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Fig. 2. – Begonia jaguarensis L. Kollmann, R. S. Lopes & Peixoto. A in Begonia jaguarensis L. Kollmann, R.S.Lopes &Peixoto (Begoniaceae), a new species from North of Espírito Santo State, Brazil
Fig. 2. – Begonia jaguarensis L. Kollmann, R. S. Lopes & Peixoto. A. Habitat; B, C. Habit (Kollmann 11441); D. Habit (Kollmann 11418); E. Habit with staminate and pistillate flowers; F. Pistil; G. Habit with staminate and pistillate flowers; H. Stamens; I. Monochasial inflorescence with open staminate flower; J. Dichasial inflorescence; K. Pistillate flowers. [Photo: L. J.-C. Kollmann]
Fig. 1. – Begonia jaguarensis L. Kollmann, R. S. Lopes & Peixoto. A in Begonia jaguarensis L. Kollmann, R.S.Lopes &Peixoto (Begoniaceae), a new species from North of Espírito Santo State, Brazil
Fig. 1. – Begonia jaguarensis L. Kollmann, R. S. Lopes & Peixoto. A. Habit; B. Stipule; C. Leaf apex; D. Inflorescence; E. Bracts; F. Staminate flower; G. Staminate flower; H. Stamens; I. Prophyll; J. Pistillate flower; K. Styles; L. Seed. [Kollmann & Lopes 11441, MBML] [Drawn by Maria Alice Rezende]
Relative width and height of handwritten letters. R code.
<p>Heigths and widths of handwritten letters of 21 writers. 500 letters of each writer.</p> <p>R code for data analysis. The codes are in beta phase, the codes have no help or explanations. Units mm/100.</p>
Illustrative dataset for the article: Vieira, R., McDonald, S., Araujo-Soares, V., Sniehotta, F., Henderson, R. (2017) "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies"
<p>This dataset is supplementary material of the manuscript "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies. McDonald et al. (2016) presents a series of novel n-of-1 studies that intended to explore the relationship between physical activity change during the retirement transition. The file contains the data of one participant. The column names correspond to the following variables:</p> <p>time: duration of follow-up (minutes);<br> minute: time of day (hours and minutes);<br> day_num: day since beginning of follow-up (the first two days were considered as adaptation phase and therefore removed); <br> PAscore: accelerometer raw score; <br> startBout: 1 (a bout of PA was initiated in this minute) or 0 (a bout of PA wasn't <br> initiated in this minute); <br> nPAbouts_day: number of PA bouts per day; <br> nPAbouts_day.l1: number of PA bouts in previous day (lag 1); <br> nPAbouts_day.l2: number of PA bouts two day before (lag 2); <br> nBoutsLast2hours: number of PA bouts in previous 2 hours; <br> retirement: 0 (before retirement) or 1 (after retirement)<br> weekday: 0 (workday) or 1 (weekend)<br> sleepLength: number of hours of sleep last night<br> sleepLength.l1: number of hours of sleep the night before<br> sleepLength.l2: number of hours of sleep two nights before<br> pers: personalised measure of partner's influence (scale 0-1)<br> periodDay: morning, evening or afternoon</p> <p>McDonald, S., Vieira, R., O'Brien, N., White, M., & Sniehotta, F. F. (2016). Does physical activity and sedentary behavior change during the retirement transition? Findings from a series of novel n-of-1 natural experiments. <em>International Journal of Behavioral Medicine, 23</em>, S261-S261.</p> <p> </p>
Individual-based simulation model of annual movement paths for the Darwin's frog (R code and data)
<p>Desprition of the R code</p> <p>I constructed an individual-based simulation model that describes the movement path of an individual<em> Rhinoderma darwinii</em> through 3-month displacement steps. This model was primarily developed to evaluate the age-specific movement behaviour of Darwin's frogs, however, I also used it to provide better estimates (i.e. alleviating for movement censoring) of age-specific annual displacements in the species. I developed several variations of this model through a combination of different random walk sub-models for juveniles and adults: uncorrelated non-stationary random walks (NRW), correlated non-stationary random walks (CRW), and stationary random walks (SRW). The NRW and CRW were modelled as a first-order Markovian process where the location of an individual <em>i</em> in time<em> t</em> depends on its spatial location in <em>t </em>- 1. The NRW is unbiased, i.e., there is no preferred direction in each movement step. In contrast, the CRW includes persistence in the directionality of movement, so there is a correlation between successive step orientations. Finally, the SRW assumes that individuals have an activity centre to which all their spatial locations are related.</p> <p>Related data are provided (y.txt, x.txt and age.txt)</p>
Data and R script for Neville, Andrews, Nettle and Bateson, 'Dissociating the effects of alternative early-life feeding schedules on the development of adult depression-like phenotypes'
<p>The R script and raw data files for the paper 'Dissociating the effects of alternative early-life feeding schedules on the development of adult depression-like phenotypes', by Vikki Neville, Clare Andrews, Daniel Nettle and Melissa Bateson.</p>
Dataset and full R script used in the data analysis of the paper "Overlooked and undervalued: Peripheral pollinators in an urban network"
