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1992-93 Parramore Island, VA Permanent Plot Baseline Data : Shrub Data
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1992-93 Parramore Island, VA Permanent Plot Baseline Data : Subplot Water Cover
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Brainport, Platooning, Baseline
<p><strong>Scenario description</strong>:</p> <p>Driving without speed advice or any services to measure base-line.</p> <p><strong>Session description</strong>:</p> <p>Baseline</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>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_Platooning_PlatoonFormation</strong>: Data sent from PlatoonService to vehicle</p> <p>This dataset contains information about the route and speed for a specific vehicle for forming a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningAction</strong>: Data logged by vehicle</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningEvent</strong>: Data logged by vehicle</p> <p>This dataset contains information about the identifiers used for each specific platooning event</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonStatus</strong>: Data sent by vehicle to PlatoonService</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystemResample</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PSInfo</strong>: Data sent by PlatoonService to the vehicle</p> <p>This dataset contains speed and route information for the vehicle to create a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Target</strong>: Data from sensors on the vehicle</p> <p>Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Brainport, Platooning, baseline of platoon formation
<p><strong>Scenario description</strong>:</p> <p>Platoon formation and platooning, from Helmond to Eindhoven and back to the Automotive Campus.<br> - Starting in urban area with speed limits of 15 and 30 km/h.<br> - Driving East on the Europaweg with speed limits of 50 and 70 km/h. This includes 3 crossings with traffic lights.<br> - Driving on the the N270, along the Automotive Campus. One crossing with traffic lights, just before the A270.<br> - Driving on the A270 (speed limit 100 km/h). Interrupted by one traffic light.<br> - U-turn at the fly-over or at the end of the A270, to return the same way to the Automotive Campus.</p> <p><strong>Session description</strong>:</p> <p>Baseline of platoon formation only, up to A270, no platooning. Without any IoT, so no speed advices.<br> - No live traffic light data available for planner<br> - Starting at default locations<br> - Meeting at the location of vehicle2 on the Automotive Campus<br> - No platooning or other automated driving.</p> <p><strong>Datasets descriptions</strong>:</p> <p><strong>AUTOPILOT_BrainPort_Platooning_DriverVehicleInteraction</strong>: Data extracted from the CAN of the vehicle</p> <p>This dataset contains e.g. throttlestatus, clutchstatus, brakestatus, brakeforce, wipersstatus, steeringwheel for the vehicle</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsAbsolute</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with absolute coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_EnvironmentSensorsRelative</strong>: Data extracted from the vehicle environment sensors</p> <p>This dataset contains information about detected object, with relative coordinates</p> <p><strong>AUTOPILOT_BrainPort_Platooning_IotVehicleMessage</strong>: Data sent between all devices, vehicles and services</p> <p>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_Platooning_PlatoonFormation</strong>: Data sent from PlatoonService to vehicle</p> <p>This dataset contains information about the route and speed for a specific vehicle for forming a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningAction</strong>: Data logged by vehicle</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatooningEvent</strong>: Data logged by vehicle</p> <p>This dataset contains information about the identifiers used for each specific platooning event</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PlatoonStatus</strong>: Data sent by vehicle to PlatoonService</p> <p>This dataset contains information about the current status of the platooning</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystem</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed, longitude, latitude, heading from the GPS</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PositioningSystemResample</strong>: Data from GPS on the vehicle</p> <p>This dataset contains speed,longitude,latitude,heading from the GPS, resampled to 100 milliseconds</p> <p><strong>AUTOPILOT_BrainPort_Platooning_PSInfo</strong>: Data sent by PlatoonService to the vehicle</p> <p>This dataset contains speed and route information for the vehicle to create a platoon</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Target</strong>: Data from sensors on the vehicle</p> <p>Target detection in the vicinity of the host vehicle, by a vehicle sensor or virtual sensor</p> <p><strong>AUTOPILOT_BrainPort_Platooning_Vehicle</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o temperature and battery state of the vehicles</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p> <p><strong>AUTOPILOT_BrainPort_Platooning_VehicleDynamics</strong>: Data from the CAN and sensors about the state of the vehicle</p> <p>This dataset contains a.o accelerations and speedlimit of the vehicle, as observed from the CAN and the external sensors</p>
