cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14839937
title: unimpeded: walcdm bao.sdss_dr16+sn.pantheon

description: cosmological model:walcdm, dataset:bao.sdss_dr16+sn.pantheon
DOI: 10.5281/zenodo.14839938
title: unimpeded: walcdm bao.sdss_dr16+sn.pantheon

description: cosmological model:walcdm, dataset:bao.sdss_dr16+sn.pantheon
DOI: 10.26036/cnp0004122
title: Chinese gut microbial reference (CGMR)

description: Construction of a Chinese population gut microbiota genetic information database using high-depth metagenomic sequencing
DOI: 10.5281/zenodo.14840037
title: unimpeded: Nlcdm bao.sdss_dr16+sn.pantheon

description: cosmological model:Nlcdm, dataset:bao.sdss_dr16+sn.pantheon
DOI: 10.5281/zenodo.14840038
title: unimpeded: Nlcdm bao.sdss_dr16+sn.pantheon

description: cosmological model:Nlcdm, dataset:bao.sdss_dr16+sn.pantheon
DOI: 10.5281/zenodo.14840049
title: unimpeded: nlcdm bao.sdss_dr16+planck_2018_CamSpec_nolens

description: cosmological model:nlcdm, dataset:bao.sdss_dr16+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14840050
title: unimpeded: nlcdm bao.sdss_dr16+planck_2018_CamSpec_nolens

description: cosmological model:nlcdm, dataset:bao.sdss_dr16+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14840055
title: unimpeded: Nlcdm bicep_keck_2018+sn.pantheon

description: cosmological model:Nlcdm, dataset:bicep_keck_2018+sn.pantheon
DOI: 10.5281/zenodo.14840056
title: unimpeded: Nlcdm bicep_keck_2018+sn.pantheon

description: cosmological model:Nlcdm, dataset:bicep_keck_2018+sn.pantheon
DOI: 10.5281/zenodo.14840071
title: unimpeded: nlcdm bicep_keck_2018+planck_2018_CamSpec_nolens

description: cosmological model:nlcdm, dataset:bicep_keck_2018+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14840072
title: unimpeded: nlcdm bicep_keck_2018+planck_2018_CamSpec_nolens

description: cosmological model:nlcdm, dataset:bicep_keck_2018+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14840077
title: unimpeded: Nlcdm bao.sdss_dr16+planck_2018_CamSpec_nolens

description: cosmological model:Nlcdm, dataset:bao.sdss_dr16+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14840078
title: unimpeded: Nlcdm bao.sdss_dr16+planck_2018_CamSpec_nolens

description: cosmological model:Nlcdm, dataset:bao.sdss_dr16+planck_2018_CamSpec_nolens
DOI: 10.6084/m9.figshare.28386947
title: studentsgratitude123.sav

description: <b>Sowing Seeds of Gratitude: </b><b>The Effect of the Trajectories of Adolescents’ Gratitude on Depression</b><b>and t</b><b>he</b><b>Mediating Role</b><b> of Self-esteem</b>
DOI: 10.5281/zenodo.14840079
title: unimpeded: nlcdm bicep_keck_2018+planck_2018_plik

description: cosmological model:nlcdm, dataset:bicep_keck_2018+planck_2018_plik
DOI: 10.5281/zenodo.14840080
title: unimpeded: nlcdm bicep_keck_2018+planck_2018_plik

description: cosmological model:nlcdm, dataset:bicep_keck_2018+planck_2018_plik
DOI: 10.5281/zenodo.14848068
title: Fiji HPV vaccine study (Clinicaltrials.gov: NCT02276521) - systems serology dataset

description: This is the dataset used in the research article titled: Systems serology analysis shows IgG1 and IgG3 memory responses six years after one dose of quadrivalent HPV vaccine, for publication in Nature Communications (2025).
DOI: 10.5281/zenodo.14848069
title: Fiji HPV vaccine study (Clinicaltrials.gov: NCT02276521) - systems serology dataset

description: This is the dataset used in the research article titled: Systems serology analysis shows IgG1 and IgG3 memory responses six years after one dose of quadrivalent HPV vaccine, for publication in Nature Communications (2025).
DOI: 10.5281/zenodo.14839745
title: unimpeded: rlcdm bao.sdss_dr16+planck_2018_CamSpec_nolens

description: cosmological model:rlcdm, dataset:bao.sdss_dr16+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14839746
title: unimpeded: rlcdm bao.sdss_dr16+planck_2018_CamSpec_nolens

