cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.17704742
title: A short-term prospective study to evaluate the clinical performance of endosseous zirconia implants

description: Endosseous zirconia implants have been established as an alternative to conventional titanium implants, especially in the aesthetic zone. This pilot study aimed to evaluate the outcome of rehabilitation with zirconia implants, in a subset of the Indian population, involving varying clinical scenarios.
DOI: 10.34808/4yn2-gq48
title: Kowalewo Pomorskie 2021 - video data - pedestrian, bicycles, vehicles

description: Kowalewo Pomorskie 2021 - video data - pedestrian, bicycles, vehicles
The data contain video traffic data (pedestrian, cyclists, vehicles) registered at 10 intersections.
The video camera was installed around bicycle and pedestrian crossings at the intersection. Camera was mounted on 7-meter-high mast. Recording date  23.09.2021. 

Location of intersections:

  Summary of road traffic and HGV share at individual intersections and cross-section.   

Sample view of the video material:
DOI: 10.6084/m9.figshare.17704763
title: VCMatch

description: The replication package for the paper titled "VCMATCH: A Ranking-based Approach forAutomatic Security Patches Localization for OSSVulnerabilities"
DOI: 10.6084/m9.figshare.17701418
title: test

description: Data of the approach.
DOI: 10.6084/m9.figshare.17701418.v3
title: test

description: Data of the approach.
DOI: 10.6084/m9.figshare.17701418.v2
title: data

description: Data of the approach.
DOI: 10.5281/zenodo.5713473
title: Data and supplementary materials for the paper "DuplicatesExtractor: Extracting Code Duplicates As Soon As They Are Introduced in the IDE"

description: This package presents all the necessary data for the paper "DuplicatesExtractor: Extracting Code Duplicates As Soon As They Are Introduced in the IDE". The package contains the following items: - The "source_code" directory contains the full source code of the plugin, along with its own README.<br> - The "DuplicatesExtractor-2021.2-1.0.zip" archive contains the pre-built version of the plugin. You can find the instructions on how to use it in the above-mentioned README in the "source_code" directory.<br> - The "supplementary.pdf" documents contains supplementary materials: the full list of the used metrics, the full list of the used projects, along with the list of the used models.<br> - The "demo.mp4" video contains the demonstration video for the tool.
DOI: 10.5281/zenodo.5713472
title: Data and supplementary materials for the paper "DuplicatesExtractor: Extracting Code Duplicates As Soon As They Are Introduced in the IDE"

description: This package presents all the necessary data for the paper "DuplicatesExtractor: Extracting Code Duplicates As Soon As They Are Introduced in the IDE". The package contains the following items: - The "source_code" directory contains the full source code of the plugin, along with its own README.<br> - The "DuplicatesExtractor-2021.2-1.0.zip" archive contains the pre-built version of the plugin. You can find the instructions on how to use it in the above-mentioned README in the "source_code" directory.<br> - The "supplementary.pdf" documents contains supplementary materials: the full list of the used metrics, the full list of the used projects, along with the list of the used models.<br> - The "demo.mp4" video contains the demonstration video for the tool.
DOI: 10.5519/qd.li72jgrj
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (8681 records)
DOI: 10.5517/ccdc.csd.cc28cv2l
title: CCDC 2096749: Experimental Crystal Structure Determination

description: Related Article: Leo D. M. Nicholls, Helma Wennemers|2021|Chem.-Eur.J.|27|17559|doi:10.1002/chem.202103197
DOI: 10.5517/ccdc.csd.cc28cv3m
title: CCDC 2096750: Experimental Crystal Structure Determination

description: Related Article: Leo D. M. Nicholls, Helma Wennemers|2021|Chem.-Eur.J.|27|17559|doi:10.1002/chem.202103197
DOI: 10.34740/kaggle/dsv/2986585
title: AutoXGB
DOI: 10.21227/99a8-aq48
title: Chinese patients’ self-statement medical  texts

description:  These medical texts data are derived from the patients' real disease cases, including the patients' diseases, symptoms and other information
DOI: 10.5281/zenodo.5810041
title: Improved production of HIV-1 subtype C protease from transgenic E coli