Controls on range shifts of coastal Californian bivalves during the peak of the last interglacial and baseline predictions for today
<p>As the most recent time in Earth history when global temperatures were warmer than at present, the peak of the last interglacial (Marine Isotope Substage [MIS] 5e; ~120,000 years ago) can serve as a pre-anthropogenic baseline for a warmer near-future world. Here we use a new compilation of 22 fossil localities in California that have been reliably dated to Marine Isotope Stage (MIS) 5e to establish baseline expectations for contemporary bivalve species movements by identifying and analyzing bivalve species with "extralimital" ranges, i.e. species that occupied the California region during MIS 5e but are now restricted to adjacent regions. We find that 15% of species (n = 142) found in MIS 5e localities have extralimital ranges and currently occupy warmer waters to the south of the California region. The majority of extralimital occurrences occur in paleo-embayments, suggesting that these sheltered habitats were more suitable habitats for warm-water species than exposed coasts during the MIS 5e. We further find that extralimital species now tend to occur in cooler, more seasonally productive coastal waters and to occupy more offshore islands when compared to the broader species pool immediately south of California. These findings suggest that high dispersal potential and pre-existing tolerances to environmental conditions similar to California's comparatively cool and seasonally productive environments may have enabled extralimital bivalves to colonize the California region during MIS 5e.</p>
Data from: Seasonal differences in baseline innate immune function are better explained by environment than annual cycle stage in a year-round breeding tropical songbird
1. Seasonal variation in innate immunity is often attributed to either temporal environmental variation or to life history trade-offs that arise from specific annual cycle stages but decoupling them is difficult in natural populations. 2. Here, we effectively decouple seasonal environmental variation from annual cycle stage effects by exploiting cross-seasonal breeding and moult in the tropical Common Bulbul Pycnonotus barbatus. We test how annual cycle stage interacts with a key seasonal environmental variable, rainfall, to determine immunity at population and individual level. If immune challenge varies with precipitation, we might expect immune function to be higher in the wet season due to increased environmental productivity. If breeding or moult imposes resource constraints on birds, depending on or independent of precipitation, we might expect lower immune indices during breeding or moult. 3. We sampled blood from 818 birds in four annual cycle stage categories: breeding, moult, simultaneous breeding and moulting, or neither. We quantified indices of innate immunity (haptoglobin, nitric oxide (NOx) and ovotransferrin concentrations, and haemagglutination and haemolysis titres) over two annual cycles of wet and dry seasons. 4. Environment (but not annual cycle stage or interactions between both) explained variation in all immune indices, except NOx. NOx concentration differed between annual cycle stages but not between seasons. However, within the wet season, haptoglobin, NOx, ovotransferrin and haemolysis differed significantly between breeding and non-breeding females. Aside from some recorded inconsistences, population level results were largely similar to results within individuals that were measured repeatedly. Unexpectedly, most immune indices were higher in the dry season and during breeding. 5. Higher immune indices may be explained if fewer or poorer quality resources force birds to increase social contact, thereby exposing individuals to novel antigens and increased infection risk, independently of environmental productivity. Breeding birds may also show higher immunity if less immune-competent and/or infected females omit breeding. We conclude that seasonal environmental variation impacts immunity more directly in natural animal populations than via resource trade-offs. In addition, immune indices were more often variable within than among individuals, but some indices are characteristic of individuals, and so may offer selective advantages if heritable.
