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Figure 6 from: Brix S, Lorz A-N, Jazdzewska AM, Hughes L, Tandberg AHS, Pabis K, Stransky B, Krapp-Schickel T, Sorbe JC, Hendrycks E, Vader W, Frutos I, Horton T, Jazdzewski K, Peart R, Beermann J, Coleman CO, Buhl-Mortensen L, Corbari L, Havermans C, Tato R, Campean AJ (2018) Amphipod family distributions around Iceland. In: Brix S, Lorz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 1-53. https://doi.org/10.3897/zookeys.731.19854
Figure 6 - Distribution map for a Synopiidae b Urothoidae, in sorted IceAGE EBS samples.
Figure 7 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 7 Amphilochus manudens. Pereopods. IINH37885. Scale bars: 0.1 mm.
Figure 6 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 6 Amphilochus manudens. Mouthparts. IINH37887. Scale bars: 0.1 mm.
Figure 2 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 2 Amphilochus anoculus sp. n. Head and mouthparts. ZMBN121952. Scale bars: 0.1 mm.
Figure 9 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 9 Amphilochus hamatus. Habitus. IINH37900. Scale bar: 0.5 mm.
Figure 14 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 14 Pictorial key to Amphilochidae in the NE Atlantic.
Figure 8 from: Tandberg AHS, Vader W (2018) On a new species of Amphilochus from deep and cold Atlantic waters, with a note on the genus Amphilochopsis (Amphipoda, Gammaridea, Amphilochidae). In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 103–134. https://doi.org/10.3897/zookeys.731.19899
Figure 8 Amphilochus manudens. Appendages from pleon and urosome. IINH37885. Scale bars: 0.1 mm.
UrbanOccupationsOETR_temettuat_animals_FAO_6_region_rural_geosample
<p><span>With the UrbanOccupationsOETR, a European Research Councill, Starting Grant funded (Grant Number 679097, Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000, </span><span><a href="https://urbanoccupations.ku.edu.tr/"><span><span>UrbanOccupationsOETR</span></span></a></span><span>) project hosted at Koç University 2016-2022, we wanted to highlight the importance of rural economic dynamics to explain differences in long-term regional economic development in the late Ottoman Empire. We provide an Excel dataset on the crop-specific agricultural mix and land area of an Ottoman region, Bursa, in the 1840s. This dataset is the result of a new geosampling methodology we devised, representing a novel development in the agricultural and overall economic history of Southeast Europe and the Middle East.</span><span> </span></p> <p><span>The 1840s serve as a good period to choose for base years mainly due to three main factors to sample economic data on a regional scale. First, due to <em>Tanzimat</em> reforms (planned and only partially accomplished transformation of the Ottoman central administration in the mid-nineteenth century), the 1840s marked a watershed of bureaucratical information gathering. Especially, the <em>temettuat</em> registers were created as a by-product to realize a drastic change in tax collection. With at least in its first iteration, the unsuccessful abolishment of tax-farming by the Tanzimat decree in 1839, the Ottoman central administration aimed to transform the existing indirect and communal taxation with direct and individual modalities. To accomplish this goal, the administration had to survey the tax base, which was in disguise due to centuries-long tax farming practices. The <em>temettuat </em>registers were conducted in the core regions of the empire with the main exception of the imperial capital, Istanbul. Second, the 1840s correspond to the last period before the beginning of drastic territorial losses, primarily in Southeast Europe, which triggered in size and frequency unprecedented waves of emigration and immigration between the core territories of the empire both in Southeast Europe as well as in Anatolia, which continued until the official demise or the implosion of the empire. Third and lastly, the 1840s serves as a very suitable point to assess the dynamics of pre-industrial and <em>ancienne</em> regime agricultural dynamics due to the lack of modern means of mechanization, irrigation, and fertilization combined with extremely rudimentary transport facilities.