cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6344454
title: Strateole-2 data set associated to the publication "A seismic network in the stratosphere"

description: NetCDF files of the pressure and temperature data of TSEN sensors, and GPS coordinates, on board EUROS gondolas of Strateole-2 project (stratospheric balloons deployed during fall 2021). One file per gondola, associated to the 4 balloons detecting the Flores quake (2021/12/14 3:20:35.8 GMT) and to a single balloon detecting the Northern Peru quake (2021/11/28 10:52:25.8 GMT). These data cover one hour before and 2 hours after the quake. The rest of the Strateole-2 data will be released by the project. This SUbset is associated to the publication "A seismic network in the stratosphere.
DOI: 10.5281/zenodo.6344453
title: Strateole-2 data set associated to the publication "A seismic network in the stratosphere"

description: NetCDF files of the pressure and temperature data of TSEN sensors, and GPS coordinates, on board EUROS gondolas of Strateole-2 project (stratospheric balloons deployed during fall 2021). One file per gondola, associated to the 4 balloons detecting the Flores quake (2021/12/14 3:20:35.8 GMT) and to a single balloon detecting the Northern Peru quake (2021/11/28 10:52:25.8 GMT). These data cover one hour before and 2 hours after the quake. The rest of the Strateole-2 data will be released by the project. This SUbset is associated to the publication "A seismic network in the stratosphere.
DOI: 10.6084/m9.figshare.19207026
title: Data

description: Microbiome and ALSFRS-R
DOI: 10.6084/m9.figshare.19207026.v3
title: Data

description: Microbiome and ALSFRS-R
DOI: 10.6084/m9.figshare.19207026.v2
title: Data

description: Microbiome and ALSFRS-R
DOI: 10.6084/m9.figshare.14604087.v3
title: hypothyroidism during pregnancy and SIBO

description: 妊娠期甲状腺功能低下和SIBO
DOI: 10.5281/zenodo.6344485
title: Pollen metabarcoding 2020 Data per bee species and plot

description: Pollen identified through metabarcoding from neeting bees 2020
DOI: 10.5281/zenodo.6344484
title: Pollen metabarcoding 2020 Data per bee species and plot

description: Pollen identified through metabarcoding from neeting bees 2020
DOI: 10.5281/zenodo.6344227
title: Mormyrops boulengeri decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6344226
title: Mormyrops boulengeri decontaminated FSCR

description: .
DOI: 10.5281/zenodo.6343571
title: Global maps of soil water characteristics parameters developed using the random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution

description: The global soil water characteristics parameters (<em>α, n, θ<sub>r</sub>, and θ<sub>s</sub></em>) maps based on van Genuchten (vG) model at 1 km resolution was developed by harnessing the technological advances in machine learning and availability of remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. We merge concepts of predictive soil mapping with a large data set of vG parameters and local information (soil, vegetation, climate) into "Covariate-based GeoTransfer Functions'' (CoGTFs) to generate global estimates of vG parameters (to highlight the impact of Geo-referenced covariates including various remote sensing maps, we use the term Geotransfer Function GTF and not pedotransfer function PTF; in the latter case, typically only soil properties are used to predict vG parameters). The vG parameters (<em>α, n, θ<sub>r</sub>, and θ<sub>s</sub></em>) dataset is provided in GeoTIFF format. A total of 16 files that represent different soil depths (0, 30, 60, and 100 cm) are provided for each parameter. Description of vG parameters and their units vG Parameters Description units <em>α</em> Inverse air entry pressure Log<sub>10</sub><em>α (m<sup>-1</sup>)</em> <em>n</em> Shape parameter Log<sub>10</sub><em>n </em>(dimensionless) <em>θ<sub>r</sub></em> Residual water content m<sup>3</sup>/m<sup>3</sup> <em>θ<sub>s</sub></em> Saturated water content m<sup>3</sup>/m<sup>3</sup> The Global vG training dataset used for this study is available here: https://doi.org/10.5281/zenodo.5547338 For more details / to cite this dataset please use: Gupta, S., Papritz, A., Lehmann, P., Hengl, T., Bonetti, S., and Or, D., (2020): Global mapping of soil water characteristics parameters – fusing curated data with machine learning and environmental covariates”. Manuscript submitted to <strong>Remote Sensing.</strong> The study was supported by ETH Zurich (Grant ETH-18 18-1). We thank Zhongwang Wei, Associate professor at Sun Yat-Sen University, for helping to collect the datasets and for insightful discussions. We would like to thank Andrea Carmintai, Professor at ETH Zurich, for the insightful discussions.
DOI: 10.15129/9c893711-a273-42f5-960c-33c5daa0ab24
title: Data for: "Three-Dimensional Imaging of Pharmaceutical Tablets Using Serial Sectioning and Raman Chemical Mapping"