description: The data set belongs to study carried out by Uraisha Ramlucken at Department of Biochemistry, University of KwaZulu-Natal, Durban, South Africa. This study was made possible through financial support from the National Research Foundation.
description: This study was made possible through financial support from the National Research Foundation.
DOI: 10.5281/zenodo.5810040
title: Improved production of HIV-1 subtype C protease from transgenic E coli

description: The data set belongs to study carried out by Uraisha Ramlucken at Department of Biochemistry, University of KwaZulu-Natal, Durban, South Africa. This study was made possible through financial support from the National Research Foundation.
description: This study was made possible through financial support from the National Research Foundation.
DOI: 10.5281/zenodo.5809552
title: Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning"

description: Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning". Predictors from the ERA5 reanalysis and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rda" format, to be read from R.
DOI: 10.34740/kaggle/dsv/2986681
title: Omicron daily cases by country (COVID-19 variant)
DOI: 10.5281/zenodo.5721783
title: Data and codes related to the article: Renard et al. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research.

description: This package contains data and codes related to the article: B. Renard, M. Thyer, D. McInerney, D. Kavetski, M. Leonard and S. Westra. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. <em>Water Resources Research</em>. <strong>R scripts</strong> The main computations of the paper have been performed using a computing code named STooDs, which is called using the bash script launchpad.sh. The R scripts in this package only perform pre-processing (create configuration files) and post-processing (analyze results) steps. Funk.R: a set of functions called by other scripts. 1_defineModel.R: define the model to be inferred and create STooDs configuration files in <em>dataset_XXX/runs.</em> 2_analyzeResults.R: analyze the outputs of STooDs runs. 3_crossValidation.R: analyze the outputs of cross-validation experiments in <em>dataset_XV</em> and <em>dataset_XV_1971-1990</em>. <strong>Data</strong> Data for the 3 cases (full dataset and 2 cross-validation experiments) are located in folders <em>dataset_XXX/data</em>. dat.txt: raw dataset in text format. dataset.RData: dataset in RData format. DMI.txt, NINO.txt, SAM.txt: 3 standard climate indices. spaceP.txt, spaceQ.txt, spaceT.txt: properties of Precipitation (P), Streamflow (Q) and Temperature (T) stations. [only for cross-validation experiments] validation.RData: left-out data used for validation.
DOI: 10.5281/zenodo.5721782
title: Data and codes related to the article: Renard et al. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research.

description: This package contains data and codes related to the article: B. Renard, M. Thyer, D. McInerney, D. Kavetski, M. Leonard and S. Westra. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. <em>Water Resources Research</em>. <strong>R scripts</strong> The main computations of the paper have been performed using a computing code named STooDs, which is called using the bash script launchpad.sh. The R scripts in this package only perform pre-processing (create configuration files) and post-processing (analyze results) steps. Funk.R: a set of functions called by other scripts. 1_defineModel.R: define the model to be inferred and create STooDs configuration files in <em>dataset_XXX/runs.</em> 2_analyzeResults.R: analyze the outputs of STooDs runs. 3_crossValidation.R: analyze the outputs of cross-validation experiments in <em>dataset_XV</em> and <em>dataset_XV_1971-1990</em>. <strong>Data</strong> Data for the 3 cases (full dataset and 2 cross-validation experiments) are located in folders <em>dataset_XXX/data</em>. dat.txt: raw dataset in text format. dataset.RData: dataset in RData format. DMI.txt, NINO.txt, SAM.txt: 3 standard climate indices. spaceP.txt, spaceQ.txt, spaceT.txt: properties of Precipitation (P), Streamflow (Q) and Temperature (T) stations. [only for cross-validation experiments] validation.RData: left-out data used for validation.
DOI: 10.5517/ccdc.csd.cc27jtm7
title: CCDC 2071749: Experimental Crystal Structure Determination

description: Related Article: Hao-Wei Chen, Chun-Xiao Jiang, Jiyang Li, Na Li, Yi Zang, Xi-Ying Wu, Wen-Xue Chen, Juan Xiong, Jia Li, Jin-Feng Hu|2021|RSC Advances|11|39781|doi:10.1039/D1RA08377E
DOI: 10.5281/zenodo.5718485
title: Validation of the DYALS (Dysphagia in Amyotrophic Lateral Sclerosis)

