Pub Date : 2024-10-16DOI: 10.1016/j.dib.2024.111031
Mohammad Fraiwan , Rami Mukbel , Dania Kanaan
Sandflies serve as carriers for numerous tropical diseases, including leishmaniasis, bartonellosis, and sandfly fever. Furthermore, sandflies are species-specific when it comes to transmitting corresponding pathogen species. Hence, accurate classification and identification of sandfly species and gender are essential for various purposes such as disease monitoring and control, population management, research and development, and epidemiological investigations. Most of the sexing and taxonomy keys are based on internal morphological features, which may lead to errors due to some features being missed by the naked eye. In this paper, we describe the process we used to collect and prepare samples of three sandfly species (Ph. alexandri, Ph. papatasi, and Ph. sergenti). The dataset described in this article contains two images per sample, representing the pharynx in the head and the genitalia in the abdomen. The dataset is organized into male and female categories for each of the three species. The sex and species were determined manually by two specialists. This dataset can be used to develop automated methods for sex identification and taxonomy. Additionally, it can be used to train students in speciation and taxonomy. To the best of our knowledge, this is the first publicly available dataset of images of this kind.
{"title":"A dataset of sandfly (Phlebotomus papatasi, Phlebotomus alexandri, and Phlebotomus sergenti) genital and pharyngeal images","authors":"Mohammad Fraiwan , Rami Mukbel , Dania Kanaan","doi":"10.1016/j.dib.2024.111031","DOIUrl":"10.1016/j.dib.2024.111031","url":null,"abstract":"<div><div>Sandflies serve as carriers for numerous tropical diseases, including leishmaniasis, bartonellosis, and sandfly fever. Furthermore, sandflies are species-specific when it comes to transmitting corresponding pathogen species. Hence, accurate classification and identification of sandfly species and gender are essential for various purposes such as disease monitoring and control, population management, research and development, and epidemiological investigations. Most of the sexing and taxonomy keys are based on internal morphological features, which may lead to errors due to some features being missed by the naked eye. In this paper, we describe the process we used to collect and prepare samples of three sandfly species (<em>Ph. alexandri, Ph. papatasi</em>, and <em>Ph. sergenti</em>). The dataset described in this article contains two images per sample, representing the pharynx in the head and the genitalia in the abdomen. The dataset is organized into male and female categories for each of the three species. The sex and species were determined manually by two specialists. This dataset can be used to develop automated methods for sex identification and taxonomy. Additionally, it can be used to train students in speciation and taxonomy. To the best of our knowledge, this is the first publicly available dataset of images of this kind.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111031"},"PeriodicalIF":1.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-15DOI: 10.1016/j.dib.2024.111019
Ki Hyun Nam
β-Glucosidase (Bgl) is a biomass-degrading enzyme that hydrolyzes cellobiose and glucose-substituted polysaccharides into glucose, playing a crucial role in enzymatic saccharification during biofuel production. Despite the wealth of structural information available on Bgl, the molecular properties of the loops above the substrate-binding pocket remain unexplored. In previous study, to better understand the molecular functions of these loop regions, four crystal structures of Thermoanaerobacterium saccharolyticum Bgl (TsaBgl) were determined. The molecular flexibility and conformational changes of the loop regions in TsaBgl were analysed, expanding our understanding of their roles in the Bgl family. The data processing and structure determination details provided here are valuable for further studies on the structural properties of these loop regions.
