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24 results for “Region of Interest”
May to July 2018 regions of interest (ROIs) of tidal marsh and tidal forest plant species to be used as ground reference data in habitat mapping
We collected field data from sites distributed in habitats along the salinity axis of the Altamaha River estuary and the Duplin River to be used as ground reference data for habitat mapping. Regions of interest (ROIs) for tidal marsh (salt, brackish, tidal fresh) and tidal fresh forest vegetation species were generated near ground control points (GCP) by digitizing vegetation areas in ArcGIS 10.4 based on field maps.These observations will be used to create habitat maps from aerial photographs of the Altamaha River estuary, GA taken following Hurricane Irma to better understand how the storm surge affected tidal vegetation and to examine any shifts in vegetation type.
Land use and land cover samples for specific regions of interest in the Brazilian Cerrado agricultural belt
<p>Land use and land cover samples for specific regions of interest in the Brazilian Cerrado agricultural belt in 2019/2020. Total of samples: 957. The process to collect them is described in Chaves, M., & Sanches, I. (2023). Improving crop mapping in Brazil's Cerrado from a data cubes-derived Sentinel-2 temporal analysis. Remote Sensing Applications: Society and Environment, 32, 101014. <a href="https://www.sciencedirect.com/science/article/pii/S2352938523000964">https://www.sciencedirect.com/science/article/pii/S2352938523000964</a> and Chaves, M., Soares, A., Mataveli, G., Sánchez, A., & Sanches, I. (2023). A semi-automated workflow for LULC mapping via Sentinel-2 data cubes and spectral indices. Automation, 4(1), 94-109. <a href="https://www.mdpi.com/2673-4052/4/1/7">https://www.mdpi.com/2673-4052/4/1/7</a>.</p>
Mare Ingenii regions of interest spectra continuum removal from Salari et al. 2023
<p>List of files considered by Salari et al. 2023:</p> <p>1) Spectral parameter map of Mare Ingenii at 600 m/pixels (1 GeoTiff file+header);</p> <p>2) ASCII file containing mare Ingenii 23 regions of interest average spectra and the corresponding standard deviations;</p> <p>2) ASCII file containing mare Ingenii 23 regions of interest average spectra continuum removal;</p>
Figs 14–18 in New and interesting records of Lepidoptera for several Russian regions
Figs 14–18. Genitalia structures of Lepidoptera. 14 – Paragabara curvicornuta Kononenko & Matov, 2010, ♀, bursa copulatrix; 15, 16 – male genitalia of Chytonix albonotata (Staudinger, 1892) (15 – genital segment, 16 – phallos); 17, 18 – male genitalia of Abraxas latifasciata Warren, 1894 (17 – genital segment, 18 – phallos). Scale bar = 1 mm.
Figs 6–13. Moths, dorsal view. 6 in New and interesting records of Lepidoptera for several Russian regions
Figs 6–13. Moths, dorsal view. 6 – Siglophora sanguinolenta (Moore, 1888), ♂, Primorsky Krai; 7 – ditto, ♀; 8 – Maliattha signifera (Walker, [1858]), ♀, Primorsky Krai; 9 – Chytonix albonotata (Staudinger, 1892), ♂, Amurskaya Oblast; 10 – Polia vespertilio (Draudt, 1934), ♂, Amurskaya Oblast; 11 – Xestia penthima (Erschoff, 1870), ♂, Yakutia; 12 – Abraxas latifasciata Warren, 1894, ♂, Primorsky Krai; 13 – Crocota niveata (Scopoli, 1763), ♂, Saratovskaya Oblast. Scale bar = 1 cm.
Linked collectors and determiners for: New synonyms, and first and interesting records of certain species of the subtribe Stenolophina from the Palaearctic, Oriental and Afrotropical regions (Coleoptera, Carabidae, Harpalini, Stenolophina).
Natural history specimen data linked to collectors and determiners held within, "New synonyms, and first and interesting records of certain species of the subtribe Stenolophina from the Palaearctic, Oriental and Afrotropical regions (Coleoptera, Carabidae, Harpalini, Stenolophina)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/19c72dc5-1ec0-4bb4-a163-ad72b1dfb4ca">https://bionomia.net/dataset/19c72dc5-1ec0-4bb4-a163-ad72b1dfb4ca</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/19c72dc5-1ec0-4bb4-a163-ad72b1dfb4ca">https://gbif.org/dataset/19c72dc5-1ec0-4bb4-a163-ad72b1dfb4ca</a>. Formatted as a Frictionless Data package.
Figs 8-9 in New synonyms, and first and interesting records of certain species of the subtribe Stenolophina from the Palaearctic, Oriental and Afrotropical regions (Coleoptera, Carabidae, Harpalini, Stenolophina)
Figs 8-9: Psychristus (Psychristus) dentatus JAEGER, 2009. Median lobe of aedeagus, lateral and dorsal aspects. (8-9) China, Yunnan, Xishuangbanna NP.
Figs 1-3 in New synonyms, and first and interesting records of certain species of the subtribe Stenolophina from the Palaearctic, Oriental and Afrotropical regions (Coleoptera, Carabidae, Harpalini, Stenolophina)
Figs 1-3: Anthracus angusticollis (PÉRINGUEY, 1908). Habitus, head and pronotum. (1-3) Madagascar, Katsepy.
Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation
<p><strong>Reference Paper: </strong></p> <p><a href="https://doi.org/10.1016/j.actaastro.2023.01.012"><strong>M. Bechini, M. Lavagna, P. Lunghi, Dataset generation and validation for spacecraft pose estimation via monocular images processing, Acta Astronautica 204 (2023) 358–369</strong></a></p> <p><a href="https://www.researchgate.net/publication/361924362_Spacecraft_Pose_Estimation_via_Monocular_Image_Processing_Dataset_Generation_and_Validation">M. Bechini, P. Lunghi, M. Lavagna. "Spacecraft Pose Estimation via Monocular Image Processing: Dataset Generation and Validation". In 9th European Conference for Aeronautics and Aerospace Sciences (EUCASS)</a></p> <p><strong>General Description:</strong></p> <p>The "<em>Tango Spacecraft Dataset for Region of Interest Estimation and Semantic Segmentation</em>" dataset here published should be used for Region of Interest (ROI) and/or semantic segmentation tasks. It is split into 30002 train images and 3002 test images representing the Tango spacecraft from Prisma mission, being the largest publicly available dataset of synthetic space-borne noise-free images tailored to ROI extraction and Semantic Segmentation tasks (up to our knowledge). The label of each image gives, for the Bounding Box annotations, the filename of the image, the ROI top-left corner (minimum x, minimum y) in pixels, the ROI bottom-right corner (maximum x, maximum y) in pixels, and the center point of the ROI in pixels. The annotation are taken in image reference frame with the origin located at the top-left corner of the image, positive x rightward and positive y downward. Concerning the Semantic Segmentation, RGB masks are provided. Each RGB mask correspond to a single image in both train and test dataset. The RGB images are such that the R channel corresponds to the spacecraft, the G channel corresponds to the Earth (if present), and the B channel corresponds to the background (deep space). Per each channel the pixels have non-zero value only in correspondence of the object that they represent (Tango, Earth, Deep Space). More information on the dataset split and on the label format are reported below. </p> <p><strong>Images Information:</strong></p> <p>The dataset comprises 30002 synthetic grayscale images of Tango spacecraft from Prisma mission that serves as train set, while the test set is formed by 3002 synthetic grayscale images of Tango spacecraft from Prisma mission in PNG format. About 1/6 of the images both in the train and in the test set have a non-black background, obtained by rendering an Earth-like model in the raytracing process used to define the images reported. The images are noise-free to increase the flexibility of the dataset. The illumination direction of the spacecraft in the scene is uniformly distributed in the 3D space in agreement with the Sun position constraints.