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Figure 7 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 7. Box plots show the distribution of relative abundances of the associations across the six facies types.
Figure 3 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 3. Sampling locations of three expeditions in 2015–2017. The first (September–October 2015) and second expeditions (July 2016) were searching for living Pontocaspian molluscs in particular. The third expedition consisted of six transects sampled by GeoEcoMar in 2017 in search of any living mollusc. In the legend w/wo means with or without.
Figure 1 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 1. Location of study area along the Romanian Black Sea coast with core locations indicated by black stars. (a) Danube Delta and RSL (modified after Vespremeanu-Stroe et al., 2017). (b) RSL bathymetry with location of study cores (modified after Dimitriu et al., 2008). Two current marine outlets are indicated by white arrows; a third outlet (Gura Portiţa) was closed in the 1970s and is indicated by a dashed white arrow.
Figure 6 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 6. Overview of the 20 most abundant mollusc species grouped according to the results of Kendall's W coefficient of concordance (for k = 4). (a) Valvata piscinalis (RGM.1309841, Core C7, depth 6 cm). (b) Dreissena polymorpha (RGM.1309827, C7 – 6 cm). (c) Dreissena bugensis (RGM.1309846, C5 – 18 cm). (d) Adacna fragilis (RGM.1309835, C2 – 18 cm). (e) Monodacna colorata s.l. (RGM.1309823, C7 – 14 cm). (f) Rissoa membranacea (RGM.1309830, C3 – 48 cm). (g) Hypanis plicata (RGM.1309845, C9 – 3 cm). (h) Clathrocaspia knipowitschii (RGM.1309843, C11 – 102 cm). (i) Mytilaster minimus (RGM.1309838, C3 – 24 cm). (j) Ecrobia maritima (RGM.1309831, C3 – 48 cm). (k) Cerastoderma glaucum (RGM.1309844, C13 – 24 cm). (l) Abra segmentum (RGM.1309821, C1 – 48 cm). (m) Parthenia interstincta (RGM.1309832, C3 – 48 cm). (n) Lentidium mediterraneum (RGM.1309837, C4 – 12 cm). (o) Retusa truncatula (RGM.1309828, C2 – 42 cm). (p) Gyraulus crista (RGM.1309840, C5 – 54 cm). (q) Potamopyrgus antipodarum (RGM.1309836, C2 – 18 cm). (r) Lithoglyphus naticoides (RGM.1309842, C5 – 18 cm). (s) Theodoxus fluviatilis (RGM.1309826, C11 – 66 cm). (t) T. fluviatilis (RGM.1309824, C11 – 78 cm). (u) Theodoxus danubialis (RGM.1309839, C3 – 24 cm). (v) T. danubialis (RGM.1309834, C2 – 30 cm). Scale bars are 1 mm.
Figure 2 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 2. Overview of core data. From left to right each core: core photograph, lithology, facies, fauna relative abundance per species group based on origin, evolution and estimated palaeosalinities.
Figure 5 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 5. NMDS ordination plot of species compositions across samples grouped into lake regions (stress = 0.173). Optimum salinity and grain size were fitted as two-dimensional smooth surfaces to illustrate the associations with species composition. Species are marked with numbers: 1 – Planorbis planorbis, 2 – Clathrocaspia knipowitschii, 3 – Potamopyrgus antipodarum, 4 – Theodoxus danubialis, 5 – Planorbarius corneus, 6 – Abra segmentum, 7 – Rissoa membranacea, 8 – Valvata macrostoma, 9 – Hypanis plicata, 10 – Mytilaster minimus, 11 – Ecrobia maritima, 12 – Parthenia interstincta, 13 – Cerastoderma glaucum and 14 – Lentidium mediterraneum.