{"title":"Data on the crystal structures of β-glucosidase from Thermoanaerobacterium saccharolyticum","authors":"Ki Hyun Nam","doi":"10.1016/j.dib.2024.111019","DOIUrl":"10.1016/j.dib.2024.111019","url":null,"abstract":"<div><div>β-Glucosidase (Bgl) is a biomass-degrading enzyme that hydrolyzes cellobiose and glucose-substituted polysaccharides into glucose, playing a crucial role in enzymatic saccharification during biofuel production. Despite the wealth of structural information available on Bgl, the molecular properties of the loops above the substrate-binding pocket remain unexplored. In previous study, to better understand the molecular functions of these loop regions, four crystal structures of <em>Thermoanaerobacterium saccharolyticum</em> Bgl (TsaBgl) were determined. The molecular flexibility and conformational changes of the loop regions in TsaBgl were analysed, expanding our understanding of their roles in the Bgl family. The data processing and structure determination details provided here are valuable for further studies on the structural properties of these loop regions.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111019"},"PeriodicalIF":1.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agriculture greatly impacts Bangladesh's economy, and vegetable cultivation plays a significant role in Agriculture by providing nourishment, and food security as well as improving the economy. The necessity of food production is growing similarly to the population growth. The farmers of Bangladesh are working hard to meet this need for food production and to gain yields. However, every year the farmers face a significant amount of loss in production due to the attack of different diseases and viruses due to the lack to technological development. The reason behind most of these losses is the lack of knowledge about diseases and being unable to detect the diseases early. Therefore, the early detection of plant disease is significant in balancing the country's economy and preventing undesirable losses. To bring a solution to this problem our dataset provides a total of 4467 images of Beans and Cowpeas leaf images which include different disease classes and fresh leaves. The dataset comprises 2,273 images of Bean and 2,194 images of Cowpea plants where each plant provides 4 classes of different disease along with the healthy leaves. This dataset will assist researchers in identifying plant diseases and farmers as well as contribute to the economy of the country.
{"title":"Comprehensive smartphone image dataset for bean and cowpea plant leaf disease detection and freshness assessment from Bangladesh vegetable fields","authors":"Mahamudul Hasan, Raiyan Gani, Mohammad Rifat Ahmmad Rashid, Raka Kamara, Taslima Khan Tarin, Sheikh Fajlay Rabbi","doi":"10.1016/j.dib.2024.111023","DOIUrl":"10.1016/j.dib.2024.111023","url":null,"abstract":"<div><div>Agriculture greatly impacts Bangladesh's economy, and vegetable cultivation plays a significant role in Agriculture by providing nourishment, and food security as well as improving the economy. The necessity of food production is growing similarly to the population growth. The farmers of Bangladesh are working hard to meet this need for food production and to gain yields. However, every year the farmers face a significant amount of loss in production due to the attack of different diseases and viruses due to the lack to technological development. The reason behind most of these losses is the lack of knowledge about diseases and being unable to detect the diseases early. Therefore, the early detection of plant disease is significant in balancing the country's economy and preventing undesirable losses. To bring a solution to this problem our dataset provides a total of 4467 images of Beans and Cowpeas leaf images which include different disease classes and fresh leaves. The dataset comprises 2,273 images of Bean and 2,194 images of Cowpea plants where each plant provides 4 classes of different disease along with the healthy leaves. This dataset will assist researchers in identifying plant diseases and farmers as well as contribute to the economy of the country.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111023"},"PeriodicalIF":1.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-12DOI: 10.1016/j.dib.2024.111034
Rajit Gupta , Gabriela Zuquim , Hanna Tuomisto
The use of satellite remote sensing has considerably improved scientific understanding of the heterogeneity of Amazonian rainforests. However, the persistent cloud cover and strong Bidirectional Reflectance Distribution Function (BRDF) effects make it difficult to produce up-to-date satellite image composites over the huge extent of Amazonia. Advanced pre-processing and pixel-based compositing over an extended time period are needed to fill the data gaps caused by clouds and to achieve consistency in pixel values across space. Recent studies have found that the multidimensional median, also known as medoid, algorithm is robust to outliers and noise, and thereby provides a useful approach for pixel-based compositing. Here we describe Landsat-7 and Landsat-8 composites covering all Amazonia that were produced using Landsat data from the years 2013–2021 and processed with Google Earth Engine (GEE). These products aggregate reflectance values over a relatively long time, and are, therefore, especially useful for identifying permanent characteristics of the landscape, such as vegetation heterogeneity that is driven by differences in geologically defined edaphic conditions. To make similar compositing possible over other areas and time periods (including shorter time periods for change detection), we make the workflow available in GEE. Visual inspection and comparison with other Landsat products confirmed that the pre-processing workflow was efficient and the composites are seamless and without data gaps, although some artifacts present in the source data remain. Basin-wide Landsat-7 and Landsat-8 composites are expected to facilitate both local and broad-scale ecological and biogeographical studies, species distribution modeling, and conservation planning in Amazonia.
