Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
15,519
datasets available to search
ShareScore release 0.9.0
Dataset results
15,519 results for “r”
Harmonized data and R code for "Coherent response of zoo- and phytoplankton assemblages to global warming since the Last Glacial Maximum"
<p>Harmonized data and R code for "<em>Coherent response of zoo- and phytoplankton assemblages to global warming since the Last Glacial Maximum</em>"<br>by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Karl-Heinz Baumann, Helmut Hillebrand and Michal Kucera (submitted to <em>Global Ecology and Biogeography</em>, 2024).</p> <p><strong>STRUCTURED ABSTRACT</strong><br><em>Aim</em>: We use the fossil record of different marine plankton groups to determine how their biodiversity changed during past climate warming comparable to projected future warming.<br><em>Location</em>: North Atlantic Ocean and adjacent seas. Time series cover a latitudinal range of 75°N to 6°S.<br>Time period: Past 24,000 years, i.e., from the Last Glacial Maximum (LGM) to the current warm period covering the last deglaciation.<br><em>Major taxa studied</em>: Planktonic foraminifera, dinoflagellates and coccolithophores.<br><em>Methods</em>: We analyse time series of fossil plankton communities using principal component analysis and generalised additive models to estimate the overall trend of temporal compositional change in each plankton group and identify periods of significant change. We further analyse local biodiversity change by analysing species richness, species gains and losses, and the effective number of species in each sample and compare alpha diversity to the LGM mean.<br><em>Results</em>: All plankton groups show remarkably similar trends in the rates and spatio-temporal dynamics of local biodiversity change and a pronounced non-linearity with climate change in the current warm period. Assemblages of planktonic foraminifera and dinoflagellates started to significantly change with the onset of global warming around 15,500 to 17,000 years ago and continued to change at the same pace during the current warm period until at least 5,000 years ago, while coccolithophores assemblages changed at a constant rate throughout the past 24,000 years seemingly irrespective of the prevailing temperature change.<br><em>Main conclusions</em>: The climate change during the transition from the LGM to the current warm period led to a long-lasting reshuffling of the zoo- and phytoplankton assemblages likely associated with the emergence of new ecological interactions and possibly a shift in the dominant drivers of plankton assemblage change from more abiotic-dominated causes during the last deglaciation to more biotic-dominated causes with the onset of the Holocene.</p> <p><strong>CONTENT</strong><br>This dataset includes the harmonized assemblage data of the three investigated plankton groups (planktonic foraminifera, dinoflagellates and coccolithophores) as well as all the R code needed to re-produce the results of this study and it's main figures.</p> <p>Scripts written by Tonke Strack</p> <p><br><strong>DATA SOURCES</strong><br>1) GMST: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. <br> <em>Nature</em> 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021).<br>2) WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, <em>Technical Editor. </em><br><em> NOAA Atlas NESDIS</em> 81, 52 (2019).<br>3) plankton assemblage data: individual data references provided in CoreList.csv</p> <p><br><strong>DATA</strong><br>1. Harmonized assemblage data<strong>*</strong>: <em>FullDataTable_PF_harmonized.txt</em><br>2. Core list of additional information on time series: <em>CoreList.csv</em><br>3. Reference lists for species names names: <em>ReferenceList_PlanktonicForaminifera.csv, ReferenceList_Dino.csv, ReferenceList_Cocco.csv</em></p> <p><br><strong>CODE</strong><br>1. <em>01_LoadData.R</em>: loads harmonized assemblage data from planktonic foraminifera, dinocyst and coccolithophores<br>2. <em>02_GMST_import.R</em>: loads loads the globally resolved surface temperature since the LGM from Osman et al. (2011)<br>3. <em>03_DataAnalysis_PCA_GAM.R</em>: PCA/GAM analysis on the plankton assemblage data (results shown in Figure 2 and 3), sensititvity analysis (results shown in Figure 4), and some summary statistics<br>4. <em>04_DataAnalysis_MH_GAM_AlternativeApproach.R</em>: alternative GAM approach using Morisita-Horn index (results shown in Figure S2, S3 and S4)<br>5. <em>05_DataAnalysis_BiodiversityChange.R</em>: local biodiversity change analysis of individual time series (results shown in Figure 5, 6 and S9)</p> <p><br>*Assemblage data of individual time series were manually downloaded, quality checked, taxonomically harmonized, and combined into one data file.