cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3888509
title: CURE-OR: Challenging Unreal and Real Environments for Object Recognition

description: As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (CURE-OR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In CURE-OR dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives. Please refer to our GitHub page for code, papers, and more information. Some data specifications are provided below: <strong>Image Name Format : </strong> "backgroundID_deviceID_objectOrientationID_objectID_challengeType_challengeLevel.jpg" <strong>Background ID: </strong> 1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office <strong>Object Orientation ID: </strong> 1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top <strong>Object ID:</strong> 1-100 <strong>Challenge Type:</strong> No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise <strong>Challenge Level: </strong> A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.
description: {"references": ["D. Temel*, J. Lee*, and G. AlRegib, \"CURE-OR: Challenging Unreal and Real Environments for Object Recognition,\" in IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, Dec. 2018", "D. Temel*, J. Lee*, and G. AlRegib, \"Object Recognition Under Multifarious Conditions: A Reliability Analysis and a Feature Similarity-Based Performance Estimation,\" in IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, Sep. 2019"]}
DOI: 10.5281/zenodo.3888508
title: CURE-OR: Challenging Unreal and Real Environments for Object Recognition

description: As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (CURE-OR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In CURE-OR dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives. Please refer to our GitHub page for code, papers, and more information. Some data specifications are provided below: <strong>Image Name Format : </strong> "backgroundID_deviceID_objectOrientationID_objectID_challengeType_challengeLevel.jpg" <strong>Background ID: </strong> 1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office <strong>Object Orientation ID: </strong> 1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top <strong>Object ID:</strong> 1-100 <strong>Challenge Type:</strong> No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise <strong>Challenge Level: </strong> A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.
description: {"references": ["D. Temel*, J. Lee*, and G. AlRegib, \"CURE-OR: Challenging Unreal and Real Environments for Object Recognition,\" in IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, Dec. 2018", "D. Temel*, J. Lee*, and G. AlRegib, \"Object Recognition Under Multifarious Conditions: A Reliability Analysis and a Feature Similarity-Based Performance Estimation,\" in IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan, Sep. 2019"]}
DOI: 10.5281/zenodo.3911142
title: Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 3/1/2016

description: Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5 km interval and from 23.8 UT 2/29/2016 to 8.9 UT 3/1/2016 at 0.1 hour interval. Errors of these values are also included. -999 represents missing value.
DOI: 10.5281/zenodo.3911141
title: Na Wind-Temperature Lidar Data at Andes Lidar Observatory on 3/1/2016

description: Measurement made by the Na Wind-Temperature Lidar at the Andes Lidar Observatory in Cerro Pachón, Chile. It includes Na density, temperature, zonal, meridional, and vertical wind, from 80 to 115 km altitude at 0.5 km interval and from 23.8 UT 2/29/2016 to 8.9 UT 3/1/2016 at 0.1 hour interval. Errors of these values are also included. -999 represents missing value.
DOI: 10.5281/zenodo.1302341
title: Data Klasifikasi Kategori Cerita Pendek

description: Data ini merupakan dataset yang digunakan bersifat publik dan diambil dari http://cerpenmu.com/. Data yang diambil berupa teks cerita pendek online berdasarkan kategori yang ditentukan, yaitu kategori cerpen anak dan kategori cerpen dongeng. Data set diambil dari cerpen yang dibuat dan diunggah antara tahun 2015 sampai dengan 2016 dengan jumlah data sebanyak 121 data cerpen.
description: Dataset merupakan bagian dari ekperimen dari jurnal JNTETI, Vol. 6, No. 3, Agustus 2017 dengan judul "Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika ". DOI: http://dx.doi.org/10.22146/jnteti.v6i3.332
DOI: 10.5281/zenodo.1302340
title: Data Klasifikasi Kategori Cerita Pendek

