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1,641 results for “similarity”
Figure 1 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 1: Standardized growth rates (GR) based on surface area (%) of Laminaria digitata (white dots) and Hedophyllum nigripes (black dots) sporophytes over two weeks in a temperature gradient (n = 5, mean ± SD). Different letters denote significant differences within each species (ANOVA with Tukey's post hoc test: α <0.05, A– D = L. digitata; a–c = H. nigripes). Asterisks indicate significant differences between standardized GR of L. digitata and H. nigripes (two-way ANOVA with Tukey's post hoc test).
Figure 5 in Looks can be deceiving: contrasting temperature characteristics of two morphologically similar kelp species co-occurring in the Arctic
Figure 5: Density of gametophytes of Laminaria digitata (A, C) and Hedophyllum nigripes (B, D) at day 7 (A, B) and day 14 (C, D) in temperature gradients between 0 and 25 °C (L. digitata) and 22 °C (H. nigripes) (n = 3–4, mean ± SD). Broken horizontal lines show the mean initial gametophyte density for each species after the acclimatization phase (day 0). †All gametophytes died.
common function paralog pairs and sequence similarity features
<p>This repository contains datasets of selected paralog pairs (Ensembl 111), labeled with various "common function" annotations, including PPI, SL, and GO datasets for both human (<em>Homo sapiens</em>) and budding yeast (<em>Saccharomyces cerevisiae</em>). These paralog pairs are characterized using different sequence similarity features, such as AlphaFold-predicted structures, Protein Language Model embeddings, and similarity searches from various databases.</p> <p> </p> <p>These datasets are used in the following manuscript: <a href="https://www.biorxiv.org/content/10.1101/2024.10.11.617835v1">Evaluating Sequence and Structural Similarity Metrics for Predicting Shared Paralog Functions</a></p> <p> </p> <p>For the corresponding analysis notebooks, see: <a href="https://github.com/cancergenetics/paralog_seq_similarity/tree/main">github.com/cancergenetics/paralog_seq_similarity/</a></p>
Working memory capacity of crows and monkeys arises from similar neuronal computations
<p>Complex cognition relies on flexible working memory, which is severely limited in its capacity. The neuronal computations underlying these capacity limits have been extensively studied in humans and in monkeys, resulting in competing theoretical models. We probed the working memory capacity of crows (<em>Corvus corone</em>) in a change detection task, developed for monkeys (<em>Macaca mulatta</em>), while we performed extracellular recordings of the prefrontal-like area nidopallium caudolaterale. We found that neuronal encoding and maintenance of information were affected by item load, in a way that is virtually identical to results obtained from monkey prefrontal cortex. Contemporary neurophysiological models of working memory employ divisive normalization as an important mechanism that may result in the capacity limitation. As these models are usually conceptualized and tested in an exclusively mammalian context, it remains unclear if they fully capture a general concept of working memory or if they are restricted to the mammalian neocortex. Here we report that carrion crows and macaque monkeys share divisive normalization as a neuronal computation that is in line with mammalian models. This indicates that computational models of working memory developed in the mammalian cortex can also apply to non-cortical associative brain regions of birds.</p>
Fig. 45. Similarity between samples from the BANGAL 0711 in The Mollusca of Galicia Bank (NE Atlantic Ocean)
Fig. 45. Similarity between samples from the BANGAL 0711 campaign, based on presence/absence data of living specimens for molluscan species and on the Bray-Curtis similarity index (A, all samples; B, beam trawl samples only). Sample labels with depth in brackets. A similar topology was obtained using the abundance data transformed to fourth root.
