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DNA sequence data for two Roscoea species, R. stenophylla and R. australis (Zingiberaceae)
<p><span>This dataset includes three genomic regions, nrITS (ITS1-5.8S–ITS2) and two chloroplast DNA (cpDNA) regions (psbA-trnH and trnL-F) for two <em>Roscoea</em> species <em>R. stenophylla</em> and <em>R. australis</em> (Zingiberaceae).</span></p>
Review data for: SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
<p>Climatology of snow water equivalent of Switzerland between winters 1962 and 2021. Obtained using quartile mapping between a model using data assimilation since 1998 and a model without data assimilation. This version of the dataset corresponds to the publication revision time. The publication has been submitted to GMD Copernicus journal as: <em>SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping</em></p>
B.Sc Datasets used for R Plots
<p>B.Sc Untersuchung der Anhydrobiose bei Nematoden aus extremen Umweltbedingungen, die für die in R erstellten Plots verwendeten Daten </p>
Datasets and R script to replicate the statistical analyses from the article entitled Multiple hosts, multiple impacts: the role of vertebrate host diversity in shaping mosquito life history and pathogen transmission
<p>Datasets and R script to replicate the statistical analyses from the article entitled " Multiple hosts, multiple impacts: the role of vertebrate host diversity in shaping mosquito life history and pathogen transmission " by Vantaux A., Moiroux N., Dabire K. R., Cohuet A., Lefevre T. 2023</p>
Output files from SPEEDY v.42 ensembles described in the paper: "Multi-decadal pacemaker simulations with an intermediate-complexity climate model" by F. Molteni, F. Kucharski and R. Farneti (part 1 of 2)
<p>The monthly-mean output from SPEEDY v.42 ensembles (either driven by prescribed sea-surface temperature (SST) or coupled to the TOM3 model) consists of a series of IEEE little-endian binary files and metadata files in text format.<br> For each year of integration (indicated by a 4-digit number YYYY) and ensemble member (indicated by a 3-digit number NNN), two binary files are present, named:<br> • attmNNN_YYYY.grd, including data on the 120x60 grid-point atmospheric grid;<br> • sftmNNN_YYYY.grd, including data on the 360x180 grid-point surface grid.<br> The metadata for these files are contained in the text files <strong>attmEEE.ctl</strong> and <strong>sftmEEE.ctl</strong> respectively, where EEE is a 3-digit ensemble identifier (usually, but not necessarily, equal to one of the ensemble-member number NNN).</p> <p><br> This repository contains data from:</p> <ul> <li>(part 1) a 41-year 5-member ensemble (653) run with prescribed SST</li> <li>(part 2) a 70-year 5-member ensemble (104) run with the coupled SPEEDY-TOM3 model.</li> </ul> <p>Integration years are 1980 to 2020 for ensemble 653 and 1951 to 2020 for ensemble 104.</p> <p><br> The structure of the binary data and metadata files follows the conventions for gridded datasets set by the GrADS diagnostic and plotting package (developed by the Center for Ocean-Land-Atmosphere Studies of George Mason University), as described here:<br> http://cola.gmu.edu/grads/gadoc/aboutgriddeddata.html</p> <p><br> In addition to the COLA-GMU web site, free version of the GrADS package for different platforms can be downloaded from the OpenGrADS web site:<br> http://opengrads.org/</p> <p><br> Specifically, the SPEEDY v.42 output consists of sequential-access files where each record contains a two-dimensional field. Three-dimensional fields are stored as a sequence of consecutive records, one for each of the 8 pressure levels where model-level data are interpolated by the post-processing routines. For each month of the year:</p> <p><br> the <strong>attmNNN_YYYY.grd</strong> files contain a sequence of <strong>9 3-D variables and 26 2-D variables</strong>;<br> the <strong>sftmNNN_YYYY.grd</strong> files contain a sequence of <strong>21 2-D variables</strong>.