description: cosmological model:rlcdm, dataset:bao.sdss_dr16+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14839861
title: unimpeded: lcdm des_y1.joint

description: cosmological model:lcdm, dataset:des_y1.joint
DOI: 10.5281/zenodo.14839862
title: unimpeded: lcdm des_y1.joint

description: cosmological model:lcdm, dataset:des_y1.joint
DOI: 10.21227/rvh5-8842
title: Metaverse Gait Authentication Dataset (MGAD)

description: The Metaverse Gait Authentication Dataset (MGAD) is a large-scale gait dataset designed for biometric authentication in virtual environments. It contains gait data from 5,000 simulated users, generated in Unity 3D and processed using OpenPose and MediaPipe to extract 16 key features, including stride length, step frequency, joint angles, ground reaction forces, and gait symmetry index. The dataset has undergone extensive preprocessing, feature engineering, and normalization to ensure high-quality, noise-free data suitable for machine learning applications. MGAD is a benchmark for evaluating gait-based authentication models in the Metaverse, enabling researchers to explore biometric security solutions in immersive environments.
DOI: 10.6084/m9.figshare.26362117.v1
title: <b>Local adaptation of a widespread plant by intraspecific trait dimensions</b>

description: <b>Data for the study on local adaptation of a widespread plant by intraspecific trait dimensions</b>
DOI: 10.5281/zenodo.14839885
title: unimpeded: mlcdm planck_2018_plik_nolens+sn.pantheon

description: cosmological model:mlcdm, dataset:planck_2018_plik_nolens+sn.pantheon
DOI: 10.5281/zenodo.14839886
title: unimpeded: mlcdm planck_2018_plik_nolens+sn.pantheon

description: cosmological model:mlcdm, dataset:planck_2018_plik_nolens+sn.pantheon
DOI: 10.5281/zenodo.14839907
title: unimpeded: lcdm bao.sdss_dr16+planck_2018_CamSpec_nolens

description: cosmological model:lcdm, dataset:bao.sdss_dr16+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14839908
title: unimpeded: lcdm bao.sdss_dr16+planck_2018_CamSpec_nolens

description: cosmological model:lcdm, dataset:bao.sdss_dr16+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14839903
title: unimpeded: lcdm bao.sdss_dr16+planck_2018_CamSpec

description: cosmological model:lcdm, dataset:bao.sdss_dr16+planck_2018_CamSpec
DOI: 10.5281/zenodo.14839904
title: unimpeded: lcdm bao.sdss_dr16+planck_2018_CamSpec

description: cosmological model:lcdm, dataset:bao.sdss_dr16+planck_2018_CamSpec
DOI: 10.5281/zenodo.14839915
title: unimpeded: lcdm bao.sdss_dr16+planck_2018_plik_nolens

description: cosmological model:lcdm, dataset:bao.sdss_dr16+planck_2018_plik_nolens
DOI: 10.5281/zenodo.14839916
title: unimpeded: lcdm bao.sdss_dr16+planck_2018_plik_nolens

description: cosmological model:lcdm, dataset:bao.sdss_dr16+planck_2018_plik_nolens
DOI: 10.6084/m9.figshare.28387004
title: IRI-Nuciferine

description: Nuciferine-IRI
DOI: 10.6084/m9.figshare.28387004.v1
title: IRI-Nuciferine

description: Nuciferine-IRI
DOI: 10.5281/zenodo.14839651
title: unimpeded: wlcdm planck_2018_plik+sn.pantheon

description: cosmological model:wlcdm, dataset:planck_2018_plik+sn.pantheon
DOI: 10.5281/zenodo.14839652
title: unimpeded: wlcdm planck_2018_plik+sn.pantheon

description: cosmological model:wlcdm, dataset:planck_2018_plik+sn.pantheon
DOI: 10.6084/m9.figshare.28387073
title: 4DFlowNet-master.zip from Non-invasive Quantification of Pressure Drops in Stenotic Intracranial Vessels – Using Deep-Learning Enhanced 4D Flow MRI to Characterize the Regional Hemodynamics of the Pulsing Brain

description: 4DFlowNet matlab code
DOI: 10.6084/m9.figshare.28387079
title: vel_struct_corr_AHR from Non-invasive Quantification of Pressure Drops in Stenotic Intracranial Vessels – Using Deep-Learning Enhanced 4D Flow MRI to Characterize the Regional Hemodynamics of the Pulsing Brain

description: Aquired high resolution phase MRStruct
DOI: 10.6084/m9.figshare.28387082
title: Supplementary Material from Non-invasive Quantification of Pressure Drops in Stenotic Intracranial Vessels – Using Deep-Learning Enhanced 4D Flow MRI to Characterize the Regional Hemodynamics of the Pulsing Brain

description: vel_struct_corr_AHR.mat
DOI: 10.6084/m9.figshare.28387082.v1
title: Supplementary Material from Non-invasive Quantification of Pressure Drops in Stenotic Intracranial Vessels – Using Deep-Learning Enhanced 4D Flow MRI to Characterize the Regional Hemodynamics of the Pulsing Brain

description: vel_struct_corr_AHR.mat
DOI: 10.6084/m9.figshare.28387085
title: mag_AHR from Non-invasive Quantification of Pressure Drops in Stenotic Intracranial Vessels – Using Deep-Learning Enhanced 4D Flow MRI to Characterize the Regional Hemodynamics of the Pulsing Brain

description: Aquired high resolution Magnitude MRStruct
DOI: 10.6084/m9.figshare.28387088
title: mrstruct_subject_20230919_user054_ALR.zip from Non-invasive Quantification of Pressure Drops in Stenotic Intracranial Vessels – Using Deep-Learning Enhanced 4D Flow MRI to Characterize the Regional Hemodynamics of the Pulsing Brain

description: Aquired low resolution Magnitude and phase MRStruct
DOI: 10.6084/m9.figshare.28387088.v1
title: mrstruct_subject_20230919_user054_ALR.zip from Non-invasive Quantification of Pressure Drops in Stenotic Intracranial Vessels – Using Deep-Learning Enhanced 4D Flow MRI to Characterize the Regional Hemodynamics of the Pulsing Brain

description: Aquired low resolution Magnitude and phase MRStruct
DOI: 10.5281/zenodo.14839665
title: unimpeded: rlcdm des_y1.joint

description: cosmological model:rlcdm, dataset:des_y1.joint
DOI: 10.5281/zenodo.14839666
title: unimpeded: rlcdm des_y1.joint

description: cosmological model:rlcdm, dataset:des_y1.joint
DOI: 10.5281/zenodo.14848181
title: AV-GPS-Dataset

description: Mehrab Abrar has created this dataset, he deserves the credit for the development of this dataset.
DOI: 10.5281/zenodo.14848182
title: AV-GPS-Dataset

description: Mehrab Abrar has created this dataset, he deserves the credit for the development of this dataset.
DOI: 10.5281/zenodo.14840154
title: unimpeded: Nlcdm planck_2018_CamSpec_nolens+sn.pantheon

description: cosmological model:Nlcdm, dataset:planck_2018_CamSpec_nolens+sn.pantheon
DOI: 10.5281/zenodo.14840155
title: unimpeded: Nlcdm planck_2018_CamSpec_nolens+sn.pantheon

description: cosmological model:Nlcdm, dataset:planck_2018_CamSpec_nolens+sn.pantheon
DOI: 10.5281/zenodo.14839761
title: unimpeded: mlcdm bao.sdss_dr16+bicep_keck_2018

description: cosmological model:mlcdm, dataset:bao.sdss_dr16+bicep_keck_2018
DOI: 10.5281/zenodo.14839762
title: unimpeded: mlcdm bao.sdss_dr16+bicep_keck_2018

description: cosmological model:mlcdm, dataset:bao.sdss_dr16+bicep_keck_2018
DOI: 10.5281/zenodo.14848085
title: CDC ATSDR Social Vulnerability Index (SVI) 2022 US archived October 28 2024

description: This is a preserved copy of the CDC ATSDR Social Vulnerability Index for 2022 (the most recent version) which was downloaded originally on 28-October-2024. The original dataset, documentation, and website were removed from the CDC website on 31-January-2025. This dataset compares SVI between census tracts across the whole USA. The file format is a .zip compressed geodatabase (.gdb) with census tract polygons that appear to use the US Census cartographic boundary file (rather than US Census Tiger/Line).

We created a comma separated value dataset from the geodatabase which can be easily used by merging census tract FIPS codes.

The overall SVI percentile ranking is stored in the variable "RPL_THEMES" as per the included documentation.