Dataset of the paper "Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)"
<p>This dataset contains 3792 images of 26 plant bug (Insecta: Heteroptera: Miridae: Mirini) species used to test the performance of a CNN in species recognition. All jpg files are 1920 pixels on the long size and additionally available as an archive file to facilitate download of the entire dataset. </p> <p>Bar code labels (unique specimen identifiers or USIs) were attached to all examined specimens used for this study. Further information such as additional photographs of habitus and genitalic structures, georeferenced coordinates of each locality, specimens dissected, notes, collecting method can be obtained from the Heteroptera Species Pages (http://research.amnh.org/pbi/heteropteraspeciespage/) which assembles available data from a specimen database and are also provided as an Excel spreadsheet (file _Adelphocoris_CNN_label_data.xlsx).</p>
Wikipedia video games similarity dataset with expert annotations
<p>A video games NLP dataset extracted from Wikipedia.</p> <p>For all articles, the figures and tables have been filtered out, as well as the categories and "see also" sections.</p> <p>The article structure, and particularly the sub-titles and paragraphs are kept in these picese.</p> <p>Provided as well are 90 seeds with recommended articles, annotated by human experts.</p>
Diverse Topologies for Evaluation of Geometric Similarity Metrics
<p>A collection of 7 datasets with each set containing 3D shapes with varying topological complexity. The datasets can be used to compare different metrics of geometric dissimilarity. Two of the datasets have topologically complex shapes that resemble designs obtained from topology optimization, a widely used design optimization method for engineering structures.</p> <p>We used this dataset for a related journal article with the following abstract: "In the early stages of engineering design, multitudes of feasible designs can be generated using structural optimization methods by varying the design requirements or user preferences for different performance objectives. Data mining such potentially large datasets is a challenging task. An unsupervised data-centric approach for exploring designs is to find clusters of similar designs and recommend only the cluster representatives for review. Design similarity can be defined not only on a purely functional level but also based on geometric properties, such as size, shape, and topology. While metrics such as chamfer distance measure the geometrical differences intuitively, it is more useful for design exploration to use metrics based on <em>geometric features</em>, which are extracted from high-dimensional 3D geometric data using dimensionality reduction techniques. If the Euclidean distance in the <em>geometric features</em> is meaningful, the features can be combined with performance attributes resulting in an aggregate feature vector that can potentially be useful in design exploration based on both geometry and performance. We propose a novel approach to evaluate such derived metrics by measuring their similarity with the metrics commonly used in 3D object classification. Furthermore, we measure clustering accuracy, which is a state-of-the-art unsupervised approach to evaluate metrics. For this purpose, we use a labeled, synthetic dataset with topologically complex designs. From our results, we conclude that Pointcloud Autoencoder is promising in encoding geometric features and developing a comprehensive design exploration method."</p> <p>For each dataset, shapes/designs are saved as surface mesh files (extension: stl) and point cloud files (extension: ply) in the folders "stls" and "plys" respectively. A brief description of the 7 different datasets is in the following table. For each dataset, the designs are named using numbers starting from 0, e.g., “0.stl, 1.stl, …, 19.stl” in the folder for the surface mesh files. Some of the datasets are labeled, i.e., each design belongs to a class. In a labeled dataset, all classes have the same number of designs, and the designs are named in the order of their class. For example, a labeled dataset with 4 designs and 2 classes contains files whose names start with {0, 1, 2, 3} where the designs {0, 1} belong to class 1, and {2, 3} belong to class 2.</p> <table> <thead> <tr> <th scope="col">Dataset name</th> <th scope="col">Directory name</th> <th scope="col">Number of designs</th> <th scope="col">Number of classes</th> </tr> </thead> <tbody> <tr> <td>Beam-rotation</td> <td>"rotate_beam"</td> <td>20</td> <td>None</td> </tr> <tr> <td>Beam-elongation</td> <td>"elongate_beam"</td> <td>20</td> <td>None</td> </tr> <tr> <td>Beam-translation</td> <td>"move_beam"</td> <td>20</td> <td>None</td> </tr> <tr> <td>Three cube trusses</td> <td>"three_cube_truss"</td> <td>150</td> <td>6</td> </tr> <tr> <td>Single cube trusses</td> <td>"single_cube_truss"</td> <td>275</td> <td>11</td> </tr> <tr> <td>Random topologies</td> <td>"three_cube_truss_random"</td> <td>1000</td> <td>50</td> </tr> <tr> <td>Topologically optimized designs</td> <td>"cube_opt_shapes"</td> <td>1500</td> <td>None</td> </tr> </tbody> </table>
