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T a b l e 4 in Changes In The Trophic Structure Of The Vertebrate Predator Community In The Cold Season In Belarussian Paazerje (Northern Belarus) With Emphasis On Depopulation Of The Wild Boar, Sus Scrofa (Artiodactyla, Suida)
T a b l e 4. Dietary overlaps (the Morisita's index) between vertebrate predators in the cold season in coniferous-small-leaved forests of Belarussian Paazerje, Northern Belarus, upper right corner — before a depopulation of the Wild Boar (1982–2011), bottom left corner — aft er a large-scale depopulation of the Wild Boar (2013–2019)
Fig. 2 in Changes In The Trophic Structure Of The Vertebrate Predator Community In The Cold Season In Belarussian Paazerje (Northern Belarus) With Emphasis On Depopulation Of The Wild Boar, Sus Scrofa (Artiodactyla, Suida)
Fig. 2. Th e Golden and White-tailed Eagles feed regularly on carrion and physical interference takes place quite often.
Fig. 1 in Changes In The Trophic Structure Of The Vertebrate Predator Community In The Cold Season In Belarussian Paazerje (Northern Belarus) With Emphasis On Depopulation Of The Wild Boar, Sus Scrofa (Artiodactyla, Suida)
Fig. 1. Dietary similarity of 17 vertebrate predators in the cold season in Belarussian Paazerje, 1972–2012.
Fig. 4 in Changes In The Trophic Structure Of The Vertebrate Predator Community In The Cold Season In Belarussian Paazerje (Northern Belarus) With Emphasis On Depopulation Of The Wild Boar, Sus Scrofa (Artiodactyla, Suida)
Fig. 4. Dietary similarity of 10 vertebrate predators in the cold season in Belarussian Paazerje, 2013–2019.
Fig. 3 in Spatial Patterns Of Bird Communities Of The Lower Dnieper Sands During The Breeding Season: Differentiation Factors
Fig. 3. The abundance (the mean number of individuals per sample) of campophilous and dendrophilous birds in groups of samples A (Clusters I–IV) and B (Clusters V–VI). N o t e. The central line represents median, the lower and upper limits of the rectangle — the first and third quartile respectively, "whiskers" — ± 1.5 of interquartile range; circles — outliers.
Fig. 1 in Spatial Patterns Of Bird Communities Of The Lower Dnieper Sands During The Breeding Season: Differentiation Factors
Fig. 1. The scheme of the study area. Аrenas of Low-Dnieper Sands: A — Kakhovska; B — Kozachelaherska; C — Oleshkivska; D — Chalbaska; E — Zburivska; F — Ivanivska; G — Kinburn Peninsula. N o t e. The first and the last sample of each census route are marked by numbers; the numbering of samples is the same as in table 1.
Fig. 1 in The Seasonal Population Dynamics Of The Cyclopoid Copepods (Cyclopoida, Cyclopidae) In Ponds Of Kyiv Region (Ukraine)
Fig. 1. Seasonal population dynamics of the cyclopids in the pond near the village Khotov: 1 — abundance of cyclopids; 2 — water temperature of pond near the village Khotov during the period of the study.
Fig. 1 in Aquatic Heteroptera Of Great Rivers Of The Ukrainian Steppe Zone And Seasonal Changes Of Abundance And Biomass
Fig. 1. Seasonal changes of abundance (A, ind./m2) and biomass (B, mg/m2) of water bugs in the Dniester and associated water bodies.
Fig. 4 in Spatial Patterns Of Bird Communities Of The Lower Dnieper Sands During The Breeding Season: Differentiation Factors
Fig. 4. The area ratio of different types of habitats on standard test plots in the groups of samples.
Fig. 1 in Avian Communities Of A Mixed Mopane-Acacia Savanna In The Cuvelai Drainage System, North-Central Namibia, During The Dry And Wet Season
Fig. 1. Seasonal changes in percentage contribution of main feeding guilds in avian assemblage in mixed Mopane-Acacia savanna (F — frugivores, G — granivores, I — insectivores, O — other guilds).