<br>Planktonic foraminifera data were harmonized following Siccha and Kuchera (2017). We merged <em>Globigerinoides ruber ruber</em> and <em>Globigerinoides ruber </em><br><em>albus</em>, because some studies only reported them together as<em> Globigerinoides ruber</em>. Also, P/D intergrades (an informal category of morphological<br>intermediates between <em>Neogloboquadrina incompta</em> and <em>Neogloboquadrina dutertrei</em>) were merged with <em>Neogloboquadrina incompta</em>.<br>Dinocyst taxonomy was harmonized following de Vernal et al. (2020) with slight additions following Zonneveld et al. (2013). Names that could not be<br>resolved using synonym lists and assigned a harmonized name following de Vernal et al. (2020) and Zonneveld et al. (2013) were treated as unidentified<br>specimens and were excluded from the assemblage analyses. These specimens were present in 4 time series and were rare taxa (relative abundances < 3%).<br>The protoperidinoids were also excluded from further assemblage analyses as this category includes all unidentified brownish cysts (de Vernal et al., 2020).<br>Coccolithophore taxonomy follows Young et al. (2003) and coccolith countings were conducted on a scanning-electron microscope (SEM) to ensure that all<br>specimens are resolved to the species level. We merged <em>Coccolithus pelagicus</em> subspecies, because they were not distinguished in all studies. <br>Species not reported in the time series data were assumed to be absent (that is, zero abundance) which is in accordance with the completeness of the counts<br>reported in the original studies. The original data were either given in absolute or relative abundances, and after excluding unnecessary columns<br>(unidentified or rare taxa that could not be harmonised) the abundances were recalculated to 100 %. In total, 41 species of planktonic foraminifera,<br>30 species of coccolithophores and 53 species of organic-walled dinocysts were observed in our study.</p> <p><strong>REFERENCES</strong><br>de Vernal, A., Radi, T., Zaragosi, S., Van Nieuwenhove, N., Rochon, A., Allan, E., . . . Richerol, T. (2020). Distribution of common modern dinoflagellate cyst taxa in surface sediments of the Northern Hemisphere in relation to environmental parameters: The new n=1968 database. <em>Mar. Micropaleontol.</em>, 159. doi:10.1016/j.marmicro.2019.101796<br>Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. S<em>ci. Data</em> 4, 170109, doi:10.1038/sdata.2017.109 (2017).<br>Young, J. R., Geisen, M., Cros, L., Kleijne, A., Sprengel, C., Probert, I., & Østergaard, J. B. (2003). A guide to extant coccolithophore taxonomy. <em>Journal of Nannoplankton Research Special Issue</em>, 1, 1-125. doi:10.58998/jnr2297<br>Zonneveld, K. A. F., Marret, F., Versteegh, G. J. M., Bogus, K., Bonnet, S., Bouimetarhan, I., . . . Young, M. (2013). Atlas of modern dinoflagellate cyst distribution based on 2405 data points. <em>Rev. Palaeobot. Palynol.</em>, 191, 1-197. doi:10.1016/j.revpalbo.2012.08.003</p>
R-XIMO experiment data
<p>The data utilized in the R-XIMO article:</p> <p>Misitano, G., Afsar, B., Lárraga, G. <em>et al.</em> Towards explainable interactive multiobjective optimization: R-XIMO. <em>Auton Agent Multi-Agent Syst</em> <strong>36</strong>, 43 (2022). https://doi.org/10.1007/s10458-022-09577-3</p> <p>Re-uploaded to Zenodo because the original link in the article does not work anymore.</p>
Fallet et al - Entomopathogenic nematodes as an effective and sustainable alternative to control the fall armyworm in Africa - Dataset and R Script
<p>We tested in field trials in Rwanda in 2022 the efficacy of entomopathogenic nematodes (EPN) to control the fall armyworm (<em>Spodoptera frugiperda </em>Smith; FAW) over a complete growing season of maize. For an exhaustive description of the method, please see the publication: Fallet et al., 2024. <em>PNAS Nexus</em>. DOI: <a href="https://doi.org/10.1093/pnasnexus/pgae122">https://doi.org/10.1093/pnasnexus/pgae122</a></p> <p>Briefly, the trials were conducted in six fields each containing 20 plots. In each field, five plots were attributed to one of the following treatments (n = 30 plots per treatment in total):</p> <ul> <li>Untreated control (negative control)</li> <li>EPN formulated in a Surfactant-Polymer-Formulation (SPF; e-nema GmbH, Schwentinental, Germany)</li> <li>EPN formulated in a carboxymethyl cellulose gel (Gel; Fallet et al., 2022. Biol. Control. DOI: https://doi.org/10.1016/j.biocontrol.2022.105086</li> <li>Cypermethrin (positive control; pyrethroid contact insecticide)</li> </ul> <p>Treatments were applied into the whorl of every plant within a plot as a 2ml spot application, starting when >30% of plants were infested with FAW. Treatments were re-applied every two weeks until the maize flowers emerged. In total, treatments were applied four times in fields I, II, II and IV, and three times in fields V and VI.</p> <p><strong>Data recorded</strong></p> <ul> <li><strong>Whorl damage:</strong><br>Whorl damage was visually assessed seven- and fourteen-days post after the first-, second- and third-treatments on 40 plants per plot using the Davis whorl damage scale (Davis et al., 1992) as described in Toepfer et al. (2021). Briefly, the Davis whorl damage scale ranges from "0" to "9", whereby a fully intact whorl is attributed a score of "0", while an almost totally destroyed whorl is attributed a score of "9". Minor damages are represented by scores ranging from 1-3, while moderate or severe damages by scores ranging from 4-6 or 7-9, respectively.</li> <li><strong>FAW infestation:</strong><br>Five days after the third application of the treatments, we evaluated FAW infestation levels. For this, ten plants were destroyed in each plot to search for caterpillars. The occurrence (yes/no) of young caterpillars (shorter than 0.5 cm) was determined as a proxy for re-infestation, whereas the number of older caterpillars (longer than 0.5 cm) was used as a proxy for treatment efficacy.</li> <li><strong>Number of cobs:</strong><br>At harvest, the number of fully-developed and underdeveloped cobs from 30 plants per plot was recorded.</li> <li><strong>Cob damage:</strong><br>Fully-devloped cobs were husked to visually evaluate cob damage, using a "0" to "3" customized damage score, where "0" represents an intact cob, while scores of "1", "2", or "3" represent minor (<5% damaged kernels), moderate (5-20% damaged kernels) or severe (>20% damaged kernels) cob damage, respectively.</li> <li><strong>Yield:</strong><br>The husked cobs from 30 plants were weighed together to estimate fresh weight (FW) yield per plot.</li> </ul> <p> </p> <p> </p>
Functioning of a canopy-dominated intertidal community during emersion: highly productive but heterotrophic at the annual scale - R code
<p>This repository contains the data and code for our paper:</p> <p>Claire Golléty, Jon Yearsley, Aline Migné, Dominique Davoult (2024). Functioning of a canopy-dominated intertidal community during emersion: highly productive but heterotrophic at the annual scale. Marine Biology<br>https://doi.org/10.1007/s00227-024-04395-5</p>
Data and R code for machine learning modelling of favourite places and routes of outdoor recreation
<p>This is a script showing the analysis used in a paper submitted for review in Landscape and Urban Planning, titled "Seeing through their eyes: Revealing recreationists’ landscape preferences through viewshed analysis and machine learning", by Carl Lehto, Marcus Hedblom, Anna Filyushkina and Thomas Ranius. </p> <p>The zip file contains an R script, data saved in .rds format and a R workspace. </p>
F I G U R E 4 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)
F I G U R E 4 Examples of functional secondary structures of the 5S gene
F I G U R E 4 in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation
F I G U R E 4 Legend on next page.
