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1,454 results for “large scale”
Poleward expanding species and non-indigenous species along the Italian, Croatian and Montenegrin coasts: a large‐scale survey based on online questionnaires.
<p>The following dataset contains information from an online survey that was carried out by three different research institutes: the Institute for Environmental Protection and Italy (ISPRA), the IRBIM- CNR, Institute of Biological Resources and Marine Biotechnologies, National Research Council, Ancona, Italy and the Institute of Oceanography and Fisheries, Split, Croatia. The survey was implemented on Google Forms and it was advertised on Facebook groups about recreational spearfishing and sea angling; some questionnaires were also snowballed in the community of recreational spearfishers and sea anglers, on Twitter. Respondents were from Italy, Croatia and Montenegro. The questionnaire was translated in Italian and Croatian. Overall, the questionnaire took approximately 10 minutes to complete.</p>
A Large Scale Empirical Study of the Impact of Spaghetti Code and Blob Anti-patterns on Program Comprehension
<p>Dataset and scripts used for the paper.</p>
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
<p>This dataset consists of 1,181,993 Tweet Ids, obtained as a result from the paper - A large-scale Twitter dataset for drug safety applications mined from publicly existing resources.</p> <p>The Tweet Ids can be hydrated using <strong>twarc</strong>, a command line tool and Python library for archiving Twitter JSON data. Please follow these instructions to install twarc - <a href="https://github.com/DocNow/twarc">https://github.com/DocNow/twarc</a></p> <p>It will take less than 4 hours to hydrate the tweet ids using twarc. </p> <p>If you intend to use the dataset, please cite the paper. </p>
AndroZooOpen: Collecting Large-scale Open Source Android Apps for the Research Community
<p>The raw collected data for the paper</p>
Fig. 1 in Large-scale migration of a school shark, Galeorhinus galeus, in the Southwestern Atlantic
Fig. 1. Schematic representation of the depth-averaged ocean circulation in the Southwestern Atlantic Ocean (adapted from Matano et al., 2010) showing the geographical locations of tagging of Galeorhinus galeus [Triangle, Nuevo Gulf (NG), 42°30'S, 65°55'W, depth 100 m], newest recapture in Uruguayan shelf waters (Star, 35°30'S, 51°48'W – depth 290 m) and the two old recapture sites (Circle, Mar del Plata, 38°24'S, 57°18'W, depth 40 m and Canal Culebra, 40°24'S, 62°06'W - depth 15 m, Irigoyen et al., 2015). Additional geographical locations are indicated with acronyms: SMG = San Matías Gulf, VP = Valdés Peninsula, AB = Anegada Bay, PM = Punta Médanos, STSF = Subtropical Shelf Front. The thick curved lines represent the path of the major ocean currents. The black line is the 200m isobath. Colours indicate bottom temperature (a, c) and bottom salinity (b, d) and black arrows indicate the bottom shelf circulation extracted from a numerical model (Palma et al., 2008). The yellow and gray lines in the left panels indicate the 11 and 17 °C isotherms respectively. The red and cyan lines in the right panels indicate the 33.93 and 33.5 isohalines.
Fig. 2. A in Large-scale migration of a school shark, Galeorhinus galeus, in the Southwestern Atlantic
Fig. 2. A) Releasing of a tagged Galeorhinus galeus, B) the recaptured tag on August 17, 2015, and C) length size distribution of school sharks captured and tagged in Nuevo Gulf (~ 42°30'S, Argentina) during February 15, 2015.
