cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34808/wzys-d880
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.ga9qyp
title: Occurrence Download

description: A dataset containing 10808 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Living Specimen, Specimen, Human Observation, Material sample)",
 "Continent is one of (Africa, South America, North America)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Selaginella acanthostachys Baker, Selaginella anceps (C.Presl) C.Presl, Selaginella apoda (L.) Spring, Selaginella bombycina Spring, Selaginella cathedrifolia Spring, Selaginella chrysoleuca Spring, Selaginella tenella (P.Beauv.) Spring, Selaginella contigua Baker, Selaginella falcata (P.Beauv.) Spring, Selaginella flagellata (L.) Spring, Selaginella flexuosa Spring, Selaginella haematodes (Kunze) Spring, Selaginella harrisii Underw. & Hieron., Selaginella hirsuta Alston ex Crabbe & Jermy, Selaginella huehuetenangensis Hieron., Selaginella kochii Hieron., Selaginella lechleri Hieron., Selaginella lychnuchus Spring ex Klotzsch, Selaginella macrostachya (Spring) Spring, Selaginella martensii Spring, Selaginella mazaruniensis Jenman, Selaginella moritziana Spring ex Klotzsch, Selaginella muscosa Spring, Selaginella nothohybrida Valdespino, Selaginella novae-hollandiae (Sw.) Spring, Selaginella oaxacana Spring, Selaginella pallescens (C.Presl) Spring, Selaginella pulcherrima Liebm. ex E.Fourn., Selaginella porphyrospora A.Braun, Selaginella potaroensis Jenman, Selaginella producta Baker, Selaginella radiata (Aubl.) Spring, Selaginella reflexa Underw., Selaginella revoluta Baker, Selaginella roraimensis Baker, Selaginella sandwithii Alston, Selaginella seemannii Baker, Selaginella simplex Baker, Selaginella umbrosa Lem. ex Hieron.)"
 ]
}

 The dataset includes 10808 records from 279 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0282011-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34808/d40f-y129
title: Tagged images with LEGO bricks - Minifig Accessories

description: The set contains images of LEGO bricks (from Minifig Accessories 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/eb13-y746
title: Tagged images with LEGO bricks - Technic Panels

description: The set contains images of LEGO bricks (from Technic Panels 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/fva4-j679
title: Tagged images with LEGO bricks - Plates

description: The set contains images of LEGO bricks (from Plates 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.2p5ep5
title: Occurrence Download

description: A dataset containing 120 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is United States of America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "MediaType is Image",
 "Month is one of (May, June, July)",
 "TaxonKey is Lonicera subspicata Hook. & Arn.",
 "Year 1950-2023"
 ]
}

 The dataset includes 120 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0289946-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34808/9maa-dd44
title: Tagged images with LEGO bricks - Technic Bricks

description: The set contains images of LEGO bricks (from Technic 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/vgfn-jz21
title: Tagged images with LEGO bricks - Pneumatics

description: The set contains images of LEGO bricks (from Pneumatics 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/wty7-0769
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: 10.34808/4aeb-1f36
title: Tagged images with bees

description: Images taken from bee hive with tagged bees. The images are prepared for training yolo5 deep neural network (supplied with the data).
The images are random frames from movies recorded in may 2017 and 2018. All images are taken from full HD video stream, split in half and rescaled to 416x416 (each half).  The images names are in format: "_frames__&lt;left|right&gt;.bmp".
The tagging of the images was done manually using objectmaker.exe tool (supplied with the data). The tags are stored in info.txt file and has the following format: "image_name number_of_tags x_coordinate_of_top_lef_corner y_coordinate_of_top_lef_corner x_coordinate_of_bottom_right_corner y_coordinate_of_bottom_right_corner". The coordinate set is provided number_of_tags times.
DOI: 10.34808/ey8c-4w87
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/gbbq-c659
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: 10.34808/5zhj-8w18
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: 10.34808/v23c-qx27
title: Video recordings of bees at entrance to hives

description: Video recordings of bees at entrance to hives from 2017-04-22, 2017-04-23 and 2018-05-22. All recordings were made using hand-held full HD camera (Samsung Galaxy S3) and encoded using H.264 video codec (Standard Baseline Profile for mov files from 2017, High Profile for mp4 files from 2018) , 30 FPS and bit rate 14478 kb/s (mov files from 2017) or 16869 kb/s (mp4 files from 2018), MPEG-4 AAC audio codec, stereo, sampling of 48000 Hz and bit rate 128 kb/s.
All recordings were done using standard handheld camera without a stand. The idea was to capture different angles, backgrounds, image sharpness, light conditions etc. Sample screenshots from the movies can be seen on the images below.
DOI: 10.15468/dl.xxmunb
title: Occurrence Download

description: A dataset containing 530 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Ambystoma mexicanum (Shaw & Nodder, 1798)"
 ]
}

 The dataset includes 530 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0294086-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34808/34by-af11
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.