Fig. 1 in Seasonal Changes In Species Diversity And Dominance Structure In Communities Of Oribatid Mites (Sarcoptiformes, Oribatei) In Megalopolis Green Areas
Fig. 1. Cluster analysis of oribatid species diversity in studied plots at April–September 2011 (plot indexes are given in Material and methods).
Fig. 2 in Seasonal Changes In Species Diversity And Dominance Structure In Communities Of Oribatid Mites (Sarcoptiformes, Oribatei) In Megalopolis Green Areas
Fig. 2. Seasonal fluctuations of numbers of registered species, mean aerial daytime temperature and relative humidity (iv — April, v — May, vi — June, vii — July, viii — August, ix — September).
Seasonal analysis comparison of three air-cooling systems in terms of thermal comfort, air quality and energy consumption for school buildings in Mediterranean climates
<p>Efficient air-cooling systems for hot climatic conditions, such as Southern Europe, are required in the context of nearly Zero Energy Buildings, nZEB. Innovative air-cooling systems such as regenerative indirect evaporative coolers, RIEC and desiccant regenerative indirect evaporative coolers, DRIEC, can be considered an interesting alternative to direct expansion air-cooling systems, DX. The main aim of the present work was to evaluate the seasonal performance of three air-cooling systems in terms of air quality, thermal comfort and energy consumption in a standard classroom. Several annual energy simulations were carried out to evaluate these indexes for four different climate zones in the Mediterranean area. The simulations were carried out with empirically validated models. The results showed that DRIEC and DX improved by 29.8% and 14.6% over RIEC regarding thermal comfort, for the warmest climatic conditions, Lampedusa and Seville. However, DX showed an energy consumption three and four times higher than DRIEC for these climatic conditions, respectively. RIEC provided the highest percentage of hours with favorable indoor air quality for all climate zones, between 46.3% and 67.5%. Therefore, the air-cooling systems DRIEC and RIEC have a significant potential to reduce energy consumption, achieving the user’s thermal comfort and improving indoor air quality.</p>
A historic global ground-based monthly seasonal aerosol climatology based in AERONET data: a database 1993-2013
<table class="ds-includeSet-table detailtable table table-striped table-hover"> <tbody> <tr class="ds-table-row odd "> <td class="metadata-key label-cell" title="dc.description.abstract"> </td> <td class="metadata-field word-break">We present an aerosol classification based upon AERONET level 2.0 almucantar retrieval products from the period 1993 to 2012. In the initial phase of this research we opto-physically identified five major types of Bulk Columnar Aerosol (BCA) - based solely upon intensive optical properties of spectral Single Scattering Albedo (SSA), spectral Indices of Refraction (real – RRI and imaginary - IRI), and two Angstrom Exponents (extinction – EAE and absorption - AAE). These BCA we classified as Maritime Aerosol, Dust Aerosol, Urban Industrial Aerosol, Biomass Burning Aerosol, and Mixed Aerosol. The classification of a particular observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance (MD) to the centroid of each reference cluster (itself a 5-D hyperellipsoid). To retain a greater number of AERONET sites in the study (200+), we kept the variable space to 5-D. To generate reference clusters, we only retained data points that lie within 2 MD from the data centroid. Our typology is based on AERONET retrieved quantities, which do not include low optical depth values (AOD=440nm < 0.4 as per AERONET criteria for almucantar scan inversion). The classifications obtained will be useful in interpreting aerosol retrievals from satellite borne instruments and as input for regional climate models. The result is a dataset describing the types of aerosol particles that are distinct from one another in optical properties, and a geographic distribution of those aerosol types. We used the typology scheme upon the qualifying AERONET data archive, and produced seasonal aerosol climatologies by aerosol type for each of the AERONET sites included in the study, regional aerosol climatology maps, and a time-integrated global aerosol climatology map based entirely upon ground-based photometric data. An internally hyperlinked compendium of the individual AERONET site aerosol climatologies was produced to contain the results of