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To understand the thermal variability in thermal response amongst healthy individuals at the cellular level using Ayurgenomics approach
<p>Uploading differential express genes across pooled (VPK) and <em>Prakritis </em>at 6hrR and 24hrR after heat shock at 43º C for 1hour.</p>
Biogenic amines for individual and social learning
<p>Individuals learn about their environment through personal observation or by observing their social conspecifics. The speed-accuracy trade-off associated with these two strategies results in two possible learning phenotypes – slow but more accurate individual learners and fast but more inaccurate social learners. While the costs and benefits of these different learning strategies are well known, we know much less about the possible neurophysiological mechanisms that underlie such cognitive variation. Neurotransmitters, based on their important role in driving cognitive differences in all taxa, are the most likely candidates that underlie the neurophysiological differences between these learning phenotypes. We measured individual honeybees for their performance on an individual and a social learning task followed by quantifying the concentrations of various neurotransmitters in their brains. Our results show that the two learning phenotypes and their accompanying differences in the speed-accuracy trade-off are consistently characterized by the opposite effects of an interaction between octopamine and serotonin and another between octopamine and glutamate. These findings challenge the general notion that social learning is simply an extension of individual learning and provide an important insight into the mechanistic underpinnings of the two different learning phenotypes and the neurophysiological mechanism that underlies slow-fast differences across taxa.</p>
Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model
<p>This dataset contains the hyperspectral images and train-test split from the article. </p> <p> </p> <p>The corresponding comet ML experiment is:</p> <p><a href="https://www.comet.com/bw4sz/deeptreeattention2/209ca047ed004d778c0f0e728e126bda?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&viewId=VWzVg9fkZDMidwu9VOZi2weM9&xAxis=epoch">CometML</a></p> <p>To protect ongoing scientific activities in the area, the geospatial position of the crops have been omitted. Please contact ForestGEO for data requests. </p> <p><a href="http://ForestGEO">https://forestgeo.si.edu/</a></p> <p>For the corresponding git repo please see: </p> <p>Contents:</p> <p>train.csv: The image crops used for model training. The image_path corresponds to the relative path to the image file in the directory.</p> <p>test.csv: The image crops used for model evaluation.</p> <p>*.tif images: A 369 band hyperspectral image using the NEON surface reflectance data cropped by the tree crown.</p> <p>The original HSI data: https://data.neonscience.org/data-products/DP3.30006.001. The location of the crown was predicted using the RGB data product and the deepforest model: https://deepforest.readthedocs.io/. </p> <p>The taxonID abbreviations follow NEON's taxonomy: https://data.neonscience.org/taxonomic-lists</p> <p>OSBS.shp: Shapefile with ensemble predictions for the full Ordway Swisher Biological Station. The ensemblaTa column is the predicted taxonID for each crown. The ens_score is the confidence prediction for that crown. The CHM_height is the extracted raster value from NEON's ecosystem structure canopy height model.</p>
Individual flowering phenology shapes plant-pollinator interactions across ecological scales affecting plant reproduction
<p>1. The balance of pollination competition and facilitation amongst co-flowering plants and abiotic resource availability can modify plant species and individual reproduction. Floral resource succession and spatial heterogeneity modulate plant-pollinator interactions across ecological scales (individual plant, local assemblage, interaction network of agroecological infrastructure across the farm). Intraspecific variation in flowering phenology can modulate the precise level of spatio-temporal heterogeneity in floral resources, pollen donor density and pollinator interactions that a plant individual is exposed to, thereby affecting reproduction.</p> <p>2. We tested how abiotic resources and multi-scale plant-pollinator interactions affected individual plant seed set, modulated by intraspecific variation in flowering phenology and spatio-temporal floral heterogeneity arising from agroecological infrastructure. We transplanted two focal insect-pollinated plant species (<em>Cyanus</em> <em>segetum</em> and <em>Centaurea</em> <em>jacea</em>, n = 288) into agroecological infrastructure (10 sown wildflower, 6 legume-grass strips) across a farm-scale experiment (125 ha).