</p> <p><br> <strong>Labels Information:</strong></p> <p>Labels for the bounding box extraction are here provided in separated JSON files. The files are formatted per each image as in the following example:</p> <ul> <li> filename : tango_img_1 # name of the image to which the data are referred</li> <li> rol_tl : [x, y] # ROI top-left corner (minimum x, minimum y) in pixels</li> <li> roi_br : [x, y] # ROI bottom-right corner (maximum x, maximum y) in pixels</li> <li> roi_cc : [x, y] # center point of the ROI in pixels</li> </ul> <p>Notice that the annotation are taken in image reference frame with the origin located at the top-left corner of the image, positive x rightward and positive y downward.To make the usage of the dataset easier, both the training set and the test set are split in two folders containing the images with earth as background and without background.</p> <p>Concerning the Semantic Segmentation Labels, they are provided as RGB masks named as "filename_mask.png" where "filename" is the filename of the image of the training set or the test set to which a specific mask is referred. The RGB images are such that the R channel corresponds to the spacecraft, the G channel corresponds to the Earth (if present), and the B channel corresponds to the background (deep space). Per each channel the pixels have non-zero value only in correspondence of the object that they represent (Tango, Earth, Deep Space). </p> <p><strong>VERSION CONTROL</strong></p> <ul> <li>v1.0: This version contains the dataset (both train and test) of full scale images with ROI annotations and RGB masks for Semantic Segmentation tasks. These images have width=height=1024 pixels. The position of tango with respect to the camera is randomly selected from a uniform distribution, but it is ensured the full visibility in all the images. </li> </ul> <p>Note: this dataset contains the same images of the <em>"Tango Spacecraft Wireframe Dataset Model for Line Segments Detection"</em> v2.0 full-scale (DOI: <a href="https://doi.org/10.5281/zenodo.6372848">https://doi.org/10.5281/zenodo.6372848</a>) and also "<em>Tango Spacecraft Dataset for Monocular Pose Estimation</em>" v1.0 (DOI: <a href="https://doi.org/10.5281/zenodo.6499007">https://doi.org/10.5281/zenodo.6499007</a>) and they can be used together by combining the annotations of the relative pose and the ones of the reprojected wireframe model of Tango, with also the ones of the ROI. <strong>These three datasets give the most comprehensive dataset of space borne synthetic images ever published</strong> (up to our knowledge).</p>
Fig. 7 in New synonyms, and first and interesting records of certain species of the subtribe Stenolophina from the Palaearctic, Oriental and Afrotropical regions (Coleoptera, Carabidae, Harpalini, Stenolophina)
Fig. 7: Anthracus angusticollis (PÉRINGUEY, 1908). Distribution.
Dataset of the paper titled Geological characteristics and high scientific interest targets of the Zhurong landing region on Mars
<p>This dataset contains the original data and crater counting data of the paper titled Geological characteristics and high scientific interest targets of the Zhurong landing region on Mars.</p>
The Use of Navigated Transcranial Magnetic Stimulation (nTMS) in the Inhibition of Neurofunctional Regions of Interest
ClinicalTrials.gov study NCT04209023. IPD Sharing: NO. Countries: 1. Publications: 7.
FIGURES 1–7. 1 in An interesting new species of Acmaeodera (s. str.) Eschscholtz 1829 from the Afrotropical Region (Coleoptera: Buprestidae)
FIGURES 1–7. 1. parameres of Holotype; 2. median lobe of Holotype; 3. ovipositor of Paratype, Tanzania–Iringa D. Ruaha N.P. Msembe; 4. protibia and tarsus of Holotype; 5. dorsal aspect of Paratype, Tanzania Iringa D. Ruaha N.P. Msembe, female 8.25 mm; 6. lateral aspect of Paratype Tanzania Mkomazi G.R. Ibaya camp, female 6.6 mm; 7. lateral aspect showing metasternum.
RBD Region Of Interest Left Posterior Pons, .nii file
Open the record for dataset details and reuse information.