Figure 8 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 8. Snapshot reconstructions of the evolution of the RSL and their mollusc biota. The names of major sand barriers are indicated in yellow, while those of deltaic lobes are in black. The names in parentheses and italic font are currently inactive lobes. Pie charts indicate the relative abundance of the three associations in the time interval of ±50 years of the indicated snapshot: blue – Association I (freshwater); green – Association II (Pontocaspian), orange – Association III (marine), grey – rest of the group. Water colours indicate a salinity gradient: blue is Black Sea influence (18 psu); green is river influence (0 psu). Note the freshening of the system and according changes in species associations with the decreasing influence of mesohaline waters from the Black Sea.
Figure 4 in A conservation palaeobiological approach to assess faunal response of threatened biota under natural and anthropogenic environmental change
Figure 4. Spatiotemporal salinity variations in the RSL. For each sample, the salinity was calculated by weighted averaging of the species' optimum salinities. Salinity categories adapted from Strydom et al. (2003). Snapshots referred to in the Discussion are indicated with letters A–F.
Data set for "Quantitative and Qualitative bibliometric scope toward the Synthesis of Rose Oxide as a Natural Product in perfumery"
<p>This is the bibliometric data for "Quantitative and Qualitative bibliometric scope toward the Synthesis of Rose Oxide as a Natural Product in perfumery" study which were derived from SCOPUS database, on 23<sup>rd</sup> September 2019, based on title search.</p>
IODP Expedition 366 Natural gamma radiation
<p>Natural gamma radiation (NGR) data in the ~0.1 to 3.0 MeV range were measured using eight custom-designed sodium iodide (thallium) [NaI(Tl)] detectors arranged along the core measurement axis at 20 cm intervals. The NGR system uses layers of passive shielding (lead) and active shielding (plastic scintillators and coincidence electronics) to reduce the cosmic-ray signal for low-count analysis of sediment core sections and to obtain the maximum signal-to-noise ratio. Data are reported on a total counts per second basis and the raw spectral files are available as compressed files for later analysis.</p>
In vitro antibacterial activity of microbial natural products against bacterial pathogens of veterinary and zoonotic relevance
<p>Supplemental material to the publication "<i>In vitro</i> antibacterial activity of microbial natural products against bacterial pathogens of veterinary and zoonotic relevance"</p>
FIGURES 3 – 8 in A new species of Stasimopus from the Eastern Cape Province of South Africa (Araneae, Mygalomorphae, Ctenizidae), with notes on its natural history
FIGURES 3 – 8. Stasimopus mandelai sp. nov., holotype male (MY 557). 3, left leg I, retrolateral aspect; 4, left leg I, prolateral aspect; 5, pedipalp, patella to bulb, retrolateral aspect; 6, pedipalp, distal portion, ventral aspect. Paratype male (MY 559). 7, leg I, retrolateral aspect; 8, pedipalp, retrolateral aspect (box showing enlarged view of palpal organ). Scale bars for legs = 2.0 mm; for pedipalps = 1.0 mm.
FIGURES 1 – 2 in A new species of Stasimopus from the Eastern Cape Province of South Africa (Araneae, Mygalomorphae, Ctenizidae), with notes on its natural history
FIGURES 1 – 2. Stasimopus mandelai sp. nov., live habitus images. 1, female; 2, holotype male. Scale bars = 5.0 mm.
FIGURES 16 – 19 in A new species of Stasimopus from the Eastern Cape Province of South Africa (Araneae, Mygalomorphae, Ctenizidae), with notes on its natural history
FIGURES 16 – 19. Stasimopus mandelai sp. nov., trapdoor and architecture of excavated burrow. 16, trapdoor closed, top view; 17, trapdoor open, lateral view; 18, closeup view of inside trapdoor, the arrow is pointing to small pits; 19, trapdoor open, oblique view.