</p> <p>3. We applied an individual-based phenologically explicit approach to match precisely the flowering period of plant individuals to the concomitant level of spatio-temporal heterogeneity in plant-pollinator interactions, potential pollen donors, floral resources and abiotic conditions (temperature, water, nitrogen).</p> <p>4. Individual plant attractiveness, assemblage floral density and conspecific pollen donor density (<em>C</em>. <em>jacea</em>) improved seed set. Network linkage density increased focal species' seed set and modified the effect of local assemblage richness and abundance on <em>C</em>. <em>segetum</em>. Mutual dependence on pollinators in networks increased <em>C</em>. <em>segetum</em> seed set, while <em>C</em>. <em>jacea</em> seed set was greatest where both specialization on pollinators and mutual dependence was high. Abiotic conditions were of little or no importance to seed set.</p> <p>5. Intra- and interspecific plant-pollinator interactions respond to spatio-temporal heterogeneity arising from agroecological management affecting wild plant species reproduction. The interplay of pollinator interactions within and between ecological scales affecting seed set implies a co-occurrence of pollinator-mediated facilitative and competitive interactions among plant species and individuals. </p>
Cations make a difference: Soil nutrient patches and fine-scale root abundance of individual species in a mountain grassland
<p><span>1. Root densities in the field vary at the centimetre scale, but we have no information </span><span>on </span><span>whether this variation is linked to variation in nutrient concentrations and availability. Roots of many species are able to proliferate in nutrient-rich patches in controlled conditions in culture, but because data on nutrient concentrations and, in particular, on their temporal stability in the field are scarce, we do not know to what extent root distribution in the soil bears traces of such a response.</span></p> <p><span>2. Here</span><span>,</span><span> we linked </span><span>centimetre</span><span>-scale measurements of soil nutrient concentrations over a period of 6 weeks with estimation of root biomass and its species composition at the same points to determine whether there is any association between the two. In addition to phosphorus, nitrate and ammonium, we determined</span><span> the</span><span> concentrations of metal cations (magnesium, calcium and potassium). We used qPCR to determine</span><span> the</span><span> quantities of individual species in the root biomass samples.</span></p> <p><span>3. We found that calcium and magnesium (and to a lesser degree phosphorus and potassium) showed fine-scale patchiness that was stable over the duration of the study (6 weeks) and </span><span>was</span><span> consistent over all these elements. Nutrient patches were associated with high root biomass and </span><span>the </span><span>occurrence of roots of several species. Such patches are formed primarily by elements with known low mobility in soil (cations, phosphorus). In contrast, nitrogen ions showed overwhelmingly high temporal variation with no relationship between root density and nitrogen concentration.</span></p> <p><span>4. Soil nutrient concentrations thus constitute a multidimensional signal. Some elements strongly </span><span>vary</span><span> in time, while some are much more stable and thus form stable patches that permit root response over periods of weeks or months. Among them, calcium and magnesium play an important role in forming soil heterogeneity at a scale comparable to the scale at which fine root densities also vary. As these cations affect a number of plant functions, their association with higher root densities confirms their role in growth dynamics of terrestrial ecosystems.</span></p>
Individual-based simulations for: Population rescue through an increase of the selfing rate under pollen limitation: Plasticity vs. evolution
<p>The file contains the individual-based simulation code that is used in the paper "Population rescue through an increase of the selfing rate under pollen limitation: plasticity vs. evolution". This study built eco-evolutionary models and individual-based simulations to explore the demographic and genetic conditions in which higher self-fertilization by plasticity and/or evolution rescues populations, following deficits due to sudden onset of pollen limitation. The code is written in C++. Please first read the README.txt file for instructions and details.</p>
