cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25625/8fqzzf/edz5x5
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/elu2ar
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/id676c
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/x8jaqp
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/sypesi
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/h8xqbw
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/g4jchk
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/nkvvml
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/usul6s
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/u5jbe0
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/dxxymg
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/m1jszq
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/vxgr7j
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/fmq17h
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/kfkibw
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/wed9jc
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/fftrcs
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/ylhknu
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/evgoai
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/y38pan
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/fizwuv
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/1cnxoo
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/alvygo
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/1ialyl
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/89viw7
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/d9rdci
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/f1uzff
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/z2abuf
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/j9fkyv
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/jglfdk
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/tm8qm3
title: log_sensor_pos.tab

description: :unav
DOI: 10.6084/m9.figshare.30530837
title: The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs

description: This article introduces a framework for Bayesian Optimization (BO) with metric movement costs, addressing a critical challenge in practical applications where input alterations incur varying costs. Our approach is a convenient plug-in that seamlessly integrates with the existing literature on batched algorithms, where designs within batches are observed following the solution of a Traveling Salesman Problem. The proposed method provides a theoretical guarantee of convergence in terms of movement costs for BO. Empirically, our method effectively reduces average movement costs over time while maintaining comparable regret performance to conventional BO methods. This framework also shows promise for broader applications in various bandit settings with movement costs.
DOI: 10.6084/m9.figshare.30530837.v1
title: The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs

description: This article introduces a framework for Bayesian Optimization (BO) with metric movement costs, addressing a critical challenge in practical applications where input alterations incur varying costs. Our approach is a convenient plug-in that seamlessly integrates with the existing literature on batched algorithms, where designs within batches are observed following the solution of a Traveling Salesman Problem. The proposed method provides a theoretical guarantee of convergence in terms of movement costs for BO. Empirically, our method effectively reduces average movement costs over time while maintaining comparable regret performance to conventional BO methods. This framework also shows promise for broader applications in various bandit settings with movement costs.
DOI: 10.25625/8fqzzf/k6mtmz
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/vqxa2y
title: log_gt_positions.tab

description: :unav
DOI: 10.6084/m9.figshare.30347514
title: Monitoring and Diagnosis for Multi-Mode Processes with Varying Operating Parameters: A Covariate-Adjusted Mixture Bayesian Network Approach

description: Process monitoring and diagnosis are crucial in many industrial applications for the timely detection of anomalies and the identification of responsible process variables. Over the past decade, Bayesian network-based methods have attracted increasing interest due to their diagnostic capabilities and compatibility with multivariate control charts. However, existing methods fail to account for process heterogeneity caused by both unobserved operational modes and observed operating parameters. To address this issue, we propose the Covariate-adjusted Mixture Bayesian Network (CaMBN) model. It uses a nonparametric mixture model to capture heterogeneous correlation structures among process variables across different operational modes, while incorporating covariates to adjust in-control parameters. A Generalized Expectation-Maximization (GEM) algorithm is further proposed for model estimation. Based on this model, a likelihood ratio-based chart and a Bayesian inference-based diagnosis procedure are proposed. The effectiveness of the proposed method is demonstrated through comprehensive numerical studies and real-world data applications to harbor tug engines. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.30347514.v2
title: Monitoring and Diagnosis for Multi-Mode Processes with Varying Operating Parameters: A Covariate-Adjusted Mixture Bayesian Network Approach

description: Process monitoring and diagnosis are crucial in many industrial applications for the timely detection of anomalies and the identification of responsible process variables. Over the past decade, Bayesian network-based methods have attracted increasing interest due to their diagnostic capabilities and compatibility with multivariate control charts. However, existing methods fail to account for process heterogeneity caused by both unobserved operational modes and observed operating parameters. To address this issue, we propose the Covariate-adjusted Mixture Bayesian Network (CaMBN) model. It uses a nonparametric mixture model to capture heterogeneous correlation structures among process variables across different operational modes, while incorporating covariates to adjust in-control parameters. A Generalized Expectation-Maximization (GEM) algorithm is further proposed for model estimation. Based on this model, a likelihood ratio-based chart and a Bayesian inference-based diagnosis procedure are proposed. The effectiveness of the proposed method is demonstrated through comprehensive numerical studies and real-world data applications to harbor tug engines. Supplementary materials for this article are available online.
DOI: 10.25625/8fqzzf/pdxuvv
title: log_sensor_pos.tab