the first phase of this work [available at https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf]. Each of these five aerosol types can be further discriminated into specific sub-types by this same scheme. For example, optical discrimination into specific sub-types of Biomass Burning aerosol may provide insight into sources exhibiting spectrally distinct smoke properties. We then use the mathematical strategies to sort the global AERONET data retrievals into the aerosol type classified against the reference standards. We believe these strategies regarding aerosol differentiation using polarization data will be useful for analysis of the newer AERONET version 3 data retrievals, and data collected from the deployment of newer CIMEL sun-photometers (with enhanced polarization measurement capabilities) to the network. The resulting AERONET-based aerosol typology is useful for applications in aerosol optics, including forward modeling or radiative transfer for remote sensing algorithms, or evaluating radiative forcing calculations in atmospheric models.</td> </tr> </tbody> </table> <p>Necessary Reference Material:</p> <p><span><span><span><span><span><span><span><span><span><span>[1] Giordano, M. E.,<em> </em><em>On Interactions of Matter and Energy: Light and Particles in a Terrestrial Atmosphere Progress on Opto-Physical Recognition and Classification of Aerosols: </em>A PhD dissertation, University of Nevada, copyright M.E. Giordano, 294 pages, December 2019. URI: <a href="http://hdl.handle.net/11714/6686" title="http://hdl.handle.net/11714/6686">http://hdl.handle.net/11714/6686</a></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://scholarworks.unr.edu/handle/11714/6686?show=full" title="https://scholarworks.unr.edu/handle/11714/6686?show=full">https://scholarworks.unr.edu/handle/11714/6686?show=full</a></span></span></span></span></span></span></span></span></span></span></p> <p>[2]<span><span><span><span><span><span><span><span><span><span> Giordano, M.E., Ward, C.S., and Hamill, P.: <em>A Compendium of Aerosol Types Based on Mahalanobis Distances and AERONET data. </em>[An internally hyperlinked compendium of seasonal aerosol and local aerosol compositions] Atmospheric Environment, 140, 213-233,2016. </span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><a href="https://doi.org/10.1016/j.atmosenv.2016.06.002" title="https://doi.org/10.1016/j.atmosenv.2016.06.002">https://doi.org/10.1016/j.atmosenv.2016.06.002</a></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf" title="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span>[3] </span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span>Hamill, P. J., Giordano, M. E., Ward, C.S., Giles, D., Holben, B.: <em>An AERONET - based aerosol</em></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><em> classification using the Mahalanobis distance,</em> Atmospheric Environment, Volume 140, September, pgs 213 -233, 2016. <a href="http://dx.doi.org/10.1016/j.atmosenv.2016.06.002">http://dx.doi.org/10.1016/j.atmosenv.2016.06.002</a>and also at</span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span>[4] Hamill, Patrick, Piedra, Patricio G., Giordano, Marco, E., 2020: <em>Simulated Polarization as a Signature of Aerosol Type</em>. Atmospheric Environment, Volume 224, 117348 article ATMENVD- 19-01763, 2020. </span></span></span></span></span></span></span></span></span></span><a href="https://doi.org/10.1016/j.atmosenv.2020.117348" title="Persistent link using digital object identifier">https://doi.org/10.1016/j.atmosenv.2020.117348</a></p> <p><span><span><span><span><span><span><span><span><span><span> </span></span></span></span></span></span></span></span></span></span></p>
Data and code for: Impacts of changing snowfall on seasonal complementarity of hydroelectric and solar power
