cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/p0ph-3e80
title: Parkes observations for project P1335 semester 2024OCTS_04

description: We request six observations for ASKAP~J1832--0911, a 44-minute Ultra-long period object, with Murriyang ultra-wide-bandwidth low-frequency receiver (UWL). The proposed observation can help us measure and understand different kinds of sub-structures of the pulse over a wide frequency range. The observation can also serve as a pilot observation to develop a method to measure wide pulses with a single-dish telescope. This observation will help us measure the physical parameters of the source and its surrounding environment, and will also help us map the system geometry so that we can model the emission mechanism.
DOI: 10.15468/dl.phh38e
title: Occurrence Download

description: A dataset containing 5688 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-100.70309 21.89075,-100.8884 21.93902,-101.02114 21.858,-100.78153 21.70803,-100.74188 21.69251,-100.81083 21.45377,-100.635 21.33999,-100.44193 21.59512,-100.41953 21.76578,-100.70309 21.89075))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 5688 records from 163 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000648-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10831339
title: Tycho Region Database of impact craters >=21 meters on the Moon.

description: This dataset presents the results of an innovative AI-driven lunar crater mapping project, focused exclusively on the Tycho region of the Moon at 21m/px (see Version V2 for the global catalog at 100m/px) , marking the first comprehensive application of artificial intelligence to detect, classify, and map lunar craters. Created through extensive work over a two-year period, this dataset leverages YOLOLens, a state-of-the-art deep learning model specifically optimized for high-resolution crater detection. YOLOLens, an innovative variant of the YOLO architecture, has been fine-tuned to handle the unique challenges of lunar surface imagery, delivering unparalleled accuracy in crater identification and localization. Detailed information on the model architecture and methodology can be found in relevant publications on:



La Grassa, Riccardo, et al. "YOLOLens: A deep learning model based on super-resolution to enhance the crater detection of the planetary surfaces." Remote Sensing 15.5 (2023): 1171,  https://doi.org/10.3390/rs15051171.

La Grassa, R, et al. "LU5M812TGT: An AI-Powered global database of impact craters ≥0.4 km on the Moon", ISPRS Journal of Photogrammetry and Remote Sensing, 2025, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2024.11.010.


The dataset preparation process involved rigorous steps in preprocessing and post-processing to enhance the quality and usability of the data. Preprocessing techniques were employed to reduce noise and enhance contrast within the complex lunar landscape, while post-processing was used to refine the accuracy of crater boundaries and dimensions detected by the model. This approach facilitated a high-confidence dataset that stands as a valuable resource for the astronomical and AI research communities.

The area analyzed in this dataset is defined by the following coordinates: longitude [-21.0000000001998046, 44.9997999998701630] and latitude [-50.9998000002697722, 39.0000000003997229].

This dataset, which includes over 6.8 million craters at a resolution of 21m/px, is a valuable resource for both astrophysicists and AI researchers. It provides precisely labeled crater data, including coordinates, dimensions, and classifications, serving as an essential benchmark for comparative analyses, model validation, and advancements in lunar and planetary science.

Notably, the global catalog created using the WAC imagery at 100m/px resolution is available in version V2 of this repository. This global dataset complements the Tycho region-specific data by offering a broader perspective on lunar crater distribution.

*********************************************************************🌕🌖🌗🌘🌑🌒🌓🌔🌕 **********************************************************************

Release of a Tycho area at 21m/px of craters catalog  with more than 6.8 million craters.



File source csv

Header: Longitude, Latitude, Diameter_w, Diameter_h, Confidence.

The coordinates are absolute in the range of [-180, +180] of Longitude and [-90, +90] of Latitude. 

