cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10039824
title: KGE-UNIT: Towards the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery

description: Key points:



	
We present a unified framework, which combines knowledge graph embedding and multi-task learning, named KGE-UNIT, for joint prediction of DTIs and DDIs. KGE-UNIT enables simultaneous prediction of multiple types of molecular interactions and enhances the performance of each task, even when data availability is limited.
	
Through KGE, KGE-UNIT can extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, which ultimately leads to an improvement in the quality of the features.
	
Based on multi-task learning, in KGE-UNIT, a novel and effective encoder-decoder predictor (i.e., task-aware CNN-based encoder and task-aware attention decoder) is proposed to fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance of all tasks.
	
The results show that KGE-UNIT outperforms other state-of-art methods for both DTIs and DDIs prediction. Moreover, through the extension of PPIs prediction, the scalability of KGE-UNIT is demonstrated.
DOI: 10.5281/zenodo.10066403
title: KGE-UNIT: Towards the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery

description: data: The datasets of KGE-UNIT
run: The retrained model weights of KGE-UNIT
run_comparison: The retrained model weights of KGE models used for comparative experiments
DOI: 10.5281/zenodo.8435763
title: KGE-UNIT: Towards the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery

description: data: The datasets of KGE-UNIT
run: The retrained model weights of KGE-UNIT
run_comparison: The retrained model weights of KGE models used for comparative experiments
DOI: 10.5281/zenodo.8435764
title: KGE-UNIT: Towards the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery

description: Key points: We present a unified framework, which combines knowledge graph embedding and multi-task learning, named KGE-UNIT, for joint prediction of DTIs and DDIs. KGE-UNIT enables simultaneous prediction of multiple types of molecular interactions and enhances the performance of each task, even when data availability is limited. Through KGE, KGE-UNIT can extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, which ultimately leads to an improvement in the quality of the features. Based on multi-task learning, in KGE-UNIT, a novel and effective encoder-decoder predictor (i.e., task-aware CNN-based encoder and task-aware attention decoder) is proposed to fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance of all tasks. The results show that KGE-UNIT outperforms other state-of-art methods for both DTIs and DDIs prediction. Moreover, through the extension of PPIs prediction, the scalability of KGE-UNIT is demonstrated.
DOI: 10.5281/zenodo.8437055
title: KGE-UNIT: Towards the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery

description: Key points: We present a unified framework, which combines knowledge graph embedding and multi-task learning, named KGE-UNIT, for joint prediction of DTIs and DDIs. KGE-UNIT enables simultaneous prediction of multiple types of molecular interactions and enhances the performance of each task, even when data availability is limited. Through KGE, KGE-UNIT can extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, which ultimately leads to an improvement in the quality of the features. Based on multi-task learning, in KGE-UNIT, a novel and effective encoder-decoder predictor (i.e., task-aware CNN-based encoder and task-aware attention decoder) is proposed to fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance of all tasks. The results show that KGE-UNIT outperforms other state-of-art methods for both DTIs and DDIs prediction. Moreover, through the extension of PPIs prediction, the scalability of KGE-UNIT is demonstrated.
DOI: 10.34740/kaggle/dsv/6865461
title: MoviesDatasets
DOI: 10.5281/zenodo.10066605
title: Ureteroscopy Lumen Segmentation Dataset

description: This is a dataset for lumen segmentation. The dataset is composed of 1,754 endoscopic images from 23 patients undergoing Ureteroscopy. The dataset is composed of a total 2,187 endoscopic images with its respective masks highlighting the lumen in .png format. The dataset is divided by folders in train/val/test, train and val folders are divided in two sub-folders (image / label) For each image there is a corresponding mask with the same name. The training and validation data is the one described in [1]. The test folder is composed of 3 patient cases, test_01, test_02 and test_03. For more details please check the referred publication associated with this dataset. 
 
J. F. Lazo et al., "A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture," 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 9203-9210, doi: 10.1109/ICPR48806.2021.9411924.
description: This work was supported by the ATLAS project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813782.
DOI: 10.5281/zenodo.10066606
title: Ureteroscopy Lumen Segmentation Dataset

description: This is a dataset for lumen segmentation. The dataset is composed of 1,754 endoscopic images from 23 patients undergoing Ureteroscopy. The dataset is composed of a total 2,187 endoscopic images with its respective masks highlighting the lumen in .png format. The dataset is divided by folders in train/val/test, train and val folders are divided in two sub-folders (image / label) For each image there is a corresponding mask with the same name. The training and validation data is the one described in [1]. The test folder is composed of 3 patient cases, test_01, test_02 and test_03. For more details please check the referred publication associated with this dataset. 
 
