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1,249 results for “R data”
CSV files and R script: writing process data of typed picture description by 15 cognitively impaired patients and 15 healthy controls
<p>Writing process data of 15 cognitively impaired patients and 15 age- and gender-matched healthy controls were obtained. Each of them completed two typed picture description tasks that were logged with Inputlog, a keystroke logging tool. Variables included time on task; number of characters, pauses and Pause-bursts per minute; proportion of pause time; duration of Pause-bursts; and pause time between words. For pause time between words, also the effect of pauses preceeding specific word categories was analyzed.</p> <p>The data were used to explore if the observation of writing behavior can assist in the screening and follow-up of mild cognitive impairment (MCI) and mild dementia due to Alzheimer’s disease (AD). This data set contains the CSV files that were used for the analyses and the corresponding R script.</p>
Data and R codes: species range-size variation in oaks
<p>We used occurrence data of 183 oak species (<em>Quercus </em>spp.) in North and South America to test how niche breadth and niche position affect the amount of suitable habitat area, and how colonization ability and post-glacial migration lags affect range filling. This dataset includes the data and R files related to the analyses. </p>
R_JAGS code for estimation and analysis of species-area-relationship (SAR) parameters from NEON (National Ecological Observatory Network) data on plant surveys
<p><span>Invasive species science is heavily geared toward the invasive agent. </span>However, management to protect native species also requires a proactive approach focused on understanding the features affecting community vulnerability to invasion impacts<span>. </span><span>Vulnerability </span><span>is likely the result of </span><span>factors acting across spatial scales, from </span><span>local to regional, and it is the combined effects of these factors that will determine the magnitude of vulnerability.</span><span> We introduce an analytical framework that quantifies the scale-dependent impact of biological invasions from the shape of the native species-area-relationship (SAR). We leverage newly available, biogeographically extensive vegetation data from the US National Ecological Observatory Network to assess plant community vulnerability to invasion impact as a function of factors acting across scales. We analyzed more than 1000 SARs widely distributed across the USA along environmental gradients and under different levels of invasion. </span>Results show that a decrease in native richness is consistently associated with invasive species cover<span>, but it is only at relatively high levels of invasion that native richness is compromised. After accounting for variation in baseline ecosystem diversity, net primary productivity, and human modification, ecoregions that are colder and wetter seem to be most vulnerable to losses of native plant species at the local level, while warmer and wetter areas seem most susceptible at the landscape level. We also document how the combined effects of cross-scale factors result in a heterogenous spatial pattern of vulnerability. </span><span>This pattern </span><span>cannot be predicted by analyses at any single scale, underscoring the importance of accounting for factors acting across scales. Simultaneously assessing differences in vulnerability between distinct plant communities at local, landscape and regional scales provided outputs that can be used to inform policy and management aimed at reducing vulnerability to the impact of plant invasions.</span></p>
Data and R code used for the GLMM and NBDA analyses in 'Captive Asian short-clawed otters (Aonyx cinereus) learn to exploit unfamiliar natural prey'
<p>Foraging plays a vital role in animal life histories, learning whether unfamiliar food items are palatable is a key part of this process. Animals that engage in extractive foraging must also learn how to overcome the protective measures of their prey. While otters (subfamily Lutrinae) are a taxon known for their extractive foraging behaviour, how they learn about prey palatability and acquire extractive foraging techniques remains poorly understood. Here we investigated: (i) how captive Asian short-clawed otters (<em>Aonyx cinereus</em>) learned to interact with, and extract meat from, unfamiliar natural prey, and (ii) how their exploitation of such prey compared to their ability to overcome artificial foraging tasks containing familiar food rewards. Network-based diffusion analysis showed that otters learned to interact with unfamiliar natural prey by observing their group mates. However, once interacting with the prey, they learned to extract the meat mainly asocially. In addition, otters took longer to overcome the protective measures of unfamiliar natural prey than those of extractive food puzzles. Asian short-clawed otter populations are declining in the wild. Increasing our understanding of how they learn to overcome novel foraging challenges could help develop pre-release training procedures as part of reintroduction programmes for otter conservation.</p>
Data and R-scripts from: Multiple stressors: negative effects of nest predation on the viability of a threatened gull in different environmental conditions