</p> <p>Within each record, grid-point data are stored as a NLONxNLAT array with longitude varying from west to east and latitude varying from south to north. The list of variables and levels is specified in the <strong>attmEEE.ctl</strong> and s<strong>ftmEEE.ctl</strong> files. These files contain descriptors which allow the data of each ensemble to be accessed as a single dataset by the GrADS package.</p> <p>Although the metadata files are specific to the GrADS package, the binary data can be read by different types of code. As example of fortran90 instructions to read the content of the <strong>attmNNN_YYY.grd</strong> and <strong>sftmNNN_YYY.grd</strong> files for one year/ens.member is as follows:</p> <p>integer, parameter :: nlon=120<br> integer, parameter :: nlat=60<br> integer, parameter :: nlev=8<br> integer, parameter :: nlon0=360<br> integer, parameter :: nlat0=180</p> <p>integer :: jmonth, jvar3d, jvar2d, jlev<br> real :: fld3d(nlon,nlat,nlev)<br> real :: fld2d(nlon,nlat), fld0(nlon0,nlat0)</p> <p>open (unit=1, file=”attmNNN_YYY.grd”, form=”formatted”, access=”sequential”)<br> open (unit=2, file=”sftmNNN_YYY.grd”, form=”formatted”, access=”sequential”)</p> <p>do jmonth=1,12</p> <p> do jvar3d=1,9<br> do jlev=1,nlev<br> read (1) fld3d(:,:,jlev)<br> …………<br> enddo<br> enddo</p> <p> do jvar2d=1,26<br> read (1) fld2d(:,:)<br> ………<br> enddo</p> <p> do jvar2d=1,21<br> read (2) fld0(:,:)<br> ………<br> enddo</p> <p>enddo</p> <p>close (1)<br> close (2)</p> <p> </p> <p> </p>
Output files from SPEEDY v.42 ensembles described in the paper: "Multi-decadal pacemaker simulations with an intermediate-complexity climate model" by F. Molteni, F. Kucharski and R. Farneti (part 2 of 2)
<p>The monthly-mean output from SPEEDY v.42 ensembles (either driven by prescribed sea-surface temperature (SST) or coupled to the TOM3 model) consists of a series of IEEE little-endian binary files and metadata files in text format.<br> For each year of integration (indicated by a 4-digit number YYYY) and ensemble member (indicated by a 3-digit number NNN), two binary files are present, named:<br> • attmNNN_YYYY.grd, including data on the 120x60 grid-point atmospheric grid;<br> • sftmNNN_YYYY.grd, including data on the 360x180 grid-point surface grid.<br> The metadata for these files are contained in the text files <strong>attmEEE.ctl</strong> and <strong>sftmEEE.ctl</strong> respectively, where EEE is a 3-digit ensemble identifier (usually, but not necessarily, equal to one of the ensemble-member number NNN).</p> <p><br> This repository contains data from:</p> <ul> <li>(part 1) a 41-year 5-member ensemble (653) run with prescribed SST</li> <li>(part 2) a 70-year 5-member ensemble (104) run with the coupled SPEEDY-TOM3 model.</li> </ul> <p>Integration years are 1980 to 2020 for ensemble 653 and 1951 to 2020 for ensemble 104.</p> <p><br> The structure of the binary data and metadata files follows the conventions for gridded datasets set by the GrADS diagnostic and plotting package (developed by the Center for Ocean-Land-Atmosphere Studies of George Mason University), as described here:<br> http://cola.gmu.edu/grads/gadoc/aboutgriddeddata.html</p> <p><br> In addition to the COLA-GMU web site, free version of the GrADS package for different platforms can be downloaded from the OpenGrADS web site:<br> http://opengrads.org/</p> <p><br> Specifically, the SPEEDY v.42 output consists of sequential-access files where each record contains a two-dimensional field. Three-dimensional fields are stored as a sequence of consecutive records, one for each of the 8 pressure levels where model-level data are interpolated by the post-processing routines. For each month of the year:</p> <p><br> the <strong>attmNNN_YYYY.grd</strong> files contain a sequence of <strong>9 3-D variables and 26 2-D variables</strong>;<br> the <strong>sftmNNN_YYYY.grd</strong> files contain a sequence of <strong>21 2-D variables</strong>.