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317
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ShareScore release 0.9.0
Dataset results
317 results for “R code”
The performance of permutations and exponential random graph models when analysing animal networks (R code and data)
<p>Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyse animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analysing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analysing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g. sex) on the number and combined strength of an individual's network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In grouping-event based networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analysing and interpreting their own network data using these methods.</p>
Data and R code for What you see is where you go: visibility influences movement decisions of a forest bird navigating a 3D structured matrix
<p>Animal spatial behaviour is often presumed to reflect responses to visual cues. However, inference of behaviour in relation to the environment is challenged by the lack of objective methods to identify the information that effectively is available to an animal from a given location. In general, animals are assumed to have unconstrained information on the environment within a detection circle of a certain radius (the perceptual range; PR). However, visual cues are only available up to the first physical obstruction within an animal's PR, making information availability a function of an animal's location within the physical environment (the effective visual perceptual range; EVPR). By using LiDAR data and viewshed analysis, we model forest birds' EVPRs at each step along a movement path. We found that the EVPR was on average 0.063% that of an unconstrained PR and, by applying a step-selection analysis, that individuals are 1.57 times more likely to move to a tree within their EVPR than to an equivalent tree outside it. This demonstrates that behavioural choices can be substantially impacted by the characteristics of an individual's EVPR and highlights that inferences made from movement data may be improved by accounting for the EVPR.</p>
Data and R code for: Tariel J., Plénet S., and Luquet É. (2020). How do developmental and parental exposures to predation affect personality and immediate behavioural plasticity in the snail Physa acuta?
<p>Data and R code of the article: Tariel J., Plénet S., and Luquet É. (2020) How do developmental and parental exposures to predation affect personality and immediate behavioural plasticity in the snail <em>Physa acuta</em>? doi:<a href="http://doi.org/10.1098/rspb.2020.1761">10.1098/rspb.2020.1761</a></p> <p>The dataset is provided (<em>data -Tariel, Plénet and Luquet (2020).csv</em>). This dataset is analyzed in the R script (<em>Juliette Tariel - R analysis.Rmd</em>). A knitted version of the R script is also provided in pdf format (<em>Juliette Tariel - R analysis.pdf</em>). Finally, a zip file is provided and contains several outputs, such as MCMCglmm objects or confint objects (<em>R outputs used in the analysis.zip</em>)</p> <p><strong>Signification of variables names:</strong></p> <ul> <li>ID: snail's identification number</li> <li>ID Family: identification number of the family of the F2 snail</li> <li>ID F1 mother: identification number of the mother of the F2 snail</li> <li>ID F1 father: identification number of the father of the F2 snail</li> <li>ID F0 grand-mother: identification number of the grand-mother of the F2 snail</li> <li>ID F0 grand-father: identification number of the grand-fathrt of the F2 snail</li> <li>Mass: total wet mass (body and shell) in grams</li> <li>Parental: parental environment (control C or predator-cue P)</li> <li>Developmental: developmental environment (C or P)</li> <li>Immediate: immediate environment (C or P)</li> <li>Trial_number</li> <li>Time: time to crawl-out of the water in seconds</li> </ul>
Supplementary Data (S1) and R Codes
<p>Supplementary Data (Data S1), including Read.me tab</p> <p>R Codes with table of contents</p>
Unvalidated R source code
<p>Univalidated R source code implmenting a performance comparison of adaptive sample size recalculation rules including in particular the new performance score by Herrmann et al. (2019)</p>
R code to perform distribution models of Plain Tyrannulet
<p>Archiving to Zenodo for peer-review</p>
Dataset and R code (Effect of Vegetation Structure on Secondary Wind Dispersal Distance of Diaspores)
Open the record for dataset details and reuse information.
Data and R code for cluster analysis and machine learning modelling of favourite places for outdoor recreation
Open the record for dataset details and reuse information.