description: Dysphagia is a common symptom during the trajectory of ALS, and it can significantly impact on the quality of life and prognosis of patients. Nowadays, no specific tool for the screening of dysphagia in ALS is validated, and the approach is heterogeneous across the Italian Centres. To validate the DYALS (Dysphagia in Amyotrophic Lateral Sclerosis) questionnaire, adapting the DYMUS (Dysphagia in Multiple Sclerosis) questionnaire, for the assessment of dysphagia in ALS patients, in order to uniform the evaluations across the Italian ALS network. We included 197 patients diagnosed with ALS following the El Escorial criteria, in sixteen Italian ALS Centres between 1st December 2019 and 1st July 2020. For each patient, we collected clinical and demographic data, and obtained ALSFRS-r score, ALSAQ-5 score, DYMUS score, and EAT-10 score. Across the 197 patients, the ratio M/F was 113/84, and the median age was 64 years (IQR 56-72.5). Bulbar patients were 20%, and spinal patients 80%. The median ALSFRSr total score of patients was 35 (IQR 28-39). DYALS score was statistically higher in bulbar ALS than in spinal ALS (median=6, IQR 4.5-9 vs median=1, IQR 0-5, z =6.253, p&lt;0.0001). DYALS questionnaire showed a high internal consistency (Cronbach’s alpha=0.88). There was a statistically significant correlation between DYALS and EAT-10 (rho=0.90, p&lt;0.0001). DYALS scale is reliable, manageable, and easily usable for the screening of dysphagia in ALS. It can be shared with all the Italian ALS Centres in order to collect uniform data for therapeutic strategies and clinical trials.
description: Funding: Ricerca Corrente 2020-2021 Ministry of Health (Italy)
DOI: 10.5281/zenodo.5718484
title: Validation of the DYALS (Dysphagia in Amyotrophic Lateral Sclerosis)

description: Dysphagia is a common symptom during the trajectory of ALS, and it can significantly impact on the quality of life and prognosis of patients. Nowadays, no specific tool for the screening of dysphagia in ALS is validated, and the approach is heterogeneous across the Italian Centres. To validate the DYALS (Dysphagia in Amyotrophic Lateral Sclerosis) questionnaire, adapting the DYMUS (Dysphagia in Multiple Sclerosis) questionnaire, for the assessment of dysphagia in ALS patients, in order to uniform the evaluations across the Italian ALS network. We included 197 patients diagnosed with ALS following the El Escorial criteria, in sixteen Italian ALS Centres between 1st December 2019 and 1st July 2020. For each patient, we collected clinical and demographic data, and obtained ALSFRS-r score, ALSAQ-5 score, DYMUS score, and EAT-10 score. Across the 197 patients, the ratio M/F was 113/84, and the median age was 64 years (IQR 56-72.5). Bulbar patients were 20%, and spinal patients 80%. The median ALSFRSr total score of patients was 35 (IQR 28-39). DYALS score was statistically higher in bulbar ALS than in spinal ALS (median=6, IQR 4.5-9 vs median=1, IQR 0-5, z =6.253, p&lt;0.0001). DYALS questionnaire showed a high internal consistency (Cronbach’s alpha=0.88). There was a statistically significant correlation between DYALS and EAT-10 (rho=0.90, p&lt;0.0001). DYALS scale is reliable, manageable, and easily usable for the screening of dysphagia in ALS. It can be shared with all the Italian ALS Centres in order to collect uniform data for therapeutic strategies and clinical trials.
description: Funding: Ricerca Corrente 2020-2021 Ministry of Health (Italy)
DOI: 10.23729/a590418d-04fc-4721-8454-debbca9d6019
title: CircVol mass and ash day 0-30

description: CircVol (https://6aika.fi/project/circvol/) was a project where new uses were developed for waste materials to promote circular economy. In this part of the project dredging mass from Matalahti in Naantali was stabilized using several recipes. More information from: Jarva, J., Eriksson, J.-E., Larkela, L, Auri, J., Kronberg, T., Tarvainen, T., Kuva, J., Järvinen, M., Hänninen, H., Lehtonen, M., Ketola, M., Huhtinen, R., 2021. CircVol-projekti: ruoppausmassojen stabiloinnin kenttäkoe Naantalin Matalahdella. Geologian Tutkimuskeskus. Työraportti 19/2021. This dataset is day 0 and day 30 scans of lab samples with just dredging mass and ash. Preview dataset found at https://doi.org/10.23729/839618b0-21c4-4f09-a6b4-37b51b05f9dd . To obtain data, please contact fairdata-pas@gtk.fi .
DOI: 10.6084/m9.figshare.17704808
title: The data associated with the paper “Data-Driven Alternating Current Optimal Power Flow: A Lagrange Multipliers Based Approaches”

