cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34808/n5te-m648
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.7m3zrr
title: Occurrence Download

description: A dataset containing 57 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Philemon albitorques P.L.Sclater, 1877)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 57 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0294098-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rqjgft
title: Occurrence Download

description: A dataset containing 167 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Eriochloa montevidensis Griseb."
 ]
}

 The dataset includes 167 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037819-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34808/59bp-be69
title: Automatically created and partially veriffied Wikipedia - WordNet mappings

description: Mapping between Wikipedia articles and WordNet synsets. The mappings between Wikipedia articles and WordNet synsets were obtained automatically using 4 algorithms of data processing. The automatically generated mappings were than a subject of verification by a group of volunteers using crowdsourcing approach through so called Games with a Purpose. The mappings are in form "Wordnet Words, Wordnet Gloss, Wikipedia URL" and map a given synset with English Wikipedia article.
DOI: 10.34808/5r8q-6v13
title: Tagged images with LEGO bricks - Plates Round Curved and Dishes

description: The set contains images of LEGO bricks (from Plates Round Curved and Dishes 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: 10.15468/dl.5e2k5w
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ponerorchis ovata (K.Y.Lang) X.H.Jin, Schuit. & W.T.Jin"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046730-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5qyakk
title: Occurrence Download

description: A dataset containing 23164 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-104.16546 53.88956,-101.69768 53.88956,-101.69768 55.31025,-104.16546 55.31025,-104.16546 53.88956))",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 23164 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0289939-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34808/hder-0h81
title: Tagged images with LEGO bricks - Technic Beams

description: The set contains images of LEGO bricks (from Technic Beams 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/snst-8b87
title: Tagged images with LEGO bricks - Plates Special

description: The set contains images of LEGO bricks (from Plates Special 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/tms3-zd71
title: Tagged images with LEGO bricks - Technic Connectors

description: The set contains images of LEGO bricks (from Technic Connectors 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/fty5-y948
title: Tagged images with LEGO bricks - Technic Beams Special

description: The set contains images of LEGO bricks (from Technic Beams Special 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/rqr7-0k30
title: Tagged images with LEGO bricks - Tiles Special

description: The set contains images of LEGO bricks (from Tiles Special 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/xj4k-5098
title: Tagged images with LEGO bricks - Bricks Curved

description: The set contains images of LEGO bricks (from Bricks Curved 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: 10.15468/dl.v4ebda
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nepeta anamurensis Gemici & Leblebici"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046753-230530130749713/datasets/export for details.
DOI: 10.34808/2pd7-g268
title: Tagged images with LEGO bricks part 2

description: The data set conatins tagged images conatining LEGO bricks used for traning LEGO bricks detecting network. The images contain random number of the same LEGO bricks on white background. Only the whole bricks are labeled.
The images lie in subdirectory denoting the id number of the bricks in the given picture. The immages were tagged with coordinates of LEGO bricks bounding boxes. The label are stored in xml files named as the image. The label were generated using labeImg software (https://github.com/tzutalin/labelImg) and are stored in PASCAL VOC format (http://host.robots.ox.ac.uk/pascal/VOC/voc2008/htmldoc/).
DOI: 10.34808/hx3q-sp50
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/skph-wh63
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: 10.34808/0zhs-wp56
title: Video of LEGO bricks on conveyor belt - Plates with studs on the side

description: 





The set contains videos of LEGO bricks (plates 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: 10.34808/2dbx-6a16
title: Tagged images with LEGO bricks

description: 





The data set conatins tagged images conatining LEGO bricks used for traning LEGO bricks detecting network. The dataset contains two types of images:
2933 photos containg from 1 to 32 LEGO bricks. The photos has random background and are taken from random angle. The photos are in 2448x3264 resolution and are stored in JPEG format.
2908 renders of LEGO bricks (1 brick on each image). The renders conatin bricks in multiple shapes and colors, on white backgroud, taken from camera facing bottom-up. The renders have 400x600 resolution and are stored in PNG format.

The images lie in proper subdirectory (photos and renders) and in further subdirectory denoting the number of bricks visible on the image. The immages were tagged with coordinates of LEGO bricks bounding boxes. The label are stored in xml files named as the image. The label were generated using labeImg software (https://github.com/tzutalin/labelImg) and are stored in PASCAL VOC format (http://host.robots.ox.ac.uk/pascal/VOC/voc2008/htmldoc/).
 



Sample photos: