Knowledge-Innovation-Centre/ESCO-Syn-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset has been created by performing TCP-SYN Flood attack. The Dataset contains 28 columns and 10,47,500 rows.

vector-institute/MultiHopRAG-syn-data-50 dataset hosted on Hugging Face and contributed by the HF Datasets community

Protein-Protein, Genetic, and Chemical Interactions for SYN (Drosophila melanogaster) curated by BioGRID (https://thebiogrid.org); DEFINITION: Synapsin

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Data was collected and processed as described in Hunter-Cevera et al. 2019, Seasons of Syn, Limnology and Oceanography. Scripts used for data processing are available at http://github.com/hsosik/NES-LTER/tree/master/.

The United-Syn-Med dataset is a specialized medical speech dataset designed to evaluate and improve Automatic Speech Recognition (ASR) systems within the healthcare domain. It comprises English medical speech recordings, with a particular focus on medical terminology and clinical conversations. The dataset is well-suited for various ASR tasks, including speech recognition, transcription, and classification, facilitating the development of models tailored for medical contexts.

This dataset supports a broad range of applications, including medical documentation automation, transcription of doctor-patient conversations, and medical knowledge extraction from audio data.

40Ar-39Ar age of plagioclase samples.

Supporting data and code from 'The time-course of real-world spatial and semantic processing'

---------------------- General Info ----------------------

If there are any bugs/issues, contact Matt Anderson: Matt.Anderson@soton.ac.uk

doi: //doi.org/10.5258/SOTON/D2036

ORCID ID (Matt Anderson): 0000-0002-7498-2719

Research funded by a University of Southampton Jubilee Scholarship, EPSRC grant EP/K005952/1, EPSRC grant EP/S016368/1, and a York University VISTA Visiting Trainee Award

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The files AggData/SEM_categorization.txt and AggData/STR_categorization.txt contain trial-by-trial response data from the semantic and spatial structure tasks respectively. The columns should be intuitively named, but here is a brief description of each:

• Ppt_No: unique participant identifier
• Scene: scene identifier in the SYNS database (see https://syns.soton.ac.uk/)
• View: view identifier in the SYNS database
• cat_agreement: proportion of participants who selected the ground-truth category, using unlimited viewing durations (see Anderson et al., 2021)
• GT_Category: Ground-truth category. In the semantic file, the numbers 1->6 correspond to Nature, Road, Residence, Farm, Beach, and Car Park / Commercial respectively. In the spatial structure file, numbers 1->4 correspond to Cluttered, Closed Off, Flat, and Tunnel respectively.
• TrialNumber: order in which the image was presented for a given participant
• imageID: unique identifier for each image
• distance_*: The next 6 variables compute various statistics of the image using coregistered ground-truth LiDAR data. These are the (i) mean across the image, standard deviation, and range. These statistics are also computed for a small patch around the fixation location.
• distance_bin: median split by the column distance_mean
• Colour_GrayScale: 1 = Grayscale, 2 = Colour
• Stereo_Cond: 1 = Mono, 2 = Stereo, 3 = Stereo-Reversed
• Pres_Time: number of frames, as a multiple of 13.33 msecs (75 Hz refresh rate)
• SceneCat: participant-selected category
• DepthCat: participant-selected depth
• ResponsePeriod: Response time (dunno why I called it period)
• Elapsed: empirical measure of computer flip interval (psychtoolbox)
• Cat_correct: 1 = correct, 0 = incorrect
• Depth_Correct: 1 = correct, 0 = incorrect

Research data for Late Paleozoic syn-collisional mafic igneous complex from southwestern Tianshan,Table S1-S5

This data-set is a supplementary material related to the generation of synthetic images of a corridor in the University of Melbourne, Australia from a building information model (BIM). This data-set was generated to check the ability of deep learning algorithms to learn task of indoor localisation from synthetic images, when being tested on real images. =============================================================================The following is the name convention used for the data-sets. The brackets show the number of images in the data-set.REAL DATAReal
---------------------> Real images (949 images)

Gradmag-Real -------> Gradmag of real data (949 images)SYNTHETIC DATASyn-Car
----------------> Cartoonish images (2500 images)

Syn-pho-real ----------> Synthetic photo-realistic images (2500 images)

Syn-pho-real-tex -----> Synthetic photo-realistic textured (2500 images)

Syn-Edge --------------> Edge render images (2500 images)

Gradmag-Syn-Car ---> Gradmag of Cartoonish images (2500 images)=============================================================================Each folder contains the images and their respective groundtruth poses in the following format [ImageName X Y Z w p q r].To generate the synthetic data-set, we define a trajectory in the 3D indoor model. The points in the trajectory serve as the ground truth poses of the synthetic images. The height of the trajectory was kept in the range of 1.5–1.8 m from the floor, which is the usual height of holding a camera in hand. Artificial point light sources were placed to illuminate the corridor (except for Edge render images). The length of the trajectory was approximately 30 m. A virtual camera was moved along the trajectory to render four different sets of synthetic images in Blender*. The intrinsic parameters of the virtual camera were kept identical to the real camera (VGA resolution, focal length of 3.5 mm, no distortion modeled). We have rendered images along the trajectory at 0.05 m interval and ± 10° tilt.The main difference between the cartoonish (Syn-car) and photo-realistic images (Syn-pho-real) is the model of rendering. Photo-realistic rendering is a physics-based model that traces the path of light rays in the scene, which is similar to the real world, whereas the cartoonish rendering roughly traces the path of light rays. The photorealistic textured images (Syn-pho-real-tex) were rendered by adding repeating synthetic textures to the 3D indoor model, such as the textures of brick, carpet and wooden ceiling. The realism of the photo-realistic rendering comes at the cost of rendering times. However, the rendering times of the photo-realistic data-sets were considerably reduced with the help of a GPU. Note that the naming convention used for the data-sets (e.g. Cartoonish) is according to Blender terminology.An additional data-set (Gradmag-Syn-car) was derived from the cartoonish images by taking the edge gradient magnitude of the images and suppressing weak edges below a threshold. The edge rendered images (Syn-edge) were generated by rendering only the edges of the 3D indoor model, without taking into account the lighting conditions. This data-set is similar to the Gradmag-Syn-car data-set, however, does not contain the effect of illumination of the scene, such as reflections and shadows.*Blender is an open-source 3D computer graphics software and finds its applications in video games, animated films, simulation and visual art. For more information please visit: http://www.blender.orgPlease cite the papers if you use the data-set:1) Acharya, D., Khoshelham, K., and Winter, S., 2019. BIM-PoseNet: Indoor camera localisation using a 3D indoor model and deep learning from synthetic images. ISPRS Journal of Photogrammetry and Remote Sensing. 150: 245-258.2) Acharya, D., Singha Roy, S., Khoshelham, K. and Winter, S. 2019. Modelling uncertainty of single image indoor localisation using a 3D model and deep learning. In ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, IV-2/W5, pages 247-254.

vector-institute/MultiHopRAG-syn-data-ctx_len-4096-100 dataset hosted on Hugging Face and contributed by the HF Datasets community

Cogne, J.P., Van Den Driessche, J. and Brun, J.P. (1993). Syn-extension rotations in the Permian St.Affrique Basin (Massif Central, France): paleomagnetic constraints. Earth and Planetary Science Letters 115: 29-42. doi: 10.1016/0012-821X(93)90210-Z. Type: [ Outcrop ] Class: [ Sedimentary ] Lithology: [ Sandstone ] Ages: [ 251 to 285 Ma N 2 ] from Earthref Magic

Data for the paper is included in Table 1-4

ruthchy/syn-length-gen-logo-data-desc-ascii_35 dataset hosted on Hugging Face and contributed by the HF Datasets community

Janes-Syn is a syntactically annotated corpus of Slovene tweets and is meant as a gold-standard training and testing dataset for syntactic annotation of Slovene computer-mediated communication and for detailed linguistic explorations which require highly accurate and reliable annotations. Words in the dataset are normalised, lemmatised, PoS-tagged and syntactically annotated with the JOS dependency model (http://eng.slovenscina.eu/tehnologije/razclenjevalnik). The annotations on all levels were manually corrected.

The corpus creation and structure are described in:

ARHAR HOLDT, Špela, FIŠER, Darja, ERJAVEC, Tomaž, KREK, Simon. Syntactic annotation of Slovene CMC : first steps. Proceedings of the 4th Conference on CMC and Social Media Corpora for the Humanities, 27-28 September 2016, Ljubljana, Slovenia, 2016, pp. 3-6. http://nl.ijs.si/janes/cmc-corpora2016/proceedings/

Janes-Syn was created from two larger corpora that are also available in the repository: Janes-Norm (http://hdl.handle.net/11356/1084) and Janes-Tag (http://hdl.handle.net/11356/1123).

A Diastereo- and Enantioselective Synthesis of α-Substituted syn-α,β-Diamino Acids

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.