<p>This contains data and R-scripts used in: </p> <ul> <li>Bård-Jørgen Bårdsen and Jan Ove Bustnes (2022). Multiple stressors: negative effects of nest predation on the viability of a threatened gull in different environmental conditions. Journal of Avian Biology.</li> </ul> <p>This study assessed the population viability of a population of the lesser black-backed gull (<em>Larus fuscus fuscus</em>) using data collected during 2005-2020 from a nature reserve in Northern Norway. The study merged results from statistical analyses of empirical data with a Leslie model. Here, we provide the underlying data, and the R-scripts used to analyse the data and run the model. The data set include information about reproduction at several stages (laying, hatching and fledgling), nest predation, and individual capture histories (used to estimate apparent survival; see <a href="https://doi.org/10.1111/jav.02953">Bårdsen and Bustnes 2022</a>).</p>
YOGData: Labelled data (YOLO and Mask R-CNN) for yogurt cup identification within production lines
<p><strong>D</strong><strong>ata abstract:</strong><br> The YogDATA dataset contains images from an industrial laboratory production line when it is functioned to quality yogurts. The case-study for the recognition of yogurt cups requires training of Mask R-CNN and YOLO v5.0 models with a set of corresponding images. Thus, it is important to collect the corresponding images to train and evaluate the class. Specifically, the YogDATA dataset includes the same labeled data for Mask R-CNN (coco format) and YOLO models. For the YOLO architecture, training and validation datsets include sets of images in jpg format and their annotations in txt file format. For the Mask R-CNN architecture, the annotation of the same sets of images are included in json file format (80% of images and annotations of each subset are in training set and 20% of images of each subset are in test set.) <br> </p> <p><strong>Paper abstract:</strong><br> The explosion of the digitisation of the traditional industrial processes and procedures is consolidating a positive impact on modern society by offering a critical contribution to its economic development. In particular, the dairy sector consists of various processes, which are very demanding and thorough. It is crucial to leverage modern automation tools and through-engineering solutions to increase their efficiency and continuously meet challenging standards. Towards this end, in this work, an intelligent algorithm based on machine vision and artificial intelligence, which identifies dairy products within production lines, is presented. Furthermore, in order to train and validate the model, the YogDATA dataset was created that includes yogurt cups within a production line. Specifically, we evaluate two deep learning models (Mask R-CNN and YOLO v5.0) to recognise and detect each yogurt cup in a production line, in order to automate the packaging processes of the products. According to our results, the performance precision of the two models is similar, estimating its at 99\%. </p> <p> </p>
Data and R code for long-term study of fire and climate effects on water quality in Clear Lake, California
<p>Long-term relationships between water quality, fire and climate for Clear Lake, California. Although the watershed has historically experienced frequent fire, the 2018 Mendocino Complex, which was the largest wildfire complex in state history, burned approximately 40% of the watershed, sparking concerns about drinking water quality and lake ecosystem health. This analysis spans approximately 1968-2021.</p>
Data from: imageseg: An R package for deep learning-based image segmentation
<p>1. Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can for example be used to assess forest structural metrics. While CNN-based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists.</p> <p>2. Here, we present R package imageseg which implements a CNN-based workflow for general-purpose image segmentation using the U-Net and U-Net++ architectures in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 2877 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1285 understory vegetation images.</p> <p>3. Overall segmentation accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively). The image segmentation models performed significantly better than commonly used thresholding methods, and generalized well to data from study areas not included in training. This indicates robustness to variation in input images and good generalization strength across forest types and biomes.</p> <p>4. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with single or multiple classes and based on color or grayscale images, e.g. for applications in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond.</p>
Data and R code from: Spatiotemporal risk factors predict landscape-scale survivorship for a northern ungulate
<p>These data and computer code (written in R, https://www.r-project.org) were created to statistically evaluate a suite of spatiotemporal covariates that could potentially explain pronghorn (Antilocapra americana) mortality risk in the Northern Sagebrush Steppe (NSS) ecosystem (50.0757<sup>o</sup> N, −108.7526<sup>o</sup> W). Known-fate data were collected from 170 adult female pronghorn monitored with GPS collars from 2003-2011, which were used to construct a time-to-event (TTE) dataset with a daily timescale and an annual recurrent origin of 11 November. Seasonal risk periods (winter, spring, summer, autumn) were defined by median migration dates of collared pronghorn. We linked this TTE dataset with spatiotemporal covariates that were extracted and collated from pronghorn seasonal activity areas (estimated using 95% minimum convex polygons) to form a final dataset. Specifically, average fence and road densities (km/km2), average snow water equivalent (SWE; kg/m2), and maximum decadal normalized difference vegetation index (NDVI) were considered as predictors. We tested for these main effects of spatiotemporal risk covariates as well as the hypotheses that pronghorn mortality risk from roads or fences could be intensified during severe winter weather (i.e., interactions: SWE*road density and SWE*fence density). We also compare an analogous frequentist implementation to estimate model-averaged risk coefficients. Ultimately, the study aimed to develop the first broad-scale, spatially explicit map of predicted annual pronghorn survivorship based on anthropogenic features and environmental gradients to identify areas for conservation and habitat restoration efforts.</p> <p> </p>