Electron Accepting Capacities of a wide variety of peat materials from around the Globe similarly explain CO2 and CH4 production
<p>In peat soils the availability of terminal electron acceptors (TEAs), both inorganic and organic, largely determines the ratio of carbon dioxide to methane formation under waterlogged, anoxic conditions. The redox properties of peat organic matter and their relationship with anoxic carbon mineralization are yet only investigated for a limited amount of peat and reference materials, although electron accepting capacities of organic matter (EACOM) largely predominate over canonical inorganic TEAs in peatlands. To address this knowledge gap, we incubated 60 peat samples from four different depths of 15 sites located in five major peatland regions (including Germany, Sweden, Russia, France and Chile) distributed around the globe covering a variety of both bog and fen type samples and characterized their capacities to serve as electron acceptors for anaerobic carbon dioxide production.<br> The dataset consists of a wide variety of recorded and calculated variables for a 56-day incubation of those samples. These variables include the formation and rates of methane, carbon dioxide, electron acceptor capacities and electron donator capacities at two different times, data on stable isotopes in delta notation (such as nitrogen, carbon and sulfur), molar element ratios for carbon/nitrogen, carbon/sulfur and nitrogen/phosphorus and elemental contents like silicon, phosphorus, sulfur, calcium and iron as well as specific fourier transformed infrared spectroscopy ratios regarding the ratios of polysaccharides and aromatic structures. The dataset was created mostly in 2019, with some additional measurements carried out in 2020 and 2021. </p>
Molecular similarity perception based on machine-learning models
<p>Molecular similarity is an particularly important notion for chemical legislation, specifically in the evaluation process for orphan drugs (i.e., drugs for rare diseases). A new molecule needs to be dissimilar from any other existing drug for a given disease to be assigned the financially advantageous status of orphan drug. Currently, there are many ways to define whether two molecules are similar or dissimilar. Thus far, the European Medicines Agency has used experts majority voting on discretional judgments of similarity when assessing new drugs for rare diseases. The decision of individual expert whether two compounds are similar is inherently subjective, depending on factors such as gender, age, state of mind, and previous experiences. It is therefore desirable, in this context, to benefit from an objective measure of similarity. To answer this need, we report a new dataset of molecular similarity assessments, that includes complex and difficult similarity scenarios. As a result, we propose new and improved models for similarity-prediction procedures, including 3D properties. These models are publicly available: <a href="https://chematlas.chimie.unistra.fr/ReadySim/">https://chematlas.chimie.unistra.fr/ReadySim/</a>.</p> <p>Software, 3D structures and pictures are available in the git related to this deposit: <a href="https://github.com/enricogandini/paper_similarity_prediction.git">https://github.com/enricogandini/paper_similarity_prediction.git</a></p> <p>The deposit contains two files.</p> <ul> <li>original_training_set.csv: this is one of the dataset published initially in [doi: 10.1186/1758-2946-6-5].