</span></p> <p><span>The temettuat surveys are invaluable resources for they provide agricultural asset- / crop-type specific agricultural mix information with cultivation area per household. However, extracting their detailed information requires a team and years. To overcome this, we developed a sampling strategy that selected five locations per subdistrict using the Analytical Hierarchy Process (AHP), considering factors of agricultural suitability (85% weight), connectivity to historical roads (within a 500-meter to the closest road or to the Danube, 15% weight, justified by its impact on suitability), and subdistrict population size (chosen villages must represent at least 5% of the subdistrict's total population).</span><span> </span></p> <p><span>Our geosampling methodology of the 1840s tax registers (<em>temettuat</em>) is based on contemporary Ottoman population registers. With this geosampling method, we aim to estimate the regional (district (<em>sancak</em>) and subdistrict (<em>kaza</em>)) level total area of cultivation and shares of the agricultural mix for key products. We are using two mid-nineteenth-century datasets: Ottoman tax (TMT) (<em>temettuat</em>) surveys for crop type and cultivation area and the population (<em>nüfus</em>) (NFS) registers for population-based sampling. Connectivity is based on a detailed and provenly accurate 1940s German military map of Turkey, <em>Deutsche Heereskarte </em>(DHK). The agricultural suitability raster is an amalgamation of the Land Capability Classification (LCC) encapsulating the variables of soil quality and quantity and the Digital Elevation Model (DEM) based on Shuttle Radar Topography Mission with 30-meter-resolution and comprising elevation, slope, and ruggedness data.</span></p> <p><span>In the end, a geosampling initiative was undertaken across six regions in Southeast Europe and Anatolia, namely Ankara, Bursa, Plovdiv, Ruse, Manisa, and Edirne, covering a total of 277 locations with 17,675 households. Our project team entered the economic data from those records into a Microsoft Access database. We employed a specially crafted data entry template to organize the tax survey data into multiple categories systematically.</span></p> <p><span>After geosampling locations, our objective extended to deriving estimates for the total number of animals within each subdistrict and region. To achieve this goal, it was imperative that the data undergo coding of the types of animals into a standardized and comparable scheme. We adopted the “Classification of Livestock for the Agricultural Census” from the “World Programme for the Census of Agriculture 2010” of the Food and Agriculture Organization of the United Nations (FAO) published in 2005. </span></p> <p><span>Every animal entry was coded into the “Classification of Livestock for the Agricultural Census” scheme, encompassing the categories of “11” – Cattle; “12” – Buffaloes; “20” – Small ruminants; “21” – Sheep; “22” – Goats; “30” - Pigs or swine; “40” – Equines; “41” - Horses; “42” – Asses; “43” – Mules and hinnies; “52” - Camels. </span></p> <p><span>To accurately reflect the total number of animals, we created a separate column where we doubled the quantities recorded as pairs (<em>çift</em> in Turkish) and then added this to the count of single animals.</span></p> <p><span>This Zenodo dataset offers animal data for the entire geosampling area per household. It includes 50,569 agricultural entries coded into to the “Classification of Livestock for the Agricultural Census”, detailing the quantity and the cultivated area, corresponding to 12,514 individuals across 12,147 households. Please note that this is a rural geosample. Although the tax surveys of the primary (urban) and secondary (subdistrict centers) locations of all regions were read and entered, they are not included in this dataset.</span></p> <p><span>The categories and descriptions of the variables of the geosample dataset are as follows:</span></p> <table> <tbody> <tr> <td> <p><strong><span>Variable</span></strong></p> </td> <td> <p><strong><span>Description</span></strong></p> </td> </tr> <tr> <td> <p><span> </span></p> <p><span>“GeoCode”</span></p> </td> <td> <p><span> </span></p> <p><span>UniqueID belonging to a specific geosampled location</span></p> </td> </tr> <tr> <td> <p><span> “Longitude” & “Latitude”</span></p> </td> <td> <p><span>Geographical coordinates used to specify the precise location of a geosampled location on the Earth's surface</span></p> </td> </tr> <tr> <td> <p><span>“Region” & “SubDistrict” & “Location”</span></p> </td> <td> <p><span>Geographic unit of entry, including region (district/<em>sancak</em>); subdistrict (<em>kaza</em>); and geosampled location as they appear in the population registers</span></p> </td> </tr> <tr> <td> <p><span>“RegisterNo”</span></p> </td> <td> <p><span>Archival code of the population register