description: Data ini merupakan dataset yang digunakan bersifat publik dan diambil dari http://cerpenmu.com/. Data yang diambil berupa teks cerita pendek online berdasarkan kategori yang ditentukan, yaitu kategori cerpen anak dan kategori cerpen dongeng. Data set diambil dari cerpen yang dibuat dan diunggah antara tahun 2015 sampai dengan 2016 dengan jumlah data sebanyak 121 data cerpen.
description: Dataset merupakan bagian dari ekperimen dari jurnal JNTETI, Vol. 6, No. 3, Agustus 2017 dengan judul "Feature Selection Klasifikasi Kategori Cerita Pendek Menggunakan Naïve Bayes dan Algoritme Genetika ". DOI: http://dx.doi.org/10.22146/jnteti.v6i3.332
DOI: 10.5281/zenodo.3759895
title: Global Storm Tide Modeling on Unstructured Meshes with ADCIRC v55 - Simulation Results and Model Setup

description: Simulation results and model setup for the paper entitled "Global Storm Tide Modeling with ADCIRC v55: Unstructured Mesh Design and Performance". Simulations conducted using ADCIRC (pre-release Version 55) on unstructured triangular meshes of the global Earth's ocean. Contains: Global Tide Harmonics: Simulated harmonic constituents of global astronomical tide on various mesh designs (*.53.nc). Local Storm Tide: ADCIRC model setup and simulation results from storm tides forced by Hurricane Katrina and Super Typhoon Haiyan on meshes with different local refinements (1.5 km, 500 m, 150 m) in the storm landfall region. ADCIRC model input files (*.13, *.14, *.15, *.22, *.221, *.222, fort.rotm) Global maximum storm tide elevations (*_maxele.63.nc) Global 3-hourly storm tide elevation time series (*.63.nc) Storm tide elevation and velocity time series (20-min intervals) at selected stations (*.61.nc, *.62.nc) Zipped archive of the ADCIRC code (adcirc-cg-GLOBAL.zip) used to produce the results archived here. See the README files for further details.
DOI: 10.5281/zenodo.3911263
title: Prosocial Decisions in Rhesus Macaques

description: The spreadsheet contains the performance of animals tested in various experimental conditions of two social studies
DOI: 10.7910/dvn/3yaseg/oz0or6
title: Moatamed_Sentinel lymph node_ Data.xlsx

description: :unav
DOI: 10.7910/dvn/3yaseg/owjqgq
title: Sentinel lymph node_ Harvard Dataverse (2).tab

description: :unav
DOI: 10.5519/qd.vvhkrkb2
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.3903066
title: CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

description: As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (&gt;2M images) traffic sign recognition dataset (CURE-TSR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. Traffic sign images in the CURE-TSR dataset were cropped from the CURE-TSD dataset, which includes around 1.7 million real-world and simulator images with more than 2 million traffic sign instances. Real-world images were obtained from the BelgiumTS video sequences and simulated images were generated with the Unreal Engine 4 game development tool. Sign types include speed limit, goods vehicles, no overtaking, no stopping, no parking, stop, bicycle, hump, no left, no right, priority to, no entry, yield, and parking. Unreal and real sequences were processed with state-of-the-art visual effect software Adobe(c) After Effects to simulate challenging conditions, which include rain, snow, haze, shadow, darkness, brightness, blurriness, dirtiness, colorlessness, sensor and codec errors. Please refer to our GitHub page for code, papers, and more information. Instructions: The name format of the provided images are as follows: "sequenceType_signType_challengeType_challengeLevel_Index.bmp" sequenceType: 01 - Real data 02 - Unreal data signType: 01 - speed_limit 02 - goods_vehicles 03 - no_overtaking 04 - no_stopping 05 - no_parking 06 - stop 07 - bicycle 08 - hump 09 - no_left 10 - no_right 11 - priority_to 12 - no_entry 13 - yield 14 - parking challengeType: 00 - No challenge 01 - Decolorization 02 - Lens blur 03 - Codec error 04 - Darkening 05 - Dirty lens 06 - Exposure 07 - Gaussian blur 08 - Noise 09 - Rain 10 - Shadow 11 - Snow 12 - Haze challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge. Index: A number shows different instances of traffic signs in the same conditions.
description: {"references": ["D. Temel, G. Kwon*, M. Prabhushankar*, and G. AlRegib, \"CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition,\" in Advances in Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Intelligent Transportation Systems, Long Beach, CA, Dec. 2017", "D. Temel, M-H. Chen, and G. AlRegib, \"Traffic Sign Detection Under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics,\" in IEEE Transactions on Intelligent Transportation Systems, Jul. 2019", "D. Temel and G. AlRegib, \"Traffic Signs in the Wild: Highlights From the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions],\" in IEEE Signal Processing Magazine, vol. 35, no. 2, pp. 154-161, Mar. 2018", "D. Temel, T. Alshawi, M.-H. Chen and G. AlRegib, \"Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions\", arXiv preprint arXiv:1902.06857 (2019)"]}
DOI: 10.5281/zenodo.3903065
title: CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