Lipidomics and metabolomics datasets for "Adverse effects of arsenic uptake in rice metabolome and lipidome revealed by untargeted liquid chromatography coupled to mass spectrometry (LC-MS) and regions of interest multivariate curve resolution"
<p><strong>Files description</strong></p> <p>Raw files for lipidomics and metabolics studies on the impact of arsenic exposure on rice growth.</p> <p>File details on the worksheets lipids_files.xlsx and metabolomics_files.xlsx</p> <p>Files have been organized as follows:</p> <p><strong>Lipidomics</strong></p> <blockquote> <p>1) Control samples: lip_controls.rar<br> 2) Watering low As exposure: lip_water_1.rar<br> 3) Watering high As exposure: lip_water_1000.rar<br> 4) Soil low As exposure: lip_soil_5.rar<br> 5) Soil high As exposure: lip_soil_50.rar<br> 6) QC samples: lip_qcs.rar</p> </blockquote> <p><strong>Metabolomics (positive ionization mode)</strong></p> <blockquote> <p>1) Control samples: met_pos_controls.rar<br> 2) Watering low As exposure: met_pos_water_1.rar<br> 3) Watering high As exposure: met_pos_water_1000.rar<br> 4) Soil low As exposure: met_pos_soil_5.rar<br> 5) Soil high As exposure: met_pos_soil_50.rar<br> 6) QC samples: met_pos_qcs.rar</p> </blockquote> <p><strong>Metabolomics (negative ionization mode)</strong></p> <blockquote> <p>1) Control samples: met_neg_controls.rar<br> 2) Watering low As exposure: met_neg_water_1.rar<br> 3) Watering high As exposure: met_neg_water_1000.rar<br> 4) Soil low As exposure: met_neg_soil_5.rar<br> 5) Soil high As exposure: met_neg_soil_50.rar<br> 6) QC samples: met_neg_qcs.rar<br> </p> </blockquote> <p> </p> <p><strong>Experimental details</strong></p> <blockquote> <p><strong>Arsenic Exposure</strong></p> <p>Arsenic was supplied through two main routes: watering with contaminated water or soil containing arsenic. In addition, this new study includes metabolomic as well as lipidomic analysis, in order to have a more global overview of arsenic exposure.</p> <p>For the watering treatment, during the first 11 days, rice was irrigated with Milli-Q water. From that day until harvesting, plants were watered with 1 and 1000 μM of As (V) for the two concentration levels of exposure, and with Milli-Q water for control samples. The lowest concentration was established at 1 μM as it is the limit of the acceptable arsenic concentration in water by European legislation. The upper concentration was set at 1000 μM, a threshold established to ensure that the experiment was performed under sub-lethal arsenic concentration for the plant, based on previous studies.</p> <p>For the soil treatment, two containers were prepared with 1 kg of soil two days before planting. Soil from the container was exposed to two arsenic concentration levels (5 and 50 mg L<sup>-1</sup>). Once sowing, rice was irrigated the whole growth period with a solution containing 0.001 μM of As (V). The lowest arsenic limit in this treatment was set at 5 mg L<sup>-1</sup> as a maximum value of common arsenic leaches without toxic characteristics, although background soil content of arsenic varies between one and 40 ppm according to the US food and drug administration (FDA) report. The highest arsenic limit was established to 50 mg L<sup>-1</sup>, as a considerably high arsenic content in the soil, slightly above the maximum frequently encountered levels.</p> <p><strong>Lipidomic Analysis</strong></p> <p>The lipidomic analysis was performed using a Waters Acquity UPLC system (Waters Corporation, MA, USA), connected to a Waters LCT Premier orthogonal accelerated time of flight mass spectrometer (Waters), operated in both positive and negative electrospray (ESI) ionization modes. Full scan spectra were acquired from 50 to 1500 Da.</p> <p>The chromatographic column employed was a Kinetex C8 (100 x 2.1 mm, 1.7 μm) (Phenomenex) under the following conditions (already used in [47]): temperature at 30˚C, injection volume at 10 μL, and flow rate at 0.3 mL min<sup>-1</sup>. Mobile phases selected were (A) MeOH 1mM ammonium formate, and (B) H<sub>2</sub>O 2mM ammonium formate, both at 0.2% formic acid. The gradient started at 80% A, increased to 90% A in 3 min, from 3 to 6 min remained at 90% A, changed to 99 % A until minute 15, remained constant 1 min, and returned to initial conditions until minute 20.</p> <p><strong>Metabolomic analysis</strong></p> <p>The metabolomic analysis was performed using a Waters Acquity UPLC system connected to a Q-Exactive (Thermo Fisher Scientific, Hemel Hempstead, UK) equipped with a quadrupole-Orbitrap mass analyzer. Electrospray (ESI) was used as an ionization source in both positive and negative ion modes. Full scan mass range was set from <em>m/z</em> 90 to 1000, and all ion fragmentation (AIF) was performed with normalized collision energy (NCE) of 35 eV.</p> <p>The column employed was an HILIC TSK gel amide-80 column (250 x 2.0 mm i.d., 5 μm) provided by Tosoh Bioscience (Tokyo, Japan), under the following experimental conditions (already employed in [45]): flow rate at 0.15 mL min<sup>-1</sup>, at room temperature, and 5 μL injection volume. Mobile phases were (A) AcN, and (B) 5 mM ammonium acetate, adjusted at pH 5.5 with acetic acid. The gradient employed was: starting conditions at 25% B, then increased until 30% B in 8 min; a 60% B was reached at 10 min, held for 2 min more and then back to 25% B until minute 14 min; lastly, a re-equilibration step was added and from 14 to 20 min at 25% B.</p> </blockquote> <p> </p> <p><strong>Funding:</strong> This research was funded by the Spanish Ministry of Science and Innovation (MCI, Grant CTQ2017-82598-P) and Severo Ochoa Project CEX2018-000794-S (funded by MCIN/AEI/ 10.13039/501100011033), and supported from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). MPC was funded by a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP). </p> <p> </p>
Figs 1–5. Moths, dorsal view. 1 in New and interesting records of Lepidoptera for several Russian regions
Figs 1–5. Moths, dorsal view. 1 – Psilogramma increta (Walker, 1865), ♂, Primorsky Krai; 2, 3 – Enispa lutefascialis (Leech, 1889), ♂, Buryatia; 4 – Metachrostis sinevi Kononenko et Matov, 2009, ♂, Primorsky Krai; 5 – Paragabara curvicornuta Kononenko et Matov, 2010, ♀, Amurskaya Oblast. Scale bar = 1 cm.