Common carp individual identification
<p>General protocol for the image based identification of individuals within close group of common carp speciesThe aim of this study was to verify the possibility of using fully-automatic non-invasive photo-identification of individual fish based on natural marks on the fish’s body without any obvious skin patterns. This approach is an alternative to stressful invasive tagging and marking techniques. Scale patterns on the body and operculum were used as discriminative features for the identification of individuals in a closed group of fish. We used common carp Cyprinus carpio. The identification method was tested on four experimental data sets: separate short-term data set (pattern variability test) and four long-term data sets (pattern stability test) common carp (32 individual fish). The accuracy of classification was 100% in both the short-term and long-term experiments. According to these results, the methods used for automatic non-invasive image-based individual-fish identification can also beused for fish species without obvious skin patterns.<strong> Four rounds of data collection were performed over the four-month experiment for carp identification. Images were taken of the whole fish out of water. Each fish was caught in the cultivation tank, anesthetized in a bucket using clave oil, and moved to the green background for imaging. A single-layer white-fabric photographic tent with controlled illumination was used for photography. The fish heads were oriented to the left for all images. Fish were tagged for long term ID.<strong> <strong> </strong></strong></strong></p> <p> </p> <p><strong>Uploaded files 01,02,03,04 are the original files (4 datasets) and in processed there are all processed files for four datasets.</strong></p>
The sampling information and detail levels of individual SVOCs along the Arctic Expedition Cruises in the Northern Hemisphere and Antarctic Expedition Cruise in the Southern Cruise
<p>This dataset comprises samples collected from the Marine Atmospheric Boundary Layer (MABL) during 3 research expeditions: the 2016 summer Arctic research cruise (74 surface seawater samples), the 2017 summer Arctic research cruise (16 atmospheric and 16 surface seawater samples), and the 2016-2017 Antarctic research cruise (40 atmospheric and 34 surface seawater samples). The sampling locations encompassed various regions, including the East China Sea to Northwest Pacific transect, the Arctic Ocean region, the East China Sea to India Ocean transect, the South Ocean, and the Antarctic offshore region. The samples were analyzed for 39 semi-volatile organic compounds (SVOCs), which included polycyclic aromatic hydrocarbons (PAHs), organochlorine pesticides (OCPs), and current-use pesticides (CUPs). All samples were collected using High-volume solid phase extraction and high throughput in situ ultrasound analysis.</p> <p>This dataset is the Supporting Information of the research article: “Global Geographical Redistribution and Source Dynamics of SVOCs in the Marine Air Boundary Layer”, Zheng et al., 2023. The unauthorized use of this database is prohibited.</p>
FIGURE 6. Styloperla spinicercia, male cercus from different individuals. A–B in The intraspecific morphological variability of Styloperla Wu, 1935 (Plecoptera: Styloperlidae)
FIGURE 6. Styloperla spinicercia, male cercus from different individuals. A–B: male from Guizhou (Mount Fanjing), the left long process with double subapical spines; C–D: male from Guangxi (Jinxiu), process on basal cercal segment is completely asymmetrical.
Mouse spontaneous behavior reflects individual variation rather than estrous state
<p>Repository containing datasets obtained for Levy et al. 2023, available online here: https://doi.org/10.1016/j.cub.2023.02.035</p> <p>Github link to related analysis code: <a href="https://github.com/dattalab/spontaneous-behavior-reflects-individuality-not-estrous">https://github.com/dattalab/spontaneous-behavior-reflects-individuality-not-estrous</a></p> <p><strong>Abstract</strong></p> <p>Behavior is shaped by both the internal state of an animal and its individual behavioral biases. Rhythmic variation in gonadal hormones during the estrous cycle is a defining feature of female internal state, one that regulates many aspects of sociosexual behavior. However, it remains unclear whether estrous state influences spontaneous behavior, and if so how these effects might relate to individual behavioral variation. Here we address this question by longitudinally characterizing the open field behavior of female mice across different phases of the estrous cycle, using