<p>Dataset and full R script used in the data analysis of the paper "<strong>Overlooked and undervalued: Peripheral pollinators in an urban network</strong>".</p> <p>Summary:</p> <p>Since insect pollinators are essential for their ecological and agricultural roles, their conservation should be a priority, particularly in the remnant green spaces within highly urbanised cities. To gain insight into the occurrence of interactions between plants and often overlooked pollinators, and into their requirements for persistence over time in urban green spaces, we studied flower visitor diversity associated with a remnant of native vegetation in Cordoba (Argentina), one of the largest cities in South America. We recorded 198 insect species from six orders (Hymenoptera, Diptera, Lepidoptera, Coleoptera, Thysanoptera, and Hemiptera) interacting with the flowers of 94 plant species. The plant-pollinator interaction network was significantly modular, with 178 pollinators playing a peripheral role (i.e., it has a few links inside its own module and rarely any to other modules). We focused on the life history traits of these peripheral pollinators, which are often neglected in ecological studies. We classified their requirements to complete the life cycle and to persist over time into three broad categories: floral rewards, places to reproduce, and additional resources for food and nests. The life cycle requirements of peripheral pollinators differ significantly across insect orders. Hymenoptera and Lepidoptera have distinct life history requirements while Diptera and Coleoptera overlap in resource use. The three life history categories highlight how pollinators displayed different foraging behaviour, reproductive strategies of immature and adult stages, and the requirement of additional food resources used by larvae and adults beyond flower rewards to complete their life cycles. Knowledge about the requirements of neglected pollinators is a benchmark that can help to identify where efforts need to be made to conserve and maintain their biodiversity, even in small urban green spaces.</p>
Eddy covariance data processing workflow example utilizing openeddy and REddyProc R packages
<p>The example dataset is provided within the folder structure required by the workflow files (version 2025-04-27; amended on 2025-07-31) related to the R package openeddy version 0.0.0.9009. Only files needed for successful processing are included. It is shared here as part of a data processing example at <a href="https://github.com/lsigut/EC_workflow">https://github.com/lsigut/EC_workflow</a> to overcome the file size limitation of GitHub.</p>
Data sets and R codes for "It's about her: male within-season movements are related to mate searching in a songbird"
<p><strong>Abstract</strong></p><p>In species with resource-defense mating systems (such as most temperate-breeding songbirds), male dispersal is often considered to be limited in both frequency and spatial extent. When dispersal occurs within a breeding season, the favored explanation is ecological resource tracking. In contrast, movements of male birds associated with temporary emigration, such as polyterritoriality (i.e., defense of an additional location after attracting a female in the initial territory), are usually attributed to mate searching. We suggest that male dispersal and polyterritoriality are functionally related, and that mate searching may be a unifying hypothesis for predicting the within-season movements of male songbirds. Here, we test three key predictions derived from this hypothesis in Wood Warblers <i>Phylloscopus sibilatrix</i>. We collected data on the spatial behavior of 107 males between 2017 and 2019, and related male movements to a new territory (both in a dispersal and polyterritorial context) to mating potential in the current territory. Most males dispersed from their territories within days or weeks after failing to attract a female, despite occupying territories in apparently suitable habitat. Probability of polyterritoriality by paired males increased after the peak fertile period of their mate. Males never dispersed following nest predation if the female remained to renest. Thus, our data are consistent with the hypothesis that both movement types are functionally related to mate searching.</p>
Data and R script for: Shoaling behaviour in response to turbidity in three-spined sticklebacks
<p class="MsoNormal"><span>Many fresh and coastal waters are becoming increasingly turbid because of human activities, which may disrupt the visually-mediated behaviours of aquatic organisms. Shoaling fish typically depend on vision to maintain collective behaviour, which has a range of benefits including protection from predators, enhanced foraging efficiency, and access to mates. Previous studies of the effects of turbidity on shoaling behaviour have focussed on changes to nearest neighbour distance and average group-level behaviours. Here, we investigated whether and how experimental shoals of three-spined sticklebacks (<em><span>Gasterosteus aculeatus</span></em>) in clear (<10 <span>Nephelometric Turbidity Units (NTU))</span> and turbid (~35 NTU<span>) </span>conditions differed in five local-level behaviours of individuals (nearest and furthest neighbour distance, heading difference with nearest neighbour, bearing angle to nearest neighbour, and swimming speed). These variables are important for the emergent group-level properties of shoaling behaviour. We found an indirect effect of turbidity on nearest-neighbour distances driven by a reduction in swimming speed, and a direct effect of turbidity which increased variability in furthest neighbour distances. In contrast, the alignment and relative position of individuals was not significantly altered in turbid compared to clear conditions. Overall, our results suggest that the shoals were usually robust to adverse effects of turbidity on collective behaviour, but group cohesion was occasionally lost during periods of instability.</span></p>
Fig. 3 in Extended distribution patterns of the Arabian burnet moth Reissita simonyi (R , 1899) (Lepidoptera: Zygaenidae) and the Arabian wall brown Lasiommata felix (W , 1929) (Lepidoptera: Nymphalidae: Satyrinae) in Southern Arabia
Fig. 3: A typical place to find Lasiommata felix near Bani Mawhab / Bait Muzaret. The artificial walls seem to be a suitable habitat for L. felix.