The documentation was originally on the CDC's website and was downloaded separately from Internet Archive (accessed on 11-February-2025): https://web.archive.org/web/20240926211307/https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI-2022-Documentation-H.pdf .
DOI: 10.5281/zenodo.14848086
title: CDC ATSDR Social Vulnerability Index (SVI) 2022 US archived October 28 2024

description: This is a preserved copy of the CDC ATSDR Social Vulnerability Index for 2022 (the most recent version) which was downloaded originally on 28-October-2024. The original dataset, documentation, and website were removed from the CDC website on 31-January-2025. This dataset compares SVI between census tracts across the whole USA. The file format is a .zip compressed geodatabase (.gdb) with census tract polygons that appear to use the US Census cartographic boundary file (rather than US Census Tiger/Line).

We created a comma separated value dataset from the geodatabase which can be easily used by merging census tract FIPS codes.

The overall SVI percentile ranking is stored in the variable "RPL_THEMES" as per the included documentation.

The documentation was originally on the CDC's website and was downloaded separately from Internet Archive (accessed on 11-February-2025): https://web.archive.org/web/20240926211307/https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI-2022-Documentation-H.pdf .
DOI: 10.5281/zenodo.14839763
title: unimpeded: rlcdm bicep_keck_2018+planck_2018_CamSpec

description: cosmological model:rlcdm, dataset:bicep_keck_2018+planck_2018_CamSpec
DOI: 10.5281/zenodo.14839764
title: unimpeded: rlcdm bicep_keck_2018+planck_2018_CamSpec

description: cosmological model:rlcdm, dataset:bicep_keck_2018+planck_2018_CamSpec
DOI: 10.5281/zenodo.14839771
title: unimpeded: rlcdm bicep_keck_2018+planck_2018_CamSpec_nolens

description: cosmological model:rlcdm, dataset:bicep_keck_2018+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14839772
title: unimpeded: rlcdm bicep_keck_2018+planck_2018_CamSpec_nolens

description: cosmological model:rlcdm, dataset:bicep_keck_2018+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14839775
title: unimpeded: rlcdm bicep_keck_2018+planck_2018_plik

description: cosmological model:rlcdm, dataset:bicep_keck_2018+planck_2018_plik
DOI: 10.5281/zenodo.14839776
title: unimpeded: rlcdm bicep_keck_2018+planck_2018_plik

description: cosmological model:rlcdm, dataset:bicep_keck_2018+planck_2018_plik
DOI: 10.5281/zenodo.14839779
title: unimpeded: mlcdm bao.sdss_dr16+sn.pantheon

description: cosmological model:mlcdm, dataset:bao.sdss_dr16+sn.pantheon
DOI: 10.5281/zenodo.14839780
title: unimpeded: mlcdm bao.sdss_dr16+sn.pantheon

description: cosmological model:mlcdm, dataset:bao.sdss_dr16+sn.pantheon
DOI: 10.6084/m9.figshare.28386995
title: Data for HVRM_model

description: The scene modeling and simulation data is included in Data.zip.
DOI: 10.5281/zenodo.14839787
title: unimpeded: rlcdm bicep_keck_2018+planck_2018_plik_nolens

description: cosmological model:rlcdm, dataset:bicep_keck_2018+planck_2018_plik_nolens
DOI: 10.5281/zenodo.14839788
title: unimpeded: rlcdm bicep_keck_2018+planck_2018_plik_nolens

description: cosmological model:rlcdm, dataset:bicep_keck_2018+planck_2018_plik_nolens
DOI: 10.57903/uj/lnzgxm
title: Reviewing published and grey literature in local European languages to supplement existing databases on floral resource traits - research data

description: The dataset was collected during the realisation of the Better-B project (https://www.better-b.eu/). The main objective was to complement the already published dataset on floral resources of plant species by searching the published and grey literature in European local languages. The data collected in 11 languages focus on plant phenology, nectar, sugar and pollen production. The dataset contains information on over 1000 species in total, of which 541 species are new to the dataset compared to the previous study.
DOI: 10.5281/zenodo.14839821
title: unimpeded: mlcdm bicep_keck_2018+planck_2018_CamSpec

description: cosmological model:mlcdm, dataset:bicep_keck_2018+planck_2018_CamSpec
DOI: 10.5281/zenodo.14839822
title: unimpeded: mlcdm bicep_keck_2018+planck_2018_CamSpec

description: cosmological model:mlcdm, dataset:bicep_keck_2018+planck_2018_CamSpec
DOI: 10.5281/zenodo.14839833
title: unimpeded: mlcdm bicep_keck_2018+planck_2018_plik