Leaf growth response to mild drought: natural variation sheds light on trait architecture
<p>Plant growth and crop yield are negatively affected by a reduction in water availability. However, a clear understanding of how growth is regulated under non-lethal drought conditions is lacking. Recent advances in genomics, phenomics and transcriptomics allow in-depth analysis of natural variation. In this study, we conducted a detailed screening of leaf growth responses to mild drought in a worldwide collection of <em>Arabidopsis thaliana</em> accessions. </p> <p>The raw phenotyping can be found in:<br> - cellularData.txt -> mature (23 days after stratification; DAS) leaf epidermis (third leaf) analysed for cell area, cell number, pavement cell area, pavement cell number, stomatal index and leaf area of the analysed leaf.</p> <p>- leaf3AreaMaturity.txt -> area of the third leaf at maturity (23DAs) in mm<sup>2.</sup></p> <p>- leaf3AreaProliferation.txt -> area of the third leaf at proliferation (last day of full cell proliferation; 8-10 DAS) in mm<sup>2</sup>.</p> <p>- rosetteArea Maturity.txt -> projected rosette area at maturity (22DAS)</p> <p>The phenotyping results have been normalised for batch effects ('experiment' in raw data)</p> <p>- allPhenotypesNormalised.txt -> contains the normalised data for all the measured phenotypes</p> <p>All datafiles indicate the accession name ('Accession'), the unique identifier for each accessions ('Ecotype_ID') as used in the 1001genomes project (www.1001genomes.org) and the treatment ('C' indicate well-watered plants, 'S' the mild-drought treated plants).</p> <p>These results and methodological results are described in Clauw et al. (2016, The Plant Cell).</p> <p>Citation:</p> <p><strong>Clauw, Pieter, Frederik Coppens, Arthur Korte, Dorota Herman, Bram Slabbinck, Stijn Dhondt, Twiggy Van Daele, et al. 2016. “Leaf Growth Response to Mild Drought: Natural Variation in Arabidopsis Sheds Light on Trait Architecture.” The Plant Cell, October. doi:10.1105/tpc.16.00483.</strong></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
System Call Logs with Natural Random Faults: Experimental Design and Application
<p>In this work we present a large dataset for use in embedded systems research which has been gathered from a realistic development environment operating in the path of an accelerated neutron beam. The dataset contains traces with events from a real-time operating system as as user events from a safety critical application. All data is carefully timestamped and in human understandable form.</p>
Chernobyl's nature: Prypjat and Dnepr rivers amazing path
<p>If someone hears about Chernobyl, then he will most probably think of the April 26th 1986 disaster in the nuclear power plant. But Sentinel-1 and Sentinel-2 caught a different eye on it. The rivers Prypjat, who passes by at the previous power plant, and Dnepr have a beautiful meandering watercourse!</p> <p>Sentinel-1 acquisitions were processed with SNAP 5.0.0 to get gecoded, calibrated (slope normalised), multilooked, despeckled sigma0 products for both polarisations starting from Sentinel-1 GRDH products.</p> <p>Sentinel-2 acquisitions were processed with sen2cor 2.3.0 to get Bottom-Of-Atmosphere reflectance images.</p> <p>Contains modified Copernicus Sentinel data 2015-7</p>
Data supplementing the article "Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13."