Fig. 5 Lateral habitus a in Rapid, large-scale species discovery in hyperdiverse taxa using 1D MinION sequencing
Fig. 5 Lateral habitus a and diagnostic features of Megaselia
Data from "Large-scale CO spirals and complex kinematics associated with the T Tauri star RU Lup"
<p>Image cubes, self-calibrated visibilities, and self-calibration and imaging scripts for data from Huang et al., 2020, "Large-scale CO spirals and complex kinematics associated with the T Tauri star RU Lup," ApJ, 898, 140 (arXiv:2007.02974) </p> <p>The raw ALMA data can be found at the ALMA archive under program ID 2018.1.01201.S (PI: Jane Huang).</p> <p><strong>Scripts:</strong></p> <p>selfcal_C43-5.py, selfcal_C43-2.py, selfcal_ACA.py: self-calibration scripts for observation of RU Lup in various ALMA configurations</p> <p>imaging.py: imaging script used for all lines</p> <p>reductionutils.py can be downloaded from <a href="https://zenodo.org/record/3628656">https://zenodo.org/record/3628656</a></p> <p><strong>Measurement sets:</strong></p> <p>RULupcontinuum.ms.tgz: Continuum visibilities for "RULupallcontinuum.image.pbcor.fits" (the high-resolution ALMA continuum data can be found on the <a href="https://bulk.cv.nrao.edu/almadata/lp/DSHARP/">DSHARP</a> page)</p> <p>RULup_12CO.ms.contsub.tgz: Continuum-subtracted visibilities for the <sup>12</sup>CO J=2-1 line toward RU Lup</p> <p>RULup_12CO_nocontsub.ms.tgz: Visibilities for the <sup>12</sup>CO J=2-1 line (no continuum subtraction) toward RU Lup, used to make the 'RULup_12COpeakintensity.fits' file.</p> <p>RULup_13CO.ms.contsub.tgz: Continuum-subtracted visibilities for the <sup>13</sup>CO J=2-1 line toward RU Lup</p> <p>RULup_C18O.ms.contsub.tgz: Continuum-subtracted visibilities for the C<sup>18</sup>O J=2-1 line toward RU Lup</p> <p>RULup_DCOp.ms.contsub.tgz: Continuum-subtracted visibilities for the DCO<sup>+</sup> J=3-2 line toward RU Lup</p> <p><strong>Images: </strong></p> <p>RULup12CO.1sigcut.pbcor.mom0.fits: Integrated intensity map of <sup>12</sup>CO J=2-1 appearing in Fig. 1</p> <p>RULup12CO.pbcor.mom1.fits: <sup>12</sup>CO J=2-1 intensity-weighted velocity map appearing in Fig. 1</p> <p>RULup13CO.pbcor.mom0.fits: Integrated intensity map of <sup>13</sup>CO J=2-1 appearing in Fig. 1</p> <p>RULup13CO.pbcor.mom1.fits: <sup>13</sup>CO J=2-1 intensity-weighted velocity map appearing in Fig. 1</p> <p>RULupC18O.pbcor.mom0.fits: Integrated intensity map of C<sup>18</sup>O J=2-1 appearing in Fig. 1</p> <p>RULupC18O.pbcor.mom1.fits: C<sup>18</sup>O J=2-1 intensity-weighted velocity map appearing in Fig. 1</p> <p>RULup12CO.image.pbcor.fits: <sup>12</sup>CO J=2-1 image cube appearing in Fig. 2</p> <p>RULupC18O.image.pbcor.fits: C<sup>18</sup>O J=2-1 image cube appearing in Fig. 2</p> <p>RULup13CO.image.pbcor.fits: <sup>12</sup>CO J=2-1 image cube appearing in Fig. 3</p> <p>RULup_12COpeakintensity.fits: Peak intensity image of <sup>12</sup>CO used to make Figure 4</p> <p>RULupDCOp.pbcor.mom0.fits: Integrated intensity map of DCO<sup>+</sup> J=3-2 appearing in Fig. 10</p> <p>RULupDCOp.image.pbcor.fits: DCO<sup>+</sup> J=3-2 image cube appearing in Fig. 11</p> <p>RULupallcontinuum.image.pbcor.fits: Continuum image appearing in Fig. 12</p> <p>RULup12COinitial.mask.image.fits: <sup>12</sup>CO CLEAN mask shown in Fig. 13</p> <p> </p>
A large-scale wide-baseline light field dataset - Part II
<p>This dataset is the Part II of a large-scale, synthetic wide-baseline light field dataset (called WLF), including 16 light fields. </p> <p>Each light field provides 9x9 angular (RGB) images and ground truth disparities. This light field dataset involves the spatial resolution (1920x1080) images only.</p> <p>The dataset is originally created for training the deep learning-based models for depth estimation. You might use this dataset for other tasks if possible. </p> <p>You might also have a try to play with this dataset using our code in https://github.com/YanWQ/LLF-Net.</p>
A Large Scale PeMS Traffic Speed Dataset
