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.
1,923
datasets available to search
ShareScore release 0.9.0
Dataset results
1,923 results for “Compound”
Confirmation of Basitarsal Compound Slit Sensilla (BCSS) in scorpion genera Chaerilus and Scorpiops (Arachnida: Scorpiones)
<p><span>Basitarsal Compound Slit Sensilla (BCSS) are a cluster of mechanosensory structures, collectively composed of striated, curved, successive slits located at the prodistal end of basitarsus, forming into a fan-shaped configuration. It can efficiently detect and amplify substrate-borne micro-vibrational signals at a certain frequency range which cause slit deformation (Wang et al., 2019), being most sensitive to surface Rayleigh waves and crucial for locating the vibration source (but requires sensilla on multiple legs) (Brownwell & Farley, 1979). This structure, to my knowledge, has only been formally reported in three species, <em>Androctonus australis </em>(Linnaeus, 1758), <em>Heterometrus silenus</em> (Simon, 1884) and <em>Smeringurus mesaensis </em>(Stahnke, 1957) (Barth & Wadepuhl, 1975; Brownwell & Farley, 1979; Wang et al., 2019). The present work constitutes an informal observation (out of curiosity) of BCSS across 3 genera, confirming its presence (with different forms) in two of those genera.</span></p> <p><span>BCSS was confidently confirmed in all <em>Scorpiops</em> and <em>Chaerilus</em> species examined (see figures on subsequent pages). There appeared to be some interspecific differences (e.g., the proximal boundary that delineates the sensillar area) within the same genus, but I have not confirmed if those differences are stable and non-overlapping (i.e., diagnostic). Sensilla are closely associated with specific ecological adaptions which shape their phenotypic configurations. One of the most famous sensilla in scorpions is the trichobothrium, which serves as an important diagnostic character for many distinct lineages. Similarly, the constellation array on the externodistal surface of pedipalp fixed finger was also found to be diagnostic in some vaejovids. It is interesting to note the different configurations of slit sensilla between genera <em>Scorpiops</em> and <em>Chaerilus</em>, where in <em>Chaerilus</em> a long, accessory slit sensillum was always present external to the main sensillar area (on or near its boundary). Occasionally, several short striations (sensilla?) were also observed on the joint tubercle between the basi- and telotarsi. Unfortunately, due to the magnification constraint: I was not able to examine genera <em>Langxie</em> and <em>Qianxie</em> (both with highly slender basitarsi); I was not able to confidently identify the striation in genus <em>Olivierus</em> albeit observing a similar depression at the same location (BCSS of the much smaller species of <em>Scorpiops</em> (<em>S. jendeki</em> and <em>S. kovariki</em>) and <em>Chaerilus</em> were even more conspicuous).</span></p>
Behavioral response of Aromia bungii adults to volatile compounds emitted by Prunus persica at different physiological status
Open the record for dataset details and reuse information.
Linked collectors and determiners for: Internal secretory structures and chemical compounds in leaves of Conyza bonariensis (L.) Cronquist and Solidago chilensis Meyen (Asteraceae, Astereae).
Natural history specimen data linked to collectors and determiners held within, "Internal secretory structures and chemical compounds in leaves of Conyza bonariensis (L.) Cronquist and Solidago chilensis Meyen (Asteraceae, Astereae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e646c99d-532a-46e5-a025-46cdcaf3808d">https://bionomia.net/dataset/e646c99d-532a-46e5-a025-46cdcaf3808d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e646c99d-532a-46e5-a025-46cdcaf3808d">https://gbif.org/dataset/e646c99d-532a-46e5-a025-46cdcaf3808d</a>. Formatted as a Frictionless Data package.
