Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

How aphids fly: take off, free flight and implications for short and long distance migration.

We used a Phantom T4040 camera at 9350-13,000 FPS and at 4.2-Mpx resolution (2560 x 1664) . The aspect ratios varied, but were typically 2048 x 1280 pixels - 2560 x 1664. Videos were captured by the Phantom Camera Control software (PCC) as Cine RAW files and converted to MP4 for analysis and viewing in slow motion. A timer recording behaviour in milliseconds is embedded in MP4 files. Filming at high FPS and in HD requires specialist flicker-free high-speed illumination lighting: we used two GSVitec™ MultiLED MX that each produced 12,000 Lux of white light (24,000 total). Videos include Drepanosiphum platanoidis (Schrank), the sycamore aphid, that feeds on Acer sp, a monophyletic group of trees ancestral to Asia, but present in Europe for the last 30 million years (Gao et al. 2020). Myzus persicae (Sulzer), the peach-potato aphid, is a medium sized aphid that is extremely polyphagous and is truly a global pest species.

opencc-by-4.0Nov 2024View details →

Patterns of object play behaviour and its functional implications in free-flying ravens (supplementary data)

This resource contains the processed data sets and R scripts associated with the article titled "Patterns of object play behaviour and its functional implications in free-flying ravens" authored by Awani Bapat, Anna E Kempf, Salome Friry, Palmyre H Boucherie, Thomas Bugnyar, published in Scientific Reports on 02-01-2025.

opencc-by-4.0Jun 2022View details →

Data from: Land use, season, and parasitism predict metal concentrations in Australian flying fox fur

There are two .csv files in this upload. The "Pteropus_metal_data_wide.csv" file contains metal concentrations (reported in ng/g) measured in fur samples collected from Pteropus flying foxes (P. alecto, P. conspicillatus, P. poliocephalus). Flying foxes were captured from 2015-2018 at multiple sites across Australia. The file also contains capture information (e.g. date, location) and biological information (e.g. species, sex, age class, parasitism) for each flying fox. The "Pteropus_metadata.csv" file provides further details on all column names in the primary data file, including the specific metals that were quantified. Detailed information on the study methods and results can be found in the associated Science of the Total Environment publication, "Land use, season, and parasitism predict metal concentrations in Australian flying fox fur" by Sánchez et al.

opencc-by-4.0Jul 2021View details →

Modulation of bioelectric cues in the evolution of flying fishes [Data set]

Assembled reference contigs for protein-coding exons and conserved non-coding regions from targeted sequence enrichment of beloniform fishes.  Current citation: Daane JM, Blum N, Lanni J, Boldt H, Iovine MK, Johnson SL, Lovejoy NR, and MP Harris. (2021). Novel regulators of growth identified in the evolution of fin proportion in flying fish. bioRxiv. doi: 10.1101/2021.03.05.434157 -contigs.tar.gz contains the assembled contigs for each species. Each contig represents a targeted region with the addition of flanking DNA sequence -cnes.tar.gz contains the targeted conserved non-coding regions isolated from the larger contigs in contigs.tar.gz -exons.tar.gz contains the targeted protein coding exons isolated from the larger contigs in contigs.tar.gz -translated_exons.tar.gz contains the translated protein coding exons from exons.tar.gz -Beloniformes.tre is the species tree  -medaka_cne_great.txt contains the associations between the assembled CNEs and neighboring protein-coding genes based on the GREAT approach

opencc-by-nc-nd-4.0Jan 2020View details →

Flying Dragon (Draco volans) lizard brain illustration

3D model of the Flying Dragon lizard brain highlighting the anatomy and the spatial arrangement of its major subdivisions. The brain reconstruction was obtained from a microCT scan of a iodine-stained specimen through manual segmentation using the software Amira 5.5.0. Other illustrations can be found <a href="https://zenodo.org/search?page=1&size=20&q=keywords:%22squamate%20brain%22">here</a>. If you are interested in reptile brain evolution and behavior, please, have a look to our recent publication: <a href="https://www.nature.com/articles/s41467-019-13405-w">"Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"</a> Simone Macrì, Yoland Savriama, Imran Khan & Nicolas Di-Poï Nature Communications 10, 5560 (2019)   Check out also our *4K* video collection of various snake and lizard 3D brains: <a href="https://www.youtube.com/playlist?list=PLgx4vtT32C8hqxG_icKiuXGtZVLVX-oG1">Snake and Lizard brain reconstructions video collection</a>   For any inquiries or additional information, please, refer to the contacts provided in the <a href="https://www.nature.com/articles/s41467-019-13405-w">article</a>.