{"title":"Seamless Landsat-7 and Landsat-8 data composites covering all Amazonia","authors":"Rajit Gupta , Gabriela Zuquim , Hanna Tuomisto","doi":"10.1016/j.dib.2024.111034","DOIUrl":"10.1016/j.dib.2024.111034","url":null,"abstract":"<div><div>The use of satellite remote sensing has considerably improved scientific understanding of the heterogeneity of Amazonian rainforests. However, the persistent cloud cover and strong Bidirectional Reflectance Distribution Function (BRDF) effects make it difficult to produce up-to-date satellite image composites over the huge extent of Amazonia. Advanced pre-processing and pixel-based compositing over an extended time period are needed to fill the data gaps caused by clouds and to achieve consistency in pixel values across space. Recent studies have found that the multidimensional median, also known as medoid, algorithm is robust to outliers and noise, and thereby provides a useful approach for pixel-based compositing. Here we describe Landsat-7 and Landsat-8 composites covering all Amazonia that were produced using Landsat data from the years 2013–2021 and processed with Google Earth Engine (GEE). These products aggregate reflectance values over a relatively long time, and are, therefore, especially useful for identifying permanent characteristics of the landscape, such as vegetation heterogeneity that is driven by differences in geologically defined edaphic conditions. To make similar compositing possible over other areas and time periods (including shorter time periods for change detection), we make the workflow available in GEE. Visual inspection and comparison with other Landsat products confirmed that the pre-processing workflow was efficient and the composites are seamless and without data gaps, although some artifacts present in the source data remain. Basin-wide Landsat-7 and Landsat-8 composites are expected to facilitate both local and broad-scale ecological and biogeographical studies, species distribution modeling, and conservation planning in Amazonia.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111034"},"PeriodicalIF":1.0,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-12DOI: 10.1016/j.dib.2024.111027
Guangyu Lei, Wei Cheng, Xipeng Yin, Yuqing Wu
The dataset presents the raw data of respiration and heartbeat of two human subjects monitored by 60GHz Frequency-Modulated Continuous Wave (FMCW) radar and the validation data obtained by the electrocardiogram (ECG) front-end. During the radar-based vital signs monitoring, the subjects were simultaneously equipped with the ECG front-end, integrated with an Arduino board, to collect muscle electric signal waveforms, which enabled the estimation of the actual number of respirations and heartbeats of the subjects during the radar monitoring. To assess the impact of radar parameters, as well as the angle and distance between the subjects and the radar, on monitoring accuracy, the dataset classifies the positions of the human subjects within the scene into symmetric and asymmetric cases. Monitoring was conducted using various radar modulation bandwidths across different angles and distances in an open environment where only the human subjects were present. The objective of this dataset is to facilitate the development of a reliable system for monitoring multiple subjects' respiration and heartbeat using a single-view FMCW radar, emphasizing enhancing the radar's ability to distinguish between different targets and improving the accuracy of respiratory and heartbeat rate monitoring.
{"title":"The dataset of multi-target vital signs monitored by FMCW radar","authors":"Guangyu Lei, Wei Cheng, Xipeng Yin, Yuqing Wu","doi":"10.1016/j.dib.2024.111027","DOIUrl":"10.1016/j.dib.2024.111027","url":null,"abstract":"<div><div>The dataset presents the raw data of respiration and heartbeat of two human subjects monitored by 60GHz Frequency-Modulated Continuous Wave (FMCW) radar and the validation data obtained by the electrocardiogram (ECG) front-end. During the radar-based vital signs monitoring, the subjects were simultaneously equipped with the ECG front-end, integrated with an Arduino board, to collect muscle electric signal waveforms, which enabled the estimation of the actual number of respirations and heartbeats of the subjects during the radar monitoring. To assess the impact of radar parameters, as well as the angle and distance between the subjects and the radar, on monitoring accuracy, the dataset classifies the positions of the human subjects within the scene into symmetric and asymmetric cases. Monitoring was conducted using various radar modulation bandwidths across different angles and distances in an open environment where only the human subjects were present. The objective of this dataset is to facilitate the development of a reliable system for monitoring multiple subjects' respiration and heartbeat using a single-view FMCW radar, emphasizing enhancing the radar's ability to distinguish between different targets and improving the accuracy of respiratory and heartbeat rate monitoring.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111027"},"PeriodicalIF":1.0,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1016/j.dib.2024.111016
Eko Prasetyo , Nanik Suciati , Ni Putu Sutramiani , Adiananda Adiananda , Ayu Putu Wiweka Krisna Dewi
The fish are incorporated with ice to preserve their freshness when sold on the market. Ordinary people can only detect its freshness with some basic freshness knowledge. Therefore, non-destructive fish freshness inspection is an innovative solution to help. This dataset provides a medium to develop a system for non-destructive detection of fish freshness. There are three data variations: sensor data, images, and organoleptic examination. This dataset includes three fish species: mackerel, tilapia, and tuna, using 21 fish of each species. Data generation was carried out for 11 days, where 800 MQ (Metal Oxide) 135 and TGS (Taguchi Gas Sensor) 2602 sensor data and 80 images were generated every day. Organoleptic examinations were carried out using the Indonesian National Standard (SNI) 2729-2013 on six parameters: eyes, gills, body surface mucus, meat, smell, and body textures. This dataset can be used to develop a fish freshness detection system, regression modeling to estimate the deterioration in fish freshness, and standard grouping of freshness classes.