Data from: Predictors of alcohol consumption among in-school adolescents in the Central Region of Ghana: a baseline information for developing cognitive-behavioural interventions
Background and purpose: Despite a recent shift in school going adolescents' engagement in health compromising behaviours has serious socio-economic implications on developing societies, it is surprising that baseline information through research that would influence planned interventions is sparse. The purpose of this study was to investigate the prevalence of alcohol drinking behaviours among in-school adolescents in the Junior High Schools (JHS) in the Central Region of Ghana. Methods and results: Using a descriptive cross-sectional design, multistage sampling procedures were used to sample 1400 school going adolescents in JHS in the Central Region. Preliminary results using simple frequencies and percentages revealed 42% alcohol drinking prevalence in the region. High prevalence of drunkenness (73%, n = 405) and early exposure to alcohol drinking when students were in primary school (52%, n = 286) were noted. Community festivals and use of alcohol as a form of medicine were enabling factors of alcohol consumption in the region. Binary logistic regression analysis also showed that geographical location was a significant predictor of alcohol drinking among school going adolescents, with students in the southern and central part of the region at greater risks of drinking alcohol than those from the northern part (OR = .696, 95% CI = 0.52-926, p = .013). However, no statistical significant variations were found in the odds of drinking alcohol within age (OR = 1.13, 95% CI = 0.86-1.48, p = .370), gender (OR = .81, 95% CI = 0.65-1.01, p = .06), religious affiliation (OR = 1.33, 95% CI = 0.94-1.89, p = .10), parental communication (OR = .86, 95% CI = 0.66-1.06, p = .13), academic performance (OR = 1.07, 95% CI = 0.79-1.45, p = .05) and socioeconomic status (OR = 1.20, 95% CI = 0.95-1.53, p = .12). Conclusions: With this baseline data, it was recommended that schools' curricula should include preventive cognitive-behavioural interventions that teach drug resistance skills and anti-drug norms. These interventions would foster the development of requisite knowledge and social skills (e.g., developing competence) for resisting social and peer influences that may trigger alcohol use and perhaps other drugs. Potentially, the motivation for alcohol use among school going adolescents in the region would be minimized, if not prevented.
Data from: Shark tooth weapons from the 19th century reflect shifting baselines in Central Pacific predator assemblies
The reefs surrounding the Gilbert Islands (Republic of Kiribati, Central Pacific), like many throughout the world, have undergone a period of rapid and intensive environmental perturbation over the past 100 years. A byproduct of this perturbation has been a reduction of the number of shark species present in their waters, even though sharks play an important in the economy and culture of the Gilbertese. Here we examine how shark communities changed over time periods that predate the written record in order to understand the magnitude of ecosystem changes in the Central Pacific. Using a novel data source, the shark tooth weapons of the Gilbertese Islanders housed in natural history museums, we show that two species of shark, the Spot-tail (Carcharhinus sorrah) and the Dusky (C. obscurus), were present in the islands during the last half of the 19th century but not reported in any historical literature or contemporary ichthyological surveys of the region. Given the importance of these species to the ecology of the Gilbert Island reefs and to the culture of the Gilbertese people, documenting these shifts in baseline fauna represents an important step toward restoring the vivid splendor of both ecological and cultural diversity.
Data from: Macroevolutionary patterning in glucocorticoids suggests different selective pressures shape baseline and stress-induced levels
Glucocorticoid (GC) hormones are important phenotypic mediators across vertebrates, but their circulating concentrations can vary markedly. Here we investigate macroevolutionary patterning in GC levels across tetrapods by testing seven specific hypotheses about GC variation, and evaluating whether the supported hypotheses reveal consistent patterns in GC evolution. If selection generally favors the "supportive" role of GCs in responding effectively to challenges, then baseline and/or stress-induced GCs may be higher in challenging contexts. Alternatively, if selection generally favors "protection" from GC-induced costs, GCs may be lower in environments where challenges are more common or severe. The predictors of baseline GCs were all consistent with supportive effects: levels were higher in smaller organisms, and in those inhabiting more energetically demanding environments. During breeding, baseline GCs were also higher in populations and species with fewer lifetime opportunities to reproduce. The predictors of stress-induced GCs were instead more consistent with the protection hypothesis: during breeding, levels were lower in organisms with fewer lifetime reproductive opportunities. Overall, these patterns indicate a surprising degree of consistency in how some selective pressures shape GCs across broad taxonomic scales; at the same time, in challenging environments selection appears to operate on baseline and stress-induced GCs in distinct ways.
Data from: Baseline and stress-induced corticosterone levels are heritable and genetically correlated in a barn owl population
The hypothalamic-pituitary-adrenal (HPA) axis is responsible for the regulation of corticosterone, a hormone that is essential in the mediation of energy allocation and physiological stress. As a continuous source of challenge and stress for organisms, the environment has promoted the evolution of physiological adaptations and led to a great variation in corticosterone profiles within or among individuals, populations and species. In order to evolve via natural selection, corticosterone levels do not only depend on the strength of selection exerted on them but also on the extent to which the regulation of corticosterone is heritable. Nevertheless, heritability of corticosterone profiles in wild populations is still poorly understood. In this study, we estimated the heritability of baseline and stress-induced corticosterone levels in barn owl (Tyto alba) nestlings from 8 years of data, using a multivariate animal model based on a behavioural pedigree. We found that baseline and stress-induced corticosterone levels are strongly genetically correlated (r = 0.68 – 0.80) and that the heritability of stress-induced corticosterone levels (h2 = 0.24 – 0.33) was moderate and similar to the heritability of baseline corticosterone levels (h2 = 0.19 – 0.30). These findings suggest that the regulation of stress-induced corticosterone and baseline levels evolve at a similar pace when selection acts with the same intensity on both traits, and that contrary to previous studies, the evolution of baseline and stress-induced level is interdependent in barn owls, as they may be strongly genetically correlated.