opencc-by-4.0Mar 2022View details →

Trap catches of carrot fly (Psila rosae) and cutworm (Agrotis ipsilon) from 142 fields in Denmark and Southern Sweden 1997-2019

The pests were caught in various vegetable crops in Denmark and Southern Sweden: Yellow sticky traps for carrot flies and pheromone traps for cutworm adults. The data is provided as both a tab-separated text file and a binary R data file. The R files provides code to read and plot the data. The two plots produced are also provided as PNG files. The data were collected by SEGES Innovation, Denmark.

opencc-by-4.0Jan 2021View details →

Dataset: A Fly in the Ointment: An Empirical Study on the Characteristics of Ethereum Smart Contracts Code Weaknesses and Vulnerabilities

Dataset: A Fly in the Ointment: An Empirical Study on the Characteristics of Ethereum Smart Contracts Code Weaknesses and Vulnerabilities Majd Soud, Grischa Liebel, Mohammad Hamdaqa majd18@ru.is, grischal@ru.is, mhamdaqa@polymtl.ca. This Dataset includes the following:  1. "labeling.xml" files that represents the data for Categories of vulnerabilities in Smart Contracts for four data sources (i.e., Common Vulnerability and Exposure (CVE), Smart Contract Weakness Classification Registry (SWC), Stack Overflow, and GitHub) XML files structure: The XML files can be opened used any editor or any code editor (e.g. Visual Studio Code). 1. Each file has a root that is <Card_Table></Card_Table> which contains all the cards we labeled.  2. Each card is represented by the <Card></Card> and contains the following:             - The tag marked by <Tag> represents the keyword that was used to search and collect the card from StackOverflow.              - The URL marked by <URL> of the URL link which contains all the information of the labeled vulnerability.               - The other tages marked by <OtherTags> that shows all the tags used in the post on Stack Overflow.              - The expert labeling for the categories of vulnerabilities in each card is represented by  <CategoryExpertLabel>             - In more details, some records has the <SecondExpertCategoryLabel> that represents the second expert labeling for the categories of vulnerabilities.             - The <CategoryAgreement> used to calculate the inter-rater agreement between the two labelers.

opencc-by-4.0Aug 2022View details →

Supplementary material for "Patterns of high-flying insect abundance are shaped by landscape type and abiotic conditions"

Abstract Insects are of increasing conservation concern as a severe decline of both biomass and biodiversity have been reported. At the same time, data on where and when they occur in the airspace is still sparse, and we currently do not know whether their density is linked to the type of landscape above which they occur. Here, we combine data of high-flying insect abundance from six locations across Switzerland representing rural, urban and mountainous landscapes, which was recorded using vertical-looking radar devices. We analysed the abundance of high-flying insects in relation to meteorological factors, daytime, and type of landscape. Air pressure was positively related to insect abundance, wind speed showed an optimum, and temperature and wind direction did not show a clear relationship. Mountainous landscapes showed a higher insect abundance than the other two landscape types. Insect abundance increased in the morning, decreased in the afternoon, had a peak after sunset, and then declined again, though the extent of this general pattern slightly differed between landscape types. We conclude that the abundance of high-flying insects is not only related to abiotic parameters, but also to the type of landscapes. Thus, conservation measures implemented on the ground should start to also account for the needs of high-flying insects.

opencc-by-4.0May 2024View details →

Data for 'Genetic variation in trophic avoidance shows fruit flies are generally attracted to bacterial pathogens'

Raw data dn R code for the analysis of data dn generation of all figures in the above referenced paper. Descriptions of each data file are included wihtin the R script.

opencc-by-4.0May 2018View details →

Fly through a 2.3Mb Nanopore Sequence

A spiral representation of the longest sequence found by <a href="https://doi.org/10.1101/312256">Alex Payne et al.</a>, which at the time of upload was the longest observed public single-pore nanopore sequence. This video shows about 30kb of the sequence at once, and zooms through the sequence at about 20kb per second in the animated GIF, or about 48kb per second in the AVI file.

opencc-by-4.0Jul 2019View details →

Data set for "Quantification of amorphous siliceous fly ash in hydrating blended cement pastes by X-ray powder diffraction"

The main data is XRD patterns originally collected as xrdml and converted into rd format. The data set for the manuscript: Quantification of amorphous siliceous fly ash in hydrating blended cement pastes by X-ray powder diffraction Xuerun Lia, Ruben Snellingsb and Karen L. Scrivenera aLaboratory of Construction Materials, Swiss Federal Institute of Technology in Lausanne (EPFL), Station 12, CH-1015 Lausanne, Switzerland bSustainable Materials Management, Flemish Institute of Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium

opencc-by-4.0Oct 2024View details →

Genome data and resources on the recombination landscape and population history of the harlequin fly