Figs 4-6 in New synonyms, and first and interesting records of certain species of the subtribe Stenolophina from the Palaearctic, Oriental and Afrotropical regions (Coleoptera, Carabidae, Harpalini, Stenolophina)
Figs 4-6: Anthracus angusticollis (PÉRINGUEY, 1908). Median lobe of aedeagus, lateral and dorsal aspects. (4-6) Madagascar, Katsepy.
Large field of view and spatial region of interest transcriptomics in frozen and FFPE tissue
GEO Series GSE268148. Homo sapiens; Mus musculus. 26 samples. Type: Expression profiling by high throughput sequencing.
Video results of the study "Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"
<p># Video Results of the Study "Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"</p> <p>This repository contains the result videos from the study titled *"Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"*. These videos showcase the performance of the algorithm on two datasets: the **Singapore Maritime Dataset (Onboard Segment)** and the **Buoy Dataset**. Please note that the original datasets are not included in this repository but are publicly available elsewhere.</p> <p>## Contents</p> <p>### 1. Singapore Maritime Dataset (Onboard Segment)<br>The videos labeled with **MVI** belong to the Singapore Maritime Dataset. These result videos demonstrate the performance of the horizon line detection algorithm on onboard maritime footage. For example:<br>- **MVI_0792_VIS_OB.avi** - Original input video (available in the Singapore Maritime Dataset)<br>- **MVI_0792_VIS_OB_R.avi** - The output video showing the results of the algorithm</p> <p>### 2. Buoy Dataset<br>The videos labeled with **buoyGT** belong to the Buoy Dataset. These results show the performance of the algorithm near maritime buoys. For example:<br>- **buoyGT_2_5_3_0.avi** - Original input video (available in the Buoy Dataset)<br>- **buoyGT_2_5_3_0_R.avi** - The output video showing the results of the algorithm</p> <p>## Video Naming Convention<br>Each result video follows a consistent naming convention related to the original input videos:<br>- **Original Video Filename**: [dataset]_[video details].avi<br>- **Result Video Filename**: [original video filename]_R.avi</p> <p>For instance:<br>- **buoyGT_2_5_3_0.avi** corresponds to **buoyGT_2_5_3_0_R.avi**<br>- **MVI_0792_VIS_OB.avi** corresponds to **MVI_0792_VIS_OB_R.avi**</p> <p>The **_R** suffix in the result videos indicates that the video contains the output of the horizon line detection algorithm.</p> <p>## How to Use<br>- To view the results for a specific video, locate the original input video in the corresponding public dataset and find the matching result video in this repository.<br>- For example, the result for the video "buoyGT_2_5_3_0.avi" can be found as "buoyGT_2_5_3_0_R.avi".</p> <p>## Datasets<br>The original datasets used in this study are not included in this repository. They are publicly available from the following sources:<br>1. **Singapore Maritime Dataset** (Onboard Segment)<br>2. **Buoy Dataset**</p> <p>Please refer to the respective dataset repositories for the original video files.</p> <p>## Citation<br>If you use these videos or the method presented in this study in your work, please cite the following:</p> <p>*Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis*.</p>
Locating Regions of Interest in Generalized Anxiety Disorder Using Function Magnetic Resonance Imaging (fMRI)
ClinicalTrials.gov study NCT01607710. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.