cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.eztkwn
title: Occurrence Download

description: A dataset containing 280 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oryza brachyantha A.Chev. & Roehr."
 ]
}

 The dataset includes 280 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037796-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sv4sxb
title: Occurrence Download

description: A dataset containing 81 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nepeta algeriensis de Noé"
 ]
}

 The dataset includes 81 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046747-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34808/0xk9-np69
title: LDRAW based renders of LEGO bricks moving on a conveyor belt with extracted models

description: The set contains renders of LEGO bricks moving on a white conveyor belt. The images were prepared for training neural network for recognition of LEGO bricks. For each brick starting position, alignment and color was selected (simulating the brick falling down on the conveyour belt) and than 10 images was created while the brick was moved across the conveyor belt. Afterwards empty frames, with no brick visible, were removed from the set. The images were saved in JPEG format. All images were generated using Blender (https://www.blender.org/) tool and were based on the 3D models from LDraw (https://www.ldraw.org/) brick library. The bricks were than extracted from the original images using OpenCV edge detection algorithms.
The colors for the LEGO bricks were selected from the following list [color (code)]: White (0xffffff), Brick Yellow (0xD9BB7B), Nougat (0xD67240), Bright Red (0xff0000), Bright Blue (0x0000ff), Bright Yellow (0xFfff00), Black (0x000000), Dark Green (0x009900), Bright Green (0x00cc00), Dark Orange (0xA83D15), Medium Blue (0x478CC6), Bright Orange (0xff6600), Bright Bluish Green (0x059D9E), Bright Yellowish-Green (0x95B90B), Bright Reddish Violet (0x990066), Sand Blue (0x5E748C), Sand Yellow (0x8D7452), Earth Blue (0x002541), Earth Green (0x003300), Sand Green (0x5F8265), Dark Red (0x80081B), Flame Yellowish Orange (0xF49B00), Reddish Brown (0x5B1C0C), Medium Stone Grey (0x9C9291), Dark Stone Grey (0x4C5156), Light Stone Grey (0xE4E4DA), Light Royal Blue (0x87C0EA), Bright Purple (0xDE378B), Light Purple (0xEE9DC3), Cool Yellow (0xFFFF99), Medium Lilac (0x2C1577), Light Nougat (0xF5C189), Dark Brown (0x300F06), Medium Nougat (0xAA7D55), Dark Azur (0x469bc3), Medium Azur (0x68c3e2), Aqua (0xd3f2ea), Medium Lavender (0xa06eb9), Lavender (0xcda4de), White Glow (0xf5f3d7), Spring Yellowish Green (0xe2f99a), Olive Green (0x77774E), Medium-Yellowish Green (0x96B93B).
The original folder contains the renders themselves, the cropped_opencv directory contains only bricks extracted from th erenders. In both cases the images were placed in a folder named after the LEGO brick code (as read from LDraw). The files naming convetion is as follows
brickID_colour_sequenceNumber_timestamp.jpg
where brickID is the LEGO brick id number as read from LDraw, color is the name of the selected brick color, sequenceNumber is the integer from 0 to 9 indicating the number of the image in the sequence and timestamp is UNIX time representation in milliseconds of the image creation time.
Sample images are presented below.
DOI: 10.34808/43yh-ck53
title: Video of LEGO bricks on conveyor belt - Bricks with studs on the side

description: 





The set contains videos of LEGO bricks (bricks with studs on the side) moving on a white conveyor belt. The videos were prepared for training neural network for recognition of LEGO bricks. The bricks were separated as much as possible and in most cases they should not overlap. The images were taken from different sides by stationary camera located over the final conveyor belt.
 
The videos were recorded using Huawei p20 Pro camera (1920x1080 resolution, H.264 high profile codec, 15 frames per second) located 14.5 cm over the belt. The bricks have random colors. The videos were placed in a seperate file for different pin type.
 
Sample images are presented below.