spectre: An R package to estimate spatially-explicit community composition using sparse data
<p>An understanding of how biodiversity is distributed across space is key to much of ecology and conservation. Many predictive modelling approaches have been developed to estimate the distribution of biodiversity over various spatial scales. Community modelling techniques may offer many benefits over single-species modelling. However, techniques capable of estimating precise species makeups of communities are highly data intensive and thus often limited in their applicability. Here we present an R package, spectre, which can predict regional community composition at a fine spatial resolution using only sparsely sampled biological data. The package can predict the presence and absence of all species in an area, both known and unknown, at the sample site scale. Underlying the spectre package is a min-conflicts optimisation algorithm that predicts species' presences and absences throughout an area using estimates of α-, β-, and γ-diversity. We demonstrate the utility of the spectre package using a spatially-explicit simulated ecosystem to assess the accuracy of the package's results. spectre offers a simple-to-use tool with which to accurately predict community compositions across varying scales, facilitating further research and knowledge acquisition into this fundamental aspect of ecology.</p>
Data and R computer code from: Summer elk calf survival in a partially migratory population
<p>These data and computer code (written in R, https://www.r-project.org) were created to statistically evaluate a suite of intrinsic and extrinsic risk factors related to calf elk and their mothers' body condition and age. Specifically, known-fate data were collected from 94 elk calves monitored from 2013-2016 in a partially migratory elk (<em>Cervus</em> <em>canadensis</em>) population in Alberta, Canada. Along with adult female data on pregnancy status, age, and body condition, we created a time-to-event dataset that allowed us to analyze calf mortality risk in a time-to-event approach. We also estimated pooled survivorship and cause-specific mortality, as well as stratifying these metrics by migration tactic (resident vs. eastern migrant). Cox proportional hazards models were used to evaluate calf mortality risk in terms of forage biomass (kg/ha), bear predation risk (from an RSF), and other factors that varied between migration tactics. We tested for differences in a number of maternal reproductive parameters (e.g., pregnancy status) and for calf explanatory variables between migrant and resident elk segments. We also use cumulative incidence functions to estimate cause-specific mortality in this multiple carnivore system. Ultimately, we hope that this work helps wildlife managers anticipate how elk calf survival and partial migration dynamics are affected by grizzly bear predation, and our study builds on a long-term partial migration study at the Ya Ha Tinda Ranch in Alberta, Canada. </p>
Text-fig. 2. Tectocarya spp. a–n: Tectocarya grandis (E.REID et M.CHANDLER) comb. n. Holotype V.22968. a: Lateral view of broken endocarp, reflected light. b–d: Longitudinal views, surface renderings from micro-CT data. e: Translucent volume renderings. f: Apical view, surface rendering. g: View of transversely broken surface showing curved locule, reflected light. h–n: Successive digital transverse sections. Note septum in the dorsal infold (arrows). o, p: Tectocarya rhenana KIRCHH., Miocene of Germany, dorsal view and transverse section [Holotype of Mastixoidea tectocaryoides KIRCHH., Alfred Mine near Konzendorf, photo by Dieter Mai] (Synonym of T. rhenana MAI, 1993). q: T. rhenana transverse section. from Mine Alfred, Düren, Germany, coll. Claire A. Brown 1952, USNM 355632. r, s: Tectocarya sp. from late Eocene of Post, Oregon, USA, physical transverse section, reflected light. UF279-50014. [Surface views of same specimen shown in Manchester and McIntosh 2007: figs 62, 63]. Scale bars 1 cm in (a–r), 0.5 cm in (s). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 2. Tectocarya spp. a–n: Tectocarya grandis (E.REID et M.CHANDLER) comb. n. Holotype V.22968. a: Lateral view of broken endocarp, reflected light. b–d: Longitudinal views, surface renderings from micro-CT data. e: Translucent volume renderings. f: Apical view, surface rendering. g: View of transversely broken surface showing curved locule, reflected light. h–n: Successive digital transverse sections. Note septum in the dorsal infold (arrows). o, p: Tectocarya rhenana KIRCHH., Miocene of Germany, dorsal view and transverse section [Holotype of Mastixoidea tectocaryoides KIRCHH., Alfred Mine near Konzendorf, photo by Dieter Mai] (Synonym of T. rhenana MAI, 1993). q: T. rhenana transverse section. from Mine Alfred, Düren, Germany, coll. Claire A. Brown 1952, USNM 355632. r, s: Tectocarya sp. from late Eocene of Post, Oregon, USA, physical transverse section, reflected light. UF279-50014. [Surface views of same specimen shown in Manchester and McIntosh 2007: figs 62, 63]. Scale bars 1 cm in (a–r), 0.5 cm in (s).