description: Figure 3: This dataset contains Raman spectra and scanning electron micrographs of the raw materials (MCC and AmberLite™ Na Ion Exchange Resin) in the format of SPC and TIF, respectively. The Raman spectra was treated with cosmic ray removal and baseline correction. Figure 4, 5, S1: This dataset contains Raman chemical maps of the MCC: AmberLite™ Na Ion Exchange Resin compact obtained at 20 µm depth increments into the sample. The data was pre-processed with cosmic ray removal and baseline correction and are in the format of SPC. Figure S2: This dataset contains Raman spectra and scanning electron micrographs of the raw materials (MCC, saccharin and eletriptan hydrobromide) in the format of SPC and TIF, respectively. The Raman spectra was treated with cosmic ray removal and baseline correction. Figure 6, 7, S8: This dataset contains Raman chemical maps of the 3-component model system compact (MCC: saccharin: eletriptan HBr 0.80: 0.15: 0.05) obtained at 20 µm depth increments into the sample. The data was pre-processed with cosmic ray removal and baseline correction and are in the format of SPC. Data embargo until 31/08/22 or on publication of the related paper
DOI: 10.5281/zenodo.6334771
title: Dataset of self-management recommendations

description: This dataset describes a list of self-management recommendations for older patients with multimorbidity and cancer, for dealing with common symptoms related to cancer treatment as well as general recommendations for healthy ageing.
DOI: 10.5281/zenodo.6344512
title: 3D Pigeon Pictures

description: 1000 photos of different angles of a pigeon in real size printed in 3D
DOI: 10.5281/zenodo.6344511
title: 3D Pigeon Pictures

description: 1000 photos of different angles of a pigeon in real size printed in 3D
DOI: 10.5281/zenodo.6342646
title: Dataset of symptoms and proms for specific cancer types and gender

description: This dataset describes a series of symptoms, potentially indicative of treatment-related complications, destabilised comorbidity or functional decline, to be used in the Geronte project for symptoms monitoring in older patients with multimorbidity during and after their cancer treatment
DOI: 10.5281/zenodo.6344388
title: Analysis of references in the IPCC AR6 WG2 Report of 2022