{"title":"DaFiF: A complete dataset for fish's freshness problems","authors":"Eko Prasetyo , Nanik Suciati , Ni Putu Sutramiani , Adiananda Adiananda , Ayu Putu Wiweka Krisna Dewi","doi":"10.1016/j.dib.2024.111016","DOIUrl":"10.1016/j.dib.2024.111016","url":null,"abstract":"<div><div>The fish are incorporated with ice to preserve their freshness when sold on the market. Ordinary people can only detect its freshness with some basic freshness knowledge. Therefore, non-destructive fish freshness inspection is an innovative solution to help. This dataset provides a medium to develop a system for non-destructive detection of fish freshness. There are three data variations: sensor data, images, and organoleptic examination. This dataset includes three fish species: mackerel, tilapia, and tuna, using 21 fish of each species. Data generation was carried out for 11 days, where 800 MQ (Metal Oxide) 135 and TGS (Taguchi Gas Sensor) 2602 sensor data and 80 images were generated every day. Organoleptic examinations were carried out using the Indonesian National Standard (SNI) 2729-2013 on six parameters: eyes, gills, body surface mucus, meat, smell, and body textures. This dataset can be used to develop a fish freshness detection system, regression modeling to estimate the deterioration in fish freshness, and standard grouping of freshness classes.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111016"},"PeriodicalIF":1.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1016/j.dib.2024.111018
Rafael Noboro Tominaga , Luan Andrade Sousa , Rodolfo Varraschim Rocha , Renato Machado Monaro , Sérgio Luciano Ávila , Maurício Barbosa de Camargo Salles , Bruno Souza Carmo
Proper monitoring of rotating machines is responsible for the efficiency in detecting, diagnosing, or even prognosing failures. Effective monitoring can lead to increased economic viability of any equipment, as it may reduce costly repairs, decrease downtime, and increase safety. Knowing the behaviour of a machine promotes better monitoring of its operation and maintenance. Data-driven algorithms have been widely used to identify failures and predict the behaviour of machines and systems. The difficulty in obtaining reliable data to test strategies or methods for this purpose is well known. Our contribution is a set of electrical current data (time series data) from a rotating machine that generates electrical energy, generically called a power generator, in a laboratory. In this machine we have the possibility of, besides monitoring its healthy behaviour, causing internal defects that can reduce its efficiency and remaining useful life.
We highlight three key lines of study with the data available here: it is possible to apply data processing tools to make discoveries not evidenced in studies; test and compare new data-driven algorithms using public and reliable data; engineering lectures can use the dataset regarding the study of electrical machines and data driving methods.
The dataset contains information mainly about the voltage and current of generators when they are subject to internal faults. These faults include short circuits between turns of winding, short circuits between windings of the same phase, and short circuits between different phases.
This dataset has a wide variety of bench configurations. The dataset comes from real generators and allows the study of phenomena that are difficult to reproduce through analytical or computational models. The time series of electrical currents are raw, no preprocessing has been done. In fact, the signals contain natural noise from an industrial environment.
In this context, the main contribution of this work is to provide a public and reliable database, which helps to speed up the development of more efficient techniques for monitoring, diagnosis, and prognostics of the behaviour of rotating electrical machines.