Text-fig. 3. Juglandaceae. Carya (a–x). Scale bars = 1 cm. a–e: USNM PAL 772346. Micro-CT scan surface rendering. a, b: Lateral, c: apical, d: basal views. e: Virtual equatorial transverse section. f–n: USNM PAL 772347. f: Lateral view, reflected light, showing path of saw cut for transverse section of (i). g: Basal view, reflected light. h: Apical view, micro-CT surface rendering. i: Physical transverse section displaying locule and cellular preservation of parts of wall. j–n: Virtual sections from micro-CT scan data. j: Transverse section at apical 1/3 of nut. Note narrow lacunae (arrows). k: Longitudinal section parallel to primary septum, traversing one of the cotyledon lobes and showing secondary septum at base. l: Longitudinal section in plane at right angles to (k) in plane of primary septum, showing divergent placental bundles arising from base of nut (arrows). m: Equatorial transverse section showing two lobes of locule separated by primary septum. n: Transverse section near base of nut showing primary and secondary septa, creating four basal lobes of locule; note diverging placental bundles (arrows). o–x: USNM PAL 772351. o: Lateral view of broken nut with exposed locule cast, reflected light. p: Same orientation of nut, micro-CT surface rendering. q: Same specimen lateral view, rotated 90° from (p), micro-CT surface rendering. r: Apical view, reflected light. s–x: Virtual sections from micro-CT in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 3. Juglandaceae. Carya (a–x). Scale bars = 1 cm. a–e: USNM PAL 772346. Micro-CT scan surface rendering. a, b: Lateral, c: apical, d: basal views. e: Virtual equatorial transverse section. f–n: USNM PAL 772347. f: Lateral view, reflected light, showing path of saw cut for transverse section of (i). g: Basal view, reflected light. h: Apical view, micro-CT surface rendering. i: Physical transverse section displaying locule and cellular preservation of parts of wall. j–n: Virtual sections from micro-CT scan data. j: Transverse section at apical 1/3 of nut. Note narrow lacunae (arrows). k: Longitudinal section parallel to primary septum, traversing one of the cotyledon lobes and showing secondary septum at base. l: Longitudinal section in plane at right angles to (k) in plane of primary septum, showing divergent placental bundles arising from base of nut (arrows). m: Equatorial transverse section showing two lobes of locule separated by primary septum. n: Transverse section near base of nut showing primary and secondary septa, creating four basal lobes of locule; note diverging placental bundles (arrows). o–x: USNM PAL 772351. o: Lateral view of broken nut with exposed locule cast, reflected light. p: Same orientation of nut, micro-CT surface rendering. q: Same specimen lateral view, rotated 90° from (p), micro-CT surface rendering. r: Apical view, reflected light. s–x: Virtual sections from micro-CT
Supplementary material 1 from: Motloung R, Robertson M, Rouget M, Wilson J (2014) Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions. NeoBiota 20: 31-48. https://doi.org/10.3897/neobiota.20.5778
Current and potential distributions of sixteen species that are not widespread in southern Africa arranged on the basis of their suitable range size : a) Acacia paradoxa, b) A. cultriformis, c) A. falciformis, d) A. pendula, e) A. rubida, f) A. stricta, g) A. retinodes, h) A. fimbriata, i) A. aneura, j) A. viscidula, k) A. acuminata, l) A. adunca, m) A. binervata, n) A. schinoides, o) A. prominens, p) A. mangium. The grey shading indicates areas that SDMs have identified as suitable by SDMs while the white ones are unsuitable.
