Pratibha, Amandeep Kaur, Meenu Khurana, R. Damaševičius
Wars, conflicts, and peace efforts have become inherent characteristics of regions, and understanding the prevailing sentiments related to these issues is crucial for finding long-lasting solutions. Twitter/`X’, with its vast user base and real-time nature, provides a valuable source to assess the raw emotions and opinions of people regarding war, conflict, and peace. This paper focuses on collecting and curating hinglish tweets specifically related to wars, conflicts, and associated taxonomy. The creation of said dataset addresses the existing gap in contemporary literature, which lacks comprehensive datasets capturing the emotions and sentiments expressed by individuals regarding wars, conflicts, and peace efforts. This dataset holds significant value and application in deep pragmatic analysis as it enables future researchers to identify the flow of sentiments, analyze the information architecture surrounding war, conflict, and peace effects, and delve into the associated psychology in this context. To ensure the dataset’s quality and relevance, a meticulous selection process was employed, resulting in the inclusion of explanable 500 carefully chosen search filters. The dataset currently has 10,040 tweets that have been validated with the help of human expert to make sure they are correct and accurate.
{"title":"Multimodal Hinglish Tweet Dataset for Deep Pragmatic Analysis","authors":"Pratibha, Amandeep Kaur, Meenu Khurana, R. Damaševičius","doi":"10.3390/data9020038","DOIUrl":"https://doi.org/10.3390/data9020038","url":null,"abstract":"Wars, conflicts, and peace efforts have become inherent characteristics of regions, and understanding the prevailing sentiments related to these issues is crucial for finding long-lasting solutions. Twitter/`X’, with its vast user base and real-time nature, provides a valuable source to assess the raw emotions and opinions of people regarding war, conflict, and peace. This paper focuses on collecting and curating hinglish tweets specifically related to wars, conflicts, and associated taxonomy. The creation of said dataset addresses the existing gap in contemporary literature, which lacks comprehensive datasets capturing the emotions and sentiments expressed by individuals regarding wars, conflicts, and peace efforts. This dataset holds significant value and application in deep pragmatic analysis as it enables future researchers to identify the flow of sentiments, analyze the information architecture surrounding war, conflict, and peace effects, and delve into the associated psychology in this context. To ensure the dataset’s quality and relevance, a meticulous selection process was employed, resulting in the inclusion of explanable 500 carefully chosen search filters. The dataset currently has 10,040 tweets that have been validated with the help of human expert to make sure they are correct and accurate.","PeriodicalId":502371,"journal":{"name":"Data","volume":"234 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139835830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coastal dunes worldwide are increasingly under pressure from the adverse effects of human activities. Therefore, more and more restoration measures are being taken to create conditions that help disturbed coastal dune ecosystems regenerate or recover naturally. However, many projects lack the (open-access) monitoring observations needed to signal whether further actions are needed, and hence lack the opportunity to "learn by doing". This submission presents an open-access data set of 37 high-resolution digital elevation models and 24 orthomosaics collected before and after the excavation of five artificial foredune trough blowouts (“notches”) in winter 2012/2013 in the Dutch Zuid-Kennemerland National Park, one of the largest coastal dune restoration projects in northwest Europe. These high-resolution data provide a valuable resource for improving understanding of the biogeomorphic processes that determine the evolution of restored dune systems as well as developing guidelines to better design future restoration efforts with foredune notching.
{"title":"Digital Elevation Models and Orthomosaics of the Dutch Noordwest Natuurkern Foredune Restoration Project","authors":"G. Ruessink, Dick Groenendijk, B. Arens","doi":"10.3390/data9020037","DOIUrl":"https://doi.org/10.3390/data9020037","url":null,"abstract":"Coastal dunes worldwide are increasingly under pressure from the adverse effects of human activities. Therefore, more and more restoration measures are being taken to create conditions that help disturbed coastal dune ecosystems regenerate or recover naturally. However, many projects lack the (open-access) monitoring observations needed to signal whether further actions are needed, and hence lack the opportunity to \"learn by doing\". This submission presents an open-access data set of 37 high-resolution digital elevation models and 24 orthomosaics collected before and after the excavation of five artificial foredune trough blowouts (“notches”) in winter 2012/2013 in the Dutch Zuid-Kennemerland National Park, one of the largest coastal dune restoration projects in northwest Europe. These high-resolution data provide a valuable resource for improving understanding of the biogeomorphic processes that determine the evolution of restored dune systems as well as developing guidelines to better design future restoration efforts with foredune notching.","PeriodicalId":502371,"journal":{"name":"Data","volume":"9 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139776255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As one of the most important topics in contemporary computer vision research, object detection has received wide attention from the precision agriculture community for diverse applications. While state-of-the-art object detection frameworks are usually evaluated against large-scale public datasets containing mostly non-agricultural objects, a specialized dataset that reflects unique properties of plants would aid researchers in investigating the utility of newly developed object detectors within agricultural contexts. This article presents AriAplBud: a close-up apple flower bud image dataset created using an unmanned aerial vehicle (UAV)-based red–green–blue (RGB) camera. AriAplBud contains 3600 images of apple flower buds at six growth stages, with 110,467 manual bounding box annotations as positive samples and 2520 additional empty orchard images containing no apple flower bud as negative samples. AriAplBud can be directly deployed for developing object detection models that accept Darknet annotation format without additional preprocessing steps, serving as a potential benchmark for future agricultural object detection research. A demonstration of developing YOLOv8-based apple flower bud detectors is also presented in this article.