whose data is being entered</span></p> </td> </tr> <tr> <td> <p><span>“HaneNo”</span></p> </td> <td> <p><span>Number of the household (specified by the registers as <em>Hane</em>), as appears in the register</span></p> </td> </tr> <tr> <td> <p><span>"HouseID"</span></p> </td> <td> <p><span>Unique ID belonging to a specific household, automatically generated by Microsoft Access</span></p> </td> </tr> <tr> <td> <p><span>"IndividualID"</span></p> </td> <td> <p><span>Unique ID belonging to a specific individual, automatically generated by Microsoft Access</span></p> </td> </tr> <tr> <td> <p><span>"AnimalID"</span></p> </td> <td> <p><span>UniqueID belonging to a specific animal of an individual</span></p> </td> </tr> <tr> <td> <p><span>"Animals"</span></p> </td> <td> <p><span>Type of the animal</span></p> </td> </tr> <tr> <td> <p><span>"AnimalClass"</span></p> </td> <td> <p><span>FAO code of the animal</span></p> </td> </tr> <tr> <td> <p><span>"CtgUnit"</span></p> </td> <td> <p><span>Specific terms describing quantity of an animal (“adet”, “res”, “tek” for a single animal, “çift” for pair of animals)</span></p> </td> </tr> <tr> <td> <p><span>"Unit"</span></p> </td> <td> <p><span>Quantity of the "CategoryUnit"</span></p> </td> </tr> <tr> <td> <p><span>“AnimalCount”</span></p> </td> <td> <p><span>"Unit" converted into precise numbers. In other words, the quantities expressed in pairs (<em>çift</em>) have been doubled, while the counts of single animals have been left unchanged.</span></p> </td> </tr> </tbody> </table> <p> </p>
Data from: Assortative mating among animals of captive and wild origin following experimental conservation releases
Captive breeding is a high profile management tool used for conserving threatened species. However, the inevitable consequence of generations in captivity is broad scale and often-rapid phenotypic divergence between captive and wild individuals, through environmental differences and genetic processes. Although poorly understood, mate choice preference is one of the changes that may occur in captivity that could have important implications for the reintroduction success of captive-bred animals. We bred wild-caught house mice for three generations to examine mating patterns and reproductive outcomes when these animals were simultaneously released into multiple outdoor enclosures with wild conspecifics. At release, there were significant differences in phenotypic (e.g. body mass) and genetic measures (e.g. Gst and F) between captive-bred and wild adult mice. Furthermore, 83% of offspring produced post-release were of same source parentage, inferring pronounced assortative mating. Our findings suggest that captive breeding may affect mating preferences, with potentially adverse implications for the success of threatened species reintroduction programmes.
Spatio-Temporal Clustering Benchmark for Collective Animal Behavior
<p>Synthetic datasets for the paper "Spatio-Temporal Clustering Benchmark for Collective Animal Behavior".</p> <p>The full_data.zip contains all 3600 datasets, and the Excel files contain information about the generated large datasets from which we sampled the respective 3600 datasets.</p>
Stronger net selection on males across animals
<p>Sexual selection is considered the major driver for the evolution of sex differences. However, the eco-evolutionary dynamics of sexual selection and their role for a population’s adaptive potential to respond to environmental change have only recently been explored. Theory predicts that sexual selection promotes adaptation at a low demographic cost only if net selection is stronger on males compared to females. We used a comparative approach to show that net selection is indeed stronger in males and provide preliminary support that this sex-bias is associated with sexual selection. Given that both sexes share the vast majority of their genes, our findings corroborate the notion that the genome is often confronted with a more stressful environment when expressed in males. Collectively, our study supports one of the long-standing key assumptions required for sexual selection to bolster adaptation, and sexual selection may therefore enable some species to track environmental change more efficiently.</p>
Figure 3 from: Vinarski M (2015) Conceptual shifts in animal systematics as reflected in the taxonomic history of a common aquatic snail species (Lymnaea stagnalis). Zoosystematics and Evolution 91(2): 91-103. https://doi.org/10.3897/zse.91.4509
Figure 3 - Illustrations of living Lymnaea stagnalis by Ginanni (1757).