description: As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (&gt;2M images) traffic sign recognition dataset (CURE-TSR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. Traffic sign images in the CURE-TSR dataset were cropped from the CURE-TSD dataset, which includes around 1.7 million real-world and simulator images with more than 2 million traffic sign instances. Real-world images were obtained from the BelgiumTS video sequences and simulated images were generated with the Unreal Engine 4 game development tool. Sign types include speed limit, goods vehicles, no overtaking, no stopping, no parking, stop, bicycle, hump, no left, no right, priority to, no entry, yield, and parking. Unreal and real sequences were processed with state-of-the-art visual effect software Adobe(c) After Effects to simulate challenging conditions, which include rain, snow, haze, shadow, darkness, brightness, blurriness, dirtiness, colorlessness, sensor and codec errors. Please refer to our GitHub page for code, papers, and more information. Instructions: The name format of the provided images are as follows: "sequenceType_signType_challengeType_challengeLevel_Index.bmp" sequenceType: 01 - Real data 02 - Unreal data signType: 01 - speed_limit 02 - goods_vehicles 03 - no_overtaking 04 - no_stopping 05 - no_parking 06 - stop 07 - bicycle 08 - hump 09 - no_left 10 - no_right 11 - priority_to 12 - no_entry 13 - yield 14 - parking challengeType: 00 - No challenge 01 - Decolorization 02 - Lens blur 03 - Codec error 04 - Darkening 05 - Dirty lens 06 - Exposure 07 - Gaussian blur 08 - Noise 09 - Rain 10 - Shadow 11 - Snow 12 - Haze challengeLevel: A number in between [01-05] where 01 is the least severe and 05 is the most severe challenge. Index: A number shows different instances of traffic signs in the same conditions.
description: {"references": ["D. Temel, G. Kwon*, M. Prabhushankar*, and G. AlRegib, \"CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition,\" in Advances in Neural Information Processing Systems (NIPS) Workshop on Machine Learning for Intelligent Transportation Systems, Long Beach, CA, Dec. 2017", "D. Temel, M-H. Chen, and G. AlRegib, \"Traffic Sign Detection Under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics,\" in IEEE Transactions on Intelligent Transportation Systems, Jul. 2019", "D. Temel and G. AlRegib, \"Traffic Signs in the Wild: Highlights From the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions],\" in IEEE Signal Processing Magazine, vol. 35, no. 2, pp. 154-161, Mar. 2018", "D. Temel, T. Alshawi, M.-H. Chen and G. AlRegib, \"Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions\", arXiv preprint arXiv:1902.06857 (2019)"]}
DOI: 10.7910/dvn/n0dp7r/qatouj
title: Compact_polytopes_dim_5_6.ipynb

description: :unav
DOI: 10.7910/dvn/n0dp7r/bsrlnt
title: Cusped_polytopes_dim_4_13.ipynb

description: :unav
DOI: 10.25919/5ef7c0e3cba06
title: Parkes pulsar observations with undefined project IDs for 2020_09