{"title":"AriAplBud: An Aerial Multi-Growth Stage Apple Flower Bud Dataset for Agricultural Object Detection Benchmarking","authors":"Wenan Yuan","doi":"10.3390/data9020036","DOIUrl":"https://doi.org/10.3390/data9020036","url":null,"abstract":"As one of the most important topics in contemporary computer vision research, object detection has received wide attention from the precision agriculture community for diverse applications. While state-of-the-art object detection frameworks are usually evaluated against large-scale public datasets containing mostly non-agricultural objects, a specialized dataset that reflects unique properties of plants would aid researchers in investigating the utility of newly developed object detectors within agricultural contexts. This article presents AriAplBud: a close-up apple flower bud image dataset created using an unmanned aerial vehicle (UAV)-based red–green–blue (RGB) camera. AriAplBud contains 3600 images of apple flower buds at six growth stages, with 110,467 manual bounding box annotations as positive samples and 2520 additional empty orchard images containing no apple flower bud as negative samples. AriAplBud can be directly deployed for developing object detection models that accept Darknet annotation format without additional preprocessing steps, serving as a potential benchmark for future agricultural object detection research. A demonstration of developing YOLOv8-based apple flower bud detectors is also presented in this article.","PeriodicalId":502371,"journal":{"name":"Data","volume":"119 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139785709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As one of the most important topics in contemporary computer vision research, object detection has received wide attention from the precision agriculture community for diverse applications. While state-of-the-art object detection frameworks are usually evaluated against large-scale public datasets containing mostly non-agricultural objects, a specialized dataset that reflects unique properties of plants would aid researchers in investigating the utility of newly developed object detectors within agricultural contexts. This article presents AriAplBud: a close-up apple flower bud image dataset created using an unmanned aerial vehicle (UAV)-based red–green–blue (RGB) camera. AriAplBud contains 3600 images of apple flower buds at six growth stages, with 110,467 manual bounding box annotations as positive samples and 2520 additional empty orchard images containing no apple flower bud as negative samples. AriAplBud can be directly deployed for developing object detection models that accept Darknet annotation format without additional preprocessing steps, serving as a potential benchmark for future agricultural object detection research. A demonstration of developing YOLOv8-based apple flower bud detectors is also presented in this article.