Fig. 4 in Extended distribution patterns of the Arabian burnet moth Reissita simonyi (R , 1899) (Lepidoptera: Zygaenidae) and the Arabian wall brown Lasiommata felix (W , 1929) (Lepidoptera: Nymphalidae: Satyrinae) in Southern Arabia
Fig. 4: Distribution of Reissita simonyi yemenicola (along the Red Sea) and Reissita simonyi simonyi (along the Indian Ocean at both sides of the Yemeni-Omani border).
Raw absorbance data and R codes for analysing enzymatic activities
<p>Raw absorbance data and sample metadata for study by Prokkola et al. (submitted 2023). See README.</p><p>Statistical analysis of data available in another repository https://doi.org/10.5281/zenodo.8014314.</p><p> </p>
Data and R code from: Fin whale song evolution in the North Atlantic
<p>Animal songs can change within and between populations as the result of different evolutionary processes. When these processes include cultural transmission, the social learning of information or behaviours from conspecifics, songs can undergo rapid evolutions because cultural novelties can emerge more frequently than genetic mutations. Understanding these song variations over large temporal and spatial scales can provide insights into the patterns, drivers and limits of song evolution that can ultimately inform on the species' capacity to adapt to rapidly changing acoustic environments.</p> <p>In this study, we analysed changes in fin whale (<em>Balaenoptera physalus</em>) songs recorded over two decades (1999–2020) across the central and eastern North Atlantic Ocean. We document a rapid replacement of song INIs (inter-note intervals) over just four singing seasons (2000/2001–2004/2005) in the southeast location of the Oceanic Northeast Atlantic (ONA) region, that co-occurred with hybrid songs (with both INIs). During the transition in song INIs (2002/2003) we show a clear geographic gradient in the occurrence of different song INIs in the whole ONA region. We also found gradual changes in song INIs (Figure 3A) and 20-Hz note (Figure 3B) and HF note (Figure 3C) peak frequencies over more than a decade with fin whales adopting song changes. These results provide evidence of vocal learning in fin whales and reveal patterns of song evolution that raise questions on the limits of song variation in this species.</p>
F I G U R E 4 in Phylogeography of Solomon Islands blossom bats reflects oceanic divides and Pleistocene connections
F I G U R E 4 (a) Phylogenetic tree of Tribe Melonycterini blossom bats produced using the quartet-based method implemented in SVDǪUARTETS, and (b) phylogenetic network of Nesonycteris blossom bats created using SPLITSTREE.
F I G U R E 2 in Phylogeography of Solomon Islands blossom bats reflects oceanic divides and Pleistocene connections
F I G U R E 2 Phylogeographic relationships among Melonycterini blossom bats (a) the Solomon Islands archipelago with bathymetric depths less than 120 m (ETOPO1, Amante & Eakins, 2009) shaded in dark grey to indicate possible land bridge connections during the Last Glacial Maximum. Islands shaded in colour represent those sampled for this study, samples were unavailable for islands shaded in light grey (EPSG: 4326–WGS 84). (b) Phylogenetic tree made using maximum likelihood methods in RAXML depicting relationships among all Nesonycteris and Melonycteris samples. Values indicate maximum likelihood bootstrap support and black circles on nodes denote values = 100. (c) Representation of the taxonomic treatment of Nesonycteris prior to this study comprising two species; (d) Alternate taxonomic treatment of Nesonycteris from the results of this study comprising four species (N. far = N. fardoulisi, N. mac = N. maccoyi); The results of STRUCTURE analyses for various datasets are presented as (e) Dataset 1, a single run, k = 4; (f) Dataset 2, a single run, k = 2; (g) Dataset 3, a single run, k = 3; and (h) Dataset 4 (five runs R1–R5, k = 4). In STRUCTURE results (e–h), each bar indicates the probability of assignment to different genetic clusters.