Wind and Precipitation Extremes in Great Britain (1979-2019) to apply the methodology for Spatiotemporal Identification of Compound Hazards
<p>The data used in this study is extracted from ERA5. ERA5 is a climate reanalysis product which was released in 2019 by ECMWF and benefits from the latest improvements in the field (Hersbach et al., 2020). ERA5 data (ECMWF, 2020) is available 1979 to present (we use up to September 2019), with a spatial resolution of 0.25deg x 0.25deg and an hourly temporal resolution. The data resolves the atmosphere using 137 levels from the surface up to a height of 80 km (ECMWF, 2020). ERA5 data are generated with a short forecast of 18 h twice a day (06:00 and 18:00 UTC) and assimilated with observed data (ECMWF, 2020). more information about ERA5 can be found <a href="https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation">here</a>.</p> <p>The two following variables are extracted from the product:</p> <ul> <li> <p>Extreme precipitation (p): accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth’s in one hour (mm). This value is averaged over a grid cell.</p> </li> <li> <p>Extreme wind (w): hourly maximum wind gust at a height of 10 m above the surface of the Earth (m s-1). The WMO (2021) defines a wind gust as the maximum of the wind averaged over 3 s intervals. As this duration is shorter than a model time step, this value is deduced from other parameters such as surface stress, surface friction, wind shear and stability. This value is averaged over a grid cell.</p> </li> </ul> <p>Importation of the raw data</p> <p>Input data is divided into 4 files for each variables representing 4 periods:</p> <ol> <li>1979-1986</li> <li>1987-1997</li> <li>1998-2008</li> <li>2009-2019</li> </ol> <pre>library(ncdf4) filer=c(paste0(getwd(),"/data/in/raindat_7986.nc"), paste0(getwd(),"/data/in/raindat_8797.nc"), paste0(getwd(),"/data/in/raindat_9808.nc"), paste0(getwd(),"/data/in/raindat_0919.nc")) filew=c(paste0(getwd(),"/data/in/windat_7986.nc"), paste0(getwd(),"/data/in/windat_8797.nc"), paste0(getwd(),"/data/in/windat_9808.nc"), paste0(getwd(),"/data/in/windat_0919.nc")) Startdate=as.POSIXct("1979-01-01 10:00:00") Enddate=as.POSIXct("1986-12-31 23:00:00") # ncr = nc_open(filer) # ncw = nc_open(filew)</pre> <p>Intermediary data</p> <p>Intermediary data are stored in the “data/interdat” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "allraininclusters1.Rdata" "allraininclusters2.Rdata" ## [3] "allraininclusters3.Rdata" "allraininclusters4.Rdata" ## [5] "extremEventsWind.Rdata" "interclustRain.Rdata" ## [7] "interclustWind.Rdata" "metaclustRain.Rdata" ## [9] "metaclustWind.Rdata" "Rain_99_AllP.Rdata" ## [11] "rainP1.Rdata" "rainP2.Rdata" ## [13] "rainP3.Rdata" "rainP4.Rdata" ## [15] "rawclustRain.Rdata" "rawclustWind.Rdata" ## [17] "timeP1.Rdata" "timeP2.Rdata" ## [19] "timeP3.Rdata" "timeP4.Rdata" ## [21] "windP1.Rdata" "windP2.Rdata" ## [23] "windP3.Rdata" "windP4.Rdata" ## [25] "Wnd_99_AllP.Rdata" </code></pre> <ul> <li> <p>allraininclustersX: [data.frame] files are used to assess more accurately the accumulated precipitation during events by collecting precipitations from timesteps in which precipitation is above and below the threshold for every grid cell and the whole duration of the cluster.</p> </li> <li> <p>99_allp: [matrix] value of extreme precipitation and extreme wind gust threshold over the whole domain (one value per grid cell)</p> </li> <li> <p>interclust: [list] files contain a list of data from wind and precipitation clusters divided in the 4 periods aggregated over space and clusters (1 value per grid cell per cluster). These files are uses to create the files “RainEv_ldat” and “Windev_ldat”.</p> </li> <li> <p>metaclust: [list] files contain a list of metadata from wind and precipitation clusters divided in the 4 periods . These files are uses to create the files “RainEv_meta” and “Windev_meta”.</p> </li> <li> <p>rainPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing precipitation data for the period X.</p> </li> <li> <p>rawclust: [list] files contain a list of data.frame from wind and precipitation clusters divided in the 4 periods. These files are uses to create the files “RainEv_hdat” and “Windev_hdat”.</p> </li> <li> <p>timePX: [vector] contain vectors of time for the 4 periods.</p> </li> <li> <p>windPX: [matrix] files contain a 3D matrix of dimension long<em>lat</em>time containing wind gust data for the period X.