Anti-predator behavior in two brown frogs: differences in the mean behaviors and in the structure of animal personality variation
<p>Predators are a major source of selection and preys have evolved the ability to flexibly respond to them. These responses often vary among species, because of their different evolutionary history, and among individuals, because of their different experiences. We analyzed both these sources of behavioral variation in two closely related brown frogs, <em>Rana dalmatina </em>and <em>R. latastei</em>. We raised tadpoles either with or without predators and tested them in open field trials both with and without predators. The effects of the raising environment were similar in the two species, whereas the effects of the testing arena differed. Both species decreased activity and increased speed and tortuosity with predators, but <em>R. dalmatina </em>moved always more and faster than <em>R. latastei </em>and it showed higher plasticity, larger variation among and within individuals, and relatively higher values in repeatability.</p>
MRAMS mesoscale model dataset, tools and animations for Jezero crater nighttime turbulence analysis
<p>This material constitutes the software tools to generate MRAMS data used in the analysis submitted for publication in JGR:Planets</p> <p>"Nocturnal turbulence at Jezero crater as determined from MEDA measurements and modeling"<br> by Pla-Garcia et al.</p> <p>The manuscript was submitted to JGR: Planets on 2 October, 2022</p>
FIG. 1 in Un visage animal dans les reliefs ninivites d'Assurbanipal ( siècle av. J.-C.)? Le cas du cheval (Equus caballus Linnaeus, 1758) et du lion (Panthera leo Linnaeus, 1758)
FIG. 1. — Relevé du relief de Khorsabad (musée du Louvre, inv. AO 19872; Botta 1849-1850: pl. 43).
Off-limb eruption and outflow animation
<p>Animation showing of limb evolution of the quiet Sun including mini-eruption and outflow from a possible null point in the corona,</p>
Animal Crossing WiFi CSI
<p>This dataset is shared as part of the paper <em>Detection and classification of animal crossings on roads using IoT based WiFi sensing</em>, submitted to the IEEE LATINCOM 2023 conference. It is distributed under the Creative Commons license Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).</p> <p>An expanded version of this dataset is available at <a href="https://ieee-dataport.org/documents/channel-state-information-data-animal-crossings-rural-roads" target="_blank" rel="noopener">IEEE Dataport</a>.</p> <h2>General description</h2> <p>Each sample from the dataset contains 500 frames of WiFi Channel State Information data, captured during a 5-second window (100 Hz sampling rate). Each frame contains the amplitude information from the 52 Wi-Fi subcarriers that transmit a data. This amounts to 26,000 features per sample.</p> <p>The non-zero amplitude values are converted to decibels, while null values are set to zero after the decibel function application to prevent negative infinite values. Subsequently, a running mean filter is applied to each frame to mitigate noise and outlier interference, ensuring a more stable representation of the signal. Additionally, we disregard zero-valued amplitudes, as these result from errors in the original signal capture process, leading to subcarriers without meaningful amplitude. Thus, zero values are not included in the running mean computation.</p> <p>We collected the CSI data using ESP32 boards, which were placed at a height of 70 cm and 12 meters apart from each other. To avoid bias towards a single environment, we collected data in four different locations, including paved and unpaved rural roads, a pasture and a gravel road. </p> <p>The parquet files can be easily read and manipulated with python libraries such as pandas.</p> <p> </p> <h2>Data labels</h2> <p>As it is intended to allow replication of the work presented, we uploaded the same separated test and training datasets used for the machine learning model. The data labels represent the following classes:</p> <p>0 - Background noise</p> <p>1 - Person</p> <p>2 - Car</p> <p>3 - Dog</p> <p>4 - Cow</p>
Animal-associated marine acidobacteria with a rich natural product repertoire
<p>This dataset contains the data associated with the manuscript: "Animal-associated marine acidobacteria with a rich natural product repertoire". This includes:</p> <p>- the list of accessions used for the antiSMASH analyses (Accessions for antiSMASH analysis.txt).</p> <p>- the scripts and data use for the transPACT analysis (.rar files).</p> <p>- the metagenome-assembled genome (MAG) recovered from a coral (Millepora sp.) metagenome belonging to the Acanthopleuribacteraceae bacterial family (TARA_CO-0003230_METAG-metabat2_a200_c2k_e500.23.fa).</p> <p>- the NMR data for the products characterised in this work (phorbactazoles, acidobactamides and calyculinamide)</p>
Pteranodon (Animated)
Pteranodon is a genus of pterosaur that included some of the largest known flying reptiles, with wingspans over 7 meters. They lived during the late Cretaceous geological period of North America in present-day Kansas, Alabama, Nebraska, Wyoming, and South Dakota. Source: Objaverse 1.0 / Sketchfab
Saint-Thurial Church - Animated
EDIT : I have changed the animation timeline. The model can loaded before the animation start Inside and outside photogrammetry model of Saint-Thurial church. Cut in half by a giant lightsaber. Source: Objaverse 1.0 / Sketchfab
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.