  The machine used to gather videos. The camera was positioned on the LEGO Duplo structure in the far side.
DOI: 10.34808/7hzd-jn36
title: LDRAW based renders of LEGO bricks moving on a conveyor belt with extracted models

description: The set contains renders of LEGO bricks moving on a white conveyor belt. The images were prepared for training neural network for recognition of LEGO bricks. For each brick starting position, alignment and color was selected (simulating the brick falling down on the conveyour belt) and than 10 images was created while the brick was moved across the conveyor belt. Afterwards empty frames, with no brick visible, were removed from the set. The images were saved in JPEG format. All images were generated using Blender (https://www.blender.org/) tool and were based on the 3D models from LDraw (https://www.ldraw.org/) brick library. The bricks were than extracted from the original images using OpenCV edge detection algorithms.
The colors for the LEGO bricks were selected from the following list [color (code)]: White (0xffffff), Brick Yellow (0xD9BB7B), Nougat (0xD67240), Bright Red (0xff0000), Bright Blue (0x0000ff), Bright Yellow (0xFfff00), Black (0x000000), Dark Green (0x009900), Bright Green (0x00cc00), Dark Orange (0xA83D15), Medium Blue (0x478CC6), Bright Orange (0xff6600), Bright Bluish Green (0x059D9E), Bright Yellowish-Green (0x95B90B), Bright Reddish Violet (0x990066), Sand Blue (0x5E748C), Sand Yellow (0x8D7452), Earth Blue (0x002541), Earth Green (0x003300), Sand Green (0x5F8265), Dark Red (0x80081B), Flame Yellowish Orange (0xF49B00), Reddish Brown (0x5B1C0C), Medium Stone Grey (0x9C9291), Dark Stone Grey (0x4C5156), Light Stone Grey (0xE4E4DA), Light Royal Blue (0x87C0EA), Bright Purple (0xDE378B), Light Purple (0xEE9DC3), Cool Yellow (0xFFFF99), Medium Lilac (0x2C1577), Light Nougat (0xF5C189), Dark Brown (0x300F06), Medium Nougat (0xAA7D55), Dark Azur (0x469bc3), Medium Azur (0x68c3e2), Aqua (0xd3f2ea), Medium Lavender (0xa06eb9), Lavender (0xcda4de), White Glow (0xf5f3d7), Spring Yellowish Green (0xe2f99a), Olive Green (0x77774E), Medium-Yellowish Green (0x96B93B).
The original folder contains the renders themselves, the cropped_opencv directory contains only bricks extracted from th erenders. In both cases the images were placed in a folder named after the LEGO brick code (as read from LDraw). The files naming convetion is as follows
brickID_colour_sequenceNumber_timestamp.jpg
where brickID is the LEGO brick id number as read from LDraw, color is the name of the selected brick color, sequenceNumber is the integer from 0 to 9 indicating the number of the image in the sequence and timestamp is UNIX time representation in milliseconds of the image creation time.
Sample images are presented below.
DOI: 10.15468/dl.c4e8t8
title: Occurrence Download

description: A dataset listing the 3516 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((174.7016 -36.84894,174.7034 -36.86906,174.71432 -36.88741,174.72616 -36.89829,174.73208 -36.89788,174.73694 -36.9043,174.75072 -36.91146,174.76133 -36.90957,174.7635 -36.90402,174.76835 -36.89842,174.76786 -36.88869,174.76456 -36.88603,174.76707 -36.87181,174.76028 -36.86974,174.75695 -36.87131,174.74031 -36.86874,174.73216 -36.87167,174.72417 -36.86984,174.72197 -36.86792,174.72165 -36.86501,174.71459 -36.8572,174.71016 -36.85196,174.7016 -36.84894))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset's 3516 records were derived from 65 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0289954-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34808/7pyd-aq72
title: LDRAW based renders of LEGO bricks moving on a conveyor belt with extracted models