description: cosmological model:mlcdm, dataset:bicep_keck_2018+planck_2018_plik
DOI: 10.5281/zenodo.14839834
title: unimpeded: mlcdm bicep_keck_2018+planck_2018_plik

description: cosmological model:mlcdm, dataset:bicep_keck_2018+planck_2018_plik
DOI: 10.5281/zenodo.14839837
title: unimpeded: rlcdm planck_2018_CamSpec_nolens+sn.pantheon

description: cosmological model:rlcdm, dataset:planck_2018_CamSpec_nolens+sn.pantheon
DOI: 10.5281/zenodo.14839838
title: unimpeded: rlcdm planck_2018_CamSpec_nolens+sn.pantheon

description: cosmological model:rlcdm, dataset:planck_2018_CamSpec_nolens+sn.pantheon
DOI: 10.5066/p9io8azj
title: Walrus Haulout and In-water Activity Levels Relative to Vessel Interactions in the Chukchi Sea, 2012-2015

description: These data were used to evaluate effects of vessel exposure on Pacific walrus (Odobenus rosmarus divergens) behaviors. We obtained greater than 120,000 hours of location and behavior (foraging, in-water not foraging, hauled out) data from 218 satellite-tagged walruses and linked them to vessel locations from the marine Automated Information System. This yielded 206 vessel-exposed walrus telemetry hours for comparison to unexposed hours which we used to assess if vessel exposure altered walrus behavior.
DOI: 10.5281/zenodo.14839843
title: unimpeded: nrunlcdm planck_2018_plik_nolens+sn.pantheon

description: cosmological model:nrunlcdm, dataset:planck_2018_plik_nolens+sn.pantheon
DOI: 10.5281/zenodo.14839844
title: unimpeded: nrunlcdm planck_2018_plik_nolens+sn.pantheon

description: cosmological model:nrunlcdm, dataset:planck_2018_plik_nolens+sn.pantheon
DOI: 10.5281/zenodo.14839839
title: unimpeded: rlcdm planck_2018_plik+sn.pantheon

description: cosmological model:rlcdm, dataset:planck_2018_plik+sn.pantheon
DOI: 10.5281/zenodo.14839840
title: unimpeded: rlcdm planck_2018_plik+sn.pantheon

description: cosmological model:rlcdm, dataset:planck_2018_plik+sn.pantheon
DOI: 10.34740/kaggle/dsv/10719123
title: cypto_data
DOI: 10.17615/m1hk-x591
title: Coding of Global Fishing Watch Research Articles (2016-2020)

description: <p>New data technologies are taking center stage in environmental governance for their potential to illuminate environmental challenges and inform policy and management directions. They hold particular promise in the oceans where ecological and human activities have long been difficult to see and know. In this piece, we turn attention to an ocean data science initiative (ODSI) that produces new data technologies with an aim of enhancing understanding of the role of such bodies in defining and experimenting with the relationship among problem framings, data mobilization, and policy and management recommendations in an era of data-driven environmental governance. The work archived here is the dataset that emerged from our analysis of the first phase research program of Global Fishing Watch (GFW), a high profile organization innovating new data technologies related to the movement of industrial fishing vessels. We review research publications GFW researchers and affiliates published between 2016-2020 and examine what concerns GFW researchers seek to address, how they develop and mobilize GFW data analysis techniques to illuminate or better understand those concerns, and the policy and management possibilities that they suggest based on findings. This dataset comprises the results of coding of GFW papers. Coding results were used for analysis that contributes to understanding organizations that work with data with a goal of improving conditions in the oceans as oceans governance actions.</p>
DOI: 10.6084/m9.figshare.28382129
title: Supplementary Material For The Paper "Can LLMs Reason About Trust?"

description: This figshare repository contains the supplementary materials and results relevant to the paper.
DOI: 10.34740/kaggle/dsv/10713302
title: California Grape
DOI: 10.34740/kaggle/dsv/10719150
title: cypto_data
DOI: 10.5281/zenodo.14839605
title: unimpeded: klcdm des_y1.joint+planck_2018_CamSpec_nolens

description: cosmological model:klcdm, dataset:des_y1.joint+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14839606
title: unimpeded: klcdm des_y1.joint+planck_2018_CamSpec_nolens

description: cosmological model:klcdm, dataset:des_y1.joint+planck_2018_CamSpec_nolens
DOI: 10.5281/zenodo.14839819
title: unimpeded: rlcdm des_y1.joint+planck_2018_plik_nolens