J. F. Lazo et al., "A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture," 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 9203-9210, doi: 10.1109/ICPR48806.2021.9411924.
description: This work was supported by the ATLAS project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813782.
DOI: 10.34740/kaggle/dsv/6865462
title: Mahanagar Yatayat Transport Dataset
DOI: 10.18154/rwth-2022-09250
title: Dataset for "High Resolution Characterization of Excavation Damaged Zone (EDZ) using Continuous and Discrete Fracture Network Inversion"

description: The Meuse/Haute-Marne Underground Research Laboratory provides the location for the experiment designed to investigate the induced fracture network around open or sealed galleries and drifts. The objective of this study is to investigate and reconstruct the hydraulic properties and the geometry of the induced fracture network to improve the insights and validate the conceptual model of the induced fracture network due to the stress redistribution during tunnel excavations. Within the presented study, the cross-hole responses of the pneumatic tests were analyzed in the first step with an equivalent porous media—3D travel time-based tomographic approach. In the next step selected 2D profiles of the 3D model domain were inverted using a discrete fracture network inversion approach. The database of the tomographic analysis is based on 18 gas injection tests and 151 pressure interferences, which were recorded between nine closely spaced boreholes. The travel time-based inversion approach allowed for the reconstruction of the 3D gas diffusivity distribution between nine boreholes with high-resolution. The applied discrete fracture network inversion approach is based on a transdimensional Markov Chain Monte Carlo (MCMC) methodology and it operates with reversible model updates (jumps) that change the problem dimensions, that is, the number, length and position of fractures within the model domain after each iteration step. The synthesis of the results between the reconstructed 3D diffusivity tomogram and the 2D fracture tomograms improved the insights into the spatial geometry of the induced fracture network around galleries.
DOI: 10.25318/9810024101-eng
title: Housing indicators by tenure including presence of mortgage payments and subsidized housing, household type including census family structure and period of construction: Canada, provinces and territories, census metropolitan areas and census agglomerations

description: Housing indicators by tenure including presence of mortgage payments and subsidized housing for Canada, provinces and territories, census metropolitan areas and census agglomerations. Includes age of primary household maintainer, household type including census family structure and period of construction.
DOI: 10.26165/juelich-data/pk8is5
title: Beta Lactoglobulin emulsions SANS and SAXS data

description: Data of beta Lactoglobulin in water and in water/oil emulsions
DOI: 10.25318/9810024101-fra
title: Indicateurs de logement selon le mode d'occupation incluant la présence de paiements hypothécaires et le logement subventionné, le type de ménage incluant la structure de la famille de recensement et la période de construction : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement

description: Indicateurs de logement selon le mode d'occupation incluant la présence de paiements hypothécaires et le logement subventionné pour le Canada, les provinces et les territoires, les régions métropolitaines de recensement et les agglomérations de recensement. Comprend l'âge du principal soutien du ménage, le type de ménage incluant la structure de la famille de recensement et la période de construction.
DOI: 10.14473/csda/dehlad
title: Naše společnost 2022 - březen (březen až květen)

description: Výzkum provedl výzkumný tým CVVM Sociologického ústavu AV ČR, v.v.i., v rámci výzkumu „Naše společnost 2022". Sběr dat provedla tazatelská síť CVVM.
DOI: 10.14473/csda/thjbm7
title: Naše společnost 2021 - listopad

description: Výzkum provedl výzkumný tým CVVM Sociologického ústavu AV ČR, v.v.i., v rámci výzkumu „Naše společnost 2022". Sběr dat provedla tazatelská síť CVVM. Předmět výzkumu: Důvěra ústavním institucím v listopadu/prosinci 2021 Nový typ koronaviru - zájem, obavy a očekávání české veřejnosti - listopad/prosinec 2021 Důvěra vybraným politikům v mezinárodním kontextu - listopad/prosinec 2021 Jak veřejnost hodnotí situaci ve světě - listopad/prosinec 2021 Nový typ koronaviru - očkování a testování - listopad/prosinec 2021 Sympatie české veřejnosti k některým zemím - listopad/prosinec 2021 Občané o vztazích ČR s některými zeměmi - listopad/prosinec 2021 Veřejnost o svých obavách z různých hrozeb a o očekáváních do budoucna - listopad/prosinec 2021 Názory české veřejnosti na sociální zabezpečení - listopad/prosinec 2021 Názory občanů na úroveň sociálního zabezpečení v ČR a alokace veřejných prostředků - listopad/prosinec 2021 Česká veřejnost o americké zahraniční politice - listopad/prosinec 2021 Češi a spoření na důchod - listopad/prosinec 2021 Názory české veřejnosti na stav důchodového systému ČR - listopad/prosinec 2021
DOI: 10.5281/zenodo.10066946
title: Dataset Vibrations emitted in presence of reproductives affect social organization in termites