unsupervised machine learning to decompose spontaneous behavior into its constituent elements1-4. We find that each female mouse exhibits a characteristic pattern of exploration that uniquely identifies it as an individual across many experimental sessions; in contrast, estrous state only negligibly impacts behavior, despite its known effects on neural circuits that regulate action selection and movement. Like female mice, male mice exhibit individual specific patterns of behavior in the open field; however, the exploratory behavior of males is significantly more variable than that expressed by females both within and across individuals. These findings suggest an underlying functional stability to the circuits that support exploration in female mice, reveal a surprising degree of specificity in individual behavior, and provide empirical support for the inclusion of both sexes in experiments querying spontaneous behaviors.</p> <p><strong>Behavioral DataFrames:</strong> holds the behavioral syllable data used for the analysis described in the manuscript:</p> <ul> <li>mean_df_female / scalar_df_female - main female dataset</li> <li>mean_df_male / scalar_df_male - main male dataset</li> <li>mean_df_female_control / mean_df_male_control - datasets for male/female control experiment presented in Fig S3.</li> </ul> <p><strong>Model files:</strong></p> <p>Additional MoSeq model files used for generating FigS2A and FigS2H. </p>
Social structure and individuals' behavior shaped by group composition in long-term tracking of rats
<p>Trajectory data and behavioral metrics from long-term tracking of groups of rats. Experiments performed in 2012 at Eötvös Loránd University, Budapest, Hungary.</p> <p>The data here is used in the following publication. See the publication for information on experimental structure and notation.<br> Nagy, Máté, Jacob Davidson, Gábor Vásárhelyi, Dániel Ábel, Enikő Kubinyi, Ahmed El Hady, and Tamás Vicsek. 2023. “Social Structure and Individuals’ Behavior Shaped by Group Composition in Long-Term Tracking of Rats.” bioRxiv. <a href="https://doi.org/10.1101/2023.03.18.533183">https://doi.org/10.1101/2023.03.18.533183</a>.</p> <p>Github repository to reproduce results in the paper: <a href="https://github.com/jacobdavidson/ratsocialgroups">github.com/jacobdavidson/ratsocialgroups</a><br> Github repositories used for tracking and computation of behavioral metrics: <a href="https://github.com/vasarhelyi">github.com/vasarhelyi</a></p> <p>The behavioral metrics, approach-avoidance networks, individual labels and group membership information, and behavioral assay data needed to reproduce the results in the publication are included in the zip file <a href="https://zenodo.org/api/files/969c78d4-1d30-4926-ac6e-141136cd3564/metrics-networks-labels.zip">metrics-networks-labels.zip</a>. This includes the following files in .csv format:</p> <p><strong>Naming/labels:</strong></p> <ul> <li><strong>uidlabels.csv</strong>: References the ranked-mass labels used in the paper to the 3-color-code labels that identify each rat from the tracking. "Group" is the membership during the original groups (Period 1), and "GroupG" is membership during the remixed groups (Period 3). <table> <tbody> <tr> <td> </td> <td> <p><strong>masslabel</strong></p> </td> <td> <p><strong>GroupMassRank-Exp7</strong></p> </td> <td> <p><strong>Group</strong></p> </td> <td> <p><strong>GroupG</strong></p> </td> </tr> <tr> <td> <p><strong>GOB</strong></p> </td> <td> <p>a5</p> </td> <td> <p>5.0</p> </td> <td> <p>A1</p> </td> <td> <p>G3</p> </td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> </li> <li><strong>groupnames.csv</strong>: Group membership information for each rat, during each experimental period <table> <tbody> <tr> <td> <p><strong>Experiments</strong></p> </td> <td> <p><strong>GOB</strong></p> </td> <td> <p><strong>OBG</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>1-7</strong></p> </td> <td> <p>A1</p> </td> <td> <p>A1</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> </ul> <p><strong>Behavioral metrics and approach-avoidance networks:</strong></p> <ul> <li><strong>metrics.csv: </strong>Behavioral metrics for each rat, calculated for each experiment. <table> <tbody> <tr> <td> <p><strong>Experiment</strong></p> </td> <td> <p><strong>metric</strong></p> </td> <td> <p><strong>GOB</strong></p> </td> <td> <p><strong>OBG</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p>AA</p> </td> <td> <p>0</p> </td> <td> <p>0.690964</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>Networks-Period1+Period3.csv: </strong>Approach-avoidance interaction networks for the groups of 7 rats in Period 1 and Period 