</li> <li>new_dataset.csv: result from a new survey organized in 2020</li> </ul> <p>The columns are the following:</p> <ul> <li>id_pair: unique identifier of the compound pair</li> <li>curated_smiles_molecule_a: first compound of the pair</li> <li>curated_smiles_molecule_b: second compound of the pair</li> <li>tanimoto_cdk_Extended: ECFP similarity measure</li> <li>TanimotoCombo: ComboScore similarity measure</li> <li>pchembl_distance: difference of activity of the compound pair</li> <li>target_name: protein to which the compound pair is binding</li> <li>simil_2D: similar based on ECFP (0 or 1)</li> <li>simil_3D: similar based on ComboScore (0 or 1)</li> <li>dissimil_2D: dissimilar based on ECFP (0 or 1)</li> <li>dissimil_3D: dissimilar based on ComboScore (0 or 1)</li> <li>pair_type: pairs are classified based on ECFP and ComboScore as similar or dissimilar in 2D and 3D - Sim2DSim3D, Sim2DDis3D, Dis2D,Sim3D, Dis2DSim3D</li> <li>n_answers: number of answers from experts</li> <li>n_similar: number of answers labeling the pair as similar compounds</li> <li>frac_similar: n_similar/n_answers</li> </ul>
Whole-genome analysis of multiple wood ant population pairs supports similar speciation histories, but different degrees of gene flow, across their European ranges
<p>The application of demographic history modelling and inference to the study of divergence between species has become a cornerstone of speciation genomics. Speciation histories are usually reconstructed by analysing single populations from each species, assuming that the inferred population history represents the actual speciation history. However, this assumption may not be met when species diverge with gene flow, e.g., when secondary contact may be confined to specific geographic regions. Here, we tested whether divergence histories inferred from heterospecific populations may vary depending on their geographic locations, using the two wood ant species <em>Formica polyctena</em> and <em>F. aquilonia</em>. We performed whole-genome resequencing of 20 individuals sampled in multiple locations across the European ranges of both species. Then, we reconstructed the histories of distinct heterospecific population pairs using a coalescent-based approach. Our analyses always supported a scenario of divergence with gene flow, suggesting that divergence started in the Pleistocene (ca. 500 kya) and occurred with continuous asymmetrical gene flow from <em>F. aquilonia</em> to <em>F. polyctena</em> until a recent time, when migration became negligible (2-19 kya). However, we found support for contemporary gene flow in a sympatric pair from Finland, where the species hybridise, but no signature of recent bidirectional gene flow elsewhere. Overall, our results suggest that divergence histories reconstructed from a few individuals may be applicable at the species level. Nonetheless, the geographical context of populations chosen to represent their species should be taken into account, as it may affect estimates of migration rates between species when gene flow is spatially heterogeneous.</p>
The genetic structure and connectivity in two sympatric rodent species with different life histories are similarly affected by land use disturbances
<p><strong>Microsatellite dataset of the wood mouse (<em>Apodemus sylvaticus)</em> and the bank vole (<em>Myodes glareolus).</em></strong></p> <p>The dataset of the wood mouse is constituted of 194 samples and 7 microsatellite markers: WM_194ind_7STRs.txt</p> <p>The dataset of the bank vole is constituted of 199 samples and 8 microsatellite markers: BV_199ind_8STRs.txt</p> <p>Each locus is encoded in the three-digit format (e.g., 126126) and each column corresponds to a locus specified in the order at the beginning of the file, following the GENEPOP format.</p> <p>Pop indicates the beginning of a new location.</p> <p> </p> <p><em><strong>Locus name in WM_194ind_7STRs.txt</strong></em></p> <p>Locus_1 AS-7-FAM<br> Locus_2 AS-12-PET<br> Locus_3 AS-20-NED<br> Locus_4 AS-34-FAM<br> Locus_5 GTTD9A-PET<br> Locus_6 AS-11-VIC<br> Locus_7 MS-AF-8-NED</p> <p> </p> <p><em><strong>Locus name in BV_199ind_8STRs.txt</strong></em></p> <p>Locus_1 Cg13B8-F_FAM<br> Locus_2 Cg6A1-F_VIC<br> Locus_3 Cg3F12-F_PET<br> Locus_4 Cg13H9-F_PET<br> Locus_5 Cg2E2-F_VIC<br> Locus_6 Cg3E10-F_FAM<br> Locus_7 Cg2A4-F_FAM<br> Locus_8 Cg3A8-F_NED</p>
Sequence Similarity Network (SSN) and Genome Neighbourhood Network (GNN) for Mycobacterium Cytochrome P450 enzymes