description: Parkes pulsar observations with undefined project IDs for 2020.
DOI: 10.21227/9g1c-9p51
title: Brazilian Flights Dataset

description: An integrated dataset containing data on departure and arrival data for Brazilian domestic flights comprising the period from the years 2000 to 2018.Composed of 13,823,630 records of flight data, each containing 50 attributes. The attributes include data regarding the airline, flight, airports, meteorological conditions, and scheduled and real times of departure and arrival.
DOI: 10.21227/y2h6-2675
title: Brazilian Flights Dataset

description: An integrated dataset containing data on departure and arrival data for Brazilian domestic flights comprising the period from the years 2000 to 2018.Composed of 13,823,630 records of flight data, each containing 50 attributes. The attributes include data regarding the airline, flight, airports, meteorological conditions, and scheduled and real times of departure and arrival.
DOI: 10.21227/gp0w-zm83
title: Measurement and Evaluation of Human Sitting and Standing Movement Biomechanics

description: Although regenerative actuators can extend the battery-powered operating durations of lower-limb exoskeletons and prostheses, these energy-efficient powertrains have been exclusively designed and evaluated for continuous level-ground walking. Building on previous research, this work analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamics modelling and simulation to estimate the biomechanical energy available for electrical regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification, whereby differences in ground reaction forces and moments between the experimental measurements and inverse dynamic simulations were minimized. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated over time to determine joint biomechanical energy. The hip generated the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee joint (0.8 ± 0.3 W/kg) and ankle joint (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming 80-kg bodyweight and previous regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~26 Joules of total electrical energy while sitting down, compared to ~19 Joules from level-ground walking. Future research should focus on activity tracking and biomechanical analyses of seniors and rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration among individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. Under Review.
DOI: 10.21227/v064-fy55
title: Brazilian Flights Dataset

description: An integrated dataset containing data on departure and arrival data for Brazilian domestic flights comprising the period from the years 2000 to 2018.Composed of 13,823,630 records of flight data, each containing 50 attributes. The attributes include data regarding the airline, flight, airports, meteorological conditions, and scheduled and real times of departure and arrival.
DOI: 10.21227/14yg-0v46
title: A Large-Scale Dataset for Active Fire Detection/Segmentation (Landsat-8)

description: This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF format with 10 bands (excluding the 15m panchromatic band). We cropped the original Landsat-8 scenes (with ~7,600 x 7,600 pixels) into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016).
DOI: 10.5519/qd.41jhot0m
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (98 records)
DOI: 10.5519/qd.buev9j19
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (215 records)
DOI: 10.21227/136x-jz63
title: Brazilian Flights Dataset

description: An integrated dataset containing data on departure and arrival data for Brazilian domestic flights comprising the period from the years 2000 to 2018.Composed of 13,823,630 records of flight data, each containing 50 attributes. The attributes include data regarding the airline, flight, airports, meteorological conditions, and scheduled and real times of departure and arrival.
DOI: 10.21227/y8cx-n567
title: A Large-Scale Dataset for Active Fire Detection/Segmentation (Landsat-8)

description: This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF format with 10 bands (excluding the 15m panchromatic band). We cropped the original Landsat-8 scenes (with ~7,600 x 7,600 pixels) into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016).
DOI: 10.21227/hpxh-v777
title: A Large-Scale Dataset for Active Fire Detection/Segmentation (Landsat-8)

description: This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF format with 10 bands (excluding the 15m panchromatic band). We cropped the original Landsat-8 scenes (with ~7,600 x 7,600 pixels) into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016).
DOI: 10.21227/j10s-yn82
title: Brazilian Flights Dataset

description: An integrated dataset containing data on departure and arrival data for Brazilian domestic flights comprising the period from the years 2000 to 2018.Composed of 13,823,630 records of flight data, each containing 50 attributes. The attributes include data regarding the airline, flight, airports, meteorological conditions, and scheduled and real times of departure and arrival.