3. <table> <tbody> <tr> <td> <p><strong>Experiment</strong></p> </td> <td> <p><strong>DayNight</strong></p> </td> <td> <p><strong>Group</strong></p> </td> <td> <p><strong>index</strong></p> </td> <td> <p><strong>winner</strong></p> </td> <td> <p><strong>loser0</strong></p> </td> <td> <p><strong>loser1</strong></p> </td> <td> <p><strong>loser2</strong></p> </td> <td> <p><strong>loser3</strong></p> </td> <td> <p><strong>loser4</strong></p> </td> <td> <p><strong>loser5</strong></p> </td> <td> <p><strong>loser6</strong></p> </td> </tr> <tr> <td> <p><strong>1</strong></p> </td> <td> <p>Day</p> </td> <td> <p>A1</p> </td> <td> <p>0</p> </td> <td> <p>GOB</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> <td> <p>0</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>Networks-Experiment8.csv</strong> and <strong>Networks-Experiment9.csv: </strong>Approach-avoidance interaction networks for the larger combined groups in the experiments during Period 2. </li> </ul> <p> </p> <p><strong>Behavioral assays results:</strong></p> <ul> <li><strong>IndividualTests.csv</strong>: Individual behavioral assay test results for the black and white box, elevated plus-maze, and canopy tests. <table> <tbody> <tr> <td> <p><strong>ID</strong></p> </td> <td> <p><strong>BWB-All Frames</strong></p> </td> <td> <p><strong>BWB-Black</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>BGP</strong></p> </td> <td> <p>7500</p> </td> <td> <p>6860</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> <li><strong>PairedTests.csv: </strong>Pairwise social interaction behavioral assay test results for tests with unfamiliar and familiar rats <table> <tbody> <tr> <td> <p><strong>rat_ID</strong></p> </td> <td> <p><strong>rat_group_N</strong></p> </td> <td> <p><strong>freq_standup_unfam1</strong></p> </td> <td> <p><strong>...</strong></p> </td> </tr> <tr> <td> <p><strong>BGP</strong></p> </td> <td> <p>2</p> </td> <td> <p>28</p> </td> <td> <p>...</p> </td> </tr> <tr> <td> <p><strong>...</strong></p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> <td> <p>...</p> </td> </tr> </tbody> </table> </li> </ul> <p> </p> <p><strong>Trajectory data</strong> is included as multiple text files for each experiment in TSV format. #t is frame number, Color_code_name identifies each rat, Real_recognition(1)_or_virtual(0) = 1 is a detection of the rat's color codes while 0 is an interpolated value, x,y, and Orientation are the position and orientation of the rat.</p> <table> <tbody> <tr> <td>#t</td> <td>Color_code_name</td> <td>Real_recognition(1)_or_virtual(0) </td> <td>x</td> <td>y</td> <td>Orientation</td> </tr> <tr> <td>0</td> <td>ORG</td> <td>0</td> <td>872.4000</td> <td>778.7000</td> <td>-79.5000</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p> </p> <p><strong>Time-lapse video of the entire experiment:</strong> <a href="https://zenodo.org/api/files/969c78d4-1d30-4926-ac6e-141136cd3564/ratlab_timelapse_entire_exp.avi?versionId=a58df78d-1291-4301-9763-86a97b9e146c">ratlab_timelapse_entire_exp.avi</a></p>
Reconstructing the landscape of gut microbial species across 29,000 diverse individuals
<p>The human gut microbiome has been linked to health and disease. Investigation of the human microbiome has largely employed 16S amplicon sequencing, with limited ability to distinguish microbes at the species level. Herein, we describe the development of Reference-based Exact Mapping (RExMap) of microbial amplicon variants that enables mapping of microbial species from standard 16S sequencing data. RExMap analysis of 16S data captures ~75% of microbial species identified by whole-genome shotgun sequencing, despite hundreds-fold less sequencing depth. RExMap re-analysis of existing 16S data from 29,349 individuals across sixteen regions from around the world reveals a detailed landscape of gut microbial species across populations and geography. Moreover, RExMap identifies a core set of fifteen gut microbes shared by humans. Core microbes are established soon after birth and closely associate with BMI across multiple independent studies. RExMap and the human microbiome dataset are presented as resources with which to explore the role of the human microbiome.</p>
FIGURE 4 in Report of intersex individuals from a southeastern Chinese Choroterpes facialis (Gillies, 1951) population (Ephemeroptera: Leptophlebiidae)
FIGURE 4 Locations of four mayfly gynandromorphisms found in Asia Yellow dot: Baetopus sp., Chövsgöl, Mongolia (after Soldán and Landa, 1981); Pink dot: Rhithrogena nepalensis Braasch, 1984, Alay mountain range, Tajikistan (after Kluge, 2015); Blue dot: Cingmina sp., Anhui Province, China (after Wu et al., 1993); Red dot: Choroterpes facialis, Fujian Province, China.