<p>This dataset was generated in the context of the Horizon 2020 MSCA IF action deCrYPtion (Grant 839116). The aim of this project is to use comparative genomics in order to propose and then test the function of uncharacterised Cytochrome P450 enzymes that are present among Mycobacterium species.</p> <p>More information about this project can be found at: https://cordis.europa.eu/project/id/839116.</p> <p>This dataset contains:</p> <p>- The FASTA sequences files obtained from the UniProt database, for members of the PF00067 protein family (CYP).</p> <p>- A set of reference FASTA sequences, matching the supplementary material from the following publication: Parvez, M. <em>et al.</em> (2016) ‘Molecular evolutionary dynamics of cytochrome P450 monooxygenases across kingdoms: Special focus on mycobacterial P450s’, <em>Scientific Reports</em>, 6(1), p. 33099. doi:<a href="https://doi.org/10.1038/srep33099">10.1038/srep33099</a>.</p> <p>- A combined FASTA files of both previously described, that was used for the generation of SSNs</p> <p>- A PNG image produced from the analysis of the Sequence Similarity Networks generated at AST78 (corresponding to 40% identity, defining CYP families)</p> <p>- A PNG image produced from the analysis of the Sequence Similarity Networks generated at AST141 (corresponding to 55% identity, defining CYP subfamilies)</p> <p>- A Cytoscape session for the Sequence Similarity Networks from the combined FASTA file generated using the Enzyme Function Initiative web tools (https://efi.igb.illinois.edu), at AST78</p> <p>- A Cytoscape session containing Sequence Similarity Networks and Genome Neighborhood Network from the combined FASTA file generated using the Enzyme Function Initiative web tools (https://efi.igb.illinois.edu), at AST141</p>
Predicting Patch Correctness Based on the Similarity of Failing Test Cases
<p>Dataset for the paper "Predicting Patch Correctness Based on the Similarity of Failing Test Cases"</p> <p>Tool name: BATS</p>
High nucleotide similarity of three Copia lineage LTR retrotransposons among plant genomes
<p>Transposable elements (TEs) are mobile genetic elements found in the majority of eukaryotic genomes. TEs deeply impact the structure and evolution of chromosomes and can induce mutations affecting coding genes. In plants, the major group of TEs is Long Terminal Repeats retrotransposons (LTR-RT). They are classified into superfamilies (<em>Gypsy</em>, <em>Copia</em>) and sub-classified into lineages. Horizontal transfer (HT), defined as the nonsexual transmission of genetic material between species, is a process allowing LTR-RTs to invade a new genome. Although this phenomenon was considered rare, recent studies demonstrate numerous transfers of LTR-RTs, suggesting that HT may be more frequent than initially estimated.</p> <p>This study aims to determine which LTR-RT lineages are shared with high similarity among 69 reference plant genomes. We identified and classified 88,450 LTR-RTs and determined 143 cases (involving 94 elements) of high similarities between pairs of genomes. Most of them involved three <em>Copia</em> lineages (<em>Oryco/Ivana</em>, <em>Retrofit/Ale</em> and <em>Tork/Tar/Ikeros</em>). A detailed analysis of three cases of high similarities involving <em>Tork/Tar/Ikeros</em> group shows a patchy distribution of the elements and phylogenetic incongruities, indicating they originated from potential HTs. Overall, our results suggest that <em>Copia</em> LTR-RTs share outstanding similarity between very distant species and may probably be more involved in HT mechanisms.</p>
Data from: The evolution of sex similarities in social signals: Climatic seasonality is associated with lower sexual dimorphism and greater elaboration of female and male signals in antbirds (Thamnophilidae)
<p>Selection on signals that mediate social competition varies with resource availability. Climate regulates resource availability, which may affect the strength of competition and selection on signals. Traditionally, this meant that more seasonal, colder, or dryer – overall harsher – environments should favor the elaboration of male signals under stronger male-male competition, increasing sexual dimorphism. However, females also use signals to compete; thus, harsher environments could strengthen competition and favor elaboration of signals in both sexes, decreasing sexual dimorphism. Alternatively, harsher environments could decrease sexual dimorphism due to scarcer resources to invest in signal elaboration in both sexes. We evaluated these contrasting hypotheses in antbirds, a family of Neotropical passerines that varies in female and male signals and occurs across diverse climatic regimes. We tested the association of sexual dimorphism of plumage coloration and songs with temperature, precipitation and their seasonality. We found that greater seasonality is associated with lower sexual dimorphism in plumage coloration and greater elaboration of visual signals in both sexes, but not acoustic signals. Our results suggest that greater seasonality may be associated with convergent elaboration of female and male visual signals, highlighting the role of signals of both sexes in the evolution of sexual dimorphism.</p>