description: :unav
DOI: 10.6084/m9.figshare.30347514.v1
title: Monitoring and Diagnosis for Multi-mode Processes with Varying Operating Parameters: A Covariate-adjusted Mixture Bayesian Network Approach

description: Process monitoring and diagnosis are crucial in many industrial applications for the timely detection of anomalies and the identification of responsible process variables. Over the past decade, Bayesian network-based methods have attracted increasing interest due to their diagnostic capabilities and compatibility with multivariate control charts. However, existing methods fail to account for process heterogeneity caused by both unobserved operational modes and observed operating parameters. To address this issue, we propose the Covariate-adjusted Mixture Bayesian Network (CaMBN) model. It utilizes a nonparametric mixture model to capture heterogeneous correlation structures among process variables across different operational modes, while incorporating covariates to adjust in-control parameters. A Generalized Expectation-Maximization (GEM) algorithm is further proposed for model estimation. Based on this model, a likelihood ratio-based chart and a Bayesian inference-based diagnosis procedure are proposed. The effectiveness of the proposed method is demonstrated through comprehensive numerical studies and real-world data applications to harbour tug engines. Supplementary materials for this article are available online.
DOI: 10.25625/8fqzzf/biwnoy
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/q5yzhy
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/xyhclc
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/pz6uhu
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/kqxonb
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/qrxqfl
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/0bm632
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/dfust4
title: log_sensor_pos.tab

description: :unav
DOI: 10.5281/zenodo.17778843
title: Seafloor Topography Predicted from SWOT Gravity Data by Deep Neural Network in the Northwestern Pacific Ocean

description: This study combines SWOT marine gravity field data with single-beam shipborne depth to construct seafloor topography models in the Northwestern Pacific using the deep neural network method (DNN)

===========================================Preprocesss-Shipborne_depth

DATA: singlbeam.xyz multibeam.xyz checkpoints.xyz

SWOT_03:https://topex.ucsd.edu/pub/GEBCO_2024/GEBCO_08:https://www.gebco.net/

===========================================DNN

CODE:deep neural network.pyDATA:Training_Dataset_SWOT.txt Ind.txt sea.zip===========================================Bathymetry model

GGM_SWOT SAS_SWOT DNN_SWOT
DOI: 10.25625/8fqzzf/ont7fa
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/fzfrne
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/rt9zj9
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/rhhrqj
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/vct1yv
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/rojjya
title: log_sensor_pos.tab

description: :unav
DOI: 10.5281/zenodo.17778842
title: Seafloor Topography Predicted from SWOT Gravity Data by Deep Neural Network in the Northwestern Pacific Ocean

description: This study combines SWOT marine gravity field data with single-beam shipborne depth to construct seafloor topography models in the Northwestern Pacific using the deep neural network method (DNN)

===========================================Preprocesss-Shipborne_depth

DATA: singlbeam.xyz multibeam.xyz checkpoints.xyz

SWOT_03:https://topex.ucsd.edu/pub/GEBCO_2024/GEBCO_08:https://www.gebco.net/

===========================================DNN

CODE:deep neural network.pyDATA:Training_Dataset_SWOT.txt Ind.txt sea.zip===========================================Bathymetry model

GGM_SWOT SAS_SWOT DNN_SWOT
DOI: 10.25625/8fqzzf/g4r3yh
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/lmq7o6
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/ui8dfm
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/zwohd6
title: log_tracks.tab

description: :unav
DOI: 10.17026/ar/9wydwi
title: PAN-00104124 - coin/coin-related, Marcus Aurelius (161-180), voor Faustina II, dupondius/as

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00104124&lt;/p&gt;
description: Modified: 2025-04-28T10:48:32+02:00
DOI: 10.25625/8fqzzf/2ieim0
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/lfefyl
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/hwe6yi
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/luevuw
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/rk1bth
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/8jwcrs
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/wyvvqn
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/eqtfje
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/zygimu
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/dituhy
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/3fuipx
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/b2n5us
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/gtgvsa
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/5oge9c
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/zolq9y
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/hr053p
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/zzbd02
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/dztwdh
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/1lvje2
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/pqwrar
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/ymaqj2
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/cvjvm2
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/sgkcgi
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/oyapcu
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/gsophp
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/sh86l7
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/okfbm0
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/h6pyrt
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/yceigi
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/qrbilf
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/95ngpv
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/2dsdxr
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/2mp5fn
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/m5fdso
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/td86ec
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/drv6yw
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/evy7lz
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/48qp21
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/ueowqd
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/kv25fl
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/zzircn
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/iage1l
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/xeruzk
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/wiqntu
title: log_gt_positions.tab

description: :unav
DOI: 10.82201/sbcbmh
title: AI & Law Basic Principles & EU AI Act

description: This presentation tackles the crucial question of accountability when machines make decisions. It uses real-world examples, like the UK Post Office Horizon scandal, to highlight the risks of unmanaged AI. The core of the presentation introduces the EU AI Act, explaining its goal to mitigate risks to fundamental rights while promoting responsible AI. It breaks down the Act's risk-based system (prohibited, high, limited, and minimal risk) and details the key requirements it places on developers and users, focusing heavily on human oversight, transparency (no black box), and compliance with data privacy laws like GDPR.
DOI: 10.25625/8fqzzf/digtkz
title: log_gt_positions.tab

description: :unav
DOI: 10.17909/zq4g-r525
title: GO-3073 Second Pointing

description: Second pointing of NIRSpec observations of the Cycle 2 program GO-3073. Observed on 2024, July 3-4
DOI: 10.25625/8fqzzf/kbl9lz
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/mwzolr
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/lkbcnc
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/0y9sj0
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/o8mch8
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/6cnf2d
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/g7ey7m
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/dozqp7
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/7cf75n
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/j91bfb
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/4niaxf
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/81ju1p
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/r7xwuk
title: log_sensor_pos.tab

description: :unav
DOI: 10.6084/m9.figshare.30132502
title: oddball-paradigm-in-the-auditory-cortex-an-open-dataset

description: This repository contains open datasets of neural recordings from the auditory cortex of rats, obtained during experiments using the oddball paradigm. The data aims to explore how the brain processes predictable versus unexpected auditory stimuli.Subjects<b>Species</b>: <i>Rattus norvegicus</i> (Sprague-Dawley)<b>Sample</b>: 37 rats<b>Ethics</b>: Approved per University of Salamanca Animal Care Committee and EU Directive 2010/63/EU<b>Anesthesia</b>: induced and maintained with urethane (1.5 g/kg, intraperitoneal), with additional doses as needed. Dexamethasone and atropine sulfate were administered at the start of surgery to reduce brain edema and bronchial secretions.<b>Acetylcholine infusion</b>: One of the barrels was filled with saline solution for current compensation (165 mM NaCl) whereas the other barrels were filled with 1 M acetylcholine chloride (Sigma, catalog no. A6625) as a concentration previously used in similar electrophysiological studies (Ayala & Malmierca, 2015; Farley et al., 1983; Habbicht & Vater, 1996). Drugs were retained by applying a -15 nA current, and were ejected when required, typically using 30–40 nA currents for 8–10 minutes, until an effect was observed, using a microiontophoresis apparatus (Neurophore BH- 2 System, Harvard Apparatus).<br>
DOI: 10.6084/m9.figshare.30132502.v6
title: oddball-paradigm-in-the-auditory-cortex-an-open-dataset

