> <p>· the year of the publication or year of the source (d_edition_year),</p> <p>· the month of the publication or month of the source (d_edition_month) and</p> <p>· the full bibliographical citation and/or explanations (source_name_complete).</p> <p>There are 57 entries in total.</p> <p>You can get in touch with us via the mail dh [at] dhi-paris.fr</p>
The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty. in Fowler's Toad (Anaxyrus fowleri) occupancy in the southern mid-Atlantic, USA
The Provincelands of Cape Cod National Seashore, Barnstable County, Massachusetts, USA. The reddish vegetation in the center of the photo is a cranberry (Vaccinium macrocarpon) bog, a wetland used for breeding by the Fowler's toad. The surrounding landscape is ideal for the Fowler's toad and supports one of the largest populations of this species in the United States. The landscape contains a patchwork of sand, pitch pine (Pinus rigida), scrub oak (Quercus ilicifolia), and dune grass (Ammophila breviligulata). Photo by Rebecca Flaherty.
ScienceDex guides
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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)
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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.