{"title":"AriAplBud: An Aerial Multi-Growth Stage Apple Flower Bud Dataset for Agricultural Object Detection Benchmarking","authors":"Wenan Yuan","doi":"10.3390/data9020036","DOIUrl":"https://doi.org/10.3390/data9020036","url":null,"abstract":"As one of the most important topics in contemporary computer vision research, object detection has received wide attention from the precision agriculture community for diverse applications. While state-of-the-art object detection frameworks are usually evaluated against large-scale public datasets containing mostly non-agricultural objects, a specialized dataset that reflects unique properties of plants would aid researchers in investigating the utility of newly developed object detectors within agricultural contexts. This article presents AriAplBud: a close-up apple flower bud image dataset created using an unmanned aerial vehicle (UAV)-based red–green–blue (RGB) camera. AriAplBud contains 3600 images of apple flower buds at six growth stages, with 110,467 manual bounding box annotations as positive samples and 2520 additional empty orchard images containing no apple flower bud as negative samples. AriAplBud can be directly deployed for developing object detection models that accept Darknet annotation format without additional preprocessing steps, serving as a potential benchmark for future agricultural object detection research. A demonstration of developing YOLOv8-based apple flower bud detectors is also presented in this article.","PeriodicalId":502371,"journal":{"name":"Data","volume":"32 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139845600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Shikov, Iuliia A. Savina, Maria N. Romanenko, A. Nizhnikov, K. S. Antonets
The Bacillus thuringiensis serovar thuringiensis strain 800/15 has been actively used as an agent in biopreparations with high insecticidal activity against the larvae of the Colorado potato beetle Leptinotarsa decemlineata and gypsy moth Lymantria dispar. In the current study, we present the first draft genome of the 800/15 strain coupled with a comparative genomic analysis of its closest reference strains. The raw sequence data were obtained by Illumina technology on the HiSeq X platform and de novo assembled with the SPAdes v3.15.4 software. The genome reached 6,524,663 bp. in size and carried 6771 coding sequences, 3 of which represented loci encoding insecticidal toxins, namely, Spp1Aa1, Cry1Ab9, and Cry1Ba8 active against the orders Lepidoptera, Blattodea, Hemiptera, Diptera, and Coleoptera. We also revealed the biosynthetic gene clusters responsible for the synthesis of secondary metabolites, including fengycin, bacillibactin, and petrobactin with predicted antibacterial, fungicidal, and growth-promoting properties. Further comparative genomics suggested the strain is not enriched with genes linked with biological activities implying that agriculturally important properties rely more on the composition of loci rather than their abundance. The obtained genomic sequence of the strain with the experimental metadata could facilitate the computational prediction of bacterial isolates’ potency from genomic data.
{"title":"Draft Genome Sequencing of the Bacillus thuringiensis var. Thuringiensis Highly Insecticidal Strain 800/15","authors":"A. Shikov, Iuliia A. Savina, Maria N. Romanenko, A. Nizhnikov, K. S. Antonets","doi":"10.3390/data9020034","DOIUrl":"https://doi.org/10.3390/data9020034","url":null,"abstract":"The Bacillus thuringiensis serovar thuringiensis strain 800/15 has been actively used as an agent in biopreparations with high insecticidal activity against the larvae of the Colorado potato beetle Leptinotarsa decemlineata and gypsy moth Lymantria dispar. In the current study, we present the first draft genome of the 800/15 strain coupled with a comparative genomic analysis of its closest reference strains. The raw sequence data were obtained by Illumina technology on the HiSeq X platform and de novo assembled with the SPAdes v3.15.4 software. The genome reached 6,524,663 bp. in size and carried 6771 coding sequences, 3 of which represented loci encoding insecticidal toxins, namely, Spp1Aa1, Cry1Ab9, and Cry1Ba8 active against the orders Lepidoptera, Blattodea, Hemiptera, Diptera, and Coleoptera. We also revealed the biosynthetic gene clusters responsible for the synthesis of secondary metabolites, including fengycin, bacillibactin, and petrobactin with predicted antibacterial, fungicidal, and growth-promoting properties. Further comparative genomics suggested the strain is not enriched with genes linked with biological activities implying that agriculturally important properties rely more on the composition of loci rather than their abundance. The obtained genomic sequence of the strain with the experimental metadata could facilitate the computational prediction of bacterial isolates’ potency from genomic data.","PeriodicalId":502371,"journal":{"name":"Data","volume":"230 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139847501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Hendriksen, Sema Tan, E. C. van Oostrom, A. Merlo, H. Bardakçi, Nilay Aksoy, Johan Garssen, G. Bruce, J. Verster
Previous studies from the Netherlands, Germany, and Argentina revealed that the 2019 coronavirus disease (COVID-19) pandemic and associated lockdown periods had a significant negative impact on the wellbeing and quality of life of students. The negative impact of lockdown periods on health correlates such as immune fitness, alcohol consumption, and mood were reflected in their academic functioning. As both the duration and intensity of lockdown measures differed between countries, it is important to replicate these findings in different countries and cultures. Therefore, the purpose of the current study was to examine the impact of the COVID-19 pandemic on immune fitness, mood, academic functioning, sleep, smoking, alcohol consumption, healthy diet, and quality of life among Turkish students. Turkish students in the age range of 18 to 30 years old were invited to complete an online survey. Data were collected from n = 307 participants and included retrospective assessments for six time periods: (1) BP (before the COVID-19 pandemic, 1 January 2020–10 March 2020), (2) NL1 (the first no lockdown period, 11 March 2020–28 April 2021), (3) the lockdown period (29 April 2021–17 May 2021), (4) NL2 (the second no lockdown period, 18 May 2021–31 December 2021), (5) NL3 (the third no lockdown period, 1 January 2022–December 2022), and (6) for the past month. In this data descriptor article, the content of the survey and the dataset are described.
