cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34808/rar1-7621
title: Tagged images with LEGO bricks - Bricks Wedged

description: The set contains images of LEGO bricks (from Bricks Wedged 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/sfqa-5866
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: 10.34808/6n2m-2m58
title: Tagged images with LEGO bricks - Bars Ladders and Fences

description: The set contains images of LEGO bricks (from Bars Ladders and Fences 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: 10.34808/703j-kf76
title: Tagged images with LEGO bricks - Technic Pins

description: The set contains images of LEGO bricks (from Technic Pins 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/jja4-ee37
title: Tagged images with LEGO bricks - Technic Steering Suspension and Engine

description: The set contains images of LEGO bricks (from Technic Steering Suspension and Engine 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/e0kr-k928
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/jvnm-f733
title: Tagged images with LEGO bricks - Bricks Sloped

description: The set contains images of LEGO bricks (from Bricks Sloped 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/zkpx-vv64
title: Tagged images with LEGO bricks - Technic Steering Suspension and Engine

description: The set contains images of LEGO bricks (from Technic Steering Suspension and Engine 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.9ff44m
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sitta insularis J.Bond, 1931, Sitala insularis Möllendorff, 1894)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 47 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0294112-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34808/2whh-rx35
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/h875-mz36
title: Tagged images with LEGO bricks - Bricks Round and Cones

description: The set contains images of LEGO bricks (from Bricks Round and Cones 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/z8g8-db76
title: Tagged images with LEGO bricks - Technic Gears

description: The set contains images of LEGO bricks (from Technic Gears 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.fawpyj
title: Occurrence Download

description: A dataset containing 72 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "TaxonKey is Erythrina mulungu Mart. ex Benth."
 ]
}

 The dataset includes 72 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037825-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mdmfrj
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Neotysonia phyllostegia (F.Muell.) Paul G.Wilson ex J.W.Green"
 ]
}

 The dataset includes 13 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046732-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34808/0tb5-gn46
title: Tagged images with LEGO bricks - Bricks

description: The set contains images of LEGO bricks (from Bricks 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: 10.34808/4cem-9244
title: Tagged images with LEGO bricks - Plants and Animals

description: The set contains images of LEGO bricks (from Plants and Animals 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/d7at-ge38
title: Tagged images with LEGO bricks - Technic Special

description: The set contains images of LEGO bricks (from Technic 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: 10.15468/dl.b4zrez
title: Occurrence Download

description: A dataset containing 669385 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Corvus frugilegus",
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "Year 2011-2021",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 669385 records from 106 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0068023-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2ym6t5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nepeta assadii Jamzad"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046758-230530130749713/datasets/export for details.
DOI: 10.15468/dl.w92me4
title: Occurrence Download

description: A dataset containing 29177 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Myodes gapperi (Vigors, 1830))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 29177 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067494-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mm9387
title: Occurrence Download

description: A dataset containing 28902 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Podarcis muralis (Laurenti, 1768)",
 "Year is greater than 2013",
 "Geometry MULTIPOLYGON (((19.51 47.46,19.51 36.21,6.41 36.21,6.41 47.46,19.51 47.46)))"
 ]
}

 The dataset includes 28902 records from 214 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037795-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fqh3r7
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lasianthaea beckeri B.L.Turner"
 ]
}

 The dataset includes 7 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004308-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rmxpvg
title: Occurrence Download

description: A dataset containing 144 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Laportea cuneata (A.Rich.) Chew"
 ]
}

 The dataset includes 144 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004083-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.