cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc29wvhk
title: CCDC 2141929: Experimental Crystal Structure Determination

description: Related Article: Subhash P. Chavan, Dinesh B. Kalbhor, Rajesh G. Gonnade|2023|Asian J.Org.Chem|12|e202300331|doi:10.1002/ajoc.202300331
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.cc2f3cqn
title: CCDC 2237571: Experimental Crystal Structure Determination

description: Related Article: Tania I. Ramírez-Candelero, Carlos A. González-González, Aydeé Fuentes-Benítes, Eduardo Díaz-Torres, Joaquín Tamariz, Rubén A. Toscano, Salvador Mastachi-Loza|2023|Asian J.Org.Chem|12|e202300341|doi:10.1002/ajoc.202300341
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: 10.48544/cf75e3a8-46c2-4e95-973e-ddf58d39a2c5
title: Vitaal & Voedzaam
DOI: 10.6084/m9.figshare.24026325.v1
title: <i>Supplementary material</i>

description: <b>Supplementary material</b> belongs to article "<b>O</b><b>ptimizing window size and directional parameters of GLCM texture feature</b><b>s</b><b>for estimating rice AGB based on UAV multispectral image</b><b>ry</b>"(Jikai Liu, Yongji Zhu, Lijuan Song, Xiangxiang Su, Jun Li, Jing Zheng, Xueqing Zhu, Lantian Ren, Wenhui Wang, and Xinwei Li).
DOI: 10.5281/zenodo.8185856
title: An Assessment of Medication Errors among Pediatric Patients in Three Hospitals in Freetown Sierra Leone: Findings and Implications for a Low-Income Country

description:  
Background
Pediatric patients are prone to medicine-related problems like medication errors (MEs), which can potentially cause harm. Yet, this has not been studied in this population in Sierra Leone. Therefore, this study investigated the prevalence and nature of MEs, including potential drug-drug interactions (pDDIs) in pediatric patients in three hospitals in Freetown, Sierra Leone.
Methods: The study was conducted in three Hospitals among pediatric patients in Freetown and consisted of two phases. Phase one was a cross-sectional retrospective review of prescriptions for completeness and accuracy against standard prescription writing guidelines. Phase two was a point prevalence inpatient chart review of MEs that were categorized into prescription, administration, and dispensing errors and pDDIs. Data was analyzed using frequency, percentages, median, and interquartile range. Kruskal-Wallis H and Mann-Whitney U tests were used to compare the prescription accuracy between the hospitals, with p<0.05 considered statistically significant.
Results: In phase one, while no prescription attained the global accuracy score (GAS) gold standard of 100%, 106 (29.0%) achieved the 80-100% mark. The patient 63 (17.2%), treatment 228 (62.3%), and prescriber 33 (9.0%) identifiers achieved an overall GAS range of 80-100%. Although the total GAS was not statistically significant (p=0.065), the date (p=0.041), patient (p=<0.001), treatment (p=0.022), and prescriber (p=<0.001) identifiers were statistically significant across the different hospitals. For phase two, the prevalence of MEs was 74 (56.1%), while that for pDDIs was 54 (40.9%). There was a statistically positive correlation between the occurrence of pDDI and the number of medicines prescribed (r=0.211, P=0.015).
Conclusion: Low GAS depicts poor compliance with prescription writing guidelines and high prescribing errors. Medication errors were observed at each phase of the medication use cycle, while clinically significant pDDIs were also reported. Thus, there is a need for training on prescription writing guidelines and medication errors.
 