description: This repository contains data on 17,419 DOIs cited in the IPCC Working Group 2 contribution to the Sixth Assessment Report, and the code to link them to the dataset built at the Curtin Open Knowledge Initiative (COKI). References were extracted from the report's PDFs (downloaded 2022-03-01) via Scholarcy and exported as RIS and BibTeX files. DOI strings were identified from RIS files by pattern matching and saved as CSV file. The list of DOIs for each chapter and cross chapter paper was processed using a custom Python script to generate a pandas DataFrame which was saved as CSV file and uploaded to Google Big Query. We used the main object table of the Academic Observatory, which combines information from Crossref, Unpaywall, Microsoft Academic, Open Citations, the Research Organization Registry and Geonames to enrich the DOIs with bibliographic information, affiliations, and open access status. A custom query was used to join and format the data and the resulting table was visualised in a Google DataStudio dashboard.<br> <br> This version of the repository also includes the set of DOIs from references in the IPCC Working Group 1 contribution to the Sixth Assessment Report as extracted by Alexis-Michel Mugabushaka and shared on Zenodo: https://doi.org/10.5281/zenodo.5475442 (CC-BY) A brief descriptive analysis was provided as a blogpost on the COKI website. <strong>The repository contains the following content:</strong> Data: <strong>data/scholarcy/RIS/</strong> - extracted references as RIS files <strong>data/scholarcy/BibTeX/</strong> - extracted references as BibTeX files <strong>IPCC_AR6_WGII_dois.csv</strong> - list of DOIs <strong>data/10.5281_zenodo.5475442/</strong> - references from IPCC AR6 WG1 report Processing: <strong>preprocessing.R</strong> - preprocessing steps for identifying and cleaning DOIs <strong>process.py</strong> - Python script for transforming data and linking to COKI data through Google Big Query Outcomes: Dataset on BigQuery - requires a google account for access and bigquery account for querying Data Studio Dashboard - interactive analysis of the generated data Zotero library of references extracted via Scholarcy <strong>PDF version of blogpost</strong> <strong>Note on licenses:</strong> <br> Data are made available under CC0 (with the exception of WG1 reference data, which have been shared under CC-BY 4.0)<br> Code is made available under Apache License 2.0
description: Archived version of Release 2022-03-10 of GitHub repository: https://github.com/Curtin-Open-Knowledge-Initiative/ipcc-ar6
DOI: 10.5281/zenodo.6327206
title: Analysis of references in the IPCC AR6 WG2 Report of 2022

description: This repository contains data on 17,419 DOIs cited in the IPCC Working Group 2 contribution to the Sixth Assessment Report, and the code to link them to the dataset built at the Curtin Open Knowledge Initiative (COKI). References were extracted from the report's PDFs (downloaded 2022-03-01) via Scholarcy and exported as RIS and BibTeX files. DOI strings were identified from RIS files by pattern matching and saved as CSV file. The list of DOIs for each chapter and cross chapter paper was processed using a custom Python script to generate a pandas DataFrame which was saved as CSV file and uploaded to Google Big Query. We used the main object table of the Academic Observatory, which combines information from Crossref, Unpaywall, Microsoft Academic, Open Citations, the Research Organization Registry and Geonames to enrich the DOIs with bibliographic information, affiliations, and open access status. A custom query was used to join and format the data and the resulting table was visualised in a Google DataStudio dashboard.<br> <br> This version of the repository also includes the set of DOIs from references in the IPCC Working Group 1 contribution to the Sixth Assessment Report as extracted by Alexis-Michel Mugabushaka and shared on Zenodo: https://doi.org/10.5281/zenodo.5475442 (CC-BY) A brief descriptive analysis was provided as a blogpost on the COKI website. <strong>The repository contains the following content:</strong> Data: <strong>data/scholarcy/RIS/</strong> - extracted references as RIS files <strong>data/scholarcy/BibTeX/</strong> - extracted references as BibTeX files <strong>IPCC_AR6_WGII_dois.csv</strong> - list of DOIs <strong>data/10.5281_zenodo.5475442/</strong> - references from IPCC AR6 WG1 report Processing: <strong>preprocessing.R</strong> - preprocessing steps for identifying and cleaning DOIs <strong>process.py</strong> - Python script for transforming data and linking to COKI data through Google Big Query Outcomes: Dataset on BigQuery - requires a google account for access and bigquery account for querying Data Studio Dashboard - interactive analysis of the generated data Zotero library of references extracted via Scholarcy <strong>PDF version of blogpost</strong> <strong>Note on licenses:</strong> <br> Data are made available under CC0 (with the exception of WG1 reference data, which have been shared under CC-BY 4.0)<br> Code is made available under Apache License 2.0
description: Archived version of Release 2022-03-10 of GitHub repository: https://github.com/Curtin-Open-Knowledge-Initiative/ipcc-ar6
DOI: 10.5281/zenodo.5705406
title: Dataset: Effect of Macrorough Sidewalls on Flow Resistance in Steep Rough Channels