{"title":"COVID-19 Lockdown Effects on Sleep, Immune Fitness, Mood, Quality of Life, and Academic Functioning: Survey Data from Turkish University Students","authors":"P. Hendriksen, Sema Tan, E. C. van Oostrom, A. Merlo, H. Bardakçi, Nilay Aksoy, Johan Garssen, G. Bruce, J. Verster","doi":"10.3390/data9020035","DOIUrl":"https://doi.org/10.3390/data9020035","url":null,"abstract":"Previous studies from the Netherlands, Germany, and Argentina revealed that the 2019 coronavirus disease (COVID-19) pandemic and associated lockdown periods had a significant negative impact on the wellbeing and quality of life of students. The negative impact of lockdown periods on health correlates such as immune fitness, alcohol consumption, and mood were reflected in their academic functioning. As both the duration and intensity of lockdown measures differed between countries, it is important to replicate these findings in different countries and cultures. Therefore, the purpose of the current study was to examine the impact of the COVID-19 pandemic on immune fitness, mood, academic functioning, sleep, smoking, alcohol consumption, healthy diet, and quality of life among Turkish students. Turkish students in the age range of 18 to 30 years old were invited to complete an online survey. Data were collected from n = 307 participants and included retrospective assessments for six time periods: (1) BP (before the COVID-19 pandemic, 1 January 2020–10 March 2020), (2) NL1 (the first no lockdown period, 11 March 2020–28 April 2021), (3) the lockdown period (29 April 2021–17 May 2021), (4) NL2 (the second no lockdown period, 18 May 2021–31 December 2021), (5) NL3 (the third no lockdown period, 1 January 2022–December 2022), and (6) for the past month. In this data descriptor article, the content of the survey and the dataset are described.","PeriodicalId":502371,"journal":{"name":"Data","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139786705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Hendriksen, Sema Tan, E. C. van Oostrom, A. Merlo, H. Bardakçi, Nilay Aksoy, Johan Garssen, G. Bruce, J. Verster
Previous studies from the Netherlands, Germany, and Argentina revealed that the 2019 coronavirus disease (COVID-19) pandemic and associated lockdown periods had a significant negative impact on the wellbeing and quality of life of students. The negative impact of lockdown periods on health correlates such as immune fitness, alcohol consumption, and mood were reflected in their academic functioning. As both the duration and intensity of lockdown measures differed between countries, it is important to replicate these findings in different countries and cultures. Therefore, the purpose of the current study was to examine the impact of the COVID-19 pandemic on immune fitness, mood, academic functioning, sleep, smoking, alcohol consumption, healthy diet, and quality of life among Turkish students. Turkish students in the age range of 18 to 30 years old were invited to complete an online survey. Data were collected from n = 307 participants and included retrospective assessments for six time periods: (1) BP (before the COVID-19 pandemic, 1 January 2020–10 March 2020), (2) NL1 (the first no lockdown period, 11 March 2020–28 April 2021), (3) the lockdown period (29 April 2021–17 May 2021), (4) NL2 (the second no lockdown period, 18 May 2021–31 December 2021), (5) NL3 (the third no lockdown period, 1 January 2022–December 2022), and (6) for the past month. In this data descriptor article, the content of the survey and the dataset are described.