<p>Data and code to reproduce analyses in manuscript entitled: Influence of changing snowfall on seasonal complementarity of hydroelectric and solar power. Submitted to Environmental Research: Infrastructure and Sustainability.</p> <p>The contents include the following scripts and files, listed below. Scripts are listed in the order needed to reproduce the analysis, though intermediate data products have been saved so it is not necessary to reproduce the initial analytical steps.</p> <ul> <li>R/ <ul> <li>eia923_860.R: extracts solar and hydropower production data; requires local download of EIA data.</li> <li>gridMET_swep.R: downloads and summarises gridmet data; does not require prior local download.</li> <li>fdr.R: function to calculate the p-value associated with a given false discovery rate as described in the associated manuscript.</li> <li>combine_data.R combines solar, hydropower, and SWE/P data</li> <li>analysis.Rmd: primary script in which analyses are conducted</li> </ul> </li> <li>data/ <ul> <li>annual_swep.csv: output from gridMET_swep.R with annual SWE/P for each watershed in the study</li> <li>monthly_hydro.csv: output from eia923_860.R</li> <li>monthly_solar.csv: output from eia923_860.R</li> <li>combined_variables.csv: combines variables above in one CSV</li> <li>watersheds_wbd_ss: shapefiles for watersheds that drain to each dam used in the study, derived as described in the manuscript.</li> </ul> </li> </ul>
Data supporting: Success of post-fire plant recovery strategies varies with shifting fire seasonality
<p><span>Wildfires are increasing in size and severity and fire seasons are lengthening, largely driven by climate and land use change.</span> <span>Many plant species from fire prone ecosystems are adapted to specific fire regimes corresponding to historical conditions and shifts beyond these bounds may have severe impacts on vegetation recovery and long-term species persistence</span><span>. Here, we conduct a meta-analysis of field-based studies across different vegetation types and climate regions to investigate how post-fire plant recruitment, reproduction and survival are affected by fires that occur outside of the historical fire season. We find that fires outside of the historical fire season may lead to decreased post-fire recruitment for many species, particularly obligate seeding species. Conversely, we find a general increase of post-fire survival in resprouting species. </span><span>Our results highlight the trade-offs that exist when considering the effects of changes in the seasonal timing of fire, an already present aspect of climate-related global fire regime change. </span></p>
Data and code from: Invasive grass indirectly alters seasonal patterns in seed predation
<p>Invasive species threaten ecosystems globally, but their impacts can be cryptic when they occur indirectly. Invader phenology can also differ from that of native species, potentially causing seasonality in invader impacts. Yet, it is unclear if invader phenology can drive seasonal patterns in indirect effects. We used a field experiment to test if an invasive grass (<em>Imperata cylindrica</em>) caused seasonal indirect effects by altering rodent foraging and seed predation patterns through time. Using seeds from native longleaf pine (<em>Pinus palustris</em>), we found seed predation was 25% greater, on average, in invaded than control plots, but this effect varied by season. Seed predation was 24% - 157% greater in invaded plots during spring and fall months, but invasion had no effect on seed predation in other months. One of the largest effects occurred in October when longleaf pine seeds are dispersed, suggesting potential effects on tree regeneration. Thus, seasonal patterns in indirect effects from invaders may cause underappreciated impacts on ecological communities.</p>
Effects of season length and uniparental care efficiency on the evolution of parental care
<p>Parental care patterns differ enormously among and even within species. In Chinese penduline tits (<em>Remiz pendulinus</em>), for example, biparental care, female-only care, male-only care, and biparental desertion all occur in the same population; moreover, the distribution of care patterns differs systematically between populations. By means of an individual-based model, we show that such diversity can readily evolve. We report five main findings. First, under a broad range of parameters, different care patterns (e.g. male care and biparental care) coexist at equilibrium. Second, for many parameters, alternative evolutionary outcomes are possible; this can explain differences in care patterns across populations. Third, rapid evolutionary transitions can occur between alternative equilibria; this can explain the often-reported evolutionary lability of parental care patterns. Fourth, season length has a strong but non-monotonic effect on the evolved care patterns. Fifth, when uniparental care efficiency is low, biparental care tends to evolve; however, in many scenarios uniparental care is still common at equilibrium. Our study sheds new light on Triver's hypothesis that the sex with the highest pre-zygotic investment is predestined to invest a lot post-zygotically as well. We also discuss the implications of climate change, which simultaneously affects season length and efficiency of parental care.</p>