description: The data set includes reach-averaged flow velocity measurements and bed and sidewall roughness parameters for flume experiments conducted at the Laboratory of Hydraulics, Hydrology, and Glaciology (VAW) at ETH Zurich.
DOI: 10.5281/zenodo.5705405
title: Dataset: Effect of Macrorough Sidewalls on Flow Resistance in Steep Rough Channels

description: The data set includes reach-averaged flow velocity measurements and bed and sidewall roughness parameters for flume experiments conducted at the Laboratory of Hydraulics, Hydrology, and Glaciology (VAW) at ETH Zurich.
DOI: 10.5281/zenodo.6334682
title: Composition of the health care professional consortium

description: Dataset on the expert panel input regarding the composition of the health care professional consortium involved in decision making and the subsequent care trajectory for older patients with multimorbidity and cancer
DOI: 10.5281/zenodo.6334780
title: Intrinsic capacity evaluation and intervention protocol

description: A protocol for the evaluation of intrinsic capacity and frailty, with subsequent interventions aimed at optimizing health status and support for older patients with multimorbidity and cancer
DOI: 10.5281/zenodo.6334724
title: Core intrinsic capacity dataset

description: Data set of core intrinsic capacity and frailty data to be included in the evaluation of older patients with cancer and multimorbidity to determine the feasibility of treatment and additional care needs
DOI: 10.5281/zenodo.6344521
title: Figures_Thesis

description: Figures_Thesis
DOI: 10.5281/zenodo.6344520
title: Figures_Thesis

description: Figures_Thesis
DOI: 10.5281/zenodo.6344524
title: Dust mass fractions for dust bins covering the 0.1 to 100 µm size range

description: This is a file containing the dust mass fractions for dust bins covering the 0.1 to 100 µm size range calculated from the BFT-supercoarse emitted dust PSD parameterization (Meng et al., 2021 GRL submitted).
DOI: 10.5281/zenodo.5762576
title: Dust mass fractions for dust bins covering the 0.1 to 100 µm size range

description: This is a file containing the dust mass fractions for dust bins covering the 0.1 to 100 µm size range calculated from the BFT-supercoarse emitted dust PSD parameterization (Meng et al., 2021 GRL submitted).
DOI: 10.5281/zenodo.6339960
title: Video-Trajectory Hand Dataset

description: This dataset consists of color and depth videos of human worker motions and their corresponding Cartesian trajectories of the worker’s hand, captured using OptiTrack motion capture system. Each motion sample comprises 4 files (RGB video, depth video, a Cartesian trajectory of worker’s hand and a label of the motion, representing one of four possible goals from 1 to 4). Total number of motion samples: 811. Structure: MPEG-4 videos of human worker motion and corresponding NumPy array files, containing Cartesian trajectories and a label of the motion. Dataset is in a .zip archive. Extract using 7-Zip or similar software. Video files (.avi) can be opened using VLC media player or any other video player that supports MPEG-4 codec. The .npy files can be loaded using Python (&gt;=3.7) and the Python library NumPy (&gt;=1.19.2).
DOI: 10.5281/zenodo.6339959
title: Video-Trajectory Hand Dataset

description: This dataset consists of color and depth videos of human worker motions and their corresponding Cartesian trajectories of the worker’s hand, captured using OptiTrack motion capture system. Each motion sample comprises 4 files (RGB video, depth video, a Cartesian trajectory of worker’s hand and a label of the motion, representing one of four possible goals from 1 to 4). Total number of motion samples: 811. Structure: MPEG-4 videos of human worker motion and corresponding NumPy array files, containing Cartesian trajectories and a label of the motion. Dataset is in a .zip archive. Extract using 7-Zip or similar software. Video files (.avi) can be opened using VLC media player or any other video player that supports MPEG-4 codec. The .npy files can be loaded using Python (&gt;=3.7) and the Python library NumPy (&gt;=1.19.2).
DOI: 10.5281/zenodo.6337847
title: Video-Trajectory Robot Dataset