Diameters (km)
DOI: 10.5281/zenodo.10831340
title: LU5M812TGT: An AI-Powered Global Database of impact craters >=0.4 km on the Moon.

description: "Check Version 2"

This dataset presents the results of a groundbreaking AI-driven lunar crater mapping project, marking the first comprehensive application of artificial intelligence to detect, classify, and map lunar craters. Created through extensive work over a two-year period, this dataset leverages YOLOLens, a state-of-the-art deep learning model specifically optimized for high-resolution crater detection. YOLOLens, an innovative variant of the YOLO architecture, has been fine-tuned to handle the unique challenges of lunar surface imagery, delivering unparalleled accuracy in crater identification and localization. Detailed information on the model architecture and methodology can be found in relevant publications on:



La Grassa, Riccardo, et al. "YOLOLens: A deep learning model based on super-resolution to enhance the crater detection of the planetary surfaces." Remote Sensing 15.5 (2023): 1171,  https://doi.org/10.3390/rs15051171.

La Grassa, R, et al. "LU5M812TGT: An AI-Powered global database of impact craters ≥0.4 km on the Moon", ISPRS Journal of Photogrammetry and Remote Sensing, 2025, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2024.11.010.


The dataset preparation process involved rigorous steps in preprocessing and post-processing to enhance the quality and usability of the data. Preprocessing techniques were employed to reduce noise and enhance contrast within the complex lunar landscape, while post-processing was used to refine the accuracy of crater boundaries and dimensions detected by the model. This approach facilitated a high-confidence dataset that stands as a valuable resource for the astronomical and AI research communities.

This dataset can serve as a critical tool for both astrophysicists and AI researchers. By providing labelled crater data with precise coordinates, dimensions, and classifications, it offers a benchmark for comparative studies, model validation, and further innovations in celestial object detection. 

***********************************************************************************************************************************************************

Release of a new global craters catalog (LU5M812TGT) with more than 5 million craters.



File source csv

Header: Longitude, Latitude, Diameter_w, Diameter_h, Confidence.

The coordinates are absolute in the range of [-180, +180] of Longitude and [-90, +90] of Latitude. 

Diameters (km)
DOI: 10.5281/zenodo.13990480
title: LU5M812TGT: An AI-Powered Global Database of impact craters >=0.4 km on the Moon.

description: This dataset presents the results of a groundbreaking AI-driven lunar crater mapping project, marking the first comprehensive application of artificial intelligence to detect, classify, and map lunar craters. Created through extensive work over a two-year period, this dataset leverages YOLOLens, a state-of-the-art deep learning model specifically optimized for high-resolution crater detection. YOLOLens, an innovative variant of the YOLO architecture, has been fine-tuned to handle the unique challenges of lunar surface imagery, delivering unparalleled accuracy in crater identification and localization. Detailed information on the model architecture and methodology can be found in relevant publications on:



La Grassa, Riccardo, et al. "YOLOLens: A deep learning model based on super-resolution to enhance the crater detection of the planetary surfaces." Remote Sensing 15.5 (2023): 1171,  https://doi.org/10.3390/rs15051171.

La Grassa, R, et al. "LU5M812TGT: An AI-Powered global database of impact craters ≥0.4 km on the Moon", ISPRS Journal of Photogrammetry and Remote Sensing, 2025, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2024.11.010.


The dataset preparation process involved rigorous steps in preprocessing and post-processing to enhance the quality and usability of the data. Preprocessing techniques were employed to reduce noise and enhance contrast within the complex lunar landscape, while post-processing was used to refine the accuracy of crater boundaries and dimensions detected by the model. This approach facilitated a high-confidence dataset that stands as a valuable resource for the astronomical and AI research communities.

This dataset can serve as a critical tool for both astrophysicists and AI researchers. By providing labelled crater data with precise coordinates, dimensions, and classifications, it offers a benchmark for comparative studies, model validation, and further innovations in celestial object detection. 

***********************************************************************************************************************************************************

Release of a new global craters catalog (LU5M812TGT) with more than 5 million craters.



File source csv

Header: Longitude, Latitude, Diameter_w, Diameter_h, Confidence.

The coordinates are absolute in the range of [-180, +180] of Longitude and [-90, +90] of Latitude. 