description: This repository contains open datasets of neural recordings from the auditory cortex of rats, obtained during experiments using the oddball paradigm. The data aims to explore how the brain processes predictable versus unexpected auditory stimuli.Subjects<b>Species</b>: <i>Rattus norvegicus</i> (Sprague-Dawley)<b>Sample</b>: 37 rats<b>Ethics</b>: Approved per University of Salamanca Animal Care Committee and EU Directive 2010/63/EU<b>Anesthesia</b>: induced and maintained with urethane (1.5 g/kg, intraperitoneal), with additional doses as needed. Dexamethasone and atropine sulfate were administered at the start of surgery to reduce brain edema and bronchial secretions.<b>Acetylcholine infusion</b>: One of the barrels was filled with saline solution for current compensation (165 mM NaCl) whereas the other barrels were filled with 1 M acetylcholine chloride (Sigma, catalog no. A6625) as a concentration previously used in similar electrophysiological studies (Ayala & Malmierca, 2015; Farley et al., 1983; Habbicht & Vater, 1996). Drugs were retained by applying a -15 nA current, and were ejected when required, typically using 30–40 nA currents for 8–10 minutes, until an effect was observed, using a microiontophoresis apparatus (Neurophore BH- 2 System, Harvard Apparatus).<br>
DOI: 10.25625/8fqzzf/bwomyq
title: log_tracks.tab

description: :unav
DOI: 10.6084/m9.figshare.30132502.v5
title: oddball-paradigm-in-the-auditory-cortex-an-open-dataset

description: This repository contains open datasets of neural recordings from the auditory cortex of rats, obtained during experiments using the oddball paradigm. The data aims to explore how the brain processes predictable versus unexpected auditory stimuli.Subjects<b>Species</b>: <i>Rattus norvegicus</i> (Sprague-Dawley)<b>Sample</b>: 37 rats<b>Ethics</b>: Approved per University of Salamanca Animal Care Committee and EU Directive 2010/63/EU<b>Anesthesia</b>: induced and maintained with urethane (1.5 g/kg, intraperitoneal), with additional doses as needed. Dexamethasone and atropine sulfate were administered at the start of surgery to reduce brain edema and bronchial secretions.<br>
DOI: 10.25625/8fqzzf/ilx68t
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/i8atha
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/k5ven3
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/wxlkzo
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/5v506p
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/ay1kfs
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/howwnl
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/xorzh6
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/aj9mej
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/nfrrky
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/kutowi
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/llogbb
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/smgxfx
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/fjm2f1
title: log_sensor_pos.tab

description: :unav
DOI: 10.13140/rg.2.2.29217.06243
title: Investigation of the WMS standard for current raster image formats and alternatives
DOI: 10.25625/8fqzzf/gzc3gg
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/xmpd2h
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/zelrmj
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/m39por
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/18im6b
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/hvsjdh
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/xdocdp
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/rmi0c4
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/4crbzs
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/dacfkd
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/fmhohc
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/jqkumz
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/ol1hxh
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/9ylf2k
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/hlt1ax
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/ht3b4w
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/iynvhy
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/nldr1w
title: log_tracks.tab

description: :unav
DOI: 10.25625/8fqzzf/fmqeqz
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/zp2rux
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/igccdw
title: log_gt_positions.tab

description: :unav
DOI: 10.25625/8fqzzf/tfpl6o
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf/3k6inz
title: log_sensor_pos.tab

description: :unav
DOI: 10.25625/8fqzzf
title: Track-to-track association in collective perception

description: Dataset for track-to-track association in a collective perception scenario with central road side unit (RSU). The dataset was created using the artery simulator (https://artery.v2x-research.eu/).
DOI: 10.15468/dl.8axdxr
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-73.80108 -38.28557,-71.76219 -38.28557,-71.76219 -37.27923,-73.80108 -37.27923,-73.80108 -38.28557))",
 "TaxonKey is Pleurodema thaul (Schneider, 1799)"
 ]
}

 The dataset includes 24 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0337757-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.