F I G U R E 2 in Assessing the diversity of Australian tarantulas (Araneae: Theraphosidae) using DNA barcoding and iterative species delimitation
F I G U R E 2 Histogram of HKY pairwise distance with bars increasing by 0.25 pairwise distance.
ALab-R 3D Reconstruction Dataset
<p>Creating per-pixel-accurate ground truth from real images for various 3D scene reconstruction tasks can be challenging, especially when the mesh topology needs to be close to production-level assets. To address this issue, we present a photorealistic synthetic dataset created from the Animal Logic ALab project and rendered using Blender. SED creator was used to generate the scenes</p>
F I G U R E 4 in Movement and habitat shift responses of juvenile Atlantic Salmon (Salmo salar) to annually permanent stream flooding
F I G U R E 4 Summary of Fulton's condition factor related to location, 2016–2017.
F I G U R E 3 Mass specific growth rates among locations, 2016–2017 in Movement and habitat shift responses of juvenile Atlantic Salmon (Salmo salar) to annually permanent stream flooding
F I G U R E 3 Mass specific growth rates among locations, 2016–2017.
finnpiatscheck/Environmental-Effects-on-a-Fig-Wasp-Community: Data sets and R scripts
<p>These are the data and R scripts used in the paper "Landscape-Level Analysis of a Fig-Pollinator-Antagonist Community: Spatial and Temporal Variation in a Fig Wasp Community and its Response to Biotic and Abiotic Factors".</p>
Data and R code for Reddin et al. 'Marine species and assemblage change foreshadowed by their thermal bias over Early Jurassic warming''
<p>This repository holds the raw and prepared datasets and R code to handle them for the manuscript Reddin et al. 'Marine species and assemblage change foreshadowed by their thermal bias over Early Jurassic warming'. Nature Communications</p>
Neutral-current DIS event samples at leading order with $E_e = 27.5\,{\rm GeV}$, $E_p = 920\,{\rm GeV}$, and $\mu_{\rm F}^2 = \mu_{\rm R}^2 = \frac{1}{2}(Q^2+H_{\rm T}^2)$
<p>Neutral-current DIS multi-jet event samples at parton level in the HDF5 event format.</p> <p>Beam energies: $E_e = 27.5\,{\rm GeV}$, $E_p = 920\,{\rm GeV}$</p> <p>Renormalisation and factorisation scale: $\mu_{\rm F}^2 = \mu_{\rm R}^2 = \frac{1}{2}(Q^2 + H_{\rm T}^2)$ with seven-point scale variation</p> <p>PDF: NNPDF40_lo_pch_as_01180</p> <p>Generation cuts: $Q^2 > 2\,{\rm GeV}^2$, $k_{\rm T} > 2\,{\rm GeV}$ in the $k_{\rm T}$-algorithm with $D=1.0$</p> <p>Generated with <a href="https://gitlab.com/hpcgen/me" target="_blank" rel="noopener">Sherpa</a> using the run cards below.</p>
Neutral-current DIS event samples at leading order with $E_e = 27.5\,{\rm GeV}$, $E_p = 820\,{\rm GeV}$, and $\mu_{\rm F}^2 = \mu_{\rm R}^2 = \frac{1}{2}(Q^2+H_{\rm T}^2)$
<p>Neutral-current DIS multi-jet event samples at parton level in the HDF5 event format.</p> <p>Beam energies: $E_e = 27.5\,{\rm GeV}$, $E_p = 820\,{\rm GeV}$</p> <p>Renormalisation and factorisation scale: $\mu_{\rm F}^2 = \mu_{\rm R}^2 = \frac{1}{2}(Q^2 + H_{\rm T}^2)$ with seven-point scale variation</p> <p>PDF: NNPDF40_lo_pch_as_01180</p> <p>Generation cuts: $Q^2 > 2\,{\rm GeV}^2$, $k_{\rm T} > 2\,{\rm GeV}$ in the $k_{\rm T}$-algorithm with $D=1.0$</p> <p>Generated with <a href="https://gitlab.com/hpcgen/me" target="_blank" rel="noopener">Sherpa</a> using the run cards below.</p>
The Three R's Of Reasonable: Relationships, Rationale, & Resources