This dataset contains phased vcf files of Chironomus riparius, ouput files of RepeatMasker, MELT, RepeatOBserver, MSMC2, iSMC and bedtools, such as supporting files.  For further details also check the GitHub page: <a href="https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024" target="_blank" rel="noopener">https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024</a> <ul> <li>phased-vcfs: Artificially phased vcf-files of five populations with four individuals each. Needed to generate multihetsep files. Input files for iSMC. <ul> <li>Hesse in Germany =  MG</li> <li>Rhône-Alpes in France = MF</li> <li>Lorraine in France = NMF</li> <li>Piemont in Italy = SI</li> <li>Andalusia in Spain = SS</li> </ul> </li> <li>multihetsep-files: Created with msmc-tools. Input files for MSMC2. </li> <li>RepeatMasker: Raw output of RepeatMasker run. Summary file and file with filtered Cla-element (a transposable element) included. </li> <li>MELT: MELT ouput with added info on population and numbered insertions reflecting all 441 detected Cla insertions. </li> <li>RepeatOBserver: Summary files on centromere predictions based on histograms and Shannon Diversity from RepeatOBserver. Genome-wise Shannon Diversity per chromosome included. </li> <li>MSMC2: Raw ouput of combined cross-coalescence and mean values if MSMC2 per populations. </li> <li>iSMC: Recombination rate rho in 10 kb windows and 100 kb windows along the genome. </li> <li>bedtools closest ismc 10 kb: Bedtools closest analysis of the distance of the next Cla-element to the recombination rate rho in 10 kb windows. </li> <li>bedtools closest ismc 100 kb: Bedtools closest analysis of the distance of the next Cla-element to the recombination rate rho in 100 kb windows.</li> <li>input-files figures: Supporting files needed to create figures. </li> </ul>