description: This dataset consists of color and depth videos of Panda robot motions and their corresponding joint and Cartesian trajectories. The dataset also includes the trajectories of a receiver robot for the purpose of an object handover. Each motion sample comprises 6 files (RGB video, depth video and 4 giver/receiver trajectories in time series form). Total number of motion samples: 38393. Structure: MPEG-4 videos of robot motion and corresponding Python serialized (or “pickled”) files, containing joint and Cartesian trajectories. Dataset is divided into four parts: simulation dataset (PandaHandover_Sim.zip), real train dataset (PandaHandover_Real_Train.zip), real validation dataset (PandaHandover_Real_Val.zip), real test dataset (PandaHandover_Real_Test.zip). Extract using 7-Zip or similar software. Video files (.avi) can be opened using VLC media player or any other video player that supports MPEG-4 codec. The .pkl files can be loaded using Python (&gt;=3.7) and the Python library Pandas (&gt;=1.1.3).
DOI: 10.5281/zenodo.6337846
title: Video-Trajectory Robot Dataset

description: This dataset consists of color and depth videos of Panda robot motions and their corresponding joint and Cartesian trajectories. The dataset also includes the trajectories of a receiver robot for the purpose of an object handover. Each motion sample comprises 6 files (RGB video, depth video and 4 giver/receiver trajectories in time series form). Total number of motion samples: 38393. Structure: MPEG-4 videos of robot motion and corresponding Python serialized (or “pickled”) files, containing joint and Cartesian trajectories. Dataset is divided into four parts: simulation dataset (PandaHandover_Sim.zip), real train dataset (PandaHandover_Real_Train.zip), real validation dataset (PandaHandover_Real_Val.zip), real test dataset (PandaHandover_Real_Test.zip). Extract using 7-Zip or similar software. Video files (.avi) can be opened using VLC media player or any other video player that supports MPEG-4 codec. The .pkl files can be loaded using Python (&gt;=3.7) and the Python library Pandas (&gt;=1.1.3).
DOI: 10.22008/fk2/38hxcw/evd4ve
title: G100_Ivigtut_61V1_S_400dpi.tif

description: :unav
DOI: 10.22008/fk2/38hxcw/qqssqh
title: G100_Ivigtut_61V1S_DescriptiveText.pdf

description: :unav
DOI: 10.22008/fk2/38hxcw/haoque
title: G100_Ivigtut_61V1_S_400dpi.pdf

description: :unav
DOI: 10.5519/odbz9i00
title: NHM Devonian Stromatoporoidea Thin Section Collection

description: This collection includes 827 thin sections belonging to 54 worldwide taxa. Many of these thin section were prepared by H. Alleyne Nicholson.
DOI: 10.22008/fk2/ecxcql/bij5h3
title: G100_Agto_67V1_N_400dpi.tif

description: :unav
DOI: 10.22008/fk2/ecxcql/lhhcks
title: G100_Agto_67V1_N_400dpi.pdf

description: :unav
DOI: 10.22008/fk2/kqu1ue/79swuz
title: G100_Bjørnesund_62V1_ N_400dpi.tif

description: :unav
DOI: 10.22008/fk2/kqu1ue/sjudpy
title: G100_Bjørnesund_62V1_ N_400dpi.pdf

description: :unav
DOI: 10.22008/fk2/jwdct3/wol4vo
title: G100_Fiskefjord_64V1_ N_400dpi.tif

description: :unav
DOI: 10.22008/fk2/jwdct3/flwlyd
title: G100_Fiskefjord_64V1_ N_400dpi.pdf

description: :unav
DOI: 10.22008/fk2/wbcvfq/pvjdta
title: G100_Kap_Brewster_70Ø1_S_400dpi.tif