Data from: Baseline immune activity is associated with date rather than with moult stage in the Arctic-breeding barnacle goose (Branta leucopsis)
Variation in immune defence in birds is often explained either by external factors such as food availability and disease pressure or by internal factors such as moult and reproductive effort. We explored these factors together in one sampling design by measuring immune activity over the time frame of the moulting period of Arctic-breeding barnacle geese (Branta leucopsis). We assessed baseline innate immunity by measuring levels of complement-mediated lysis and natural antibody-mediated agglutination together with total and differential leukocyte counts. Variation in immune activity during moult was strongly associated with calendar date and to a smaller degree with the growth stage of wing feathers. We suggest that the association with calendar date reflected temporal changes in the external environment. This environmental factor was further explored by comparing the immune activity of geese in the Arctic population with conspecifics in the temperate climate zone at comparable moult stages. In the Arctic environment, which has a lower expected disease load, geese exhibited significantly lower values of complement-mediated lysis, their blood contained fewer leukocytes, and levels of phagocytic cells and reactive leukocytes were relatively low. This suggests that lower baseline immune activity could be associated with lower disease pressure. We conclude that in our study species, external factors such as food availability and disease pressure have a greater effect on temporal variation of baseline immune activity than internal factors such as moult stage.
Data from: Cough frequency during treatment associated with baseline cavitary volume and proximity to the airway in pulmonary TB
Background: Cough frequency, and its duration, is a lab-free biomarker that can be used in low-resource settings and has been associated with transmission and treatment response. Radiological characteristics associated with increased cough frequency may be important in understanding transmission. The relationship between cough frequency and cavitary lung disease has never been studied. Methods: We analyzed 41 human immunodeficiency virus-negative adults with culture-confirmed, drug-susceptible pulmonary tuberculosis throughout treatment. Cough recordings were based on the Cayetano Cough Monitor and sputum samples were evaluated using microscopic-observation drug susceptibility broth culture, among culture-positive samples bacillary burden was assessed by time to positivity. Computerized tomography scans were analyzed by a U.S. board-certified radiologist and an automated-computer algorithm. The algorithm evaluates cavity volume and cavitary proximity to the airway. Computerized tomography scans were taken within one month of treatment initiation. We compared small cavities (≤7-mL) versus large cavities (>7-mL) and cavities located closer to (≤10-mm) and farther (>10-mm) from the airway to cough frequency and cough cessation until treatment day 62. Results: Cough frequency during treatment was two-fold higher in participants with large cavity volumes (Rate Ratio [RR]=1.98, p=0.01) and cavities located closer to the airway (RR=2.44, p=0.001). Comparably, cough ceased three times faster in smaller cavities (adjusted hazard ratio [HR]=2.89, p=0.06) and those farther from the airway (adjusted HR=3.61, p=0.02). Similar results are found for bacillary burden and culture conversion during treatment. Conclusions: Cough frequency during treatment is greater and lasts for longer in patients with larger cavities, especially those closer to the airway.
Data from: Historical baselines and the future of shell calcification for a foundation species in a changing ocean
Seawater pH and the availability of carbonate ions is decreasing due to anthropogenic carbon dioxide emissions, posing challenges for calcifying marine species. Marine mussels are of particular concern given their role as foundation species worldwide. Here, we document shell growth and calcification patterns in Mytilus californianus, the California mussel, over millennial and decadal scales. By comparing shell thickness across the largest modern shells, the largest mussels collected in the 1960s-1970s and shells from two Native American midden sites (1000-2420 years BP), we found that modern shells are thinner overall, thinner per age category, and thinner per unit length. Thus, the largest individuals of this species are calcifying less now than in the past. Comparisons of shell thickness in smaller individuals over the past 10-40 years, however, do not show significant shell thinning. Given our sampling strategy, these results are unlikely to simply reflect within-site variability or preservation effects. Review of environmental and biotic drivers known to affect shell calcification suggest declining ocean pH as a likely explanation for the observed shell thinning. Further future decreases in shell thickness could have significant negative impacts on M. californianus survival and, in turn, negatively impact the species rich complex that occupies mussel beds.