</p> <p>Within each record, grid-point data are stored as a NLONxNLAT array with longitude varying from west to east and latitude varying from south to north. The list of variables and levels is specified in the <strong>attmEEE.ctl</strong> and s<strong>ftmEEE.ctl</strong> files. These files contain descriptors which allow the data of each ensemble to be accessed as a single dataset by the GrADS package.</p> <p>Although the metadata files are specific to the GrADS package, the binary data can be read by different types of code. As example of fortran90 instructions to read the content of the <strong>attmNNN_YYY.grd</strong> and <strong>sftmNNN_YYY.grd</strong> files for one year/ens.member is as follows:</p> <p>integer, parameter :: nlon=120<br> integer, parameter :: nlat=60<br> integer, parameter :: nlev=8<br> integer, parameter :: nlon0=360<br> integer, parameter :: nlat0=180</p> <p>integer :: jmonth, jvar3d, jvar2d, jlev<br> real :: fld3d(nlon,nlat,nlev)<br> real :: fld2d(nlon,nlat), fld0(nlon0,nlat0)</p> <p>open (unit=1, file=”attmNNN_YYY.grd”, form=”formatted”, access=”sequential”)<br> open (unit=2, file=”sftmNNN_YYY.grd”, form=”formatted”, access=”sequential”)</p> <p>do jmonth=1,12</p> <p> do jvar3d=1,9<br> do jlev=1,nlev<br> read (1) fld3d(:,:,jlev)<br> …………<br> enddo<br> enddo</p> <p> do jvar2d=1,26<br> read (1) fld2d(:,:)<br> ………<br> enddo</p> <p> do jvar2d=1,21<br> read (2) fld0(:,:)<br> ………<br> enddo</p> <p>enddo</p> <p>close (1)<br> close (2)</p>
Physiological constraints and cognitive chunking in Zebra Finch songs: Data and R script
<p>Learned bird songs often have a hierarchical organization. In the case of zebra finches, each bird's song is made up of a string of notes delivered in a stereotyped sequence to form a "motif", and motifs are repeated to form a song bout. During song learning, young males copy "chunks" of two or more consecutive notes from their tutors' songs. These chunks are represented as distinct units within memory (during learning) and within motor systems (during song production). During song performance, motifs may deviate from the learned sequence by stopping short, starting late, or by skipping, inserting or repeating notes. We measured acoustic and temporal variables related to the respiratory and vocal physiology of song production and asked how they related to deviations from each bird's "canonical" sequence. The best predictor of deviations from that sequence was the duration of the silent interval between notes, when inspiration normally occurs. Deviations from the canonical motif occurred less often after higher-pitched notes, perhaps because a high-low sequence forms a prosodic unit. Premature stops often followed louder and longer notes, suggesting that respiratory and muscular physiology influence the location of such stops. Boundaries between the learned chunks of a male's motif predicted where and how often non-canonical starts occurred. Physiological and cognitive elements also interacted to define the segmentation of zebra finch song sequences. Long silent intervals between notes were associated both with physiology (inspirations) and with the cognitive boundaries of learned chunks – and hence with deviations from the canonical motif.</p>
Anthropogenic Specular Interference in the Operational GOES-R Fire Product