Data and R code linked to the paper "The human metatarsal from Sedia del Diavolo"
<p>Data and R code to reproduce the results reported in "The human metatarsal from Sedia del Diavolo"</p>
FI GU R E 1 Schematic drawing of Apodera angatakere and codes of the measurements taken. (A) Length of the test; (B) width of test; (C) width of the pseudostome; (D) length of the neck; (E) maximal width of the neck; (F) width of the test at the constriction; (G) length of the test without the neck in Superficially described and ignored for 92 years, rediscovered and emended: Apodera angatakere (Amoebozoa: Arcellinida: Hyalospheniformes) is a new flagship testate amoeba taxon from Aotearoa (New Zealand)
FI GU R E 1 Schematic drawing of Apodera angatakere and codes of the measurements taken. (A) Length of the test; (B) width of test; (C) width of the pseudostome; (D) length of the neck; (E) maximal width of the neck; (F) width of the test at the constriction; (G) length of the test without the neck
R code: Large-scale assessment of bird data quality in a citizen science platform
<p>R code archived to Zenodo for publication</p>
Supplemental R code and csv files for statistical analysis on Doi et al. "Effects of species traits and ecosystem characteristics on species detection by eDNA metabarcoding in lake fish communities"
<p>Supplemental R code and csv files for statistical analysis on Doi et al. "Effects of species traits and ecosystem characteristics on species detection by eDNA metabarcoding in lake fish communities"</p>
R Code for Count data, spatial data, environmental data - Dee Estuary Waders 1970-2020
<p>R Code for analysis of spatio temporal data of waders on the Dee Estuary</p>
R code and data for "Multiple imputation and direct estimation for qPCR data with non-detects"
<p>R code and data to reproduce figures and tables in the manuscript: Multiple imputation and direct estimation for qPCR data with non-detects.</p>
Oil palm yield census data and R-code_v1
<p>Census datasets 1-28</p> <p>R-code for processing and plotting</p>
R code to perform distribution models of Plain Tyrannulet
<p>Archiving to Zenodo for publication</p>
Data and R Code from "PAT-GEOM: A Software Package for the Analysis of Animal Patterns" (published in Methods in Ecology and Evolution)
<p>Datasets for Figures 2 and 4 in the article "PAT-GEOM: A Software Package for the Analysis of Animal Patterns" (published in Methods in Ecology and Evolution) and the R code used to perform the analysis described in the article.</p>
Datasets and R source code of manuscript "Adding insult to injury: anthropogenic noise intensifies predation risk by an invasive freshwater fish species" by Fernandez Declerck et al.
<p>Datasets and R source code of manuscript "Adding insult to injury: anthropogenic noise intensifies predation risk by an invasive freshwater fish species" by Fernandez Declerck et al. (submitted)</p> <p> Project<br> ├── README<br> ├── data_functional_response.txt<br> ├── data_prey_behaviour.txt<br> ├── R_script.R<br> └── BINV-D-22-00447_Playback.wav</p> <p>Main dataset: 'data_functional_response.txt'. Dataset for the functional response of the predator. Fish behaviour was recorded under two noise conditions (either boat noise or ambient noise). The dataset corresponds to data frame "d" in the script. Variables description:<br> - id: identity of the fish<br> - condition: noise condition, either "boat noise" or "ambient noise"<br> - prey_number: number of chironomid larvae introduced in the tank<br> - prey_captured : number of chironomid larvae consumed<br> - fish_mass : fish body mass (g)<br> - swim_distance: swim distance (m)</p> <p>Secondary dataset: 'data_prey_behaviour.txt'. Dataset for control experiment on prey behaviour. Prey behaviour was recorded under two noise conditions (either boat noise or ambient noise). The dataset corresponds to data frame "f" in the script. We used 20 replicates with 10 replicates for ambient noise condition, and 10 replicates for boat noise condition. Two focal prey larva were observed per replicates. Each prey larva was observed during two time periods (corresponding to two noise sequences). Variables description:<br> - condition: noise condition, either "boat noise" or "ambient noise"<br> - replicate: number of the replicate<br> - unique_id: identity of each focal larva<br> - noise_sequence: number of the noise sequence (either second or third)<br> - prop_inactive: proportion of time spent inactive by the focal larva<br> - prop_active: proportion of time spent active by the focal larva</p> <p>R source code 'code R_script.R'. Complete analysis as one single R script. See comments for additional information.</p> <p>The last file `BINV-D-22-00447_Playback.wav` is an audio file (Waveform Audio File Format). It is the soundtrack used in the playback experiments.</p>
Highlighting the potential of multilevel statistical models for analysis of individual agroforestry systems (companion working R code)
<p>A companion working R code and crop yield dataset that illustrate key concepts presented in a scientific publication titled 'Highlighting the potential of multilevel statistical models for analysis of individual agroforestry systems' published in Agroforestry Systems (July 2023). For the shape file of tree strips, please refer to the publication's supplementary material (adjust the name accordingly). </p>
Dataset and R code from RDA WG Discipline-Specific Guidance on DMP - Online Survey
<p>Dataset from the Online Survey of the <a href="https://www.rd-alliance.org/groups/discipline-specific-guidance-data-management-plans-wg">Research Data Alliance's Discipline-Specific Guidance for Data Management Plans Working Group</a>.</p> <p><br> The data was collected from November 8, 2021 to January 14, 2022.</p> <p>The survey was divided into the following areas after a brief introduction on "Purpose of this survey" and "Use of the information you provide."</p> <ul> <li>Demographics</li> <li>Data Description and Collection</li> <li>Data Documentation & Quality</li> <li>Data Archiving, Publishing & Sharing After the Project</li> <li>Guidelines, Principles & Best Practices</li> <li>Follow-up interviews and group discussions</li> </ul> <p>The analysis of the online survey was focused on the four areas: Natural Sciences, Life Sciences, Humanities & Social Sciences, and Engineering. The results of the evaluation will be presented in a separate publication.</p> <p>In addition to the data, the variables and values are also published here.</p> <p>The online survey questions can be accessed here: https://doi.org/10.5281/zenodo.7443373</p> <p>A more detailed analysis and description can be found in the paper "Discipline-specific Aspects in Data Management Planning" submitted to Data Science Journal (2022-12-15).</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.