Diameters (km)
DOI: 10.5281/zenodo.14715016
title: Tycho Region Database of impact craters >=21 meters on the Moon.

description: This dataset presents the results of an innovative AI-driven lunar crater mapping project, focused exclusively on the Tycho region of the Moon at 21m/px (see Version V2 for the global catalog at 100m/px) , marking the first comprehensive application of artificial intelligence to detect, classify, and map lunar craters. Created through extensive work over a two-year period, this dataset leverages YOLOLens, a state-of-the-art deep learning model specifically optimized for high-resolution crater detection. YOLOLens, an innovative variant of the YOLO architecture, has been fine-tuned to handle the unique challenges of lunar surface imagery, delivering unparalleled accuracy in crater identification and localization. Detailed information on the model architecture and methodology can be found in relevant publications on:



La Grassa, Riccardo, et al. "YOLOLens: A deep learning model based on super-resolution to enhance the crater detection of the planetary surfaces." Remote Sensing 15.5 (2023): 1171,  https://doi.org/10.3390/rs15051171.

La Grassa, R, et al. "LU5M812TGT: An AI-Powered global database of impact craters ≥0.4 km on the Moon", ISPRS Journal of Photogrammetry and Remote Sensing, 2025, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2024.11.010.


The dataset preparation process involved rigorous steps in preprocessing and post-processing to enhance the quality and usability of the data. Preprocessing techniques were employed to reduce noise and enhance contrast within the complex lunar landscape, while post-processing was used to refine the accuracy of crater boundaries and dimensions detected by the model. This approach facilitated a high-confidence dataset that stands as a valuable resource for the astronomical and AI research communities.

The area analyzed in this dataset is defined by the following coordinates: longitude [-21.0000000001998046, 44.9997999998701630] and latitude [-50.9998000002697722, 39.0000000003997229].

This dataset, which includes over 6.8 million craters at a resolution of 21m/px, is a valuable resource for both astrophysicists and AI researchers. It provides precisely labeled crater data, including coordinates, dimensions, and classifications, serving as an essential benchmark for comparative analyses, model validation, and advancements in lunar and planetary science.

Notably, the global catalog created using the WAC imagery at 100m/px resolution is available in version V2 of this repository. This global dataset complements the Tycho region-specific data by offering a broader perspective on lunar crater distribution.

*********************************************************************🌕🌖🌗🌘🌑🌒🌓🌔🌕 **********************************************************************

Release of a Tycho area at 21m/px of craters catalog  with more than 6.8 million craters.



File source csv

Header: Longitude, Latitude, Diameter_w, Diameter_h, Confidence.

The coordinates are absolute in the range of [-180, +180] of Longitude and [-90, +90] of Latitude. 

Diameters (km)
DOI: 10.5281/zenodo.14715112
title: PC_cCMV_RhesusMacaque

description: This is the data repository for the pentameric glycoprotein complex project.
DOI: 10.5281/zenodo.14715113
title: PC_cCMV_RhesusMacaque

description: This is the data repository for the pentameric glycoprotein complex project.
DOI: 10.15468/dl.xrnran
title: Occurrence Download

description: A dataset containing 99 species occurrences available in GBIF matching the query:
{
 "and" : [
 "RecordedBy is DODGE",
 "StateProvince is WIsconsin"
 ]
}

 The dataset includes 99 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000650-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4trd5x
title: Occurrence Download

description: A dataset containing 140934 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Country is one of (Canada, Mexico, United States of America)",
 "EventDate is greater than or equal to 2024-12-27",
 "EventDate is less than or equal to 2025-01-26",
 {
 "not" : {
 "Issue" : [
 "is one of (Basis of record invalid, Coordinate reprojection failed, Coordinate reprojection suspicious, Coordinate precision invalid, Coordinate uncertainty meters invalid, Country mismatch, Country invalid, Geodetic datum invalid, Presumed swapped coordinate, Presumed negated longitude, Presumed negated latitude, Country coordinate mismatch)"
 ]
 }
 }
 ]
}