R code and associated data for: A review of riverine ecosystem service quantification: research gaps and recommendations
<p>This publication contains the R code and associated data used in the Journal of Applied Ecology publication entitled "A review of riverine ecosystem service quantification: research gaps and recommendations". </p>
Data and R script for 'Early-life begging effort reduces adult body mass but strengthens behavioural defence of the rate of energy intake in European starlings (Sturnus vulgaris)'
<p>Data files and R script for Dunn et al. "Early-life begging effort reduces adult body mass but strengthens behavioural defence of the rate of energy intake in European starlings (<em>Sturnus vulgaris</em>)"</p> <p>Includes a single R script that produces all the analyses in the paper. The script makes use of three different .csv data files.</p>
R code and data for: How much is enough? Minimum sample sizes in community ecology
<p>minimum_sample_sizes_code.R includes the complete set of instructions used to carry out the analyses in the related manuscript, plus save the computed data files and generate the text figures. data_files.tar.gz includes all of the files generated by the R code.</p>
Simulation Data & R scripts for: "Introducing recurrent events analyses to assess species interactions based on camera trap data: a comparison with time-to-first-event approaches"
<p><strong>Files descriptions:</strong></p> <p>All csv files refer to results from the different models (PAMM, AARs, Linear models, MRPPs) on each iteration of the simulation. One row being one iteration. <br>"results_perfect_detection.csv" refers to the results from the first simulation part with all the observations.<br>"results_imperfect_detection.csv" refers to the results from the first simulation part with randomly thinned observations to mimick imperfect detection.</p> <p>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>PAMM30: p-value of the PAMM running on the 30-days survey.<br>PAMM7: p-value of the PAMM running on the 7-days survey.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p>"results_int_dir_perf_det.csv" refers to the results from the second simulation part, with all the observations.<br>"results_int_dir_imperf_det.csv" refers to the results from the second simulation part, with randomly thinned observations to mimick imperfect detection.<br>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of A on B.<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of B on A.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2_BAB: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>AAR2_ABA: ratio value for the Avoidance-Attraction-Ratio calculating ABA/AA.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p><strong>Scripts files description:</strong><br>1_Functions: R script containing the functions:<br> - MRPP from Karanth et al. (2017) adapted here for time efficiency.<br> - MRPP from Murphy et al. (2021) adapted here for time efficiency.<br> - Version of the ct_to_recurrent() function from the recurrent package adapted to process parallized on the simulation datasets.<br> - The simulation() function used to simulate two species observations with reciprocal effect on each other.<br>2_Simulations: R script containing the parameters definitions for all iterations (for the two parts of the simulations), the simulation paralellization and the random thinning mimicking imperfect detection.<br>3_Approaches comparison: R script containing the fit of the different models tested on the simulated data.<br>3_1_Real data comparison: R script containing the fit of the different models tested on the real data example from Murphy et al. 2021.<br>4_Graphs: R script containing the code for plotting results from the simulation part and appendices.<br>5_1_Appendix - Check for similarity between codes for Karanth et al 2017 method: R script containing Karanth et al. (2017) and Murphy et al. (2021) codes lines and the adapted version for time-efficiency matter and a comparison to verify similarity of results.<br>5_2_Appendix - Multi-response procedure permutation difference: R script containing R code to test for difference of the MRPPs approaches according to the species on which permutation are done.</p>
Data from: hespdiv: an R package for spatially constrained, hierarchical and contiguous regionalization in palaeobiogeography
<p>This is data for the '"hespdiv": an R package for spatially constrained, hierarchical, and contiguous regionalization in palaeobiogeography' paper. It contains datasets used, their metada, dataset processing scripts, a list of references to data contributors, and R files containing some of the results presented in the paper.</p>
Phenotype variation in Niphargus (Amphipoda: Niphargidae): possible explanations and open challenges: data and R code
<p>Data and R code for performing the analyses of phylogenetic signal presented in the manuscript titled "Phenotype variation in Niphargus (Amphipoda: Niphargidae): possible explanations and open challenges. Data contains phylogenetic tree (Delić et al., 2023) and functional trait data in the RDS format (Premate & Fišer, 2024). The R code is available in the html format.</p> <p>References/data sources:</p> <p>Delić, T., Borko, S., Premate, E., Rexhepi, B., Alther, R., Knuesel, M., ... & Altermatt, F. (2023). Evolutionary origin of morphologically cryptic species imprints co-occurrence and sympatry patterns. <em>bioRxiv</em>, 2023-09.</p> <p>Premate, E., & Fišer, C. (2024). Functional trait dataset of European groundwater Amphipoda: Niphargidae and Typhlogammaridae. <em>Scientific Data</em>, <em>11</em>(1), 188.</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.