Data from: Seasonality, body size and maturation time in the neotropical grasshopper Sphenarium histrio across an altitudinal gradient
<p>In insects, male mating success and female fecundity usually increase with body size. However, natural selection favors faster maturation, reducing the risk of pre-reproductive death when the reproductive season is short in habitats located at high altitudes or far from the equator. Also, if males that mature earlier than females under these conditions increase their mating opportunities, protandry may evolve in their populations. Nonetheless, since body size is strongly correlated with maturation time in insects, a faster sexual maturation is reached at the expense of having a small body size. We analyzed the differences in the adult body size of males and females of the grasshopper Sphenarium histrio in three sites across an altitudinal gradient in southern Mexico. We also evaluated the possibility of protandry in these sampling sites using a common garden experiment. Male and female grasshoppers collected from low altitude sites in the field and reared in the laboratory were larger than those from a high altitude, suggesting genetic differentiation. Grasshoppers from a high altitude hatched earlier, had a shorter development time, presented fewer instars, and were smaller than grasshoppers from the other sampling sites. Moreover, development time in the three sampling sites was shorter in males than in females, suggesting protandry. Interestingly, the males from the three sites showed similar growth rates, but the females from low and high altitudes, respectively, had the fastest and slowest growth rates. In general, the adaptive value of the evolution of protandry has been focused on males. However, it may be that the growth rates of females in these sites could modify the degree of protandry as a response to their risk of pre-reproductive death and the potential benefits associated with multiple matings.</p> <p>The xlsx file contains the data for all the statistical analyses.</p>
BirdVox-full-season: 6672 hours of audio from migratory birds
<p><strong>BirdVox-full-season: 6672 hours of audio from migratory birds</strong></p> <p><strong>================================================</strong></p> <p>Version 1.1, May 2022.</p> <p> </p> <p>The full-season dataset contains 6671 hours of audio from the Fall 2015 migration season in Tompkins County, NY.</p> <p> </p> <p><strong>Created By</strong></p> <p><strong>---------------</strong></p> <p>Andrew Farnsworth (1), Benjamin Mark Van Doren (1), Steve Kelling (1), Vincent Lostanlen (2), Justin Salamon (3), Aurora Cramer (4), Juan Pablo Bello (4)</p> <p>(1): Cornell Lab of Ornithology (CLO)</p> <p>(2): Laboratoire des Sciences du Numérique de Nantes (LS2N), CNRS</p> <p>(3): Adobe Research</p> <p>(4): New York University</p> <p>https://wp.nyu.edu/birdvox</p> <p> </p> <p><strong>Data acquisition</strong></p> <p><strong>--------------------</strong></p> <p>In 2015, we placed nine bioacoustic sensors - Recording and Observing Bird Identification Node (ROBIN) developed by the Cornell Lab of Ornithology - in residential areas of Tompkins County, NY, USA, primarily surrounding the town of Ithaca, NY, USA. All sensors had the same hardware configurationcomprising a Knowles EK23132 microphone element, an analog-to-digital converter, a Raspberry Pi Model B single-board computer, a solid-state memory card, and a battery. The microphone element is omnidirectional and has an approximately flat sensitivity of 53±5 dB between 2 and 10 kHz; that is, the frequency range of flight calls. The microphone element sits at the bottom of a small horn-shaped enclosure oriented upwards. In turn, this enclosure sits inside a hard plastic housing, whose purpose is to reject lateral sound sources, such as insects or car engines.