<p><strong>Dataset for the research paper "Anthropogenic Specular Interference in the Operational GOES-R Fire Product".</strong></p> <p>Large reflective structures like solar power plants and commercial greenhouses sometimes reflect sunlight directly into GOES-R sensors. These anthropogenic specular reflections, or "sparkles", cause commission errors in operational GOES-R ABI products like the Fire Detection and Characterization Algorithm (FDCA). Using the <em>abi-sparkle</em> library for Python (Dove-Robinson, 2023), we generated a dataset containing both detected anthropogenic specular reflection pixels and the coincident FDCA commission errors caused by them for the GOES-16 CONUS domain during the 2020 calendar year.</p> <p>The dataset consists of two exported PostgreSQL tables: <em>sparkle_pixels_g16_abi_conus_2020</em>, which contains the detected anthropogenic specular reflection pixels at 500 m resolution, and <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>, which contains clustered FDCA false alarm fire pixels caused by anthropogenic specular reflection at 2 km resolution. The FDCA pixels were only processed for fire mask codes 10-15 and 30-35; see Table 3.11 in Schmidt et al., 2013 for fire code definitions.</p> <p>Each row in <em>sparkle_pixels_g16_abi_conus_2020 </em>is a detected specular reflection pixel in a GOES-16 CONUS image from the 2020 calendar year with a unique numeric ID <em>sparkle_id </em>and associated metadata from the detection algorithm <em>abi-sparkle</em>. The column <em>sparkle_geom </em>is a PostGIS geometry ST_Point object that can be used to plot the pixels on a map.</p> <p>FDCA fire pixels at 2 km resolution were clustered based on their connectivity in a 3x3 pixel kernel and assigned a UUID <em>fire_cluster_id </em>in the table <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>. Only the fire clusters that overlapped with sparkle pixels in time and space were retained in the table. In this way, each row of <em>fdca_commission_error_clusters_g16_abi_conus_2020 </em>is a unique cluster of errant FDCA fire pixels caused by anthropogenic specular reflection in every available scan start time for the GOES-16 CONUS domain in 2020. Every fire cluster centroid has a PostGIS geometry object <em>fire_cluster_centroid_geom </em>that can be used to plot the errant fire pixel clusters on a map.</p> <p>The two tables relate with the column <em>sparkle_ids </em>in <em>fdca_commission_error_clusters_g16_abi_conus_2020</em>, which is an array of overlapping sparkle IDs from the <em>sparkle_pixels_g16_abi_conus_2020 </em>table. The combined dataset may therefore be generated with a simple SQL INNER JOIN:</p> <p><em>SELECT * FROM fdca_commission_error_clusters_g16_abi_conus_2020 fcecgac<br> INNER JOIN sparkle_pixels_g16_abi_conus_2020 spgac ON spgac.sparkle_id = ANY(fcecgac.sparkle_ids);</em></p> <p>The tables can be imported into a PostgreSQL database version 12 or newer with PostGIS extensions installed. For example, to import the tables into a database in a Linux environment, run the following commands:</p> <p><em>gunzip -c sparkle_pixels_g16_abi_conus_2020.sql.gz | psql -d your_database_name<br> gunzip -c fdca_commission_error_clusters_g16_abi_conus_2020.sql.gz | psql -d your_database_name</em></p>
Figure 7 in Rhyacophila siparantum sp. nov. (Trichoptera: Rhyacophilidae), a new species of the R. philopotamoides species group from the Republic of Kosovo with molecular and ecological notes
Figure 7. Enlarged aedeagus and parameres of Rhyacophila siparantum sp. nov.
Figure 1 in Rhyacophila siparantum sp. nov. (Trichoptera: Rhyacophilidae), a new species of the R. philopotamoides species group from the Republic of Kosovo with molecular and ecological notes
Figure 1. Male of Rhyacophila siparantum sp. nov.
Figure 6 in Rhyacophila siparantum sp. nov. (Trichoptera: Rhyacophilidae), a new species of the R. philopotamoides species group from the Republic of Kosovo with molecular and ecological notes
Figure 6. Dorsal profile of Rhyacophila siparantum sp. nov.
Figure 5 in Rhyacophila siparantum sp. nov. (Trichoptera: Rhyacophilidae), a new species of the R. philopotamoides species group from the Republic of Kosovo with molecular and ecological notes
Figure 5. Lateral profile of Rhyacophila siparantum sp. nov.
(b) Brenthis ino (Rottemburg, 1775) (Foto R. Gibbons). in Erstnachweis von Brenthis daphne (Denis & Schiffermüller, 1775) im Kanton Zürich (Lepidoptera: Nymphalidae)
(b) Brenthis ino (Rottemburg, 1775) (Foto R. Gibbons).