{"title":"COVID-19 Lockdown Effects on Sleep, Immune Fitness, Mood, Quality of Life, and Academic Functioning: Survey Data from Turkish University Students","authors":"P. Hendriksen, Sema Tan, E. C. van Oostrom, A. Merlo, H. Bardakçi, Nilay Aksoy, Johan Garssen, G. Bruce, J. Verster","doi":"10.3390/data9020035","DOIUrl":"https://doi.org/10.3390/data9020035","url":null,"abstract":"Previous studies from the Netherlands, Germany, and Argentina revealed that the 2019 coronavirus disease (COVID-19) pandemic and associated lockdown periods had a significant negative impact on the wellbeing and quality of life of students. The negative impact of lockdown periods on health correlates such as immune fitness, alcohol consumption, and mood were reflected in their academic functioning. As both the duration and intensity of lockdown measures differed between countries, it is important to replicate these findings in different countries and cultures. Therefore, the purpose of the current study was to examine the impact of the COVID-19 pandemic on immune fitness, mood, academic functioning, sleep, smoking, alcohol consumption, healthy diet, and quality of life among Turkish students. Turkish students in the age range of 18 to 30 years old were invited to complete an online survey. Data were collected from n = 307 participants and included retrospective assessments for six time periods: (1) BP (before the COVID-19 pandemic, 1 January 2020–10 March 2020), (2) NL1 (the first no lockdown period, 11 March 2020–28 April 2021), (3) the lockdown period (29 April 2021–17 May 2021), (4) NL2 (the second no lockdown period, 18 May 2021–31 December 2021), (5) NL3 (the third no lockdown period, 1 January 2022–December 2022), and (6) for the past month. In this data descriptor article, the content of the survey and the dataset are described.","PeriodicalId":502371,"journal":{"name":"Data","volume":"42 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139846621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Shikov, Iuliia A. Savina, Maria N. Romanenko, A. Nizhnikov, K. S. Antonets
The Bacillus thuringiensis serovar thuringiensis strain 800/15 has been actively used as an agent in biopreparations with high insecticidal activity against the larvae of the Colorado potato beetle Leptinotarsa decemlineata and gypsy moth Lymantria dispar. In the current study, we present the first draft genome of the 800/15 strain coupled with a comparative genomic analysis of its closest reference strains. The raw sequence data were obtained by Illumina technology on the HiSeq X platform and de novo assembled with the SPAdes v3.15.4 software. The genome reached 6,524,663 bp. in size and carried 6771 coding sequences, 3 of which represented loci encoding insecticidal toxins, namely, Spp1Aa1, Cry1Ab9, and Cry1Ba8 active against the orders Lepidoptera, Blattodea, Hemiptera, Diptera, and Coleoptera. We also revealed the biosynthetic gene clusters responsible for the synthesis of secondary metabolites, including fengycin, bacillibactin, and petrobactin with predicted antibacterial, fungicidal, and growth-promoting properties. Further comparative genomics suggested the strain is not enriched with genes linked with biological activities implying that agriculturally important properties rely more on the composition of loci rather than their abundance. The obtained genomic sequence of the strain with the experimental metadata could facilitate the computational prediction of bacterial isolates’ potency from genomic data.
{"title":"Draft Genome Sequencing of the Bacillus thuringiensis var. Thuringiensis Highly Insecticidal Strain 800/15","authors":"A. Shikov, Iuliia A. Savina, Maria N. Romanenko, A. Nizhnikov, K. S. Antonets","doi":"10.3390/data9020034","DOIUrl":"https://doi.org/10.3390/data9020034","url":null,"abstract":"The Bacillus thuringiensis serovar thuringiensis strain 800/15 has been actively used as an agent in biopreparations with high insecticidal activity against the larvae of the Colorado potato beetle Leptinotarsa decemlineata and gypsy moth Lymantria dispar. In the current study, we present the first draft genome of the 800/15 strain coupled with a comparative genomic analysis of its closest reference strains. The raw sequence data were obtained by Illumina technology on the HiSeq X platform and de novo assembled with the SPAdes v3.15.4 software. The genome reached 6,524,663 bp. in size and carried 6771 coding sequences, 3 of which represented loci encoding insecticidal toxins, namely, Spp1Aa1, Cry1Ab9, and Cry1Ba8 active against the orders Lepidoptera, Blattodea, Hemiptera, Diptera, and Coleoptera. We also revealed the biosynthetic gene clusters responsible for the synthesis of secondary metabolites, including fengycin, bacillibactin, and petrobactin with predicted antibacterial, fungicidal, and growth-promoting properties. Further comparative genomics suggested the strain is not enriched with genes linked with biological activities implying that agriculturally important properties rely more on the composition of loci rather than their abundance. The obtained genomic sequence of the strain with the experimental metadata could facilitate the computational prediction of bacterial isolates’ potency from genomic data.","PeriodicalId":502371,"journal":{"name":"Data","volume":" June","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139787352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Igor Bezerra Reis, Rafael Ângelo Santos Leite, Mateus Miranda Torres, Alcides Gonçalves da Silva Neto, Francisco José da Silva e Silva, A. Teles
A registered trademark represents one of a company’s most valuable intellectual assets, acting as a safeguard against possible reputational damage and financial losses resulting from infringements of this intellectual property. To be registered, a mark must be unique and distinctive in relation to other trademarks which are already registered. In this paper, we describe the CMAD, an acronym for Conflicting Marks Archive Dataset. This dataset has been meticulously organized into pairs of marks (Number of pairs = 18,355) involved in copyright infringement across word, figurative and mixed marks. Organizations sought to register these marks with the National Institute of Industrial Property (INPI) in Brazil, and had their applications denied after analysis by intellectual property specialists. The robustness of this dataset is ensured by the intrinsic similarity of the conflicting marks, since the decisions were made by INPI specialists. This characteristic provides a reliable basis for the development and testing of tools designed to analyze similarity between marks, thus contributing to the evolution of practices and computer-based solutions in the field of intellectual property.