<p>These data supplement the article Einhäuser, W., & Nuthmann, A. (2016). Salient in space, salient in time: Fixation probability predicts fixation duration during natural scene viewing. Journal of Vision, 16(11):13, 1-17, doi:10.1167/16.11.13.</p> <p>The data can be used freely for academic purposes, provided the aforementioned reference is appropriately cited.</p> <p>The following files are available for experiment 2 of the article:</p> <p>allData.mat</p> <p>Includes the datamatrix allData with the following columns:</p> <p>1) Line used for analysis in the article (0 - no, 1-yes).<br> Possible reasons for exclusion:<br> a. fixation duration smaller than 50ms or larger 1000ms<br> b. fixation adjacent to a blink (preceding or following)<br> c. fixation outside the image</p> <p>2) ID of observer (1-24)</p> <p>3) ID of condition (1:grayscale, 2: reduced luminance, 3: reduced contrast, 4: equalized luminance, 5: equalized contrast, 6: phasenoise)</p> <p>4) ID of image (48 unique numbers between 1 and 135)</p> <p>5) horizontal eye position</p> <p>6) vertical eye position</p> <p>7) fixation duration in ms</p> <p>8) value of empirical map generated from search condition of experiment 1 at fixated location</p> <p>9) value of empirical map generated from preference condition of experiment 1 at fixated location</p> <p>10) value of empirical map generated from memorization condition of experiment 1 at fixated location</p> <p>11) value of empirical map generated from joining memorization and preference condition of experiment 1 at fixated location</p> <p>12) value of empirical map generated from condition 1 at fixated location</p> <p>13) value of empirical map generated from condition 2 at fixated location</p> <p>14) value of empirical map generated from condition 3 at fixated location</p> <p>15) value of empirical map generated from condition 4 at fixated location</p> <p>16) value of empirical map generated from condition 5 at fixated location</p> <p>17) value of empirical map generated from condition 6 at fixated location</p> <p>18) value of empirical map generated from condition 1 at fixated location leaving out the current observer</p> <p>19) value of empirical map generated from condition 2 at fixated location leaving out the current observer</p> <p>20) value of empirical map generated from condition 3 at fixated location leaving out the current observer</p> <p>21) value of empirical map generated from condition 4 at fixated location leaving out the current observer</p> <p>22) value of empirical map generated from condition 5 at fixated location leaving out the current observer</p> <p>23) value of empirical map generated from condition 6 at fixated location leaving out the current observer</p> <p>24) luminance at fixation</p> <p>25) luminance contrast at fixation</p> <p>26) edge density at fixation</p> <p>27) eccentricity of fixation</p> <p> </p> <p>usedData.Rdata</p> <p>- for all lines that are used for analysis (allData(:,1)==1) a field in an R dataframe is created, which contains the following fields (for details, see description of matlab file above):</p> <p>obsNum: the ID of the observer (1-24)</p> <p>condNum: the ID of the condition (1-6)</p> <p>imgNum: the ID of the image (48 unique numbers between 1 and 135)</p> <p>fixDur: fixation duration</p> <p>LUM, LCG, ED, ECC: luminance, contrast, edge density and eccentricity at fixation</p> <p>empMapFromSearch, empMapFromPref, empMapFromMem, empMapFromJoint: values of empirical maps generated from data of experiment 1 (search, preference, memorization task as well as combination of the latter two) at fixation</p> <p>empMapFromC1 through empMapFromC6: value of empirical map generated from condition 1 through 6 at fixated location</p> <p>empMapFromC1loo through empMapFromC6loo - value of empirical map generated from condition 1 through 6 at fixated location leaving out the current observer</p> <p>x,y - coordinates of fixation</p> <p> </p> <p>modelsFigure7.R - computes all models for figure 7 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p>modelsFigure8.R - computes all models for figure 8 of the aforementioned article (Note: depending on your system, this can take substantial time; depending on the version of the lme-package results may deviate slightly from those given in the paper)</p> <p> </p>
Data supplementing the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.