{"title":"Electrical signals dataset from fixed-speed and variable-speed synchronous generators under healthy and faulty conditions","authors":"Rafael Noboro Tominaga , Luan Andrade Sousa , Rodolfo Varraschim Rocha , Renato Machado Monaro , Sérgio Luciano Ávila , Maurício Barbosa de Camargo Salles , Bruno Souza Carmo","doi":"10.1016/j.dib.2024.111018","DOIUrl":"10.1016/j.dib.2024.111018","url":null,"abstract":"<div><div>Proper monitoring of rotating machines is responsible for the efficiency in detecting, diagnosing, or even prognosing failures. Effective monitoring can lead to increased economic viability of any equipment, as it may reduce costly repairs, decrease downtime, and increase safety. Knowing the behaviour of a machine promotes better monitoring of its operation and maintenance. Data-driven algorithms have been widely used to identify failures and predict the behaviour of machines and systems. The difficulty in obtaining reliable data to test strategies or methods for this purpose is well known. Our contribution is a set of electrical current data (time series data) from a rotating machine that generates electrical energy, generically called a power generator, in a laboratory. In this machine we have the possibility of, besides monitoring its healthy behaviour, causing internal defects that can reduce its efficiency and remaining useful life.</div><div>We highlight three key lines of study with the data available here: it is possible to apply data processing tools to make discoveries not evidenced in studies; test and compare new data-driven algorithms using public and reliable data; engineering lectures can use the dataset regarding the study of electrical machines and data driving methods.</div><div>The dataset contains information mainly about the voltage and current of generators when they are subject to internal faults. These faults include short circuits between turns of winding, short circuits between windings of the same phase, and short circuits between different phases.</div><div>This dataset has a wide variety of bench configurations. The dataset comes from real generators and allows the study of phenomena that are difficult to reproduce through analytical or computational models. The time series of electrical currents are raw, no preprocessing has been done. In fact, the signals contain natural noise from an industrial environment.</div><div>In this context, the main contribution of this work is to provide a public and reliable database, which helps to speed up the development of more efficient techniques for monitoring, diagnosis, and prognostics of the behaviour of rotating electrical machines.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111018"},"PeriodicalIF":1.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.dib.2024.111010
Théo Le Saint , Jean Nabucet , Cécile Sulmon , Julien Pellen , Karine Adeline , Laurence Hubert-Moy
A dataset was produced for 117 urban trees in four monospecific tree rows in the city of Rennes, northwestern France. The trees were measured in nine 2- to 3-day measurement sessions from Apr-Sep 2021. The dataset includes (i) leaf traits (i.e., contents of pigments, water and dry matter) measured in situ and in the laboratory; (ii) plant area density measured in situ under the canopy and (iii) georeferenced data that describe the location, geometry and species of the trees. The dataset provides an original overview of dynamics of the contents of pigments, water and dry matter and plant area density for four tree species grown under urban conditions. It can be used for several purposes, such as identifying trees’ responses/behaviors in relation to their urban environment or climate conditions.
{"title":"A spatio-temporal dataset for ecophysiological monitoring of urban trees","authors":"Théo Le Saint , Jean Nabucet , Cécile Sulmon , Julien Pellen , Karine Adeline , Laurence Hubert-Moy","doi":"10.1016/j.dib.2024.111010","DOIUrl":"10.1016/j.dib.2024.111010","url":null,"abstract":"<div><div>A dataset was produced for 117 urban trees in four monospecific tree rows in the city of Rennes, northwestern France. The trees were measured in nine 2- to 3-day measurement sessions from Apr-Sep 2021. The dataset includes (i) leaf traits (<em>i.e.</em>, contents of pigments, water and dry matter) measured <em>in situ</em> and in the laboratory; (ii) plant area density measured <em>in situ</em> under the canopy and (iii) georeferenced data that describe the location, geometry and species of the trees. The dataset provides an original overview of dynamics of the contents of pigments, water and dry matter and plant area density for four tree species grown under urban conditions. It can be used for several purposes, such as identifying trees’ responses/behaviors in relation to their urban environment or climate conditions.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111010"},"PeriodicalIF":1.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.dib.2024.111012
Asko Lõhmus
Diversity and abundance of breeding birds are frequently reported and analysed as indicators of environmental change. However, such data available for forests typically contain either relative abundances based on snapshot observations or have been collected in small sample plots, which limit their distributional and ecological analysis across landscapes. I present a spatial dataset from three adjacent landscapes in Estonia (hemiboreal Europe), which has been obtained by standard multiple-visit mapping of nesting territories in 2020–2022. The records constitute the most likely centroids of distinct nesting territories of all 98 breeding species detected; these have been extracted and interpreted based on observations from an average 7–8 visits per season, and quality-assessed for three levels of spatial accuracy. One landscape was mapped in all three years, the others in either 2021 or 2022. The total area mapped was 14.3 km2, including 86 % woodlands of diverse types and origins; a woodland characteristics dataset accompanies the bird data to facilitate habitat analysis. The paper describes the study plots; technical protocols of fieldwork and record interpretation; limitations (notably the likely missing of 10–20 % of pairs in most species); and possibilities to use the data in basic and applied ecological research. The main values of the dataset are that (i) it provides landscape-scale distribution map for the whole breeding assemblage of birds at high spatial precision, (ii) has accompanying woodland habitat data, and (iii) it also includes a repeatedly mapped landscape for detecting temporal variation in bird distributions.