description: The data associated with the paper “Data-Driven Alternating Current Optimal Power Flow: A Lagrange Multipliers Based Approaches”<br>
DOI: 10.6084/m9.figshare.17704808.v1
title: The data associated with the paper “Data-Driven Alternating Current Optimal Power Flow: A Lagrange Multipliers Based Approaches”

description: The data associated with the paper “Data-Driven Alternating Current Optimal Power Flow: A Lagrange Multipliers Based Approaches”<br>
DOI: 10.6084/m9.figshare.16695262
title: ViralFlow: an automated workflow for SARS-CoV-2 genome assembly, lineage assignment, mutations and intrahost variants detection

description: The COVID-19 pandemic, a disease caused by the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), emerged in 2019 and quickly spread worldwide. Genomic surveillance has become the gold standard methodology to monitor and study this emerging virus. The current deluge of SARS-CoV-2 genomic data being generated worldwide has put additional pressure on the urgent need for streamlined bioinformatics workflows for data analysis. Here, we describe a workflow developed by our group to process and analyze large-scale SARS-CoV-2 Illumina amplicon sequencing data. This workflow automates all the steps involved in SARS-CoV-2 genomic analysis: data processing, genome assembly, PANGO lineage assignment, mutation analysis and the screening of intrahost variants. The workflow presented here (https://github.com/dezordi/ViralFlow) is available through Docker or Singularity images, allowing implementation in laptops for small scale analyses or in high processing capacity servers or clusters. Moreover, the low requirements for memory and CPU cores makes it a versatile tool for SARS-CoV-2 genomic analysis.
DOI: 10.6084/m9.figshare.16695262.v3
title: ViralFlow: an automated workflow for SARS-CoV-2 genome assembly, lineage assignment, mutations and intrahost variants detection

description: The COVID-19 pandemic, a disease caused by the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), emerged in 2019 and quickly spread worldwide. Genomic surveillance has become the gold standard methodology to monitor and study this emerging virus. The current deluge of SARS-CoV-2 genomic data being generated worldwide has put additional pressure on the urgent need for streamlined bioinformatics workflows for data analysis. Here, we describe a workflow developed by our group to process and analyze large-scale SARS-CoV-2 Illumina amplicon sequencing data. This workflow automates all the steps involved in SARS-CoV-2 genomic analysis: data processing, genome assembly, PANGO lineage assignment, mutation analysis and the screening of intrahost variants. The workflow presented here (https://github.com/dezordi/ViralFlow) is available through Docker or Singularity images, allowing implementation in laptops for small scale analyses or in high processing capacity servers or clusters. Moreover, the low requirements for memory and CPU cores makes it a versatile tool for SARS-CoV-2 genomic analysis.
DOI: 10.6084/m9.figshare.16695262.v2
title: ViralFlow: an automated workflow for SARS-CoV-2 genome assembly, lineage assignment, mutations and intrahost variants detection

description: The COVID-19 pandemic, a disease caused by the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), emerged in 2019 and quickly spread worldwide. Genomic surveillance has become the gold standard methodology to monitor and study this emerging virus. The current deluge of SARS-CoV-2 genomic data being generated worldwide has put additional pressure on the urgent need for streamlined bioinformatics workflows for data analysis. Here, we describe a workflow developed by our group to process and analyze large-scale SARS-CoV-2 Illumina amplicon sequencing data. This workflow automates all the steps involved in SARS-CoV-2 genomic analysis: data processing, genome assembly, PANGO lineage assignment, mutation analysis and the screening of intrahost variants. The workflow presented here (https://github.com/dezordi/ViralFlow) is available through Docker or Singularity images, allowing implementation in laptops for small scale analyses or in high processing capacity servers or clusters. Moreover, the low requirements for memory and CPU cores makes it a versatile tool for SARS-CoV-2 genomic analysis.
DOI: 10.5281/zenodo.5808943
title: Hard Layer