 The dataset includes 140934 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000623-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vtyu34
title: Occurrence Download

description: A dataset containing 8997707 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Plantae",
 "Country is one of (Norway)"
 ]
}

 The dataset includes 8997707 records from 610 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000634-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zw9hde
title: Occurrence Download

description: A dataset containing 4814191 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((180 -44.3,173 -44.3,173 -47.5,170 -47.5,157 -47.5,157 -45.9,150 -45.9,150 -47.5,143 -47.5,143 -45.8,140 -45.8,140 -44.5,137 -44.5,137 -43,135 -43,135 -41.7,131 -41.7,131 -40.1,115 -40.1,92 -40.1,92 -41.4,78 -41.4,78 -42.3,69 -42.3,69 -43.3,47 -43.3,47 -41.7,30 -41.7,12 -41.7,12 -40.3,10 -40.3,10 -38.3,-5 -38.3,-5 -38.9,-9 -38.9,-9 -40.2,-13 -40.2,-13 -41.4,-21 -41.4,-21 -42.5,-39 -42.5,-39 -40.7,-49 -40.7,-49 -48.6,-54 -48.6,-54 -55.7,-62.7972582608082 -55.7,-64 -55.7,-64 -57.8,-71 -57.8,-71 -58.9,-80 -58.9,-80 -40,-125 -40,-167 -40,-167 -42.6,-171 -42.6,-171 -44.3,-180 -44.3,-180 -90, 0 -90,180 -90,180 -44.3))",
 "TaxonKey is Animalia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 4814191 records from 1081 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000641-250121130708018/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15157562
title: TABLE 1 in On some tropical hydroids (Cnidaria: Hydrozoa), with descriptions of four new species

description: TABLE 1. Cnidome composition of Corymorpha balssi Stechow, 1932 (in µm).
Small, spherical capsulesLarge, spherical capsulesDesmonemesElliptical capsulesType S 1 stenotelesType S 2 stenotelesType S 3 stenotelesType S 4 stenotelesType S 5 stenotelesPOLYPUpper part of caulus-11.0–12.7-----(12.0–13.8) × (10.7–11.7)-Aboral tentacles-11.0–12.7(5.1–5.3) × (3.8–4.1)(9.0–10.3) × (3.4–4.1)-(6.9–7.6) × (5.3–5.7)-(12.7–13.1) × (11.0–11.4)-Oral tentacles--(5.1–5.5) × (3.8–4.1)(9.0–9.6) × (3.4–3.8)-(6.2–7.2) × (4.8–5.7)-(12.7–13.8) × (11.0–11.4)-MEDUSACapitate tentacle--(6.7–7.1) × (4.8–5.0)(9.3–9.8) × (3.4–3.8)--(8.9–9.6) × (7.1–7.6)(13.1–13.6) × (11.2–11.4)-Simple tentacles-11.7–13.8-(9.3–9.8) × (3.4–3.8)--(8.6–9.3) × (6.6–7.2)(13.4–13.8) × (11.0–11.7)(18.3–20.3) × (16.5–18.6)Manubrium/mouth----(5.7–5.9) × (4.3–4.5)--(13.8–14.8) × (12.1–13.4)-Exumbrella7.1–7.6--------
description: Published as part of Galea, Horia R. & Maggioni, Davide, 2024, On some tropical hydroids (Cnidaria: Hydrozoa), with descriptions of four new species, pp. 8 in Zootaxa 5428 (1) on page 8, DOI: 10.11646/zootaxa.5428.1.1, http://zenodo.org/record/10842930
DOI: 10.5281/zenodo.15157566
title: TABLE 2 in On some tropical hydroids (Cnidaria: Hydrozoa), with descriptions of four new species