description: cosmological model:rlcdm, dataset:des_y1.joint+planck_2018_plik_nolens
DOI: 10.5281/zenodo.14839820
title: unimpeded: rlcdm des_y1.joint+planck_2018_plik_nolens

description: cosmological model:rlcdm, dataset:des_y1.joint+planck_2018_plik_nolens
DOI: 10.5281/zenodo.14839767
title: unimpeded: mlcdm bao.sdss_dr16+des_y1.joint

description: cosmological model:mlcdm, dataset:bao.sdss_dr16+des_y1.joint
DOI: 10.5281/zenodo.14839768
title: unimpeded: mlcdm bao.sdss_dr16+des_y1.joint

description: cosmological model:mlcdm, dataset:bao.sdss_dr16+des_y1.joint
DOI: 10.5281/zenodo.14847681
title: Data set for the paper entitled "Impact of an earthquake event on submarine groundwater discharge in Jeju Island, Korea"
DOI: 10.5281/zenodo.14847883
title: Impact of electron cyclotron wave resonance plasma on defect reduction in ZnO thin films

description: A detailed description of the files related to each image with graphs is included in README.pdf and README.txt. All data were measured and calculated as outlined in the manuscript, with no additional non-standard data filtering applied. Measurement errors are indicated in the graphs and discussed in the manuscript, with references provided.
DOI: 10.5281/zenodo.14847884
title: Impact of electron cyclotron wave resonance plasma on defect reduction in ZnO thin films

description: A detailed description of the files related to each image with graphs is included in README.pdf and README.txt. All data were measured and calculated as outlined in the manuscript, with no additional non-standard data filtering applied. Measurement errors are indicated in the graphs and discussed in the manuscript, with references provided.
DOI: 10.5281/zenodo.14839719
title: unimpeded: nrunlcdm des_y1.joint+sn.pantheon

description: cosmological model:nrunlcdm, dataset:des_y1.joint+sn.pantheon
DOI: 10.5281/zenodo.14839720
title: unimpeded: nrunlcdm des_y1.joint+sn.pantheon

description: cosmological model:nrunlcdm, dataset:des_y1.joint+sn.pantheon
DOI: 10.5281/zenodo.14847680
title: Data set for the paper entitled "Impact of an earthquake event on submarine groundwater discharge in Jeju Island, Korea"
DOI: 10.5281/zenodo.14839729
title: unimpeded: rlcdm des_y1.joint+sn.pantheon

description: cosmological model:rlcdm, dataset:des_y1.joint+sn.pantheon
DOI: 10.5281/zenodo.14839730
title: unimpeded: rlcdm des_y1.joint+sn.pantheon

description: cosmological model:rlcdm, dataset:des_y1.joint+sn.pantheon
DOI: 10.5281/zenodo.14844272
title: UGV_platform_sensor_dataset_VIAS_2nd collection round [2]

description:  







Description



The dataset contains RGB-D image streams and 3D LIDAR point cloud gathered in the second visit to VIAS metro facilities. 3 Intel D-435f and LIVOX 3D LIDAR were mounted on wheeled platform mounted in rails. To visualize the gathered data in RVIZ follow the next steps:



Launch roscore:  $ roscore

Publish static livox frame:  $ rosrun tf static_transform_publisher 0 0 0 0 0 0 1 camera1_link livox_frame 100

Play in loop desired rosbag:  $ rosbag play 2024-09-17-XX-XX-XX_X.bag -l 

Launch RVIZ and open vias.rviz config in settings: $ rviz


The following topics have been recorded on the bag files:

Intel camera looking at rail (bolts and clips) is camera 3.



/camera3/aligned_depth_to_color/camera_info

/camera3/aligned_depth_to_color/image_raw

/camera3/aligned_depth_to_color/image_raw/compressed

/camera3/aligned_depth_to_color/image_raw/compressedDepth

/camera3/color/camera_info

/camera3/color/image_raw

/camera3/color/image_raw/compressed

/camera3/color/metadata

/camera3/depth/camera_info

/camera3/depth/image_rect_raw

/camera3/depth/image_rect_raw/compressed

/camera3/depth/image_rect_raw/compressedDepth

/camera3/depth/metadata

/camera3/realsense2_camera_manager/bond


Point-cloud data from LIVOX 3D Lidar can be seen in: 



/livox/imu

/livox/lidar