Keywords:  Pediatrics, Prescription, Medication errors, Drug-drug interactions, Sierra Leone
DOI: 10.5281/zenodo.8185857
title: An Assessment of Medication Errors among Pediatric Patients in Three Hospitals in Freetown Sierra Leone: Findings and Implications for a Low-Income Country

description:  
Background
Pediatric patients are prone to medicine-related problems like medication errors (MEs), which can potentially cause harm. Yet, this has not been studied in this population in Sierra Leone. Therefore, this study investigated the prevalence and nature of MEs, including potential drug-drug interactions (pDDIs) in pediatric patients in three hospitals in Freetown, Sierra Leone.
Methods: The study was conducted in three Hospitals among pediatric patients in Freetown and consisted of two phases. Phase one was a cross-sectional retrospective review of prescriptions for completeness and accuracy against standard prescription writing guidelines. Phase two was a point prevalence inpatient chart review of MEs that were categorized into prescription, administration, and dispensing errors and pDDIs. Data was analyzed using frequency, percentages, median, and interquartile range. Kruskal-Wallis H and Mann-Whitney U tests were used to compare the prescription accuracy between the hospitals, with p<0.05 considered statistically significant.
Results: In phase one, while no prescription attained the global accuracy score (GAS) gold standard of 100%, 106 (29.0%) achieved the 80-100% mark. The patient 63 (17.2%), treatment 228 (62.3%), and prescriber 33 (9.0%) identifiers achieved an overall GAS range of 80-100%. Although the total GAS was not statistically significant (p=0.065), the date (p=0.041), patient (p=<0.001), treatment (p=0.022), and prescriber (p=<0.001) identifiers were statistically significant across the different hospitals. For phase two, the prevalence of MEs was 74 (56.1%), while that for pDDIs was 54 (40.9%). There was a statistically positive correlation between the occurrence of pDDI and the number of medicines prescribed (r=0.211, P=0.015).
Conclusion: Low GAS depicts poor compliance with prescription writing guidelines and high prescribing errors. Medication errors were observed at each phase of the medication use cycle, while clinically significant pDDIs were also reported. Thus, there is a need for training on prescription writing guidelines and medication errors.
 
Keywords:  Pediatrics, Prescription, Medication errors, Drug-drug interactions, Sierra Leone
DOI: 10.34740/kaggle/dsv/7043661
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.8192555
title: Localized Medical VQA

description: This dataset contains 3 sub-datasets with questions about regions for the Medical Visual Question Answering (VQA) task. Traditionally, questions are asked about the entire image. In these datasets, we ask questions about randomly generated regions in an image for fundus images as well as cataract surgery frames and surgeries performed with the DaVinci robot.
All three datasets were created using publicly available datasets.
For more information and code, visit our GitHub page.
If you use this dataset, please cite our work:

@inproceedings{tascon2023localized,
  title={Localized Questions in Medical Visual Question Answering},
  author={Tascon-Morales, Sergio and M{\'a}rquez-Neila, Pablo and Sznitman, Raphael},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={361--370},
  year={2023},
  organization={Springer}
}
DOI: 10.5281/zenodo.8192556
title: Localized Medical VQA

description: This dataset contains 3 sub-datasets with questions about regions for the Medical Visual Question Answering (VQA) task. Traditionally, questions are asked about the entire image. In these datasets, we ask questions about randomly generated regions in an image for fundus images as well as cataract surgery frames and surgeries performed with the DaVinci robot.
All three datasets were created using publicly available datasets.
For more information and code, visit our GitHub page.
If you use this dataset, please cite our work:

@inproceedings{tascon2023localized,
  title={Localized Questions in Medical Visual Question Answering},
  author={Tascon-Morales, Sergio and M{\'a}rquez-Neila, Pablo and Sznitman, Raphael},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={361--370},
  year={2023},
  organization={Springer}
}
DOI: 10.5281/zenodo.10203116
title: The supplemental data for the paper: "Methodology of generation of CFD meshes and  4D shape reconstruction of coronary arteries from patient-specific dynamic CT"

description: The supplemental data for the paper: "Methodology of generation of CFD meshes and  4D shape reconstruction of coronary arteries from patient-specific dynamic CT"
A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.
DOI: 10.5281/zenodo.10203115
title: The supplemental data for the paper: "Methodology of generation of CFD meshes and  4D shape reconstruction of coronary arteries from patient-specific dynamic CT"

description: The supplemental data for the paper: "Methodology of generation of CFD meshes and  4D shape reconstruction of coronary arteries from patient-specific dynamic CT"
A video file (minimum play resolution is HD to see the mesh) showing the movement of the LCA throughout the heart cycle and .STL files for 10--100% (increment of 10\%) of the heart cycle phase.
DOI: 10.5281/zenodo.10203840
title: hafferia_zeledoni_zanfona_1.fasta.gz