F I G U R E 3 in Phylogeography of Solomon Islands blossom bats reflects oceanic divides and Pleistocene connections
F I G U R E 3 Pairwise Nei's genetic distances for Melonycteris and Nesonycteris blossom bats from the Solomon Islands and Bismarck archipelagos. Nei's genetic distance was calculated using the R package StAMPP. Values surrounded by a yellow or green rectangle are pairwise distances between samples from New Georgia group islands, and Greater Bukida islands, respectively.
Figs 7a–r in Five New Spathidiids (Ciliophora: Bromeliads Haptoria) from Caribbean Tank
Figs 7a–r. Spathidium bromeliophilum from life (a–e, o–p) and after protargol impregnation (f–n, q, r). a – right side view of a representative specimen, length 135 μm. Arrowhead marks end of bristle tail of brush row 3; b, c – the ∞-shaped oral bulge, which is slightly dumbbell-shaped and/or cuneate, is studded with extrusomes. The oblique fibre bundles originate from the circumoral dikinetids; d – two views of the same extrusome, length 5 µm; e – surface view, showing the two size-types of cortical granules, diameter 0.2 µm and 0.4 µm; f–j – variability of shape of body and oral bulge whose ventral third is often curved laterally; k, l – a specimen engulfing a Colpoda (k) and another that has just engulfed a Vorticella; m, n – ciliary pattern of right and left side and nuclear apparatus of holotype specimen, length 138 μm; o, p – detail and overview of a resting cyst with inactive contractile vacuole, diameter 42 µm; q, r – ciliary pattern of anterior dorsal and ventral side, showing the isostichad dorsal brush and the wide spacing of the dikinetids in row 3. B(1–3) – dorsal brush (rows), BA – oral basket, BU – oral bulge, CK – circumoral kinety, CV – contractile vacuole, E – extrusomes, EP – excretory pores, FV – food vacuole, LD – lipid droplets, MA – macronuclear nodules, MI – micronuclei. Scale bars: 40 μm (a, f–n), 20 μm (q–r) and 2 μm (o).
R scripts, input and output data for: Season of death, pathogen persistence and wildlife behaviour alter number of anthrax secondary infections from environmental reservoirs
<p>An important part of infectious disease management is predicting factors that influence disease outbreaks, such as <em>R</em>, the number of secondary infections arising from an infected individual. Estimating <em>R</em> is particularly challenging for environmentally transmitted pathogens given time lags between cases and subsequent infections. Here, we calculated <em>R</em> for <em>Bacillus anthracis</em> infections arising from anthrax carcass sites in Etosha National Park, Namibia. Combining host behavioural data, pathogen concentrations, and simulation models, we show that <em>R</em> is spatially and temporally variable, driven by spore concentrations at death, host visitation rates and early preference for foraging at infectious sites. While spores were detected up to a decade after death, most secondary infections occurred within two years. Transmission simulations under scenarios combining site infectiousness and host exposure risk under different environmental conditions led to dramatically different outbreak dynamics, from pathogen extinction (<em>R</em><1) to explosive outbreaks (<em>R</em>>10). These transmission heterogeneities may explain variation in anthrax outbreak dynamics observed globally, and more generally, the critical importance of environmental variation underlying host-pathogens interactions. Notably, our approach allowed us to estimate the lethal dose of a highly virulent pathogen non-invasively from observational studies and epidemiological data, useful when experiments on wildlife are undesirable or impractical.</p>
Code and example images from: recolorize: An R package for flexible color segmentation of biological images
<p>Color pattern variation provides biological information in fields ranging from disease ecology to speciation dynamics. Comparing color pattern geometries across images requires color segmentation, where pixels in an image are assigned to one of a set of color classes shared by all images. Manual methods for color segmentation are slow and subjective, while automated methods can struggle with high technical variation in aggregate image sets. We present recolorize, an R package toolbox for human-subjective color segmentation with functions for batch-processing low-variation image sets and additional tools for handling images from diverse (high variation) sources. The package also includes export options for a variety of formats and color analysis packages. This paper illustrates recolorize for three example datasets, including high variation, batch processing, and combining with reflectance spectra, and demonstrates the downstream use of methods that rely on this output.</p>
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.