cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21786557
title: IV.9_SNP_mv20.vcf

description: De novo mutations observed in individual IV.9. Sequenced reads were aligned to the hg19 reference human genome downloaded from UCSC and aligned using Burrows–Wheeler alignment (BWA-MEM). <em>De novo</em> mutations were called using standard GATK4 best practices pipeline. All samples were jointly called, but only high-confidence mendelian violation sites with minimum GQ=20 for each trio member were selected. Subsequently, additional filters were applied: i) coverage of each trio member ≥10; ii) absence of reads confirming alternative allele in parents; iii) number of reads confirming reference and alternative allele in proband ≥5; iv) allele frequency of the alternative allele in the proband ≥0.3. Additionally, clustered mutations (distance between mutations ≤1000bp) were filtered out, as potential false positives. Mutations present in any other family member were also excluded. To eliminate false positive mutations coming from the sequencing of proband fibroblasts, we used other fibroblasts from the same individual (used in MA experiments) as additional confirmation of mutation presence.
DOI: 10.6084/m9.figshare.21786557.v2
title: IV.9_SNP_mv20.vcf

description: De novo mutations observed in individual IV.9. Sequenced reads were aligned to the hg19 reference human genome downloaded from UCSC and aligned using Burrows–Wheeler alignment (BWA-MEM). <em>De novo</em> mutations were called using standard GATK4 best practices pipeline. All samples were jointly called, but only high-confidence mendelian violation sites with minimum GQ=20 for each trio member were selected. Subsequently, additional filters were applied: i) coverage of each trio member ≥10; ii) absence of reads confirming alternative allele in parents; iii) number of reads confirming reference and alternative allele in proband ≥5; iv) allele frequency of the alternative allele in the proband ≥0.3. Additionally, clustered mutations (distance between mutations ≤1000bp) were filtered out, as potential false positives. Mutations present in any other family member were also excluded. To eliminate false positive mutations coming from the sequencing of proband fibroblasts, we used other fibroblasts from the same individual (used in MA experiments) as additional confirmation of mutation presence.
DOI: 10.6084/m9.figshare.21786557.v1
title: IV.9_SNP_mv20,vcf

description: De novo mutations observed in individual IV.9. Sequenced reads were aligned to the hg19 reference human genome downloaded from UCSC and aligned using Burrows–Wheeler alignment (BWA-MEM). <em>De novo</em> mutations were called using standard GATK4 best practices pipeline. All samples were jointly called, but only high-confidence mendelian violation sites with minimum GQ=20 for each trio member were selected. Subsequently, additional filters were applied: i) coverage of each trio member ≥10; ii) absence of reads confirming alternative allele in parents; iii) number of reads confirming reference and alternative allele in proband ≥5; iv) allele frequency of the alternative allele in the proband ≥0.3. Additionally, clustered mutations (distance between mutations ≤1000bp) were filtered out, as potential false positives. Mutations present in any other family member were also excluded. To eliminate false positive mutations coming from the sequencing of proband fibroblasts, we used other fibroblasts from the same individual (used in MA experiments) as additional confirmation of mutation presence.
DOI: 10.6084/m9.figshare.21786542
title: IV.6_SNP_mv20.vcf

description: De novo mutations observed in individual IV.6. Sequenced reads were aligned to the hg19 reference human genome downloaded from UCSC and aligned using Burrows–Wheeler alignment (BWA-MEM). <em>De novo</em> mutations were called using standard GATK4 best practices pipeline. All samples were jointly called, but only high-confidence mendelian violation sites with minimum GQ=20 for each trio member were selected. Subsequently, additional filters were applied: i) coverage of each trio member ≥10; ii) absence of reads confirming alternative allele in parents; iii) number of reads confirming reference and alternative allele in proband ≥5; iv) allele frequency of the alternative allele in the proband ≥0.3. Additionally, clustered mutations (distance between mutations ≤1000bp) were filtered out, as potential false positives. Mutations present in any other family member were also excluded. To eliminate false positive mutations coming from the sequencing of proband fibroblasts, we used other fibroblasts from the same individual (used in MA experiments) as additional confirmation of mutation presence.
DOI: 10.6084/m9.figshare.21786542.v2
title: IV.6_SNP_mv20.vcf