description: :unav
DOI: 10.22008/fk2/wbcvfq/ks39a9
title: G100_Kap_Brewster_70Ø1_S_400dpi.pdf

description: :unav
DOI: 10.15151/esrf-dc-745664887
title: Speckle-Based Imaging of Pultruded Carbon Fibre Tube

description: Pultruded carbon fibre tubes imaged as verification data for a directional dark-field speckle-based imaging technique.
DOI: 10.22008/fk2/7ckyd6/uauqfo
title: G100_Mellemfjord_69V1_ N_400dpi.tif

description: :unav
DOI: 10.22008/fk2/7ckyd6/d68fp6
title: G100_Mellemfjord_69V1_ N_400dpi.pdf

description: :unav
DOI: 10.22008/fk2/eyogd8/nklltb
title: G100_Rolige_Bræ_70Ø4_N_400dpi.tif

description: :unav
DOI: 10.22008/fk2/eyogd8/4vjv8m
title: G100_Rolige_Bræ_70Ø4_N_400dpi.pdf

description: :unav
DOI: 10.5281/zenodo.6007431
title: Desenzano, Villa romana, Rilievo fotogrammetrico

description: Nell’ambito del progetto e-Archeo, gestito dalla società ALES, si è svolta l’attività di rilievo fotogrammetrico di dettaglio della villa romana di Desenzano, con l’obiettivo di creare un’importante risorsa per la valorizzazione e al contempo di determinare un punto di partenza per l’elaborazione delle piante di fase e per la ricostruzione e modellazione 3D dell’edificio.<br> Per il rilievo della villa si è proceduto con l’acquisizione del dataset delle immagini tramite drone (Mavic 2 pro della DJI), ottenendo fotogrammi: nadirali, frontali e obliqui. Nel contempo è stata registrata la posizione dei punti di appoggio e di controllo, da usare nell’elaborazione fotogrammetrica per georiferire i rilievi.<br> Per la fase successiva di acquisizione ed elaborazione dei dati del progetto è stato utilizzato il software Agisoft Metashape (Structure-from-Motion). In seguito all’allineamento e all’ottimizzazione delle camere è stata generata la nuvola di punti densa, il meshing delle superfici e la texturizzazione del modello 3D. Dal set di dati ottenuto è stato possibile ricavare l’ortofoto dell’area della villa e di dettaglio dei settori A e B e il DTM dell’area della villa, quest’ultimo utile alla ricostruzione del paesaggio antico.<br> Gruppo di lavoro: F. Soriano e A. Zemignani.
DOI: 10.5281/zenodo.6007430
title: Desenzano, Villa romana, Rilievo fotogrammetrico

description: Nell’ambito del progetto e-Archeo, gestito dalla società ALES, si è svolta l’attività di rilievo fotogrammetrico di dettaglio della villa romana di Desenzano, con l’obiettivo di creare un’importante risorsa per la valorizzazione e al contempo di determinare un punto di partenza per l’elaborazione delle piante di fase e per la ricostruzione e modellazione 3D dell’edificio.<br> Per il rilievo della villa si è proceduto con l’acquisizione del dataset delle immagini tramite drone (Mavic 2 pro della DJI), ottenendo fotogrammi: nadirali, frontali e obliqui. Nel contempo è stata registrata la posizione dei punti di appoggio e di controllo, da usare nell’elaborazione fotogrammetrica per georiferire i rilievi.<br> Per la fase successiva di acquisizione ed elaborazione dei dati del progetto è stato utilizzato il software Agisoft Metashape (Structure-from-Motion). In seguito all’allineamento e all’ottimizzazione delle camere è stata generata la nuvola di punti densa, il meshing delle superfici e la texturizzazione del modello 3D. Dal set di dati ottenuto è stato possibile ricavare l’ortofoto dell’area della villa e di dettaglio dei settori A e B e il DTM dell’area della villa, quest’ultimo utile alla ricostruzione del paesaggio antico.<br> Gruppo di lavoro: F. Soriano e A. Zemignani.