opencc-by-4.0Jul 2023View details →

Calcium imaging of odor responses in the fruit fly mushroom body

Abstract This dataset contains olfactory responses in the third stage of the olfactory circuit in fruit flies: the mushroom body. The responses are recorded with the GCaMP3 sensor. The methods used to collect the data and the procedures to process them are presented in detail in Campbell et al., 2013, Journal of Neuroscience. The dataset was also used in a recent manuscript by Srinivasan et al., 2023. Methods Please refer to Campbell et al., 2013, Journal of Neuroscience for details. Here, we present a description of how the data was collected, the odors presented, and the analysis, excerpted from Campbell et al., 2013. Animal preparation Flies carrying the genetically encoded calcium sensor UAS-GCaMP3 (Tian et al., 2009) were crossed with OK107-Gal4 flies (Connolly et al., 1996) to drive GCaMP3 expression in essentially all KCs (Lee and Luo, 1999; Aso et al., 2009). All experiments were conducted on female F1 heterozygotes from this cross, aged 2–5 d post-eclosion. Procedures for animal preparation were as described previously (Turner et al., 2008; Murthy and Turner, 2010; Honegger et al., 2011). Flies were anesthetized temporarily on ice and inserted into a small hole cut in the recording platform. The animal’s head was tilted forward, exposing the olfactory organs to the odor delivery nozzle located on the underside of the plat- form. The fly was fixed in place with fast-drying epoxy (Devcon 5 min epoxy). The top of the fly was bathed in oxygenated saline (Wilson et al., 2004) and the cuticle overlying the brain was dissected away. Air sacs overlying the MBs were pushed aside, but we did not attempt to remove the perineural sheath. To minimize movement of the brain inside the head capsule, we removed the pulsatile organ at the neck and the probos- cis retractor muscles that pass over the caudal aspect of the optic lobes.   Odor delivery  The following chemicals were used as stimuli: 2-heptanone (CAS #110-43- 0), 3-octanol (CAS #589-98-0), 6-methyl-5-hepten-2-one (CAS #110-93-0), ␣-humulene (CAS #6753-98-6), benzaldehyde (CAS #100-52-7), ethyl lactate (CAS #97-64-3), ethyl octanoate (CAS #106-32-1), hexanal (CAS #66-25-1), isoamyl acetate (CAS #123-92-2), 4-methylcyclo- hexanol (CAS #589-91-3), methyl octanoate (CAS #111-11-5), diethyl suc- cinate (CAS #123-25-1), pentanal (CAS #110-62-3), butyl acetate (CAS #123-86-4), 1-octen-3-ol (CAS #3391-86-4), 1-hepten-3-ol (CAS #4938-52- 7), and pentyl acetate (CAS #628-63-7).  Odors were presented using a custom-built delivery system that uses serial air dilutions to control odor concentration while maintaining a constant total airflow of 1 L/min at the fly. Experiments were conducted at an odor dilution of 1:100 or, where appropriate, adjusted to match the concentrations used behaviorally. We used a photo-ionization detector (Aurora Scientific) to match concentrations between the imaging rig and the T-maze and to monitor odor delivery throughout each imaging ex- periment. Odor pulses were created by switching between clean and odorized air streams using a synchronous two-way valve (N-Research).  This final valve was located 50 cm from the fly, leading to a delay of 300 ms between valve switching and the odor reaching the fly. The flow path was 1/8 inch in diameter throughout, which enabled the system to work near atmospheric pressure at these flow rates. The distance of the valve from the fly and the large tubing diameter virtually eliminated pressure transients caused by valve switching, as measured by the photo-ionization detector and a hot-wire anemometer. Calcium imaging Two-photon imaging was performed using a Prairie Ultima system (Prairie Technologies) and a Ti-Sapphire laser (Chameleon XR; Coher- ent) tuned to 920 nm delivering 8 –10 mW at the sample. All images were acquired with Olympus water-immersion objectives (LUMPlanFl/IR, 60x, numerical aperture 0.9; LUMPlanFl/IR, 40x, numerical aperture 0.8). Imaging planes were selected to maximize the number of visibleKCs. Typically imaging frames were 300 x 300 pixels, acquired with a pixel dwell time of 1.6 s, yielding frame rates near 3.8 Hz. On average, 120 KCs (range: 60 –170) were monitored in one plane.  Custom MATLAB (MathWorks) routines were used to control odor presentation and synchronize stimulus delivery with data acquisition.  Data were acquired in 20 s sweeps with a 1 s odor pulse triggered 8 s after sweep onset. The interstimulus interval was 25 s. Stimuli were presented in randomly interleaved fashion, adjusted so that the same odor was never presented twice in succession. Imaging analysis Data were analyzed using MATLAB and R (http://www.R-project.org).  To correct for motion within the field of view, frames were aligned using 2D image registration approaches. In many cases, a Fourier-based sub-pixel translation correction was sufficient (Guizar-Sicairos et al., 2008).  Some animals required an affine transform to cope with global distortions, such as rotational movement of the brain (Thirion, 1998). Where necessary a nonrigid transform was used to correct more localized dis- tortions (Klein et al., 2010).  Fluorescent neural tissue was automatically segmented from the surrounding regions. Pixel intensity values from the area outside this boundary were considered to represent background (tissue autofluorescence plus shot noise) and the mean pixel intensity value from the back- ground was then subtracted from the overall image.  To quantify the response of the KCs a small, circular region of interest 6 – 8 pixels in diameter was applied to each cell body. This allowed aver- aging of the pixel intensity values from each cell, treating individual KCs as separate units. Care was taken to ensure that each selected cell re- mained within its region of interest over the whole imaging session.  Response amplitudes were calculated as the mean change in fluorescence (dF/F) in the 0.5– 4.5 s window after stimulus onset. A statistical test originally described in Honegger et al. (2011) was used to determine whether a KC responded significantly on a given trial.  Briefly, the SD of the baseline activity was obtained 8 s before stimulus onset. The response time course was then smoothed using a five-point running average to control for outliers. The peak dF/F in the 0.5– 4.5 s window after stimulus onset was determined. The response was judged to be significant if this peak was 2.33 SDs greater than the baseline, which corresponds to a one-tailed significance test where alpha = 0.01. References Aso Y, Grübel K, Busch S, Friedrich AB, Siwanowicz I, Tanimoto H (2009) The mushroom body of adult Drosophila characterized by GAL4 drivers.  J Neurogenet 23:156 –172.  Connolly JB, Roberts IJ, Armstrong JD, Kaiser K, Forte M, Tully T, O’Kane CJ (1996) Associative learning disrupted by impaired Gs signaling in Drosophila mushroom bodies. Science 274:2104 –2107. Honegger KS, Campbell RA, Turner GC (2011) Cellular-resolution population imaging reveals robust sparse coding in the Drosophila mushroom body. J Neurosci 31:11772–11785. Lee T, Luo L (1999) Mosaic analysis with a repressible cell marker for studies of gene function in neuronal morphogenesis. Neuron 22:451– 461. Murthy M, Turner GC (2010) In vivo whole-cell recordings in the Drosophila brain. In: Drosophila neurobiology methods: a laboratory manual (Zhang B, Waddell S, Freeman M, eds). Cold Spring Harbor, NY: Cold Spring Harbor Laboratory. Srinivasan, S., Daste, S., Modi, M., Turner, G., Fleischmann, A. & Navlakha, S (2023). Stochastic coding: a conserved feature of odor representations and its implications for odor discrimination. bioRxiv. Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2:243–260. Tian L, Hires SA, Mao T, Huber D, Chiappe ME, Chalasani SH, Petreanu L, Akerboom J, McKinney SA, Schreiter ER, Bargmann CI, Jayaraman V, Svoboda K, Looger LL (2009) Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat Methods 6:875–881. Turner GC, Bazhenov M, Laurent G (2008) Olfactory representations by Drosophila mushroom body neurons. J Neurophysiol 99:734 –746. Wilson RI, Turner GC, Laurent G (2004) Transformation of olfactory representations in the Drosophila antennal lobe. Science 303:366–370. Usage notes The files are all in csv format, and can be easily opened in R or Python or other programming languages. Please see the README.md file for directions on how to use the data. The dataset included here is broken into two parts. The main dataset was the one that was chiefly used in the Campbell and Srinivasan papers, with the second part containing 7 additional datasets that were used in some figures. A fuller description is available in the README.md file.