<p>Central to applying the principle of optimisation in the system of radiological protection is the evaluation of what level of radiation exposure should be considered “as low as reasonably achievable” (ALARA) in a given circumstance. Determining what is “reasonable” is an abstract although somewhat intuitive concept with many potential answers depending on both the situation and those involved, whether individuals or organisations. There are common themes across exposure scenarios and within existing radiation protection guidance related to the determination of reasonableness. However, despite the fairly consistent and agreeable nature, there remains a gap in how to apply these themes in real situations. For example, without measurable goalposts (or a transparent process for setting such goalposts) for determining what constitutes ALARA, we can find ourselves misinterpreting the optimisation process as keeping exposures “as low as possible.” We thus propose herein, by consolidating and building on existing ideas, an easily understandable and actionable “reasonableness” framework. This simple, yet broadly applicable tool is intended to help radiation protection practitioners systematically reflect on all of the factors that make up “reasonable” before making a decision, although the process and decision itself will necessarily retain the complexity of the prevailing circumstance. The proposed “Rs” of Reasonable represent Relationships (stakeholders, transparency, and empathy), Rationale (contextual, scientific, and ethical justifications), and Resources (technology/technique, finances, time).</p>
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
<p>Diet analysis integrates a wide variety of visual, chemical and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, e.g., highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package <i>mvtweedie</i> that extends capabilities of widely used packages in R (<i>mgcv</i> and <i>ggplot2</i>) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted puffins (<i>Fratercula cirrhata</i>) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980-2000) and then Pacific herring and capelin (2000-2020), while wolves (<i>Canis lupus ligoni</i>) in Southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines. </p>
High-quality video files for Hermsen, R, "Emergent multilevel selection in a simple spatial model of the evolution of altruism" (2021)
<p>The supplementary movies published with the article<br> <br> R. Hermsen<em>, Emergent multilevel selection in a simple spatial model of the evolution of altruism</em><br> <br> have a relatively low resolution. Here, the same three movies are provided at a higher resolution.</p> <p>Note: In Version 1 of this deposit, Movie 1 was incorrect: it visualized a different simulation run than intended. This is corrected in Version 2.</p>
R scripts for "Using LiDAR to assess the influence of fine-scale habitat structure on the abundance of the bearded reedling (Panurus biarmicus)"
<p>Dataset which contains R scripts and R package SDMaps used for data preparation and modelling in the methodology described in my MSc thesis "Using LiDAR to assess the influence of fine-scale habitat structure on the abundance of the bearded reedling (<em>Panurus biarmicus</em>)".</p> <p>The project involved creating Species Distribution Models to test whether and how fine-scale habitat structure - in the form of LiDAR derived variable - influences the relative abundance of the bearded reedling in the Netherlands, beyond other variables such as land use, soil type and climate data.</p> <p>These scripts can serve as an example of how the SDMaps package can be used to build Species Distribution Models that include LiDAR derived variables, and to replicate some of the methodologies described in the thesis. The SDMaps package contains an installation guide, manual, tutorial and examples.</p>
Data and R script for "Fear and cultural background drive sexual prejudice in France – A sentiment analysis approach"
<p>Data:</p> <p>corpus_integral.csv</p> <p>FEEL_1.csv</p> <p>mauvais.txt</p> <p>neg_hetero_corrected.txt</p> <p>participant_info_used.txt</p> <p>pos_hetero_corrected.txt</p> <p>R script:</p> <p>polarities.R</p> <p>sentiments_discrete.R</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.