{"title":"A high-precision dataset of breeding bird distributions in forested landscapes in Estonia","authors":"Asko Lõhmus","doi":"10.1016/j.dib.2024.111012","DOIUrl":"10.1016/j.dib.2024.111012","url":null,"abstract":"<div><div>Diversity and abundance of breeding birds are frequently reported and analysed as indicators of environmental change. However, such data available for forests typically contain either relative abundances based on snapshot observations or have been collected in small sample plots, which limit their distributional and ecological analysis across landscapes. I present a spatial dataset from three adjacent landscapes in Estonia (hemiboreal Europe), which has been obtained by standard multiple-visit mapping of nesting territories in 2020–2022. The records constitute the most likely centroids of distinct nesting territories of all 98 breeding species detected; these have been extracted and interpreted based on observations from an average 7–8 visits per season, and quality-assessed for three levels of spatial accuracy. One landscape was mapped in all three years, the others in either 2021 or 2022. The total area mapped was 14.3 km<sup>2</sup>, including 86 % woodlands of diverse types and origins; a woodland characteristics dataset accompanies the bird data to facilitate habitat analysis. The paper describes the study plots; technical protocols of fieldwork and record interpretation; limitations (notably the likely missing of 10–20 % of pairs in most species); and possibilities to use the data in basic and applied ecological research. The main values of the dataset are that (i) it provides landscape-scale distribution map for the whole breeding assemblage of birds at high spatial precision, (ii) has accompanying woodland habitat data, and (iii) it also includes a repeatedly mapped landscape for detecting temporal variation in bird distributions.</div></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":"57 ","pages":"Article 111012"},"PeriodicalIF":1.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.dib.2024.110990
S. Ma Lu , S. Zainali , T.E.K. Zidane , T. Hörndahl , S. Tekie , A. Khosravi , M. Guezgouz , B. Stridh , A. Avelin , P.E. Campana
Agrivoltaic systems emerge as a promising solution to the ongoing conflict between allocating agricultural land for food production and establishing solar parks. This field experiment, conducted during the spring and summer seasons of 2023, aims to showcase barley production in a vertical agrivoltaic system compared to open-field reference conditions at Kärrbo Prästgård, near Västerås, Sweden. The dataset presented in this article encompasses both barley kernel and straw yields, kernel crude protein levels, starch content in kernels and thousand kernel weight. All collected data underwent analysis of variance (ANOVA) with Tukey pairwise comparison when possible, using dedicated software RStudio 4.3.2. This dataset article illustrates the effects of the vertical agrivoltaic design system on barley productivity. Interested researchers can benefit from this data to better comprehend barley yield under this specific agrivoltaic design and conduct further analyses and comparisons with yields from different locations or design configurations. The experimental data holds the potential to foster collaborations and advance research in agrivoltaic systems, providing a valuable resource for anyone interested in the subject. It was observed that the mean barley yield in all the different areas of the vertical agrivoltaic system were higher than the one in the control area. Additionally, weather and solar irradiance data collected during the growing season are provided in the repository for further usage.
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