description: This dataset comes from the first phase of MAIL methodology as described in D2.3. Here a top-down stepwise approach is followed, in which areas that are not Marginal Lands (MLs) are incrementally removed, based on thresholds of various marginality criteria and indicators. These results are an intermediate layer, named “ML HARD” including all potential MLs after the exclusion of all LULC types that are not fulfilling the definition of MLs.
description: {"references": ["MAIL deliverable 2.3 \"Report on Methodology development\", 2020"]}
DOI: 10.5281/zenodo.5808942
title: Hard Layer

description: This dataset comes from the first phase of MAIL methodology as described in D2.3. Here a top-down stepwise approach is followed, in which areas that are not Marginal Lands (MLs) are incrementally removed, based on thresholds of various marginality criteria and indicators. These results are an intermediate layer, named “ML HARD” including all potential MLs after the exclusion of all LULC types that are not fulfilling the definition of MLs.
description: {"references": ["MAIL deliverable 2.3 \"Report on Methodology development\", 2020"]}
DOI: 10.5281/zenodo.5809011
title: Final MLs

description: This dataset comes from the second phase of MAIL methodology as described in D2.3 where all participating datasets assigned with a weighted value per pixel. This value based on a Pairwise Comparison Matrix (PCM) of ranks where the value of each indicator (i.e., slope, moisture, salinity, productivity, soil contamination, pH etc.) are grouped into 3 classes according to their physical characteristics. The final step in the mapping of MLs, is the reclassification of the resulting product of the weighted overlay into 3 classes, depicting marginality: <em>1)</em> <em>Marginal lands with high plantation suitability, 2) Marginal lands with low plantation suitability </em>and<em> 3) Potentially unsuitable lands</em>. The subdivision of these categories was carried out with three different methodological approaches; The first is to calculate the maximum and minimum value obtained in the MLs layer and divide the difference by three. This will obtain three ranges of equal magnitude. The second methodological approach is to calculate the 25<sup>th</sup> and 75<sup>th</sup> percentiles of the values obtained in the MLs layer. This approach would penalize the “Marginal lands with high plantation suitability” and the "Potentially unsuitable lands" and increasing the number of pixels belonging to the middle layer "Marginal lands with low plantation suitability". The third one is to calculate the 33<sup>rd</sup> and 66<sup>th</sup> percentile of the values obtained in the layer MLs. In this case, the total number of pixels is divided equally into the three categories. But with the difference with respect to the a) method that in this case the thresholds are established with the maximum number of pixels and not with the maximum and minimum values.
description: {"references": ["MAIL deliverable D2.3 \"Report on Methodology development\""]}
DOI: 10.5281/zenodo.5809010
title: Final MLs

description: This dataset comes from the second phase of MAIL methodology as described in D2.3 where all participating datasets assigned with a weighted value per pixel. This value based on a Pairwise Comparison Matrix (PCM) of ranks where the value of each indicator (i.e., slope, moisture, salinity, productivity, soil contamination, pH etc.) are grouped into 3 classes according to their physical characteristics. The final step in the mapping of MLs, is the reclassification of the resulting product of the weighted overlay into 3 classes, depicting marginality: <em>1)</em> <em>Marginal lands with high plantation suitability, 2) Marginal lands with low plantation suitability </em>and<em> 3) Potentially unsuitable lands</em>. The subdivision of these categories was carried out with three different methodological approaches; The first is to calculate the maximum and minimum value obtained in the MLs layer and divide the difference by three. This will obtain three ranges of equal magnitude. The second methodological approach is to calculate the 25<sup>th</sup> and 75<sup>th</sup> percentiles of the values obtained in the MLs layer. This approach would penalize the “Marginal lands with high plantation suitability” and the "Potentially unsuitable lands" and increasing the number of pixels belonging to the middle layer "Marginal lands with low plantation suitability". The third one is to calculate the 33<sup>rd</sup> and 66<sup>th</sup> percentile of the values obtained in the layer MLs. In this case, the total number of pixels is divided equally into the three categories. But with the difference with respect to the a) method that in this case the thresholds are established with the maximum number of pixels and not with the maximum and minimum values.
description: {"references": ["MAIL deliverable D2.3 \"Report on Methodology development\""]}