FIGURE 3 in Report of intersex individuals from a southeastern Chinese Choroterpes facialis (Gillies, 1951) population (Ephemeroptera: Leptophlebiidae)
FIGURE 3 Abdominal terminals of two intersexes of Choroterpes facialis and two normal imagos A: Normal male (showing normal penes and forceps, dorsal view, dorsal portion of abdominal segment IX removed); B: Predominately male intersex (showing penes, sternum IX and white interior, dorsal view); C: Predominately female intersex (showing penes, sternum IX and eggs inside, dorsal view); D: Normal female (showing sternum IX, ventral view). Arrows in A–C indicating the penis.
FIGURE 2 in Report of intersex individuals from a southeastern Chinese Choroterpes facialis (Gillies, 1951) population (Ephemeroptera: Leptophlebiidae)
FIGURE 2 Head of two intersexes of Choroterpes facialis and two normal imagos (dorsal view) A: Normal male; B: Predominately male intersex; C: Predominately female intersex; D: Normal female
Sequences of nuclear ribosomal internal transcribed spacer (ITS) for five Toxicodendron vernicifluum individuals
<p>This dataset includes the aligned sequences of nuclear ribosomal internal transcribed spacer (ITS) for five <em>Toxicodendron vernicifluum</em> individuals sampled from China.</p>
Cortical glutamate and GABA are related to compulsive behaviour in individuals with obsessive compulsive disorder and healthy controls
<p>This link contains the Data Source files, the code, and raw MRS data analysed in <a href="https://www.researchsquare.com/article/rs-2152573/v1">this article</a>. </p>
FIG. 1 in Evaluation of Tagging Methods for Unique Identification of Individuals in Three Aquatic Eurycea Salamander Species
FIG. 1. VIE tag orientation used on three aquatic salamanders (Eurycea spp.) was either vertical or horizontal. Tags were read left to right (tail to head) for vertical orientation and top to bottom (dorsal to ventral) for horizontal orientation.
FIG. 3 in Evaluation of Tagging Methods for Unique Identification of Individuals in Three Aquatic Eurycea Salamander Species
FIG. 3. Examples of the different type of scenarios with tags in the study with time series photos and corresponding scores for expert readability (Read), breakage (Break), and percent remaining (%). (A) Depicts E. rathbuni with near faultless horizontal VIE tag, black-blue-pink. (B) A typical horizontal VIE tag on Texas Blind Salamander, purple-yellow-pink. (C) A vertical VIE tag on Texas Blind Salamander showing how the elastomer spreads into costal grooves and breaks along those lines, orange-green-red. (D) San Marcos Salamander with vertical VIE tag showing partial tag migration between month 6 and 9. Bottom portion of second yellow line migrates toward third green line, yellow-yellow-green. This individual had a previous horizontal green mark for sex. (E) Comal Springs Salamander with vertical VIE tag portraying partial tag loss of pink line between month 6 and 9, yellow-pink-red. (F) A Texas Blind Salamander, part of pilot tagging group tagged in June 2018, that was followed during the study, depicting a long-term horizontal VIE tag, orange-yellow-orange.
FIG. 2 in Evaluation of Tagging Methods for Unique Identification of Individuals in Three Aquatic Eurycea Salamander Species
FIG. 2. Body size ranges for three aquatic salamanders (Eurycea spp.) varied by species and animal age. (A) Largest F1 Texas Blind Salamander, in the first group to receive PIT tags. (B) Average size representative of Texas Blind Salamanders to receive horizontal VIA and VIE tags, approximately 2–5 plus years of age. (C) 8.4 mm PIT tag. (D) Average size of vertical VIE-tagged Texas Blind Salamanders, approximately nine months to 1.5 years of age. (E) Average size of San Marcos Salamanders used in study, 2 plus years of age. (F) Average size of Comal Springs Salamanders used in study, 2 plus years of age.
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.