</p> </li> </ul> <p>Output data</p> <p>Output data contains metadata and raw data of single and compound hazard clusters are stored in the “data/out” folder which contains the following files in Rdata format:</p> <pre><code>## [1] "compoundclusters.csv" "CompoundRW_79-19.v3x.Rdata" ## [3] "extremEvents_Rain.Rdata" "extremEvents_Wind.Rdata" ## [5] "Rain_stfprint.Rdata" "rainclusters.csv" ## [7] "RainEv_hdat_1979-2019.Rdata" "RainEv_ldat_1979-2019.Rdata" ## [9] "Rainev_ldatp_1979-2019.Rdata" "RainEv_meta_1979-2019.Rdata" ## [11] "RainEv_metap_1979-2019.Rdata" "Wind_stfprint.Rdata" ## [13] "windcluster.csv" "WindEv_hdat_1979-2019.Rdata" ## [15] "WindEv_ldat_1979-2019.Rdata" "WindEv_meta_1979-2019.Rdata" </code></pre> <ul> <li> <p>CompoundRW: [data.frame] contains metadata for the compound hazard clusters identified</p> </li> <li> <p>_hdat: [data.frame] hourly data of precipitation and wind gust clusters.</p> </li> <li> <p>_ldat: [data.frame] aggregated data over space and clusters (1 value per grid cell per cluster) for wind gust and precipitation clusters.Rain_ldatp contains aggregated values including non-extreme timesteps. Created from allraininclustersX.</p> </li> <li> <p>_meta:[data.frame] metadata for wind gust and precipitation clusters</p> </li> <li> <p>stfprint: [data.frame] files containing duration*footprint of each hazard clusters during all clusters</p> </li> <li> <p>sptdf: [data.frame] data.frame containing spatial, temporal, cluster and intensity information</p> </li> </ul> <p>Codes assiciated to the method are availaible here: https://github.com/Alowis/SI-CH</p>
BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space
<p>BonMOLière is a set of small-sized libraries of readily purchasable compounds, optimized to produce genuine hits in biological screens across the protein space. The corresponding research article has been published in <em>Int. J. Mol. Sci.</em> 2021, 22(15), 7773, DOI: <a href="https://doi.org/10.3390/ijms22157773">https://doi.org/10.3390/ijms22157773</a></p>
A large-ensemble simulation of yields and meteorological drivers to evaluate spatial compounding crop failures in Europe
<p>The dataset consists of a subset from Vogel et al. (2021), comprising large-ensemble simulations of winter wheat yields aggregated at the country level for 20 European countries. The winter wheat yields were simulated by the APSIM-Wheat model (version 7.10) (Zhang et al. 2014) driven by meteorological data from the EC-Earth global climate model (Hazeleger et al., 2010; Van der Wiel et al., 2019). To investigate meteorological drivers of crop failure, the dataset also includes monthly means of daily precipitation, vapour pressure deficit, and maximum temperature fields, of two leading European producers, i.e. France and Germany. For more details, see the description in Vogel et al. (2021).</p> <p>By using this data, you also agree to cite the reference below:</p> <p>Vogel, J., Rivoire, P., Deidda, C., Rahimi, L., Sauter, C. A., Tschumi, E., van der Wiel, K., Zhang, T., Zscheischler, J. (2021). Identifying meteorological drivers of extreme impacts: an application to simulated crop yields. Earth System Dynamics,12(1),151-172.</p>
Figs. 55–60. Opopaea deserticola Simon, compound microscope. 55–58. Male palp. 59, 60. Female epigastric region. 55. Prolateral view. 56. Retrolateral view. 57. Bulb, prolateral view. 58. Bulb, retrolateral view. 59. Ventral view. 60 in The Goblin Spider Genera Opopaea and Epectris (Araneae, Oonopidae) in the New World
Figs. 55–60. Opopaea deserticola Simon, compound microscope. 55–58. Male palp. 59, 60. Female epigastric region. 55. Prolateral view. 56. Retrolateral view. 57. Bulb, prolateral view. 58. Bulb, retrolateral view. 59. Ventral view. 60. Dorsal view.
Figs. 145–150. Epectris apicalis Simon, compound microscope. 145–148. Male palp. 149, 150. Female epigastric region. 145. Prolateral view. 146. Retrolateral view. 147. Bulb, prolateral view. 148. Bulb, retrolateral view. 149. Ventral view. 150 in The Goblin Spider Genera Opopaea and Epectris (Araneae, Oonopidae) in the New World
Figs. 145–150. Epectris apicalis Simon, compound microscope. 145–148. Male palp. 149, 150. Female epigastric region. 145. Prolateral view. 146. Retrolateral view. 147. Bulb, prolateral view. 148. Bulb, retrolateral view. 149. Ventral view. 150. Dorsal view.
FIG. 7. — A, B, Janstockia phallusiella n. gen., n in A new genus of notodelphyid copepod (Crustacea, Copepoda, Cyclopoida) from a compound ascidian host collected in the Suez Canal
FIG. 7. — A, B, Janstockia phallusiella n. gen., n. sp.; A, leg 1; B, leg 2; C, Ophioseides cardiocephalus Hesse, 1864, leg 1. Scale bars: 50 μm.
FIG. 5. — Janstockia phallusiella n. gen., n in A new genus of notodelphyid copepod (Crustacea, Copepoda, Cyclopoida) from a compound ascidian host collected in the Suez Canal
FIG. 5. — Janstockia phallusiella n. gen., n. sp.; A, urosome, ventral view; B, copulatory pore and paired sperm ducts, with blind projections; C, cluster of setae representing incorporated caudal rami, showing setular ornamentation, dorsal view; D, urosome, dorsal view; E, mandibular palp; F, maxillule; G, maxilliped; H, tip of maxilliped. Scale bars: A, D, 200 μm; B, C, F, H, 25 μm; E, G, 50 μm.