<p>This repository is a traffic speed data set collected by California Freeway Performance Measurement System (PeMS). In this data set, it has 11160 time series corresponding to <strong>11160</strong> sensors/locations. In three data files (i.e., <em>pems-4w.csv</em>, <em>pems-8w.csv</em>, and <em>pems-12w.csv</em>), each time series is indeed a time series with <strong>288</strong> (5-minute) time points per day during the first <strong>4 weeks</strong> (PeMS-4W), first <strong>8 weeks</strong> (PeMS-8W), and first <strong>12 weeks</strong> (PeMS-12W) of 2018, respectively. Note that the original data was downloaded from PeMS system with 30-second traffic speed records. Our data is a subset of file <em>speed.h5</em> created by article <a href="https://arxiv.org/pdf/2004.08038.pdf">Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting</a> and its original link is available at <a href="https://bit.ly/2SsGleK">here</a>.</p> <p>Our research work on this data set is:</p> <p>Xinyu Chen, Yixian Chen, Nicolas Saunier, Lijun Sun (2021). Scalable low-rank tensor learning for spatiotemporal traffic data imputation. Transportation Research Part C: Emerging Technologies, 129: 103226. [<a href="https://doi.org/10.1016/j.trc.2021.103226">DOI</a>] [<a href="https://arxiv.org/abs/2008.03194">preprint</a>] [<a href="https://doi.org/10.5281/zenodo.3939792">data</a>] [<a href="http://github.com/xinychen/tensor-learning">Python code</a>]</p>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs rat dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html">http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>) and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (drug matrix dataset)
<p>This data was generated by Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics [Internet]. 2006 Oct;7(7):1025–1044. Available from: http://dx.doi.org/10.2217/14622416.7.7.1025 PMID: 17054413. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="https://cran.r-project.org/web/packages/ToxicoGx/">cran.r-project.org/web/packages/ToxicoGx</a>) and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p> <p> </p> <p> </p>
Large-scale RNAi screening uncovers new therapeutic targets in the human parasite Schistosoma mansoni
<p><span><span><span><span><span><span><span><span><span><span><span>Schistosomes kill 250,000 people every year. Treatment of schistosomiasis relies on a single drug (praziquantel). Unfortunately, a scarcity of molecular tools has hindered the discovery of new drug targets. Here, we describe a large-scale RNA interference screen in adult Schistosoma mansoni examining the function of 2,216 genes. We discovered 250 genes with phenotypes affecting neuromuscular function, tissue integrity, stem cell maintenance, and parasite survival. Leveraging these data, we prioritize compounds with activity against the parasites and uncover a pair of protein kinases (TAO and STK25) that cooperate to maintain muscle-specific mRNA transcription. Loss of either of these kinases results in paralysis and worm death in a mammalian host. These studies will expedite therapeutic development and invigorate studies of these neglected parasites. Large-scale RNAi screening uncovers new therapeutic targets in the human parasite Schistosoma mansoni.</span></span></span></span></span></span></span></span></span></span></span></p> <p> </p>
INCLUDE: A Large Scale Dataset for Indian Sign Language Recognition
<p><strong>Dataset Details</strong>: The INCLUDE dataset has 4292 videos (the paper mentions 4287 videos but 5 videos were added later). The videos used for training are mentioned in train.csv (3475), while that used for testing is mentioned in test.csv (817 files). Each video is a recording of 1 ISL sign, signed by deaf students from St. Louis School for the Deaf, Adyar, Chennai.</p> <p>INCLUDE50 has 766 train videos and 192 test videos.</p> <p><strong>Train-Test Split:</strong> Please download the train-test split for INCLUDE and INCLUDE50 from here: <a href="https://drive.google.com/file/d/1tGjOR9xbk279ZUYnmk3BE-ktcWBdS24w/view?usp=sharing">Train-Test Split</a></p> <p><strong>Publication Link</strong><em>: <a href="https://dl.acm.org/doi/10.1145/3394171.3413528">https://dl.acm.org/doi/10.1145/3394171.3413528</a></em></p> <p><strong>AI4Bharat website</strong><em>: <a href="https://ai4bharat.org/include-dataset">https://sign-language.ai4bharat.org/</a></em></p> <p><strong>Download Instructions</strong></p> <p>For ease of access, we have prepared a Shell Script to download all the parts of the dataset and extract them to form the complete INCLUDE dataset.