description: The set contains renders of LEGO bricks moving on a white conveyor belt. The images were prepared for training neural network for recognition of LEGO bricks. For each brick starting position, alignment and color was selected (simulating the brick falling down on the conveyour belt) and than 10 images was created while the brick was moved across the conveyor belt. Afterwards empty frames, with no brick visible, were removed from the set. The images were saved in JPEG format. All images were generated using Blender (https://www.blender.org/) tool and were based on the 3D models from LDraw (https://www.ldraw.org/) brick library. The bricks were than extracted from the original images using OpenCV edge detection algorithms.
The colors for the LEGO bricks were selected from the following list [color (code)]: White (0xffffff), Brick Yellow (0xD9BB7B), Nougat (0xD67240), Bright Red (0xff0000), Bright Blue (0x0000ff), Bright Yellow (0xFfff00), Black (0x000000), Dark Green (0x009900), Bright Green (0x00cc00), Dark Orange (0xA83D15), Medium Blue (0x478CC6), Bright Orange (0xff6600), Bright Bluish Green (0x059D9E), Bright Yellowish-Green (0x95B90B), Bright Reddish Violet (0x990066), Sand Blue (0x5E748C), Sand Yellow (0x8D7452), Earth Blue (0x002541), Earth Green (0x003300), Sand Green (0x5F8265), Dark Red (0x80081B), Flame Yellowish Orange (0xF49B00), Reddish Brown (0x5B1C0C), Medium Stone Grey (0x9C9291), Dark Stone Grey (0x4C5156), Light Stone Grey (0xE4E4DA), Light Royal Blue (0x87C0EA), Bright Purple (0xDE378B), Light Purple (0xEE9DC3), Cool Yellow (0xFFFF99), Medium Lilac (0x2C1577), Light Nougat (0xF5C189), Dark Brown (0x300F06), Medium Nougat (0xAA7D55), Dark Azur (0x469bc3), Medium Azur (0x68c3e2), Aqua (0xd3f2ea), Medium Lavender (0xa06eb9), Lavender (0xcda4de), White Glow (0xf5f3d7), Spring Yellowish Green (0xe2f99a), Olive Green (0x77774E), Medium-Yellowish Green (0x96B93B).
The original folder contains the renders themselves, the cropped_opencv directory contains only bricks extracted from th erenders. In both cases the images were placed in a folder named after the LEGO brick code (as read from LDraw). The files naming convetion is as follows
brickID_colour_sequenceNumber_timestamp.jpg
where brickID is the LEGO brick id number as read from LDraw, color is the name of the selected brick color, sequenceNumber is the integer from 0 to 9 indicating the number of the image in the sequence and timestamp is UNIX time representation in milliseconds of the image creation time.
Sample images are presented below.
DOI: 10.34808/hymr-sd39
title: LDRAW based renders of LEGO bricks moving on a conveyor belt with extracted models

description: The set contains renders of LEGO bricks moving on a white conveyor belt. The images were prepared for training neural network for recognition of LEGO bricks. For each brick starting position, alignment and color was selected (simulating the brick falling down on the conveyour belt) and than 10 images was created while the brick was moved across the conveyor belt. Afterwards empty frames, with no brick visible, were removed from the set. The images were saved in JPEG format. All images were generated using Blender (https://www.blender.org/) tool and were based on the 3D models from LDraw (https://www.ldraw.org/) brick library. The bricks were than extracted from the original images using OpenCV edge detection algorithms.
The colors for the LEGO bricks were selected from the following list [color (code)]: White (0xffffff), Brick Yellow (0xD9BB7B), Nougat (0xD67240), Bright Red (0xff0000), Bright Blue (0x0000ff), Bright Yellow (0xFfff00), Black (0x000000), Dark Green (0x009900), Bright Green (0x00cc00), Dark Orange (0xA83D15), Medium Blue (0x478CC6), Bright Orange (0xff6600), Bright Bluish Green (0x059D9E), Bright Yellowish-Green (0x95B90B), Bright Reddish Violet (0x990066), Sand Blue (0x5E748C), Sand Yellow (0x8D7452), Earth Blue (0x002541), Earth Green (0x003300), Sand Green (0x5F8265), Dark Red (0x80081B), Flame Yellowish Orange (0xF49B00), Reddish Brown (0x5B1C0C), Medium Stone Grey (0x9C9291), Dark Stone Grey (0x4C5156), Light Stone Grey (0xE4E4DA), Light Royal Blue (0x87C0EA), Bright Purple (0xDE378B), Light Purple (0xEE9DC3), Cool Yellow (0xFFFF99), Medium Lilac (0x2C1577), Light Nougat (0xF5C189), Dark Brown (0x300F06), Medium Nougat (0xAA7D55), Dark Azur (0x469bc3), Medium Azur (0x68c3e2), Aqua (0xd3f2ea), Medium Lavender (0xa06eb9), Lavender (0xcda4de), White Glow (0xf5f3d7), Spring Yellowish Green (0xe2f99a), Olive Green (0x77774E), Medium-Yellowish Green (0x96B93B).
The original folder contains the renders themselves, the cropped_opencv directory contains only bricks extracted from th erenders. In both cases the images were placed in a folder named after the LEGO brick code (as read from LDraw). The files naming convetion is as follows
brickID_colour_sequenceNumber_timestamp.jpg
where brickID is the LEGO brick id number as read from LDraw, color is the name of the selected brick color, sequenceNumber is the integer from 0 to 9 indicating the number of the image in the sequence and timestamp is UNIX time representation in milliseconds of the image creation time.
Sample images are presented below.
DOI: 10.34808/pb7j-n716
title: Tagged images with LEGO bricks - Plates Angled