FIG. 4. — Janstockia phallusiella n. gen., n in A new genus of notodelphyid copepod (Crustacea, Copepoda, Cyclopoida) from a compound ascidian host collected in the Suez Canal
FIG. 4. — Janstockia phallusiella n. gen., n. sp., scanning electron micrographs; A, mandibular palp, ventral view in situ; B, maxillule, ventral view in situ; C, hirsute lobes of maxillule; D, maxilliped. Scale bars: A-C, 10 μm; D, 20 μm.
FIG. 1. — Janstockia phallusiella n. gen., n in A new genus of notodelphyid copepod (Crustacea, Copepoda, Cyclopoida) from a compound ascidian host collected in the Suez Canal
FIG. 1. — Janstockia phallusiella n. gen., n. sp.; A, habitus of paratype (MNHN-Cp2178), lateral view; B, habitus of holotype (MNHN-Cp2177), ventral view; C, habitus of holotype, dorsal view showing lateral expansions of first pedigerous somite (arrow). Scale bars: A, 1.0 mm; B, C, 0.5 mm.
FIG. 9 in A new genus of notodelphyid copepod (Crustacea, Copepoda, Cyclopoida) from a compound ascidian host collected in the Suez Canal
FIG. 9. — Schematic of leg 2 of Janstockia phallusiella n. gen., n. sp. Abbreviations: ba, basis; en1-3, endopodal segments 1 to 3; ex1-3, exopodal segments 1 to 3; ics, intercoxal sclerite; os, outer basal seta.
FIG. 6. — Janstockia phallusiella n. gen., n in A new genus of notodelphyid copepod (Crustacea, Copepoda, Cyclopoida) from a compound ascidian host collected in the Suez Canal
FIG. 6. — Janstockia phallusiella n. gen., n. sp., scanning electron micrographs; A, leg 1, anteroventral; B, detail of leg 1 exopod; C, rami of leg 2; D, leg 4; E, detail of leg 3 rami; F, detail of leg 4 rami. Scale bars: A, 20 μm; B, C, E, F, 10 μm; D, 50 μm.
FIG. 8 in A new genus of notodelphyid copepod (Crustacea, Copepoda, Cyclopoida) from a compound ascidian host collected in the Suez Canal
FIG. 8. — Ophioseides cardiocephalus Hesse, 1864; A, antennule; B, tip of antennule showing setal elements; C, antenna; D, detail of antennal endopod, showing claw and accessory setae; E, oral appendages in situ. Abbreviations: mn, mandible; mx, maxilla; mxl, maxillule. Scale bars: A, C-E, 50 μm; B, 25 μm.
FIG. 3. — Janstockia phallusiella n. gen., n in A new genus of notodelphyid copepod (Crustacea, Copepoda, Cyclopoida) from a compound ascidian host collected in the Suez Canal
FIG. 3. — Janstockia phallusiella n. gen., n. sp., scanning electron micrographs; A, cephalosome and anterior part of trunk, ventral view; B, rostrum, labrum and oral area, ventral view; C, detail of trunk surface showing integumental ornamentation; D, genital openings, ventral view. Scale bars: A, 200 μm; B, 100 μm; C, 10 mm; D, 50 μm.
Data and code for "Hotspots and drivers of compound marine heatwave and low net primary production extremes"
<p>This repository provides the code and data for the study "Hotspots and drivers of compound marine heatwave and low net primary production extremes". All processed data required to produce the figures in this study are provided. However, not all raw data are provided, because of too large file sizes. For more information, please contact natacha.legrix@unibe.ch. </p>
On the correspondence between the transcriptomic response of a compound and its effects on its targets
<p>Dataset used in the manuscript "On the correspondence between the transcriptomic response of a compound and its effects on its targets".</p> <p>More info at https://github.com/enveda/transcriptomic-target-correlation</p>
Figure 2 in Predatory mites, a green pesticide, and an entomopathogenic compound: A proposed IPM tactic based on pest species diversity indices and population dynamics
Figure 2. Schematic diagram of the experiment's plantation and IPM methodology, C.n: Cydnoseius negevi, A.s: Amblyseius swirskii, and P.p Phytoseiulus persimilis. (Photo credits: Dr. Zidan has created this diagram on www.biorender.com).
Figure 1 in Predatory mites, a green pesticide, and an entomopathogenic compound: A proposed IPM tactic based on pest species diversity indices and population dynamics
Figure 1. Google Earth map photography of the experimental locations (pointed with pin) – i) Om Sabir, Kom Hamada, El Beheira Governorate (30° 29' 50.6" N, 30° 46' 18.8" E), and ii) Kom Oshim, Fayoum Governorate (29° 34' 40.9" N, 30° 55' 38.3" E).
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.