</p> <p><em>You can find the script here: <a href="http://bit.ly/include_dl">http://bit.ly/include_dl</a></em></p> <p><strong>Paper Abstract:</strong> <em>Indian Sign Language (ISL) is a complete language with its own grammar, syntax, vocabulary and several unique linguistic attributes. It is used by over 5 million deaf people in India. Currently, there is no publicly available dataset on ISL to evaluate Sign Language Recognition (SLR) approaches. In this work, we present the Indian Lexicon Sign Language Dataset - INCLUDE - an ISL dataset that contains 0.27 million frames across 4,287 videos over 263 word signs from 15 different word categories. INCLUDE is recorded with the help of experienced signers to provide close resemblance to natural conditions. A subset of 50 word signs is chosen across word categories to define INCLUDE-50 for rapid evaluation of SLR methods with hyperparameter tuning. The best performing model achieves an accuracy of 94.5% on the INCLUDE-50 dataset and 85.6% on the INCLUDE dataset</em></p>
Figure 7 from: Li Y, Liu C, Lin L, Li Y, Xiao J, Loh K-H (2020) Pleistocene isolation caused by sea-level fluctuations shaped genetic characterization of Pampus minor over a large-scale geographical distribution. ZooKeys 969: 137-154. https://doi.org/10.3897/zookeys.969.52069
Figure 7 BSPs showing NefT (Nef = effective population size; T = generation time) changes over time for P. minor based on Cytb sequences. The upper and lower limits of the blue line represent the 95% confidence intervals of highest posterior densities (HPD) analysis. The solid black line represents median estimates of NefT.
Figure 5 from: Li Y, Liu C, Lin L, Li Y, Xiao J, Loh K-H (2020) Pleistocene isolation caused by sea-level fluctuations shaped genetic characterization of Pampus minor over a large-scale geographical distribution. ZooKeys 969: 137-154. https://doi.org/10.3897/zookeys.969.52069
Figure 5 Matrix of pairwise FST values between 11 P. minor populations based on Cytb sequences. * significant at p < 0.05 by the permutation test, ** extremely significant at p < 0.01 by the permutation test.
Figure 6 from: Li Y, Liu C, Lin L, Li Y, Xiao J, Loh K-H (2020) Pleistocene isolation caused by sea-level fluctuations shaped genetic characterization of Pampus minor over a large-scale geographical distribution. ZooKeys 969: 137-154. https://doi.org/10.3897/zookeys.969.52069
Figure 6 The expected mismatch distributions under a sudden expansion model (solid gray line) and the observed pairwise difference (black bars) of Cytb haplotypes of P. minor.
Figure 4 from: Li Y, Liu C, Lin L, Li Y, Xiao J, Loh K-H (2020) Pleistocene isolation caused by sea-level fluctuations shaped genetic characterization of Pampus minor over a large-scale geographical distribution. ZooKeys 969: 137-154. https://doi.org/10.3897/zookeys.969.52069
Figure 4 Unrooted minimum spanning tree showing the genetic relationships among the Cytb haplotypes of P. minor. Circle sizes are proportional to haplotype frequency. Perpendicular tick marks on the lines joining the haplotypes represent the number of nucleotide substitutions.
Figure 1 from: Li Y, Liu C, Lin L, Li Y, Xiao J, Loh K-H (2020) Pleistocene isolation caused by sea-level fluctuations shaped genetic characterization of Pampus minor over a large-scale geographical distribution. ZooKeys 969: 137-154. https://doi.org/10.3897/zookeys.969.52069
Figure 1 Sampling locations of P. minor. Populations are marked by abbreviations that correspond to Table 1.
Figure 2 from: Li Y, Liu C, Lin L, Li Y, Xiao J, Loh K-H (2020) Pleistocene isolation caused by sea-level fluctuations shaped genetic characterization of Pampus minor over a large-scale geographical distribution. ZooKeys 969: 137-154. https://doi.org/10.3897/zookeys.969.52069
Figure 2 Composition and distribution of 22 Cytb haplotypes in the Chinese and Malaysian populations.
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.