description: The set contains images of LEGO bricks (from Plates Angled category). The images were prepared for training neural network for recognition and labeling of LEGO bricks. The images contain one brick each. The images were taken from different sides by handheld camera hovering over the bricks lying on a white, non reflective surface.
The images were extracted from photos taken using Huawei p20 Pro camera (2160x3840 resolution, JPEG file format). The bricks were illuminated using two top-down facing 1600lm, 4000K LED lamps. The shutter speed and ISO were set to 1/100 and 50 respectively (to match the lamps frequency). Each file is limited to a bounding box as detected using LegoSorter app (https://github.com/legosorter). The bounding boxes were created using YOLO trained neural network designated to detect (but not differentiate) LEGO bricks.
The bricks have random colors. The photos are organized using official LEGO part numbers, photos of each brick located in a folder named after the part number. 
Sample images are presented below.
DOI: 10.34808/gw0r-ka59
title: Tagged images with LEGO bricks - Windscreens and Fuselage

description: The set contains images of LEGO bricks (from Windscreens and Fuselage category). The images were prepared for training neural network for recognition and labeling of LEGO bricks. The images contain one brick each. The images were taken from different sides by handheld camera hovering over the bricks lying on a white, non reflective surface.
The images were extracted from photos taken using Huawei p20 Pro camera (2160x3840 resolution, JPEG file format). The bricks were illuminated using two top-down facing 1600lm, 4000K LED lamps. The shutter speed and ISO were set to 1/100 and 50 respectively (to match the lamps frequency). Each file is limited to a bounding box as detected using LegoSorter app (https://github.com/legosorter). The bounding boxes were created using YOLO trained neural network designated to detect (but not differentiate) LEGO bricks.
The bricks have random colors. The photos are organized using official LEGO part numbers, photos of each brick located in a folder named after the part number. 
Sample images are presented below.
DOI: 10.34808/rcza-jy08
title: LEGO bricks for training classification network

description: The data set contains images of 447 different classes of LEGO bricks used for training LEGO bricks classification network. The dataset contains two types of images: photos (10%) and renders (90%) aggregated into respective directories. Each directory (photos and renders) contains 447 directories labeled as the official brick type number. The images contains only bricks on white background, the renders were augmented randomly so that they resemble the real pictures.
Sample images are presented below. Real photos are on the left side and renders on the right side.
DOI: 10.34808/xfgk-6f77
title: LDRAW based renders of LEGO bricks moving on a conveyor belt with extracted models

description: The set contains renders of LEGO bricks moving on a white conveyor belt. The images were prepared for training neural network for recognition of LEGO bricks. For each brick starting position, alignment and color was selected (simulating the brick falling down on the conveyour belt) and than 10 images was created while the brick was moved across the conveyor belt. Afterwards empty frames, with no brick visible, were removed from the set. The images were saved in JPEG format. All images were generated using Blender (https://www.blender.org/) tool and were based on the 3D models from LDraw (https://www.ldraw.org/) brick library. The bricks were than extracted from the original images using OpenCV edge detection algorithms.
The colors for the LEGO bricks were selected from the following list [color (code)]: White (0xffffff), Brick Yellow (0xD9BB7B), Nougat (0xD67240), Bright Red (0xff0000), Bright Blue (0x0000ff), Bright Yellow (0xFfff00), Black (0x000000), Dark Green (0x009900), Bright Green (0x00cc00), Dark Orange (0xA83D15), Medium Blue (0x478CC6), Bright Orange (0xff6600), Bright Bluish Green (0x059D9E), Bright Yellowish-Green (0x95B90B), Bright Reddish Violet (0x990066), Sand Blue (0x5E748C), Sand Yellow (0x8D7452), Earth Blue (0x002541), Earth Green (0x003300), Sand Green (0x5F8265), Dark Red (0x80081B), Flame Yellowish Orange (0xF49B00), Reddish Brown (0x5B1C0C), Medium Stone Grey (0x9C9291), Dark Stone Grey (0x4C5156), Light Stone Grey (0xE4E4DA), Light Royal Blue (0x87C0EA), Bright Purple (0xDE378B), Light Purple (0xEE9DC3), Cool Yellow (0xFFFF99), Medium Lilac (0x2C1577), Light Nougat (0xF5C189), Dark Brown (0x300F06), Medium Nougat (0xAA7D55), Dark Azur (0x469bc3), Medium Azur (0x68c3e2), Aqua (0xd3f2ea), Medium Lavender (0xa06eb9), Lavender (0xcda4de), White Glow (0xf5f3d7), Spring Yellowish Green (0xe2f99a), Olive Green (0x77774E), Medium-Yellowish Green (0x96B93B).
The original folder contains the renders themselves, the cropped_opencv directory contains only bricks extracted from th erenders. In both cases the images were placed in a folder named after the LEGO brick code (as read from LDraw). The files naming convetion is as follows
brickID_colour_sequenceNumber_timestamp.jpg
where brickID is the LEGO brick id number as read from LDraw, color is the name of the selected brick color, sequenceNumber is the integer from 0 to 9 indicating the number of the image in the sequence and timestamp is UNIX time representation in milliseconds of the image creation time.
Sample images are presented below.
DOI: 10.34808/4v54-jt71
title: LDRAW based renders of LEGO bricks moving on a conveyor belt with extracted models