Code and data related to semi-synchronous growth changes and the similarity of tree-ring patterns
<p>This dataset contains the data and code related to Visser, RM. 2021 On the similarity of tree-ring patterns: Assessing the influence of semi-synchronous growth changes on the Gleichläufigkeitskoeffizient for big tree-ring data sets. <em>Archaeometry 63(1)</em>: 204–215. DOI: <a href="https://doi.org/10.1111/arcm.12600">https://doi.org/10.1111/arcm.12600</a>.</p>
Text-fig. 4. Extant Fraxinus fruits and other groups with similar fruits. a: Ventilago leiocarpa BENTH. (KUN 06190258); b: Liriodendron chinense (HEMSL.) SARG. (KUN 0040571); c: Plenckia populnea REISSEK (K 000537359); d: Fraxinus nigra MARSHALL (KUN 0937878); e: F. anomala TORR. ex S.WATSON (RSA 0064862); f: F. gooddingii LITTLE (USFS 0030124); g: F. platypoda OLIV. (KUN 0027753); h: F. malacophylla HEMSL. (K 000901679); i: F. chinensis ROXB. (KUN 0027530). Scale bar = 1 cm. in Fraxinus L. (Oleaceae) Fruits From The Early Oligocene Of Southwest China And Their Biogeographic Implications
Text-fig. 4. Extant Fraxinus fruits and other groups with similar fruits. a: Ventilago leiocarpa BENTH. (KUN 06190258); b: Liriodendron chinense (HEMSL.) SARG. (KUN 0040571); c: Plenckia populnea REISSEK (K 000537359); d: Fraxinus nigra MARSHALL (KUN 0937878); e: F. anomala TORR. ex S.WATSON (RSA 0064862); f: F. gooddingii LITTLE (USFS 0030124); g: F. platypoda OLIV. (KUN 0027753); h: F. malacophylla HEMSL. (K 000901679); i: F. chinensis ROXB. (KUN 0027530). Scale bar = 1 cm.
Text-fig. 9. Wataria kvacekii sp. nov., UF 279-24556. a: Wood ring-porous, earlywood with 2–3 rows of wide pores, vessels solitary and in radial multiples of 2, axial parenchyma scanty vasicentric and some apotracheal diffuse-in-aggregates, TS. b: Series of vessel elements with simple perforations, axial parenchyma strands adjacent to vessels, RLS. c: Alternate intervessel pitting, vessel element end walls horizontal, RLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e, f: Rays with tile cells, storied axial parenchyma, some strands chambered crystalliferous, TLS. g: Detail of ray, TLS. h: Storied imperforate elements. Scale bars: 200 µm in a; 100 µm in b, e; 50 µm in c, d, f, h; 20 µm in g. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 9. Wataria kvacekii sp. nov., UF 279-24556. a: Wood ring-porous, earlywood with 2–3 rows of wide pores, vessels solitary and in radial multiples of 2, axial parenchyma scanty vasicentric and some apotracheal diffuse-in-aggregates, TS. b: Series of vessel elements with simple perforations, axial parenchyma strands adjacent to vessels, RLS. c: Alternate intervessel pitting, vessel element end walls horizontal, RLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e, f: Rays with tile cells, storied axial parenchyma, some strands chambered crystalliferous, TLS. g: Detail of ray, TLS. h: Storied imperforate elements. Scale bars: 200 µm in a; 100 µm in b, e; 50 µm in c, d, f, h; 20 µm in g.
Text-fig. 8. Pterocaryoxylon sp., a–c, e: UF 279-85024; d, f: UF 279-24551. a, b: Wood semi-ring-porous, vessels solitary and in short radial multiples, axial parenchyma scanty vasicentric, marginal, and in narrow lines, TS. c: Crowded alternate intervessel pitting, simple perforation plate (PP), TLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e: Rays mostly 1–3 cells wide, occasionally 4 cells, uniseriate rays probably mostly square to upright cells, TLS. f: Rays heterocellular, body cells procumbent. Scale bars: 200 µm in a, b; 100 µm in e, f; 50 µm in c; 20 µm in d. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 8. Pterocaryoxylon sp., a–c, e: UF 279-85024; d, f: UF 279-24551. a, b: Wood semi-ring-porous, vessels solitary and in short radial multiples, axial parenchyma scanty vasicentric, marginal, and in narrow lines, TS. c: Crowded alternate intervessel pitting, simple perforation plate (PP), TLS. d: Vessel-axial parenchyma pitting similar to intervessel pitting, RLS. e: Rays mostly 1–3 cells wide, occasionally 4 cells, uniseriate rays probably mostly square to upright cells, TLS. f: Rays heterocellular, body cells procumbent. Scale bars: 200 µm in a, b; 100 µm in e, f; 50 µm in c; 20 µm in d.
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.