description: These data were collected from video recordings analyzed with an automated individual tracking system. Variables related to the spatial distribution of individuals and their activity were extracted from the identity and coordinates of each individual for each image.
DOI: 10.5281/zenodo.7383145
title: Dataset Vibrations emitted in presence of reproductives affect social organization in termites

description: These data were collected from video recordings analyzed with an automated individual tracking system. Variables related to the spatial distribution of individuals and their activity were extracted from the identity and coordinates of each individual for each image.
DOI: 10.5281/zenodo.7383146
title: Dataset Vibrations emitted in presence of reproductives affect social organization in termites

description: These data were collected from video recordings analyzed with an automated individual tracking system. Variables related to the spatial distribution of individuals and their activity were extracted from the identity and coordinates of each individual for each image.
DOI: 10.25318/9810024201-eng
title: Housing indicators by tenure including presence of mortgage payments and subsidized housing, household size and period of construction: Canada, provinces and territories, census metropolitan areas and census agglomerations

description: Housing indicators by tenure including presence of mortgage payments and subsidized housing for Canada, provinces and territories, census metropolitan areas and census agglomerations. Includes age of primary household maintainer, household size and period of construction.
DOI: 10.15787/vtt1/geztad
title: Simulation-based approach to investigate the electric scooter rider protection during traffic accidents (05-116)

description: Project Description: Last decade, the popularity of rideshare electric scooters increased considerably. However, there have also been concerns regarding the safety of riders (2.4 to 18 times more people per trip are injured on e-scooter sharing than on bicycle sharing) and the impacts these vehicles have on those who share roads and sidewalks with them. While non-collision induced falls seems to be the main cause of scooter injuries (~60-80%), the collisions with vehicles and pedestrians represent the causes of other scooter injuries. This project started with a comprehensive literature review of e-scooter injuries reported in the current literature as well as an overview of current e-scooter company policies, state laws, and local laws. Based on this literature review, two e-scooter impact studies were investigated in detail using computer simulations: 1) the impact between an e-scooter and a bump – the most common cause of riders’ injuries, 2) the impact between an e-scooter and vehicle- the most common cause of riders’ fatalities. The simulation injury results showed similarities with the injuries reported in the literatures, so the models and numerical approach developed in this project could be used by industry to develop safety equipment and by government agencies to propose appropriate state/local laws. Data Scope: Please see Safe-D_05-116_Project Description document below. Data Specification: Please see Safe-D_05-116_Project Description document below.
DOI: 10.15151/esrf-dc-1388457372
title: SAXS data related to the manuscript "Morphological analysis of polydisperse nanoplatelets using SAXS"

description: In the work related to this dataset, we propose a fast and quantitative method for the in situ characterization of dispersions of polydisperse nanoplatelets on the example of graphene nanoplatelets (GNPs) and gibbsite. The method relies on synchrotron small-angle X-ray scattering (SAXS). The SAXS data are fitted with a polydisperse form factor for platelets, which yields size distributions in both thickness and radius.
DOI: 10.25318/9810024201-fra
title: Indicateurs de logement selon le mode d'occupation incluant la présence de paiements hypothécaires et le logement subventionné, la taille du ménage et la période de construction : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement

description: Indicateurs de logement selon le mode d'occupation incluant la présence de paiements hypothécaires et le logement subventionné pour le Canada, les provinces et les territoires, les régions métropolitaines de recensement et les agglomérations de recensement. Comprend l'âge du principal soutien du ménage, la taille du ménage et la période de construction.
DOI: 10.25345/c56h4d17v
title: MassIVE MSV000093243 - Cytoskeleton-associated protein 4 (CKAP4) affects podocyte cytoskeleton dynamics in diabetic kidney disease
title: Cytoskeleton-associated protein 4 (CKAP4) stabilizes the endoplasmic reticulum, by regulating its folding and anchoring to the microtubular cytoskeleton. Since podocytes are highly dependent on an intact cytoskeleton to exert their function, the role of CKAP4 was explored during normal conditions and in diabetic kidney disease (DKD). Methods. The differences in CKAP4 gene expression between patients with chronic kidney disease (CKD) and controls were explored in publicly available databases. Expression of CKAP4 in patients with DKD was investigated with in situ hybridization. Localization in human kidney tissue was determined using immunofluorescence, qPCR, and western blot. The CKAP4 homolog was knocked down (KD) in zebrafish. Proteinuria and glomerular morphology were investigated. CKAP4 KD and overexpression in podocytes were investigated using proteomics, immunofluorescence, and western blot. Results. Glomerular CKAP4 gene expression was reduced in DKD patients compared to healthy individuals, but not in other CKDs. The glomerular expression of CKAP4 was most pronounced in podocytes. CKAP4 KD zebrafish became proteinuric and podocyte FPE (foot process effacement) was observed. CKAP4 KD in podocytes in vitro led to disruption of actin stress fibers, microtubular rearrangement and loss of integrin expression. Conclusion. CKAP4 reduction causes podocyte FPE and proteinuria in vivo. Loss of CKAP4 disrupts the podocyte actin cytoskeleton, its microtubular orientation, and integrin-based glomerular basement membrane attachment. This indicates that CKAP4 is a crucial connector between cell body and structural elements in podocytes. The reduction of CKAP4 observed in glomeruli from DKD patients may thus be coupled to impaired podocyte function and proteinuria.
DOI: 10.5281/zenodo.10066845
title: Autonomous Intraluminal Navigation Dataset

description: This is a dataset for lumen segmentation. The dataset is composed of 1,873 images taken from endoscopic videos of 10 different phantoms. For each endoscopic image there is its corresponding mask in png format. The dataset is divided by folders in train/val/test. The training and validation data is the one used for training the networks described in [1]. The test folder is composed of two folders from a single phantom case. For more details please check the referred publication associated with this dataset. 
 
[1]. J. F. Lazo et al., "Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 6952-6959, doi: 10.1109/IROS47612.2022.9982141.
description: This work was supported by the ATLAS project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813782.This work was also partially supported by French State Funds managed by the Agence Nationale de la Recherche (ANR) through the Investissements d'Avenir Program under Grant ANR-11-LABX-0004 (Labex CAMI) and Grant ANR-10-IAHU-02 (IHU-Strasbourg)
DOI: 10.5281/zenodo.10066844
title: Autonomous Intraluminal Navigation Dataset

description: This is a dataset for lumen segmentation. The dataset is composed of 1,873 images taken from endoscopic videos of 10 different phantoms. For each endoscopic image there is its corresponding mask in png format. The dataset is divided by folders in train/val/test. The training and validation data is the one used for training the networks described in [1]. The test folder is composed of two folders from a single phantom case. For more details please check the referred publication associated with this dataset. 
 
[1]. J. F. Lazo et al., "Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing," 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 6952-6959, doi: 10.1109/IROS47612.2022.9982141.
description: This work was supported by the ATLAS project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813782.This work was also partially supported by French State Funds managed by the Agence Nationale de la Recherche (ANR) through the Investissements d'Avenir Program under Grant ANR-11-LABX-0004 (Labex CAMI) and Grant ANR-10-IAHU-02 (IHU-Strasbourg)
DOI: 10.5519/qd.osz4plkr
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (125 records)
DOI: 10.17037/data.00002840
title: Sexual and Physical Abuse among Patients with Severe Mental Illness in Kampala and Masaka – Coding Framework Data

description: A spreadsheet containing selected transcript quotations collected from 18 in-depth interview participants. The interview transcripts cover a range of topics, including the experience of living with severe mental illness, relapse of mental illness episodes, and abuse/victimisation and its various forms. Themes covers include physical, sexual & psychological victimisation; causes of vulnerability to victimisation of people with severe mental illness; what can be done to reduce victimisation; experience of living with severe mental illness; mental illness treatment experience and challenges.
description: Qualitative, in-depth interviews were held with 18 participants, selected from a purposive sample of patients who have suffered from severe mental illness but had experienced symptom remission. These participants had suffered victimisation before acquiring a mental illness or while suffering a mental illness. Voice recordings were transcribed verbatim and the transcripts anonymised before analysis. An interpretative phenomenological analysis (IPA) was conducted, by which the aim was to explore in detail participants’ life-worlds and the meanings they attach to these, in this case victimisation experiences of persons living with severe mental illness.
DOI: 10.5517/ccdc.csd.cc2frw47
title: CCDC 2257269: Experimental Crystal Structure Determination

description: Related Article: Helene Lebel, Gary Mathieu, Emna Azek, Calvine Lai|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2frw58
title: CCDC 2257270: Experimental Crystal Structure Determination

description: Related Article: Helene Lebel, Gary Mathieu, Emna Azek, Calvine Lai|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.