description: De novo mutations observed in individual IV.6. Sequenced reads were aligned to the hg19 reference human genome downloaded from UCSC and aligned using Burrows–Wheeler alignment (BWA-MEM). <em>De novo</em> mutations were called using standard GATK4 best practices pipeline. All samples were jointly called, but only high-confidence mendelian violation sites with minimum GQ=20 for each trio member were selected. Subsequently, additional filters were applied: i) coverage of each trio member ≥10; ii) absence of reads confirming alternative allele in parents; iii) number of reads confirming reference and alternative allele in proband ≥5; iv) allele frequency of the alternative allele in the proband ≥0.3. Additionally, clustered mutations (distance between mutations ≤1000bp) were filtered out, as potential false positives. Mutations present in any other family member were also excluded. To eliminate false positive mutations coming from the sequencing of proband fibroblasts, we used other fibroblasts from the same individual (used in MA experiments) as additional confirmation of mutation presence.
DOI: 10.6084/m9.figshare.21786542.v1
title: IV.6_SNP&gt;mv20.vcf

description: De novo mutations observed in individual IV.6. Sequenced reads were aligned to the hg19 reference human genome downloaded from UCSC and aligned using Burrows–Wheeler alignment (BWA-MEM). <em>De novo</em> mutations were called using standard GATK4 best practices pipeline. All samples were jointly called, but only high-confidence mendelian violation sites with minimum GQ=20 for each trio member were selected. Subsequently, additional filters were applied: i) coverage of each trio member ≥10; ii) absence of reads confirming alternative allele in parents; iii) number of reads confirming reference and alternative allele in proband ≥5; iv) allele frequency of the alternative allele in the proband ≥0.3. Additionally, clustered mutations (distance between mutations ≤1000bp) were filtered out, as potential false positives. Mutations present in any other family member were also excluded. To eliminate false positive mutations coming from the sequencing of proband fibroblasts, we used other fibroblasts from the same individual (used in MA experiments) as additional confirmation of mutation presence.
DOI: 10.5281/zenodo.5121822
title: Problem instances for "An Iterated Greedy Matheuristic for Scheduling in Steelmaking-Continuous Casting Process"

description: A test upload for archiving
DOI: 10.5281/zenodo.5121821
title: Problem instances for "An Iterated Greedy Matheuristic for Scheduling in Steelmaking-Continuous Casting Process"

description: A test upload for archiving
DOI: 10.34740/kaggle/dsv/4781724
title: License Plate Text Recognition Dataset
DOI: 10.6084/m9.figshare.19246273
title: A comparative study of water quality using two quality indices and a risk index in a drinking water distribution network

description: This study compares the Canadian Council Water Quality Index (CCME WQI) and the Arithmetic Water Quality Index (WAWQI) methodologies for determining the quality of water in the city of Azogues (Ecuador). Additionally, a drinking water quality risk index (IRCA) was determined to evaluate the degree of risk of disease occurrence related to water consumption. The data generated came from the analyses of twelve physicochemical parameters (pH, turbidity, colour, total dissolved solids, electrical conductivity, total hardness, alkalinity, nitrates, phosphates, sulfates, chlorides, residual chlorine) from 172 samples of water over six months. The calculated average value of CCME WQI (97.59 ± 1.08) indicates that 100% of the drinking system was of ‘excellent’ quality. The WAWQI average value was calculated to be 26.36 ± 1.13 indicating that 16.67% of the distribution system was of ‘excellent’ quality and 83.33% of the distribution water was of ‘good’ quality. The IRCA calculated in all the distribution zones is between 0 and 5% and therefore, the distributed water is considered suitable for human consumption and is rated at the no-risk level. Furthermore, WAWQI is influenced by parameters with low maximum allowed concentration (for example, turbidity value 1 NTU in the Ecuadorian standard was used instead of 5 NTU recommended by the WHO); conversely, CCME-WQI is influenced by parameters with a high maximum allowed concentration (no parameter exceeded the norm in this study). The IRCA is a support instrument to guarantee that the water supplied by the provider companies complies with the characteristics established for drinking water.
DOI: 10.6084/m9.figshare.19246273.v1
title: A comparative study of water quality using two quality indices and a risk index in a drinking water distribution network

description: This study compares the Canadian Council Water Quality Index (CCME WQI) and the Arithmetic Water Quality Index (WAWQI) methodologies for determining the quality of water in the city of Azogues (Ecuador). Additionally, a drinking water quality risk index (IRCA) was determined to evaluate the degree of risk of disease occurrence related to water consumption. The data generated came from the analyses of twelve physicochemical parameters (pH, turbidity, colour, total dissolved solids, electrical conductivity, total hardness, alkalinity, nitrates, phosphates, sulfates, chlorides, residual chlorine) from 172 samples of water over six months. The calculated average value of CCME WQI (97.59 ± 1.08) indicates that 100% of the drinking system was of ‘excellent’ quality. The WAWQI average value was calculated to be 26.36 ± 1.13 indicating that 16.67% of the distribution system was of ‘excellent’ quality and 83.33% of the distribution water was of ‘good’ quality. The IRCA calculated in all the distribution zones is between 0 and 5% and therefore, the distributed water is considered suitable for human consumption and is rated at the no-risk level. Furthermore, WAWQI is influenced by parameters with low maximum allowed concentration (for example, turbidity value 1 NTU in the Ecuadorian standard was used instead of 5 NTU recommended by the WHO); conversely, CCME-WQI is influenced by parameters with a high maximum allowed concentration (no parameter exceeded the norm in this study). The IRCA is a support instrument to guarantee that the water supplied by the provider companies complies with the characteristics established for drinking water.
DOI: 10.6084/m9.figshare.21702048.v1
title: Nitrogen removal based on anammox-based processes applied to mature landfill leachate diluted with domestic wastewater: a review