description: The set contains renders of LEGO bricks moving on a white conveyor belt. The images were prepared for training neural network for recognition of LEGO bricks. For each brick starting position, alignment and color was selected (simulating the brick falling down on the conveyour belt) and than 10 images was created while the brick was moved across the conveyor belt. Afterwards empty frames, with no brick visible, were removed from the set. The images were saved in JPEG format. All images were generated using Blender (https://www.blender.org/) tool and were based on the 3D models from LDraw (https://www.ldraw.org/) brick library. The bricks were than extracted from the original images using OpenCV edge detection algorithms.
The colors for the LEGO bricks were selected from the following list [color (code)]: White (0xffffff), Brick Yellow (0xD9BB7B), Nougat (0xD67240), Bright Red (0xff0000), Bright Blue (0x0000ff), Bright Yellow (0xFfff00), Black (0x000000), Dark Green (0x009900), Bright Green (0x00cc00), Dark Orange (0xA83D15), Medium Blue (0x478CC6), Bright Orange (0xff6600), Bright Bluish Green (0x059D9E), Bright Yellowish-Green (0x95B90B), Bright Reddish Violet (0x990066), Sand Blue (0x5E748C), Sand Yellow (0x8D7452), Earth Blue (0x002541), Earth Green (0x003300), Sand Green (0x5F8265), Dark Red (0x80081B), Flame Yellowish Orange (0xF49B00), Reddish Brown (0x5B1C0C), Medium Stone Grey (0x9C9291), Dark Stone Grey (0x4C5156), Light Stone Grey (0xE4E4DA), Light Royal Blue (0x87C0EA), Bright Purple (0xDE378B), Light Purple (0xEE9DC3), Cool Yellow (0xFFFF99), Medium Lilac (0x2C1577), Light Nougat (0xF5C189), Dark Brown (0x300F06), Medium Nougat (0xAA7D55), Dark Azur (0x469bc3), Medium Azur (0x68c3e2), Aqua (0xd3f2ea), Medium Lavender (0xa06eb9), Lavender (0xcda4de), White Glow (0xf5f3d7), Spring Yellowish Green (0xe2f99a), Olive Green (0x77774E), Medium-Yellowish Green (0x96B93B).
The original folder contains the renders themselves, the cropped_opencv directory contains only bricks extracted from th erenders. In both cases the images were placed in a folder named after the LEGO brick code (as read from LDraw). The files naming convetion is as follows
brickID_colour_sequenceNumber_timestamp.jpg
where brickID is the LEGO brick id number as read from LDraw, color is the name of the selected brick color, sequenceNumber is the integer from 0 to 9 indicating the number of the image in the sequence and timestamp is UNIX time representation in milliseconds of the image creation time.
Sample images are presented below.
DOI: 10.34808/aaq3-j752
title: Tagged images with LEGO bricks - Windows and Doors

description: The set contains images of LEGO bricks (from Windows and Doors category). The images were prepared for training neural network for recognition and labeling of LEGO bricks. The images contain one brick each. The images were taken from different sides by handheld camera hovering over the bricks lying on a white, non reflective surface.
The images were extracted from photos taken using Huawei p20 Pro camera (2160x3840 resolution, JPEG file format). The bricks were illuminated using two top-down facing 1600lm, 4000K LED lamps. The shutter speed and ISO were set to 1/100 and 50 respectively (to match the lamps frequency). Each file is limited to a bounding box as detected using LegoSorter app (https://github.com/legosorter). The bounding boxes were created using YOLO trained neural network designated to detect (but not differentiate) LEGO bricks.
The bricks have random colors. The photos are organized using official LEGO part numbers, photos of each brick located in a folder named after the part number. 
Sample images are presented below.