Experimental data and R scripts for simulations and analysis
<p>Zip file including the experimental data and R scripts for simulations and analysis used in the following article: "Colonisation debt: when invasion history impacts current range expansion by Morel-Journel, T., Haond M., Dunan L., Mailleret L. and Vercken E.</p>
R Notebook and Dataset for "Usage-based perspective on argument realisation: A corpus study of Indonesian BUY verbs in applicative construction with -kan" (1.0.0)
<p>This repository contains the dataset and R codes for our paper that has been published in <a href="http://www.aa.tufs.ac.jp/en/publications/nusa">NUSA</a> (<i>Linguistic studies of languages in and around Indonesia</i>) special volume (74) on "Applicatives in Austronesian Languages".</p><h4>How to cite the paper</h4><p>Rajeg, Gede Primahadi Wijaya & I Wayan Arka. 2023. Usage-based perspective on argument realisation: A corpus study of Indonesian BUY verbs in applicative construction with -<i>kan</i>. In Jocelyn Aznar, Christian Döhler & Jozina Vander Klok (eds.), <i>NUSA (special issue on "Applicatives in Austronesian Languages")</i>, vol. 74, 83–114. <a href="https://tufs.repo.nii.ac.jp/records/2000019">https://tufs.repo.nii.ac.jp/records/2000019</a>.</p><h4>Description of the repository</h4><p>The .qmd file contains the R codes used to produce the quantitative analyses in the paper, including the statistical figures. This .qmd file also interweaves some text narratives with the codes. The file is published as a webpage at: <a href="https://gederajeg.github.io/applicative-buy/">https://gederajeg.github.io/applicative-buy/</a></p><p>The raw, annotated concordance data is located in the <a href="https://github.com/gederajeg/applicative-buy/tree/main/data">data</a> directory.</p><p>The statistical figures can also be accessed individually <a href="https://github.com/gederajeg/applicative-buy/tree/main/nusa-applicative-code_files/figure-html">here</a>.</p>
Data and R code for: "Global meta-analysis shows reduced quality of food crops under inadequate animal pollination"
<p>R code, data, and metadata for the study "Global meta-analysis shows reduced quality of food crops under inadequate animal pollination"</p>
Supplementary information for the manuscript 'GFViz: A tutorial on creating interactive visualization of genomic features using R Tidyverse and plotly'
<p>This is the supplementary information of the manuscript 'GFViz: A tutorial on creating interactive visualization of genomic features using R Tidyverse and plotly' published as a part of the thesis 'Annotating and making use of the <em>Avena sativa</em> cv. Sang reference genome' by Nikos Tsardakas Renhuldt.</p>
Performance and process-based evaluation of BARPA-R
<p>Supporting data for figures in GMD draft paper: Performance and process-based evaluation of BARPA-R</p>
Recolonizing native wildlife facilitates exotic plant invasion into Singapore's rain forests: Data and R script
<p>Halting biological invasions and rewilding extirpated fauna are conservation interventions to bolster biodiversity, species interactions, and ecosystems. These actions are often considered separately and the potential for reintroduced wildlife to facilitate invasive plants has been largely overlooked. Here, we investigate the role of Singapore's recolonizing native wild pigs (<em>Sus scrofa</em>) in facilitating an invasive weed <em>Miconia crenata </em>into tropical rain forests, which are normally highly resistant to invasion. We conducted line-transect surveys in 11 Singaporean rain forests and used generalized linear mixed models to consider the contribution of pigs' soil disturbances, human forest paths, and other environmental covariates, on the density of <em>M. crenata</em>. We found that <em>M. crenata</em> was more abundant at forest edges and invasion into forest interior was facilitated by pigs, paths, and canopy gaps, but that these effects were all additive, not synergistic (i.e. not multiplicative). These<span> results highlight how modern invasions are driven by multiple disturbances as well as propagule pressure (e.g. urban birds dispersing seeds at forest edges where they establish in pig soil disturbances). </span>Singapore's extensive native forest restoration efforts may have provided plentiful edge and secondary forests that are well suited to pigs and <em>M. crenata</em>, which in turn undermine the aims of fostering later-successional native plant communities. To prevent negative externalities, we suggest that plant restoration and rewilding projects consider the potential role of wildlife in facilitating non-native plants, and couple these actions with preliminary screening of unintended consequences and continued monitoring, as well as limiting human-mediated weed invasion to minimize propagule sources.</p>
FIG. 8 in New data on the distribution of the genus Roeseliana Zeuner, 1941 (Orthoptera, Tettigoniidae, Tettigoniinae) in the southwestern Balkans, with description of R. epirotica n. sp.
FIG. 8. — Locality of Mazarakia, Epirus, Greece.
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.