description: The anammox process is considered a sustainable, low-cost, and promising alternative for the treatment of effluents with high concentrations of nitrogen (N) and low concentrations of chemical oxygen demand (COD). Mature landfill leachates are rich in nitrogen and a potential source of water and soil contamination, potentially causing eutrophication of surface water bodies. In developing countries, leachates have been treated together with municipal effluents in WWTP. However, there are few reports on nitrogen removal in these systems. This review evaluated studies using anammox-based processes, including partial nitrification and anammox (PN/A), focusing on mature leachates and their co-treatment with municipal wastewater. We aimed to identify anammox-based treatment strategies leading to high N and COD removal efficiencies. For PN/A, sequential batch reactors have been successfully used to treat different effluents such as leachates. Recirculation and leachate dilution in anammox-based processes were strategies successfully adopted to achieve high nitrogen removal. Temperatures above 30°C and pH values from 7.8–8.5 had positive effects on the performance of co-treatment processes. The best performances in the co-treatment processes using PN/A were achieved for dilution rates between 2 and 11.5 parts of municipal wastewater to 1 part of mature leachate, reaching N and COD removal efficiencies above 90% and 86%, respectively. Inhibition of nitrite-oxidizing bacteria, acclimatization of anammox bacteria for long periods, as well as extracellular polymeric substances production by anammox bacteria are important factors in the performance of PN/A systems for the co-treatment of mature leachate and domestic wastewater.
DOI: 10.6084/m9.figshare.21702048
title: Nitrogen removal based on anammox-based processes applied to mature landfill leachate diluted with domestic wastewater: a review

description: The anammox process is considered a sustainable, low-cost, and promising alternative for the treatment of effluents with high concentrations of nitrogen (N) and low concentrations of chemical oxygen demand (COD). Mature landfill leachates are rich in nitrogen and a potential source of water and soil contamination, potentially causing eutrophication of surface water bodies. In developing countries, leachates have been treated together with municipal effluents in WWTP. However, there are few reports on nitrogen removal in these systems. This review evaluated studies using anammox-based processes, including partial nitrification and anammox (PN/A), focusing on mature leachates and their co-treatment with municipal wastewater. We aimed to identify anammox-based treatment strategies leading to high N and COD removal efficiencies. For PN/A, sequential batch reactors have been successfully used to treat different effluents such as leachates. Recirculation and leachate dilution in anammox-based processes were strategies successfully adopted to achieve high nitrogen removal. Temperatures above 30°C and pH values from 7.8–8.5 had positive effects on the performance of co-treatment processes. The best performances in the co-treatment processes using PN/A were achieved for dilution rates between 2 and 11.5 parts of municipal wastewater to 1 part of mature leachate, reaching N and COD removal efficiencies above 90% and 86%, respectively. Inhibition of nitrite-oxidizing bacteria, acclimatization of anammox bacteria for long periods, as well as extracellular polymeric substances production by anammox bacteria are important factors in the performance of PN/A systems for the co-treatment of mature leachate and domestic wastewater.
DOI: 10.5281/zenodo.7071383
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714
DOI: 10.5281/zenodo.7093283
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714
DOI: 10.5281/zenodo.7108221
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714
DOI: 10.5281/zenodo.7113965
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714
DOI: 10.5281/zenodo.7125182
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714
DOI: 10.5281/zenodo.7139658
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714
DOI: 10.5281/zenodo.7144635
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714
DOI: 10.5281/zenodo.7158215
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714
DOI: 10.5281/zenodo.7071382
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714
DOI: 10.5281/zenodo.7292890
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714
DOI: 10.5281/zenodo.7298730
title: Software Similarity Dataset

description: This dataset contains the post-processed data for software similarity learning. More information is given: SoftwareSim_Github post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software. final_data: All information obtained by Somef & Inspect4py as well as cleaning. Each file represents software in the format given --&gt; Function_Name: [[Called Function], [Function Tokens]] lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given: Property Example Graph_1 kakaobrain_helo_word Graph_2 mblondel_soft-dtw miniLM 0.4503 Sbert 0.7204 TSDAE 0.5714