{"title":"Conflicting Marks Archive Dataset: A Dataset of Conflicting Marks from the Brazilian Intellectual Property Office","authors":"Igor Bezerra Reis, Rafael Ângelo Santos Leite, Mateus Miranda Torres, Alcides Gonçalves da Silva Neto, Francisco José da Silva e Silva, A. Teles","doi":"10.3390/data9020033","DOIUrl":"https://doi.org/10.3390/data9020033","url":null,"abstract":"A registered trademark represents one of a company’s most valuable intellectual assets, acting as a safeguard against possible reputational damage and financial losses resulting from infringements of this intellectual property. To be registered, a mark must be unique and distinctive in relation to other trademarks which are already registered. In this paper, we describe the CMAD, an acronym for Conflicting Marks Archive Dataset. This dataset has been meticulously organized into pairs of marks (Number of pairs = 18,355) involved in copyright infringement across word, figurative and mixed marks. Organizations sought to register these marks with the National Institute of Industrial Property (INPI) in Brazil, and had their applications denied after analysis by intellectual property specialists. The robustness of this dataset is ensured by the intrinsic similarity of the conflicting marks, since the decisions were made by INPI specialists. This characteristic provides a reliable basis for the development and testing of tools designed to analyze similarity between marks, thus contributing to the evolution of practices and computer-based solutions in the field of intellectual property.","PeriodicalId":502371,"journal":{"name":"Data","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139789781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Igor Bezerra Reis, Rafael Ângelo Santos Leite, Mateus Miranda Torres, Alcides Gonçalves da Silva Neto, Francisco José da Silva e Silva, A. Teles
A registered trademark represents one of a company’s most valuable intellectual assets, acting as a safeguard against possible reputational damage and financial losses resulting from infringements of this intellectual property. To be registered, a mark must be unique and distinctive in relation to other trademarks which are already registered. In this paper, we describe the CMAD, an acronym for Conflicting Marks Archive Dataset. This dataset has been meticulously organized into pairs of marks (Number of pairs = 18,355) involved in copyright infringement across word, figurative and mixed marks. Organizations sought to register these marks with the National Institute of Industrial Property (INPI) in Brazil, and had their applications denied after analysis by intellectual property specialists. The robustness of this dataset is ensured by the intrinsic similarity of the conflicting marks, since the decisions were made by INPI specialists. This characteristic provides a reliable basis for the development and testing of tools designed to analyze similarity between marks, thus contributing to the evolution of practices and computer-based solutions in the field of intellectual property.
{"title":"Conflicting Marks Archive Dataset: A Dataset of Conflicting Marks from the Brazilian Intellectual Property Office","authors":"Igor Bezerra Reis, Rafael Ângelo Santos Leite, Mateus Miranda Torres, Alcides Gonçalves da Silva Neto, Francisco José da Silva e Silva, A. Teles","doi":"10.3390/data9020033","DOIUrl":"https://doi.org/10.3390/data9020033","url":null,"abstract":"A registered trademark represents one of a company’s most valuable intellectual assets, acting as a safeguard against possible reputational damage and financial losses resulting from infringements of this intellectual property. To be registered, a mark must be unique and distinctive in relation to other trademarks which are already registered. In this paper, we describe the CMAD, an acronym for Conflicting Marks Archive Dataset. This dataset has been meticulously organized into pairs of marks (Number of pairs = 18,355) involved in copyright infringement across word, figurative and mixed marks. Organizations sought to register these marks with the National Institute of Industrial Property (INPI) in Brazil, and had their applications denied after analysis by intellectual property specialists. The robustness of this dataset is ensured by the intrinsic similarity of the conflicting marks, since the decisions were made by INPI specialists. This characteristic provides a reliable basis for the development and testing of tools designed to analyze similarity between marks, thus contributing to the evolution of practices and computer-based solutions in the field of intellectual property.","PeriodicalId":502371,"journal":{"name":"Data","volume":"172 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139849652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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