description: TABLE 2. Measurements (in µm) of Antennella flava, sp. nov.
MSNMCoe372Ahydrothecate internodes- length290–395- diameter at transverse node35–70- nematotheca, length65–105- nematotheca, aperture diameter35–55Hydrothecate internodes- length270–330- hydrotheca, abcauline wall155–175- hydrotheca, free adcauline wall120–125- hydrotheca, aperture diameter190–205- mesial nematotheca, length100–105- mesial nematotheca, aperture diameter55–60- apophysis lateral nematothecae, length70–75- lateral nematotheca, length75–85- lateral nematotheca, aperture diameter50–60- axillar nematotheca, length55–65- axillar nematotheca, aperture diameter35–40Female gonotheca- cauline apophysis, length45–50- proximal segment, length50–60- gonothecal length485–505- gonothecal width270–305- diameter at aperture155–170- nematotheca, length110–125- nematotheca, aperture diameter55–60Male gonotheca- apophysis, length40–50- basal segment, length40–55- gonothecal length435–545- gonothecal width215–255- diameter at aperture65–80- nematotheca, length85–100- nematotheca, aperture diameter55–60
description: Published as part of Galea, Horia R. & Maggioni, Davide, 2024, On some tropical hydroids (Cnidaria: Hydrozoa), with descriptions of four new species, pp. 29 in Zootaxa 5428 (1) on page 29, DOI: 10.11646/zootaxa.5428.1.1, http://zenodo.org/record/10842930
DOI: 10.5281/zenodo.15157568
title: TABLE 3 in On some tropical hydroids (Cnidaria: Hydrozoa), with descriptions of four new species

description: TABLE 3. List of stations.
Station #LocalityDive siteLatitudeLongitude1PemuteranBeach-8.143380°114.658050°2PemuteranBeach-8.144146°114.657994°3PemuteranPontoon-8.142954°114.660906°4KubuBoga shipwreck-8.249684°115.576176°5TulambenLiberty shipwreck-8.274168°115.592647°6TulambenCoral garden-8.276825°115.594071°7TulambenDrop-off-8.278284°115.597062°8TulambenMelasti-8.291824°115.609316°9TulambenSeraya-8.295692°115.612838°10TulambenSidem-8.307683°115.621221°11AmedMelasti-8.330810°115.639825°12AmedGhost Bay-8.332965°115.643044°13AmedPyramids-8.333536°115.653598°14AmedJemeluk wall-8.335974°115.659258°15AmedLipah beach-8.350152°115.683861°16AmedJapanese shipwreck-8.363970°115.700446°17PadangbaiJetty-8.508310°115.518818°18PadangbaiJepun shipwreck-8.528118°115.514784°19PererenanBeach-8.650831°115.119820°
description: Published as part of Galea, Horia R. & Maggioni, Davide, 2024, On some tropical hydroids (Cnidaria: Hydrozoa), with descriptions of four new species, pp. 33 in Zootaxa 5428 (1) on page 33, DOI: 10.11646/zootaxa.5428.1.1, http://zenodo.org/record/10842930
DOI: 10.5281/zenodo.15157569
title: TABLE 3 in On some tropical hydroids (Cnidaria: Hydrozoa), with descriptions of four new species