description: hafferia_zeledoni_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.10203839
title: hafferia_zeledoni_zanfona_1.fasta.gz

description: hafferia_zeledoni_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.10203850
title: mareca_strepera_zanfona_1.fasta.gz

description: mareca_strepera_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.10203849
title: mareca_strepera_zanfona_1.fasta.gz

description: mareca_strepera_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.8409782
title: Data from interlaboratory comparison of air thermometer calibrations, ATM ILC, 2019-2021

description: The dataset contains data from an interlaboratory comparison conducted in EURAMET in 2019-2021, with the final report published in Oct 2023. The report and the ILC protocol are available in a different upload. 


This dataset consists of 3 files:



	
participant information.xlsx: information on the setups used by each participant, their role in the ILC, temperatures measured at, the probes they measured on, the volume of chambers and the nature of the thermostat (bath, climate chamber or a subchamber).
	
preprocessed data and drift.xlsx: preprocessed probe resistance, reference temperature, link corrected resistance, unilateral degree of equivalence, normalised error, pressure (if available), humidity (if available), pilot measurements in liquid and air for each probe, the list of points excluded from the consensus computations.
	
selfheating.xlsx: probe-by-probe selfheating data from a number of participants (but not all).
DOI: 10.5281/zenodo.8409783
title: Data from interlaboratory comparison of air thermometer calibrations, ATM ILC, 2019-2021

description: The dataset contains data from an interlaboratory comparison conducted in EURAMET in 2019-2021, with the final report published in Oct 2023. The report and the ILC protocol are available in a different upload. 


This dataset consists of 3 files:



	
participant information.xlsx: information on the setups used by each participant, their role in the ILC, temperatures measured at, the probes they measured on, the volume of chambers and the nature of the thermostat (bath, climate chamber or a subchamber).
	
preprocessed data and drift.xlsx: preprocessed probe resistance, reference temperature, link corrected resistance, unilateral degree of equivalence, normalised error, pressure (if available), humidity (if available), pilot measurements in liquid and air for each probe, the list of points excluded from the consensus computations.
	
selfheating.xlsx: probe-by-probe selfheating data from a number of participants (but not all).
DOI: 10.5281/zenodo.10203866
title: marsdenia_floribunda_zanfona_1.fasta.gz

description: marsdenia_floribunda_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.10203867
title: marsdenia_floribunda_zanfona_1.fasta.gz

description: marsdenia_floribunda_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.10203861
title: Large spots DeepMIB project, synthetic dataset for testing 2D patch-wise segmentation

description: A complete DeepMIB project with a synthetic dataset generated for quick tests of the patch-wise segmentation (classification) approachesThe dataset includes a trained Resnet18 network for detection of patches that belong to large white spots on black background.The network can be opened by loading "2D_LargeSpots_Patchwise_Resnet18.mibCfg" by

MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load 
Drag and drop of the config file into DeepMIB window
Microscopy Image Browser: https://mib.helsinki.fi
DOI: 10.5281/zenodo.10203862
title: Large spots DeepMIB project, synthetic dataset for testing 2D patch-wise segmentation

description: A complete DeepMIB project with a synthetic dataset generated for quick tests of the patch-wise segmentation (classification) approachesThe dataset includes a trained Resnet18 network for detection of patches that belong to large white spots on black background.The network can be opened by loading "2D_LargeSpots_Patchwise_Resnet18.mibCfg" by

MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load 
Drag and drop of the config file into DeepMIB window
Microscopy Image Browser: https://mib.helsinki.fi
DOI: 10.5281/zenodo.10203764
title: Small spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation

description: A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches.The dataset includes a trained U-net network for detection of small random spots of 2 colors on a black background.
The network can be opened by loading "2D_SmallSpots_3cl_Unet.mibCfg" file by

MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load 
Drag and drop of the config file into DeepMIB window
Microscopy Image Browser: https://mib.helsinki.fi
DOI: 10.5281/zenodo.10203765
title: Small spots DeepMIB project, synthetic dataset for testing 2D semantic segmentation

description: A complete DeepMIB project with a synthetic dataset generated for quick tests of semantic segmentation approaches.The dataset includes a trained U-net network for detection of small random spots of 2 colors on a black background.
The network can be opened by loading "2D_SmallSpots_3cl_Unet.mibCfg" file by

MIB->Menu->Tools->Deep learning segmentation->Options tab->Config files->Load 
Drag and drop of the config file into DeepMIB window
Microscopy Image Browser: https://mib.helsinki.fi
DOI: 10.23729/d5eb75ec-c3c6-454e-a2ab-52f898823f9b
title: KYT BROCTIO bentonite 2

description: As a part of the KYT BROCTIO project (preview: https://doi.org/10.23729/0eb206fd-08fb-4c1f-a36d-336bcb647496 PAS:https://doi.org/10.23729/eb57d89f-73d0-498f-b1fc-785c130d23c1) bentonite samples compacted with the VTT triaxial equipment were imaged. This was done to check the samples for homogeneity and structure integrity. Preview dataset found at https://doi.org/10.23729/2895b127-79fc-4ac9-aa14-fb5db8fe5b1b . To obtain data, please contact fairdata-pas@gtk.fi .
DOI: 10.4121/14791686.v2
title: Distribution centers 1980-2020 Geodata

description: This is an older and partial version of the dataset:Nefs, Merten (2023): Dutch Distribution Centres 2021 Geodata. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/19361018For updated information, please refer to this new dataset.<br>Geodataset including ca. 10 thousand logistic buildings in the East-Southeast corridor of the Netherlands, as well as the business estates they are in, measured between 1980-2020. Interactive zoomable map with timeline slider available on: http://mertennefs.eu/landscapes-of-trade/<br>The dataset is compiled to establish a comprehensive overview of the growth of the logistic complex in the busiest freight corridor of the Netherlands. Building footprint in this area has quadrupled between 1980 and 2020. The data was compiled as part of PhD research project Landscapes of Trade (information on www.landscapesoftrade.nl).<br><em>Disclaimer: Ca. 9 out of 10 buildings are new greenfield developments, while 1 out of 10 is developed on an existing business site (brownfield). Therefore, the data give a 90% accurate representation of the growth of the logistic complex. There are also functional grey areas: some buildings clearly have a logistic typology and activity, but according to business microdata fall in another category, for example automobile assembly and service, or production of food.</em>
DOI: 10.5281/zenodo.10202994
title: Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF

description: This repository contains namelists needed to reproduce the WRF(V4.4) simulations analyzed in "Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF"
DOI: 10.5281/zenodo.10202995
title: Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF

description: This repository contains namelists needed to reproduce the WRF(V4.4) simulations analyzed in "Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parameterization in WRF"
DOI: 10.6084/m9.figshare.24631791
title: 3T3 training matrix

description: Training matrix containing RPM of 10 epigenomic and transcriptomic datasets scored within ~5000 R-loops and random regions in 3T3 cell line.
DOI: 10.6084/m9.figshare.24631791.v1
title: 3T3 training matrix

description: Training matrix containing RPM of 10 epigenomic and transcriptomic datasets scored within ~5000 R-loops and random regions in 3T3 cell line.
DOI: 10.6084/m9.figshare.24631803
title: E14 training matrix

description: Training matrix containing RPM of 10 epigenomic and transcriptomic datasets scored within ~5000 R-loops and random regions in E14 cell line.
DOI: 10.6084/m9.figshare.24631803.v1
title: E14 training matrix

description: Training matrix containing RPM of 10 epigenomic and transcriptomic datasets scored within ~5000 R-loops and random regions in E14 cell line.
DOI: 10.5281/zenodo.10203843
title: Dataset Turini et al., 2023. Immunity Transfer in Mule Foals Fed with Good IgG Quality Colostrum.

description: Dataset manuscript Turini et al., 2023. Immunity Transfer in Mule Foals Fed with Good IgG Quality Colostrum.
DOI: 10.5281/zenodo.10203844
title: Dataset Turini et al., 2023. Immunity Transfer in Mule Foals Fed with Good IgG Quality Colostrum.