<p>These data supplement the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.</p> <p>Use is free for academic purposes, provided the aforementioned article is appropriately cited.</p> <p>The directory contains the following files</p> <p>stimuli.tar.gz - stimuli used in this study; note that this is based on the MONS database, but some deviations from the final version of the database do exist.</p> <p>ratings.mat contains the variables<br> arousal - mean arousal rating<br> valence - mean valence rating<br> valence2 - squared mean valence rating (after subtracting midpoint)<br> motivationalValue - mean motivation rating<br> motivaionalValue2 - squared mean motivation rating (after subtracting midpoint)</p> <p>All variables are 104x3, where the first dimension is the stimulus number, and the second dimension the motivation ground truth (aversive, neutral, appetitive)</p> <p><br> Experiment 1</p> <p>fixationsExperiment1.mat contains the variables fixationX, fixationY, fixationDuration, fixaitonOnset, fixationInitial, which contain for each fixation horizontal and vertical coordinate, the duration, the time of the onset relative to the trial onset and whether it is the initial fixation. All variables have dimensions 16x104x3x50, where the first dimension is the observer, the second the scene, the third the condition and the forth a counter of fixations. Whenever there are less than 50 fixations the remainder are filled with NaN.</p> <p><br> boundingBoxesExperiment1.mat contains for each critical object the bounding box coordinates x,y of upper left corner and width and height as variables boundingBoxX, boundingBoxY, boundingBoxW, boundingBoxH respectively. Note that this is relative to the eyetracker coordinates of experiment 1 (full display 1024x768, presentation in the center) and will therefore not match the coordinates of the images in the archive or the bounding box coordinates of experiment 2. Dimensions are 104x3, the dimensions representing scene number and condition, respectively.</p> <p><br> figure2.m uses these data to computes figure 2 of the article from these data</p> <p><br> dataForExperiment1.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of table 1. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object.</p> <p><br> table1.R computes and prints the models for table 1</p> <p> </p> <p>Experiment 2</p> <p>fixationsExperiment2.mat contains fixation data for experiment 2. Variable names as in experiment 1. Dimensions are 18x99x3x3x50, where the first dimension is the observer, the second the image number, the third the visual condition, the third the motivational condition and the fifth the fixation count. Since only one visual condition was shown to each observer per motivational condition, there is an additional variable 'hasData', which is 1 if the image was presented to the observer in this condition and 0 otherwise. Since fixations can be outside the image and will therefore be excluded, there is also an additional variable fixationNumber to keep a correct count of the fixation number in the trial.</p> <p>boundingBoxesExperiment2.mat contains bounding box data for experiment 2 in image (and fixation) coordinates. Notation as for experiment 1, but coordinates refer to image and eyetracking coordinates used for experiment 2 and therefore can differ occasionally.</p> <p><br> figure3and4.m generates figures 3 and 4 of the article from these data files.</p> <p>dataForExperiment2.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of tables 2 amd 3. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. The fields imgMot and imgVis contain the motivational ground truth and the salience manipulation, respectively.</p> <p>table2.R uses the Rdata file to compute the models for table 2 of the article and print summary results</p> <p>table3.R uses the Rdata file to compute the models for table 3 of the article and print summary results. Note that the computation can take substantial time; results might deviate slightly depending on the exact version of R and its libraries used.</p> <p> </p>
Dataset supplementing Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006
<p>These data supplement the publication</p> <p>Stoll, J., Thrun, M., Nuthmann, A., & Einhäuser, W. (2015). Overt attention in natural scenes: Objects dominate features. Vision Research, 107, 36-48. doi: 10.1016/j.visres.2014.11.006</p> <p>and be used freely for scientific purposes provided the aforementioned paper is appropriately cited.</p> <p>Note that the image files cannot be provided on this site due to copyright restrictions.</p> <p>The dataset contains the following files:</p> <p>maps_01.mat - maps_72.mat:</p> <p>For each image the 6 maps used in the paper are contained, the maps of experiment 1 are labelled as in the paper (AWS, OOM, nOOM, PVL,UNI), AWS2 is the AWS map for the modified stimuli of experiments 2 and 3.</p> <p>exp?_fixations.mat contains all fixations of the respective experiment.</p> <p>For experiment 1, there are the variables xFix, yFix, durFix, which contain the x position, the y condition, and the fixation duration of each fixation. Dimensions are images x subjects x fixation number, where the first fixation is the 0th (initial) fixation. The variable condition (image x subject) contains the condition in which the respective image was shown to the subject. For the main analysis only the "0" condition was used, refer to the paper's appendix for the other conditions.</p> <p>For experiment 2 and 3, variables are called xFixByImage, yFixByImage, dFixByImage and the dimensions are subject x image x fixation number. In addition tFixByImage contains the start of the fixation relative to trial onset (negative for the 0th fixation).<br> In both cases, empty entries are filled with nans.</p> <p><br> computeROC.m is a helper function called by other functions.</p> <p><br> figure1.m through figure7.m reproduce the figures from the paper to exemplify data usage.</p> <p> </p>
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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.