opencc-by-4.0Oct 2023View details →

High-resolution spatiotemporal modelling of sand fly abundance in Cyprus in 2015

The expected population size of P. papatasi in Cyprus in 2015 was simulated using the stochastic climate-driven population dynamics model of the species presented in Erguler et al. (2019). The model was simulated with air temperature and relative humidity obtained from WRF-ARW. Two sets of parameters, one for Steni and one for Geri - each with 1000 alternative configurations - were used to simulate the average number of adult females per day per trap (a proxy to expected population size).

opencc-by-nc-nd-4.0Jan 2020View details →

Golden Flying Snake (Chrysopelea ornata) brain illustration

3D model of the Golden Flying Snake brain highlighting the anatomy and the spatial arrangement of its major subdivisions. The brain reconstruction was obtained from a microCT scan of a iodine-stained specimen through manual segmentation using the software Amira 5.5.0. Other illustrations can be found <a href="https://zenodo.org/search?page=1&size=20&q=keywords:%22squamate%20brain%22">here</a>. If you are interested in reptile brain evolution and behavior, please, have a look to our recent publication: <a href="https://www.nature.com/articles/s41467-019-13405-w">"Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"</a> Simone Macrì, Yoland Savriama, Imran Khan & Nicolas Di-Poï Nature Communications 10, 5560 (2019)   Check out also our *4K* video collection of various snake and lizard 3D brains: <a href="https://www.youtube.com/playlist?list=PLgx4vtT32C8hqxG_icKiuXGtZVLVX-oG1">Snake and Lizard brain reconstructions video collection</a>   For any inquiries or additional information, please, refer to the contacts provided in the <a href="https://www.nature.com/articles/s41467-019-13405-w">article</a>.

Figs 3-6 in The fast-running flies (Diptera, Hybotidae, Tachydromiinae) of Singapore and adjacent regions

Figs 3-6. Elaphropeza biuncinata (Melander, 1928). 3. head, ♂, lateral view. 4. head, ♂, frontal view. 5. Platypalpus sp. scutum dorsal view. 6. E. biuncinata thorax, lateral view.

Fig. 237 in The fast-running flies (Diptera, Hybotidae, Tachydromiinae) of Singapore and adjacent regions

Fig. 237. Species accumulation curve: number of species in relation to the number of sampled habitats (x-axis).

Figs 220-224 in The fast-running flies (Diptera, Hybotidae, Tachydromiinae) of Singapore and adjacent regions

Figs 220-224. Stilpon neesoonensis sp. nov., ♂. 220. head lateral, lateral view. 221. mid leg, anterior view. 222. right epandrial lamella, lateral view. 223. epandrium with cerci, dorsal view. 224. left surstylus and cercus, dorso-lateral view.

Figs 207-209 in The fast-running flies (Diptera, Hybotidae, Tachydromiinae) of Singapore and adjacent regions

Figs 207-209. Stilpon goesi Shamshev & Grootaert, 2006, ♂ terminalia. 207. right surstylus, lateral view. 208. epandrium with cerci, dorsal view. 209. left surstylus, lateral view. Scale bar= 0.1 mm (after Shamshev & Grootaert 2006).