Myotis nattereri baseline trajectory
<p>Myotis nattereri baseline trajectory x(t), y(t), z(t)</p>
Pipistrellus pipistrellus baseline trajectory
<p>Pipistrellus pipistrellus baseline trajectory x(t), y(t), z(t)</p>
SIASAR databases: Nicaragua (Baseline), Honduras, Panama and Dominican Republic (last accessed: December 21, 2016)
<p>Databases contain the rural water and sanitation data collected and approved by SIASAR community. All data is available at www.siasar.org (last accessed: December 21, 2016)</p> <p>Each country's database contains four files *csv:<br> <br> i) The Water System ("_SIS") contains data related to the water infrastructure;<br> <br> ii) The Service Provision ("_PSE") contains data related to the water service provider;<br> <br> iii) The Community ("_COM") contains general data of the community and specific data related to the provision of water, sanitation and hygiene services.<br> <br> iv) The System - Provider - Community ("_SISCOMPSE") contains data needed to link the water system, the service provider and the community. </p> <p>These files are prepared to be exploited in the construction of SIASAR composite indicators v1, as described in http://hdl.handle.net/2117/77587</p>
Baseline models and optimized CNN models for 8 datasets
<p>Datasets:</p> <ul> <li>Asirra</li> <li>CIFAR-10</li> <li>CIFAR-100</li> <li>GTSRB</li> <li>HASYv2</li> <li>MNIST</li> <li>STL-10</li> <li>SVHN</li> </ul>
Supplementary Figure 1 QQ plot of rate of decline from baseline in ODI score (ODI1: 7D, ODI2: 14D, ODI3: 21D, ODI4: 30D).
<p><span>QQ plot of rate of decline from baseline in ODI score (ODI1: 7D, ODI2: 14D, ODI3: 21D, ODI4: 30D).</span></p>
DCASE 2024 Task 9: Language-Queried Audio Source Separation | Pre-trained Weights for the Baseline System
<p><strong>== Descriptions ==</strong></p> <p>We trained the AudioSep [1] model using the <a href="../records/10887496">development set</a> (Clotho and augmented FSD50K datasets) for 200k steps with a batch size of 16 using one Nvidia A100 GPU (around 1 day). Model details can be found in the <a href="https://arxiv.org/abs/2308.05037">AudioSep paper</a>.</p> <p>Pre-trained weights for the baseline system:</p> <ul> <li>audiosep_16k,baseline,step=200000.ckpt</li> </ul> <p>Baseline codebase:</p> <ul> <li>GitHub: <a href="https://github.com/Audio-AGI/dcase2024_task9_baseline">https://github.com/Audio-AGI/dcase2024_task9_baseline</a></li> </ul> <p><strong>== Reference ==</strong></p> <p>[1] Liu X, Kong Q, Zhao Y, et al. Separate anything you describe. arXiv:2308.05037, 2023.</p> <p><strong>== Contact ==</strong></p> <p>Xubo Liu, xubo.liu@surrey.ac.uk</p>
Fig. 5. Dendrogram generated from PATN analysis using Gower association measure and baseline dataset comprising 24 samples and 161 in Simonachne, a new genus for Australia segregated from Ancistrachne s.l. (Poaceae: Panicoideae: Paniceae) and a new subtribe Cleistochloinae
Fig. 5. Dendrogram generated from PATN analysis using Gower association measure and baseline dataset comprising 24 samples and 161 morphological characters. Three main clusters were resolved, viz. subtribes Cleistochloinae and subtribe Neurachninae sensu Clayton and Renvoize (1986) and 'paniculate inflorescence group' and 'paniculate inflorescence group'. Classification strategy set at flexible UPGMA agglomerative hierarchical fusion technique with Beta = −0.10. Size of symbols and letters indicates depth of field.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.