description: TABLE 3. List of stations.
Station #LocalityDive siteLatitudeLongitude1PemuteranBeach-8.143380°114.658050°2PemuteranBeach-8.144146°114.657994°3PemuteranPontoon-8.142954°114.660906°4KubuBoga shipwreck-8.249684°115.576176°5TulambenLiberty shipwreck-8.274168°115.592647°6TulambenCoral garden-8.276825°115.594071°7TulambenDrop-off-8.278284°115.597062°8TulambenMelasti-8.291824°115.609316°9TulambenSeraya-8.295692°115.612838°10TulambenSidem-8.307683°115.621221°11AmedMelasti-8.330810°115.639825°12AmedGhost Bay-8.332965°115.643044°13AmedPyramids-8.333536°115.653598°14AmedJemeluk wall-8.335974°115.659258°15AmedLipah beach-8.350152°115.683861°16AmedJapanese shipwreck-8.363970°115.700446°17PadangbaiJetty-8.508310°115.518818°18PadangbaiJepun shipwreck-8.528118°115.514784°19PererenanBeach-8.650831°115.119820°
description: Published as part of Galea, Horia R. & Maggioni, Davide, 2024, On some tropical hydroids (Cnidaria: Hydrozoa), with descriptions of four new species, pp. 33 in Zootaxa 5428 (1) on page 33, DOI: 10.11646/zootaxa.5428.1.1, http://zenodo.org/record/10842930
DOI: 10.5281/zenodo.15157572
title: TABLE 1 in Helminths of small rodents (Heteromyidae and Cricetidae) in the Yucatan Peninsula, Mexico: an integrative taxonomic approach to their inventory

description: TABLE 1. Study sites in the Yucatan Peninsula (Mexico) where small rodents were trapped.
SiteMunicipality, stateTrapping sitesGeographical coordinatesCollection dateHobonil ranchTzucacab, YucatanMedium semi-deciduous tropical forest20°01’10.4’’ N, 89°01’23.4’’ WMarch 2017Ich ha lol xaan ecological parkHampolol, CampecheSub-evergreen medium tropical forest19°56’47.3” N, 90°22’26.1” WMay 2017Molas farmMerida, YucatanLow deciduous tropical forest and pastures20°47’54.5’’ N, 89°37’56.9’’ WJuly 2017Dzununcan farmMerida, YucatanLow deciduous tropical forest20°51’28.3’’ N, 89°38’34.4’’ WAugust 2017San Francisco cattle ranchPanaba, YucatanLow deciduous tropical forest and pastures21°21’50.3” N, 88°19’39.8” WAugust 2017Santa Cruz cattle ranchTizimin, YucatanSemi-deciduous tropical forest21°08’54.4’’ N, 87°54’37.8’’ WSeptember 2018VallazooValladolid, YucatanSecondary forest and zoo facilities20°43’29.8’’ N, 88°12’26.4’’ WSeptember 2018Aak ecological parkMerida, YucatanLow deciduous tropical forest20°07’48.1’’ N, 89°37’16.5’’ WNovember 2018Papam ranchHunucma, YucatanPastures and secondary forest21°02’45.0’’ N, 89°53’18.9’’ WJanuary 2019Kuncheil cattle ranchSotuta, YucatanCultivated grasslands and secondary forest20°39’14.7’’ N, 89°02’27.1’’ WMarch 2019Jolie Jungle eco-hotelPuerto Morelos, Quintana RooDeciduous tropical forest20°55’33.6’’ N, 87°07’39.4’’ WAugust 2019Balam Nah eco-hotelFelipe Carrillo Puerto, Quintana RooDeciduous tropical forest19°28’56.5’’ N, 88°03’52.6’’ WSeptember 2019David Gustavo communityBacalar, Quintana RooCultivated grasslands and secondary forest18°52’50.3’’ N, 88°41’25.5’’ WSeptember 2019Zoh Laguna communityCalakmul, CampecheCultivated grasslands and secondary forest18°35’21.9’’ N, 89° 25’ 17.4’’ WSeptember 2019
description: Published as part of Panti-May, Jesús Alonso, Moguel-Chin, Wilson Isaias, Hernández-Mena, David Iván, Cárdenas-Vargas, Miguel Humberto, Torres-Castro, Marco, García-Prieto, Luis, Digiani, Maria Celina, Hernández-Betancourt, Silvia F. & Vidal-Martínez, Víctor Manuel, 2023, Helminths of small rodents (Heteromyidae and Cricetidae) in the Yucatan Peninsula, Mexico: an integrative taxonomic approach to their inventory, pp. 207 in Zootaxa 5357 (2) on page 207, DOI: 10.11646/zootaxa.5357.2.3, http://zenodo.org/record/10017978