description: Dataset manuscript Turini et al., 2023. Immunity Transfer in Mule Foals Fed with Good IgG Quality Colostrum.
DOI: 10.5281/zenodo.10203860
title: Optical properties of marine aerosols with varying water content at wavelengths 532 and 1064 nm, modelled with a morphologically realistic aerosol model

description: The data contain computational results obtained with the ADDA program at wavelengths 532 nm and 1064 nm, for particle sizes 0.04, 0.06, ..., 1.5 micrometers (where size = volume-equivalent dry radius), and for salt mass fractions 0.91, 0.94, 0.97, 1.00. The content of the data files is described in the README file.
DOI: 10.5281/zenodo.10203859
title: Optical properties of marine aerosols with varying water content at wavelengths 532 and 1064 nm, modelled with a morphologically realistic aerosol model

description: The data contain computational results obtained with the ADDA program at wavelengths 532 nm and 1064 nm, for particle sizes 0.04, 0.06, ..., 1.5 micrometers (where size = volume-equivalent dry radius), and for salt mass fractions 0.91, 0.94, 0.97, 1.00. The content of the data files is described in the README file.
DOI: 10.5281/zenodo.10203920
title: Pannexin-1 promotes the invasion of pituitary adenoma by modulating ATP release
DOI: 10.5281/zenodo.10203919
title: Pannexin-1 promotes the invasion of pituitary adenoma by modulating ATP release
DOI: 10.5066/f7q81b46
title: North American Breeding Bird Survey Dataset 1966 - 2014, version 2014.0

description: The 1966-2014 North American Breeding Bird Survey dataset contains avian point count data for more than 700 North American bird taxa (species, races, and unidentified species groupings). These data are collected annually during the breeding season, primarily June and May, along thousands of randomly established roadside survey routes in the United States and Canada. Routes are about 24.5 miles (39.2 km) long with counting locations placed at regular intervals, for a total of 50 stops. At each stop, a person highly skilled in avian identification conducts a 3-minute point count, recording every bird seen within a quarter-mile (400-m) radius and every bird heard. Surveys begin 30 minutes before local sunrise and take approximately 5 hours to complete. A route is sampled once per year, with the total number of routes sampled per year growing over time; about 600 routes were sampled in 1966, while in recent decades approximately 3000 routes have been sampled annually. In addition to avian count data, this dataset also contains date route sampled, survey start and end times, start and end weather conditions, a unique observer identification number, route identification information, route location information including geographic coordinates of route start point, and an indicator of sample quality.
DOI: 10.5066/f71r6nk8
title: North American Breeding Bird Survey Dataset 1966 - 2015, version 2015.0

description: The 1966-2015 North American Breeding Bird Survey dataset contains avian point count data for more than 700 North American bird taxa (primarily species, but also some races and unidentified species groupings). These data are collected annually during the breeding season, primarily June and May, along thousands of randomly established roadside survey routes in the United States and Canada. Routes are about 24.5 miles (39.2 km) long with counting locations placed at regular intervals, for a total of 50 stops. At each stop, a person highly skilled in avian identification conducts a 3-minute point count, recording every bird seen within a quarter-mile (400-m) radius and every bird heard. Surveys begin 30 minutes before local sunrise and take approximately 5 hours to complete. A route is sampled once per year, with the total number of routes sampled per year growing over time; about 600 routes were sampled in 1966, while in recent decades approximately 3000 routes have been sampled annually. In addition to avian count data, this dataset also contains date route sampled, survey start and end times, start and end weather conditions, a unique observer identification number, route identification information, route location information including geographic coordinates of route start point, and an indicator of sample quality.
DOI: 10.6084/m9.figshare.24631818
title: Data and Codes

description: Data and codes for Daniel Muth's dissertation submitted to Central European University, Vienna.
DOI: 10.6084/m9.figshare.24631818.v1
title: Data and Codes

description: Data and codes for Daniel Muth's dissertation submitted to Central European University, Vienna.