</p> <p>The analog-to-digital converter encodes the monophonic signal recorded by the microphone into a linear pulse-code modulation sequence at a sample rate of 24 kHz and a sample depth of 16 bits. This sample rate corresponds to an appropriate Nyquist bound to capture the diversity of avian flight calls, which occur almost exculsively below 11 kHz in frequency. The single-board computer streams this sequence under the form of 20-second buffers, which are progressively appended to a lossless audio file in FLAC format. This acquisition procedure is repeated every night from civil twilight dawn to dusk between August 3rd, 2015 and December 8th, 2015. This temporal period corresponds to the general pattern of the timing of nocturnal bird migration, which birds usually initiate 30-45 minutes after local sunset and cease in the hours around dawn; this is not always the case for cessation.</p> <p>This duty cycle corresponds to roughly 1,500 hours of audio per sensor, and thus 13,500 hours for the entire sensor network. However, due to intermittent failures of sensing hardware, a common feature of many autonomous recording platforms, we retrieved only 6,651 hours successfully.</p> <p>We gathered FLAC files according to their location of provenance or "unit". Note that unit09 was never deployed: hence, the unit IDs are 01, 02, 03, 04, 05, 06, 07, 08, and 10.</p> <p>Despite hard plastic housing and deployment locations that attempted to physically facilitate avoidance of non-target signal capture, these sensors captured audio of nocturally migrating birds as well as additional features of this soundscape including human activities (anthrophony), meteorological phenomena (e.g. geophony) and non-targeted avian and other biological signals (biophony). These latter signals include non-human mammals (e.g. White-tailed Deer, flying squirrel, canines), diurnal vocalizations of resident and migrant birds that are not flight calls (e.g. Blue Jays, American Crow, American Goldfinch), anurans (e.g. spring peepers), and many insect species (e.g. Fork-tailed Bush-Katydid, Snowy Tree Cricket). </p> <p> </p> <p><strong>Derivative Datasets</strong></p> <p><strong>----------------------------</strong></p> <p>A representative subset of these audio recordings were selected for annotation. Ornithologist Andrew Farnsworth used the Raven software to pinpoint and label every avian flight call in time and frequency. He found 26138 sound events, of which 21546 are flight calls from Passeriformes. Of those, 13385 are identifiable in terms of family, and 8669 are identifiable in terms of both family and species. The annotation process took over 600 hours. This subset of recordings and the corresponding annotations have been released as BirdVox-296h (<a href="https://doi.org/10.5281/zenodo.4415480">https://doi.org/10.5281/zenodo.4415480</a>). The isolated flight calls and annotations have been released as BirdVox-14SD (<a href="https://doi.org/10.5281/zenodo.3667093">https://doi.org/10.5281/zenodo.3667093</a>) and its follow-up release BirdVox-25SD (<a href="https://doi.org/10.5281/zenodo.5889214">https://doi.org/10.5281/zenodo.5889214</a>).</p> <p> </p> <p><strong>Feedback</strong></p> <p><strong>-------------</strong></p> <p>Please help us improve BirdVox-full-night by sending your feedback to:</p> <p>vincent.lostanlen@ls2n.fr and af27@cornell.edu</p> <p>In case of a problem, please include as many details as possible.</p> <p> </p> <p><strong>Acknowledgement</strong></p> <p><strong>--------------------------</strong></p> <p>We thank the following people for their contributions to the development, construction, maintenance, deployment, and acquisition of the ROBIN units: Jessie Barry, Ian Davies, Tom Fredericks, Jeff Gerbracht, Sara Keen, Holger Klinck, Anne Klingensmith, Ray Mack, Peter Marchetto, Ed Moore, Matt Robbins, Ken Rosenberg, and Chris Tessaglia-Hymes, Chris Wood. Initial data collection activities were supported by NSF 1125098, Wolf Creek Foundation, and Leon Levy Foundation; analyses were supported largely by NSF 1633206 as well as Leon Levy Foundation and NSF 1661329.</p> <p>Cornell University is located on the traditional homelands of the Gayogo̱hó꞉nǫ' (Guy-yo-KO-no) (the Cayuga Nation). The Gayogo̱hó꞉nǫ' are members of the Haudenosaunee (Ho-di-no-so-ni) Confederacy, an alliance of six sovereign Nations with a historic and contemporary presence on this land. The Confederacy precedes the establishment of Cornell University, New York state, and the United States of America. We acknowledge the painful history of Gayogo̱hó꞉nǫ' dispossession, and honor the ongoing connection of Gayogo̱hó꞉nǫ' people, past and present, to these lands and waters. We acknowledge that the land on which the data was collected is the unceded territory of the Cayuga nation.</p> <p> </p>
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)
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.